[
{"raw_link": "https://ourworldindata.org/how-much-are-people-across-the-world-paying-for-their-carbon-emissions", "title": "How much are people across the world paying for their carbon emissions?", "context": "Home\nCO2 & Greenhouse Gas Emissions\nHow much are people across the world paying for their carbon emissions?\nAround 30% of the world’s emissions have some carbon price, but how much extra are people paying?\nBy\nHannah Ritchie\n(writing)\nand\nPablo Rosado\n(data)\nJune 22, 2026\nBrowse past versions\nCite this article\nReuse our work freely\nRecently, I was in a meeting with a group of people who do not normally spend much time in the same room: environmentalists, climate scientists, and economists.\nOver several days, we debated and tried to untangle some of the most contested issues around climate change: how drastic the impacts would be, what solutions would look like, and what this would mean for global development. The discussions were intense. There were disagreements left, right, and center.\nDespite all of the differences in the room, it struck me that there was one thing everyone agreed on: those who emit greenhouse gases should pay for the damage they cause — there should be a price on carbon.\n1\nHow, then, is the world acting on this rare consensus between environmentalists and economists?\nWe’ve made some progress. In 2010, just 7% of the world’s carbon dioxide (CO\n2\n) emissions were covered by a carbon price, either through a tax or a carbon trading scheme. Last year, 30% were.\nWe wrote about the coverage of carbon markets and how this has changed over time in a\nprevious article\n.\nBut to be effective, carbon prices need to be high enough to make a meaningful difference to the price of goods and services — enough to incentivize innovation and to make cleaner alternatives meaningfully cheaper.\nWhat is the price of carbon across these markets?\nThe chart below shows the global picture.\n2\nCarbon pricing schemes are lined up from the most expensive on the left to the cheapest on the right. The width of each step shows the share of the world’s CO\n2\nemissions it covers. This not only reflects differences across countries, but is also specific to the sectors or fuels within countries that are priced.\n3\nOn the very left-hand side of the chart, you can see that a small share of emissions — less than 0.5% of the total — are taxed at more than $100 per tonne. This is the emissions-weighted price in Uruguay, Sweden, Norway, Finland, Switzerland, and Hungary.\nMany countries in Western Europe charge prices in the $65–90 range, mostly due to the European Union’s Emissions Trading Scheme. These emissions account for around 5% of the total.\nThe majority of carbon markets charge very little: most charge less than $10 per tonne, and some barely a dollar.\nAt 29%, the red line drops to zero: that’s because the remaining 71% of emissions have no carbon price at all.\nDownload\nCarbon price vs. share of global CO₂ emissions covered\nExplore this data in an interactive visualization, and see the distribution for previous years.\nWhy does this matter?\nThe rationale for a carbon price is simple: the true cost of burning fossil fuels is not reflected in their market price. The greenhouse gas emissions they generate cause damage, but those costs are not necessarily felt by those who caused them. The point of a carbon tax is to reflect those damages in the price of products and fuels, so that those responsible for the emissions pay a fair price.\nMy colleague Max Roser wrote a more\nin-depth explainer\nabout the argument for a carbon price.\nThere is no consensus on what that fair price should be. Hundreds of academic papers have tried to estimate the “social cost of carbon” — the cost in damages of emitting one tonne of CO₂ today. They span a wide range, but median estimates tend to be greater than $100 per tonne of CO\n2\n.\n4\n71% of emissions have no carbon price at all.\nThis is far higher than the price in almost every carbon market across the world, as we just saw in the chart above.\nHow do carbon prices actually change how much we pay for energy?\nAt $5 per tonne, the cost of filling your car with petrol would increase by less than 1%.\n5\nThat’s a small increase compared to the typical fluctuations a driver would see at the petrol pump from month to month. In unremarkable years, price swings of 10% to 15% are common in oil markets. And during fuel crises, price increases are even more extreme: the recent war in Iran has led to a 50%\nprice hike\n.\nAt a carbon price of $10 per tonne, the increase would still be only 2%.\n6\nAgain, barely anything.\nIt’s only when carbon prices get into the $100 per tonne range — which is broadly in line with estimates of climate damages — that price changes start to register: petrol prices increase by around 15%. That’s about the same magnitude as regular swings in oil markets.\n7\nYet almost no carbon market charges this much.\nThe unexpected allies at that climate meeting didn’t just agree that there should be a price on carbon, but that it should be far higher than most people are paying today.\nAcknowledgments\nMany thanks to Carl Edward Rasmussen for the suggestion of this visualization.\nThanks also to Max Roser and Edouard Mathieu for editorial feedback and comments, and to Geoffroy Dolphin for help with the data.\nContinue reading on Our World in Data\nWhich countries have put a price on carbon?\nPutting a price on carbon helps us account for the real costs of fossil fuels in the market. Which countries have a carbon tax or trading system?\nThe argument for a carbon price\nWe are paying a price for fossil fuels, but that price is not paid by those who burn the fossil fuels — we need to change that.\nHow much in subsidies do fossil fuels receive?\nEstimates range from less than $1 trillion to $7 trillion. Where do these numbers come from?\nEndnotes\nMost economists from across the political spectrum agree on the effectiveness of a carbon tax.\nThe\nIGM Forum Survey\n, run by Chicago Booth, polls around 40 prominent economists from across the political spectrum on key issues.\nWhen asked whether a carbon tax would be a less expensive way to reduce emissions than other policy choices, almost all consistently agree or strongly agree.\nThis is based on\na dataset\npublished by Geoffroy Dolphin and Magnus Merkle.\nDolphin, G., Merkle, M. (2024).\nEmissions-weighted Carbon Price: Source and Methods\n.\nScientific Data\n.\nPrices shown are the emissions-weighted average across sectors that have a carbon price.\nAs an example, China has a carbon trading scheme for only some of its sectors (primarily the electricity sector). On the chart, this shows as approximately 15% of the world’s emissions. China’s total economy-wide emissions are\n32% of the global total\n. So only around half of its emissions are priced.\nIn a database published by Richard Tol, which included around 11,500 estimates across 147 studies, the median cost was $103 per tonne of CO2. The mean was $165.\nTol, R. S. (2024). Database for the meta-analysis of the social cost of carbon (v2026. 1). arXiv preprint arXiv:2402.09125.\nAnother meta-analysis, published in 2024, yielded a figure of $132.\nF.C. Moore, M.A. Drupp, J. Rising, S. Dietz, I. Rudik, & G. Wagner, Synthesis of evidence yields high social cost of carbon due to structural model variation and uncertainties. PNAS.\nI’ve calculated based on the price change per liter of petrol.\nBurning one liter of petrol\nemits around\n2.3 kilograms of CO\n2\n, or 0.0023 tonnes. At a price of $5 per tonne of CO\n2\n, that’s an additional cost of around 1 cent [0.0023 * 5 = $0.0115].\nPetrol prices typically vary from around $0.82 per liter\nin the US\nto around $2 per liter\nin the EU\n. Let’s assume $1.50, which is typical in a country like China.\nA $0.01 price increase on a $1.50 liter of petrol is just a 0.8% increase. In the EU, it would be just 0.6%, and in the US, 1.4%.\nAt $10 per tonne, the price increase for a $1.50 liter of petrol would be just $0.02, which is 2%.\nFor $100 per tonne, it would be an increase of $0.23.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Pablo Rosado (2026) - “How much are people across the world paying for their carbon emissions?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260622-061340/how-much-are-people-across-the-world-paying-for-their-carbon-emissions.html' [Online Resource] (archived on June 22, 2026).\nBibTeX citation\n@article{owid-how-much-are-people-across-the-world-paying-for-their-carbon-emissions,\nauthor = {Hannah Ritchie and Pablo Rosado},\ntitle = {How much are people across the world paying for their carbon emissions?},\njournal = {Our World in Data},\nyear = {2026},\nnote = {https://archive.ourworldindata.org/20260622-061340/how-much-are-people-across-the-world-paying-for-their-carbon-emissions.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "how-much-are-people-across-the-world-paying-for-their-carbon-emissions", "source_url": "https://ourworldindata.org/how-much-are-people-across-the-world-paying-for-their-carbon-emissions", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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"Low-income countries"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2024", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": 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"0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Carbon price (US$ per tonne 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"Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Carbon price (US$ per tonne of CO₂ in 2025) on covered emissions (which can be economy-wide or sector-specific)": "0", "Share of global CO₂ emissions covered by a given price": "0", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", 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To do this, they rely on two metrics:\n- Carbon prices applied at the sectoral level (e.g. electricity, or road transport).\n- Each sector's contribution to a country's CO₂ emissions (e.g. what percentage of a country's emissions come from electricity, or road transport).\nThey then weight each sector's carbon price by the relevant sector's contribution to CO₂ emissions, and aggregate these figures to get an economy-wide weighted carbon price.", "A full technical note on the methodology is provided by the authors [in this report](https://www.rff.org/publications/working-papers/emissions-weighted-carbon-price-sources-and-methods/)."], "descriptionProcessing": "This indicator is derived from the source's economy-wide emissions-weighted carbon price, which averages prices across all of a country's CO₂ emissions (including those not subject to any carbon price). 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{"raw_link": "https://ourworldindata.org/five-million-children-die-every-year-what-do-they-die-from", "title": "Five million children die every year — what do they die from?", "context": "Home\nChild & Infant Mortality\nFive million children die every year — what do they die from?\nTo reduce child mortality, we need to understand what children are dying from.\nBy\nHannah Ritchie\n(writing)\n,\nSophia Mersmann\n(visualization)\n,\nand\nFiona Spooner\n(data)\nMay 25, 2026\nBrowse past versions\nCite this article\nReuse our work freely\nPeople often ask me for my favorite statistic. My answer changes, depending on what I’m obsessed with at any given moment. But my least favorite has been the same for years: five million children die every year.\nNot just because of the pain and loss that it represents, but especially because most of these deaths are avoidable.\nBut to reduce mortality, we need to understand what children are dying from.\nMy colleagues Sophia Mersmann and Fiona Spooner have built a beautiful interactive visualization that helps us do this for any country in the world. I’ll use it in this article to look at a few specific views. At the end, you’ll find the full interactive version that shows the data for any country you’re interested in.\nThe first version of this visualization gives us a global overview of the 4.7 million children younger than five who died in 2023. Each box represents a cause of death, and its size is proportional to the number of children who died from it.\nThere is an almost equal share split between infectious diseases — such as pneumonia, malaria, diarrheal diseases, and measles — and birth disorders, which are dominated by preterm births and neonatal suffocation. Both of these categories account for over 40% of child deaths.\nNon-communicable diseases — congenital defects, malnutrition, and cancer — are responsible for 9%, and 5% die from injuries and accidents such as drowning, road incidents, or fires.\nHow does this differ between low- and high-income countries?\nLet’s look at a low- and a high-income country to compare: Nigeria and the United Kingdom. Both the rate of childhood death and the distribution of causes vary significantly.\nA baby born in Nigeria is more than\n20 times as likely\nto die before their fifth birthday as one born in the UK.\nThe next visualization shows what children in Nigeria die from. Infectious diseases are a far more common cause of death than they are in the world as a whole. They account for 56% of child deaths. The largest single cause was malaria — a disease that\ncan be prevented\nor treated with the right resources.\nThe same is true for many other infectious diseases: there are vaccines for rotavirus, the leading cause of diarrheal diseases (but only 57% of Nigerian children\nget the jab\n), for measles (\nonly one-third\n), and for meningitis, and\nwhooping cough\n.\nOne-third die from birth complications, including neonatal suffocation, preterm births, and neonatal infections. Only half of births in Nigeria\nare attended\nby a skilled health professional; increasing this would reduce the risk of complications and save lives.\nIn high-income countries, deaths from infectious diseases are far rarer. In countries like the UK, birth complications and non-communicable diseases dominate, as the visualization below shows. This is not because these are worse in richer countries; it’s because other causes of death — those most easily preventable — are less common.\nRead my article:\nWhere in the world are babies at the lowest risk of dying?\nAlmost 40% of deaths are due to preterm births. That was also my finding from writing about\ndifferences in infant mortality rates\nbetween rich countries: countries where babies were less likely to be born prematurely tended to have lower infant mortality rates.\nPreterm births vary between countries for a number of reasons, including smoking and obesity rates, maternal age, fertility treatments, and how high-risk pregnancies are handled by doctors. But these factors don’t fully explain the differences. Many preterm births happen spontaneously,\nwithout a known cause\n, which is why progress has been slower than for other causes of death, such as infectious diseases.\nWhat do older children die from?\nIn health statistics, young children — under five years old — are often grouped separately from older children. This is for a few reasons.\nDeath rates tend to be higher among younger children; the earliest days and weeks of life are the most dangerous. In 2023, the number of children who died before five was around five times higher than for those aged 5 to 14.\nThe leading causes of death are quite different, too. In the treemap below, you can see what these older children died from, across the world as a whole.\nInfectious diseases still dominate, particularly in lower-income countries. But injuries — particularly drowning and road incidents — become much more prominent. As do childhood cancers.\nAgain, many of these deaths can be prevented. Childhood cancer deaths in countries like the UK and the US\nhave fallen\nsixfold over the last 70 years. Safer roads are possible too: the number of children dying on British roads\nhas fallen\nby 90% since 1980.\nSo when I say that most of the world’s child deaths could be prevented, I’m not just talking about protecting children against infectious diseases (although this is often the most cost-effective way to save many lives). There are many other areas where we’ve made progress and can go even further.\nRead my article on\nhow Britain built some of the world’s safest roads\n.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nExplore what children die from in your own country\nThe first step toward reducing child deaths is knowing where and why they happen.\nThis tool, built by my colleagues Sophia and Fiona, helps us do this.\nYou can explore the causes of child deaths in your country, break down the numbers by age and gender, and understand how this has changed over more than 40 years.\nAcknowledgments\nMany thanks to Max Roser and Edouard Mathieu for feedback and suggestions on this article, to Tuna Acisu for data assistance, and Daniel Bachler, Marcel Gerber, and Marwa Boukarim for technical and design feedback on the visualization.\nContinue reading on Our World in Data\nWhat do people die from in different countries?\nExplore causes of death data for all countries, spanning more than four decades.\nDoes the news reflect what we die from?\nWhat do Americans die from, and what do the New York Times, Washington Post, and Fox News report on?\nCauses of Death\nTo find ways to save lives, it’s essential to know what people are dying from. Explore global data and research on causes of death.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie, Sophia Mersmann, and Fiona Spooner (2026) - “Five million children die every year — what do they die from?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260525-123112/five-million-children-die-every-year-what-do-they-die-from.html' [Online Resource] (archived on May 25, 2026).\nBibTeX citation\n@article{owid-five-million-children-die-every-year-what-do-they-die-from,\nauthor = {Hannah Ritchie and Sophia Mersmann and Fiona Spooner},\ntitle = {Five million children die every year — what do they die from?},\njournal = {Our World in Data},\nyear = {2026},\nnote = {https://archive.ourworldindata.org/20260525-123112/five-million-children-die-every-year-what-do-they-die-from.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "five-million-children-die-every-year-what-do-they-die-from", "source_url": "https://ourworldindata.org/five-million-children-die-every-year-what-do-they-die-from", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "To reduce child mortality, we need to understand what children are dying from.", "numeric_mentions": ["25,", "2026", "4.7 million", "2023", "40%", "9%", "5%", "20", "56%", "57%", "2023,", "5", "14", "70 years", "90%", "1980", "40 years", "20260525", "123112"], "numeric_evidence": [{"title": "Child mortality rate", "source_url": "https://ourworldindata.org/grapher/child-mortality.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Under-five mortality rate (selected)"], "row_count_total": 16835, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1957", "Under-five mortality rate (selected)": "37.13"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1958", "Under-five mortality rate (selected)": "36.52"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1959", "Under-five mortality rate (selected)": "35.95"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1960", "Under-five mortality rate (selected)": "35.32"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Under-five mortality rate (selected)": "34.76"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Under-five mortality rate (selected)": "34.23"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Under-five mortality rate (selected)": "33.68"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Under-five mortality rate (selected)": "33.17"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Under-five mortality rate (selected)": "32.65"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Under-five mortality rate (selected)": "32.15"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Under-five mortality rate (selected)": "31.66"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Under-five mortality rate (selected)": "31.16"}, {"Entity": "Afghanistan", "Code": 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"Under-five mortality rate (selected)": "7.86"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Under-five mortality rate (selected)": "7.61"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Under-five mortality rate (selected)": "7.55"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Under-five mortality rate (selected)": "7.69"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Under-five mortality rate (selected)": "7.97"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Under-five mortality rate (selected)": "8.33"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Under-five mortality rate (selected)": "8.73"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Under-five mortality rate (selected)": "9.16"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Under-five mortality rate (selected)": "9.52"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Under-five mortality rate (selected)": "9.83"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Under-five mortality rate (selected)": "10.02"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Under-five mortality rate (selected)": "10.08"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Under-five mortality rate (selected)": "10.04"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Under-five mortality rate (selected)": "9.92"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Under-five mortality rate (selected)": "9.83"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Under-five mortality rate (selected)": "9.79"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Under-five mortality rate (selected)": "8.48"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Under-five mortality rate (selected)": "8.85"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Under-five mortality rate (selected)": "9.17"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Under-five mortality rate (selected)": "9.23"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Under-five mortality rate (selected)": "9.4"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Under-five mortality rate (selected)": "9.47"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Under-five mortality rate (selected)": "9.34"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Under-five mortality rate (selected)": "9.03"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Under-five mortality rate (selected)": "8.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Under-five mortality rate (selected)": "7.88"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Under-five mortality rate (selected)": "7.14"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Under-five mortality rate (selected)": "6.55"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Under-five mortality rate (selected)": "6.18"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Under-five mortality rate (selected)": "5.98"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Under-five mortality rate (selected)": "5.69"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Under-five mortality rate (selected)": "5.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Under-five mortality rate (selected)": "5.23"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Under-five mortality rate (selected)": "5.11"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Under-five mortality rate (selected)": "5.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Under-five mortality rate (selected)": "4.76"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Under-five mortality rate (selected)": "4.6"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Under-five mortality rate (selected)": "4.42"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "child-mortality", "metadata_url": "https://ourworldindata.org/grapher/child-mortality.metadata.json", "chart_title": "Child mortality rate", "chart_subtitle": "Estimated share of newborns who die before age 5.", "chart_note": null, "chart_citation": "Gapminder (2015); UN Inter-agency Group for Child Mortality Estimation (2025)", "original_chart_url": "https://ourworldindata.org/grapher/child-mortality", "owid_column_metadata": {"Child mortality rate": {"titleShort": "Child mortality rate", "titleLong": "Child mortality rate - Gapminder; UN IGME – Long-run data", "descriptionShort": "The long-run estimated share of newborns who die before reaching the age of five.", "descriptionKey": ["Child mortality, the death of children under the age of five, is still extremely common in our world today. The historical data makes clear that it doesn’t have to be this way: societies can protect their children and reduce child mortality to very low rates. For child mortality to reach low levels, many things have to go right at the same time: good healthcare, good nutrition, clean water and sanitation, maternal health, and high living standards. We can, therefore, think of child mortality as a proxy indicator of a country’s living conditions.", "The chart shows our long-run data on child mortality, which allows you to see how child mortality has changed in countries around the world. It combines data from two sources: Gapminder and the UN Inter-agency Group for Child Mortality Estimation (UN IGME).", "[Gapminder](https://www.gapminder.org/data/documentation/gd005/) provides estimates of child mortality rates from 1800 to 2015. The full list of sources used can be found in [their documentation](https://www.gapminder.org/data/documentation/gd005/).", "[UN IGME](https://childmortality.org/all-cause-mortality/data) provides estimates of child mortality rates for some countries from 1932 onward.", "For years where data from both sources is available, we prioritize the UN IGME data. See [this page](https://docs.google.com/spreadsheets/d/1n-WO7yEbi6sXPpeWrorSEVu8w_Yu5dM0n97q1h16L0g/edit?gid=0#gid=0) for more details on which source is used for each data point.", "This indicator is calculated as the number of children under the age of five who died in a given year, divided by the number of newborns in that year."], "descriptionProcessing": "This indicator is a combination of data from two sources:\n  - Gapminder, which provides estimates of child mortality rates for the years 1800 to 2015.\n  - The UN Inter-agency Group for Child Mortality Estimation (UN IGME) provides estimates of child mortality rates, for some countries from 1932 onward.\n\nFor years where data from both sources is available, we prioritize the UN IGME data. See [this page](https://docs.google.com/spreadsheets/d/1n-WO7yEbi6sXPpeWrorSEVu8w_Yu5dM0n97q1h16L0g/edit?gid=0#gid=0) for more details on which source is used for each data point.\n\nIn the Gapminder dataset we remove rows where the source is labelled as \"Guesstimate\" or \"Model based on Life Expectancy\" to try and ensure we use the best available data.\n\nWe remove data for Austria before 1830 from the Gapminder dataset, as there is a jump in 1830 that is likely an error.", "shortUnit": "%", "unit": "deaths per 100 live births", "timespan": "1751-2023", "type": "Numeric", "owidVariableId": 1027766, "shortName": "child_mortality_rate", "lastUpdated": "2025-04-25", "citationShort": "Gapminder (2015); UN Inter-agency Group for Child Mortality Estimation (2025) – processed by Our World in Data", "citationLong": "Gapminder (2015); UN Inter-agency Group for Child Mortality Estimation (2025) – processed by Our World in Data. “Child mortality rate – Gapminder; UN IGME – Long-run data” [dataset]. United Nations Inter-agency Group for Child Mortality Estimation, “United Nations Inter-agency Group for Child Mortality Estimation”; Gapminder, “Child mortality rate under age five v7”; Gapminder based on UN IGME & UN WPP, “Under-five Mortality v11”; Various sources, “Population” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1027766.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Share of one-year-olds vaccinated against rotavirus", "source_url": "https://ourworldindata.org/grapher/share-of-one-year-olds-who-received-the-rotavirus-vaccine.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Rotavirus (RotAC)"], "row_count_total": 1719, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Rotavirus (RotAC)": "46"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Rotavirus (RotAC)": "49"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Rotavirus (RotAC)": "53"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Rotavirus (RotAC)": "50"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Rotavirus (RotAC)": "53"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Rotavirus (RotAC)": "55"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2024", "Rotavirus (RotAC)": "56"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Rotavirus (RotAC)": "0.55681306"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Rotavirus (RotAC)": "1.9772316"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Rotavirus (RotAC)": "4.484932"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Rotavirus (RotAC)": "8.256735"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Rotavirus (RotAC)": "14.721178"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Rotavirus (RotAC)": "29.85668"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Rotavirus (RotAC)": "37.090595"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Rotavirus (RotAC)": "41.147476"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Rotavirus (RotAC)": "42.472027"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Rotavirus (RotAC)": "44.75137"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Rotavirus (RotAC)": "47.201824"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Rotavirus (RotAC)": "49.558533"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2021", "Rotavirus (RotAC)": "50.93665"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2022", "Rotavirus (RotAC)": "49.923843"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2023", "Rotavirus (RotAC)": "56.747242"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2006", "Rotavirus (RotAC)": "0"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2007", "Rotavirus (RotAC)": "0"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2008", "Rotavirus (RotAC)": "0"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2009", "Rotavirus (RotAC)": "1"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2010", "Rotavirus (RotAC)": "2"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2011", "Rotavirus (RotAC)": "2"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2012", "Rotavirus (RotAC)": "5"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2013", "Rotavirus (RotAC)": "12"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2014", "Rotavirus (RotAC)": "29"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2015", "Rotavirus (RotAC)": "38"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2016", "Rotavirus (RotAC)": "42"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2017", "Rotavirus (RotAC)": "43"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2018", "Rotavirus (RotAC)": "46"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2019", "Rotavirus (RotAC)": "49"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2020", "Rotavirus (RotAC)": "51"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2021", "Rotavirus (RotAC)": "53"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2022", "Rotavirus (RotAC)": "53"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2023", "Rotavirus (RotAC)": "62"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2024", "Rotavirus (RotAC)": "65"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Rotavirus (RotAC)": "98"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Rotavirus (RotAC)": "98"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "Rotavirus (RotAC)": "97"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Rotavirus (RotAC)": "97"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "Rotavirus (RotAC)": "98"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2006", "Rotavirus (RotAC)": "11"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2007", "Rotavirus (RotAC)": "18"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2008", "Rotavirus (RotAC)": "37"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2009", "Rotavirus (RotAC)": "56"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2010", "Rotavirus (RotAC)": "63"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2011", "Rotavirus (RotAC)": "69"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2012", "Rotavirus (RotAC)": "71"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2013", "Rotavirus (RotAC)": "72"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2014", "Rotavirus (RotAC)": "72"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2015", "Rotavirus (RotAC)": "76"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2016", "Rotavirus (RotAC)": "73"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2017", "Rotavirus (RotAC)": "71"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2018", "Rotavirus (RotAC)": "73"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2019", "Rotavirus (RotAC)": "74"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2020", "Rotavirus (RotAC)": "71"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2021", "Rotavirus (RotAC)": "70"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", 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"Rotavirus (RotAC)": "33"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2014", "Rotavirus (RotAC)": "91"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2015", "Rotavirus (RotAC)": "93"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2016", "Rotavirus (RotAC)": "94"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2017", "Rotavirus (RotAC)": "94"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2018", "Rotavirus (RotAC)": "93"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2019", "Rotavirus (RotAC)": "92"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2020", "Rotavirus (RotAC)": "92"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2021", "Rotavirus (RotAC)": "92"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2022", "Rotavirus (RotAC)": "92"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2023", "Rotavirus (RotAC)": "93"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2024", "Rotavirus (RotAC)": "94"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2008", "Rotavirus (RotAC)": 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"Code": "UZB", "Year": "2021", "Rotavirus (RotAC)": "52"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2022", "Rotavirus (RotAC)": "62"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2023", "Rotavirus (RotAC)": "89"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2024", "Rotavirus (RotAC)": "99"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2021", "Rotavirus (RotAC)": "29"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2022", "Rotavirus (RotAC)": "53"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2023", "Rotavirus (RotAC)": "73"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2024", "Rotavirus (RotAC)": "77"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2006", "Rotavirus (RotAC)": "13"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2007", "Rotavirus (RotAC)": "29"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2008", "Rotavirus (RotAC)": "50"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2009", "Rotavirus (RotAC)": "54"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2010", "Rotavirus (RotAC)": "49"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2011", "Rotavirus (RotAC)": "66"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2012", "Rotavirus (RotAC)": "76"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2013", "Rotavirus (RotAC)": "77"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2014", "Rotavirus (RotAC)": "76"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2015", "Rotavirus (RotAC)": "84"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2016", "Rotavirus (RotAC)": "47"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2017", "Rotavirus (RotAC)": "18"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2018", "Rotavirus (RotAC)": "0"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2019", "Rotavirus (RotAC)": "0"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2020", "Rotavirus (RotAC)": "0"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2021", "Rotavirus (RotAC)": "0"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2022", "Rotavirus (RotAC)": "0"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2023", "Rotavirus (RotAC)": "0"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2024", "Rotavirus (RotAC)": "0"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2006", "Rotavirus (RotAC)": "0"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2007", "Rotavirus (RotAC)": "0"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2008", "Rotavirus (RotAC)": "0"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2009", "Rotavirus (RotAC)": "0"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2010", "Rotavirus (RotAC)": "0"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2011", "Rotavirus (RotAC)": "0"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2012", "Rotavirus (RotAC)": "2"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2013", "Rotavirus (RotAC)": "4"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2014", "Rotavirus (RotAC)": "14"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2015", "Rotavirus (RotAC)": "21"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2016", "Rotavirus (RotAC)": "25"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2017", "Rotavirus (RotAC)": "27"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2018", "Rotavirus (RotAC)": "27"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2019", "Rotavirus (RotAC)": "28"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2020", "Rotavirus (RotAC)": "34"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2021", "Rotavirus (RotAC)": "39"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2022", "Rotavirus (RotAC)": "47"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2023", "Rotavirus (RotAC)": "57"}, {"Entity": "West and Central Africa (UNICEF)", "Code": "", "Year": "2024", "Rotavirus (RotAC)": "62"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2006", "Rotavirus (RotAC)": "0"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2007", "Rotavirus (RotAC)": "0"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2008", "Rotavirus (RotAC)": "1"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2009", "Rotavirus (RotAC)": "1"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2010", "Rotavirus (RotAC)": "1"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2011", "Rotavirus (RotAC)": "1"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2012", "Rotavirus (RotAC)": "1"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2013", "Rotavirus (RotAC)": "4"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2014", "Rotavirus (RotAC)": "1"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2015", "Rotavirus (RotAC)": "1"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2016", "Rotavirus (RotAC)": "1"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2017", "Rotavirus (RotAC)": "1"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2018", "Rotavirus (RotAC)": "2"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2019", "Rotavirus (RotAC)": "2"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2020", "Rotavirus (RotAC)": "2"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2021", "Rotavirus (RotAC)": "2"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2022", "Rotavirus (RotAC)": "4"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2023", "Rotavirus (RotAC)": "7"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2024", "Rotavirus (RotAC)": "8"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Rotavirus (RotAC)": "1"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Rotavirus (RotAC)": "2"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Rotavirus (RotAC)": "4"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Rotavirus (RotAC)": "6"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Rotavirus (RotAC)": "8"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Rotavirus (RotAC)": "9"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Rotavirus (RotAC)": "11"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Rotavirus (RotAC)": "14"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Rotavirus (RotAC)": "18"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Rotavirus (RotAC)": "22"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Rotavirus (RotAC)": "24"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Rotavirus (RotAC)": "27"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Rotavirus (RotAC)": "35"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Rotavirus (RotAC)": "40"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Rotavirus (RotAC)": "47"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Rotavirus (RotAC)": "49"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Rotavirus (RotAC)": "52"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Rotavirus (RotAC)": "56"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Rotavirus (RotAC)": "59"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Rotavirus (RotAC)": "23"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Rotavirus (RotAC)": "56"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Rotavirus (RotAC)": "55"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Rotavirus (RotAC)": "47"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Rotavirus (RotAC)": "52"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Rotavirus (RotAC)": "59"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Rotavirus (RotAC)": "53"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Rotavirus (RotAC)": "59"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Rotavirus (RotAC)": "57"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Rotavirus (RotAC)": "58"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2022", "Rotavirus (RotAC)": "61"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2023", "Rotavirus (RotAC)": "48"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2024", "Rotavirus (RotAC)": "42"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Rotavirus (RotAC)": "73"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Rotavirus (RotAC)": "82"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Rotavirus (RotAC)": "94"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Rotavirus (RotAC)": "96"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Rotavirus (RotAC)": "91"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Rotavirus (RotAC)": "90"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Rotavirus (RotAC)": "87"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Rotavirus (RotAC)": "87"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Rotavirus (RotAC)": "50"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Rotavirus (RotAC)": "58"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Rotavirus (RotAC)": "58"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Rotavirus (RotAC)": "48"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Rotavirus (RotAC)": "87"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Rotavirus (RotAC)": "91"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Rotavirus (RotAC)": "91"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Rotavirus (RotAC)": "90"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Rotavirus (RotAC)": "92"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Rotavirus (RotAC)": "88"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Rotavirus (RotAC)": "86"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Rotavirus (RotAC)": "55"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Rotavirus (RotAC)": "55"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Rotavirus (RotAC)": "85"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "share-of-one-year-olds-who-received-the-rotavirus-vaccine", "metadata_url": "https://ourworldindata.org/grapher/share-of-one-year-olds-who-received-the-rotavirus-vaccine.metadata.json", "chart_title": "Share of one-year-olds vaccinated against rotavirus", "chart_subtitle": "The share of one-year-old children who received the final recommended dose of rotavirus vaccine.", "chart_note": "Rotavirus is a virus that causes severe diarrhea, mostly in infants and young children. It can lead to dehydration and death if not treated.", "chart_citation": "WHO & UNICEF (2025); UN, World Population Prospects (2024)", "original_chart_url": "https://ourworldindata.org/grapher/share-of-one-year-olds-who-received-the-rotavirus-vaccine", "owid_column_metadata": {"Share of one-year-olds vaccinated against rotavirus": {"titleShort": "Share of one-year-olds vaccinated against rotavirus", "titleLong": "Share of one-year-olds vaccinated against rotavirus", "descriptionShort": "Share of one-year-olds who have had the final recommended dose (2nd or 3rd) of the rotavirus vaccine in a given year.", "descriptionKey": ["This chart shows official estimates of national immunization coverage published by the WHO and UNICEF. The estimates include all WHO member states, even those that did not report 2023 data. For non-reporting countries, WHO uses statistical methods to extrapolate from previously reported data, ensuring global coverage can be assessed.", "Global and regional vaccination coverage is calculated using population-weighted averages. In 2023, approximately 5% of countries did not report data, requiring extrapolation from their 2022 data to maintain complete global estimates.", "These estimates combine several sources: official administrative data from health facilities, coverage surveys that meet WHO quality standards, and other relevant information like vaccine supply issues or schedule changes. The accuracy of these estimates depends on how complete and reliable each country’s reporting systems are."], "shortUnit": "%", "unit": "%", "timespan": "2006-2024", "type": "Numeric", "owidVariableId": 1077446, "shortName": "coverage__antigen_rotac", "lastUpdated": "2025-07-15", "nextUpdate": "2026-07-15", "citationShort": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data. “Share of one-year-olds vaccinated against rotavirus” [dataset]. WHO & UNICEF, “WHO Immunization Data - Vaccination coverage”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077446.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Share of children vaccinated, by vaccine", "source_url": "https://ourworldindata.org/grapher/vaccination-coverage-who-unicef.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Measles, first dose (MCV1)", "Hepatitis B (HepB3)", "Diphtheria/tetanus/pertussis (DTP3)", "Inactivated polio (IPV1)", "Polio (Pol3)", "H. influenzae type b (Hib3)", "Rubella (RCV1)", "Pneumococcal conjugate (PCV3)", "Rotavirus (RotAC)"], "row_count_total": 9203, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Measles, first dose (MCV1)": "11", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "4", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Measles, first dose (MCV1)": "", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "3", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "3", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Measles, first dose (MCV1)": "8", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "5", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "5", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Measles, first dose (MCV1)": "9", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "5", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "5", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Measles, first dose (MCV1)": "14", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "16", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "16", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Measles, first dose (MCV1)": "14", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "15", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "15", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Measles, first dose (MCV1)": "14", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "11", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "11", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Measles, first 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"Measles, first dose (MCV1)": "20", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "25", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "25", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Measles, first dose (MCV1)": "19", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "23", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "23", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Measles, first dose (MCV1)": "22", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "21", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "21", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Measles, first dose (MCV1)": "25", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "18", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "18", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Measles, first dose (MCV1)": "40", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "12", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "8", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Measles, first dose (MCV1)": "41", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "20", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "20", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Measles, first dose (MCV1)": "42", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "31", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "31", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Measles, first dose (MCV1)": "38", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "28", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "28", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Measles, first dose (MCV1)": "31", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "27", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "28", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Measles, first dose (MCV1)": "31", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "27", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "27", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Measles, first dose (MCV1)": "27", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "24", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "24", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Measles, first dose (MCV1)": "37", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "33", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "35", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Measles, first dose (MCV1)": "35", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "36", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "36", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Measles, first dose (MCV1)": "39", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "41", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "41", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Measles, first dose (MCV1)": "48", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "50", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "50", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Measles, first dose (MCV1)": "50", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "58", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "58", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Measles, first dose (MCV1)": "53", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "58", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "58", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Measles, first dose (MCV1)": "55", "Hepatitis B (HepB3)": "63", "Diphtheria/tetanus/pertussis (DTP3)": "63", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "63", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Measles, first dose (MCV1)": "59", "Hepatitis B (HepB3)": "64", "Diphtheria/tetanus/pertussis (DTP3)": "64", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "64", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Measles, first dose (MCV1)": "60", "Hepatitis B (HepB3)": "63", "Diphtheria/tetanus/pertussis (DTP3)": "63", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "63", "H. influenzae type b (Hib3)": "63", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Measles, first dose (MCV1)": "62", "Hepatitis B (HepB3)": "66", "Diphtheria/tetanus/pertussis (DTP3)": "66", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "66", "H. influenzae type b (Hib3)": "66", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Measles, first dose (MCV1)": "64", "Hepatitis B (HepB3)": "68", "Diphtheria/tetanus/pertussis (DTP3)": "68", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "68", "H. influenzae type b (Hib3)": "68", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Measles, first dose (MCV1)": "59", "Hepatitis B (HepB3)": "67", "Diphtheria/tetanus/pertussis (DTP3)": "67", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "67", "H. influenzae type b (Hib3)": "67", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Measles, first dose (MCV1)": "57", "Hepatitis B (HepB3)": "64", "Diphtheria/tetanus/pertussis (DTP3)": "64", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "64", "H. influenzae type b (Hib3)": "64", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Measles, first dose (MCV1)": "60", "Hepatitis B (HepB3)": "62", "Diphtheria/tetanus/pertussis (DTP3)": "62", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "63", "H. influenzae type b (Hib3)": "62", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "49", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Measles, first dose (MCV1)": "62", "Hepatitis B (HepB3)": "64", "Diphtheria/tetanus/pertussis (DTP3)": "64", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "65", "H. influenzae type b (Hib3)": "64", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "64", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Measles, first dose (MCV1)": "64", "Hepatitis B (HepB3)": "66", "Diphtheria/tetanus/pertussis (DTP3)": "66", "Inactivated polio (IPV1)": "65", "Polio (Pol3)": "66", "H. influenzae type b (Hib3)": "66", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "62", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Measles, first dose (MCV1)": "64", "Hepatitis B (HepB3)": "64", "Diphtheria/tetanus/pertussis (DTP3)": "64", "Inactivated polio (IPV1)": "61", "Polio (Pol3)": "64", "H. influenzae type b (Hib3)": "64", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "63", "Rotavirus (RotAC)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Measles, first dose (MCV1)": "66", "Hepatitis B (HepB3)": "68", "Diphtheria/tetanus/pertussis (DTP3)": "68", "Inactivated polio (IPV1)": "66", "Polio (Pol3)": "67", "H. influenzae type b (Hib3)": "68", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "64", "Rotavirus (RotAC)": "46"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Measles, first dose (MCV1)": "57", "Hepatitis B (HepB3)": "65", "Diphtheria/tetanus/pertussis (DTP3)": "65", "Inactivated polio (IPV1)": "63", "Polio (Pol3)": "63", "H. influenzae type b (Hib3)": "65", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "61", "Rotavirus (RotAC)": "49"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Measles, first dose (MCV1)": "57", "Hepatitis B (HepB3)": "61", "Diphtheria/tetanus/pertussis (DTP3)": "61", "Inactivated polio (IPV1)": "56", "Polio (Pol3)": "61", "H. influenzae type b (Hib3)": "61", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "59", "Rotavirus (RotAC)": "53"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Measles, first dose (MCV1)": "51", "Hepatitis B (HepB3)": "55", "Diphtheria/tetanus/pertussis (DTP3)": "55", "Inactivated polio (IPV1)": "56", "Polio (Pol3)": "55", "H. influenzae type b (Hib3)": "55", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "53", "Rotavirus (RotAC)": "50"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Measles, first dose (MCV1)": "56", "Hepatitis B (HepB3)": "58", "Diphtheria/tetanus/pertussis (DTP3)": "58", "Inactivated polio (IPV1)": "61", "Polio (Pol3)": "60", "H. influenzae type b (Hib3)": "58", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "55", "Rotavirus (RotAC)": "53"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Measles, first dose (MCV1)": "55", "Hepatitis B (HepB3)": "60", "Diphtheria/tetanus/pertussis (DTP3)": "60", "Inactivated polio (IPV1)": "59", "Polio (Pol3)": "59", "H. influenzae type b (Hib3)": "60", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "58", "Rotavirus (RotAC)": "55"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2024", "Measles, first dose (MCV1)": "55", "Hepatitis B (HepB3)": "59", "Diphtheria/tetanus/pertussis (DTP3)": "59", "Inactivated polio (IPV1)": "59", "Polio (Pol3)": "59", "H. influenzae type b (Hib3)": "59", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "57", "Rotavirus (RotAC)": "56"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1980", "Measles, first dose (MCV1)": "8.788675", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "9.280128", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "10.659865", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1981", "Measles, first dose (MCV1)": "13.584435", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "14.024124", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "14.85983", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.0024084074", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1982", "Measles, first dose (MCV1)": "16.1695", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "15.810309", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "17.51523", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.005234502", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1983", "Measles, first dose (MCV1)": "22.506445", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "22.324041", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "23.841959", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.0078658825", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1984", "Measles, first dose (MCV1)": "27.358686", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "28.290375", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "28.822533", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.0060665742", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Measles, first dose (MCV1)": "37.62574", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "36.273804", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "36.026085", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.0069829165", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Measles, first dose (MCV1)": "43.977665", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "40.47414", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "41.26465", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.007285251", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Measles, first dose (MCV1)": "48.090973", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "46.71855", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "46.598835", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.007155202", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Measles, first dose (MCV1)": "53.309116", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "50.22169", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "50.10822", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.006547526", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Measles, first dose (MCV1)": "58.952354", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "55.134563", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "54.993004", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.0064208126", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Measles, first dose (MCV1)": "60.099186", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "59.96432", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "60.26462", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.006057423", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Measles, first dose (MCV1)": "57.623325", "Hepatitis B (HepB3)": "0.1474861", "Diphtheria/tetanus/pertussis (DTP3)": "52.78793", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "54.664852", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.0061144107", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Measles, first dose (MCV1)": "54.89886", "Hepatitis B (HepB3)": "0.41907984", "Diphtheria/tetanus/pertussis (DTP3)": "53.432983", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "54.051056", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.006174817", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Measles, first dose (MCV1)": "55.636944", "Hepatitis B (HepB3)": "0.5242127", "Diphtheria/tetanus/pertussis (DTP3)": "53.492268", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "53.637333", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.0059950105", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Measles, first dose (MCV1)": "58.456398", "Hepatitis B (HepB3)": "4.6038184", "Diphtheria/tetanus/pertussis (DTP3)": "57.247498", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "58.064297", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.005968022", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Measles, first dose (MCV1)": "58.0215", "Hepatitis B (HepB3)": "5.896559", "Diphtheria/tetanus/pertussis (DTP3)": "56.69431", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "57.036182", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.39410242", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1996", "Measles, first dose (MCV1)": "58.00786", "Hepatitis B (HepB3)": "6.760703", "Diphtheria/tetanus/pertussis (DTP3)": "54.87563", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "56.40393", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.43630525", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1997", "Measles, first dose (MCV1)": "57.545433", "Hepatitis B (HepB3)": "10.124232", "Diphtheria/tetanus/pertussis (DTP3)": "53.40453", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "56.899475", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "0.43574384", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1998", "Measles, first dose (MCV1)": "55.333866", "Hepatitis B (HepB3)": "10.557017", "Diphtheria/tetanus/pertussis (DTP3)": "52.293278", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "54.613285", "H. influenzae type b (Hib3)": "0.18968733", "Rubella (RCV1)": "0.429669", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1999", "Measles, first dose (MCV1)": "53.57708", "Hepatitis B (HepB3)": "11.379977", "Diphtheria/tetanus/pertussis (DTP3)": "53.57892", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "55.011646", "H. influenzae type b (Hib3)": "0.18304695", "Rubella (RCV1)": "6.8811803", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Measles, first dose (MCV1)": "54.72067", "Hepatitis B (HepB3)": "12.694634", "Diphtheria/tetanus/pertussis (DTP3)": "55.335575", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "56.550716", "H. influenzae type b (Hib3)": "2.7899144", "Rubella (RCV1)": "7.0053377", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Measles, first dose (MCV1)": "56.288155", "Hepatitis B (HepB3)": "14.137585", "Diphtheria/tetanus/pertussis (DTP3)": "56.13366", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "58.199753", "H. influenzae type b (Hib3)": "2.5885217", "Rubella (RCV1)": "7.032908", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Measles, first dose (MCV1)": "57.418213", "Hepatitis B (HepB3)": "29.480455", "Diphtheria/tetanus/pertussis (DTP3)": "57.02247", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "61.47698", "H. influenzae type b (Hib3)": "10.481474", "Rubella (RCV1)": "6.745995", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Measles, first dose (MCV1)": "59.886723", "Hepatitis B (HepB3)": "32.59442", "Diphtheria/tetanus/pertussis (DTP3)": "59.627296", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "63.543938", "H. influenzae type b (Hib3)": "11.454642", "Rubella (RCV1)": "6.8071265", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Measles, first dose (MCV1)": "62.025993", "Hepatitis B (HepB3)": "38.011642", "Diphtheria/tetanus/pertussis (DTP3)": "62.330452", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "64.98652", "H. influenzae type b (Hib3)": "14.202113", "Rubella (RCV1)": "8.536828", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Measles, first dose (MCV1)": "63.863575", "Hepatitis B (HepB3)": "44.71034", "Diphtheria/tetanus/pertussis (DTP3)": "65.32099", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "67.4109", "H. influenzae type b (Hib3)": "15.3655405", "Rubella (RCV1)": "8.508334", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Measles, first dose (MCV1)": "66.37255", "Hepatitis B (HepB3)": "49.655983", "Diphtheria/tetanus/pertussis (DTP3)": "67.22611", "Inactivated polio 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"Diphtheria/tetanus/pertussis (DTP3)": "47", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "47", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1992", "Measles, first dose (MCV1)": "46", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "50", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "50", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1993", "Measles, first dose (MCV1)": "51", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "54", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "54", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1994", "Measles, first dose (MCV1)": "31", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "33", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "33", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1995", "Measles, first dose (MCV1)": "40", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "44", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "44", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1996", "Measles, first dose (MCV1)": "43", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "47", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "47", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1997", "Measles, first dose (MCV1)": "41", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "40", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "40", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1998", "Measles, first dose (MCV1)": "66", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "68", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "68", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1999", "Measles, first dose (MCV1)": "74", "Hepatitis B (HepB3)": "9", "Diphtheria/tetanus/pertussis (DTP3)": "71", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "71", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Measles, first dose (MCV1)": "70", "Hepatitis B (HepB3)": "14", "Diphtheria/tetanus/pertussis (DTP3)": "74", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "74", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Measles, first dose (MCV1)": "72", "Hepatitis B (HepB3)": "19", "Diphtheria/tetanus/pertussis (DTP3)": "73", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "73", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Measles, first dose (MCV1)": "62", "Hepatitis B (HepB3)": "31", "Diphtheria/tetanus/pertussis (DTP3)": "65", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "64", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Measles, first dose (MCV1)": "64", "Hepatitis B 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"Hepatitis B (HepB3)": "78", "Diphtheria/tetanus/pertussis (DTP3)": "78", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "78", "H. influenzae type b (Hib3)": "73", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Measles, first dose (MCV1)": "71", "Hepatitis B (HepB3)": "79", "Diphtheria/tetanus/pertussis (DTP3)": "79", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "79", "H. influenzae type b (Hib3)": "75", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Measles, first dose (MCV1)": "69", "Hepatitis B (HepB3)": "78", "Diphtheria/tetanus/pertussis (DTP3)": "78", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "78", "H. influenzae type b (Hib3)": "75", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Measles, first dose (MCV1)": "65", "Hepatitis B (HepB3)": "76", "Diphtheria/tetanus/pertussis (DTP3)": "76", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "76", "H. influenzae type b (Hib3)": "74", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Measles, first dose (MCV1)": "68", "Hepatitis B (HepB3)": "76", "Diphtheria/tetanus/pertussis (DTP3)": "76", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "77", "H. influenzae type b (Hib3)": "75", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Measles, first dose (MCV1)": "66", "Hepatitis B (HepB3)": "69", "Diphtheria/tetanus/pertussis (DTP3)": "69", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "69", "H. influenzae type b (Hib3)": "69", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "41", "Rotavirus (RotAC)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Measles, first dose (MCV1)": "63", "Hepatitis B (HepB3)": "67", "Diphtheria/tetanus/pertussis (DTP3)": "67", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "68", "H. influenzae type b (Hib3)": "67", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "67", "Rotavirus (RotAC)": "23"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Measles, first dose (MCV1)": "68", "Hepatitis B (HepB3)": "71", "Diphtheria/tetanus/pertussis (DTP3)": "71", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "72", "H. influenzae type b (Hib3)": "71", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "71", "Rotavirus (RotAC)": "56"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Measles, first dose (MCV1)": "64", "Hepatitis B (HepB3)": "70", "Diphtheria/tetanus/pertussis (DTP3)": "70", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "70", "H. influenzae type b (Hib3)": "70", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "69", "Rotavirus (RotAC)": "55"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Measles, first dose (MCV1)": "61", "Hepatitis B (HepB3)": "63", "Diphtheria/tetanus/pertussis (DTP3)": "63", "Inactivated polio (IPV1)": "12", "Polio (Pol3)": "64", "H. influenzae type b (Hib3)": "63", "Rubella (RCV1)": "61", "Pneumococcal conjugate (PCV3)": "63", "Rotavirus (RotAC)": "47"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Measles, first dose (MCV1)": "62", "Hepatitis B (HepB3)": "63", "Diphtheria/tetanus/pertussis (DTP3)": "63", "Inactivated polio (IPV1)": "61", "Polio (Pol3)": "64", "H. influenzae type b (Hib3)": "63", "Rubella (RCV1)": "62", "Pneumococcal conjugate (PCV3)": "62", "Rotavirus (RotAC)": "52"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Measles, first dose (MCV1)": "56", "Hepatitis B (HepB3)": "59", "Diphtheria/tetanus/pertussis (DTP3)": "59", "Inactivated polio (IPV1)": "58", "Polio (Pol3)": "59", "H. influenzae type b (Hib3)": "59", "Rubella (RCV1)": "56", "Pneumococcal conjugate (PCV3)": "58", "Rotavirus (RotAC)": "59"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Measles, first dose (MCV1)": "53", "Hepatitis B (HepB3)": "54", "Diphtheria/tetanus/pertussis (DTP3)": "54", "Inactivated polio (IPV1)": "52", "Polio (Pol3)": "54", "H. influenzae type b (Hib3)": "54", "Rubella (RCV1)": "53", "Pneumococcal conjugate (PCV3)": "51", "Rotavirus (RotAC)": "53"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Measles, first dose (MCV1)": "54", "Hepatitis B (HepB3)": "60", "Diphtheria/tetanus/pertussis (DTP3)": "60", "Inactivated polio (IPV1)": "59", "Polio (Pol3)": "60", "H. influenzae type b (Hib3)": "60", "Rubella (RCV1)": "54", "Pneumococcal conjugate (PCV3)": "58", "Rotavirus (RotAC)": "59"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Measles, first dose (MCV1)": "53", "Hepatitis B (HepB3)": "57", "Diphtheria/tetanus/pertussis (DTP3)": "57", "Inactivated polio (IPV1)": "55", "Polio (Pol3)": "57", "H. influenzae type b (Hib3)": "57", "Rubella (RCV1)": "53", "Pneumococcal conjugate 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(RCV1)": "45", "Pneumococcal conjugate (PCV3)": "45", "Rotavirus (RotAC)": "48"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2024", "Measles, first dose (MCV1)": "41", "Hepatitis B (HepB3)": "42", "Diphtheria/tetanus/pertussis (DTP3)": "42", "Inactivated polio (IPV1)": "45", "Polio (Pol3)": "42", "H. influenzae type b (Hib3)": "42", "Rubella (RCV1)": "41", "Pneumococcal conjugate (PCV3)": "41", "Rotavirus (RotAC)": "42"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1983", "Measles, first dose (MCV1)": "56", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "49", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "46", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1984", "Measles, first dose (MCV1)": "57", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "59", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "50", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Measles, first dose (MCV1)": "58", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "66", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "61", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Measles, first dose (MCV1)": "58", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "66", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "74", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Measles, first dose (MCV1)": "80", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "83", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "81", "H. influenzae type 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type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Measles, first dose (MCV1)": "80", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "79", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "80", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Measles, first dose (MCV1)": "85", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "83", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "83", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Measles, first dose (MCV1)": "91", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "86", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "87", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Measles, first dose (MCV1)": "96", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "90", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "90", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Measles, first dose (MCV1)": "86", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "86", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "84", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Measles, first dose (MCV1)": "86", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "84", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "83", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Measles, first dose (MCV1)": "86", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "83", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "82", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Measles, first dose (MCV1)": "86", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "84", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "83", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Measles, first dose (MCV1)": "85", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "84", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "84", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Measles, first dose (MCV1)": "85", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "85", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "85", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Measles, first dose (MCV1)": "84", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "85", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "86", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Measles, first dose (MCV1)": "84", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "84", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "85", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Measles, first dose (MCV1)": "84", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "83", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "85", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Measles, first dose (MCV1)": "85", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "83", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "84", "H. influenzae type b (Hib3)": "83", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Measles, first dose (MCV1)": "85", "Hepatitis B (HepB3)": "82", "Diphtheria/tetanus/pertussis (DTP3)": "82", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "84", "H. influenzae type b (Hib3)": "82", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Measles, first dose (MCV1)": "85", "Hepatitis B (HepB3)": "81", "Diphtheria/tetanus/pertussis (DTP3)": "81", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "83", "H. influenzae type b (Hib3)": "81", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Measles, first dose (MCV1)": "93", "Hepatitis B (HepB3)": "80", "Diphtheria/tetanus/pertussis (DTP3)": "80", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "77", "H. influenzae type b (Hib3)": "80", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Measles, first dose (MCV1)": "87", "Hepatitis B (HepB3)": "87", "Diphtheria/tetanus/pertussis (DTP3)": "87", "Inactivated polio 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"Inactivated polio (IPV1)": "", "Polio (Pol3)": "83", "H. influenzae type b (Hib3)": "81", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Measles, first dose (MCV1)": "82", "Hepatitis B (HepB3)": "78", "Diphtheria/tetanus/pertussis (DTP3)": "78", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "70", "H. influenzae type b (Hib3)": "78", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Measles, first dose (MCV1)": "80", "Hepatitis B (HepB3)": "79", "Diphtheria/tetanus/pertussis (DTP3)": "79", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "74", "H. influenzae type b (Hib3)": "79", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Measles, first dose (MCV1)": "85", "Hepatitis B (HepB3)": "86", "Diphtheria/tetanus/pertussis (DTP3)": "86", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "78", "H. influenzae type b (Hib3)": "86", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "77", "Rotavirus (RotAC)": "73"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Measles, first dose (MCV1)": "90", "Hepatitis B (HepB3)": "90", "Diphtheria/tetanus/pertussis (DTP3)": "90", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "90", "H. influenzae type b (Hib3)": "90", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "81", "Rotavirus (RotAC)": "82"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Measles, first dose (MCV1)": "97", "Hepatitis B (HepB3)": "95", "Diphtheria/tetanus/pertussis (DTP3)": "95", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "91", "H. influenzae type b (Hib3)": "95", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "94", "Rotavirus (RotAC)": "94"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Measles, first dose (MCV1)": "96", "Hepatitis B (HepB3)": "94", "Diphtheria/tetanus/pertussis (DTP3)": "94", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "92", "H. influenzae type b (Hib3)": "94", "Rubella (RCV1)": "96", "Pneumococcal conjugate (PCV3)": "94", "Rotavirus (RotAC)": "96"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Measles, first dose (MCV1)": "94", "Hepatitis B (HepB3)": "90", "Diphtheria/tetanus/pertussis (DTP3)": "90", "Inactivated polio (IPV1)": "36", "Polio (Pol3)": "90", "H. influenzae type b (Hib3)": "90", "Rubella (RCV1)": "94", "Pneumococcal conjugate (PCV3)": "90", "Rotavirus (RotAC)": "91"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Measles, first dose (MCV1)": "93", "Hepatitis B (HepB3)": "88", "Diphtheria/tetanus/pertussis (DTP3)": "88", "Inactivated polio (IPV1)": "73", "Polio (Pol3)": "89", "H. influenzae type b (Hib3)": "88", "Rubella (RCV1)": "93", "Pneumococcal conjugate (PCV3)": "89", "Rotavirus (RotAC)": "90"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Measles, first dose (MCV1)": "96", "Hepatitis B (HepB3)": "84", "Diphtheria/tetanus/pertussis (DTP3)": "84", "Inactivated polio (IPV1)": "84", "Polio (Pol3)": "83", "H. influenzae type b (Hib3)": "84", "Rubella (RCV1)": "96", "Pneumococcal conjugate (PCV3)": "85", "Rotavirus (RotAC)": "87"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Measles, first dose (MCV1)": "90", "Hepatitis B (HepB3)": "91", "Diphtheria/tetanus/pertussis (DTP3)": "91", "Inactivated polio (IPV1)": "80", "Polio (Pol3)": "87", "H. influenzae type b (Hib3)": "91", "Rubella (RCV1)": "90", "Pneumococcal conjugate (PCV3)": "89", "Rotavirus (RotAC)": "87"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Measles, first dose (MCV1)": "88", "Hepatitis B (HepB3)": "92", "Diphtheria/tetanus/pertussis (DTP3)": "92", "Inactivated polio (IPV1)": "78", "Polio (Pol3)": "94", "H. influenzae type b (Hib3)": "92", "Rubella (RCV1)": "88", "Pneumococcal conjugate (PCV3)": "87", "Rotavirus (RotAC)": "50"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Measles, first dose (MCV1)": "83", "Hepatitis B (HepB3)": "90", "Diphtheria/tetanus/pertussis (DTP3)": "90", "Inactivated polio (IPV1)": "77", "Polio (Pol3)": "91", "H. influenzae type b (Hib3)": "90", "Rubella (RCV1)": "83", "Pneumococcal conjugate (PCV3)": "87", "Rotavirus (RotAC)": "58"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Measles, first dose (MCV1)": "88", "Hepatitis B (HepB3)": "91", "Diphtheria/tetanus/pertussis (DTP3)": "91", "Inactivated polio (IPV1)": "87", "Polio (Pol3)": "90", "H. influenzae type b (Hib3)": "91", "Rubella (RCV1)": "88", "Pneumococcal conjugate (PCV3)": "91", "Rotavirus (RotAC)": "58"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Measles, first dose (MCV1)": "56", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "39", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "38", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Measles, first dose (MCV1)": "58", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "46", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "46", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Measles, first dose (MCV1)": "60", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "53", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "53", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Measles, first dose (MCV1)": "62", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "60", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "61", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Measles, first dose (MCV1)": "78", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "63", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "63", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Measles, first dose (MCV1)": "83", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "75", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "82", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Measles, first dose (MCV1)": "88", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "84", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "86", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Measles, first dose (MCV1)": "88", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "85", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "87", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Measles, first dose (MCV1)": "87", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "87", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "88", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Measles, first dose (MCV1)": "87", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "88", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "89", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Measles, first dose (MCV1)": "87", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "87", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "88", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Measles, first dose (MCV1)": "86", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "86", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "86", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Measles, first dose (MCV1)": "86", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "85", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "85", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Measles, first dose (MCV1)": "87", "Hepatitis B (HepB3)": "", "Diphtheria/tetanus/pertussis (DTP3)": "87", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "87", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Measles, first dose (MCV1)": "87", "Hepatitis B (HepB3)": "1", "Diphtheria/tetanus/pertussis (DTP3)": "88", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "88", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Measles, first dose (MCV1)": "88", "Hepatitis B (HepB3)": "9", "Diphtheria/tetanus/pertussis (DTP3)": "90", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "90", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Measles, first dose (MCV1)": "84", "Hepatitis B (HepB3)": "16", "Diphtheria/tetanus/pertussis (DTP3)": "86", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "86", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Measles, first dose (MCV1)": "79", "Hepatitis B (HepB3)": "24", "Diphtheria/tetanus/pertussis (DTP3)": "84", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "84", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Measles, first dose (MCV1)": "77", "Hepatitis B (HepB3)": "31", "Diphtheria/tetanus/pertussis (DTP3)": "81", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "81", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Measles, first dose (MCV1)": "75", "Hepatitis B (HepB3)": "79", "Diphtheria/tetanus/pertussis (DTP3)": "78", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "78", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Measles, first dose (MCV1)": "73", "Hepatitis B (HepB3)": "76", "Diphtheria/tetanus/pertussis (DTP3)": "75", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "76", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Measles, first dose (MCV1)": "70", "Hepatitis B (HepB3)": "73", "Diphtheria/tetanus/pertussis (DTP3)": "71", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "73", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Measles, first dose (MCV1)": "68", "Hepatitis B (HepB3)": "70", "Diphtheria/tetanus/pertussis (DTP3)": "68", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "70", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Measles, first dose (MCV1)": "66", "Hepatitis B (HepB3)": "68", "Diphtheria/tetanus/pertussis (DTP3)": "65", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "67", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Measles, first dose (MCV1)": "67", "Hepatitis B (HepB3)": "65", "Diphtheria/tetanus/pertussis (DTP3)": "68", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "69", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Measles, first dose (MCV1)": "68", "Hepatitis B (HepB3)": "68", "Diphtheria/tetanus/pertussis (DTP3)": "70", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "71", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Measles, first dose (MCV1)": "69", "Hepatitis B (HepB3)": "72", "Diphtheria/tetanus/pertussis (DTP3)": "73", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "73", "H. influenzae type b (Hib3)": "", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Measles, first dose (MCV1)": "70", "Hepatitis B (HepB3)": "75", "Diphtheria/tetanus/pertussis (DTP3)": "75", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "75", "H. influenzae type b (Hib3)": "75", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Measles, first dose (MCV1)": "76", "Hepatitis B (HepB3)": "73", "Diphtheria/tetanus/pertussis (DTP3)": "73", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "69", "H. influenzae type b (Hib3)": "73", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Measles, first dose (MCV1)": "90", "Hepatitis B (HepB3)": "90", "Diphtheria/tetanus/pertussis (DTP3)": "89", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "89", "H. influenzae type b (Hib3)": "90", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Measles, first dose (MCV1)": "92", "Hepatitis B (HepB3)": "94", "Diphtheria/tetanus/pertussis (DTP3)": "93", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "93", "H. influenzae type b (Hib3)": "94", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Measles, first dose (MCV1)": "97", "Hepatitis B (HepB3)": "97", "Diphtheria/tetanus/pertussis (DTP3)": "95", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "95", "H. influenzae type b (Hib3)": "97", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "21", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Measles, first dose (MCV1)": "93", "Hepatitis B (HepB3)": "95", "Diphtheria/tetanus/pertussis (DTP3)": "95", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "95", "H. influenzae type b (Hib3)": "95", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "95", "Rotavirus (RotAC)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Measles, first dose (MCV1)": "92", "Hepatitis B (HepB3)": "91", "Diphtheria/tetanus/pertussis (DTP3)": "91", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "92", "H. influenzae type b (Hib3)": "91", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "91", "Rotavirus (RotAC)": "48"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Measles, first dose (MCV1)": "86", "Hepatitis B (HepB3)": "87", "Diphtheria/tetanus/pertussis (DTP3)": "87", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "88", "H. influenzae type b (Hib3)": "87", "Rubella (RCV1)": "", "Pneumococcal conjugate (PCV3)": "87", "Rotavirus (RotAC)": "87"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Measles, first dose (MCV1)": "95", "Hepatitis B (HepB3)": "90", "Diphtheria/tetanus/pertussis (DTP3)": "90", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "90", "H. influenzae type b (Hib3)": "90", "Rubella (RCV1)": "95", "Pneumococcal conjugate (PCV3)": "90", "Rotavirus (RotAC)": "91"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Measles, first dose (MCV1)": "90", "Hepatitis B (HepB3)": "89", "Diphtheria/tetanus/pertussis (DTP3)": "89", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "89", "H. influenzae type b (Hib3)": "89", "Rubella (RCV1)": "90", "Pneumococcal conjugate (PCV3)": "89", "Rotavirus (RotAC)": "91"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Measles, first dose (MCV1)": "88", "Hepatitis B (HepB3)": "89", "Diphtheria/tetanus/pertussis (DTP3)": "89", "Inactivated polio (IPV1)": "", "Polio (Pol3)": "89", "H. influenzae type b (Hib3)": "89", "Rubella (RCV1)": "88", "Pneumococcal conjugate (PCV3)": "89", "Rotavirus (RotAC)": "90"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Measles, first dose (MCV1)": "85", "Hepatitis B (HepB3)": "90", "Diphtheria/tetanus/pertussis (DTP3)": "90", "Inactivated polio (IPV1)": "65", "Polio (Pol3)": "90", "H. influenzae type b (Hib3)": "90", "Rubella (RCV1)": "85", "Pneumococcal conjugate (PCV3)": "90", "Rotavirus (RotAC)": "92"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Measles, first dose (MCV1)": "85", "Hepatitis B (HepB3)": "86", "Diphtheria/tetanus/pertussis (DTP3)": "86", "Inactivated polio (IPV1)": "86", "Polio (Pol3)": "86", "H. influenzae type b (Hib3)": "86", "Rubella (RCV1)": "85", "Pneumococcal conjugate (PCV3)": "86", "Rotavirus (RotAC)": "88"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Measles, first dose (MCV1)": "88", "Hepatitis B (HepB3)": "88", "Diphtheria/tetanus/pertussis (DTP3)": "88", "Inactivated polio (IPV1)": "88", "Polio (Pol3)": "88", "H. influenzae type b (Hib3)": "88", "Rubella (RCV1)": "88", "Pneumococcal conjugate (PCV3)": "88", "Rotavirus (RotAC)": "86"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Measles, first dose (MCV1)": "90", "Hepatitis B (HepB3)": "90", "Diphtheria/tetanus/pertussis (DTP3)": "90", "Inactivated polio (IPV1)": "90", "Polio (Pol3)": "90", "H. influenzae type b (Hib3)": "90", "Rubella (RCV1)": "90", "Pneumococcal conjugate (PCV3)": "90", "Rotavirus (RotAC)": "55"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Measles, first dose (MCV1)": "90", "Hepatitis B (HepB3)": "90", "Diphtheria/tetanus/pertussis (DTP3)": "90", "Inactivated polio (IPV1)": "90", "Polio (Pol3)": "90", "H. influenzae type b (Hib3)": "90", "Rubella (RCV1)": "90", "Pneumococcal conjugate (PCV3)": "90", "Rotavirus (RotAC)": "55"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Measles, first dose (MCV1)": "90", "Hepatitis B (HepB3)": "91", "Diphtheria/tetanus/pertussis (DTP3)": "91", "Inactivated polio (IPV1)": "88", "Polio (Pol3)": "91", "H. influenzae type b (Hib3)": "91", "Rubella (RCV1)": "90", "Pneumococcal conjugate (PCV3)": "91", "Rotavirus (RotAC)": "85"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "vaccination-coverage-who-unicef", "metadata_url": "https://ourworldindata.org/grapher/vaccination-coverage-who-unicef.metadata.json", "chart_title": "Share of children vaccinated, by vaccine", "chart_subtitle": "Share of one-year-olds who have been vaccinated against a disease or a pathogen.", "chart_note": "This includes diphtheria, pertussis and tetanus (3rd dose), measles (1st dose), hepatitis B (3rd dose), polio (3rd dose), Haemophilus influenzae b (3rd dose), rubella (1st dose), rotavirus (final dose), pneumococcal conjugate (3rd dose), and inactivated polio (first dose).", "chart_citation": "WHO & UNICEF (2025); UN, World Population Prospects (2024)", "original_chart_url": "https://ourworldindata.org/grapher/vaccination-coverage-who-unicef", "owid_column_metadata": {"Share of one-year-olds who have had one dose of the measles vaccine": {"titleShort": "Share of one-year-olds who have had one dose of the measles vaccine", "titleLong": "Share of one-year-olds who have had one dose of the measles vaccine", "descriptionShort": "Share of one-year-olds who have had the first dose of the measles vaccine in a given year.", "descriptionKey": ["Measles is one of the most contagious diseases. The first dose of measles vaccine is critical for building immunity. In countries where the national schedule recommends the first dose at 12 months or later based on local epidemiology, these estimates reflect the percentage of children who received their first dose as recommended.", "This chart shows official estimates of national immunization coverage published by the WHO and UNICEF. The estimates include all WHO member states, even those that did not report 2023 data. For non-reporting countries, WHO uses statistical methods to extrapolate from previously reported data, ensuring global coverage can be assessed.", "Global and regional vaccination coverage is calculated using population-weighted averages. In 2023, approximately 5% of countries did not report data, requiring extrapolation from their 2022 data to maintain complete global estimates.", "These estimates combine several sources: official administrative data from health facilities, coverage surveys that meet WHO quality standards, and other relevant information like vaccine supply issues or schedule changes. The accuracy of these estimates depends on how complete and reliable each country’s reporting systems are."], "shortUnit": "%", "unit": "%", "timespan": "1980-2024", "type": "Numeric", "owidVariableId": 1077440, "shortName": "coverage__antigen_mcv1", "lastUpdated": "2025-07-15", "nextUpdate": "2026-07-15", "citationShort": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data. “Share of one-year-olds who have had one dose of the measles vaccine” [dataset]. WHO & UNICEF, “WHO Immunization Data - Vaccination coverage”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077440.metadata.json"}, "Share of one-year-olds who have had three doses of the hepatitis B vaccine": {"titleShort": "Share of one-year-olds who have had three doses of the hepatitis B vaccine", "titleLong": "Share of one-year-olds who have had three doses of the hepatitis B vaccine", "descriptionShort": "Share of one-year-olds who have had three doses of the hepatitis B vaccine in a given year.", "descriptionKey": ["This chart shows official estimates of national immunization coverage published by the WHO and UNICEF. The estimates include all WHO member states, even those that did not report 2023 data. For non-reporting countries, WHO uses statistical methods to extrapolate from previously reported data, ensuring global coverage can be assessed.", "Global and regional vaccination coverage is calculated using population-weighted averages. In 2023, approximately 5% of countries did not report data, requiring extrapolation from their 2022 data to maintain complete global estimates.", "These estimates combine several sources: official administrative data from health facilities, coverage surveys that meet WHO quality standards, and other relevant information like vaccine supply issues or schedule changes. The accuracy of these estimates depends on how complete and reliable each country’s reporting systems are."], "shortUnit": "%", "unit": "%", "timespan": "1985-2024", "type": "Numeric", "owidVariableId": 1077435, "shortName": "coverage__antigen_hepb3", "lastUpdated": "2025-07-15", "nextUpdate": "2026-07-15", "citationShort": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data. “Share of one-year-olds who have had three doses of the hepatitis B vaccine” [dataset]. WHO & UNICEF, “WHO Immunization Data - Vaccination coverage”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077435.metadata.json"}, "Share of one-year-olds who have had three doses of the diphtheria, tetanus and pertussis vaccine": {"titleShort": "Share of one-year-olds who have had three doses of the diphtheria, tetanus and pertussis vaccine", "titleLong": "Share of one-year-olds who have had three doses of the diphtheria, tetanus and pertussis vaccine", "descriptionShort": "Share of one-year-olds who have had three doses of the combined diphtheria, tetanus and pertussis vaccine in a given year.", "descriptionKey": ["This chart shows official estimates of national immunization coverage published by the WHO and UNICEF. The estimates include all WHO member states, even those that did not report 2023 data. For non-reporting countries, WHO uses statistical methods to extrapolate from previously reported data, ensuring global coverage can be assessed.", "Global and regional vaccination coverage is calculated using population-weighted averages. In 2023, approximately 5% of countries did not report data, requiring extrapolation from their 2022 data to maintain complete global estimates.", "These estimates combine several sources: official administrative data from health facilities, coverage surveys that meet WHO quality standards, and other relevant information like vaccine supply issues or schedule changes. The accuracy of these estimates depends on how complete and reliable each country’s reporting systems are."], "shortUnit": "%", "unit": "%", "timespan": "1980-2024", "type": "Numeric", "owidVariableId": 1077436, "shortName": "coverage__antigen_dtpcv3", "lastUpdated": "2025-07-15", "nextUpdate": "2026-07-15", "citationShort": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data. “Share of one-year-olds who have had three doses of the diphtheria, tetanus and pertussis vaccine” [dataset]. WHO & UNICEF, “WHO Immunization Data - Vaccination coverage”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077436.metadata.json"}, "Share of one-year-olds who have had the one dose of the inactivated polio vaccine": {"titleShort": "Share of one-year-olds who have had the one dose of the inactivated polio vaccine", "titleLong": "Share of one-year-olds who have had the one dose of the inactivated polio vaccine", "descriptionShort": "Share of one-year-olds who have had the first dose of the inactivated polio vaccine in a given year.", "descriptionKey": ["In countries where both types of polio vaccine are used — IPV (inactivated polio vaccine, given as an injection) and OPV (oral polio vaccine, given as drops) — WHO and UNICEF count any baby under 1 year old who received at least one routine dose of IPV in their estimate of “first dose” coverage (meaning the first dose of IPV). In countries that only use IPV, “first dose” simply refers to the first IPV dose given.", "This chart shows official estimates of national immunization coverage published by the WHO and UNICEF. The estimates include all WHO member states, even those that did not report 2023 data. For non-reporting countries, WHO uses statistical methods to extrapolate from previously reported data, ensuring global coverage can be assessed.", "Global and regional vaccination coverage is calculated using population-weighted averages. In 2023, approximately 5% of countries did not report data, requiring extrapolation from their 2022 data to maintain complete global estimates.", "These estimates combine several sources: official administrative data from health facilities, coverage surveys that meet WHO quality standards, and other relevant information like vaccine supply issues or schedule changes. The accuracy of these estimates depends on how complete and reliable each country’s reporting systems are."], "shortUnit": "%", "unit": "%", "timespan": "2015-2024", "type": "Numeric", "owidVariableId": 1077439, "shortName": "coverage__antigen_ipv1", "lastUpdated": "2025-07-15", "nextUpdate": "2026-07-15", "citationShort": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data. “Share of one-year-olds who have had the one dose of the inactivated polio vaccine” [dataset]. WHO & UNICEF, “WHO Immunization Data - Vaccination coverage”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077439.metadata.json"}, "Share of one-year-olds who have had three doses of the polio vaccine": {"titleShort": "Share of one-year-olds who have had three doses of the polio vaccine", "titleLong": "Share of one-year-olds who have had three doses of the polio vaccine", "descriptionShort": "Share of one-year-olds who have had the third dose of either the oral or inactivated polio vaccine in a given year.", "descriptionKey": ["This chart shows official estimates of national immunization coverage published by the WHO and UNICEF. The estimates include all WHO member states, even those that did not report 2023 data. For non-reporting countries, WHO uses statistical methods to extrapolate from previously reported data, ensuring global coverage can be assessed.", "Global and regional vaccination coverage is calculated using population-weighted averages. In 2023, approximately 5% of countries did not report data, requiring extrapolation from their 2022 data to maintain complete global estimates.", "These estimates combine several sources: official administrative data from health facilities, coverage surveys that meet WHO quality standards, and other relevant information like vaccine supply issues or schedule changes. The accuracy of these estimates depends on how complete and reliable each country’s reporting systems are."], "shortUnit": "%", "unit": "%", "timespan": "1980-2024", "type": "Numeric", "owidVariableId": 1077444, "shortName": "coverage__antigen_pol3", "lastUpdated": "2025-07-15", "nextUpdate": "2026-07-15", "citationShort": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data. “Share of one-year-olds who have had three doses of the polio vaccine” [dataset]. WHO & UNICEF, “WHO Immunization Data - Vaccination coverage”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077444.metadata.json"}, "Share of one-year-olds vaccinated against Haemophilus influenzae type b": {"titleShort": "Share of one-year-olds vaccinated against Haemophilus influenzae type b", "titleLong": "Share of one-year-olds vaccinated against Haemophilus influenzae type b", "descriptionShort": "Share of one-year-olds who have had three doses of Haemophilus influenzae type b vaccine in a given year.", "descriptionKey": ["This chart shows official estimates of national immunization coverage published by the WHO and UNICEF. The estimates include all WHO member states, even those that did not report 2023 data. For non-reporting countries, WHO uses statistical methods to extrapolate from previously reported data, ensuring global coverage can be assessed.", "Global and regional vaccination coverage is calculated using population-weighted averages. In 2023, approximately 5% of countries did not report data, requiring extrapolation from their 2022 data to maintain complete global estimates.", "These estimates combine several sources: official administrative data from health facilities, coverage surveys that meet WHO quality standards, and other relevant information like vaccine supply issues or schedule changes. The accuracy of these estimates depends on how complete and reliable each country’s reporting systems are."], "shortUnit": "%", "unit": "%", "timespan": "1990-2024", "type": "Numeric", "owidVariableId": 1077437, "shortName": "coverage__antigen_hib3", "lastUpdated": "2025-07-15", "nextUpdate": "2026-07-15", "citationShort": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data. “Share of one-year-olds vaccinated against Haemophilus influenzae type b” [dataset]. WHO & UNICEF, “WHO Immunization Data - Vaccination coverage”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077437.metadata.json"}, "Share of one-year-olds vaccinated against rubella": {"titleShort": "Share of one-year-olds vaccinated against rubella", "titleLong": "Share of one-year-olds vaccinated against rubella", "descriptionShort": "Share of one-year-olds who have had one dose of rubella vaccine in a given year.", "descriptionKey": ["Rubella coverage estimates are based on WHO and UNICEF data for the first dose of the measles-rubella vaccine, as the WHO recommends this combined vaccine.", "This chart shows official estimates of national immunization coverage published by the WHO and UNICEF. The estimates include all WHO member states, even those that did not report 2023 data. For non-reporting countries, WHO uses statistical methods to extrapolate from previously reported data, ensuring global coverage can be assessed.", "Global and regional vaccination coverage is calculated using population-weighted averages. In 2023, approximately 5% of countries did not report data, requiring extrapolation from their 2022 data to maintain complete global estimates.", "These estimates combine several sources: official administrative data from health facilities, coverage surveys that meet WHO quality standards, and other relevant information like vaccine supply issues or schedule changes. The accuracy of these estimates depends on how complete and reliable each country’s reporting systems are."], "shortUnit": "%", "unit": "%", "timespan": "1980-2024", "type": "Numeric", "owidVariableId": 1077445, "shortName": "coverage__antigen_rcv1", "lastUpdated": "2025-07-15", "nextUpdate": "2026-07-15", "citationShort": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data. “Share of one-year-olds vaccinated against rubella” [dataset]. WHO & UNICEF, “WHO Immunization Data - Vaccination coverage”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077445.metadata.json"}, "Share of one-year-olds who have had the third dose of the pneumococcal conjugate vaccine": {"titleShort": "Share of one-year-olds who have had the third dose of the pneumococcal conjugate vaccine", "titleLong": "Share of one-year-olds who have had the third dose of the pneumococcal conjugate vaccine", "descriptionShort": "Share of one-year-olds who have had the third dose of the pneumococcal conjugate vaccine in a given year.", "descriptionKey": ["In some countries the vaccine schedule may recommend two doses in before the age of one and a booster at a later date. These later vaccine schedules are also counted under this variable.", "This chart shows official estimates of national immunization coverage published by the WHO and UNICEF. The estimates include all WHO member states, even those that did not report 2023 data. For non-reporting countries, WHO uses statistical methods to extrapolate from previously reported data, ensuring global coverage can be assessed.", "Global and regional vaccination coverage is calculated using population-weighted averages. In 2023, approximately 5% of countries did not report data, requiring extrapolation from their 2022 data to maintain complete global estimates.", "These estimates combine several sources: official administrative data from health facilities, coverage surveys that meet WHO quality standards, and other relevant information like vaccine supply issues or schedule changes. The accuracy of these estimates depends on how complete and reliable each country’s reporting systems are."], "shortUnit": "%", "unit": "%", "timespan": "2008-2024", "type": "Numeric", "owidVariableId": 1077443, "shortName": "coverage__antigen_pcv3", "lastUpdated": "2025-07-15", "nextUpdate": "2026-07-15", "citationShort": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data. “Share of one-year-olds who have had the third dose of the pneumococcal conjugate vaccine” [dataset]. WHO & UNICEF, “WHO Immunization Data - Vaccination coverage”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077443.metadata.json"}, "Share of one-year-olds vaccinated against rotavirus": {"titleShort": "Share of one-year-olds vaccinated against rotavirus", "titleLong": "Share of one-year-olds vaccinated against rotavirus", "descriptionShort": "Share of one-year-olds who have had the final recommended dose (2nd or 3rd) of the rotavirus vaccine in a given year.", "descriptionKey": ["This chart shows official estimates of national immunization coverage published by the WHO and UNICEF. The estimates include all WHO member states, even those that did not report 2023 data. For non-reporting countries, WHO uses statistical methods to extrapolate from previously reported data, ensuring global coverage can be assessed.", "Global and regional vaccination coverage is calculated using population-weighted averages. In 2023, approximately 5% of countries did not report data, requiring extrapolation from their 2022 data to maintain complete global estimates.", "These estimates combine several sources: official administrative data from health facilities, coverage surveys that meet WHO quality standards, and other relevant information like vaccine supply issues or schedule changes. 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WHO & UNICEF, “WHO Immunization Data - Vaccination coverage”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077446.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "5a72985c8b6763b1f4ed"},
{"raw_link": "https://ourworldindata.org/south-koreas-population-is-set-to-shrink-what-would-it-take-to-stop-the-decline", "title": "South Korea’s population is set to shrink: what would it take to stop the decline?", "context": "Home\nPopulation Growth\nSouth Korea’s population is set to shrink: what would it take to stop the decline?\nHow much would fertility rates, life expectancy, or migration rates need to change to stop the population from shrinking?\nBy\nHannah Ritchie\n(writing)\n,\nSophia Mersmann\n(data visualization)\n,\nand\nDaniel Bachler\n(concept & modeling)\nMay 18, 2026\nBrowse past versions\nCite this article\nReuse our work freely\nSouth Korea’s population is expected to decline substantially in the coming decades. According to the United Nations’s projections, shown in the chart below, the country’s population will more than halve.\nIn 2026, there are around 52 million people in South Korea. By 2100, the UN projects there will be just 22 million.\nSouth Korea’s population would not only be less than half its peak size, but also much older. Below, you can see what the population pyramid would look like in 2100. The top — for people in their 70s, 80s, and 90s — would be very “heavy” compared to the bottom half.\nThere would be almost as many people of retirement age as those in the working-age population.\nThese projections rely on assumptions about how the drivers of population change — births, deaths, and migration — will change in the coming decades.\nBelow, we’ve plotted the UN’s assumptions for the three key metrics in this population model: fertility rate, life expectancy, and net migration.\nThe UN assumes that the fertility rate — the average number of children per woman — will rebound from its current low point of 0.7 births per woman to 1 by the middle of the century and to 1.3 by 2100.\nFor life expectancy, the UN projects an increase to 91 years by the end of the century.\nAnd for net migration, the UN projects a decline from 1.7% annually today to almost zero by 2050 and thereafter.\nNo one knows with certainty how many children people will have in 2080, or what the migration rate will be in 2060. So it’s worth asking what the population will look like if things turn out differently from what the UN assumes.\nMy colleagues Daniel Bachler and Sophia Mersmann have built a population simulation tool that helps to answer this — not just in South Korea, but in any country.\nThis tool is deliberately simple: small enough to run in a browser and nevertheless capable of modeling whole populations based on the key demographic inputs. It is far less detailed than the models expert demographers use, and our aim is not to replace them. We find this tool useful for developing an understanding of the difference that changes in demographic variables can make to the size and structure of future populations.\nIf you want to dig deeper into the methodology, here’s the\ntechnical documentation\non how this population model works.\nYou can explore the model for any country in the world at the very end of this page.\nI’ve used the model to explore a simple question: what would need to happen for South Korea’s population to remain roughly constant?\nHow much would fertility rates, life expectancy, or migration rates need to change?\nI will investigate the levers one by one, so in each scenario, I’ll keep the UN’s assumptions for the other two metrics as they are.\nBy how much would the fertility rate need to rise to keep the population from shrinking?\nOne way to boost the population is for people to have more children.\nSouth Korea has seen a particularly dramatic decline in fertility rates over the last 60 years. In 1960, the average woman had six children. Since then, it has declined to just 0.75 children.\nBy how much would the fertility rate need to increase for South Korea to keep its population constant over this century?\nOur population model shows one possible path: if the rate were to increase to 2.1 children per woman by 2050 and remain at that level, the country’s population would stay close to current levels.\nIn other words, South Korea would need to see a substantial “baby boom” in the next few decades. You can adjust the path with the controls in the visualization. As you’ll see, a smaller increase in fertility rates would slow the decline, but it wouldn’t stop it.\nBy how much would life expectancy need to increase to keep the population from shrinking?\nAnother driver of population growth is increasing lifespans.\nBy how much would life expectancy need to increase in a future in which the UN’s expectation for the future of fertility rates and migration rates becomes true? The visualization below shows the answer: by about 50 years.\nOur population model shows that if average life expectancy increases to 130 years by 2050, South Korea could keep its population constant for several more decades.\n1\nBut ultimately, low fertility rates will lead to a decline, even if life expectancy increases substantially.\nReaching 130 years of life expectancy within the next few decades would require a dramatic acceleration in the rate of improvement.\nIn recent decades, the countries with the\nhighest life expectancy\nhave taken four years to increase average life expectancy by one year. At those rates, it would take 188 years to increase life expectancy from 83 to 130 years. South Korea would need to do it in 25.\nRelying on increased life expectancy to stabilize population size also raises questions about whether it’s the size of the population that matters, or its age structure.\nUnder that scenario, by the end of the century, South Korea’s median age would be 82 years. If the retirement age remained at 65, there would be more than two pensioners for every working person.\nUnless medical innovations made it feasible for people to work well into their 80s, 90s, or 100s, the working-age population would be greatly outnumbered by retirees.\nBy how much would immigration need to increase to keep the population from shrinking?\nSouth Korea’s final option is to look elsewhere. Rather than relying on population increase from births or longer lives, it could make it easier for people to move to the country.\nCurrently, around 1.3 people move in per 1,000 residents each year. That’s net migration, which is the balance of people entering and leaving.\nHow much would net migration rates need to increase if South Korea wanted to maintain its current population size?\nThe projection shows that it would require a net migration rate of about 9‰ — 9 immigrants per 1,000 people annually (the “‰” symbol means “per 1,000”). This is about seven times higher than the current rate.\nHow realistic are these migration rates?\nMost countries have far lower rates. Net migration in the United States and the United Kingdom\nhas fluctuated\nbetween 3 and 6 per 1,000 people over the last 30 years. Many other high-income countries have\nsimilar rates\n.\nNet migration rates above 8‰ are much less common, but not unheard of. As you can see in the chart below, Canada has maintained rates between 6 and 10 per 1,000 for most of the last 20 years. In Germany, rates have reached those levels over the last decade.\nWhat would be rare is maintaining such high immigration rates for more than 70 years. And given that most other countries are also shrinking, it would be increasingly difficult to attract such high numbers of immigrants.\nWhat did I learn about South Korea’s prospects of avoiding a shrinking population from this tool?\nThe gains in life expectancy or migration would be extremely large; the scale of these changes seems implausible. The most realistic of the three scenarios is an increase in fertility rates; they would need to increase to 2.3 children per woman within a few decades and stay there. Those rates are not unprecedented by any means — in fact, they’re well below the country’s historical average — but such a strong reversal would be unique following the huge decline in fertility rates across the world over the last half-century.\nI think it’s very unlikely that South Korea can maintain its current population levels over the course of this century.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nExplore your own projections\nIn this article, I’ve explored some very specific scenarios for South Korea.\nBut Sophia and Daniel’s tool — which you can see below — lets you see what would happen to population projections and the age pyramid under very different assumptions. You can adjust future fertility rates, life expectancy, and migration rates for South Korea, or any other country you are interested in.\nMethodology\nThis tool is built on a simple population model; we describe the technical details underpinning it and provide a walkthrough explainer in our technical documentation.\nPopulation tool: How will populations across the world change in the 21st century?\nWe created an interactive tool that lets you test how changes in fertility rates, life expectancy, and migration rates will change future populations.\nPopulation and Demography Data Explorer\nExplore historical population data and the UN’s other projection scenarios (low and high), and the demographic assumptions that underpin them for any country.\nAcknowledgments\nMany thanks to Mallika Snyder for her valuable feedback and suggestions on this tool. We’d also like to thank Max Roser and Edouard Mathieu for comments and editorial feedback, and Marwa Boukarim for input on design and visualization.\nEndnotes\nIt’s worth noting what this would mean in practice. Using these assumptions, almost no one in this population model would die in most years in South Korea. That seems implausible, but it actually points to the fact that the gains in health and life expectancy would need to be incredibly huge if this were to be the factor that stabilized South Korea’s population.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie, Sophia Mersmann, and Daniel Bachler (2026) - “South Korea’s population is set to shrink: what would it take to stop the decline?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260603-102953/south-koreas-population-is-set-to-shrink-what-would-it-take-to-stop-the-decline.html' [Online Resource] (archived on June 3, 2026).\nBibTeX citation\n@article{owid-south-koreas-population-is-set-to-shrink-what-would-it-take-to-stop-the-decline,\nauthor = {Hannah Ritchie and Sophia Mersmann and Daniel Bachler},\ntitle = {South Korea’s population is set to shrink: what would it take to stop the decline?},\njournal = {Our World in Data},\nyear = {2026},\nnote = {https://archive.ourworldindata.org/20260603-102953/south-koreas-population-is-set-to-shrink-what-would-it-take-to-stop-the-decline.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "south-koreas-population-is-set-to-shrink-what-would-it-take-to-stop-the-decline", "source_url": "https://ourworldindata.org/south-koreas-population-is-set-to-shrink-what-would-it-take-to-stop-the-decline", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "How much would fertility rates, life expectancy, or migration rates need to change to stop the population from shrinking?", "numeric_mentions": ["18,", "2026", "2026,", "52 million", "2100,", "22 million", "2100", "70", "80", "90", "0.7", "1", "1.3", "91 years", "1.7%", "2050", "2080,", "2060", "60 years", "1960,", "0.75", "2.1", "50 years", "130 years", "2050,", "188 years", "83", "25", "82 years", "65,", "100", "1,000", "9", "3", "6", "30 years", "8", "10", "20 years", "70 years", "2.3", "21", "20260603", "102953", "3,"], "numeric_evidence": [{"title": "Country with highest female life expectancy", "source_url": "https://ourworldindata.org/grapher/maximum-life-expectancy-sex-female.csv", "file_type": "csv", "columns": ["Entity", "Year", "Life expectancy", "Country with yearly maximum life expectancy - Sex: female"], "row_count_total": 184, "rows_head": [{"Entity": "Australia 1921", "Year": "1921", "Life expectancy": "63.18", "Country with yearly maximum life expectancy - 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Sex: female": "Denmark"}, {"Entity": "Denmark 1861", "Year": "1861", "Life expectancy": "49.01", "Country with yearly maximum life expectancy - Sex: female": "Denmark"}, {"Entity": "Denmark 1862", "Year": "1862", "Life expectancy": "48.5", "Country with yearly maximum life expectancy - Sex: female": "Denmark"}, {"Entity": "Denmark 1863", "Year": "1863", "Life expectancy": "48.52", "Country with yearly maximum life expectancy - Sex: female": "Denmark"}, {"Entity": "Denmark 1904", "Year": "1904", "Life expectancy": "57.43", "Country with yearly maximum life expectancy - Sex: female": "Denmark"}, {"Entity": "Denmark 1910", "Year": "1910", "Life expectancy": "59.64", "Country with yearly maximum life expectancy - Sex: female": "Denmark"}, {"Entity": "Denmark 1914", "Year": "1914", "Life expectancy": "60.11", "Country with yearly maximum life expectancy - Sex: female": "Denmark"}, {"Entity": "Denmark 1915", "Year": "1915", "Life expectancy": "59.89", "Country with yearly maximum life expectancy - Sex: female": "Denmark"}, {"Entity": "Denmark 1918", "Year": "1918", "Life expectancy": "57.28", "Country with yearly maximum life expectancy - Sex: female": "Denmark"}, {"Entity": "Hong Kong 2011", "Year": "2011", "Life expectancy": "86.0892", "Country with yearly maximum life expectancy - Sex: female": "Hong Kong"}, {"Entity": "Hong Kong 2013", "Year": "2013", "Life expectancy": "86.562", "Country with yearly maximum life expectancy - Sex: female": "Hong Kong"}, {"Entity": "Hong Kong 2014", "Year": "2014", "Life expectancy": "86.7554", "Country with yearly maximum life expectancy - Sex: female": "Hong Kong"}, {"Entity": "Hong Kong 2015", "Year": "2015", "Life expectancy": "86.982", "Country with yearly maximum life expectancy - Sex: female": "Hong Kong"}, {"Entity": "Hong Kong 2016", "Year": "2016", "Life expectancy": "87.264", "Country with yearly maximum life expectancy - Sex: female": "Hong Kong"}, {"Entity": "Hong Kong 2017", "Year": "2017", "Life expectancy": "87.6226", "Country with yearly maximum life expectancy - Sex: female": "Hong Kong"}, {"Entity": "Hong Kong 2018", "Year": "2018", "Life expectancy": "87.9357", "Country with yearly maximum life expectancy - Sex: female": "Hong Kong"}, {"Entity": "Hong Kong 2019", "Year": "2019", "Life expectancy": "88.2395", "Country with yearly maximum life expectancy - Sex: female": "Hong Kong"}, {"Entity": "Hong Kong 2023", "Year": "2023", "Life expectancy": "88.1294", "Country with yearly maximum life expectancy - Sex: female": "Hong Kong"}, {"Entity": "Iceland 1842", "Year": "1842", "Life expectancy": "45.2", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1853", "Year": "1853", "Life expectancy": "52.31", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1889", "Year": "1889", "Life expectancy": "54.36", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1892", "Year": "1892", "Life expectancy": "55.59", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1893", "Year": "1893", "Life expectancy": "57.13", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1896", "Year": "1896", "Life expectancy": "55.89", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1899", "Year": "1899", "Life expectancy": "53.45", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1906", "Year": "1906", "Life expectancy": "59.11", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1912", "Year": "1912", "Life expectancy": "59.38", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1913", "Year": "1913", "Life expectancy": "61.23", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1917", "Year": "1917", "Life expectancy": "61.52", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1919", "Year": "1919", "Life expectancy": "60.38", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1940", "Year": "1940", "Life expectancy": "68.74", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1946", "Year": "1946", "Life expectancy": "71.7", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1949", "Year": "1949", "Life expectancy": "73.21", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1950", "Year": "1950", "Life expectancy": "73.5168", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1952", "Year": "1952", "Life expectancy": "74.8242", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1954", "Year": "1954", "Life expectancy": "75.6009", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1955", "Year": "1955", "Life expectancy": "75.8877", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1958", "Year": "1958", "Life expectancy": "75.9493", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1961", "Year": "1961", "Life expectancy": "76.1792", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1962", "Year": "1962", "Life expectancy": "76.0576", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1963", "Year": "1963", "Life expectancy": "75.9672", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1964", "Year": "1964", "Life expectancy": "76.3245", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1975", "Year": "1975", "Life expectancy": "78.6537", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1976", "Year": "1976", "Life expectancy": "79.7894", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1977", "Year": "1977", "Life expectancy": "79.2244", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1978", "Year": "1978", "Life expectancy": "79.2677", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1979", "Year": "1979", "Life expectancy": "79.2596", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1980", "Year": "1980", "Life expectancy": "80.0593", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1981", "Year": "1981", "Life expectancy": "79.3836", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Iceland 1983", "Year": "1983", "Life expectancy": "80.1398", "Country with yearly maximum life expectancy - Sex: female": "Iceland"}, {"Entity": "Japan 1982", "Year": "1982", "Life expectancy": "79.7247", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1984", "Year": "1984", "Life expectancy": "80.2603", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1985", "Year": "1985", "Life expectancy": "80.5448", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1986", "Year": "1986", "Life expectancy": "80.9786", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1987", "Year": "1987", "Life expectancy": "81.431", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1988", "Year": "1988", "Life expectancy": "81.3282", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1989", "Year": "1989", "Life expectancy": "81.8003", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1990", "Year": "1990", "Life expectancy": "81.8645", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1991", "Year": "1991", "Life expectancy": "82.1772", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1992", "Year": "1992", "Life expectancy": "82.2972", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1993", "Year": "1993", "Life expectancy": "82.4479", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1994", "Year": "1994", "Life expectancy": "82.8995", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1995", "Year": "1995", "Life expectancy": "82.78", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1996", "Year": "1996", "Life expectancy": "83.4974", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1997", "Year": "1997", "Life expectancy": "83.7321", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1998", "Year": "1998", "Life expectancy": "83.9208", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1999", "Year": "1999", "Life expectancy": "83.9158", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2000", "Year": "2000", "Life expectancy": "84.5192", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2001", "Year": "2001", "Life expectancy": "84.8445", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2002", "Year": "2002", "Life expectancy": "85.143", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2003", "Year": "2003", "Life expectancy": "85.2435", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2004", "Year": "2004", "Life expectancy": "85.5007", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2005", "Year": "2005", "Life expectancy": "85.4076", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2006", "Year": "2006", "Life expectancy": "85.6959", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2007", "Year": "2007", "Life expectancy": "85.8781", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2008", "Year": "2008", "Life expectancy": "85.9491", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2009", "Year": "2009", "Life expectancy": "86.3206", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2010", "Year": "2010", "Life expectancy": "86.2318", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2012", "Year": "2012", "Life expectancy": "86.3506", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2020", "Year": "2020", "Life expectancy": "87.7266", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2021", "Year": "2021", "Life expectancy": "87.5914", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2022", "Year": "2022", "Life expectancy": "87.0874", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Netherlands 1936", "Year": "1936", "Life expectancy": "67.41", "Country with yearly maximum life expectancy - Sex: female": "Netherlands"}, {"Entity": "Norway 1846", "Year": "1846", "Life expectancy": "49.67", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1847", "Year": "1847", "Life expectancy": "46.08", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1849", "Year": "1849", "Life expectancy": "49.82", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1850", "Year": "1850", "Life expectancy": "51.28", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1851", "Year": "1851", "Life expectancy": "51.49", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1852", "Year": "1852", "Life expectancy": "49.94", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1854", "Year": "1854", "Life expectancy": "53.3", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1855", "Year": "1855", "Life expectancy": "52.26", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1856", "Year": "1856", "Life expectancy": "51.84", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1857", "Year": "1857", "Life expectancy": "51.6", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1858", "Year": "1858", "Life expectancy": "53.09", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1859", "Year": "1859", "Life expectancy": "51.28", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1860", "Year": "1860", "Life expectancy": "51.3", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1864", "Year": "1864", "Life expectancy": "49.95", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1865", "Year": "1865", "Life expectancy": "51.85", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1866", "Year": "1866", "Life expectancy": "51.47", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1867", "Year": "1867", "Life expectancy": "49.55", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1868", "Year": "1868", "Life expectancy": "48.95", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1869", "Year": "1869", "Life expectancy": "50.99", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1870", "Year": "1870", "Life expectancy": "52.55", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1871", "Year": "1871", "Life expectancy": "51.4", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1873", "Year": "1873", "Life expectancy": "51.16", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1874", "Year": "1874", "Life expectancy": "49.23", "Country with yearly maximum life expectancy - Sex: female": "Norway"}], "rows_tail": [{"Entity": "Japan 1982", "Year": "1982", "Life expectancy": "79.7247", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1984", "Year": "1984", "Life expectancy": "80.2603", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1985", "Year": "1985", "Life expectancy": "80.5448", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1986", "Year": "1986", "Life expectancy": "80.9786", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1987", "Year": "1987", "Life expectancy": "81.431", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1988", "Year": "1988", "Life expectancy": "81.3282", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1989", "Year": "1989", "Life expectancy": "81.8003", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1990", "Year": "1990", "Life expectancy": "81.8645", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1991", "Year": "1991", "Life expectancy": "82.1772", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1992", "Year": "1992", "Life expectancy": "82.2972", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1993", "Year": "1993", "Life expectancy": "82.4479", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1994", "Year": "1994", "Life expectancy": "82.8995", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1995", "Year": "1995", "Life expectancy": "82.78", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1996", "Year": "1996", "Life expectancy": "83.4974", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1997", "Year": "1997", "Life expectancy": "83.7321", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1998", "Year": "1998", "Life expectancy": "83.9208", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 1999", "Year": "1999", "Life expectancy": "83.9158", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2000", "Year": "2000", "Life expectancy": "84.5192", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2001", "Year": "2001", "Life expectancy": "84.8445", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2002", "Year": "2002", "Life expectancy": "85.143", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2003", "Year": "2003", "Life expectancy": "85.2435", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2004", "Year": "2004", "Life expectancy": "85.5007", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2005", "Year": "2005", "Life expectancy": "85.4076", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2006", "Year": "2006", "Life expectancy": "85.6959", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2007", "Year": "2007", "Life expectancy": "85.8781", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2008", "Year": "2008", "Life expectancy": "85.9491", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2009", "Year": "2009", "Life expectancy": "86.3206", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2010", "Year": "2010", "Life expectancy": "86.2318", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2012", "Year": "2012", "Life expectancy": "86.3506", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2020", "Year": "2020", "Life expectancy": "87.7266", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2021", "Year": "2021", "Life expectancy": "87.5914", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Japan 2022", "Year": "2022", "Life expectancy": "87.0874", "Country with yearly maximum life expectancy - Sex: female": "Japan"}, {"Entity": "Netherlands 1936", "Year": "1936", "Life expectancy": "67.41", "Country with yearly maximum life expectancy - Sex: female": "Netherlands"}, {"Entity": "Norway 1846", "Year": "1846", "Life expectancy": "49.67", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1847", "Year": "1847", "Life expectancy": "46.08", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1849", "Year": "1849", "Life expectancy": "49.82", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1850", "Year": "1850", "Life expectancy": "51.28", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1851", "Year": "1851", "Life expectancy": "51.49", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1852", "Year": "1852", "Life expectancy": "49.94", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1854", "Year": "1854", "Life expectancy": "53.3", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1855", "Year": "1855", "Life expectancy": "52.26", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1856", "Year": "1856", "Life expectancy": "51.84", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1857", "Year": "1857", "Life expectancy": "51.6", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1858", "Year": "1858", "Life expectancy": "53.09", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1859", "Year": "1859", "Life expectancy": "51.28", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1860", "Year": "1860", "Life expectancy": "51.3", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1864", "Year": "1864", "Life expectancy": "49.95", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1865", "Year": "1865", "Life expectancy": "51.85", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1866", "Year": "1866", "Life expectancy": "51.47", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1867", "Year": "1867", "Life expectancy": "49.55", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1868", "Year": "1868", "Life expectancy": "48.95", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1869", "Year": "1869", "Life expectancy": "50.99", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1870", "Year": "1870", "Life expectancy": "52.55", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1871", "Year": "1871", "Life expectancy": "51.4", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1873", "Year": "1873", "Life expectancy": "51.16", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1874", "Year": "1874", "Life expectancy": "49.23", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1875", "Year": "1875", "Life expectancy": "49.17", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1876", "Year": "1876", "Life expectancy": "48.31", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1877", "Year": "1877", "Life expectancy": "51.2", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1878", "Year": "1878", "Life expectancy": "53.18", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1879", "Year": "1879", "Life expectancy": "54.36", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1880", "Year": "1880", "Life expectancy": "53.27", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1881", "Year": "1881", "Life expectancy": "51.85", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1883", "Year": "1883", "Life expectancy": "50.7", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1884", "Year": "1884", "Life expectancy": "52", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1885", "Year": "1885", "Life expectancy": "52.3", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1886", "Year": "1886", "Life expectancy": "52.88", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1897", "Year": "1897", "Life expectancy": "55.64", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1900", "Year": "1900", "Life expectancy": "55.14", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1901", "Year": "1901", "Life expectancy": "56.43", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1902", "Year": "1902", "Life expectancy": "57.98", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1903", "Year": "1903", "Life expectancy": "56.45", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1905", "Year": "1905", "Life expectancy": "56.17", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1908", "Year": "1908", "Life expectancy": "57.56", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1911", "Year": "1911", "Life expectancy": "59.63", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1934", "Year": "1934", "Life expectancy": "67.69", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1935", "Year": "1935", "Life expectancy": "67.3", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1938", "Year": "1938", "Life expectancy": "68.98", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1939", "Year": "1939", "Life expectancy": "69.04", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1945", "Year": "1945", "Life expectancy": "70.45", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1947", "Year": "1947", "Life expectancy": "71.67", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1948", "Year": "1948", "Life expectancy": "72.86", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1951", "Year": "1951", "Life expectancy": "74.2921", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1953", "Year": "1953", "Life expectancy": "75.0707", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1956", "Year": "1956", "Life expectancy": "75.5262", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1957", "Year": "1957", "Life expectancy": "75.5347", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1959", "Year": "1959", "Life expectancy": "75.7854", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1960", "Year": "1960", "Life expectancy": "75.8561", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1965", "Year": "1965", "Life expectancy": "76.5274", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1966", "Year": "1966", "Life expectancy": "76.7095", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1967", "Year": "1967", "Life expectancy": "76.9442", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1968", "Year": "1968", "Life expectancy": "76.79", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1969", "Year": "1969", "Life expectancy": "76.6741", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1970", "Year": "1970", "Life expectancy": "77.3203", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Norway 1974", "Year": "1974", "Life expectancy": "77.9665", "Country with yearly maximum life expectancy - Sex: female": "Norway"}, {"Entity": "Sweden 1840", "Year": "1840", "Life expectancy": "46.14", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1841", "Year": "1841", "Life expectancy": "47.24", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1844", "Year": "1844", "Life expectancy": "46.91", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1845", "Year": "1845", "Life expectancy": "48.5", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1848", "Year": "1848", "Life expectancy": "47.29", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1872", "Year": "1872", "Life expectancy": "51.88", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1882", "Year": "1882", "Life expectancy": "50.03", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1887", "Year": "1887", "Life expectancy": "52.84", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1888", "Year": "1888", "Life expectancy": "53.5", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1890", "Year": "1890", "Life expectancy": "51.77", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1891", "Year": "1891", "Life expectancy": "52.5", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1894", "Year": "1894", "Life expectancy": "53.05", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1895", "Year": "1895", "Life expectancy": "55.29", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1898", "Year": "1898", "Life expectancy": "56.03", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1907", "Year": "1907", "Life expectancy": "58.05", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1909", "Year": "1909", "Life expectancy": "59.48", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1916", "Year": "1916", "Life expectancy": "59.28", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1920", "Year": "1920", "Life expectancy": "60.24", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1923", "Year": "1923", "Life expectancy": "64.05", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1941", "Year": "1941", "Life expectancy": "68.26", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1942", "Year": "1942", "Life expectancy": "70.36", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1943", "Year": "1943", "Life expectancy": "70.08", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1971", "Year": "1971", "Life expectancy": "77.3865", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1972", "Year": "1972", "Life expectancy": "77.5328", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}, {"Entity": "Sweden 1973", "Year": "1973", "Life expectancy": "77.7288", "Country with yearly maximum life expectancy - Sex: female": "Sweden"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "maximum-life-expectancy-sex-female", "metadata_url": "https://ourworldindata.org/grapher/maximum-life-expectancy-sex-female.metadata.json", "chart_title": "Country with highest female life expectancy", "chart_subtitle": "The country with the highest female life expectancy at birth, in each year.", "chart_note": "Records are only shown for countries in the Human Mortality Database.", "chart_citation": "Human Mortality Database (2025); UN, World Population Prospects (2024)", "original_chart_url": "https://ourworldindata.org/grapher/maximum-life-expectancy-sex-female", "owid_column_metadata": {"Maximum life expectancy - Sex: female": {"titleShort": "Maximum life expectancy", "titleLong": "Maximum life expectancy - HMD; UN WPP", "descriptionShort": "Maximum life expectancy recorded in a given year (among females).", "descriptionKey": ["Period life expectancy is a metric that summarizes death rates across all age groups in one particular year. 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 lives as the age-specific death rates seen in that particular year.", "Records are only shown for countries in the Human Mortality Database. Prior to 1950, we use HMD (2025) data. From 1950 onwards, we use UN WPP (2024) data."], "shortUnit": "years", "unit": "years", "timespan": "1840-2023", "type": "Numeric", "owidVariableId": 1118409, "shortName": "life_expectancy__sex_female", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Mortality Database (2025); UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "Human Mortality Database (2025); UN, World Population Prospects (2024) – processed by Our World in Data. “Maximum life expectancy – HMD; UN WPP” [dataset]. Human Mortality Database, “Human Mortality Database”; United Nations, “World Population Prospects” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118409.metadata.json"}, "Country with yearly maximum life expectancy - Sex: female": {"titleShort": "Country with yearly maximum life expectancy - Sex: female", "titleLong": "Country with yearly maximum life expectancy - Sex: female", "descriptionShort": "Name of the country with the yearly maximum life expectancy registered among females.", "descriptionProcessing": "This indicator is meant to be used as an auxiliary indicator.", "unit": "", "timespan": "1840-2023", "type": "String", "owidVariableId": 1118412, "shortName": "country_with_max_le__sex_female", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Mortality Database (2025); UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "Human Mortality Database (2025); UN, World Population Prospects (2024) – processed by Our World in Data. “Country with yearly maximum life expectancy - Sex: female” [dataset]. Human Mortality Database, “Human Mortality Database”; United Nations, “World Population Prospects” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118412.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "47bd1e52f7c8b78db3be"},
{"raw_link": "https://ourworldindata.org/childhood-stunting-fell-dramatically-over-the-20th-century", "title": "Childhood stunting fell dramatically over the 20th century", "context": "Home\nHunger & Undernourishment\nChildhood stunting fell dramatically over the 20th century\nWhat can countries with high stunting rates today learn from Japan’s experience of going from 70% to 5%?\nBy\nHannah Ritchie\n(writing)\nand\nTuna Acisu\n(data)\nMay 11, 2026\nBrowse past versions\nCite this article\nReuse our work freely\nOne in four children\nin the world\ntoday suffers from stunting. That’s\n150 million children\nunder five years old.\nA child is considered stunted if they are too short for their age. It is a consequence of malnutrition. Here, we’re not simply talking about children who are slightly smaller than their peers, but those who are shorter than the medically acceptable range for healthy growth.\n1\nStunting reflects poor nutrition and frequent exposure to disease or illness, which reduces their ability to retain nutrients and increases their requirements. Stunting suggests that a child’s development has been hindered, and its impacts are not limited to childhood: it affects both physical and cognitive progress and can persist throughout a person’s life.\nFor more technical details, read my\nshort explainer\non childhood stunting.\nLooking at rates\nacross the world\ntoday, we see huge differences: stunting has been almost eliminated in some countries, but there are still many in Asia and Africa where it affects more than a third of children.\nThis raises the question of when and how stunting was reduced in the places where it’s rare today.\nA new crucial dataset gives us some answers.\nUntil now, researchers had only high-quality data on stunting rates dating back to the 1990s. But a major effort by Eric Schneider and colleagues filled this gap by carefully collecting records that allow us to see how things have changed over more than a century.\n2\nThe data they published in their\nrecent paper\nshows that childhood stunting fell dramatically across many countries over the 20th century. You can explore this data via the chart below.\nBy the 1990s, when many high-income nations had reduced stunting rates to under 10%, it was still a huge problem in many low-to-middle-income countries.\nHowever, they have also made progress. In the chart below, you can see stunting rates for a selection of middle-income countries. You can explore many more countries via the interactive chart.\nNote that this historical data is based on children aged 2 to 10, and the latest data point is from the 2010s. Commonly updated metrics focus on children under 5, making it difficult to directly compare these long-run historical rates to those today. However, the broad picture is similar:\nmodern stunting rates\nin rich countries are incredibly low, while in low- and middle-income countries, it’s typical for more than a fifth of children to be stunted (and in some cases more than a third).\nThis long-run data, in itself, shows that child health can improve dramatically, and that high rates of stunting are not inevitable.\nTo understand how progress is possible, I want to zoom in on one country: Japan.\nAt the beginning of the 20th century, more than 70% of Japanese children were stunted. Today, very few are.\nThe fact that we have this long-run data showing Japan’s path from widespread to low levels of stunting means we can contextualize and understand where many low- and middle-income countries now sit in this historical trajectory.\nThe chart below shows Japan’s decline in stunting over the last hundred years alongside rates across a selection of countries in the 2010s. Japan has such high-quality data that we can plot this as an annual series.\n3\nNote that the data is based on the birth year of each cohort of children, not the measurement year (which is why the gap during the Second World War appears earlier than expected).\nYou can see, for example, that today Burundi and East Timor have rates comparable to Japan in the 1920s. Malawi and India to Japan in the 1940s.\nUnderstanding the interventions and changes in Japan at each level of stunting could offer valuable lessons for countries at different stages of their own journey.\nDownload\nHow did Japan reduce stunting?\nIt’s useful to break Japan’s timeline into a few key periods: the early 1900s, before the Second World War, when stunting declined at a moderate pace; the war itself, when progress stalled; and then the post-war period, where stunting fell dramatically.\nPre-war period\nIn the early 1900s, stunting rates fell from around 70% to 40–45% by the early 1940s. Rates were falling at around 0.8 percentage points per year, on average.\nAt the beginning of the 20th century, more than 70% of Japanese children were stunted.\nWhile improvements in nutrition played a crucial role in reducing stunting after the war, the research suggests that, in this pre-war period, tackling infectious disease was more important.\n4\nOver the first half of the 20th century, deaths from gastrointestinal diseases in Japan fell substantially. Across the country, rates fell by roughly 40%, and in major cities they more than halved in just a decade.\n5\nA big part of this progress came from the expansion of piped water.\n6\nIn Tokyo, the share of households with access to a water supply increased from around 30% in 1920 to 80% in the mid-1930s.\nResearchers have estimated that this clean water buildout accounted for 30% to 40% of the reduction in child mortality and infectious disease deaths over this period.\n5\nWhat does this mean for countries with pre-war-Japan levels of stunting today?\nIf we look at some of the countries with very high stunting rates, we see that child deaths from diarrheal diseases are common. Some of them are highlighted in the chart below, where several stand out as having higher death rates than other countries at similar incomes.\nJapan’s history suggests that these countries could reduce childhood stunting by focusing on controlling infectious diseases through the expansion of clean water, safe sanitation, and hygiene facilities.\nWorld War II reversal\nBefore moving on to Japan’s steep decline in stunting after the Second World War, it’s worth noting that progress stalled and may even have reversed during the war’s latter stages.\nThe research\nsuggests that\nthere was a temporary, and quite severe, reversal in nutritional levels.\n7\nThe average daily supply of calories dropped from around 2,100 kcal in 1941 to less than 1,800 kcal in 1945.\n8\nSurveys from Tokyo in the final stages of the war\nreported that\nthe vast majority of children were eating one meal a day or less.\nData collection was much more limited during the war, which is why you see a gap in the earlier time series for Japan. But this chart also shows that stunting levels were higher after the war than before it.\nWhat’s interesting about this cohort of stunted Japanese children is that they managed to catch up with their peers later in childhood.\n9\nBy adulthood, they were just as tall. This suggests that when conditions improve, children can recover from early nutritional shocks during later childhood, not just in the first few years of life.\n10\nThis has important implications for policy: if catch-up growth is possible (which is what this Japanese data suggests), preventing deficiencies in the first place is cheaper and more beneficial, but improving nutrition throughout childhood still gives children the opportunity to close the gap.\nRapid post-war progress\nAfter the war, the decline in stunting accelerated, averaging around 2.2 percentage points per year.\nThe pre-war period tackled disease; the post-war period tackled disease\nand\ndiet at the same time, which is why progress was nearly three times faster.\nJapanese public health researchers later described Japan as “the paradise of parasites” during this period.\n11\nAs many as 7 in 10 people living in rural areas were infected with\nroundworm\nand\nhookworm\n, parasites that live in the intestine and prevent nutrients from being absorbed properly.\n12\nOther infectious diseases were also rampant.\nBut in the thirty years following the war, things changed. Japan effectively eliminated diseases such as\nhookworm\n,\nascariasis\n, and\nmalaria\n, and saw a significant drop in diarrheal disease. This was the result of significant investments in scaling up clean water supplies and sanitation,\ndeworming programs\nin schools, and hygiene education.\nBetween 1950 and 1980, the share of people with clean piped water increased from less than 30% to over 80%.\n13\nAnother, unappreciated change was the transition from “nightsoil” to chemical fertilizers.\nFor much of the 20th century, Japanese agriculture relied heavily on human excrement (nightsoil) as a nutrient source.\n14\nBefore the Second World War, some of this was displaced by synthetic chemical fertilizers, but during the war, these supplies were almost completely cut off. This practice was a key source of parasitic infection, particularly hookworm, among rural populations. Parasite eggs from the excrement could survive in the soil for weeks; people then ingested them either through eating vegetables and other foods grown in the fields, or through larvae penetrating the skin of people working in the fields.\nIn the post-war decades, chemical fertilizers gradually replaced nightsoil again, breaking the transmission chain of these parasites through agriculture. Exposure to infectious diseases dropped significantly.\nIn most countries with stunting rates in the 5% to 40% range today, tackling infectious diseases through expanded access to safe water, sanitation, and hygiene practices is crucial. Death rates attributable to a lack of these resources are still\nvery high\nrelative to where Japan is today. Diseases such as\nfilariasis\n, caused by parasitic worms, are\nstill common\n.\nBetween 1950 and 1980, the share of people with clean piped water increased from less than 30% to over 80%.\nJapan achieved this by building out infrastructure and education programs on the importance of hygiene practices. This was a crucial part of the country’s strategy, particularly in schools.\nReductions in stunting were so fast, not just because disease burden declined and children could retain nutrients more easily, but also because nutritional intakes improved.\nOver the second half of the 20th century, food consumption in Japan shifted from being heavily reliant on staples such as rice and cereals to one with more meat, seafood, and fruit. In the chart below, you can see this transition, measured as the average daily supply of different food groups since 1961 (the earliest year for which data is available).\nNot only did total calorie intake increase after the war, but this diversification also increased consumption of crucial micronutrients and high-quality proteins, which had previously been missing from a monotonous, cereal-dominant diet.\nThe American occupation of Japan in the years after the war\nhad a positive influence\non these nutritional trends. From the late 1940s to the early 1950s, the United States provided large food aid programs. These programs increased total consumption but also made foods such as bread and milk staples in the Japanese diet, where they hadn’t been before.\nThe Japanese government made childhood nutrition a priority. The post-war period marked a huge scale-up in school lunches.\n15\nYou can see this rollout in the chart below.\nIn 1948, half of school children\nwere receiving\npartial lunches; that is, meals with milk and side dishes. By the 1960s, almost all children were receiving complete lunches, which were even more substantial.\nDownload\nChildren’s nutrition dramatically improved, and so did their mothers’.\nOne reason why Japanese stunting rates were previously so high was because of maternal malnutrition. A malnourished mother greatly increases the risk of a baby being born underweight. Between 1900 and 1970, average birth weight in Japan increased by 250 grams.\n16\nIn a number of low-to-middle-income countries today, many babies are born with\nlow birthweights\n. In countries such as India, it’s more than one-quarter. Improving maternal health is therefore essential to make sure that children are not at a disadvantage before they’re even born.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nOther countries can make progress too\nJapan’s progress on stunting was impressive. But it is not unique.\nSome countries in recent decades have achieved declines as steep as Japan’s post-war transformation. In the chart below, you can see data for Ethiopia, Nepal, and Peru over the past few decades. These are often held up as\nmodern exemplars\nof countries making rapid progress. What’s notable is that they span South America, Africa, and Asia.\nWhile this new long-run dataset on stunting rates over the 20th century helps us understand that it is not inevitable, we should also be honest about the challenges in ending stunting everywhere.\nCountries like Burundi and India are where Japan was decades ago. Japan's experience shows that transformation is possible, but it requires simultaneous action on disease, diet, and infrastructure sustained over a generation. Nonetheless, that seems like an investment worth making. The future of hundreds of millions of children depends on it.\nAcknowledgments\nMany thanks to Eric Schneider and Juliana Jaramillo Echeverri for the provision of data and feedback on this article. We’d also like to thank Max Roser and Edouard Mathieu for comments and editorial feedback.\nContinue reading on Our World in Data\nWhat is childhood stunting?\nStunting is an important marker of childhood malnutrition. But what is it, and how is it measured?\nHalf of all child deaths are linked to malnutrition\nImproving the nutrition of mothers and children could save many lives at a relatively low cost.\nAlmost three billion people cannot afford a healthy diet\nA healthy, nutritious diet is much more expensive than a calorie sufficient one.\nEndnotes\nThe World Health Organization (WHO) publishes growth standards showing the expected height trajectory for healthy children. A child is considered stunted if their height-for-age falls more than two standard deviations below the median — roughly the bottom 2.5% of the reference distribution.\nSo, a child is stunted if their height is more than two standard deviations below the median for their age.\nSchneider, E. B., Jaramillo Echeverri, J., Purcell, M., A’Hearn, B., Arthi, V., Blum, M., ... & Roberts, E. (2026). The decline of child stunting in 122 countries: a systematic review of child growth studies since the 19th century. BMJ Global Health, 11(2), e018607.\nThis annual series was provided through personal communication with the lead author of the paper, Eric Schneider.\nSchneider, E. B., & Ogasawara, K. (2018). Disease and child growth in industrialising Japan: Critical windows and the growth pattern, 1917–39. Explorations in Economic History.\nOgasawara, K., Shirota, S., & Kobayashi, G. (2018). Public health improvements and mortality in interwar Tokyo: a Bayesian disease mapping approach. Cliometrica.\nOgasawara, K., & Matsushita, Y. (2018). Public health and multiple-phase mortality decline: Evidence from industrializing Japan. Economics & Human Biology.\nSchneider, E. (2022). Can stunted children recover after the first 1,000 days?\nLSE Blogs\n.\nSchneider, E. B., Ogasawara, K., & Colec, T. J. (2021). Health Shocks, Recovery and the First Thousand Days: The Effect of the Second World War on the Growth Pattern of Height in Japanese Children.\nSchneider, E. B., Ogasawara, K., & Cole, T. J. (2021). Health shocks, recovery, and the first thousand days: The effect of the second world war on height growth in Japanese children. Population and Development Review.\nThis challenges the strong version of the “first 1,000 days” hypothesis, which holds that nutritional deficits in the first years of life are largely irreversible.\nKojima, S., & Takeuchi, T. (2006). Global parasite control initiative of Japan (Hashimoto Initiative). Parasitology International.\nBay, A. R. (2022). Total prevention: a history of schistosomiasis in Japan. Medical History.\nKasai, T., Nakatani, H., Takeuchi, T., & Crump, A. (2007). Research and control of parasitic diseases in Japan: current position and future perspectives. Trends in parasitology.\nNoriko, Y. 129 Between Nature and Human: History of the Use of “Night Soil” in Japan. In Handbook of Environmental History in Japan (pp. 129-141). Routledge.\nTanaka, N., & Miyoshi, M. (2012). School lunch program for health promotion among children in Japan. Asia Pacific journal of clinical nutrition.\nWeights in 1900 come from this paper: Misawa, T. (1909). A few statistical facts from Japan. The Pedagogical Seminary, 16(1), 104-112.\nAnd weights in 1970 come from here: Kato, N., Sauvaget, C., Yoshida, H., Yokoyama, T., & Yoshiike, N. (2021). Factors associated with birthweight decline in Japan (1980–2004). BMC pregnancy and childbirth.\nIt’s worth noting that the birthweight of babies in Japan has fallen from its peak in recent decades, which may partly reflect the fact that many young women in Japan are considered underweight by body mass index measures. The average BMI has actually\nfallen slightly\nin recent decades.\nTakemoto, Y., Ota, E., Yoneoka, D., Mori, R., & Takeda, S. (2016). Japanese secular trends in birthweight and the prevalence of low birthweight infants during the last three decades: a population-based study. Scientific reports.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Tuna Acisu (2026) - “Childhood stunting fell dramatically over the 20th century” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260604-101727/childhood-stunting-fell-dramatically-over-the-20th-century.html' [Online Resource] (archived on June 4, 2026).\nBibTeX citation\n@article{owid-childhood-stunting-fell-dramatically-over-the-20th-century,\nauthor = {Hannah Ritchie and Tuna Acisu},\ntitle = {Childhood stunting fell dramatically over the 20th century},\njournal = {Our World in Data},\nyear = {2026},\nnote = {https://archive.ourworldindata.org/20260604-101727/childhood-stunting-fell-dramatically-over-the-20th-century.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "childhood-stunting-fell-dramatically-over-the-20th-century", "source_url": "https://ourworldindata.org/childhood-stunting-fell-dramatically-over-the-20th-century", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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Sex: both sexes": "44.6", "GDP per capita": "1981.7102", "Population": "40578847", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "1983.8126", "Population": "41454762", "World region according to OWID": "Asia"}, {"Entity": "Albania", "Code": "ALB", "Year": "1990", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "5560.857", "Population": "3277965", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1991", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "4027.9055", "Population": "3282608", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "3761.1555", "Population": "3282506", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "4145.92", "Population": "3277831", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "4517.799", "Population": "3269417", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "5151.3975", "Population": "3258573", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "5563.793", "Population": "3245680", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "4943.004", "Population": "3229665", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "5387.5684", "Population": "3210134", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "6086.009", "Population": "3188598", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "6582.0166", "Population": "3166147", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "7232.9907", "Population": "3147943", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "7590.49", "Population": "3134095", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "8025.2812", "Population": "3118093", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "8483.294", "Population": "3098662", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "26.7", "GDP per capita": "8964.318", "Population": "3076156", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "9564.029", "Population": "3050805", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "10262.967", "Population": "3022887", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "11056.352", "Population": "2992933", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "23.2", "GDP per capita": "11430.622", "Population": "2961487", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "11829.054", "Population": "2928731", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "12153.114", "Population": "2911499", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "12463.57", "Population": "2910003", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "12873.483", "Population": "2907571", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "13366.56", "Population": "2903748", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "13876.819", "Population": "2898634", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "14643.489", "Population": "2897868", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "11.3", "GDP per capita": "15359.461", "Population": "2898245", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "16170.99", "Population": "2894229", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "16761.193", "Population": "2885011", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "16457.787", "Population": "2871950", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "18212.871", "Population": "2849641", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "19388.873", "Population": "2827614", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "20481.035", "Population": "2811660", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "21641.074", "Population": "2791756", "World region according to OWID": "Europe"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1987", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "16.9", "GDP per capita": "", "Population": "23443627", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1990", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "11728.546", "Population": "25375815", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1991", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "11314.864", "Population": "25987937", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1992", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "22.9", "GDP per capita": "11241.415", "Population": "26628570", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1993", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "10743.706", "Population": "27277042", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1994", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "10414.035", "Population": "27887277", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1995", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "22.6", "GDP per capita": "10588.443", "Population": "28470194", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1996", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "10808.879", "Population": "29033045", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1997", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "10725.968", "Population": "29579299", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1998", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "11094.888", "Population": "30054135", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1999", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "11292.037", "Population": "30474360", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "23.6", "GDP per capita": "11558.221", "Population": "30903894", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "11742.595", "Population": "31331226", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "24", "GDP per capita": "12213.126", "Population": "31750832", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "12835.182", "Population": "32175813", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "13226.765", "Population": "32628286", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "13738.496", "Population": "33109258", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "15.4", "GDP per capita": "13920.694", "Population": "33623505", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "14114.675", "Population": "34189420", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "14206.776", "Population": "34816963", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "14104.428", "Population": "35490442", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "14496.421", "Population": "36188237", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "14641.964", "Population": "36903374", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "11.7", "GDP per capita": "14697.54", "Population": "37646165", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "14778.191", "Population": "38414176", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "15073.763", "Population": "39205035", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "15239.518", "Population": "40019528", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "15511.686", "Population": "40850719", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "15427.664", "Population": "41689302", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "15343.426", "Population": "42505033", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "9.8", "GDP per capita": "15199.199", "Population": "43294551", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "14194.155", "Population": "44042094", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "14496.865", "Population": "44761099", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2022", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "14782.2", "Population": "45477391", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "15159.324", "Population": "46164222", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2024", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "15501.92", "Population": "46814302", "World region according to OWID": "Africa"}, {"Entity": "Andorra", "Code": "AND", "Year": "1990", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "50036.3", "Population": "52616", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1991", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "47624.97", "Population": "56691", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1992", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "45246.53", "Population": "60217", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1993", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "42605.66", "Population": "63293", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1994", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "42716.367", "Population": "64631", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1995", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "44375.023", "Population": "63936", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1996", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "46386.09", "Population": "64006", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1997", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "50032.35", "Population": "64722", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1998", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "51092.223", "Population": "65400", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1999", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "52921.047", "Population": "65732", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2000", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "54809.145", "Population": "65703", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2001", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - 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Sex: both sexes": "", "GDP per capita": "21405.117", "Population": "8161972574", "World region according to OWID": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1991", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "50.4", "GDP per capita": "", "Population": "14430205", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1996", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "50.4", "GDP per capita": "", "Population": "17313181", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1997", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "55.2", "GDP per capita": "", "Population": "17873943", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "57.3", "GDP per capita": "", "Population": "21456379", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "57", "GDP per capita": "", "Population": "22790082", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "46.6", "GDP per capita": "", "Population": "27582902", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "46.4", "GDP per capita": "", "Population": "29312955", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - 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Sex: both sexes": "", "GDP per capita": "2220.5654", "Population": "10017633", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "2268.9697", "Population": "10325186", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "52.5", "GDP per capita": "2299.333", "Population": "10647955", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": "", "GDP per capita": "2383.8762", "Population": "10983604", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - 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Sex: both sexes": "25.9", "GDP per capita": "5215.253", "Population": "16634366", "World region according to OWID": "Africa"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "share-of-children-younger-than-5-who-suffer-from-stunting", "metadata_url": "https://ourworldindata.org/grapher/share-of-children-younger-than-5-who-suffer-from-stunting.metadata.json", "chart_title": "Malnutrition: Share of children who are stunted", "chart_subtitle": "Share of children younger than five years old who are defined as stunted. Stunting is when a child is significantly shorter than the average for their age. It is a consequence of poor nutrition or repeated infection.", "chart_note": null, "chart_citation": "World Health Organization - Global Health Observatory (2026)", "original_chart_url": "https://ourworldindata.org/grapher/share-of-children-younger-than-5-who-suffer-from-stunting", "owid_column_metadata": {"Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes": {"titleShort": "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes", "titleLong": "Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes", "unit": "%", "timespan": "1983-2024", "type": "Numeric", "owidVariableId": 1256621, "shortName": "stunting_prevalence_among_children_under_5_years_of_age__pct_height_for_age__lt__2_sd__survey_based_estimates__sex_both_sexes", "lastUpdated": "2026-05-22", "nextUpdate": "2027-05-22", "citationShort": "World Health Organization - Global Health Observatory (2026) – with minor processing by Our World in Data", "citationLong": "World Health Organization - Global Health Observatory (2026) – with minor processing by Our World in Data. “Stunting prevalence among children under 5 years of age (% height-for-age <-2 SD), survey-based estimates - Sex: both sexes – WHO” [dataset]. World Health Organization, “Global Health Observatory” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1256621.metadata.json"}, "GDP per capita, PPP (constant 2021 international $)": {"titleShort": "GDP per capita", "titleLong": "GDP per capita - World Bank – In constant international-$", "descriptionShort": "Average economic output per person in a country or region per year. This data is adjusted for inflation and differences in living costs between countries.", "descriptionKey": ["GDP per capita is a comprehensive measure of people's average income. It helps compare income levels across countries and track how they change over time. It is especially useful for understanding trends in economic growth and living standards.", "GDP per capita is calculated as the value of all final goods and services produced each year in a country (the gross domestic product), divided by the population. It represents the average economic output per person.", "This indicator shows the large inequality between people in different countries. In the poorest countries, average incomes are below $1,000 per year; in rich countries, they are more than 50 times higher.", "This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "This data comes from the World Bank and starts in 1990. For estimates going back several centuries, explore our chart of GDP per capita from the [Maddison Project Database](https://ourworldindata.org/grapher/gdp-per-capita-maddison-project-database)."], "shortUnit": "$", "unit": "international-$ in 2021 prices", "timespan": "1990-2024", "type": "Numeric", "owidVariableId": 1204826, "shortName": "ny_gdp_pcap_pp_kd", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "Eurostat, OECD, IMF, and World Bank (2026) – with minor processing by Our World in Data", "citationLong": "Eurostat, OECD, IMF, and World Bank (2026) – with minor processing by Our World in Data. “GDP per capita – World Bank – In constant international-$” [dataset]. Eurostat, OECD, IMF, and World Bank, “World Development Indicators 125” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1204826.metadata.json"}, "Population": {"titleShort": "Population", "titleLong": "Population", "descriptionShort": "Population by country, available from 10,000 BCE to 2100, based on data and estimates from different sources.", "descriptionKey": ["Population is the most commonly used metric throughout Our World in Data. It is used directly to understand population growth over time, and indirectly to calculate per-capita indicators, making it easier to compare countries of different sizes.", "We construct this indicator by combining multiple sources covering different periods.\n  - HYDE v3.3 (2023): historical estimates from 10,000 BCE to 1799.\n  - Gapminder v7 (2022): for 1800-1949.\n  - UN World Population Prospects (2024): for 1950 onwards, including 2100 projections.\n  - Gapminder Systema Globalis (2023): additional source for former countries (Yugoslavia, USSR, etc.)", "Breaks in the data may occur at the boundaries between sources due to their methodological differences.", "You can read more about the sources and methodology in our [dedicated article](https://ourworldindata.org/population-sources). We also provide a table of sources showing the source we use for each country-year.", "We calculate geographical aggregates (continents, income groups, etc.) by summing individual country populations. For years before 1800, we rely directly on HYDE's values for continents to ensure historical consistency."], "descriptionProcessing": "### Combination of different sources\nWe construct our long-run population data by combining multiple sources:\n\n- 10,000 BCE–1799: historical estimates by HYDE (v3.3).\n\n- 1800–1949: historical estimates by Gapminder (v7).\n\n- 1950–2023: population records from the United Nations World Population Prospects (2024 revision).\n\n- 2024-2100: Projections based on Medium variant by the UN World Population Prospects (2024 revision).\n\n**Geographical aggregates**\n\n- For most years, we calculate aggregates by summing the population of member countries.\n- We do this based on [our definition of continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and the [World Bank’s income groups](https://ourworldindata.org/grapher/world-bank-income-groups).\n- The only exception is before 1800, where we use HYDE's estimates for continents (but not income groups).\n\nFor most of the years, we've estimated regional aggregates by summing the population of countries in each region. We've relied on [our continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and [World Bank income group definitions](https://ourworldindata.org/grapher/world-bank-income-groups). The only exception is before 1800, where we've used HYDE's estimates on continents (but not income groups).\n\n**World**\n- Before 1800: we use data from HYDE.\n- 1800-1950: we estimate the global population by summing all available countries in the dataset.\n- After 1950, we rely on estimates from the United Nations World Population Prospects.", "shortUnit": "", "unit": "people", "timespan": "-10000-2100", "type": "Integer", "owidVariableId": 953899, "shortName": "population", "lastUpdated": "2024-07-15", "nextUpdate": "2026-07-15", "citationShort": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data", "citationLong": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data. “Population” [dataset]. PBL Netherlands Environmental Assessment Agency, “History Database of the Global Environment 3.3”; Gapminder, “Population v7”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update”; Gapminder, “Systema Globalis” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/953899.metadata.json"}, "World region according to OWID": {"titleShort": "World region according to OWID", "titleLong": "World region according to OWID", "descriptionShort": "Regions defined by Our World in Data, which are used in OWID charts and maps.", "unit": "", "timespan": "2023-2023", "type": "Continent", "owidVariableId": 900801, "shortName": "owid_region", "lastUpdated": "2023-01-01", "citationShort": "Our World in Data – processed by Our World in Data", "citationLong": "Our World in Data – processed by Our World in Data. “World region according to OWID” [dataset]. Our World in Data, “Regions” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/900801.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Malnutrition: Number of children who are stunted", "source_url": "https://ourworldindata.org/grapher/number-children-stunted-who.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes"], "row_count_total": 5275, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2332200"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2319500"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2386700"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2465300"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2473800"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2473399.9"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2481800"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2423300"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2372900"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2359900"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2336900"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2344500"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2375000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2404199.8000000003"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2449400"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2487500"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2507800"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2531500"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2559200"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2591000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2637300"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2676200"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2709400"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2770900"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2024", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2849800"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "1990", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "43047398"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "1991", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "44144200"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "1992", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "45078500"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "1993", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "45878200"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "1994", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "46517400"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "1995", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "47059800"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "1996", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "47613900"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "1997", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "48068700"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "1998", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "48463500"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "1999", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "48976700"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2000", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "49674200"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2001", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "50493103"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2002", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "51349600"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2003", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "52164898"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2004", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "52907898"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2005", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "53630200"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2006", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "54277897"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2007", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "54710500"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2008", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "54978100"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2009", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "55126300"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2010", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "55140900"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2011", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "55119800"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2012", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "55160800"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2013", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "55277603"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2014", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "55423800"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2015", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "55550198"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2016", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "55576397"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2017", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "55461900"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2018", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "55296300"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2019", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "55174698"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2020", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "55291603"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2021", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "55837200"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2022", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "56903200"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2023", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "57958200"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2024", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "59121700"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "85300.006"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "82300"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "78200"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "73300.004"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "67900"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "61700"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "55300"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "49200.002"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "43400"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "38300"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "33900"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "30700"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "28500"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "27000"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "25799.999"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "24600.001"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "23200.002"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "21300"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "19000"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "16800"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "14800"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "13200"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "12100"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "11200"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "10500"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "681500"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "641599.9500000001"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "605700"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "578900.0399999999"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "562800"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "550799.97"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "541600"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "534700.04"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "529400.05"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "525200"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "519599.99999999994"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "513599.99999999994"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "511100"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "510499.99999999994"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "510499.99999999994"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "510300"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "507200"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "499100"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "487100"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "473299.98"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "458800"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "445400"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2022", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "434700"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "425200.02"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2024", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "414100"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "1990", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "13317500"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "1991", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "13442800"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "1992", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "13445300"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "1993", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "13336100"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "1994", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "13135799"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "1995", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "12830700"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "1996", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "12443000"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "1997", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "12004701"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "1998", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "11559299"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "1999", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "11145100"}], "rows_tail": [{"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2015", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "13804600"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2016", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "13458401"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2017", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "12960700"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2018", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "12309900"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2019", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "11658200"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2020", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "10954100"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2021", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "10190400"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2022", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "9524700"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2023", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "8951900.5"}, {"Entity": "Western Pacific (WHO)", "Code": "WHO_WPAC", "Year": "2024", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "8415700"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1990", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "260910800"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1991", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "258102500.00000003"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1992", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "252643700"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1993", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "246353090"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1994", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "239654000"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1995", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "232183500"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1996", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "225180590"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1997", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "219213500"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1998", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "214094100"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1999", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "209953200"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "207205410"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "205712400"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "204654400"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "203388600"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "201922500"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "200287800"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "198258300"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "195678910"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "192931090"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "190101410"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "186873700"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "183495800"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "180440800"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "177516590"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "174448000"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "171224800"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "167876900"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "163968290"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "159833000"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "156070900"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "152999500"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "150787900"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "150111000"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "149813200"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "150194900"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1951700"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1985600"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2023699.9999999998"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2057799.9999999998"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2088800"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2116000"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2143100"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2165800"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2179100"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2187399.9"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2187899.8"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2185000"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2188600"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2208600"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2242200"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2295800"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2363600"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2435400"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2503300"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2572100.1999999997"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2648399.8"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2734000"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2022", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2841800"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2023", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "2941200"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2024", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "3054600"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "989000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1011900.1000000001"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1032700"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1049500"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1064300.1"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1079700"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1095200"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1106400"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1114200"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1116699.9"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1112300"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1102100"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1090800"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1080600"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1070600"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1058400"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1040400"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1016099.8999999999"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "991300"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "971799.97"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "960700.0399999999"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "963900"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "984800"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1009400.0000000001"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "1036100"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "593000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "619800"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "645800"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "668200"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "684000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "694600"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "700200"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "700700"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "702500"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "705700.0399999999"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "710100"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "712799.97"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "709300"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "698300"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "677599.9700000001"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "650400.0399999999"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "619000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "587400"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "559300"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "539800"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "527500"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "526200"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "534400.05"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "541799.96"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Stunting numbers among children under 5 years of age (millions), model-based estimates - Sex: both sexes": "553200"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "number-children-stunted-who", "metadata_url": "https://ourworldindata.org/grapher/number-children-stunted-who.metadata.json", "chart_title": "Malnutrition: Number of children who are stunted", "chart_subtitle": "The number of children younger than five who are stunted – significantly shorter than the average for their age, as a consequence of poor nutrition or repeated infection.", "chart_note": null, "chart_citation": "World Health Organization - Global Health Observatory (2026)", "original_chart_url": "https://ourworldindata.org/grapher/number-children-stunted-who", "owid_column_metadata": {"Stunting numbers among children under 5 years of age (millions), model-based estimates - 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Reuse should follow the source and license information in this chart metadata."}, {"title": "Childhood stunting rates", "source_url": "https://ourworldindata.org/grapher/long-run-childhood-stunting-rates.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Long-run childhood stunting rates"], "row_count_total": 635, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Long-run childhood stunting rates": "60.9936"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Long-run childhood stunting rates": "56.342453"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Long-run childhood stunting rates": "46.9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Long-run childhood stunting rates": "23.4"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Long-run childhood stunting rates": "11.4"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1985", "Long-run childhood stunting rates": "19.920166"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1995", "Long-run childhood stunting rates": "23.066668"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Long-run childhood stunting rates": "13.55"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Long-run childhood stunting rates": "10.1"}, {"Entity": "Angola", "Code": "AGO", "Year": "1955", "Long-run childhood stunting rates": "74.84369"}, {"Entity": "Angola", "Code": "AGO", "Year": "1995", "Long-run childhood stunting rates": "64.5"}, {"Entity": "Angola", "Code": "AGO", "Year": "2005", "Long-run childhood stunting rates": "32.55866"}, {"Entity": "Angola", "Code": "AGO", "Year": "2015", "Long-run childhood stunting rates": "41.2"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1995", "Long-run childhood stunting rates": "11.971466"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2005", "Long-run childhood stunting rates": "6.7000003"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2015", "Long-run childhood stunting rates": "6.8"}, {"Entity": "Armenia", "Code": "ARM", "Year": "1995", "Long-run childhood stunting rates": "20.05"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2005", "Long-run childhood stunting rates": "20"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2015", "Long-run childhood stunting rates": "7.1000004"}, {"Entity": "Aruba", "Code": "ABW", "Year": "1965", "Long-run childhood stunting rates": "3.2705603"}, {"Entity": "Australia", "Code": "AUS", "Year": "1895", "Long-run childhood stunting rates": "27.038742"}, {"Entity": "Australia", "Code": "AUS", "Year": "1905", "Long-run childhood stunting rates": "21.798351"}, {"Entity": "Australia", "Code": "AUS", "Year": "1945", "Long-run childhood stunting rates": "4.301838"}, {"Entity": "Australia", "Code": "AUS", "Year": "1975", "Long-run childhood stunting rates": "2.1078272"}, {"Entity": "Australia", "Code": "AUS", "Year": "1985", "Long-run childhood stunting rates": "1.0634296"}, {"Entity": "Australia", "Code": "AUS", "Year": "1995", "Long-run childhood stunting rates": "0.0000010359116"}, {"Entity": "Australia", "Code": "AUS", "Year": "2005", "Long-run childhood stunting rates": "1.9"}, {"Entity": "Austria", "Code": "AUT", "Year": "2005", "Long-run childhood stunting rates": "0.7701558"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "1965", "Long-run childhood stunting rates": "19.464993"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "1995", "Long-run childhood stunting rates": "24.179619"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2005", "Long-run childhood stunting rates": "23.348133"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "1975", "Long-run childhood stunting rates": "16.198936"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "1985", "Long-run childhood stunting rates": "13.998072"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "1995", "Long-run childhood stunting rates": "12.834354"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "1965", "Long-run childhood stunting rates": "86.79235"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "1985", "Long-run childhood stunting rates": "77.92541"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "1995", "Long-run childhood stunting rates": "65.99836"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2005", "Long-run childhood stunting rates": "49.51869"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2015", "Long-run childhood stunting rates": "34.24489"}, {"Entity": "Barbados", "Code": "BRB", "Year": "1965", "Long-run childhood stunting rates": "6.555627"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2005", "Long-run childhood stunting rates": "5.7999997"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1965", "Long-run childhood stunting rates": "0.8849011"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2005", "Long-run childhood stunting rates": "3.6"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1915", "Long-run childhood stunting rates": "38.647"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1965", "Long-run childhood stunting rates": "2.0278273"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1975", "Long-run childhood stunting rates": "1.6060262"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1995", "Long-run childhood stunting rates": "1.0596621"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2015", "Long-run childhood stunting rates": "1.6"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2005", "Long-run childhood stunting rates": "21.85"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2015", "Long-run childhood stunting rates": "16.9"}, {"Entity": "Benin", "Code": "BEN", "Year": "1995", "Long-run childhood stunting rates": "49.949997"}, {"Entity": "Benin", "Code": "BEN", "Year": "2005", "Long-run childhood stunting rates": "42.1"}, {"Entity": "Benin", "Code": "BEN", "Year": "2015", "Long-run childhood stunting rates": "38.149998"}, {"Entity": "Bermuda", "Code": "BMU", "Year": "1965", "Long-run childhood stunting rates": "2.0247045"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "1985", "Long-run childhood stunting rates": "69.255936"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "1995", "Long-run childhood stunting rates": "55.1"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2005", "Long-run childhood stunting rates": "39"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "1985", "Long-run childhood stunting rates": "52.8"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "1995", "Long-run childhood stunting rates": "39.8"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2005", "Long-run childhood stunting rates": "25.650002"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2015", "Long-run childhood stunting rates": "16.300001"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "1995", "Long-run childhood stunting rates": "10.9"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2005", "Long-run childhood stunting rates": "8.6"}, {"Entity": "Botswana", "Code": "BWA", "Year": "1995", "Long-run childhood stunting rates": "34.79737"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2005", "Long-run childhood stunting rates": "25.9"}, {"Entity": "Brazil", "Code": "BRA", "Year": "1965", "Long-run childhood stunting rates": "27.750547"}, {"Entity": "Brazil", "Code": "BRA", "Year": "1985", "Long-run childhood stunting rates": "21.3"}, {"Entity": "Brazil", "Code": "BRA", "Year": "1995", "Long-run childhood stunting rates": "8.039012"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2005", "Long-run childhood stunting rates": "6"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2015", "Long-run childhood stunting rates": "6"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2005", "Long-run childhood stunting rates": "20.909878"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2005", "Long-run childhood stunting rates": "8.303947"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2015", "Long-run childhood stunting rates": "6.2974043"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "1985", "Long-run childhood stunting rates": "49.7"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "1995", "Long-run childhood stunting rates": "54.8"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2005", "Long-run childhood stunting rates": "39.782455"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2015", "Long-run childhood stunting rates": "27.168993"}, {"Entity": "Burundi", "Code": "BDI", "Year": "1985", "Long-run childhood stunting rates": "62.6"}, {"Entity": "Burundi", "Code": "BDI", "Year": "1995", "Long-run childhood stunting rates": "69.4"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2005", "Long-run childhood stunting rates": "65.34456"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2015", "Long-run childhood stunting rates": "60.4"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "1995", "Long-run childhood stunting rates": "61.85"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2005", "Long-run childhood stunting rates": "48.933334"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2015", "Long-run childhood stunting rates": "30.400002"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "1985", "Long-run childhood stunting rates": "40.5"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "1995", "Long-run childhood stunting rates": "50.300003"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2005", "Long-run childhood stunting rates": "41.466667"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2015", "Long-run childhood stunting rates": "33.65"}, {"Entity": "Canada", "Code": "CAN", "Year": "1945", "Long-run childhood stunting rates": "18.056099"}, {"Entity": "Canada", "Code": "CAN", "Year": "1965", "Long-run childhood stunting rates": "10.770369"}, {"Entity": "Canada", "Code": "CAN", "Year": "1975", "Long-run childhood stunting rates": "2.0625596"}, {"Entity": "Cape Verde", "Code": "CPV", "Year": "1985", "Long-run childhood stunting rates": "29.602942"}, {"Entity": "Cape Verde", "Code": "CPV", "Year": "1995", "Long-run childhood stunting rates": "22.97694"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "1995", "Long-run childhood stunting rates": "53.800003"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2005", "Long-run childhood stunting rates": "51.266666"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2015", "Long-run childhood stunting rates": "45.850002"}, {"Entity": "Chad", "Code": "TCD", "Year": "1995", "Long-run childhood stunting rates": "51.1"}, {"Entity": "Chad", "Code": "TCD", "Year": "2005", "Long-run childhood stunting rates": "50.3"}, {"Entity": "Chad", "Code": "TCD", "Year": "2015", "Long-run childhood stunting rates": "35.80985"}, {"Entity": "Chile", "Code": "CHL", "Year": "1985", "Long-run childhood stunting rates": "13.532548"}, {"Entity": "Chile", "Code": "CHL", "Year": "1995", "Long-run childhood stunting rates": "2.578746"}, {"Entity": "Chile", "Code": "CHL", "Year": "2005", "Long-run childhood stunting rates": "1.5455285"}, {"Entity": "Chile", "Code": "CHL", "Year": "2015", "Long-run childhood stunting rates": "1.2902175"}, {"Entity": "China", "Code": "CHN", "Year": "1955", "Long-run childhood stunting rates": "50.767757"}, {"Entity": "China", "Code": "CHN", "Year": "1975", "Long-run childhood stunting rates": "27.836983"}, {"Entity": "China", "Code": "CHN", "Year": "1985", "Long-run childhood stunting rates": "28.417568"}, {"Entity": "China", "Code": "CHN", "Year": "1995", "Long-run childhood stunting rates": "18.371563"}, {"Entity": "China", "Code": "CHN", "Year": "2005", "Long-run childhood stunting rates": "7.0105414"}, {"Entity": "China", "Code": "CHN", "Year": "2015", "Long-run childhood stunting rates": "4.0525875"}, {"Entity": "Colombia", "Code": "COL", "Year": "1955", "Long-run childhood stunting rates": "90.4689"}, {"Entity": "Colombia", "Code": "COL", "Year": "1985", "Long-run childhood stunting rates": "26.78255"}, {"Entity": "Colombia", "Code": "COL", "Year": "1995", "Long-run childhood stunting rates": "21.25"}, {"Entity": "Colombia", "Code": "COL", "Year": "2005", "Long-run childhood stunting rates": "14.950001"}, {"Entity": "Colombia", "Code": "COL", "Year": "2015", "Long-run childhood stunting rates": "13.3"}, {"Entity": "Comoros", "Code": "COM", "Year": "1985", "Long-run childhood stunting rates": "43.91842"}, {"Entity": "Comoros", "Code": "COM", "Year": "1995", "Long-run childhood stunting rates": "48.05"}, {"Entity": "Comoros", "Code": "COM", "Year": "2005", "Long-run childhood stunting rates": "31.400002"}, {"Entity": "Congo", "Code": "COG", "Year": "1985", "Long-run childhood stunting rates": "33.6664"}, {"Entity": "Congo", "Code": "COG", "Year": "2005", "Long-run childhood stunting rates": "31.2"}, {"Entity": "Congo", "Code": "COG", "Year": "2015", "Long-run childhood stunting rates": "21.5"}], "rows_tail": [{"Entity": "Spain", "Code": "ESP", "Year": "1995", "Long-run childhood stunting rates": "1.387596"}, {"Entity": "Spain", "Code": "ESP", "Year": "2005", "Long-run childhood stunting rates": "1.16643"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "1925", "Long-run childhood stunting rates": "55.535503"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "1985", "Long-run childhood stunting rates": "34.550003"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "1995", "Long-run childhood stunting rates": "24.620567"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2005", "Long-run childhood stunting rates": "18.201544"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2015", "Long-run childhood stunting rates": "17.3"}, {"Entity": "Sudan", "Code": "SDN", "Year": "2005", "Long-run childhood stunting rates": "41.238583"}, {"Entity": "Sudan", "Code": "SDN", "Year": "2015", "Long-run childhood stunting rates": "40.925"}, {"Entity": "Suriname", "Code": "SUR", "Year": "1965", "Long-run childhood stunting rates": "21.594645"}, {"Entity": "Suriname", "Code": "SUR", "Year": "1995", "Long-run childhood stunting rates": "14.4"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2005", "Long-run childhood stunting rates": "9.1"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2015", "Long-run childhood stunting rates": "9.6"}, {"Entity": "Sweden", "Code": "SWE", "Year": "1955", "Long-run childhood stunting rates": "3.3240466"}, {"Entity": "Sweden", "Code": "SWE", "Year": "1965", "Long-run childhood stunting rates": "0.94899136"}, {"Entity": "Sweden", "Code": "SWE", "Year": "1975", "Long-run childhood stunting rates": "0.41482794"}, {"Entity": "Sweden", "Code": "SWE", "Year": "1985", "Long-run childhood stunting rates": "1.1451504"}, {"Entity": "Syria", "Code": "SYR", "Year": "1985", "Long-run childhood stunting rates": "34.1"}, {"Entity": "Syria", "Code": "SYR", "Year": "1995", "Long-run childhood stunting rates": "28.553743"}, {"Entity": "Syria", "Code": "SYR", "Year": "2005", "Long-run childhood stunting rates": "26.8"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "1925", "Long-run childhood stunting rates": "71.714905"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "1935", "Long-run childhood stunting rates": "61.398613"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "1965", "Long-run childhood stunting rates": "24.857235"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "1975", "Long-run childhood stunting rates": "6.9828033"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "1985", "Long-run childhood stunting rates": "4.1016803"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "1975", "Long-run childhood stunting rates": "26.879902"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "1995", "Long-run childhood stunting rates": "47.216156"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2005", "Long-run childhood stunting rates": "35.95"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2015", "Long-run childhood stunting rates": "21.600002"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "1985", "Long-run childhood stunting rates": "58.8"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "1995", "Long-run childhood stunting rates": "58.149998"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2005", "Long-run childhood stunting rates": "43.8"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2015", "Long-run childhood stunting rates": "35.461365"}, {"Entity": "Thailand", "Code": "THA", "Year": "1985", "Long-run childhood stunting rates": "22.402967"}, {"Entity": "Thailand", "Code": "THA", "Year": "1995", "Long-run childhood stunting rates": "18.978205"}, {"Entity": "Thailand", "Code": "THA", "Year": "2005", "Long-run childhood stunting rates": "15.05"}, {"Entity": "Thailand", "Code": "THA", "Year": "2015", "Long-run childhood stunting rates": "10.6"}, {"Entity": "Togo", "Code": "TGO", "Year": "1985", "Long-run childhood stunting rates": "44.7"}, {"Entity": "Togo", "Code": "TGO", "Year": "1995", "Long-run childhood stunting rates": "44.230747"}, {"Entity": "Togo", "Code": "TGO", "Year": "2005", "Long-run childhood stunting rates": "32.621014"}, {"Entity": "Togo", "Code": "TGO", "Year": "2015", "Long-run childhood stunting rates": "30.199999"}, {"Entity": "Tonga", "Code": "TON", "Year": "1985", "Long-run childhood stunting rates": "1.6302066"}, {"Entity": "Tonga", "Code": "TON", "Year": "2005", "Long-run childhood stunting rates": "7.4674406"}, {"Entity": "Tonga", "Code": "TON", "Year": "2015", "Long-run childhood stunting rates": "2.2"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "1985", "Long-run childhood stunting rates": "4"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "1995", "Long-run childhood stunting rates": "6.5"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2005", "Long-run childhood stunting rates": "6.8999996"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "1985", "Long-run childhood stunting rates": "24.3"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "1995", "Long-run childhood stunting rates": "14.555344"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2005", "Long-run childhood stunting rates": "8.722105"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2015", "Long-run childhood stunting rates": "8.2"}, {"Entity": "Turkey", "Code": "TUR", "Year": "1985", "Long-run childhood stunting rates": "30.300001"}, {"Entity": "Turkey", "Code": "TUR", "Year": "1995", "Long-run childhood stunting rates": "18.167458"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2005", "Long-run childhood stunting rates": "14.366667"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2015", "Long-run childhood stunting rates": "6.2"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "1965", "Long-run childhood stunting rates": "41.285637"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "1995", "Long-run childhood stunting rates": "27.900002"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2005", "Long-run childhood stunting rates": "20.900002"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2015", "Long-run childhood stunting rates": "9.3"}, {"Entity": "Turks and Caicos Islands", "Code": "TCA", "Year": "2015", "Long-run childhood stunting rates": "0.3"}, {"Entity": "Tuvalu", "Code": "TUV", "Year": "2005", "Long-run childhood stunting rates": "9.547664"}, {"Entity": "Tuvalu", "Code": "TUV", "Year": "2015", "Long-run childhood stunting rates": "6"}, {"Entity": "Uganda", "Code": "UGA", "Year": "1985", "Long-run childhood stunting rates": "52.100002"}, {"Entity": "Uganda", "Code": "UGA", "Year": "1995", "Long-run childhood stunting rates": "53.55"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2005", "Long-run childhood stunting rates": "38.98"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2015", "Long-run childhood stunting rates": "29.26"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1975", "Long-run childhood stunting rates": "6.8426104"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1995", "Long-run childhood stunting rates": "22.2"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2005", "Long-run childhood stunting rates": "5.8226137"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1905", "Long-run childhood stunting rates": "45.374622"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1915", "Long-run childhood stunting rates": "19.58044"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1945", "Long-run childhood stunting rates": "8.835714"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1955", "Long-run childhood stunting rates": "4.21069"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1965", "Long-run childhood stunting rates": "4.6541424"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1975", "Long-run childhood stunting rates": "3.4212284"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1985", "Long-run childhood stunting rates": "2.4371676"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1995", "Long-run childhood stunting rates": "1.1560502"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2005", "Long-run childhood stunting rates": "3.5633385"}, {"Entity": "United States", "Code": "USA", "Year": "1905", "Long-run childhood stunting rates": "16.449593"}, {"Entity": "United States", "Code": "USA", "Year": "1915", "Long-run childhood stunting rates": "9.144431"}, {"Entity": "United States", "Code": "USA", "Year": "1925", "Long-run childhood stunting rates": "3.0455945"}, {"Entity": "United States", "Code": "USA", "Year": "1935", "Long-run childhood stunting rates": "3.6784"}, {"Entity": "United States", "Code": "USA", "Year": "1965", "Long-run childhood stunting rates": "6.5225053"}, {"Entity": "United States", "Code": "USA", "Year": "1985", "Long-run childhood stunting rates": "3.2"}, {"Entity": "United States", "Code": "USA", "Year": "1995", "Long-run childhood stunting rates": "2.6999998"}, {"Entity": "United States", "Code": "USA", "Year": "2005", "Long-run childhood stunting rates": "2.3600001"}, {"Entity": "United States", "Code": "USA", "Year": "2015", "Long-run childhood stunting rates": "3.2"}, {"Entity": "Uruguay", "Code": "URY", "Year": "1955", "Long-run childhood stunting rates": "15.705527"}, {"Entity": "Uruguay", "Code": "URY", "Year": "1985", "Long-run childhood stunting rates": "22.489412"}, {"Entity": "Uruguay", "Code": "URY", "Year": "1995", "Long-run childhood stunting rates": "16.157953"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2005", "Long-run childhood stunting rates": "10.308847"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2015", "Long-run childhood stunting rates": "8.05"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1955", "Long-run childhood stunting rates": "12.184454"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1995", "Long-run childhood stunting rates": "32.25"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2005", "Long-run childhood stunting rates": "22"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2015", "Long-run childhood stunting rates": "10"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "1995", "Long-run childhood stunting rates": "28.24887"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2005", "Long-run childhood stunting rates": "30.75"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1955", "Long-run childhood stunting rates": "34.884087"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1965", "Long-run childhood stunting rates": "41.798187"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1985", "Long-run childhood stunting rates": "16.46662"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1995", "Long-run childhood stunting rates": "19.281368"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2005", "Long-run childhood stunting rates": "15.9035015"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "1955", "Long-run childhood stunting rates": "65.08407"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "1985", "Long-run childhood stunting rates": "64.81594"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "1995", "Long-run childhood stunting rates": "48.36337"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2005", "Long-run childhood stunting rates": "33.80216"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2015", "Long-run childhood stunting rates": "24.679432"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1975", "Long-run childhood stunting rates": "43.54657"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1985", "Long-run childhood stunting rates": "56.9"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1995", "Long-run childhood stunting rates": "64.18652"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Long-run childhood stunting rates": "57.436172"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Long-run childhood stunting rates": "55.5"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Long-run childhood stunting rates": "60.978836"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Long-run childhood stunting rates": "46.050003"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Long-run childhood stunting rates": "36.6"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Long-run childhood stunting rates": "31.600002"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Long-run childhood stunting rates": "40.55"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Long-run childhood stunting rates": "37.266666"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Long-run childhood stunting rates": "27.833334"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "long-run-childhood-stunting-rates", "metadata_url": "https://ourworldindata.org/grapher/long-run-childhood-stunting-rates.metadata.json", "chart_title": "Childhood stunting rates", "chart_subtitle": "Share of 2-to-10-year-olds who are defined as stunted. Stunting is when a child is significantly shorter than the average for their age. It is a consequence of poor nutrition or repeated infection.", "chart_note": null, "chart_citation": "Schneider et al. (2026). The decline of child stunting in 122 countries: a systematic review of child growth studies since the 19th century.", "original_chart_url": "https://ourworldindata.org/grapher/long-run-childhood-stunting-rates", "owid_column_metadata": {"Long-run childhood stunting rates": {"titleShort": "Long-run childhood stunting rates", "titleLong": "Long-run childhood stunting rates", "descriptionShort": "Share of children between 2 and 10 years old who are stunted, so shorter than would be expected for their age. Stunting is a sign of long-term malnutrition or poor health in early childhood.", "descriptionKey": ["Stunting rates are an important indicator of child health and nutrition. High rates can reflect poor nutrition and frequent exposure to disease or illness, which increase a child’s nutrient requirements and affect their ability to retain nutrients. This can hinder physical and cognitive development, and can persist throughout someone’s life.", "Children are considered stunted if their height-for-age is more than two standard deviations below the median of the World Health Organization (WHO) [child growth standards](https://www.who.int/tools/child-growth-standards/standards).", "This data comes from [an article by Schneider et al. (2026)](https://gh.bmj.com/content/11/2/e018607) that compiles historical studies and the UN Joint Malnutrition Estimates (JME) database to provide a long-term perspective on child stunting rates.", "Schneider et al. compile stunting rates among children between 2 and 10 years old. This data is therefore not directly comparable to more recent datasets (such as the UN JME database) which provide stunting rates for children under 5 years old.", "This chart includes only data from high-quality studies, which e.g. have larger sample sizes or look at the national or sub-national level (as opposed to local or small-scale studies). The full dataset includes additional data points and is available via the original paper [here](https://doi.org/10.5281/zenodo.18262234)", "The year shown is the birth decade of the children in each study, plotted at the midpoint of that decade. So the 1955 data point includes children born between 1950 and 1959."], "shortUnit": "%", "unit": "%", "timespan": "1895-2015", "type": "Numeric", "owidVariableId": 1210239, "shortName": "stunting_rate", "lastUpdated": "2026-03-27", "citationShort": "Schneider et al. (2026) – with minor processing by Our World in Data", "citationLong": "Schneider et al. (2026) – with minor processing by Our World in Data. “Long-run childhood stunting rates” [dataset]. Schneider et al., “Worldwide Historical Child Stunting Dataset” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1210239.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "24c64a8378dcde2b3dce"},
{"raw_link": "https://ourworldindata.org/most-people-care-about-farm-animals-our-food-system-doesnt-reflect-that", "title": "Most people care about farm animals — our food system doesn't reflect that", "context": "Home\nAnimal Welfare\nMost people care about farm animals — our food system doesn't reflect that\nSurveys worldwide show that most people find common animal farming practices unacceptable, even where meat consumption is high.\nBy\nPablo Rosado\nApril 20, 2026\nBrowse past versions\nCite this article\nReuse our work freely\nIn a world that often feels deeply polarized, it is rare to find a topic where almost everyone agrees. The treatment of farm animals is one of them. Surveys show that a strong majority of people, regardless of their diet, oppose common practices in animal agriculture.\nThe chart below shows results from a survey of British adults on whether several common farming practices are acceptable. For every practice listed, less than 13% of respondents rated it “acceptable”. A small minority approved of procedures such as castrating newborn calves or trimming the tails of newborn piglets, but large majorities judged each practice as “not acceptable”.\nDownload\nExplore the data\nin an interactive chart\n.\nSome of the practices mentioned in the survey — such as killing newborn chicks, keeping animals in cages, or amputating body parts — may sound extreme. As\na recent US survey suggests\n, most consumers believe ​​that the meat they buy comes from animals raised in good conditions.\nBut in modern agriculture, these practices are widespread:\nmost farmed animals\nglobally are raised in factory farms, including around 85% in the UK, and 99% in the US.\n1\nFor example, in both countries, nearly all male chicks born in the egg industry are killed, including in free-range systems.\n2\nIn the appendix at the end of this article, we describe these and other practices in more detail, including some estimates of how common they are in the UK.\nOther surveys show similarly large opposition to common farming practices. In a recent US survey, at most one in five respondents rated each practice as “acceptable”. The researchers noted that this view was broadly shared across age, gender, income, political affiliation, ethnicity, and region. As the following chart shows, the vast majority rated each practice as either somewhat or very unacceptable.\nIn\nanother US survey\n, around two in five of respondents agreed on banning slaughterhouses and factory farming, and close to a third supported banning animal farming altogether.\nThese views are not limited to the UK and the US. At the end of this article, we include a list of related surveys showing that concern for animal welfare extends across many other countries.\nGiven all this, and with more plant-based foods now on the market, including meat alternatives, one might expect people in these countries to be shifting away from consuming animal products. But the data shows a different story.\nMeat consumption remains high\nAt a global level, meat consumption is not only high but also increasing. Each year, hundreds of billions of\nland animals\n,\nfish\n, and\ncrustaceans\nare farmed and killed to produce food.\nIn the UK, only a small share of adults identify as vegetarian or vegan, while the share of people who consume meat or fish remains around 90%. In fact, due to a gradual shift from red meat to poultry, the number of animals slaughtered in the UK has actually increased by around 20% in the last decade.\n3\nThe same pattern holds for the US:\nmost people\ncontinue to eat meat, and\nmore animals\nare slaughtered now than ever before.\nThis highlights that the widespread opposition to common farming practices we saw earlier is not limited to vegans and vegetarians: the vast majority of survey respondents regularly consume meat. It would therefore seem that for most people, reducing meat consumption would not be contrary to their values — it would be, in many ways, more consistent with them.\nThere is a clear gap between what people want — meat produced without suffering — and what the food system delivers. Understanding this gap is the first step to closing it.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nClosing the gap\nThe data suggests we do not need to convince people to care about animals. The majority already do. But changing behavior on a large scale is difficult, especially in a food system that makes animal products cheap and convenient while hiding many of the associated welfare costs.\nThere is a clear gap between what people want — meat produced without suffering — and what the food system delivers.\nIf we want to close the gap between our food system and our values, we can’t just rely on changing people’s diets. Several other levers need to move together, including innovation and policy.\nFood companies offer products that look and taste similar to meat and fish, but are made from plants. And soon, we will be able to buy cultivated meat, produced without raising and slaughtering animals.\n4\nOnce these new products reach price parity with conventional foods, consumers may be more willing to adopt them, as\na recent US survey\nsuggested. The challenge now is to make alternative proteins cheaper, tastier, and more widely available.\nIn the meantime, as long as animals are still part of the system, policy can ensure welfare standards improve, phasing out the worst practices that most people oppose.\nOne of the biggest opportunities of our generation is to build a system where the food we eat reflects the values we already hold.\nAcknowledgments\nI would like to thank Max Roser and Hannah Ritchie for their valuable suggestions and feedback on this article.\nContinue reading on Our World in Data\nHow many animals get slaughtered every day?\nHundreds of millions of animals get killed for meat every day.\nHow many animals are factory-farmed?\nThe majority of farm animals in the world are factory-farmed.\nDo better cages or cage-free environments really improve the lives of hens?\nResearch suggests that moving hens from battery cages to cage-free environments reduces the time animals spend in pain substantially.\nAppendix 1: Common farming practices\nHere is an overview of common farming practices used today worldwide in meat, egg, and dairy production that impact animal welfare. They are designed to manage large numbers of animals efficiently and profitably, at the cost of the animals experiencing substantial pain and distress.\nThere is no comprehensive data on the prevalence of these practices: farmers are often not required to report them, and national statistics are scarce. We therefore include some estimates of how many animals may be affected each year in the UK alone, taken from a separate article by Bryant Research:\n5\nKilling newborn male chicks using gas or mechanical maceration\n. Male chicks cannot lay eggs and are not profitable to raise for meat, so they are typically killed on the day they hatch, either using gas or using a machine with fast-rotating blades. This practice, commonly known as “chick culling”, is estimated to lead to the deaths of around 99% of male chicks in the UK (about 42 million animals annually).\nKeeping egg-laying hens in small cages\n. Hens are often kept in cages to reduce space requirements and to make flock management easier. In many countries, this means battery cages that give each bird roughly the area of an A4 sheet of printer paper, with wire floors and little room to move or stretch their wings. The UK has moved away from these systems to some extent: about 23% of hens (around 11 million animals) are kept in enriched cages, which provide slightly more space and include features such as perches and nesting areas.\nCutting the beaks of young chicks\n. The tips of chicks’ beaks are trimmed, usually within the first week of life, to reduce injuries from feather-pecking in large, high-density flocks. This practice, called “beak trimming” or sometimes “debeaking”, is considered common in the UK, although its exact prevalence is not known.\nCastrating young male calves\n. Young male calves are often castrated, either by placing a tight rubber ring around the scrotum, by using a clamp that crushes the blood vessels above the testicles, or by removing the testicles with a knife. This is typically done without any pain relief. The purpose is to make the animals easier to manage, prevent unwanted breeding, and produce higher-value meat. No estimate for its prevalence in the UK is available, but it is considered common practice.\nRemoving the horn buds of calves using a hot iron\n. This procedure, called “disbudding”, involves burning the horn-producing tissue of young calves to prevent horn growth and reduce the risk of injuries to other animals and farm workers. It causes acute pain and is often carried out without pain relief. In the UK, around 90% of calves are disbudded, affecting about three million animals each year. If calves are not disbudded when young, farmers may later remove fully developed horns, a practice known as “dehorning”, which is more invasive and painful. Similar practices are often applied to goats.\nKilling newborn male dairy calves\n. Male calves in dairy systems are often killed shortly after birth because they cannot produce milk and have low economic value. In the UK, around 18% of dairy calves are killed at a very young age (about 80,000 animals).\nCutting or grinding the teeth of newborn piglets\n. Piglets’ teeth are shortened to reduce injuries to other piglets and to the sow’s udder. No estimate for its prevalence in the UK is available, but it remains widely used there and in many other countries.\nCutting the tails of newborn piglets\n. The tails of newborn piglets are partially amputated, typically without pain relief. This is done to reduce the risk of tail-biting in crowded or stressful conditions. Around 85% of UK piglets (about 7.7 million animals) undergo this practice, known as “tail docking”.\nKeeping pregnant sows in narrow crates\n. During pregnancy, sows are kept individually in metal-barred crates, only slightly larger than their bodies: this means that for weeks, they can only stand up and lie down, but cannot turn around. These crates are used to reduce aggression between sows when keeping them in dense conditions. In the UK, around 60% of sows (about 200,000 animals) are kept in these conditions.\nCastrating male piglets\n. Male piglets are castrated to prevent unwanted breeding and to reduce the risk of ‘boar taint’, an unpleasant smell and taste in the meat. In many countries, this is typically done by surgically removing the testicles without pain relief. It is common practice in much of the world, but has become rare in the UK, where pigs are usually slaughtered at a younger age, before boar taint typically develops.\nEarly separation of cows and calves\n. In dairy systems, calves are usually separated from their mothers within hours or days of birth to allow milk collection, causing significant distress to both the mother and her calf. It is common practice globally, including the UK.\nMarking animals for identification.\nFarmed land animals are routinely marked so they can be identified and managed. This often involves piercing the ear with a plastic tag (“ear tagging”) or cutting out small sections of the ear (“ear notching”). In some regions, farmers also mark animals by burning the skin with a hot iron (“branding”). These procedures cause acute pain at the time they are performed. Ear tagging and similar identification methods are used around the world and affect the majority of farmed land animals, including in the UK.\nMarking and handling animals in aquaculture.\nFarmed fish in intensive systems are routinely handled and sometimes physically altered for identification and management — for example, through fin clipping, tagging, or other marking techniques. These procedures can cause acute stress and pain, and are widely used in intensive aquaculture systems worldwide, including in the UK.\nTransporting animals over long distances.\nFarmed animals are routinely moved between farms and to slaughterhouses, often over many hours in crowded vehicles, and in some countries without feed, water, or adequate temperature control. This is standard practice worldwide, including in the UK.\nThere are also other routine procedures involved in food production that have major welfare implications, such as feed withdrawal before slaughter, and the slaughter process itself, which varies in the level of pain and distress animals may experience depending on the species and method used.\nIn addition, there are other painful practices that occur in specific sectors, for example, force-feeding ducks and geese to produce foie gras, or removing the eyestalks of shrimp to stimulate reproduction in aquaculture.\nTo accelerate progress in reducing farm animal suffering, we need reliable data on how often these practices are used, and robust estimates of the intensity and duration of the pain they cause, as the\nWelfare Footprint Institute\naims to quantify.\nShow more\nAppendix 2: Available surveys on people’s views on common farming practices\nHere is a selection of surveys and petitions related to animal welfare:\nAcceptability of Common Farming Practices\n– Bryant Research. Conducted in 2021, with around 1,000 participants in the UK. Large majorities described common farming practices as unacceptable. The strongest opposition was to confinement cages for chickens and pigs, which around 94% and 96% rated as unacceptable.\nKnowledge and attitudes to factory farming practices in the UK and US: Can minds and behaviour be changed?\n6\n. Conducted in 2025, with around 2,000 participants in the UK and 2,000 in the US. The researchers found substantial knowledge gaps about common farming practices. When asked about 12 practices, at least three-quarters of respondents rated every practice unacceptable. A significant share of participants supported banning factory farming (56% in the UK; 45% in the US).\nPublic Acceptability Of Standard U.S. Animal Agriculture Practices\n7\n– Faunalytics (2025). Conducted in 2025, with around 1,000 participants in the US. Large majorities rated each of 12 standard animal agriculture practices as (somewhat or very) unacceptable. Opposition was broad across demographic groups.\nAnimals, Food, and Technology (AFT)\n8\n– Sentience Institute. Conducted in 2017, 2019, 2020, 2021, 2023, and 2025, with around 1,000-1,500 participants each year in the US. The latest version resulted in\na majority of people\nreporting discomfort with the way animals are used in the food industry, and\nrelatively strong support\nfor banning slaughterhouses and factory farming. About a third supported even banning animal farming as a whole.\nOn-farm animal welfare for certain animals: modernisation of EU legislation\n– European Commission. Public (not representative) consultation conducted in 2025, with over ~190,000 responses (75% from Germany). Respondents showed strong support for phasing out cages across species, found the systematic killing of day-old male chicks ethically problematic, and agreed that current rules do not ensure animals can express normal behavior.\nAttitudes of Europeans towards animal welfare\n– European Commission. Conducted in\n2007\n(\ndata\n),\n2015\n(\ndata\n), and\n2023\n(\ndata\n), with around 26,000 participants in the EU; about 1,000 per country. This is a Eurobarometer omnibus survey which includes a list of questions about animal welfare. According to the latest version, 84% of Europeans believe that the welfare of farmed animals should be better protected in their country than it is now. A similar number support limiting the transport time of animals.\nPublic attitudes towards aquatic animal welfare\n– Eurogroup for Animals and Compassion in World Farming. Conducted in 2024, with around 12,000 participants from countries in the EU, the US, and China. Among the EU participants (around 9,000), the majority agreed that fish feel pain (71%), feel negative emotions like fear (60%), and are sentient (60%); 91% agreed that the welfare of fish should be protected as much or more than other farmed animals.\nSupport for the end of caged hens\n– Bryant Research. Conducted in 2023, with around 1,000 participants in Canada. Over three-quarters found battery cages or enriched cages unacceptable.\nConsumer attitudes on electrical stunning of sea bream and sea bass in Greece’s largest markets\n– Centre for Aquaculture Progress. Conducted in 2024, with around 1600 participants in Greece, Italy, Spain, and France. 80% said they were willing to pay more for fish that are slaughtered with higher welfare standards.\nCages in farming\n– Compassion in World Farming / YouGov. Conducted in 2020, with around 24,000 participants in 28 European countries. The survey asks specifically about whether using cages in farming is cruel to the animals being farmed. In all countries, at least 48% (and in most countries more than 70%) agreed that using cages in farming is cruel to the animals being farmed. In almost all countries, a majority supported banning cages.\nAcceptability of pig housing systems\n9\n. Conducted in 2021 with around 1,000 participants in Germany. Free-range systems were rated most acceptable, while fully indoor slatted-floor systems were generally rejected. Even when presented with trade-offs (such as cost or environmental benefits), participants only made small compromises and continued to prefer higher-welfare options.\nWhat do Brits think of UK farming practices?\n– YouGov. Conducted in 2020, with around 1,700 participants in the UK. This survey does not measure approval, but awareness. It shows that many people don’t know how widespread common farming practices are (e.g., 78% didn’t know how often carbon dioxide is used in slaughter).\nAnimal welfare priorities poll\n– Humane World for Animals / Focaldata. Conducted in 2023, with around 6,000 UK participants. 63% feel the Government should bring in legislation to phase out intensive farming to protect the environment and animals. 71% say policies improving animal welfare would reflect their values.\nEnd the Cage Age\n– European Citizens’ Initiative. This initiative calls on the European Commission to propose legislation to prohibit the use of cages for EU farmed animals for a range of farmed species. It was launched in 2018 and has been signed by around 1.4 million Europeans.\nDeath by a thousand cuts\n10\n. Published in 2025, with around 300 current or former farmers in the UK. It reveals that, for farmers, sending animals to the slaughterhouse is “hard” (68%) or even “a horrible day” (49%).\nAnd here are some additional polls on general attitudes towards animals and animal rights:\nCauses and protesting\n– YouGov. Conducted in 2023, with around 1,000 participants in the US. The survey asks about 17 different causes, and “Animal rights” is supported by 49% of people (only surpassed by “Racial equality”, \"Religious freedom”, and “Free speech”). It is not among the causes people support the most, but it is one of the causes with the lowest opposition.\nDo you think that animals are conscious?\n– YouGov. Conducted in 2024, with around 3,700 participants in the US. Participants were asked if they think animals are conscious. 53% replied “Definitely”, and 30% “Probably”. Only 17% are unsure or think they are not.\nShow more\nEndnotes\nAs my colleague Hannah explains in\na separate article\n, there is no specific definition of a “factory farm”. Under reasonable assumptions, 85% of animals in the UK are factory farmed, according to\nCompassion in World Farming\n, and 99% in the US, according to\nthe Sentience Institute\n.\nAccording to\nsome estimates\n, the practice of “chick culling” amounts to 40–45 million male chicks killed each year in the UK alone.\nSome countries\nhave banned this practice, but it is still commonplace in most of the world.\nThis is called\nthe small body problem\n: smaller animals must be killed\nin greater numbers\nto produce the same amount of meat as larger animals.\nCultivated meat is real meat grown from animal cells rather than produced by raising and slaughtering animals. Cells are grown in controlled conditions to produce muscle and fat, using the same biological processes that occur inside an animal. It has already been used to produce animal foods such as chicken, beef burgers, salmon, and pork fat. In 2025, the UK started selling pet food made of cultivated chicken meat.\nPrevalence of ‘Unacceptable’ UK Farming Practices\n- Bryant Research (2025).\nOstarek, M., Rogers, C., & Nadel, S. (2026). Knowledge and attitudes to factory farming practices in the UK and US: Can minds and behaviour be changed?\nZenodo\n.\nPolanco, A., & Troy, A. (2025).\nPublic Acceptability Of Standard U.S. Animal Agriculture Practices. Faunalytics.\nAnthis, Jacy Reese; Ladak, Ali (2025), “Animals, Food, and Technology (AFT) Survey 2017–2025”,\nMendeley Data, V2\n.\nSchütz A, Busch G, Sonntag WI (2023) Systematically analysing the acceptability of pig farming systems with different animal welfare levels when considering intra-sustainability trade-offs: Are citizens willing to compromise? PLoS ONE 18(3): e0282530.\nhttps://doi.org/10.1371/journal.pone.0282530\nFlores, C., Knowles, R., Bryant, C. et al. ‘Death by a thousand cuts’: The Role of Moral Distress and Moral Injury in Farmer Mental Ill-Health. J Agric Environ Ethics 38, 18 (2025).\nhttps://doi.org/10.1007/s10806-025-09955-3\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nPablo Rosado (2026) - “Most people care about farm animals — our food system doesn't reflect that” Published online at OurWorldinData.org. 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"2.3423371", "Strongly disagree": "1.2285732"}, {"Entity": "Farmed animals have roughly the same ability to feel pain as humans", "Year": "2023", "Strongly agree": "35.676704", "Agree": "28.278345", "Somewhat agree": "18.442717", "No opinion": "7.6579533", "Somewhat disagree": "6.8531523", "Disagree": "2.460025", "Strongly disagree": "0.63110185"}, {"Entity": "Farmed animals have roughly the same ability to feel pain as humans", "Year": "2025", "Strongly agree": "33.955875", "Agree": "31.495932", "Somewhat agree": "15.4743", "No opinion": "6.2880263", "Somewhat disagree": "5.840574", "Disagree": "4.2923393", "Strongly disagree": "2.6529527"}, {"Entity": "I am comfortable with how animals are used in the food industry", "Year": "2019", "Strongly agree": "6.938881", "Agree": "12.892467", "Somewhat agree": "20.469193", "No opinion": "7.57746", "Somewhat disagree": "23.636919", "Disagree": "14.70021", "Strongly disagree": "13.784874"}, {"Entity": "I am comfortable with how animals are used in the food industry", "Year": "2020", "Strongly agree": "8.148005", "Agree": "16.410686", "Somewhat agree": "22.912737", "No opinion": "8.440914", "Somewhat disagree": "20.852654", "Disagree": "13.009778", "Strongly disagree": "10.225225"}, {"Entity": "I am comfortable with how animals are used in the food industry", "Year": "2021", "Strongly agree": "8.106115", "Agree": "14.723615", "Somewhat agree": "20.870136", "No opinion": "7.600967", "Somewhat disagree": "18.88854", "Disagree": "14.601599", "Strongly disagree": "15.209028"}, {"Entity": "I am comfortable with how animals are used in the food industry", "Year": "2023", "Strongly agree": "8.42012", "Agree": "17.216478", "Somewhat agree": "21.266014", "No opinion": "8.564143", "Somewhat disagree": "20.845255", "Disagree": "12.331484", "Strongly disagree": "11.356505"}, {"Entity": "I am comfortable with how animals are used in the food industry", "Year": "2025", "Strongly agree": "8.442548", "Agree": "18.44621", "Somewhat agree": "25.705534", "No opinion": "6.358801", "Somewhat disagree": "19.01174", "Disagree": "11.647988", "Strongly disagree": "10.387178"}, {"Entity": "I have some discomfort with the way animals are used in the food industry", "Year": "2017", "Strongly agree": "18.629978", "Agree": "21.058264", "Somewhat agree": "25.145632", "No opinion": "6.780434", "Somewhat disagree": "10.66255", "Disagree": "12.891478", "Strongly disagree": "4.8316665"}, {"Entity": "I have some discomfort with the way animals are used in the food industry", "Year": "2019", "Strongly agree": "21.86819", "Agree": "22.253395", "Somewhat agree": "29.308071", "No opinion": "5.910837", "Somewhat disagree": "8.8079605", "Disagree": "7.140382", "Strongly disagree": "4.7111635"}, {"Entity": "I have some discomfort with the way animals are used in the food industry", "Year": "2020", "Strongly agree": "17.315737", "Agree": "21.643908", "Somewhat agree": "27.762957", "No opinion": "6.7214174", "Somewhat disagree": "10.8382635", "Disagree": "10.046057", "Strongly disagree": "5.6716604"}, {"Entity": "I have some discomfort with the way animals are used in the food industry", "Year": "2021", "Strongly agree": "20.543255", "Agree": "21.289755", "Somewhat agree": "27.78348", "No opinion": "6.708187", "Somewhat disagree": "8.637475", "Disagree": "9.32373", "Strongly disagree": "5.714117"}, {"Entity": "I have some discomfort with the way animals are used in the food industry", "Year": "2023", "Strongly agree": "15.766266", "Agree": "20.828419", "Somewhat agree": "28.764124", "No opinion": "8.500409", "Somewhat disagree": "11.868469", "Disagree": "8.673764", "Strongly disagree": "5.5985494"}, {"Entity": "I have some discomfort with the way animals are used in the food industry", "Year": "2025", "Strongly agree": "15.107932", "Agree": "21.853613", "Somewhat agree": "28.403809", "No opinion": "7.2690783", "Somewhat disagree": "10.815273", "Disagree": "10.272309", "Strongly disagree": "6.2779865"}, {"Entity": "Most farmed animals are treated well", "Year": "2017", "Strongly agree": "5.703358", "Agree": "18.290638", "Somewhat agree": "27.772379", "No opinion": "10.801693", "Somewhat disagree": "17.643312", "Disagree": "11.719742", "Strongly disagree": "8.068878"}, {"Entity": "Most farmed animals are treated well", "Year": "2019", "Strongly agree": "8.03992", "Agree": "17.84541", "Somewhat agree": "23.245094", "No opinion": "8.811576", "Somewhat disagree": "17.872913", "Disagree": "13.914207", "Strongly disagree": "10.27088"}, {"Entity": "Most farmed animals are treated well", "Year": "2020", "Strongly agree": "11.18575", "Agree": "20.86973", "Somewhat agree": "24.065838", "No opinion": "9.747713", "Somewhat disagree": "15.905344", "Disagree": "10.636075", "Strongly disagree": "7.5895495"}, {"Entity": "Most farmed animals are treated well", "Year": "2021", "Strongly agree": "11.228006", "Agree": "17.154978", "Somewhat agree": "23.852747", "No opinion": "8.907317", "Somewhat disagree": "14.425922", "Disagree": "13.336452", "Strongly disagree": "11.094577"}, {"Entity": "Most farmed animals are treated well", "Year": "2023", "Strongly agree": "9.135772", "Agree": "19.395727", "Somewhat agree": "23.474758", "No opinion": "9.824133", "Somewhat disagree": "17.261168", "Disagree": "12.889203", "Strongly disagree": "8.01924"}, {"Entity": "Most farmed animals are treated well", "Year": "2025", "Strongly agree": "12.997591", "Agree": "22.970037", "Somewhat agree": "24.026361", "No opinion": "7.698583", "Somewhat disagree": "15.292945", "Disagree": "8.526839", "Strongly disagree": "8.487642"}, {"Entity": "The meat, dairy, and eggs I purchase usually come from animals treated humanely", "Year": "2017", "Strongly agree": "8.612174", "Agree": "21.19059", "Somewhat agree": "30.208782", "No opinion": "20.391989", "Somewhat disagree": "10.752523", "Disagree": "6.456737", "Strongly disagree": "2.3872051"}, {"Entity": "The meat, dairy, and eggs I purchase usually come from animals treated humanely", "Year": "2019", "Strongly agree": "14.550616", "Agree": "23.283188", "Somewhat agree": "25.848106", "No opinion": "19.232012", "Somewhat disagree": "10.834192", "Disagree": "3.792304", "Strongly disagree": "2.4595802"}, {"Entity": "The meat, dairy, and eggs I purchase usually come from animals treated humanely", "Year": "2020", "Strongly agree": "14.648304", "Agree": "25.484167", "Somewhat agree": "24.41229", "No opinion": "20.145761", "Somewhat disagree": "8.526274", "Disagree": "5.3140583", "Strongly disagree": "1.4691455"}, {"Entity": "The meat, dairy, and eggs I purchase usually come from animals treated humanely", "Year": "2021", "Strongly agree": "16.059912", "Agree": "24.708395", "Somewhat agree": "25.199097", "No opinion": "15.851454", "Somewhat disagree": "9.309359", "Disagree": "6.160492", "Strongly disagree": "2.7112906"}, {"Entity": "The meat, dairy, and eggs I purchase usually come from animals treated humanely", "Year": "2023", "Strongly agree": "14.151612", "Agree": "26.60497", "Somewhat agree": "27.245993", "No opinion": "16.154379", "Somewhat disagree": "8.684178", "Disagree": "5.8590646", "Strongly disagree": "1.2998041"}, {"Entity": "The meat, dairy, and eggs I purchase usually come from animals treated humanely", "Year": "2025", "Strongly agree": "16.984646", "Agree": "29.168753", "Somewhat agree": "25.132553", "No opinion": "13.114136", "Somewhat disagree": "10.17067", "Disagree": "3.8476977", "Strongly disagree": "1.5815468"}], "rows_tail": [], "sampling_note": "Stored first 40 rows and last 40 rows when the table is larger.", "grapher_slug": "survey-animal-pain-sentience", "metadata_url": "https://ourworldindata.org/grapher/survey-animal-pain-sentience.metadata.json", "chart_title": "Public attitudes to livestock treatment and animal pain in the United States", "chart_subtitle": "The surveys measured attitudes towards animal farming in the United States. 1,000-1,500 adults were surveyed each year. Results are weighted to be representative of age, gender, region, ethnicity, and income.", "chart_note": "Survey questions have been shortened for visualization purposes.", "chart_citation": "Sentience Institute (2025)", "original_chart_url": "https://ourworldindata.org/grapher/survey-animal-pain-sentience", "owid_column_metadata": {"Livestock treatment and animal pain - Share of respondents that answered 'Strongly agree'": {"titleShort": "Livestock treatment and animal pain - Share of respondents that answered 'Strongly agree'", "titleLong": "Livestock treatment and animal pain - Share of respondents that answered 'Strongly agree'", "descriptionShort": "Weighted share of survey respondents selecting a response.", "descriptionKey": ["The survey has been repeated in 2017, 2019, 2020, 2021, 2023, and 2025, with 7,165 participants across six waves. Responses are weighted to be representative of the US population.", "The questions related to livestock treatment and animal pain are:\n- \"Farmed animals have roughly the same ability to feel pain and discomfort as humans.\"\n- \"Farmed animals have substantially less ability to feel pain and discomfort than humans.\"\n- \"I am comfortable with the way animals are used in the food industry.\"\n- \"I have some discomfort with the way animals are used in the food industry.\"\n- \"Most farmed animals are treated well. For example, the animals are given enough space and kept in good health.\"\n- \"The animal-based foods I purchase (meat, dairy, and/or eggs) usually come from animals that are treated humanely. For example, the animals are given enough space and kept in good health.\"\n- \"The factory farming of animals is one of the most important social issues in the world today.\""], "shortUnit": "%", "unit": "%", "timespan": "2017-2025", "type": "Numeric", "owidVariableId": 1205662, "shortName": "livestock_treatment_and_animal_pain__strongly_agree", "lastUpdated": "2026-03-02", "nextUpdate": "2027-03-02", "citationShort": "Sentience Institute (2025) – processed by Our World in Data", "citationLong": "Sentience Institute (2025) – processed by Our World in Data. “Livestock treatment and animal pain - Share of respondents that answered 'Strongly agree'” [dataset]. Sentience Institute, “Animals, Food, and Technology” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205662.metadata.json"}, "Livestock treatment and animal pain - Share of respondents that answered 'Agree'": {"titleShort": "Livestock treatment and animal pain - Share of respondents that answered 'Agree'", "titleLong": "Livestock treatment and animal pain - Share of respondents that answered 'Agree'", "descriptionShort": "Weighted share of survey respondents selecting a response.", "descriptionKey": ["The survey has been repeated in 2017, 2019, 2020, 2021, 2023, and 2025, with 7,165 participants across six waves. Responses are weighted to be representative of the US population.", "The questions related to livestock treatment and animal pain are:\n- \"Farmed animals have roughly the same ability to feel pain and discomfort as humans.\"\n- \"Farmed animals have substantially less ability to feel pain and discomfort than humans.\"\n- \"I am comfortable with the way animals are used in the food industry.\"\n- \"I have some discomfort with the way animals are used in the food industry.\"\n- \"Most farmed animals are treated well. For example, the animals are given enough space and kept in good health.\"\n- \"The animal-based foods I purchase (meat, dairy, and/or eggs) usually come from animals that are treated humanely. For example, the animals are given enough space and kept in good health.\"\n- \"The factory farming of animals is one of the most important social issues in the world today.\""], "shortUnit": "%", "unit": "%", "timespan": "2017-2025", "type": "Numeric", "owidVariableId": 1205657, "shortName": "livestock_treatment_and_animal_pain__agree", "lastUpdated": "2026-03-02", "nextUpdate": "2027-03-02", "citationShort": "Sentience Institute (2025) – processed by Our World in Data", "citationLong": "Sentience Institute (2025) – processed by Our World in Data. “Livestock treatment and animal pain - Share of respondents that answered 'Agree'” [dataset]. Sentience Institute, “Animals, Food, and Technology” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205657.metadata.json"}, "Livestock treatment and animal pain - Share of respondents that answered 'Somewhat agree'": {"titleShort": "Livestock treatment and animal pain - Share of respondents that answered 'Somewhat agree'", "titleLong": "Livestock treatment and animal pain - Share of respondents that answered 'Somewhat agree'", "descriptionShort": "Weighted share of survey respondents selecting a response.", "descriptionKey": ["The survey has been repeated in 2017, 2019, 2020, 2021, 2023, and 2025, with 7,165 participants across six waves. Responses are weighted to be representative of the US population.", "The questions related to livestock treatment and animal pain are:\n- \"Farmed animals have roughly the same ability to feel pain and discomfort as humans.\"\n- \"Farmed animals have substantially less ability to feel pain and discomfort than humans.\"\n- \"I am comfortable with the way animals are used in the food industry.\"\n- \"I have some discomfort with the way animals are used in the food industry.\"\n- \"Most farmed animals are treated well. For example, the animals are given enough space and kept in good health.\"\n- \"The animal-based foods I purchase (meat, dairy, and/or eggs) usually come from animals that are treated humanely. For example, the animals are given enough space and kept in good health.\"\n- \"The factory farming of animals is one of the most important social issues in the world today.\""], "shortUnit": "%", "unit": "%", "timespan": "2017-2025", "type": "Numeric", "owidVariableId": 1205659, "shortName": "livestock_treatment_and_animal_pain__somewhat_agree", "lastUpdated": "2026-03-02", "nextUpdate": "2027-03-02", "citationShort": "Sentience Institute (2025) – processed by Our World in Data", "citationLong": "Sentience Institute (2025) – processed by Our World in Data. “Livestock treatment and animal pain - Share of respondents that answered 'Somewhat agree'” [dataset]. Sentience Institute, “Animals, Food, and Technology” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205659.metadata.json"}, "Livestock treatment and animal pain - Share of respondents that answered 'No opinion'": {"titleShort": "Livestock treatment and animal pain - Share of respondents that answered 'No opinion'", "titleLong": "Livestock treatment and animal pain - Share of respondents that answered 'No opinion'", "descriptionShort": "Weighted share of survey respondents selecting a response.", "descriptionKey": ["The survey has been repeated in 2017, 2019, 2020, 2021, 2023, and 2025, with 7,165 participants across six waves. Responses are weighted to be representative of the US population.", "The questions related to livestock treatment and animal pain are:\n- \"Farmed animals have roughly the same ability to feel pain and discomfort as humans.\"\n- \"Farmed animals have substantially less ability to feel pain and discomfort than humans.\"\n- \"I am comfortable with the way animals are used in the food industry.\"\n- \"I have some discomfort with the way animals are used in the food industry.\"\n- \"Most farmed animals are treated well. For example, the animals are given enough space and kept in good health.\"\n- \"The animal-based foods I purchase (meat, dairy, and/or eggs) usually come from animals that are treated humanely. For example, the animals are given enough space and kept in good health.\"\n- \"The factory farming of animals is one of the most important social issues in the world today.\""], "shortUnit": "%", "unit": "%", "timespan": "2017-2025", "type": "Numeric", "owidVariableId": 1205660, "shortName": "livestock_treatment_and_animal_pain__no_opinion", "lastUpdated": "2026-03-02", "nextUpdate": "2027-03-02", "citationShort": "Sentience Institute (2025) – processed by Our World in Data", "citationLong": "Sentience Institute (2025) – processed by Our World in Data. “Livestock treatment and animal pain - Share of respondents that answered 'No opinion'” [dataset]. Sentience Institute, “Animals, Food, and Technology” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205660.metadata.json"}, "Livestock treatment and animal pain - Share of respondents that answered 'Somewhat disagree'": {"titleShort": "Livestock treatment and animal pain - Share of respondents that answered 'Somewhat disagree'", "titleLong": "Livestock treatment and animal pain - Share of respondents that answered 'Somewhat disagree'", "descriptionShort": "Weighted share of survey respondents selecting a response.", "descriptionKey": ["The survey has been repeated in 2017, 2019, 2020, 2021, 2023, and 2025, with 7,165 participants across six waves. Responses are weighted to be representative of the US population.", "The questions related to livestock treatment and animal pain are:\n- \"Farmed animals have roughly the same ability to feel pain and discomfort as humans.\"\n- \"Farmed animals have substantially less ability to feel pain and discomfort than humans.\"\n- \"I am comfortable with the way animals are used in the food industry.\"\n- \"I have some discomfort with the way animals are used in the food industry.\"\n- \"Most farmed animals are treated well. For example, the animals are given enough space and kept in good health.\"\n- \"The animal-based foods I purchase (meat, dairy, and/or eggs) usually come from animals that are treated humanely. For example, the animals are given enough space and kept in good health.\"\n- \"The factory farming of animals is one of the most important social issues in the world today.\""], "shortUnit": "%", "unit": "%", "timespan": "2017-2025", "type": "Numeric", "owidVariableId": 1205661, "shortName": "livestock_treatment_and_animal_pain__somewhat_disagree", "lastUpdated": "2026-03-02", "nextUpdate": "2027-03-02", "citationShort": "Sentience Institute (2025) – processed by Our World in Data", "citationLong": "Sentience Institute (2025) – processed by Our World in Data. “Livestock treatment and animal pain - Share of respondents that answered 'Somewhat disagree'” [dataset]. Sentience Institute, “Animals, Food, and Technology” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205661.metadata.json"}, "Livestock treatment and animal pain - Share of respondents that answered 'Disagree'": {"titleShort": "Livestock treatment and animal pain - Share of respondents that answered 'Disagree'", "titleLong": "Livestock treatment and animal pain - Share of respondents that answered 'Disagree'", "descriptionShort": "Weighted share of survey respondents selecting a response.", "descriptionKey": ["The survey has been repeated in 2017, 2019, 2020, 2021, 2023, and 2025, with 7,165 participants across six waves. Responses are weighted to be representative of the US population.", "The questions related to livestock treatment and animal pain are:\n- \"Farmed animals have roughly the same ability to feel pain and discomfort as humans.\"\n- \"Farmed animals have substantially less ability to feel pain and discomfort than humans.\"\n- \"I am comfortable with the way animals are used in the food industry.\"\n- \"I have some discomfort with the way animals are used in the food industry.\"\n- \"Most farmed animals are treated well. For example, the animals are given enough space and kept in good health.\"\n- \"The animal-based foods I purchase (meat, dairy, and/or eggs) usually come from animals that are treated humanely. For example, the animals are given enough space and kept in good health.\"\n- \"The factory farming of animals is one of the most important social issues in the world today.\""], "shortUnit": "%", "unit": "%", "timespan": "2017-2025", "type": "Numeric", "owidVariableId": 1205658, "shortName": "livestock_treatment_and_animal_pain__disagree", "lastUpdated": "2026-03-02", "nextUpdate": "2027-03-02", "citationShort": "Sentience Institute (2025) – processed by Our World in Data", "citationLong": "Sentience Institute (2025) – processed by Our World in Data. “Livestock treatment and animal pain - Share of respondents that answered 'Disagree'” [dataset]. Sentience Institute, “Animals, Food, and Technology” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205658.metadata.json"}, "Livestock treatment and animal pain - Share of respondents that answered 'Strongly disagree'": {"titleShort": "Livestock treatment and animal pain - Share of respondents that answered 'Strongly disagree'", "titleLong": "Livestock treatment and animal pain - Share of respondents that answered 'Strongly disagree'", "descriptionShort": "Weighted share of survey respondents selecting a response.", "descriptionKey": ["The survey has been repeated in 2017, 2019, 2020, 2021, 2023, and 2025, with 7,165 participants across six waves. Responses are weighted to be representative of the US population.", "The questions related to livestock treatment and animal pain are:\n- \"Farmed animals have roughly the same ability to feel pain and discomfort as humans.\"\n- \"Farmed animals have substantially less ability to feel pain and discomfort than humans.\"\n- \"I am comfortable with the way animals are used in the food industry.\"\n- \"I have some discomfort with the way animals are used in the food industry.\"\n- \"Most farmed animals are treated well. For example, the animals are given enough space and kept in good health.\"\n- \"The animal-based foods I purchase (meat, dairy, and/or eggs) usually come from animals that are treated humanely. For example, the animals are given enough space and kept in good health.\"\n- \"The factory farming of animals is one of the most important social issues in the world today.\""], "shortUnit": "%", "unit": "%", "timespan": "2017-2025", "type": "Numeric", "owidVariableId": 1205663, "shortName": "livestock_treatment_and_animal_pain__strongly_disagree", "lastUpdated": "2026-03-02", "nextUpdate": "2027-03-02", "citationShort": "Sentience Institute (2025) – processed by Our World in Data", "citationLong": "Sentience Institute (2025) – processed by Our World in Data. “Livestock treatment and animal pain - Share of respondents that answered 'Strongly disagree'” [dataset]. Sentience Institute, “Animals, Food, and Technology” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205663.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Public opinion on common farming practices in the US", "source_url": "https://ourworldindata.org/grapher/public-opinion-on-common-farming-practices-in-the-us.csv", "file_type": "csv", "columns": ["Entity", "Year", "Very acceptable", "Somewhat acceptable", "Neither acceptable nor unacceptable", "Somewhat unacceptable", "Very unacceptable"], "row_count_total": 12, "rows_head": [{"Entity": "Calves are permanently separated from their mothers at birth", "Year": "2025", "Very acceptable": "36.390877", "Somewhat acceptable": "100.04458", "Neither acceptable nor unacceptable": "110.66772", "Somewhat unacceptable": "324.13116", "Very unacceptable": "477.76566"}, {"Entity": "Calves' horn buds are removed with a knife or hot iron, sometimes without pain relief", "Year": "2025", "Very acceptable": "39.745327", "Somewhat acceptable": "90.25624", "Neither acceptable nor unacceptable": "94.16657", "Somewhat unacceptable": "228.50061", "Very unacceptable": "596.33124"}, {"Entity": "Chickens are bred to grow fast and struggle to walk and stand", "Year": "2025", "Very acceptable": "34.221863", "Somewhat acceptable": "86.505554", "Neither acceptable nor unacceptable": "102.70333", "Somewhat unacceptable": "294.97003", "Very unacceptable": "530.59924"}, {"Entity": "Chickens are kept in cages, with less room than a sheet of paper per bird", "Year": "2025", "Very acceptable": "20.425714", "Somewhat acceptable": "76.43033", "Neither acceptable nor unacceptable": "61.232517", "Somewhat unacceptable": "268.88165", "Very unacceptable": "622.0298"}, {"Entity": "Chickens are kept indoors with little space per bird", "Year": "2025", "Very acceptable": "29.132803", "Somewhat acceptable": "70.522285", "Neither acceptable nor unacceptable": "93.21231", "Somewhat unacceptable": "283.89566", "Very unacceptable": "572.23694"}, {"Entity": "Live chickens are hung upside down, stunned, have their throats slit, then boiled", "Year": "2025", "Very acceptable": "34.99388", "Somewhat acceptable": "85.48248", "Neither acceptable nor unacceptable": "117.37836", "Somewhat unacceptable": "209.1278", "Very unacceptable": "602.01746"}, {"Entity": "Newborn calves are castrated without pain relief", "Year": "2025", "Very acceptable": "40.939495", "Somewhat acceptable": "70.841354", "Neither acceptable nor unacceptable": "81.65424", "Somewhat unacceptable": "216.82234", "Very unacceptable": "638.74255"}, {"Entity": "Newborn chickens' beaks are cut off without pain relief", "Year": "2025", "Very acceptable": "33.301254", "Somewhat acceptable": "74.216515", "Neither acceptable nor unacceptable": "86.498665", "Somewhat unacceptable": "203.45374", "Very unacceptable": "651.52985"}, {"Entity": "Newborn male chicks are killed in meat-grinders", "Year": "2025", "Very acceptable": "33.436512", "Somewhat acceptable": "42.834072", "Neither acceptable nor unacceptable": "88.962975", "Somewhat unacceptable": "209.38632", "Very unacceptable": "674.3801"}, {"Entity": "Newborn piglets' tails are cut off without pain relief", "Year": "2025", "Very acceptable": "26.649277", "Somewhat acceptable": "83.82052", "Neither acceptable nor unacceptable": "85.683365", "Somewhat unacceptable": "239.21658", "Very unacceptable": "613.63025"}, {"Entity": "Pigs are kept in cages, unable to turn around for weeks", "Year": "2025", "Very acceptable": "28.489779", "Somewhat acceptable": "70.88107", "Neither acceptable nor unacceptable": "69.042786", "Somewhat unacceptable": "212.38618", "Very unacceptable": "668.2002"}, {"Entity": "Pigs are killed in CO₂ gas chambers", "Year": "2025", "Very acceptable": "46.04483", "Somewhat acceptable": "118.11393", "Neither acceptable nor unacceptable": "143.5882", "Somewhat unacceptable": "229.12692", "Very unacceptable": "512.1261"}], "rows_tail": [], "sampling_note": "Stored first 12 rows and last 12 rows when the table is larger.", "grapher_slug": 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{"raw_link": "https://ourworldindata.org/battery-price-decline", "title": "Battery costs have declined by 99% in the last three decades, making electrified transport a reality", "context": "Home\nEnergy\nBattery costs have declined by 99% in the last three decades, making electrified transport a reality\nBatteries have become much cheaper, making energy storage far more affordable.\nBy\nHannah Ritchie\nand\nPablo Rosado\n(data work)\nFirst published in 2024; updated and rewritten in March 2026.\nBrowse past versions\nCite this article\nReuse our work freely\nOver 20 million electric cars\nwere sold\nglobally in 2025. Most of these cars sold for around $40,000, but some are now as cheap as $10,000.\n1\nEven just two decades ago, these prices and sales figures would have been impossible. That’s because the batteries were far too expensive.\nThe chart below shows the decline in lithium-ion\nbattery cell\nprices since 1991. Note that this is shown on a logarithmic scale.\nThe price declined by more than 99%. In 1991, lithium-ion batteries cost around $9,200 per kilowatt-hour — 33 years later, they cost just $78.\nLet’s put that in perspective. The battery cells you’d find in a standard electric car today, which give around 220 to 250 miles (350 to 400 kilometers) of range, cost around $5,000.\n2\nJust a decade ago, this would have cost over $20,000, as much as many would pay for the entire car itself. And back in 1991, almost $600,000.\n3\nWhat’s promising is that the drop in prices continues: they’ve fallen by a third in just the last few years.\nThis data focuses on the cost of the battery cells. The full battery pack — including cooling, casing, and control systems — costs a bit more.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nWhy have prices fallen?\nHow did batteries get so much cheaper?\nFor technologies like batteries, prices fall with production. That's because they follow a learning curve: as cumulative production grows, innovators and engineers find incremental improvements in chemistry, manufacturing, and supply chains, driving a continuous fall in prices. Batteries did not become cheaper because of one big breakthrough, but thanks to thousands of small ones.\nMy colleague, Max Roser, explains learning curves in more detail in\nhis article\non renewable energy technologies.\nIn the chart below, we’ve plotted the price of lithium-ion batteries against their\ncumulative\nproduction globally. Both axes are logarithmic.\nDownload\nBack in 1991, the market was tiny: just 130 kilowatt-hours had been produced worldwide. Just enough to power two of today's electric cars. Since then, production has grown dramatically, and as more batteries were produced, prices fell (which in turn created more demand and further increased production). By the end of 2023, global cumulative production had increased by a factor of 27 million from 1991 levels.\nIn the early 1990s, the price decline was much slower than you see for the rest of the curve. This was for several reasons. The market at the time was incredibly immature and relied on expensive and niche supply chains. The electronics company Sony largely held a monopoly over early technology, reducing market competition and making cost reductions a lower priority than improvements in scaling, safety, and battery lifespan.\nBetween 1991 and 2023, global cumulative production increased by a factor of 27 million.\nIt wasn’t until the late 1990s that other competitors — particularly South Korean brands such as Samsung and LG, and later Chinese manufacturers — entered the market, which brought fierce price competition and large-scale automated production.\nFrom 1998 onwards, every time the global cumulative battery production doubled, the price dropped by roughly 19%. This is similar to\nthe learning rate\nof solar panels; every time global production doubled, prices fell by around 20%.\nNot all of this progress was driven by innovation for electric transport and renewable energy. In the 1990s and early 2000s, the main market for lithium-ion batteries was consumer electronics. The development of smaller batteries in products such as phones and laptops came first; only later did they become viable for cars, buses, and larger energy storage.\nBut price was not the only barrier. Falling costs were needed to make electric vehicles affordable, and better energy density was needed to make them practical. This energy density — how much electrical energy a battery can store for its volume — has more than tripled since the 1990s.\n4\nEvery time the global cumulative battery production has doubled, the price has dropped by roughly 19%.\nThat means more energy for less space and weight. Cars can now have larger batteries with enough capacity to go hundreds of miles without becoming impractically heavy or inefficient. Lighter batteries open up opportunities for the electrification of trucks, and even small airplanes and ships, where weight is crucial.\n5\nThe\nhuge decline in the cost of solar power\n— particularly during the 2010s — transformed it from one of the most expensive sources of electricity to the cheapest. What has been missing, not only for the deployment of renewable energy but for the takeoff of electrified transport, has been cheap storage.\nThat cheaper storage is now arriving. Three decades ago, lithium-ion batteries were a niche technology for mobile phones and early laptops. Today, they power tens of millions of cars and store electricity in homes and on power grids worldwide.\nThe half-a-million-dollar battery was never going to transform transport. The $5,000 battery is.\nAcknowledgments\nMany thanks to Max Roser and Edouard Mathieu for editorial feedback and comments on this article, and to Marwa Boukarim for design help with the visualizations.\nContinue reading on Our World in Data\nLearning curves: What does it mean for a technology to follow Wright’s Law?\nTechnologies that follow Wright’s Law get cheaper at a consistent rate, as the cumulative production of that technology increases.\nWhy did renewables become so cheap so fast?\nIn most places, power from new renewables is now cheaper than new fossil fuels.\nTracking global data on electric vehicles\nExplore data on electric car sales and stocks worldwide.\nEndnotes\nSome of the bestselling electric cars in China\nare sold\nlocally for the equivalent of around $10,000. In other markets, they would be more expensive.\nStandard electric cars in a country like the United Kingdom\ntend to cost\nsomewhere between $35,000 and $45,000. Smaller cars are a bit cheaper.\nElectric cars vary in the size of their battery, ranging from just a few tens of kilowatt-hours for small cars designed for city-driving, to over 100 kWh for long-range cars. The average car sold today is somewhere in the range of 50 to 80 kWh.\nTo calculate the cost, I’ve assumed the battery is 63 kWh. 63 kWh multiplied by $78 per kWh = $4,914. Once you include the rest of the battery pack — cooling systems, casing, and electronics — it might have been as much as $6,000 or $7,000.\nThe\ntypical efficiency\nof an electric car is around 3.5 to 4 miles per kWh. That gives a stated range of around 221 to 252 miles for a 63 kWh battery. Depending on driving style, speed, and weather, the real-world range can be around 20% lower than the advertised range.\n9,210 per kWh = $580,000.\nFor the entire battery pack, it’s probably around $6,000 or $7,000.\n63 kWh multiplied by $9,210 per kWh = $580,000. Again, once you include the rest of the battery pack, it might have been as much as $800,000.\nIn 1991, you could only get 200 watt-hours of capacity per liter of battery. You can now get over 700 watt-hours.\nZiegler, M. S., & Trancik, J. E. (2021). Re-examining rates of lithium-ion battery technology improvement and cost decline. Energy & Environmental Science.\nDavis, S. J., Lewis, N. S., Shaner, M., Aggarwal, S., Arent, D., Azevedo, I. L., ... & Caldeira, K. (2018). Net-zero emissions energy systems. Science.\nFinger, D. F., Braun, C., & Bil, C. (2020). Impact of battery performance on the initial sizing of hybrid-electric general aviation aircraft. Journal of Aerospace Engineering.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Pablo Rosado (2026) - “Battery costs have declined by 99% in the last three decades, making electrified transport a reality” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-090244/battery-price-decline.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-battery-price-decline,\nauthor = {Hannah Ritchie and Pablo Rosado},\ntitle = {Battery costs have declined by 99% in the last three decades, making electrified transport a reality},\njournal = {Our World in Data},\nyear = {2026},\nnote = {https://archive.ourworldindata.org/20260518-090244/battery-price-decline.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "battery-price-decline", "source_url": "https://ourworldindata.org/battery-price-decline", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Batteries have become much cheaper, making energy storage far more affordable.", "numeric_mentions": ["99%", "2024", "2026", "20 million", "2025", "40,000,", "10,000", "1", "1991", "1991,", "9,200", "33 years", "78", "220", "250", "350", "400", "5,000", "2", "20,000,", "600,000", "3", "130", "2023,", "27 million", "1990", "1998", "19%", "20%", "2000", "4", "5", "2010", "35,000", "45,000", "100", "50", "80", "63", "4,914", "6,000", "7,000", "3.5", "221", "252", "9,210", "580,000", "800,000", "200", "700", "2021", "2018", "2020", "20260518", "090244", "18,"], "numeric_evidence": [{"title": "Price of lithium-ion battery cells", "source_url": "https://ourworldindata.org/grapher/price-of-lithium-ion-battery-cells.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Price of lithium-ion battery cells"], "row_count_total": 34, "rows_head": 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"OWID_WRL", "Year": "2000", "Price of lithium-ion battery cells": "2690.6272"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Price of lithium-ion battery cells": "1878.0293"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Price of lithium-ion battery cells": "1345.5295"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Price of lithium-ion battery cells": "1040.3679"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Price of lithium-ion battery cells": "930.90393"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Price of lithium-ion battery cells": "802.5369"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Price of lithium-ion battery cells": "687.29584"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Price of lithium-ion battery cells": "655.836"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Price of lithium-ion battery cells": "687.6569"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Price of lithium-ion battery cells": "612.57623"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Price of lithium-ion battery cells": "550.72064"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Price of lithium-ion battery cells": "524.59796"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Price of lithium-ion battery cells": "539.6925"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Price of lithium-ion battery cells": "490.8935"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Price of lithium-ion battery cells": "434.2699"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Price of lithium-ion battery cells": "331.94275"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Price of lithium-ion battery cells": "259"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Price of lithium-ion battery cells": "187"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Price of lithium-ion battery cells": "157"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Price of lithium-ion battery cells": "132"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Price of lithium-ion battery cells": "123"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Price of lithium-ion battery cells": "119"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Price of lithium-ion battery cells": "132"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Price of lithium-ion battery cells": "111"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Price of lithium-ion battery cells": "78"}], "rows_tail": [], "sampling_note": "Stored first 34 rows and last 34 rows when the table is larger.", "grapher_slug": "price-of-lithium-ion-battery-cells", "metadata_url": "https://ourworldindata.org/grapher/price-of-lithium-ion-battery-cells.metadata.json", "chart_title": "Price of lithium-ion battery cells", "chart_subtitle": "Representative estimate of the price of battery 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{"raw_link": "https://ourworldindata.org/the-human-cost-of-unsafe-abortions", "title": "The human cost of unsafe abortions", "context": "The human cost of unsafe abortions\nRomania’s history offers a rare natural experiment on what happens when abortion laws change rapidly. What can the rest of the world learn from this?\nBy\nHannah Ritchie\nMarch 23, 2026\nBrowse past versions\nCite this article\nReuse our work freely\nIn the two decades from 1965 to 1985, maternal mortality fell sharply across Europe. Rates in my own country, the United Kingdom,\nfell by\nmore than two-thirds.\nRomania was the exception. Its rates increased by almost 150%.\nBy the late 1980s, Romanian women were dying at rates several times higher than in other Eastern European countries, and ten times higher than those in Western Europe, as the chart below shows.\nWhy were Romanian women dying at much higher rates than in neighboring countries? To understand this, we need to go back three decades.\nIn 1957, Romania legalized abortion. Surgical procedures became readily available, affordable, and relatively safe.\n1\nAt the same time, there was very little access to contraception, which made unintended pregnancies extremely common. As a result, many women relied on abortion to control their fertility. By the mid-1960s, more than a million abortions were performed each year, four times the number of babies born.\n2\nYou can see the impact of this in the next chart. In the decade from 1957 to the late 1960s, births fell by around 40%.\n3\nBut things changed when Nicolae Ceaușescu became president in 1965. Worried about declining births in Romania, Ceaușescu implemented “Decree 770”, which put tight restrictions on abortions and contraception. Abortion was banned except for women who were over 45 years old, had at least four children (later raised to five), faced life-threatening complications, or had been victims of rape.\n4\nThe impact of these restrictions was dramatic. Births nearly doubled from 1966 to 1967. Fertility rates increased from less than 2 births per woman to more than 3.5, as you can see in the chart below.\nEventually, they fell again as couples found other ways to manage births, but it took many decades for them to drop back to pre-ban levels.\nSo Ceaușescu’s plan to\nboost population growth\nworked. But making abortion illegal for most women meant that many turned to more dangerous alternatives.\nWe know abortions continued because women and those who performed them were imprisoned or heavily fined when caught. Records suggest that many of these people were not medically trained.\n5\nThese procedures often relied on non-sterile metal instruments, improvised tools, or toxic substances to induce a miscarriage. As a result, many women suffered from severe internal bleeding, perforation of the uterus, or life-threatening conditions caused by infections. Because these procedures were illegal, women often delayed seeking medical help when complications arose, turning treatable problems into fatal ones.\nAs you can see in the chart below, maternal mortality rates in Romania increased substantially in the decades following the abortion ban. This reflects the expansion of illegal abortion methods and practitioners, and partly explains why births gradually fell over the same period. A growing fraction of maternal deaths were caused by unsafe abortion; by the 1980s, these deaths accounted for more than 80% of the total.\n6\nDownload\nIn 1989, Ceaușescu was overthrown, and just as quickly as abortion was criminalized, restrictions were lifted again. Maternal deaths declined as abortions moved from illegal methods to regulated ones.\n7\nOver the course of the two-and-a-half decades when abortion was restricted, it’s estimated that around 10,000 Romanian women died from unsafe terminations.\n8\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nWhat can we learn from Romania’s experience?\nRomania’s sudden policy changes created a rare natural experiment: when abortion was legal, maternal deaths were low; when it was banned, they rose; and when it was legalized again, they fell.\nIf we want to draw lessons from Romania’s history, we need to keep in mind that its situation was unusual.\nOver the two-and-a-half decades when abortion was restricted, it’s estimated that around 10,000 Romanian women died from unsafe terminations.\nNo country today has anywhere close to the abortion rates that Romania had in the early 1960s. I estimate that around 20% of women of reproductive age in Romania were having an abortion\neach year\nat that time.\n9\nGlobally, the country with the highest rate today is less than half that of Romania in the 1960s.\n10\nMost countries in Europe and North America have rates closer to 1%.\n11\n​​Romania was an extreme case, and we shouldn’t expect changes today to have such dramatic consequences. But the mechanism still applies.\nIt is estimated that around 40% of women in the world\nlive in countries\nwhere abortion is either completely illegal or severely restricted. Yet abortion rates are\nstill higher\nin these places than they are in Western Europe and many other places where abortion is legal.\nWhere abortions are illegal, most take place in unsafe and unsanitary conditions. In countries with tight restrictions, three-quarters of abortions are estimated to be unsafe, compared to far less than 10% in North America and North and Western Europe.\n12\nHow does this affect the health risks for women? Safe abortions have very low mortality rates,\ntypically below\n1 death per 100,000 abortions.\n13\nIn regions where the majority of abortions are unsafe, mortality rates can be several hundred times higher; in Western and Middle Africa, around 1-in-200 abortions result in the woman dying.\n14\nEstimates suggest that unsafe abortions account for 8% of maternal deaths globally.\n15\nThis means that an estimated 23,000 women die every year.\n16\nRomania learned about the consequences of unsafe abortion at an enormous cost. In many parts of the world, that lesson continues.\nAcknowledgments\nMany thanks to Max Roser and Edouard Mathieu for editorial feedback and comments on this article.\nContinue reading on Our World in Data\nIf we can make maternal deaths as rare as in the healthiest countries, we can save 275,000 mothers each year\nMaternal mortality was much more common in the past. It is much lower today, but global inequalities are still large.\nMaternal Mortality\nWhat could be more tragic than a mother losing her life in the moment that she is giving birth to her newborn? Why are mothers dying and what can be done to prevent these deaths?\nThe rise in reported maternal mortality rates in the US is largely due to a change in measurement\nMaternal 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.\nEndnotes\nTeitelbaum, M. S. (1972). Fertility effects of the abolition of legal abortion in Romania. Population Studies.\nHorga, M., Gerdts, C., & Potts, M. (2013). The remarkable story of Romanian women's struggle to manage their fertility. Journal of Family Planning and Reproductive Health Care.\nBy the mid-1960s, births\nhad fallen\nto around 260,000 per year.\nBirth rates\nfell by slightly more than 40%.\nThe policy initially had an age threshold of 45 years, which was later lowered to 40 years, then raised again to 45.\nThe government became deeply involved in women's and families' reproductive decisions in other ways, too. Working women had to get a monthly gynaecological exam; if they failed to attend, they lost their rights to medical care, pensions, and social support. If a married couple did not have a child within two years — and there were no medical reasons preventing them from doing so — they had to pay extra taxes.\nHord, C., David, H. P., Donnay, F., & Wolf, M. (1991). Reproductive health in Romania: reversing the Ceausescu legacy.\nStudies in family planning\n.\nHord, C., David, H. P., Donnay, F., & Wolf, M. (1991). Reproductive health in Romania: reversing the Ceausescu legacy. Studies in Family Planning.\nThe drop in deaths might have been even larger if it weren’t for the fact that abortion clinics were overwhelmed in the year after legalization. The demand from women was high, and medical support was limited; abortion clinics had either been severely restricted or closed completely.\nI’ve seen this figure cited in several places, but failed to find a solid original reference.\nHowever, based on my own calculations, this seems reasonable as an estimate.\nBased on the previous chart in this article, mortality from abortions over the period when abortion was restricted averaged around 80 to 120 deaths per 100,000 live births. Earlier in the period, it was around 30, and by the end, it had reached over 140. An average of 100 deaths per 100,000 live births seems like a reasonable estimate.\nUsing the\nnumber of births\nfrom 1966 to 1989 (the period of restriction), dividing by 100,000 and multiplying by 100 (the number of abortion deaths per 100,000 births), I calculated a figure of\naround 9,500 deaths in that 24-year period\n. Approximately 10,000 deaths, therefore, seems like a reasonable estimate.\nI’ve based this on the number of girls and women aged 15 to 49 years (often described as the “reproductive age”). In 1965, there were around 4.9 million Romanian women in this age bracket.\nSome women could have multiple abortions in a given year, so this 20% figure is a simplification assuming that women only had one.\nAt those rates, the average woman would have had four abortions in her lifetime.\nHorga, M., Gerdts, C., & Potts, M. (2013). The remarkable story of Romanian women's struggle to manage their fertility. Journal of Family Planning and Reproductive Health Care.\nThe study by Bearak et al. (2022) estimated that Georgia had the highest rate in the world, at 80 abortions per 1,000 women aged 15 to 49 years per year.\nBearak, J. M., Popinchalk, A., Beavin, C., Ganatra, B., Moller, A. B., Tunçalp, Ö., & Alkema, L. (2022). Country-specific estimates of unintended pregnancy and abortion incidence: a global comparative analysis of levels in 2015–2019. BMJ Global Health.\nThis study is based on data from the period 2010 to 2014, so it’s a bit outdated. However, I could not find a more recent global study of this question.\nGanatra, B., Gerdts, C., Rossier, C., Johnson, B. R., Tunçalp, Ö., Assifi, A., ... & Alkema, L. (2017). Global, regional, and subregional classification of abortions by safety, 2010–14: estimates from a Bayesian hierarchical model. The Lancet.\nIn countries like the United States, there are around 0.5 deaths per 100,000.\nSteenland, M. W., Mercon, K., Brown, B. P., & Thoma, M. E. (2026). Pregnancy-and Abortion-Related Mortality in the US, 2018-2021. JAMA Network Open.\nEstimates are that case fatality rates are between 450 and 500 per 100,000 abortions.\nGanatra, B., Gerdts, C., Rossier, C., Johnson, B. R., Tunçalp, Ö., Assifi, A., ... & Alkema, L. (2017). Global, regional, and subregional classification of abortions by safety, 2010–14: estimates from a Bayesian hierarchical model. The Lancet.\nCresswell, J. A., Alexander, M., Chong, M. Y., Link, H. M., Pejchinovska, M., Gazeley, U., ... & Say, L. (2025). Global and regional causes of maternal deaths 2009–20: a WHO systematic analysis. The Lancet Global Health.\nSome earlier figures from the 2010s suggested that abortions were responsible for as much as 13% of maternal deaths globally.\nWorld Health Organization (2011). Unsafe abortion: global and regional estimates of the incidence of unsafe abortion and associated mortality in 2008.\nIn 2023, there were 286,000\nmaternal deaths\nglobally. 8% of this figure is 23,000.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2026) - “The human cost of unsafe abortions” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-093348/the-human-cost-of-unsafe-abortions.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-the-human-cost-of-unsafe-abortions,\nauthor = {Hannah Ritchie},\ntitle = {The human cost of unsafe abortions},\njournal = {Our World in Data},\nyear = {2026},\nnote = {https://archive.ourworldindata.org/20260518-093348/the-human-cost-of-unsafe-abortions.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "the-human-cost-of-unsafe-abortions", "source_url": "https://ourworldindata.org/the-human-cost-of-unsafe-abortions", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Romania’s history offers a rare natural experiment on what happens when abortion laws change rapidly. What can the rest of the world learn from this?", "numeric_mentions": ["23,", "2026", "1965", "1985,", "150%", "1980", "1957,", "1", "1960", "2", "1957", "40%", "3", "770", "45 years", "4", "1966", "1967", "3.5", "5", "80%", "6", "1989,", "7", "10,000", "8", "20%", "9", "10", "1%", "11", "10%", "12", "100,000", "13", "200", "14", "8%", "15", "23,000", "16", "275,000", "20 years", "1972", "2013", "260,000", "40 years", "45", "1991", "80", "120", "30,", "140", "100", "1989", "9,500", "24", "49 years", "1965,", "4.9 million", "2022", "1,000", "2015", "2019", "2010", "2014,", "2017", "0.5", "2018", "2021", "450", "500", "2025", "2009", "20", "13%", "2011", "2008", "2023,", "286,000"], "numeric_evidence": [{"title": "Maternal mortality ratio", "source_url": "https://ourworldindata.org/grapher/maternal-mortality.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Maternal mortality ratio", "World region according to OWID", "Maternal mortality ratio (Annotations)"], 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"883.7139", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Maternal mortality ratio": "833.4772", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Maternal mortality ratio": "820.68536", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Maternal mortality ratio": "785.35406", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Maternal mortality ratio": "775.6927", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Maternal mortality ratio": "749.8157", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Maternal mortality ratio": "681.80756", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Maternal mortality ratio": "663.4275", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Maternal mortality ratio": "644.2726", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Maternal mortality ratio": "620.40753", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Maternal mortality ratio": "904.12787", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Maternal mortality ratio": "883.07526", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Maternal mortality ratio": "910.0019", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Maternal mortality ratio": "891.8916", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Maternal mortality ratio": "855.7746", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Maternal mortality ratio": "850.23047", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Maternal mortality ratio": "841.73444", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Maternal mortality ratio": "866.1556", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Maternal mortality ratio": "856.2383", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Maternal mortality ratio": "823.59766", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Maternal mortality ratio": "801.83624", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1996", "Maternal mortality ratio": "797.6708", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1997", "Maternal mortality ratio": "782.36926", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1998", "Maternal mortality ratio": "804.27374", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1999", "Maternal mortality ratio": "739.20233", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Maternal mortality ratio": "718.2771", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Maternal mortality ratio": "705.3272", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Maternal mortality ratio": "692.7808", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Maternal mortality ratio": "668.3644", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Maternal mortality ratio": "652.6978", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Maternal mortality ratio": "636.8363", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Maternal mortality ratio": "619.63403", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Maternal mortality ratio": "611.9057", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Maternal mortality ratio": "607.67474", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Maternal mortality ratio": "595.9811", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Maternal mortality ratio": "583.5715", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Maternal mortality ratio": "559.11273", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Maternal mortality ratio": "544.01935", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Maternal mortality ratio": "535.4149", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Maternal mortality ratio": "527.16296", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Maternal mortality ratio": "511.22266", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Maternal mortality ratio": "504.3417", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Maternal mortality ratio": "496.15768", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Maternal mortality ratio": "487.91956", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Maternal mortality ratio": "478.50122", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Maternal mortality ratio": "464.0011", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1985", "Maternal mortality ratio": "47.698692", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1986", "Maternal mortality ratio": "41.96449", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1987", "Maternal mortality ratio": "39.203156", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1988", "Maternal mortality ratio": "36.728065", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1989", "Maternal mortality ratio": "34.94203", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1990", "Maternal mortality ratio": "32.874947", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1991", "Maternal mortality ratio": "29.07421", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "Maternal mortality ratio": "27.674513", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "Maternal mortality ratio": "26.515451", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "Maternal mortality ratio": "24.24055", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "Maternal mortality ratio": "22.034124", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "Maternal mortality ratio": "18.68523", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "Maternal mortality ratio": "18.388054", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "Maternal mortality ratio": "15.522337", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "Maternal mortality ratio": "14.890729", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Maternal mortality ratio": "14.326414", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Maternal mortality ratio": "12.525534", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Maternal mortality ratio": "12.407263", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Maternal mortality ratio": "12.1191635", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Maternal mortality ratio": "10.631596", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Maternal mortality ratio": "10.830499", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Maternal mortality ratio": "10.76677", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Maternal mortality ratio": "10.195825", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Maternal mortality ratio": "9.739404", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Maternal mortality ratio": "9.097263", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Maternal mortality ratio": "8.516676", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Maternal mortality ratio": "8.045227", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Maternal mortality ratio": "7.796703", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Maternal mortality ratio": "7.2153625", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Maternal mortality ratio": "7.0098605", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Maternal mortality ratio": "6.878285", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Maternal mortality ratio": "6.6560845", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Maternal mortality ratio": "6.689698", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Maternal mortality ratio": "5.4292927", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Maternal mortality ratio": "5.305023", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Maternal mortality ratio": "8.276446", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1985", "Maternal mortality ratio": "255.586", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1986", "Maternal mortality ratio": "240.39912", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1987", "Maternal mortality ratio": "236.62263", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1988", "Maternal mortality ratio": "223.13094", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1989", "Maternal mortality ratio": "216.49147", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1990", "Maternal mortality ratio": "205.81978", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1991", "Maternal mortality ratio": "203.99115", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1992", "Maternal mortality ratio": "203.64143", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1993", "Maternal mortality ratio": "203.94144", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1994", "Maternal mortality ratio": "202.64119", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1995", "Maternal mortality ratio": "203.82845", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1996", "Maternal mortality ratio": "192.42587", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}], "rows_tail": [{"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Maternal mortality ratio": "258.04745", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Maternal mortality ratio": "248.80334", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Maternal mortality ratio": "241.46191", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Maternal mortality ratio": "231.28264", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Maternal mortality ratio": "228.95949", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Maternal mortality ratio": "223.14148", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Maternal mortality ratio": "219.4542", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Maternal mortality ratio": "215.49913", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Maternal mortality ratio": "213.16835", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Maternal mortality ratio": "215.09969", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Maternal mortality ratio": "214.92525", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Maternal mortality ratio": "212.23637", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1985", "Maternal mortality ratio": "466.11166", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1986", "Maternal mortality ratio": "471.0639", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1987", "Maternal mortality ratio": "437.03156", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1988", "Maternal mortality ratio": "416.40182", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1989", "Maternal mortality ratio": "407.2671", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1990", "Maternal mortality ratio": "383.90952", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1991", "Maternal mortality ratio": "369.3595", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1992", "Maternal mortality ratio": "368.42075", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1993", "Maternal mortality ratio": "347.5976", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1994", "Maternal mortality ratio": "357.9511", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1995", "Maternal mortality ratio": "341.94156", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1996", "Maternal mortality ratio": "336.23206", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1997", "Maternal mortality ratio": "324.082", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1998", "Maternal mortality ratio": "315.1185", "World region according to OWID": "Asia", "Maternal 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"Maternal mortality ratio": "209.69948", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Maternal mortality ratio": "196.47777", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Maternal mortality ratio": "183.0726", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Maternal mortality ratio": "167.64091", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Maternal mortality ratio": "168.44948", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Maternal mortality ratio": "155.33047", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Maternal mortality ratio": "156.6496", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Maternal mortality ratio": "155.16437", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Maternal mortality ratio": "157.6561", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Maternal mortality ratio": "150.95755", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Maternal mortality ratio": "156.02693", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Maternal mortality ratio": "163.92967", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Maternal mortality ratio": "171.83197", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Maternal mortality ratio": "179.46648", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Maternal mortality ratio": "175.63083", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Maternal mortality ratio": "181.33215", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Maternal mortality ratio": "183.39972", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Maternal mortality ratio": "555.443", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Maternal mortality ratio": "573.41504", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Maternal mortality ratio": "560.2397", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Maternal mortality ratio": "580.4884", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Maternal mortality ratio": "588.8861", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Maternal mortality ratio": "602.9902", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Maternal mortality ratio": "607.2958", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Maternal mortality ratio": "599.1928", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Maternal mortality ratio": "576.1547", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Maternal mortality ratio": "543.74713", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Maternal mortality ratio": "523.1933", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Maternal mortality ratio": "508.71634", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Maternal mortality ratio": "500.6629", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Maternal mortality ratio": "491.66293", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Maternal mortality ratio": "468.4201", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Maternal mortality ratio": "418.6942", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Maternal mortality ratio": "362.62503", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Maternal mortality ratio": "337.08258", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Maternal mortality ratio": "308.1953", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Maternal mortality ratio": "311.69516", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Maternal mortality ratio": "309.2504", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Maternal mortality ratio": "306.0923", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Maternal mortality ratio": "295.85287", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Maternal mortality ratio": "319.58072", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Maternal mortality ratio": "326.5901", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Maternal mortality ratio": "268.49405", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Maternal mortality ratio": "232.77356", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Maternal mortality ratio": "220.6309", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Maternal mortality ratio": "191.35355", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Maternal mortality ratio": "168.83693", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Maternal mortality ratio": "165.72624", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Maternal mortality ratio": "155.3573", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Maternal mortality ratio": "155.82332", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Maternal mortality ratio": "144.87115", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Maternal mortality ratio": "128.84006", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Maternal mortality ratio": "134.66542", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Maternal mortality ratio": "431.54065", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Maternal mortality ratio": "387.47693", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Maternal mortality ratio": "366.37717", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Maternal mortality ratio": "374.7746", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Maternal mortality ratio": "358.34622", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Maternal mortality ratio": "337.5501", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Maternal mortality ratio": "346.73132", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Maternal mortality ratio": "349.56744", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Maternal mortality ratio": "346.98022", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Maternal mortality ratio": "352.07843", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Maternal mortality ratio": "363.99518", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Maternal mortality ratio": "361.6028", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Maternal mortality ratio": "323.441", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Maternal mortality ratio": "321.5364", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Maternal mortality ratio": "384.5703", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Maternal mortality ratio": "387.75034", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Maternal mortality ratio": "589.1738", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Maternal mortality ratio": "442.7154", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Maternal mortality ratio": "533.8988", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Maternal mortality ratio": "488.08743", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Maternal mortality ratio": "533.07275", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Maternal mortality ratio": "558.164", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Maternal mortality ratio": "656.42145", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Maternal mortality ratio": "684.827", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Maternal mortality ratio": "669.8878", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Maternal mortality ratio": "618.3297", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Maternal mortality ratio": "562.12067", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Maternal mortality ratio": "527.61646", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Maternal mortality ratio": "495.25714", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Maternal mortality ratio": "440.85205", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Maternal mortality ratio": "408.11902", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Maternal mortality ratio": "399.7575", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Maternal mortality ratio": "366.381", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Maternal mortality ratio": "358.5035", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Maternal mortality ratio": "393.1763", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Maternal mortality ratio": "356.7589", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "maternal-mortality", "metadata_url": "https://ourworldindata.org/grapher/maternal-mortality.metadata.json", "chart_title": "Maternal mortality ratio", "chart_subtitle": "Estimated number of women who die from maternal conditions per 100,000 live births, based on data from death certificates, large-scale surveys, and statistical modeling.", "chart_note": "Prior to 1985, only reported data are available, which are likely to underestimate the true maternal mortality rate. From 1985, estimates are shown, which aim to adjust for underreporting and misclassification.", "chart_citation": "UN MMEIG (2023) and other sources", "original_chart_url": "https://ourworldindata.org/grapher/maternal-mortality", "owid_column_metadata": {"Maternal mortality ratio": {"titleShort": "Maternal mortality ratio", "titleLong": "Maternal mortality ratio", "descriptionShort": "The estimated number of women who die from maternal conditions per 100,000 live births, based on data from death certificates, large-scale surveys, and statistical modeling.", "descriptionKey": ["Maternal deaths are defined as a death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and site of pregnancy,\nfrom any cause related or aggravated by the pregnancy or its management, but not from accidental or incidental causes."], "descriptionProcessing": "- The dataset combines three sources: WHO Mortality Database (before 1985), Gapminder (before 1985, if WHO Mortality Database data are unavailable), UN MMEIG (1985 onwards).\n  The WHO Mortality Database and Gapminder contain reported figures from countries, and are likely to underestimate the true maternal mortality figures. The UN MMEIG aims to estimates the true rate, by adjusting for underreporting and misclassification. Sudden jumps in mortality rate in 1985 are a consequence of switching data sources (from reported to estimated figures).\n- For the years between 1950 - 1985 we calculated the maternal mortality ratio and maternal mortality rate based\n  on the number of maternal deaths from the WHO mortality database and live births and female population of reproductive age from the UN WPP.\n- Where the reported maternal deaths in the WHO Mortality Database differed significantly from the estimated figures in the UN MMEIG data, we opted not to include them.\n- Where a data point is attached to a range of years in the Gapminder data set, we used the midpoint of the range.\n- The UN MMEIG data shown (post 1985) is the point estimate - this means there is a 50% chance that the true measure lies above this point,\n  and a 50% chance that the true value lies below this point.\n- We calculated regional aggregates by summing the maternal deaths and live births of all countries in the region and then calculating the MMR based on these figures.", "shortUnit": "", "unit": "deaths per 100,000 live births", "timespan": "1751-2020", "type": "Numeric", "owidVariableId": 959831, "shortName": "mmr", "lastUpdated": "2024-07-08", "nextUpdate": "2026-07-22", "citationShort": "UN MMEIG (2023) and other sources – with major processing by Our World in Data", "citationLong": "UN MMEIG (2023); WHO Mortality Database (2025); UN, World Population Prospects (2024); Gapminder (2010) – with major processing by Our World in Data. “Maternal mortality ratio” [dataset]. UN MMEIG (WHO, UNICEF, UNFPA, World Bank Group and UNDESA/ Population Division), “Trends in maternal mortality 2020”; WHO Mortality Database, “WHO Mortality Database”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update”; Gapminder, “Maternal mortality ratio V1” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/959831.metadata.json"}, "World region according to OWID": {"titleShort": "World region according to OWID", "titleLong": "World region according to OWID", "descriptionShort": "Regions defined by Our World in Data, which are used in OWID charts and maps.", "unit": "", "timespan": "2023-2023", "type": "Continent", "owidVariableId": 900801, "shortName": "owid_region", "lastUpdated": "2023-01-01", "citationShort": "Our World in Data – processed by Our World in Data", "citationLong": "Our World in Data – processed by Our World in Data. “World region according to OWID” [dataset]. Our World in Data, “Regions” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/900801.metadata.json"}, "959831-annotations": {"titleShort": "959831-annotations", "titleLong": "959831-annotations", "type": "SeriesAnnotation", "citationShort": " – processed by Our World in Data", "citationLong": " – processed by Our World in Data. “959831-annotations” [dataset].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/undefined.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Population growth rate", "source_url": "https://ourworldindata.org/grapher/population-growth-rates.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Population growth rate", "Population growth rate (%) (Projected)"], "row_count_total": 38203, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1950", "Population growth rate": "1.275", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1951", "Population growth rate": "1.36", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1952", "Population growth rate": "1.374", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1953", "Population growth rate": "1.335", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1954", "Population growth rate": "1.394", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1955", "Population growth rate": "1.485", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1956", "Population growth rate": "1.55", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1957", "Population growth rate": "1.596", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1958", "Population growth rate": "1.525", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1959", "Population growth rate": "1.789", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1960", "Population growth rate": "1.913", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Population growth rate": "2.01", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Population growth rate": "2.078", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Population growth rate": "2.132", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Population growth rate": "2.19", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Population growth rate": "2.276", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Population growth rate": "2.263", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Population growth rate": "2.349", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Population growth rate": "2.372", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1969", "Population growth rate": "2.413", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1970", "Population growth rate": "2.477", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1971", "Population growth rate": "2.382", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1972", "Population growth rate": "2.502", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Population growth rate": "2.567", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Population growth rate": "2.488", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Population growth rate": "2.344", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Population growth rate": "2.086", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Population growth rate": "2.17", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Population growth rate": "1.851", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Population growth rate": "-1.199", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Population growth rate": "-6.144", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Population growth rate": "-13.883", "Population growth rate (%) (Projected)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Population growth rate": "-2.164", "Population growth rate (%) (Projected)": ""}, {"Entity": 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"ZWE", "Year": "1964", "Birth rate": "46.557"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1965", "Birth rate": "46.375"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "Birth rate": "46.06"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "Birth rate": "45.822"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "Birth rate": "45.667"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "Birth rate": "45.523"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "Birth rate": "45.54"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "Birth rate": "45.611"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "Birth rate": "45.995"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "Birth rate": "46.384"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "Birth rate": "46.395"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Birth rate": "46.623"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Birth rate": "46.524"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Birth rate": "46.51"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Birth rate": "46.766"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Birth rate": "46.305"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Birth rate": "44.84"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Birth rate": "46.313"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Birth rate": "45.723"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Birth rate": "44.988"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Birth rate": "43.721"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Birth rate": "42.336"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Birth rate": "40.936"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Birth rate": "39.499"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Birth rate": "37.653"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Birth rate": "36.387"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Birth rate": "35.51"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Birth rate": "34.913"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Birth rate": "34.269"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Birth rate": "32.379"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Birth rate": "32.025"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Birth rate": "31.665"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Birth rate": "32.405"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Birth rate": "33.372"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Birth rate": "34.47"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Birth rate": "35.615"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Birth rate": "36.128"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Birth rate": "36.463"}, {"Entity": "Zimbabwe", 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{"raw_link": "https://ourworldindata.org/why-cheap-waste-management-is-key-to-stopping-plastic-pollution", "title": "Why cheap waste management is key to stopping plastic pollution", "context": "Home\nPlastic Pollution\nWhy cheap waste management is key to stopping plastic pollution\nImproving waste management in low- and middle-income countries could cut global pollution by 98%.\nBy\nHannah Ritchie\n(writing)\nand\nVeronika Samborska\n(data)\nMarch 16, 2026\nBrowse past versions\nCite this article\nReuse our work freely\nOf every 5 kilograms of plastic waste produced globally, 1 kilogram ends up polluting the environment.\nThis has serious consequences for people and other animals alike. It pollutes waterways, harms wildlife, and burning plastic generates toxic air that millions breathe.\nBut this terrible pollution is not inevitable.\nIn countries with good waste management systems, far less plastic pollutes the environment. Across high-income countries, plastic pollution per person is 100 times\nlower than in lower-income countries.\nIf every country managed its waste in this way, the world would cut plastic pollution by more than 98%.\n1\nWhy is this gap so large?\nIn the chart below, you see two key metrics: how much plastic\nwaste\nis generated and how much plastic\npollution\nis produced per person. These estimates are taken from research by Joshua Cottom and colleagues.\n2\nClearly, people in high-income countries don’t produce 100 times less pollution than those in lower-income countries because they\nuse\nless plastic. Per person, they use much more.\nThe fact that more waste doesn’t automatically translate into more pollution is also clear when we look at\nthe relationship betweeen waste and pollution by country\n.\nDownload\nThe huge difference in pollution rates is a consequence of how waste is managed. In high-income countries, most waste is collected and sent to controlled landfills or to facilities that incinerate or recycle it.\nIn many low- and middle-income countries, people find themselves in a very different situation: less than half of solid household\nwaste is collected\n. People often have little choice but to burn or dump it. But even the waste that\nis\ncollected is\noften left\nin open dumps, where it’s at risk of leaking into the environment.\nMost pollution, then, comes from uncollected waste and poorly managed disposal sites. You can see this in the chart.\n3\nWhat, then, is causing plastic pollution in rich countries? Roughly half\ncomes from\nlittering: people thoughtlessly chucking their plastic bottles, wrappers, and bags. If we built a world where people don’t do this, we could increase that 98% reduction to 99%.\nDownload\nWhat does this mean for our options to tackle plastic pollution?\nCutting plastic use in rich countries has very little impact on global plastic pollution: the world’s high-income countries\ngenerate less than\n0.5% of the total.\nReducing use in low- and middle-income countries could certainly help. But even large reductions wouldn’t get close to eliminating pollution. If one in every five kilograms of plastic waste in these countries ends up as pollution, even\nhalving\nplastic waste would still leave tens of millions of tonnes leaking into the environment each year.\n4\nIf every country managed its waste the way high-income countries do, the world would cut plastic pollution by over 98%.\nImproving waste management systems in low- and middle-income countries is therefore crucial. Getting there does not require fancy solutions. It needs investment in very basic infrastructure in the right places.\nIn a study published in\nNature Sustainability\n, Malak Anshassi and Timothy Townsend estimate that high-income countries typically spend about $50 per person on waste management.\n5\nIn low-income countries, it’s $1 at most.\n6\nThis is where investment makes the biggest difference: each dollar spent upgrading systems in a low- or lower-middle-income country prevents roughly\n25,000 times more plastic pollution\nthan the same dollar spent on advanced infrastructure in a rich country.\nSince capital is usually the constraint, focusing on\nbasic\ninfrastructure — collection and controlled landfills — beats expensive options like incinerators and recycling plants.\n7\nFor those passionate about ending plastic pollution, this is where attention and resources could make the biggest difference.\nTo most, this won’t sound like a particularly attractive way to spend money. Who really wants to invest in waste collection trucks and landfills? Not many. But for those passionate about ending plastic pollution, this is where attention and resources could make the biggest difference. Making the case for waste management and ways to make these processes and infrastructure cheaper could be the best thing you do to stop bottles clogging the world’s rivers and toxic pollution filling the air.\nWe already have the knowledge and tools to reduce global plastic pollution to just 2% of its current levels. With the right focus and investment, most of it is preventable.\nAcknowledgments\nMany thanks to Max Roser and Edouard Mathieu for editorial feedback and comments on this article.\nContinue reading on Our World in Data\nHow much plastic waste ends up in the ocean?\nAround 0.5% of plastic waste ends up in the ocean. Most of it stays close to the shoreline.\nWhere does the plastic in our oceans come from?\nWhich countries and rivers emit the most plastic to the ocean? What does this mean for solutions to tackle plastic pollution?\nPlastic Pollution\nHow much plastic ends up in the ocean? Where does it come from?\nEndnotes\nThe world generated around 52 million tonnes of plastic pollution in 2020.\nIf all countries had the same rate of plastic pollution as high-income countries — 0.12 kilograms per person — this would generate 960,000 tonnes of plastic pollution each year [0.12 * 8 billion people / 1000 to convert to tonnes]. That’s a reduction of around 98% [(52,000,000 - 960,000) / 52,000,000 = 98%].\nCottom, J. W., Cook, E., & Velis, C. A. (2024). A local-to-global emissions inventory of macroplastic pollution. Nature, 633(8028), 101-108.\nThis combines estimates of the amount of plastic used and waste generated in each country with data on how waste is managed to understand how much pollution is generated and where in the system it comes from. This relies on some assumptions about how much plastic waste stored in sanitary landfills or open dumps might leak into the environment.\nThis is the most comprehensive global assessment of plastic pollution to date, but its results and conclusions are similar to those of previous studies. All point towards a similar conclusion: that most plastic pollution and ocean plastics are generated in low- to middle-income countries, even though they use less plastic per person.\nJambeck, 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. Science.\nMeijer, L. J. J., van Emmerik, T., van der Ent, R., Schmidt, C. & Lebreton, L. (2021). More than 1000 rivers account for 80% of global riverine plastic emissions into the ocean. Science Advances.\nLebreton, L. C. M. et al. (2017). River plastic emissions to the world’s oceans. Nature Communications.\nIn rich countries, littering dominates, not because people litter more, but because other sources are so small.\nThe “one in every five kilograms” figure varies by the particular income group: in low-income countries, it’s one-in-two, in lower-middle income it’s one-in-three, and in upper-middle income it’s one-in-seven.\nLow and middle-income countries combined\nproduce around\n200 million tonnes of plastic waste each year. If they halved this, it would be 100 million tonnes. If one in five kilograms becomes pollution, that would still mean 20 million tonnes each year.\nAnshassi, M., & Townsend, T. G. (2025). Improving waste systems in the global south to tackle international environmental impacts. Nature Sustainability.\nThe authors give the contrast of Italy and the Netherlands, which spend $54 and US$60 per person, respectively, to Mexico and Hungary at US$2 and US$7 per person. In low-income countries, spending levels are likely lower than this: in some countries, it’s likely less than $1 per person.\nNote that this is only true if waste management involves controlled systems: reliable collection and sanitary landfills. If countries invest in more collection, but then put it in open dumps or openly burn it, it simply moves the pollution. It does not stop it.\nMacLeod, M. (2024). Waste management won’t solve the plastics problem—we need to cut consumption. Nature.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Veronika Samborska (2026) - “Why cheap waste management is key to stopping plastic pollution” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260324-152047/why-cheap-waste-management-is-key-to-stopping-plastic-pollution.html' [Online Resource] (archived on March 24, 2026).\nBibTeX citation\n@article{owid-why-cheap-waste-management-is-key-to-stopping-plastic-pollution,\nauthor = {Hannah Ritchie and Veronika Samborska},\ntitle = {Why cheap waste management is key to stopping plastic pollution},\njournal = {Our World in Data},\nyear = {2026},\nnote = {https://archive.ourworldindata.org/20260324-152047/why-cheap-waste-management-is-key-to-stopping-plastic-pollution.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "why-cheap-waste-management-is-key-to-stopping-plastic-pollution", "source_url": "https://ourworldindata.org/why-cheap-waste-management-is-key-to-stopping-plastic-pollution", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Improving waste management in low- and middle-income countries could cut global pollution by 98%.", "numeric_mentions": ["98%", "16,", "2026", "5", "1", "100", "2", "3", "99%", "0.5%", "4", "50", "6", "25,000", "7", "2%", "52 million", "2020", "0.12", "960,000", "8 billion", "1000", "52,000,000", "2024", "633", "8028", "101", "108", "2015", "2021", "80%", "2017", "200 million", "100 million", "20 million", "2025", "54", "60", "20260324", "152047", "24,"], "numeric_evidence": [{"title": "Plastic pollution versus plastic waste generation per person", "source_url": "https://ourworldindata.org/grapher/plastic-waste-vs-pollution.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Plastic pollution per capita", "Plastic waste generation per capita", "World Bank's 2025 income classification"], "row_count_total": 253, "rows_head": 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"World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Plastic pollution per capita": "8.739351", "Plastic waste generation per capita": "33.999477", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2020", "Plastic pollution per capita": "5.183191", "Plastic waste generation per capita": "43.08616", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Americas (UN M49)", "Code": "", "Year": "2020", "Plastic pollution per capita": "4.5673437", "Plastic waste generation per capita": "53.56487", "World Bank's 2025 income classification": ""}, {"Entity": "Andorra", "Code": "AND", "Year": "2020", "Plastic pollution per capita": "0.14984356", "Plastic waste generation per capita": "81.45857", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Angola", "Code": "AGO", "Year": "2020", "Plastic pollution per capita": "16.48874", "Plastic waste generation per capita": "29.233612", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Anguilla", "Code": "AIA", "Year": "2020", "Plastic pollution per capita": "14.054703", "Plastic waste generation per capita": "34.092785", "World Bank's 2025 income classification": ""}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2020", "Plastic pollution per capita": "0.77469116", "Plastic waste generation per capita": "75.72931", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2020", "Plastic pollution per capita": "7.7870283", "Plastic waste generation per capita": "53.38393", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2020", "Plastic pollution per capita": "5.653116", "Plastic waste generation per capita": "37.50392", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2020", "Plastic pollution per capita": "0.6353624", "Plastic waste generation per capita": "90.22238", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Asia (UN M49)", "Code": "", "Year": "2020", "Plastic pollution per capita": "6.254765", "Plastic waste generation per capita": "27.736652", "World Bank's 2025 income classification": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2020", "Plastic pollution per capita": "0.08576395", "Plastic waste generation per capita": "54.58498", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Austria", "Code": "AUT", "Year": "2020", "Plastic pollution per capita": "0.13914104", "Plastic waste generation per capita": "67.51663", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2020", "Plastic pollution per capita": "8.844375", "Plastic waste generation per capita": "33.813522", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Bahamas", "Code": "BHS", "Year": "2020", "Plastic pollution per capita": "0.58742434", "Plastic waste generation per capita": "83.99232", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2020", "Plastic pollution per capita": "0.038585633", "Plastic waste generation per capita": "57.640438", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2020", "Plastic pollution per capita": "10.460064", "Plastic waste generation per capita": "19.46173", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2020", "Plastic pollution per capita": "0.49374634", "Plastic waste generation per capita": "75.52421", "World Bank's 2025 income 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"41.910778", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2020", "Plastic pollution per capita": "6.7803245", "Plastic waste generation per capita": "41.236485", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "British Virgin Islands", "Code": "VGB", "Year": "2020", "Plastic pollution per capita": "0.777337", "Plastic waste generation per capita": "89.74809", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2020", "Plastic pollution per capita": "0.17532015", "Plastic waste generation per capita": "98.08689", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2020", "Plastic pollution per capita": "7.2664776", "Plastic waste generation per capita": "40.170273", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": 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{"Entity": "French Polynesia", "Code": "PYF", "Year": "2020", "Plastic pollution per capita": "0.18779983", "Plastic waste generation per capita": "78.10096", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Gabon", "Code": "GAB", "Year": "2020", "Plastic pollution per capita": "3.4365788", "Plastic waste generation per capita": "33.542747", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Gambia", "Code": "GMB", "Year": "2020", "Plastic pollution per capita": "11.344165", "Plastic waste generation per capita": "17.482418", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "Georgia", "Code": "GEO", "Year": "2020", "Plastic pollution per capita": "7.425445", "Plastic waste generation per capita": "45.038437", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Germany", "Code": "DEU", "Year": "2020", "Plastic pollution per capita": "0.092645116", 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{"Entity": "Ireland", "Code": "IRL", "Year": "2020", "Plastic pollution per capita": "0.13298433", "Plastic waste generation per capita": "55.997574", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Isle of Man", "Code": "IMN", "Year": "2020", "Plastic pollution per capita": "0.10367511", "Plastic waste generation per capita": "46.436024", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Israel", "Code": "ISR", "Year": "2020", "Plastic pollution per capita": "0.069108404", "Plastic waste generation per capita": "58.479454", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Italy", "Code": "ITA", "Year": "2020", "Plastic pollution per capita": "0.12916295", "Plastic waste generation per capita": "67.154465", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Jamaica", "Code": "JAM", "Year": "2020", "Plastic pollution per capita": "8.396294", "Plastic waste 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"Upper-middle-income countries"}, {"Entity": "Monaco", "Code": "MCO", "Year": "2020", "Plastic pollution per capita": "0.041476604", "Plastic waste generation per capita": "80.894035", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Mongolia", "Code": "MNG", "Year": "2020", "Plastic pollution per capita": "9.856271", "Plastic waste generation per capita": "35.0269", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Morocco", "Code": "MAR", "Year": "2020", "Plastic pollution per capita": "10.514465", "Plastic waste generation per capita": "34.33464", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Mozambique", "Code": "MOZ", "Year": "2020", "Plastic pollution per capita": "10.372215", "Plastic waste generation per capita": "15.322708", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "Myanmar", "Code": "MMR", "Year": "2020", "Plastic pollution 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income classification": "High-income countries"}, {"Entity": "New Caledonia", "Code": "NCL", "Year": "2020", "Plastic pollution per capita": "0.3481924", "Plastic waste generation per capita": "79.41013", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "New Zealand", "Code": "NZL", "Year": "2020", "Plastic pollution per capita": "0.10268797", "Plastic waste generation per capita": "73.429985", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Nicaragua", "Code": "NIC", "Year": "2020", "Plastic pollution per capita": "14.679978", "Plastic waste generation per capita": "29.343771", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Niger", "Code": "NER", "Year": "2020", "Plastic pollution per capita": "5.021193", "Plastic waste generation per capita": "7.6309223", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2020", "Plastic pollution per capita": "16.96176", "Plastic waste generation per capita": "26.982248", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Niue", "Code": "NIU", "Year": "2020", "Plastic pollution per capita": "15.34829", "Plastic waste generation per capita": "29.586117", "World Bank's 2025 income classification": ""}, {"Entity": "Norfolk Island", "Code": "NFK", "Year": "2020", "Plastic pollution per capita": "0.27140737", "Plastic waste generation per capita": "76.93648", "World Bank's 2025 income classification": ""}, {"Entity": "North Korea", "Code": "PRK", "Year": "2020", "Plastic pollution per capita": "13.342949", "Plastic waste generation per capita": "25.292168", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "North Macedonia", "Code": "MKD", "Year": "2020", "Plastic pollution per capita": "7.7310867", "Plastic waste generation per capita": "42.55271", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Northern Cyprus", "Code": "OWID_CYN", "Year": "2020", "Plastic pollution per capita": "0.26655626", "Plastic waste generation per capita": "57.994938", "World Bank's 2025 income classification": ""}, {"Entity": "Northern Mariana Islands", "Code": "MNP", "Year": "2020", "Plastic pollution per capita": "0.54976916", "Plastic waste generation per capita": "89.11835", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Norway", "Code": "NOR", "Year": "2020", "Plastic pollution per capita": "0.100299284", "Plastic waste generation per capita": "43.903088", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Oceania (UN M49)", "Code": "", "Year": "2020", "Plastic pollution per capita": "3.6913612", "Plastic waste generation per capita": "48.56264", "World Bank's 2025 income classification": ""}, {"Entity": "Oman", "Code": "OMN", "Year": "2020", "Plastic pollution per capita": "0.65866864", "Plastic waste generation per capita": "54.015583", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Pakistan", "Code": "PAK", "Year": "2020", "Plastic pollution per capita": "11.301396", "Plastic waste generation per capita": "21.74189", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Palau", "Code": "PLW", "Year": "2020", "Plastic pollution per capita": "11.017211", "Plastic waste generation per capita": "45.64472", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Palestine", "Code": "PSE", "Year": "2020", "Plastic pollution per capita": "5.6615186", "Plastic waste generation per capita": "41.090416", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Panama", "Code": "PAN", "Year": "2020", "Plastic pollution per capita": "9.635644", "Plastic waste generation per capita": "41.40283", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Papua New Guinea", "Code": "PNG", "Year": "2020", "Plastic pollution per capita": "13.36103", "Plastic waste generation per capita": "21.307005", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Paracel Islands", "Code": "", "Year": "2020", "Plastic pollution per capita": "18.24816", "Plastic waste generation per capita": "34.682068", "World Bank's 2025 income classification": ""}, {"Entity": "Paraguay", "Code": "PRY", "Year": "2020", "Plastic pollution per capita": "23.352188", "Plastic waste generation per capita": "46.055584", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Peru", "Code": "PER", "Year": "2020", "Plastic pollution per capita": "8.178159", "Plastic waste generation per capita": "37.08425", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Philippines", "Code": "PHL", "Year": "2020", "Plastic pollution per capita": "7.3591537", "Plastic waste generation per capita": "31.88892", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Pitcairn Islands", "Code": "", "Year": "2020", "Plastic pollution per capita": "12.153927", "Plastic waste generation per capita": "17.550352", "World Bank's 2025 income classification": ""}, {"Entity": "Poland", "Code": "POL", "Year": "2020", "Plastic pollution per capita": "0.18434705", "Plastic waste generation per capita": "53.899822", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2020", "Plastic pollution per capita": "0.11472551", "Plastic waste generation per capita": "56.47036", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Puerto Rico", "Code": "PRI", "Year": "2020", "Plastic pollution per capita": "0.58367896", "Plastic waste generation per capita": "69.126335", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2020", "Plastic pollution per capita": "0.062016428", "Plastic waste generation per capita": "75.04647", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Reunion", "Code": "REU", "Year": "2020", "Plastic pollution per capita": "0.4090952", "Plastic waste generation per capita": "53.904644", "World Bank's 2025 income classification": ""}, {"Entity": "Romania", "Code": "ROU", "Year": "2020", "Plastic pollution per capita": "7.3003125", "Plastic waste generation per capita": "34.255993", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Russia", "Code": "RUS", "Year": "2020", "Plastic pollution per capita": "11.712518", "Plastic waste generation per capita": "41.9666", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2020", "Plastic pollution per capita": "15.339281", "Plastic waste generation per capita": "20.802486", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "Saint Barthelemy", "Code": "BLM", "Year": "2020", "Plastic pollution per capita": "0.45287883", "Plastic waste generation per capita": "52.805973", "World Bank's 2025 income classification": ""}, {"Entity": "Saint Helena", "Code": "SHN", "Year": "2020", "Plastic pollution per capita": "16.471945", "Plastic waste generation per capita": "32.607822", "World Bank's 2025 income classification": ""}, {"Entity": "Saint Kitts and Nevis", "Code": "KNA", "Year": "2020", "Plastic pollution per capita": "0.90526706", "Plastic waste generation per capita": "87.768936", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Saint Lucia", "Code": "LCA", "Year": "2020", "Plastic pollution per capita": "6.62196", "Plastic waste generation per capita": "56.71632", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Saint Martin (French part)", "Code": "MAF", "Year": "2020", "Plastic pollution per capita": "1.9404747", "Plastic waste generation per capita": "50.318676", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Saint Pierre and Miquelon", "Code": "SPM", "Year": "2020", "Plastic pollution per capita": "0.24340081", "Plastic waste generation per capita": "69.39064", "World Bank's 2025 income classification": ""}, {"Entity": "Saint Vincent and the Grenadines", "Code": "VCT", "Year": "2020", "Plastic pollution per capita": "5.8190236", "Plastic waste generation per capita": "48.539845", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Samoa", "Code": "WSM", "Year": "2020", "Plastic pollution per capita": "12.240864", "Plastic waste generation per capita": "33.95619", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "San Marino", "Code": "SMR", "Year": "2020", "Plastic pollution per capita": "0.14121714", "Plastic waste generation per capita": "79.38529", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Sao Tome and Principe", "Code": "STP", "Year": "2020", "Plastic pollution per capita": "16.890089", "Plastic waste generation per capita": "33.612106", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2020", "Plastic pollution per capita": "0.09572041", "Plastic waste generation per capita": "75.27483", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2020", "Plastic pollution per capita": "13.0353", "Plastic waste generation per capita": "21.548824", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Serbia", "Code": "SRB", "Year": "2020", "Plastic pollution per capita": "17.033463", "Plastic waste generation per capita": "47.667347", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Seychelles", "Code": "SYC", "Year": "2020", "Plastic pollution per capita": "1.1786499", "Plastic waste generation per capita": "85.82099", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2020", "Plastic pollution per capita": "7.2817273", "Plastic waste generation per capita": "11.224377", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2020", "Plastic pollution per capita": "0.02964416", "Plastic waste generation per capita": "63.05804", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Sint Maarten (Dutch part)", "Code": "SXM", "Year": "2020", "Plastic pollution per capita": "0.487552", "Plastic waste generation per capita": "78.746254", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Slovakia", "Code": "SVK", "Year": "2020", "Plastic pollution per capita": "0.19916192", "Plastic waste generation per capita": "68.13317", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2020", "Plastic pollution per capita": "0.2514575", "Plastic waste generation per capita": "58.348034", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Solomon Islands", "Code": "SLB", "Year": "2020", "Plastic pollution per capita": "13.203426", "Plastic waste generation per capita": "19.10275", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Somalia", "Code": "SOM", "Year": "2020", "Plastic pollution per capita": "13.583317", "Plastic waste generation per capita": "19.072376", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2020", "Plastic pollution per capita": "10.371117", "Plastic waste generation per capita": "40.75479", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2020", "Plastic pollution per capita": "0.055418983", "Plastic waste generation per capita": "48.69923", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2020", "Plastic pollution per capita": "20.366692", "Plastic waste generation per capita": "32.962162", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "Spain", "Code": "ESP", "Year": "2020", "Plastic pollution per capita": "0.13198704", "Plastic waste generation per capita": "73.52711", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2020", "Plastic pollution per capita": "12.629981", "Plastic waste generation per capita": "35.268265", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Sudan", "Code": "SDN", "Year": "2020", "Plastic pollution per capita": "11.924007", "Plastic waste generation per capita": "24.63625", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2020", "Plastic pollution per capita": "6.914282", "Plastic waste generation per capita": "40.01604", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Svalbard and Jan Mayen", "Code": "SJM", "Year": "2020", "Plastic pollution per capita": "0.30016464", "Plastic waste generation per capita": "74.35056", "World Bank's 2025 income classification": ""}, {"Entity": "Sweden", "Code": "SWE", "Year": "2020", "Plastic pollution per capita": "0.088868305", "Plastic waste generation per capita": "46.240276", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2020", "Plastic pollution per capita": "0.08500185", "Plastic waste generation per capita": "43.746487", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Syria", "Code": "SYR", "Year": "2020", "Plastic pollution per capita": "15.798999", "Plastic waste generation per capita": "33.99665", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2020", "Plastic pollution per capita": "0.05875851", "Plastic waste generation per capita": "61.547928", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2020", "Plastic pollution per capita": "16.348816", "Plastic waste generation per capita": "29.174982", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2020", "Plastic pollution per capita": "12.635989", "Plastic waste generation per capita": "19.060566", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Thailand", "Code": "THA", "Year": "2020", "Plastic pollution per capita": "13.933423", "Plastic waste generation per capita": "47.967552", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Togo", "Code": "TGO", "Year": "2020", "Plastic pollution per capita": "11.740352", "Plastic waste generation per capita": "18.318016", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "Tokelau", "Code": "TKL", "Year": "2020", "Plastic pollution per capita": "9.128647", "Plastic waste generation per capita": "12.089662", "World Bank's 2025 income classification": ""}, {"Entity": "Tonga", "Code": "TON", "Year": "2020", "Plastic pollution per capita": "10.720667", "Plastic waste generation per capita": "33.90128", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2020", "Plastic pollution per capita": "4.591176", "Plastic waste generation per capita": "61.76152", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2020", "Plastic pollution per capita": "10.689528", "Plastic waste generation per capita": "35.907883", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2020", "Plastic pollution per capita": "5.7868023", "Plastic waste generation per capita": "47.996128", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2020", "Plastic pollution per capita": "11.3705635", "Plastic waste generation per capita": "32.48831", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Turks and Caicos Islands", "Code": "TCA", "Year": "2020", "Plastic pollution per capita": "0.54006255", "Plastic waste generation per capita": "79.30824", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Tuvalu", "Code": "TUV", "Year": "2020", "Plastic pollution per capita": "10.142396", "Plastic waste generation per capita": "29.655687", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2020", "Plastic pollution per capita": "13.907193", "Plastic waste generation per capita": "19.327776", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2020", "Plastic pollution per capita": "9.512135", "Plastic waste generation per capita": "32.573418", "World Bank's 2025 income classification": "Upper-middle-income countries"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2020", "Plastic pollution per capita": "0.099733666", "Plastic waste generation per capita": "111.069664", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2020", "Plastic pollution per capita": "0.06896565", "Plastic waste generation per capita": "54.752804", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "United States", "Code": "USA", "Year": "2020", "Plastic pollution per capita": "0.14185095", "Plastic waste generation per capita": "74.34526", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "United States Virgin Islands", "Code": "VIR", "Year": "2020", "Plastic pollution per capita": "0.7760323", "Plastic waste generation per capita": "78.27857", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2020", "Plastic pollution per capita": "5.1852093", "Plastic waste generation per capita": "35.108326", "World Bank's 2025 income classification": ""}, {"Entity": "Uruguay", "Code": "URY", "Year": "2020", "Plastic pollution per capita": "0.28037092", "Plastic waste generation per capita": "57.908627", "World Bank's 2025 income classification": "High-income countries"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2020", "Plastic pollution per capita": "11.746851", "Plastic waste generation per capita": "30.92816", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2020", "Plastic pollution per capita": "20.101683", "Plastic waste generation per capita": "31.01355", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Vatican", "Code": "VAT", "Year": "2020", "Plastic pollution per capita": "0.031234784", "Plastic waste generation per capita": "65.59585", "World Bank's 2025 income classification": ""}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2020", "Plastic pollution per capita": "8.775086", "Plastic waste generation per capita": "49.284683", "World Bank's 2025 income classification": ""}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "Plastic pollution per capita": "7.817499", "Plastic waste generation per capita": "37.737564", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2020", "Plastic pollution per capita": "10.276126", "Plastic waste generation per capita": "13.399278", "World Bank's 2025 income classification": ""}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "2020", "Plastic pollution per capita": "8.535897", "Plastic waste generation per capita": "28.840668", "World Bank's 2025 income classification": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Plastic pollution per capita": "6.65102", "Plastic waste generation per capita": "33.124847", "World Bank's 2025 income classification": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Plastic pollution per capita": "10.140191", "Plastic waste generation per capita": "28.098106", "World Bank's 2025 income classification": "Low-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Plastic pollution per capita": "11.374153", "Plastic waste generation per capita": "16.633657", "World Bank's 2025 income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Plastic pollution per capita": "10.394113", "Plastic waste generation per capita": "13.585236", "World Bank's 2025 income classification": "Lower-middle-income countries"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "plastic-waste-vs-pollution", "metadata_url": "https://ourworldindata.org/grapher/plastic-waste-vs-pollution.metadata.json", "chart_title": "Plastic pollution versus plastic waste generation per person", "chart_subtitle": "Plastic pollution is plastic emitted into the environment, either through open burning or as solid debris. Based on modelled estimates for the year 2020.", "chart_note": null, "chart_citation": "Cottom et al. (2024)", "original_chart_url": "https://ourworldindata.org/grapher/plastic-waste-vs-pollution", "owid_column_metadata": {"Total plastic pollution per capita": {"titleShort": "Total plastic pollution per capita", "titleLong": "Total plastic pollution per capita", "descriptionShort": "Estimated total amount of plastic waste released to the environment per person each year through debris and open burning from municipal sources such as households, shops, and offices.", "descriptionKey": ["Plastic pollution is plastic that is no longer contained because it escapes from collection, disposal, or recycling and enters the environment.", "This data covers only macroplastics, which are plastic pieces larger than 5 millimetres.", "Total plastic pollution is the sum of debris (unburned plastic that escapes into the environment as physical items) and plastic burned in open, uncontrolled fires.", "Plastic pollution is attributed to five land-based sources: uncollected waste, littering, losses during collection and transport, uncontrolled disposal sites (open dumps), and rejects from sorting and reprocessing.", "Per-capita values are calculated by dividing the total annual value by the relevant population (for example, the population of a country, region or income group).", "Cottom et al. (2024) developed the [SPOT model](https://www.nature.com/articles/s41586-024-07758-6) model, which first fills gaps in municipal waste data using statistical predictions. It then estimates how plastic flows through the waste system and quantifies the uncertainty in those estimates. The model produces results for around 50,700 municipalities, which are subsequently aggregated to country and regional totals.", "Values are model-based estimates and come with uncertainty. They should be interpreted as approximate estimates rather than exact measurements."], "shortUnit": "kg/person", "unit": "kilograms per person", "timespan": "2020-2020", "type": "Numeric", "owidVariableId": 1134067, "shortName": "plas_em_per_cap", "lastUpdated": "2026-01-14", "nextUpdate": "2027-01-14", "citationShort": "Cottom et al. (2024) – with minor processing by Our World in Data", "citationLong": "Cottom et al. (2024) – with minor processing by Our World in Data. “Total plastic pollution per capita” [dataset]. Cottom et al., “A local-to-global emissions inventory of macroplastic pollution” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1134067.metadata.json"}, "Plastic waste generation per capita": {"titleShort": "Plastic waste generation per capita", "titleLong": "Plastic waste generation per capita", "descriptionShort": "Estimated amount of plastic waste generated per person per year from municipal sources such as households, shops, and offices.", "descriptionKey": ["Municipal plastic waste is the plastic fraction of municipal solid waste (for example, packaging and consumer products discarded with household waste).", "Cottom et al. (2024) developed the [SPOT model](https://www.nature.com/articles/s41586-024-07758-6) model, which first fills gaps in municipal waste data using statistical predictions. It then estimates how plastic flows through the waste system and quantifies the uncertainty in those estimates. The model produces results for around 50,700 municipalities, which are subsequently aggregated to country and regional totals.", "This data covers plastic that comes from land-based municipal solid waste (everyday waste from households and similar sources). It does not include pollution from making plastic, textiles, sea-based sources (like fishing gear), electronic waste, or plastic that is exported as waste and then lost elsewhere.", "Per-capita values are calculated by dividing the total annual value by the relevant population (for example, the population of a country, region or income group)."], "descriptionProcessing": "We calculated per capita plastic waste generation by dividing total plastic waste generation by the population of each country or region.", "shortUnit": "kg/person", "unit": "kilograms per person", "timespan": "2020-2020", "type": "Numeric", "owidVariableId": 1134053, "shortName": "pwg_per_cap", "lastUpdated": "2026-01-14", "nextUpdate": "2027-01-14", "citationShort": "Cottom et al. (2024) – with minor processing by Our World in Data", "citationLong": "Cottom et al. (2024) – with minor processing by Our World in Data. “Plastic waste generation per capita” [dataset]. Cottom et al., “A local-to-global emissions inventory of macroplastic pollution” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1134053.metadata.json"}, "World Bank's latest income classification": {"titleShort": "World Bank's 2025 income classification", "titleLong": "World Bank's 2025 income classification", "descriptionShort": "Income classification based on the country's income for the latest year informed.", "descriptionKey": ["The World Bank creates a yearly classification of countries by income, for all countries with population over 30,000.", "This classification stays the same throughout the World Bank's fiscal year (from July 1 to June 30) even if the income data for a country changes.", "Low-income countries are those with a gross national income (GNI) per capita of $1,135 or less in 2024.", "Lower-middle-income countries are those with a GNI per capita between $1,136 and $4,495 in 2024.", "Upper-middle-income countries are those with a GNI per capita between $4,496 and $13,935 in 2024.", "High-income countries are those with a GNI per capita of more than $13,935 in 2024.", "Venezuela, classified as an upper-middle income country until the fiscal year 2021, has been unclassified since then due to the unavailability of data. Ethiopia is currently in a temporary status of unclassification."], "unit": "", "timespan": "2024-2024", "type": "Ordinal", "owidVariableId": 1077023, "shortName": "classification_latest", "lastUpdated": "2025-07-01", "nextUpdate": "2026-07-01", "citationShort": "World Bank (2025) – with major processing by Our World in Data", "citationLong": "World Bank (2025) – with major processing by Our World in Data. “World Bank's 2025 income classification” [dataset]. World Bank, “Income Classifications” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077023.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Share of total municipal solid waste that is collected", "source_url": "https://ourworldindata.org/grapher/share-waste-collected.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Share of total municipal solid waste collected"], "row_count_total": 239, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Share of total municipal solid waste collected": "52"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Share of total municipal solid waste collected": "77"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Share of total municipal solid waste collected": "92"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Andorra", "Code": "AND", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Angola", "Code": "AGO", "Year": "2020", "Share of total municipal solid waste collected": "23.1"}, {"Entity": "Anguilla", "Code": "AIA", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2020", "Share of total municipal solid waste collected": "98.61"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2020", "Share of total municipal solid waste collected": "89.91"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2020", "Share of total municipal solid waste collected": "80"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Australia", "Code": "AUS", "Year": "2020", "Share of total municipal solid waste collected": "99"}, {"Entity": "Austria", "Code": "AUT", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2020", "Share of total municipal solid waste collected": "56"}, {"Entity": "Bahamas", "Code": "BHS", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2020", "Share of total municipal solid waste collected": "52"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2020", "Share of total municipal solid waste collected": "90"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2020", "Share of total municipal solid waste collected": "85.2"}, {"Entity": "Benin", "Code": "BEN", "Year": "2020", "Share of total municipal solid waste collected": "23"}, {"Entity": "Bermuda", "Code": "BMU", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2020", "Share of total municipal solid waste collected": "94"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2020", "Share of total municipal solid waste collected": "57.6"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2020", "Share of total municipal solid waste collected": "73.99"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2020", "Share of total municipal solid waste collected": "23.6"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2020", "Share of total municipal solid waste collected": "91.3"}, {"Entity": "British Virgin Islands", "Code": "VGB", "Year": "2020", "Share of total municipal solid waste collected": "97.2"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2020", "Share of total municipal solid waste collected": "94.95234"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2020", "Share of total municipal solid waste collected": "98.3"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2020", "Share of total municipal solid waste collected": "34.52"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2020", "Share of total municipal solid waste collected": "75"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2020", "Share of total municipal solid waste collected": "61.6"}, {"Entity": "Canada", "Code": "CAN", "Year": "2020", "Share of total municipal solid waste collected": "99"}, {"Entity": "Cape Verde", "Code": "CPV", "Year": "2020", "Share of total municipal solid waste collected": "70.6"}, {"Entity": "Cayman Islands", "Code": "CYM", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "Chad", "Code": "TCD", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "Channel Islands", "Code": "OWID_CIS", "Year": "2020", "Share of total municipal solid waste collected": "99.91863"}, {"Entity": "Chile", "Code": "CHL", "Year": "2020", "Share of total municipal solid waste collected": "95"}, {"Entity": "China", "Code": "CHN", "Year": "2020", "Share of total municipal solid waste collected": "94"}, {"Entity": "Christmas Island", "Code": "CXR", "Year": "2020", "Share of total municipal solid waste collected": "94.95234"}, {"Entity": "Cocos Islands", "Code": "CCK", "Year": "2020", "Share of total municipal solid waste collected": "94.95234"}, {"Entity": "Colombia", "Code": "COL", "Year": "2020", "Share of total municipal solid waste collected": "98"}, {"Entity": "Comoros", "Code": "COM", "Year": "2020", "Share of total municipal solid waste collected": "20"}, {"Entity": "Congo", "Code": "COG", "Year": "2020", "Share of total municipal solid waste collected": "45.6861"}, {"Entity": "Cook Islands", "Code": "COK", "Year": "2020", "Share of total municipal solid waste collected": "94.95234"}, {"Entity": "Costa Rica", "Code": "CRI", "Year": "2020", "Share of total municipal solid waste collected": "90.4"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2020", "Share of total municipal solid waste collected": "45.6861"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2020", "Share of total municipal solid waste collected": "97.76"}, {"Entity": "Cuba", "Code": "CUB", "Year": "2020", "Share of total municipal solid waste collected": "76.9"}, {"Entity": "Curacao", "Code": "CUW", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2020", "Share of total municipal solid waste collected": "92.42"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Dhekelia", "Code": "", "Year": "2020", "Share of total municipal solid waste collected": "97.6901"}, {"Entity": "Djibouti", "Code": "DJI", "Year": "2020", "Share of total municipal solid waste collected": "65.89891"}, {"Entity": "Dominica", "Code": "DMA", "Year": "2020", "Share of total municipal solid waste collected": "94"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2020", "Share of total municipal solid waste collected": "69"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2020", "Share of total municipal solid waste collected": "66.05336"}, {"Entity": "Ecuador", "Code": "ECU", "Year": "2020", "Share of total municipal solid waste collected": "81"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2020", "Share of total municipal solid waste collected": "62.5"}, {"Entity": "El Salvador", "Code": "SLV", "Year": "2020", "Share of total municipal solid waste collected": "78.8"}, {"Entity": "Equatorial Guinea", "Code": "GNQ", "Year": "2020", "Share of total municipal solid waste collected": "23.6"}, {"Entity": "Eritrea", "Code": "ERI", "Year": "2020", "Share of total municipal solid waste collected": "45.6861"}, {"Entity": "Estonia", "Code": "EST", "Year": "2020", "Share of total municipal solid waste collected": "86.89"}, {"Entity": "Eswatini", "Code": "SWZ", "Year": "2020", "Share of total municipal solid waste collected": "23.6"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2020", "Share of total municipal solid waste collected": "50"}, {"Entity": "Falkland Islands", "Code": "FLK", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Faroe Islands", "Code": "FRO", "Year": "2020", "Share of total municipal solid waste collected": "99.91863"}, {"Entity": "Fiji", "Code": "FJI", "Year": "2020", "Share of total municipal solid waste collected": "90.747826"}, {"Entity": "Finland", "Code": "FIN", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "France", "Code": "FRA", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "French Guiana", "Code": "GUF", "Year": "2020", "Share of total municipal solid waste collected": "97.6901"}, {"Entity": "French Polynesia", "Code": "PYF", "Year": "2020", "Share of total municipal solid waste collected": "51"}, {"Entity": "Gabon", "Code": "GAB", "Year": "2020", "Share of total municipal solid waste collected": "23.6"}, {"Entity": "Gambia", "Code": "GMB", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "Georgia", "Code": "GEO", "Year": "2020", "Share of total municipal solid waste collected": "60"}, {"Entity": "Germany", "Code": "DEU", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2020", "Share of total municipal solid waste collected": "85"}, {"Entity": "Gibraltar", "Code": "GIB", "Year": "2020", "Share of total municipal solid waste collected": "97.6901"}, {"Entity": "Greece", "Code": "GRC", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Greenland", "Code": "GRL", "Year": "2020", "Share of total municipal solid waste collected": "98.792366"}, {"Entity": "Grenada", "Code": "GRD", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Guadeloupe", "Code": "GLP", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Guam", "Code": "GUM", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Guatemala", "Code": "GTM", "Year": "2020", "Share of total municipal solid waste collected": "77.7"}, {"Entity": "Guernsey", "Code": "GGY", "Year": "2020", "Share of total municipal solid waste collected": "97.6901"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "Guinea-Bissau", "Code": "GNB", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "Guyana", "Code": "GUY", "Year": "2020", "Share of total municipal solid waste collected": "89"}, {"Entity": "Haiti", "Code": "HTI", "Year": "2020", "Share of total municipal solid waste collected": "11"}, {"Entity": "Honduras", "Code": "HND", "Year": "2020", "Share of total municipal solid waste collected": "68"}, {"Entity": "Hong Kong", "Code": "HKG", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Hungary", "Code": "HUN", "Year": "2020", "Share of total municipal solid waste collected": "99.94"}, {"Entity": "Iceland", "Code": "ISL", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "India", "Code": "IND", "Year": "2020", "Share of total municipal solid waste collected": "52"}, {"Entity": "Indonesia", "Code": "IDN", "Year": "2020", "Share of total municipal solid waste collected": "80"}, {"Entity": "Iran", "Code": "IRN", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Iraq", "Code": "IRQ", "Year": "2020", "Share of total municipal solid waste collected": "76"}, {"Entity": "Ireland", "Code": "IRL", "Year": "2020", "Share of total municipal solid waste collected": "92.04"}, {"Entity": "Isle of Man", "Code": "IMN", "Year": "2020", "Share of total municipal solid waste collected": "96"}, {"Entity": "Israel", "Code": "ISR", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Italy", "Code": "ITA", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Jamaica", "Code": "JAM", "Year": "2020", "Share of total municipal solid waste collected": "76"}, {"Entity": "Japan", "Code": "JPN", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Jersey", "Code": "JEY", "Year": "2020", "Share of total municipal solid waste collected": "97.6901"}, {"Entity": "Jordan", "Code": "JOR", "Year": "2020", "Share of total municipal solid waste collected": "95"}, {"Entity": "Kazakhstan", "Code": "KAZ", "Year": "2020", "Share of total municipal solid waste collected": "77"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2020", "Share of total municipal solid waste collected": "40"}, {"Entity": "Kiribati", "Code": "KIR", "Year": "2020", "Share of total municipal solid waste collected": "54"}, {"Entity": "Kosovo", "Code": "OWID_KOS", "Year": "2020", "Share of total municipal solid waste collected": "39"}, {"Entity": "Kuwait", "Code": "KWT", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Kyrgyzstan", "Code": "KGZ", "Year": "2020", "Share of total municipal solid waste collected": "77.26673"}, {"Entity": "Laos", "Code": "LAO", "Year": "2020", "Share of total municipal solid waste collected": "55"}, {"Entity": "Latvia", "Code": "LVA", "Year": "2020", "Share of total municipal solid waste collected": "84.36"}, {"Entity": "Lebanon", "Code": "LBN", "Year": "2020", "Share of total municipal solid waste collected": "100"}], "rows_tail": [{"Entity": "Lebanon", "Code": "LBN", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Lesotho", "Code": "LSO", "Year": "2020", "Share of total municipal solid waste collected": "20"}, {"Entity": "Liberia", "Code": "LBR", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "Libya", "Code": "LBY", "Year": "2020", "Share of total municipal solid waste collected": "90.42822"}, {"Entity": "Liechtenstein", "Code": "LIE", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Lithuania", "Code": "LTU", "Year": "2020", "Share of total municipal solid waste collected": "98.69"}, {"Entity": "Luxembourg", "Code": "LUX", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Macao", "Code": "MAC", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Madagascar", "Code": "MDG", "Year": "2020", "Share of total municipal solid waste collected": "18"}, {"Entity": "Malawi", "Code": "MWI", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "Malaysia", "Code": "MYS", "Year": "2020", "Share of total municipal solid waste collected": "95"}, {"Entity": "Maldives", "Code": "MDV", "Year": "2020", "Share of total municipal solid waste collected": "38.2"}, {"Entity": "Mali", "Code": "MLI", "Year": "2020", "Share of total municipal solid waste collected": "40"}, {"Entity": "Malta", "Code": "MLT", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Marshall Islands", "Code": "MHL", "Year": "2020", "Share of total municipal solid waste collected": "60"}, {"Entity": "Martinique", "Code": "MTQ", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Mauritania", "Code": "MRT", "Year": "2020", "Share of total municipal solid waste collected": "65.28197"}, {"Entity": "Mauritius", "Code": "MUS", "Year": "2020", "Share of total municipal solid waste collected": "98"}, {"Entity": "Mayotte", "Code": "MYT", "Year": "2020", "Share of total municipal solid waste collected": "97.6901"}, {"Entity": "Mexico", "Code": "MEX", "Year": "2020", "Share of total municipal solid waste collected": "93.4"}, {"Entity": "Micronesia (country)", "Code": "FSM", "Year": "2020", "Share of total municipal solid waste collected": "8"}, {"Entity": "Moldova", "Code": "MDA", "Year": "2020", "Share of total municipal solid waste collected": "77.26673"}, {"Entity": "Monaco", "Code": "MCO", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Mongolia", "Code": "MNG", "Year": "2020", "Share of total municipal solid waste collected": "90.747826"}, {"Entity": "Montenegro", "Code": "MNE", "Year": "2020", "Share of total municipal solid waste collected": "96.99"}, {"Entity": "Montserrat", "Code": "MSR", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Morocco", "Code": "MAR", "Year": "2020", "Share of total municipal solid waste collected": "86"}, {"Entity": "Mozambique", "Code": "MOZ", "Year": "2020", "Share of total municipal solid waste collected": "52.5"}, {"Entity": "Myanmar", "Code": "MMR", "Year": "2020", "Share of total municipal solid waste collected": "60"}, {"Entity": "Namibia", "Code": "NAM", "Year": "2020", "Share of total municipal solid waste collected": "23.6"}, {"Entity": "Nauru", "Code": "NRU", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Nepal", "Code": "NPL", "Year": "2020", "Share of total municipal solid waste collected": "94"}, {"Entity": "Netherlands", "Code": "NLD", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Netherlands Antilles", "Code": "ANT", "Year": "2020", "Share of total municipal solid waste collected": "97.6901"}, {"Entity": "New Caledonia", "Code": "NCL", "Year": "2020", "Share of total municipal solid waste collected": "67"}, {"Entity": "New Zealand", "Code": "NZL", "Year": "2020", "Share of total municipal solid waste collected": "97"}, {"Entity": "Nicaragua", "Code": "NIC", "Year": "2020", "Share of total municipal solid waste collected": "92.3"}, {"Entity": "Niger", "Code": "NER", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2020", "Share of total municipal solid waste collected": "45.6861"}, {"Entity": "Niue", "Code": "NIU", "Year": "2020", "Share of total municipal solid waste collected": "94.95234"}, {"Entity": "Norfolk Island", "Code": "NFK", "Year": "2020", "Share of total municipal solid waste collected": "95"}, {"Entity": "North Korea", "Code": "PRK", "Year": "2020", "Share of total municipal solid waste collected": "77.100365"}, {"Entity": "North Macedonia", "Code": "MKD", "Year": "2020", "Share of total municipal solid waste collected": "76.61"}, {"Entity": "Northern Mariana Islands", "Code": "MNP", "Year": "2020", "Share of total municipal solid waste collected": "99.29029"}, {"Entity": "Norway", "Code": "NOR", "Year": "2020", "Share of total municipal solid waste collected": "99"}, {"Entity": "Oman", "Code": "OMN", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Pakistan", "Code": "PAK", "Year": "2020", "Share of total municipal solid waste collected": "52"}, {"Entity": "Palau", "Code": "PLW", "Year": "2020", "Share of total municipal solid waste collected": "77"}, {"Entity": "Palestine", "Code": "PSE", "Year": "2020", "Share of total municipal solid waste collected": "92.2"}, {"Entity": "Panama", "Code": "PAN", "Year": "2020", "Share of total municipal solid waste collected": "84.9"}, {"Entity": "Papua New Guinea", "Code": "PNG", "Year": "2020", "Share of total municipal solid waste collected": "60"}, {"Entity": "Paraguay", "Code": "PRY", "Year": "2020", "Share of total municipal solid waste collected": "51"}, {"Entity": "Peru", "Code": "PER", "Year": "2020", "Share of total municipal solid waste collected": "82.93"}, {"Entity": "Philippines", "Code": "PHL", "Year": "2020", "Share of total municipal solid waste collected": "66.05336"}, {"Entity": "Poland", "Code": "POL", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Puerto Rico", "Code": "PRI", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Reunion", "Code": "REU", "Year": "2020", "Share of total municipal solid waste collected": "97.6901"}, {"Entity": "Romania", "Code": "ROU", "Year": "2020", "Share of total municipal solid waste collected": "90"}, {"Entity": "Russia", "Code": "RUS", "Year": "2020", "Share of total municipal solid waste collected": "84.74976"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "Saint Helena", "Code": "SHN", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Saint Kitts and Nevis", "Code": "KNA", "Year": "2020", "Share of total municipal solid waste collected": "98"}, {"Entity": "Saint Lucia", "Code": "LCA", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Saint Martin (French part)", "Code": "MAF", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Saint Pierre", "Code": "", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Saint Vincent and the Grenadines", "Code": "VCT", "Year": "2020", "Share of total municipal solid waste collected": "91"}, {"Entity": "Samoa", "Code": "WSM", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "San Marino", "Code": "SMR", "Year": "2020", "Share of total municipal solid waste collected": "98.792366"}, {"Entity": "Sao Tome and Principe", "Code": "STP", "Year": "2020", "Share of total municipal solid waste collected": "48.4"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2020", "Share of total municipal solid waste collected": "21.4"}, {"Entity": "Serbia", "Code": "SRB", "Year": "2020", "Share of total municipal solid waste collected": "74.67"}, {"Entity": "Seychelles", "Code": "SYC", "Year": "2020", "Share of total municipal solid waste collected": "95"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2020", "Share of total municipal solid waste collected": "44"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Sint Maarten (Dutch part)", "Code": "SXM", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Slovakia", "Code": "SVK", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2020", "Share of total municipal solid waste collected": "93.84"}, {"Entity": "Solomon Islands", "Code": "SLB", "Year": "2020", "Share of total municipal solid waste collected": "12"}, {"Entity": "Somalia", "Code": "SOM", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2020", "Share of total municipal solid waste collected": "23.6"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2020", "Share of total municipal solid waste collected": "99.92"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "Spain", "Code": "ESP", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2020", "Share of total municipal solid waste collected": "52"}, {"Entity": "Sudan", "Code": "SDN", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2020", "Share of total municipal solid waste collected": "80"}, {"Entity": "Svalbard", "Code": "", "Year": "2020", "Share of total municipal solid waste collected": "75.86"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2020", "Share of total municipal solid waste collected": "99"}, {"Entity": "Syria", "Code": "SYR", "Year": "2020", "Share of total municipal solid waste collected": "80"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2020", "Share of total municipal solid waste collected": "99.9789"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2020", "Share of total municipal solid waste collected": "38.25"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2020", "Share of total municipal solid waste collected": "48"}, {"Entity": "Thailand", "Code": "THA", "Year": "2020", "Share of total municipal solid waste collected": "90.747826"}, {"Entity": "Togo", "Code": "TGO", "Year": "2020", "Share of total municipal solid waste collected": "36.47612"}, {"Entity": "Tokelau", "Code": "TKL", "Year": "2020", "Share of total municipal solid waste collected": "95"}, {"Entity": "Tonga", "Code": "TON", "Year": "2020", "Share of total municipal solid waste collected": "71"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2020", "Share of total municipal solid waste collected": "95"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2020", "Share of total municipal solid waste collected": "88.12"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2020", "Share of total municipal solid waste collected": "84.74976"}, {"Entity": "Turks and Caicos Islands", "Code": "TCA", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Tuvalu", "Code": "TUV", "Year": "2020", "Share of total municipal solid waste collected": "47"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2020", "Share of total municipal solid waste collected": "39"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2020", "Share of total municipal solid waste collected": "75.86"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2020", "Share of total municipal solid waste collected": "98.14322"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "United States", "Code": "USA", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "United States Virgin Islands", "Code": "VIR", "Year": "2020", "Share of total municipal solid waste collected": "92.902275"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2020", "Share of total municipal solid waste collected": "98"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2020", "Share of total municipal solid waste collected": "77.26673"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2020", "Share of total municipal solid waste collected": "12"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2020", "Share of total municipal solid waste collected": "100"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "Share of total municipal solid waste collected": "72"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Share of total municipal solid waste collected": "20"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Share of total municipal solid waste collected": "20"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Share of total municipal solid waste collected": "45.6861"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "share-waste-collected", "metadata_url": "https://ourworldindata.org/grapher/share-waste-collected.metadata.json", "chart_title": "Share of total municipal solid waste that is collected", "chart_subtitle": null, "chart_note": null, "chart_citation": "Anshassi and Townsend (2025)", "original_chart_url": "https://ourworldindata.org/grapher/share-waste-collected", "owid_column_metadata": {"Share of total municipal solid waste collected": {"titleShort": "Share of total municipal solid waste collected", "titleLong": "Share of total municipal solid waste collected", "descriptionShort": "Share of total municipal solid waste generated that is captured by organised collection services and enters a managed waste stream (regardless of final treatment or disposal method).", "descriptionKey": ["Municipal solid waste is everyday, non-hazardous waste from households and similar municipal sources (for example, shops, offices, and institutions).", "Collected waste is waste that is picked up by organized collection services (such as public, private, or contracted waste collectors) and enters a managed waste system. This is true regardless of how the waste is ultimately disposed of or treated.", "This is expressed as a share of all waste generated in a country (by mass), not just the portion that was collected.", "The indicator is based on waste generation and management data from the World Bank’s What a Waste 2.0 database, combined with mismanagement rates from Lebreton and Andrady (2019). The authors used World Bank data from 2016, standardised it, and projected the results to 2020.", "For countries with missing data, the authors used proxy countries with similar characteristics (same region, classification level, and income level) to fill gaps in waste composition and management statistics."], "shortUnit": "%", "unit": "%", "timespan": "2020-2020", "type": "Numeric", "owidVariableId": 1134086, "shortName": "collected", "lastUpdated": "2026-01-20", "citationShort": "Anshassi and Townsend (2025) – with minor processing by Our World in Data", "citationLong": "Anshassi and Townsend (2025) – with minor processing by Our World in Data. “Share of total municipal solid waste collected” [dataset]. Anshassi and Townsend, “Improving waste systems in the global south to tackle international environmental impacts” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1134086.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "How solid municipal waste is managed", "source_url": "https://ourworldindata.org/grapher/municipal-waste-management-method.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Open dumps", "Open burning", "Controlled landfills", "Sanitary landfills", "Incinerated", "Composted", "Recycled"], "row_count_total": 239, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Open dumps": "57.664207", "Open burning": "36.685463", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "4.2143216", "Recycled": "1.4360082"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Open dumps": "55.454403", "Open burning": "21.11593", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "8.270926", "Composted": "3.6382716", "Recycled": "11.520468"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Open dumps": "2", "Open 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"47.410145", "Sanitary landfills": "19.106901", "Incinerated": "0.1259742", "Composted": "0.39374", "Recycled": "4.6599994"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2020", "Open dumps": "0.65319103", "Open burning": "0.65319103", "Controlled landfills": "98.61473", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0.078888", "Recycled": "0"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2020", "Open dumps": "22.842161", "Open burning": "2.5225", "Controlled landfills": "13.04699", "Sanitary landfills": "56.193752", "Incinerated": "0", "Composted": "0", "Recycled": "5.3946004"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2020", "Open dumps": "85", "Open burning": "5", "Controlled landfills": "10", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "0"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2020", "Open dumps": "22.445189", "Open burning": "22.445189", "Controlled landfills": "44.890377", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "10.219251"}, {"Entity": "Australia", "Code": "AUS", "Year": "2020", "Open dumps": "0", "Open burning": "0", "Controlled landfills": "0", "Sanitary landfills": "48.6487", "Incinerated": "9.6723", "Composted": "0", "Recycled": "41.678997"}, {"Entity": "Austria", "Code": "AUT", "Year": "2020", "Open dumps": "0", "Open burning": "0", "Controlled landfills": "0", "Sanitary landfills": "5.2", "Incinerated": "37.9", "Composted": "31.24", "Recycled": "25.66"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2020", "Open dumps": "67", "Open burning": "11", "Controlled landfills": "22", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "0"}, {"Entity": "Bahamas", "Code": "BHS", "Year": "2020", "Open dumps": "23.705072", "Open burning": "4.5981703", "Controlled landfills": "47.410145", "Sanitary landfills": "19.106901", "Incinerated": "0.1259742", "Composted": "0.39374", "Recycled": "4.6599994"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2020", "Open dumps": "23", "Open burning": "0", "Controlled landfills": "46", "Sanitary landfills": "23", "Incinerated": "0", "Composted": "0", "Recycled": "8"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2020", "Open dumps": "48.635", "Open burning": "48.635", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "2.73", "Recycled": "0"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2020", "Open dumps": "2.725", "Open burning": "2.725", "Controlled landfills": "86.45", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "8.1"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2020", "Open dumps": "7.1", "Open burning": "0", "Controlled landfills": "76.9", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "16"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2020", "Open dumps": "0", "Open burning": "0", "Controlled landfills": "0", "Sanitary landfills": "3.17", "Incinerated": "43.39", "Composted": "19.137", "Recycled": "34.303"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2020", "Open dumps": "63.632", "Open burning": "7.4", "Controlled landfills": "0", "Sanitary landfills": "28.967999", "Incinerated": "0", "Composted": "0", "Recycled": "0"}, {"Entity": "Benin", "Code": "BEN", "Year": "2020", "Open dumps": "47.125", "Open burning": "47.125", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "5.75"}, {"Entity": "Bermuda", "Code": "BMU", "Year": "2020", "Open dumps": "0", "Open burning": "0", "Controlled landfills": "12.1", "Sanitary landfills": "0", "Incinerated": "67.6", "Composted": "18.3", "Recycled": "2"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2020", "Open dumps": "1.5", "Open burning": "1.5", "Controlled landfills": "94.885", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "1.3066", "Recycled": "0.80840003"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2020", "Open dumps": "42.57952", "Open burning": "10.6", "Controlled landfills": "21.223042", "Sanitary landfills": "18.39168", "Incinerated": "0", "Composted": "0.2304", "Recycled": "6.9753594"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2020", "Open dumps": "42.124805", "Open burning": "11.233981", "Controlled landfills": "28.794107", "Sanitary landfills": "17.846388", "Incinerated": "0", "Composted": "0", "Recycled": "0.00071770296"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2020", "Open dumps": "24.941", "Open burning": "24.941", "Controlled landfills": "49.882", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "0.236"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2020", "Open dumps": "18.167858", "Open burning": "3.9074175", "Controlled landfills": "27.796032", "Sanitary landfills": "48.667896", "Incinerated": "0", "Composted": "0.1826", "Recycled": "1.2782"}, {"Entity": "British Virgin Islands", "Code": "VGB", "Year": "2020", "Open 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"Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0.7013025", "Recycled": "1.1747417"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2020", "Open dumps": "50", "Open burning": "50", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "0"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2020", "Open dumps": "68.6648", "Open burning": "19.2", "Controlled landfills": "0", "Sanitary landfills": "11.8888", "Incinerated": "0", "Composted": "0", "Recycled": "0.2464"}, {"Entity": "Canada", "Code": "CAN", "Year": "2020", "Open dumps": "0", "Open burning": "0", "Controlled landfills": "0", "Sanitary landfills": "72.606705", "Incinerated": "2.97", "Composted": "4.0392", "Recycled": "20.3841"}, {"Entity": "Cape Verde", "Code": "CPV", "Year": "2020", "Open dumps": "63.721294", "Open burning": "31.37113", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "1.6405045", "Recycled": "3.2670681"}, {"Entity": "Cayman Islands", "Code": "CYM", "Year": "2020", "Open dumps": "20.122631", "Open burning": "20.122631", "Controlled landfills": "40.245262", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "19.509478"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2020", "Open dumps": "58.83529", "Open burning": "39.288666", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0.7013025", "Recycled": "1.1747417"}, {"Entity": "Chad", "Code": "TCD", "Year": "2020", "Open dumps": "58.83529", "Open burning": "39.288666", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0.7013025", "Recycled": "1.1747417"}, {"Entity": "Channel Islands", "Code": "OWID_CIS", "Year": "2020", "Open dumps": "0", "Open burning": "0", "Controlled landfills": "39.186153", "Sanitary landfills": "0.081370294", "Incinerated": "16.42853", "Composted": "15.896881", "Recycled": "28.40707"}, {"Entity": "Chile", "Code": "CHL", "Year": "2020", "Open dumps": "10.533875", "Open burning": "2.525375", "Controlled landfills": "86.038246", "Sanitary landfills": "0", "Incinerated": "0.133", "Composted": "0.41799998", "Recycled": "0.3515"}, {"Entity": "China", "Code": "CHN", "Year": "2020", "Open dumps": "22.217602", "Open burning": "1.5", "Controlled landfills": "31.2752", "Sanitary landfills": "14.1376", "Incinerated": "28.0496", "Composted": "2.82", "Recycled": "0"}, {"Entity": "Christmas Island", "Code": "CXR", "Year": "2020", "Open dumps": "21.556334", "Open burning": "8.545649", "Controlled landfills": "43.112667", "Sanitary landfills": "13.010684", "Incinerated": "0", "Composted": "1.0747982", "Recycled": "12.69987"}, {"Entity": "Cocos Islands", "Code": "CCK", "Year": "2020", "Open dumps": "21.556334", "Open burning": "8.545649", "Controlled landfills": "43.112667", "Sanitary landfills": "13.010684", "Incinerated": "0", "Composted": "1.0747982", "Recycled": "12.69987"}, {"Entity": "Colombia", "Code": "COL", "Year": "2020", "Open dumps": "4.42", "Open burning": "0.5", "Controlled landfills": "1", "Sanitary landfills": "77.22401", "Incinerated": "0", "Composted": "0", "Recycled": "16.856"}, {"Entity": "Comoros", "Code": "COM", "Year": "2020", "Open dumps": "54.844425", "Open burning": "44.126934", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0.38452694", "Recycled": "0.64411557"}, {"Entity": "Congo", "Code": "COG", "Year": "2020", "Open dumps": "44.01512", "Open burning": "44.01512", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "11.969759"}, {"Entity": "Cook Islands", "Code": "COK", "Year": "2020", "Open dumps": "21.556334", "Open burning": "8.545649", "Controlled landfills": "43.112667", "Sanitary landfills": "13.010684", "Incinerated": "0", "Composted": "1.0747982", "Recycled": "12.69987"}, {"Entity": "Costa Rica", "Code": "CRI", 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"Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "0"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2020", "Open dumps": "75.2", "Open burning": "24.8", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "0"}, {"Entity": "Svalbard", "Code": "", "Year": "2020", "Open dumps": "23.875376", "Open burning": "6.035", "Controlled landfills": "47.75075", "Sanitary landfills": "17.840376", "Incinerated": "2.070978", "Composted": "0", "Recycled": "2.42752"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2020", "Open dumps": "0", "Open burning": "0", "Controlled landfills": "0", "Sanitary landfills": "0.8", "Incinerated": "51.2", "Composted": "15.63", "Recycled": "32.37"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2020", "Open dumps": "0", "Open burning": "0", "Controlled landfills": "0", "Sanitary landfills": "1", "Incinerated": "46.53", "Composted": "20.79", "Recycled": "31.68"}, {"Entity": "Syria", "Code": "SYR", "Year": "2020", "Open dumps": "72.2", "Open burning": "5", "Controlled landfills": "16.4", "Sanitary landfills": "3.2", "Incinerated": "0", "Composted": "1.2", "Recycled": "2"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2020", "Open dumps": "0", "Open burning": "0", "Controlled landfills": "0", "Sanitary landfills": "35.813545", "Incinerated": "64.18645", "Composted": "0", "Recycled": "0"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2020", "Open dumps": "69.125", "Open burning": "30.875", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "0"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2020", "Open dumps": "66.535995", "Open burning": "33.464", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "0"}, {"Entity": "Thailand", "Code": "THA", "Year": "2020", "Open dumps": "56.98861", "Open burning": "2.3130426", "Controlled landfills": "16.87704", "Sanitary landfills": "6.1254783", "Incinerated": "0.3629913", "Composted": "0", "Recycled": "17.332834"}, {"Entity": "Togo", "Code": "TGO", "Year": "2020", "Open dumps": "66.85197", "Open burning": "31.76194", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0.65657014", "Recycled": "0.7295224"}, {"Entity": "Tokelau", "Code": "TKL", "Year": "2020", "Open dumps": "79.925", "Open burning": "2.5", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0.95", "Recycled": "16.625"}, {"Entity": "Tonga", "Code": "TON", "Year": "2020", "Open dumps": "64.2", "Open burning": "35.8", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "0"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2020", "Open dumps": "85.6", "Open burning": "1.6", "Controlled landfills": "12", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0.8", "Recycled": "0"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2020", "Open dumps": "88.95", "Open burning": "2.5", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "4.75", "Recycled": "3.8"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2020", "Open dumps": "41.9631", "Open burning": "3.1903", "Controlled landfills": "6.3806", "Sanitary landfills": "47.5848", "Incinerated": "0", "Composted": "0.8812", "Recycled": "0"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2020", "Open dumps": "25", "Open burning": "3.812559", "Controlled landfills": "50", "Sanitary landfills": "21.18744", "Incinerated": "0", "Composted": "0", "Recycled": "0"}, {"Entity": "Turks and Caicos Islands", "Code": "TCA", "Year": "2020", "Open dumps": "23.705072", "Open burning": "4.5981703", "Controlled landfills": "47.410145", "Sanitary landfills": "19.106901", "Incinerated": "0.1259742", "Composted": "0.39374", "Recycled": "4.6599994"}, {"Entity": "Tuvalu", "Code": "TUV", "Year": "2020", "Open dumps": "49.8825", "Open burning": "43.0675", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "7.05"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2020", "Open dumps": "64.43", "Open burning": "30.5", "Controlled landfills": "0", "Sanitary landfills": "2.73", "Incinerated": "0", "Composted": "0", "Recycled": "2.34"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2020", "Open dumps": "23.875376", "Open burning": "6.035", "Controlled landfills": "47.75075", "Sanitary landfills": "17.840376", "Incinerated": "2.070978", "Composted": "0", "Recycled": "2.42752"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2020", "Open dumps": "60.848797", "Open burning": "0", "Controlled landfills": "0", "Sanitary landfills": "9.823075", "Incinerated": "0", "Composted": "8.83289", "Recycled": "19.628643"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2020", "Open dumps": "0", "Open burning": "0", "Controlled landfills": "0", "Sanitary landfills": "25.14", "Incinerated": "31.38", "Composted": "16.23", "Recycled": "27.25"}, {"Entity": "United States", "Code": "USA", "Year": "2020", "Open dumps": "0", "Open burning": "0", "Controlled landfills": "0", "Sanitary landfills": "52.6", "Incinerated": "12.8", "Composted": "0", "Recycled": "34.6"}, {"Entity": "United States Virgin Islands", "Code": "VIR", "Year": "2020", "Open dumps": "23.705072", "Open burning": "4.5981703", "Controlled landfills": "47.410145", "Sanitary landfills": "19.106901", "Incinerated": "0.1259742", "Composted": "0.39374", "Recycled": "4.6599994"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2020", "Open dumps": "19.277", "Open burning": "2.127", "Controlled landfills": "60.466", "Sanitary landfills": "10.29", "Incinerated": "0", "Composted": "0", "Recycled": "7.84"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2020", "Open dumps": "73.18002", "Open burning": "26.81998", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "0"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2020", "Open dumps": "48.458", "Open burning": "47.102", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "4.44"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2020", "Open dumps": "91.362144", "Open burning": "8.637856", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "0"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "Open dumps": "36.32", "Open burning": "36.32", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "10.8", "Recycled": "16.56"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Open dumps": "27.75", "Open burning": "22.75", "Controlled landfills": "47.9", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "1.6"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Open dumps": "53.50691", "Open burning": "45.165977", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "1.3271148"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Open dumps": "46.34511", "Open burning": "46.34511", "Controlled landfills": "0", "Sanitary landfills": "0", "Incinerated": "0", "Composted": "0", "Recycled": "7.309776"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "municipal-waste-management-method", "metadata_url": "https://ourworldindata.org/grapher/municipal-waste-management-method.metadata.json", "chart_title": "How solid municipal waste is managed", "chart_subtitle": "Share of solid municipal waste, by mass, that is managed by different methods. Open dumps and open burning are most polluting as they increase the risk of waste escaping into the environment, and generate the most air pollution.", "chart_note": null, "chart_citation": "Anshassi and Townsend (2025)", "original_chart_url": "https://ourworldindata.org/grapher/municipal-waste-management-method", "owid_column_metadata": {"Share of total municipal solid waste sent to open dumps": {"titleShort": "Open dumps", "titleLong": "Open dumps", "descriptionShort": "Share of total municipal solid waste generated that ends up in open dumps or other uncontrolled disposal sites with little or no engineering to contain waste (including waste dumped after collection and waste dumped directly when uncollected).", "descriptionKey": ["Municipal solid waste is everyday, non-hazardous waste from households and similar municipal sources (for example, shops, offices, and institutions).", "This is expressed as a share of all waste generated in a country (by mass), not just the portion that was collected.", "Open dumps are uncontrolled disposal sites with little or no infrastructure to safely contain waste. For example, they typically have minimal covering of waste, no systems to manage contaminated water (leachate), and no systems to manage landfill gas emissions.", "The indicator is based on waste generation and management data from the World Bank’s What a Waste 2.0 database, combined with mismanagement rates from Lebreton and Andrady (2019). The authors used World Bank data from 2016, standardised it, and projected the results to 2020.", "For countries with missing data, the authors used proxy countries with similar characteristics (same region, classification level, and income level) to fill gaps in waste composition and management statistics."], "descriptionProcessing": "We estimate this as the share of waste that is collected multiplied by the share of collected waste sent to open dumps, plus the share of waste that is uncollected multiplied by the share of uncollected waste that is openly dumped.", "shortUnit": "%", "unit": "%", "timespan": "2020-2020", "type": "Numeric", "owidVariableId": 1134088, "shortName": "open_dump_landfill", "lastUpdated": "2026-01-20", "citationShort": "Anshassi and Townsend (2025) – with major processing by Our World in Data", "citationLong": "Anshassi and Townsend (2025) – with major processing by Our World in Data. “Open dumps” [dataset]. Anshassi and Townsend, “Improving waste systems in the global south to tackle international environmental impacts” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1134088.metadata.json"}, "Share of total municipal solid waste burned in the open": {"titleShort": "Open burning", "titleLong": "Open burning", "descriptionShort": "Share of total municipal solid waste generated that is burned in open, uncontrolled fires (as opposed to engineered incineration).", "descriptionKey": ["Municipal solid waste is everyday, non-hazardous waste from households and similar municipal sources (for example, shops, offices, and institutions).", "This is expressed as a share of all waste generated in a country (by mass), not just the portion that was collected.", "Open-air burning is when waste is burned in open, uncontrolled fires (rather than in purpose-built incineration facilities with pollution controls).", "The indicator is based on waste generation and management data from the World Bank’s What a Waste 2.0 database, combined with mismanagement rates from Lebreton and Andrady (2019). The authors used World Bank data from 2016, standardised it, and projected the results to 2020.", "For countries with missing data, the authors used proxy countries with similar characteristics (same region, classification level, and income level) to fill gaps in waste composition and management statistics."], "descriptionProcessing": "We estimate this as the share of waste that is collected multiplied by the share of collected waste burned in the open, plus the share of waste that is uncollected multiplied by the share of uncollected waste burned in the open.", "shortUnit": "%", "unit": "%", "timespan": "2020-2020", "type": "Numeric", "owidVariableId": 1134092, "shortName": "open_air_burning", "lastUpdated": "2026-01-20", "citationShort": "Anshassi and Townsend (2025) – with major processing by Our World in Data", "citationLong": "Anshassi and Townsend (2025) – with major processing by Our World in Data. “Open burning” [dataset]. Anshassi and Townsend, “Improving waste systems in the global south to tackle international environmental impacts” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1134092.metadata.json"}, "Share of total municipal solid waste sent to controlled landfills": {"titleShort": "Controlled landfills", "titleLong": "Controlled landfills", "descriptionShort": "Share of total municipal solid waste generated that is disposed of in controlled landfills, which have some operational and pollution controls but are less engineered than sanitary landfills.", "descriptionKey": ["Municipal solid waste is everyday, non-hazardous waste from households and similar municipal sources (for example, shops, offices, and institutions).", "This is expressed as a share of all waste generated in a country (by mass), not just the portion that was collected.", "Controlled landfills have some basic operational and pollution controls (such as some covering of waste and site management), but have less advanced infrastructure than sanitary landfills.", "The indicator is based on waste generation and management data from the World Bank’s What a Waste 2.0 database, combined with mismanagement rates from Lebreton and Andrady (2019). The authors used World Bank data from 2016, standardised it, and projected the results to 2020.", "For countries with missing data, the authors used proxy countries with similar characteristics (same region, classification level, and income level) to fill gaps in waste composition and management statistics."], "descriptionProcessing": "We estimate this as the share of waste that is collected multiplied by the share of collected waste sent to controlled landfills, plus the share of waste that is uncollected multiplied by the share of uncollected waste that ends up in controlled landfills.", "shortUnit": "%", "unit": "%", "timespan": "2020-2020", "type": "Numeric", "owidVariableId": 1134091, "shortName": "controlled_landfill", "lastUpdated": "2026-01-20", "citationShort": "Anshassi and Townsend (2025) – with major processing by Our World in Data", "citationLong": "Anshassi and Townsend (2025) – with major processing by Our World in Data. “Controlled landfills” [dataset]. Anshassi and Townsend, “Improving waste systems in the global south to tackle international environmental impacts” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1134091.metadata.json"}, "Share of total municipal solid waste sent to sanitary landfills": {"titleShort": "Sanitary landfills", "titleLong": "Sanitary landfills", "descriptionShort": "Share of total municipal solid waste generated that is disposed of in sanitary landfills—engineered facilities designed to contain waste (for example with liners and leachate management and often landfill-gas control).", "descriptionKey": ["Municipal solid waste is everyday, non-hazardous waste from households and similar municipal sources (for example, shops, offices, and institutions).", "This is expressed as a share of all waste generated in a country (by mass), not just the portion that was collected.", "Sanitary landfills are engineered disposal sites with advanced infrastructure designed to safely contain waste and prevent pollution. They typically have protective liners to prevent contamination of soil and groundwater, systems to manage contaminated water (leachate), and in many cases systems to capture or control landfill gas emissions.", "The indicator is based on waste generation and management data from the World Bank’s What a Waste 2.0 database, combined with mismanagement rates from Lebreton and Andrady (2019). The authors used World Bank data from 2016, standardised it, and projected the results to 2020.", "For countries with missing data, the authors used proxy countries with similar characteristics (same region, classification level, and income level) to fill gaps in waste composition and management statistics."], "descriptionProcessing": "We estimate this as the share of waste that is collected multiplied by the share of collected waste sent to sanitary landfills, plus the share of waste that is uncollected multiplied by the share of uncollected waste that ends up in sanitary landfills.", "shortUnit": "%", "unit": "%", "timespan": "2020-2020", "type": "Numeric", "owidVariableId": 1134090, "shortName": "sanitary_landfill", "lastUpdated": "2026-01-20", "citationShort": "Anshassi and Townsend (2025) – with major processing by Our World in Data", "citationLong": "Anshassi and Townsend (2025) – with major processing by Our World in Data. “Sanitary landfills” [dataset]. Anshassi and Townsend, “Improving waste systems in the global south to tackle international environmental impacts” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1134090.metadata.json"}, "Share of total municipal solid waste incinerated": {"titleShort": "Incinerated", "titleLong": "Incinerated", "descriptionShort": "Share of total municipal solid waste generated that is incinerated in engineered facilities (controlled combustion, typically with emissions controls).", "descriptionKey": ["Municipal solid waste is everyday, non-hazardous waste from households and similar municipal sources (for example, shops, offices, and institutions).", "This is expressed as a share of all waste generated in a country (by mass), not just the portion that was collected.", "Municipal solid waste incineration is controlled burning of waste in purpose-built incinerators. These facilities typically have emissions controls to reduce air pollution, and some also recover energy from the burning process.", "The indicator is based on waste generation and management data from the World Bank’s What a Waste 2.0 database, combined with mismanagement rates from Lebreton and Andrady (2019). The authors used World Bank data from 2016, standardised it, and projected the results to 2020.", "For countries with missing data, the authors used proxy countries with similar characteristics (same region, classification level, and income level) to fill gaps in waste composition and management statistics."], "descriptionProcessing": "We estimate this as the share of waste that is collected multiplied by the share of collected waste that is incinerated, because waste must be collected to be treated in engineered incineration facilities.", "shortUnit": "%", "unit": "%", "timespan": "2020-2020", "type": "Numeric", "owidVariableId": 1134089, "shortName": "mswi_incineration", "lastUpdated": "2026-01-20", "citationShort": "Anshassi and Townsend (2025) – with major processing by Our World in Data", "citationLong": "Anshassi and Townsend (2025) – with major processing by Our World in Data. “Incinerated” [dataset]. Anshassi and Townsend, “Improving waste systems in the global south to tackle international environmental impacts” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1134089.metadata.json"}, "Share of total municipal solid waste composted": {"titleShort": "Composted", "titleLong": "Composted", "descriptionShort": "Share of total municipal solid waste generated that is composted (biological processing of organic waste into compost).", "descriptionKey": ["Municipal solid waste is everyday, non-hazardous waste from households and similar municipal sources (for example, shops, offices, and institutions).", "This is expressed as a share of all waste generated in a country (by mass), not just the portion that was collected.", "Composting is a biological process that breaks down organic waste (such as food scraps and garden waste) to produce compost, which can be used to improve soil quality.", "The indicator is based on waste generation and management data from the World Bank’s What a Waste 2.0 database, combined with mismanagement rates from Lebreton and Andrady (2019). The authors used World Bank data from 2016, standardised it, and projected the results to 2020.", "For countries with missing data, the authors used proxy countries with similar characteristics (same region, classification level, and income level) to fill gaps in waste composition and management statistics."], "descriptionProcessing": "We estimate this as the share of waste that is collected multiplied by the share of collected waste that is composted, because waste must be collected to be composted through formal systems.", "shortUnit": "%", "unit": "%", "timespan": "2020-2020", "type": "Numeric", "owidVariableId": 1134094, "shortName": "composting", "lastUpdated": "2026-01-20", "citationShort": "Anshassi and Townsend (2025) – with major processing by Our World in Data", "citationLong": "Anshassi and Townsend (2025) – with major processing by Our World in Data. “Composted” [dataset]. Anshassi and Townsend, “Improving waste systems in the global south to tackle international environmental impacts” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1134094.metadata.json"}, "Share of total municipal solid waste recycled": {"titleShort": "Recycled", "titleLong": "Recycled", "descriptionShort": "Share of total municipal solid waste generated that is recycled (collected and sorted materials reprocessed into secondary materials).", "descriptionKey": ["Municipal solid waste is everyday, non-hazardous waste from households and similar municipal sources (for example, shops, offices, and institutions).", "This is expressed as a share of all waste generated in a country (by mass), not just the portion that was collected.", "Recycling is the process of collecting and reprocessing waste materials (such as paper, plastic, glass, and metals) to create new products.", "The indicator is based on waste generation and management data from the World Bank’s What a Waste 2.0 database, combined with mismanagement rates from Lebreton and Andrady (2019). The authors used World Bank data from 2016, standardised it, and projected the results to 2020.", "For countries with missing data, the authors used proxy countries with similar characteristics (same region, classification level, and income level) to fill gaps in waste composition and management statistics."], "descriptionProcessing": "We estimate this as the share of waste that is collected multiplied by the share of collected waste that is recycled, because waste must be collected to be recycled through formal systems.", "shortUnit": "%", "unit": "%", "timespan": "2020-2020", "type": "Numeric", "owidVariableId": 1134093, "shortName": "recycling", "lastUpdated": "2026-01-20", "citationShort": "Anshassi and Townsend (2025) – with major processing by Our World in Data", "citationLong": "Anshassi and Townsend (2025) – with major processing by Our World in Data. “Recycled” [dataset]. 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{"raw_link": "https://ourworldindata.org/deadliest-animals", "title": "What are the world’s deadliest animals, and can we protect ourselves against them?", "context": "Home\nCauses of Death\nWhat are the world’s deadliest animals, and can we protect ourselves against them?\nDeaths from other animals are mostly caused by just two types: mosquitoes and snakes.\nBy\nHannah Ritchie\nand\nFiona Spooner\nMarch 9, 2026\nBrowse past versions\nCite this article\nReuse our work freely\nOne and a half million people are killed by animals every year. Almost one million by other animals, and more than half a million from direct conflict among ourselves.\nAlmost all of these deaths from other animals are caused by just two types: mosquitoes and snakes.\nIn the chart below, we’ve brought together estimates of the number of people killed by different animals.\nThese numbers are estimates, and some come with significant uncertainty. That’s why we’ve published a\ndetailed methodology\nexplaining our sources and how they compare. Despite this uncertainty, we feel confident about the relative orders of magnitude across different animals.\n1\nDownload\nThe biggest killers, by far, are mosquitoes. They have been one of our biggest threats\nfor millennia\n, and still kill approximately 760,000 people every year.\n2\nOver 80% of those deaths are the result of\nmalaria\n, which is transmitted and spread by the\nAnopheles\nmosquito. Malaria still kills close to\nhalf a million children\nevery year.\nAnother 100,000 people die every year from other mosquito-borne diseases, including dengue fever and yellow fever (spread by the mosquito species\nAedes aegypti\n)\nand Japanese encephalitis.\nAlmost all deaths from other animals are caused by just two types: mosquitoes and snakes.\nSnakes are one of the most common phobias, and you can see why. They are the second largest killers. The death toll from venomous snakes is surprisingly uncertain, as many of these deaths occur in rural areas where death records are often poor.\n3\nBut the figure is likely to be around 100,000 deaths per year. That means snakes kill more than all animals below them on the list combined.\n4\nMost of those remaining deaths are caused by dogs, the animals that humans have grown to love as domesticated pets. The majority are\ndue to rabies\n, rather than direct wounds.\nNear the bottom of the list, we reach the animals that dominate our nightmares — sharks and wolves. They make for gripping headlines and\nblockbuster films\n. But in reality, shark and wolf attacks are very rare.\nOf course, they don’t kill fewer people because they’re less dangerous. We’d rather be locked in a room with a mosquito than a lion. The real difference is exposure: it’s much easier to avoid large predators than it is to avoid disease-carrying insects and parasites.\n5\nThe good news is that most deaths from animals — especially the largest killers — are preventable. We have\nbednets\nand insecticide sprays to reduce exposure to mosquitoes, and medication to treat malaria if someone does become infected. New techniques, such as the\nWolbachia method\n, have been developed to stop the spread of dengue fever. Antivenoms can\noften save\nsomeone from a potentially fatal snakebite.\n6\nThe problem is that\nnot everyone has access\nto these preventive and treatment methods when they need them.\n7\nIf these small killers received the same global attention as large predators, more effort might go into stopping them. That is one reason why these comparisons are useful: as a reminder of what people are actually dying from, and where the most lives could be saved.\nThere are other diseases — in particular,\nneglected tropical diseases\n— that could also be dramatically reduced with better access to treatment.\nIn many regions, deaths from mosquitoes have decreased dramatically. Malaria was once prevalent in countries that are\nnow free of it\n. If we could achieve this in all parts of the world, the number of deaths caused by other animals would be almost six times smaller.\n8\nIf we were to also eliminate deaths from snakes through the use of antivenoms and better diagnostics, the death toll would be again reduced by almost two-thirds.\n9\nMethodology\nIf you’re interested in digging deeper, we provide a more detailed methodological document that lays out the uncertainties and sources behind these numbers.\nAcknowledgments\nMany thanks to Max Roser and Edouard Mathieu for editorial feedback and comments on this article, and Pablo Rosado for technical support.\nContinue reading on Our World in Data\nMalaria was common across half the world – since then it has been eliminated in many regions\nMalaria has been eliminated from large parts of Europe, the Americas, East Asia, Australia, and the Caribbean.\nMalaria\nThe deadly disease transmitted by mosquitoes is one of the leading causes of death in children. How did we eliminate the disease in some world regions and how can we continue progress against malaria?\nNeglected Tropical Diseases\nNeglected tropical diseases affect millions of people despite the existence of cheap interventions to control them.\nEndnotes\nWe’ve also rounded these figures to not overstate their accuracy.\nIt has often\nbeen claimed\nthat “mosquitoes have killed half of the humans who ever lived”. While the exact figure isn’t known, further investigation into the numbers suggests that this is unlikely to be true. Tim Harford and team covered this on\nan episode of BBC More or Less\n. What’s undeniable is that they have killed\nmany\npeople through the spread of infectious diseases.\nOur former colleague, Saloni Dattani, wrote\na great article\nabout snakebites in India, and the range of available estimates.\nAll the other animals on the list sum to 81,000 deaths.\nThis is even true\namong\nlarge animals. Hippos kill far fewer people than elephants or big cats. But among typical safari animals, they actually have the\nhighest chance of killing you\nin a given encounter. Their low death toll is simply explained by the fact that humans rarely come into contact with them.\nNot all venomous snakes have an effective antivenom, so based on current treatments, many but not all snakebite deaths could potentially be averted.\nWilliams, D. J., Faiz, M. A., Abela-Ridder, B., Ainsworth, S., Bulfone, T. C., Nickerson, A. D., ... & Warrell, D. A. (2019). Strategy for a globally coordinated response to a priority neglected tropical disease: Snakebite envenoming. PLoS neglected tropical diseases.\nBawaskar, H. S., & Bawaskar, P. H. (2019). Snakebite envenoming. The Lancet.\nThe death toll from non-human animals was around 920,000. Mosquitoes caused 760,000 of those. If these dropped to zero, the total would be 160,000, which is 5.8-times smaller.\nSnakebites caused around 100,000 deaths. If the total (after eliminating deaths from mosquitoes) was 160,000, eliminating a further 100,000 would reduce the death toll by almost two-thirds.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Fiona Spooner (2026) - “What are the world’s deadliest animals, and can we protect ourselves against them?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260309-000239/deadliest-animals.html' [Online Resource] (archived on March 9, 2026).\nBibTeX citation\n@article{owid-deadliest-animals,\nauthor = {Hannah Ritchie and Fiona Spooner},\ntitle = {What are the world’s deadliest animals, and can we protect ourselves against them?},\njournal = {Our World in Data},\nyear = {2026},\nnote = {https://archive.ourworldindata.org/20260309-000239/deadliest-animals.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "deadliest-animals", "source_url": "https://ourworldindata.org/deadliest-animals", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Deaths from other animals are mostly caused by just two types: mosquitoes and snakes.", "numeric_mentions": ["9,", "2026", "1", "760,000", "2", "80%", "100,000", "3", "4", "5", "6", "7", "8", "9", "81,000", "2019", "920,000", "160,000,", "5.8", "20260309", "000239"], "numeric_evidence": [{"grapher_slug": "child-deaths-from-malaria-number", "source_url": "https://ourworldindata.org/grapher/child-deaths-from-malaria-number", "parse_error": "Failed to fetch https://ourworldindata.org/grapher/child-deaths-from-malaria-number.csv"}, {"title": "Share of children sleeping under insecticide-treated nets", "source_url": "https://ourworldindata.org/grapher/children-sleeping-under-treated-bednet.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Use of insecticide-treated bed nets (% of under-5 population)"], "row_count_total": 292, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Use of insecticide-treated bed nets (% of under-5 population)": "4.6"}, {"Entity": "Angola", "Code": "AGO", "Year": "2001", "Use of insecticide-treated bed nets (% of under-5 population)": "2.3"}, {"Entity": "Angola", "Code": "AGO", "Year": "2007", "Use of insecticide-treated bed nets (% of under-5 population)": "17.7"}, {"Entity": "Angola", "Code": "AGO", "Year": "2011", "Use of insecticide-treated bed nets (% of under-5 population)": "25.9"}, {"Entity": "Angola", "Code": "AGO", "Year": "2016", "Use of insecticide-treated bed nets (% of under-5 population)": "21.7"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "1"}, {"Entity": "Benin", "Code": "BEN", "Year": "2001", "Use of insecticide-treated bed nets (% of under-5 population)": "7"}, {"Entity": "Benin", "Code": "BEN", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "20.3"}, {"Entity": "Benin", "Code": "BEN", "Year": "2012", "Use of insecticide-treated bed nets (% of under-5 population)": "69.7"}, {"Entity": "Benin", "Code": "BEN", "Year": "2014", "Use of insecticide-treated bed nets (% of under-5 population)": "72.7"}, {"Entity": "Benin", "Code": "BEN", "Year": "2018", "Use of insecticide-treated bed nets (% of under-5 population)": "76.3"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2012", "Use of insecticide-treated bed nets (% of under-5 population)": "30.9"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2003", "Use of insecticide-treated bed nets (% of under-5 population)": "1.9"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "10"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "47.4"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2014", "Use of insecticide-treated bed nets (% of under-5 population)": "75.3"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2018", "Use of insecticide-treated bed nets (% of under-5 population)": "54.4"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "1"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2005", "Use of insecticide-treated bed nets (% of under-5 population)": "8"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "45"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2012", "Use of insecticide-treated bed nets (% of under-5 population)": "53.8"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2016", "Use of insecticide-treated bed nets (% of under-5 population)": "39.9"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2017", "Use of insecticide-treated bed nets (% of under-5 population)": "39.9"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2005", "Use of insecticide-treated bed nets (% of under-5 population)": "4"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "4.2"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "1"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2004", "Use of insecticide-treated bed nets (% of under-5 population)": "1.1"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "13"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2011", "Use of insecticide-treated bed nets (% of under-5 population)": "21"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2014", "Use of insecticide-treated bed nets (% of under-5 population)": "54.8"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2018", "Use of insecticide-treated bed nets (% of under-5 population)": "59.8"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "2"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "15"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "36.4"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2019", "Use of insecticide-treated bed nets (% of under-5 population)": "50.6"}, {"Entity": "Chad", "Code": "TCD", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "1"}, {"Entity": "Chad", "Code": "TCD", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "9.8"}, {"Entity": "Chad", "Code": "TCD", "Year": "2015", "Use of insecticide-treated bed nets (% of under-5 population)": "36.4"}, {"Entity": "Chad", "Code": "TCD", "Year": "2019", "Use of insecticide-treated bed nets (% of under-5 population)": "54.3"}, {"Entity": "Colombia", "Code": "COL", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "0.7"}, {"Entity": "Comoros", "Code": "COM", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "9"}, {"Entity": "Comoros", "Code": "COM", "Year": "2012", "Use of insecticide-treated bed nets (% of under-5 population)": "41.1"}, {"Entity": "Congo", "Code": "COG", "Year": "2005", "Use of insecticide-treated bed nets (% of under-5 population)": "6"}, {"Entity": "Congo", "Code": "COG", "Year": "2012", "Use of insecticide-treated bed nets (% of under-5 population)": "31.5"}, {"Entity": "Congo", "Code": "COG", "Year": "2015", "Use of insecticide-treated bed nets (% of under-5 population)": "60.5"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "1"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2004", "Use of insecticide-treated bed nets (% of under-5 population)": "4"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2005", "Use of insecticide-treated bed nets (% of under-5 population)": "1.3"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "3"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2012", "Use of insecticide-treated bed nets (% of under-5 population)": "37.2"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2016", "Use of insecticide-treated bed nets (% of under-5 population)": "59.7"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2001", "Use of insecticide-treated bed nets (% of under-5 population)": "1"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2007", "Use of insecticide-treated bed nets (% of under-5 population)": "5.8"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "38.1"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2014", "Use of insecticide-treated bed nets (% of under-5 population)": "55.8"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2018", "Use of insecticide-treated bed nets (% of under-5 population)": "51"}, {"Entity": "Djibouti", "Code": "DJI", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "1"}, {"Entity": "Djibouti", "Code": "DJI", "Year": "2009", "Use of insecticide-treated bed nets (% of under-5 population)": "19.9"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2002", "Use of insecticide-treated bed nets (% of under-5 population)": "8"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "41"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2016", "Use of insecticide-treated bed nets (% of under-5 population)": "55.7"}, {"Entity": "Equatorial Guinea", "Code": "GNQ", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "1"}, {"Entity": "Equatorial Guinea", "Code": "GNQ", "Year": "2011", "Use of insecticide-treated bed nets (% of under-5 population)": "23"}, {"Entity": "Eritrea", "Code": "ERI", "Year": "2002", "Use of insecticide-treated bed nets (% of under-5 population)": "4"}, {"Entity": "Eritrea", "Code": "ERI", "Year": "2008", "Use of insecticide-treated bed nets (% of under-5 population)": "48.9"}, {"Entity": "Eritrea", "Code": "ERI", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "20.4"}, {"Entity": "Eswatini", "Code": "SWZ", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "0"}, {"Entity": "Eswatini", "Code": "SWZ", "Year": "2007", "Use of insecticide-treated bed nets (% of under-5 population)": "0.6"}, {"Entity": "Eswatini", "Code": "SWZ", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "1.5"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2005", "Use of insecticide-treated bed nets (% of under-5 population)": "1.5"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2007", "Use of insecticide-treated bed nets (% of under-5 population)": "33"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2011", "Use of insecticide-treated bed nets (% of under-5 population)": "30.1"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2015", "Use of insecticide-treated bed nets (% of under-5 population)": "45.3"}, {"Entity": "Gabon", "Code": "GAB", "Year": "2008", "Use of insecticide-treated bed nets (% of under-5 population)": "55.1"}, {"Entity": "Gabon", "Code": "GAB", "Year": "2012", "Use of insecticide-treated bed nets (% of under-5 population)": "38.8"}, {"Entity": "Gambia", "Code": "GMB", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "15"}, {"Entity": "Gambia", "Code": "GMB", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "49"}, {"Entity": "Gambia", "Code": "GMB", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "33.3"}, {"Entity": "Gambia", "Code": "GMB", "Year": "2013", "Use of insecticide-treated bed nets (% of under-5 population)": "47"}, {"Entity": "Gambia", "Code": "GMB", "Year": "2017", "Use of insecticide-treated bed nets (% of under-5 population)": "62.4"}, {"Entity": "Gambia", "Code": "GMB", "Year": "2018", "Use of insecticide-treated bed nets (% of under-5 population)": "56"}, {"Entity": "Gambia", "Code": "GMB", "Year": "2020", "Use of insecticide-treated bed nets (% of under-5 population)": "44"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2003", "Use of insecticide-treated bed nets (% of under-5 population)": "3.9"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "22"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2008", "Use of insecticide-treated bed nets (% of under-5 population)": "38.7"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2011", "Use of insecticide-treated bed nets (% of under-5 population)": "39"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2014", "Use of insecticide-treated bed nets (% of under-5 population)": "46.6"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2016", "Use of insecticide-treated bed nets (% of under-5 population)": "52.2"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2018", "Use of insecticide-treated bed nets (% of under-5 population)": "48.6"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2019", "Use of insecticide-treated bed nets (% of under-5 population)": "54.1"}, {"Entity": "Guatemala", "Code": "GTM", "Year": "1999", "Use of insecticide-treated bed nets (% of under-5 population)": "1"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2003", "Use of insecticide-treated bed nets (% of under-5 population)": "4"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2005", "Use of insecticide-treated bed nets (% of under-5 population)": "1.4"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2007", "Use of insecticide-treated bed nets (% of under-5 population)": "5"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2008", "Use of insecticide-treated bed nets (% of under-5 population)": "5"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2012", "Use of insecticide-treated bed nets (% of under-5 population)": "26"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2016", "Use of insecticide-treated bed nets (% of under-5 population)": "67.9"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2018", "Use of insecticide-treated bed nets (% of under-5 population)": "26.6"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2021", "Use of insecticide-treated bed nets (% of under-5 population)": "38.2"}, {"Entity": "Guinea-Bissau", "Code": "GNB", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "7"}, {"Entity": "Guinea-Bissau", "Code": "GNB", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "39"}, {"Entity": "Guinea-Bissau", "Code": "GNB", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "35.5"}, {"Entity": "Guinea-Bissau", "Code": "GNB", "Year": "2014", "Use of insecticide-treated bed nets (% of under-5 population)": "80.6"}, {"Entity": "Guinea-Bissau", "Code": "GNB", "Year": "2019", "Use of insecticide-treated bed nets (% of under-5 population)": "93.6"}, {"Entity": "Guyana", "Code": "GUY", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "5.5"}, {"Entity": "Guyana", "Code": "GUY", "Year": "2005", "Use of insecticide-treated bed nets (% of under-5 population)": "6.7"}, {"Entity": "Guyana", "Code": "GUY", "Year": "2009", "Use of insecticide-treated bed nets (% of under-5 population)": "24.4"}, {"Entity": "Guyana", "Code": "GUY", "Year": "2014", "Use of insecticide-treated bed nets (% of under-5 population)": "7.4"}, {"Entity": "Guyana", "Code": "GUY", "Year": "2020", "Use of insecticide-treated bed nets (% of under-5 population)": "11.2"}, {"Entity": "Haiti", "Code": "HTI", "Year": "2012", "Use of insecticide-treated bed nets (% of under-5 population)": "12"}, {"Entity": "Haiti", "Code": "HTI", "Year": "2017", "Use of insecticide-treated bed nets (% of under-5 population)": "18.2"}, {"Entity": "India", "Code": "IND", "Year": "2016", "Use of insecticide-treated bed nets (% of under-5 population)": "5"}, {"Entity": "India", "Code": "IND", "Year": "2021", "Use of insecticide-treated bed nets (% of under-5 population)": "4.4"}, {"Entity": "Indonesia", "Code": "IDN", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "0"}, {"Entity": "Indonesia", "Code": "IDN", "Year": "2007", "Use of insecticide-treated bed nets (% of under-5 population)": "3.7"}, {"Entity": "Iraq", "Code": "IRQ", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "0"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "3"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2003", "Use of insecticide-treated bed nets (% of under-5 population)": "6"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2009", "Use of insecticide-treated bed nets (% of under-5 population)": "46.7"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "42.2"}], "rows_tail": [{"Entity": "Namibia", "Code": "NAM", "Year": "2009", "Use of insecticide-treated bed nets (% of under-5 population)": "34"}, {"Entity": "Namibia", "Code": "NAM", "Year": "2013", "Use of insecticide-treated bed nets (% of under-5 population)": "5.6"}, {"Entity": "Niger", "Code": "NER", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "1"}, {"Entity": "Niger", "Code": "NER", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "7.4"}, {"Entity": "Niger", "Code": "NER", "Year": "2009", "Use of insecticide-treated bed nets (% of under-5 population)": "43"}, {"Entity": "Niger", "Code": "NER", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "63.7"}, {"Entity": "Niger", "Code": "NER", "Year": "2012", "Use of insecticide-treated bed nets (% of under-5 population)": "20.1"}, {"Entity": "Niger", "Code": "NER", "Year": "2015", "Use of insecticide-treated bed nets (% of under-5 population)": "95.5"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2003", "Use of insecticide-treated bed nets (% of under-5 population)": "1.2"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2008", "Use of insecticide-treated bed nets (% of under-5 population)": "5.5"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "28.9"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2011", "Use of insecticide-treated bed nets (% of under-5 population)": "16.4"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2013", "Use of insecticide-treated bed nets (% of under-5 population)": "16.6"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2014", "Use of insecticide-treated bed nets (% of under-5 population)": "25.4"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2015", "Use of insecticide-treated bed nets (% of under-5 population)": "43.6"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2017", "Use of insecticide-treated bed nets (% of under-5 population)": "49.1"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2018", "Use of insecticide-treated bed nets (% of under-5 population)": "52.2"}, {"Entity": "Pakistan", "Code": "PAK", "Year": "2007", "Use of insecticide-treated bed nets (% of under-5 population)": "0.2"}, {"Entity": "Pakistan", "Code": "PAK", "Year": "2018", "Use of insecticide-treated bed nets (% of under-5 population)": "0.4"}, {"Entity": "Papua New Guinea", "Code": "PNG", "Year": "2018", "Use of insecticide-treated bed nets (% of under-5 population)": "51.5"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "5"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2005", "Use of insecticide-treated bed nets (% of under-5 population)": "12.6"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2008", "Use of insecticide-treated bed nets (% of under-5 population)": "56.5"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "69.6"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2011", "Use of insecticide-treated bed nets (% of under-5 population)": "69.6"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2013", "Use of insecticide-treated bed nets (% of under-5 population)": "74.1"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2015", "Use of insecticide-treated bed nets (% of under-5 population)": "67.7"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2017", "Use of insecticide-treated bed nets (% of under-5 population)": "68"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2020", "Use of insecticide-treated bed nets (% of under-5 population)": "55.6"}, {"Entity": "Sao Tome and Principe", "Code": "STP", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "23"}, {"Entity": "Sao Tome and Principe", "Code": "STP", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "42"}, {"Entity": "Sao Tome and Principe", "Code": "STP", "Year": "2009", "Use of insecticide-treated bed nets (% of under-5 population)": "56"}, {"Entity": "Sao Tome and Principe", "Code": "STP", "Year": "2014", "Use of insecticide-treated bed nets (% of under-5 population)": "61.1"}, {"Entity": "Sao Tome and Principe", "Code": "STP", "Year": "2019", "Use of insecticide-treated bed nets (% of under-5 population)": "62.6"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "2"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2005", "Use of insecticide-treated bed nets (% of under-5 population)": "7.2"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "16.4"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2009", "Use of insecticide-treated bed nets (% of under-5 population)": "29.2"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2011", "Use of insecticide-treated bed nets (% of under-5 population)": "34.5"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2013", "Use of insecticide-treated bed nets (% of under-5 population)": "45.8"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2014", "Use of insecticide-treated bed nets (% of under-5 population)": "43.2"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2015", "Use of insecticide-treated bed nets (% of under-5 population)": "55.4"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2016", "Use of insecticide-treated bed nets (% of under-5 population)": "66.6"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2017", "Use of insecticide-treated bed nets (% of under-5 population)": "60.7"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2018", "Use of insecticide-treated bed nets (% of under-5 population)": "56.4"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2019", "Use of insecticide-treated bed nets (% of under-5 population)": "65.4"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2021", "Use of insecticide-treated bed nets (% of under-5 population)": "46.5"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "2"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2005", "Use of insecticide-treated bed nets (% of under-5 population)": "5"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2008", "Use of insecticide-treated bed nets (% of under-5 population)": "25.8"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "30.3"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2013", "Use of insecticide-treated bed nets (% of under-5 population)": "49"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2016", "Use of insecticide-treated bed nets (% of under-5 population)": "44.1"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2017", "Use of insecticide-treated bed nets (% of under-5 population)": "59.5"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2019", "Use of insecticide-treated bed nets (% of under-5 population)": "59.1"}, {"Entity": "Solomon Islands", "Code": "SLB", "Year": "1999", "Use of insecticide-treated bed nets (% of under-5 population)": "53"}, {"Entity": "Solomon Islands", "Code": "SLB", "Year": "2002", "Use of insecticide-treated bed nets (% of under-5 population)": "42"}, {"Entity": "Solomon Islands", "Code": "SLB", "Year": "2007", "Use of insecticide-treated bed nets (% of under-5 population)": "40"}, {"Entity": "Solomon Islands", "Code": "SLB", "Year": "2015", "Use of insecticide-treated bed nets (% of under-5 population)": "69.6"}, {"Entity": "Somalia", "Code": "SOM", "Year": "1999", "Use of insecticide-treated bed nets (% of under-5 population)": "0.3"}, {"Entity": "Somalia", "Code": "SOM", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "11.4"}, {"Entity": "South Asia (WB)", "Code": "WB_SA", "Year": "2019", "Use of insecticide-treated bed nets (% of under-5 population)": "4.387759"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "21"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2009", "Use of insecticide-treated bed nets (% of under-5 population)": "25.3"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2013", "Use of insecticide-treated bed nets (% of under-5 population)": "45.8"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2017", "Use of insecticide-treated bed nets (% of under-5 population)": "41.7"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2007", "Use of insecticide-treated bed nets (% of under-5 population)": "2.9"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2016", "Use of insecticide-treated bed nets (% of under-5 population)": "3.5"}, {"Entity": "Sub-Saharan Africa (WB)", "Code": "WB_SSA", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "37.305553"}, {"Entity": "Sub-Saharan Africa (WB)", "Code": "WB_SSA", "Year": "2019", "Use of insecticide-treated bed nets (% of under-5 population)": "52.66592"}, {"Entity": "Sudan", "Code": "SDN", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "0.4"}, {"Entity": "Sudan", "Code": "SDN", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "30"}, {"Entity": "Sudan", "Code": "SDN", "Year": "2009", "Use of insecticide-treated bed nets (% of under-5 population)": "25.3"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "3"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "43.4"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "1.9"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2005", "Use of insecticide-treated bed nets (% of under-5 population)": "1"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "1999", "Use of insecticide-treated bed nets (% of under-5 population)": "2"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2004", "Use of insecticide-treated bed nets (% of under-5 population)": "10"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2005", "Use of insecticide-treated bed nets (% of under-5 population)": "16"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2008", "Use of insecticide-treated bed nets (% of under-5 population)": "25.7"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "63.6"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2012", "Use of insecticide-treated bed nets (% of under-5 population)": "72"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2016", "Use of insecticide-treated bed nets (% of under-5 population)": "54.4"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2017", "Use of insecticide-treated bed nets (% of under-5 population)": "54.6"}, {"Entity": "Togo", "Code": "TGO", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "2"}, {"Entity": "Togo", "Code": "TGO", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "38"}, {"Entity": "Togo", "Code": "TGO", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "57.1"}, {"Entity": "Togo", "Code": "TGO", "Year": "2014", "Use of insecticide-treated bed nets (% of under-5 population)": "42.8"}, {"Entity": "Togo", "Code": "TGO", "Year": "2017", "Use of insecticide-treated bed nets (% of under-5 population)": "69.7"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "9.7"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2009", "Use of insecticide-treated bed nets (% of under-5 population)": "32.8"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2011", "Use of insecticide-treated bed nets (% of under-5 population)": "42.8"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2015", "Use of insecticide-treated bed nets (% of under-5 population)": "74.3"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2016", "Use of insecticide-treated bed nets (% of under-5 population)": "62"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2018", "Use of insecticide-treated bed nets (% of under-5 population)": "60.3"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2019", "Use of insecticide-treated bed nets (% of under-5 population)": "60.3"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2002", "Use of insecticide-treated bed nets (% of under-5 population)": "13"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2007", "Use of insecticide-treated bed nets (% of under-5 population)": "55.7"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2013", "Use of insecticide-treated bed nets (% of under-5 population)": "51"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2000", "Use of insecticide-treated bed nets (% of under-5 population)": "16"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2005", "Use of insecticide-treated bed nets (% of under-5 population)": "13"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "5"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2011", "Use of insecticide-treated bed nets (% of under-5 population)": "9.4"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Use of insecticide-treated bed nets (% of under-5 population)": "1"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Use of insecticide-treated bed nets (% of under-5 population)": "7.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "23"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Use of insecticide-treated bed nets (% of under-5 population)": "28.5"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Use of insecticide-treated bed nets (% of under-5 population)": "41"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Use of insecticide-treated bed nets (% of under-5 population)": "50"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Use of insecticide-treated bed nets (% of under-5 population)": "57"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Use of insecticide-treated bed nets (% of under-5 population)": "40.6"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Use of insecticide-treated bed nets (% of under-5 population)": "51.6"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Use of insecticide-treated bed nets (% of under-5 population)": "51.6"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Use of insecticide-treated bed nets (% of under-5 population)": "3.1"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Use of insecticide-treated bed nets (% of under-5 population)": "17.3"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Use of insecticide-treated bed nets (% of under-5 population)": "9.7"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Use of insecticide-treated bed nets (% of under-5 population)": "26.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Use of insecticide-treated bed nets (% of under-5 population)": "9"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Use of insecticide-treated bed nets (% of under-5 population)": "14.9"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "children-sleeping-under-treated-bednet", "metadata_url": "https://ourworldindata.org/grapher/children-sleeping-under-treated-bednet.metadata.json", "chart_title": "Share of children sleeping under insecticide-treated nets", "chart_subtitle": "The share of children under five years old who slept under an insecticide-treated bednet to prevent malaria infection the previous night.", "chart_note": null, "chart_citation": "Demographic and Health Surveys (DHS) and UNICEF, via World Bank (2026)", "original_chart_url": "https://ourworldindata.org/grapher/children-sleeping-under-treated-bednet", "owid_column_metadata": {"Use of insecticide-treated bed nets (% of under-5 population)": {"titleShort": "Use of insecticide-treated bed nets (% of under-5 population)", "titleLong": "Use of insecticide-treated bed nets (% of under-5 population)", "shortUnit": "%", "unit": "% of under-5 population", "timespan": "1999-2021", "type": "Numeric", "owidVariableId": 1205115, "shortName": "sh_mlr_nets_zs", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", 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{"raw_link": "https://ourworldindata.org/work-employment", "title": "Work and Employment", "context": "Work and Employment\nBy\nBertha Rohenkohl\n,\nPablo Arriagada\n,\nand\nEsteban Ortiz-Ospina\nContents\nWork is at the center of most people’s lives. For billions of adults around the world, it is the main way to earn a living, support their families, and contribute to society.\nWhat share of the population in different countries is part of the labor force? How is employment split between people who work for an employer and those who run their own business or work independently? And how many people want a job but can’t find one?\nOn this page, we look at these different aspects of work. We explain the key concepts and present data showing how labor market participation, unemployment, and employment are measured, and how the trends differ across countries.\nWe focus on paid work among people of working age. Other important aspects, such as\nunpaid care work\n,\nchild labor\n, and long-run trends in\nworking hours\n, are covered separately.\nUnderstanding labor force statistics: participation, unemployment, and employment\nEmployment and unemployment figures come up regularly in public discussions, and the terms themselves will be familiar to most of us. But in labor force statistics, these terms have specific meanings, and the exact definitions matter for understanding what the numbers show.\nWithout a clear grasp of the definitions and who is counted where, it is easy to misread the data — for example, by taking a rise or fall in unemployment to only reflect changes in how many people have jobs, when it can also reflect changes in how many people are counted as looking for work in the first place.\nIn this section, we walk through this and other common sources of confusion, laying out the key definitions and showing how they shape the interpretation of employment trends.\nThe labor force: who is included and who is not\nThe labor force includes those of working age who are “economically active”, either because they are working or because they are actively looking for work.\nThe diagram here illustrates this classification. The labor force, shown in purple, includes people of working age who are employed (dark blue) and those who do not have a job but are actively seeking one (light blue).\n1\nThe labor force excludes people who are not of working age (dark red), as well as people of working age who are neither employed nor actively seeking work (orange). The orange group “outside the labor force” includes students, retirees, unpaid caregivers, as well as those who are not actively seeking work, perhaps due to long-term illness or other reasons.\nDownload\nExplore this diagram as an interactive chart\nShare of population by labor force status\nHow is “working age” defined?\nThere is no single definition of “working age”. The age range used in labor statistics varies by country and by data source.\nThe most common lower age limit is 15, but some sources start at 14, 16, or 18, often reflecting national laws on minimum working ages and how labor force surveys are designed. Some sources also set an upper age limit, commonly around 64, reflecting statutory retirement ages or pension eligibility rules; but since these vary (and can change over time), the exact cutoff is not the same across countries or even within a country over time.\nFor international comparisons, the most widely used cross-country series are produced by the\nInternational Labour Organization (ILO)\n. In its harmonized series, the working-age population is typically defined as everyone aged 15 and older, with no upper age limit, and most of our charts on this page follow this definition.\n2\nHow labor force surveys measure who is working and who is not\nIn most countries, headline labor statistics are produced using dedicated labor force surveys (LFS). In these surveys, national statistical offices ask people of working age about their work situation: more specifically, what they have been doing for work recently and, if they don’t work, whether they were looking for a job.\n3\nWhere dedicated labor force surveys don’t exist, countries sometimes use other household surveys or population censuses that ask related questions about work.\n4\nLabor force surveys focus on people’s recent work situation. The definition of “recent” is fixed using a clearly defined window of time, typically the past week. This observation window is called the\nreference period\n.\nBased on the survey responses, statistical offices classify everyone of working age into one of three categories:\nA person is counted as\nemployed\nif they did any work for pay or profit during the reference period. This definition is broad: even one hour of paid work counts, as does self-employment or helping without pay in a family farm or family business. It can also include work to produce goods for one’s own use, such as subsistence farming (depending on the statistical standards).\n5\nBut unpaid activities done in the household for the own benefit of the family, such as cooking, cleaning, or caring for family members, are not counted as employment.\nA person is counted as\nunemployed\nif they did not have a job during the reference period, but were actively looking for one (for example, by applying for jobs or contacting employers) and were available to start soon.\nPeople who were not working, not actively searching for a job, or not available to start soon are considered to be\noutside the\nlabor force\n. As mentioned above, this includes groups such as college students, retired people, or caregivers.\nKey indicator: the labor force participation rate\nOne metric that is commonly discussed is the labor force participation rate. This indicator measures the share of the working-age population that is either employed or unemployed, based on the classification explained above.\nIt answers a basic but important question: out of all the adults of working age, how many are actually taking part in the labor market?\nDownload\nThe map shows labor force participation rates around the world. As we can see, there are large differences across countries — in some countries, more than 70% of adults are working or looking for work, while in others, fewer than half are active in the labor market.\n6\nYou can see another version of this map for adults\naged 15–64\n.\nThe differences in labor force participation across countries often reflect demographic differences, because work follows life-course patterns: more people in education at younger ages, more people retired and not in the labor force\nat older ages\n.\nThey also reflect social norms and policies that shape who works and who doesn’t. In particular, in many countries, women are much\nmore likely than men\nto be out of the labor force, often because they spend more time on\nunpaid care and housework\n; and female labor participation rates also vary with education systems, pension rules, and the availability of childcare. You can read more about this on our page on\nWomen’s Employment\n.\nRelated charts\nExplore labor force participation data\nParticipation rates by age groups and sex\nLabor force participation rate by age\nShare of the working-age population (ages 15 and over) who are economically active (employed or unemployed).\nKey indicator: the unemployment rate\nIt is common to think of the unemployment rate as the share of adults who are out of work. In labor force statistics, however, it has a more specific meaning. The unemployment rate measures the share of people in the labor force who do not have a job,\nand\nare both actively looking for work and available to start soon.\nIt gives a simple indication of how difficult it is for people who want a job to find one, which is why it is widely followed in public discussions and policy debates.\nDownload\nThe next map plots unemployment rates across the world. The data shows that in recent years, most countries have had unemployment rates in the range of around 2% to 10%. This pattern holds for countries of very different income levels.\nThis is something that often surprises people: some poor countries have similar or even lower unemployment rates than rich countries. For example, Burkina Faso and Australia have roughly the same unemployment rate (around\n3.5% and 4 % in 2025\n).\nBear in mind that the map shows ILO modeled estimates, which can be quite different from the numbers published by national statistical offices. We explain the difference\nin the section below\n.\nThis pattern is explained by the fact that, despite the levels being similar, the underlying dynamics and work patterns are very different. In poorer countries, most people work in informal or subsistence activities, even when the work is irregular, because they cannot afford to be unemployed (or outside the labor force) for long. In richer countries, a much larger share of workers are in formal wage employment, and unemployment more often reflects job loss and active job search within a formal labor market. We explain this distinction between formal and informal employment and explore the cross-country pattern in more detail\nin the section below.\nIn the map, we focus on overall unemployment rates, but the picture can look very different for specific groups. In many countries,\nwomen have higher unemployment rates\n, and\nyoung people are more likely to be unemployed\n.\nExplore related charts\nFemale unemployment rate\nYouth unemployment rate\nWhat the unemployment rate measures, and what it misses\nAs we explained above, the concept of “unemployment” has a specific meaning in official statistics. A person is counted as unemployed if they had no work during the reference period, have recently taken active steps to find work, and are available to start work soon. Current ILO guidance operationalises this with a “recent” search window of four weeks, and a “short availability” window of two weeks.\n7\nBecause the definition depends on a specific reference period, which covers a relatively short window, the timing and frequency of the surveys are crucial. Seasonal workers, for example, may be classified as employed or unemployed depending on when the survey takes place.\nAs a result, unemployment rates can vary depending on the time of year and the frequency of surveys. It can be quite different if we look at monthly, quarterly, or annual data.\n8\nJob-search behaviour is another factor that shapes both the measurement and interpretation of unemployment figures. “Actively looking” for work requires concrete steps — for example, applying for jobs, registering with employment services, answering adverts, contacting employers, or taking steps to start a business. In essence, the requirement to be “actively looking” sets a higher bar for what counts as a job-search effort and goes beyond just wanting a job.\n9\nPeople who want to work but have stopped searching because prospects are poor, previous searches failed, costs or caring responsibilities limit their options, or they lack confidence that a costly job search will pay off, become “discouraged workers”. Because they are not actively searching, they are not counted as unemployed and are instead recorded as outside the labor force.\nThis helps explain why, especially during economic downturns, unemployment numbers can understate joblessness: when people stop looking for work, they move\noutside\nthe labor force, and the unemployment rate can fall even though employment has not risen.\nUnderstanding this point clarifies what the unemployment rate actually measures: not the share of all adults without work, but the share of people\nin the labor force\n— those actively engaged in the job market — who are unemployed.\nKey Indicator: the employment rate\nAnother way to understand activity in the labor market is to look at how many adults actually have jobs. This is what the employment rate (also known as the employment-to-population ratio) captures.\nWhile the unemployment rate tells us how many people in the\nlabor force\nare without a job, this indicator looks at the entire\nworking-age population\n. It answers a question that many people intuitively care about: among those who could work (because they are of working age), how many are employed?\nDownload\nThis indicator complements the unemployment rate by using a broader reference group. It therefore reflects both differences in employment and differences in labor market participation.\nBecause of this, the two indicators are related, but they tell different stories: countries with low unemployment rates often have higher employment rates, but this is not always the case.\nConsider, for instance, Italy. In 2025, Italy had one of the lowest employment rates in Europe, at around\n46%\n, while its unemployment rate was\nnot far from the European Union average\n.\nThis reflects the fact that a larger share of\nolder people\nand\nwomen\nin Italy are outside the labor force. Lower participation reduces the employment rate but does not affect the unemployment rate, since people who are not actively looking for work are not counted as unemployed.\n10\nHow people work: employment relationships and job types\nEmployment statistics try to answer a simple question: how many people work? The answer to this question, clearly, depends on what we mean by “work”.\nIn practice, what constitutes “having a job” or “being employed” can mean very different things. Some people work for an employer, others run their own company, and some work without pay in a family business or farm. Some work long hours every day, others only for a few hours a week.\nIn official statistics, all of these people are considered employed. Under the ILO framework, anyone who worked for pay or profit for at least one hour during the survey reference period (usually a week) is considered employed.\n11\nBecause employment takes many forms, it is useful to look not only at how many people work, but also\nhow\nthey work.\nEmployment status\nOne way to classify workers is by their employment status. In this context, “status” describes the nature of a person’s employment relationship, that is, who they work for or with. It’s not about the sector they work in, their skill level, or the quality of their work.\nHere, the focus is on whether people work for others, for themselves, or within a family business or family farm.\nThe ILO distinguishes four main categories:\nEmployees\n— people who work for an employer in exchange for wages or a salary.\n12\nEmployers\n— self-employed people who run their own business\nand\nhire other people as staff.\nIndependent own-account workers\n— self-employed people who don’t hire others. For example, in low-income countries, this often includes small farmers, informal street vendors, and people running one-person businesses from home. In high-income countries, this includes freelancers, consultants, and solo tradespeople, such as plumbers or electricians.\n13\nContributing family workers\n— people who work without pay in a\nfamily\nbusiness or farm, including those producing goods either for sale or for their own use.\n14\nThe chart here shows how workers are distributed across these groups. You can add or remove countries.\nThe main insight from this chart is that in poorer countries, many people work on their own, often as small farmers, solo traders, or in small family businesses. Much of this self-employment is a way for people to earn a living when\nstable wage jobs are scarce\n, rather than running a business by choice. In contrast, in\nricher countries\n, most people work as employees (for an employer).\n15\nUnderstanding these differences in employment status is important for interpreting labor markets and for comparing countries. Headline indicators like the employment rate group all employed people together, but this gives an incomplete picture because employment relationships shape the risks, protections, and opportunities people face at work.\nNote that this classification of workers by status doesn’t distinguish between full-time and part-time jobs, between secure and precarious contracts (such as temporary or unstable jobs), or between formal and informal employment.\nWe’ll come back to informal work in the next section, but here we want to briefly mention another related concept:\nvulnerable employment\n. The ILO defines this group as the sum of “independent own account workers” and “contributing family workers.” These are workers who tend to face more job insecurity and irregular incomes. But even within this category, people’s work circumstances can look very different. For example, some own-account workers in rich countries are highly paid professionals or freelancers who have autonomy over their work and stability — people who wouldn’t usually be described as “vulnerable”.\nInformal work\nWhen we think about employment, we often picture people in regular, registered jobs — those with written contracts, stable pay, and access to benefits. But a large share of work around the world happens outside this formal framework. These jobs are considered\ninformal\n.\nIn official statistics, both formal and informal jobs are included when counting total employment. The ILO framework first classifies people as employed, unemployed, or outside the labor force, and only then, within the employed group, distinguishes between\nformal\nand\ninformal\nemployment.\nInformality is about the job, not the worker. In general, informal work lacks formal arrangements, either in law or in practice. These jobs are often not covered by labor laws, social security, or worker protections such as sick pay and annual leave. They may also lack written contracts.\nCommon examples include street vendors, domestic cleaners or caregivers, casual construction workers, small-scale farmers, and home-based craft workers.\nBut informal employment is not the same as working in the informal sector. A person in a registered, “formal” company can still have an informal job if it lacks legal or social protection.\nA concrete example of informal employment\nWhile most of us can probably picture some types of informal employment, others are harder to imagine if you have never seen or experienced them. To make the concept more concrete, it can be helpful to look at a specific real-world example from a non-rich country where many of these jobs exist, and compare how these would be classified in richer countries.\nImagine a busy intersection in Bogotá, Colombia. At the traffic lights, a man approaches cars with a small bucket and cloth, offering to clean windshields in exchange for a few coins. There is no fixed price, no clear expectation, and no guarantee that anyone will pay him. But he shows up at the same intersection every day, often for many hours, because this is how he earns a living. Some drivers give him small coins when they choose to, but on slow days, he earns very little.\nIn a labor force survey, he would probably report that he “worked for pay or profit for at least one hour during the reference week”, which meets the ILO definition of employment, and he would not report that he is actively searching for a job. He works on his own, brings his own materials, is not paid by an employer, and expects to earn income directly from the activity. By the standards used in labor statistics, he therefore fits squarely within the employed category, specifically as an independent own-account worker.\nThis may seem surprising because, while cleaning windshields is work, this arrangement doesn’t look like “having a job,” even though it meets the statistical definition of employment.\nIn many non-rich countries, millions of people make their living through irregular, low-paid, and highly precarious activities like the one in this example. These jobs are part of what labor statistics typically consider\ninformal\nemployment.\nIn high-income countries, labor statistics rely on the same broad ILO definitions, so the exact same situation would be classified in the same way. The difference is that situations like this are much less common. People who lack a formal job are more likely to receive social assistance or temporary support, including unemployment benefits or subsidies, while they search for work. As a result, marginal income-earning activities are more often short-term stopgaps rather than a main livelihood.\nInformal employment does exist in richer countries, but it often looks quite different. In high-income countries, it often takes the form of undeclared pay, misclassification, or atypical contracts, rather than visible and highly precarious own-account work, as in the example. The same statistical category of informality can therefore capture very different realities across countries.\nMeasuring informal employment\nMeasuring informality is difficult, and definitions vary across countries. National statistical offices often rely on a mix of criteria, such as whether a worker has a written contract, access to social security, or employment benefits. However, countries differ in which of these criteria they apply, so estimates are not always comparable.\n16\nInformal jobs can also be hard to capture in surveys, because people may not be willing to report these jobs in the first place. Workers might worry about tax implications or other consequences of declaring informal activity.\nThese difficulties are reflected in the data itself: cross-country estimates of informal work are incomplete, with informal employment not measured separately in many high-income countries such as the US, Canada or the UK, where open informality is less visible.\n17\nDespite these limitations, the available data provide a useful picture of how widespread informal employment remains around the world. As the chart shows, informal work is an essential source of income for many people. In the Democratic Republic of Congo, for example, nearly 97% of the people who work do so in an informal job. Some of these workers are employees paid by an employer in an informal setting, but that’s a minority — the\nmajority\nare independent “own account workers” or “contributing family workers”.\nInformal work is far more prevalent in\nlower-income countries\n, where it often serves as the main source of income for workers and their families. But in richer countries, as mentioned above, there is also informal employment — for example, in undeclared domestic work or delivery jobs without contracts or benefits.\nReducing informality and extending legal and social protection to all workers is one of the targets of the\nSustainable Development Goals (SDG 8)\n.\nExplore related charts\nShare of female workers in informal employment\nShare of workers in informal employment in the agricultural sector\nShare of employed people working in jobs that lack basic social or legal protection and employment benefits. Does not include illegal and illicit activities. The agricultural sector includes agriculture, forestry, and fishing.\nEmployment by economic sector\nIn the previous sections, we looked at employment from two angles: who is counted in the labor force, and their working arrangements. Here, we look at the data from a different perspective. Instead of focusing on who people work for, we focus on\nwhat\nwork they do in the economy.\n​​A common way to do this is to classify jobs by broad sectors of activity, such as agriculture, industry, and services. These categories describe the main activity of the workplace, not the specific tasks an individual performs.\nLooking at employment by economic sector helps understand how employment patterns change as countries develop. As incomes rise, workers tend to move out\nfrom agriculture\ninto industry and then\ninto services\n— a shift often referred to as\n“structural transformation\n”.\nThe chart here shows this. In lower-income countries, a large share of workers are employed in agriculture (often as independent own-account or informal workers, as shown in the previous charts). In richer countries, by contrast, most employment is in services, reflecting jobs that are more likely to be wage-based and formal. This mirrors the differences above in employment status and informality:\nstructural change\nin the sectoral composition of jobs often goes together with a shift away from independent and subsistence work toward wage employment.\nBut it is important not to assume that “moving out of agriculture” is equivalent to “moving into formality”. Many middle-income countries have a sectoral mix that is closer to rich countries than to poor ones, yet informality remains widespread. Colombia is a good example of this — industry and services make up\nmore than 85 percent\nof employment, but close to\n60 percent of workers\nremain in informal employment.\nData quality and measurement\nInternational labor statistics are incomplete and uneven\nComprehensive labor force survey data isn’t always available. In many countries, national labor statistics are patchy or missing entirely for some years. And even when data does exist, it can be noisy or inconsistent with international measurement standards. These are the two main reasons why the International Labour Organization (ILO) produces modeled estimates.\nTo produce modeled estimates, the ILO applies a shared harmonization standard (currently the\n13th ICLS\n), adjusts reported data to improve comparability, and uses statistical models to estimate missing values (we explain this in more detail below).\nThe map here gives you an overview of the coverage of the ILO’s unemployment rate estimates across countries and time. It distinguishes between years where both national survey values and modeled estimates exist (and whether these estimates agree), and those where only one or neither is available.\n18\nAs the map shows, coverage is uneven across countries and over time. Using the slider, you can see how this has changed. With improvements in data collection, most high-income countries, and many middle-income countries, now report national survey unemployment estimates on an annual basis, and these tend to closely match the ILO’s modeled figures.\nIndia illustrates how changes in data collection can expand coverage. In 2017, the country\nintroduced a new annual labor force survey\n(the Periodic Labour Force Survey, PLFS), replacing an earlier survey that was conducted roughly every five years.\nIn contrast, large parts of Africa and Asia still lack regular, representative labor force surveys, which means unemployment estimates for many years rely primarily on ILO modeling.\n19\nHow does the ILO produce “modeled estimates” of labor statistics?\nThe ILO’s goal is to create a comprehensive dataset covering every country and year, allowing for cross-country comparisons and the calculation of regional and global aggregates based on a single measurement standard.\nBefore modeling begins, the ILO collects the data that already exists. This typically starts with labor force survey microdata — the anonymized individual responses — when countries make them available. Where labor surveys are missing, the ILO may rely on alternative sources, such as household surveys or, more rarely, population censuses.\nOnce the existing data is\ntriaged and harmonized\n, the ILO identifies remaining gaps and uses statistical models to estimate missing values for specific countries or years. These models draw on data from the same country at other points in time (interpolation), as well as from similar countries, such as those that are geographically close or at similar income levels\nThe models also relate labor market indicators to other variables, such as GDP, population, demographic structure, and education, using data from sources including the UN, World Bank, and IMF. The outputs of these models are the ILO “modeled estimates.”\nThe ILO provides a high-level description of this approach, but there is no detailed public documentation or code that specifies the exact model structure, variables used, or how different inputs are weighted. They only provide a general overview, which you can read\nhere\n.\n20\nHow do “modeled estimates” compare to national data as reported by countries?\nThe data visualization here compares unemployment rates from national estimates (as reported by countries) and modeled estimates (as produced by the ILO).\nYou can use the dropdown menu to select a different metric: labor force participation rates and employment rates.\nWhen labor force surveys are regular and reliable, modeled estimates are identical or very close to the national estimates. But where surveys are sparse — often in poorer countries or those affected by crises — the modeled results rely more heavily on statistical assumptions, and there are substantial differences between the national and modeled estimates.\nIn some countries, such as Mali and Rwanda, for instance, you can see that better data coverage, harmonized definitions, and improved ILO models have brought the two series closer together in recent years. Looking further back in time, however, this convergence is much less common. In earlier years, differences between national and modeled estimates were often much larger, especially across sub-Saharan Africa and parts of Asia.\nThe modeled estimates remain the most comprehensive and comparable source for making global and regional comparisons of labor statistics, but it is important to emphasize that they should be interpreted with care when analyzing individual countries' trends in isolation or when ranking countries directly.\nNotice, in particular, that the ILO approach assumes that labor markets follow predictable patterns that can be captured by broader regional or economic trends, which may not always be true. The ILO models also sometimes smooth out irregular jumps in the data, treating them as possible “measurement noise” rather than genuine shocks. However, in some cases — such as during a crisis or conflict — sharp movements may be genuine.\nWhat you need to know when using work and employment data from the ILO\nWhere does data on work and employment come from?\nMost national data on work and employment comes from national labor force surveys. These surveys ask people of working age about their current or recent work situation, such as whether they have a job, are looking for one, and what kind of work they do.\nLabor force surveys are the main data source of this kind of information. But when they aren’t available, researchers try to fill gaps by using other sources, such as general household surveys that include questions about work or, more rarely, population censuses that collect basic data on employment.\nThe\nInternational Labour Organization (ILO)\nacts as a global hub for this data, compiling and harmonizing results reported by individual countries.\nHow do international standards make labor data more comparable?\nInternational labor statistics typically follow a set of standards agreed upon at the\nInternational Conference of Labour Statisticians (ICLS)\n— a meeting organized by the ILO roughly every five years.\nThese ICLS standards set out internationally agreed definitions relating to core concepts, measurement rules, and methodologies. According to the ILO, they reflect expert consensus as “best practices” and provide a shared language for measuring and monitoring the functioning of labor markets around the world.\nHowever, labor markets evolve over time. People do different kinds of work today than they did in the past, employment relations evolve, and statistical definitions are updated to better reflect new realities. As a result, ICLS standards are not fixed — they are revised and refined.\nRead more\nWhat are the main ICLS standards in use today?\nRead more about the 13th and 19th ICLS.\nToday, two different sets of standards are especially relevant:\nThe\n13th ICLS standards\n(adopted in 1982) are still used today by many countries and by the ILO to harmonize international estimates. You can read more about it in this\nICLS resolution document\n.\nThe\n19th ICLS standards\n(adopted in 2013) updated many of the key definitions, including what counts as employment and unemployment, and how to treat subsistence work and other forms of unpaid work. Many countries (especially rich countries) are already using these newer standards. You can read about the specific definitions of the 19th ICLS in this\nresolution document\n.\nOne major difference between the 13th and 19th ICLS was in the definition of employment. According to the newer 19th ICLS, producing goods for own consumption (such as subsistence farming) is\nnot\ncounted as employment anymore. This change led to lower employment figures for some countries when they adopted the 19th ICLS, especially in those with a large share of people working in subsistence farming.\n21\nLater conferences, including the 20th and 21st ICLS, have continued refining these standards.\nNational statistical agencies that run labor force surveys are encouraged to design and report their data in line with ICLS standards.\nBut countries adopt standards at different speeds, so data from different sources — or even for the same country in different years — may be based on different sets of standards, and are not always directly comparable. When comparing any labor market statistics across countries and over time, it is important to check which definitions and ICLS standards (if any) are being used.\nHow does the ILO harmonize data across countries?\nMost estimates published by the ILO are harmonized to one of the standards agreed at the International Conference of Labour Statisticians (ICLS). For example, both the ILO-published\nLabor Force Statistics\nand\nModelled Estimates\nseries follow the definitions from the 13th ICLS.\nIdeally, to compare data across countries, we should ensure that the data is based on the same set of standards.\nWhen a country’s definition of an indicator differs from the chosen standards (for example, if a country reports estimates based on the 19th ICLS and another follows the 13th ICLS), the ILO reclassifies or adjusts the country-reported figures to match their chosen classification.\n22\nThis process relies on the assumption that survey responses can be reclassified in a consistent way, which sometimes is straightforward but other times may not hold well in practice.\nEven when countries use the same ICLS standards, surveys can differ in some details — such as what the exact thresholds used to define “working age” are. The ILO takes extra steps to harmonize these, when possible, and this is done for the dataset of modeled estimates.\nWhy do figures differ between data sources?\nIt is not unusual for different data sources to report different labor statistics, even when they refer to the same indicator for the same country and year.\nOne reason for this is that data sources use different definitions. Some figures come directly from countries’ own surveys, while others — such as the modeled estimates published by the ILO — use harmonized definitions or adjust the data to make it more comparable across countries and over time. Even when figures are based on similar labor force surveys, the numbers can differ. This can happen, for example, when countries adopt new international standards — such as moving from the 13th ICLS to the 19th ICLS — or when different age thresholds are used to define\nthe working-age population\n. As a result, the labor force survey figures published by countries can vary over time, depending on which specific definitions are used, and are also not always identical to the ILO’s harmonized estimates.\nAnother common reason is that estimates might come from different types of data sources. On this page, we primarily show figures based on labor force surveys, which are the main source used for international comparisons, but some countries also report figures based on administrative data.\nA good example of this is the unemployment rate. Some governments publish figures on “registered unemployment\", which is based on data from administrative records from job centers or benefit agencies.\n23\nRegistered unemployment typically includes people who are eligible for benefits and who choose (or are required) to register. For this reason, it usually underestimates unemployment compared to survey-based measures such as those following the ILO definitions. But the rules of eligibility and registration can vary a lot across countries — for example, by things such as previous work history, length of unemployment, and job search requirements. These rules can vary widely and make it difficult to compare across countries.\nWhen figures come from different sources, their overlap with ILO-based estimates is often only partial. In unemployment statistics, for example, some people recorded as unemployed in administrative registers do not meet the ILO definition, while others who meet the ILO definition never appear in the registers at all. These differences are well documented in countries\nsuch as Germany\n: someone who works up to 15 hours per week can be counted as unemployed in administrative statistics, but under ILO definitions, this person is classified as employed.\nAcknowledgments\nWe thank Hannah Ritchie, Edouard Mathieu, and Andy Dickerson for their helpful suggestions and ideas for this page.\nFeatured Data on\nWork & Employment\nData Insights on\nWork & Employment\nEndnotes\n“Actively seeking” work means taking concrete steps to find a job, such as applying for jobs or contacting employers, rather than only wanting to work. You can read more about this\nin this other section\n.\nIncluding older age groups doesn’t mean they are expected to work. It reflects a descriptive goal: to capture everyone who may participate in the labor market and who, in many countries, does. People above retirement age who are not working or looking for a job are still counted as outside the labor force. The European Union’s Eurostat, for example, has labor statistics for people aged 15 to 89, and publishes indicators for multiple age ranges within this bracket.\nThe fact that work statistics are produced from data coming from surveys may seem surprising, since it can feel more natural to obtain the relevant information from employers or administrative records. In practice, such records (when they exist) only cover part of the labor market — usually waged, formally registered jobs — and they can differ widely in how jobs are defined and recorded between countries. For this reason, headline national labor statistics and ILO estimates are based on surveys that ask people directly about their work, using common definitions across countries.\nA limitation of labor force surveys is that they do not capture all population groups equally well, which can affect how representative labor market statistics are. In particular, migrant and minority groups are often identified using self-reported information. Some respondents may choose not to disclose this information, making labor market outcomes for these groups harder to measure. In some cases, population censuses or other household surveys can help fill gaps, but these sources are typically less frequent or less detailed than labor force surveys. In contexts where migrants face threats, legal uncertainty, or enforcement crackdowns, they may be less willing to respond or disclose information, and this can lead to\nsubstantial data gaps\n.\nThe exact definition of employment follows international standards that are set by the International Conference of Labour Statisticians (ICLS), and which have changed over time. We explain this further in\nHow do international standards make labor data comparable?\nThe most used standards today are the 13th ICLS and 19th ICLS. One important difference lies in how they define employment, particularly in how they classify the production of goods for one's own use or consumption, such as subsistence farming. This was included as employment under the 13th ICLS, but not under the newer 19th ICLS, which\ndifferentiates\nbetween the concepts of “work for pay or profit” and other types of work (which can be unpaid or for one's own use).\nPeople not in the labor force are also described as not economically active, or inactive. The\ninactivity\nrate can be calculated as 100 minus the labor force participation rate.\nThese reference periods are specified in the 19th ICLS. Earlier standards, such as the 13th ICLS, required a reference period but were less prescriptive about its length. You can read more about the differences in standards in our\nsection below\n.\nFor annual data, these timing effects tend to cancel out in the annual average (for example, taking the mean of monthly rates), so annual series are typically less affected by seasonal volatility than monthly or quarterly series.\nNevertheless, it’s important to keep in mind that, because unemployment statistics are a snapshot that relies on short reference periods, headline unemployment figures by themselves do not distinguish short-term from long-term unemployment (commonly defined as lasting 12 months or more), nor do they show flows into and out of unemployment. To study duration or transitions, we need survey questions about spell length or panel data that follow the same individuals over time.\nEurostat publishes long-term unemployment data\nhere\n, and you can read more about it in these references:\nMachin, S. and Manning, A. (1999).\nThe causes and consequences of longterm unemployment in Europe\n. Handbook of labor economics, vol 3, pp.3085-3139.\nKrueger, A.B., Cramer, J. and Cho, D. (2014).\nAre the long-term unemployed on the margins of the labor market?\n. Brookings papers on economic activity, 2014(1), pp.229-299.\nDuell, N., Thurau, L. and Vetter, T. (2016).\nLong-term unemployment in the EU: Trends and policies\n. Gütersloh: Bertelsmann Stiftung.\nCountries differ in which job-search activities they count and in how questions are worded; those differences can affect reported unemployment. International standards and guidance from the ILO aim to harmonise definitions and survey practice to reduce these comparability issues, but some measurement differences remain.\nAnother important point to keep in mind when looking at employment rates is that this metric doesn’t capture people who are employed but working fewer hours than they would like (also known as time-related underemployment). The ILO refers to this and other situations, such as the potential labor force, as “labor underutilization”. You can read more about it on the\nILO website\n.\nEmployment is defined by the ILO according to international standards, which we explain in a\nsection below\n. Earlier standards (13th ICLS) also counted some unpaid production of goods for own use (such as subsistence farming) as employment. Newer standards (19th ICLS) separate this from employment for pay or profit.\nEconomists and statistical agencies often distinguish between\nwages\nand\nsalaries\n. Wages are payments linked to a specific unit of time or output, such as per hour or per task, while salaries are regular fixed payments, usually set by an employment contract and paid monthly or annually.\nIn everyday language, many own-account workers are described as “independent workers” or “independent contractors”. In the ILO framework, however, the category “own-account workers” refers specifically to self-employed people who do not hire others; national legal definitions of “independent contractor” may differ.\nIn estimates based on the 13th ICLS standards, production for own use (such as subsistence farming) was counted as employment. The 19th ICLS revised this definition: own-use production is no longer included, and only contributing family workers who produce goods for the market or exchange are considered.\nOne caveat is that employment categories are not perfectly comparable across countries. Some high-income countries classify people who run incorporated businesses as employees rather than self-employed.\nComparability issues can also arise from the different criteria used to define the size of the business, the conditions for when a business must be formally registered with the government, whether paid domestic workers are included, and whether someone with a secondary informal job (but a main formal job) is counted.\nInformality is often less visible in richer countries because stronger enforcement and social protections reduce open informal work, while remaining informal activity is concentrated in hidden forms — such as undeclared pay or misreporting — that are harder to capture in surveys.\nTo put together this chart, we combined the datasets of national country-reported survey estimates and ILO modeled estimates, and compared the values when both were available for any given country-year. We classified estimates as “agreeing” if the absolute difference was less than 0.1 percentage points. Notice that some countries have no published modeled estimates in some years — for example, Ukraine, Palestine, and Sudan in recent years. This happens when the ILO cannot retrieve national data and when it deems that the modeled results wouldn’t be reliable, such as in periods of conflicts or major disruptions.\nChina\nis a notable case: in recent years, national and modeled estimates often differ, or only modeled estimates are available. This reflects the fact that\nChina’s national figures\nare not available for all years and typically refer only to urban areas, whereas the ILO’s modeled estimates aim to produce a consistent national series that includes both urban and rural areas.\nThe ILO reports in this overview document that they estimate many different versions (specifications) of the models, and select the best performing ones via a “cross-validation method”: repeatedly training models on random parts of the data and selecting those that best predict missing values and produce more stable results. However, the public ILO documentation doesn’t list the exact variables used to model different indicators (only that they may vary), nor the precise rules for when modeled estimates are based on a country’s own history versus information from similar countries. For some indicators (such as labor force participation), the ILO notes that it groups countries by “broad economic similarity and geographic proximity” (p.5), but we could not find more details than that.\nYou can read more in:\nILO Modelled Estimates. Methodological overview. (2025). ILO Department of Statistics.\nAvailable at:\nhttps://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/\nRead more about other changes in this\nILO report\n.\nA description of the “microdata processing” steps is provided in this\nILO document\n.\nFor some countries, the OECD compiles and publishes these figures in its\nData Explorer\n.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this topic page, please also cite the underlying data sources. This topic page can be cited as:\nBertha Rohenkohl, Pablo Arriagada, and Esteban Ortiz-Ospina (2026) - “Work and Employment” Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/work-employment' [Online Resource]\nBibTeX citation\n@article{owid-work-employment,\nauthor = {Bertha Rohenkohl and Pablo Arriagada and Esteban Ortiz-Ospina},\ntitle = {Work and Employment},\njournal = {Our World in Data},\nyear = {2026},\nnote = {https://ourworldindata.org/work-employment}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "work-employment", "source_url": "https://ourworldindata.org/work-employment", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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Employment is defined in line with the 13th ICLS and includes the production of goods for own use, such as subsistence farming.", "chart_citation": "UN, World Population Prospects (2024); ILO Modelled Estimates, via World Bank (2026)", "original_chart_url": "https://ourworldindata.org/grapher/population-by-labor-force-status-bars", "owid_column_metadata": {"Number of people below working age": {"titleShort": "Below working age", "titleLong": "Below working age", "descriptionShort": "Population below working age (under 15 years old).", "descriptionKey": ["This data comes from UN World Population Prospects and it is a combination of population estimates and projections to match International Labour Organization data."], "shortUnit": "", "unit": "people", "timespan": "1991-2025", "type": "Integer", "owidVariableId": 1205631, "shortName": "number_below_working_age", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "UN, World Population Prospects (2024) – processed by Our World in Data. “Below working age” [dataset]. United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205631.metadata.json"}, "Number of employed people": {"titleShort": "Employed", "titleLong": "Employed", "descriptionShort": "Working-age population (ages 15 and over) who are employed.", "descriptionKey": ["This data comes from the ILO Modelled Estimates series. The International Labour Organization (ILO) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).", "This data follows the standards of the 13th International Classification of Labour Statisticians (ICLS). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the 19th ICLS, data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat)."], "descriptionProcessing": "We calculated the number of employed people as the employment-to-population ratio (coming from ILO Modelled Estimates via World Bank) multiplied by the population over age 15 (coming from UN World Population Prospects).", "shortUnit": "", "unit": "people", "timespan": "1991-2025", "type": "Integer", "owidVariableId": 1205633, "shortName": "number_employed", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "ILO Modelled Estimates, via World Bank (2026); UN, World Population Prospects (2024) – with major processing by Our World in Data", "citationLong": "ILO Modelled Estimates, via World Bank (2026); UN, World Population Prospects (2024) – with major processing by Our World in Data. “Employed” [dataset]. ILO Modelled Estimates, via World Bank, “World Development Indicators 125”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205633.metadata.json"}, "Number of unemployed people": {"titleShort": "Unemployed", "titleLong": "Unemployed", "descriptionShort": "Working-age population (ages 15 and over) who are unemployed.", "descriptionKey": ["This data comes from the ILO Modelled Estimates series. The International Labour Organization (ILO) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).", "This data follows the standards of the 13th International Classification of Labour Statisticians (ICLS). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the 19th ICLS, data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat)."], "descriptionProcessing": "We calculated the number of unemployed people as the unemployment rate (coming from ILO Modelled Estimates via World Bank) multiplied by the labor force. The labor force was calculated as the labor force participation rate (coming from ILO Modelled Estimates via World Bank) multiplied by the population over age 15 (coming from UN World Population Prospects).", "shortUnit": "", "unit": "people", "timespan": "1991-2025", "type": "Integer", "owidVariableId": 1205634, "shortName": "number_unemployed", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "ILO Modelled Estimates, via World Bank (2026); UN, World Population Prospects (2024) – with major processing by Our World in Data", "citationLong": "ILO Modelled Estimates, via World Bank (2026); UN, World Population Prospects (2024) – with major processing by Our World in Data. “Unemployed” [dataset]. ILO Modelled Estimates, via World Bank, “World Development Indicators 125”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205634.metadata.json"}, "Number of people out of the labor force": {"titleShort": "Outside the labor force", "titleLong": "Outside the labor force", "descriptionShort": "Working-age population (ages 15 and over) who are not economically active (not employed and not unemployed).", "descriptionKey": ["This data comes from the ILO Modelled Estimates series. The International Labour Organization (ILO) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).", "This data follows the standards of the 13th International Classification of Labour Statisticians (ICLS). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the 19th ICLS, data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat)."], "descriptionProcessing": "We calculated the number of people out of the labor force as the total population (coming from UN World Population Prospects) minus the labor force and the population below working age (also from UN World Population Prospects). The labor force was calculated as the labor force participation rate (coming from ILO Modelled Estimates via World Bank) multiplied by the population over age 15 (coming from UN World Population Prospects).", "shortUnit": "", "unit": "people", "timespan": "1991-2025", "type": "Integer", "owidVariableId": 1205635, "shortName": "number_out_of_labor_force", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "UN, World Population Prospects (2024); ILO Modelled Estimates, via World Bank (2026) – with major processing by Our World in Data", "citationLong": "UN, World Population Prospects (2024); ILO Modelled Estimates, via World Bank (2026) – with major processing by Our World in Data. “Outside the labor force” [dataset]. United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update”; ILO Modelled Estimates, via World Bank, “World Development Indicators 125” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205635.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Labor force participation rate", "source_url": "https://ourworldindata.org/grapher/labor-participation-rate.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Labor force participation rate"], "row_count_total": 7186, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Labor force participation rate": "47.186"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Labor force participation rate": "47.14"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Labor force participation rate": "47.087"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Labor force participation rate": "47.024"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Labor force participation rate": "46.95"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Labor force participation rate": "46.876"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Labor force participation rate": "46.803"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Labor force participation rate": "46.732"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Labor force participation rate": "46.667"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Labor force participation rate": "46.609"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Labor force participation rate": "46.562"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Labor force participation rate": "46.526"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Labor force participation rate": "46.505"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Labor force participation rate": "46.497"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Labor force participation rate": "46.506"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Labor force participation rate": "46.532"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Labor force participation rate": "46.571"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Labor force participation rate": "46.622"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Labor force participation rate": "46.682"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Labor force participation rate": "46.746"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Labor force participation rate": "46.815"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Labor force participation rate": "46.884"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Labor force participation rate": "46.956"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Labor force participation rate": "47.026"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Labor force participation rate": "47.096"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Labor force participation rate": "47.165"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Labor force participation rate": "47.235"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Labor force participation rate": "47.305"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Labor force participation rate": "45.566"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Labor force participation rate": "43.814"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Labor force participation rate": "41.579"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Labor force participation rate": "40.684"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Labor force participation rate": "37.64"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Labor force participation rate": "37.588"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2024", "Labor force participation rate": "37.547"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2025", "Labor force participation rate": "37.457"}, {"Entity": "Albania", "Code": "ALB", "Year": "1990", "Labor force participation rate": "63.69"}, {"Entity": "Albania", "Code": "ALB", "Year": "1991", "Labor force participation rate": "68.312"}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "Labor force participation rate": "69.36"}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "Labor force participation rate": "67.876"}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "Labor force participation rate": "66.602"}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "Labor force participation rate": "64.732"}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "Labor force participation rate": "63.466"}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "Labor force participation rate": "64.955"}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "Labor force participation rate": "63.712"}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "Labor force participation rate": "62.061"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Labor force participation rate": "61.163"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Labor force participation rate": "60.132"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Labor force participation rate": "59.61"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Labor force participation rate": "58.557"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Labor force participation rate": "57.496"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Labor force participation rate": "56.428"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Labor force participation rate": "55.354"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Labor force participation rate": "54.275"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Labor force participation rate": "53.192"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Labor force participation rate": "54.993"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Labor force participation rate": "55.202"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Labor force participation rate": "59.938"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Labor force participation rate": "56.995"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Labor force participation rate": "52.418"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Labor force participation rate": "53.42"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Labor force participation rate": "55.497"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Labor force participation rate": "57.314"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Labor force participation rate": "58.057"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Labor force participation rate": "59.296"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Labor force participation rate": "60.306"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Labor force participation rate": "59.367"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Labor force participation rate": "59.684"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "Labor force participation rate": "62.161"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Labor force participation rate": "63.882"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "Labor force participation rate": "64.006"}, {"Entity": "Albania", "Code": "ALB", "Year": "2025", "Labor force participation rate": "64.263"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1990", "Labor force participation rate": "45.624"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1991", "Labor force participation rate": "45.824"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1992", "Labor force participation rate": "45.799"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1993", "Labor force participation rate": "45.788"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1994", "Labor force participation rate": "45.761"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1995", "Labor force participation rate": "45.601"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1996", "Labor force participation rate": "45.446"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1997", "Labor force participation rate": "45.059"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1998", "Labor force participation rate": "44.719"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1999", "Labor force participation rate": "44.492"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Labor force participation rate": "44.275"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Labor force participation rate": "44.076"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Labor force participation rate": "43.864"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Labor force participation rate": "43.661"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Labor force participation rate": "43.498"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Labor force participation rate": "43.348"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Labor force participation rate": "43.247"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Labor force participation rate": "43.177"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Labor force participation rate": "43.149"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Labor force participation rate": "43.164"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Labor force participation rate": "43.507"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Labor force participation rate": "43.321"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Labor force participation rate": "43.11"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Labor force participation rate": "42.874"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Labor force participation rate": "42.603"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Labor force participation rate": "42.459"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Labor force participation rate": "42.333"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Labor force participation rate": "42.325"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Labor force participation rate": "42.281"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Labor force participation rate": "42.203"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Labor force participation rate": "38.042"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Labor force participation rate": "38.425"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2022", "Labor force participation rate": "41.795"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "Labor force participation rate": "41.507"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2024", "Labor force participation rate": "41.161"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2025", "Labor force participation rate": "40.784"}, {"Entity": "Angola", "Code": "AGO", "Year": "1990", "Labor force participation rate": "77.269"}, {"Entity": "Angola", "Code": "AGO", "Year": "1991", "Labor force participation rate": "77.307"}, {"Entity": "Angola", "Code": "AGO", "Year": "1992", "Labor force participation rate": "77.342"}, {"Entity": "Angola", "Code": "AGO", "Year": "1993", "Labor force participation rate": "77.374"}, {"Entity": "Angola", "Code": "AGO", "Year": "1994", "Labor force participation rate": "77.401"}, {"Entity": "Angola", "Code": "AGO", "Year": "1995", "Labor force participation rate": "77.415"}, {"Entity": "Angola", "Code": "AGO", "Year": "1996", "Labor force participation rate": "77.415"}, {"Entity": "Angola", "Code": "AGO", "Year": "1997", "Labor force participation rate": "77.408"}, {"Entity": "Angola", "Code": "AGO", "Year": "1998", "Labor force participation rate": "77.394"}, {"Entity": "Angola", "Code": "AGO", "Year": "1999", "Labor force participation rate": "77.38"}, {"Entity": "Angola", "Code": "AGO", "Year": "2000", "Labor force participation rate": "77.362"}, {"Entity": "Angola", "Code": "AGO", "Year": "2001", "Labor force participation rate": "77.342"}], "rows_tail": [{"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Labor force participation rate": "61.77"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Labor force participation rate": "61.640343"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Labor force participation rate": "61.484386"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Labor force participation rate": "61.298046"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Labor force participation rate": "61.163765"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Labor force participation rate": "61.029152"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Labor force participation rate": "59.644558"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Labor force participation rate": "60.38425"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Labor force participation rate": "60.736332"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Labor force participation rate": "61.19854"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Labor force participation rate": "61.13241"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2025", "Labor force participation rate": "60.97926"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1990", "Labor force participation rate": "44.229"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1991", "Labor force participation rate": "44.189"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1992", "Labor force participation rate": "44.142"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1993", "Labor force participation rate": "44.14"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1994", "Labor force participation rate": "44.118"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1995", "Labor force participation rate": "44.479"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1996", "Labor force participation rate": "44.855"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1997", "Labor force participation rate": "45.226"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1998", "Labor force participation rate": "45.595"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1999", "Labor force participation rate": "45.991"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Labor force participation rate": "45.198"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Labor force participation rate": "44.442"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Labor force participation rate": "43.724"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Labor force participation rate": "43.048"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Labor force participation rate": "42.423"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Labor force participation rate": "41.865"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Labor force participation rate": "41.361"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Labor force participation rate": "40.894"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Labor force participation rate": "40.467"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Labor force participation rate": "40.077"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Labor force participation rate": "39.719"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Labor force participation rate": "38.924"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Labor force participation rate": "38.213"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Labor force participation rate": "37.564"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Labor force participation rate": "36.972"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Labor force participation rate": "32.068"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Labor force participation rate": "32.282"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Labor force participation rate": "32.47"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Labor force participation rate": "32.612"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Labor force participation rate": "32.729"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Labor force participation rate": "32.487"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Labor force participation rate": "32.308"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2022", "Labor force participation rate": "33.025"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2023", "Labor force participation rate": "33.106"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2024", "Labor force participation rate": "33.185"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2025", "Labor force participation rate": "33.103"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Labor force participation rate": "59.654"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Labor force participation rate": "59.631"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Labor force participation rate": "59.613"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Labor force participation rate": "59.601"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Labor force participation rate": "59.599"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Labor force participation rate": "59.605"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Labor force participation rate": "59.613"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Labor force participation rate": "59.609"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Labor force participation rate": "59.598"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Labor force participation rate": "59.582"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Labor force participation rate": "59.552"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Labor force participation rate": "59.498"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Labor force participation rate": "59.441"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Labor force participation rate": "59.391"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Labor force participation rate": "59.345"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Labor force participation rate": "59.301"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Labor force participation rate": "59.256"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Labor force participation rate": "59.21"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Labor force participation rate": "59.161"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Labor force participation rate": "59.111"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Labor force participation rate": "59.062"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Labor force participation rate": "59.018"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Labor force participation rate": "58.972"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Labor force participation rate": "58.914"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Labor force participation rate": "58.843"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Labor force participation rate": "58.763"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Labor force participation rate": "58.674"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Labor force participation rate": "58.577"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Labor force participation rate": "58.475"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Labor force participation rate": "59.544"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Labor force participation rate": "60.872"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Labor force participation rate": "60.099"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Labor force participation rate": "59.532"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Labor force participation rate": "61.961"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Labor force participation rate": "61.902"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2025", "Labor force participation rate": "61.933"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Labor force participation rate": "66.989"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Labor force participation rate": "66.849"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Labor force participation rate": "66.896"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Labor force participation rate": "66.968"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Labor force participation rate": "66.854"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Labor force participation rate": "66.674"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Labor force participation rate": "66.431"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Labor force participation rate": "66.175"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Labor force participation rate": "65.939"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Labor force participation rate": "65.746"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Labor force participation rate": "65.614"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Labor force participation rate": "65.524"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Labor force participation rate": "65.493"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Labor force participation rate": "65.519"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Labor force participation rate": "65.568"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Labor force participation rate": "65.624"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Labor force participation rate": "65.658"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Labor force participation rate": "65.687"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Labor force participation rate": "65.746"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Labor force participation rate": "65.83"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Labor force participation rate": "65.925"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Labor force participation rate": "66.013"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Labor force participation rate": "66.092"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Labor force participation rate": "66.148"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Labor force participation rate": "66.158"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Labor force participation rate": "66.13"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Labor force participation rate": "66.07"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Labor force participation rate": "65.989"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Labor force participation rate": "65.894"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Labor force participation rate": "65.795"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Labor force participation rate": "64.665"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Labor force participation rate": "65.397"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Labor force participation rate": "65.214"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Labor force participation rate": "67.786"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Labor force participation rate": "67.725"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2025", "Labor force participation rate": "67.718"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "labor-participation-rate", "metadata_url": "https://ourworldindata.org/grapher/labor-participation-rate.metadata.json", "chart_title": "Labor force participation rate", "chart_subtitle": "Share of the working-age population (ages 15 and over) who are economically active (employed or unemployed).", "chart_note": "All figures correspond to modeled ILO estimates. Employment is defined in line with the 13th ICLS and includes the production of goods for own use, such as subsistence farming.", "chart_citation": "ILO Modelled Estimates, via World Bank (2026)", "original_chart_url": "https://ourworldindata.org/grapher/labor-participation-rate", "owid_column_metadata": {"Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate)": {"titleShort": "Labor force participation rate", "titleLong": "Labor force participation rate", "descriptionShort": "Share of the working-age population (ages 15 and over) who are economically active (employed or unemployed).", "descriptionKey": ["The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.", "People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.", "When defining the labor force, the definition of “working age” varies across countries, depending on national laws and practices. In the ILO modeled estimates shown here, this is harmonized to refer to people aged 15 and older.", "This data comes from the ILO Modelled Estimates series. The International Labour Organization (ILO) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).", "This data follows the standards of the 13th International Classification of Labour Statisticians (ICLS). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the 19th ICLS, data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat)."], "shortUnit": "%", "unit": "%", "timespan": "1990-2025", "type": "Numeric", "owidVariableId": 1205327, "shortName": "sl_tlf_cact_zs", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "ILO Modelled Estimates, via World Bank (2026) – processed by Our World in Data", "citationLong": "ILO Modelled Estimates, via World Bank (2026) – processed by Our World in Data. “Labor force participation rate – ILO” [dataset]. ILO Modelled Estimates, via World Bank, “World Development Indicators 125” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205327.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Labor force participation rate", "source_url": "https://ourworldindata.org/grapher/labor-force-participation-rate.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Labor force participation rate"], "row_count_total": 7186, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Labor force participation rate": "47.186"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Labor force participation rate": "47.14"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Labor force participation rate": "47.087"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Labor force participation rate": "47.024"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Labor force participation rate": "46.95"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Labor force participation rate": "46.876"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Labor force participation rate": "46.803"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Labor force participation rate": "46.732"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Labor force participation rate": "46.667"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Labor force participation rate": "46.609"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Labor force participation rate": "46.562"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Labor force participation rate": "46.526"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Labor force participation rate": "46.505"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Labor force participation rate": "46.497"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Labor force participation rate": "46.506"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Labor force participation rate": "46.532"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Labor force participation rate": "46.571"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Labor force participation rate": "46.622"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Labor force participation rate": "46.682"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Labor force participation rate": "46.746"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Labor force participation rate": "46.815"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Labor force participation rate": "46.884"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Labor force participation rate": "46.956"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Labor force participation rate": "47.026"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Labor force participation rate": "47.096"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Labor force participation rate": "47.165"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Labor force participation rate": "47.235"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Labor force participation rate": "47.305"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Labor force participation rate": "45.566"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Labor force participation rate": "43.814"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Labor force participation rate": "41.579"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Labor force participation rate": "40.684"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Labor force participation rate": "37.64"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Labor force participation rate": "37.588"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2024", "Labor force participation rate": "37.547"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2025", "Labor force 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"62.061"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Labor force participation rate": "61.163"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Labor force participation rate": "60.132"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Labor force participation rate": "59.61"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Labor force participation rate": "58.557"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Labor force participation rate": "57.496"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Labor force participation rate": "56.428"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Labor force participation rate": "55.354"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Labor force participation rate": "54.275"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Labor force participation rate": "53.192"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Labor force participation rate": "54.993"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Labor force participation rate": "55.202"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Labor force participation rate": "59.938"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Labor force participation rate": "56.995"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Labor force participation rate": "52.418"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Labor force participation rate": "53.42"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Labor force participation rate": "55.497"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Labor force participation rate": "57.314"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Labor force participation rate": "58.057"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Labor force participation rate": "59.296"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Labor force participation rate": "60.306"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Labor force participation rate": "59.367"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Labor force participation rate": "59.684"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "Labor force participation rate": "62.161"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Labor force participation rate": "63.882"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "Labor force participation rate": "64.006"}, {"Entity": "Albania", "Code": "ALB", "Year": "2025", "Labor force participation rate": "64.263"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1990", "Labor force participation rate": "45.624"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1991", "Labor force participation rate": "45.824"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1992", "Labor force participation rate": "45.799"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1993", "Labor force participation rate": "45.788"}, {"Entity": "Algeria", "Code": "DZA", "Year": 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"42.603"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Labor force participation rate": "42.459"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Labor force participation rate": "42.333"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Labor force participation rate": "42.325"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Labor force participation rate": "42.281"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Labor force participation rate": "42.203"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Labor force participation rate": "38.042"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Labor force participation rate": "38.425"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2022", "Labor force participation rate": "41.795"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "Labor force participation rate": "41.507"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2024", "Labor force participation rate": "41.161"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2025", "Labor force participation rate": "40.784"}, {"Entity": "Angola", "Code": "AGO", "Year": "1990", "Labor force participation rate": "77.269"}, {"Entity": "Angola", "Code": "AGO", "Year": "1991", "Labor force participation rate": "77.307"}, {"Entity": "Angola", "Code": "AGO", "Year": "1992", "Labor force participation rate": "77.342"}, {"Entity": "Angola", "Code": "AGO", "Year": "1993", "Labor force participation rate": "77.374"}, {"Entity": "Angola", "Code": "AGO", "Year": "1994", "Labor force participation rate": "77.401"}, {"Entity": "Angola", "Code": "AGO", "Year": "1995", "Labor force participation rate": "77.415"}, {"Entity": "Angola", "Code": "AGO", "Year": "1996", "Labor force participation rate": "77.415"}, {"Entity": "Angola", "Code": "AGO", "Year": "1997", "Labor force participation rate": "77.408"}, {"Entity": "Angola", "Code": "AGO", "Year": "1998", "Labor force participation rate": "77.394"}, {"Entity": "Angola", "Code": "AGO", "Year": "1999", "Labor force participation rate": "77.38"}, {"Entity": "Angola", "Code": "AGO", "Year": "2000", "Labor force participation rate": "77.362"}, {"Entity": "Angola", "Code": "AGO", "Year": "2001", "Labor force participation rate": "77.342"}], "rows_tail": [{"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Labor force participation rate": "61.77"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Labor force participation rate": "61.640343"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Labor force participation rate": "61.484386"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Labor force participation rate": "61.298046"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Labor force participation rate": "61.163765"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Labor force participation rate": "61.029152"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Labor force participation rate": "59.644558"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Labor force participation rate": "60.38425"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Labor force participation rate": "60.736332"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Labor force participation rate": "61.19854"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Labor force participation rate": "61.13241"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2025", "Labor force participation rate": "60.97926"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1990", "Labor force participation rate": "44.229"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1991", "Labor force participation rate": "44.189"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1992", "Labor force participation rate": "44.142"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1993", "Labor force participation rate": "44.14"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1994", "Labor force participation rate": "44.118"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1995", "Labor force participation rate": "44.479"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1996", "Labor force participation rate": "44.855"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1997", "Labor force participation rate": "45.226"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1998", "Labor force participation rate": "45.595"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1999", "Labor force participation rate": "45.991"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Labor force participation rate": "45.198"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Labor force participation rate": "44.442"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Labor force participation rate": "43.724"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Labor force participation rate": "43.048"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Labor force participation rate": "42.423"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Labor force participation rate": "41.865"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Labor force participation rate": "41.361"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Labor force participation rate": "40.894"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Labor force participation rate": "40.467"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Labor force participation rate": "40.077"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Labor force participation rate": "39.719"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Labor force participation rate": "38.924"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Labor force participation rate": "38.213"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Labor force participation rate": "37.564"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Labor force participation rate": "36.972"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Labor force participation rate": "32.068"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Labor force participation rate": "32.282"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Labor force participation rate": "32.47"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Labor force participation rate": "32.612"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Labor force participation rate": "32.729"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Labor force participation rate": "32.487"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Labor force participation rate": "32.308"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2022", "Labor force participation rate": "33.025"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2023", "Labor force participation rate": "33.106"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2024", "Labor force participation rate": "33.185"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2025", "Labor force participation rate": "33.103"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Labor force participation rate": "59.654"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Labor force participation rate": "59.631"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Labor force participation rate": "59.613"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Labor force participation rate": "59.601"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Labor force participation rate": "59.599"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Labor force participation rate": "59.605"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Labor force participation rate": "59.613"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Labor force participation rate": "59.609"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Labor force participation rate": "59.598"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Labor force participation rate": "59.582"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Labor force participation rate": "59.552"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Labor force participation rate": "59.498"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Labor force participation rate": "59.441"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Labor force participation rate": "59.391"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Labor force participation rate": "59.345"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Labor force participation rate": "59.301"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Labor force participation rate": "59.256"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Labor force participation rate": "59.21"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Labor force participation rate": "59.161"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Labor force participation rate": "59.111"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Labor force participation rate": "59.062"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Labor force participation rate": "59.018"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Labor force participation rate": "58.972"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Labor force participation rate": "58.914"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Labor force participation rate": "58.843"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Labor force participation rate": "58.763"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Labor force participation rate": "58.674"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Labor force participation rate": "58.577"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Labor force participation rate": "58.475"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Labor force participation rate": "59.544"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Labor force participation rate": "60.872"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Labor force participation rate": "60.099"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Labor force participation rate": "59.532"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Labor force participation rate": "61.961"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Labor force participation rate": "61.902"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2025", "Labor force participation rate": "61.933"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Labor force participation rate": "66.989"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Labor force participation rate": "66.849"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Labor force participation rate": "66.896"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Labor force participation rate": "66.968"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Labor force participation rate": "66.854"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Labor force participation rate": "66.674"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Labor force participation rate": "66.431"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Labor force participation rate": "66.175"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Labor force participation rate": "65.939"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Labor force participation rate": "65.746"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Labor force participation rate": "65.614"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Labor force participation rate": "65.524"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Labor force participation rate": "65.493"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Labor force participation rate": "65.519"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Labor force participation rate": "65.568"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Labor force participation rate": "65.624"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Labor force participation rate": "65.658"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Labor force participation rate": "65.687"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Labor force participation rate": "65.746"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Labor force participation rate": "65.83"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Labor force participation rate": "65.925"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Labor force participation rate": "66.013"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Labor force participation rate": "66.092"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Labor force participation rate": "66.148"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Labor force participation rate": "66.158"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Labor force participation rate": "66.13"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Labor force participation rate": "66.07"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Labor force participation rate": "65.989"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Labor force participation rate": "65.894"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Labor force participation rate": "65.795"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Labor force participation rate": "64.665"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Labor force participation rate": "65.397"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Labor force participation rate": "65.214"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Labor force participation rate": "67.786"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Labor force participation rate": "67.725"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2025", "Labor force participation rate": "67.718"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "labor-force-participation-rate", "metadata_url": "https://ourworldindata.org/grapher/labor-force-participation-rate.metadata.json", "chart_title": "Labor force participation rate", "chart_subtitle": "Share of the working-age population (ages 15 and over) who are economically active (employed or unemployed).", "chart_note": "All figures correspond to modeled ILO estimates. Employment is defined in line with the 13th ICLS and includes the production of goods for own use, such as subsistence farming.", "chart_citation": "ILO Modelled Estimates, via World Bank (2026)", "original_chart_url": "https://ourworldindata.org/grapher/labor-force-participation-rate", "owid_column_metadata": {"Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate)": {"titleShort": "Labor force participation rate", "titleLong": "Labor force participation rate", "descriptionShort": "Share of the working-age population (ages 15 and over) who are economically active (employed or unemployed).", "descriptionKey": ["The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.", "People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.", "When defining the labor force, the definition of “working age” varies across countries, depending on national laws and practices. In the ILO modeled estimates shown here, this is harmonized to refer to people aged 15 and older.", "This data comes from the ILO Modelled Estimates series. The International Labour Organization (ILO) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).", "This data follows the standards of the 13th International Classification of Labour Statisticians (ICLS). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the 19th ICLS, data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat)."], "shortUnit": "%", "unit": "%", "timespan": "1990-2025", "type": "Numeric", "owidVariableId": 1205327, "shortName": "sl_tlf_cact_zs", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "ILO Modelled Estimates, via World Bank (2026) – processed by Our World in Data", "citationLong": "ILO Modelled Estimates, via World Bank (2026) – processed by Our World in Data. “Labor force participation rate – ILO” [dataset]. ILO Modelled Estimates, via World Bank, “World Development Indicators 125” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205327.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "b4cea1edbed86acb337f"},
{"raw_link": "https://ourworldindata.org/religion", "title": "Religion", "context": "Religion\nBy\nHannah Ritchie\n,\nLucas Rodés-Guirao\n,\nPablo Arriagada\n,\nand\nEsteban Ortiz-Ospina\nPublished in February 2026.\nContents\nMost people in the world are religious. Religion — the beliefs, institutions, and practices that societies hold — play an important role in how billions of people live and think. For many, it plays a crucial role in their overall well-being and connection to others. It affects how cultures form and interact, how social attitudes evolve, and the policies that countries put in place or remove. Throughout the history of religion, it has played a crucial role in the global dynamics of conflict and cooperation, and it continues to do so today.\nReligiosity, just like urbanization or migration, is an important demographic indicator that helps us to understand the world and how it’s changing.\nThere are also prominent debates about whether religion is in decline or whether, in particular demographics, there is a resurgence.\n1\nThese debates are often tied to political arguments and can therefore shape societal narratives and outcomes. Data can help us interrogate some of these patterns.\nOn this page, we explore patterns of religious affiliation, participation, and belief. We look at data on changes in religiosity — and the potential decline of religion — across countries and over time.\nReligious affiliation: Which religions do people say they belong to?\nOne way to understand how religious a society is, and what religions people identify with, is to simply ask people directly.\nAt the bottom of this article, we discuss some of the limitations of relying on self-reported survey data.\nGlobally, most people say they belong to a religion, with Christianity and Islam accounting for the largest shares, but patterns vary widely across countries and regions.\nThat’s what the data on religious affiliation in this section captures. It relies on survey and census responses. Someone is described as being religious or non-religious based on their self-identification. If they report in a survey that they are Christian, Muslim, or Hindu, this is what is recorded, regardless of their actual practices or specific beliefs.\nIn this section, we look at this same affiliation data from several angles: how many people identify with a religion, which religions they identify with, and how these patterns vary across countries and regions.\nMost of this data comes from the Pew Research Center. Its large global analyses are based on more than 2,700 sources of data, including national censuses, large-scale demographic surveys, general population surveys, and population registers.\n2\nHow many people say they are part of a religion?\nIn 2020, three-quarters of people globally said they were religious.\n3\nThat means they were self-affiliated with at least one of the world’s religions.\nBut there are huge variations in religiosity across the world. In the map below, you can see the share of people religiously affiliated by country.\nAcross Africa, the Middle East, and South America, the vast majority of people are religious. Rates tend to be well over 90%.\nAcross Asia, Europe, and North America, rates are more mixed. In some Asian countries, such as India, Pakistan, and Bangladesh, almost everyone says they follow a religion. But you can see that in China, rates are much lower. There, only around 10% do.\nIn Europe, you also see large differences. Eastern Europe tends to be more religious. But even within Western and Southern Europe, there’s a lot of variation. More than 80% of people in Portugal, Italy, Denmark, and Ireland identify with a religion, compared to less than half in the Netherlands.\nIn the bar chart below, you can compare the rates of specific countries. Again, the large differences are clear, even for countries in a similar region.\nThe largest religious groups in the world\nGlobally, Christians are the largest group based on self-identified religious identity. You can see this in the chart.\nAround 2.3 billion people identified as Christian, which was only slightly higher than the number that identified as Muslim.\nThose who are not affiliated with any religion were the third largest group, and were more than all other religions — except Christianity and Islam — combined. Two-thirds of the 1.9 billion who are religiously unaffiliated are from China.\n4\nBeyond the global totals, religious affiliation is also unevenly distributed across the world's regions.\nMost people who identify as Hindu or Buddhist\nlive in Asia\n. People who report having no religious affiliation are\nalso concentrated there\n, reflecting the fact that this group is the largest in countries such as China, Japan, and South Korea.\nPeople who identify as Muslim are more widely distributed: while\nmost live in Asia\n, large shares also live in Sub-Saharan Africa, North Africa, and the Middle East.\nPeople who identify as Christian are the\nmost geographically dispersed\n, with substantial shares living in Asia, Sub-Saharan Africa, Europe, and the Americas.\nFinally, people who identify as Jewish make up a much smaller share of the global population and are concentrated mainly in North America and the Middle East.\nReligious affiliation across countries\nThe most common religious affiliation in each country\nReligious affiliation differs widely across countries, both in how diverse religious identities are and in which groups are most common.\nBefore digging into detailed breakdowns, we can look at the most common religion in each country. That is, the one that has the most self-identified followers. This is mapped below.\nAcross the Americas, Europe, and Southern Africa, Christianity is the dominant religion. The few exceptions are mostly countries where the largest group is those who don’t follow any religion at all.\nAcross Northern Africa and the Middle East, Islam is the most common. And across Asia, there is much more variation. In India, Hinduism is the most common. For its neighbors, it’s Islam or Buddhism. In East Asia — including China, Japan, and South Korea — the non-religious are the largest group.\nThe composition of religious affiliation in each country\nWhat does the religious composition of countries look like\nbeyond\nthe most common affiliation?\nYou can see the full breakdown in the chart below.\nIn some countries, there is substantial diversity. In the UK, for example, no religious group accounts for more than half of the population: just under 50% are Christians; 40% follow no religion, and the remaining 10% is a mix of many others.\nIn a country like Egypt, there is much less diversity: 95% of the population identifies as Muslim, and the remaining 5% as Christian. In China, approximately 90% of the population has no religious affiliation.\nReligious participation: How much does religion matter in people’s lives?\nMost people in the world say they are religious, but the significance of religion varies a lot from person to person. Someone might believe in particular religious principles, but not actively participate in rituals or engage much with a religious community (and vice versa). For others, religion is a huge part of their lives and is a defining factor in key decisions.\nSo it’s useful to look not just at how many people say they are affiliated with a religion, but how important it is to them, and how often they participate in rituals and services.\nHow important is religion in people’s lives?\nThe following chart shows people’s survey responses to the question of how important religion is in their lives. This data comes from the\nIntegrated Values Surveys\n.\n5\nIn Indonesia, Egypt, Nigeria, and Ethiopia, almost everyone says that religion is “very important”. At the other end of the spectrum — in countries like Japan and China, where most people are\nnot\nreligious — most say that it’s “not very” or “not at all” important.\nThe majority of Americans — around 60% of them — say it’s “very” or “rather” important. That’s the opposite of countries like the United Kingdom and Germany, where around two-thirds say that it’s not important.\nWe might expect religion to be seen as more important in countries where more people identify as religious. This is broadly true, but there is some variation: in some places, many people report a religious affiliation while relatively few say religion plays a very important role in their lives.\nIn the scatterplot, you can see these two metrics plotted against each other.\n6\nThere is a very clear and strong relationship, but with interesting regional patterns. Many European countries have high rates of affiliation, but very few people consider it to be very important to them. Take Denmark as an example. More than 80% of Danes say that they are religious, but only 5% say it’s “very important”.\nIn Europe, there are only a few countries where more than 50% of people say it’s very important. By contrast, there is no African country in this dataset where this figure is lower than 85%.\nHow often do people pray or attend religious services?\nThe frequency of religious rituals and practices also provides some indication of the role that religion plays in people’s lives.\nThe chart below shows the percentage of people who report praying at least several times per week, excluding events such as weddings and funerals.\n7\nAcross many countries in Europe, one-quarter to one-third of the population does so. Across much of Africa, the Middle East, and South-East Asia, the share can be three times higher.\nThe chart below shows the share who attend religious services at least once a month.\nAcross the world, people are more likely to pray than to attend religious services. 62% of Americans pray at least once a month, but only 39% attend a religious service. In Pakistan, 90% of the population prays, but only 61% attend services frequently.\nReligious attitudes: What do people think and feel about religion?\nReligiosity can also be measured based on people’s overall attitudes towards religion, both in terms of their feelings about their own religion’s institutions, but also how they feel about people who follow different religions.\nThese attitudes can matter a lot for social cohesion, trust, and even conflict within a given society or country. In this section, we look at survey data on these attitudes to understand levels of trust within religious communities and people’s comfort with religious diversity.\nTrust in religious institutions\nMany religions are organized around institutions — for example, churches, mosques, or temple communities — which function as organizational centers that host worship, provide religious guidance, and foster communal ties.\nThe chart below shows how much trust people place in these religious institutions. It is based on surveys asking whether respondents trust them “completely” or “somewhat”. In the survey wording, the institutions are labeled as “churches”, but it is intended to capture trust in religious institutions broadly, including mosques, temples, and similar organizations.\nChina and Japan stand out as having low levels of trust in these institutions, but both are countries where people who are non-religious are the largest group. So that’s not particularly surprising: trust correlates quite strongly with levels of religiosity.\nTrust is extremely high across South Asia, the Middle East, and a small number of countries in Africa for which we have data from this survey. In these countries, trust in “the churches” tends to be far higher than trust\nin other organizations\n, such as the police, companies, educational institutions, and the media.\nTolerance and trust in people who follow other religions\nAs countries become more demographically diverse, building and maintaining trust among people from\ndifferent\nreligions can be important for a country’s stability and social cohesion.\nThe chart below shows the share of people who say that they “trust completely” or “trust somewhat” people of another religion.\nTrust is particularly high in Nordic countries, the United Kingdom, Canada, and the United States. There, three-quarters of people or more say that they trust others.\nAt the other end of the scale, rates can be less than one-in-five in countries like Japan and China.\nYou\ncan see here\nhow trust in people of another religion compares to people’s trust in other groups. Trust in people of another religion tends to correlate strongly with overall trust in others within a society. That means this indicator may largely reflect aggregate trust levels within a country, rather than cross-religion trust specifically.\nAnother way to understand people’s tolerance or trust is to ask whether they would or would not want particular groups as neighbors. In the chart below, you can see the share of people who said they would\nnot\nwant neighbors who followed a different religion from them.\nIn many countries, the share was extremely small. In the United Kingdom, Germany, the United States, Australia, Brazil, and Kenya, fewer than 5% said they would be unhappy.\nIn others, the responses were very different. Across most of Asia, well over one-quarter, and sometimes more than half of people said they would not want neighbors who follow a different religion. These are often countries with a long history of conflict or discrimination between different religious groups.\nChanges in religiosity over time: How are religious identities and practices shifting?\nSo far, we’ve mostly examined a snapshot of religious affiliation, beliefs, and practices across the world today. But how have these patterns changed over time, and how might we expect them to change in the future? Are societies becoming more or less religious?\nIn this section, we’ll look at changes in religious affiliation over the past decade and how we might expect religious identity to change in the future.\nRecent changes in religious affiliation\nMany countries lack high-quality long-term data on religious identity. For now, the only globally comparable time series comes from Pew’s estimates for 2010 and 2020, which we use here to examine recent changes.\nWhere did religious affiliation decline or increase over that decade?\nThe chart below shows the\npercentage point change\nin the share of people affiliated with any religion over the 10-year period. Note that this is different from the percentage change. If 70% of a country’s population identified as religious in 2010, and this dropped to 60% in 2020, then the value would be negative 10 percentage points.\nThe countries in red all saw a drop in religiosity. Australia experienced the largest decline, with a 17-point drop. Chile and Uruguay were not far behind. The United States also saw a substantial decline of 13 points.\nMany countries — particularly those in Northern Africa, the Middle East, and South-East Asia — saw little change. Almost everyone identified as being religious in 2010, and this was still true in 2020. A small number of countries across Sub-Saharan Africa saw an increase of up to 5 percentage points.\nOverall, countries across the Americas, Europe, East Asia, and Oceania have seen lower levels of religious affiliation over time. The rest of the world has not.\nReligious identity and development\nOne relationship we see when we plot religiosity metrics against development or income indicators is that more people identify as non-religious in more developed countries.\nThe scatterplot below shows the share of people who are\nnot\nreligious in each country, measured against gross domestic product (GDP) per capita.\nAs you can see, rates of non-religiosity tend to be higher in countries with higher incomes.\nMany studies have documented this relationship between income and the decline of religion.\n8\nBut the specific reasons or causal drivers of this are still debated.\n9\nIn a follow-up article, we’ll look at some of the research on these drivers and dynamics in more detail.\nWhat is clear is that this relationship is not universal. In the chart, you can see that countries where Islam is the most popular religion — such as those in the Middle East — tend to maintain very high levels of religious identity, despite having high income levels.\nFuture changes in religious affiliation\nIt’s interesting to consider how religiosity and the composition of global religions will change in the future. This will largely reflect broader demographic trends across the world.\nOne clear pattern from the data is that countries with almost universal levels of religiosity today tend to have higher fertility rates — and faster population growth — than those with lower levels of religious affiliation. Countries with extremely low levels of religious identification often have very low fertility rates, often well below the “replacement” level. In other words, their populations are shrinking.\nThis relationship is shown in the chart below. Countries where religious affiliation is low — such as China, Hong Kong, Japan, and South Korea — tend to cluster at low fertility rates, often well below two children per woman. On the right-hand side, you can see that there are religious countries with low fertility, but there are no countries where the non-religious are the majority and fertility is high.\nThis matters for how global patterns of religiosity change over time. Even if religious affiliation declines in some countries, population growth is still high in places where religion remains widespread. Over time, this demographic dynamic can offset — or even outweigh — declines elsewhere.\nFor this reason, many projections (including those from\nthe Pew Research Center\n) suggest non-religious groups\nwill actually shrink\nas a share of the global population in the coming decades, and religion will remain widespread globally.\n10\nThat’s not because populations are becoming more religious everywhere, but because population growth is fastest in the most religious parts of the world.\nExplore the data\nGlobal data on religious affiliation from Pew Research Center\nThis page features a range of data on religious affiliation: the share of people who are religious, what religion they belong to, and how these patterns have been changing over time.\nThis is based on self-identification: what people\nsay\nabout their religious affiliation.\nIn this chart, you can explore this data in more granular detail for each country.\nHere are a few insights from these key indicators:\nLevels of religiosity\ncan vary from\nas high as almost 100% in some countries (such as India) and as low as 10% in others (such as China). You can add and remove other countries to compare.\nThe share of people who are religious\nhas declined substantially\nin many countries between 2010 and 2020, including the United States, Canada, Australia, and much of Europe.\nThe geographical distribution of particular religions varies a lot. Christianity\nis very widespread\n, with high population shares across Europe, North and South America, and much of Africa. Islam is more concentrated, but still\nhas high shares\nacross multiple regions, including North Africa, the Middle East, and Southeast Asia. Religions such as Hinduism and Buddhism are very geographically concentrated;\nHinduism in South Asia\nand\nBuddhism in South-East and East Asia\n.\nRates and absolute numbers can give a very different perspective. While\njust 15% of people\nin India identify as Muslim, compared to 97% in its neighbor, Pakistan, it has\nalmost the same number\nof Muslims in total.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nData quality and biases\nMuch of the data used in this topic page is based on self-reports. For religious affiliation, people are asked whether they follow a particular religion and what that religion is. Even data on service attendance or prayer is based on how often someone reports doing so.\nIn some sense, relying on self-reported data is unavoidable. People’s beliefs are often internal and private; only they can say what they believe or follow. Self-reported survey data also allows researchers to explore religiosity across various dimensions among large populations: affiliation, importance, specific behaviors, and tolerance for other religions.\nBut it also has its limitations and biases.\n11\nIt’s worth keeping these in mind when using this data to understand differences across countries and changes over time.\nIn countries or communities where religiosity is particularly salient and participation is socially desirable, people may overstate expected behaviors or beliefs. That creates a systematic bias toward expected norms.\nPeople may interpret religious affiliation in different ways. When asked whether they follow a particular religion, some people may respond with their family's or parents’ religion, even if their own beliefs have changed over time. Being “religious” or “non religious” will also differ based on cultural context. As\nnoted elsewhere\n, many people in East Asia said they were religiously unaffiliated, but\nmany still hold\nsome cultural and religious beliefs or carry out specific practices.\n12\nHow questions are framed, the interview structure, and the questions asked beforehand can also make a difference.\n13\nAsking someone, “What is your religion?” may be more likely to force a positive response, than starting with the question “Do you follow a religion?”.\nPew Research\nnotes that\nAmericans reported higher levels of religious participation when the interview was conducted over the phone than when it was completed independently online. If people are asked political questions first, for example, respondents may be more primed to give a politically aligned answer.\nThese are just some examples of the biases that can affect survey responses. While this survey data allows us to understand global patterns in religious identity and behaviors — and can give some insights into changes over time — it may be inadequate to assess smaller differences between countries, especially with large cultural and political differences.\nFeatured Data on\nReligion\nRead more of our work on society & community\nTrust\nTrust is essential for effective cooperation. How does trust vary between different societies and locations and what matters for levels of trust?\nLoneliness and Social Connections\nIn this topic page, we explore data on loneliness and social connections and review available evidence on the link between social connections and well-being.\nMarriages and Divorces\nHow is the institution of marriage changing? What percentage of marriages end in divorce? Explore global data on marriages and divorces.\nEndnotes\nFor example, there is a debate about a resurgence in religious affiliation\namong young men\nin America.\nConrad Hackett et al. (2025). How the Global Religious Landscape Changed From 2010 to 2020. Pew Research Center.\nHackett, Conrad, Marcin Stonawski, Yunping Tong, Stephanie Kramer, Anne Fengyan Shi and Dalia Fahmy. 2025. “How the Global Religious Landscape Changed From 2010 to 2020.” Pew Research Center. doi:\n10.58094/fj71-ny11\n.\nIn 2020, around 1.3 billion people in China were unaffiliated with any religion. That’s 68% of the 1.9 billion global total.\nEVS (2021): EVS Trend File 1981-2017. GESIS Data Archive, Cologne. ZA7503 Data file Version 3.0.0, doi:10.4232/1.14021.\nHaerpfer, C., Inglehart, R., Moreno, A., Welzel, C., Kizilova, K., Diez-Medrano J., M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2022. World Values Survey Trend File (1981-2022) Cross-National Data-Set. Madrid, Spain & Vienna, Austria: JD Systems Institute & WVSA Secretariat. Data File Version 2.0.0, doi:10.14281/18241.27.\nNote that we also investigated this relationship when we looked at people who say religion is only “important” rather than “very important”. It holds true there, too: the share saying that religion is “important” was far higher in countries where the share of people who are religiously affiliated was high.\nThe list of possible responses to the question “Apart from weddings and funerals, about how often do you pray?” was: “several times a day”, “once a day”, “several times a week”, “only when attending religious services”, “only on special holy days”, “once a year”, “less often”, “never, practically never”, “don’t know” or “no answer”. Missing data have been excluded from these response totals.\nThe share who answered “don’t know” or “no answer” combined was less than 1% in most countries, but reached 7% in a handful of outliers.\nPasquale, F. L., & Kosmin, B. A. (2013). Atheism and the secularization thesis.\nScheitle, C. P., & Corcoran, K. E. (2023). Predictors of Adopting an Atheistic Worldview: An Analysis of Survey Data Containing a Retrospective Measure of Belief in God. Socius.\nStrulik, H. (2016). Secularization and Long‐Run Economic Growth. Economic Inquiry.\nRuck, D. J., Bentley, R. A., & Lawson, D. J. (2018). Religious change preceded economic change in the 20th century. Science advances.\nPaldam, M., & Gundlach, E. (2013). The religious transition. A long-run perspective. Public Choice, 156(1), 105-123.\nInglehart, R., & Baker, W. E. (2000). Modernization, cultural change, and the persistence of traditional values. American sociological review.\nNote that these Pew projections are from 2015, and are now slightly outdated (although the main takeaway likely holds true). We’re currently waiting for their updated estimates.\nThis paper by Conrad Hackett provides a good overview.\nHackett, C. (2014). Seven things to consider when measuring religious identity. Religion, 44(3), 396-413.\nZhang, C., Brenner, P. S., & He, L. (2022). Measuring religious non-affiliation in China: a comparison of major national surveys in China. International Journal of Public Opinion Research.\nBrenner, P. S., LaPlante, J., & Reed, T. L. (2024). Sources of Inconsistency in the Measurement of Religious Affiliation: Evidence from a Survey Experiment and Cognitive Interviews. Sociology of Religion.\nBaker, J. O., Hill, J. P., & Porter, N. (2017). Assessing measures of religion and secularity with crowdsourced data from Amazon’s Mechanical Turk. Faithful Measures.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this topic page, please also cite the underlying data sources. This topic page can be cited as:\nHannah Ritchie, Lucas Rodés-Guirao, Pablo Arriagada, and Esteban Ortiz-Ospina (2026) - “Religion” Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/religion' [Online Resource]\nBibTeX citation\n@article{owid-religion,\nauthor = {Hannah Ritchie and Lucas Rodés-Guirao and Pablo Arriagada and Esteban Ortiz-Ospina},\ntitle = {Religion},\njournal = {Our World in Data},\nyear = {2026},\nnote = {https://ourworldindata.org/religion}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "religion", "source_url": "https://ourworldindata.org/religion", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "What share of the world is religious, and how is this changing? Explore global data and research on trends in religiosity.", "numeric_mentions": ["2026", "1", "2,700", "2", "2020,", "3", "90%", "10%", "80%", "2.3 billion", "1.9 billion", "4", "50%", "40%", "95%", "5%", "5", "60%", "6", "85%", "7", "62%", "39%", "61%", "2010", "10", "70%", "2010,", "10 percentage points", "17", "13", "2020", "5 percentage points", "8", "9", "100%", "15%", "97%", "11", "12", "2025", "10.58094", "1.3 billion", "68%", "2021", "1981", "2017", "3.0", "0,", "10.4232", "1.14021", "2022", "2.0", "10.14281", "18241.27", "1%", "7%", "2013", "2023", "2016", "2018", "20", "156", "105", "123", "2000", "2015,", "2014", "44", "396", "413", "2024"], "numeric_evidence": [{"title": "religious-composition", "source_url": "https://ourworldindata.org/grapher/religious-composition.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Share of the population who are religious"], "row_count_total": 428, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Share of the population who are religious": "99.99135"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Share of the population who are religious": "99.99155"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Share of the population who are religious": "97.50711"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Share of the population who are religious": "97.88416"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Share of the population who are religious": "90.46257"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Share of the population who are religious": "92.347115"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Share of the population who are religious": "98.915276"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Share of the population who are religious": "98.733795"}, {"Entity": "Angola", "Code": "AGO", "Year": "2010", "Share of the population who are religious": "95.52315"}, {"Entity": "Angola", "Code": "AGO", "Year": "2020", "Share of the population who are religious": "93.88519"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2010", "Share of the population who are religious": "90.27249"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2020", "Share of the population who are religious": "90.76709"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2010", "Share of the population who are religious": "98.79385"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2020", "Share of the population who are religious": "98.82212"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2010", "Share of the population who are religious": "94.38775"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2020", "Share of the population who are religious": "94.126015"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2010", "Share of the population who are religious": "68.00084"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2020", "Share of the population who are religious": "68.42117"}, {"Entity": "Asia-Pacific (Pew)", "Code": "PEW_APA", "Year": "2010", "Share of the population who are religious": "66.980705"}, {"Entity": "Asia-Pacific (Pew)", "Code": "PEW_APA", "Year": "2020", "Share of the population who are religious": "67.15467"}, {"Entity": "Australia", "Code": "AUS", "Year": "2010", "Share of the population who are religious": "75.14132"}, {"Entity": "Australia", "Code": "AUS", "Year": "2020", "Share of the population who are religious": "57.65598"}, {"Entity": "Austria", "Code": "AUT", "Year": "2010", "Share of the population who are religious": "86.49473"}, {"Entity": "Austria", "Code": "AUT", "Year": "2020", "Share of the population who are religious": "77.60261"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2010", "Share of the population who are religious": "98.9316"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2020", "Share of the population who are religious": "95.24005"}, {"Entity": "Bahamas", "Code": "BHS", "Year": "2010", "Share of the population who are religious": "98.084206"}, {"Entity": "Bahamas", "Code": "BHS", "Year": "2020", "Share of the population who are religious": "98.14312"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2010", "Share of the population who are religious": "99.63177"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2020", "Share of the population who are religious": "99.679924"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2010", "Share of the population who are religious": "100"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2020", "Share of the population who are religious": "100"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2010", "Share of the population who are religious": "79.19351"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2020", "Share of the population who are religious": "79.861046"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2010", "Share of the population who are religious": "95.01992"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2020", "Share of the population who are religious": "86.22826"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2010", "Share of the population who are religious": "67.98737"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2020", "Share of the population who are religious": "61.016556"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2010", "Share of the population who are religious": "83.993416"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2020", "Share of the population who are religious": "84.3704"}, {"Entity": "Benin", "Code": "BEN", "Year": "2010", "Share of the population who are religious": "95.600876"}, {"Entity": "Benin", "Code": "BEN", "Year": "2020", "Share of the population who are religious": "95.11409"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2010", "Share of the population who are religious": "99.971535"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2020", "Share of the population who are religious": "99.95632"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2010", "Share of the population who are religious": "96.0117"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2020", "Share of the population who are religious": "90.45234"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2010", "Share of the population who are religious": "98.893036"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2020", "Share of the population who are religious": "98.95426"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2010", "Share of the population who are religious": "85.33732"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2020", "Share of the population who are religious": "85.27779"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2010", "Share of the population who are religious": "91.88428"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2020", "Share of the population who are religious": "86.52672"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2010", "Share of the population who are religious": "99.84345"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2020", "Share of the population who are religious": "99.843796"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2010", "Share of the population who are religious": "91.59275"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2020", "Share of the population who are religious": "89.96784"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2010", "Share of the population who are religious": "99.3757"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2020", "Share of the population who are religious": "99.4242"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2010", "Share of the population who are religious": "98.14089"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2020", "Share of the population who are religious": "98.74705"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2010", "Share of the population who are religious": "99.90671"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2020", "Share of the population who are religious": "99.92096"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2010", "Share of the population who are religious": "96.92445"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2020", "Share of the population who are religious": "97.920135"}, {"Entity": "Canada", "Code": "CAN", "Year": "2010", "Share of the population who are religious": "76.14522"}, {"Entity": "Canada", "Code": "CAN", "Year": "2020", "Share of the population who are religious": "65.37057"}, {"Entity": "Cape Verde", "Code": "CPV", "Year": "2010", "Share of the population who are religious": "86.40841"}, {"Entity": "Cape Verde", "Code": "CPV", "Year": "2020", "Share of the population who are religious": "79.3838"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2010", "Share of the population who are religious": "99.00177"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2020", "Share of the population who are religious": "99.54755"}, {"Entity": "Chad", "Code": "TCD", "Year": "2010", "Share of the population who are religious": "97.310486"}, {"Entity": "Chad", "Code": "TCD", "Year": "2020", "Share of the population who are religious": "96.86534"}, {"Entity": "Channel Islands", "Code": "OWID_CIS", "Year": "2010", "Share of the population who are religious": "85.94011"}, {"Entity": "Channel Islands", "Code": "OWID_CIS", "Year": "2020", "Share of the population who are religious": "85.783844"}, {"Entity": "Chile", "Code": "CHL", "Year": "2010", "Share of the population who are religious": "86.41838"}, {"Entity": "Chile", "Code": "CHL", "Year": "2020", "Share of the population who are religious": "69.73393"}, {"Entity": "China", "Code": "CHN", "Year": "2010", "Share of the population who are religious": "12.577994"}, {"Entity": "China", "Code": "CHN", "Year": "2020", "Share of the population who are religious": "10.376869"}, {"Entity": "Colombia", "Code": "COL", "Year": "2010", "Share of the population who are religious": "92.755844"}, {"Entity": "Colombia", "Code": "COL", "Year": "2020", "Share of the population who are religious": "90.0564"}, {"Entity": "Comoros", "Code": "COM", "Year": "2010", "Share of the population who are religious": "99.86997"}, {"Entity": "Comoros", "Code": "COM", "Year": "2020", "Share of the population who are religious": "99.87"}, {"Entity": "Congo", "Code": "COG", "Year": "2010", "Share of the population who are religious": "94.36776"}, {"Entity": "Congo", "Code": "COG", "Year": "2020", "Share of the population who are religious": "94.80081"}, {"Entity": "Costa Rica", "Code": "CRI", "Year": "2010", "Share of the population who are religious": "92.45545"}, {"Entity": "Costa Rica", "Code": "CRI", "Year": "2020", "Share of the population who are religious": "89.901405"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2010", "Share of the population who are religious": "89.991684"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2020", "Share of the population who are religious": "94.435646"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2010", "Share of the population who are religious": "95.32783"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2020", "Share of the population who are religious": "93.34217"}, {"Entity": "Cuba", "Code": "CUB", "Year": "2010", "Share of the population who are religious": "76.46236"}, {"Entity": "Cuba", "Code": "CUB", "Year": "2020", "Share of the population who are religious": "78.39216"}, {"Entity": "Curacao", "Code": "CUW", "Year": "2010", "Share of the population who are religious": "94.1174"}, {"Entity": "Curacao", "Code": "CUW", "Year": "2020", "Share of the population who are religious": "94.07378"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2010", "Share of the population who are religious": "93.51679"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2020", "Share of the population who are religious": "93.42991"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2010", "Share of the population who are religious": "31.46232"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2020", "Share of the population who are religious": "27.186573"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2010", "Share of the population who are religious": "98.57817"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2020", "Share of the population who are religious": "98.82445"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2010", "Share of the population who are religious": "85.50286"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2020", "Share of the population who are religious": "83.36909"}, {"Entity": "Djibouti", "Code": "DJI", "Year": "2010", "Share of the population who are religious": "98.88505"}, {"Entity": "Djibouti", "Code": "DJI", "Year": "2020", "Share of the population who are religious": "98.89869"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2010", "Share of the population who are religious": "85.05826"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2020", "Share of the population who are religious": "80.39891"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2010", "Share of the population who are religious": "99.82361"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2020", "Share of the population who are religious": "99.93614"}, {"Entity": "Ecuador", "Code": "ECU", "Year": "2010", "Share of the population who are religious": "95.73057"}, {"Entity": "Ecuador", "Code": "ECU", "Year": "2020", "Share of the population who are religious": "91.608604"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2010", "Share of the population who are religious": "99.995415"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2020", "Share of the population who are religious": "99.995346"}, {"Entity": "El Salvador", "Code": "SLV", "Year": "2010", "Share of the population who are religious": "88.37601"}, {"Entity": "El Salvador", "Code": "SLV", "Year": "2020", "Share of the population who are religious": "88.118004"}, {"Entity": "Equatorial Guinea", "Code": "GNQ", "Year": "2010", "Share of the population who are religious": "95.012695"}, {"Entity": "Equatorial Guinea", "Code": "GNQ", "Year": "2020", "Share of the population who are religious": "95.012695"}, {"Entity": "Eritrea", "Code": "ERI", "Year": "2010", "Share of the population who are religious": "98.96114"}, {"Entity": "Eritrea", "Code": "ERI", "Year": "2020", "Share of the population who are religious": "98.93885"}, {"Entity": "Estonia", "Code": "EST", "Year": "2010", "Share of the population who are religious": "68.493004"}, {"Entity": "Estonia", "Code": "EST", "Year": "2020", "Share of the population who are religious": "56.39712"}], "rows_tail": [{"Entity": "Poland", "Code": "POL", "Year": "2010", "Share of the population who are religious": "97.349045"}, {"Entity": "Poland", "Code": "POL", "Year": "2020", "Share of the population who are religious": "91.354416"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2010", "Share of the population who are religious": "92.7721"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2020", "Share of the population who are religious": "86.21906"}, {"Entity": "Puerto Rico", "Code": "PRI", "Year": "2010", "Share of the population who are religious": "89.681725"}, {"Entity": "Puerto Rico", "Code": "PRI", "Year": "2020", "Share of the population who are religious": "89.62008"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2010", "Share of the population who are religious": "99.69968"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2020", "Share of the population who are religious": "99.72638"}, {"Entity": "Reunion", "Code": "REU", "Year": "2010", "Share of the population who are religious": "97.939476"}, {"Entity": "Reunion", "Code": "REU", "Year": "2020", "Share of the population who are religious": "97.912895"}, {"Entity": "Romania", "Code": "ROU", "Year": "2010", "Share of the population who are religious": "99.79016"}, {"Entity": "Romania", "Code": "ROU", "Year": "2020", "Share of the population who are religious": "99.0604"}, {"Entity": "Russia", "Code": "RUS", "Year": "2010", "Share of the population who are religious": "86.96957"}, {"Entity": "Russia", "Code": "RUS", "Year": "2020", "Share of the population who are religious": "79.80176"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2010", "Share of the population who are religious": "99.10367"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2020", "Share of the population who are religious": "98.992065"}, {"Entity": "Saint Lucia", "Code": "LCA", "Year": "2010", "Share of the population who are religious": "93.9735"}, {"Entity": "Saint Lucia", "Code": "LCA", "Year": "2020", "Share of the population who are religious": "94.098724"}, {"Entity": "Saint Vincent and the Grenadines", "Code": "VCT", "Year": "2010", "Share of the population who are religious": "92.12687"}, {"Entity": "Saint Vincent and the Grenadines", "Code": "VCT", "Year": "2020", "Share of the population who are religious": "92.13609"}, {"Entity": "Samoa", "Code": "WSM", "Year": "2010", "Share of the population who are religious": "99.85518"}, {"Entity": "Samoa", "Code": "WSM", "Year": "2020", "Share of the population who are religious": "99.93578"}, {"Entity": "Sao Tome and Principe", "Code": "STP", "Year": "2010", "Share of the population who are religious": "87.54924"}, {"Entity": "Sao Tome and Principe", "Code": "STP", "Year": "2020", "Share of the population who are religious": "90.30676"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2010", "Share of the population who are religious": "99.914116"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2020", "Share of the population who are religious": "99.8879"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2010", "Share of the population who are religious": "99.99642"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2020", "Share of the population who are religious": "99.979294"}, {"Entity": "Serbia", "Code": "SRB", "Year": "2010", "Share of the population who are religious": "98.77815"}, {"Entity": "Serbia", "Code": "SRB", "Year": "2020", "Share of the population who are religious": "95.99113"}, {"Entity": "Seychelles", "Code": "SYC", "Year": "2010", "Share of the population who are religious": "97.75706"}, {"Entity": "Seychelles", "Code": "SYC", "Year": "2020", "Share of the population who are religious": "97.63955"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2010", "Share of the population who are religious": "99.903625"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2020", "Share of the population who are religious": "99.98796"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2010", "Share of the population who are religious": "83.3984"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2020", "Share of the population who are religious": "80.10392"}, {"Entity": "Slovakia", "Code": "SVK", "Year": "2010", "Share of the population who are religious": "75.30627"}, {"Entity": "Slovakia", "Code": "SVK", "Year": "2020", "Share of the population who are religious": "74.67033"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2010", "Share of the population who are religious": "73.556"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2020", "Share of the population who are religious": "67.73261"}, {"Entity": "Solomon Islands", "Code": "SLB", "Year": "2010", "Share of the population who are religious": "99.707535"}, {"Entity": "Solomon Islands", "Code": "SLB", "Year": "2020", "Share of the population who are religious": "99.672485"}, {"Entity": "Somalia", "Code": "SOM", "Year": "2010", "Share of the population who are religious": "99.979034"}, {"Entity": "Somalia", "Code": "SOM", "Year": "2020", "Share of the population who are religious": "99.98388"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2010", "Share of the population who are religious": "94.1929"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2020", "Share of the population who are religious": "96.877884"}, {"Entity": "South America", "Code": "OWID_SAM", "Year": "2010", "Share of the population who are religious": "92.08667"}, {"Entity": "South America", "Code": "OWID_SAM", "Year": "2020", "Share of the population who are religious": "87.7113"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2010", "Share of the population who are religious": "58.66052"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2020", "Share of the population who are religious": "51.73638"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2010", "Share of the population who are religious": "99.55081"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2020", "Share of the population who are religious": "99.5597"}, {"Entity": "Spain", "Code": "ESP", "Year": "2010", "Share of the population who are religious": "81.030624"}, {"Entity": "Spain", "Code": "ESP", "Year": "2020", "Share of the population who are religious": "73.64026"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2010", "Share of the population who are religious": "99.9821"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2020", "Share of the population who are religious": "99.93012"}, {"Entity": "Sub-Saharan Africa (Pew)", "Code": "PEW_SSA", "Year": "2010", "Share of the population who are religious": "96.912766"}, {"Entity": "Sub-Saharan Africa (Pew)", "Code": "PEW_SSA", "Year": "2020", "Share of the population who are religious": "97.40853"}, {"Entity": "Sudan", "Code": "SDN", "Year": "2010", "Share of the population who are religious": "99.87445"}, {"Entity": "Sudan", "Code": "SDN", "Year": "2020", "Share of the population who are religious": "99.43678"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2010", "Share of the population who are religious": "92.6273"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2020", "Share of the population who are religious": "91.890724"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2010", "Share of the population who are religious": "78.529175"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2020", "Share of the population who are religious": "71.09362"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2010", "Share of the population who are religious": "79.915665"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2020", "Share of the population who are religious": "69.234856"}, {"Entity": "Syria", "Code": "SYR", "Year": "2010", "Share of the population who are religious": "98.06593"}, {"Entity": "Syria", "Code": "SYR", "Year": "2020", "Share of the population who are religious": "98.01996"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2010", "Share of the population who are religious": "77.226135"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2020", "Share of the population who are religious": "76.908165"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2010", "Share of the population who are religious": "99.92545"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2020", "Share of the population who are religious": "99.92545"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2010", "Share of the population who are religious": "97.822075"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2020", "Share of the population who are religious": "96.52184"}, {"Entity": "Thailand", "Code": "THA", "Year": "2010", "Share of the population who are religious": "99.9289"}, {"Entity": "Thailand", "Code": "THA", "Year": "2020", "Share of the population who are religious": "99.987366"}, {"Entity": "Togo", "Code": "TGO", "Year": "2010", "Share of the population who are religious": "92.43137"}, {"Entity": "Togo", "Code": "TGO", "Year": "2020", "Share of the population who are religious": "91.98721"}, {"Entity": "Tonga", "Code": "TON", "Year": "2010", "Share of the population who are religious": "99.71973"}, {"Entity": "Tonga", "Code": "TON", "Year": "2020", "Share of the population who are religious": "99.418655"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2010", "Share of the population who are religious": "97.450226"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2020", "Share of the population who are religious": "97.55145"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2010", "Share of the population who are religious": "99.62952"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2020", "Share of the population who are religious": "99.55869"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2010", "Share of the population who are religious": "99.81739"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2020", "Share of the population who are religious": "97.471"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2010", "Share of the population who are religious": "99.4922"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2020", "Share of the population who are religious": "99.92811"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2010", "Share of the population who are religious": "99.984535"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2020", "Share of the population who are religious": "99.90512"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2010", "Share of the population who are religious": "86.76028"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2020", "Share of the population who are religious": "84.85543"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2010", "Share of the population who are religious": "99.69131"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2020", "Share of the population who are religious": "99.66416"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2010", "Share of the population who are religious": "71.21594"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2020", "Share of the population who are religious": "59.78184"}, {"Entity": "United States", "Code": "USA", "Year": "2010", "Share of the population who are religious": "83.5452"}, {"Entity": "United States", "Code": "USA", "Year": "2020", "Share of the population who are religious": "70.270004"}, {"Entity": "United States Virgin Islands", "Code": "VIR", "Year": "2010", "Share of the population who are religious": "96.276276"}, {"Entity": "United States Virgin Islands", "Code": "VIR", "Year": "2020", "Share of the population who are religious": "95.96832"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2010", "Share of the population who are religious": "63.73621"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2020", "Share of the population who are religious": "47.590195"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2010", "Share of the population who are religious": "99.39568"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2020", "Share of the population who are religious": "99.44801"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2010", "Share of the population who are religious": "98.91526"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2020", "Share of the population who are religious": "98.633545"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2010", "Share of the population who are religious": "91.568924"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2020", "Share of the population who are religious": "90.27225"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2010", "Share of the population who are religious": "37.855167"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "Share of the population who are religious": "32.32635"}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "2010", "Share of the population who are religious": "99.71657"}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "2020", "Share of the population who are religious": "99.74705"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Share of the population who are religious": "76.70783"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Share of the population who are religious": "75.83811"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Share of the population who are religious": "99.924385"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Share of the population who are religious": "99.938446"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Share of the population who are religious": "99.92061"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Share of the population who are religious": "99.94025"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Share of the population who are religious": "87.5171"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Share of the population who are religious": "89.46011"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "religious-composition", "metadata_url": "https://ourworldindata.org/grapher/religious-composition.metadata.json", "chart_title": null, "chart_subtitle": "Estimates are based on how people describe their own religious identity. This self-identification is taken regardless of their practices or beliefs. Estimates include people who are affiliated with any religion.", "chart_note": null, "chart_citation": "Pew Research Center (2025)", "original_chart_url": "https://ourworldindata.org/grapher/religious-composition", "owid_column_metadata": {"Share of the population -\n Any religion": {"titleShort": "Share of the population who are religious", "titleLong": "Share of the population who are religious", "descriptionShort": "Estimates of the percentage of people affiliated to any religion.", "descriptionKey": ["These estimates are [sourced from](https://www.pewresearch.org/religion/2025/06/09/global-religious-change-methodology/) more than 2,700 censuses and surveys.", "People are categorized based on how they describe their own religious identity. If someone identifies with a religious group, they're classified as being part of that group regardless of their practices or beliefs.", "The religiously unaffiliated population includes people who say they do not identify with any religion or that they are atheist or agnostic in surveys and censuses.", "Some people categorised as “non-religious” or “religiously unaffiliated” may engage in activities and hold beliefs that can be considered religious or spiritual, even though they don't describe themselves as belonging to any religion. This is particularly important for Chinese data, since “religiously unaffiliated” is by far the largest group. Pew [discusses this](https://www.pewresearch.org/religion/2023/08/30/measuring-religion-in-china/) in detail.", "While censuses often provide information on people of all ages, most surveys only report on the religious composition of adults. In such cases, researchers use indirect demographic methods to estimate this data for children. For example, Pew uses data on the age structure and fertility rates of women in different religious groups to estimate the proportion of each religious group in the child population. This assumes that children share their mother's religion.", "Pew's methodology has changed over time, as improved data sources have become available. That means its latest estimates for 2010 — shown in this dataset — may differ from its earlier publications. You can see these changes and the reasons for these revisions in [its updated methodology](https://www.pewresearch.org/religion/2025/06/09/why-we-revised-our-estimates-for-2010/)."], "shortUnit": "%", "unit": "percentage", "timespan": "2010-2020", "type": "Numeric", "owidVariableId": 1119756, "shortName": "share__religion_any_religion", "lastUpdated": "2025-10-31", "nextUpdate": "2026-10-31", "citationShort": "Pew Research Center (2025) – with minor processing by Our World in Data", "citationLong": "Pew Research Center (2025) – with minor processing by Our World in Data. “Share of the population who are religious” [dataset]. Pew Research Center, “Global Religious Composition Estimates for 2010 and 2020”; Various sources, “Population” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1119756.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Number of people by religion", "source_url": "https://ourworldindata.org/grapher/number-of-people-by-religion.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Buddhists", "Hindus", "Muslims", "Jews", "Christians", "No religion", "Other religions"], "row_count_total": 428, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "28210000", "Jews": "< 10000", "Christians": "30000", "No religion": "< 10000", "Other religions": "30000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "39020000", "Jews": "< 10000", "Christians": "< 10000", "No religion": "< 10000", "Other religions": "40000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Buddhists": "8", "Hindus": "134", "Muslims": "48085", "Jews": "9", "Christians": "53658", "No religion": "2712", "Other religions": "2615"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Buddhists": "10", "Hindus": "147", "Muslims": "61814", "Jews": "6", "Christians": "70314", "No religion": "3007", "Other religions": "2784"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "2050000", "Jews": "< 10000", "Christians": "590000", "No religion": "280000", "Other religions": "< 10000"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "2140000", "Jews": "< 10000", "Christians": "510000", "No religion": "220000", "Other religions": "< 10000"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "35670000", "Jews": "< 10000", "Christians": "110000", "No religion": "390000", "Other religions": "10000"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "43330000", "Jews": "< 10000", "Christians": "130000", "No religion": "560000", "Other religions": "20000"}, {"Entity": "Angola", "Code": "AGO", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "40000", "Jews": "< 10000", "Christians": "21330000", "No religion": "1040000", "Other religions": "880000"}, {"Entity": "Angola", "Code": "AGO", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "90000", "Jews": "< 10000", "Christians": "31120000", "No religion": "2050000", "Other religions": "190000"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2010", "Buddhists": "10000", "Hindus": "< 10000", "Muslims": "410000", "Jews": "180000", "Christians": "36360000", "No religion": "4020000", "Other religions": "300000"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2020", "Buddhists": "10000", "Hindus": "< 10000", "Muslims": "420000", "Jews": "170000", "Christians": "39970000", "No religion": "4170000", "Other religions": "440000"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "< 10000", "Jews": "< 10000", "Christians": "2860000", "No religion": "40000", "Other religions": "40000"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "< 10000", "Jews": "< 10000", "Christians": "2810000", "No religion": "30000", "Other religions": "40000"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "< 10000", "Jews": "< 10000", "Christians": "80000", "No religion": "< 10000", "Other religions": "10000"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "< 10000", "Jews": "< 10000", "Christians": "90000", "No religion": "< 10000", "Other religions": "10000"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2010", "Buddhists": "33567", "Hindus": "104488", "Muslims": "115052", "Jews": "583", "Christians": "23597", "No religion": "135635", "Other religions": "10948"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2020", "Buddhists": "31543", "Hindus": "116880", "Muslims": "135131", "Jews": "686", "Christians": "25190", "No religion": "148004", "Other religions": "11248"}, {"Entity": "Asia-Pacific (Pew)", "Code": "PEW_APA", "Year": "2010", "Buddhists": "336320000", "Hindus": "1043560000", "Muslims": "1022290000", "Jews": "180000", "Christians": "253420000", "No religion": "1363120000", "Other religions": "109360000"}, {"Entity": "Asia-Pacific (Pew)", "Code": "PEW_APA", "Year": "2020", "Buddhists": "316110000", "Hindus": "1166710000", "Muslims": "1187660000", "Jews": "190000", "Christians": "268840000", "No religion": "1492750000", "Other religions": "112540000"}, {"Entity": "Australia", "Code": "AUS", "Year": "2010", "Buddhists": "600000", "Hindus": "320000", "Muslims": "550000", "Jews": "110000", "Christians": "14860000", "No religion": "5500000", "Other religions": "190000"}, {"Entity": "Australia", "Code": "AUS", "Year": "2020", "Buddhists": "670000", "Hindus": "760000", "Muslims": "900000", "Jews": "110000", "Christians": "12040000", "No religion": "10900000", "Other religions": "360000"}, {"Entity": "Austria", "Code": "AUT", "Year": "2010", "Buddhists": "20000", "Hindus": "< 10000", "Muslims": "450000", "Jews": "10000", "Christians": "6730000", "No religion": "1130000", "Other religions": "20000"}, {"Entity": "Austria", "Code": "AUT", "Year": "2020", "Buddhists": "30000", "Hindus": "10000", "Muslims": "740000", "Jews": "< 10000", "Christians": "6080000", "No religion": "2000000", "Other religions": "60000"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "8880000", "Jews": "10000", "Christians": "150000", "No religion": "100000", "Other religions": "< 10000"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": 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{"Entity": "Ukraine", "Code": "UKR", "Year": "2020", "Buddhists": "150000", "Hindus": "< 10000", "Muslims": "250000", "Jews": "40000", "Christians": "37280000", "No religion": "6770000", "Other religions": "190000"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2010", "Buddhists": "10000", "Hindus": "750000", "Muslims": "5210000", "Jews": "< 10000", "Christians": "910000", "No religion": "20000", "Other religions": "30000"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2020", "Buddhists": "20000", "Hindus": "1110000", "Muslims": "6890000", "Jews": "< 10000", "Christians": "1350000", "No religion": "30000", "Other religions": "40000"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2010", "Buddhists": "280000", "Hindus": "930000", "Muslims": "3310000", "Jews": "280000", "Christians": "39300000", "No religion": "18140000", "Other religions": "760000"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2020", "Buddhists": "310000", "Hindus": "1140000", "Muslims": "4290000", "Jews": "300000", "Christians": "33250000", "No religion": "27090000", "Other religions": "980000"}, {"Entity": "United States", "Code": "USA", "Year": "2010", "Buddhists": "3580000", "Hindus": "1790000", "Muslims": "2770000", "Jews": "5710000", "Christians": "243500000", "No religion": "51180000", "Other religions": "2530000"}, {"Entity": "United States", "Code": "USA", "Year": "2020", "Buddhists": "4380000", "Hindus": "3040000", "Muslims": "4050000", "Jews": "5730000", "Christians": "217270000", "No religion": "100910000", "Other religions": "4050000"}, {"Entity": "United States Virgin Islands", "Code": "VIR", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "< 10000", "Jews": "< 10000", "Christians": "100000", "No religion": "< 10000", "Other religions": "< 10000"}, {"Entity": "United States Virgin Islands", "Code": "VIR", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "< 10000", "Jews": "< 10000", "Christians": "80000", "No religion": "< 10000", "Other religions": "< 10000"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "< 10000", "Jews": "20000", "Christians": "2020000", "No religion": "1200000", "Other religions": "70000"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "< 10000", "Jews": "20000", "Christians": "1510000", "No religion": "1780000", "Other religions": "90000"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2010", "Buddhists": "20000", "Hindus": "60000", "Muslims": "27180000", "Jews": "< 10000", "Christians": "950000", "No religion": "170000", "Other religions": "< 10000"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "32120000", "Jews": "< 10000", "Christians": "930000", "No religion": "190000", "Other religions": "340000"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "< 10000", "Jews": "< 10000", "Christians": "200000", "No religion": "< 10000", "Other religions": "40000"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "< 10000", "Jews": "< 10000", "Christians": "250000", "No religion": "< 10000", "Other religions": "50000"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "10000", "Jews": "10000", "Christians": "25750000", "No religion": "2430000", "Other religions": "600000"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "10000", "Jews": "< 10000", "Christians": "25060000", "No religion": "2770000", "Other religions": "590000"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2010", "Buddhists": "24130000", "Hindus": "60000", "Muslims": "80000", "Jews": "< 10000", "Christians": "7690000", "No religion": "54350000", "Other religions": "1160000"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "Buddhists": "22580000", "Hindus": "40000", "Muslims": "70000", "Jews": "< 10000", "Christians": "8170000", "No religion": "66370000", "Other religions": "850000"}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "410000", "Jews": "< 10000", "Christians": "< 10000", "No religion": "< 10000", "Other religions": "< 10000"}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "550000", "Jews": "< 10000", "Christians": "< 10000", "No religion": "< 10000", "Other religions": "< 10000"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Buddhists": "342750000", "Hindus": "1051540000", "Muslims": "1675800000", "Jews": "13910000", "Christians": "2147230000", "No religion": "1635250000", "Other religions": "154110000"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Buddhists": "324190000", "Hindus": "1177860000", "Muslims": "2022590000", "Jews": "14780000", "Christians": "2268860000", "No religion": "1905360000", "Other religions": "172170000"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "26700000", "Jews": "< 10000", "Christians": "30000", "No religion": "20000", "Other religions": "< 10000"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "36090000", "Jews": "< 10000", "Christians": "20000", "No religion": "20000", "Other religions": "< 10000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "70000", "Jews": "< 10000", "Christians": "13640000", "No religion": "10000", "Other religions": "240000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "100000", "Jews": "< 10000", "Christians": "18730000", "No religion": "10000", "Other religions": "220000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "70000", "Jews": "< 10000", "Christians": "11280000", "No religion": "1670000", "Other religions": "330000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Buddhists": "< 10000", "Hindus": "< 10000", "Muslims": "80000", "Jews": "< 10000", "Christians": "13540000", "No religion": "1640000", "Other religions": "260000"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "number-of-people-by-religion", "metadata_url": "https://ourworldindata.org/grapher/number-of-people-by-religion.metadata.json", "chart_title": "Number of people by religion", "chart_subtitle": "Estimates are based on how people describe their own religious identity. This self-identification is taken regardless of their practices or beliefs.", "chart_note": "\"Other religions\" include Baha'is, Daoists (also spelled Taoists), Jains, Shintoists, Sikhs, Wiccans, Zoroastrians, and many small groups, some of which can be described as folk or traditional religions.", "chart_citation": "Pew Research Center (2025)", "original_chart_url": "https://ourworldindata.org/grapher/number-of-people-by-religion", "owid_column_metadata": {"Number of people - Buddhists": {"titleShort": "Number of people\nwho are Buddhists", "titleLong": "Number of people\nwho are Buddhists", "descriptionShort": "Estimates of the number of people who are buddhists.", "descriptionKey": ["These estimates are [sourced from](https://www.pewresearch.org/religion/2025/06/09/global-religious-change-methodology/) more than 2,700 censuses and surveys.", "People are categorized based on how they describe their own religious identity. If someone identifies with a religious group, they're classified as being part of that group regardless of their practices or beliefs.", "While censuses often provide information on people of all ages, most surveys only report on the religious composition of adults. In such cases, researchers use indirect demographic methods to estimate this data for children. For example, Pew uses data on the age structure and fertility rates of women in different religious groups to estimate the proportion of each religious group in the child population. This assumes that children share their mother's religion.", "Pew's methodology has changed over time, as improved data sources have become available. That means its latest estimates for 2010 — shown in this dataset — may differ from its earlier publications. You can see these changes and the reasons for these revisions in [its updated methodology](https://www.pewresearch.org/religion/2025/06/09/why-we-revised-our-estimates-for-2010/)."], "unit": "people", "timespan": "2010-2020", "type": "NumberOrString", "owidVariableId": 1119765, "shortName": "count__religion_buddhists", "lastUpdated": "2025-10-31", "nextUpdate": "2026-10-31", "citationShort": "Pew Research Center (2025) – processed by Our World in Data", "citationLong": "Pew Research Center (2025) – processed by Our World in Data. “Number of people\nwho are Buddhists” [dataset]. Pew Research Center, “Global Religious Composition Estimates for 2010 and 2020” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1119765.metadata.json"}, "Number of people - Hindus": {"titleShort": "Number of people\nwho are Hindus", "titleLong": "Number of people\nwho are Hindus", "descriptionShort": "Estimates of the number of people who are hindus.", "descriptionKey": ["These estimates are [sourced from](https://www.pewresearch.org/religion/2025/06/09/global-religious-change-methodology/) more than 2,700 censuses and surveys.", "People are categorized based on how they describe their own religious identity. If someone identifies with a religious group, they're classified as being part of that group regardless of their practices or beliefs.", "While censuses often provide information on people of all ages, most surveys only report on the religious composition of adults. In such cases, researchers use indirect demographic methods to estimate this data for children. For example, Pew uses data on the age structure and fertility rates of women in different religious groups to estimate the proportion of each religious group in the child population. This assumes that children share their mother's religion.", "Pew's methodology has changed over time, as improved data sources have become available. That means its latest estimates for 2010 — shown in this dataset — may differ from its earlier publications. You can see these changes and the reasons for these revisions in [its updated methodology](https://www.pewresearch.org/religion/2025/06/09/why-we-revised-our-estimates-for-2010/)."], "unit": "people", "timespan": "2010-2020", "type": "NumberOrString", "owidVariableId": 1119767, "shortName": "count__religion_hindus", "lastUpdated": "2025-10-31", "nextUpdate": "2026-10-31", "citationShort": "Pew Research Center (2025) – processed by Our World in Data", "citationLong": "Pew Research Center (2025) – processed by Our World in Data. “Number of people\nwho are Hindus” [dataset]. Pew Research Center, “Global Religious Composition Estimates for 2010 and 2020” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1119767.metadata.json"}, "Number of people - Muslims": {"titleShort": "Number of people\nwho are Muslims", "titleLong": "Number of people\nwho are Muslims", "descriptionShort": "Estimates of the number of people who are muslims.", "descriptionKey": ["These estimates are [sourced from](https://www.pewresearch.org/religion/2025/06/09/global-religious-change-methodology/) more than 2,700 censuses and surveys.", "People are categorized based on how they describe their own religious identity. If someone identifies with a religious group, they're classified as being part of that group regardless of their practices or beliefs.", "While censuses often provide information on people of all ages, most surveys only report on the religious composition of adults. In such cases, researchers use indirect demographic methods to estimate this data for children. For example, Pew uses data on the age structure and fertility rates of women in different religious groups to estimate the proportion of each religious group in the child population. This assumes that children share their mother's religion.", "Pew's methodology has changed over time, as improved data sources have become available. That means its latest estimates for 2010 — shown in this dataset — may differ from its earlier publications. You can see these changes and the reasons for these revisions in [its updated methodology](https://www.pewresearch.org/religion/2025/06/09/why-we-revised-our-estimates-for-2010/)."], "unit": "people", "timespan": "2010-2020", "type": "NumberOrString", "owidVariableId": 1119769, "shortName": "count__religion_muslims", "lastUpdated": "2025-10-31", "nextUpdate": "2026-10-31", "citationShort": "Pew Research Center (2025) – processed by Our World in Data", "citationLong": "Pew Research Center (2025) – processed by Our World in Data. “Number of people\nwho are Muslims” [dataset]. Pew Research Center, “Global Religious Composition Estimates for 2010 and 2020” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1119769.metadata.json"}, "Number of people - Jews": {"titleShort": "Number of people\nwho are Jews", "titleLong": "Number of people\nwho are Jews", "descriptionShort": "Estimates of the number of people who are jews.", "descriptionKey": ["These estimates are [sourced from](https://www.pewresearch.org/religion/2025/06/09/global-religious-change-methodology/) more than 2,700 censuses and surveys.", "People are categorized based on how they describe their own religious identity. If someone identifies with a religious group, they're classified as being part of that group regardless of their practices or beliefs.", "While censuses often provide information on people of all ages, most surveys only report on the religious composition of adults. In such cases, researchers use indirect demographic methods to estimate this data for children. For example, Pew uses data on the age structure and fertility rates of women in different religious groups to estimate the proportion of each religious group in the child population. This assumes that children share their mother's religion.", "Pew's methodology has changed over time, as improved data sources have become available. That means its latest estimates for 2010 — shown in this dataset — may differ from its earlier publications. You can see these changes and the reasons for these revisions in [its updated methodology](https://www.pewresearch.org/religion/2025/06/09/why-we-revised-our-estimates-for-2010/)."], "unit": "people", "timespan": "2010-2020", "type": "NumberOrString", "owidVariableId": 1119768, "shortName": "count__religion_jews", "lastUpdated": "2025-10-31", "nextUpdate": "2026-10-31", "citationShort": "Pew Research Center (2025) – processed by Our World in Data", "citationLong": "Pew Research Center (2025) – processed by Our World in Data. “Number of people\nwho are Jews” [dataset]. Pew Research Center, “Global Religious Composition Estimates for 2010 and 2020” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1119768.metadata.json"}, "Number of people - Christians": {"titleShort": "Number of people\nwho are Christians", "titleLong": "Number of people\nwho are Christians", "descriptionShort": "Estimates of the number of people who are christians.", "descriptionKey": ["These estimates are [sourced from](https://www.pewresearch.org/religion/2025/06/09/global-religious-change-methodology/) more than 2,700 censuses and surveys.", "People are categorized based on how they describe their own religious identity. If someone identifies with a religious group, they're classified as being part of that group regardless of their practices or beliefs.", "While censuses often provide information on people of all ages, most surveys only report on the religious composition of adults. In such cases, researchers use indirect demographic methods to estimate this data for children. For example, Pew uses data on the age structure and fertility rates of women in different religious groups to estimate the proportion of each religious group in the child population. This assumes that children share their mother's religion.", "Pew's methodology has changed over time, as improved data sources have become available. That means its latest estimates for 2010 — shown in this dataset — may differ from its earlier publications. You can see these changes and the reasons for these revisions in [its updated methodology](https://www.pewresearch.org/religion/2025/06/09/why-we-revised-our-estimates-for-2010/)."], "unit": "people", "timespan": "2010-2020", "type": "NumberOrString", "owidVariableId": 1119766, "shortName": "count__religion_christians", "lastUpdated": "2025-10-31", "nextUpdate": "2026-10-31", "citationShort": "Pew Research Center (2025) – processed by Our World in Data", "citationLong": "Pew Research Center (2025) – processed by Our World in Data. “Number of people\nwho are Christians” [dataset]. Pew Research Center, “Global Religious Composition Estimates for 2010 and 2020” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1119766.metadata.json"}, "Number of people - No religion": {"titleShort": "Number of people\nwho are not religious", "titleLong": "Number of people\nwho are not religious", "descriptionShort": "Estimates of the number of people who are religiously unaffiliated.", "descriptionKey": ["These estimates are [sourced from](https://www.pewresearch.org/religion/2025/06/09/global-religious-change-methodology/) more than 2,700 censuses and surveys.", "People are categorized based on how they describe their own religious identity. If someone identifies with a religious group, they're classified as being part of that group regardless of their practices or beliefs.", "The religiously unaffiliated population includes people who say they do not identify with any religion or that they are atheist or agnostic in surveys and censuses.", "Some people categorised as “non-religious” or “religiously unaffiliated” may engage in activities and hold beliefs that can be considered religious or spiritual, even though they don't describe themselves as belonging to any religion. This is particularly important for Chinese data, since “religiously unaffiliated” is by far the largest group. Pew [discusses this](https://www.pewresearch.org/religion/2023/08/30/measuring-religion-in-china/) in detail.", "While censuses often provide information on people of all ages, most surveys only report on the religious composition of adults. In such cases, researchers use indirect demographic methods to estimate this data for children. For example, Pew uses data on the age structure and fertility rates of women in different religious groups to estimate the proportion of each religious group in the child population. This assumes that children share their mother's religion.", "Pew's methodology has changed over time, as improved data sources have become available. That means its latest estimates for 2010 — shown in this dataset — may differ from its earlier publications. You can see these changes and the reasons for these revisions in [its updated methodology](https://www.pewresearch.org/religion/2025/06/09/why-we-revised-our-estimates-for-2010/)."], "unit": "people", "timespan": "2010-2020", "type": "NumberOrString", "owidVariableId": 1119771, "shortName": "count__religion_religiously_unaffiliated", "lastUpdated": "2025-10-31", "nextUpdate": "2026-10-31", "citationShort": "Pew Research Center (2025) – processed by Our World in Data", "citationLong": "Pew Research Center (2025) – processed by Our World in Data. “Number of people\nwho are not religious” [dataset]. Pew Research Center, “Global Religious Composition Estimates for 2010 and 2020” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1119771.metadata.json"}, "Number of people - Other religions": {"titleShort": "Number of people\naffiliated to other religions", "titleLong": "Number of people\naffiliated to other religions", "descriptionShort": "Estimates of the number of people affiliated to other religions. Other religions include Baha'is, Daoists (also spelled Taoists), Jains, Shintoists, Sikhs, Wiccans, Zoroastrians and many small groups, some of which can be described as folk or traditional religions.", "descriptionKey": ["These estimates are [sourced from](https://www.pewresearch.org/religion/2025/06/09/global-religious-change-methodology/) more than 2,700 censuses and surveys.", "People are categorized based on how they describe their own religious identity. If someone identifies with a religious group, they're classified as being part of that group regardless of their practices or beliefs.", "While censuses often provide information on people of all ages, most surveys only report on the religious composition of adults. In such cases, researchers use indirect demographic methods to estimate this data for children. For example, Pew uses data on the age structure and fertility rates of women in different religious groups to estimate the proportion of each religious group in the child population. This assumes that children share their mother's religion.", "Pew's methodology has changed over time, as improved data sources have become available. That means its latest estimates for 2010 — shown in this dataset — may differ from its earlier publications. You can see these changes and the reasons for these revisions in [its updated methodology](https://www.pewresearch.org/religion/2025/06/09/why-we-revised-our-estimates-for-2010/)."], "unit": "people", "timespan": "2010-2020", "type": "NumberOrString", "owidVariableId": 1119770, "shortName": "count__religion_other_religions", "lastUpdated": "2025-10-31", "nextUpdate": "2026-10-31", "citationShort": "Pew Research Center (2025) – processed by Our World in Data", "citationLong": "Pew Research Center (2025) – processed by Our World in Data. “Number of people\naffiliated to other religions” [dataset]. Pew Research Center, “Global Religious Composition Estimates for 2010 and 2020” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1119770.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "What is the most common religious affiliation in each country?", "source_url": "https://ourworldindata.org/grapher/most-common-religion.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Most popular religion in a country"], "row_count_total": 428, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Angola", "Code": "AGO", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Angola", "Code": "AGO", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2010", "Most popular religion in a country": "Religiously Unaffiliated"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2020", "Most popular religion in a country": "Religiously Unaffiliated"}, {"Entity": "Asia-Pacific (Pew)", "Code": "PEW_APA", "Year": "2010", "Most popular religion in a country": "Religiously Unaffiliated"}, {"Entity": "Asia-Pacific (Pew)", "Code": "PEW_APA", "Year": "2020", "Most popular religion in a country": "Religiously Unaffiliated"}, {"Entity": "Australia", "Code": "AUS", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Australia", "Code": "AUS", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Austria", "Code": "AUT", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Austria", "Code": "AUT", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Bahamas", "Code": "BHS", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Bahamas", "Code": "BHS", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Benin", "Code": "BEN", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Benin", "Code": "BEN", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2010", "Most popular religion in a country": "Buddhists"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2020", "Most popular religion in a country": "Buddhists"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2010", "Most popular religion in a country": "Buddhists"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2020", "Most popular religion in a country": "Buddhists"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Canada", "Code": "CAN", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Canada", "Code": "CAN", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Cape Verde", "Code": "CPV", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Cape Verde", "Code": "CPV", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Chad", "Code": "TCD", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Chad", "Code": "TCD", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Channel Islands", "Code": "OWID_CIS", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Channel Islands", "Code": "OWID_CIS", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Chile", "Code": "CHL", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Chile", "Code": "CHL", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "China", "Code": "CHN", "Year": "2010", "Most popular religion in a country": "Religiously Unaffiliated"}, {"Entity": "China", "Code": "CHN", "Year": "2020", "Most popular religion in a country": "Religiously Unaffiliated"}, {"Entity": "Colombia", "Code": "COL", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Colombia", "Code": "COL", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Comoros", "Code": "COM", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Comoros", "Code": "COM", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Congo", "Code": "COG", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Congo", "Code": "COG", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Costa Rica", "Code": "CRI", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Costa Rica", "Code": "CRI", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Cuba", "Code": "CUB", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Cuba", "Code": "CUB", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Curacao", "Code": "CUW", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Curacao", "Code": "CUW", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2010", "Most popular religion in a country": "Religiously Unaffiliated"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2020", "Most popular religion in a country": "Religiously Unaffiliated"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Djibouti", "Code": "DJI", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Djibouti", "Code": "DJI", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Ecuador", "Code": "ECU", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Ecuador", "Code": "ECU", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "El Salvador", "Code": "SLV", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "El Salvador", "Code": "SLV", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Equatorial Guinea", "Code": "GNQ", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Equatorial Guinea", "Code": "GNQ", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Eritrea", "Code": "ERI", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Eritrea", "Code": "ERI", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Estonia", "Code": "EST", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Estonia", "Code": "EST", "Year": "2020", "Most popular religion in a country": "Christians"}], "rows_tail": [{"Entity": "Poland", "Code": "POL", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Poland", "Code": "POL", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Puerto Rico", "Code": "PRI", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Puerto Rico", "Code": "PRI", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Reunion", "Code": "REU", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Reunion", "Code": "REU", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Romania", "Code": "ROU", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Romania", "Code": "ROU", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Russia", "Code": "RUS", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Russia", "Code": "RUS", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Saint Lucia", "Code": "LCA", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Saint Lucia", "Code": "LCA", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Saint Vincent and the Grenadines", "Code": "VCT", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Saint Vincent and the Grenadines", "Code": "VCT", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Samoa", "Code": "WSM", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Samoa", "Code": "WSM", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Sao Tome and Principe", "Code": "STP", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Sao Tome and Principe", "Code": "STP", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Serbia", "Code": "SRB", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Serbia", "Code": "SRB", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Seychelles", "Code": "SYC", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Seychelles", "Code": "SYC", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2010", "Most popular religion in a country": "Buddhists"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2020", "Most popular religion in a country": "Buddhists"}, {"Entity": "Slovakia", "Code": "SVK", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Slovakia", "Code": "SVK", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Solomon Islands", "Code": "SLB", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Solomon Islands", "Code": "SLB", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Somalia", "Code": "SOM", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Somalia", "Code": "SOM", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "South America", "Code": "OWID_SAM", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "South America", "Code": "OWID_SAM", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2010", "Most popular religion in a country": "Religiously Unaffiliated"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2020", "Most popular religion in a country": "Religiously Unaffiliated"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Spain", "Code": "ESP", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Spain", "Code": "ESP", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2010", "Most popular religion in a country": "Buddhists"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2020", "Most popular religion in a country": "Buddhists"}, {"Entity": "Sub-Saharan Africa (Pew)", "Code": "PEW_SSA", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Sub-Saharan Africa (Pew)", "Code": "PEW_SSA", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Sudan", "Code": "SDN", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Sudan", "Code": "SDN", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Syria", "Code": "SYR", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Syria", "Code": "SYR", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2010", "Most popular religion in a country": "Other Religions"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2020", "Most popular religion in a country": "Other Religions"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Thailand", "Code": "THA", "Year": "2010", "Most popular religion in a country": "Buddhists"}, {"Entity": "Thailand", "Code": "THA", "Year": "2020", "Most popular religion in a country": "Buddhists"}, {"Entity": "Togo", "Code": "TGO", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Togo", "Code": "TGO", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Tonga", "Code": "TON", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Tonga", "Code": "TON", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "United States", "Code": "USA", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "United States", "Code": "USA", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "United States Virgin Islands", "Code": "VIR", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "United States Virgin Islands", "Code": "VIR", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2020", "Most popular religion in a country": "Religiously Unaffiliated"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2010", "Most popular religion in a country": "Religiously Unaffiliated"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "Most popular religion in a country": "Religiously Unaffiliated"}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Most popular religion in a country": "Muslims"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Most popular religion in a country": "Muslims"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Most popular religion in a country": "Christians"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Most popular religion in a country": "Christians"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Most popular religion in a country": "Christians"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "most-common-religion", "metadata_url": "https://ourworldindata.org/grapher/most-common-religion.metadata.json", "chart_title": "What is the most common religious affiliation in each country?", "chart_subtitle": "Religious affiliation with the highest population share in 2020.", "chart_note": "Data is based on how people describe their own religious identity. This self-identification is taken regardless of their practices or beliefs.", "chart_citation": "Pew Research Center (2025)", "original_chart_url": "https://ourworldindata.org/grapher/most-common-religion", "owid_column_metadata": {"Most popular religion in a country": {"titleShort": "Most popular religion in a country", "titleLong": "Most popular religion in a country", "descriptionShort": "The most popular religion in each country and year, based on share of population.", "descriptionKey": ["These estimates are [sourced from](https://www.pewresearch.org/religion/2025/06/09/global-religious-change-methodology/) more than 2,700 censuses and surveys.", "People are categorized based on how they describe their own religious identity. If someone identifies with a religious group, they're classified as being part of that group regardless of their practices or beliefs.", "The religiously unaffiliated population includes people who say they do not identify with any religion or that they are atheist or agnostic in surveys and censuses.", "Some people categorised as “non-religious” or “religiously unaffiliated” may engage in activities and hold beliefs that can be considered religious or spiritual, even though they don't describe themselves as belonging to any religion. This is particularly important for Chinese data, since “religiously unaffiliated” is by far the largest group. Pew [discusses this](https://www.pewresearch.org/religion/2023/08/30/measuring-religion-in-china/) in detail.", "While censuses often provide information on people of all ages, most surveys only report on the religious composition of adults. In such cases, researchers use indirect demographic methods to estimate this data for children. For example, Pew uses data on the age structure and fertility rates of women in different religious groups to estimate the proportion of each religious group in the child population. This assumes that children share their mother's religion.", "Pew's methodology has changed over time, as improved data sources have become available. That means its latest estimates for 2010 — shown in this dataset — may differ from its earlier publications. You can see these changes and the reasons for these revisions in [its updated methodology](https://www.pewresearch.org/religion/2025/06/09/why-we-revised-our-estimates-for-2010/)."], "unit": "", "timespan": "2010-2020", "type": "Ordinal", "owidVariableId": 1119755, "shortName": "most_popular_religion", "lastUpdated": "2025-10-31", "nextUpdate": "2026-10-31", "citationShort": "Pew Research Center (2025) – processed by Our World in Data", "citationLong": "Pew Research Center (2025) – processed by Our World in Data. “Most popular religion in a country” [dataset]. Pew Research Center, “Global Religious Composition Estimates for 2010 and 2020” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1119755.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "d217d44a00aa04450c1e"},
{"raw_link": "https://ourworldindata.org/biofuels-demand-global-aviation", "title": "Could biofuels meet demand for global aviation?", "context": "Home\nEnergy\nCould biofuels meet demand for global aviation?\nTo fuel all of the world’s aviation demand, global biofuels would need to more than triple and be exclusively used for air travel.\nBy\nHannah Ritchie\nand\nPablo Rosado\nJanuary 26, 2026\nBrowse past versions\nCite this article\nReuse our work freely\nMost of the world’s liquid biofuels currently go into cars and trucks, not planes.\nBut how will this change in the future? Electric vehicles\nlook like\nthe leading decarbonization solution for road transport. Plummeting\ncosts of batteries\nhave made electric cars (and increasingly trucks) competitive with petrol and diesel ones.\nIn a previous article, we looked at how biofuel land could be used more efficiently for road transport. We\nfound that\nthe world could electrify all its cars and trucks if we installed solar panels on less than one-third of the land used to grow biofuels.\nThe outlook for aviation is less clear. Short-haul flights might go electric. Long-haul ones will be more challenging to electrify (although some analysts remain optimistic).\n1\nHydrogen is one possible alternative to jet fuel, but it is still far from commercial scale.\nAnother option that gets a lot of attention is biofuels. A small amount of biofuels is already blended into jet fuel supplies in some countries. In 2023, Virgin Atlantic made headlines when it flew the\nfirst transatlantic flight\npowered entirely by biofuels. For many airlines, biofuels are currently the most practical decarbonization option.\n2\nBut how much biofuel does aviation actually use today? How much would be needed to replace fossil jet fuel — and could popular sources such as waste cooking oils ever meet that demand?\nIn this article, we put the key numbers into perspective.\nBiofuels provide less than 1% of global aviation energy demand\nTwo statements summarize the current situation with biofuels and aviation today.\nFirst, only a tiny share of global biofuel production is used for air travel — about 1% of it.\n3\nMost is used for road transport.\nSecond, biofuels make up only a very small fraction of aviation fuel itself. We estimate around 0.4%.\n4\nIn other words, aviation still runs almost entirely on fossil jet fuel. Despite decades of innovation and discussions about moving to more sustainable options, the fuel mix has barely changed.\nDownload\nAll of the world’s liquid biofuels could power just a fraction of the aviation fleet\nLet’s imagine we\nwent all-in on the electrification of road transport and biofuels were no longer needed for cars and trucks. Would the world’s biofuels power every plane instead?\nThe answer is no.\nIn 2024, the world\nproduced\nan estimated 1,400 terawatt-hours (TWh) of energy in the form of liquid biofuels. The global aviation fleet consumed 3,932 TWh.\n5\nThat means we would need to almost triple global biofuel production to meet today’s demand.\nThis is in the most optimistic case. Reallocating all of the world’s bioethanol and biodiesel to aviation is not a one-to-one swap. Producing jet-equivalent fuel from bio-based inputs also yields non-jet co-products such as naphtha, fuel gas, or diesel, and involves conversion losses. Depending on the pathway, perhaps only 30% to 80% of it ends up as jet fuel. If we assume 50%, only around 700 TWh of energy would be available for aviation — enough to meet just one-sixth of today’s demand.\nNote that very short flights — traveling less than 500 kilometers — that might be electrified in the medium-term account for just 5% of aviation’s fuel burn.\n6\nSo the quantity of biofuels needed would be similar, even if electric planes become a reality very soon.\nDownload\nKeep in mind that at least\n32 million hectares\n— a Germany-sized area of land — is already used to produce liquid biofuels. Based on the current mix of biofuel crops we grow, we’d need between three and six times the area of Germany.\nBut we’d also expect aviation demand to continue growing; how much will depend on trends in air travel demand and improvements in aircraft efficiency. In the past, efficiency gains have been impressive: the amount of fuel burned per passenger-kilometer has dropped by more than 60% since 1990.\n7\nIn\na paper\npublished in\nNature Sustainability\n, Candelaria Bergero and colleagues modeled aviation energy demand in 2050 under different assumptions.\n8\nThey estimated that energy demand ranges from as little as 1,200 TWh in a scenario where plane efficiency improves dramatically, to 7,600 TWh in a “business-as-usual” scenario.\n9\nIn the business-as-usual case, biofuel production would need to increase five to tenfold to meet aviation demand with today’s fuel mix.\n10\nEven in optimistic scenarios, aviation efficiency would need to improve at over four times the historical rate to make biofuels a dominant solution.\nThe aviation industry itself acknowledges this gap — particularly if biofuels are expected to carry the decarbonization burden alone, without alternatives such as hydrogen.\n11\nWaste cooking oils and fats could only supply a small amount of the demand from aviation\nOne of the major drawbacks of biofuels\nproduced from food crops\nis the amount of land they use. Growing fuel on fertile land has a high opportunity cost: that land could be used to produce food or freed up for nature to recover.\nThere is one biofuel source that avoids this problem: used cooking oils and fats. These are residues left behind, mostly from cooking vegetable oils and animal fats. Since these are waste, they are harnessing a resource that would otherwise need to be managed and disposed of.\nMany early trials of aviation biofuels have relied on these waste sources. But could they ever supply a meaningful share of global aviation energy?\nThe amount of waste oils and fats generated worldwide is uncertain. In their recent\nAgricultural Outlook\n, the OECD and FAO estimated that global waste oils and fats production was around 25 million tonnes in 2023.\n12\nConverted into aviation fuel, this could provide at most around 150 TWh of energy — just 4% of aviation demand today.\n13\nThis, however, represents an upper bound. It assumes\nall\nof the world’s waste oils are collected and processed, and that all of them are allocated to aviation.\nSome sources suggest that global waste oil and fat production is slightly larger.\n14\nBut these differences would only increase its share of global aviation demand by a few percentage points. Using any of these estimates, the maximum is still likely to be well below 10%.\nWaste oils can, therefore, make a small contribution to jet fuels. However, the majority would still have to come from elsewhere: biofuels from food crops, hydrogen, or electricity for shorter flights.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nBiofuels might meet a small amount of aviation demand, but they won’t supply it all\nThis analysis aimed to put the numbers in perspective: how much biofuel aviation uses today, and what it would take to replace a substantial share of jet fuel in the future.\nThe conclusions are clear. It is extremely unlikely that the world could run the\nentire\nglobal aviation fleet on biofuels. With today’s crops and technologies — corn, sugar crops, vegetable oils, and waste oils — supply falls far short of demand. Global biofuel supply would need to almost triple, and all of it would need to be allocated to air travel.\nAnother consideration is the economics of biofuels. Replacing jet fuels with biofuels is\nmore expensive\n, which also reduces the likelihood of a potential transition.\nBiofuels can contribute to decarbonizing aviation, but only to a limited extent. Any credible pathway to deep emissions cuts will need other technologies — such as hydrogen and electrification — to cover what biofuels cannot.\nAcknowledgments\nWe would like to thank Max Roser and Edouard Mathieu for editorial feedback and comments on this article.\nContinue reading on Our World in Data\nPutting solar panels on land used for biofuels would produce enough electricity for all cars and trucks to go electric\nThe world dedicates a Poland-sized area of land to liquid biofuels. Is there a more efficient way to generate energy?\nBioenergy and Biofuels\nHow much bioenergy does the world produce, and what is it used for?\nWhat share of global CO₂ emissions come from aviation?\nAviation accounts for 2.5% of global CO₂ emissions. But it has contributed around 4% to global warming to date.\nEndnotes\nAfonso, F., Sohst, M., Diogo, C. M., Rodrigues, S. S., Ferreira, A., Ribeiro, I., ... & Suleman, A. (2023). Strategies towards a more sustainable aviation: A systematic review. Progress in Aerospace Sciences.\nNeste\nis just one example. It provides fuel from used cooking oil and animal fat waste, which can be blended into existing jet fuel supplies.\nIn 2024, the International Energy Agency\nestimated that\n1.8 billion liters of liquid biofuel were for “biojet” fuel. Total production was 118 billion liters. That means biojet fuel was only 1%.\nThe International Energy Agency (IEA)\nestimated that\naviation energy demand in 2024 was 14.16 exajoules (EJ). That’s equivalent to 3,932 terawatt-hours (TWh).\nWe estimate that biofuels used for aviation are equivalent to around 17 TWh. The IEA\nestimates that\n1.8 billion liters of biojet fuel were used in 2024. With an energy content of 34 MJ per litre, this is equivalent to 61 billion MJ (or 0.061 EJ). That is equivalent to 17 TWh.\n17 TWh is 0.43% of the 3,930 TWh demand [17/ 3,930 = 0.43%].\nThis is very similar to figures reported by the International Air Transport Association (IATA). It\nreported that\n, “In 2024, SAF production reached 1Mt (1.250 billion liters), doubling the amounts produced in 2023, representing\n0.3%\nof global jet fuel use.”\nBy 2025, the IATA expected that this could be 0.6% of global demand.\nThe International Energy Agency (IEA)\nestimated that\naviation energy demand in 2024 was 14.16 exajoules (EJ). That’s equivalent to 3,932 terawatt-hours (TWh).\nThese figures are also roughly in line with estimates\npublished in a paper\nby Bergero et al. (2023). They projected that energy demand in 2025 would be 13.1 EJ, equivalent to 3642 TWh.\nDobruszkes, F., Mattioli, G., & Gozzoli, E. (2024). The elephant in the room: Long-haul air services and climate change. Journal of Transport Geography.\nBergero et al. (2023) report an energy intensity of 2.85 MJ per passenger-kilometer-equivalent in 1990. By 2021, this was 1.13 MJ. That’s a 60% reduction.\nThe data for the underlying paper can be found\nhere\n.\nBergero, C., Gosnell, G., Gielen, D., Kang, S., Bazilian, M., & Davis, S. J. (2023). Pathways to net-zero emissions from aviation. Nature Sustainability.\nIn the business-as-usual scenario, efficiency continues to improve at 1% per year — the average rate of improvement in recent decades. In 2050, it reaches 27.2 exajoules (EJ), which is equivalent to 7,562 TWh [27.2 * 278 = 7,562].\nIn the “ambitious” scenario, efficiency improves dramatically, at an average rate of 4% per year. Energy demand in this scenario is 4.17 EJ in 2050, which is equivalent to 1,159 TWh. The numbers in the main text have been rounded for simplicity.\nThe world currently produces 1,400 TWh in the form of liquid biofuels. To reach 7,600 TWh, this would need to increase 5.4-fold. If we assume that only around half of biofuels can be converted into jet fuel, it would be around 10-fold.\nIn its\nGlobal Feedstock Assessment for SAF Production - Outlook to 2050\nreport, the International Air Transport Association (IATA) states that: “[...] around 400 Mt of SAF is forecast to be possible to produce in 2050. Although this would be a major achievement, it is 100 Mt of SAF short of what will be needed in 2050. Sustainable biomass feedstocks are largely available, though access can be limited, underlining the need for e-SAF. Still, the major barrier to reaching the 500 Mt needed in 2050 is the pace of technology rollout.”\nOECD/FAO (2025), OECD-FAO Agricultural Outlook 2025-2034, Paris and Rome,\nhttps://doi.org/10.1787/601276cd-en\n.\nWe’ve taken the specific data from\nthis interactive chart\n, accompanying the report.\nTo convert the 25 million tonnes of waste oils and fats into jet fuel energy, we use HEFA conversion yields reported by\nIEA Bioenergy\n. They describe a typical configuration in which around 13% of the incoming oil ends up as jet fuel, and a jet-maximising configuration in which this can rise to around 50%. Applying these yields and using an energy content of 43 MJ/kg for jet fuel, this corresponds to 39 to 149 TWh of energy.\nSince global aviation demand is around 3,900 TWh, this would supply 1% to 4% of demand.\nA study by\nEcofys\n, for example, had estimates of 34 million tonnes, rather than the 25 million tonnes we assumed above.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Pablo Rosado (2026) - “Could biofuels meet demand for global aviation?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260126-093829/biofuels-demand-global-aviation.html' [Online Resource] (archived on January 26, 2026).\nBibTeX citation\n@article{owid-biofuels-demand-global-aviation,\nauthor = {Hannah Ritchie and Pablo Rosado},\ntitle = {Could biofuels meet demand for global aviation?},\njournal = {Our World in Data},\nyear = {2026},\nnote = {https://archive.ourworldindata.org/20260126-093829/biofuels-demand-global-aviation.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "biofuels-demand-global-aviation", "source_url": "https://ourworldindata.org/biofuels-demand-global-aviation", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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{"raw_link": "https://ourworldindata.org/us-crime-rates", "title": "How have crime rates in the United States changed over the last 50 years?", "context": "Home\nHomicides\nHow have crime rates in the United States changed over the last 50 years?\nBoth violent and property crime are far below their 1990s peak, but some crimes see periodic rises.\nBy\nHannah Ritchie\nand\nFiona Spooner\nJanuary 19, 2026\nBrowse past versions\nCite this article\nReuse our work freely\nWe all want to live somewhere we feel safe.\nCrime is clearly a concern for many people. Nearly 60% of Americans\nsay that\nreducing crime should be a top priority for the US president and Congress.\n1\nA similar share\nsays that\nviolent crime is very important when making decisions about which political candidates to vote for.\nWe’re citing American survey data here because this article focuses on crime in the US. But crime ranks as a top issue in many other countries, as well.\nTracking crime rates is crucial to understanding whether strategies to reduce offenses are effective and for identifying areas of concern.\nUnfortunately, the quality of crime data is often too poor to get a clear perspective of changes over the long term and to compare rates between countries.\nThis is true even for the crime with the most complete coverage, homicide.\n2\nComparisons are even more complicated for less severe crimes.\nHowever, a few countries have higher-quality and longer-term estimates.\nIn this article, we’ll focus on the United States. The\ndataset published by the FBI\ngoes back to 1979, giving us an almost 50-year perspective on how crime has changed.\n3\nSince not all crimes are recorded, and reporting methodologies can change over time, the FBI produces a dataset that attempts to adjust for this. That means this data might be slightly different from the raw reported crime figures that you see elsewhere.\nHow have rates of\nviolent crime\nchanged in the United States?\nSeveral crimes fall within the category of violent crimes. In US statistics, this includes homicide (murder and non-negligent manslaughter), rape,\nrobbery\n, and\naggravated assault\n.\n4\nIn the chart below, you can see national rates of violent crime since 1979. This is measured as the number of offenses per 100,000 people.\n5\nViolent crime rates increased during the 1980s, reaching a peak in the early 1990s at around 750 offenses per 100,000. Since then, rates have more than halved. Over the past three decades, rates have fluctuated slightly from year to year, but the overall trend has been downward.\nHas this decline been the result of a drop in a specific type of\nviolent crime\n, or have rates fallen across all categories?\nIn this second chart, you can again see the aggregate measure of violent crime rates, alongside the specific rates for homicide, aggravated assault, and violent robbery.\nThe FBI changed its definition of rape in 2013, so it’s not possible to show a consistent series over the same period. In the dropdown below, we discuss this data in more detail and show the impact of this change on rates in the United States.\nThe first thing to notice is the differences in scale on the y-axis. Homicides are far less common than assaults or robberies.\nBut overall, there is a fairly consistent pattern across these crimes over these 50 years. Rates peaked in the early 1990s (although homicide and robbery rates were already high through the 1980s) and have seen a substantial decline since then. Homicide rates have approximately halved; aggravated assaults have dropped by 40%; and robberies by almost 80%.\nAgain, there is some variability over this period. Homicide and assault rates increased in 2020 and 2021, but have since fallen again.\nData on rape and sexual assault\nIn 2013, the FBI\nchanged its definition\nof rape in its Uniform Crime Reporting Program. This was the first time it had done so in its 80-year history for a Part 1 offense. That means the data is not comparable over the entire 50-year period.\nThe legacy definition of rape was narrower in scope. It was defined as the “carnal knowledge of a female forcibly and against her will”. In other words, the forceful penetration of a woman against her consent. It did not include male victims, nor did it include other sexual assault offenses.\nThe revised definition is broader in two ways. First, it includes male and non-binary victims. Second, it includes other sexual assault crimes, including oral and anal sex against someone’s consent, and sexual assault with an object.\nWe have plotted both series — using the older, legacy definition, and the revised definition — in the chart below.\nYou can see that there is data available for both the legacy and revised definitions for several years. During those years, rape rates are, as we’d expect, higher in the revised definition, since it includes a broader set of victims and crimes.\nThe FBI\npublished a breakdown\nof how the number of these crimes varied in 2013, using both the legacy and revised definitions. The number of rapes — defined as forcible penetration — with female victims was the same: 26,994 in each. But the count under the revised definition additionally included 611 cases of rape of male victims, 7,602 cases classified as “sodomy” (oral or anal sexual intercourse without consent), and 3,043 counts of “sexual assault with an object”.\nThis change in definition means we cannot get a complete understanding of how rape rates have changed since 1979. But the data does show that rape of\nwomen\n(using the narrower definition) became less common from a peak in the early 1990s, through to the early 2010s. After that point, rates start to climb slightly. The revised definition shows an increase to 2020, before falling back to rates equivalent to the mid-2010s.\nShow more\nHow have rates of\nproperty crime\nand theft changed in the United States?\nWhat about non-violent crimes? These tend to fall under the banner of\nproperty crimes\n. Property crimes involve the stealing, damage, or destruction of someone’s property. This includes offenses such as burglary, larceny (which is more like pickpocketing, and without breaking and entering), theft, and arson.\nIn the chart below, you can see how property crime rates have changed over the past half-century.\nThe trend is similar to what we saw for violent crime. Rates were high throughout the 1980s and into the early 1990s, but have fallen by 60% in the last 30 years. Unlike violent crime rates, there has been less variability from year to year, resulting in a fairly consistent downward trend.\nAt the peak, there were around 5,000 offenses per 100,000 people. That’s one for every 20th person in the population, each year. Since then, it has fallen to fewer than 1 per 50 people.\nAgain, the chart below looks at trends for different types of property crime.\n6\nLarceny and burglary have mirrored the steady decline since the 1990s.\nMotor vehicle theft has been on a bumpier road, with a more distinct peak around the 1990s, a rebound in the early 2000s, and a smaller uptick in rates over the last five years. To put these numbers in absolute terms: in 1991, around 1.7 million vehicles were stolen in the United States. In recent years, that number has fluctuated between 700,000 and 1 million.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nAmericans are less likely to be victims of crime than they were 30 years ago\nThe overall trend in both violent and property crimes has been downward over the last 30 years. Americans in the 1990s were at least twice as likely to be victims of crime as they are today. This is also true at a global level. While we don’t have consistent data on theft and assaults, we see that global homicide rates\nhave gone down\nin recent decades.\nThis is not necessarily how the public perceives it.\nThe polling agency Gallup has conducted\nnumerous surveys\nasking Americans how they perceive changes in crime rates since 1993. In 23 out of the 27 annual surveys, the majority said that they believed crime rates had increased from the previous year.\nAmericans in the 1990s were at least twice as likely to be victims of crime as they are today.\nIt is not true that crime has fallen\nevery\nyear; there have been periods of elevated rates along the way. The start of the 2020s is a clear example. But rates in most years have indeed been lower than in the previous year. Most of the time, the popular perception was therefore at odds with the data.\nOne reason why people often think that crime is rising is that they constantly see individual events on the news.\n7\nAs we showed in a\nrecent article\n, homicides dominate coverage related to causes of death in leading news outlets across the political spectrum. Homicides received 40% to 50% of “causes of death” coverage, despite making up less than 1% of deaths.\nThis disconnect between perceptions and the reality of crime rates is understandable. It’s impossible to get a sense of the broader changes in crime from individual stories alone. To get a sense of perspective, we need to step back and look at consistent data over time.\nContinue reading on Our World in Data\nDoes the news reflect what we die from?\nWhat do Americans die from, and what do the New York Times, Washington Post, and Fox News report on?\nHomicide data: how sources differ and when to use which one\nThere are several ways to measure homicides. What approaches do different sources take? And when is which approach best?\nEndnotes\nThis is based on Pew Research survey data for 2024.\nGramlich (2024).\nWhat the data says about crime in the U.S\n. Pew Research Center.\nCountries may use different definitions for homicide records (if they have consistent records at all). That’s why different national and international datasets often\nyield different estimates\nfor the same country, and why we usually caution against comparing countries with very distinct coverage.\nFederal Bureau of Investigation (2025). Estimated crime totals for the nation and all 50 states, 1979–2024. Summary Reporting System (SRS) Estimates.\nIn this long-run dataset, the Summary Reporting System (SRS), if multiple crimes are committed in the same offense, the FBI only records the most serious crime in the dataset. For example, if a burglary resulted in a homicide, the homicide would be the crime that would appear in the data. This is called the “hierarchy rule”.\nOver the last few decades, the FBI has been transitioning to a separate reporting program — The National Incident-Based Reporting System (NIBRS) — which does not use the hierarchy rule, and can record up to 10 offenses per criminal incident. Unfortunately, estimates from this data do not extend as far back in time, and are much patchier in earlier years, as it took agencies some time to move to the new, more detailed system.\nSeveral studies have compared crime rates between the Summary Reporting System, which we use in this article, and the NIBRS, which includes multiple crimes. You can find an in-depth discussion of the results\nhere\n. They find relatively small differences in crime rates. Overall, the transition to the NIBRS increased national crime rates by around 2%. It had differential impacts on specific types of crimes, with a “less than a 0.1% increase for rape, a 0.6% increase for robbery and aggravated assault, a 1.0% increase for burglary, a 2.6% increase for larceny, and a 2.7% increase for motor vehicle theft.”\nThe analysis concludes that “these results suggest that NIBRS does not significantly alter crime rates at the national level, but individual local agencies, especially in low-crime areas, may see notable increases in the reported numbers and rates of some offenses due to low base rates of crime.”\nAs we explain later in the article, the time series for rape includes some methodological changes that affect its comparability over time. These crimes\nare\nincluded in the aggregate of “violent crimes”. In 2013, there was a methodological change in how rape cases were recorded, which is more encompassing, so we’d expect some increase in the number of cases thereafter (even if the actual number of crimes did not change). This may be one of the factors contributing to the small increase in rates of violent crime in the chart around this time.\nWe do not expect that differences in rape definition over time affect the overall direction or trend of violent crime in the past three decades.\nUnfortunately, individual counts of arson are not available from the source, so this category of crime is not shown. As the\nFBI states\n:\n“Although arson data are included in the trend and clearance tables, sufficient data are not available to estimate totals for this offense.”\nThis is related to an “availability bias” or “\navailability heuristic\n”, where we give greater importance to recent examples of something that come to mind. We are biased towards things we can quickly recall.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Fiona Spooner (2026) - “How have crime rates in the United States changed over the last 50 years?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-090244/us-crime-rates.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-us-crime-rates,\nauthor = {Hannah Ritchie and Fiona Spooner},\ntitle = {How have crime rates in the United States changed over the last 50 years?},\njournal = {Our World in Data},\nyear = {2026},\nnote = {https://archive.ourworldindata.org/20260518-090244/us-crime-rates.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "us-crime-rates", "source_url": "https://ourworldindata.org/us-crime-rates", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Both violent and property crime are far below their 1990s peak, but some crimes see periodic rises.", "numeric_mentions": ["50 years", "1990", "19,", "2026", "60%", "1", "2", "1979,", "50", "3", "4", "1979", "100,000", "5", "1980", "750", "2013,", "40%", "80%", "2020", "2021,", "80", "26,994", "611", "7,602", "3,043", "2010", "2020,", "30 years", "5,000", "20", "6", "2000", "1991,", "1.7 million", "700,000", "1 million", "1993", "23", "27", "7", "50%", "1%", "2024", "2025", "10", "2%", "0.1%", "0.6%", "1.0%", "2.6%", "2.7%", "20260518", "090244", "18,"], "numeric_evidence": [{"title": "Estimated violent crime rate in the United States", "source_url": "https://ourworldindata.org/grapher/violent-crime-rate-us.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Estimated violent crime rate per 100,000 population"], "row_count_total": 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"Estimated violent crime rate per 100,000 population": "713.59094"}, {"Entity": "United States", "Code": "USA", "Year": "1995", "Estimated violent crime rate per 100,000 population": "684.4633"}, {"Entity": "United States", "Code": "USA", "Year": "1996", "Estimated violent crime rate per 100,000 population": "636.6358"}, {"Entity": "United States", "Code": "USA", "Year": "1997", "Estimated violent crime rate per 100,000 population": "610.978"}, {"Entity": "United States", "Code": "USA", "Year": "1998", "Estimated violent crime rate per 100,000 population": "567.58496"}, {"Entity": "United States", "Code": "USA", "Year": "1999", "Estimated violent crime rate per 100,000 population": "522.9527"}, {"Entity": "United States", "Code": "USA", "Year": "2000", "Estimated violent crime rate per 100,000 population": "506.52988"}, {"Entity": "United States", "Code": "USA", "Year": "2001", "Estimated violent crime rate per 100,000 population": "504.51855"}, {"Entity": "United States", "Code": "USA", "Year": "2002", "Estimated violent crime rate per 100,000 population": "494.37704"}, {"Entity": "United States", "Code": "USA", "Year": "2003", "Estimated violent crime rate per 100,000 population": "475.83508"}, {"Entity": "United States", "Code": "USA", "Year": "2004", "Estimated violent crime rate per 100,000 population": "463.15555"}, {"Entity": "United States", "Code": "USA", "Year": "2005", "Estimated violent crime rate per 100,000 population": "469.0428"}, {"Entity": "United States", "Code": "USA", "Year": "2006", "Estimated violent crime rate per 100,000 population": "479.33542"}, {"Entity": "United States", "Code": "USA", "Year": "2007", "Estimated violent crime rate per 100,000 population": "471.77393"}, {"Entity": "United States", "Code": "USA", "Year": "2008", "Estimated violent crime rate per 100,000 population": "458.61417"}, {"Entity": "United States", "Code": "USA", "Year": "2009", "Estimated violent crime rate per 100,000 population": "431.87872"}, {"Entity": "United States", "Code": "USA", "Year": "2010", "Estimated violent crime rate per 100,000 population": "404.50235"}, {"Entity": "United States", "Code": "USA", "Year": "2011", "Estimated violent crime rate per 100,000 population": "387.05975"}, {"Entity": "United States", "Code": "USA", "Year": "2012", "Estimated violent crime rate per 100,000 population": "387.75375"}, {"Entity": "United States", "Code": "USA", "Year": "2013", "Estimated violent crime rate per 100,000 population": "369.13336"}, {"Entity": "United States", "Code": "USA", "Year": "2014", "Estimated violent crime rate per 100,000 population": "361.55386"}, {"Entity": "United States", "Code": "USA", "Year": "2015", "Estimated violent crime rate per 100,000 population": "373.73718"}, {"Entity": "United States", "Code": "USA", "Year": "2016", "Estimated violent crime rate per 100,000 population": "397.52084"}, {"Entity": "United States", "Code": "USA", "Year": "2017", "Estimated violent crime rate per 100,000 population": "394.8597"}, {"Entity": "United States", "Code": "USA", "Year": "2018", "Estimated violent crime rate per 100,000 population": "383.363"}, {"Entity": "United States", "Code": "USA", "Year": "2019", "Estimated violent crime rate per 100,000 population": "380.8343"}, {"Entity": "United States", "Code": "USA", "Year": "2020", "Estimated violent crime rate per 100,000 population": "398.53363"}, {"Entity": "United States", "Code": "USA", "Year": "2021", "Estimated violent crime rate per 100,000 population": "386.97678"}, {"Entity": "United States", "Code": "USA", "Year": "2022", "Estimated violent crime rate per 100,000 population": "388.7349"}, {"Entity": "United States", "Code": "USA", "Year": "2023", "Estimated violent crime rate per 100,000 population": "379.53604"}, {"Entity": "United States", "Code": "USA", "Year": "2024", "Estimated violent crime rate per 100,000 population": "359.1019"}], "rows_tail": [], "sampling_note": "Stored first 46 rows and last 46 rows when the table is 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The descending order of violent crimes are homicide, rape, robbery, and aggravated assault, followed by the property crimes of burglary, larceny-theft, and motor vehicle theft. For example, if a robbery occurs during which a homicide takes place, only the homicide is counted."], "unit": "violent crimes per 100,000 population", "timespan": "1979-2024", "type": "Numeric", "owidVariableId": 1130272, "shortName": "violent_crime_per_100k", "lastUpdated": "2025-11-21", "nextUpdate": "2026-11-21", "citationShort": "FBI (2025) – processed by Our World in Data", "citationLong": "FBI (2025) – processed by Our World in Data. “Estimated violent crime rate per 100,000 population” [dataset]. FBI, “Summary Reporting System (SRS) Estimates” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1130272.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Violent crime rates in the United States", "source_url": "https://ourworldindata.org/grapher/types-violent-crime-rate-us.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Violent crime", "Homicide", "Aggravated assault", "Robbery (taking something through force or violence)"], "row_count_total": 46, "rows_head": [{"Entity": "United States", "Code": "USA", "Year": "1979", "Violent crime": "548.85754", "Homicide": "9.750158", "Aggravated assault": "285.99857", "Robbery (taking something through force or violence)": "218.40172"}, {"Entity": "United States", "Code": "USA", "Year": "1980", "Violent crime": "596.6383", "Homicide": "10.22413", "Aggravated assault": "298.49222", "Robbery (taking something through force or violence)": "251.09468"}, {"Entity": "United States", "Code": "USA", "Year": "1981", "Violent crime": "593.4743", "Homicide": "9.814102", "Aggravated assault": "289.32428", "Robbery (taking something through force or violence)": "258.38718"}, {"Entity": "United States", "Code": "USA", "Year": "1982", "Violent crime": "570.8213", "Homicide": "9.069151", "Aggravated assault": "288.98694", "Robbery (taking something through force or violence)": "238.76343"}, {"Entity": "United States", "Code": "USA", "Year": "1983", "Violent crime": "538.1224", "Homicide": "8.258623", "Aggravated assault": "279.43387", "Robbery (taking something through force or violence)": "216.67422"}, {"Entity": "United States", "Code": "USA", "Year": "1984", "Violent crime": "539.9269", "Homicide": "7.92622", "Aggravated assault": "290.61774", "Robbery (taking something through force or violence)": "205.66446"}, {"Entity": "United States", "Code": "USA", "Year": "1985", "Violent crime": "558.06396", "Homicide": "7.975663", "Aggravated assault": "303.9822", "Robbery (taking something through force or violence)": "209.25775"}, {"Entity": "United States", "Code": "USA", "Year": "1986", "Violent crime": "620.14374", "Homicide": "8.583997", "Aggravated assault": "347.4418", "Robbery (taking something through force or violence)": "226.0311"}, {"Entity": "United States", "Code": "USA", "Year": "1987", "Violent crime": "612.4915", "Homicide": "8.2942295", "Aggravated assault": "352.9208", "Robbery (taking something through force or violence)": "213.67218"}, {"Entity": "United States", "Code": "USA", "Year": "1988", "Violent crime": "640.58386", "Homicide": "8.456068", "Aggravated assault": "372.22733", "Robbery (taking something through force or violence)": "222.07373"}, {"Entity": "United States", "Code": "USA", "Year": "1989", "Violent crime": "666.89984", "Homicide": "8.710829", "Aggravated assault": "385.58868", "Robbery (taking something through force or violence)": "234.31157"}, {"Entity": "United States", "Code": "USA", "Year": "1990", "Violent crime": "729.61395", "Homicide": "9.3953285", "Aggravated assault": "422.85114", "Robbery (taking something through force or violence)": "256.25742"}, {"Entity": "United States", "Code": "USA", "Year": "1991", "Violent crime": "758.1771", "Homicide": "9.796826", "Aggravated assault": "433.3633", "Robbery (taking something through force or violence)": "272.74384"}, {"Entity": "United States", "Code": "USA", "Year": "1992", "Violent crime": "757.66626", "Homicide": "9.316562", "Aggravated assault": "441.8991", "Robbery (taking something through force or violence)": "263.68616"}, {"Entity": "United States", "Code": "USA", "Year": "1993", "Violent crime": "747.14777", "Homicide": "9.514218", "Aggravated assault": "440.52893", "Robbery (taking something through force or violence)": "255.97925"}, {"Entity": "United States", "Code": "USA", "Year": "1994", "Violent crime": "713.59094", "Homicide": "8.960268", "Aggravated assault": "427.60794", "Robbery (taking something through force or violence)": "237.75826"}, {"Entity": "United States", "Code": "USA", "Year": "1995", "Violent crime": "684.4633", "Homicide": "8.221358", "Aggravated assault": "418.2623", "Robbery (taking something through force or violence)": "220.89108"}, {"Entity": "United States", "Code": "USA", "Year": "1996", "Violent crime": "636.6358", "Homicide": "7.406819", "Aggravated assault": "391.00198", "Robbery (taking something through force or violence)": "201.93677"}, {"Entity": "United States", "Code": "USA", "Year": "1997", "Violent crime": "610.978", "Homicide": "6.80064", "Aggravated assault": "382.09995", "Robbery (taking something through force or violence)": "186.17047"}, {"Entity": "United States", "Code": "USA", "Year": "1998", "Violent crime": "567.58496", "Homicide": "6.2808976", "Aggravated assault": "361.36548", "Robbery (taking something through force or violence)": "165.47246"}, {"Entity": "United States", "Code": "USA", "Year": "1999", "Violent crime": "522.9527", "Homicide": "5.692161", "Aggravated assault": "334.34937", "Robbery (taking something through force or violence)": "150.12277"}, {"Entity": "United States", "Code": "USA", "Year": "2000", "Violent crime": "506.52988", "Homicide": "5.538304", "Aggravated assault": "323.9641", "Robbery (taking something through force or violence)": "144.98373"}, {"Entity": "United States", "Code": "USA", "Year": "2001", "Violent crime": "504.51855", "Homicide": "5.6207547", "Aggravated assault": "318.60043", "Robbery (taking something through force or violence)": "148.45108"}, {"Entity": "United States", "Code": "USA", "Year": "2002", "Violent crime": "494.37704", "Homicide": "5.6355796", "Aggravated assault": "309.54434", "Robbery (taking something through force or violence)": "146.12642"}, {"Entity": "United States", "Code": "USA", "Year": "2003", "Violent crime": "475.83508", "Homicide": "5.683847", "Aggravated assault": "295.41354", "Robbery (taking something through force or violence)": "142.4521"}, {"Entity": "United States", "Code": "USA", "Year": "2004", "Violent crime": "463.15555", "Homicide": "5.498935", "Aggravated assault": "288.56165", "Robbery (taking something through force or violence)": "136.71399"}, {"Entity": "United States", "Code": "USA", "Year": "2005", "Violent crime": "469.0428", "Homicide": "5.645734", "Aggravated assault": "290.7924", "Robbery (taking something through force or violence)": "140.78519"}, {"Entity": "United States", "Code": "USA", "Year": "2006", "Violent crime": "479.33542", "Homicide": "5.7812586", "Aggravated assault": "291.9507", "Robbery (taking something through force or violence)": "150.04953"}, {"Entity": "United States", "Code": "USA", "Year": "2007", "Violent crime": "471.77393", "Homicide": "5.6786466", "Aggravated assault": "287.23383", "Robbery (taking something through force or violence)": "148.30658"}, {"Entity": "United States", "Code": "USA", "Year": "2008", "Violent crime": "458.61417", "Homicide": "5.415055", "Aggravated assault": "277.47278", "Robbery (taking something through force or violence)": "145.88022"}, {"Entity": "United States", "Code": "USA", "Year": "2009", "Violent crime": "431.87872", "Homicide": "5.015854", "Aggravated assault": "264.6569", "Robbery (taking something through force or violence)": "133.13788"}, {"Entity": "United States", "Code": "USA", "Year": "2010", "Violent crime": "404.50235", "Homicide": "4.759315", "Aggravated assault": "252.75385", "Robbery (taking something through force or violence)": "119.31876"}, {"Entity": "United States", "Code": "USA", "Year": "2011", "Violent crime": "387.05975", "Homicide": "4.7052546", "Aggravated assault": "241.48024", "Robbery (taking something through force or violence)": "113.8594"}, {"Entity": "United States", "Code": "USA", "Year": "2012", "Violent crime": "387.75375", "Homicide": "4.7331142", "Aggravated assault": "242.7757", "Robbery (taking something through force or violence)": "113.11907"}, {"Entity": "United States", "Code": "USA", "Year": "2013", "Violent crime": "369.13336", "Homicide": "4.5242057", "Aggravated assault": "229.63118", "Robbery (taking something through force or violence)": "109.03497"}, {"Entity": "United States", "Code": "USA", "Year": "2014", "Violent crime": "361.55386", "Homicide": "4.441415", "Aggravated assault": "229.24805", "Robbery (taking something through force or violence)": "101.25353"}, {"Entity": "United States", "Code": "USA", "Year": "2015", "Violent crime": "373.73718", "Homicide": "4.949569", "Aggravated assault": "238.10068", "Robbery (taking something through force or violence)": "102.247574"}, {"Entity": "United States", "Code": "USA", "Year": "2016", "Violent crime": "397.52084", "Homicide": "5.384255", "Aggravated assault": "248.2892", "Robbery (taking something through force or violence)": "102.9038"}, {"Entity": "United States", "Code": "USA", "Year": "2017", "Violent crime": "394.8597", "Homicide": "5.318823", "Aggravated assault": "249.21611", "Robbery (taking something through force or violence)": "98.60029"}, {"Entity": "United States", "Code": "USA", "Year": "2018", "Violent crime": "383.363", "Homicide": "5.0121293", "Aggravated assault": "248.24396", "Robbery (taking something through force or violence)": "86.10002"}, {"Entity": "United States", "Code": "USA", "Year": "2019", "Violent crime": "380.8343", "Homicide": "5.0769053", "Aggravated assault": "250.36308", "Robbery (taking something through force or violence)": "81.772316"}, {"Entity": "United States", "Code": "USA", "Year": "2020", "Violent crime": "398.53363", "Homicide": "6.5465975", "Aggravated assault": "279.68115", "Robbery (taking something through force or violence)": "73.93376"}, {"Entity": "United States", "Code": "USA", "Year": "2021", "Violent crime": "386.97678", "Homicide": "6.787307", "Aggravated assault": "272.23227", "Robbery (taking something through force or violence)": "65.52088"}, {"Entity": "United States", "Code": "USA", "Year": "2022", "Violent crime": "388.7349", "Homicide": "6.5351973", "Aggravated assault": "273.0162", "Robbery (taking something through force or violence)": "67.06251"}, {"Entity": "United States", "Code": "USA", "Year": "2023", "Violent crime": "379.53604", "Homicide": "5.91052", "Aggravated assault": "266.59482", "Robbery (taking something through force or violence)": "67.09169"}, {"Entity": "United States", "Code": "USA", "Year": "2024", "Violent crime": "359.1019", "Homicide": "4.979257", "Aggravated assault": "256.07257", "Robbery (taking something through force or violence)": "60.55435"}], "rows_tail": [], "sampling_note": "Stored first 46 rows and last 46 rows when the table is larger.", "grapher_slug": "types-violent-crime-rate-us", "metadata_url": "https://ourworldindata.org/grapher/types-violent-crime-rate-us.metadata.json", "chart_title": "Violent crime rates in the United States", "chart_subtitle": "Estimated rates per 100,000 people. Violent crime includes four offenses: murder and non-negligent manslaughter, rape, robbery, and aggravated assault. Data for rape is not shown due to the lack of comparable data over time.", "chart_note": "The FBI takes reported crime data and adjusts it for gaps and underreporting to provide consistent long-term estimates over time. That means it may be different to reported crime figures.", "chart_citation": "FBI (2025)", "original_chart_url": "https://ourworldindata.org/grapher/types-violent-crime-rate-us", "owid_column_metadata": {"Estimated violent crime rate per 100,000 population": {"titleShort": "Violent crime", "titleLong": "Violent crime", "descriptionShort": "Violent crime is composed of four offenses: murder and nonnegligent manslaughter, rape, robbery, and aggravated assault.", "descriptionKey": ["This data reflects the hierarchy rule, which requires that only the most serious offense in a case be counted. The descending order of violent crimes are homicide, rape, robbery, and aggravated assault, followed by the property crimes of burglary, larceny-theft, and motor vehicle theft. For example, if a robbery occurs during which a homicide takes place, only the homicide is counted."], "unit": "violent crimes per 100,000 population", "timespan": "1979-2024", "type": "Numeric", "owidVariableId": 1130272, "shortName": "violent_crime_per_100k", "lastUpdated": "2025-11-21", "nextUpdate": "2026-11-21", "citationShort": "FBI (2025) – processed by Our World in Data", "citationLong": "FBI (2025) – processed by Our World in Data. “Violent crime” [dataset]. FBI, “Summary Reporting System (SRS) Estimates” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1130272.metadata.json"}, "Estimated homicide rate per 100,000 population": {"titleShort": "Homicide", "titleLong": "Homicide", "descriptionShort": "Homicide includes murder and non-negligent manslaughter, the willful killing of one human being by another.", "descriptionKey": ["This data reflects the hierarchy rule, which requires that only the most serious offense in a case be counted. The descending order of violent crimes are homicide, rape, robbery, and aggravated assault, followed by the property crimes of burglary, larceny-theft, and motor vehicle theft. For example, if a robbery occurs during which a homicide takes place, only the homicide is counted.", "This excludes unsucessful attempts, suicides, deaths caused by negligence or accidental deaths. This data also excludes so called 'justifiable homicides', which are defined as the (1) killing of a felon by a law enforcement officer in the line of duty or (2) the killing of a felon, during the commission of a felony, by a private citizen."], "unit": "homicides per 100,000 population", "timespan": "1979-2024", "type": "Numeric", "owidVariableId": 1130271, "shortName": "homicide_per_100k", "lastUpdated": "2025-11-21", "nextUpdate": "2026-11-21", "citationShort": "FBI (2025) – processed by Our World in Data", "citationLong": "FBI (2025) – processed by Our World in Data. “Homicide” [dataset]. FBI, “Summary Reporting System (SRS) Estimates” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1130271.metadata.json"}, "Estimated aggravated assault rate per 100,000 population": {"titleShort": "Aggravated assault", "titleLong": "Aggravated assault", "descriptionShort": "An unlawful attack by one person upon another for the purpose of inflicting severe or aggravated bodily injury.", "descriptionKey": ["This data reflects the hierarchy rule, which requires that only the most serious offense in a case be counted. The descending order of violent crimes are homicide, rape, robbery, and aggravated assault, followed by the property crimes of burglary, larceny-theft, and motor vehicle theft. For example, if a robbery occurs during which a homicide takes place, only the homicide is counted.", "This type of assault usually is accompanied by the use of a weapon or by means likely to produce death or great bodily harm. Simple assaults are excluded, simple assaults are defined as assaults and attempted assaults where no weapon was used or no serious or aggravated injury resulted to the victim. Stalking, intimidation, coercion, and hazing are included."], "unit": "aggravated assaults per 100,000 population", "timespan": "1979-2024", "type": "Numeric", "owidVariableId": 1130274, "shortName": "aggravated_assault_per_100k", "lastUpdated": "2025-11-21", "nextUpdate": "2026-11-21", "citationShort": "FBI (2025) – processed by Our World in Data", "citationLong": "FBI (2025) – processed by Our World in Data. “Aggravated assault” [dataset]. FBI, “Summary Reporting System (SRS) Estimates” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1130274.metadata.json"}, "Estimated robbery rate per 100,000 population": {"titleShort": "Robbery (taking something through force or violence)", "titleLong": "Robbery (taking something through force or violence)", "descriptionShort": "Robbery is the (successful or unsuccessful) attempt to take something of value from another person, either by using force or by threatening the victim.", "descriptionKey": ["This data reflects the hierarchy rule, which requires that only the most serious offense in a case be counted. The descending order of violent crimes are homicide, rape, robbery, and aggravated assault, followed by the property crimes of burglary, larceny-theft, and motor vehicle theft. For example, if a robbery occurs during which a homicide takes place, only the homicide is counted."], "unit": "robberies per 100,000 population", "timespan": "1979-2024", "type": "Numeric", "owidVariableId": 1130273, "shortName": "robbery_per_100k", "lastUpdated": "2025-11-21", "nextUpdate": "2026-11-21", "citationShort": "FBI (2025) – processed by Our World in Data", "citationLong": "FBI (2025) – processed by Our World in Data. “Robbery (taking something through force or violence)” [dataset]. FBI, “Summary Reporting System (SRS) Estimates” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1130273.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Estimated number of rapes per 100,000 in the United States", "source_url": "https://ourworldindata.org/grapher/rates-rape-united-states.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Revised definition", "Legacy definition"], "row_count_total": 46, "rows_head": [{"Entity": "United States", "Code": "USA", "Year": "1979", "Revised definition": "", "Legacy definition": "34.707108"}, {"Entity": "United States", "Code": "USA", "Year": "1980", "Revised definition": "", "Legacy definition": "36.82728"}, {"Entity": "United States", "Code": "USA", "Year": "1981", "Revised definition": "", "Legacy definition": "35.953083"}, {"Entity": "United States", "Code": "USA", "Year": "1982", "Revised definition": "", "Legacy definition": "34.001762"}, {"Entity": "United States", "Code": "USA", "Year": "1983", "Revised definition": "", "Legacy definition": "33.755646"}, {"Entity": "United States", "Code": "USA", "Year": "1984", "Revised definition": "", "Legacy definition": "35.71845"}, {"Entity": "United States", "Code": "USA", "Year": "1985", "Revised definition": "", "Legacy definition": "36.848354"}, {"Entity": "United States", "Code": "USA", "Year": "1986", "Revised definition": "", "Legacy definition": "38.086826"}, {"Entity": "United States", "Code": "USA", "Year": "1987", "Revised definition": "", "Legacy definition": "37.60428"}, {"Entity": "United States", "Code": "USA", "Year": "1988", "Revised definition": "", "Legacy definition": "37.826744"}, {"Entity": "United States", "Code": "USA", "Year": "1989", "Revised definition": "", "Legacy definition": "38.28875"}, {"Entity": "United States", "Code": "USA", "Year": "1990", "Revised definition": "", "Legacy definition": "41.110073"}, {"Entity": "United States", "Code": "USA", "Year": "1991", "Revised definition": "", "Legacy definition": "42.27313"}, {"Entity": "United States", "Code": "USA", "Year": "1992", "Revised definition": "", "Legacy definition": "42.76443"}, {"Entity": "United States", "Code": "USA", "Year": "1993", "Revised definition": "", "Legacy definition": "41.12535"}, {"Entity": "United States", "Code": "USA", "Year": "1994", "Revised definition": "", "Legacy definition": "39.26446"}, {"Entity": "United States", "Code": "USA", "Year": "1995", "Revised definition": "", "Legacy definition": "37.088577"}, {"Entity": "United States", "Code": "USA", "Year": "1996", "Revised definition": "", "Legacy definition": "36.290207"}, {"Entity": "United States", "Code": "USA", "Year": "1997", "Revised definition": "", "Legacy definition": "35.90698"}, {"Entity": "United States", "Code": "USA", "Year": "1998", "Revised definition": "", "Legacy definition": "34.466118"}, {"Entity": "United States", "Code": "USA", "Year": "1999", "Revised definition": "", "Legacy definition": "32.788418"}, {"Entity": "United States", "Code": "USA", "Year": "2000", "Revised definition": "", "Legacy definition": "32.043705"}, {"Entity": "United States", "Code": "USA", "Year": "2001", "Revised definition": "", "Legacy definition": "31.84627"}, {"Entity": "United States", "Code": "USA", "Year": "2002", "Revised definition": "", "Legacy definition": "33.0707"}, {"Entity": "United States", "Code": "USA", "Year": "2003", "Revised definition": "", "Legacy definition": "32.28561"}, {"Entity": "United States", "Code": "USA", "Year": "2004", "Revised definition": "", "Legacy definition": "32.380993"}, {"Entity": "United States", "Code": "USA", "Year": "2005", "Revised definition": "", "Legacy definition": "31.819479"}, {"Entity": "United States", "Code": "USA", "Year": "2006", "Revised definition": "", "Legacy definition": "31.553934"}, {"Entity": "United States", "Code": "USA", "Year": "2007", "Revised definition": "", "Legacy definition": "30.554886"}, {"Entity": "United States", "Code": "USA", "Year": "2008", "Revised definition": "", "Legacy definition": "29.84611"}, {"Entity": "United States", "Code": "USA", "Year": "2009", "Revised definition": "", "Legacy definition": "29.06811"}, {"Entity": "United States", "Code": "USA", "Year": "2010", "Revised definition": "", "Legacy definition": "27.67043"}, {"Entity": "United States", "Code": "USA", "Year": "2011", "Revised definition": "", "Legacy definition": "27.014856"}, {"Entity": "United States", "Code": "USA", "Year": "2012", "Revised definition": "", "Legacy definition": "27.125881"}, {"Entity": "United States", "Code": "USA", "Year": "2013", "Revised definition": "35.92287", "Legacy definition": "25.943014"}, {"Entity": "United States", "Code": "USA", "Year": "2014", "Revised definition": "37.009804", "Legacy definition": "26.610859"}, {"Entity": "United States", "Code": "USA", "Year": "2015", "Revised definition": "39.30674", "Legacy definition": "28.439377"}, {"Entity": "United States", "Code": "USA", "Year": "2016", "Revised definition": "40.94359", "Legacy definition": "29.98399"}, {"Entity": "United States", "Code": "USA", "Year": "2017", "Revised definition": "41.724495", "Legacy definition": ""}, {"Entity": "United States", "Code": "USA", "Year": "2018", "Revised definition": "44.006886", "Legacy definition": ""}, {"Entity": "United States", "Code": "USA", "Year": "2019", "Revised definition": "43.62197", "Legacy definition": ""}, {"Entity": "United States", "Code": "USA", "Year": "2020", "Revised definition": "38.37211", "Legacy definition": ""}, {"Entity": "United States", "Code": "USA", "Year": "2021", "Revised definition": "42.43633", "Legacy definition": ""}, {"Entity": "United States", "Code": "USA", "Year": "2022", "Revised definition": "42.120983", "Legacy definition": ""}, {"Entity": "United States", "Code": "USA", "Year": "2023", "Revised definition": "39.938988", "Legacy definition": ""}, {"Entity": "United States", "Code": "USA", "Year": "2024", "Revised definition": "37.4957", "Legacy definition": ""}], "rows_tail": [], "sampling_note": "Stored first 46 rows and last 46 rows when the table is larger.", "grapher_slug": "rates-rape-united-states", "metadata_url": "https://ourworldindata.org/grapher/rates-rape-united-states.metadata.json", "chart_title": "Estimated number of rapes per 100,000 in the United States", "chart_subtitle": "In 2013, the FBI changed its definition of rape within its crime reporting program. The legacy definition was narrower, and only included the forcible penetration of females without consent. The revised definition includes male and non-binary victims, and a broader criteria of crimes, including non-consensual oral or anal sex, or sexual assault with an object.", "chart_note": "The FBI takes reported crime data and adjusts it for gaps and underreporting to provide consistent long-term estimates over time. That means it may be different to reported crime figures.", "chart_citation": "FBI (2025)", "original_chart_url": "https://ourworldindata.org/grapher/rates-rape-united-states", "owid_column_metadata": {"Estimated number of rapes according to the revised definition per 100,000 population": {"titleShort": "Revised definition", "titleLong": "Revised definition", "descriptionKey": ["This data reflects the hierarchy rule, which requires that only the most serious offense in a case be counted. The descending order of violent crimes are homicide, rape, robbery, and aggravated assault, followed by the property crimes of burglary, larceny-theft, and motor vehicle theft. For example, if a robbery occurs during which a homicide takes place, only the homicide is counted."], "unit": "rapes per 100,000 population", "timespan": "2013-2024", "type": "Numeric", "owidVariableId": 1132534, "shortName": "rape_revised_per_100k", "lastUpdated": "2025-11-21", "nextUpdate": "2026-11-21", "citationShort": "FBI (2025) – processed by Our World in Data", "citationLong": "FBI (2025) – processed by Our World in Data. “Revised definition” [dataset]. FBI, “Summary Reporting System (SRS) Estimates” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132534.metadata.json"}, "Estimated number of rapes according to the legacy definition per 100,000 population": {"titleShort": "Legacy definition", "titleLong": "Legacy definition", "descriptionKey": ["This data reflects the hierarchy rule, which requires that only the most serious offense in a case be counted. The descending order of violent crimes are homicide, rape, robbery, and aggravated assault, followed by the property crimes of burglary, larceny-theft, and motor vehicle theft. For example, if a robbery occurs during which a homicide takes place, only the homicide is counted."], "unit": "rapes per 100,000 population", "timespan": "1979-2016", "type": "Numeric", "owidVariableId": 1132533, "shortName": "rape_legacy_per_100k", "lastUpdated": "2025-11-21", "nextUpdate": "2026-11-21", "citationShort": "FBI (2025) – processed by Our World in Data", "citationLong": "FBI (2025) – processed by Our World in Data. “Legacy definition” [dataset]. FBI, “Summary Reporting System (SRS) Estimates” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132533.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "829fa3eb51be5a1d9052"},
{"raw_link": "https://ourworldindata.org/biofuel-land-solar-electric-vehicles", "title": "Putting solar panels on land used for biofuels would produce enough electricity for all cars and trucks to go electric", "context": "Home\nEnergy\nBioenergy and Biofuels\nPutting solar panels on land used for biofuels would produce enough electricity for all cars and trucks to go electric\nThe world dedicates a Poland-sized area of land to liquid biofuels. Is there a more efficient way to generate energy?\nBy\nHannah Ritchie\nand\nPablo Rosado\nJanuary 12, 2026\nBrowse past versions\nCite this article\nReuse our work freely\nElectric vehicles might be promoted as the key technological solution for low-carbon transport today, but they weren’t always the obvious option. Back in the early 2000s, it was biofuels.\n1\nRather than extracting and burning oil, we could grow crops like cereals and sugarcane, and turn them into viable fuels.\nWhile we might expect biofuels to be a solution of the past due to the cost-competitiveness and\nrise of electric cars\n, the world produces more biofuels than ever. And this rise is expected to continue.\nIn this article, we give a sense of perspective on how much land is used to produce biofuels, and what the potential of that land could be if we used it for other forms of energy. We’ll focus on what would happen if we used that land for solar panels, and then how many electric vehicles could be powered as a result.\nWe’ll mostly focus on road transport, as that is where 99% of biofuels are currently used. The world generates small amounts of “biojet fuel” — used in aviation — but this accounts for only 1% of the total.\n2\nWhile aviation biofuels will increase in the coming years, in the near-to-medium-term, they’ll still be small compared to fuel for cars and trucks. By 2028, the IEA\nprojects that aviation\nmight consume around 2% of global biofuels.\nTo be clear: we’re not proposing that we should replace all biofuel land with solar panels. There are many ways we could utilise this land, whether for food production, some biofuel production, or rewilding. Maybe some combination of all of the above. But to make informed decisions about how to use our land effectively, we need to get a perspective on the potential of each option. That’s what we aim to do here for solar power and electrified transport.\nFor this analysis, we draw on a range of sources and, at times, produce our own estimates. We’ve written\na full methodological document\nthat explains our assumptions and guides you through each calculation.\nWhich countries produce biofuels, and what are the impacts?\nBefore we get into the calculations, it’s worth a quick overview of where biofuels are produced today, and what their impacts are.\nIf you’re already familiar with biofuels, you might want to skip to the next section.\nSome might imagine that biofuels have lost their relevance. But historical policies supporting them are still in place. As shown in the chart below, the world produces more biofuels than ever, and this trend is\nexpected to continue\n. Global production is focused in a relatively small number of markets, with the United States, Brazil, and the European Union dominating. Since there are no signs of policies changing in these regions, we would not expect the rise of biofuels to end.\nMost of the world’s biofuels come from sugarcane (mostly grown in Brazil), cereal crops such as corn (mostly grown in the United States and the European Union), and oil crops such as soybean and palm oil (which are grown in the US, Brazil, and Indonesia).\nIn the map below, you can get a view of where the world’s biofuels are grown.\nCollectively, these biofuels\nproduce around\n4% of the world’s energy demand for transport. While that does push some oil from the energy mix, the climate benefits of biofuels are not always as clear as people might assume.\nOnce we consider the climate impact of growing the food and manufacturing the fuel, the carbon savings relative to petrol can be small for some crops.\n3\nBut more importantly, when the\nopportunity costs\nof the land used to grow those crops are taken into account, they might be\nworse\nfor the climate.\n4\nThat’s because agricultural land use is not “free”. If we chose not to use it for agriculture, then it could be rewilded and reforested, which would sequester carbon from the atmosphere.\nFrom a climate perspective, freeing up that cropland from biofuels would be one alternative. However, another option is to utilise it for another form of energy, which could offer a much greater climate benefit.\nHow much land do biofuels use?\nThis\nshould\nbe easy to estimate. If you know how much land in the United States (or any other country) is used for corn, and what fraction of corn is for biofuels, you can calculate the amount of land used for biofuels.\nWhat makes things complicated is that biofuels often produce co-products that are allocated to other uses, such as animal feed. Not all of the corn or soybeans turn into liquid that can be put in a car; some residues can then be fed to pigs and chickens. How you adjust this land used for biofuels and their co-products can lead to quite different results.\nA\nrecent analysis\nfrom researchers at Cerulogy estimated that biofuels are grown on 61 million hectares of land.\n5\nBut when they split this allocation between land for biofuels and land for animal feed, the land use for biofuels\nalone\nwas 32 million hectares. The other 29 million hectares would be allocated for land use for animal feed.\nThere are much higher published figures. The Union for the Promotion of Oil and Protein Plants\nestimates that\nas much as 112 million hectares are “used to supply feedstock for biofuels”.\n6\nBy this definition, there is no adjustment for dual use of that land or the land use of co-products. That’s\none\nof the reasons why the figures are much higher. Even taking this into account, the numbers are still higher, and the honest answer is that we don’t know why.\nFor this article, we’re going to assume a net land use of\n32 million hectares\n. This is\nconservative\n, and that is deliberate. As we’ll soon see, the amount of solar power we could generate, or the number of electric vehicles we could power on this land, is extremely large. And that’s with us being fairly ungenerous about the amount of land available. Larger land use figures could also be credible; in that case, the potential would be even higher.\nHow large is 32 million hectares? Imagine an area like the one in the box below: 640 kilometers across, and 500 kilometers high. For context, that’s about\nthe size of\nGermany, Poland, the Philippines, Finland, or Italy.\nDownload\nHow much solar power could you produce on that land, and how many cars could you run?\nCould we use those 32 million hectares of land differently to produce even\nmore\nenergy than we currently get from biofuels?\nThe answer is yes. If we put solar panels on that land, we could produce roughly 32,000 terawatt-hours of electricity each year.\n7\nThat’s 23 times more than the energy that is currently produced\nin the form\nof all liquid biofuels.\n8\nYou can see this comparison in the chart.\nSome estimates suggest the gap between the energy generated by solar power and biofuels is even larger.\nDownload\n32,000 terawatt-hours is a big number. The world\ngenerated\n31,000 TWh of electricity in 2024. So, these new solar panels would produce enough to meet the world’s current electricity demand.\nAgain, our proposal isn’t that we\nshould\ncover all of this land in solar panels, or that it could easily power the world on its own. We don’t account for the fact that we’d need energy storage and other options to make sure that power is available where and when it’s needed (not just when the sun is shining). We’re just trying to get a sense of perspective for how much electricity\ncould\nbe produced by using that land in more efficient ways.\nIf we put solar panels on that land, we could produce roughly 32,000 terawatt-hours of electricity each year.\nThese comparisons might seem surprising at first. But they can be explained by the fact that growing crops is a very inefficient process. Plants convert\nless than 1%\nof sunlight into biomass through photosynthesis.\n9\nEven more energy is then lost when we turn those plants into liquid fuels. Crops such as sugarcane tend to perform better than others, like maize or soybeans, but even they are still inefficient.\nBy comparison, solar panels convert 15% to 20% of sunlight into electricity, with some recent designs\nachieving as much\nas 25%.\n10\nThat means replacing crops with solar panels will generate a lot more energy.\nNow, you might think that we’re comparing very different things here: energy from liquid biofuels meant to decarbonize\ntransport\n, and solar, which could decarbonize\nelectricity\n. But with the rise of affordable and high-quality electric vehicles, solar power can be a way to decarbonize transport, too.\nRun the numbers, and we find that you could power\nall\nof the world’s cars and trucks on this solar energy if transport were electrified.\nOf course, these vehicles would need to be electrified in the first place. This is happening — electric car\nsales are rising\n, and electric trucks are now starting to get some attention — but it will take time for most vehicles on the road to be electric. For now, we’ll imagine that they are.\nWe estimate that the total electricity needed to power all cars and trucks is around 7,000 TWh per year, comprising 3,500 TWh for cars and a similar amount for trucks. We’ve added this comparison to the chart.\nYou could power all of the world’s cars and trucks on this solar energy if transport were electrified.\nThat’s less than one-quarter of the 32,000 TWh that solar panels could produce on biofuel land. Consider those options. The world could meet 3% or 4% of transport demand with biofuels. Or it could meet\nall\nroad transport demand on just one-quarter of that land. The other three-quarters could be used for other things, such as food production, biofuels for aviation, or it could be left alone to rewild.\nIt’s worth noting that in this scenario — unlike using solar for bulk electricity needs — we would need much less additional energy storage solutions, because every car and truck is essentially a big battery in itself.\nDownload\nThe reason these comparisons are even more stark than biofuels versus solar is that most of the energy consumed in a petrol car is wasted; either as heat (if you put your hand over the bonnet, you will often notice that it’s extremely warm after driving) or from friction when braking. An electric car is much more efficient without a combustion engine, and thanks to regenerative braking (which uses braking energy to recharge the battery). That means that driving one mile in an electric car uses\njust one-third\nof the energy of driving one mile in a combustion engine car.\nPut these two efficiencies together, and we find that you could drive 70 times as many miles in a solar-powered electric car as you could in one running on biofuels from the same amount of land.\nThis “70 times” figure is conservative as it’s based on total biofuel energy production and land use, which includes more efficient crops such as sugarcane. The comparison between solar-powered electric cars and corn ethanol biofuels can be 100, or even 200 times.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nLand use comes at a cost, so we should think carefully about how to use it well\nOur point here is\nnot\nthat we should cover all of our biofuel land in solar panels. There are reasons why the comparisons above are simpler than the real world, and why dedicating\nall\nof that land to solar power would not be ideal.\n11\nThe world could meet 3% or 4% of transport demand with biofuels. Or it could meet all road transport demand on just one-quarter of that land.\nWhat we do want to challenge is how we think and talk about land use. People rightly question the impact of solar or wind farms on landscapes, but rarely consider the land use of existing biofuel crops, which do very little to decarbonize our energy supplies. Whether we’ll run out of land for solar or wind is a common concern, but when we run the numbers, it’s clear that there\nis\nmore than enough; we’re just using it for other things. Stacking up the comparative benefits of those other things allows us to make better choices, if they’re available.\nIn this article, we wanted to run the numbers and get some perspective on how we could use that Germany- or Poland-sized area of land in the most efficient way. What’s clear is that we could produce a huge amount of electricity from solar on just a fraction of that land. We could power an entire global electric car and truck fleet on just one-quarter of it.\nLand use\ncomes at a cost: for the climate, ecosystems, and other species we share the planet with. That means we should think carefully about how to use it well. That might mean a mix of biofuels for aviation, and solar power for road transport and electricity grids. It might mean going all-in on solar. Or it could mean using some of it for solar power, and leaving the rest alone. Sometimes, the most thoughtful option is not using land at all and letting it return to nature.\nMethodology\nIf you’re interested in digging deeper, we provide a methodological document where we go through our sources and calculations in detail.\nAcknowledgments\nWe would like to thank Max Roser and Edouard Mathieu for editorial feedback and comments on this article. We also thank Marwa Boukarim for help and support with the visualizations.\nContinue reading on Our World in Data\nCould biofuels meet demand for global aviation?\nTo fuel all of the world’s aviation demand, global biofuels would need to more than triple and be exclusively used for air travel.\nBioenergy and Biofuels\nHow much bioenergy does the world produce, and what is it used for?\nIf the world adopted a plant-based diet, we would reduce global agricultural land use from 4 to 1 billion hectares\nWe could reduce the amount of land used for grazing and croplands used to grow animal feed.\nEndnotes\nOther options didn’t rely on switching fuels, such as improving car efficiency and expanding public transport, but these only go so far.\nHere’s\na quote\nfrom the Intergovernmental Panel on Climate Change in 2007: “Within the transport sector there are five mitigation options with a clear link between sustainable development, adaptation and mitigation. These areas are biofuels, energy efficient, public transport, non-motorised transport and urban planning.”\nIn 2024, the International Energy Agency\nestimates that\n1.8 billion litres of liquid biofuel were for “biojet” fuel. Total production was 118 billion litres. That means biojet fuel was only 1%.\nMost of this biojet fuel comes from waste fats and oils, which also don’t have the same land use dilemmas as bioethanol and biodiesel used for road transport.\nCarbon savings for sugarcane feedstocks tend to be much larger than they are for corn, wheat, and palm oil feedstocks.\nThis can vary a lot, depending on location, crop type, and production system. But this meta-analysis finds that some, such as sugarcane ethanol from Brazil, can achieve more than 60% savings (if no land use change is involved), but some crops produce almost no savings at all.\nJeswani, H. K., Chilvers, A., & Azapagic, A. (2020). Environmental sustainability of biofuels: a review. Proceedings of the Royal Society A.\nThese results can be very sensitive to the methodology and life-cycle assessment tools.\nPereira, L. G., Cavalett, O., Bonomi, A., Zhang, Y., Warner, E., & Chum, H. L. (2019). Comparison of biofuel life-cycle GHG emissions assessment tools: The case studies of ethanol produced from sugarcane, corn, and wheat. Renewable and Sustainable Energy Reviews.\nSearchinger, T. D., Wirsenius, S., Beringer, T., & Dumas, P. (2018). Assessing the efficiency of changes in land use for mitigating climate change. Nature, 564(7735), 249-253.\nFehrenbach, H., & Bürck, S. (2022). Carbon opportunity costs of biofuels in Germany—An extended perspective on the greenhouse gas balance including foregone carbon storage. Frontiers in Climate.\nSandford et al. (2024). Diverted harvest: Environmental Risk from Growth in International Biofuel Demand. Cerulogy.\nThey estimate that 8% of global croplands supply feedstock for biofuel production. Using their estimate of 1.4 billion hectares of total cropland, this would be 112 million hectares.\nThis is based on the power density of modern solar panels — how much energy can be produced for a given area. For more details on these calculations, see\nour full methodological document\n.\nThis 1424 TWh is based on data from the\nEnergy Institute\n. We converted this from petajoules (EJ) to TWh using a conversion factor of 0.27778.\nCroce, R., Carmo-Silva, E., Cho, Y. B., Ermakova, M., Harbinson, J., Lawson, T., ... & Zhu, X. G. (2024). Perspectives on improving photosynthesis to increase crop yield. The Plant Cell.\nOni, A. M., Mohsin, A. S., Rahman, M. M., & Bhuian, M. B. H. (2024). A comprehensive evaluation of solar cell technologies, associated loss mechanisms, and efficiency enhancement strategies for photovoltaic cells. Energy Reports.\nFor example, global biofuel land is not located precisely where solar electricity or electric vehicle demand is expected to be.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Pablo Rosado (2026) - “Putting solar panels on land used for biofuels would produce enough electricity for all cars and trucks to go electric” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-083815/biofuel-land-solar-electric-vehicles.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-biofuel-land-solar-electric-vehicles,\nauthor = {Hannah Ritchie and Pablo Rosado},\ntitle = {Putting solar panels on land used for biofuels would produce enough electricity for all cars and trucks to go electric},\njournal = {Our World in Data},\nyear = {2026},\nnote = {https://archive.ourworldindata.org/20260518-083815/biofuel-land-solar-electric-vehicles.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "biofuel-land-solar-electric-vehicles", "source_url": "https://ourworldindata.org/biofuel-land-solar-electric-vehicles", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "The world dedicates a Poland-sized area of land to liquid biofuels. Is there a more efficient way to generate energy?", "numeric_mentions": ["12,", "2026", "2000", "1", "99%", "1%", "2", "2028,", "2%", "4%", "3", "4", "61 million", "5", "32 million", "29 million", "112 million", "6", "640", "500", "32,000", "7", "23", "8", "31,000", "2024", "9", "15%", "20%", "25%", "10", "7,000", "3,500", "3%", "70", "100,", "200", "11", "1 billion", "2007", "2024,", "1.8 billion", "118 billion", "60%", "2020", "2019", "2018", "564", "7735", "249", "253", "2022", "8%", "1.4 billion", "1424", "0.27778", "20260518", "083815", "18,"], "numeric_evidence": [{"title": "Biofuel energy production", "source_url": "https://ourworldindata.org/grapher/biofuel-production.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Biofuels"], "row_count_total": 1610, "rows_head": [{"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Biofuels": "0.3051023"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Biofuels": "0.3051023"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Biofuels": "0.30529028"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Biofuels": "0.068616726"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Biofuels": "0.068616726"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Biofuels": "0.068616726"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1996", "Biofuels": "0.06880471"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1997", "Biofuels": "0.102925085"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1998", "Biofuels": "0.102925085"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1999", "Biofuels": "0.068616726"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Biofuels": "0.06880471"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Biofuels": "0.068616726"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Biofuels": "0.24095866"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Biofuels": "0.251716"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Biofuels": "0.28782013"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Biofuels": "0.2943336"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Biofuels": "0.35644424"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Biofuels": "0.3456951"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Biofuels": "0.37789974"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Biofuels": "0.5937582"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Biofuels": "0.8238055"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Biofuels": "1.1333025"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Biofuels": "1.0127013"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Biofuels": "1.3421129"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Biofuels": "1.478777"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Biofuels": "1.3343786"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Biofuels": "1.2191871"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Biofuels": "1.1253604"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Biofuels": "0.8885908"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Biofuels": "0.9744526"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Biofuels": "0.9650717"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2021", "Biofuels": "0.9954881"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2022", "Biofuels": "1.0745708"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2023", "Biofuels": "1.1433736"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2024", "Biofuels": "1.148281"}, {"Entity": "Africa (EI)", "Code": "", "Year": "1990", "Biofuels": "0.3051023"}, {"Entity": "Africa (EI)", "Code": "", "Year": "1991", "Biofuels": "0.3051023"}, {"Entity": "Africa (EI)", "Code": "", "Year": "1992", "Biofuels": "0.30529028"}, {"Entity": "Africa (EI)", "Code": "", "Year": "1993", "Biofuels": "0.068616726"}, {"Entity": "Africa (EI)", "Code": "", "Year": "1994", "Biofuels": "0.068616726"}, {"Entity": "Africa (EI)", "Code": "", "Year": "1995", "Biofuels": "0.068616726"}, {"Entity": "Africa (EI)", "Code": "", "Year": "1996", "Biofuels": "0.06880471"}, {"Entity": "Africa (EI)", "Code": "", "Year": "1997", "Biofuels": "0.102925085"}, {"Entity": "Africa (EI)", "Code": "", "Year": "1998", "Biofuels": "0.102925085"}, {"Entity": "Africa (EI)", "Code": "", "Year": "1999", "Biofuels": "0.068616726"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2000", "Biofuels": "0.06880471"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2001", "Biofuels": "0.068616726"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2002", "Biofuels": "0.24095866"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2003", "Biofuels": "0.251716"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2004", "Biofuels": "0.28782013"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2005", "Biofuels": "0.2943336"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2006", "Biofuels": "0.35644424"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2007", "Biofuels": "0.3456951"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2008", "Biofuels": "0.37789974"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2009", "Biofuels": "0.5937582"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2010", "Biofuels": "0.8238055"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2011", "Biofuels": "1.1333025"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2012", "Biofuels": "1.0127013"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2013", "Biofuels": "1.3421129"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2014", "Biofuels": "1.478777"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2015", "Biofuels": "1.3343786"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2016", "Biofuels": "1.2191871"}, {"Entity": "Africa (EI)", "Code": "", "Year": "2017", "Biofuels": 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"Code": "ZWE", "Year": "2012", "Land area": "38685000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Land area": "38685000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Land area": "38685000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Land area": "38685000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Land area": "38685000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Land area": "38685000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Land area": "38685000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Land area": "38685000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Land area": "38685000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Land area": "38685000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Land area": "38685000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Land area": "38685000"}], "sampling_note": "Stored first 120 rows and last 120 rows when 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Food and Agriculture Organization of the United Nations, “Land, Inputs and Sustainability: Land Use” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1195972.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "890c8f5cca1a4935e39e"},
{"raw_link": "https://ourworldindata.org/bioenergy-biofuels", "title": "Bioenergy and Biofuels", "context": "Bioenergy and Biofuels\nBy\nHannah Ritchie\nand\nPablo Rosado\nContents\nBiofuels are fuels produced from biomass and organic matter, which can be made from crops such as cereals or sugarcane, wood, grasses, or biological waste such as animal fats and oils.\nThese biomass sources can be converted into energy supplies in various ways. They can, for example, be burned to produce electricity in a power plant. They can also be processed into liquid fuels and used in cars, trucks, and planes. It’s these two “modern” applications that the data and research on this page focus on.\nThis page focuses on what is called\nmodern bioenergy\n.\nTraditional bioenergy\n, which is the direct burning of wood, crops, and other biological waste, remains a primary source of energy for cooking and heating for billions of people. That is covered separately in our work on\nEnergy Access\n.\nElectricity from bioenergy\nElectricity can be generated from biomass using various methods. The most common method is combustion: biomass, such as wood pellets, wood chips, and forest residues, is burned in a power plant, just like you’d do with coal. This creates steam, which turns a turbine, and this generates power.\nBioenergy accounts for a relatively small share of the world’s electricity, but, as we’ll see, in some countries, it’s a substantial source of power.\nHow much of the world’s electricity comes from bioenergy?\nThe chart below shows bioenergy’s share of the\nglobal\nelectricity mix.\nIn 2000, it produced around 1% of the world’s electricity. This doubled over the next two decades, but has stagnated at just over 2% in the last five years.\nIn comparison to\nother sources\nof electricity, the growth of bioenergy has been relatively slow. It took almost 20 years for its share to increase one percentage point, from 1% to 2%. Solar power is now\ngrowing its share\nby more than one percentage point every year.\nWhich countries rely on bioenergy for electricity?\nSome countries get a substantial share of their electricity from bioenergy. The map below shows the distribution across the world.\nLuxembourg generates almost one-third from biomass. In Uruguay, Estonia, and Denmark, around one-fifth.\nBioenergy is much more common in Europe and South America than in other regions, for several reasons.\nMany countries in Europe had existing or older coal plant infrastructure; these could be easily retrofitted to use biomass instead of coal. Several, including Finland, Sweden, Estonia, and Latvia, also have\nlarge forestry sectors\nthat produce wood residues and lower-grade wood, which could be diverted towards electricity production. Bioenergy has made up a substantial share of their electricity generation for decades, so this is not a recent change. Policies in\nthe European Union\n— where biomass is counted as a renewable source — have often encouraged a switch to bioenergy through subsidies and feed-in tariffs.\nSouth America — as we’ll soon see — produces a lot of\nliquid\nbiofuels for transport. In an effort to reduce their dependence on oil, several countries in South America have implemented large-scale programs to promote the production of bioethanol. The most notable is the\n“Proálcool” program\nin Brazil. Sugarcane is their primary biofuel crop, and it leaves behind a fibrous residue that can be used for electricity production.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nLiquid biofuels for transport\nBiofuels are also produced in liquid form and used as fuel in transport. As we’ll see, most of this biofuel comes from growing crops, such as corn, sugarcane, oil palm, and soybeans. Waste cooking oil and animal fats are also used to produce liquid fuels.\nToday, most liquid biofuels are used in road transport. They can be blended with petrol or diesel at various concentrations, displacing some oil from the energy mix. But increasingly, they are promoted as a potential solution to decarbonizing sectors such as aviation or shipping.\nHow has global liquid biofuel production changed over time?\nLiquid biofuels have grown dramatically over the past few decades. As you can see in the chart below, global production has grown sevenfold in the last 20 years.\nThis has been driven by policies in a few key regions — particularly the United States, Brazil, and the European Union — that have actively promoted biofuel production.\nWhich countries produce biofuels?\nWhere in the world are these biofuels produced? You can see production — measured in terms of energy content — by country in the map below. Both the United States and Brazil stand out as the world’s largest producers.\nOnly a handful of countries (or regions, if we consider the European Union collectively) produce most of the world’s biofuels.\nThe United States is the world’s largest producer, as you can see in the chart below. Next are Brazil, the EU, Indonesia, and China. These five countries/regions alone produce more than 80% of the global total.\nYou can see how global production of liquid biofuels has changed over the last few decades, broken down by country.\nAll\nof the major producers saw substantial growth over that time (and continue to do so).\nInterest and policies oriented towards biofuels started in the United States in the 1990s as a way to reduce its dependence on foreign oil markets. In 2005, it introduced its\nRenewable Fuel Standard\n, which mandated that a minimum volume of the country’s transport fuel be sourced from biofuels. In 2007, it extended it further. This led to a rapid growth in biofuel production, mostly from corn (maize).\nBrazil started\nusing bioethanol\ndecades earlier. As you can see, by the early 1990s, they were already producing significant volumes. However, production ramped up in the early 2000s, again, as a means to reduce the country’s dependence on oil for transportation. Most of its biofuels come from sugarcane.\nWhat crops are used for biofuels?\nWhat are the biomass sources of these fuels?\nIn the chart below, you can see a breakdown\nfrom the\nInternational Energy Agency. This shows each feedstock’s share of global liquid biofuels by volume (i.e., liters).\nMost of the world’s biofuels — over 90% of them — come from food crops, not waste oils or fats. The largest source is corn (maize), which accounts for just over one-third of the total. Most of this comes from the United States, where biofuel demand\nhas driven\nmost of its growth in corn production over the last few decades.\nSugarcane — mostly produced in Brazil — is the second largest source, followed by oil crops including palm oil, soy oil, and rapeseed. Used cooking oil and animal fats make up around 12%.\nDownload\nHow much land is used for biofuel production?\nWhat makes this slightly complicated to answer is that biofuels often produce co-products that are allocated to other uses, such as animal feed. How you adjust this land used for biofuels and their co-products can lead to quite different results.\nA\nrecent analysis\nfrom researchers at Cerulogy estimated that biofuels are grown on 61 million hectares of land.\n1\nBut when they split this allocation between land for biofuels and land for animal feed, the land use for biofuels\nalone\nwas 32 million hectares. The other 29 million hectares would be allocated for land use for animal feed.\nThere are much higher published figures. The Union for the Promotion of Oil and Protein Plants\nestimates that\nas much as 112 million hectares are “used to supply feedstock for biofuels”.\n2\nBy this definition, there is no adjustment for dual use of that land or the land use of co-products. That’s\none\nof the reasons why the figures are much higher. Even taking this into account, the numbers are still higher, and it’s unclear why. The methodology is not fully transparent.\nPutting the more recent figure of 32 million hectares into context: imagine an area like the one in the box below: 640 kilometers across, and 500 kilometers high. For context, that’s about\nthe size of\nGermany, Poland, the Philippines, Finland, or Italy.\nDownload\nYou can read more about the land use of biofuels, including some analysis on whether this land could be used for energy more efficiently, in our article:\nPutting solar panels on land used for biofuels would produce enough electricity for all cars and trucks to go electric\nThe world dedicates a Poland-sized area of land to liquid biofuels. Is there a more efficient way to generate energy?\nWhat are biofuels used for?\nCollectively, biofuels\nproduce around\n4% of the world’s energy demand for transport.\nThe vast majority of biofuel use is for\nroad\ntransport. The International Energy Agency reports around 99% of it.\n3\nLess than 1% was used for aviation in 2023. But within aviation’s fuel supply, biofuel’s contribution is even smaller. We estimate that only around 0.4% of aviation’s energy demand comes from biofuels.\n4\nYou can see both of those points in the chart below.\nDownload\nYou can read more about the contribution and potential of biofuels for aviation in our article:\nCould biofuels meet demand for global aviation?\nTo fuel all of the world’s aviation demand, global biofuels would need to more than triple and be exclusively used for air travel.\nResearch & Writing\nJanuary 12, 2026\nPutting solar panels on land used for biofuels would produce enough electricity for all cars and trucks to go electric\nThe world dedicates a Poland-sized area of land to liquid biofuels. Is there a more efficient way to generate energy?\nHannah Ritchie and Pablo Rosado\nJanuary 26, 2026\nCould biofuels meet demand for global aviation?\nTo fuel all of the world’s aviation demand, global biofuels would need to more than triple and be exclusively used for air travel.\nHannah Ritchie and Pablo Rosado\nSee all articles on this topic\nData Insights on\nBioenergy and Biofuels\nEndnotes\nSandford et al. (2024). Diverted harvest: Environmental Risk from Growth in International Biofuel Demand. Cerulogy.\nThey estimate that 8% of global croplands supply feedstock for biofuel production. Using their estimate of 1.4 billion hectares of total cropland, this would be 112 million hectares.\nIn 2024, the International Energy Agency\nestimates that\n1.8 billion liters of liquid biofuel were for “biojet” fuel. Total production was 118 billion liters. That means biojet fuel was only 1%. We can probably assume that the rest was used for road transport, since its use in marine transport is also negligible.\nThe International Energy Agency (IEA)\nestimated that\naviation energy demand in 2024 was 14.16 exajoules (EJ). That’s equivalent to 13.6 exajoules (EJ), or 3,932 terawatt-hours (TWh).\nWe estimate that biofuels used for aviation are equivalent to around 17 TWh. The IEA\nestimates that\n1.8 billion liters of biojet fuel were used in 2024. With an energy content of 34 MJ per litre, this is equivalent to 61 billion MJ (or 0.061 EJ). That is equivalent to 17 TWh.\n17 TWh is 0.43% of the 3,930 TWh demand [17/ 3,930 = 0.43%].\nThis is very similar to figures reported by the International Air Transport Association (IATA). It\nreported that\nin 2024, “In 2024, SAF production reached 1Mt (1.250 billion liters), doubling the amounts produced in 2023, representing\n0.3%\nof global jet fuel use.”\nBy 2025, the IATA expected that this could be 0.6% of global demand.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this topic page, please also cite the underlying data sources. This topic page can be cited as:\nHannah Ritchie and Pablo Rosado (2026) - “Bioenergy and Biofuels” Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/bioenergy-biofuels' [Online Resource]\nBibTeX citation\n@article{owid-bioenergy-biofuels,\nauthor = {Hannah Ritchie and Pablo Rosado},\ntitle = {Bioenergy and Biofuels},\njournal = {Our World in Data},\nyear = {2026},\nnote = {https://ourworldindata.org/bioenergy-biofuels}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "bioenergy-biofuels", "source_url": "https://ourworldindata.org/bioenergy-biofuels", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "How much bioenergy does the world produce, and what is it used for?", "numeric_mentions": ["2000,", "1%", "2%", "20 years", "80%", "1990", "2005,", "2007,", "2000", "90%", "12%", "61 million", "1", "32 million", "29 million", "112 million", "2", "640", "500", "4%", "99%", "3", "2023", "0.4%", "4", "12,", "2026", "26,", "2024", "8%", "1.4 billion", "2024,", "1.8 billion", "118 billion", "14.16", "13.6", "3,932", "17", "34", "61 billion", "0.061", "0.43%", "3,930", "1.250 billion", "2023,", "0.3%", "2025,", "0.6%"], "numeric_evidence": [{"title": "Share of electricity production by source", "source_url": "https://ourworldindata.org/grapher/share-elec-by-source.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Coal", "Gas", "Hydropower", "Solar", "Wind", "Oil", "Nuclear", "Other renewables", "Bioenergy"], "row_count_total": 7887, "rows_head": [{"Entity": "ASEAN (Ember)", "Code": "", "Year": "2000", "Coal": "20.073397", "Gas": "43.36783", "Hydropower": "13.3145", "Solar": "0", "Wind": "0", "Oil": "17.224627", "Nuclear": "0", "Other renewables": "4.469849", "Bioenergy": "1.549794"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2001", "Coal": "21.294035", "Gas": "47.00437", "Hydropower": "13.411835", "Solar": "0", "Wind": "0", "Oil": "12.646572", "Nuclear": "0", "Other renewables": "4.048483", "Bioenergy": "1.5947074"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2002", "Coal": "21.567904", "Gas": "48.22826", "Hydropower": "12.3040695", "Solar": "0", "Wind": "0", "Oil": "12.537224", "Nuclear": "0", "Other renewables": "3.8343453", "Bioenergy": "1.5281978"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2003", "Coal": "22.258347", "Gas": "49.423958", "Hydropower": "11.610299", "Solar": "0", "Wind": "0", "Oil": "11.649574", "Nuclear": "0", "Other renewables": "3.4344316", "Bioenergy": "1.6233908"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2004", "Coal": "23.15902", "Gas": "49.165344", "Hydropower": "10.670345", "Solar": "0", "Wind": "0", "Oil": "11.979219", "Nuclear": "0", "Other renewables": "3.3366222", "Bioenergy": "1.6894547"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2005", "Coal": "23.825285", "Gas": "48.461777", "Hydropower": "10.308411", "Solar": "0", "Wind": "0.0038264329", "Oil": "12.5621805", "Nuclear": "0", "Other renewables": "3.1568072", "Bioenergy": "1.6817173"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2006", "Coal": "25.235302", "Gas": "48.005196", "Hydropower": "11.060549", "Solar": "0.0073103425", "Wind": "0.0091379285", "Oil": "10.994755", "Nuclear": "0", "Other renewables": "3.130654", "Bioenergy": "1.557103"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2007", "Coal": "26.89212", "Gas": "47.32806", "Hydropower": "10.924385", "Solar": "0.0069087017", "Wind": "0.010363053", "Oil": "10.093613", "Nuclear": "0", "Other renewables": "2.9776504", "Bioenergy": "1.7669004"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2008", "Coal": "25.172195", "Gas": "48.528046", "Hydropower": "11.581892", "Solar": "0.0066229547", "Wind": "0.009934432", "Oil": "9.80694", "Nuclear": "0", "Other renewables": "3.1508708", "Bioenergy": "1.7434928"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2009", "Coal": "26.064331", "Gas": "49.445217", "Hydropower": "11.798517", "Solar": "0.006404406", "Wind": "0.011207711", "Oil": "7.792561", "Nuclear": "0", "Other renewables": "3.1413612", "Bioenergy": "1.7403973"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2010", "Coal": "27.296684", "Gas": "47.641445", "Hydropower": "11.657397", "Solar": "0.010191306", "Wind": "0.014559008", "Oil": "8.578167", "Nuclear": "0", "Other renewables": "2.8084328", "Bioenergy": "1.9931282"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2011", "Coal": "29.450401", "Gas": "42.545357", "Hydropower": "13.889857", "Solar": "0.01394564", "Wind": "0.01812933", "Oil": "9.220858", "Nuclear": "0", "Other renewables": "2.695692", "Bioenergy": "2.165758"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2012", "Coal": "31.25153", "Gas": "43.156445", "Hydropower": "14.842191", "Solar": "0.066961125", "Wind": "0.018027995", "Oil": "5.9711294", "Nuclear": "0", "Other renewables": "2.5355089", "Bioenergy": "2.1582086"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2013", "Coal": "31.478807", "Gas": "44.097267", "Hydropower": "15.569215", "Solar": "0.17508632", "Wind": "0.059994616", "Oil": "4.0820823", "Nuclear": "0", "Other renewables": "2.329995", "Bioenergy": "2.207557"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2014", "Coal": "32.702526", "Gas": "43.19942", "Hydropower": "15.140573", "Solar": "0.25579593", "Wind": "0.07176176", "Oil": "4.1390324", "Nuclear": "0", "Other renewables": "2.3577209", "Bioenergy": "2.133176"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2015", "Coal": "35.39786", "Gas": "41.657574", "Hydropower": "13.410051", "Solar": "0.3130408", "Wind": "0.13227077", "Oil": "4.562239", "Nuclear": "0", "Other renewables": "2.3257608", "Bioenergy": "2.2012057"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2016", "Coal": "37.579174", "Gas": "39.1782", "Hydropower": "14.86888", "Solar": "0.52922606", "Wind": "0.16196588", "Oil": "3.1640084", "Nuclear": "0", "Other renewables": "2.2417314", "Bioenergy": "2.2768068"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2017", "Coal": "38.07062", "Gas": "35.923565", "Hydropower": "17.805515", "Solar": "0.62506825", "Wind": "0.2530038", "Oil": "2.5826232", "Nuclear": "0", "Other renewables": "2.3147366", "Bioenergy": "2.4248679"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2018", "Coal": "40.52156", "Gas": "33.79427", "Hydropower": "17.494701", "Solar": "0.63304734", "Wind": "0.32399324", "Oil": "2.257682", "Nuclear": "0", "Other renewables": "2.3127701", "Bioenergy": "2.6619732"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2019", "Coal": "43.068466", "Gas": "34.177814", "Hydropower": "14.445861", "Solar": "1.1771625", "Wind": "0.5266488", "Oil": "1.809854", "Nuclear": "0", "Other renewables": "2.2099645", "Bioenergy": "2.5842328"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2020", "Coal": "45.41634", "Gas": "30.107845", "Hydropower": "15.929886", "Solar": "1.8049083", "Wind": "0.5118396", "Oil": "1.2634357", "Nuclear": "0", "Other renewables": "2.36434", "Bioenergy": "2.6014025"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2021", "Coal": "43.63855", "Gas": "29.46088", "Hydropower": "16.833925", "Solar": "3.1351295", "Wind": "0.74250793", "Oil": "1.0994996", "Nuclear": "0", "Other renewables": "2.241354", "Bioenergy": "2.8481534"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2022", "Coal": "41.612778", "Gas": "29.080238", "Hydropower": "18.45009", "Solar": "3.0615087", "Wind": "1.10509", "Oil": "1.2458867", "Nuclear": "0", "Other renewables": "2.2200031", "Bioenergy": "3.224407"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2023", "Coal": "43.60655", "Gas": "29.169516", "Hydropower": "16.318176", "Solar": "3.02916", "Wind": "1.3483888", "Oil": "1.0592238", "Nuclear": "0", "Other renewables": "2.1506643", "Bioenergy": "3.318325"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2024", "Coal": "44.64134", "Gas": "28.524305", "Hydropower": "16.374407", "Solar": "3.0626206", "Wind": "1.3386486", "Oil": "0.95332485", "Nuclear": "0", "Other renewables": "2.0116708", "Bioenergy": "3.0936835"}, {"Entity": "ASEAN (Ember)", "Code": "", "Year": "2025", "Coal": "44.561813", "Gas": "27.38402", "Hydropower": "17.438406", "Solar": "3.3821223", "Wind": "1.4403037", "Oil": "1.1081506", "Nuclear": "0", "Other renewables": "1.9556887", "Bioenergy": "2.729496"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Coal": "0", "Gas": "0", "Hydropower": "64.583336", "Solar": "0", "Wind": "0", "Oil": "35.416668", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Coal": "5.7971015", "Gas": "0", "Hydropower": "72.46377", "Solar": "0", "Wind": "0", "Oil": "21.739132", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Coal": "5.633803", "Gas": "0", "Hydropower": "78.873245", "Solar": "0", "Wind": "0", "Oil": "15.492958", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Coal": "9.89011", "Gas": "0", "Hydropower": "69.23077", "Solar": "0", "Wind": "0", "Oil": "20.87912", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Coal": "7.5949364", "Gas": "0", "Hydropower": "70.88608", "Solar": "0", "Wind": "0", "Oil": "21.518988", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Coal": "7.317073", "Gas": "0", "Hydropower": "71.95122", "Solar": "0", "Wind": "0", "Oil": "20.731709", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Coal": "16.666668", "Gas": "0", "Hydropower": "71.111115", "Solar": "0", "Wind": "0", "Oil": "12.222222", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Coal": "14.851486", "Gas": "0", "Hydropower": "74.25742", "Solar": "0", "Wind": "0", "Oil": "10.891089", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Coal": "16.666666", "Gas": "0", "Hydropower": "69.230774", "Solar": "0", "Wind": "0", "Oil": "14.102565", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Coal": "6.741573", "Gas": "0", "Hydropower": "87.64045", "Solar": "0", "Wind": "0", "Oil": "5.6179776", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Coal": "6.8181815", "Gas": "0", "Hydropower": "85.22727", "Solar": "0", "Wind": "0", "Oil": "7.9545455", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Coal": "12.987014", "Gas": "0", "Hydropower": "77.92208", "Solar": "0", "Wind": "0", "Oil": "9.090909", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Coal": "7.9545455", "Gas": "0", "Hydropower": "80.681816", "Solar": "3.4090908", "Wind": "0", "Oil": "7.9545455", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Coal": "10.81081", "Gas": "0", "Hydropower": "77.47748", "Solar": "2.7027025", "Wind": "0", "Oil": "9.009009", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Coal": 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{"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Coal": "0", "Gas": "21.5781", "Hydropower": "0", "Solar": "0", "Wind": "0", "Oil": "78.4219", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Coal": "0", "Gas": "31.400282", "Hydropower": "0", "Solar": "0", "Wind": "0", "Oil": "68.59972", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Coal": "0", "Gas": "32", "Hydropower": "0", "Solar": "0", "Wind": "0", "Oil": "68", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Coal": "0", "Gas": "38.51175", "Hydropower": "0", "Solar": "0.1305483", "Wind": "0", "Oil": "61.3577", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Coal": "0", "Gas": "39.23611", "Hydropower": "0", "Solar": "2.0833333", "Wind": "0", "Oil": "58.680553", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Coal": "0", "Gas": "32.848232", "Hydropower": "0", "Solar": "3.3264034", "Wind": "0", "Oil": "63.825363", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Coal": "0", "Gas": "23.456789", "Hydropower": "0", "Solar": "4.6913576", "Wind": "0", "Oil": "71.85185", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Coal": "0", "Gas": "9.169054", "Hydropower": "0", "Solar": "13.753581", "Wind": "0", "Oil": "77.07736", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Coal": "0", "Gas": "9.756098", "Hydropower": "0", "Solar": "14.634146", "Wind": "0", "Oil": "75.60976", "Nuclear": "0", "Other renewables": "0", "Bioenergy": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Coal": "0", "Gas": "13.945578", 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"USA", "Year": "2009", "Biofuels": "262.63998"}, {"Entity": "United States", "Code": "USA", "Year": "2010", "Biofuels": "309.4932"}, {"Entity": "United States", "Code": "USA", "Year": "2011", "Biofuels": "347.36652"}, {"Entity": "United States", "Code": "USA", "Year": "2012", "Biofuels": "331.92157"}, {"Entity": "United States", "Code": "USA", "Year": "2013", "Biofuels": "348.46423"}, {"Entity": "United States", "Code": "USA", "Year": "2014", "Biofuels": "370.0955"}, {"Entity": "United States", "Code": "USA", "Year": "2015", "Biofuels": "381.23242"}, {"Entity": "United States", "Code": "USA", "Year": "2016", "Biofuels": "407.63672"}, {"Entity": "United States", "Code": "USA", "Year": "2017", "Biofuels": "420.88632"}, {"Entity": "United States", "Code": "USA", "Year": "2018", "Biofuels": "435.04752"}, {"Entity": "United States", "Code": "USA", "Year": "2019", "Biofuels": "429.92654"}, {"Entity": "United States", "Code": "USA", "Year": "2020", "Biofuels": "393.43954"}, {"Entity": "United States", "Code": "USA", "Year": "2021", "Biofuels": "425.18463"}, {"Entity": "United States", "Code": "USA", "Year": "2022", "Biofuels": "452.0822"}, {"Entity": "United States", "Code": "USA", "Year": "2023", "Biofuels": "497.89624"}, {"Entity": "United States", "Code": "USA", "Year": "2024", "Biofuels": "532.58624"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1990", "Biofuels": "68.512"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1991", "Biofuels": "76.8156"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1992", "Biofuels": "70.37044"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1993", "Biofuels": "68.25598"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1994", "Biofuels": "74.95961"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1995", "Biofuels": "76.39261"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1996", "Biofuels": "85.029236"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1997", "Biofuels": "93.439804"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1998", "Biofuels": "85.30479"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1999", "Biofuels": "78.68598"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2000", "Biofuels": "65.069145"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2001", "Biofuels": "69.95166"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2002", "Biofuels": "78.49688"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2003", "Biofuels": "93.25523"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2004", "Biofuels": "95.18144"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2005", "Biofuels": "106.80752"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2006", "Biofuels": "123.84488"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2007", "Biofuels": "161.1925"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2008", "Biofuels": "211.17262"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2009", "Biofuels": "214.67964"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2010", "Biofuels": "244.79805"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2011", "Biofuels": "244.94052"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2012", "Biofuels": "259.37454"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2013", "Biofuels": "296.08154"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2014", "Biofuels": "329.0537"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2015", "Biofuels": "312.72162"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2016", "Biofuels": "324.36276"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2017", "Biofuels": "323.79294"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2018", "Biofuels": "397.78952"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2019", "Biofuels": "440.20374"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2020", "Biofuels": "421.26093"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2021", "Biofuels": "438.0463"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2022", "Biofuels": "465.60687"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2023", "Biofuels": "514.1076"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2024", "Biofuels": "565.33716"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1990", "Biofuels": "86.5863"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1991", "Biofuels": "97.41143"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1992", "Biofuels": "93.686874"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1993", "Biofuels": "95.28714"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1994", "Biofuels": "105.83975"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1995", "Biofuels": "109.823586"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1996", "Biofuels": "110.976395"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1997", "Biofuels": "127.39432"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1998", "Biofuels": "121.57153"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1999", "Biofuels": "117.51924"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Biofuels": "112.215546"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Biofuels": "122.578415"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Biofuels": "144.04039"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Biofuels": "178.25037"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Biofuels": "201.25858"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Biofuels": "237.02116"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Biofuels": "301.89563"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Biofuels": "410.08588"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Biofuels": "556.3209"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Biofuels": "614.0576"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Biofuels": "706.45715"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Biofuels": "751.1705"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Biofuels": "764.31354"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Biofuels": "832.6453"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Biofuels": "907.98663"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Biofuels": "909.044"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Biofuels": "943.0741"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Biofuels": "970.6383"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Biofuels": "1084.535"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Biofuels": "1140.2383"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Biofuels": "1074.6621"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Biofuels": "1132.0234"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Biofuels": "1196.7405"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Biofuels": "1313.8804"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Biofuels": "1424.5156"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "biofuel-production", "metadata_url": "https://ourworldindata.org/grapher/biofuel-production.metadata.json", "chart_title": "Biofuel energy production", "chart_subtitle": "Total biofuel production is measured in terawatt-hours per year. Biofuel production includes both bioethanol and biodiesel.", "chart_note": null, "chart_citation": "Energy Institute - Statistical Review of World Energy (2025)", "original_chart_url": "https://ourworldindata.org/grapher/biofuel-production", "owid_column_metadata": {"Biofuels production - TWh": {"titleShort": "Biofuels production", "titleLong": "Biofuels production", "descriptionShort": "Includes biogasoline (such as ethanol) and biodiesel. Volumes have been adjusted for energy content.", "shortUnit": "TWh", "unit": "terawatt-hours", "timespan": "1990-2024", "type": "Numeric", "owidVariableId": 1077519, "shortName": "biofuels_production_twh", "lastUpdated": "2025-06-27", "nextUpdate": "2026-06-27", "citationShort": "Energy Institute - Statistical Review of World Energy (2025) – with major processing by Our World in Data", "citationLong": "Energy Institute - Statistical Review of World Energy (2025) – with major processing by Our World in Data. “Biofuels production” [dataset]. Energy Institute, “Statistical Review of World Energy” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077519.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "2a78cd6da3b48a95bb18"},
{"raw_link": "https://ourworldindata.org/medicine-biotech", "title": "Medicine and Biotechnology", "context": "Medicine and Biotechnology\nBy\nTuna Acisu\n,\nSaloni Dattani\n,\nFiona Spooner\n,\nVeronika Samborska\n,\nHannah Ritchie\n,\nand\nMax Roser\nCite this work\nReuse this work\nFor much of human history, people relied on trial and error to find cures for diseases and health conditions using natural plants and remedies, with little idea of which ingredients worked or why. Natural cures were occasionally effective, but contamination and inconsistency were common, and treatments couldn’t be produced reliably at scale.\nBut in the recent past, scientific advances changed this. The industrial production of chemical drugs made it possible to purify and mass-produce medicines safely and reliably, including anesthetics, painkillers, chemotherapy for cancer, and later,\nstatins\nto prevent heart disease. Vaccines brought diseases like\nsmallpox\nand\npolio\nunder control. The “\nGolden Age of Antibiotics\n” turned once-deadly infections into treatable conditions. And biotechnology — using living organisms like yeast and bacteria to develop products — paved the way for gene therapies, engineered proteins, and\nmonoclonal antibodies\n.\nToday, drug development relies less on guesswork and more on prediction and design. It’s supported by advances in tools like microscopy, genetic sequencing, and other technologies that help scientists understand diseases at the level of cells and molecules. New medicines undergo careful clinical trials to test their safety and effectiveness, and are reviewed by agencies like the\nUS Food and Drug Administration\nbefore they reach patients.\nTogether, these advances have reduced death rates from infectious diseases,\ncancers\n,\ncardiovascular diseases\n, and other chronic illnesses,\nraising life expectancy further\n. This progress can continue if we invest in new ways to treat conditions we still can’t cure, and develop safer, more effective medicines for the ones we can. Ensuring everyone can benefit from these treatments means making them affordable and deliverable across the world.\nThis page brings together key data and research on medicine and biotechnology.\nRelated topics\nHealthcare spending\nResearch and development\nTechnological change\nVaccination\nAntibiotics and antibiotic resistance\nResearch & Writing\nNovember 4, 2024\nHPV vaccination: How the world can eliminate cervical cancer\nHPV vaccines offer a rare opportunity to effectively eliminate one type of cancer. By taking this opportunity, it’s possible to save hundreds of thousands of women each year.\nSaloni Dattani and Veronika Samborska\nJuly 20, 2020\nOur history is a battle against the microbes: we lost terribly before science, public health, and vaccines allowed us to protect ourselves\nFor most of history, we were losing the battle against microbes. Vaccines were one of the breakthroughs that turned it around.\nMax Roser\nJune 9, 2025\nChildhood leukemia: how a deadly cancer became treatable\nBefore the 1970s, most children affected by leukemia would quickly die from it. Now, most children in rich countries are cured.\nSaloni Dattani\nMore Articles on Medicine and Biotechnology\nAugust 26, 2024\nAntipsychotic medications: a timeline of innovations and remaining challenges\nSaloni Dattani\nDecember 23, 2024\nWhat was the Golden Age of Antibiotics, and how can we spark a new one?\nSaloni Dattani\nMay 19, 2025\nMeasles vaccines save millions of lives each year\nSaloni Dattani and Fiona Spooner\nJune 17, 2024\nTrachoma: how a common cause of blindness can be prevented worldwide\nSaloni Dattani and Fiona Spooner\nKey Charts on Medicine & Biotechnology\nSee all charts on this topic\nCost of sequencing a full human genome\nGlobal research & development funding for infectious disease technologies\nNew drugs approved in the United States\nNew drugs approved in the United States, by designations\nAverage clinical trial study length, by phase\nClinical trials by type of intervention\nCost per billion pairs of DNA sequencing\nNumber of clinical trials\nNumber of clinical trials by purpose\nNumber of clinical trials by sponsor\nNumber of clinical trials by status\nNumber of clinical trials by study type\nNumber of clinical trials with results posted\nNumber of entries in biological sequence databases\nDealt with anxiety or depression by taking prescribed medication\nShare of Stage 4 cancers diagnosed by different pathways in England\nShare of stage 1 cancers diagnosed by each pathway in England\nChart 1 of 17\nFeatured Data on\nMedicine & Biotechnology\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this topic page, please also cite the underlying data sources. This topic page can be cited as:\nTuna Acisu, Saloni Dattani, Fiona Spooner, Veronika Samborska, Hannah Ritchie, and Max Roser (2025) - “Medicine and Biotechnology” Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/medicine-biotech' [Online Resource]\nBibTeX citation\n@article{owid-medicine-biotech,\nauthor = {Tuna Acisu and Saloni Dattani and Fiona Spooner and Veronika Samborska and Hannah Ritchie and Max Roser},\ntitle = {Medicine and Biotechnology},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://ourworldindata.org/medicine-biotech}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "medicine-biotech", "source_url": "https://ourworldindata.org/medicine-biotech", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Innovations in medicines and biotechnology have increased life expectancy and saved many lives. Explore how this happened, and how more progress can be made.", "numeric_mentions": ["4,", "2024", "20,", "2020", "9,", "2025", "1970", "26,", "23,", "19,", "17,", "4", "1", "17"], "numeric_evidence": [{"title": "Life expectancy", "source_url": "https://ourworldindata.org/grapher/life-expectancy.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Life expectancy"], "row_count_total": 21565, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1950", "Life expectancy": "28.1563"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1951", "Life expectancy": "28.5836"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1952", "Life expectancy": "29.0138"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1953", "Life expectancy": "29.4521"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1954", "Life expectancy": "29.6975"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1955", "Life expectancy": "30.366"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1956", "Life expectancy": "30.8303"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1957", "Life expectancy": "31.3451"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1958", "Life expectancy": "31.84"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1959", "Life expectancy": "32.3365"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1960", "Life expectancy": "32.7987"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Life expectancy": "33.291"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Life expectancy": "33.7565"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Life expectancy": "34.2008"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Life expectancy": "34.6726"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Life expectancy": "35.1245"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Life expectancy": "35.5831"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Life expectancy": "36.042"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Life expectancy": "36.5101"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1969", "Life expectancy": "36.979"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1970", "Life expectancy": "37.4601"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1971", "Life expectancy": "37.9324"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1972", "Life expectancy": "38.4226"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Life expectancy": "38.951"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Life expectancy": "39.4687"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Life expectancy": "39.9944"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Life expectancy": "40.5184"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Life expectancy": "41.0821"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Life expectancy": "40.0859"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Life expectancy": "38.8441"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Life expectancy": "39.2581"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Life expectancy": "39.4058"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Life expectancy": "36.0577"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Life expectancy": "36.5174"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Life expectancy": "31.4732"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Life expectancy": "32.1316"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Life expectancy": "38.4001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Life expectancy": "38.8312"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Life expectancy": "43.2381"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Life expectancy": "44.4961"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Life expectancy": "45.1183"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Life expectancy": "45.5207"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Life expectancy": "46.5691"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Life expectancy": "51.0212"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Life expectancy": "50.9689"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Life expectancy": "52.1032"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Life expectancy": "52.8302"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Life expectancy": "53.2123"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Life expectancy": "52.487"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Life expectancy": "54.5324"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Life expectancy": "55.0047"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Life expectancy": "55.5113"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Life expectancy": "56.2251"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Life expectancy": "57.1713"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Life expectancy": "57.8098"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Life expectancy": "58.2468"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Life expectancy": "58.5533"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Life expectancy": "58.9563"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Life expectancy": "59.7081"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Life expectancy": "60.2478"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Life expectancy": "60.7018"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Life expectancy": "61.2503"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Life expectancy": "61.7349"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Life expectancy": "62.1878"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Life expectancy": "62.2599"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Life expectancy": "62.2695"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Life expectancy": "62.6459"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Life expectancy": "62.4062"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Life expectancy": "62.4434"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Life expectancy": "62.9411"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Life expectancy": "61.4537"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Life expectancy": "60.4174"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Life expectancy": "65.617"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Life expectancy": "66.0346"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1770", "Life expectancy": "26.4"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1925", "Life expectancy": "26.4"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1950", "Life expectancy": "37.2455"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1951", "Life expectancy": "37.7751"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1952", "Life expectancy": "38.2546"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1953", "Life expectancy": "38.7309"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1954", "Life expectancy": "39.0876"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1955", "Life expectancy": "39.5474"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1956", "Life expectancy": "39.9762"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1957", "Life expectancy": "39.8561"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1958", "Life expectancy": "40.2405"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1959", "Life expectancy": "41.194"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1960", "Life expectancy": "41.3961"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1961", "Life expectancy": "41.7756"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1962", "Life expectancy": "42.2531"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1963", "Life expectancy": "42.7175"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1964", "Life expectancy": "43.0898"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1965", "Life expectancy": "43.3551"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1966", "Life expectancy": "43.4601"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1967", "Life expectancy": "43.7471"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1968", "Life expectancy": "44.2523"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1969", "Life expectancy": "44.5077"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1970", "Life expectancy": "44.9859"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1971", "Life expectancy": "45.3999"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1972", "Life expectancy": "45.4595"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1973", "Life expectancy": "46.101"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1974", "Life expectancy": "46.4118"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1975", "Life expectancy": "46.807"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1976", "Life expectancy": "47.5487"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1977", "Life expectancy": "48.0406"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1978", "Life expectancy": "48.4287"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1979", "Life expectancy": "48.9562"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1980", "Life expectancy": "49.4094"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1981", "Life expectancy": "49.8226"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1982", "Life expectancy": "50.2133"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1983", "Life expectancy": "49.5464"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1984", "Life expectancy": "49.7402"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Life expectancy": "50.1332"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Life expectancy": "50.5889"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Life expectancy": "51.1887"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Life expectancy": "51.0598"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Life expectancy": "51.7063"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Life expectancy": "51.6566"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Life expectancy": "51.4937"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Life expectancy": "51.4261"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Life expectancy": "51.8296"}], "rows_tail": [{"Entity": "Zambia", "Code": "ZMB", "Year": "1978", "Life expectancy": "56.3349"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1979", "Life expectancy": "56.0499"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1980", "Life expectancy": "55.4961"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1981", "Life expectancy": "54.986"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1982", "Life expectancy": "54.361"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1983", "Life expectancy": "53.6737"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1984", "Life expectancy": "52.8772"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Life expectancy": "52.0014"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Life expectancy": "51.1489"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Life expectancy": "50.4211"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Life expectancy": "49.6737"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Life expectancy": "48.8817"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Life expectancy": "48.204"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Life expectancy": "47.4431"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Life expectancy": "46.9679"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Life expectancy": "46.6781"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Life expectancy": "46.3439"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Life expectancy": "46.0509"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Life expectancy": "45.7565"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Life expectancy": "45.7429"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Life expectancy": "45.7095"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Life expectancy": "45.924"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Life expectancy": "46.5771"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Life expectancy": "47.4077"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Life expectancy": "48.4614"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Life expectancy": "49.6158"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Life expectancy": "50.5398"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Life expectancy": "51.6147"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Life expectancy": "52.6928"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Life expectancy": "53.73"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Life expectancy": "54.835"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Life expectancy": "55.8862"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Life expectancy": "56.896"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Life expectancy": "57.8431"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Life expectancy": "58.704"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Life expectancy": "59.4452"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Life expectancy": "60.1091"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Life expectancy": "60.7284"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Life expectancy": "61.1285"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Life expectancy": "61.5644"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Life expectancy": "62.1381"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Life expectancy": "62.9145"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Life expectancy": "63.3607"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Life expectancy": "62.3631"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Life expectancy": "65.2791"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Life expectancy": "66.3487"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1950", "Life expectancy": "49.4155"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1951", "Life expectancy": "49.8355"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1952", "Life expectancy": "50.2394"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1953", "Life expectancy": "50.6334"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1954", "Life expectancy": "51.024"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1955", "Life expectancy": "51.4106"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1956", "Life expectancy": "51.8011"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1957", "Life expectancy": "52.1959"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1958", "Life expectancy": "52.5823"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1959", "Life expectancy": "53.022"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1960", "Life expectancy": "53.4922"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1961", "Life expectancy": "53.9663"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1962", "Life expectancy": "54.4535"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1963", "Life expectancy": "54.9416"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1964", "Life expectancy": "55.4311"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1965", "Life expectancy": "55.9054"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "Life expectancy": "56.3595"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "Life expectancy": "56.7664"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "Life expectancy": "57.1449"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "Life expectancy": "57.4813"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "Life expectancy": "57.7608"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "Life expectancy": "57.9959"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "Life expectancy": "58.1755"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "Life expectancy": "58.0872"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "Life expectancy": "58.1937"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Life expectancy": "58.2495"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Life expectancy": "57.6277"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Life expectancy": "56.9962"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Life expectancy": "54.7297"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Life expectancy": "54.9184"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Life expectancy": "59.0621"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Life expectancy": "59.4463"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Life expectancy": "59.859"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Life expectancy": "59.9888"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Life expectancy": "60.6223"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Life expectancy": "60.9012"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Life expectancy": "60.9824"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Life expectancy": "60.7239"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Life expectancy": "60.2414"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Life expectancy": "59.4458"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Life expectancy": "58.3191"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Life expectancy": "57.0371"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Life expectancy": "55.6021"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Life expectancy": "53.9764"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Life expectancy": "52.5367"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Life expectancy": "51.1435"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Life expectancy": "48.9815"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Life expectancy": "48.5361"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Life expectancy": "47.5508"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Life expectancy": "46.3786"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Life expectancy": "46.0339"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Life expectancy": "44.5249"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Life expectancy": "45.9381"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Life expectancy": "45.373"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Life expectancy": "46.0426"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Life expectancy": "46.5701"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Life expectancy": "47.0559"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Life expectancy": "47.5756"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Life expectancy": "48.4559"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Life expectancy": "49.7496"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Life expectancy": "51.9252"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Life expectancy": "53.9112"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Life expectancy": "55.3855"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Life expectancy": "56.8422"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Life expectancy": "58.106"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Life expectancy": "58.9895"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Life expectancy": "59.7601"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Life expectancy": "60.2626"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Life expectancy": "60.9055"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Life expectancy": "61.0603"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Life expectancy": "61.53"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Life expectancy": "60.1347"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Life expectancy": "62.3601"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Life expectancy": "62.7748"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "life-expectancy", "metadata_url": "https://ourworldindata.org/grapher/life-expectancy.metadata.json", "chart_title": "Life expectancy", "chart_subtitle": null, "chart_note": null, "chart_citation": "Riley (2005); Zijdeman et al. (2015); HMD (2025); UN WPP (2024)", "original_chart_url": "https://ourworldindata.org/grapher/life-expectancy", "owid_column_metadata": {"Period life expectancy at birth": {"titleShort": "Life expectancy", "titleLong": "Life expectancy - Riley; Zijdeman et al.; HMD; UN WPP – Long-run data", "descriptionShort": "Period life expectancy is the number of years the average person born in a certain year would live if they experienced the same chances of dying at each age as people did that year.", "descriptionKey": ["Across the world, people are living longer. In 1900, the global average life expectancy was 32 years. By 2023, this had more than doubled to 73 years.", "Countries around the world made big improvements, and life expectancy more than doubled in every region. This wasn’t just due to falling child mortality; people started living longer at all ages.", "Even after World War II, there have been large drops in life expectancy, such as during the Great Leap Forward famine in China, the HIV/AIDS epidemic in sub-Saharan Africa, the Rwandan genocide, or the COVID-19 pandemic.", "Period life expectancy is an indicator that summarizes death rates across all age groups in one particular year. It shows how long the average baby born in that year would be expected to live if they experienced the same chances of dying at each age as people did in that year.", "This chart shows long-run estimates of life expectancy compiled by our team from several data sources. Before 1950, for country-level data, we rely on the [Human Mortality Database (2025)](https://www.mortality.org/Data/ZippedDataFiles) combined with [Zijdeman (2015)](https://clio-infra.eu/Indicators/LifeExpectancyatBirthTotal.html). For regional data, we use [Riley (2005)](https://doi.org/10.1111/j.1728-4457.2005.00083.x). From 1950 onward, we use the [United Nations World Population Prospects (2024)](https://population.un.org/wpp/downloads).", "Detailed information on the source of each data point can be found on [this page](https://docs.google.com/spreadsheets/d/1LnrU1V3p2wq7sAPY4AHRdH1urol3cKev7prEvlLfSU4/edit?gid=0#gid=0)."], "descriptionProcessing": "This chart combines data from several sources. For country-level data before 1950, we use the Human Mortality Database (2025) data and Zijdeman et al. (2015). For country-years where these sources overlap, we use the Human Mortality Database.\n\nFor regional data, before 1950, we use Riley's (2005) estimates.\n\nFrom 1950 onwards, we use the United Nations World Population Prospects (2024) for both country-level and regional data.\n\nDetailed information on the source of each data point can be found on [this page](https://docs.google.com/spreadsheets/d/1LnrU1V3p2wq7sAPY4AHRdH1urol3cKev7prEvlLfSU4/edit?gid=0#gid=0).", "shortUnit": "years", "unit": "years", "timespan": "1543-2023", "type": "Numeric", "owidVariableId": 1118466, "shortName": "life_expectancy_0", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Riley (2005); Zijdeman et al. (2015); HMD (2025); UN WPP (2024) – with major processing by Our World in Data", "citationLong": "Riley (2005); Zijdeman et al. (2015); HMD (2025); UN WPP (2024) – with major processing by Our World in Data. “Life expectancy – Riley; Zijdeman et al.; HMD; UN WPP – Long-run data” [dataset]. Human Mortality Database, “Human Mortality Database”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update”; Zijdeman et al., “Life Expectancy at birth v2”; James C. Riley, “Estimates of Regional and Global Life Expectancy, 1800-2001” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118466.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Average clinical trial study length, by phase", "source_url": "https://ourworldindata.org/grapher/average-study-length-by-phase.csv", "file_type": "csv", "columns": ["Entity", "Year", "Average study length by phase"], "row_count_total": 185, "rows_head": [{"Entity": "Early phase 1", "Year": "2002", "Average study length by phase": "562.5"}, {"Entity": "Early phase 1", "Year": "2003", "Average study length by phase": "800.5"}, {"Entity": "Early phase 1", "Year": "2004", "Average study length by phase": "152.5"}, {"Entity": "Early phase 1", "Year": "2005", "Average study length by phase": "243.5"}, {"Entity": "Early phase 1", "Year": "2006", "Average study length by phase": "457"}, {"Entity": "Early phase 1", "Year": "2007", "Average study length by phase": "546"}, {"Entity": "Early phase 1", "Year": "2008", "Average study length by phase": "441"}, {"Entity": "Early phase 1", "Year": "2009", "Average study length by phase": "578"}, {"Entity": "Early phase 1", "Year": "2010", "Average study length by phase": "579"}, {"Entity": "Early phase 1", "Year": "2011", "Average study length by phase": "485"}, {"Entity": "Early phase 1", "Year": "2012", "Average study length by phase": "609"}, {"Entity": "Early phase 1", "Year": "2013", "Average study length by phase": "670"}, {"Entity": "Early phase 1", "Year": "2014", "Average study length by phase": "699"}, {"Entity": "Early phase 1", "Year": "2015", "Average study length by phase": "549"}, {"Entity": "Early phase 1", "Year": "2016", "Average study length by phase": "694.5"}, {"Entity": "Early phase 1", "Year": "2017", "Average study length by phase": "640"}, {"Entity": "Early phase 1", "Year": "2018", "Average study length by phase": "758"}, {"Entity": "Early phase 1", "Year": "2019", "Average study length by phase": "673.5"}, {"Entity": "Early phase 1", "Year": "2020", "Average study length by phase": "728"}, {"Entity": "Early 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{"Entity": "Phase 2", "Year": "2001", "Average study length by phase": "975"}, {"Entity": "Phase 2", "Year": "2002", "Average study length by phase": "990"}, {"Entity": "Phase 2", "Year": "2003", "Average study length by phase": "760"}, {"Entity": "Phase 2", "Year": "2004", "Average study length by phase": "883"}, {"Entity": "Phase 2", "Year": "2005", "Average study length by phase": "731"}, {"Entity": "Phase 2", "Year": "2006", "Average study length by phase": "759"}, {"Entity": "Phase 2", "Year": "2007", "Average study length by phase": "791"}, {"Entity": "Phase 2", "Year": "2008", "Average study length by phase": "731"}, {"Entity": "Phase 2", "Year": "2009", "Average study length by phase": "854"}, {"Entity": "Phase 2", "Year": "2010", "Average study length by phase": "821"}, {"Entity": "Phase 2", "Year": "2011", "Average study length by phase": "883"}, {"Entity": "Phase 2", "Year": "2012", "Average study length by phase": "975"}, {"Entity": "Phase 2", "Year": "2013", "Average study 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"Average study length by phase": "802"}, {"Entity": "Phase 2/3 (combined)", "Year": "2021", "Average study length by phase": "811"}, {"Entity": "Phase 2/3 (combined)", "Year": "2022", "Average study length by phase": "718.5"}, {"Entity": "Phase 2/3 (combined)", "Year": "2023", "Average study length by phase": "729.5"}, {"Entity": "Phase 2/3 (combined)", "Year": "2024", "Average study length by phase": "798"}, {"Entity": "Phase 3", "Year": "1998", "Average study length by phase": "745.5"}, {"Entity": "Phase 3", "Year": "1999", "Average study length by phase": "973"}, {"Entity": "Phase 3", "Year": "2000", "Average study length by phase": "731"}, {"Entity": "Phase 3", "Year": "2001", "Average study length by phase": "881"}, {"Entity": "Phase 3", "Year": "2002", "Average study length by phase": "639"}, {"Entity": "Phase 3", "Year": "2003", "Average study length by phase": "638"}, {"Entity": "Phase 3", "Year": "2004", "Average study length by phase": "594"}, {"Entity": "Phase 3", "Year": "2005", "Average study length by phase": "578"}, {"Entity": "Phase 3", "Year": "2006", "Average study length by phase": "638"}, {"Entity": "Phase 3", "Year": "2007", "Average study length by phase": "671"}, {"Entity": "Phase 3", "Year": "2008", "Average study length by phase": "701"}, {"Entity": "Phase 3", "Year": "2009", "Average study length by phase": "731"}, {"Entity": "Phase 3", "Year": "2010", "Average study length by phase": "730"}, {"Entity": "Phase 3", "Year": "2011", "Average study length by phase": "792"}, {"Entity": "Phase 3", "Year": "2012", "Average study length by phase": "766"}, {"Entity": "Phase 3", "Year": "2013", "Average study length by phase": "792"}, {"Entity": "Phase 3", "Year": "2014", "Average study length by phase": "822"}, {"Entity": "Phase 3", "Year": "2015", "Average study length by phase": "850"}, {"Entity": "Phase 3", "Year": "2016", "Average study length by phase": "882"}, {"Entity": "Phase 3", "Year": "2017", "Average study length by phase": "864"}, {"Entity": "Phase 3", "Year": "2018", "Average study length by phase": "921.5"}, {"Entity": "Phase 3", "Year": "2019", "Average study length by phase": "937"}, {"Entity": "Phase 3", "Year": "2020", "Average study length by phase": "909"}, {"Entity": "Phase 3", "Year": "2021", "Average study length by phase": "868"}, {"Entity": "Phase 3", "Year": "2022", "Average study length by phase": "999"}, {"Entity": "Phase 3", "Year": "2023", "Average study length by phase": "923"}, {"Entity": "Phase 3", "Year": "2024", "Average study length by phase": "994"}, {"Entity": "Phase 4", "Year": "1998", "Average study length by phase": "853"}, {"Entity": "Phase 4", "Year": "1999", "Average study length by phase": "1005"}, {"Entity": "Phase 4", "Year": "2000", "Average study length by phase": "945"}, {"Entity": "Phase 4", "Year": "2001", "Average study length by phase": "762"}, {"Entity": "Phase 4", "Year": "2002", "Average study length by phase": "518"}, {"Entity": "Phase 4", "Year": "2003", "Average study length by phase": "456"}, {"Entity": "Phase 4", "Year": "2004", "Average study length by phase": "547.5"}, {"Entity": "Phase 4", "Year": "2005", "Average study length by phase": "608"}, {"Entity": "Phase 4", "Year": "2006", "Average study length by phase": "669"}, {"Entity": "Phase 4", "Year": "2007", "Average study length by phase": "700"}, {"Entity": "Phase 4", "Year": "2008", "Average study length by phase": "700"}, {"Entity": "Phase 4", "Year": "2009", "Average study length by phase": "700"}, {"Entity": "Phase 4", "Year": "2010", "Average study length by phase": "699"}, {"Entity": "Phase 4", "Year": "2011", "Average study length by phase": "671"}, {"Entity": "Phase 4", "Year": "2012", "Average study length by phase": "731"}, {"Entity": "Phase 4", "Year": "2013", "Average study length by phase": "761"}, {"Entity": "Phase 4", "Year": "2014", "Average study length by phase": "730"}, {"Entity": "Phase 4", "Year": "2015", "Average study length by phase": "760"}, {"Entity": "Phase 4", "Year": "2016", "Average study length by phase": "701"}, {"Entity": "Phase 4", "Year": "2017", "Average study length by phase": "722.5"}, {"Entity": "Phase 4", "Year": "2018", "Average study length by phase": "730"}, {"Entity": "Phase 4", "Year": "2019", "Average study length by phase": "729"}, {"Entity": "Phase 4", "Year": "2020", "Average study length by phase": "745"}, {"Entity": "Phase 4", "Year": "2021", "Average study length by phase": "731"}, {"Entity": "Phase 4", "Year": "2022", "Average study length by phase": "667.5"}, {"Entity": "Phase 4", "Year": "2023", "Average study length by phase": "746.5"}, {"Entity": "Phase 4", "Year": "2024", "Average study length by phase": "703"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "average-study-length-by-phase", "metadata_url": "https://ourworldindata.org/grapher/average-study-length-by-phase.metadata.json", "chart_title": "Average clinical trial study length, by phase", "chart_subtitle": null, "chart_note": "ClinicalTrials.gov was established in 2000 and data on trials starting before that is incomplete. There can also be a delay in registration, so latest years might not have complete data yet.", "chart_citation": "ClinicalTrials.gov (2025)", "original_chart_url": "https://ourworldindata.org/grapher/average-study-length-by-phase", "owid_column_metadata": {"Average study length by phase": {"titleShort": "Average study length by phase", "titleLong": "Average study length by phase", "descriptionShort": "Median length of completed clinical trials by phase. The phase refers to the phase of drug development the drug is currently in (early phase 1, phase 1, phase 2, phase 3 or phase 4). The study length is the time from start date to completion date, given in days.\n", "descriptionKey": ["Clinical trials are conducted in different phases of the development of a new treatment or intervention, such as early phase 1, phase 1, phase 2, phase 3, or phase 4.", "Early phase 1 trials are exploratory trials that test the effect of an intervention on the human body, often with a small number of participants. Not every treatment has an early phase 1 trial.", "Phase 1 trials test the safety and dosage of a new treatment, phase 2 trials test the effectiveness and side effects, and phase 3 trials compare the new treatment to standard treatments.", "Phase 4 trials are conducted after a treatment has been approved and are used to monitor the long-term effects and side-effects of the treatment. Not every treatment has a phase 4 trial.", "\\\"Phase 1/phase 2\\\" trials are a combination of phase 1 and phase 2 trials, and \"phase 2/phase 3\" trials are a combination of phase 2 and phase 3 trials.", "The average length is calculated by taking the difference between the start date and completion date of each trial, grouped by phase and completion year.", "This data comes from the ClinicalTrials.gov database. It only includes interventional clinical trials that are marked as \"completed\" and have a valid \"completion date\".", "Registration in the ClinicalTrials.gov database is mandatory for trials in the United States and for treatments that seek FDA approval, but voluntary for other trials conducted in other countries."], "descriptionProcessing": "- First we calculate the length of each trial by taking the difference between the start date and completion date of each trial.\n- Then we group the trials by phase and completion year, and calculate the median length of each group.", "shortUnit": "days", "unit": "days", "timespan": "1976-2025", "type": "Numeric", "owidVariableId": 1103691, "shortName": "avg_study_length_days", "lastUpdated": "2025-07-28", "nextUpdate": "2026-07-28", "citationShort": "ClinicalTrials.gov (2025) – with major processing by Our World in Data", "citationLong": "ClinicalTrials.gov (2025) – with major processing by Our World in Data. “Average study length by phase” [dataset]. ClinicalTrials.gov, “Clinical Trials (ClinicalTrials.gov)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1103691.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Clinical trials by type of intervention", "source_url": "https://ourworldindata.org/grapher/trials-by-intervention.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Device", "Biological", "Dietary supplement", "Radiation", "Genetic", "Drugs", "Surgical", "Diagnostic", "Behavioral"], "row_count_total": 29, "rows_head": [{"Entity": "World", "Code": "OWID_WRL", "Year": "1997", "Device": "2", "Biological": "8", "Dietary supplement": "1", "Radiation": "0", "Genetic": "0", "Drugs": "66", "Surgical": "4", "Diagnostic": "0", "Behavioral": "7"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1998", "Device": "3", "Biological": "11", "Dietary supplement": "4", "Radiation": "1", "Genetic": "0", "Drugs": "118", "Surgical": "10", "Diagnostic": "0", "Behavioral": "16"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1999", "Device": "9", "Biological": "24", "Dietary supplement": "3", "Radiation": "2", "Genetic": "0", "Drugs": "133", "Surgical": "15", "Diagnostic": "0", "Behavioral": "24"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Device": "9", "Biological": "42", "Dietary supplement": "4", "Radiation": "20", "Genetic": "1", "Drugs": "281", "Surgical": "59", "Diagnostic": "1", "Behavioral": "27"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Device": "18", "Biological": "84", "Dietary supplement": "6", "Radiation": "16", "Genetic": "2", "Drugs": "438", "Surgical": "78", "Diagnostic": "0", "Behavioral": "45"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Device": "23", "Biological": "94", "Dietary supplement": "12", "Radiation": "34", "Genetic": "3", "Drugs": "707", "Surgical": "118", "Diagnostic": "0", "Behavioral": "72"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Device": "48", "Biological": "114", "Dietary supplement": "10", "Radiation": "36", "Genetic": "3", "Drugs": "1147", "Surgical": "133", "Diagnostic": "0", "Behavioral": "121"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Device": "89", "Biological": "162", "Dietary supplement": "31", "Radiation": "50", "Genetic": "5", "Drugs": "1675", "Surgical": "208", "Diagnostic": "0", "Behavioral": "172"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Device": "188", "Biological": "216", "Dietary supplement": "44", "Radiation": "52", "Genetic": "12", "Drugs": "2447", "Surgical": "289", "Diagnostic": "2", "Behavioral": "336"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Device": "254", "Biological": "332", "Dietary supplement": "88", "Radiation": "64", "Genetic": "16", "Drugs": "2955", "Surgical": "459", "Diagnostic": "3", "Behavioral": "424"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Device": "400", "Biological": "409", "Dietary supplement": "122", "Radiation": "59", "Genetic": "27", "Drugs": "3767", "Surgical": "567", "Diagnostic": "2", "Behavioral": "545"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Device": "552", "Biological": "492", "Dietary supplement": "226", "Radiation": "65", "Genetic": "45", "Drugs": "4478", "Surgical": "610", "Diagnostic": "5", "Behavioral": "601"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Device": "751", "Biological": "584", "Dietary supplement": "307", "Radiation": "123", "Genetic": "40", "Drugs": "4923", "Surgical": "728", "Diagnostic": "3", "Behavioral": "716"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Device": "840", "Biological": "646", "Dietary supplement": "366", "Radiation": "111", "Genetic": "51", "Drugs": "4917", "Surgical": "819", "Diagnostic": "7", "Behavioral": "820"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Device": "982", "Biological": "620", "Dietary supplement": "451", "Radiation": "115", "Genetic": "57", "Drugs": "5106", "Surgical": "912", "Diagnostic": "12", "Behavioral": "973"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Device": "1144", "Biological": "743", "Dietary supplement": "499", "Radiation": "134", "Genetic": "66", "Drugs": "5304", "Surgical": "962", "Diagnostic": "17", "Behavioral": "1053"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Device": "1293", "Biological": "727", "Dietary supplement": "530", "Radiation": "159", "Genetic": "54", "Drugs": "5248", "Surgical": "1078", "Diagnostic": "24", "Behavioral": "1194"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Device": "1463", "Biological": "723", "Dietary supplement": "541", "Radiation": "151", "Genetic": "56", "Drugs": "5653", "Surgical": "1134", "Diagnostic": "36", "Behavioral": "1426"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Device": "1885", "Biological": "783", "Dietary supplement": "640", "Radiation": "189", "Genetic": "47", "Drugs": "5669", "Surgical": "1382", "Diagnostic": "63", "Behavioral": "1578"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Device": "2221", "Biological": "764", "Dietary supplement": "603", "Radiation": "246", "Genetic": "75", "Drugs": "5874", "Surgical": "1454", "Diagnostic": "114", "Behavioral": "1854"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Device": "2399", "Biological": "744", "Dietary supplement": "633", "Radiation": "217", "Genetic": "64", "Drugs": "5800", "Surgical": "1529", "Diagnostic": "252", "Behavioral": "2087"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Device": "2516", "Biological": "750", "Dietary supplement": "730", "Radiation": "232", "Genetic": "48", "Drugs": "5727", "Surgical": "1677", "Diagnostic": "446", "Behavioral": "2362"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Device": "2836", "Biological": "830", "Dietary supplement": "743", "Radiation": "241", "Genetic": "57", "Drugs": "5861", "Surgical": "1761", "Diagnostic": "725", "Behavioral": "2562"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Device": "2453", "Biological": "772", "Dietary supplement": "651", "Radiation": "237", "Genetic": "61", "Drugs": "5331", "Surgical": "1754", "Diagnostic": "926", "Behavioral": "2442"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Device": "2587", "Biological": "889", "Dietary supplement": "612", "Radiation": "250", "Genetic": "70", "Drugs": "5622", "Surgical": "1709", "Diagnostic": "994", "Behavioral": "2429"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Device": "2899", "Biological": "967", "Dietary supplement": "794", "Radiation": "279", "Genetic": "68", "Drugs": "5737", "Surgical": "1869", "Diagnostic": "1025", "Behavioral": "2818"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Device": "3005", "Biological": "928", "Dietary supplement": "762", "Radiation": "218", "Genetic": "74", "Drugs": "5774", "Surgical": "1913", "Diagnostic": "944", "Behavioral": "3027"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Device": "2610", "Biological": "775", "Dietary supplement": "689", "Radiation": "184", "Genetic": "52", "Drugs": "5193", "Surgical": "1713", "Diagnostic": "881", "Behavioral": "2865"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2025", "Device": "756", "Biological": "230", "Dietary supplement": "207", "Radiation": "56", "Genetic": "13", "Drugs": "1780", "Surgical": "489", "Diagnostic": "227", "Behavioral": "883"}], "rows_tail": [], "sampling_note": "Stored first 29 rows and last 29 rows when the table is larger.", "grapher_slug": "trials-by-intervention", "metadata_url": "https://ourworldindata.org/grapher/trials-by-intervention.metadata.json", "chart_title": "Clinical trials by type of intervention", "chart_subtitle": "Annual number of completed clinical trials registered globally in the ClinicalTrials.gov database by the type of treatment they investigate. Treatments can include drugs, biologicals, devices, radiation or surgical treatment, but also behavioral interventions, diagnostic tests, dietary supplement or a combination of multiple of these.", "chart_note": "Some studies include multiple types of interventions, and are counted multiple times (once for each intervention type). ClinicalTrials.gov was established in 2000 and data on trials starting before that is incomplete. There can also be a delay in registration, so latest years might not have complete data yet.", "chart_citation": "ClinicalTrials.gov (2025)", "original_chart_url": "https://ourworldindata.org/grapher/trials-by-intervention", "owid_column_metadata": {"Number of clinical trials with device-based interventions": {"titleShort": "Device", "titleLong": "Device", "descriptionShort": "Annual number of completed clinical trials registered in the ClinicalTrials.gov database that involve devices used for treatment. These interventions include the use of medical devices, implants, or other technological treatments.\n", "descriptionKey": ["Device-based interventions involve the use of medical devices, implants, or other technological treatments. These can include pacemakers, prosthetics, or other medical devices. Using a placebo device also counts as a device-based intervention.", "If multiple interventions are given, the study is counted towards all interventions listed.", "This data comes from the ClinicalTrials.gov database. It only includes interventional or observational clinical trials that are marked as \"completed\" and have a valid \"completion date\". Expanded access studies, which provide access to investigational drugs or devices for patients with serious conditions, are not included in this data.\n", "Registration in the ClinicalTrials.gov database is mandatory for trials in the United States and for treatments that seek FDA approval, but voluntary for other trials conducted in other countries."], "shortUnit": "", "unit": "trials", "timespan": "1918-2031", "type": "Integer", "owidVariableId": 1103682, "shortName": "device", "lastUpdated": "2025-07-28", "nextUpdate": "2026-07-28", "citationShort": "ClinicalTrials.gov (2025) – with major processing by Our World in Data", "citationLong": "ClinicalTrials.gov (2025) – with major processing by Our World in Data. “Device” [dataset]. ClinicalTrials.gov, “Clinical Trials (ClinicalTrials.gov)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1103682.metadata.json"}, "Number of clinical trials with biological interventions": {"titleShort": "Biological", "titleLong": "Biological", "descriptionShort": "Annual number of completed clinical trials registered in the ClinicalTrials.gov database that involve biological interventions. Biological interventions can include vaccines or other biological treatments.\n", "descriptionKey": ["Biological interventions are treatments that involve giving biological products to participants. These can include vaccines, blood products, or other biological treatments.", "If multiple interventions are given, the study is counted towards all interventions listed.", "This data comes from the ClinicalTrials.gov database. It only includes interventional or observational clinical trials that are marked as \"completed\" and have a valid \"completion date\". Expanded access studies, which provide access to investigational drugs or devices for patients with serious conditions, are not included in this data.\n", "Registration in the ClinicalTrials.gov database is mandatory for trials in the United States and for treatments that seek FDA approval, but voluntary for other trials conducted in other countries."], "shortUnit": "", "unit": "trials", "timespan": "1918-2031", "type": "Integer", "owidVariableId": 1103687, "shortName": "biological", "lastUpdated": "2025-07-28", "nextUpdate": "2026-07-28", "citationShort": "ClinicalTrials.gov (2025) – with major processing by Our World in Data", "citationLong": "ClinicalTrials.gov (2025) – with major processing by Our World in Data. “Biological” [dataset]. ClinicalTrials.gov, “Clinical Trials (ClinicalTrials.gov)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1103687.metadata.json"}, "Number of clinical trials with dietary supplement interventions": {"titleShort": "Dietary supplement", "titleLong": "Dietary supplement", "descriptionShort": "Annual number of completed clinical trials registered in the ClinicalTrials.gov database that involve dietary supplements. These interventions include giving patients vitamins, minerals, or other nutritional supplements.\n", "descriptionKey": ["Dietary supplement interventions involve giving participants vitamins, minerals, or other nutritional supplements.", "If multiple interventions are given, the study is counted towards all interventions listed.", "This data comes from the ClinicalTrials.gov database. It only includes interventional or observational clinical trials that are marked as \"completed\" and have a valid \"completion date\". Expanded access studies, which provide access to investigational drugs or devices for patients with serious conditions, are not included in this data.\n", "Registration in the ClinicalTrials.gov database is mandatory for trials in the United States and for treatments that seek FDA approval, but voluntary for other trials conducted in other countries."], "shortUnit": "", "unit": "trials", "timespan": "1918-2031", "type": "Integer", "owidVariableId": 1103684, "shortName": "dietary_supplement", "lastUpdated": "2025-07-28", "nextUpdate": "2026-07-28", "citationShort": "ClinicalTrials.gov (2025) – with major processing by Our World in Data", "citationLong": "ClinicalTrials.gov (2025) – with major processing by Our World in Data. “Dietary supplement” [dataset]. ClinicalTrials.gov, “Clinical Trials (ClinicalTrials.gov)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1103684.metadata.json"}, "Number of clinical trials with radiation interventions": {"titleShort": "Radiation", "titleLong": "Radiation", "descriptionShort": "Annual number of completed clinical trials registered in the ClinicalTrials.gov database that involve radiation as a part of the treatment.\n", "descriptionKey": ["Radiation interventions involve the use of radiation as a part of the treatment, such as radiation therapy for cancer.", "If multiple interventions are given, the study is counted towards all interventions listed.", "This data comes from the ClinicalTrials.gov database. It only includes interventional or observational clinical trials that are marked as \"completed\" and have a valid \"completion date\". Expanded access studies, which provide access to investigational drugs or devices for patients with serious conditions, are not included in this data.\n", "Registration in the ClinicalTrials.gov database is mandatory for trials in the United States and for treatments that seek FDA approval, but voluntary for other trials conducted in other countries."], "shortUnit": "", "unit": "trials", "timespan": "1918-2031", "type": "Integer", "owidVariableId": 1103681, "shortName": "radiation", "lastUpdated": "2025-07-28", "nextUpdate": "2026-07-28", "citationShort": "ClinicalTrials.gov (2025) – with major processing by Our World in Data", "citationLong": "ClinicalTrials.gov (2025) – with major processing by Our World in Data. “Radiation” [dataset]. ClinicalTrials.gov, “Clinical Trials (ClinicalTrials.gov)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1103681.metadata.json"}, "Number of clinical trials with genetic interventions": {"titleShort": "Genetic", "titleLong": "Genetic", "descriptionShort": "Annual number of completed clinical trials registered in the ClinicalTrials.gov database that involve genetic interventions. These interventions include gene transfers, gene therapy, or stem-cell therapy.\n", "descriptionKey": ["Genetic interventions involve treating patients with a gene-based therapy, including gene transfers, recombinant DNA or stem-cell therapy.", "If multiple interventions are given, the study is counted towards all interventions listed.", "This data comes from the ClinicalTrials.gov database. It only includes interventional or observational clinical trials that are marked as \"completed\" and have a valid \"completion date\". Expanded access studies, which provide access to investigational drugs or devices for patients with serious conditions, are not included in this data.\n", "Registration in the ClinicalTrials.gov database is mandatory for trials in the United States and for treatments that seek FDA approval, but voluntary for other trials conducted in other countries."], "shortUnit": "", "unit": "trials", "timespan": "1918-2031", "type": "Integer", "owidVariableId": 1103689, "shortName": "genetic", "lastUpdated": "2025-07-28", "nextUpdate": "2026-07-28", "citationShort": "ClinicalTrials.gov (2025) – with major processing by Our World in Data", "citationLong": "ClinicalTrials.gov (2025) – with major processing by Our World in Data. “Genetic” [dataset]. ClinicalTrials.gov, “Clinical Trials (ClinicalTrials.gov)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1103689.metadata.json"}, "Number of clinical trials with pharmaceutical interventions": {"titleShort": "Drugs", "titleLong": "Drugs", "descriptionShort": "Annual number of completed clinical trials registered in the ClinicalTrials.gov database that involve a pharmaceutical treatment, such as a drug or medication.\n", "descriptionKey": ["Pharmaceutical interventions involve giving participants a chemical drug or medication. Using a placebo drug also counts as a pharmaceutical intervention.", "If multiple interventions are given, the study is counted towards all interventions listed.", "This data comes from the ClinicalTrials.gov database. It only includes interventional or observational clinical trials that are marked as \"completed\" and have a valid \"completion date\". Expanded access studies, which provide access to investigational drugs or devices for patients with serious conditions, are not included in this data.\n", "Registration in the ClinicalTrials.gov database is mandatory for trials in the United States and for treatments that seek FDA approval, but voluntary for other trials conducted in other countries."], "shortUnit": "", "unit": "trials", "timespan": "1918-2031", "type": "Integer", "owidVariableId": 1103688, "shortName": "drug", "lastUpdated": "2025-07-28", "nextUpdate": "2026-07-28", "citationShort": "ClinicalTrials.gov (2025) – with major processing by Our World in Data", "citationLong": "ClinicalTrials.gov (2025) – with major processing by Our World in Data. “Drugs” [dataset]. ClinicalTrials.gov, “Clinical Trials (ClinicalTrials.gov)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1103688.metadata.json"}, "Number of clinical trials with surgical interventions": {"titleShort": "Surgical", "titleLong": "Surgical", "descriptionShort": "Annual number of completed clinical trials registered in the ClinicalTrials.gov database that involve surgical interventions.\n", "descriptionKey": ["Surgical interventions involve procedures that are performed on patients, such as surgeries or other invasive procedures.", "If multiple interventions are given, the study is counted towards all interventions listed.", "This data comes from the ClinicalTrials.gov database. It only includes interventional or observational clinical trials that are marked as \"completed\" and have a valid \"completion date\". Expanded access studies, which provide access to investigational drugs or devices for patients with serious conditions, are not included in this data.\n", "Registration in the ClinicalTrials.gov database is mandatory for trials in the United States and for treatments that seek FDA approval, but voluntary for other trials conducted in other countries."], "shortUnit": "", "unit": "trials", "timespan": "1918-2031", "type": "Integer", "owidVariableId": 1103680, "shortName": "procedure", "lastUpdated": "2025-07-28", "nextUpdate": "2026-07-28", "citationShort": "ClinicalTrials.gov (2025) – with major processing by Our World in Data", "citationLong": "ClinicalTrials.gov (2025) – with major processing by Our World in Data. “Surgical” [dataset]. ClinicalTrials.gov, “Clinical Trials (ClinicalTrials.gov)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1103680.metadata.json"}, "Number of clinical trials with diagnostic interventions": {"titleShort": "Diagnostic", "titleLong": "Diagnostic", "descriptionShort": "Annual number of completed clinical trials registered in the ClinicalTrials.gov database that involve diagnostic interventions. These interventions include tests or procedures used to diagnose a condition.\n", "descriptionKey": ["Diagnostic interventions involve tests or procedures used to diagnose a condition. These can include blood tests, in vitro diagnostic tests, imaging tests, or other diagnostic procedures.", "If multiple interventions are given, the study is counted towards all interventions listed.", "This data comes from the ClinicalTrials.gov database. It only includes interventional or observational clinical trials that are marked as \"completed\" and have a valid \"completion date\". Expanded access studies, which provide access to investigational drugs or devices for patients with serious conditions, are not included in this data.\n", "Registration in the ClinicalTrials.gov database is mandatory for trials in the United States and for treatments that seek FDA approval, but voluntary for other trials conducted in other countries."], "shortUnit": "", "unit": "trials", "timespan": "1918-2031", "type": "Integer", "owidVariableId": 1103685, "shortName": "diagnostic_test", "lastUpdated": "2025-07-28", "nextUpdate": "2026-07-28", "citationShort": "ClinicalTrials.gov (2025) – with major processing by Our World in Data", "citationLong": "ClinicalTrials.gov (2025) – with major processing by Our World in Data. “Diagnostic” [dataset]. ClinicalTrials.gov, “Clinical Trials (ClinicalTrials.gov)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1103685.metadata.json"}, "Number of clinical trials with behavioral interventions": {"titleShort": "Behavioral", "titleLong": "Behavioral", "descriptionShort": "Annual number of completed clinical trials registered globally in the ClinicalTrials.gov database that involve behavioral interventions. Behavioral interventions can include lifestyle changes, counseling, or other non-pharmaceutical treatments\n", "descriptionKey": ["Behavioral interventions are non-pharmaceutical treatments that focus on changing behaviors or habits. They can include lifestyle changes, counseling, or other non-pharmaceutical treatments.", "If multiple interventions are given, the study is counted towards all interventions listed.", "This data comes from the ClinicalTrials.gov database. It only includes interventional or observational clinical trials that are marked as \"completed\" and have a valid \"completion date\". Expanded access studies, which provide access to investigational drugs or devices for patients with serious conditions, are not included in this data.\n", "Registration in the ClinicalTrials.gov database is mandatory for trials in the United States and for treatments that seek FDA approval, but voluntary for other trials conducted in other countries."], "shortUnit": "", "unit": "trials", "timespan": "1918-2031", "type": "Integer", "owidVariableId": 1103686, "shortName": "behavioral", "lastUpdated": "2025-07-28", "nextUpdate": "2026-07-28", "citationShort": "ClinicalTrials.gov (2025) – with major processing by Our World in Data", "citationLong": "ClinicalTrials.gov (2025) – with major processing by Our World in Data. “Behavioral” [dataset]. ClinicalTrials.gov, “Clinical Trials (ClinicalTrials.gov)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1103686.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "7fda80e947e2b5aae6dc"},
{"raw_link": "https://ourworldindata.org/top-of-the-charts-2025", "title": "Top of the Charts: our most popular work in 2025", "context": "Top of the Charts: our most popular work in 2025\nA look back at the most popular charts, articles, data insights, and more from Our World in Data in 2025.\nBy\nOur World in Data team\nDecember 5, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nAs 2025 draws to a close, we want to look back on the year and share the pieces of our work that were most popular and engaging with you, our readers.\nWe continue to reach tens of millions of readers each year through our website. We also have a wide reach on other platforms. Across social media, our posts this year have been viewed nearly 60 million times. Our work also gets hundreds of millions of views on websites like Wikipedia, and many thousands of citations by media around the world each year.\nFrom the charts with the most views, to the articles and data insights with the most reads, to our most-popular posts on social media, here we give you the “Top of the Charts” from Our World in Data in 2025. We hope you enjoy!\nIf you do, please\nshare this list or your favorite parts\nwith someone who you think would, too. Sharing with others is one of the best ways you can support our work.\nThanks, and have a great holiday season!\n– The Our World in Data team\nDownload\nSee our most popular work in 2025\nYou can navigate using the links below.\nCharts\nArticles\nData insights\nTopic pages\nSocial media posts\nDatasets\nCharts\nHere are our top five most-viewed interactive charts in 2025.\n1\nCO\n2\nemissions\nLife expectancy\nDemocracy\nTemperature anomalies\nEconomic growth\nWe’ve made it easy for you to share these — just click the “Share” button in the bottom right of the chart. You can also\nembed them\nin any website.\nYou can\nreuse all of our charts for free\nunder our permissive Creative Commons license.\nExplore all of our nearly 14,000 charts\nCovering 123 different topics\n1. CO\n2\nemissions\n2. Life expectancy\n3. Democracy\n4. Temperature anomalies\n5. Economic growth\nArticles\nHere are the top five most-read articles that we published this year.\n2\nThe baby boom in seven charts\nDoes the news reflect what we die from?\nAir pollution kills millions every year — where does it come from?\nDeath rates from cardiovascular disease have fallen dramatically — what were the breakthroughs behind this?\nWhere in the world are babies at the lowest risk of dying?\nStay up to date with all our latest articles\nSubscribe to The OWID Brief newsletter\n1. The baby boom in seven charts\nBy Saloni Dattani and Lucas Rodés-Guirao\nThe baby boom reshaped family life and drove population growth in many countries.\nOne of the striking aspects of the baby boom is that it happened in many high-income countries at the same time — even in those not directly involved in World War II, such as Sweden. You can see this in the chart.\nWhat caused the baby boom? This is still widely debated by demographers. Various theories have been put forward, including economic factors, such as rising wages and lower housing costs, as well as declining maternal mortality and societal changes.\nIt’s likely that multiple factors played a role and that no single explanation fully accounts for the surge in births.\nIn this article, Saloni Dattani and Lucas Rodés-Guirao explore the key patterns of the baby boom in seven charts.\nRead the article\nDownload\n2. Does the news reflect what we die from?\nBy Hannah Ritchie, Tuna Acisu, and Edouard Mathieu\nMore than 80% of people surveyed say they follow the news because they “want to know what is going on in the world around them.”\nIt’s not just that people expect the news to inform them about what’s going on in the world — most think that it does. And this is what media outlets themselves promise to do.\nHowever, as we discuss in this article, the media focuses on just a fraction of our world.\nWe investigate this through the lens of health, looking at causes of death in the United States and reporting on these causes in the New York Times, Washington Post, and Fox News.\nOur point is not that we should want or expect the media’s coverage to perfectly match the real distribution of deaths, although we’d argue that it would be better if it were less skewed.\nWe wrote this article so that you, the reader, are aware of a significant disconnect between what we often hear and what actually happens.\nIt’s easy to conflate what we see in the news with the reality of our world, and keeping this mismatch in mind can help you avoid falling into this trap.\nRead the article\nDownload\n3. Air pollution kills millions every year — where does it come from?\nBy Hannah Ritchie and Pablo Rosado\nMillions of people die prematurely from air pollution every year, as you can see in the chart.\nThis problem has existed since humans started burning materials for fuel — first wood and biomass, then fossil fuels.\nBut it’s an environmental and public health problem that we can make progress on. We know this because the world has already been successful in reducing air pollutants, and many countries that used to be highly polluted now have much cleaner air than they used to.\nTo tackle air pollution effectively — to focus our efforts on the interventions that will have the biggest impact — we need to understand where it’s coming from.\nIn this article, Hannah Ritchie and Pablo Rosado overview the many sources of different pollutants — including sulfur dioxide, ammonia, nitrous oxide, and black carbon — and explain how we can reduce their harmful impacts.\nRead the article\nDownload\n4. Death rates from cardiovascular disease have fallen dramatically — what were the breakthroughs behind this?\nBy Saloni Dattani\nIn 1945 — at just 63 years old — President Franklin D. Roosevelt was sitting for a portrait when he raised a hand to his head and whispered, “I have a terrific pain in the back of my head.”\nMinutes later, he lost consciousness and died from a massive brain hemorrhage — a consequence of uncontrolled high blood pressure and heart disease, which doctors at the time couldn’t treat.\nRoosevelt wasn’t alone. Mid-twentieth-century medicine, even for some of the world's most powerful people, often lacked the tools to treat or sometimes even diagnose specific cardiovascular diseases.\nToday, pills could have driven down Roosevelt’s blood pressure within weeks. The hypertension that struck him and many others without warning, often known as “the silent killer”, is routinely diagnosed and treated.\nCardiovascular diseases are still the leading cause of death worldwide. But the story reflects a remarkable and often overlooked fact: the risk of dying from cardiovascular diseases has fallen dramatically in recent decades.\nThis progress was built on decades of biomedical research, surgical advances, public health efforts, and lifestyle changes — many of these are highlighted in the chart.\nIn this article, Saloni Dattani looks at how and why deaths from cardiovascular disease have declined.\nRead the article\nDownload\n5. Where in the world are babies at the lowest risk of dying?\nBy Hannah Ritchie\nWhich country is the safest for a baby to be born in?\nAnswering this question might seem easy: divide the number of infants who die by the total number of infants born, make a map of these rates, and find the lowest number.\nBut while these comparisons are very helpful in identifying the huge differences across countries at different income levels, things get more complicated when it comes to the small differences between the countries with the lowest mortality rates.\nThis is because countries measure infant deaths slightly differently, specifically, the number of live births that are recorded.\nIn this article, Hannah Ritchie explains these differences in measurement and how they affect mortality rates. She also looks at what explains some of the differences in outcomes for babies in different countries.\nRead the article\nDownload\nData insights\nHere are the top five most-read data insights that we published this year.\n1\nGlobal sales of combustion engine cars have peaked\nThe twin baby boom\nThe world has probably passed “peak air pollution”\nSuicide rates are higher in men than women\nLife expectancy has increased at all ages\nThese are our bite-sized insights on the world and how it’s changing. We now have a\ncatalog\nof nearly 400 insights. You can find them\nin our search\n, too.\nSubscribe to our Data Insights newsletter\nTo receive these insights right in your inbox, every few days\n1. Global sales of combustion engine cars have peaked\nBy Hannah Ritchie\nDownload\nTo decarbonize road transport, the world must move away from petrol and diesel cars and towards electric vehicles and other forms of low-carbon transport.\nThis transition has already started. In fact, global sales of combustion engine cars are well past the peak and are now falling.\nAs you can see in the chart, global sales peaked in 2018. This is calculated based on data from the\nInternational Energy Agency\n. Bloomberg New Energy Finance\nestimates\nthis peak occurred one year earlier, in 2017.\nSales of electric cars, on the other hand, are growing quickly.\nExplore more data on electric car sales across the world\n2. The twin baby boom\nBy Saloni Dattani and Lucas Rodés-Guirao\nDownload\nThe share of births that are twins has changed over time.\nThe chart shows data for France, Canada, the United States, and England & Wales in the\nHuman Multiple Births Database\n.\nAs you can see, twin births have risen dramatically since the 1980s.\nOne reason is the use of reproductive technologies such as in vitro fertilization (IVF), which have made it possible for many more couples to conceive. During procedures like IVF, multiple eggs can be used at the same time to maximize the chances of a successful pregnancy, which can lead to twin births.\nAnother reason for the rise in twin births is that\nthe average age of women at childbirth has risen\n. Older women are\nmore likely to have twin births\n, even without using reproductive technologies.\nTwin births are a chance event, but data shows they can also be influenced by societal changes and reproductive technologies.\nExplore trends in twin births for other countries\n3. The world has probably passed “peak air pollution”\nBy Hannah Ritchie\nDownload\nGlobal emissions of local air pollutants have probably passed their peak.\nThe chart shows estimates of global emissions of pollutants such as sulphur dioxide (which causes acid rain), nitrogen oxides, and black and organic carbon.\nThese pollutants are harmful to human health and can also damage ecosystems.\nIt looks like emissions have peaked for almost all of these pollutants. Global air pollution is now falling, and we can save many lives by accelerating this decline.\nThe exception is ammonia, which is mainly produced by agriculture. Its emissions are still rising.\nThese estimates come from the\nCommunity Emissions Data System (CEDS)\n.\nAir pollution has not peaked everywhere in the world; explore the data for your country\n4. Suicide rates are higher in men than women\nBy Hannah Ritchie\nDownload\nGlobally,\nmore than 700,000 people\ndie from suicide every year.\nUnderstanding the factors that increase the risk of suicide can help us provide the most effective interventions and support systems.\nOne thing we do know is that more men die from suicide than women. In the chart, you can see male suicide rates (on the vertical axis) plotted against female rates. One dot is one country. Since all of the dots lie above the line, male suicide rates were higher in all countries included in this dataset.\nThe size of this gender gap varies by country. In the United States, rates among men are four times higher than among women. In South Korea and Japan, they’re around double. Some countries lie closer to the line, meaning the gap is smaller.\nThe exact reasons for this gender gap are still debated. Factors could include the lethality of different methods, stigma around seeking help, different social pressures, and alcohol and drug abuse.\nEvery suicide is a tragedy. However, suicide death rates\nhave\ndeclined\nin many countries, and we know that they can be reduced further with greater understanding and support. If you are dealing with suicidal thoughts, you can receive immediate help by visiting resources such as\nfindahelpline.com\n.\nRead our article on how suicide statistics can vary across sources\n5. Life expectancy has increased at all ages\nBy Esteban Ortiz-Ospina\nDownload\nIt’s a common misconception that life expectancy has increased only because fewer children die. Historical mortality records show that adults today also live much longer than adults in the past.\nIt’s true that child mortality rates were much higher in the past, and\ntheir decline\nhas greatly improved overall life expectancy. But in recent decades, improvements in survival at older ages have been even more important.\nThe chart shows the\nperiod life expectancy\nin France for people of different ages. This measures how long someone at each of those ages would live, on average, if they experienced the death rates recorded in that year. For example, the last point on the top dark-red line shows that an 80-year-old in 2023 could expect to live to about 90, assuming mortality rates stayed as they were in 2023.\nAs you can see, life expectancy in France has risen at every age. In 1816, someone who had reached the age of 10 could expect to live to 57. By 2023, this had increased to 84. For those aged 65, it rose from 76 in 1816 to 87 in 2023.\nThe\ndata for many other countries\nshows the same. This remarkable shift is the result of advances in medicine, public health, and living standards.\nExplore the data and read more about how life expectancy is measured\nTopic pages\nHere are our top five most-viewed topic pages in 2025.\n2\nCO₂ and Greenhouse Gas Emissions\nPopulation Growth\nLife Expectancy\nPlastic Pollution\nPoverty\nThese are the “homepage” for a given topic, collecting all of our data, research, and writing in one place. They’re a great starting point for exploring a new topic.\nExplore all 123 topics that we cover on Our World in Data\n1. CO₂ and Greenhouse Gas Emissions\nHuman emissions of greenhouse gases are the primary driver of\nclimate change\ntoday.\n3\nCO\n2\nand other greenhouse gases like methane and nitrous oxide are emitted when we\nburn fossil fuels\n, produce materials such as steel, cement, and plastics, and\ngrow the food we eat\n. If we want to reduce these emissions, we need to transform our energy systems, industries, and food systems.\nAt the same time\n, we need to tackle\nenergy poverty\n,\nlow standards of living\n, and\npoor nutrition\n, which all remain enormous problems for billions of people.\nTechnological advances could allow us to do both. The\nprices of solar\n, wind, and\nbatteries\nhave plummeted in recent decades, increasingly undercutting the cost of fossil fuel alternatives. Further progress could allow us to provide cheap, clean energy for everyone. Political change is essential to create a system that supports rapid decarbonization.\nEmissions are still rising in many parts of the world. However, several countries have managed to\ncut their emissions\nin recent decades. With affordable low-carbon technologies, other countries can increase their living standards without the high-carbon pathway that rich countries followed in the past.\nOn this page, you can find our data, visualizations, and writing on CO\n2\nand other greenhouse gas emissions.\nExplore the page\n2. Population Growth\nPopulation growth is one of the most important topics we cover on Our World in Data.\nFor most of human history, the global population was a tiny fraction of what it is today. Over the last few centuries, the human population has gone through an extraordinary change. In 1800, there were one billion people. Today, there are more than 8 billion of us.\nBut after a period of very fast population growth, demographers expect the world population to peak by the end of this century.\nOn this page, you will find all of our data, charts, and writing on changes in population growth. This includes how populations are distributed worldwide, how this has changed, and what demographers expect for the future.\nExplore the page\n3. Life Expectancy\nAcross the world, people are living longer.\nIn 1900, the average life expectancy of a newborn was 32 years. By 2021, this had more than doubled to 71 years.\nBut where, when, how, and why has this dramatic change occurred?\nTo understand it, we can look at data on life expectancy worldwide.\nThe large reduction in child mortality has played an important role in increasing life expectancy. But life expectancy has increased\nat all ages\n. Infants, children, adults, and the elderly are all less likely to die than in the past, and death is being delayed.\nThis remarkable shift results from advances in medicine, public health, and living standards. Along with it, many predictions of the ‘limit’ of life expectancy have been broken.\nOn this page, you will find global data and research on life expectancy and related measures of longevity: the probability of death at a given age, the sex gap in life expectancy, lifespan inequality within countries, and more.\nExplore the page\n4. Plastic Pollution\nPlastic production has sharply increased over the last 70 years. In 1950, the world produced just two million tonnes. It now produces over 450 million tonnes.\nPlastic has added much value to our lives: it’s a cheap, versatile, and sterile material used in various applications, including construction, home appliances, medical instruments, and food packaging.\nHowever, when plastic waste is mismanaged — not recycled, incinerated, or kept in sealed landfills — it becomes an environmental pollutant. One to two million tonnes of plastic enter our oceans yearly, affecting wildlife and ecosystems.\nImproving\nthe management of plastic waste\nacross the world – especially in poorer countries, where most of the ocean plastics come from – is therefore critical to tackling this problem.\nOn this page, you can find all of our data, visualizations, and writing on plastic pollution.\nExplore the page\n5. Poverty\nGlobal poverty is one of the most pressing problems that the world faces today. The poorest in the world are often\nundernourished\nand without access to basic services such as\nelectricity\nand\nsafe drinking water\n; they have less access to\neducation\nand suffer from\nmuch poorer health\n.\nIn order to make progress against such poverty in the future, we need to understand poverty around the world today and how it has changed.\nOn this page, you can find all our data, visualizations, and writing relating to poverty. This work aims to help you understand the scale of the problem today; where progress has been achieved and where it has not; what can be done to make progress against poverty in the future; and the methods behind the data on which this knowledge is based.\nExplore the page\nSocial media posts\nHere are our top five most-popular social media posts in 2025, based on the number of “likes” they received.\nDoes the news reflect what we die from? (Instagram)\nHow do the rights of LGBT+ people vary across the world? (Instagram)\nVaccines reduced measles cases across US states (Reddit)\nWhat do governments spend money on? (Instagram)\nHomophobic attitudes have fallen in Western Europe and the United States (Reddit)\nWe have a presence on most social media platforms to make it easy to follow our work:\nX/Twitter\n,\nInstagram\n,\nFacebook\n,\nLinkedIn\n,\nThreads\n, and\nBluesky\n. Our combined audience across those is nearly 800,000, and our posts this year have been viewed nearly 60 million times.\nWe also often post our work on Reddit in subreddits like\nr/dataisbeautiful\nand\nr/Infographics\n.\n1. Does the news reflect what we die from? (Instagram)\nThis post to share our article “\nDoes the news reflect what we die from?\n” turned out to be our most-popular social media post of all time on any platform.\nIt has nearly 126,000 likes, 6 million views, and 67,000 shares, and helped substantially increase our follower count. Thanks to everyone who liked, commented on, and shared it!\nSee the post on Instagram\nDownload\n2. How do the rights of LGBT+ people vary across the world? (Instagram)\nThis post shared data that our colleagues\nPablo Arriagada\nand\nBastian Herre\nhad updated from\nEqualdex\n, a collaborative knowledge base crowdsourcing LGBT+ rights data by country and region.\nExplore more of that data in\nthis article\n.\nSee the post on Instagram\nDownload\n3. Vaccines reduced measles cases across US states (Reddit)\nOur colleague\nFiona Spooner\nposted this as “OC” (original content) on the “Data is Beautiful” subreddit to share her article with\nSaloni Dattani\n, “\nMeasles vaccines save millions of lives each year\n”.\nSee the post on r/dataisbeautiful\nDownload\n4. What do governments spend money on? (Instagram)\nThis post shared updated data and writing on our\nGovernment Spending topic page\n, work led by\nBertha Rohenkohl\nand\nPablo Arriagada\n.\nSee the post on Instagram\nDownload\n5. Homophobic attitudes have fallen in Western Europe and the United States (Reddit)\nThis post shared\na data insight\nwritten by our colleague\nSimon van Teutem\n.\nSee the post on r/UpliftingNews\nDownload\nDatasets\nHere are the top five most-downloaded datasets on Our World in Data this year.\n4\nCOVID-19\nCO₂ emissions\nEconomic growth\nLife expectancy\nRenewable electricity\nWe’ve made it\neasy to download and reuse our data\n— with an API and enhanced download options (just click the “Download” button at the bottom of any chart).\nOur work would not be possible without the researchers and data providers we rely on, so we ask you to always\nrespect their license terms and cite them\nappropriately.\nThis is crucial to allow data providers to continue doing their work, enhancing, maintaining, and updating valuable data.\nFind the data that matters to you\nUsing our recently improved search\n1. COVID-19\nThe COVID-19 pandemic has had a profound impact on the world, causing tens of millions of deaths, overwhelming healthcare systems, and disrupting societies and economies.\nReliable data\nhas been crucial to effectively track and respond to the pandemic and guide public health efforts, research, and policies.\nFor several years during the pandemic,\nour team at Our World in Data\npublished daily updates on a range of crucial indicators and developed two global datasets on\ntesting\nand\nvaccination\n.\nOur COVID-19 Data Explorer became a go-to source for people to understand the extent and spread of the disease, and it contains our most-downloaded dataset to this day.\nBesides the data on testing and vaccination our team collected, this dataset relies on many sources, including\nThe Economist\n,\nWHO\n, the\nOxford Covid-19 Government Response Tracker\n, the\nHuman Mortality Database\n,\nKarlinsky and Kobak (2021)\n, and more.\n2. CO₂ emissions\nData from the\nGlobal Carbon Budget\n3. Economic growth\nData from the\nWorld Bank\n, IMF, OECD, and Eurostat\n4. Life expectancy\nData from\nthe UN\n,\nHuman Mortality Database\n,\nZijdeman et al. (2015)\n, and\nRiley (2005)\n5. Renewable electricity\nData from the Energy Institute’s\nStatistical Review of World Energy\nEndnotes\nBased on cumulative page views on Our World in Data.\nBased on cumulative page views.\nIPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, In press, doi:10.1017/9781009157896.\nBased on the cumulative number of clicks on \"Download\" buttons in charts and data explorers.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nOur World in Data team (2025) - “Top of the Charts: our most popular work in 2025” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260622-064358/top-of-the-charts-2025.html' [Online Resource] (archived on June 22, 2026).\nBibTeX citation\n@article{owid-top-of-the-charts-2025,\nauthor = {Our World in Data team},\ntitle = {Top of the Charts: our most popular work in 2025},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260622-064358/top-of-the-charts-2025.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "top-of-the-charts-2025", "source_url": "https://ourworldindata.org/top-of-the-charts-2025", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "A look back at the most popular charts, articles, data insights, and more from Our World in Data in 2025.", "numeric_mentions": ["2025", "5,", "60 million", "1", "2", "14,000", "123", "3", "4", "5", "80%", "1945", "63 years", "400", "2018", "2017", "1980", "700,000", "80", "2023", "90,", "1816,", "10", "57", "2023,", "84", "65,", "76", "1816", "87", "1800,", "8 billion", "1900,", "32 years", "2021,", "71 years", "70 years", "1950,", "450 million", "2025,", "800,000,", "126,000", "6 million", "67,000", "19", "2021", "2015", "2005", "10.1017", "9781009157896", "20260622", "064358", "22,", "2026"], "numeric_evidence": [{"title": "Annual CO₂ emissions", "source_url": "https://ourworldindata.org/grapher/annual-co2-emissions-per-country.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Annual CO₂ emissions"], "row_count_total": 29384, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1949", "Annual CO₂ emissions": "14656"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1950", "Annual CO₂ emissions": "84272"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1951", "Annual CO₂ emissions": "91600"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1952", "Annual CO₂ emissions": "91600"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1953", "Annual CO₂ emissions": "106256"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1954", "Annual CO₂ emissions": "106256"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1955", "Annual CO₂ emissions": "153888"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1956", "Annual CO₂ emissions": "183200"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1957", "Annual CO₂ emissions": "293120"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1958", "Annual CO₂ emissions": "329760"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1959", "Annual CO₂ emissions": "384571"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1960", "Annual CO₂ emissions": "413885"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Annual CO₂ emissions": "490798"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Annual CO₂ emissions": "688594"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Annual CO₂ emissions": "706736"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Annual CO₂ emissions": "838551"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Annual CO₂ emissions": "1006917"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Annual CO₂ emissions": "1091159"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Annual CO₂ emissions": "1281865"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Annual CO₂ emissions": "1223391"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1969", "Annual CO₂ emissions": "941232"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1970", "Annual CO₂ emissions": "1670397"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1971", "Annual CO₂ emissions": "1893554"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1972", "Annual CO₂ emissions": "1530347"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Annual CO₂ emissions": "1635454"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Annual CO₂ emissions": "1913152"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Annual CO₂ emissions": "2121383"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Annual CO₂ emissions": "1980859"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Annual CO₂ emissions": "2384175"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Annual CO₂ emissions": "2153300"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Annual CO₂ emissions": "2232754"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Annual CO₂ emissions": "1756302"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Annual CO₂ emissions": "1978463"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Annual CO₂ emissions": "2094580.9"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Annual CO₂ emissions": "2519954"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Annual CO₂ emissions": "2821540"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Annual CO₂ emissions": "3501422"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Annual CO₂ emissions": "3133645"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Annual CO₂ emissions": "3113826"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Annual CO₂ emissions": "2856896"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Annual CO₂ emissions": "2764855"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Annual CO₂ emissions": "2024326.1"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Annual CO₂ emissions": "1914301"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Annual CO₂ emissions": "1482054"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Annual CO₂ emissions": "1486943"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Annual CO₂ emissions": "1453829"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Annual CO₂ emissions": "1417327"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Annual CO₂ emissions": "1370104"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Annual CO₂ emissions": "1304152"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Annual CO₂ emissions": "1278504"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Annual CO₂ emissions": "1091640"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Annual CO₂ emissions": "1047127.94"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Annual CO₂ emissions": "1069098"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Annual CO₂ emissions": "1341065"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Annual CO₂ emissions": "1559679"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Annual CO₂ emissions": "1237247"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Annual CO₂ emissions": "1889507"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Annual CO₂ emissions": "2159318"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Annual CO₂ emissions": "2799909"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Annual CO₂ emissions": "4254490"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Annual CO₂ emissions": "6388232"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Annual CO₂ emissions": "8364803.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Annual CO₂ emissions": "11409623"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Annual CO₂ emissions": "9731202"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Annual CO₂ emissions": "8891447"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Annual CO₂ emissions": "8697668"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Annual CO₂ emissions": "9384400"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Annual CO₂ emissions": "8605932"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Annual CO₂ emissions": "9311054"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Annual CO₂ emissions": "10191504"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Annual CO₂ emissions": "10400110"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Annual CO₂ emissions": "11118626"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Annual CO₂ emissions": "9868841"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Annual CO₂ emissions": "10169889"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Annual CO₂ emissions": "10516319"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2024", "Annual CO₂ emissions": "10825998"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1884", "Annual CO₂ emissions": "21984"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1885", "Annual CO₂ emissions": "36640"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1886", "Annual CO₂ emissions": "47632"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1887", "Annual CO₂ emissions": "47632"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1888", "Annual CO₂ emissions": "80608"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1889", "Annual CO₂ emissions": "131904"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1890", "Annual CO₂ emissions": "296784"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1891", "Annual CO₂ emissions": "296784"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1892", "Annual CO₂ emissions": "479984"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1893", "Annual CO₂ emissions": 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"13443295"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Annual CO₂ emissions": "13701154"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "annual-co2-emissions-per-country", "metadata_url": "https://ourworldindata.org/grapher/annual-co2-emissions-per-country.metadata.json", "chart_title": "Annual CO₂ emissions", "chart_subtitle": "Carbon dioxide (CO₂) emissions from fossil fuels and industry. Land-use change emissions are not included.", "chart_note": null, "chart_citation": "Global Carbon Budget (2025)", "original_chart_url": "https://ourworldindata.org/grapher/annual-co2-emissions-per-country", "owid_column_metadata": {"Annual CO₂ emissions": {"titleShort": "Annual CO₂ emissions", "titleLong": "Annual CO₂ emissions", "descriptionShort": "Annual total emissions of carbon dioxide (CO₂), excluding land-use change, measured in tonnes.", "descriptionKey": ["This data is based on territorial emissions, meaning the emissions produced within a country's borders, but not those from imported goods. For example, emissions from imported steel are counted in the country where the steel is produced. To learn more and look at emissions adjusted for trade, read our article: [How do CO₂ emissions compare when we adjust for trade?](https://ourworldindata.org/consumption-based-co2)", "Emissions from international aviation and shipping are not included in the data for any individual country or region. They are only counted in the global total."], "descriptionProcessing": "- Global emissions are converted from tonnes of carbon to tonnes of carbon dioxide (CO₂) using a factor of 3.664. This is the conversion factor [recommended by the Global Carbon Project](https://globalcarbonbudgetdata.org/downloads/jGJH0-data/Global+Carbon+Budget+v2024+Dataset+Descriptions.pdf). It reflects that one tonne of carbon, when fully oxidized, forms 3.664 tonnes of CO₂, based on the relative molecular weights of carbon and oxygen in CO₂.\n- Emissions from the 1991 Kuwaiti oil fires are included in Kuwait's emissions for that year.", "shortUnit": "t", "unit": "tonnes", "timespan": "1750-2024", "type": "Numeric", "owidVariableId": 1119906, "shortName": "emissions_total", "lastUpdated": "2025-11-13", "nextUpdate": "2026-11-13", "citationShort": "Global Carbon Budget (2025) – with major processing by Our World in Data", "citationLong": "Global Carbon Budget (2025) – with major processing by Our World in Data. “Annual CO₂ emissions” [dataset]. Global Carbon Project, “Global Carbon Budget v15” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1119906.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Life expectancy", "source_url": "https://ourworldindata.org/grapher/life-expectancy.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Life expectancy"], "row_count_total": 21565, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1950", "Life expectancy": "28.1563"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1951", "Life expectancy": "28.5836"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1952", "Life expectancy": "29.0138"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1953", "Life expectancy": "29.4521"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1954", "Life expectancy": "29.6975"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1955", "Life expectancy": "30.366"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1956", "Life expectancy": "30.8303"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1957", "Life expectancy": "31.3451"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1958", "Life expectancy": "31.84"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1959", "Life expectancy": "32.3365"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1960", "Life expectancy": "32.7987"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Life expectancy": "33.291"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Life expectancy": "33.7565"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Life expectancy": "34.2008"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Life expectancy": "34.6726"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Life expectancy": "35.1245"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Life expectancy": "35.5831"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Life expectancy": "36.042"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Life expectancy": "36.5101"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1969", "Life expectancy": "36.979"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1970", "Life expectancy": "37.4601"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1971", "Life expectancy": "37.9324"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1972", "Life expectancy": "38.4226"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Life expectancy": "38.951"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Life expectancy": "39.4687"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Life expectancy": "39.9944"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Life expectancy": "40.5184"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Life expectancy": "41.0821"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Life expectancy": "40.0859"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Life expectancy": "38.8441"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Life expectancy": "39.2581"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Life expectancy": "39.4058"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Life expectancy": "36.0577"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Life expectancy": "36.5174"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Life expectancy": "31.4732"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Life expectancy": "32.1316"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Life expectancy": "38.4001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Life expectancy": "38.8312"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Life expectancy": "43.2381"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Life expectancy": "44.4961"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Life expectancy": "45.1183"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Life 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{"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Life expectancy": "57.1713"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Life expectancy": "57.8098"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Life expectancy": "58.2468"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Life expectancy": "58.5533"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Life expectancy": "58.9563"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Life expectancy": "59.7081"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Life expectancy": "60.2478"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Life expectancy": "60.7018"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Life expectancy": "61.2503"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Life expectancy": "61.7349"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Life expectancy": "62.1878"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Life expectancy": "62.2599"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Life expectancy": "62.2695"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Life expectancy": "62.6459"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Life expectancy": "62.4062"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Life expectancy": "62.4434"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Life expectancy": "62.9411"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Life expectancy": "61.4537"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Life expectancy": "60.4174"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Life expectancy": "65.617"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Life expectancy": "66.0346"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1770", "Life expectancy": "26.4"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1925", "Life 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"Africa", "Code": "OWID_AFR", "Year": "1961", "Life expectancy": "41.7756"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1962", "Life expectancy": "42.2531"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1963", "Life expectancy": "42.7175"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1964", "Life expectancy": "43.0898"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1965", "Life expectancy": "43.3551"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1966", "Life expectancy": "43.4601"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1967", "Life expectancy": "43.7471"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1968", "Life expectancy": "44.2523"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1969", "Life expectancy": "44.5077"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1970", "Life expectancy": "44.9859"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1971", "Life expectancy": "45.3999"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1972", "Life expectancy": "45.4595"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1973", "Life expectancy": "46.101"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1974", "Life expectancy": "46.4118"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1975", "Life expectancy": "46.807"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1976", "Life expectancy": "47.5487"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1977", "Life expectancy": "48.0406"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1978", "Life expectancy": "48.4287"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1979", "Life expectancy": "48.9562"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1980", "Life expectancy": "49.4094"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1981", "Life expectancy": "49.8226"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1982", "Life expectancy": "50.2133"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1983", "Life expectancy": "49.5464"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1984", "Life expectancy": "49.7402"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Life expectancy": "50.1332"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Life expectancy": "50.5889"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Life expectancy": "51.1887"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Life expectancy": "51.0598"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Life expectancy": "51.7063"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Life expectancy": "51.6566"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Life expectancy": "51.4937"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Life expectancy": "51.4261"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Life expectancy": "51.8296"}], "rows_tail": [{"Entity": "Zambia", "Code": "ZMB", "Year": "1978", "Life expectancy": "56.3349"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1979", "Life expectancy": "56.0499"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1980", "Life expectancy": "55.4961"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1981", "Life expectancy": "54.986"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1982", "Life expectancy": "54.361"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1983", "Life expectancy": "53.6737"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1984", "Life expectancy": "52.8772"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Life expectancy": "52.0014"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Life expectancy": "51.1489"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Life expectancy": "50.4211"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Life expectancy": "49.6737"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Life expectancy": "48.8817"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Life expectancy": "48.204"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Life expectancy": "47.4431"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Life expectancy": "46.9679"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Life expectancy": "46.6781"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Life expectancy": "46.3439"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Life expectancy": "46.0509"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Life expectancy": "45.7565"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Life expectancy": "45.7429"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Life expectancy": "45.7095"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Life expectancy": "45.924"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Life expectancy": "46.5771"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Life expectancy": "47.4077"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Life expectancy": "48.4614"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Life expectancy": "49.6158"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Life expectancy": "50.5398"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Life expectancy": "51.6147"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Life expectancy": "52.6928"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Life expectancy": "53.73"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Life expectancy": "54.835"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Life expectancy": "55.8862"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Life expectancy": "56.896"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Life expectancy": "57.8431"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Life expectancy": "58.704"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Life expectancy": "59.4452"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Life expectancy": "60.1091"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Life expectancy": "60.7284"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Life expectancy": "61.1285"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Life expectancy": "61.5644"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Life expectancy": "62.1381"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Life expectancy": "62.9145"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Life expectancy": "63.3607"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Life expectancy": "62.3631"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Life expectancy": "65.2791"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Life expectancy": "66.3487"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1950", "Life expectancy": "49.4155"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1951", "Life expectancy": "49.8355"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1952", "Life expectancy": "50.2394"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1953", "Life expectancy": "50.6334"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1954", "Life expectancy": "51.024"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1955", "Life expectancy": "51.4106"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1956", "Life expectancy": "51.8011"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1957", "Life expectancy": "52.1959"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1958", "Life expectancy": "52.5823"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1959", "Life expectancy": "53.022"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1960", "Life expectancy": "53.4922"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1961", "Life expectancy": "53.9663"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1962", "Life expectancy": "54.4535"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1963", "Life expectancy": "54.9416"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1964", "Life expectancy": "55.4311"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1965", "Life expectancy": "55.9054"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "Life expectancy": "56.3595"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "Life expectancy": "56.7664"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "Life expectancy": "57.1449"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "Life expectancy": "57.4813"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "Life expectancy": "57.7608"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "Life expectancy": "57.9959"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "Life expectancy": "58.1755"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "Life expectancy": "58.0872"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "Life expectancy": "58.1937"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Life expectancy": "58.2495"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Life expectancy": "57.6277"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Life expectancy": "56.9962"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Life expectancy": "54.7297"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Life expectancy": "54.9184"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Life expectancy": "59.0621"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Life expectancy": "59.4463"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Life expectancy": "59.859"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Life expectancy": "59.9888"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Life expectancy": "60.6223"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Life expectancy": "60.9012"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Life expectancy": "60.9824"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Life expectancy": "60.7239"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Life expectancy": "60.2414"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Life expectancy": "59.4458"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Life expectancy": "58.3191"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Life expectancy": "57.0371"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Life expectancy": "55.6021"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Life expectancy": "53.9764"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Life expectancy": "52.5367"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Life expectancy": "51.1435"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Life expectancy": "48.9815"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Life expectancy": "48.5361"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Life expectancy": "47.5508"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Life expectancy": "46.3786"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Life expectancy": "46.0339"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Life expectancy": "44.5249"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Life expectancy": "45.9381"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Life expectancy": "45.373"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Life expectancy": "46.0426"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Life expectancy": "46.5701"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Life expectancy": "47.0559"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Life expectancy": "47.5756"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Life expectancy": "48.4559"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Life expectancy": "49.7496"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Life expectancy": "51.9252"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Life expectancy": "53.9112"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Life expectancy": "55.3855"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Life expectancy": "56.8422"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Life expectancy": "58.106"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Life expectancy": "58.9895"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Life expectancy": "59.7601"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Life expectancy": "60.2626"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Life expectancy": "60.9055"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Life expectancy": "61.0603"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Life expectancy": "61.53"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Life expectancy": "60.1347"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Life expectancy": "62.3601"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Life expectancy": "62.7748"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "life-expectancy", "metadata_url": "https://ourworldindata.org/grapher/life-expectancy.metadata.json", "chart_title": "Life expectancy", "chart_subtitle": null, "chart_note": null, "chart_citation": "Riley (2005); Zijdeman et al. (2015); HMD (2025); UN WPP (2024)", "original_chart_url": "https://ourworldindata.org/grapher/life-expectancy", "owid_column_metadata": {"Period life expectancy at birth": {"titleShort": "Life expectancy", "titleLong": "Life expectancy - Riley; Zijdeman et al.; HMD; UN WPP – Long-run data", "descriptionShort": "Period life expectancy is the number of years the average person born in a certain year would live if they experienced the same chances of dying at each age as people did that year.", "descriptionKey": ["Across the world, people are living longer. In 1900, the global average life expectancy was 32 years. By 2023, this had more than doubled to 73 years.", "Countries around the world made big improvements, and life expectancy more than doubled in every region. This wasn’t just due to falling child mortality; people started living longer at all ages.", "Even after World War II, there have been large drops in life expectancy, such as during the Great Leap Forward famine in China, the HIV/AIDS epidemic in sub-Saharan Africa, the Rwandan genocide, or the COVID-19 pandemic.", "Period life expectancy is an indicator that summarizes death rates across all age groups in one particular year. It shows how long the average baby born in that year would be expected to live if they experienced the same chances of dying at each age as people did in that year.", "This chart shows long-run estimates of life expectancy compiled by our team from several data sources. Before 1950, for country-level data, we rely on the [Human Mortality Database (2025)](https://www.mortality.org/Data/ZippedDataFiles) combined with [Zijdeman (2015)](https://clio-infra.eu/Indicators/LifeExpectancyatBirthTotal.html). For regional data, we use [Riley (2005)](https://doi.org/10.1111/j.1728-4457.2005.00083.x). From 1950 onward, we use the [United Nations World Population Prospects (2024)](https://population.un.org/wpp/downloads).", "Detailed information on the source of each data point can be found on [this page](https://docs.google.com/spreadsheets/d/1LnrU1V3p2wq7sAPY4AHRdH1urol3cKev7prEvlLfSU4/edit?gid=0#gid=0)."], "descriptionProcessing": "This chart combines data from several sources. For country-level data before 1950, we use the Human Mortality Database (2025) data and Zijdeman et al. (2015). For country-years where these sources overlap, we use the Human Mortality Database.\n\nFor regional data, before 1950, we use Riley's (2005) estimates.\n\nFrom 1950 onwards, we use the United Nations World Population Prospects (2024) for both country-level and regional data.\n\nDetailed information on the source of each data point can be found on [this page](https://docs.google.com/spreadsheets/d/1LnrU1V3p2wq7sAPY4AHRdH1urol3cKev7prEvlLfSU4/edit?gid=0#gid=0).", "shortUnit": "years", "unit": "years", "timespan": "1543-2023", "type": "Numeric", "owidVariableId": 1118466, "shortName": "life_expectancy_0", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Riley (2005); Zijdeman et al. (2015); HMD (2025); UN WPP (2024) – with major processing by Our World in Data", "citationLong": "Riley (2005); Zijdeman et al. (2015); HMD (2025); UN WPP (2024) – with major processing by Our World in Data. “Life expectancy – Riley; Zijdeman et al.; HMD; UN WPP – Long-run data” [dataset]. Human Mortality Database, “Human Mortality Database”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update”; Zijdeman et al., “Life Expectancy at birth v2”; James C. Riley, “Estimates of Regional and Global Life Expectancy, 1800-2001” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118466.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"grapher_slug": "democracy-index-eiu", "source_url": "https://ourworldindata.org/grapher/democracy-index-eiu", "parse_error": "Failed to fetch https://ourworldindata.org/grapher/democracy-index-eiu.csv"}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "c7c3104020e9b1f03f7b"},
{"raw_link": "https://ourworldindata.org/wild-mammals-birds-biomass", "title": "Almost all of the world’s mammal biomass is humans and livestock", "context": "Home\nBiodiversity\nAlmost all of the world’s mammal biomass is humans and livestock\nHumans and livestock make up 95% of the world’s mammal biomass; wild mammals are just 5%.\nBy\nHannah Ritchie\nand\nFiona Spooner\nDecember 1, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nA diverse range of mammals once roamed the planet. This changed dramatically with the arrival of humans, who have become the dominant species through our own populations, as well as the animals we breed and raise for food.\nThere are various ways to compare the distribution or abundance of different types of mammals. One way is to compare them based on the\nnumber\nof individuals. In these terms, very small animals vastly outnumber larger animals, but this doesn’t necessarily give us an idea of how much ecological and biological resources different animals use.\nAnother metric that ecologists often use is biomass — the total weight of all animals of a given species. This not only takes into account the number of animals but also factors in their size.\n1\nIt gives more weight to larger animals at higher levels of the ecological “pyramid”: these rely on well-functioning bases below them.\nLet’s then look at the breakdown of the global mammal kingdom in these terms.\n2\nIt’s shown in the chart below. This data is sourced from the study by Lior Greenspoon and colleagues.\n3\nEach square represents one percent of the world’s mammal biomass, including both land and marine animals. For context, that 1% is equal to around 11 million tonnes.\nDownload\nThe dominance of humans is clear. We account for more than one-third of mammal biomass. Our biomass is more than seven times greater than all wild mammals combined.\nOur livestock and pets, which are primarily cattle, account for 59%.\nThat leaves just 5% as wild mammals, which includes thousands of different species, from elephants and deer to lions and whales.\nBeyond the totals for humans, livestock, and wild animals, there are a few striking comparisons that we found surprising. Farmed pigs weigh as much as all of the world’s whales, orcas, sea otters, seals, and dolphins combined. All the dogs in the world, including pets and feral dogs, weigh as much as all wild mammals on land.\nAt the end of this article, we provide more details on the assumptions made in the underlying study, and give some cross-checks on the credibility of these numbers.\nChickens and other poultry outweigh wild birds\nWhen we show people the chart above, one question often comes up: what about chickens? Of course, chickens are not mammals. But we can make a similar comparison between poultry and wild birds.\nLike mammals, poultry livestock collectively weigh much more than all the world’s wild birds. You can see this in the next chart.\n4\nDownload\nThe\nsize\nof the difference between poultry and wild birds, though, is much less certain than it is for humans and livestock versus wild mammals. That’s because estimates for the number of wild birds vary a lot.\nOur biomass is more than seven times greater than all wild mammals combined.\nThe underlying study that this data comes from uses several methods to estimate the weight of wild birds globally. The different techniques yield quite different estimates: 5 million tonnes in one, and 24 million tonnes in the other. They take the geometric mean of the two, which is their final estimate shown below. It suggests that poultry weigh more than twice as much as wild birds.\nBut this result is clearly very sensitive to the choice of methodology. If we used the “5 million tonnes” figure, then wild birds would account for just 14%. If we assumed 24 million tonnes, they’d be 44%. We explain this uncertainty in more detail in our appendix at the end of this article.\nUsing any method, the overall direction of the result is the same: chickens and other livestock birds weigh more than their wild cousins.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nWild mammals have declined, but the total amount of mammal biomass has increased a lot\nHow did humans come to dominate the mammal kingdom?\nA huge decline in the number and size of wild mammals has played a major role. Estimates suggest that the biomass of wild mammals\nhas declined\nby roughly 85% over the last 100,000 years, and particularly since the migration of human populations across the planet.\n5\nBut that’s not the only reason. It’s not that the abundance of wild mammals was replaced one by one by humans and livestock. In fact, human activity has dramatically increased the total amount of mammal biomass on the planet.\nAround 100,000 years ago, the total biomass of land mammals summed up to approximately 120 million tonnes, essentially all of it in the form of\nwild\nanimals.\n6\nBy 10,000 years ago, this had fallen to 90 million tonnes. But the most dramatic changes followed the advent of agriculture. Wild mammal populations and biomass continued to decline, while human and livestock populations gradually increased.\nIn this section, we’re focusing on terrestrial mammals, so marine mammal figures have not been included.\nBy 1850, the total mammal biomass on land — including wild animals, humans, and livestock — had increased to an estimated 250 million tonnes.\n7\nSince then, this has continued to increase rapidly. Today, mammals weigh roughly 1100 million tonnes, which represents a quadrupling since 1850. Wild mammals declined, but this was more than offset by the huge rise in biomass of humans and farmed mammals.\nFarmed pigs weigh as much as all of the world’s whales, orcas, sea otters, seals, and dolphins combined.\nHumans achieved this by harnessing external resources and energy inputs that weren’t available to wild animal populations before. We’ve used fossil fuels and agricultural innovations\nto harness\nsynthetic fertilizers. We’ve engineered extremely productive crop varieties\nthat grow much faster\n— and supply more energy — than conventional plants. We’ve cleared land to make space for raising livestock at higher densities than you’d find them in the wild. Essentially, we’ve added huge amounts of energy to the system that was there in the absence of human populations.\nBut while the mammal kingdom is more “vast” than ever before, this has, at least so far, come at the cost of diversity. Wild mammals have shrunk not just in relative terms, but also in absolute terms.\nAcknowledgments\nThanks to Marwa Boukarim for her help on design and visualization, and to Max Roser and Edouard Mathieu for feedback and comments on this article.\nContinue reading on Our World in Data\nJust ten species make up almost half the weight of all wild mammals on Earth\nA small number of species dominate the distribution of wild mammal biomass.\nThe largest mammals have always been at the greatest risk of extinction – this is still the case today\nHumans hunted many of the world’s large mammals to extinction. This threat still exists today, but it doesn’t have to be that way.\nWild mammals have declined by 85% since the rise of humans, but there is a possible future where they flourish\nWild mammal biomass has declined by 85% since the rise of humans. But we can turn things around by reducing the amount of land we use for agriculture.\nAppendix: Estimation methods and uncertainties\nIn the sections below, we detail the methods and figures that the authors of the scientific papers used in producing the statistics shown in this article. We do this to help make the work and methodology more transparent and understandable.\nUnless otherwise stated, the methods we’re describing are those of the paper by Lior Greenspoon et al. (2023).\n8\nHumans\nHow do researchers estimate the total biomass of humans?\nTo calculate the biomass of humans, the authors take global population figures for 2021 from the UN Population Division’s\nWorld Population Prospects\nand multiply this by an age-weighted average body mass of 50 kilograms per person. This is relatively small for an adult, but when averaged across children and adults, it seems like a reasonable estimate.\nIn 2021, the global population\nwas around\n7.95 billion. Multiplying by 50 kilograms gives a total weight of 398 million tonnes.\nLivestock\nHow do researchers estimate the total biomass of cattle, pigs, and sheep?\nTo calculate the biomass of farm livestock, the authors take livestock population figures for 2018 from the Food and Agriculture Organization of the United Nations. The data for all animals, excluding cattle and pigs, is available\nhere\n. The data for cattle and pigs is\nhere\n.\nTo get the total mass of these animals, they multiply each type by their average respective weight in different world regions. These weights come from\nthis inventory guideline report\nfrom the Intergovernmental Panel on Climate Change (IPCC), and are listed\nhere\n.\nPets\nHow do researchers estimate the total biomass of cats, dogs, and other pets?\nThe authors produce biomass estimates for four types of pets: dogs, cats, rats, and mice. You can find their table of assumptions and estimates\nhere\n.\nFor\ndogs\n, the global population is estimated to be 987 million. This comes from\nthe chapter\n“The dog-human-wildlife interface: assessing the scope of the problem” in the book\nFree-Ranging Dogs and Wildlife Conservation\n.\nThis includes domesticated and feral dogs (which is where a lot of the uncertainty comes from). For example, around one-quarter of the global total comes from rural China, where there is approximately one dog for every 2.9 humans.\nWe were initially skeptical about these figures and found it difficult to trace the exact source for each estimate. However, upon further examination of the literature, commonly cited figures\nrange from\napproximately\n700\nto 900 million.\nWe therefore cautiously concluded that a population estimate of 1 billion does not seem unreasonable, although it’s on the higher end.\nTo get a total weight, they assume an average weight of 22 kilograms per dog.\n9\nFor context, this is roughly the weight of an average Border Collie, Australian Shepherd, or English Springer Spaniel.\nIn the original study, Greenspoon et al. (2023) give a final estimate of 20 million tonnes. We find it plausible that it falls within the range of 15 to 20 million tonnes, so their estimate is on the higher end.\nFor\ncats\n, they assume a global population of 600 million, and an average mass of 3.75 kilograms.\n10\nBased on personal experience, this seems like a fair estimate for the average cat. The total biomass of cats — at 2.3 million tonnes — is not large enough to feature in the main graphic in this article.\nThe same is true for rats and mice, which collectively weigh an order of magnitude less.\nWild mammals\nUncertainties in the biomass of wild mammals\nThe estimates of wild mammal biomass have the largest uncertainty.\nIn a separate article, we look at the distribution of wild mammals specifically, using the estimates from this paper. There, we provide more detail on population estimates and their methodologies.\nBut briefly, the authors use global population reports — which give estimates of the total number of animals — for roughly 6% of land mammal species. They then use the average weights of these animals to calculate their biomass. The biomass of the remaining wild land mammal species was estimated using a Support Vector Regression model. It might seem odd to base total estimates based on records for just 6% of species, but this small fraction dominates total mammal biomass; the remaining 94% make up a relatively small fraction.\nThis does introduce significant uncertainty; however, the overall conclusions of this article and the visualization would not change under different assumptions. Wild mammals’ share of total biomass might change by as much as a few percentage points in either direction, but the fact that they make up far less than 10% remains true.\nPoultry and wild birds\nThe uncertainty in estimates of wild birds is particularly high\nUnlike the mammal figures, the data for poultry and wild birds comes from the earlier article by Bar-On et al. (2018).\n5\nThey calculated the biomass of\nwild birds\nusing two slightly different sources and methodologies. In the first, they used wild bird population figures from a 1997 paper by Gaston and Blackburn, who extrapolated bird densities (the number of birds per unit area) across different environments and regions to derive a global total.\n11\nUsing slightly different models and extrapolations gave a similar figure of around 200 to 400 billion wild birds globally.\nTo calculate the combined\nweight\nof these birds, they used data on the relationship between bird population density and body weight among British birds.\n12\nBased on this, they arrived at a weighted average of around 80 grams per bird. If we multiply this by 300 billion (the mid-point population estimate), we get a weight of 24 million tonnes.\n13\nThey also applied another methodology, using the individual mass and population densities of around 900 bird species, then extrapolating that out to all known bird species. This gave a result of 5 million tonnes.\nIn their paper, they took the geometric mean of the two estimates (24 million and 5 million) to get a final estimate of 11 million tonnes.\nThe fact that the two methodologies yielded pretty different results — the first being more than four times higher — highlights the level of uncertainty in these wild population estimates. Just to make this even clearer: a more recent paper produced a much lower estimate of wild bird abundance of just 50 billion.\n14\nHowever, the uncertainty was huge, ranging from 3.9 billion to 2.1\ntrillion,\nresulting in a strong response from other researchers.\n15\nThey calculated the weight of\npoultry\nin a similar way to other livestock species (see the section above on “Livestock”). They took population figures from the Food and Agriculture Organization of the United Nations and multiplied them by their respective weights, detailed in\nthis inventory guideline report\nfrom the Intergovernmental Panel on Climate Change (IPCC). This gave a result of around 30 million tonnes.\nThe final result is, then, very sensitive to the assumptions made in these comparisons. Using any of the estimates, poultry outweigh wild birds. However, using the 24 million tonnes for wild birds from the first methodology means the gap is relatively close. Using the 5 million tonnes figure would mean that wild birds were just 14% of total bird biomass.\nEndnotes\nTo calculate the biomass of a taxonomic group, the researchers multiplied the average weight of a given animal by the number of individuals in that group. In humans, for example, they take the average weight of a person and multiply it by the human population. Sometimes this is given in tonnes of\ncarbon\n. To estimate that, you can take the “wet biomass” — the total of an animal when it’s alive — and divide by six.\nUnfortunately, due to data availability, not all of these estimates are for the same year. 2018 is the year used to calculate the weight of livestock. For humans, the year was 2021. For wild mammals, there is even greater variability. These differences are worth noting, but are likely to have only marginal impacts on the final figures.\nGreenspoon, L., Krieger, E., Sender, R., Rosenberg, Y., Bar-On, Y. M., Moran, U., ... & Milo, R. (2023). The global biomass of wild mammals. Proceedings of the National Academy of Sciences.\nIn an earlier version of this article, we presented data from an earlier paper from Bar-On et al. (2018). The results are similar, but the Greenspoon et al. (2023) paper gives updated estimates.\nBar-On, Y. M., Phillips, R., & Milo, R. (2018). The biomass distribution on Earth. Proceedings of the National Academy of Sciences.\nFor the bird comparison, we’ve used data from the study by Bar-On et al. (2018).\nBar-On, Y. M., Phillips, R., & Milo, R. (2018). The biomass distribution on Earth. Proceedings of the National Academy of Sciences.\nBar-On, Y. M., Phillips, R., & Milo, R. (2018). The biomass distribution on Earth. Proceedings of the National Academy of Sciences.\nBarnosky, A. D. (2008). Megafauna biomass tradeoff as a driver of Quaternary and future extinctions. Proceedings of the National Academy of Sciences.\nThis figure comes from Greenspoon et al. (2025). The global biomass of mammals since 1850. Nature Communications.\nThe authors attempt to quantify the change in global mammal biomass since 1850. These figures, especially for wild mammals, are highly uncertain, especially further back in time. They estimate that humans and livestock weighed 200 million tonnes, and wild land mammals, 50 million tonnes. The 200 million figure is likely to be much more certain than the latter.\nGreenspoon, L., Krieger, E., Sender, R., Rosenberg, Y., Bar-On, Y. M., Moran, U., ... & Milo, R. (2023). The global biomass of wild mammals. Proceedings of the National Academy of Sciences.\nThis comes from an older paper from Woodall et al. (1988).\nP. F. Woodall, I. P. Johnstone, Dimensions and allometry of testes, epididymides and spermatozoa in the domestic dog (Canis familiaris). Journal of Reproduction and Fertility.\nPopulation estimates come from the following source.\nYoung et al. (2011), Urban carnivores: Ecology, conflict, conservation. The Journal of Wildlife Management.\nGaston KJ, Blackburn TM (1997). How many birds are there? Biodiversity Conservation.\nNee, S., Read, A. F., Greenwood, J. J., & Harvey, P. H. (1991). The relationship between abundance and body size in British birds. Nature.\nIn the original paper, the authors calculate this weight in tonnes of carbon. To convert our wet weight to tonnes of carbon, you’d divide by 6. That would give 4 million tonnes of carbon.\nCallaghan, C. T., Nakagawa, S., & Cornwell, W. K. (2021). Global abundance estimates for 9,700 bird species. Proceedings of the National Academy of Sciences.\nRobinson, O. J., Socolar, J. B., Stuber, E. F., Auer, T., Berryman, A. J., Boersch-Supan, P. H., ... & Johnston, A. (2022). Extreme uncertainty and unquantifiable bias do not inform population sizes. Proceedings of the National Academy of Sciences.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Fiona Spooner (2025) - “Almost all of the world’s mammal biomass is humans and livestock” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260130-093223/wild-mammals-birds-biomass.html' [Online Resource] (archived on January 30, 2026).\nBibTeX citation\n@article{owid-wild-mammals-birds-biomass,\nauthor = {Hannah Ritchie and Fiona Spooner},\ntitle = {Almost all of the world’s mammal biomass is humans and livestock},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260130-093223/wild-mammals-birds-biomass.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "wild-mammals-birds-biomass", "source_url": "https://ourworldindata.org/wild-mammals-birds-biomass", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Humans and livestock make up 95% of the world’s mammal biomass; wild mammals are just 5%.", "numeric_mentions": ["95%", "5%", "1,", "2025", "1", "2", "3", "1%", "11 million", "59%", "4", "5 million", "24 million", "14%", "44%", "85%", "100,000 years", "5", "120 million", "6", "10,000 years", "90 million", "1850,", "250 million", "7", "1100 million", "1850", "2023", "8", "2021", "50", "2021,", "7.95 billion", "398 million", "2018", "987 million", "2.9", "700", "900 million", "1 billion", "22", "9", "20 million", "15", "600 million", "3.75", "10", "2.3 million", "6%", "94%", "10%", "1997", "11", "200", "400 billion", "12", "80", "300 billion", "13", "900", "50 billion", "14", "3.9 billion", "2.1\ntrillion", "30 million", "2008", "200 million", "50 million", "1988", "2011", "1991", "4 million", "9,700", "2022", "20260130", "093223", "30,", "2026"], 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{"raw_link": "https://ourworldindata.org/end-progress-extreme-poverty", "title": "The end of progress against extreme poverty?", "context": "Home\nPoverty\nThe end of progress against extreme poverty?\nIn the last three decades, the world has made progress against extreme poverty faster than ever before. But unless the poorest economies start growing, this period of progress against the worst form of poverty is over.\nBy\nMax Roser\nNovember 17, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nIn the last decades, the world has made fantastic progress against extreme poverty. In 1990, 2.3 billion people lived in extreme poverty. Since then, the number of extremely poor people has declined by\n1.5 billion people\n.\nThis means on any average day\nin the last 35 years,\nabout 115,000 people left extreme poverty behind.\n1\nLeaving the very worst poverty behind\ndoesn’t mean\na life free of want, but it does mean a big change. Additional income matters most for those who have the least. It means having the chance to leave hunger behind, to gain access to clean water, to access better healthcare, and to have at least some electricity — for light at night and perhaps even to cook and heat.\nCan we expect this rapid progress to continue?\nUnfortunately, we cannot. Based on\ncurrent trends\n, progress against extreme poverty will come to a halt. As we’ll see, the number of people in extreme poverty is projected to decline, from 831 million people in 2025 to 793 million people in 2030. After 2030, the number of extremely poor people is expected to increase.\nTo understand why the rapid progress against deep poverty will not continue into the future, we need to know why the world made progress in the past.\nBased on current trends, progress against extreme poverty will come to a halt.\nExtreme poverty declined in the last three decades because, back in the 1990s, the majority of the poorest people on the planet lived in countries that subsequently achieved very fast economic growth. In Indonesia and China, more than two-thirds of the population lived in extreme poverty. But these economies then grew rapidly, so that by today, the share has declined to\nless than 10%\n. Other large Asian countries — including India, Pakistan, Bangladesh, and the Philippines — also achieved strong growth, and as a consequence, the share living in extreme poverty\ndeclined rapidly\n. Much of the progress happened in Asia, but conditions in other regions improved too: the share living in extreme poverty\nalso declined\nin Ghana, Cape Verde, Cameroon, Panama, Bolivia, Mexico, Brazil, and many other countries.\nThis chart shows the economic change in these countries over the past decades. As incomes increased, the share of people in extreme poverty declined.\nWhat is different today is that the majority of the world’s poorest people are stuck in economies that have been stagnating for a long time. Consider the case of Madagascar. In the long run, the country has not seen any growth at all: GDP per capita in Madagascar is\nabout the same\ntoday as it was in 1950. As a consequence, the number of people in extreme poverty\nincreased\nin line with the country’s population growth. In richer countries, it is possible to reduce poverty by reducing inequality through redistribution, but a country like Madagascar cannot reduce its share of people in extreme poverty through redistribution. This is because the mean income\nis lower\nthan the poverty line; if everyone had the same income, everyone would be living in extreme poverty.\nThe situation is similar in other countries, as the chart below shows: in the Democratic Republic of Congo, Mozambique, Malawi, Burundi, and the Central African Republic, more than half of the population lives in extreme poverty. As their economies\nhave stagnated\n, the deep poverty that most people live in has remained largely unchanged for decades.\nThis is why we have to expect the end of progress against extreme poverty based on current trends. If the poorest economies remain stagnant, hundreds of millions of people will continue to live in extreme poverty.\nI’m always skeptical when people say that we are at a juncture in history where the future looks much different than the past. But when it comes to the fight against extreme poverty, I fear it is true. Today, the majority of the world’s poorest people are living in economies that have not achieved economic growth in the recent past.\nThe projection below makes this difference clear: the future we can expect looks very different from the recent past.\nThis chart is based on the latest available projection made by the researchers at the World Bank.\n2\nUp to 2030, this projection is based on the latest growth projections from the World Bank and the IMF. From 2031 onward, poverty projections are based on the average growth rates observed from 2015 to 2024.\n3\nThese projections show that we cannot expect a continuation of the strong decline of the past. After 2030, the number of people in extreme poverty is projected to increase.\nBased on current trends, we have to expect the end of progress against extreme poverty.\nThe chart also shows how the geographic distribution of poverty has shifted. Three decades ago, most extremely poor people lived in Asia; today, most are in Sub-Saharan Africa. In the coming years, this trend is expected to continue. Growth in Asia will largely end extreme poverty in the region, while the economic stagnation and population growth in several African countries will mean that the number of people in extreme poverty there will stagnate or even increase.\nOf course, this is not just a concern until 2040: without rising incomes in the poorest regions, extreme poverty will remain a reality.\nThe UN was right to make the “eradication of extreme poverty for all people everywhere”\ngoal number one\nof the Sustainable Development Goals (SDGs). Unfortunately, the world is clearly not on track to achieve this most important goal.\nToday, the majority of the world’s poorest people are living in economies that have not achieved economic growth in the recent past.\nCrucially, the expectations are dire for the very poorest regions in the world, but much less so for those who have left extreme poverty behind. Based on current growth, we can be optimistic that the world will continue to make progress relative to\nhigher\npoverty thresholds. The number of people living on $5 or $10 a day will likely\ncontinue to decline\n. This is one reason why those who say the international poverty line needs to be higher are wrong. Economic growth is most important for the very poorest, and without a very low poverty line, we cannot see whether growth is lifting the poorest out of poverty. This is the reason we, at Our World in Data, have always published data on a\nwide range\nof definitions of poverty. One poverty line is not enough, and we need to rely on several poverty lines — higher and lower than the international poverty line — to understand how the world is changing.\n4\nIt’s no news that we should expect an end to progress against extreme poverty. This article is an update of\nan article I published in 2019,\nin which I wrote the same: the fact that the poorest economies are not growing means that the rapid progress against extreme poverty seen in the last decades will end.\nAlthough this prospect has been known for years, it has hardly received the attention it deserves. Progress against extreme poverty was one of humanity's most outstanding achievements of the past decades — the end of it would be one of the very worst realities of the coming ones.\nImportantly, however, these projections are not predictions; their purpose is not to describe what the world in 2030 or 2040 will certainly look like. These projections describe what we have to expect based on current trends; they tell us about our present world rather than the reality of tomorrow. Current trends don’t have to become future facts: many countries left extreme poverty behind in the past, because they had a moment\nat which they broke out of stagnation\n.\n5\nWhat these projections tell us, however, is that if the poorest countries do not start to grow, a very bleak future is ahead of us: a future in which extreme poverty remains the reality for hundreds of millions for many years to come.\n$3 a day: A new poverty line has shifted the World Bank’s data on extreme poverty. What changed, and why?\nThe international poverty line that defines extreme poverty was last updated in 2025. My colleagues Joe Hasell, Bertha Rohenkohl, and Pablo Arriagada explain in their article what this update means for the measurement and our understanding of extreme poverty.\nTopic page: Poverty\nYou can find all our work on poverty on our dedicated topic page.\nAcknowledgments\nI would like to thank Bertha Rohenkohl, Pablo Arriagada, Simon van Teutem, and Edouard Mathieu for their helpful comments on drafts of this essay and the visualizations.\nNote on the earlier version of this article\nThis is an update of an article that I published in May 2019 on Our World in Data, titled “\nAs the world’s poorest economies are stagnating, half a billion are expected to be in extreme poverty in 2030\n”.\nBased on even earlier projections, I also made this a central point of my presentation to the UN back in 2018, for which you can find my slides\nhere\n.\nEndnotes\nPeople in extreme poverty in 1990 (data\nhere\n): 2,310,000,000 people. In 2025: 808,210,000 people.\nDaily reduction of the number of people in extreme poverty: 117,557 per day = (2,310,000,000-808,210,000)/(35*365)\nDifferent projections from different research teams made in recent years have\nshown the same\n.\nTo generate the poverty projections up to 2030, World Bank researchers take the last observed distribution of income or consumption expenditure based on household surveys and model the shift in line with the projected growth in real GDP per capita from national accounts data. The GDP forecasts are those of the World Bank's Global Economic Prospects (June 2025) and the IMF's World Economic Outlook (April 2025). Projections for 2031 onwards are based on average annual GDP per capita growth data observed from 2015 to 2024. For both cases, the method also assumes that income inequality doesn’t change over time. You can read more about this projection method in the World Bank Poverty, Prosperity, and Planet Report 2024 (Annex 1).\nLakner, C., Foster, E. M., Jolliffe, D., Ibarra, G. L., & Tetteh Baah, S. K. (2025) — Reproducibility Package for Global Poverty Revisited Using 2021 PPPs And New Data On Consumption. World Bank.\nOn this point, see, for example,\nthis earlier article\nof mine.\nThe big lesson from the\nhistory of extreme poverty\nis that it is the growth of an entire economy that lifts individuals out of poverty. The key to ending extreme poverty globally will be that the poorest countries achieve the difficult task of economic growth. To end poverty — especially when considering poverty lines that are higher than the average income in a country — an economy\nneeds\nto grow.\nBut it’s not only about macroeconomic performance. Social policy and direct household-level support, too, make an important difference. Even in very poor economies, there is scope for targeted policies to support the very poorest. In an analysis of how today’s richest countries left extreme poverty behind, Martin Ravallion emphasizes the role the expanded social protection policies played at the time.\nSee Martin Ravallion (2015) – The Economics of Poverty: History, Measurement, and Policy and section 8 in Martin Ravallion (2016) – Are the world’s poorest being left behind? In the Journal of Economic Growth. Online here\nhttps://link.springer.com/article/10.1007/s10887-016-9126-7\nAnd in our time, we also have an opportunity that was not there in the past, when almost everyone was desperately poor. The fact that some people are very poor, while others are very rich, means that it is possible to redistribute from the rich to the poor globally. The non-profit organization GiveDirectly makes possible what its name suggests: you can give cash directly to the poorest people in the world.\nGrowth, national, and international redistribution. There are ways to continue the progress against the worst poverty.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nMax Roser (2025) - “The end of progress against extreme poverty?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-093348/end-progress-extreme-poverty.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-end-progress-extreme-poverty,\nauthor = {Max Roser},\ntitle = {The end of progress against extreme poverty?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-093348/end-progress-extreme-poverty.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "end-progress-extreme-poverty", "source_url": "https://ourworldindata.org/end-progress-extreme-poverty", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "In the last three decades, the world has made progress against extreme poverty faster than ever before. 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"2894229", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Share in extreme poverty": "0.2723694546148181", "GDP per capita": "16761.193", "Population": "2885011", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Share in extreme poverty": "0.3409224562346935", "GDP per capita": "16457.787", "Population": "2871950", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Share in extreme poverty": "", "GDP per capita": "18212.871", "Population": "2849641", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "Share in extreme poverty": "", "GDP per capita": "19388.873", "Population": "2827614", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Share in extreme poverty": "", "GDP per capita": "20481.035", "Population": "2811660", "World region according 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"Year": "1993", "Share in extreme poverty": "", "GDP per capita": "10743.706", "Population": "27277042", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1994", "Share in extreme poverty": "", "GDP per capita": "10414.035", "Population": "27887277", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1995", "Share in extreme poverty": "11.806999891996384", "GDP per capita": "10588.443", "Population": "28470194", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1996", "Share in extreme poverty": "", "GDP per capita": "10808.879", "Population": "29033045", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1997", "Share in extreme poverty": "", "GDP per capita": "10725.968", "Population": "29579299", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1998", "Share in extreme poverty": "", "GDP per capita": "11094.888", "Population": "30054135", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1999", "Share in extreme poverty": "", "GDP per capita": "11292.037", "Population": "30474360", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Share in extreme poverty": "", "GDP per capita": "11558.221", "Population": "30903894", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Share in extreme poverty": "", "GDP per capita": "11742.595", "Population": "31331226", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Share in extreme poverty": "", "GDP per capita": "12213.126", "Population": "31750832", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Share in extreme poverty": "", "GDP per capita": "12835.182", "Population": "32175813", "World 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"Code": "DZA", "Year": "2009", "Share in extreme poverty": "", "GDP per capita": "14104.428", "Population": "35490442", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Share in extreme poverty": "", "GDP per capita": "14496.421", "Population": "36188237", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Share in extreme poverty": "0", "GDP per capita": "14641.964", "Population": "36903374", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Share in extreme poverty": "", "GDP per capita": "14697.54", "Population": "37646165", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Share in extreme poverty": "", "GDP per capita": "14778.191", "Population": "38414176", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Share in extreme poverty": "", "GDP per capita": "15073.763", "Population": "39205035", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Share in extreme poverty": "", "GDP per capita": "15239.518", "Population": "40019528", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Share in extreme poverty": "", "GDP per capita": "15511.686", "Population": "40850719", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Share in extreme poverty": "", "GDP per capita": "15427.664", "Population": "41689302", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Share in extreme poverty": "", "GDP per capita": "15343.426", "Population": "42505033", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Share in extreme poverty": "", "GDP per capita": "15199.199", "Population": "43294551", "World 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"Population": "63936", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1996", "Share in extreme poverty": "", "GDP per capita": "46386.09", "Population": "64006", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1997", "Share in extreme poverty": "", "GDP per capita": "50032.35", "Population": "64722", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1998", "Share in extreme poverty": "", "GDP per capita": "51092.223", "Population": "65400", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1999", "Share in extreme poverty": "", "GDP per capita": "52921.047", "Population": "65732", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2000", "Share in extreme poverty": "", "GDP per capita": "54809.145", "Population": "65703", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2001", "Share in extreme poverty": "", "GDP per capita": "59109.016", "Population": "65874", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2002", "Share in extreme poverty": "", "GDP per capita": "61188.637", "Population": "66528", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2003", "Share in extreme poverty": "", "GDP per capita": "63656.195", "Population": "69507", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2004", "Share in extreme poverty": "", "GDP per capita": "64353.484", "Population": "74342", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2005", "Share in extreme poverty": "", "GDP per capita": "65114.805", "Population": "77436", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2006", "Share in extreme poverty": "", "GDP 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"Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2012", "Share in extreme poverty": "", "GDP per capita": "58172.133", "Population": "76858", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2013", "Share in extreme poverty": "", "GDP per capita": "57332.16", "Population": "75220", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2014", "Share in extreme poverty": "", "GDP per capita": "59929.246", "Population": "73755", "World region according to OWID": "Europe"}], "rows_tail": [{"Entity": "World (excluding China)", "Code": "", "Year": "2017", "Share in extreme poverty": "14.087669551372528", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding China)", "Code": "", "Year": "2018", "Share in extreme poverty": "13.429884612560272", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding 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"35.74796915054321", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "1998", "Share in extreme poverty": "36.0106498003006", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "1999", "Share in extreme poverty": "35.342058539390564", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2000", "Share in extreme poverty": "33.982884883880615", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2001", "Share in extreme poverty": "32.81261622905731", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2002", "Share in extreme poverty": "30.945226550102234", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2003", "Share in extreme poverty": "29.04365062713623", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2004", "Share in extreme poverty": "26.79445445537567", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2005", "Share in extreme poverty": "24.75351244211197", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2006", "Share in extreme poverty": "23.700065910816193", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2007", "Share in extreme poverty": "22.20028042793274", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2008", "Share in extreme poverty": "21.050718426704407", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2009", "Share in extreme poverty": "20.22738605737686", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2010", "Share in extreme poverty": "18.689104914665222", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2011", "Share in extreme poverty": "17.066286504268646", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2012", "Share in extreme poverty": "16.050858795642853", "GDP per capita": "", "Population": "", "World 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""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2018", "Share in extreme poverty": "11.389302462339401", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2019", "Share in extreme poverty": "11.211827397346497", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2020", "Share in extreme poverty": "11.792800575494766", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2021", "Share in extreme poverty": "12.021751701831818", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World (excluding India)", "Code": "", "Year": "2022", "Share in extreme poverty": "11.891724169254303", "GDP per capita": "", "Population": "", "World region according to OWID": ""}, {"Entity": "World 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"67.05899834632874", "GDP per capita": "2365.6296", "Population": "8373920", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Share in extreme poverty": "", "GDP per capita": "2110.5833", "Population": "8576270", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Share in extreme poverty": "", "GDP per capita": "2119.9565", "Population": "8785768", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Share in extreme poverty": "56.06018304824829", "GDP per capita": "2197.1956", "Population": "9004059", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Share in extreme poverty": "", "GDP per capita": "2223.4575", "Population": "9237064", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Share in extreme poverty": "56.623685359954834", "GDP per capita": "2157.5735", "Population": "9482414", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Share in extreme poverty": "", "GDP per capita": "2198.1892", "Population": "9740013", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Share in extreme poverty": "", "GDP per capita": "2220.5654", "Population": "10017633", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Share in extreme poverty": "", "GDP per capita": "2268.9697", "Population": "10325186", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Share in extreme poverty": "67.98156499862671", "GDP per capita": "2299.333", "Population": "10647955", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Share in extreme poverty": "", "GDP per capita": "2383.8762", "Population": "10983604", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Share in extreme poverty": "68.93101930618286", "GDP per capita": "2471.7207", "Population": "11338201", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Share in extreme poverty": "", "GDP per capita": "2564.4756", "Population": "11718819", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Share in extreme poverty": "71.96303009986877", "GDP per capita": "2673.4614", "Population": "12129560", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Share in extreme poverty": "", "GDP per capita": "2796.353", "Population": "12565093", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Share in extreme poverty": "", "GDP per capita": "2908.1436", "Population": "13021327", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Share in extreme poverty": "", "GDP per capita": "3065.8442", "Population": "13490395", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Share in extreme poverty": "71.36719226837158", "GDP per capita": "3266.5076", "Population": "13965592", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Share in extreme poverty": "", "GDP per capita": "3335.6433", "Population": "14437796", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Share in extreme poverty": "", "GDP per capita": "3474.5525", "Population": "14913631", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Share in extreme poverty": "", "GDP per capita": "3535.2246", "Population": "15399000", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Share in extreme poverty": "", "GDP per capita": "3585.5774", "Population": "15895320", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Share in extreme poverty": "67.89283752441406", "GDP per capita": "3576.9255", "Population": "16399092", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Share in extreme poverty": "", "GDP per capita": "3598.1716", "Population": "16914428", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Share in extreme poverty": "", "GDP per capita": "3612.5059", "Population": "17441328", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Share in extreme poverty": "", "GDP per capita": "3646.9597", "Population": "17973574", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Share in extreme poverty": "", "GDP per capita": "3591.5642", "Population": "18513839", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Share in extreme poverty": "", "GDP per capita": "3391.5955", "Population": "19059394", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Share in extreme poverty": "", "GDP per capita": "3503.035", "Population": "19603610", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Share in extreme poverty": "71.6561496257782", "GDP per capita": "3585.1238", "Population": "20152935", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Share in extreme poverty": "", "GDP per capita": "3673.4841", "Population": "20723967", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Share in extreme poverty": "", "GDP per capita": "3708.069", "Population": "20723967", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Share in extreme poverty": "", "GDP per capita": "6082.8423", "Population": "10137287", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Share in extreme poverty": "", "GDP per capita": "6254.275", "Population": "10404820", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Share in extreme poverty": "", "GDP per capita": "5532.0376", "Population": "10702697", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Share in extreme poverty": "", "GDP per capita": "5509.083", "Population": "10860285", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Share in extreme poverty": "", "GDP per capita": "6010.742", "Population": "10873146", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Share in extreme poverty": "", "GDP per capita": "5964.5894", "Population": "10974607", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Share in extreme poverty": "", "GDP per capita": "6474.16", "Population": "11158364", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Share in extreme poverty": "", "GDP per capita": "6524.0625", "Population": "11369833", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Share in extreme poverty": "", "GDP per capita": "6582.3486", "Population": "11594299", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Share in extreme poverty": "", "GDP per capita": "6423.709", "Population": "11783454", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Share in extreme poverty": "", "GDP per capita": "6170.334", "Population": "11892055", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Share in extreme poverty": "", "GDP per capita": "6217.4116", "Population": "11971904", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Share in extreme poverty": "", "GDP per capita": "5610.1914", "Population": "12087661", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Share in extreme poverty": "", "GDP per capita": "4601.6606", "Population": "12232324", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Share in extreme poverty": "", "GDP per capita": "4287.598", "Population": "12365901", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Share in extreme poverty": "", "GDP per capita": "4004.6646", "Population": "12483433", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Share in extreme poverty": "", "GDP per capita": "3819.2334", "Population": "12636442", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Share in extreme poverty": "", "GDP per capita": "3631.5376", "Population": "12804062", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Share in extreme poverty": "", "GDP per capita": "2954.0994", "Population": "12959154", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Share in extreme poverty": "", "GDP per capita": "3299.4138", "Population": "13142791", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Share in extreme poverty": "", "GDP per capita": "3885.3938", "Population": "13356551", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Share in extreme poverty": "35.71699857711792", "GDP per capita": "4358.926", "Population": "13595421", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Share in extreme poverty": "", "GDP per capita": "5003.4873", "Population": "13817887", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Share in extreme poverty": "", "GDP per capita": "5031.6875", "Population": "14013811", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Share in extreme poverty": "", "GDP per capita": "5081.1123", "Population": "14207367", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Share in extreme poverty": "", "GDP per capita": "5102.7144", "Population": "14399008", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Share in extreme poverty": "", "GDP per capita": "5070.4023", "Population": "14600297", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Share in extreme poverty": "44.65687274932861", "GDP per capita": "5234.384", "Population": "14812484", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Share in extreme poverty": "", "GDP per capita": "5415.4697", "Population": "15034457", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Share in extreme poverty": "49.21989440917969", "GDP per capita": "4993.8438", "Population": "15271377", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Share in extreme poverty": "", "GDP per capita": "4527.7197", "Population": "15526887", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Share in extreme poverty": "", "GDP per capita": "4827.089", "Population": "15797220", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Share in extreme poverty": "", "GDP per capita": "5036.761", "Population": "16069061", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Share in extreme poverty": "", "GDP per capita": "5218.0225", "Population": "16340829", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Share in extreme poverty": "", "GDP per capita": "5215.253", "Population": "16340829", "World region according to OWID": "Africa"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "the-share-of-people-living-in-extreme-poverty-vs-gdp-per-capita", "metadata_url": "https://ourworldindata.org/grapher/the-share-of-people-living-in-extreme-poverty-vs-gdp-per-capita.metadata.json", "chart_title": "Share of population living in extreme poverty vs. GDP per capita", "chart_subtitle": "Extreme poverty is defined as living below the International Poverty Line of $3 per day. This data is adjusted for inflation and differences in living costs between countries.", "chart_note": "This data is expressed in international-$ at 2021 prices. Depending on the country and year, poverty data relates to income (measured after taxes and benefits) or consumption, per capita.", "chart_citation": "World Bank Poverty and Inequality Platform (2026); Eurostat, OECD, IMF, and World Bank (2026)", "original_chart_url": "https://ourworldindata.org/grapher/the-share-of-people-living-in-extreme-poverty-vs-gdp-per-capita", "owid_column_metadata": {"Share of population in poverty ($3 a day, 2021 prices)": {"titleShort": "Share in extreme poverty", "titleLong": "Share in extreme poverty", "descriptionShort": "Percentage of population living in households with an income or consumption below $3 per day.", "descriptionKey": ["The World Bank defines extreme poverty as living on less than $3 per day. This threshold, known as the \"International Poverty Line\", is set so that poverty can be compared across countries. This indicator plays an important and successful role in focusing the world's attention on the very poorest people. The UN uses this indicator to track progress towards [ending extreme poverty by 2030](https://ourworldindata.org/sdgs/no-poverty).", "Two centuries ago, most of the world's population was extremely poor. Many believed that widespread poverty was inevitable. But this turned out to be wrong. Economic growth is possible, and poverty can decline. With this poverty line, we can track whether countries are leaving the worst poverty behind.", "This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "Many people, today and in the past, have no formal monetary income. This data accounts for that by including the estimated value of non-market income, such as food grown by subsistence farmers for their own use.", "The data comes from the World Bank's Poverty and Inequality Platform (PIP), which draws on national household surveys. Regional and global estimates are extrapolated to the year of the data release using GDP growth estimates and forecasts. For more details about the methodology, please refer to the [World Bank PIP documentation](https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#nowcasts).", "Depending on the country and year, the data refers either to income (after taxes and benefits) or to consumption, per capita. These are not perfectly comparable — consumption tends to be more evenly distributed than income. For most countries, we have only one option available. But when there is a mix of consumption and income data points, we process the data to keep one observation per country and year."], "descriptionProcessing": "For most countries in the dataset, estimates relate to disposable income or consumption, for all available years. Several countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\n\nIn most of our charts, we present the data with some data points dropped to present a single series for each country. This allows us to make readable visualizations that combine multiple countries. In choosing which data points to keep, we strike a balance between maintaining comparability over time and showing as long a time series as possible. As such, the exact approach varies across countries.", "shortUnit": "%", "unit": "%", "timespan": "1963-2026", "type": "Numeric", "owidVariableId": 1220228, "shortName": "headcount_ratio__ppp_version_2021__poverty_line_300__welfare_type_income_or_consumption__table_income_or_consumption_consolidated__survey_comparability_no_spells", "lastUpdated": "2026-03-24", "nextUpdate": "2026-09-20", "citationShort": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data", "citationLong": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data. “Share in extreme poverty – World Bank” [dataset]. World Bank Poverty and Inequality Platform, “World Bank Poverty and Inequality Platform (PIP) 20260324_2021, 20260324_2017” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1220228.metadata.json"}, "GDP per capita, PPP (constant 2021 international $)": {"titleShort": "GDP per capita", "titleLong": "GDP per capita - World Bank – In constant international-$", "descriptionShort": "Average economic output per person in a country or region per year. This data is adjusted for inflation and differences in living costs between countries.", "descriptionKey": ["GDP per capita is a comprehensive measure of people's average income. It helps compare income levels across countries and track how they change over time. It is especially useful for understanding trends in economic growth and living standards.", "GDP per capita is calculated as the value of all final goods and services produced each year in a country (the gross domestic product), divided by the population. It represents the average economic output per person.", "This indicator shows the large inequality between people in different countries. In the poorest countries, average incomes are below $1,000 per year; in rich countries, they are more than 50 times higher.", "This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "This data comes from the World Bank and starts in 1990. For estimates going back several centuries, explore our chart of GDP per capita from the [Maddison Project Database](https://ourworldindata.org/grapher/gdp-per-capita-maddison-project-database)."], "shortUnit": "$", "unit": "international-$ in 2021 prices", "timespan": "1990-2024", "type": "Numeric", "owidVariableId": 1204826, "shortName": "ny_gdp_pcap_pp_kd", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "Eurostat, OECD, IMF, and World Bank (2026) – with minor processing by Our World in Data", "citationLong": "Eurostat, OECD, IMF, and World Bank (2026) – with minor processing by Our World in Data. “GDP per capita – World Bank – In constant international-$” [dataset]. Eurostat, OECD, IMF, and World Bank, “World Development Indicators 125” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1204826.metadata.json"}, "Population (historical)": {"titleShort": "Population", "titleLong": "Population", "descriptionShort": "Population by country, available from 10,000 BCE to 2023, based on data and estimates from different sources.", "descriptionKey": ["Population is the most commonly used metric throughout Our World in Data. It is used directly to understand population growth over time, and indirectly to calculate per-capita indicators, making it easier to compare countries of different sizes.", "We construct this indicator by combining multiple sources covering different periods.\n  - HYDE v3.3 (2023): historical estimates from 10,000 BCE to 1799.\n  - Gapminder v7 (2022): for 1800-1949.\n  - UN World Population Prospects (2024): for 1950 onwards, including 2100 projections.\n  - Gapminder Systema Globalis (2023): additional source for former countries (Yugoslavia, USSR, etc.)", "Breaks in the data may occur at the boundaries between sources due to their methodological differences.", "You can read more about the sources and methodology in our [dedicated article](https://ourworldindata.org/population-sources). We also provide a table of sources showing the source we use for each country-year.", "We calculate geographical aggregates (continents, income groups, etc.) by summing individual country populations. For years before 1800, we rely directly on HYDE's values for continents to ensure historical consistency."], "descriptionProcessing": "### Combination of different sources\nWe construct our long-run population data by combining multiple sources:\n\n- 10,000 BCE–1799: historical estimates by HYDE (v3.3).\n\n- 1800–1949: historical estimates by Gapminder (v7).\n\n- 1950–2023: population records from the United Nations World Population Prospects (2024 revision).\n\n**Geographical aggregates**\n\n- For most years, we calculate aggregates by summing the population of member countries.\n- We do this based on [our definition of continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and the [World Bank’s income groups](https://ourworldindata.org/grapher/world-bank-income-groups).\n- The only exception is before 1800, where we use HYDE's estimates for continents (but not income groups).\n\nFor most of the years, we've estimated regional aggregates by summing the population of countries in each region. We've relied on [our continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and [World Bank income group definitions](https://ourworldindata.org/grapher/world-bank-income-groups). The only exception is before 1800, where we've used HYDE's estimates on continents (but not income groups).\n\n**World**\n- Before 1800: we use data from HYDE.\n- 1800-1950: we estimate the global population by summing all available countries in the dataset.\n- After 1950, we rely on estimates from the United Nations World Population Prospects.", "shortUnit": "", "unit": "people", "timespan": "-10000-2023", "type": "Integer", "owidVariableId": 953903, "shortName": "population_historical", "lastUpdated": "2024-07-15", "nextUpdate": "2026-07-15", "citationShort": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data", "citationLong": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data. “Population – HYDE, Gapminder, UN – Long-run data” [dataset]. PBL Netherlands Environmental Assessment Agency, “History Database of the Global Environment 3.3”; Gapminder, “Population v7”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update”; Gapminder, “Systema Globalis” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/953903.metadata.json"}, "World region according to OWID": {"titleShort": "World region according to OWID", "titleLong": "World region according to OWID", "descriptionShort": "Regions defined by Our World in Data, which are used in OWID charts and maps.", "unit": "", "timespan": "2023-2023", "type": "Continent", "owidVariableId": 900801, "shortName": "owid_region", "lastUpdated": "2023-01-01", "citationShort": "Our World in Data – processed by Our World in Data", "citationLong": "Our World in Data – processed by Our World in Data. “World region according to OWID” [dataset]. Our World in Data, “Regions” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/900801.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "GDP per capita", "source_url": "https://ourworldindata.org/grapher/gdp-per-capita-maddison-project-database.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "GDP per capita", "GDP per capita (Annotations)"], "row_count_total": 21586, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1950", "GDP per capita": "1156", "GDP per capita (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1951", "GDP per capita": "1170", "GDP per capita (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1952", "GDP per capita": "1189", "GDP per capita (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1953", "GDP per capita": "1240", "GDP per capita (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1954", "GDP per capita": "1245", "GDP per capita (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", 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(Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "GDP per capita": "2268", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "GDP per capita": "2283", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "GDP per capita": "2275", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "GDP per capita": "2235", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "GDP per capita": "2163", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "GDP per capita": "1946", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "GDP per capita": "1964", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "GDP per capita": "1930", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "GDP per capita": "2064", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "GDP per capita": "2243", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "GDP per capita": "2240", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "GDP per capita": "2190", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "GDP per capita": "2067", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "GDP per capita": "2128", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "GDP per capita": "2107", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "GDP per capita": "2004", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "GDP per capita": "2114", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "GDP per capita": "2181", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "GDP per capita": "2160", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "GDP per capita": "2222.2554", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "GDP per capita": "1971.4456", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "GDP per capita": "1948.9105", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "GDP per capita": "2110.712", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "GDP per capita": "2102.8599", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "GDP per capita": "2292.4875", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "GDP per capita": "2326.5388", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "GDP per capita": "2369.019", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "GDP per capita": "2323.0093", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "GDP per capita": "2211.1963", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "GDP per capita": "2193.7385", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "GDP per capita": "2025.3177", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "GDP per capita": "1700.9558", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "GDP per capita": "1604.503", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "GDP per capita": "1496.0343", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "GDP per capita": "1455.7286", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "GDP per capita": "1422.1553", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "GDP per capita": "1197.5261", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "GDP per capita": "1285.0466", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "GDP per capita": "1401.8566", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "GDP per capita": "1515", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "GDP per capita": "1749.8566", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "GDP per capita": "1766.3789", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "GDP per capita": "1789.0048", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "GDP per capita": "1798.7872", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "GDP per capita": "1782.8301", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "GDP per capita": "1843.9463", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "GDP per capita": "1900.1992", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "GDP per capita": "1753.0244", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "GDP per capita": "1585.9728", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "GDP per capita": "1687.2532", "GDP per capita (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "GDP per capita": "1703.5294", "GDP per capita (Annotations)": ""}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "gdp-per-capita-maddison-project-database", "metadata_url": "https://ourworldindata.org/grapher/gdp-per-capita-maddison-project-database.metadata.json", "chart_title": "GDP per capita", "chart_subtitle": "GDP per capita is a country's gross domestic product divided by its population. This data is adjusted for inflation and differences in living costs between countries.", "chart_note": "This data is expressed in international-$ at 2011 prices.", "chart_citation": "Bolt and van Zanden – Maddison Project Database 2023", "original_chart_url": "https://ourworldindata.org/grapher/gdp-per-capita-maddison-project-database", "owid_column_metadata": {"GDP per capita": {"titleShort": "GDP per capita", "titleLong": "GDP per capita - Maddison Project Database – Long-run data in constant international-$", "descriptionShort": "Average economic output per person in a country or region per year. This data is adjusted for inflation and differences in living costs between countries.", "descriptionKey": ["GDP per capita is a comprehensive measure of people's average income. It helps compare income levels across countries and track how they change over time. It is especially useful for understanding trends in economic growth and living standards.", "GDP per capita is calculated as the value of all final goods and services produced each year in a country (the gross domestic product), divided by the population. It represents the average economic output per person.", "This indicator shows the large inequality between people in different countries. In the poorest countries, average incomes are below $1,000 per year; in rich countries, they are more than 50 times higher.", "This data comes from the [Maddison Project Database](https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2023), which provides GDP per capita estimates for the _very long run_. Some country series extend as far back as 1 CE, and regional estimates start in 1820.", "This work builds on the efforts of many researchers who have carefully reconstructed historical data on economic growth and population for individual countries. You can find the full list of sources in [the original dataset](https://dataverse.nl/api/access/datafile/421302).", "This data is expressed in constant international dollars at 2011 prices to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "This dataset combines multiple purchasing power parity (PPP) benchmarks to ensure historical consistency and comparability over time. 1990 PPPs are used up to 1990, and 2011 PPPs are used from 2011 onward. For the years in between, they adjust the series to smoothly connect the two benchmarks. This approach preserves consistency with the original long-run estimates calculated by Angus Maddison.", "Time series for former countries and territories are calculated forward by estimating values based on their last official borders.", "For more frequently updated estimates since 1990, explore our chart of GDP per capita from the [World Bank](https://ourworldindata.org/grapher/gdp-per-capita-worldbank)."], "shortUnit": "$", "unit": "international-$ in 2011 prices", "timespan": "1-2022", "type": "Numeric", "owidVariableId": 900793, "shortName": "gdp_per_capita", "lastUpdated": "2024-04-26", "nextUpdate": "2027-04-22", "citationShort": "Bolt and van Zanden – Maddison Project Database 2023 – with minor processing by Our World in Data", "citationLong": "Bolt and van Zanden – Maddison Project Database 2023 – with minor processing by Our World in Data. “GDP per capita – Maddison Project Database – Long-run data in constant international-$” [dataset]. Bolt and van Zanden, “Maddison Project Database 2023” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/900793.metadata.json"}, "900793-annotations": {"titleShort": "900793-annotations", "titleLong": "900793-annotations", "type": "SeriesAnnotation", "citationShort": " – processed by Our World in Data", "citationLong": " – processed by Our World in Data. “900793-annotations” [dataset].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/undefined.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Distribution of population between different poverty lines", "source_url": "https://ourworldindata.org/grapher/distribution-of-population-poverty-thresholds.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Above $10 a day", "$8.30-$10 a day", "$4.20-$8.30 a day", "$3-$4.20 a day", "Below $3 a day"], "row_count_total": 2833, "rows_head": [{"Entity": "Albania", "Code": "ALB", "Year": "1996", "Above $10 a day": "900034", "$8.30-$10 a day": "411154", "$4.20-$8.30 a day": "1480630", "$3-$4.20 a day": "282193", "Below $3 a day": "94022"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Above $10 a day": "818304", "$8.30-$10 a day": "372084", "$4.20-$8.30 a day": "1401061", "$3-$4.20 a day": "331580", "Below $3 a day": "127981"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Above $10 a day": "1071754", "$8.30-$10 a day": "434202", "$4.20-$8.30 a day": "1221763", "$3-$4.20 a day": "203957", "Below $3 a day": "79811"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Above $10 a day": "1216764", "$8.30-$10 a day": "450136", "$4.20-$8.30 a day": "1107202", "$3-$4.20 a day": "148829", "Below $3 a day": "24383"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Above $10 a day": "1133125", "$8.30-$10 a day": "414508", "$4.20-$8.30 a day": "1126111", "$3-$4.20 a day": "135626", "Below $3 a day": "51338"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Above $10 a day": "1182598", "$8.30-$10 a day": "301570", "$4.20-$8.30 a day": "942923", "$3-$4.20 a day": "244708", "Below $3 a day": "101968"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Above $10 a day": "1491058", "$8.30-$10 a day": "345170", "$4.20-$8.30 a day": "766174", "$3-$4.20 a day": "98441", "Below $3 a day": "30450"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Above $10 a day": "1482809", "$8.30-$10 a day": "322584", "$4.20-$8.30 a day": "726750", "$3-$4.20 a day": "126649", "Below $3 a day": "30677"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Above $10 a day": "1463928", "$8.30-$10 a day": "319338", "$4.20-$8.30 a day": "740231", "$3-$4.20 a day": "86535", "Below $3 a day": "38253"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Above $10 a day": "1700356", "$8.30-$10 a day": "278202", "$4.20-$8.30 a day": "537060", "$3-$4.20 a day": "65776", "Below $3 a day": "26339"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Above $10 a day": "1693852", "$8.30-$10 a day": "322009", "$4.20-$8.30 a day": "506450", "$3-$4.20 a day": "38496", "Below $3 a day": "6994"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Above $10 a day": "1743268", "$8.30-$10 a day": "281307", "$4.20-$8.30 a day": "450224", "$3-$4.20 a day": "45061", "Below $3 a day": "8620"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1988", "Above $10 a day": "5385347", "$8.30-$10 a day": "2395524", "$4.20-$8.30 a day": "10071758", "$3-$4.20 a day": "3300355", "Below $3 a day": "2956552"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1995", "Above $10 a day": "6985447", "$8.30-$10 a day": "2922750", "$4.20-$8.30 a day": "11472917", "$3-$4.20 a day": "3727602", "Below $3 a day": "3361476"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Above $10 a day": "15487653", "$8.30-$10 a day": "6051384", "$4.20-$8.30 a day": "13760960", "$3-$4.20 a day": "1729655", "Below $3 a day": "0"}, {"Entity": "Angola", "Code": "AGO", "Year": "2000", "Above $10 a day": "3765529", "$8.30-$10 a day": "1174425", "$4.20-$8.30 a day": "4790189", "$3-$4.20 a day": "2184184", "Below $3 a day": "4396533"}, {"Entity": "Angola", "Code": "AGO", "Year": "2008", "Above $10 a day": "4748918", "$8.30-$10 a day": "1472034", "$4.20-$8.30 a day": "7367891", "$3-$4.20 a day": "3551088", "Below $3 a day": "4856783"}, {"Entity": "Angola", "Code": "AGO", "Year": "2018", "Above $10 a day": "4458374", "$8.30-$10 a day": "1836811", "$4.20-$8.30 a day": "8289672", "$3-$4.20 a day": "4525471", "Below $3 a day": "12370168"}, {"Entity": "Argentina (urban)", "Code": "", "Year": "1980", "Above $10 a day": "20610433", "$8.30-$10 a day": "870664", "$4.20-$8.30 a day": "1730419", "$3-$4.20 a day": "0", "Below $3 a day": "0"}, {"Entity": "Argentina (urban)", "Code": "", "Year": "1986", "Above $10 a day": "23989926", "$8.30-$10 a day": "755977", "$4.20-$8.30 a day": "1013383", "$3-$4.20 a day": "226773", "Below $3 a day": "332861"}, {"Entity": "Argentina (urban)", "Code": "", "Year": "1987", "Above $10 a day": "23277624", "$8.30-$10 a day": "993109", "$4.20-$8.30 a day": "2017945", "$3-$4.20 a day": "169502", "Below $3 a day": "399412"}, {"Entity": "Argentina (urban)", "Code": "", "Year": "1991", "Above $10 a day": "22089585", "$8.30-$10 a day": "1880842", "$4.20-$8.30 a day": "3879151", "$3-$4.20 a day": "760802", "Below $3 a day": "371430"}, {"Entity": "Argentina (urban)", "Code": "", "Year": "1992", "Above $10 a day": "22222769", "$8.30-$10 a day": "1980101", "$4.20-$8.30 a day": "3953057", "$3-$4.20 a day": "584596", "Below $3 a day": "735109"}, {"Entity": "Argentina (urban)", "Code": "", "Year": "1993", "Above $10 a day": "22807483", "$8.30-$10 a day": "1946355", "$4.20-$8.30 a day": "3663017", "$3-$4.20 a day": "672930", "Below $3 a day": "872657"}, {"Entity": "Argentina (urban)", "Code": "", "Year": "1994", "Above $10 a day": "22880865", "$8.30-$10 a day": "1937081", "$4.20-$8.30 a day": "3989685", "$3-$4.20 a day": "815989", "Below $3 a day": "823992"}, {"Entity": "Argentina (urban)", "Code": "", "Year": "1995", "Above $10 a day": "21506815", "$8.30-$10 a day": "2010647", "$4.20-$8.30 a day": "4862890", "$3-$4.20 a day": "963027", "Below $3 a day": "1583247"}, {"Entity": "Argentina (urban)", "Code": "", "Year": "1996", "Above $10 a day": "21303204", "$8.30-$10 a day": "1976099", "$4.20-$8.30 a day": "5294645", "$3-$4.20 a day": "1119848", "Below $3 a day": "1698788"}, {"Entity": "Argentina (urban)", "Code": "", "Year": "1997", "Above $10 a day": "22115209", "$8.30-$10 a day": "1838531", "$4.20-$8.30 a day": "5194749", "$3-$4.20 a day": "1040300", "Below $3 a day": "1659963"}, {"Entity": "Argentina (urban)", "Code": "", "Year": "1998", "Above $10 a day": "21589245", "$8.30-$10 a day": "1978131", "$4.20-$8.30 a day": "5573317", "$3-$4.20 a day": "1327814", "Below $3 a day": "1828057"}, {"Entity": "Argentina (urban)", "Code": "", "Year": "1999", "Above $10 a day": "21443340", "$8.30-$10 a day": "2132551", "$4.20-$8.30 a day": "5841307", "$3-$4.20 a day": "1390745", "Below $3 a day": "1933869"}, {"Entity": "Argentina (urban)", "Code": "", "Year": "2000", "Above $10 a day": "20886801", "$8.30-$10 a day": "2033881", "$4.20-$8.30 a day": "6242013", "$3-$4.20 a day": "1585508", "Below $3 a day": "2437749"}, {"Entity": "Argentina (urban)", "Code": "", "Year": "2001", "Above $10 a day": "19648337", "$8.30-$10 a day": "2454947", "$4.20-$8.30 a day": "5789307", "$3-$4.20 a day": "2125666", 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"235585342", "$4.20-$8.30 a day": "1137835445", "$3-$4.20 a day": "648612751", "Below $3 a day": "1537265167"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2004", "Above $10 a day": "1823798194", "$8.30-$10 a day": "252057178", "$4.20-$8.30 a day": "1178232958", "$3-$4.20 a day": "667370592", "Below $3 a day": "1435319574"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2005", "Above $10 a day": "1910034395", "$8.30-$10 a day": "267089353", "$4.20-$8.30 a day": "1221858712", "$3-$4.20 a day": "680253829", "Below $3 a day": "1341928767"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2006", "Above $10 a day": "2004664991", "$8.30-$10 a day": "279399177", "$4.20-$8.30 a day": "1234110930", "$3-$4.20 a day": "668477340", "Below $3 a day": "1300445930"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2007", "Above $10 a day": "2107992509", "$8.30-$10 a day": "292600802", "$4.20-$8.30 a day": "1255689248", "$3-$4.20 a day": "664568773", "Below $3 a day": "1232962172"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2008", "Above $10 a day": "2201532368", "$8.30-$10 a day": "301620521", "$4.20-$8.30 a day": "1271859808", "$3-$4.20 a day": "663976017", "Below $3 a day": "1183594070"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2009", "Above $10 a day": "2272674905", "$8.30-$10 a day": "314569640", "$4.20-$8.30 a day": "1295250160", "$3-$4.20 a day": "657416064", "Below $3 a day": "1151153551"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2010", "Above $10 a day": "2385727801", "$8.30-$10 a day": "329022106", "$4.20-$8.30 a day": "1321310926", "$3-$4.20 a day": "645645759", "Below $3 a day": "1076078496"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2011", "Above $10 a day": "2486357289", "$8.30-$10 a day": "346199964", "$4.20-$8.30 a day": "1357685504", "$3-$4.20 a day": "641388238", "Below $3 a day": "994263917"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2012", "Above $10 a day": "2594636219", "$8.30-$10 a day": "354806206", "$4.20-$8.30 a day": "1374534612", "$3-$4.20 a day": "627557254", "Below $3 a day": "946720557"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2013", "Above $10 a day": "2723608524", "$8.30-$10 a day": "378488109", "$4.20-$8.30 a day": "1434046536", "$3-$4.20 a day": "609801847", "Below $3 a day": "824117832"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2014", "Above $10 a day": "2824721129", "$8.30-$10 a day": "392314687", "$4.20-$8.30 a day": "1432929650", "$3-$4.20 a day": "591587868", "Below $3 a day": "800237642"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2015", "Above $10 a day": "2928782394", "$8.30-$10 a day": "402971329", "$4.20-$8.30 a day": "1438163173", "$3-$4.20 a day": "572171241", "Below $3 a day": "771564599"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2016", "Above $10 a day": "3024227367", "$8.30-$10 a day": "409634568", "$4.20-$8.30 a day": "1440213208", "$3-$4.20 a day": "553431141", "Below $3 a day": "757429652"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2017", "Above $10 a day": "3117058524", "$8.30-$10 a day": "410126133", "$4.20-$8.30 a day": "1443597161", "$3-$4.20 a day": "545845925", "Below $3 a day": "738238177"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2018", "Above $10 a day": "3217260918", "$8.30-$10 a day": "416281003", "$4.20-$8.30 a day": "1440238249", "$3-$4.20 a day": "528697505", "Below $3 a day": "720097173"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2019", "Above $10 a day": "3298896907", "$8.30-$10 a day": "419703470", "$4.20-$8.30 a day": "1443936080", "$3-$4.20 a day": "510120513", "Below $3 a day": "716321206"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2020", "Above $10 a day": "3276284430", "$8.30-$10 a day": "430502377", "$4.20-$8.30 a day": "1483864742", "$3-$4.20 a day": "500592347", "Below $3 a day": "760886920"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2021", "Above $10 a day": "3441238957", "$8.30-$10 a day": "415421088", "$4.20-$8.30 a day": "1387252109", "$3-$4.20 a day": "480226440", "Below $3 a day": "782172574"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2022", "Above $10 a day": "3480654396", "$8.30-$10 a day": "417677338", "$4.20-$8.30 a day": "1402982439", "$3-$4.20 a day": "482220643", "Below $3 a day": "780587296"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2023", "Above $10 a day": "3591117670", "$8.30-$10 a day": "405318089", "$4.20-$8.30 a day": "1363124090", "$3-$4.20 a day": "477254868", "Below $3 a day": "789173379"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2024", "Above $10 a day": "3673691231", "$8.30-$10 a day": "402407559", "$4.20-$8.30 a day": "1346142855", "$3-$4.20 a day": "475626704", "Below $3 a day": "793004995"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2025", "Above $10 a day": "3740525371", "$8.30-$10 a day": "402653164", "$4.20-$8.30 a day": "1335727821", "$3-$4.20 a day": "473977794", "Below $3 a day": "793879114"}, {"Entity": "World (excluding India)", "Code": "", "Year": "2026", "Above $10 a day": "3804272079", "$8.30-$10 a day": "403652902", "$4.20-$8.30 a day": "1330212530", "$3-$4.20 a day": "474515769", "Below $3 a day": "790879520"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1998", "Above $10 a day": "3511768", "$8.30-$10 a day": "1632888", "$4.20-$8.30 a day": "7774873", "$3-$4.20 a day": "2919600", "Below $3 a day": "2606883"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Above $10 a day": "2121175", "$8.30-$10 a day": "1162301", "$4.20-$8.30 a day": "9411364", "$3-$4.20 a day": "5120925", "Below $3 a day": "5162569"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Above $10 a day": "2369797", "$8.30-$10 a day": "1266219", "$4.20-$8.30 a day": "9141570", "$3-$4.20 a day": "7383212", "Below $3 a day": "10065510"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Above $10 a day": "579696", "$8.30-$10 a day": "216078", "$4.20-$8.30 a day": "1374218", "$3-$4.20 a day": "784487", "Below $3 a day": "5027171"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Above $10 a day": "428326", "$8.30-$10 a day": "177946", "$4.20-$8.30 a day": "1205509", "$3-$4.20 a day": "946672", "Below $3 a day": "5615468"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Above $10 a day": "623603", "$8.30-$10 a day": "257868", "$4.20-$8.30 a day": "1789043", "$3-$4.20 a day": "1285850", "Below $3 a day": "5047689"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Above $10 a day": "635861", "$8.30-$10 a day": "254507", "$4.20-$8.30 a day": "1849894", "$3-$4.20 a day": "1372857", "Below $3 a day": "5369289"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Above $10 a day": "325996", "$8.30-$10 a day": "144401", "$4.20-$8.30 a day": "1502931", "$3-$4.20 a day": "1525178", "Below $3 a day": "7428029"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Above $10 a day": "596078", "$8.30-$10 a day": "249803", "$4.20-$8.30 a day": "1570175", "$3-$4.20 a day": "1185837", "Below $3 a day": "7991321"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Above $10 a day": "599186", "$8.30-$10 a day": "226470", "$4.20-$8.30 a day": "1458644", "$3-$4.20 a day": "1177514", "Below $3 a day": "8885505"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Above $10 a day": "674864", "$8.30-$10 a day": "234171", "$4.20-$8.30 a day": "1689529", "$3-$4.20 a day": "1400178", "Below $3 a day": "9966852"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Above $10 a day": "967070", "$8.30-$10 a day": "346924", "$4.20-$8.30 a day": "2224148", "$3-$4.20 a day": "1727140", "Below $3 a day": "11133807"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Above $10 a day": "804041", "$8.30-$10 a day": "397676", "$4.20-$8.30 a day": "2541724", "$3-$4.20 a day": "1968678", "Below $3 a day": "14440819"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Above $10 a day": "1611194", "$8.30-$10 a day": "666991", "$4.20-$8.30 a day": "4039744", "$3-$4.20 a day": "2421618", "Below $3 a day": "4855877"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Above $10 a day": "1510649", "$8.30-$10 a day": "634425", "$4.20-$8.30 a day": "3515700", "$3-$4.20 a day": "2536917", "Below $3 a day": "6614791"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Above $10 a day": "1604886", "$8.30-$10 a day": "496593", "$4.20-$8.30 a day": "3302140", "$3-$4.20 a day": "2351198", "Below $3 a day": "7516551"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "distribution-of-population-poverty-thresholds", "metadata_url": "https://ourworldindata.org/grapher/distribution-of-population-poverty-thresholds.metadata.json", "chart_title": "Distribution of population between different poverty lines", "chart_subtitle": "This data is adjusted for inflation and differences in living costs between countries.", "chart_note": "This data is expressed in international-$ at 2021 prices. Depending on the country and year, it relates to income (measured after taxes and benefits) or to consumption, per capita.", "chart_citation": "World Bank Poverty and Inequality Platform (2026)", "original_chart_url": "https://ourworldindata.org/grapher/distribution-of-population-poverty-thresholds", "owid_column_metadata": {"Number of people not in poverty (above $10 a day, 2021 prices)": {"titleShort": "Above $10 a day", "titleLong": "Above $10 a day", "descriptionShort": "Number of people living in households with an income or consumption above $10 per day.", "descriptionKey": ["This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "Many people, today and in the past, have no formal monetary income. This data accounts for that by including the estimated value of non-market income, such as food grown by subsistence farmers for their own use.", "The data comes from the World Bank's Poverty and Inequality Platform (PIP), which draws on national household surveys. Regional and global estimates are extrapolated to the year of the data release using GDP growth estimates and forecasts. For more details about the methodology, please refer to the [World Bank PIP documentation](https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#nowcasts).", "Depending on the country and year, the data refers either to income (after taxes and benefits) or to consumption, per capita. These are not perfectly comparable — consumption tends to be more evenly distributed than income. For most countries, we have only one option available. But when there is a mix of consumption and income data points, we process the data to keep one observation per country and year."], "descriptionProcessing": "For most countries in the dataset, estimates relate to disposable income or consumption, for all available years. Several countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\n\nIn most of our charts, we present the data with some data points dropped to present a single series for each country. This allows us to make readable visualizations that combine multiple countries. In choosing which data points to keep, we strike a balance between maintaining comparability over time and showing as long a time series as possible. 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World Bank Poverty and Inequality Platform, “World Bank Poverty and Inequality Platform (PIP) 20260324_2021, 20260324_2017” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1218627.metadata.json"}, "Number of people in poverty (between $8.30 and $10 a day, 2021 prices)": {"titleShort": "$8.30-$10 a day", "titleLong": "$8.30-$10 a day", "descriptionShort": "Number of people living in households with an income or consumption between $8.30 and $10 per day.", "descriptionKey": ["This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "Many people, today and in the past, have no formal monetary income. This data accounts for that by including the estimated value of non-market income, such as food grown by subsistence farmers for their own use.", "The data comes from the World Bank's Poverty and Inequality Platform (PIP), which draws on national household surveys. Regional and global estimates are extrapolated to the year of the data release using GDP growth estimates and forecasts. For more details about the methodology, please refer to the [World Bank PIP documentation](https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#nowcasts).", "Depending on the country and year, the data refers either to income (after taxes and benefits) or to consumption, per capita. These are not perfectly comparable — consumption tends to be more evenly distributed than income. For most countries, we have only one option available. But when there is a mix of consumption and income data points, we process the data to keep one observation per country and year."], "descriptionProcessing": "For most countries in the dataset, estimates relate to disposable income or consumption, for all available years. Several countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\n\nIn most of our charts, we present the data with some data points dropped to present a single series for each country. This allows us to make readable visualizations that combine multiple countries. In choosing which data points to keep, we strike a balance between maintaining comparability over time and showing as long a time series as possible. As such, the exact approach varies across countries.", "shortUnit": "", "unit": "people", "timespan": "1963-2026", "type": "Numeric", "owidVariableId": 1219387, "shortName": "headcount_between__ppp_version_2021__poverty_line_830_and_1000__welfare_type_income_or_consumption__table_income_or_consumption_consolidated__survey_comparability_no_spells", "lastUpdated": "2026-03-24", "nextUpdate": "2026-09-20", "citationShort": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data", "citationLong": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data. “$8.30-$10 a day – World Bank” [dataset]. World Bank Poverty and Inequality Platform, “World Bank Poverty and Inequality Platform (PIP) 20260324_2021, 20260324_2017” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1219387.metadata.json"}, "Number of people in poverty (between $4.20 and $8.30 a day, 2021 prices)": {"titleShort": "$4.20-$8.30 a day", "titleLong": "$4.20-$8.30 a day", "descriptionShort": "Number of people living in households with an income or consumption between $4.20 and $8.30 per day.", "descriptionKey": ["This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "Many people, today and in the past, have no formal monetary income. This data accounts for that by including the estimated value of non-market income, such as food grown by subsistence farmers for their own use.", "The data comes from the World Bank's Poverty and Inequality Platform (PIP), which draws on national household surveys. Regional and global estimates are extrapolated to the year of the data release using GDP growth estimates and forecasts. For more details about the methodology, please refer to the [World Bank PIP documentation](https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#nowcasts).", "Depending on the country and year, the data refers either to income (after taxes and benefits) or to consumption, per capita. These are not perfectly comparable — consumption tends to be more evenly distributed than income. For most countries, we have only one option available. But when there is a mix of consumption and income data points, we process the data to keep one observation per country and year."], "descriptionProcessing": "For most countries in the dataset, estimates relate to disposable income or consumption, for all available years. Several countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\n\nIn most of our charts, we present the data with some data points dropped to present a single series for each country. This allows us to make readable visualizations that combine multiple countries. In choosing which data points to keep, we strike a balance between maintaining comparability over time and showing as long a time series as possible. As such, the exact approach varies across countries.", "shortUnit": "", "unit": "people", "timespan": "1963-2026", "type": "Numeric", "owidVariableId": 1219379, "shortName": "headcount_between__ppp_version_2021__poverty_line_420_and_830__welfare_type_income_or_consumption__table_income_or_consumption_consolidated__survey_comparability_no_spells", "lastUpdated": "2026-03-24", "nextUpdate": "2026-09-20", "citationShort": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data", "citationLong": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data. “$4.20-$8.30 a day – World Bank” [dataset]. World Bank Poverty and Inequality Platform, “World Bank Poverty and Inequality Platform (PIP) 20260324_2021, 20260324_2017” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1219379.metadata.json"}, "Number of people in poverty (between $3 and $4.20 a day, 2021 prices)": {"titleShort": "$3-$4.20 a day", "titleLong": "$3-$4.20 a day", "descriptionShort": "Number of people living in households with an income or consumption between $3 and $4.20 per day.", "descriptionKey": ["This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "Many people, today and in the past, have no formal monetary income. This data accounts for that by including the estimated value of non-market income, such as food grown by subsistence farmers for their own use.", "The data comes from the World Bank's Poverty and Inequality Platform (PIP), which draws on national household surveys. Regional and global estimates are extrapolated to the year of the data release using GDP growth estimates and forecasts. For more details about the methodology, please refer to the [World Bank PIP documentation](https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#nowcasts).", "Depending on the country and year, the data refers either to income (after taxes and benefits) or to consumption, per capita. These are not perfectly comparable — consumption tends to be more evenly distributed than income. For most countries, we have only one option available. But when there is a mix of consumption and income data points, we process the data to keep one observation per country and year."], "descriptionProcessing": "For most countries in the dataset, estimates relate to disposable income or consumption, for all available years. Several countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\n\nIn most of our charts, we present the data with some data points dropped to present a single series for each country. This allows us to make readable visualizations that combine multiple countries. In choosing which data points to keep, we strike a balance between maintaining comparability over time and showing as long a time series as possible. As such, the exact approach varies across countries.", "shortUnit": "", "unit": "people", "timespan": "1963-2026", "type": "Numeric", "owidVariableId": 1219370, "shortName": "headcount_between__ppp_version_2021__poverty_line_300_and_420__welfare_type_income_or_consumption__table_income_or_consumption_consolidated__survey_comparability_no_spells", "lastUpdated": "2026-03-24", "nextUpdate": "2026-09-20", "citationShort": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data", "citationLong": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data. “$3-$4.20 a day – World Bank” [dataset]. World Bank Poverty and Inequality Platform, “World Bank Poverty and Inequality Platform (PIP) 20260324_2021, 20260324_2017” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1219370.metadata.json"}, "Number of people in poverty ($3 a day, 2021 prices)": {"titleShort": "Below $3 a day", "titleLong": "Below $3 a day", "descriptionShort": "Number of people living in households with an income or consumption below $3 per day.", "descriptionKey": ["The World Bank defines extreme poverty as living on less than $3 per day. This threshold, known as the \"International Poverty Line\", is set so that poverty can be compared across countries. This indicator plays an important and successful role in focusing the world's attention on the very poorest people. The UN uses this indicator to track progress towards [ending extreme poverty by 2030](https://ourworldindata.org/sdgs/no-poverty).", "Two centuries ago, most of the world's population was extremely poor. Many believed that widespread poverty was inevitable. But this turned out to be wrong. Economic growth is possible, and poverty can decline. With this poverty line, we can track whether countries are leaving the worst poverty behind.", "This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "Many people, today and in the past, have no formal monetary income. This data accounts for that by including the estimated value of non-market income, such as food grown by subsistence farmers for their own use.", "The data comes from the World Bank's Poverty and Inequality Platform (PIP), which draws on national household surveys. Regional and global estimates are extrapolated to the year of the data release using GDP growth estimates and forecasts. For more details about the methodology, please refer to the [World Bank PIP documentation](https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#nowcasts).", "Depending on the country and year, the data refers either to income (after taxes and benefits) or to consumption, per capita. These are not perfectly comparable — consumption tends to be more evenly distributed than income. For most countries, we have only one option available. But when there is a mix of consumption and income data points, we process the data to keep one observation per country and year."], "descriptionProcessing": "For most countries in the dataset, estimates relate to disposable income or consumption, for all available years. Several countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\n\nIn most of our charts, we present the data with some data points dropped to present a single series for each country. This allows us to make readable visualizations that combine multiple countries. In choosing which data points to keep, we strike a balance between maintaining comparability over time and showing as long a time series as possible. As such, the exact approach varies across countries.", "shortUnit": "", "unit": "people", "timespan": "1963-2026", "type": "Numeric", "owidVariableId": 1217712, "shortName": "headcount__ppp_version_2021__poverty_line_300__welfare_type_income_or_consumption__table_income_or_consumption_consolidated__survey_comparability_no_spells", "lastUpdated": "2026-03-24", "nextUpdate": "2026-09-20", "citationShort": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data", "citationLong": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data. “Below $3 a day – World Bank” [dataset]. World Bank Poverty and Inequality Platform, “World Bank Poverty and Inequality Platform (PIP) 20260324_2021, 20260324_2017” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1217712.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "0cc395038fa4a094a99d"},
{"raw_link": "https://ourworldindata.org/timeline-anxiety-medications", "title": "Anxiety is one of the world’s most common health issues. How have treatments evolved over the last 70 years?", "context": "Home\nMental Health\nAnxiety is one of the world’s most common health issues. How have treatments evolved over the last 70 years?\nAnxiety affects hundreds of millions of people every year. What treatments are available, and how have they changed over time?\nBy\nHannah Ritchie\nand\nTuna Acisu\nNovember 10, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nAnxiety is the\nmost common\nmental health condition globally. It’s estimated that\n4% to 5%\nof people in the world have an anxiety disorder at any given time.\nLong-term surveys in the United States suggest that around\none-third of people\nexperience an anxiety disorder at some point in their lives.\n1\nThere is often poor data on the prevalence of mental health conditions, especially in lower-income countries. Even in rich countries, these figures might be an undercount due to the stigma that many feel in admitting they struggle with mental health.\nThat means most of us will either struggle with anxiety ourselves or know someone close who has or will. This is also true for the two of us, and seeing how big an impact it can have on people’s everyday lives, we know that having effective treatments that alleviate or at least reduce symptoms can be life-changing.\nIn this article, we examine the history of pharmacological treatments — specifically, drugs — used to treat anxiety since the 1950s. These have changed a lot in the five decades until the early 2000s, but there have been no new anxiety medications approved since 2004.\n2\nWhile there has been a slowdown in the number of new drugs\napproved\nfor anxiety (which is the focus of this article), the usage and number of prescriptions for anti-anxiety medications have likely continued to increase in recent decades.\nIt can be hard to get concrete and consistent data on this because, as we’ll see, many of the most recent drugs are primarily antidepressants; so even when prescription figures are available, it’s usually not clear whether they are being used to tackle depression or anxiety. In some cases, it can be both. Even in just the last few years, there have been\nnoticeable increases\nin the percentage of American adults receiving mental health treatment, which includes taking medications.\nFor context, around one in six American adults\ntakes medication\nfor any mental health issue each year.\nA 50-year history of anxiety medications in the United States\nAcross the world, a range of treatments are used to alleviate anxiety. Building a consistent timeline for all countries would be difficult, so here we focus on the timeline of medication approvals in the United States. At least 22 drugs have been officially approved for anxiety treatment there since 1955.\nIn the chart below, we’ve created a timeline. Each dot represents a single drug, with its official name and trade name (which you may have heard more about) labeled. Tuna Acisu and Saloni Dattani produced this dataset based on data from the\nUS Food and Drug Administration\n(FDA).\n3\nMost of us will either struggle with anxiety ourselves or have someone close to us who has or will.\nThe date allocated to each drug is when the FDA first approved it. This means the FDA found the molecule to be a safe and effective treatment for a particular condition. Some of these drugs were first approved explicitly for anxiety, but others were used for other conditions and were only approved for anxiety later. For example, “duloxetine” was approved for the treatment of depression in 2004, which is the date shown on the timeline. It got licensed for anxiety three years later.\nEach drug is colored according to its therapeutic class, determined by its active ingredient and mechanism of action in treating anxiety.\nNote that here we only focus on drugs officially approved for anxiety treatment by the FDA. That means “off-label” medications — which means a doctor decides that a particular drug could be useful for a patient, despite it not being\nofficially\napproved for anxiety — are not included.\nThe timeline can be split into three big parts: tranquilizers in the 1950s, the benzodiazepine era up to the mid-1980s, and the era of antidepressants in the following two decades.\nDownload\nTranquilizers\nThe first medication on our timeline is meprobamate, commonly known as Miltown. It’s a tranquilizer, which was the main treatment for anxiety in the 1950s. Miltown has often been framed as the first “blockbuster” psychiatric drug in the United States, due to its popularity and how it was promoted to “consumers”.\n4\nSeveral\nhistorical accounts\nof anxiety treatment suggest that 1 in 20 Americans was taking Miltown in the late 1950s, but we were unable to find an official or academic source for these claims.\nMiltown was marketed as a “minor tranquilizer”: it was said to take the edge off anxiety symptoms without the full effects of sedation that previous tranquilizers caused. It worked by slowing activity in the central nervous system, often reducing the acute symptoms of anxiety.\nWhile it was safer than previous drugs, it still carried significant risks of overdose and addiction and was later replaced by safer treatments.\nIn this era, anxiety was often treated as a short-term nervous reaction. That’s why treatments like tranquillizers were used: they targeted the physical symptoms of the nervous system rather than the underlying causes.\nBut as you can see in the timeline, antidepressants, such as monoamine oxidase inhibitors (MAOIs) and tricyclic antidepressants (TCAs) started being used in the early 1960s. These can alter the patient’s mood and affect their anxiety by changing brain chemistry. Their effectiveness suggested that anxiety had a neurological basis that could be changed in the long term through medications, rather than relying only on short-term treatments that targeted nervous symptoms. This represented a clear change in the overall scientific understanding of anxiety disorders.\nThe benzodiazepine revolution of the 1960s and ‘70s\nThe major treatment revolution of the 1960s and ‘70s was in the development of benzodiazepine anxiolytics.\n5\nIn the timeline above, you can see them in the red box. These became the go-to anxiety treatment for decades. Valium and Xanax are two of the most well-known drugs in this class.\nAnxiolytic is the technical term for drugs that reduce anxiety. Other drugs that don’t have “anxiolytic” in their name, such as antidepressants, can also have an anxiolytic\neffect\n. It’s just not included in their drug class.\nBenzodiazepines work by enhancing the effect of GABA, the brain’s main calming neurotransmitter. By amplifying this neurotransmitter, these drugs reduce brain activity, producing relaxation and relief from anxiety.\nA crucial feature of benzodiazepines is that they can work very quickly, reducing symptoms within hours or even minutes. That’s very different from many antidepressants, which can take weeks to have an effect.\nMany studies on benzodiazepines have found positive results in reducing symptoms compared to a placebo.\n6\nBenzodiazepines are often effective in treating acute physical symptoms such as panic attacks, but do not necessarily provide a long-term solution.\nThe main concerns with these drugs are tolerability, abuse, dependence, and withdrawal symptoms.\n7\nGrowing concerns about these problems led to them falling out of favour among many clinicians, especially with the arrival of SSRI antidepressants.\n5\nTheir use for anxiety has declined in the late twentieth century, even though some argue that benzodiazepines can be just as effective and tolerable for many people as the drugs that followed.\n8\nThe use of antidepressants since the 1990s\nIn the late 1980s, a new generation of anxiety treatments was born: selective serotonin reuptake inhibitors (SSRIs). You can see them in the green box in our timeline.\nSSRIs were initially introduced to treat depression, but were also effective across several anxiety disorders. They work by blocking the reabsorption of serotonin — a “messenger” in the brain that plays a key role in mood regulation, anxiety, and sleep — so that more of it remains available.\nWhen someone struggles with an anxiety disorder, the amygdala in the brain (their “fear alarm system”) tends to be overactive, while their prefrontal cortex (which helps to regulate that fear) is underactive. When SSRIs increase serotonin availability, they reduce the noise from the amygdala and strengthen the prefrontal cortex to regulate emotions of fear. That can reduce anxiety symptoms and panic responses.\nMany studies show that SSRI antidepressants are effective in treating anxiety disorders. A\nCochrane Review\n, which included 37 studies focused on “generalized anxiety disorder”, concluded that they “have high confidence that antidepressants are more effective than placebo at improving treatment response”.\n9\nCochrane Reviews are often seen as the “gold standard” of meta-analyses and medical literature reviews. It’s rare for them to suggest positive results with “high confidence”.\nOther large meta-analyses have found similar results for the treatment of general anxiety disorder.\n10\nSSRIs have also been shown to help with more specific anxiety disorders, such as social anxiety, but with smaller benefits.\n11\nA different\nCochrane Review\nfound that they were effective in treating\nsocial\nanxiety relative to a placebo, but this was based on “very low‐ to moderate‐quality evidence.”\n12\nThis gives us lower confidence than the Cochrane Review on treating\ngeneralized\nanxiety disorder, which was based on higher-quality evidence. Still, it does suggest SSRIs likely have some positive impact on social anxiety, too.\nOne key reason why doctors are much more likely to prescribe SSRIs over benzodiazepines is that they don’t have the same potential for abuse and dependency. Unlike previous treatments, which often had a more acute and immediate effect, SSRIs tend to act more slowly. It can frequently take a month or more for people to feel the positive impacts on their symptoms and mood. However, they tend to be more sustainable, long-term solutions, meaning people can safely take them for longer, compared to previous drugs.\nSome of the most commonly prescribed medications for anxiety today are SSRIs approved in the 1990s. We both know several people who have taken sertraline (Zoloft) to manage their symptoms, for example.\nA similar drug type, serotonin-norepinephrine reuptake inhibitors (SNRIs), also became available in the 1990s and early 2000s. They block the reabsorption of serotonin, but also block another neurotransmitter called norepinephrine. This can improve the patient’s energy levels, motivation, and focus — key symptoms for someone struggling with anxiety.\nThe last new anxiety medication that was approved in the US was duloxetine, an SNRI. This was in 2004, more than two decades ago.\nThe timeline we’ve just walked through is specific to the US. But we’d find similar patterns for other countries, such as the UK or others in Europe. There would, however, be some differences, especially in the specific dates of approval. There are also some drugs approved in Europe — etifoxine is one example — which are not approved in the US, and vice versa. Citalopram is another example; it’s\napproved\nfor panic disorder treatment in the UK, but is\nonly used\n“off-label” for anxiety disorders in the US.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nInnovation in anxiety treatments has not stopped, but it has slowed\nNo new drugs have been approved for treating anxiety in almost twenty years. But these two silent decades don’t mean there have been no developments in the treatment of anxiety.\n13\nFirst, there have been incremental changes in formulations and dosage regimes for existing medications. For example, the US FDA approved a delayed-release form of duloxetine — the last drug on the timeline — and an extended-release version of lorazepam, which was first approved in 1977. Europe provides another example, where the formulation Duloxetine Lilly\nwas approved\nin 2014. However, these are adaptations of existing drugs rather than discoveries.\nWhile existing treatments can be life-changing for some, we still have some way to go to develop effective treatments for everyone.\nSecond, some newer antidepressant drugs are also prescribed “\noff-label\n” for anxiety. This means doctors decide, based on their judgment, that a particular drug could be useful for a patient with anxiety, despite it not being officially approved for that purpose. There are a surprising number of these; we found at least twelve when working on this timeline. It’s worth noting that the lack of approval for anxiety specifically can have implications for their availability and affordability, as it can affect insurance coverage.\nThird, several promising new drug innovations are at various stages of development and clinical trials.\n14\nSome of these target the same pathways as SSRIs and SNRIs, but in improved ways. Others take entirely novel approaches. Not all of these will succeed and become key treatments outside of the lab, but it seems likely that at least some will.\nFinally, there have been developments in non-pharmaceutical treatments in recent decades. This article focuses on drugs, but there is an increasing number of other approaches, including cognitive behavioral therapy, non-invasive neurostimulation, and virtual reality exposure technologies.\nDespite these, it’s hard to dispute that progress in the last few decades has slowed relative to the previous 50 years. While existing treatments can be very effective for some people — in fact, life-changing for some — we still have some way to go to develop effective treatments for\neveryone\nwho struggles with an anxiety disorder, and ensure these treatments are available to them.\nAcknowledgments\nMany thanks to Saloni Dattani for helping to prepare the dataset on which this timeline relies. Thanks also to Max Roser and Edouard Mathieu for their feedback and comments on this article and its visualization.\nContinue reading on Our World in Data\nAntipsychotic medications: a timeline of innovations and remaining challenges\nScientists have developed effective and safer antipsychotic medications, but much improvement is still needed.\nHow do researchers study the prevalence of mental illnesses?\nGlobal data on mental health is essential to understand the scale and patterns of these illnesses, and how to reduce them. How do researchers collect this data, and how reliable is it?\nHow are mental illnesses defined?\nMental illnesses are a range of conditions that significantly affect people’s lives. What are their symptoms?\nEndnotes\nThis is based on the US\nNational Comorbidity Survey (NCS)\n, which is widely referenced in the scientific literature.\nGarakani, A., Murrough, J. W., Freire, R. C., Thom, R. P., Larkin, K., Buono, F. D., & Iosifescu, D. V. (2020). Pharmacotherapy of anxiety disorders: current and emerging treatment options. Frontiers in psychiatry.\nWhile no new drugs have been approved since 2004, some other new antidepressants are used \"off-label\" for anxiety. \"Off-label\" means a doctor decides that a particular drug could be useful for a patient, despite it not being officially approved for anxiety treatment.\nA database of medications approved in the US can be found in the FDA’s\nOrange Book\n, and\nDrugs@FDA\n.\nKocsis, J. H. (2009). Happy pills in America: From Miltown to Prozac. The Journal of Clinical Investigation.\nLópez-Muñoz, F., Álamo, C., & García-García, P. (2011). The discovery of chlordiazepoxide and the clinical introduction of benzodiazepines: half a century of anxiolytic drugs. Journal of Anxiety Disorders.\nSlee, A., Nazareth, I., Bondaronek, P., Liu, Y., Cheng, Z., & Freemantle, N. (2019). Pharmacological treatments for generalised anxiety disorder: a systematic review and network meta-analysis. The Lancet.\nFernandes, H., Novais, C., Sousa-Pinto, B., Soares-da-Silva, P., & Azevedo, L. F. (2025). Comparative efficacy and safety of benzodiazepines in the treatment of patients with generalized anxiety disorder: a systematic review and network meta-analysis. Psychotherapy and Psychosomatics.\nBalon, R., & Starcevic, V. (2020). Role of benzodiazepines in anxiety disorders. Anxiety disorders: Rethinking and understanding recent discoveries.\nThis Cochrane Review does find evidence that it’s effective relative to a placebo in treating panic disorder, but with lower-quality evidence:\nBreilmann, J., Girlanda, F., Guaiana, G., Barbui, C., Cipriani, A., Castellazzi, M., ... & Koesters, M. (2019). Benzodiazepines versus placebo for panic disorder in adults. Cochrane database of systematic reviews.\nStudies suggest that the number of overdoses involving Benzodiazepines has continued to increase over the last few decades.\nBachhuber, M. A., Hennessy, S., Cunningham, C. O., & Starrels, J. L. (2016). Increasing benzodiazepine prescriptions and overdose mortality in the United States, 1996–2013. American journal of public health, 106(4), 686-688.\nIn 2020, the US Food and Drug Administration\nupdated its warning labels\non benzodiazepine drugs to communicate these risks more clearly:\n“To address the serious risks of abuse, addiction, physical dependence, and withdrawal reactions, on September 23, 2020 FDA required the Boxed Warning be updated for all benzodiazepine medicines. Benzodiazepines are widely used to treat many conditions, including anxiety, insomnia, and seizures.\nBenzodiazepines can be an important treatment option for treating disorders for which these drugs are indicated. However, even when taken at recommended dosages, their use can lead to misuse, abuse, and addiction. Abuse and misuse can result in overdose or death, especially when benzodiazepines are combined with other medicines, such as opioid pain relievers, alcohol, or illicit drugs. Physical dependence can occur when benzodiazepines are taken steadily for several days to weeks, even as prescribed. Stopping them abruptly or reducing the dosage too quickly can result in withdrawal reactions, including seizures, which can be life-threatening.”\nBalon, R., & Starcevic, V. (2020). Role of benzodiazepines in anxiety disorders. Anxiety disorders: Rethinking and understanding recent discoveries.\nKopcalic, K., Arcaro, J., Pinto, A., Ali, S., Barbui, C., Curatoli, C., ... & Guaiana, G. (2025). Antidepressants versus placebo for generalised anxiety disorder (GAD). Cochrane Database of Systematic Reviews.\nSlee, A., Nazareth, I., Bondaronek, P., Liu, Y., Cheng, Z., & Freemantle, N. (2019). Pharmacological treatments for generalised anxiety disorder: a systematic review and network meta-analysis. The Lancet.\nBaldwin, D., Woods, R., Lawson, R., & Taylor, D. (2011). Efficacy of drug treatments for generalised anxiety disorder: systematic review and meta-analysis. Bmj, 342.\nWilliams, T., McCaul, M., Schwarzer, G., Cipriani, A., Stein, D. J., & Ipser, J. (2020). Pharmacological treatments for social anxiety disorder in adults: a systematic review and network meta-analysis. Acta Neuropsychiatrica.\nWilliams, T., Hattingh, C. J., Kariuki, C. M., Tromp, S. A., van Balkom, A. J., Ipser, J. C., & Stein, D. J. (2017). Pharmacotherapy for social anxiety disorder (SAnD). Cochrane Database of Systematic Reviews.\nTadros, E., Keerthana, S., Padder, S., Totlani, J., Hirsch, D., Kaidbay, D. N., ... & IsHak, W. W. (2025). Anxiety disorders, PTSD and OCD: systematic review of approved psychiatric medications (2008–2024) and pipeline phase III medications. Drugs in Context.\nIsHak, W. W., Meyer, A., Freire, L., Totlani, J., Murphy, N., Renteria, S., ... & Danovitch, I. (2024). Overview of psychiatric medications in the pipeline in phase III trials as of June 1, 2024: a systematic review. Innovations in clinical neuroscience.\nTadros, E., Keerthana, S., Padder, S., Totlani, J., Hirsch, D., Kaidbay, D. N., ... & IsHak, W. W. (2025). Anxiety disorders, PTSD and OCD: systematic review of approved psychiatric medications (2008–2024) and pipeline phase III medications. Drugs in Context, 14, 2024-11.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Tuna Acisu (2025) - “Anxiety is one of the world’s most common health issues. How have treatments evolved over the last 70 years?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20251125-173858/timeline-anxiety-medications.html' [Online Resource] (archived on November 25, 2025).\nBibTeX citation\n@article{owid-timeline-anxiety-medications,\nauthor = {Hannah Ritchie and Tuna Acisu},\ntitle = {Anxiety is one of the world’s most common health issues. How have treatments evolved over the last 70 years?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20251125-173858/timeline-anxiety-medications.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "timeline-anxiety-medications", "source_url": "https://ourworldindata.org/timeline-anxiety-medications", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Anxiety affects at least hundreds of millions of people every year. 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{"raw_link": "https://ourworldindata.org/vaping-vs-smoking-health-risks", "title": "While vaping is not risk-free, it is less harmful than tobacco", "context": "Home\nSmoking\nWhile vaping is not risk-free, it is less harmful than tobacco\nAnswers to some of the most frequently asked questions about vaping and its effects.\nBy\nHannah Ritchie\nNovember 3, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nA decade ago, most smokers thought vaping was less harmful than tobacco. But ten years on, the opposite is true: the majority say that vapes are just as or even more dangerous.\n1\nYou can see this shift in opinion in the chart below.\n2\nDownload\nIt’s not surprising that sentiment towards vaping has become more negative — the public discourse has shifted substantially. Look at the news, and it’s easy to see why people would think that vaping is just as bad, if not worse.\nHere are just three headlines published in popular newspapers (The Sun, The Telegraph, and Daily Mail) in the last few years:\nDownload\nHeadlines from\nThe Telegraph\n,\nDaily Mail\n, and\nThe Sun\nnewspapers.\n3\nThe rising popularity of e-cigarettes may be another reason for the backlash against them.\nIn the United Kingdom today, almost as many people vape\nas smoke tobacco\n: around one-in-ten adults.\n4\nBut the trajectory of these products has been very different. Half of adults smoked cigarettes in the 1970s, and since then, rates have plummeted. Vapes, on the other hand, are a relatively recent addition. You can see this in the chart below.\nIs this a positive development? Or are people just substituting one harmful habit with an equally harmful or even worse one?\nIn this article, I address some of the most frequently asked questions about vaping and its effects. Are British smokers right? Is vaping actually more harmful than tobacco? Is there evidence for its effectiveness as a tool to quit? Could it be a “gateway” to smoking among young people?\nWhat are the health risks of vaping compared to cigarettes?\nThis is the crucial question. If vaping is just as bad as cigarettes, then substituting one for the other would not be positive for public health, or for individuals who smoke.\nResearch suggests that this is not the case. While we can’t say that vaping carries\nzero\nrisk, the evidence clearly indicates that it is\nmuch\nless harmful than smoking.\nLook at the news, and it’s easy to see why people would think that vaping is just as bad, if not worse.\nThis should not be surprising: tobacco is extremely bad for health. It not only\nincreases the risk\nof lung cancer — American men who smoke are around 21 times more likely to die from lung cancer — but a range of other diseases too, including strokes, heart disease, and several other cancers.\n5\nIn the UK, smoking is the\nleading risk factor\nfor early death, ahead of obesity, high blood pressure, cholesterol, and other behavioral and environmental factors. If you’re a smoker who wants to improve your health, quitting is probably the single best thing you can do.\nTo understand the\ncomparative\nrisks of tobacco and vaping, we need to understand what’s in each and what makes them harmful.\nWhen you light a\ncigarette\n, you burn dry tobacco leaves. This burning generates thousands of chemicals, some of which are toxic or carcinogenic. These include nicotine (which makes them addictive), tar, carbon monoxide, benzene, cadmium, and polycyclic aromatic hydrocarbons (which are strong carcinogens), to name a few. Inhaling these substances is bad for the lungs, but as mentioned above, it can also damage other organs.\nVaping is different.\nE-cigarettes\nheat a liquid, which creates an aerosol. This vapor is inhaled, but since no burning occurs — the liquid is\nheated,\nbut there is no combustion — fewer toxins are released compared to smoking.\nThat doesn’t mean vaping is completely harmless, though. Many vapes contain nicotine, which makes them addictive. While nicotine is relatively benign for most people and has little long-term health impact, it can have short-term impacts on blood pressure and heart rate.\n6\nIt’s also not recommended for pregnant women, since there is some evidence that it can cross the placenta and affect fetal development — I say more on this in the additional information section below.\nVapes also contain solvents and flavorings that can create harmful byproducts, and small amounts of metals can be released from the heating coil. Breathing in aerosols can irritate the lungs and exacerbate asthma or bronchitis. But overall, the health impacts are far lower than smoking tobacco.\nPeople often note that we have less data on the\nlong-term\nhealth impacts of vaping. This is certainly true compared to cigarettes. However, vapes’ earliest adopters have been using them for two decades — they were first commercialized in the early 2000s. If there were significant long-term health impacts, we’d expect to see signs of those by now, especially if they were anywhere close to those of tobacco. And while we don’t have very long-term epidemiological data, we do have a good understanding of the ingredients, the levels of exposure, and how these are likely to affect the human body.\nSeveral additional health concerns are often raised; you might have seen them in media headlines. I review the evidence on these in the “detailed questions” section below.\nFor those who don’t want to read through all of the details in my detailed FAQ below, the summary is quite simple: many health bodies suggest that vaping is much less harmful than tobacco. While they advise against it for young people or non-smokers, they do recommend it as a “quitting tool” for smokers for the health benefits of switching.\n7\nI don’t think I can put it\nmore succinctly\nthan this:\n“The key points about vaping (e-cigarettes) can be easily summarised. If you smoke, vaping is much safer; if you don’t smoke, don’t vape.”\n–\nChris Whitty, Chief Medical Officer for England\nJust so that my position is clear for the rest of the article: I do not want to “promote” e-cigarettes, just as Chris Whitty would not recommend them to someone who has never smoked or vaped. However, based on the research, vaping does appear to be an effective tool that helps people quit cigarettes.\nWhat motivated me to write this research overview was the finding in the opening chart — that most smokers think vaping is equally or even more harmful. That misconception robs some people of the best option they might have to quit. I hope, then, that this article helps to close the gap in understanding and supports more smokers who want to stop but have struggled to do so.\nFrequently asked questions on the health impacts of vaping\nDo vapes deliver more nicotine than cigarettes?\nIt’s often claimed that vaping is more addictive than smoking and delivers more nicotine.\nMore specifically, a\ncommon claim\nis that a single vape delivers as much nicotine as 50 cigarettes.\nOf course, it’s hard to know whether the comparison to 50 cigarettes is a lot, without knowing how long a single disposable vape might last.\nLet’s then compare typical nicotine absorption levels for vapers and smokers.\nA typical packet of 20 cigarettes contains around 200 to 300 milligrams (mg) of nicotine (10 to 15 mg per cigarette). However, only a fraction of that is absorbed into the bloodstream (most is lost in smoke). That would mean 1 to 1.5 mg per cigarette, or 20 to 30 mg per pack.\n8\nVapes can contain different levels of nicotine. A vape at the legal limit in the UK contains 20 mg of nicotine per milliliter (ml) of liquid. A typical vape contains 2 ml, which means 40 mg of nicotine for the entire vape.\nAbout half\nof this nicotine is absorbed by the user, which means they receive about 20 mg per vape: similar to the lower end for a pack of 20 cigarettes.\nThe total intake of nicotine depends on how much people smoke. The average daily cigarette consumption among smokers in Great Britain\nis around\n10 to 11 per day. That means a pack of 20 cigarettes — and 20 mg of nicotine — every two days.\nA typical 2ml vape provides around 600 “puffs”. Based on industry and seller estimates (which are obviously rough), this\nwould last\none to two days for a moderate to average user: roughly equivalent to a 20-pack of cigarettes for the average smoker. Just like cigarette usage varies among smokers, so does how quickly a vape can last. Lighter vapers can use one for up to a week, giving them much less nicotine than the average smoker. Heavy vapers can consume a vape in less than a day, and will be taking in large amounts of nicotine.\nIf vapers are using e-cigarettes with lower levels of nicotine — which many do — then they are likely to be taking in slightly less nicotine than they’d get from cigarettes.\n9\nOverall, it is reasonable to conclude that the nicotine exposure for the\ntypical\nvaper is very similar to that of the average cigarette smoker, at least in countries like the UK. This was also the conclusion of an\nevidence review\nfrom the Royal College of Physicians:\n“When smoking a cigarette, typically 8-10 puffs are taken over 8 or 9 minutes, resulting in a peak in blood nicotine levels of around 10–20 ng/ml. E-cigarette users typically inhale in groups of puffs that are smaller than those of cigarette smokers, so peak levels with ad lib use are usually lower than that of smokers. However, on average, daily intake of nicotine is similar for smokers and e-cigarette users.”\n10\nAre e-cigarettes harmful to pregnant women?\nSome studies and headlines have raised concerns about vaping during pregnancy.\nEarlier in the article, I referenced a headline in the newspaper,\nThe Sun\n: “\nDANGER ZONE: Vaping while pregnant is NO SAFER than smoking and can leave your baby ‘deformed’, study suggests\n”.\nLet’s look at the study that this headline is referring to.\n11\nIn it, five pregnant mice were exposed to e-cigarette vapor during pregnancy. Another seven pregnant mice were not. The babies of mice exposed to nicotine tended to exhibit delayed development, with less mature lungs and shorter bones, suggesting a slower skeletal development.\nThis was a study in a very small population of mice. In the experiment, nicotine-laden vapor was pumped into a chamber for four hours a day. A study on the offspring of 12 mice is difficult to extrapolate, and it is surely a leap to assume that the impact on humans would be the same.\nWhat do studies among pregnant women suggest?\nUnfortunately, the quality of available studies is low. In a\nrecent meta-analysis\nof 26 studies, all but one were rated as “poor”.\n12\nThe results of these deficient studies were extremely mixed. In studies comparing pregnancy outcomes between women who vaped and those who used no nicotine products or tobacco, more studies than not found no difference in key metrics such as birthweight, size, or prematurity.\n13\nHowever, many studies that compared women who vaped and those who smoked also found no obvious difference in outcomes.\nBoth of these things cannot be true because we know that smoking tobacco increases pregnancy risks compared to not using nicotine or cigarettes at all.\nIt’s plausible that vaping can increase the risk of some pregnancy outcomes, such as low birthweight or prematurity. Most vapes do contain nicotine, and there is some evidence that nicotine can cross into the placenta and affect fetal development, resulting in lower-birthweight babies. However, vapes do not contain many of the chemicals that make cigarettes so harmful.\nBased on this, we should expect vaping to carry lower risks than cigarettes, but not zero compared to not using any products at all. But without high-quality studies to rely on, it’s hard to say with confidence.\nIt’s no different from a lot of general public health advice on vaping: it can be a great replacement for smokers, but is not recommended for people who don’t smoke (especially pregnant women).\nDo vapers get “popcorn lung”?\nA popular concern has been that e-cigarettes cause a condition known as “popcorn lung”.\nThese concerns have even been\npromoted by\npublic health organizations such as the American Lung Association (this was published in 2016; perhaps they would not promote the same misinformation today, but they also did not issue a correction).\n”Popcorn lung” is formally known as bronchiolitis obliterans, a condition that causes scarring of the lungs and narrowing of the airways. The name “popcorn lung” has been given because workers in a popcorn factory developed the condition. It was thought that inhaling diacetyl, a popular flavoring ingredient, was the cause. This has been disputed, so it’s unclear whether this was the real cause.\n14\nThe reason people suggest that e-cigarettes cause popcorn lung is that diacetyl has been used as a flavoring in vapes.\nHowever, there have been\nno reported cases\nof “popcorn lung” among vapers. When it comes to the comparison with smoking, it is worth noting that cigarette smokers ingest as much as ten times more diacetyl than vapers, and no cases of “popcorn lung” have been reported among tobacco smokers, even those who have smoked heavily for many decades.\n15\nImportantly, diacetyl\nhas been banned\nas an ingredient in e-cigarettes in many countries.\nIs vaping an effective tool to quit cigarettes?\nThe majority of smokers say that they would like to quit.\n16\nMost have tried to, often several times.\n17\nThe problem is that letting go of an addictive habit is hard.\nThis is why I wrote this article: Research suggests that e-cigarettes are the\nmost\neffective quitting tool.\n18\nPeriodically, researchers\npublish\n“Cochrane Reviews” on the effectiveness of vapes as a way to stop smoking.\n19\nCochrane Reviews are independent assessments that attempt to answer important medical questions by examining the scientific literature as a whole.\nTheir latest review — published in 2025 — found “high certainty evidence” that people who used vapes were more likely to stop smoking than those using other nicotine replacement therapies, like patches or gums.\n20\nThis confidence level is rare for these Cochrane assessments.\nThey also judged that there is moderate evidence that vapes that contained nicotine were more effective as a quitting tool than those without.\nThe changes in the last decade are consistent with this finding. If we look at\nsuccess rates\nfor stopping tobacco smoking in Britain, we see a gradual increase over the last decade or so, both among adults and young people.\nThere are several reasons why success rates have increased — for example, the COVID-19 pandemic may have played a role, as people spent less time around other smokers and more people set health goals — but this rise also coincides with the growing popularity of vaping. Before 2013, the most popular “quitting aids” were nicotine replacement therapies (patches or gums), but since then, e-cigarettes have become\nthe most common\nby far.\n“The key points about vaping can be easily summarised. If you smoke, vaping is much safer; if you don’t smoke, don’t vape.” — Chris Whitty, Chief Medical Officer for England\nMore than half of British smokers who quit in the last five years say they\nused e-cigarettes\nin their final, successful attempt. That amounts to 2.4 million people. 60% of those ex-smokers still use vapes, but 32% have since quit vaping too. E-cigarettes have not been the only factor in more people quitting, but they have likely played some role.\nSo far, we’ve talked about the use of vaping to quit cigarettes altogether: the potential benefits or risks of completely substituting one for the other. However, many people who vape continue to smoke. If they are vaping\nand\nstill smoking the same number of cigarettes, then there are no health benefits to using e-cigarettes. Their overall consumption would have actually increased. However, some current smokers\nuse vapes to\nsmoke\nfewer\ncigarettes, even if they don’t cut them out completely. That reduction in cigarette use is still beneficial for their health.\nWhat do current smokers think about the harms of vapes compared to cigarettes?\nIf vapes are an effective and less harmful tool that helps people quit cigarettes, then current smokers need to know this. Survey data suggests that they do not.\nThe chart below shows how different groups view the relative harms of vapes and cigarettes. This survey data comes from\nAction on Smoking and Health\n(ASH).\nDownload\nOn the left, you can see that ex-smokers and current smokers who\ndo\nvape are more likely to say that vaping is\nless\nharmful than cigarettes. That’s not surprising, and might be one of the reasons why they started vaping in the first place.\nBut, if you ask smokers who do not currently vape — they either used to, or have never used them — they say the opposite: that e-cigarettes are\nmore\nharmful. This is shown in the two bars on the right.\nE-cigarettes are the most effective quitting tool.\nThese differences might partly explain why\nsmokers\nappear to be increasingly negative towards vapes, which we saw in the opening of this article. Those who smoked in 2015 and thought vapes would be effective ways to quit no longer smoke, and therefore weren’t surveyed as “current smokers” in 2025. That leaves those who have been more reluctant to use tools like vaping to quit.\nThis perception gap among current smokers matters. There is evidence that people’s perceptions of the health risks of vaping affect their willingness to switch.\n21\nFor example, in a study of young adult smokers, those who knew vaping was less harmful were more likely to have made the switch from cigarettes to e-cigarettes at a follow-up six years later.\n22\nWhen smokers are asked why they haven’t tried e-cigarettes, around one-third either say it’s because they are concerned that they’re not safe enough or don’t want to substitute one addiction for another. See the chart below, which shows the\nmain\nreason cigarette smokers give for not trying e-cigarettes.\nAn additional 13% are skeptical that vapes would help them quit or cut back on cigarettes. Indeed, e-cigarettes won’t work for anyone, but as we saw earlier, they are the most effective tool available.\nThat means around half of smokers who are unwilling to try e-cigarettes give reasons that are at odds with much of the scientific evidence and public health advice.\nDownload\nHow common is vaping among people who have never smoked?\nTobacco smokers who switch to e-cigarettes might see large health benefits, but one concern is that many people who have never smoked take up vaping. If the health risks of vaping are not\nzero,\nthen that could represent a public health problem.\nIs it common for people who have never smoked to take up vaping?\nAs a reminder, just under 10% of British adults use vapes (we’ll discuss vaping among young people soon). That’s around 4.7 million people.\nYou can see this breakdown of vapers in the chart below, based on their smoking history.\nThe majority are either current or ex-smokers. There are around 300,000 people who vape who are “never smokers”, which is a relatively small share of the total.\nThis represents around 1% of people in Great Britain who have never smoked and now vape. 99% of “never smokers” do not vape, but this figure does appear to be growing quickly.\nAs smoking rates decline and younger generations come through, the share of vapers who are “never smokers” will increase over time — and could eventually become the dominant group.\nHow common is vaping among young people, and how often is it a “gateway” to tobacco?\nThe biggest concern that most people raise about vaping is its impact on young people. This is for good reasons.\nFirst, most people who become addicted to smoking start in their teenage years.\n23\nSecond, young people are more likely to vape than people in older age groups. The chart below plots the share of people in each age group who vape in the UK. Nearly 30% of those aged 16–24 vape (not necessarily daily, but at least occasionally), compared to 14% of those in their late thirties or 10% in their late fifties.\nDownload\nLet’s then go through the main concerns.\nThe first is that e-cigarettes are a “gateway” to tobacco. Teenagers start smoking vapes, then progress to cigarettes, which they get hooked on. Given the huge health risks of smoking, it would be extremely detrimental if vaping were a key driver.\nIt’s true that young people who vape are more likely to go on to smoke than young people who don’t vape. This is well-established within the scientific literature.\n24\nWhat’s less clear is the\ncausal\nlink; it may not be that vaping is what\ncaused\nthem to start smoking. It could be that, for some underlying reason, young people who are more likely to start vaping are the same young people who are more likely to take up smoking. These young people who vape are possibly more prone to “risky” or addictive behaviors than those who don’t, and that likely makes them more open to smoking cigarettes, too.\nOverall, the literature on this causal relationship is inconclusive. One\nrecent study\n, published in\nAddiction\n, suggested with “very low certainty evidence” that, at the population level, an increase in vaping rates is associated with a decline in smoking rates.\n25\nThis study has received pushback from other researchers in the same journal, who argue that the available evidence — or lack of reliable evidence — does not support the conclusion that vaping actually helps to reduce future smoking among young people.\n26\nSince very few reviews tackle this “causal” question and those that do attempt to, at best, find only “very low certainty evidence” of vapes displacing cigarettes (which is contested), it’s hard to know exactly how the rise of vaping has affected young people’s behaviors around cigarettes.\nWe can gain\nsome\ninsight into this question through population-level data on smoking rates. If vaping\nwere\na widespread reason for young people to start smoking tobacco, then we might expect to see more youngsters using cigarettes over time. Vaping rates have increased quite dramatically among young people, and\nwith almost 20%\nof 15-year-olds regularly using them, have reached a level where we would expect to see an effect in the smoking data.\nIn England, we don’t see such an effect: smoking rates\ncontinue to fall\n(or hold constant at their lowest levels in decades). You can see this in the chart below. Only a few per cent of 15-year-olds smoke regularly, which is a huge drop from the 1980s, when it was over 20%. The share of pupils (aged 11 to 15) who have\never\nsmoked has also\nfallen dramatically\n, from 50% to 12% since the 1990s. The same is true for other nations in Great Britain.\nTo be clear: this doesn’t mean vaping has been a driver of this decline in smoking. Smoking rates have fallen for a variety of reasons, and the initial drop predates e-cigarettes. What it does suggest is that any potential “gateway” effect has not\nyet\nbeen large enough to offset the decline in smoking.\nThe number of cigarettes smoked per regular smoker has also continued to decline, from 39 in 2006 to 9. Smoking less still has many health benefits, even if people smoke regularly.\nThese trends are reflected in the United States, where smoking rates among young people have continued to fall, reaching a low of 1.7% high school students in 2024. As Jamie Hartmann-Boyce, assistant professor of health policy and management at the University of Massachusetts Amherst,\nsays\n:\n“The smoking rates among kids have declined steeply, and whether or not that's due to vaping or something else is up in the air. But it's difficult to argue that—in the U.S. population—youth vaping is en masse causing kids to smoke. The data doesn't support that so far.”\nEvidence from New Zealand is still contested, with some researchers suggesting that vaping is responsible for a\nslowdown in the decline\nof youth smoking rates.\n27\nOthers dispute this.\n28\nTo me, there is a clear argument to be made that e-cigarettes can positively impact many adults. In older populations, the majority of people who vape are ex- or current smokers; only a small percentage have never smoked. However, the net impact on teenagers is less clear. That’s because most of those who vape are teens who haven’t had time to become smokers. It’s hard, then, to know the counterfactual of how many would go on to smoke cigarettes if vapes didn’t exist.\nThat leads to another concern: that a large population of young people is becoming addicted to nicotine through vaping. Many are those who would never have smoked in the pre-vaping era. I think this is broadly true. The significance of this problem depends on how harmful we think nicotine is on its own. It is far less dangerous than many of the other ingredients in cigarettes, but most health experts would argue that it isn’t risk-free.\nThat’s why public health advice almost always tries to strike a balance between recommending e-cigarettes as an effective tool to quit tobacco without encouraging vaping use among young people and non-smokers. The challenge for public communication is that emphasizing the small health risks of vaping for non-smokers or young people can make it seem particularly “unsafe” — or even more harmful than cigarettes — which isn’t true.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nSmoking is uniquely bad for human health, and almost anything that can help people quit will improve public health\nI started this article with survey data; it showed that most British smokers think that vaping is more or equally harmful than cigarettes.\nThe evidence is clear that this is incorrect.\nA smoker who replaces cigarettes with e-cigarettes will reduce their health risks and potentially live a longer and healthier life. The fact that many smokers believe the opposite means that many will not try e-cigarettes as a way to quit. That’s a shame because e-cigarettes are the most effective quitting tool available.\nA smoker who replaces cigarettes with e-cigarettes will reduce their health risks and potentially live a longer and healthier life.\nAt the same time, given the current evidence we have about vaping rates among young people, policymakers have very valid concerns about how to best promote e-cigarettes as a safer cessation tool without actively encouraging millions of teens to start. Striking this balance is difficult, but crucial. The stakes are high.\nSmoking is the leading risk factor for early death in many countries and kills\nmore than six million\npeople every year. The mistaken belief that vaping is just as harmful means fewer smokers make the switch that could save their lives. Closing this perception gap could save millions.\nUpdate note\nA previous version of this article referenced one study as being a Cochrane Review. While this study and its protocol\nwere registered\nin the Cochrane Library, the final study\nwas published\nin the journal\nAddiction\n(not in Cochrane). This has been corrected.\nThe section on vaping associations in young people has also been updated to reflect disagreements within the scientific literature on the causal impact of vaping on youth smoking rates.\nAcknowledgments\nMany thanks to Max Roser, Edouard Mathieu, Simon van Teutem, and Fiona Spooner for their valuable suggestions and feedback on this article, and to Marwa Boukarim for work on its visualizations.\nContinue reading on Our World in Data\nSmoking: How large of a global problem is it? And how can we make progress against it?\nEvery year, around eight million people die prematurely as a result of smoking. But there are things we can do to prevent this.\nSmoking\nTobacco smoking is one of the world’s largest health problems today.\nHow do researchers estimate the death toll caused by each risk factor, whether it’s smoking, obesity, or air pollution?\nRisk factors are important to understand because they can help us identify how to save lives. How do researchers estimate their impact?\nEndnotes\nThis additional study, based on more than 28,000 survey respondents in England, found the same. From 2014 to 2023, there was a substantial increase in the share that said vapes were more harmful. Most adult smokers in 2023 thought e-cigarettes were at least as harmful as cigarettes.\nJackson, S. E., Tattan-Birch, H., East, K., Cox, S., Shahab, L., & Brown, J. (2024). Trends in harm perceptions of E-cigarettes vs cigarettes among adults who smoke in England, 2014-2023. JAMA Network Open, 7(2), e240582-e240582.\nAs I’ll discuss later, part of this may be a selection effect: many of the smokers who thought vapes were\nless\nharmful in 2015 have perhaps\nstopped\nsmoking since then. So the remaining smokers in 2025 include those who have been more reluctant or have struggled to quit. They might be less willing to try alternatives that could help them quit (and might possibly be trying to find justifications for why).\nIf you think that your teen vaping is better than them smoking – think again\n—\nThe Telegraph\nDANGER ZONE: Vaping while pregnant is NO SAFER than smoking and can leave your baby ‘deformed’, study suggests\n—\nThe Sun\nVaping 'more dangerous than smoking', bombshell first-of-its-kind study reveals - it raises risk of THREE deadly diseases\n— Daily Mail\nRecent data on e-cigarette use\nsuggests that\naround 10% of adults in the UK use them, with just under 8% using them daily. Cigarette smoking is only slightly higher,\nat around\n12%.\nOza, S., Thun, M. J., Henley, S. J., Lopez, A. D., & Ezzati, M. (2011).\nHow many deaths are attributable to smoking in the United States? Comparison of methods for estimating smoking-attributable mortality when smoking prevalence changes\n. Preventive medicine, 52(6), 428-433.\nRoyal College of Physicians.\nE-cigarettes and harm reduction: An evidence review\n. RCP, 2024.\nHere’s\nPublic Health England\n: “Alternative nicotine delivery devices such as vaping products can play a vital role in reducing the huge health burden caused by cigarette smoking, which remains: (i) the largest single risk factor for death and years of life lived in ill-health globally, (ii) a leading cause of health inequalities in England, and (iii) the second most important risk factor for death and disability-adjusted life years globally.”\nHere is\nthe Royal College of Physicians\n: “E-cigarettes should be promoted as an effective means of helping people who smoke to quit smoking tobacco.”\nHealth Canada\n: “If you've tried approved methods to quit and are still smoking, switching completely to vaping nicotine is less harmful than continuing to smoke. Youth and people who don't smoke, shouldn't vape.”\nHukkanen, J., Jacob III, P., & Benowitz, N. L. (2005). Metabolism and disposition kinetics of nicotine. Pharmacological reviews.\nAs you can see from the graph on “E-liquid strength” in\nthese surveys\nin England, many vapers use e-cigarettes with a nicotine content of 6mg, 7mg, or 12mg, which is lower than the 20mg standard we assumed above.\nThe Royal College of Physicians quotes 8 to 10 puffs over 8 or 9 minutes. Having spoken to smokers and looking at more online references, this seems conservative. That means the nicotine intake from cigarettes could be higher.\nOzekin, Y. H., Saal, M. L., Pineda, R. H., Moehn, K., Ordonez-Erives, M. A., Figueroa, M. F. D., ... & Vladar, E. K. (2023). Intrauterine exposure to nicotine through maternal vaping disrupts embryonic lung and skeletal development via the Kcnj2 potassium channel. Developmental biology.\nUssher, M., Fleming, J., & Brose, L. (2024). Vaping during pregnancy: a systematic review of health outcomes. BMC Pregnancy and Childbirth.\nCohn, A. M., Elmasry, H., Wild, R. C., Johnson, A. L., Abudayyeh, H., Kurti, A., & Coleman-Cowger, V. H. (2023). Birth outcomes associated with E-cigarette and non–E-Cigarette tobacco product use during pregnancy: an examination of PATH Data Waves 1–5. Nicotine and Tobacco Research.\nGalbraith, D., & Weill, D. (2009). Popcorn lung and bronchiolitis obliterans: a critical appraisal. International archives of occupational and environmental health.\nLogue, J. M., Sleiman, M., Montesinos, V. N., Russell, M. L., Litter, M. I., Benowitz, N. L., ... & Destaillats, H. (2017). Emissions from electronic cigarettes: assessing vapers’ intake of toxic compounds, secondhand exposures, and the associated health impacts. Environmental science & technology.\nIn the UK,\naround two-thirds\nof smokers say they want to quit. The\nsame is true\nin the United States. And,\nin Australia\n.\nIn any\ngiven year\n, more than one-third of British smokers try to quit. Over years or even decades, more than half of smokers will likely have tried at least once.\nJackson, S. E., Brown, J., Buss, V., & Shahab, L. (2025). Prevalence of popular smoking cessation aids in England and associations with quit success. JAMA Network Open.\nJackson, S. E., Brown, J., Buss, V., & Shahab, L. (2025). Prevalence of popular smoking cessation aids in England and associations with quit success. JAMA Network Open.\nHere is the latest review, and its predecessor, published in 2022.\nLindson, N., Butler, A. R., McRobbie, H., Bullen, C., Hajek, P., Wu, A. D., ... & Hartmann-Boyce, J. (2025). Electronic cigarettes for smoking cessation.\nCochrane Database of Systematic Reviews\n.\nHartmann-Boyce J, Lindson N, Butler AR, McRobbie H, Bullen C, Begh R, Theodoulou A, Notley C, Rigotti NA, Turner T, Fanshawe TR, Hajek P (2022). Electronic cigarettes for smoking cessation.\nCochrane Database of Systematic Reviews\n.\nLindson, N., Butler, A. R., McRobbie, H., Bullen, C., Hajek, P., Wu, A. D., ... & Hartmann-Boyce, J. (2025). Electronic cigarettes for smoking cessation. Cochrane Database of Systematic Reviews.\nMcNeill, A, Simonavičius, E, Brose, LS, Taylor, E, East, K, Zuikova, E, Calder, R and Robson, D (2022). Nicotine vaping in England: an evidence update including health risks and perceptions, September 2022. A report commissioned by the Office for Health Improvement and Disparities. London: Office for Health Improvement and Disparities.\nEast, Katherine, et al. (2025). Perceived Harm of Vaping Relative to Smoking and Associations With Subsequent Smoking and Vaping Behaviors Among Young Adults: Evidence From a UK Cohort Study. Nicotine and Tobacco Research.\nThe US Centers for Disease Control and Prevention (CDC)\nstates that\n9 out of 10 adults who smoke daily tried their first cigarette before the age of 18.\nIn England, two-thirds of smokers\nstart before\nthe age of 18, and 83% before the age of 20.\nAn umbrella review — which is bacially a review of reviews — found that most studies found a positive association between youth vaping and smoking. They found that young people who vape are around three times as likely to smoke cigarettes as those who don’t vape.\nWhile they discuss a “causal relationship” in this paper, it’s difficult to demonstrate this causality since most of the underlying studies are observational, and often contain confounding factors. See\nmore discussion\non the merits and limitations of this umbrella review from experts.\nGolder, S., Hartwell, G., Barnett, L. M., Nash, S. G., Petticrew, M., & Glover, R. E. (2025). Vaping and harm in young people: umbrella review. Tobacco Control.\nA previous version of this article referenced this study as being a Cochrane Review. While this\nstudy and its protocol\nwere registered in the Cochrane Library, the final study was published in the journal\nAddiction\n(not as a Cochrane Review).\nBegh, R., Conde, M., Fanshawe, T. R., Kneale, D., Shahab, L., Zhu, S., ... & Hartmann‐Boyce, J. (2025). Electronic cigarettes and subsequent cigarette smoking in young people: A systematic review.\nAddiction\n.\nEgger, S., & McKee, M. (2025). Unreliable evidence from problematic risk of bias assessments: Comment on Begh et al.,‘Electronic cigarettes and subsequent cigarette smoking in young people: A systematic review’.\nAddiction\n.\nYou can also read the original authors’ response to this critique.\nHartmann-Boyce, J., Begh, R., Conde, M., Shahab, L., Jackson, S. E., Pesko, M. F., ... & Lindson, N. (2025). Response to comment from Egger and McKee entitled'Unreliable evidence from problematic risk of bias assessments: Comment on Begh et al., 'Electronic cigarettes and subsequent cigarette smoking in young people: A systematic review\"'.\nAddiction\n.\nEgger, S., David, M., McCool, J., Hardie, L., Weber, M. F., Luo, Q., & Freeman, B. (2025). Trends in smoking prevalence among 14–15-year-old adolescents before and after the emergence of vaping in New Zealand; an interrupted time series analysis of repeated cross-sectional data, 1999–2023. The Lancet Regional Health–Western Pacific.\nWalker, N., Parag, V., Wong, S. F., Youdan, B., Broughton, B., Bullen, C., & Beaglehole, R. (2020). Use of e-cigarettes and smoked tobacco in youth aged 14–15 years in New Zealand: findings from repeated cross-sectional studies (2014–19). The Lancet Public Health.\nChan, G. C., Sun, T., Vu, G., & Hall, W. (2025). Caution is needed when interpreting interrupted time series findings on vaping and smoking. The Lancet Regional Health–Western Pacific.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2025) - “While vaping is not risk-free, it is less harmful than tobacco” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-090244/vaping-vs-smoking-health-risks.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-vaping-vs-smoking-health-risks,\nauthor = {Hannah Ritchie},\ntitle = {While vaping is not risk-free, it is less harmful than tobacco},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-090244/vaping-vs-smoking-health-risks.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "vaping-vs-smoking-health-risks", "source_url": "https://ourworldindata.org/vaping-vs-smoking-health-risks", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Answers to some of the most frequently asked questions about vaping and its effects.", "numeric_mentions": ["3,", "2025", "1", "2", "3", "4", "1970", "21", "5", "6", "2000", "7", "50", "20", "200", "300", "10", "15", "1.5", "30", "8", "40", "11", "600", "9", "12", "26", "13", "2016", "14", "16", "17", "18", "19", "2013,", "2.4 million", "60%", "32%", "2015", "22", "13%", "10%", "4.7 million", "300,000", "1%", "99%", "23", "30%", "24", "14%", "25", "20%", "1980", "50%", "12%", "1990", "39", "2006", "1.7%", "2024", "27", "28", "28,000", "2014", "2023,", "2023", "8%", "2011", "52", "428", "433", "2005", "2009", "2017", "2022", "18,", "83%", "1999", "2020", "15 years"], "numeric_evidence": [{"grapher_slug": "number-of-deaths-by-risk-factor", "source_url": "https://ourworldindata.org/grapher/number-of-deaths-by-risk-factor", "parse_error": "Failed to fetch https://ourworldindata.org/grapher/number-of-deaths-by-risk-factor.csv"}, {"title": "Number of adults in Great Britain who vape, by smoking history", "source_url": "https://ourworldindata.org/grapher/british-vapers-by-smoking-history.csv", "file_type": "csv", "columns": ["Entity", "Year", "Never smokers", "Ex-smokers", "Current smokers"], "row_count_total": 12, "rows_head": [{"Entity": "Great Britain", "Year": "2012", "Never smokers": "30000", "Ex-smokers": "200000", "Current smokers": "600000"}, {"Entity": "Great Britain", "Year": "2013", "Never smokers": "80000", "Ex-smokers": "400000", "Current smokers": "900000"}, {"Entity": "Great Britain", "Year": "2014", "Never smokers": "40000", "Ex-smokers": "700000", "Current smokers": "1400000"}, {"Entity": "Great Britain", "Year": "2015", "Never smokers": "50000", "Ex-smokers": "1000000", "Current smokers": "1600000"}, {"Entity": "Great Britain", "Year": "2016", "Never smokers": "50000", "Ex-smokers": "1400000", "Current smokers": "1500000"}, {"Entity": "Great Britain", "Year": "2017", "Never smokers": "90000", "Ex-smokers": "1500000", "Current smokers": "1300000"}, {"Entity": "Great Britain", "Year": "2018", "Never smokers": "130000", "Ex-smokers": "1600000", "Current smokers": "1400000"}, {"Entity": "Great Britain", "Year": "2019", "Never smokers": "220000", "Ex-smokers": "2000000", "Current smokers": "1500000"}, {"Entity": "Great Britain", "Year": "2020", "Never smokers": "90000", "Ex-smokers": "1900000", "Current smokers": "1200000"}, {"Entity": "Great Britain", "Year": "2021", "Never smokers": "180000", "Ex-smokers": "2400000", "Current smokers": "1100000"}, {"Entity": "Great Britain", "Year": "2022", "Never smokers": "350000", "Ex-smokers": "2500000", "Current smokers": "1500000"}, {"Entity": "Great Britain", "Year": "2023", "Never smokers": "320000", "Ex-smokers": "2700000", "Current smokers": "1700000"}], "rows_tail": [], "sampling_note": "Stored first 12 rows and last 12 rows when the table is larger.", "grapher_slug": "british-vapers-by-smoking-history", "metadata_url": "https://ourworldindata.org/grapher/british-vapers-by-smoking-history.metadata.json", "chart_title": "Number of adults in Great Britain who vape, by smoking history", "chart_subtitle": "\"Smoking\" refers to smoking tobacco cigarettes.", "chart_note": null, "chart_citation": "Action on Smoking and Health (ASH)", "original_chart_url": "https://ourworldindata.org/grapher/british-vapers-by-smoking-history", "owid_column_metadata": {"Number of \"never smokers\" who use vapes": {"titleShort": "Never smokers", "titleLong": "Never smokers", "unit": "", "timespan": "2012-2023", "type": "Integer", "owidVariableId": 1104702, "shortName": "number_vapes_never_smokers", "lastUpdated": "2025-09-08", "citationShort": "UK Office for National Statistics (ONS) and Action on Smoking and Health (ASH) – processed by Our World in Data", "citationLong": "UK Office for National Statistics (ONS) and Action on Smoking and Health (ASH) – processed by Our World in Data. “Never smokers” [dataset]. 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A \"regular smoker\" is defined as smoking at least one cigarette per week.", "chart_note": null, "chart_citation": "NHS England (2024)", "original_chart_url": "https://ourworldindata.org/grapher/smoking-rates-young-people-england", "owid_column_metadata": {"Share of 15 year olds who smoke regularly": {"titleShort": "Regular smokers (15 years)", "titleLong": "Regular smokers (15 years)", "shortUnit": "%", "unit": "%", "timespan": "1982-2023", "type": "Integer", "owidVariableId": 1108425, "shortName": "_15yo_regular_smokers", "lastUpdated": "2025-10-09", "citationShort": "NHS England (2024) – processed by Our World in Data", "citationLong": "NHS England (2024) – processed by Our World in Data. “Regular smokers (15 years)” [dataset]. 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{"raw_link": "https://ourworldindata.org/material-footprint-limitations", "title": "Resource use matters, but material footprints are a poor way to measure it", "context": "Home\nMetals & Minerals\nResource use matters, but material footprints are a poor way to measure it\nAdding up the weight of very different materials doesn’t tell us about their scarcity, environmental, or socioeconomic impacts.\nBy\nHannah Ritchie\nOctober 20, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nWhat do a tonne of potatoes, gravel, coal, and copper have in common? Not much, except that they all weigh the same, and are treated exactly the same in a metric called the “material footprint”.\nThe material footprint sums up the weight of all the resources used within an economy. So if a country’s material footprint is 60 million tonnes, it extracts 60 million tonnes of “stuff” per year. This includes both non-renewable resources like metals and fossil fuels, and “renewable” ones like crops and wood. The scarcity or environmental impact of different resources is not considered, so every kilogram of stuff is considered just as important as every kilogram of something else.\n1\nNote that the “material footprint” can differ from “domestic material consumption” as it attempts to adjust for traded goods, capturing total\nconsumption\nwithin an economy.\nSome readers may not be familiar with this metric, but it has gained increasing popularity in environmental discussions and international policy. It’s included as a key metric in the United Nations’ Sustainable Development Goals, which is why we have charts on it in our\nSDG Tracker\n. This metric is tracked in per capita terms and is shown in the chart below.\nIt is also used in the\nplanetary pressures index\nby the UN Development Programme, and you’ll find many reports on it by the OECD, European agencies, and others.\n2\nHowever, for reasons I’ll explain in this article, I don’t find this metric helpful in understanding the sustainability of resource use or its environmental impacts. I fear that rather than helping us tackle some of our biggest environmental and resource challenges, it obscures our understanding and takes our focus away from the most pressing problems.\nIt’s\nnot\nthat resource use doesn’t matter — it’s that the material footprint fails to capture\nwhy\nThere are at least three reasons why we should be measuring and monitoring our resource use:\nTo see if we risk\nrunning out of a particular resource\n. If we’ve depleted the world’s copper, cobalt, or lithium and are at risk of running out, then we need to know about it. But to assess this, we need to know how much of that specific material we’re using,\nand\nhow much is left. We’d need to know how much copper, cobalt, or lithium we use each year and the state of our global reserves. To do that, we need to look at specific mineral datasets (which exist and are published by organizations such as the US Geological Survey or British Geological Survey). We have\na lot of this data\non Our World in Data. This is also true for “natural” ecosystems or populations we’re depleting. If we’re concerned about the depletion of Atlantic bluefin tuna, we must look at how much of that population or species we’re catching, how many are left, and how quickly populations regenerate. Our team\nalso shows this data\non fish catch and depletion for specific species. Looking at a metric that throws the weight of tuna together with wood, coal, and gravel does not help understand the scarcity of any of them.\nTo measure the environmental impact of e\nxtracting and consuming resources\n. Mining uses land, can disrupt landscapes, and cause pollution. Burning fossil fuels generates carbon emissions and air pollution. Beef production can drive deforestation and biodiversity loss. These impacts are extremely important to monitor (we cover most, if not all, of them here on Our World in Data). But material footprints don’t tell us much about the environmental impact. The production of a tonne of gravel does not have the same impact as a tonne of uranium or pork.\nTo measure the socioeconomic consequences of\ne\nxtracting and consuming resources\n. Mining can be associated with unsafe working practices, and some supply chains rely on exploitative labor. But, again, the material footprint does nothing to help us identify and improve these conditions. Cobalt and gold mining are associated with poor working conditions in\ncountries\nlike the Democratic Republic of Congo, but material footprints don’t tell us that. In fact, many of these precious minerals are extracted in relatively small quantities, so they barely show on a whole-economy material footprint. Some of the\nmost documented\nexploitative practices have been in textile supply chains. In terms of material footprint, clothing has a very\nlow\n“material intensity”, so judging by this metric, it would be deemed a more “responsible” way to spend your money.\nResource use\ndoes\nmatter for these reasons, but the material footprint, at best, captures them poorly and, at worst, hides some of the most negative impacts.\nMost of our material footprint comes from non-metallic minerals and biomass\nThe chart below shows the breakdown of the European Union's material footprint. More than 70% is made up of biomass (our food and wood for industry and construction) and non-metallic minerals for construction and infrastructure.\nThis should already raise some questions.\nA tonne of gravel does not have the same impact as a tonne of uranium or pork.\nBiomass is a\nrenewable\nresource (if managed sustainably). I can grow and harvest potatoes, tomatoes, and wheat today and then replant them for next year. The “net” change in the biomass we produce is often zero over longer timescales; it’s not being depleted like other resources. To compare this in terms of\nweight\nto fossil fuels and other minerals, which are\nnot\nrenewable, mixes materials that are too different to be bundled together.\nNon-metallic minerals, such as gravel — which dominate Europe’s footprint — do not have zero environmental impact. Mining for materials such as sand can disrupt ecosystems, disturb riverbeds, and affect natural flood defenses. However, they tend to have a much lower environmental impact than the other categories. As the European Environment Agency\nputs it\n:\n“Non-metallic minerals account for a large part of the total material footprint, yet they have less environmental and climate impact than metals and fossil fuels. This is because they are mostly composed of inert materials such as gravel, limestone.”\nIf we consider a large material footprint problematic, then it follows that we should focus on using less sand, gravel, wood, and limestone. However, this would achieve far less in addressing most of the resource constraints and environmental and social impacts that we care about than tackling other (smaller) categories like fossil fuels and particular metal ores.\nDownload\nHousing and food are the two sectors behind most of the EU’s material footprint\nSince non-metallic minerals and biomass dominate the EU’s material footprint, we shouldn’t be surprised that housing and food have the biggest impact when we look at the footprint by the end-use sector.\nThe chart below shows this breakdown: more than half (52%) of the total footprint is linked to housing, and 19% to food. These two sectors alone account for almost three-quarters of the material footprint. Again, most of this is from non-metallic minerals like gravel and sand, and biomass for food (mostly crops).\nA lot of the things some might classify as “non-essential” goods, such as cars, stuff we buy for our homes, and clothing, are small by comparison.\n3\nDownload\nIt’s interesting to read the European Environment Agency’s\nanalysis of\nwhat this breakdown means for policy and action.\nOn housing, the agency states:\n“The very high material footprint of housing means that no significant reduction in the EU’s material footprint can be achieved without addressing our built environment. On the other hand, the environmental benefit from avoiding extraction of non-metallic minerals is relatively small.”\nSo, to substantially reduce our material footprint, we need to rethink our homes — maybe the materials we use to build or their size — but the environmental benefits of doing so are pretty small. Again, that raises the question of why we would make this the focal point of action if there are few benefits.\nOn food, the policy implications are also unclear:\n“The potential for a radical reduction of the food sector’s material footprint is rather low as it is composed of food items essential to our societies. However, dietary shifts and the management of food waste can contribute to reducing the food sector’s material footprint”.\nWe need food to eat, so\nsubstantially\ncutting back is hard. The two obvious proposals are reducing food waste and shifting to more plant-based diets (less material-intensive, because you don’t have to produce food for the animals first). These are both strong recommendations that I have written a lot about before.\nHere is\nmy article\non food waste, and\nhere\nyou’ll find many articles and datasets about the environmental benefits of dietary change.\nBut what’s crucial is that there is already a\nlong list of reasons\nwhy we would want to make these changes: the fact that food is responsible for up to one-third of the world’s greenhouse gas emissions; that it uses half of the world’s habitable land; that it’s the leading driver of water use, water pollution, biodiversity loss, and deforestation; and the fact that we raise and slaughter more than 70 billion land\nanimals for food\nevery year. All these problems can be improved by shifting to more plant-based diets and reducing food waste.\nOf all the arguments to make this shift, the “material footprint” is the least convincing. When it comes to sustainability, it’s much less obvious why I should care more about the\nweight\nof the amount of wheat, corn, or lentils we grow than I do about the ecosystems destroyed, forests cut down, animals raised under cruel conditions, or the rivers polluted.\n“Luxury” goods we associate with overconsumption tend to have a relatively low material footprint\nA common explanation for measuring material footprints is that many of us overconsume and need to do so less. On a personal level, I am also very conscious of my consumption. I think carefully about what I buy and its impact. I still wear clothes that are many, many years old, and I hold on to my mobile phone for as long as I can.\nWhen I speak to others about this, they often mention the same items and industries: consumer technology and “fast fashion” are always in the spotlight.\nBut, surprisingly, thinking carefully about these purchases is not advice that follows from looking at material footprints. The chart below shows the breakdown of the EU’s material consumption, this time by final product. We see that these consumer products account for a very small fraction of the total footprint.\n4\nTextiles and clothing (which includes footwear and non-clothing textiles) account for only 1% of the total. Computers and consumer electronics are just 0.8%. Surprisingly, rubber and plastic products are just 0.2%.\nDramatically reducing our use of items traditionally associated with excess consumption would barely change our material footprint. My sense is that most people are unaware of this.\nYou might also notice that fishing is just 0.1% of the EU’s material footprint. This suggests that fish consumption — and overfishing — is basically not a problem, which is false.\nThe material footprint leads us to counterintuitive policy recommendations that many environmentalists would strongly object to. Here’s the European Environment Agency\nagain\n:\n“Services require the lowest material use per euro spent among all domains, followed by clothing and household goods. Therefore, consumption patterns directly affect the EU’s material footprint and one way to reduce it is to promote expenditure patterns that are less material intensive.”\nSpending more money on services than physical “stuff” makes sense if we want to reduce our material footprints. However, since clothing and households also have a low material intensity, we could also reduce our footprint by spending much more on clothes, televisions, phones, and other consumer goods and less on essentials such as food and housing.\n“Spend more of your money on clothes and iPhones” to minimize your environmental footprint is not advice I’ve heard before (and is not advice\nI’d\ngive either). Yet this is what the European Environment Agency implies when it suggests “promoting expenditure patterns that are less material-intensive”. That advice comes directly from the results of material footprints.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nSustainability means much more than just carbon footprints, so we should track lots of environmental impacts\nOne motivation for measuring material footprints was to extend the focus of sustainability beyond carbon footprints. I share this sentiment. I\nwrote a book\nabout seven environmental problems, and climate change was just one of them.\nHowever, there are better ways to understand these environmental concerns than summing up the weight of the different resources we use.\nI have written many articles on measuring sustainability and environmental impacts that go beyond carbon emissions. At\nOur World in Dat\na, we have deliberately made our environment section extensive (see our list of topics below). We’ve covered\nland use\n,\nwater use\n,\neutrophication\n,\ndeforestation\n,\nfertilizer overuse\n,\nbiodiversity loss\n,\nfood waste\n, and much more.\nDownload\nResource use matters, and we need to monitor issues such as the risk of running out of some materials or the mining and socioeconomic impacts of others. There is a lot that we can do to make our economies more material-efficient and to shift from a model of continual extraction to a more circular one where we reuse materials.\n5\nI’ve\nwritten about this\nopportunity before as we shift from fossil fuels to low-carbon energy.\nAdding up the weight of very different materials doesn’t tell us about their scarcity, environmental, or socioeconomic impacts.\nMany metrics — like the ones listed in the screenshot above — do a better job at capturing the negative impacts. If we’re concerned about the scarcity of copper, we should be tracking how much we use and how much is available. If we’re worried about the environmental and social impacts of mining — water use, pollution, exploitation in supply chains — then we should be tracking these directly. However, the material footprint can downplay these issues because metal ores and fossil fuels make up a small fraction of the total in regions like the EU.\nDespite the many limitations of the material footprint, almost all of the underlying individual indicators are useful. To calculate the final material footprint, researchers need to know the tonnes of copper, gold, cobalt, gravel, wood, and Atlantic tuna. On their own, these datasets are extremely valuable and could help us focus on specific resource challenges. It’s when they’re combined into a single number that this value is lost.\nComparing resource quantities\nwithin\na common context can also be informative. For example, knowing how much mined materials we’ll need\nfor different energy sources\ncan help us understand some of the implications of the energy transition. The same applies to the amount of crops (including feed) required for different dietary choices.\nKnowing how much uranium the world uses each year is useful. Creating a metric that suggests it should be treated the same as bananas is not.\nAcknowledgments\nThank you to Max Roser and Edouard Mathieu for their valuable comments and suggestions on this article and its visualizations.\nContinue reading on Our World in Data\nLiving Planet Index: what does it really mean?\nThe Living Planet Index is the biodiversity metric that always claims the headlines. It’s often misinterpreted. How should we understand it?\nWhich countries have the critical minerals needed for the energy transition?\nAn overview of the distribution of critical minerals for clean energy.\nDo we only have 60 harvests left?\nClaims 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.\nEndnotes\nSome models and calculations apply conversion factors, such as ore-to-metal ratios for minerals and metals. However, the point remains that things are considered equally, only based on mass.\nUnited Nations Statistics Division.\nGoal 12: Ensure sustainable consumption and production patterns\n.\nUNDP Human Development Report.\nPlanetary pressures–adjusted Human Development Index (PHDI)\n. United Nations Development Programme.\nOECD,\nMaterial Consumption\n.\nOf course, some of these goods would be considered “essential”: we need some clothes, basic resources in our homes, and transport (even if that’s in the form of public transport or cycling), but many argue that these are sectors where we “overconsume” and some purchases have become non-essential.\nThis data\ncomes from Eurostat\n.\nIn fact, some footprinting metrics, such as “Domestic material consumption” would still count one tonne of recycled material within the material footprint, hence increasing recycling rates and circularity would not actually help to reduce the footprint. Others, such as the material footprint or “Raw material consumption”, do attempt to treat raw extraction and recycled materials separately. However, these flows can be difficult to separate, especially where data availability is challenging.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2025) - “Resource use matters, but material footprints are a poor way to measure it” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-093348/material-footprint-limitations.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-material-footprint-limitations,\nauthor = {Hannah Ritchie},\ntitle = {Resource use matters, but material footprints are a poor way to measure it},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-093348/material-footprint-limitations.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "material-footprint-limitations", "source_url": "https://ourworldindata.org/material-footprint-limitations", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Adding up the weight of very different materials doesn’t tell us about their scarcity, environmental, or socioeconomic impacts.", "numeric_mentions": ["20,", "2025", "60 million", "1", "2", "70%", "52%", "19%", "3", "70 billion", "4", "1%", "0.8%", "0.2%", "0.1%", "5", "60", "100,", "60,", "30 years", "12", "20260518", "093348", "18,", "2026"], "numeric_evidence": [{"title": "Material footprint per capita", "source_url": "https://ourworldindata.org/grapher/material-footprint-per-capita.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC"], "row_count_total": 23, "rows_head": [{"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "9.34"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "9.36"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "9.43"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "9.69"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "10.09"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "10.34"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "10.68"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "10.97"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "10.99"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "10.91"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "11.39"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "11.96"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "12.19"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "12.48"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "12.52"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "12.35"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "12.36"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "12.32"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "12.29"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "11.75"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "12"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "12.27"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": "12.28"}], "rows_tail": [], "sampling_note": "Stored first 23 rows and last 23 rows when the table is larger.", "grapher_slug": "material-footprint-per-capita", "metadata_url": "https://ourworldindata.org/grapher/material-footprint-per-capita.metadata.json", "chart_title": "Material footprint per capita", "chart_subtitle": "Material footprint is the quantity of material needed to meet a country's material demand. It is material production, adjusted for trade. The total material footprint is the sum of the material footprint for biomass, fossil fuels, metal ores, and non-metal ores, given in tonnes per year.", "chart_note": null, "chart_citation": "United Nations Environment Programme", "original_chart_url": "https://ourworldindata.org/grapher/material-footprint-per-capita", "owid_column_metadata": {"12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC": {"titleShort": "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC", "titleLong": "12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC", "descriptionShort": "Material footprint per capita, by type of raw material (tonnes)", "shortUnit": "", "unit": "tonnes", "timespan": "2000-2022", "type": "Numeric", "owidVariableId": 992778, "shortName": "_12_2_1__en_mat_ftprpc", "lastUpdated": "2024-08-27", "nextUpdate": "2026-08-27", "citationShort": "United Nations Environment Programme – processed by Our World in Data", "citationLong": "United Nations Environment Programme – processed by Our World in Data. “12.2.1 - Material footprint per capita, by type of raw material (tonnes) - EN_MAT_FTPRPC” [dataset]. 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Reuse should follow the source and license information in this chart metadata."}, {"title": "Raw material consumption in the EU-27 by the final product", "source_url": "https://ourworldindata.org/grapher/material-footprint-end-products.csv", "file_type": "csv", "columns": ["Entity", "Year", "Share of total material consumption"], "row_count_total": 63, "rows_head": [{"Entity": "Advertising services", "Year": "2022", "Share of total material consumption": "0"}, {"Entity": "Agricultural products", "Year": "2022", "Share of total material consumption": "5.64"}, {"Entity": "Air transport services", "Year": "2022", "Share of total material consumption": "0.16"}, {"Entity": "Architectural and engineering services", "Year": "2022", "Share of total material consumption": "0.28"}, {"Entity": "Basic metals", "Year": "2022", "Share of total material consumption": "0.31"}, {"Entity": "Chemical products", "Year": "2022", "Share of total material consumption": "0.74"}, {"Entity": "Coke and refined 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{"Entity": "Machinery repair", "Year": "2022", "Share of total material consumption": "0.54"}, {"Entity": "Membership organisation services", "Year": "2022", "Share of total material consumption": "0.19"}, {"Entity": "Metal products", "Year": "2022", "Share of total material consumption": "0.94"}, {"Entity": "Mining and quarrying", "Year": "2022", "Share of total material consumption": "2.23"}, {"Entity": "Other financial services", "Year": "2022", "Share of total material consumption": "0.03"}, {"Entity": "Other machinery", "Year": "2022", "Share of total material consumption": "1.57"}, {"Entity": "Other non-metallic mineral products", "Year": "2022", "Share of total material consumption": "1.69"}, {"Entity": "Other personal services", "Year": "2022", "Share of total material consumption": "0.28"}, {"Entity": "Other technical services", "Year": "2022", "Share of total material consumption": "0.06"}, {"Entity": "Other transport equipment", "Year": "2022", "Share of total material consumption": "0.58"}, {"Entity": "Paper products", "Year": "2022", "Share of total material consumption": "0.29"}, {"Entity": "Pharmaceutical products", "Year": "2022", "Share of total material consumption": "0.47"}, {"Entity": "Postal services", "Year": "2022", "Share of total material consumption": "0.02"}, {"Entity": "Printing and recording services", "Year": "2022", "Share of total material consumption": "0.03"}, {"Entity": "Public admin and defence", "Year": "2022", "Share of total material consumption": "2.78"}, {"Entity": "Publishing services", "Year": "2022", "Share of total material consumption": "0.19"}, {"Entity": "Real estate services", "Year": "2022", "Share of total material consumption": "3.46"}, {"Entity": "Rental and leasing services", "Year": "2022", "Share of total material consumption": "0.09"}, {"Entity": "Repair of computers and household goods", "Year": "2022", "Share of total material consumption": "0.05"}, {"Entity": "Residential care services", "Year": "2022", "Share of total material consumption": "0.75"}, {"Entity": "Retail trade (except vehicles)", "Year": "2022", "Share of total material consumption": "1.8"}, {"Entity": "Rubber and plastic products", "Year": "2022", "Share of total material consumption": "0.22"}, {"Entity": "Scientific research", "Year": "2022", "Share of total material consumption": "1.36"}, {"Entity": "Security and investigation services", "Year": "2022", "Share of total material consumption": "0.15"}, {"Entity": "Sewage management", "Year": "2022", "Share of total material consumption": "0.8"}, {"Entity": "Sport and recreation", "Year": "2022", "Share of total material consumption": "0.23"}, {"Entity": "Telecommunications services", "Year": "2022", "Share of total material consumption": "0.3"}, {"Entity": "Textiles and clothing", "Year": "2022", "Share of total material consumption": "0.99"}, {"Entity": "Travel agency services", "Year": "2022", "Share of total material consumption": "0.12"}, {"Entity": "Vehicle sales and repair", "Year": "2022", "Share of total material consumption": "0.51"}, {"Entity": "Vehicles", "Year": "2022", "Share of total material consumption": "2.57"}, {"Entity": "Video, TV and radio services", "Year": "2022", "Share of total material consumption": "0.16"}, {"Entity": "Warehousing", "Year": "2022", "Share of total material consumption": "0.26"}, {"Entity": "Water transport services", "Year": "2022", "Share of total material consumption": "0.06"}, {"Entity": "Water treatment and supply", "Year": "2022", "Share of total material consumption": "0.18"}, {"Entity": "Wholesale trade (except vehicles)", "Year": "2022", "Share of total material consumption": "1.43"}, {"Entity": "Wood products (minus furniture)", "Year": "2022", "Share of total material consumption": "0.4"}], "rows_tail": [], "sampling_note": "Stored first 63 rows and last 63 rows when the table is larger.", "grapher_slug": "material-footprint-end-products", "metadata_url": 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The raw material consumption is the mass of any resources — including renewable and non-renewable ones — used in the production of these products. This adjusts for trade so it includes all resource extraction, including those overseas.", "chart_note": "Due to the number of final products in the dataset, not all can be shown on the chart at the same time.", "chart_citation": "Eurostat (2025)", "original_chart_url": "https://ourworldindata.org/grapher/material-footprint-end-products", "owid_column_metadata": {"Share of economy's raw material consumption": {"titleShort": "Share of total material consumption", "titleLong": "Share of total material consumption", "shortUnit": "%", "unit": "%", "timespan": "2022-2022", "type": "Numeric", "owidVariableId": 1077513, "shortName": "share_material_footprint", "lastUpdated": "2025-10-23", "citationShort": "Eurostat (2025) – processed by Our World in Data", "citationLong": "Eurostat (2025) – processed by Our World in Data. “Share of total material consumption” [dataset]. 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Reuse should follow the source and license information in this chart metadata."}, {"title": "Eutrophying emissions per 100 grams of protein", "source_url": "https://ourworldindata.org/grapher/eutrophying-emissions-protein.csv", "file_type": "csv", "columns": ["Entity", "Year", "Eutrophying emissions per 100g protein"], "row_count_total": 31, "rows_head": [{"Entity": "Apples", "Year": "2010", "Eutrophying emissions per 100g protein": "48.333332"}, {"Entity": "Bananas", "Year": "2010", "Eutrophying emissions per 100g protein": "36.555557"}, {"Entity": "Beef (beef herd)", "Year": "2010", "Eutrophying emissions per 100g protein": "151.15848"}, {"Entity": "Beef (dairy herd)", "Year": "2010", "Eutrophying emissions per 100g protein": "185.05066"}, {"Entity": "Berries & Grapes", "Year": "2010", "Eutrophying emissions per 100g protein": "61.2"}, {"Entity": "Brassicas", "Year": "2010", "Eutrophying emissions per 100g protein": "45.545456"}, {"Entity": "Cassava", "Year": "2010", "Eutrophying emissions per 100g protein": "7.6666665"}, {"Entity": "Cheese", "Year": "2010", "Eutrophying emissions per 100g protein": "44.551632"}, {"Entity": "Citrus Fruit", "Year": "2010", "Eutrophying emissions per 100g protein": "37.333332"}, {"Entity": "Coffee", "Year": "2010", "Eutrophying emissions per 100g protein": "138.15"}, {"Entity": "Dark Chocolate", "Year": "2010", "Eutrophying emissions per 100g protein": "174.16"}, {"Entity": "Eggs", "Year": "2010", "Eutrophying emissions per 100g protein": "19.61067"}, {"Entity": "Fish (farmed)", "Year": "2010", "Eutrophying emissions per 100g protein": "103.1002"}, {"Entity": "Groundnuts", "Year": "2010", "Eutrophying emissions per 100g protein": "5.4010696"}, {"Entity": "Lamb & Mutton", "Year": "2010", "Eutrophying emissions per 100g protein": "48.54073"}, {"Entity": "Maize", "Year": "2010", "Eutrophying emissions per 100g protein": "4.2421055"}, {"Entity": "Milk", "Year": "2010", "Eutrophying emissions per 100g protein": "32.272728"}, {"Entity": "Nuts", "Year": "2010", "Eutrophying emissions per 100g protein": "11.726883"}, {"Entity": "Oatmeal", "Year": "2010", "Eutrophying emissions per 100g protein": "8.638461"}, {"Entity": "Onions & Leeks", "Year": "2010", "Eutrophying emissions per 100g protein": "24.923077"}, {"Entity": "Other Pulses", "Year": "2010", "Eutrophying emissions per 100g protein": "7.9775805"}, {"Entity": "Peas", "Year": "2010", "Eutrophying emissions per 100g protein": "3.3843384"}, {"Entity": "Pig Meat", "Year": "2010", "Eutrophying emissions per 100g protein": "47.20643"}, {"Entity": "Potatoes", "Year": "2010", "Eutrophying emissions per 100g protein": "20.470589"}, {"Entity": "Poultry Meat", "Year": "2010", "Eutrophying emissions per 100g protein": "28.117783"}, {"Entity": "Prawns (farmed)", "Year": "2010", "Eutrophying emissions per 100g protein": "153.83887"}, {"Entity": "Rice", "Year": "2010", "Eutrophying emissions per 100g protein": "49.394367"}, {"Entity": "Root Vegetables", "Year": "2010", "Eutrophying emissions per 100g protein": "16.1"}, {"Entity": "Tofu", "Year": "2010", "Eutrophying emissions per 100g protein": "3.85"}, {"Entity": "Tomatoes", "Year": "2010", "Eutrophying emissions per 100g protein": "68.27273"}, {"Entity": "Wheat & Rye", "Year": "2010", "Eutrophying emissions per 100g protein": "5.8688526"}], "rows_tail": [], "sampling_note": "Stored first 31 rows and last 31 rows when the table is larger.", "grapher_slug": "eutrophying-emissions-protein", "metadata_url": "https://ourworldindata.org/grapher/eutrophying-emissions-protein.metadata.json", "chart_title": "Eutrophying emissions per 100 grams of protein", "chart_subtitle": "Eutrophying emissions represent runoff of excess nutrients into the surrounding environment and waterways, which affect and pollute ecosystems. 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{"raw_link": "https://ourworldindata.org/does-the-news-reflect-what-we-die-from", "title": "Does the news reflect what we die from?", "context": "Home\nCauses of Death\nDoes the news reflect what we die from?\nWhat do Americans die from, and what do the New York Times, Washington Post, and Fox News report on?\nBy\nHannah Ritchie\n,\nTuna Acisu\n,\nand\nEdouard Mathieu\nOctober 6, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nAcknowledgments\nFor this work, we relied on\nMedia Cloud\n, an open-access platform for media analysis. We would like to thank their team, particularly Emily Boardman Ndulue and Fernando Bermejo, for making this invaluable resource available and for their help with this project.\nMore than 80% of people —\nincluding surveyed\nAmericans, Brits, Germans, and Italians — say they follow the news because they “want to know what is going on in the world around them.”\n1\nIt’s not just that people\nexpect\nthe news to inform them about what’s going on in the world. Most think that it\ndoes\n. When\nasked what\nemotions the news generates, “informed” was the most common response.\n2\nThis is what media outlets themselves promise to do. Here are several quotes from the New York Times’s\nmission statement\n:\n“We seek the truth and help people understand the world. [...]\nWe help a global audience understand a vast and diverse world.”\nHowever, as we’ll see in this article, the media focuses on a particular sliver of our world, leaving much of the “vast and diverse world” largely out of their reporting. We’ll investigate this through the lens of health, looking at causes of death and reporting in the United States.\nAs we’ll discuss, our point is not that we should want or expect the media’s coverage to perfectly match the real distribution of deaths, although we’d argue that it would be better if it were less skewed. We wrote this article so that you, the reader, are aware of a significant disconnect between what we often hear and what actually happens.\nIt’s easy to conflate what we see in the news with the reality of our world, and keeping this mismatch in mind can help you avoid falling into this trap.\nCounting deaths and mentions in popular media\nWe focused on causes of death and media coverage in the United States in 2023.\nThe full list of all causes of death is very long, and since many causes are very rare, we didn’t investigate all of them. But our analysis accounts for 76% of all deaths in the US in 2023.\n3\nIt includes the 12 leading causes of death in the US, plus homicide, drug overdoses, and terrorism, since they receive a lot of attention in the media.\nWe used data from the US\nCenters for Disease Control and Prevention\n(CDC) to calculate each cause’s share of the total.\n4\nWe then compared this to the relative share of articles that mentioned these causes of death in three media outlets: the New York Times, the Washington Post, and the news website of Fox News. We selected these three because they are among the biggest\nnational\nnews organizations, are extremely popular, and are seen as being on different parts of the political spectrum.\nTo count the number of mentions, we relied on\nMedia Cloud\n, an open-source platform regularly used for media analysis. In an\nextended methodology document\n, we provide many more details on how we constructed the data. Two things are important to mention here.\nFor each cause of death, we included synonyms in our search. So, when searching for mentions of “homicide”, we also included mentions of related terms such as “murder”, “killer”, and other terms. For “heart disease”, we included terms like “heart attack”, “cardiac arrest”, “heart failure”, and many others.\nWe only counted articles where a cause of death — or its related terms — was mentioned\nmore than once\n. This ensures that our analysis is focused on reporting on causes of death rather than just articles that mention a cause of death in passing. Additionally, this approach reduces the number of false positives and noise in our results.\nWhat do Americans die from, and what do they read about in the news?\nYou can see the results of our analysis in the chart below.\nThere are two big takeaways from this analysis. The first one is that the actual distribution of deaths shown on the left is very different from the causes of death that the media talks about.\nThe second insight is how similar the distribution of coverage is between the three media outlets. While there are some differences (Fox News was a bit more likely to mention homicides, for example, while the NYT did the same for terrorism), these are much smaller than we might expect. While right- and left-wing media might differ in\nhow\nthey cover particular topics,\nwhat\nthey choose to write or talk about is similar.\nThe insight in this comparison, then, is not about differences between partisan media. It’s about the difference between\nactual\ncauses of death and what the news tells Americans about. Those differences — as we can see in the chart — are huge.\nHeart disease and cancer accounted for 56% of deaths among these 15 causes, but together they received just 7% of the media coverage. Other chronic issues, such as strokes, respiratory problems, diabetes, and kidney and liver disease, were also very underrepresented in the news.\nRare — but dramatic — events such as homicides and terrorism received more than half of all media coverage, despite being much smaller causes of death in the US. Terrorism, in particular, is a very rare cause of death, with 16 deaths in 2023.\n5\nDownload\nHow over- and underrepresented are different causes of death in the media?\nAnother way to visualize this data is to measure how over- or underrepresented each cause is.\nHeart disease and cancer accounted for 56% of deaths but received just 7% of the coverage\nTo do this, we calculate the ratio between a cause’s share of deaths and its share of news articles. In the chart below, we’ve done this for coverage in the New York Times (the results are similar for the other two outlets).\nIt highlights that homicides and terrorism are extremely overrepresented. Homicides received 43 times more coverage than their share of deaths; terrorism received over 18,000 times more.\nAt the other end, we see that conditions like heart disease, stroke, and liver disease are very underrepresented.\nDownload\nWhy is the media so biased towards dramatic risks?\nThe fact that the media focuses on dramatic, emotive events — and much less on “everyday”, more common mortality risks — has been found in several studies.\n6\nThese studies have shown that this mismatch has existed for a long time, and that genuine changes in death rates between causes of death account for a tiny fraction of the changes in media coverage.\n7\nOur analysis adds to the evidence, with updated data for 2023.\nWhy does this mismatch exist?\nHomicides received 43 times more coverage than their share of deaths; terrorism received over 18,000 times more\nOne explanation is that the news, true to its name, tells us what’s\nnew\n. The fact that nearly 2000 Americans die from heart disease\nevery single day\nmeans it is not novel or new. The headline tomorrow would be the same as it was today, which was the same as yesterday. Rarer events like terrorist attacks, plane crashes, homicides, or disasters each have their unique headline.\nPeople who die from common health risks quickly become mere numbers. On the other hand, those who die in rarer events have a face, a name, and a story that can be told. As humans, this makes us much more likely to click and read, making these stories ideal for the media to write about.\nWhile we would like news organizations to focus much more on the larger problems that societies face — that is what we try to do at Our World in Data — it would be wrong to put all of the blame on them. They respond to what readers want, and many want emotive and engaging stories (even if their circumstances are terrible and upsetting).\nEven outside the news, some of the most successful television series are intense, crime-filled dramas. Disaster movies pull in record numbers at the box office. One of the most popular podcast genres is “true crime,” where we spend hours listening to the gripping, scary tales of serial killers or con artists.\nIt's not surprising, then, that we’re much more likely to click on a news story about the latest murder or disaster than one about heart or kidney disease. And because media organizations need traffic and attention to survive, they and the public are stuck in a reinforcing feedback loop where rare events are always in the headlines and chronic problems get drowned out.\nThis is not just a problem with the modern media environment. The audience for this type of media has always been there. What’s changed is the reporting frequency: rather than getting one newspaper in the morning, we are bombarded with updates almost in real-time. We also now receive news from a much larger geographical area; it’s not just about what’s happened in our own town, but also about what’s happened on the other side of the country, or even the world.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nDoes this bias really matter?\nOur point is not that we think the New York Times, Washington Post, or Fox News’ coverage should\nexactly match the distribution of causes of death. A newspaper that constantly covers heart disease and kidney failure would be a boring one that soon goes out of business. Even though our mission at Our World in Data is to cover the world’s largest problems, our own writing and data publications also don’t precisely match the scale of those problems. We expect we will be closer to the real distribution than the mainstream media, but there will still be some mismatch.\nThe reason we’re doing this analysis is to make you or other readers more aware of this selection bias. The frequency of news coverage doesn’t reflect what’s happening across millions or billions of people, but it’s easy to fall into the trap of thinking it does.\nOur colleague Max Roser wrote about this in his article:\nThe limits of our personal experience and the value of statistics\n.\nWhy, then, do we think that this bias matters? Does it actually affect people’s perceptions of problems?\nIn a\nlarge survey\namong US adults, people who consumed local crime news “often” were more than three times more likely to say they were “extremely concerned” about crime affecting them or their family than those who rarely or never read local crime news.\n8\nThe frequency of news coverage doesn’t reflect what’s happening across millions or billions of people, but it’s easy to fall into the trap of thinking it does\nNearly six-in-ten Americans\nstill see\ninternational terrorism as a critical threat to the United States, despite the domestic impact on the US being relatively low for two decades. People are often far more anxious about flying than driving, even though commercial airline crashes are\nincredibly rare\n.\nThe information we’re exposed to profoundly impacts how we perceive the world, even if our perspective is less skewed than the media's.\nBut there’s one final reason why this bias matters. It makes it hard for us to understand how causes of death\nare changing over time\n. If we’re constantly bombarded with stories of the latest murders and crimes, we might easily think that these are happening more and more. That is a widespread sentiment. In 23 of the 27\nGallup surveys\nconducted since 1993, most Americans said there was more crime than the year before. In reality, rates of crime — including\nhomicides\nand other\nviolent crime\n— have fallen a lot.\nAnd if we don’t hear about what’s happening to heart disease rates, treatments, or the odds of surviving cancer, we might wrongly imagine that no progress has been made. Yet childhood cancer deaths\nhave plummeted\nover the last 50 years. Even among adults, death rates from cancer have\nfallen dramatically\nsince the 1990s. So too have\ndeath rates\nfrom heart disease.\nThis perception gap about the world matters, and the media is not doing a good job of trying to close it.\nMethodology\nIf you’re interested in digging deeper, we provide a more detailed methodological document about how this data was generated, and a few additional analyses.\nCorrection note\nThis article was initially published on October 6, 2025, and was updated on October 9. This update corrected an error, whereby “Drug and overdose” deaths were also included within the US CDC category of “Accidents”. This meant that they were double-counted. We have corrected this, and the change made only a small difference to the final numbers. The relative share of deaths from accidents changed from 9.5% to 7.8%, and the share of other causes increased slightly. We thank Karl Pettersson for flagging this.\nContinue reading on Our World in Data\nThe limits of our personal experience and the value of statistics\nThe world is huge; to get a clear idea of what our world is like, we have to rely on carefully collected, well-documented statistics.\nHow are causes of death registered around the world?\nIn many countries, when people die, the cause of their death is officially registered in their country’s national system. How is this determined?\nEndnotes\nRespondents could choose to “agree” with multiple answers. This survey was from 2015, but as we’ll see, more recent data suggests that even in 2025, most Americans think the news keeps them informed about what’s happening worldwide.\nIn the Pew Research survey, 46% said it made them feel informed “extremely often or often” with a further 43% “sometimes”. That was more common than any other emotion. The other high-ranking ones were negative emotions such as anger or sadness.\nIn 2023,\nthere were\napproximately 3 million (3,090,964) deaths in the United States. 2.3 million (2,350,117) died from the twelve leading causes plus drug overdoses, homicides and terrorism. You can find these results in our intermediate and final data files, which are available in\nour methodology document\n. That means the combined share was around 76% of the total [2,305,117 / 3,090,964 * 100 = 76%].\nWe used mortality data from\nCDC Wonder\nfor all causes except terrorism (which isn’t reported there). For this, we relied on data from the\nGlobal Terrorism Index\n.\nThis figure is sourced from the Institute for Economics and Peace (IEP)’s\nGlobal Terrorism Index 2024\nReport. It states on page 38: “The impact of terrorism improved in North America over the past year, owing to an improvement in score in Canada. There was one attack and death from terrorism in Canada in 2023, down from the peak of 12 deaths and eight attacks in 2018. By contrast, the impact of terrorism increased in the US, with 16 deaths from seven incidents.”\nIsch, C. (2025). Media bias in portrayals of mortality risks: Comparison of newspaper coverage to death rates. Social Science & Medicine, 364, 117542.\nPilar, M. R., Eyler, A. A., Moreland-Russell, S., & Brownson, R. C. (2020). Actual causes of death in relation to media, policy, and funding attention: Examining public health priorities. Frontiers in Public Health, 8, 279.\nBomlitz, L. J., & Brezis, M. (2008). Misrepresentation of health risks by mass media. Journal of Public Health, 30(2), 202-204.\nIsch, C. (2025). Media bias in portrayals of mortality risks: Comparison of newspaper coverage to death rates. Social Science & Medicine, 364, 117542.\nThis survey was conducted by Pew Research in 2024. It asked US adults whether they were extremely/very concerned, somewhat concerned, or not at all concerned about crime in their local community affecting them or their family.\n33% of those who “often” get local crime news were “extremely concerned”. The share among those who “sometimes” get this type of news was 19%. It was just 10% among those who rarely consume it.\nhttps://www.pewresearch.org/short-reads/2024/08/29/the-link-between-local-news-coverage-and-americans-perceptions-of-crime\n.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie, Tuna Acisu, and Edouard Mathieu (2025) - “Does the news reflect what we die from?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20251125-173858/does-the-news-reflect-what-we-die-from.html' [Online Resource] (archived on November 25, 2025).\nBibTeX citation\n@article{owid-does-the-news-reflect-what-we-die-from,\nauthor = {Hannah Ritchie and Tuna Acisu and Edouard Mathieu},\ntitle = {Does the news reflect what we die from?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20251125-173858/does-the-news-reflect-what-we-die-from.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "does-the-news-reflect-what-we-die-from", "source_url": "https://ourworldindata.org/does-the-news-reflect-what-we-die-from", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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""}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "2015", "Homicide rate per 100,000 population": "12.4050865", "World region according to OWID": ""}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "2016", "Homicide rate per 100,000 population": "12.379274", "World region according to OWID": ""}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "2017", "Homicide rate per 100,000 population": "12.257181", "World region according to OWID": ""}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "2018", "Homicide rate per 100,000 population": "11.756515", "World region according to OWID": ""}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "2019", "Homicide rate per 100,000 population": "11.47677", "World region according to OWID": ""}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "2020", "Homicide rate per 100,000 population": "11.048939", "World region according to OWID": ""}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "2021", "Homicide rate per 100,000 population": "11.295057", "World region according to OWID": ""}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "2022", "Homicide rate per 100,000 population": "11.187279", "World region according to OWID": ""}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "2023", "Homicide rate per 100,000 population": "10.802414", "World region according to OWID": ""}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "2024", "Homicide rate per 100,000 population": "10.5003195", "World region according to OWID": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "Homicide rate per 100,000 population": "3.9603949", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "Homicide rate per 100,000 population": "5.8270354", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "Homicide rate per 100,000 population": "3.4256952", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "Homicide rate per 100,000 population": "7.948279", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "Homicide rate per 100,000 population": "8.380375", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "Homicide rate per 100,000 population": "41.304665", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "Homicide rate per 100,000 population": "20.559898", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "Homicide rate per 100,000 population": "16.684462", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Homicide rate per 100,000 population": "4.1375203", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Homicide rate per 100,000 population": "6.9886894", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Homicide rate per 100,000 population": "6.891948", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Homicide rate per 100,000 population": "5.3237762", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Homicide rate per 100,000 population": "4.2276373", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Homicide rate per 100,000 population": "5.006243", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Homicide rate per 100,000 population": "3.1139286", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Homicide rate per 100,000 population": "3.4735007", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Homicide rate per 100,000 population": "3.1073225", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Homicide rate per 100,000 population": "2.870187", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Homicide rate per 100,000 population": "4.063206", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Homicide rate per 100,000 population": "4.8772044", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Homicide rate per 100,000 population": "5.3951817", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Homicide rate per 100,000 population": "4.264726", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Homicide rate per 100,000 population": "4.614723", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Homicide rate per 100,000 population": "2.2079382", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Homicide rate per 100,000 population": "2.7261436", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Homicide rate per 100,000 population": "2.0012128", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Homicide rate per 100,000 population": "2.2803986", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Homicide rate per 100,000 population": "2.2530255", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Homicide rate per 100,000 population": "2.1239898", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Homicide rate per 100,000 population": "2.3160856", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "Homicide rate per 100,000 population": "1.6975479", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Homicide rate per 100,000 population": "1.3870834", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "Homicide rate per 100,000 population": "1.7551677", "World region according to OWID": "Europe"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Homicide rate per 100,000 population": "0.92792225", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Homicide rate per 100,000 population": "0.79264295", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Homicide rate per 100,000 population": "0.9420696", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Homicide rate per 100,000 population": "0.78049177", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Homicide rate per 100,000 population": "0.7018856", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Homicide rate per 100,000 population": "0.75873816", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Homicide rate per 100,000 population": "1.3892517", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Homicide rate per 100,000 population": "1.2495389", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Homicide rate per 100,000 population": "1.4717499", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Homicide rate per 100,000 population": "1.3643339", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Homicide rate per 100,000 population": "1.326782", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Homicide rate per 100,000 population": "1.2569172", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Homicide rate per 100,000 population": "1.3363123", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Homicide rate per 100,000 population": "1.1987653", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Homicide rate per 100,000 population": "1.4849432", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Homicide rate per 100,000 population": "1.5526875", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2022", "Homicide rate per 100,000 population": "1.7481215", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "Homicide rate per 100,000 population": "1.1610724", "World region according to OWID": "Africa"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2001", "Homicide rate per 100,000 population": "1.7527409", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2002", "Homicide rate per 100,000 population": "12.267466", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2003", "Homicide rate per 100,000 population": "5.2658834", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2004", "Homicide rate per 100,000 population": "7.0400224", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2005", "Homicide rate per 100,000 population": "10.597524", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2006", "Homicide rate per 100,000 population": "7.0954065", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2007", "Homicide rate per 100,000 population": "1.7821182", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2008", "Homicide rate per 100,000 population": "5.3736477", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2009", "Homicide rate per 100,000 population": "3.6017864", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2010", "Homicide rate per 100,000 population": "9.053297", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2011", "Homicide rate per 100,000 population": "9.108214", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2012", "Homicide rate per 100,000 population": "3.6704657", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2013", "Homicide rate per 100,000 population": "5.5549383", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2014", "Homicide rate per 100,000 population": "5.6110425", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2015", "Homicide rate per 100,000 population": "7.564583", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2016", "Homicide rate per 100,000 population": "0", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2017", "Homicide rate per 100,000 population": "13.569573", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2018", "Homicide rate per 100,000 population": "25.53601", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2019", "Homicide rate per 100,000 population": "0", "World region according to OWID": "Oceania"}, {"Entity": "Americas (UN)", "Code": "", "Year": "2000", "Homicide rate per 100,000 population": "15.686963", "World region according to OWID": ""}, {"Entity": "Americas (UN)", "Code": "", "Year": "2001", "Homicide rate per 100,000 population": "16.44783", "World region according to OWID": ""}, {"Entity": "Americas (UN)", "Code": "", "Year": "2002", "Homicide rate per 100,000 population": "16.481121", "World region according to OWID": ""}, {"Entity": "Americas (UN)", "Code": "", "Year": "2003", "Homicide rate per 100,000 population": "16.299654", "World region according to OWID": ""}, {"Entity": "Americas (UN)", "Code": "", "Year": "2004", "Homicide rate per 100,000 population": "15.260684", "World region according to OWID": ""}, {"Entity": "Americas (UN)", "Code": "", "Year": "2005", "Homicide rate per 100,000 population": "14.974931", "World region according to OWID": ""}, {"Entity": "Americas (UN)", "Code": "", "Year": "2006", "Homicide rate per 100,000 population": "15.202533", "World region according to OWID": ""}, {"Entity": "Americas (UN)", "Code": "", "Year": "2007", "Homicide rate per 100,000 population": "14.83073", "World region according to OWID": ""}, {"Entity": "Americas (UN)", "Code": "", "Year": "2008", "Homicide rate per 100,000 population": "15.758094", "World region according to OWID": ""}, {"Entity": "Americas (UN)", "Code": "", "Year": "2009", "Homicide rate per 100,000 population": "16.459183", "World region according to OWID": ""}, {"Entity": "Americas (UN)", "Code": "", "Year": "2010", "Homicide rate per 100,000 population": "16.790705", "World region according to OWID": ""}, {"Entity": "Americas (UN)", "Code": "", "Year": "2011", "Homicide rate per 100,000 population": "16.994877", "World region according to OWID": ""}, {"Entity": "Americas (UN)", "Code": "", "Year": "2012", "Homicide rate per 100,000 population": "17.112413", "World region according to OWID": ""}, {"Entity": "Americas (UN)", "Code": "", "Year": "2013", "Homicide rate per 100,000 population": "16.566425", "World region according to OWID": ""}], "rows_tail": [{"Entity": "Vietnam", "Code": "VNM", "Year": "2007", "Homicide rate per 100,000 population": "1.3965716", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2008", "Homicide rate per 100,000 population": "1.2820544", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2009", "Homicide rate per 100,000 population": "1.4260927", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2010", "Homicide rate per 100,000 population": "1.5276401", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2011", "Homicide rate per 100,000 population": "1.5350128", "World region according to OWID": "Asia"}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2000", "Homicide rate per 100,000 population": "4.782262", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2001", "Homicide rate per 100,000 population": "4.833886", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2002", "Homicide rate per 100,000 population": "4.837426", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2003", "Homicide rate per 100,000 population": "4.8297124", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2004", "Homicide rate per 100,000 population": "4.8459616", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2005", "Homicide rate per 100,000 population": "5.1756663", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2006", "Homicide rate per 100,000 population": "4.9649296", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2007", "Homicide rate per 100,000 population": "5.0083475", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2008", "Homicide rate per 100,000 population": "4.772474", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2009", "Homicide rate per 100,000 population": "3.730781", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2010", "Homicide rate per 100,000 population": "3.7009094", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2011", "Homicide rate per 100,000 population": "4.9018893", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2012", "Homicide rate per 100,000 population": "4.9815645", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2013", "Homicide rate per 100,000 population": "5.053801", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2014", "Homicide rate per 100,000 population": "4.7623963", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2015", "Homicide rate per 100,000 population": "4.600902", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2016", "Homicide rate per 100,000 population": "4.9626846", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2017", "Homicide rate per 100,000 population": "5.172242", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2018", "Homicide rate per 100,000 population": "5.242862", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2019", "Homicide rate per 100,000 population": "4.8711433", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2020", "Homicide rate per 100,000 population": "4.2111936", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2021", "Homicide rate per 100,000 population": "4.422855", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2022", "Homicide rate per 100,000 population": "4.5598226", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2023", "Homicide rate per 100,000 population": "4.8514276", "World region according to OWID": ""}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2024", "Homicide rate per 100,000 population": "4.64461", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2000", "Homicide rate per 100,000 population": "1.6165633", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2001", "Homicide rate per 100,000 population": "1.5634084", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2002", "Homicide rate per 100,000 population": "1.602488", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2003", "Homicide rate per 100,000 population": "1.4284827", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2004", "Homicide rate per 100,000 population": "1.449924", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2005", "Homicide rate per 100,000 population": "1.3624419", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2006", "Homicide rate per 100,000 population": "1.2564192", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2007", "Homicide rate per 100,000 population": "1.2537518", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2008", "Homicide rate per 100,000 population": "1.240188", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2009", "Homicide rate per 100,000 population": "1.1010277", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2010", "Homicide rate per 100,000 population": "1.0841254", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2011", "Homicide rate per 100,000 population": "1.0972776", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2012", "Homicide rate per 100,000 population": "1.0098387", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2013", "Homicide rate per 100,000 population": "1.0002885", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2014", "Homicide rate per 100,000 population": "1.0063837", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2015", "Homicide rate per 100,000 population": "1.1065139", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2016", "Homicide rate per 100,000 population": "1.0888176", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2017", "Homicide rate per 100,000 population": "1.0107424", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2018", "Homicide rate per 100,000 population": "0.97864795", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2019", "Homicide rate per 100,000 population": "0.90394616", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2020", "Homicide rate per 100,000 population": "0.9442572", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2021", "Homicide rate per 100,000 population": "0.9079678", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2022", "Homicide rate per 100,000 population": "0.99279004", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2023", "Homicide rate per 100,000 population": "1.045743", "World region according to OWID": ""}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2024", "Homicide rate per 100,000 population": "1.0449004", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Homicide rate per 100,000 population": "6.898169", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Homicide rate per 100,000 population": "7.0128365", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Homicide rate per 100,000 population": "6.9597163", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Homicide rate per 100,000 population": "6.783185", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Homicide rate per 100,000 population": "6.5602446", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Homicide rate per 100,000 population": "6.3594284", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Homicide rate per 100,000 population": "6.245216", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Homicide rate per 100,000 population": "6.0780063", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Homicide rate per 100,000 population": "6.077181", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Homicide rate per 100,000 population": "6.1254582", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Homicide rate per 100,000 population": "6.107562", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Homicide rate per 100,000 population": "6.172122", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Homicide rate per 100,000 population": "6.197366", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Homicide rate per 100,000 population": "6.096792", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Homicide rate per 100,000 population": "6.087443", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Homicide rate per 100,000 population": "5.919705", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Homicide rate per 100,000 population": "5.9631624", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Homicide rate per 100,000 population": "5.891153", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Homicide rate per 100,000 population": "5.6498275", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Homicide rate per 100,000 population": "5.394625", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Homicide rate per 100,000 population": "5.3042336", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Homicide rate per 100,000 population": "5.525062", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Homicide rate per 100,000 population": "5.2951646", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Homicide rate per 100,000 population": "5.2296734", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Homicide rate per 100,000 population": "5.144926", "World region according to OWID": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1998", "Homicide rate per 100,000 population": "3.334054", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1999", "Homicide rate per 100,000 population": "5.381141", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Homicide rate per 100,000 population": "3.5517478", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Homicide rate per 100,000 population": "3.2810755", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Homicide rate per 100,000 population": "2.9237163", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Homicide rate per 100,000 population": "3.8183067", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Homicide rate per 100,000 population": "4.1077375", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Homicide rate per 100,000 population": "4.984973", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Homicide rate per 100,000 population": "5.6834073", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Homicide rate per 100,000 population": "5.8097186", "World region according to OWID": "Asia"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Homicide rate per 100,000 population": "7.0638075", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Homicide rate per 100,000 population": "8.569656", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Homicide rate per 100,000 population": "9.392565", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Homicide rate per 100,000 population": "8.896668", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Homicide rate per 100,000 population": "9.689528", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Homicide rate per 100,000 population": "9.195976", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Homicide rate per 100,000 population": "11.006154", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Homicide rate per 100,000 population": "7.955972", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Homicide rate per 100,000 population": "5.329719", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Homicide rate per 100,000 population": "6.0190997", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Homicide rate per 100,000 population": "5.82861", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Homicide rate per 100,000 population": "5.977367", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Homicide rate per 100,000 population": "5.9341693", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Homicide rate per 100,000 population": "5.6302366", "World region 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"Homicide rate per 100,000 population": "10.965643", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Homicide rate per 100,000 population": "10.814334", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Homicide rate per 100,000 population": "8.483398", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Homicide rate per 100,000 population": "5.3232317", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Homicide rate per 100,000 population": "7.0994935", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Homicide rate per 100,000 population": "4.88212", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Homicide rate per 100,000 population": "5.1730795", "World region according to OWID": 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people, in both sexes aged all ages": "279.01453"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "264.95486"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "184.45703"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1961", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "378.4812"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1962", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "381.81592"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1963", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "401.75586"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1964", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "357.03195"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1966", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "470.8961"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1969", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "318.32715"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1970", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "554.5336"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1971", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "565.9357"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1972", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "597.7632"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1973", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "579.50885"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1974", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "628.1369"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1975", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "587.698"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1976", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "617.3869"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1977", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "573.5276"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1978", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "408.92725"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1983", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "269.01956"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1985", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "337.97183"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1986", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "308.56305"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1987", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "334.39246"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1988", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "362.64005"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1989", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "264.45505"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1990", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "302.57874"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1991", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "265.8481"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1992", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "306.7622"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1993", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "298.78052"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1994", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "266.757"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1995", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "268.94742"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1998", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "282.2647"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1999", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "294.9562"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2000", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "213.94537"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2001", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "235.6646"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2002", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "215.96507"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2003", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "257.58002"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2004", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "238.383"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2005", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "255.59813"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2006", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "221.31737"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2007", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "199.60213"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2008", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "221.87457"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2009", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "157.05225"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2010", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "125.37917"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2011", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "162.99594"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2012", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "183.7352"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2013", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "177.7924"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2014", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "218.22522"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2015", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "181.09926"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2016", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "176.05775"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2017", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "192.16563"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2018", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "187.48418"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2019", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "216.84155"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2020", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "197.00922"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2021", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "158.47339"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1966", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "335.62057"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1967", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "375.84045"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1968", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "396.51794"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1969", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "428.27173"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1970", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "418.71698"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1977", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "405.9636"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1978", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "401.04254"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1979", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "417.43887"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1980", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "399.34442"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1981", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "399.25558"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1982", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "370.11154"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1983", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "402.15405"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1984", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "410.7449"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1985", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "380.31046"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1986", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "378.52267"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1987", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "379.20407"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1988", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "353.71768"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1989", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "341.54858"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1990", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "347.03247"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1991", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "331.42654"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1992", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "336.09177"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1993", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "328.75882"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1994", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "295.6187"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1995", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "305.76575"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1996", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "287.45767"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1997", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "234.82921"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1998", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "235.62079"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1999", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "237.77393"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2000", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "216.99747"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2001", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "215.14868"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2002", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "212.10309"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2003", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "207.15826"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2004", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "194.51419"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2005", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "188.04074"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2006", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "182.73055"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2007", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "187.65993"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2008", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "174.23799"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2009", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "169.95134"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2010", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "179.86206"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2011", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "173.71593"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2012", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "168.57495"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2013", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "161.67715"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2014", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "155.36261"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2015", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "159.68933"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2016", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "165.11478"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2017", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "155.17032"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2018", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "150.7534"}], "rows_tail": [{"Entity": "Uruguay", "Code": "URY", "Year": "1999", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "210.35536"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2000", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "196.2997"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2001", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "195.16841"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2002", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "187.07153"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2003", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "195.43385"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2004", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "187.66515"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2005", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in 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"Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "141.84737"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2013", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "133.36638"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2014", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "127.17292"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2015", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "133.26936"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2016", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "132.5821"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2017", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged 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all ages": "457.0909"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1989", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "449.84366"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1990", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "452.51248"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1991", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "480.50836"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1992", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "505.5781"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1993", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "558.98517"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1994", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "595.4329"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1995", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "587.1021"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1996", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "581.9719"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1997", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "550.1595"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1998", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "587.0876"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1999", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "543.78925"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2000", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "578.819"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2001", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "562.49927"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2002", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "580.9394"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2003", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "560.19635"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2004", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "528.3416"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2005", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "568.2264"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2009", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "513.3439"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2010", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "529.81573"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2011", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "536.1273"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2012", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "528.57666"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2013", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "507.85742"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2014", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "506.98273"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2015", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "487.6102"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2016", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "484.2495"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2017", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "483.45926"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2018", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "456.01877"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2019", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "441.96405"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2020", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "667.87646"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2021", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "524.6609"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2022", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "564.03345"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2023", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "594.05206"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1955", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "220.51321"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1956", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "233.86784"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1957", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "241.36119"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1958", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "239.57635"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1959", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "244.75569"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1960", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "238.46555"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1961", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "232.88805"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1962", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "239.82538"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1963", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "245.61946"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1964", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "251.63261"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1965", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "250.58118"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1966", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "249.61572"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1967", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "263.10034"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1968", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "286.15848"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1969", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "285.36688"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1970", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "271.92053"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1971", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "265.73242"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1972", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "277.04257"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1973", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "272.77307"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1974", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "281.9253"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1975", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "291.65042"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1976", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "304.0038"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1977", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "294.18048"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1978", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "299.33224"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1979", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "281.30106"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1980", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "292.81915"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1981", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "281.18713"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1982", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "284.76086"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1983", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "317.93726"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1985", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "253.39572"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1986", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "237.62524"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1987", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "259.34503"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1988", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "262.46536"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1989", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "250.11455"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1990", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "316.0102"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1992", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "315.00735"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1993", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "304.55466"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1994", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "308.47415"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1996", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "264.6826"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1997", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "247.59302"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1998", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "243.20886"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1999", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "240.75316"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2000", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "231.7668"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2001", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "233.36794"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2002", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "216.02838"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2003", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "227.60887"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2004", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "215.55257"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2005", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "210.79869"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2006", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "209.23705"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2007", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "206.16945"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2008", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "210.36505"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2009", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "203.0272"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2010", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "208.44048"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2011", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "209.00166"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2012", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "201.83017"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2013", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "196.01802"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2014", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "212.63083"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2015", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "208.01509"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2016", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "224.29936"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "cardiovascular-disease-death-rate-who-mdb", "metadata_url": "https://ourworldindata.org/grapher/cardiovascular-disease-death-rate-who-mdb.metadata.json", "chart_title": "Death rate from cardiovascular diseases", "chart_subtitle": "Reported 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{"raw_link": "https://ourworldindata.org/us-foreign-aid-saved-millions", "title": "Foreign aid from the United States saved millions of lives each year", "context": "Home\nForeign Aid\nForeign aid from the United States saved millions of lives each year\nFor decades, these aid programs received bipartisan support and made a difference. Cutting them will cost lives.\nBy\nSimon van Teutem\nand\nHannah Ritchie\nSeptember 29, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nIn total dollars spent, the United States was the world’s largest foreign aid donor in 2023. It gave $62 billion, about the same as the next three largest donors combined: Germany, Japan, and the United Kingdom. The chart below shows the ten largest givers.\nDownload\nThe US had a huge impact on the\nglobal\nbudget, despite giving only a small share of its income. Of the ten countries that gave the most in raw figures, the US gave the smallest share of its national income: 0.24% of gross national income (GNI).\nBut because the US is so large, what America decides to contribute (or not) matters a lot.\nMany Americans, and others, understandably wonder what impact this aid has had. Have their tax dollars made a difference?\nAid programs financed by American taxpayers saved approximately three million people every year\nAmerican foreign aid has contributed in many ways, from fostering economic growth and alleviating poverty in low-income countries to boosting food production and reducing malnutrition.\n1\nBut in this article, we’ll focus on one especially tangible way to measure the outcome of foreign aid: the number of lives it has saved.\nWe conclude that aid programs financed by American taxpayers saved approximately three million people annually. As we will see, it’s hard to estimate this precisely, so our “best estimate” of three million could plausibly be closer to two or four million.\nThis was a huge achievement that Americans can rightly be proud of. As we’ll see later, it comes at a comparatively low cost: Americans tend to vastly overestimate how much is spent on foreign aid, and support rises once they learn the real figures.\nNote on America’s aid structure\nMost of America’s foreign assistance for global health and humanitarian response came from the\nUnited States Agency for International Development (USAID)\n. USAID was the main American agency for delivering development assistance abroad.\n2\nUSAID alone had a budget of $43.8 billion, and made up around 61% of total US foreign assistance.\n3\nThe rest was channeled through other agencies, such as the Department of State. Most of the interventions covered in this article were funded through USAID.\nThe current US administration has dismantled USAID entirely and imposed deep cuts on many other foreign aid programs.\nMillions of lives were saved by USAID every year\nEstimating the number of lives saved by foreign aid is not straightforward. Health data from low-income countries is often limited, and we can never directly observe what would have happened without aid. In some cases, finances might come from elsewhere to fill the gap, but in others, it will go unfilled. That means all estimates come with considerable uncertainty, but they are precise enough to give us a sense of the impact.\nThe chart below is based on a\nrecent analysis\nby Charles Kenny and Justin Sandefur of the Center for Global Development, in which they estimated the number of lives saved by American foreign aid.\n4\nDownload\nThese figures are gross estimates. They reflect the number of lives that US foreign aid helped to save, rather than the exact number that would have been lost if the US had not provided this support. In some cases, other governments, charities, or communities might have stepped in to fill the gap.\nKenny and Sandefur focused on interventions targeting HIV/AIDS, tuberculosis, malaria, vaccines, and humanitarian assistance. Their estimates range from 2.3 to 5.6 million lives saved annually, with their central estimate of 3.3 million. The breakdown is shown in the chart.\nAIDS programs saved the largest number of lives: over 1.5 million per year. Between a quarter and half a million were saved by vaccines, tuberculosis, malaria, and humanitarian response each.\nNote that there is possibly some overlap between a few of the categories. For example, one intervention to tackle tuberculosis is giving infants the\nBacillus Calmette-Guérin (BCG) vaccine\n, which provides some protection against tuberculosis. So, there’s possibly some “double-counting” between the vaccines and tuberculosis categories. However, we would expect that these overlapping numbers are not large, and certainly not large enough to change the overall takeaway, which is that American foreign aid saved\naround\nthree million lives per year.\nKenny and Sandefur’s estimates do not include lives saved from other aid programs, including improved access to\nclean water and sanitation\n, better nutrition, and family planning. We expect these to\nhave\nsaved lives, so the true figure might be higher.\n5\nFor these (and other) reasons, Kenny and Sandefur make it clear that these numbers are uncertain; their upper-bound estimates are more than twice their lower-bound ones.\nSince this was just one analysis, it’s worth comparing these results to other estimates. A recent paper, published in\nThe Lancet\n— which received a lot of attention — estimated that USAID averted 92 million deaths in the 21 years from 2001 to 2021.\n6\nOn average, that would be approximately 46 million per decade or 4 to 5 million lives saved annually.\nSeveral experts have flagged methodological limitations in this study, so we would not recommend basing estimates from this paper alone. Still, it does give a similar figure of at least several million per year.\n7\nPEPFAR has likely saved more than 20 million lives from AIDS\nWe can also compare the estimated deaths averted from\nspecific\ndiseases in these aggregate analyses to studies that look at HIV, tuberculosis, or malaria individually.\nThe US President’s Emergency Plan for AIDS Relief — better known as PEPFAR — is widely considered one of history's most successful international aid initiatives.\n8\nLaunched in 2003, PEPFAR funds testing,\nantiretroviral therapy\n, prevention, and care in dozens of low- and middle-income countries, focusing on the hardest-hit regions of Sub-Saharan Africa. In 2023 alone,\nover 20 million people\nglobally received antiretroviral therapy through PEPFAR.\nIn many of these countries, HIV/AIDS disproportionately affects women — especially young women. And without treatment, the virus can be passed from mother to child during pregnancy, childbirth, or breastfeeding. One of PEPFAR’s most transformative impacts has been interrupting this transmission chain for millions of mothers. Millions of babies who would have been born with HIV were instead born healthy. This means many of the lives saved by US aid were children’s.\nSo, how many lives has this program saved? The State Department administers PEPFAR and says the program has saved\n25 million lives\n.\n9\nBut it’s also worth looking at independent analyses; a recent assessment finds that it has saved between 7.5 and 30 million lives.\n10\nAgain, this highlights how uncertain these estimates are but suggests that the State Department’s estimate of 25 million could be reasonable, as does Kenny and Sandefur’s estimate of around 1.6 million per year.\nThe chart below shows the estimated number of deaths from HIV/AIDS and the estimated number of deaths averted by\nantiretroviral therapy\n(ART) globally. PEPFAR wasn’t the only initiative expanding access to antiretroviral treatment, but was by far the largest. As the global rollout of ART gained momentum, HIV/AIDS deaths, which had been rising sharply, quickly plateaued — and then began a steep decline.\nRepublican President George W. Bush launched PEPFAR. Reflecting later on the moral imperative behind the decision, he wrote in his memoir\nDecision Points\n:\n“I considered America a generous nation with a moral responsibility to do our part to help relieve poverty and despair.”\nThrough PEPFAR, Americans did just that.\nAmerican aid to tackle malaria has likely saved over 100,000 lives every year\nIn addition to its leadership in fighting HIV/AIDS, the US has played a vital role in the global effort against malaria through the President’s Malaria Initiative and contributions to\nThe Global Fund\n.\nFounded in 2005, this program supports malaria prevention and treatment in 27 African countries, the region\nhit hardest\nby the disease. It funds life-saving interventions: bednets, insecticide spraying, seasonal preventive medicine for children, and access to diagnostics and treatment. These efforts have contributed to\nlarge declines\nin malaria deaths in recent decades.\nKenny and Sandefur estimate that US foreign aid helped prevent around 293,000 malaria deaths yearly.\n11\nThis is a reasonable central estimate given the range within the literature. Studies provide higher and lower estimates, but these typically range from around 100,000 lives to 600,000 per year. In the footnote, we’ve included some more details of these studies.\n12\nA figure of around 300,000 seems credible. Even if the true number were a few hundred thousand higher or lower, the conclusion would be the same: USAID has saved millions of lives yearly.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nMany Americans support foreign aid for health programs, but overestimate how much the US spends\nIf American aid has saved several million lives each year, it would be reasonable to assume that taking that aid away will cost lives, possibly just as many if no other interventions or resources fill the gap.\n13\nPrograms that took decades to build are being ripped up in a matter of months\nThat hypothetical has become a reality this year, with the\ncomplete shutdown\nof USAID and cuts to other assistance programs. This has already had life-changing impacts on the people who would have received medicines, treatments, and humanitarian support. In countries like Uganda, clinics have already reported shortages of life-saving medication.\n14\nPrograms that took decades to build are being ripped up in a matter of months.\nIf we think that saving these lives in other parts of the world is fundamentally a good thing and worth protecting aid budgets for — not just in the US but in other rich countries, too — then public attitudes to foreign aid matter. Political representatives tend to pursue policies that are popular among the public.\nOpposition to foreign aid, as a whole, is high across the American public. There is no disputing that. However, most Americans support giving aid for the life-saving interventions we looked at in this article. When people\nwere asked\nwhat\ntypes\nof aid programs they think the US government should spend money on, the share supporting health programs was extremely high.\n15\n83% of respondents said that the US should give foreign aid for “providing medicine and medical supplies to developing countries”.\nThis has strong bipartisan support. The share among Democrats was 91%, and among Republicans it was 77%.\nHowever, Americans might still support aid cuts because they think the US spends too much. Indeed, recent survey data show that most Americans overestimate how much the US spends, often by a huge amount.\nThe US aid budget has been around 1% of total federal spending. But when KFF — a policy research and polling organization —\nasked people\nwhat share they thought was spent on aid, 30% believed that the US spent one-third or more of its budget on aid. And a staggering 15% thought that it spent more than half. That means they thought the US spent in a week what it actually gave for the entire year.\nDownload\nIt’s worth noting that the survey design may have nudged respondents toward higher estimates, for example, by including several high-percentage response options, but none for 1% or less. Even so, the fact that more than half of the participants believed foreign aid accounts for over 10% of the federal budget points to a broader tendency to overestimate.\nA 2016 Ipsos survey found a similar pattern: on average, Americans believed that more than 10% of the national budget — excluding military spending — went toward foreign aid.\n16\nPerhaps most interesting (and encouraging) is that when respondents were told that the actual number was just 1% of spending, the share of people who said the US spent “too much” on aid dropped from 58% to 34%. Many Republicans, who are more skeptical of foreign assistance, also shifted to a more pro-aid position.\nMost Americans vastly overestimate how much is spent on foreign aid, and support rises once they learn the real figures\nIt’s understandable: if you thought a third of the federal budget was being spent on foreign aid, you might be frustrated with the government’s priorities and the persistence of poverty and disease. But that is an inaccurate picture of how much is spent and whether it provides any benefit.\nNot all aid is incredibly effective, but the very best aid\nis,\nand a small share of government budgets can save millions of lives.\nAcknowledgments\nThanks to Max Roser, Edouard Mathieu, Saloni Dattani, and Jack Ramm for their feedback and comments on this article and its visualizations.\nContinue reading on Our World in Data\nFor many of us, it doesn’t cost much to improve someone’s life, and we can do much more of it\nMost countries spend less than 1% of their national income on foreign aid; even small increases could make a big difference.\nHow much foreign aid is spent domestically rather than overseas?\nIn many countries, a significant share of aid is spent domestically on hosting refugees, offering student scholarships, and administrative costs.\nWhat is foreign aid? How “Official Development Assistance” is measured\nForeign aid measurement is complicated — what exactly counts as Official Development Assistance, what doesn’t, and how much is actually spent abroad?\nEndnotes\nKarras, G. (2006).\nForeign aid and long‐run economic growth: empirical evidence for a panel of developing countries.\nJournal of International Development: The Journal of the Development Studies Association, 18(1), 15-28.\nSsozi, J., Asongu, S., & Amavilah, V. H. (2019).\nThe effectiveness of development aid for agriculture in Sub-Saharan Africa.\nJournal of Economic Studies, 46(2), 284-305.\nCavalcanti, D. M., de Sales, L. D. O. F., da Silva, A. F., Basterra, E. L., Pena, D., Monti, C., ... & Rasella, D. (2025). Evaluating the impact of two decades of USAID interventions and projecting the effects of defunding on mortality up to 2030: a retrospective impact evaluation and forecasting analysis. The Lancet. (p.5 of the Supplementary appendix)\nUSAID spent $43.8 billion; the remainder of American aid totaled $28.1 billion.\nKenny, C., & Sandefur, J. (2025). How many lives does US foreign aid save?. Center for Global Development.\nSome of the benefits of these programs could also already be captured in the lives saved from particular diseases. For example, better nutrition often\nreduces the risk\nof dying from malaria or tuberculosis. Therefore, some of the “lives saved” from these programs may already be included.\nKenny and Sandefur do provide some rough estimates for how many lives these could have saved, and estimate that water and sanitation could have saved 23,200 lives per year; family planning saved 8,340 women from maternal deaths per year; and 71,000 to 193,000 from nutrition per year.\nCavalcanti, D. M., de Sales, L. D. O. F., da Silva, A. F., Basterra, E. L., Pena, D., Monti, C., ... & Rasella, D. (2025).\nEvaluating the impact of two decades of USAID interventions and projecting the effects of defunding on mortality up to 2030: a retrospective impact evaluation and forecasting analysis\n. The Lancet.\nOne concern is that it “controls” for variables — like domestic health spending — that may themselves be affected by aid. If government health budgets are reduced because aid fills the gap (a phenomenon known as “crowding out”), then controlling for that spending could lead to overestimating aid’s impact, ignoring what might have happened in its absence.\nRatevosian, J., Millett, G., Honermann, B., Bennett, S., Connor, C., Bekker, L. G., & Beyrer, C. (2025).\nPEPFAR under review: what's at stake for PEPFAR's future.\nThe Lancet, 405(10479), 603-605.\nU.S. State Department.\nOur Priorities – PEPFAR\n.\nPiper, K., Sargeant, L. L., Aitken, C., Randall, A., Tsai, B., Kasten, D., Hatfield-Dodds, Z., Scholl, K., Collier, C., & Mago, R. (2025).\nA citizen review of PEPFAR effectiveness: Yesterday, today, and tomorrow\n.\nTheir approach compares today’s death rates to those in 2000, before PMI began. If those earlier rates had continued, around 600,000 more people would have died in 2023. Since the US accounted for nearly half of all global malaria aid in 2022, Kenny and Sandefur attribute just under half of these averted deaths to its efforts. It’s a rough method, but one that’s commonly used in the literature.\nWinskill, P., Slater, H. C., Griffin, J. T., Ghani, A. C., & Walker, P. G. (2017).\nThe US President's Malaria Initiative, Plasmodium falciparum transmission and mortality: A modelling study.\nPLoS Medicine, 14(11), e1002448.\nOne widely cited analysis focused on children under five — the group\nmost vulnerable\nto malaria — and found that in a set of PMI-supported countries, their mortality rate dropped from 28.9 to 24.3 deaths per 1,000 person-years.\nJakubowski, A., Stearns, S. C., Kruk, M. E., Angeles, G., & Thirumurthy, H. (2017).\nThe US President’s Malaria Initiative and under-5 child mortality in sub-Saharan Africa: A difference-in-differences analysis.\nPLoS medicine, 14(6), e1002319.\nUsing this decline, Kenny and Sandefur offer a second estimate: they apply the drop across all children under five in PMI-supported countries. The result is a rough estimate of 600,000 lives saved each year from PMI alone.\nSome other studies find lower figures. One analysis covering 2005 to 2016 estimates that PMI saved around 940,000 lives (or 94,000 per year).\nWinskill, P., Slater, H. C., Griffin, J. T., Ghani, A. C., & Walker, P. G. (2017).\nThe US President's Malaria Initiative, Plasmodium falciparum transmission and mortality: A modelling study.\nPLoS Medicine, 14(11), e1002448.\nA more recent analysis projected that in 2025, PMI would prevent around 104,000 deaths across its focus countries.\nSymons, T. L., Lubinda, J., McPhail, M., Saddler, A., van den Berg, M., Baggen, H., Berman, Y., Hafsia, S., Jayaseelen, R., Amratia, P., Browne, A., Cameron, E., Vargas-Ruiz, C., Rumisha, S. F., Golding, N., Weiss, D. J., & Gething, P. W. (2025).\nEstimating the potential malaria morbidity and mortality avertable by the President’s Malaria Initiative in 2025: A geospatial modelling analysis\n[Preprint].\nThese more modest figures reflect a narrower scope. Both studies look only at PMI, while a large chunk of US malaria funding is channeled through The Global Fund, which might explain the difference.\nIn 2023, the US government provided approximately $795 million in direct bilateral funding for global malaria control through PMI (Source:\nBeat Malaria\n). The same year, it contributed $2.0 billion to the Global Fund to Fight AIDS, Tuberculosis and Malaria. (Source:\nKFF\n), which has historically disbursed 29 percent of its funds to malaria projects (Source:\nGlobal Fund\n). Assuming this stays constant, the US contributed $580 million to tackle malaria via the Global Fund in 2023.\nThis distinction is highlighted by Charles Kenny and Justin Sandefur in their\narticle\n: “Where possible, we examine both of the questions “how many lives were saved by US financing?’ (gross lives saved) and ‘how many would have been lost absent US financing?” (net lives saved). The number of people who would be dead if the US hadn’t assisted isn’t necessarily all of the lives saved by that assistance because of effects including ‘crowding out’ and “crowding in”—international investments displacing or spurring more domestic support for lifesaving interventions—and market effects like generating lower prices. If the US pays for 1,000 vaccine doses that save 3 lives, we’re counting those 3 lives in the “gross” calculation. But perhaps 300 of those vaccinations would’ve happened without US aid, so the “net” calculation only attributes 1 life saved to US aid.”\nKenny, C. (2025, July 8).\nThese USAID awards were saving lives. Reverse the cuts or reissue them.\nCenter for Global Development.\nPew Research Center, May, 2025, “Majorities of Americans Support Several – But Not All – Types of Foreign Aid”.\nIpsos (2017).\nGlobal Impact of Development Aid\n.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSimon van Teutem and Hannah Ritchie (2025) - “Foreign aid from the United States saved millions of lives each year” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-090244/us-foreign-aid-saved-millions.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-us-foreign-aid-saved-millions,\nauthor = {Simon van Teutem and Hannah Ritchie},\ntitle = {Foreign aid from the United States saved millions of lives each year},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-090244/us-foreign-aid-saved-millions.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "us-foreign-aid-saved-millions", "source_url": "https://ourworldindata.org/us-foreign-aid-saved-millions", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "For decades, these aid programs received bipartisan support and made a difference. Cutting them will cost lives.", "numeric_mentions": ["29,", "2025", "2023", "62 billion", "0.24%", "1", "2", "43.8 billion", "61%", "3", "4", "2.3", "5.6 million", "3.3 million", "1.5 million", "5", "92 million", "21 years", "2001", "2021", "6", "46 million", "5 million", "7", "20 million", "8", "2003,", "25 million", "9", "7.5", "30 million", "10", "1.6 million", "100,000", "2005,", "27", "293,000", "11", "600,000", "12", "300,000", "13", "14", "15", "83%", "91%", "77%", "1%", "30%", "15%", "10%", "2016", "16", "58%", "34%", "2006", "18", "28", "2019", "46", "284", "305", "2030", "28.1 billion", "23,200", "8,340", "71,000", "193,000", "405", "10479", "603", "605", "2000,", "2022,", "2017", "28.9", "24.3", "1,000", "2005", "940,000"], "numeric_evidence": [{"title": "Share of newborns vaccinated against tuberculosis", "source_url": "https://ourworldindata.org/grapher/bcg-immunization-coverage-for-tb-among-1-year-olds.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Tuberculosis vaccine (BCG)"], "row_count_total": 7423, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Tuberculosis vaccine (BCG)": "10"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Tuberculosis vaccine (BCG)": "10"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Tuberculosis vaccine (BCG)": "11"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Tuberculosis vaccine (BCG)": "17"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Tuberculosis vaccine (BCG)": "18"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Tuberculosis vaccine (BCG)": "27"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Tuberculosis vaccine (BCG)": "40"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Tuberculosis vaccine (BCG)": "38"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Tuberculosis vaccine (BCG)": "30"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Tuberculosis vaccine 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"Tuberculosis vaccine (BCG)": "97"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Tuberculosis vaccine (BCG)": "98"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Tuberculosis vaccine (BCG)": "99"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Tuberculosis vaccine (BCG)": "97"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Tuberculosis vaccine (BCG)": "99"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Tuberculosis vaccine (BCG)": "97"}], "rows_tail": [{"Entity": "Yemen", "Code": "YEM", "Year": "1993", "Tuberculosis vaccine (BCG)": "57"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1994", "Tuberculosis vaccine (BCG)": "41"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1995", "Tuberculosis vaccine (BCG)": "60"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1996", "Tuberculosis vaccine (BCG)": "58"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1997", "Tuberculosis vaccine (BCG)": "54"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1998", "Tuberculosis vaccine (BCG)": "66"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1999", "Tuberculosis vaccine (BCG)": "79"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Tuberculosis vaccine (BCG)": "82"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Tuberculosis vaccine (BCG)": "78"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Tuberculosis vaccine (BCG)": "73"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Tuberculosis vaccine (BCG)": "66"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Tuberculosis vaccine (BCG)": "64"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Tuberculosis vaccine (BCG)": "66"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Tuberculosis vaccine (BCG)": "67"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Tuberculosis vaccine (BCG)": "64"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Tuberculosis vaccine (BCG)": "60"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Tuberculosis vaccine (BCG)": "58"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Tuberculosis vaccine (BCG)": "65"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Tuberculosis vaccine (BCG)": "59"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Tuberculosis vaccine (BCG)": "64"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Tuberculosis vaccine (BCG)": "67"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Tuberculosis vaccine (BCG)": "70"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Tuberculosis vaccine (BCG)": "42"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Tuberculosis vaccine (BCG)": "67"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Tuberculosis vaccine (BCG)": "59"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Tuberculosis vaccine (BCG)": "55"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Tuberculosis vaccine (BCG)": "59"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Tuberculosis vaccine (BCG)": "59"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Tuberculosis vaccine (BCG)": "60"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2022", "Tuberculosis vaccine (BCG)": "63"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2023", "Tuberculosis vaccine (BCG)": "54"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2024", "Tuberculosis vaccine (BCG)": "50"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1981", "Tuberculosis vaccine (BCG)": "72"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1982", "Tuberculosis vaccine (BCG)": "70"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1983", "Tuberculosis vaccine (BCG)": "68"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1984", "Tuberculosis vaccine (BCG)": "71"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Tuberculosis vaccine (BCG)": "92"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Tuberculosis vaccine (BCG)": "97"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Tuberculosis vaccine (BCG)": "97"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Tuberculosis vaccine (BCG)": "97"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Tuberculosis vaccine (BCG)": "97"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Tuberculosis vaccine (BCG)": "94"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Tuberculosis vaccine (BCG)": "92"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Tuberculosis vaccine (BCG)": "98"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Tuberculosis vaccine (BCG)": "97"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Tuberculosis vaccine (BCG)": "96"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Tuberculosis vaccine (BCG)": "94"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Tuberculosis vaccine (BCG)": "94"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Tuberculosis vaccine (BCG)": "94"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Tuberculosis vaccine (BCG)": "93"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Tuberculosis vaccine (BCG)": "93"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Tuberculosis vaccine (BCG)": "92"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Tuberculosis vaccine (BCG)": "92"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Tuberculosis vaccine (BCG)": "91"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Tuberculosis vaccine (BCG)": "91"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Tuberculosis vaccine (BCG)": "92"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Tuberculosis vaccine (BCG)": "92"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Tuberculosis vaccine (BCG)": "92"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Tuberculosis vaccine (BCG)": "92"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Tuberculosis vaccine (BCG)": "99"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Tuberculosis vaccine (BCG)": "97"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Tuberculosis vaccine (BCG)": "99"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Tuberculosis vaccine (BCG)": "99"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Tuberculosis vaccine (BCG)": "91"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Tuberculosis vaccine (BCG)": "85"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Tuberculosis vaccine (BCG)": "92"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Tuberculosis vaccine (BCG)": "91"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Tuberculosis vaccine (BCG)": "90"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Tuberculosis vaccine (BCG)": "64"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Tuberculosis vaccine (BCG)": "65"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Tuberculosis vaccine (BCG)": "67"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Tuberculosis vaccine (BCG)": "69"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Tuberculosis vaccine (BCG)": "76"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Tuberculosis vaccine (BCG)": "94"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Tuberculosis vaccine (BCG)": "94"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Tuberculosis vaccine (BCG)": "89"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Tuberculosis vaccine (BCG)": "90"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Tuberculosis vaccine (BCG)": "91"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Tuberculosis vaccine (BCG)": "91"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Tuberculosis vaccine (BCG)": "91"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Tuberculosis vaccine (BCG)": "96"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Tuberculosis vaccine (BCG)": "96"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Tuberculosis vaccine (BCG)": "92"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Tuberculosis vaccine (BCG)": "88"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Tuberculosis vaccine (BCG)": "86"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Tuberculosis vaccine (BCG)": "84"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Tuberculosis vaccine (BCG)": "82"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Tuberculosis vaccine (BCG)": "80"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Tuberculosis vaccine (BCG)": "78"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Tuberculosis vaccine (BCG)": "76"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Tuberculosis vaccine (BCG)": "80"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Tuberculosis vaccine (BCG)": "84"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Tuberculosis vaccine (BCG)": "87"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Tuberculosis vaccine (BCG)": "91"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Tuberculosis vaccine (BCG)": "90"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Tuberculosis vaccine (BCG)": "99"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Tuberculosis vaccine (BCG)": "98"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Tuberculosis vaccine (BCG)": "98"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Tuberculosis vaccine (BCG)": "99"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Tuberculosis vaccine (BCG)": "90"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Tuberculosis vaccine (BCG)": "95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Tuberculosis vaccine (BCG)": "88"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Tuberculosis vaccine (BCG)": "92"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Tuberculosis vaccine (BCG)": "96"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Tuberculosis vaccine (BCG)": "96"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Tuberculosis vaccine (BCG)": "96"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "bcg-immunization-coverage-for-tb-among-1-year-olds", "metadata_url": "https://ourworldindata.org/grapher/bcg-immunization-coverage-for-tb-among-1-year-olds.metadata.json", "chart_title": "Share of newborns vaccinated against tuberculosis", "chart_subtitle": "Share of newborns who have received the Bacillus Calmette-Guérin (BCG) vaccine against tuberculosis. This is calculated only for countries where the vaccine is recommended for all newborns, typically where there is higher TB incidence.", "chart_note": "Coverage estimates include only countries where the WHO recommends vaccination for all newborns. Tuberculosis (TB) is a bacterial infection that primarily affects the lungs but can also affect other parts of the body. It is the leading infectious cause of death worldwide.", "chart_citation": "WHO & UNICEF (2025); UN, World Population Prospects (2024)", "original_chart_url": "https://ourworldindata.org/grapher/bcg-immunization-coverage-for-tb-among-1-year-olds", "owid_column_metadata": {"Share of newborns vaccinated against tuberculosis": {"titleShort": "Share of newborns vaccinated against tuberculosis", "titleLong": "Share of newborns vaccinated against tuberculosis", "descriptionShort": "Share of newborns who have had one dose of the tuberculosis (BCG) vaccine in a given year.", "descriptionKey": ["The WHO recommends BCG vaccination at birth for all newborns in countries or settings with a high incidence of TB and/or high leprosy burden. Countries with low TB incidence or leprosy burden may choose to selectively vaccinate newborns in high-risk groups.", "This chart shows official estimates of national immunization coverage published by the WHO and UNICEF. The estimates include all WHO member states, even those that did not report 2023 data. For non-reporting countries, WHO uses statistical methods to extrapolate from previously reported data, ensuring global coverage can be assessed.", "Global and regional vaccination coverage is calculated using population-weighted averages. In 2023, approximately 5% of countries did not report data, requiring extrapolation from their 2022 data to maintain complete global estimates.", "These estimates combine several sources: official administrative data from health facilities, coverage surveys that meet WHO quality standards, and other relevant information like vaccine supply issues or schedule changes. The accuracy of these estimates depends on how complete and reliable each country’s reporting systems are."], "shortUnit": "%", "unit": "%", "timespan": "1980-2024", "type": "Integer", "owidVariableId": 1077433, "shortName": "coverage__antigen_bcg", "lastUpdated": "2025-07-15", "nextUpdate": "2026-07-15", "citationShort": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data. “Share of newborns vaccinated against tuberculosis” [dataset]. WHO & UNICEF, “WHO Immunization Data - Vaccination coverage”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077433.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "HIV/AIDS deaths averted by antiretroviral therapy", "source_url": "https://ourworldindata.org/grapher/hivaids-deaths-and-averted-due-to-art.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Deaths averted due to antiretroviral therapy", "HIV/AIDS deaths"], "row_count_total": 5940, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "13.11604"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "15.64595"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "19.23207"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "25.38882"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "32.6106"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "39.21881"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "45.98785"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "53.22236"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "60.99987"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "69.58483"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "78.80464"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "84.58985"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "94.02258"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "109.36147"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "123.22548"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "135.409"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "151.74098"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "168.64323"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "181.07874"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Deaths averted due to antiretroviral therapy": "4.16118", "HIV/AIDS deaths": "197.71815"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Deaths averted due to antiretroviral therapy": "13.22638", "HIV/AIDS deaths": "209.17273"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Deaths averted due to antiretroviral therapy": "23.34316", "HIV/AIDS deaths": "220.6069"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Deaths averted due to antiretroviral therapy": "32.17337", "HIV/AIDS deaths": "239.95451"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Deaths averted due to antiretroviral therapy": "37.56673", "HIV/AIDS deaths": "261.7979"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Deaths averted due to antiretroviral therapy": "42.82465", "HIV/AIDS deaths": "285.6313"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Deaths averted due to antiretroviral therapy": "50.68639", "HIV/AIDS deaths": "312.34625"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Deaths averted due to antiretroviral therapy": "65.33326", "HIV/AIDS deaths": "328.2478"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Deaths averted due to antiretroviral therapy": "97.56781", "HIV/AIDS deaths": "329.9947"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Deaths averted due to antiretroviral therapy": "113.58154", "HIV/AIDS deaths": "351.93124"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Deaths averted due to antiretroviral therapy": "110.48019", "HIV/AIDS deaths": "396.11554"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Deaths averted due to antiretroviral therapy": "110.152", "HIV/AIDS deaths": "441.3039"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Deaths averted due to antiretroviral therapy": "106.70383", "HIV/AIDS deaths": "493.18436"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Deaths averted due to antiretroviral therapy": "103.30062", "HIV/AIDS deaths": "540.34955"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Deaths averted due to antiretroviral therapy": "101.00451", "HIV/AIDS deaths": "587.03296"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2024", "Deaths averted due to antiretroviral therapy": "123.57879", "HIV/AIDS deaths": "622.5749"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "330036.72"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "416715.56"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "512851.56"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "617284.2"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "725080.94"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Deaths averted due to antiretroviral therapy": "-3320.4688", "HIV/AIDS deaths": "837816.3"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1996", "Deaths averted due to antiretroviral therapy": "-2342.9531", "HIV/AIDS deaths": "953950.75"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1997", "Deaths averted due to antiretroviral therapy": "1988.0391", "HIV/AIDS deaths": "1063449.2"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1998", "Deaths averted due to antiretroviral therapy": "3948.8406", "HIV/AIDS deaths": "1172092.1"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1999", "Deaths averted due to antiretroviral therapy": "2671.4666", "HIV/AIDS deaths": "1278173.6"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Deaths averted due to antiretroviral therapy": "6041.6807", "HIV/AIDS deaths": "1368729.8"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Deaths averted due to antiretroviral therapy": "11759.441", "HIV/AIDS deaths": "1447893.1"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Deaths averted due to antiretroviral therapy": "20275.287", "HIV/AIDS deaths": "1512357.8"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Deaths averted due to antiretroviral therapy": "34795.32", "HIV/AIDS deaths": "1559542.8"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Deaths averted due to antiretroviral therapy": "84272.27", "HIV/AIDS deaths": "1559964.4"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Deaths averted due to antiretroviral therapy": "173498.84", "HIV/AIDS deaths": "1505618.5"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Deaths averted due to antiretroviral 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"Year": "2013", "Deaths averted due to antiretroviral therapy": "980904.6", "HIV/AIDS deaths": "723162.9"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Deaths averted due to antiretroviral therapy": "1036153.7", "HIV/AIDS deaths": "673053.8"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Deaths averted due to antiretroviral therapy": "1080086.2", "HIV/AIDS deaths": "633714.9"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Deaths averted due to antiretroviral therapy": "1122481.1", "HIV/AIDS deaths": "600934.1"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Deaths averted due to antiretroviral therapy": "1162040.4", "HIV/AIDS deaths": "568387.7"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Deaths averted due to antiretroviral therapy": "1198001.9", "HIV/AIDS deaths": "537047.8"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Deaths averted due to antiretroviral therapy": "1216766.4", "HIV/AIDS deaths": 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"HIV/AIDS deaths": "0.33694"}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "0.48451"}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "0.68289"}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "0.94775"}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "1.29506"}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "1.73952"}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "2.29633"}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "2.986"}, {"Entity": 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"Year": "2006", "Deaths averted due to antiretroviral therapy": "6.25804", "HIV/AIDS deaths": "10.93441"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Deaths averted due to antiretroviral therapy": "8.51816", "HIV/AIDS deaths": "12.05386"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Deaths averted due to antiretroviral therapy": "12.62721", "HIV/AIDS deaths": "11.69436"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Deaths averted due to antiretroviral therapy": "16.82388", "HIV/AIDS deaths": "11.58354"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Deaths averted due to antiretroviral therapy": "18.58761", "HIV/AIDS deaths": "14.19443"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Deaths averted due to antiretroviral therapy": "21.35719", "HIV/AIDS deaths": "16.0425"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Deaths averted due to antiretroviral therapy": "26.85774", "HIV/AIDS deaths": "15.28306"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Deaths averted due to antiretroviral therapy": "32.79282", "HIV/AIDS deaths": "13.66141"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Deaths averted due to antiretroviral therapy": "37.19497", "HIV/AIDS deaths": "13.62423"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Deaths averted due to antiretroviral therapy": "40.07778", "HIV/AIDS deaths": "15.28252"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Deaths averted due to antiretroviral therapy": "43.19578", "HIV/AIDS deaths": "16.20452"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Deaths averted due to antiretroviral therapy": "45.94895", "HIV/AIDS deaths": "17.40882"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Deaths averted due to antiretroviral therapy": "45.16736", "HIV/AIDS deaths": "20.82005"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Deaths averted due to antiretroviral therapy": "42.77485", "HIV/AIDS deaths": "25.2884"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Deaths averted due to antiretroviral therapy": "43.63497", "HIV/AIDS deaths": "26.10833"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Deaths averted due to antiretroviral therapy": "46.63191", "HIV/AIDS deaths": "24.25711"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "Deaths averted due to antiretroviral therapy": "49.83729", "HIV/AIDS deaths": "22.03654"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Deaths averted due to antiretroviral therapy": "50.80004", "HIV/AIDS deaths": "21.83712"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "Deaths averted due to antiretroviral therapy": "51.15083", "HIV/AIDS deaths": "21.88783"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1990", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "21.18665"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1991", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "25.23257"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1992", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "30.04797"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1993", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "35.52625"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1994", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "41.90431"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1995", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "49.06796"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1996", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "57.37834"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1997", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "67.04661"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1998", "Deaths averted due to antiretroviral therapy": "12.83174", "HIV/AIDS deaths": "65.58839"}, {"Entity": "Algeria", 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"25549.771"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Deaths averted due to antiretroviral therapy": "44973.098", "HIV/AIDS deaths": "24654.82"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Deaths averted due to antiretroviral therapy": "47620.105", "HIV/AIDS deaths": "23304.2"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Deaths averted due to antiretroviral therapy": "49870.625", "HIV/AIDS deaths": "22392.223"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Deaths averted due to antiretroviral therapy": "52028.703", "HIV/AIDS deaths": "21846.227"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Deaths averted due to antiretroviral therapy": "54197.383", "HIV/AIDS deaths": "21893.162"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Deaths averted due to antiretroviral therapy": "58495.45", "HIV/AIDS deaths": "20470.453"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Deaths averted due to antiretroviral therapy": "61842.812", "HIV/AIDS deaths": "19905.488"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Deaths averted due to antiretroviral therapy": "62602.68", "HIV/AIDS deaths": "22089.783"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Deaths averted due to antiretroviral therapy": "64011.312", "HIV/AIDS deaths": "22708.465"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Deaths averted due to antiretroviral therapy": "66428.96", "HIV/AIDS deaths": "22405.121"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Deaths averted due to antiretroviral therapy": "70991.9", "HIV/AIDS deaths": "19737.826"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Deaths averted due to antiretroviral therapy": "73829.87", "HIV/AIDS deaths": "18493.479"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Deaths averted due to antiretroviral therapy": "75670.266", "HIV/AIDS deaths": "17375.988"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Deaths averted due to antiretroviral therapy": "76692.98", "HIV/AIDS deaths": "16372.746"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "40074.203"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "52678.164"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "66054.58"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "79674.625"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Deaths averted due to antiretroviral therapy": "0", "HIV/AIDS deaths": "93256.59"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Deaths averted due to antiretroviral therapy": "-3320.4688", "HIV/AIDS deaths": "105438.09"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Deaths averted due to antiretroviral 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"Deaths averted due to antiretroviral therapy": "1845.3438", "HIV/AIDS deaths": "149705.45"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Deaths averted due to antiretroviral therapy": "2935.5781", "HIV/AIDS deaths": "145665.75"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Deaths averted due to antiretroviral therapy": "7297.2305", "HIV/AIDS deaths": "137260.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Deaths averted due to antiretroviral therapy": "12428.992", "HIV/AIDS deaths": "124750.22"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Deaths averted due to antiretroviral therapy": "19817.64", "HIV/AIDS deaths": "113651.99"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Deaths averted due to antiretroviral therapy": "28038.297", "HIV/AIDS deaths": "101609.4"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Deaths averted due to antiretroviral therapy": "36182.594", "HIV/AIDS deaths": "89808.55"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Deaths averted due to antiretroviral therapy": "47971.97", "HIV/AIDS deaths": "74554.234"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Deaths averted due to antiretroviral therapy": "62152.332", "HIV/AIDS deaths": "57886.293"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Deaths averted due to antiretroviral therapy": "71000.92", "HIV/AIDS deaths": "46271.043"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Deaths averted due to antiretroviral therapy": "76492.766", "HIV/AIDS deaths": "39000.344"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Deaths averted due to antiretroviral therapy": "79163.99", "HIV/AIDS deaths": "34399.55"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Deaths averted due to antiretroviral therapy": "80430.86", "HIV/AIDS deaths": "32618.66"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Deaths averted due to antiretroviral therapy": "82433.81", "HIV/AIDS deaths": 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"87156.305", "HIV/AIDS deaths": "17933.617"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Deaths averted due to antiretroviral therapy": "84959.89", "HIV/AIDS deaths": "16723.037"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "hivaids-deaths-and-averted-due-to-art", "metadata_url": "https://ourworldindata.org/grapher/hivaids-deaths-and-averted-due-to-art.metadata.json", "chart_title": "HIV/AIDS deaths averted by antiretroviral therapy", "chart_subtitle": "Estimated annual number of deaths from HIV/AIDS and the estimated number of deaths averted by antiretroviral therapy (ART). 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The World Health Organization (WHO) now advises ART for everyone diagnosed with HIV.", "Antiretroviral therapy (ART) has significantly reduced the number of AIDS-related deaths since its introduction in 1996.", "By suppressing HIV replication, ART improves the health and longevity of people living with HIV, thereby preventing deaths that would have occurred in its absence."], "unit": "deaths", "timespan": "1990-2024", "type": "Numeric", "owidVariableId": 1134332, "shortName": "deaths_averted_art__age_total__sex_total__estimate_central", "lastUpdated": "2026-01-19", "citationShort": "Joint United Nations Programme on HIV/AIDS (2026) – with minor processing by Our World in Data", "citationLong": "Joint United Nations Programme on HIV/AIDS (2026) – with minor processing by Our World in Data. “Lives saved due to ART – central estimate” [dataset]. 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Reuse should follow the source and license information in this chart metadata."}, {"title": "Malaria deaths by world region", "source_url": "https://ourworldindata.org/grapher/global-malaria-deaths-by-world-region.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Deaths from malaria"], "row_count_total": 126, "rows_head": [{"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Deaths from malaria": "840000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Deaths from malaria": "838000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Deaths from malaria": "797000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Deaths from malaria": "774000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Deaths from malaria": "750000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Deaths from malaria": "723000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Deaths from malaria": "715000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Deaths from malaria": "698000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Deaths from malaria": "678000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Deaths from malaria": "671000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Deaths from malaria": "646000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Deaths from malaria": "608000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Deaths from malaria": "575000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Deaths from malaria": "556000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Deaths from malaria": "534000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Deaths from malaria": "527000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Deaths from malaria": "528000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Deaths from malaria": "542000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Deaths from malaria": "533000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Deaths from malaria": "534000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Deaths from malaria": "602000"}, {"Entity": "Americas", "Code": "", "Year": "2000", "Deaths from malaria": "909"}, {"Entity": "Americas", "Code": "", "Year": "2001", "Deaths from malaria": "832"}, {"Entity": "Americas", "Code": "", "Year": "2002", "Deaths from malaria": "764"}, {"Entity": "Americas", "Code": "", "Year": "2003", "Deaths from malaria": "726"}, {"Entity": "Americas", "Code": "", "Year": "2004", "Deaths from malaria": "711"}, {"Entity": "Americas", "Code": "", "Year": "2005", "Deaths from malaria": "687"}, {"Entity": "Americas", "Code": "", "Year": "2006", "Deaths from malaria": "581"}, {"Entity": "Americas", "Code": "", "Year": "2007", "Deaths from malaria": "503"}, {"Entity": "Americas", "Code": "", "Year": "2008", "Deaths from malaria": "470"}, {"Entity": 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"4000"}, {"Entity": "Western Pacific", "Code": "", "Year": "2014", "Deaths from malaria": "3800"}], "rows_tail": [{"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Deaths from malaria": "715000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Deaths from malaria": "698000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Deaths from malaria": "678000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Deaths from malaria": "671000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Deaths from malaria": "646000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Deaths from malaria": "608000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Deaths from malaria": "575000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Deaths from malaria": "556000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Deaths from malaria": "534000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Deaths 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"Year": "2018", "Deaths from malaria": "571"}, {"Entity": "Americas", "Code": "", "Year": "2019", "Deaths from malaria": "509"}, {"Entity": "Americas", "Code": "", "Year": "2020", "Deaths from malaria": "409"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2000", "Deaths from malaria": "13700"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2001", "Deaths from malaria": "13900"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2002", "Deaths from malaria": "13200"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2003", "Deaths from malaria": "12200"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2004", "Deaths from malaria": "10600"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2005", "Deaths from malaria": "11500"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2006", "Deaths from malaria": "11500"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2007", "Deaths from malaria": "9800"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2008", "Deaths from malaria": "7200"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2009", "Deaths from malaria": "6900"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2010", "Deaths from malaria": "8800"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2011", "Deaths from malaria": "8000"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2012", "Deaths from malaria": "8100"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2013", "Deaths from malaria": "7700"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2014", "Deaths from malaria": "7900"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2015", "Deaths from malaria": "8300"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2016", "Deaths from malaria": "9500"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2017", "Deaths from malaria": "10200"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2018", "Deaths from malaria": "10900"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2019", "Deaths from malaria": "11500"}, {"Entity": "Eastern Mediterranean", "Code": "", "Year": "2020", "Deaths from malaria": "12300"}, {"Entity": "Europe", "Code": "OWID_EUR", "Year": "2000", "Deaths from malaria": "0"}, {"Entity": "Europe", "Code": "OWID_EUR", "Year": "2001", "Deaths from malaria": "0"}, {"Entity": "Europe", "Code": "OWID_EUR", "Year": "2002", "Deaths from malaria": "0"}, {"Entity": "Europe", "Code": "OWID_EUR", "Year": "2003", "Deaths from malaria": "0"}, {"Entity": "Europe", "Code": "OWID_EUR", "Year": "2004", "Deaths from malaria": "0"}, {"Entity": "Europe", "Code": "OWID_EUR", "Year": "2005", "Deaths from malaria": "0"}, {"Entity": "Europe", "Code": "OWID_EUR", "Year": "2006", "Deaths from malaria": "0"}, {"Entity": "Europe", "Code": "OWID_EUR", "Year": "2007", "Deaths from malaria": "0"}, {"Entity": "Europe", "Code": "OWID_EUR", "Year": "2008", "Deaths from 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{"raw_link": "https://ourworldindata.org/wolbachia-neglected-tropical-diseases", "title": "How Wolbachia bacteria could help us tackle some of the world’s most neglected tropical diseases", "context": "Home\nNeglected Tropical Diseases\nHow Wolbachia bacteria could help us tackle some of the world’s most neglected tropical diseases\nA common bacterium can dramatically reduce the spread of dengue fever and other tropical diseases.\nBy\nHannah Ritchie\nSeptember 22, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nThe animals that pose the biggest threat to humans are not lions, sharks, or snakes; they are tiny mosquitoes. Mosquitoes kill\nmore than\n600,000 people every year from malaria alone, but they also carry and spread a host of other tropical diseases.\nOne of those is dengue fever. It’s a common disease found in the tropics and subtropics, and around\n60 million recorded cases\noccur each year.\nFor most people, the symptoms of dengue fever are extremely unpleasant: a fever, severe headache, nausea, joint and muscle pain, and sometimes a rash. Some die from it. Around 25,000 people\ndie from dengue\neach year, almost all of them in Asia. To put this in perspective, in most years over the past decade,\nfewer than\n25,000 people died globally in all natural disasters combined.\n1\nEnding dengue fever would be like ending the death toll from floods, wildfires, hurricanes, and other disasters.\nUnlike some other tropical diseases, dengue fever has no specific antiviral treatment, and while vaccines exist,\nnone provide\nuniversal protection comparable to vaccines for diseases like measles or polio.\nThere are a few vaccines against dengue, but these are used more selectively. One, for example, is sometimes given to those who have already been infected by dengue before, to protect against future infection. Another is not particularly effective in some younger children, but recommended for older ones.\nHowever, one promising solution could dramatically reduce the spread of infections and the number of people experiencing a severe form of the disease: the Wolbachia method.\nWolbachia\nis a tiny bacterium that naturally occurs in around half of all insect species, including fruit flies, bees, beetles, moths, dragonflies, butterflies, and some mosquitoes.\n“Far North Queensland is now essentially a dengue-free area for the first time in well over 100 years”\nThe mosquito most commonly spreading dengue fever —\nAedes aegypti\n—\ndoesn’t\nnaturally contain Wolbachia. But scientists discovered that when these mosquitoes are bred to carry it, they are much less likely to transmit viruses from person to person. This doesn’t just apply to dengue fever but also to other diseases such as yellow fever, Zika, and chikungunya. This new method promises to finally give humanity an effective tool against several tropical neglected diseases.\nIn this article, I’ll focus on how Wolbachia can be used against dengue fever. I'll examine how this innovative new method works, how effective it is in reducing transmission, and how it can be rolled out across the tropics to protect as many people as possible.\nHow the Wolbachia method works\nTo understand how Wolbachia\ncould stop the spread of disease, we must first understand how mosquitoes transmit them. Mosquitoes do not carry these tropical diseases naturally. Instead, female mosquitoes can pick up the virus when they bite someone already infected with dengue fever, yellow fever, or Zika. When they then bite their next victim, they can pass it on.\nThe spread is from human to human, but mosquitoes act as the messenger.\nThe Wolbachia\nmethod\nworks because it stops the viral infection from developing in the mosquito. It boosts its immune system, and since mosquitoes need resources to survive, it reduces the available resources (such as cholesterol) that viruses need to grow. Wolbachia makes it much harder for viruses to thrive, and if they’re not present in the mosquito, they can’t be passed on to the next human.\nBut how do scientists\ndevelop a whole population\nof Wolbachia-positive mosquitoes?\nFirst, they extract Wolbachia bacteria from insects that naturally carry them. Around half of insects do, so we’re never in short supply. Then, under a microscope, they inject these bacteria into\nAedes aegypti\nembryos. This is an incredibly delicate process that requires very fine needles and lots of expertise. Even then, many of the embryos don’t survive. Those that do, though, are raised into adult mosquitoes.\nThe second step is to develop a colony of Wolbachia-positive mosquitoes in the lab. This is done by taking the females with Wolbachia and having them mate with male mosquitoes.\nWolbachia is passed on not only to males but also to mosquito offspring that the females produce.\nOnce a big enough population of Wolbachia-carrying mosquitoes has been established, they’re released into the wild, where they mix with natural populations. This part of the process can require large teams of volunteers. In their lifetimes, mosquitoes usually don’t fly more than 150 meters from where they hatch, so you need many spaced releases to cover an area that’s even just a few tens of square kilometers. Over several generations — typically within three to six months — more and more mosquitoes within the local environment become “Wolbachia-positive” until almost all of the mosquitoes carry the bacteria. That means they do not carry or transmit viruses like dengue fever to humans.\nIn the chart below, I summarized this process.\nAs I’ll come on to later, these wild releases require many mosquitoes. There are now “factories” for this; one in Medellín, Colombia, produces 30 million Wolbachia-positive mosquitoes weekly.\nDownload\nThis method tends to be self-sustaining, so once a successful “release” of mosquitoes has occurred, it doesn’t need to be repeated.\nThe method does not involve genetic modification of mosquito species; it simply relies on a natural bacterium that’s extremely common among insects and many other mosquito species. Humans are exposed to these bacteria daily through the food they eat and the insects that bite them. There have been no reported health risks associated with exposure to it.\nWolbachia programs reduce the number of dengue infections by more than three-quarters\nMany things should work in theory; the question is whether they also work in practice. How about the Wolbachia method?\nOne of the\nmost famous trials\nwas conducted by the World Mosquito Program in Yogyakarta, a city in Indonesia that is badly affected by dengue fever and other neglected tropical diseases.\n2\nWorking with the local community — a population of around 310,000 people — the study site was split into 24 areas, each measuring around one square kilometer.\nThis was the method's first randomized control trial (RCT). RCTs are often considered the “gold standard” for testing the efficacy of particular interventions.\nWolbachia-infected mosquitoes reduced dengue fever infections by 77% and hospitalizations by 86% in Indonesian trials\nTwelve of those areas were randomly selected to have Wolbachia-infected mosquitoes released. The other 12 did not get the Wolbachia treatment (and were therefore the “control”). By comparing dengue fever infections in the areas that did and didn’t receive Wolbachia, researchers could learn how effective it was in stopping the spread. Note that this involved releases of the mosquitoes every few weeks over nine months, so it wasn’t a single “one-time” release.\nThe results are staggering. Wolbachia\nreduced dengue fever's incidence (the number of new infections) by 77%, and the number of dengue hospitalizations was 86% lower in the areas with Wolbachia treatment. The figure below summarizes these results.\nDownload\nWe can also see this effect when comparing dengue fever infection rates over time. In the chart below, we can see dengue fever cases in areas that received the Wolbachia treatment as the solid red line and those that\ndidn’t\nget the treatment as the blue line. In the period before the Wolbachia release in 2016, all of these areas experienced large outbreaks at similar times. But after the deployment of Wolbachia\n(the shaded zone at the end), the areas with the treatment had significantly lower levels than those without. That suggests that it was effective in reducing the spread of the virus.\nBut as you can see in the chart, even in areas with the Wolbachia treatment, the number of dengue fever infections didn’t drop to zero. That’s why combining it with other control methods, such as effective vaccines, sprays, and bednets, might still be needed to ensure everyone is protected. These other interventions would be required at a much lower scale than in a scenario without any Wolbachia treatment, and the results would be far better than what could be achieved with sprays or nets alone.\nDownload\nIndonesia is not the only country where we have evidence that this method works.\nThe first Wolbachia\nreleases by the World Mosquito Program were in\nNorthern Queensland\n, Australia, back in 2011. Over the last 14 years, monitoring has shown that Wolbachia-positive mosquitoes continue to self-sustain. There are no signs of local dengue transmission in the areas with the highest rollout. As Dr Richard Gair, a physician in the local area, put it:\n“Far North Queensland is now essentially a dengue-free area for the first time in well over 100 years.”\nPrograms in Brazil and Colombia reduced new dengue infections by more than two-thirds, and in some cases, more than 90% in the areas where they were released. In Brazil, the same method also led to a substantial decline in the incidence of the Zika virus and chikungunya.\n3\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nHow can Wolbachia programs be scaled across the tropics?\nThese results seem too good to be true. The Wolbachia method has been shown to dramatically reduce the spread of not just dengue fever but also other neglected diseases. Wolbachia is self-sustaining, unlike other management tools such as vaccines, mosquito sprays, or bednets, which must be readministered or delivered periodically. Once Wolbachia-positive mosquito populations have been established, they remain so for long periods. There are no obvious biodiversity concerns, and this method does not involve genetic modification (which often receives public and political pushback and involves additional regulatory hurdles).\nWhat’s stopping them from being scaled across much of the tropics, where tens of millions of people are affected by these diseases every year?\nThe first potential barrier is\npublic and political acceptance\n. In each trial program, researchers invested heavily in community engagement through local media and key stakeholders to explain to the local population what was involved, how it worked, and the impacts. People could share their concerns and have their questions answered. This was particularly successful in building community trust, and support for the trials was high. In Yogyakarta,\npublic acceptance\nwas at 88%, and in other parts of Indonesia where interventions took place, this was over 90%. In Colombia, Australia, and Brazil,\nlevels of support\nwere similarly high.\nThese trials were clearly carefully planned and small enough to develop public trust. This might be harder if they were to be rolled out across much larger areas. However, the difficulty in building confidence in these initial trials is that these Wolbachia programs were extremely new and experimental to some extent. As Wolbachia became more common and the benefits were more widely known, the amount of engagement needed to help locals understand the costs and benefits would fall. I expect this will not be a blocker, and will be more welcome to many than alternative health measures such as vaccines or chemical sprays.\nThe challenge of\nfunding\nthese programs is a bigger barrier. Most initial programs have been financed through institutional grants or philanthropic donations.\n4\nThese financial commitments are viable at the trial stage, but generous donations will not be sufficient to scale these programs across the tropics. Other forms of finance — most likely government funding in each country — will be needed for this next stage.\nThe Wolbachia method requires a substantial\nupfront\ncost to breed or buy mosquitoes and develop labor-intensive release programs. But the “running” costs are very low once they've been deployed. Only a few studies are looking at the cost-effectiveness of Wolbachia, but early evidence suggests a payback of $1.35 to $3.40 for every dollar spent.\n5\nA study looking at the cost-effectiveness of Wolbachia programs in a densely-populated city in Indonesia estimated that it cost around $1500 to prevent one disability-adjusted life year (DALY), a measure of disease burden. By global health standards, that is relatively cheap, especially in middle-income countries where many incredibly cheap life-saving interventions, such as childhood vaccinations, are already widely used. So these programs — particularly in areas with a lot of dengue fever — should pay themselves back and be a good investment for most governments, but that doesn’t mean securing the capital costs is easy.\nScaling Wolbachia programs means producing billions of mosquitoes weekly — Medellín already makes 30 million every week\nThe most obvious barriers to me are\nlogistical challenges\nneeded to apply this method at scale. Extracting the bacteria from other insects and microinjecting them into tiny mosquito embryos is incredibly labour-intensive and requires rare skills and dexterity. However, a mass breeding program must be developed in much larger volumes. This means that applying this method in large, densely populated regions is a much bigger task than in many of the trials we looked at above. Colombia\nhas\ninvested in giant mosquito-breeding programs, and the size is quite astounding: its Medellín factory produces more than\n30 million mosquitoes every week\n. If we expand Wolbachia across high-impact tropical areas, we’d need to scale this to billions of mosquitoes weekly.\nIn the future, countries would either have to develop their own mosquito-breeding factories or import mosquito eggs internationally, which also introduces challenges around preservation, risks of infection, and biosafety concerns.\nThe last logistical hurdle is\ndistribution\n. In the trials, mosquitoes were released every 50 meters. They therefore relied on a large number of program staff and community volunteers. Scaling this across large cities efficiently and sustainably is a challenge. There is an opportunity for emerging technologies, such as drones, to make this process less labor-intensive and more optimized, but these solutions are still\nat the trial stage\n.\nThe Wolbachia\nmethod is an extremely promising solution that could protect hundreds of millions of people from some of the world’s most neglected diseases, which currently have no cure.\nAcknowledgments\nThanks to Max Roser, Edouard Mathieu, and Simon van Teutem for their feedback and comments on this article and its visualizations.\nContinue reading on Our World in Data\nTrachoma: how a common cause of blindness can be prevented worldwide\nThe world has seen a large decline in trachoma, but millions are still at risk. How can we make more progress against it?\nMalaria was common across half the world – since then it has been eliminated in many regions\nMalaria has been eliminated from large parts of Europe, the Americas, East Asia, Australia, and the Caribbean.\nOur history is a battle against the microbes: we lost terribly before science, public health, and vaccines allowed us to protect ourselves\nFor most of history, we were losing the battle against microbes. Vaccines were one of the breakthroughs that turned it around.\nEndnotes\nThis is based on data from EM-DAT, the most comprehensive dataset on disasters worldwide. In most years over the past decade (2013 to 2023), the death toll was lower than 25,000. Deaths in 2022 and 2023 were much higher (upwards of 76,000) as a result of particularly devastating\nearthquakes in Turkey and Syria\n, and the\nDerna dam collapse\nin Libya. Note that this data captures some deaths from heatwaves, but does not fully account for the premature deaths attributed to cold or heat.\nUtarini, A., Indriani, C., Ahmad, R. A., Tantowijoyo, W., Arguni, E., Ansari, M. R., ... & Simmons, C. P. (2021). Efficacy of Wolbachia-infected mosquito deployments for the control of dengue. New England Journal of Medicine, 384(23), 2177-2186.\nPinto, S. B., Riback, T. I., Sylvestre, G., Costa, G., Peixoto, J., Dias, F. B., ... & Moreira, L. A. (2021).\nEffectiveness of Wolbachia-infected mosquito deployments in reducing the incidence of dengue and other Aedes-borne diseases in Niterói, Brazil: A quasi-experimental study\n. PLoS neglected tropical diseases.\nThe World Mosquito Program provides\na full list\nof its financial supporters. These include foundations such as The Bill and Melinda Gates Foundation, the Rotary Foundation, and the Wellcome Trust, as well as aid agencies such as USAID and aid programs from the New Zealand and Australian governments.\nBrady, O.J., Kharisma, D.D., Wilastonegoro, N.N. et al. The cost-effectiveness of controlling dengue in Indonesia using wMel Wolbachia released at scale: a modelling study. BMC Med 18, 186 (2020).\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2025) - “How Wolbachia bacteria could help us tackle some of the world’s most neglected tropical diseases” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20251125-173858/wolbachia-neglected-tropical-diseases.html' [Online Resource] (archived on November 25, 2025).\nBibTeX citation\n@article{owid-wolbachia-neglected-tropical-diseases,\nauthor = {Hannah Ritchie},\ntitle = {How Wolbachia bacteria could help us tackle some of the world’s most neglected tropical diseases},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20251125-173858/wolbachia-neglected-tropical-diseases.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "wolbachia-neglected-tropical-diseases", "source_url": "https://ourworldindata.org/wolbachia-neglected-tropical-diseases", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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Reuse should follow the source and license information in this chart metadata."}, {"grapher_slug": "dengue-incidence", "source_url": "https://ourworldindata.org/grapher/dengue-incidence", "parse_error": "Failed to fetch https://ourworldindata.org/grapher/dengue-incidence.csv"}, {"title": "Dengue fever deaths", "source_url": "https://ourworldindata.org/grapher/dengue-fever-deaths.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Total deaths from dengue among both sexes"], "row_count_total": 4422, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Total deaths from dengue among both sexes": "0.07"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Total deaths from dengue among both sexes": "0.08"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Total deaths from dengue among both sexes": "0.08"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Total deaths from dengue among both sexes": "0.09"}, {"Entity": "Afghanistan", 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{"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Total deaths from dengue among both sexes": "17.91"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Total deaths from dengue among both sexes": "14.27"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Total deaths from dengue among both sexes": "16.61"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Total deaths from dengue among both sexes": "17.46"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Total deaths from dengue among both sexes": "18.960001"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Total deaths from dengue among both sexes": "19.96"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Total deaths from dengue among both sexes": "21.67"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Total deaths from dengue among both sexes": "23.699999"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Total deaths from dengue among both 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{"Entity": "Albania", "Code": "ALB", "Year": "2005", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Total 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"Algeria", "Code": "DZA", "Year": "2002", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Total deaths from 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"DZA", "Year": "2021", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2000", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2001", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2002", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2003", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2004", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2005", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2006", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2007", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2008", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2009", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2010", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2011", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2012", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2013", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2014", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2015", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2016", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2017", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2018", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2019", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2020", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Andorra", "Code": "AND", "Year": "2021", "Total deaths from dengue among both sexes": "0"}, {"Entity": "Angola", "Code": "AGO", "Year": "2000", "Total deaths from dengue among both sexes": "0.03"}, {"Entity": "Angola", "Code": "AGO", "Year": "2001", "Total deaths from dengue among both sexes": "0.03"}, {"Entity": "Angola", "Code": "AGO", "Year": "2002", "Total deaths from dengue among both sexes": "0.03"}, {"Entity": "Angola", "Code": "AGO", "Year": "2003", "Total deaths from dengue among both sexes": "0.04"}, {"Entity": "Angola", "Code": "AGO", "Year": "2004", "Total deaths from dengue among both sexes": "0.05"}, {"Entity": "Angola", "Code": "AGO", "Year": "2005", "Total deaths from dengue among both sexes": "0.06"}, {"Entity": "Angola", "Code": "AGO", "Year": "2006", "Total deaths from dengue among both sexes": "0.06"}, {"Entity": "Angola", "Code": "AGO", "Year": "2007", "Total deaths from dengue among both sexes": "0.08"}, {"Entity": "Angola", "Code": "AGO", "Year": "2008", "Total deaths from dengue among both sexes": "0.09"}, {"Entity": "Angola", "Code": "AGO", "Year": "2009", "Total deaths from dengue among both sexes": "0.09"}], "rows_tail": [{"Entity": "Venezuela", "Code": "VEN", "Year": "2012", "Total deaths from dengue among both sexes": "58.17"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2013", "Total deaths from dengue among both sexes": "59.27"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2014", "Total deaths from dengue among both sexes": "63.97"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2015", "Total deaths from dengue among both sexes": "65.59"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2016", "Total deaths from dengue among both sexes": "96.02"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2017", "Total deaths from dengue among both sexes": "85.98"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2018", "Total deaths from dengue among both sexes": "79.11"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2019", "Total deaths from dengue among both sexes": "72.61"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2020", "Total deaths from dengue among both sexes": "68.35"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2021", "Total deaths from dengue among both sexes": "67.66"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2000", "Total deaths from dengue among both sexes": "66.22"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2001", "Total deaths from dengue among both sexes": "62.4"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2002", "Total deaths from dengue among both sexes": "58.64"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2003", "Total deaths from dengue among both sexes": "57.67"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2004", "Total deaths from dengue among both sexes": "56.33"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2005", "Total deaths from dengue among both sexes": "55.64"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2006", "Total deaths from dengue among both sexes": "56.73"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2007", "Total deaths from dengue among both sexes": "79.95"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2008", "Total deaths from dengue among both sexes": "61.3"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2009", "Total deaths from dengue among both sexes": "62.37"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2010", "Total deaths from dengue among both sexes": "65.75"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2011", "Total deaths from dengue among both sexes": "67.53"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2012", "Total deaths from dengue among both sexes": "70.13"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2013", "Total deaths from dengue among both sexes": "69.56"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2014", "Total deaths from dengue among both sexes": "72.19"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2015", "Total deaths from dengue among both sexes": "75.17"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2016", "Total deaths from dengue among both sexes": "99.36"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2017", "Total deaths from dengue among both sexes": "103.7"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2018", "Total deaths from dengue among both sexes": "80.2"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2019", "Total deaths from dengue among both sexes": "82.22"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "Total deaths from dengue among both sexes": "88.11"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2021", "Total deaths from dengue among both sexes": "96.57"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Total deaths from dengue among both sexes": "17693.91"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Total deaths from dengue among both sexes": "18457.34"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Total deaths from dengue among both sexes": "19344.969"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Total deaths from dengue among both sexes": "19448.842"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Total deaths from dengue among both sexes": "20042.818"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Total deaths from dengue among both sexes": "20000.49"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Total deaths from dengue among both sexes": "20059.48"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Total deaths from dengue among both sexes": "22777.32"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Total deaths from dengue among both sexes": "22640.53"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Total deaths from dengue among both sexes": "23217.16"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Total deaths from dengue among both sexes": "24824.08"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Total deaths from dengue among both sexes": "25166.61"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Total deaths from dengue among both sexes": "26976.242"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Total deaths from dengue among both sexes": "28131.541"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Total deaths from dengue among both sexes": "27626.15"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Total deaths from dengue among both sexes": "28669.91"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Total deaths from dengue among both sexes": "27236.69"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Total deaths from dengue among both sexes": "28115.762"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Total deaths from dengue among both sexes": "27221.762"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Total deaths from dengue among both sexes": "26231.951"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Total deaths from dengue among both sexes": "25634.889"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Total deaths from dengue among both sexes": "24048.22"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Total deaths from dengue among both sexes": "0.08"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Total deaths from dengue among both sexes": "0.08"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Total deaths from dengue among both sexes": "0.08"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Total deaths from dengue among both sexes": "0.08"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Total deaths from dengue among both sexes": "0.07"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Total deaths from dengue among both sexes": "0.08"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Total deaths from dengue among both sexes": "0.08"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Total deaths from dengue among both sexes": "0.08"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Total deaths from dengue among both sexes": "0.08"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Total deaths from dengue among both sexes": "0.09"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Total deaths from dengue among both sexes": "0.09"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Total deaths from dengue among both sexes": "0.09"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Total deaths from dengue among both sexes": "0.09"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Total deaths from dengue among both sexes": "0.09"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Total deaths from dengue among both sexes": "0.1"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Total deaths from dengue among both sexes": "0.1"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Total deaths from dengue among both sexes": "0.11"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Total deaths from dengue among both sexes": "0.11"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Total deaths from dengue among both sexes": "0.11"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Total deaths from dengue among both sexes": "375.54"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Total deaths from dengue among both sexes": "0.12"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Total deaths from dengue among both sexes": "0.12"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Total deaths from dengue among both sexes": "0.02"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Total deaths from dengue among both sexes": "0.02"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Total deaths from dengue among both sexes": "0.02"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Total deaths from dengue among both sexes": "0.02"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Total deaths from dengue among both sexes": "0.03"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Total deaths from dengue among both sexes": "0.03"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Total deaths from dengue among both sexes": "0.03"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Total deaths from dengue among both sexes": "0.04"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Total deaths from dengue among both sexes": "0.04"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Total deaths from dengue among both sexes": "0.04"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Total deaths from dengue among both sexes": "0.04"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Total deaths from dengue among both sexes": "0.05"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Total deaths from dengue among both sexes": "0.06"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Total deaths from dengue among both sexes": "0.06"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Total deaths from dengue among both sexes": "0.07"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Total deaths from dengue among both sexes": "0.07"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Total deaths from dengue among both sexes": "0.08"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Total deaths from dengue among both sexes": "0.08"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Total deaths from dengue among both sexes": "0.08"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Total deaths from dengue among both sexes": "0.09"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Total deaths from dengue among both sexes": "0.1"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Total deaths from dengue among both sexes": "0.11"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Total deaths from dengue among both sexes": "0.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Total deaths from dengue among both sexes": "0.02"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Total deaths from dengue among both sexes": "0.02"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Total deaths from dengue among both sexes": "0.02"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Total deaths from dengue among both sexes": "0.02"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "dengue-fever-deaths", "metadata_url": "https://ourworldindata.org/grapher/dengue-fever-deaths.metadata.json", "chart_title": "Dengue fever deaths", "chart_subtitle": "Estimated annual number of deaths from dengue fever. 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{"raw_link": "https://ourworldindata.org/britain-safest-roads-history", "title": "How Britain built some of the world’s safest roads", "context": "Home\nCauses of Death\nHow Britain built some of the world’s safest roads\nThe death rate per mile driven has declined 22-fold since 1950.\nBy\nHannah Ritchie\nSeptember 8, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nA century ago, these were the cars on Britain’s roads. Forget driving lessons or tests; to get behind the wheel legally, all you needed was a paper license, which cost the equivalent of around 25 pence today.\nCars had no seatbelts and, of course, no airbags. There were no mirrors to see traffic behind. No flashing indicators, so your signal to turn left or right was simply sticking your arm out. The brakes were poor, and emergency braking was impossible. Steering was stiff and clunky, and the headlights were weak, making it difficult to see much at night.\nCombine this with the lack of pavements for pedestrians, the lack of signs or traffic signals, and the absence of enforced rules, and you can understand why it was a dangerous time to be on the roads.\nThroughout the 1920s and ‘30s, between 5,000 and 7,000 people died in road accidents each year.\nDownload\nSource:\nWolverhampton City Archives via Wikimedia Commons\nDownload\nSource:\nMark Crombie, Flickr vintage topographical postcards collection\nFast-forward to today. Around 1,700 people die in road incidents each year in the UK, about a quarter of the number that used to be. That’s despite there being 16 times more vehicles on the road and 33 times as many miles driven.\nPer mile driven, the death rate declined 22-fold since 1950.\nYou can see all of this in the chart below.\nIf road deaths per mile driven were still as high as in 1950, then the UK would not see 1,700 road deaths per year, but 37,000.\n1\nToday, the United Kingdom has one of the lowest road death rates in the world.\n2\nYou can see it compared to other countries in the chart.\nHow did road traffic become so safe in the UK? In this article, I want to journey through the history of policies, norms, and transport innovations that have saved thousands of lives yearly. These lessons help identify what works and what doesn’t, so that other parts of the world can make their roads much safer. Globally, around\n1.2 million people\ndie in road accidents every year. Yet this is one of the world’s most overlooked health problems, even though we already know how to prevent many of these deaths.\nMany of the UK’s interventions and policies are reflected in the more general guidance in reports such as those\npublished by\nthe World Bank’s Global Road Safety Facility.\n3\nThese reports take a more in-depth look at the policies and interventions that are effective (or not) in reducing road fatalities. They align closely with the lessons I have drawn from Britain’s history.\nAnarchy and blackouts: Britain’s roads until the end of World War II\nLet’s go back to the period before the Second World War.\nSpeed limits on Britain’s roads had existed since the early 1900s, but they were rarely enforced, so hardly anyone followed them. In 1930, the government decided that speed limits should be abolished if no one was willing to follow them. The country’s own transport minister, Herbert Morrison,\neven admitted\nto ignoring them in parliament:\n“… there was not one of their Lordships who observed the speed limit [and] I venture to say that as legislators we are not entitled to enforce and to continue speed limits.”\nJust four years later, following concerns about the number of pedestrians killed on busy urban roads, a limit of 30 miles per hour was reintroduced in built-up areas. Road deaths stayed relatively stable throughout the 1930s until the start of the Second World War.\nDuring the first few years of the war, road deaths increased. Mandatory nighttime blackouts prevented cars from using their headlights, and streetlights were turned off completely. The chart shows that pedestrians, especially, were paying with their lives.\nAs the King’s Surgeon\nwrote in\nthe British Medical Journal in 1939:\n“frightening the nation into blackout regulations, the Luftwaffe was able to kill 600 British citizens a month without ever taking to the air”.\nThe high risk to pedestrians in the first half of the 20th century was not only true in the UK. The\nLiterary Digest\nwas a weekly magazine in the United States; in the 1920s to 1940s, the humor section\noften featured\njokes about how dangerous cars were to pedestrians.\nHere’s one published in the Nashville Banner:\n“With so many automobiles, the supply of pedestrians will soon be much short of the demand.”\nAnd another:\nTRAFFIC COP: “Hey you! Didn’t you hear me yelling for you to stop?”\nAUTO FIEND: “Oh! Was that you yelling? I thought that was just somebody I had run over.”\nCars were becoming increasingly popular, but at the cost of those walking alongside them.\nThe climb to a post-war peak in the mid-1960s\nOver the next few decades, road deaths steadily climbed to their post-war peak in 1966.\nBut it’d be wrong to conclude that the UK government did little to improve road safety at that time. Fatality\nrates\ndeclined, but this was not enough to offset the rapid rise in the number of cars on the roads and the number of miles being driven.\nThe chart below shows the\nchange\nin each indicator from 1950 to 1966: the number of registered vehicles almost tripled, miles driven more than doubled, and deaths increased by just over 50%. That means rates actually went down.\nWhat were some of the policies that successfully limited the number of deaths?\nIn the footnote, I’ll list a few small interventions for which there is mixed evidence, and instead focus on the larger ones with a more substantial evidence base.\n4\nOne of the most important changes during this period was the introduction of motorways (also known as “highways”). I’d previously assumed that motorways were more\ndangerous than city or other rural roads because cars drive much faster.\nBut it’s the opposite. You can see this in the most recent data for the UK, shown in the chart below. If we look at the number of deaths per billion miles driven, we see that motorways are roughly four times safer than urban roads, and more than five times safer than rural roads. This is not specific to the UK:\namong 24 OECD countries\n, approximately 5% of road deaths occurred on motorways.\n5\nIn almost all countries, it was less than 10%.\nDownload\nRural roads are the most dangerous. In 18 countries, more than half of deaths occurred on rural roads, and it was more than two-thirds in Spain, Sweden, Finland, Ireland, and New Zealand.\nMotorways are safer for several reasons. First, there are fewer road users — just cars, no pedestrians or cyclists. Second, there are fewer stops and starts. Third, there are usually physical barriers in the middle, separating vehicles traveling in different directions and reducing the risk of head-on collisions, which are much more common on narrow rural roads.\nOne key to road safety is having well-designed motorway infrastructure. The first motorway in the UK opened in 1958, and the growth of the network since then has saved many lives.\nIf motorways were the big infrastructural change of the 1950s, roundabouts were the innovation of the 1960s. In 1966, the UK government\nimplemented\nthe “priority rule”, which requires drivers at roundabouts to give priority to vehicles already in the roundabout. This rule continues to this day.\nAgain, I previously underestimated the importance of roundabouts because I assumed that the high and constant traffic flow might increase the risk of collisions. However, there is good evidence that well-designed roundabout systems make our roads much safer than the alternatives: intersections with stop signs (2-way or 4-way) and traffic light systems.\nStudies have found that replacing traditional intersections with roundabouts substantially reduces injuries and deaths from collisions. The magnitude of reduction depends on the context and road conditions beforehand. A study in the US found that the conversion of 24 intersections to roundabouts reduced the number of injury crashes by 76%.\n6\nIn Europe, studies have found a 35% to 40% reduction in rates of serious injury crashes, and a reduction in deaths of 50% to 70%.\n7\nA meta-analysis across 44 studies found that converting junctions to roundabouts was associated with a two-thirds reduction of fatal accidents.\n8\nAgain, roundabouts are safer for several reasons.\nThe risk of a side-on or head-on collision is much higher at traffic lights and stop sign interventions. These collisions tend to be much more dangerous and lead to higher fatality rates. On a roundabout, a collision will likely be at a much less severe angle, and head-on collisions are much rarer. Vehicles also tend to travel at higher speeds, especially at traffic light intersections, whereas effective roundabouts force vehicles to slow down. For this reason, roundabouts that are small and do not force cars to drive\naround\nthem are ineffective\nbecause they can basically drive right through, without slowing down.\nStop-sign intersections — especially 4-way ones — can cause confusion about which vehicles have right-of-way. Finally, both of these road designs force many stop-and-go movements, increasing the likelihood of crashes when drivers brake too late. Roundabouts allow a more continuous flow of traffic.\n9\nWhile roundabouts reduce risks for drivers, passengers, and pedestrians, the data for cyclists is less clear. There is some evidence that large multi-lane roundabouts actually increase risks compared to traffic light intersections.\nThe battle against drunk driving\nThe other major change instigated in the late 1960s was the war against drunk driving.\n10\nIt’s now incredible to hear stories from my grandparents’ generation about how common this used to be. Surveys from the late 1970s\nsuggest that\nover half of male drivers and two-thirds of young male drivers would do it every week.\n11\nThose rates would be unthinkable in Britain today. That’s not just because of the\nlegal\nramifications: it’s also no longer\nsocially\nacceptable to drink and drive. More than 90% of Brits\nsay that\ndrink-driving is unacceptable, and they’d feel ashamed if they got caught.\nIn a\nlarge survey\nof road users across Europe, around 10% of car drivers in the UK said that they had driven after having at least one drink in the last month. 8% said they drove while being over the legal limit. So drink-driving still happens, but it’s much less normalized than it used to be.\nThis shift happened through legal actions and public education campaigns.\n12\nIn 1967, the UK introduced a drink-driving law, which set an objective and measurable limit for how much alcohol was allowed in someone’s bloodstream. They also introduced breathalyzers and told police to stop and test people liberally. Before 1967, there was a law against drink-driving, but it was vague and hard to enforce. It was illegal to be \"under the influence of drink or drugs to such an extent as to be incapable of having proper control of the vehicle\", but without a legal limit or standardized testing, police often struggled to prove that someone was impaired due to alcohol, especially in court. This is a clear example of better data measurement having saved many lives.\nOver time, the consequences for getting caught increased, including a permanent driving ban, hefty fines, or even jail time for serious offenses.\nWhile the legal consequences were ramping up, the government launched\nhard-hitting ad campaigns\nto highlight the suffering that could be caused by drunk driving. Slogans such as “drinking and driving wrecks lives” became well-known. Advertisements where a night out ended with someone’s loved ones being killed increased the stigma of prioritizing one more drink over a person’s life. The\nevidence suggests\nthat public information campaigns alone produce mixed (and often unimpressive) results, but they can make a real difference when paired with strong enforcement.\nThis battle has been incredibly successful. In 1967, 1,640 people died in drunk-driving incidents. This has fallen by 82%\nto roughly\n300 per year. As you can see in the chart below, collisions involving drunk drivers\nhave fallen\neven more quickly than the overall reduction in road collisions.\nThis reduction matters a lot because drink-driving incidents are particularly fatal. In 2022, just 4% of road collisions involved drunk drivers, but these incidents resulted in 18% of road\ndeaths\n.\n13\nThe rise of motorcycle helmets, seatbelts, and safer cars\nTackling drunk driving helped to reduce the number of collisions, but much of the drop in deaths during the 1980s and 1990s was about protecting drivers and passengers when a vehicle\ndid\ncrash.\nThis rise in protection within cars was pre-dated by mandatory helmets on motorcycles in 1973. At the time, early data suggested that wearing a helmet dramatically reduced the likelihood of a serious or life-threatening head injury in a motorcycle incident. In the decades since, the evidence for this has only gotten stronger.\n14\nIn the US, states with less stringent helmet laws have more head injuries.\n15\nA meta-analysis of studies across Africa suggests that helmet-wearing reduced the risk of a severe head injury by up to 88%.\n16\nA decade later, in 1983, wearing seatbelts in the front seats of vehicles\nbecame mandatory\n. A few years later, belts became mandatory for children in the back seats, and by 1991, it was mandatory for everyone in the car. Like motorcycle helmets, seatbelts make a huge difference to someone’s odds of surviving a crash.\n17\nBut it wasn’t just the provision of seatbelts that changed. By the late 1990s, the\nEuropean New Car Assessment Programme\n(NCAP) was introduced, spearheaded by the UK. It provided detailed safety assessments and tests for new cars. This programme was voluntary, but vehicles could receive a “safety rating” depending on how well drivers, passengers, and children would be protected during tests such as front-on and side collisions. This meant that people now factored in the safety rating of cars when choosing a new one, and manufacturers had to develop safer cars. Tests also showed the effectiveness of airbags, seatbelts, and “crumple zones” — the parts that absorb energy during a collision to protect occupants.\nThese innovations made a real difference to the odds that someone would survive a crash. It was during the 1990s and early 2000s that the number of deaths among drivers and passengers fell particularly strongly.\nMaking roads safer for kids: stricter speed limits and traffic-calmed zones\nThe most dramatic decline has been the reduction in the number of pedestrians killed on Britain’s roads. Since 1990, pedestrian deaths have fallen by 75% from around 1,600 to 400 per year.\nSpeed limits have played an incredibly important role, not just for pedestrians on urban roads but for all road users. As the\nWorld Bank’s report\nputs it:\n“[...] there are no other risk factors that have such a substantial and pervasive impact on safety as speed. Speed has an impact on both the likelihood of a crash occurring, and severity of the outcome when crashes do occur.”\nThe relationship between speed and health impacts follows a “power law”: a 5% increase in average speed typically leads to a 10% increase in injury, and a 20% increase in deaths.\n18\nSpeed limits should be set based on the most vulnerable road users. They can be higher on motorways because everyone is protected within a vehicle. On urban roads, limits need to be set based on the risks to pedestrians and cyclists. In areas with many children, their vulnerability calls for even stricter limits.\nIn the late 1990s, the UK introduced 20-mile-per-hour zones around schools. These expanded throughout the 2000s and are now common across the country. Across the board, the enforcement of speed limits has become much tighter. These interventions can be very effective.\n19\nIf a pedestrian is hit by a vehicle at or below 20 miles per hour, they have a good chance of surviving. Above this, their chances fall dramatically. A study across 40 cities in Europe found that a 20-mph speed limit reduced the rate of fatalities by 37%.\n20\nIn some areas, including cities in the UK, it was as much as 70%.\n21\nLike the war against drunk driving, these legal limits were also accompanied by emotionally visceral public campaigns. I still remember\nthe TV advert\nI saw several times a week as a child. It featured a young girl, dead at the side of the road, with her voiceover telling drivers: “If you hit me at 40 miles per hour, there’s an 80% chance I’ll die. Hit me at 30, and there’s an 80% chance I’ll live”. These are the types of messages that stay with people and can change behaviors and attitudes.\nMore recent data suggests that the percentages are slightly lower than quoted in those old adverts. For example,\n2011 data from the US\nsuggested that the fatality risk at 42mph was 50%.\nAs a consequence, the drop in child deaths on the UK’s roads has been dramatic. You can see this in the chart below. In 1980, over 600 children were killed. By 2021, this had fallen to less than 50.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nEvery year, about 1.2 million die on the world’s roads — we know how to bring this number down\nA lot has changed since the first British drivers got their cars in the 1920s. The roads are different. The vehicles are different. People’s attitudes to driving are different.\nThese changes transformed Britain’s roads from chaotic to some of the safest in the world, saving thousands of lives every year.\nWorldwide, around\n1.2 million people\ndie from road injuries every year. Most of them are in low- and middle-income countries, with much higher road death rates. If every country could lower its rates to those of the UK, Sweden, or Norway, this number would be just under 200,000.\n22\nWe’d save one million lives every year.\nLearning from the history of how roads became safer would make a massive difference to people's health worldwide. It would mean that hundreds of thousands of parents, children, or partners don’t have to receive the dreaded phone call that their loved one will never come home.\nAcknowledgments\nMany thanks to Max Roser, Saloni Dattani, and Edouard Mathieu for their valuable suggestions and feedback on this article.\nContinue reading on Our World in Data\nAir pollution kills millions every year — where does it come from?\nA breakdown of the sources of many air pollutants that damage our health and ecosystems.\nMany countries have eliminated lead from paint. How do we achieve the same everywhere?\nPaint is an important source of lead exposure. Which countries have regulations on its use?\nEndnotes\nThe death rate in 1950 was 111 deaths per billion miles driven. In 2023, 334.4 billion miles were driven. That means the number of deaths would be [111 * 334.4 = 37,000].\nIn this article, I will largely use the terms “Britain” and the “United Kingdom” interchangeably for simplicity. However, these terms differ slightly: Britain includes England, Wales, and Scotland, while the United Kingdom includes Northern Ireland.\n“Turner, B., Job, S. and Mitra, S. (2021). Guide for Road Safety Interventions: Evidence of What Works and What Does Not Work. Washington, DC., USA: World Bank.”\nIn 1951, Britain introduced zebra crossings. Within a year, pedestrian deaths had fallen by 11%, which is often used as evidence to support their effectiveness. However, evidence from across countries finds more mixed results. What seems key is that the zebra crossing is visible to drivers at a distance. This often means that having a\nraised\ncrossing, or islands in the middle of the road, is more effective than purely markings on the road itself.\nIn 1953, “Lollipop wardens,” individuals who stop traffic around school zones to help children cross the road safely, were introduced.\nIn 1960, the government introduced the “\nMOT test\n”, a check that vehicles must pass to ensure they are safe enough to be on the road. Again, the benefits of MOT tests appear to be context-specific. The UK now has a relatively modern vehicle fleet, meaning mechanical faults that lead to accidents are much less likely. In the UK,\njust 1–2% of crashes\nare caused by a vehicle defect. In the US, it’s just 2-3%. From this data, we might assume that MOT tests are incredibly effective (hence why rates are so low). However, some studies suggest that the impact of MOTs on crash rates for modern\ncars\nis low; an\nexperiment in Norway\nfound no difference in crash rates between cars that had routine inspections and those that didn’t. This was\nnot the case\nfor trucks and heavier vehicles; less frequent inspections have been linked to higher crash rates.\nThe benefits of MOT tests for modern cars\ncould be relatively small\n. However, a far higher percentage of crashes in low- to middle-income countries result from mechanical fault. Here, the introduction of more routine inspection could make a difference.\nITF (2024), Road Safety Annual Report 2024, OECD Publishing, Paris.\nRetting, R. A., Persaud, B. N., Garder, P. E., & Lord, D. (2001). Crash and injury reduction following installation of roundabouts in the United States. American journal of public health, 91(4), 628.\nDe Brabander, B., Nuyts, E., & Vereeck, L. (2005). Road safety effects of roundabouts in Flanders. Journal of Safety Research, 36(3), 289-296.\nElvik, R. (2003). Effects on road safety of converting intersections to roundabouts: review of evidence from non-US studies. Transportation Research Record, 1847(1), 1-10.\nElvik, R. (2017). Road safety effects of roundabouts: A meta-analysis. Accident Analysis & Prevention, 99, 364-371.\nRetting, R. A., Mandavilli, S., McCartt, A. T., & Russell, E. R. (2006). Roundabouts, traffic flow and public opinion. Traffic engineering and control, 47(7), 268-272.\nIn\nan article\nin\nWorks in Progress\n, Nick Cowen looks at how this was achieved in Britain in more detail.\nThese figures seem very high to me, but are\nrepeatedly referenced\nin UK government reports and documentation. The original surveys for this data are unavailable online, so we don’t know the exact phrasing of the questions. I suspect that “drunk driving” in this case meant driving after\nany\nalcohol consumption, and did not necessarily mean they were driving “drunk”.\nAnother piece of evidence that made me less skeptical was that self-reported rates of drink-driving are still high in many other European countries. In a large survey of road users across Europe, 37% of car drivers in Luxembourg said they had driven after having at least one drink in the last 30 days. In Portugal, it was 28%; in Spain, 24%. The rate among male drivers, and particularly\nyoung\nmale drivers, is likely to be higher.\nThe UK Department for Transport has an extensive document outlining these interventions and changes in public attitudes over time.\nBullmore et al. (2012).\nDepartment for Transport: how thirty years of drink drive communications saved almost 2,000 lives\n.\nIn 2022, there were 106,000 collisions in Great Britain; just 4600 of those involved drink driving, which is around 4%. In that year, around 1700 people died on Britain’s roads, and 300 of those — which is 18% — happened in drink driving collisions.\nYu, W. Y., Chen, C. Y., Chiu, W. T., & Lin, M. R. (2011). Effectiveness of different types of motorcycle helmets and effects of their improper use on head injuries. International journal of epidemiology, 40(3), 794-803.\nGanga, A., Kim, E. J., Tang, O. Y., Feler, J. R., Sastry, R. A., Anderson, M. N., ... & Sullivan, P. Z. (2023). The burden of unhelmeted motorcycle injury: A nationwide scoring-based analysis of helmet safety legislation. Injury, 54(3), 848-856.\nOlsen, C. S., Thomas, A. M., Singleton, M., Gaichas, A. M., Smith, T. J., Smith, G. A., ... & Cook, L. J. (2016). Motorcycle helmet effectiveness in reducing head, face and brain injuries by state and helmet law. Injury epidemiology, 3, 1-11.\nAbdi, N., Robertson, T., Petrucka, P., & Crizzle, A. M. (2022). Do motorcycle helmets reduce road traffic injuries, hospitalizations and mortalities in low and lower-middle income countries in Africa? A systematic review and meta-analysis. BMC public health, 22(1), 824.\nEvans, L. (1986). The effectiveness of safety belts in preventing fatalities. Accident Analysis & Prevention, 18(3), 229-241.\nSpeed management: a road safety manual for decision-makers and practitioners. Geneva, Global Road Safety Partnership, 2008\nRetting, R. A., Ferguson, S. A., & McCartt, A. T. (2003). A review of evidence-based traffic engineering measures designed to reduce pedestrian–motor vehicle crashes. American journal of public health, 93(9), 1456-1463.\nThis study looks at a review of 30km/h speed limits, which is around 19 mph.\nYannis, G., & Michelaraki, E. (2024). Review of city-wide 30 km/h speed limit benefits in Europe. Sustainability, 16(11), 4382.\nMusial, S., Bunn, S. & Lally, C. (2025)\nPublic health impacts of 20 mph limits and zones\n. Parliamentary Office of Science and Technology.\nThe global\ndeath rate from road injuries\nwas 15.4 per 100,000 in 2021. This figure was 2.4 per 100,000 in the UK. To calculate the number of deaths if the global average converged to the UK rate: [1.2 million / 15.4 * 2.4] = 189,000.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2025) - “How Britain built some of the world’s safest roads” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-095641/britain-safest-roads-history.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-britain-safest-roads-history,\nauthor = {Hannah Ritchie},\ntitle = {How Britain built some of the world’s safest roads},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-095641/britain-safest-roads-history.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "britain-safest-roads-history", "source_url": "https://ourworldindata.org/britain-safest-roads-history", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "The death rate per mile driven has declined 22-fold since 1950.", "numeric_mentions": ["22", "1950", "8,", "2025", "25", "1920", "30", "5,000", "7,000", "1,700", "16", "33", "1950,", "37,000", "1", "2", "1.2 million", "3", "1900", "1930,", "1930", "1939", "600", "20", "1940", "1960", "1966", "50%", "4", "24", "5%", "5", "10%", "18", "1958,", "1966,", "76%", "6", "35%", "40%", "70%", "7", "44", "8", "9", "10", "1970", "11", "90%", "8%", "12", "1967,", "1,640", "82%", "300", "2022,", "4%", "18%", "13", "1980", "1990", "1973", "14", "15", "88%", "1983,", "1991,", "17", "2000", "1990,", "75%", "1,600", "400", "20%", "19", "40", "37%", "21", "80%", "30,"], "numeric_evidence": [{"title": "Number of road deaths, vehicles and miles driven in Great Britain", "source_url": "https://ourworldindata.org/grapher/uk-number-vehicles-miles-road-deaths.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "All road deaths", "Vehicles", "Miles driven", "Road deaths per billion miles driven"], "row_count_total": 98, "rows_head": [{"Entity": "United Kingdom", "Code": "GBR", "Year": "1926", "All road deaths": "4886", "Vehicles": "1700000", "Miles driven": "", "Road deaths per billion miles driven": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1927", "All road deaths": "5329", "Vehicles": "1900000", "Miles driven": "", "Road deaths per billion miles driven": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1928", "All road deaths": "6138", "Vehicles": "2000000", "Miles driven": "", "Road deaths per billion miles driven": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1929", "All road deaths": "6696", "Vehicles": "2200000", "Miles driven": "", "Road deaths per billion miles driven": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1930", "All road deaths": "7305", "Vehicles": "2300000", "Miles 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"United Kingdom", "Code": "GBR", "Year": "1942", "All road deaths": "6926", "Vehicles": "1800000", "Miles driven": "", "Road deaths per billion miles driven": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1943", "All road deaths": "5796", "Vehicles": "1500000", "Miles driven": "", "Road deaths per billion miles driven": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1944", "All road deaths": "6416", "Vehicles": "1600000", "Miles driven": "", "Road deaths per billion miles driven": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1945", "All road deaths": "5256", "Vehicles": "2600000", "Miles driven": "", "Road deaths per billion miles driven": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1946", "All road deaths": "5062", "Vehicles": "3100000", "Miles driven": "", "Road deaths per billion miles driven": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1947", "All road deaths": "4881", "Vehicles": "3500000", "Miles driven": "", "Road deaths per billion miles driven": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1948", "All road deaths": "4513", "Vehicles": "3700000", "Miles driven": "", "Road deaths per billion miles driven": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1949", "All road deaths": "4773", "Vehicles": "4100000", "Miles driven": "", "Road deaths per billion miles driven": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1950", "All road deaths": "5012", "Vehicles": "4400000", "Miles driven": "45000000000", "Road deaths per billion miles driven": "111"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1951", "All road deaths": "5250", "Vehicles": "4700000", "Miles driven": "50000000000", "Road deaths per billion miles driven": "105"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1952", "All road deaths": "4706", "Vehicles": "5000000", "Miles driven": "52000000000", "Road deaths per billion miles driven": "91"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1953", "All road deaths": "5090", "Vehicles": "5300000", "Miles driven": "53000000000", "Road deaths per billion miles driven": "96"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1954", "All road deaths": "5010", "Vehicles": "5800000", "Miles driven": "55000000000", "Road deaths per billion miles driven": "91"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1955", "All road deaths": "5526", "Vehicles": "6500000", "Miles driven": "59000000000", "Road deaths per billion miles driven": "94"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1956", "All road deaths": "5367", "Vehicles": "7000000", "Miles driven": "60000000000", "Road deaths per billion miles driven": "89"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1957", "All road deaths": "5550", "Vehicles": "7500000", "Miles driven": "60000000000", "Road deaths per billion miles driven": "93"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1958", "All road deaths": "5970", "Vehicles": "8000000", "Miles driven": "67000000000", "Road deaths per billion miles driven": "89"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1959", "All road deaths": "6520", "Vehicles": "8700000", "Miles driven": "73000000000", "Road deaths per billion miles driven": "89"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1960", "All road deaths": "6970", "Vehicles": "9400000", "Miles driven": "77000000000", "Road deaths per billion miles driven": "91"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1961", "All road deaths": "6908", "Vehicles": "10000000", "Miles driven": "83000000000", "Road deaths per billion miles driven": "83"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1962", "All road deaths": "6709", "Vehicles": "10600000", "Miles driven": "86000000000", "Road deaths per billion miles driven": "78"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1963", "All road deaths": "6922", "Vehicles": "11400000", "Miles driven": "90000000000", "Road deaths per billion miles driven": "77"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1964", "All road deaths": "7820", "Vehicles": "12400000", "Miles driven": "100000000000", "Road deaths per billion miles driven": "78"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1965", "All road deaths": "7952", "Vehicles": "12900000", "Miles driven": "105000000000", "Road deaths per billion miles driven": "76"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1966", "All road deaths": "7985", "Vehicles": "13300000", "Miles driven": "111000000000", "Road deaths per billion miles driven": "72"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1967", "All road deaths": "7319", "Vehicles": "14100000", "Miles driven": "115000000000", "Road deaths per billion miles driven": "64"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1968", "All road deaths": "6810", "Vehicles": "14400000", "Miles driven": "120000000000", "Road deaths per billion miles driven": "57"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1969", "All road deaths": "7365", "Vehicles": "14800000", "Miles driven": "123000000000", "Road deaths per billion miles driven": "60"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1970", "All road deaths": "7499", "Vehicles": "15000000", "Miles driven": "127000000000", "Road deaths per billion miles driven": "59"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1971", "All road deaths": "7699", "Vehicles": "15500000", "Miles driven": "134000000000", "Road deaths per billion miles driven": "57"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1972", "All road deaths": "7763", "Vehicles": "16100000", "Miles driven": "141000000000", "Road deaths per billion miles driven": "55"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1973", "All road deaths": "7406", "Vehicles": "17000000", "Miles driven": "148000000000", "Road deaths per billion miles driven": "50"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1974", "All road deaths": "6883", "Vehicles": "17300000", "Miles driven": "145000000000", "Road deaths per billion miles driven": "47"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1975", "All road deaths": "6366", "Vehicles": "17500000", "Miles driven": "147000000000", "Road deaths per billion miles driven": "43"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1976", "All road deaths": "6570", "Vehicles": "17800000", "Miles driven": "154000000000", "Road deaths per billion miles driven": "43"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1977", "All road deaths": "6614", "Vehicles": "", "Miles driven": "157000000000", "Road deaths per billion miles driven": "42"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1978", "All road deaths": "6831", "Vehicles": "17800000", "Miles driven": "163000000000", "Road deaths per billion miles driven": "42"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1979", "All road deaths": "6352", "Vehicles": "18600000", "Miles driven": "161836126956", "Road deaths per billion miles driven": "39"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1980", "All road deaths": "5953", "Vehicles": "19200000", "Miles driven": "172132247608", "Road deaths per billion miles driven": "35"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1981", "All road deaths": "5846", "Vehicles": "19400000", "Miles driven": "175444156061", "Road deaths per billion miles driven": "33"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1982", "All road deaths": "5937", "Vehicles": "19800000", "Miles driven": "180725811193", "Road deaths per billion miles driven": "33"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1983", "All road deaths": "5445", "Vehicles": "20200000", "Miles driven": "182956533772", "Road deaths per billion miles driven": "30"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1984", "All road deaths": "5599", "Vehicles": "20800000", "Miles driven": "192295742788", "Road deaths per billion miles driven": "29"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1985", "All road deaths": "5165", "Vehicles": "21200000", "Miles driven": "196185526450", "Road deaths per billion miles driven": "26"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1986", "All road deaths": "5385", "Vehicles": "21700000", "Miles driven": "205524735466", "Road deaths per billion miles driven": "26"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1987", "All road deaths": "5125", "Vehicles": "22200000", "Miles driven": "221332418590", "Road deaths per billion miles driven": "23"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1988", "All road deaths": "5052", "Vehicles": "23300000", "Miles driven": "236698928169", "Road deaths per billion miles driven": "21"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1989", "All road deaths": "5373", "Vehicles": "24200000", "Miles driven": "256060854511", "Road deaths per billion miles driven": "21"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1990", "All road deaths": "5217", "Vehicles": "24700000", "Miles driven": "258546339279", "Road deaths per billion miles driven": "20"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1991", "All road deaths": "4568", "Vehicles": "24500000", "Miles driven": "258950851925", "Road deaths per billion miles driven": "18"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1992", "All road deaths": "4229", "Vehicles": "24851000", "Miles driven": "259021066870", "Road deaths per billion miles driven": "16"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1993", "All road deaths": "3814", "Vehicles": "24800000", "Miles driven": "258703994460", "Road deaths per billion miles driven": "15"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1994", "All road deaths": "3650", "Vehicles": "25231451", "Miles driven": "264428442667", "Road deaths per billion miles driven": "14"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1995", "All road deaths": "3621", "Vehicles": "25369353", "Miles driven": "269590386726", "Road deaths per billion miles driven": "13"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1996", "All road deaths": "3598", "Vehicles": "26301921", "Miles driven": "276632724916", "Road deaths per billion miles driven": "13"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1997", "All road deaths": "3599", "Vehicles": "26973790", "Miles driven": "282354737200", "Road deaths per billion miles driven": "13"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1998", "All road deaths": "3421", "Vehicles": "27538415", "Miles driven": "287349973096", "Road deaths per billion miles driven": "12"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1999", "All road deaths": "3423", "Vehicles": "28367560", "Miles driven": "292690042886", "Road deaths per billion miles driven": "12"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2000", "All road deaths": "3409", "Vehicles": "28897581", "Miles driven": "292136449492", "Road deaths per billion miles driven": "12"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2001", "All road deaths": "3450", "Vehicles": "29747130", "Miles driven": "296091352504", "Road deaths per billion miles driven": "12"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2002", "All road deaths": "3431", "Vehicles": "30556673", "Miles driven": "302980381004", "Road deaths per billion miles driven": "11"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2003", "All road deaths": "3508", "Vehicles": "31207359", "Miles driven": "304845143409", "Road deaths per billion miles driven": "12"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2004", "All road deaths": "3221", "Vehicles": "32258856", "Miles driven": "308300549243", "Road deaths per billion miles driven": "10"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2005", "All road deaths": "3201", "Vehicles": "32897383", "Miles driven": "308578205314", "Road deaths per billion miles driven": "10"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2006", "All road deaths": "3172", "Vehicles": "33070484", "Miles driven": "313235167194", "Road deaths per billion miles driven": "10"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2007", "All road deaths": "2946", "Vehicles": "33650981", "Miles driven": "315916421155", "Road deaths per billion miles driven": "9"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2008", "All road deaths": "2538", "Vehicles": "33883382", "Miles driven": "313460143512", "Road deaths per billion miles driven": "8"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2009", "All road deaths": "2222", "Vehicles": "33958430", "Miles driven": "310330422185", "Road deaths per billion miles driven": "7"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2010", "All road deaths": "1850", "Vehicles": "34120148", "Miles driven": "306295156213", "Road deaths per billion miles driven": "6"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2011", "All road deaths": "1901", "Vehicles": "34228594", "Miles driven": "307452168751", "Road deaths per billion miles driven": "6"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2012", "All road deaths": "1754", "Vehicles": "34522322", "Miles driven": "307913729294", "Road deaths per billion miles driven": "6"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2013", "All road deaths": "1713", "Vehicles": "35034487", "Miles driven": "309003123075", "Road deaths per billion miles driven": "6"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2014", "All road deaths": "1775", "Vehicles": "35633107", "Miles driven": "318076322122", "Road deaths per billion miles driven": "6"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2015", "All road deaths": "1730", "Vehicles": "36467487", "Miles driven": "323756669927", "Road deaths per billion miles driven": "5"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2016", "All road deaths": "1792", 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Reuse should follow the source and license information in this chart metadata."}, {"title": "Road deaths in Great Britain by road user", "source_url": "https://ourworldindata.org/grapher/britain-road-deaths-user.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Pedestrians", "Drivers and passengers", "Motorcyclists", "Cyclists"], "row_count_total": 97, "rows_head": [{"Entity": "United Kingdom", "Code": "GBR", "Year": "1927", "Pedestrians": "2774", "Drivers and passengers": "736", "Motorcyclists": "1175", "Cyclists": "644"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1928", "Pedestrians": "3255", "Drivers and passengers": "797", "Motorcyclists": "1395", "Cyclists": "691"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1929", "Pedestrians": "3523", "Drivers and passengers": "796", "Motorcyclists": "1582", "Cyclists": "795"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1930", "Pedestrians": "3722", "Drivers and passengers": "864", "Motorcyclists": 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{"raw_link": "https://ourworldindata.org/pesticide-bans-suicide-prevention", "title": "Bans on highly toxic pesticides could be an effective way to save lives from suicide", "context": "Home\nSuicides\nBans on highly toxic pesticides could be an effective way to save lives from suicide\nPesticide poisoning is a common method of suicide in many low- to middle-income countries. Banning highly toxic pesticides and substituting them with less fatal ones can save lives.\nBy\nHannah Ritchie\nSeptember 1, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nNote on content\nThis article contains an in-depth discussion of suicide deaths, including motivations and methods, which may not be suitable or helpful for people who are struggling with serious mental health issues.\nIf you are dealing with suicidal thoughts, you can receive immediate help by visiting resources such as\nfindahelpline.com\n.\nPreventing suicides can seem like a much more difficult problem than many of those we cover on Our World in Data. The number of people dying from suicide each year has\nbarely changed\nin decades.\n1\nMental health problems, a key risk factor for suicide, are complex and don’t have a single or simple fix. You’d be forgiven for thinking that reducing suicide deaths would be slow, if it were possible at all.\nPesticides are used in 14% to 20% of suicide deaths globally — 100,000 to 150,000 deaths each year\nHowever, many countries have shown us that this assumption is false. There’s a lot we can do to prevent suicides. Sri Lanka is one of the most dramatic examples of this.\nAt the turn of the millennium, Sri Lanka had the second-highest suicide rate in the world (after Greenland). But look at the chart below: rates have fallen by almost two-thirds since then. Despite it having a growing population, the\nnumber\nof Sri Lankans dying from suicide has\nmore than halved\n, saving thousands of lives every year.\n2\nThe biggest driver of this decline is something that most of us rarely think about: a reduction in self-poisonings from pesticides. This is a common method used in many low- to middle-income countries, where most suicides occur. The figures on this, like a lot of data on suicides globally, are uncertain. Many of the studies I’ll discuss in this article rely on data from police records, which tend to be an undercount due to stigma in reporting.\n3\nBut to give some sense of the magnitude of the problem: several studies estimate that pesticides are used in 14% to 20% of suicide deaths globally.\n4\nThat would mean 100,000 to 150,000 deaths each year.\n5\nHow did Sri Lanka reduce these deaths over the last 30 years? Many studies suggest that it was because the country banned some of the most toxic pesticides, while allowing them to be substituted for less harmful ones.\n6\nIn this article, I look at the evidence and arguments for banning highly toxic pesticides to reduce suicide deaths, including some of the concerns that these bans might raise.\nThe decline of pesticide suicide deaths in Sri Lanka\nLet’s first look at the case study of Sri Lanka. Its suicide rates have fallen dramatically over the last 25 years. But why?\nTo answer this question, it’s helpful to look at the change in suicide methods over this period. A study by Duleeka Knipe and colleagues studied trends in suicide deaths in Sri Lanka from 1975 through to 2012.\n7\nTo get the breakdown by method and demographics, it relied on recorded data from the\nDepartment of Police’s Division of Statistics\n. Due to the stigma that is often attached to suicide deaths — and the challenges of collecting national data, especially in low-income countries in the 1980s or 1990s — it would be reasonable to expect that these deaths are a conservative estimate.\nIn the chart, you can see the suicide rate across different methods. What’s immediately clear is how dominant self-poisonings (mostly from pesticides) have been, but also the fact that this is where most of the decline in total suicide rates has come from. As recently as the late 1990s, 40 people per 100,000 were dying from self-poisonings each year. This has fallen by more than two-thirds to just over 10.\nDownload\nAgain, some readers might be surprised that pesticides are the leading method of suicide. This is not a big issue in high-income countries, but it is in low- to middle-income countries. Pesticides have weaker regulations, meaning the general public can buy them like any other good. They’re often used in small-scale farming and stored on-site, which is frequently the family’s home. That means they’re easily accessible to many people.\nWhat then caused this decline in pesticide poisoning deaths?\nMany point to Sri Lanka’s ban on highly hazardous pesticides (HHPs) as the key driver. Pesticides are\ntypically classified\nby the WHO and the UN’s Food and Agriculture Organization based on their hazards to human health and the environment. HHPs rate highly on this classification and are more fatal than most other pesticides if ingested.\nSri Lanka did not ban all HHPs in a single swoop. It gradually prohibited a range of HHPs over several decades. In the chart below, I’ve overlaid the dates of these bans onto the overall suicide rate trend. No single pesticide phase-out dropped rates by two-thirds alone. Instead, researchers attribute different substances to different parts of the overall trend.\nThe ban on parathion and methyl parathion in the 1980s is attributed to stopping a continued\nincrease\nin suicide rates, which had been climbing for decades. The 1995 ban on WHO Class I toxicity pesticides led to the first sustained downturn. There was another step-change in 1998 due to the ban on endosulfan, and further bans on paraquat continued this decline into the late 2010s.\nResearchers can also use more complex analyses to understand what role these bans had on the decline in suicide rates. These studies tend to find that they were a key driver.\n8\nTo be clear: this doesn’t mean they were the\nonly\nreason for the reduction, but in a world without those bans, the fall in suicides would be much less steep, and many more people would be dying from pesticide poisonings every year.\nBans on the most toxic pesticides have reduced suicide deaths in other countries\nSri Lanka is not the only country to have experienced a decline in suicides from self-poisonings. In particular, this has been the case across several Asian countries. The chart shows the change in\ntotal\nsuicide rates across a selection of countries in South and East Asia. These countries have banned at least some HHPs over this period (some also banned selected ones before 2000). As you can see, rates have fallen to varying degrees over the past few decades.\nAgain, the ban of HHPs is unlikely to be the\nonly\ndriver of this decline, but a few studies suggest it has been important.\n9\nEven in countries where rates had stopped rising or were already falling, introducing a ban on particular HHPs often changed the\nrate\nof the decline.\nIt’s important to note that in some countries, these bans did\nnot\nlead to a conclusive reduction in suicides. Nepal’s suicide rate, for example, has\nnot changed\n, and studies suggest that the ingestion of\nbanned\npesticides is still one of the leading methods used.\n10\nThis suggests that these illegal substances are still entering and being used in the country.\nSo, the success of these bans in driving down suicide rates depended a lot on the context: how common self-poisonings already were;\nwhat\npesticides were banned, and whether other highly toxic pesticides were still available; how stringently the bans were enforced; and how other risk factors for suicide were changing. However, across several countries, these bans appear to have had some positive impact on saving lives.\nPeople often switch to other methods, but these tend to be less fatal\nWhen I was looking at the initial research on this, I had some doubts that these bans could lead to a decline in\ntotal\nsuicide rates.\nFirst, Sri Lanka did not ban pesticides completely. It banned certain HHPs, which could be substituted for other chemical pesticides. If someone\nreally\nwanted to die by suicide, wouldn’t they just consume the new pesticide anyway?\nThe answer is yes: many people did drink substituted pesticides. But, consuming these lower toxicity alternatives was much less deadly. A study from Nepal found that aluminum phosphide (a pesticide used before the ban) had a fatality rate of around 50%.\n11\nThat meant that half of those ingesting it would die. The pesticides used\nafter\nthe ban had a fatality rate of 7%. That’s seven times lower.\nSome of the banned HHPs had even higher fatality rates than this, up to 80%. So even if the total number of people who consumed pesticides did not change at all, we would still expect the death rate to fall because they were much less likely to die as a result.\nMy second doubt was whether people simply switched from self-poisoning to another method. If someone were determined to die, we might expect them to seek out another way in the absence of toxic pesticides. For some people, this was the case. Earlier, we saw that in Sri Lanka, while suicide rates from self-poisonings had fallen a lot, there was an increase in suicide from hanging.\n12\nBut this increase was much smaller than the decline from pesticides, so it\nwasn’t\nthe case that most, or even many, people made this switch. The decline in pesticide poisonings more than offset the increase in hangings, and so fewer people still died from suicide overall. This was also true in Bangladesh.\nThe size of this substitution effect can vary by country. Studies from India suggest a significant change in the methods used. After the pesticide endosulfan was banned nationwide in 2011, there was a strong decline in deaths from self-poisoning. But, over that same period, there was also a strong increase in suicides by hanging. By 2014, pesticide death rates were 48% lower than expected under a scenario without a ban.\n13\nBut the\ntotal\nsuicide rate was only 10% lower. That’s because the rise in hanging had offset a substantial fraction of the lives “saved” from self-poisoning. This was particularly true for men, where total rates did not decline much. In women, there appeared to be a much smaller substitution effect.\nMy final doubt was whether removing HHPs would actually reduce suicide deaths, or simply delay them. If someone attempted suicide with a less toxic pesticide or a less fatal method, and did not die, wouldn’t they just try again? Broader research on suicide suggests that for most people, the answer is no.\nAn analysis combining the results of 177 studies over 30 years found that 1.6% of people who had attempted suicide tried again within one year, and 3.6% had tried again within five years.\n14\nThis suggests that more than 96% of people who attempt suicide and survive do not do so again in the next five years. If most people do not re-attempt suicide, then making sure that the method used in the first attempt has a low fatality risk could save many lives. In this case, that means removing access to highly toxic pesticides.\nThis leads to an important point underpinning some of the justification for these bans. People attempt suicide for a variety of reasons. Some have been struggling with mental health problems and suicidal ideation for a long time. A substantial fraction, though, does so as an impulsive act. Particularly during periods of acute crisis, some people attempt self-harm quickly as a way to alleviate the immediate emotional stress, without necessarily wanting to die. The attempt is not premeditated, and the time between the thought and the act can be incredibly short.\n15\nFor someone living in rural Sri Lanka, this might mean reaching for a highly toxic pesticide that is easily accessible at home. Interviews with Sri Lankans who survived self-poisoning attempts suggested that 85% chose their method based on accessibility: it was there and available in these moments of distress.\n16\nIf a large number of suicide attempts are the result of brief, impulsive acts, then it would make sense that removing the most fatal methods would make a difference. People would have a higher chance of survival and could get the help and support needed to prevent future attempts.\nCould these pesticide bans create more harm and potentially increase suicides if they reduce food production?\nSo far, we’ve discussed pesticide use as a problem: they’re toxic and cost lives when consumed. However, when used effectively, pesticides can also bring important benefits in the form of food security, agricultural productivity, and farmer incomes.\nFarmers can see vast amounts of their harvest wiped out if they lack ways to manage pests and disease. For many, that doesn’t just mean less food; it also means a big chunk of the year’s income is gone.\nI’ve\nwritten previously\nabout the benefits of agricultural technologies such as fertilizers, better seeds, pesticides, and irrigation for food production, poverty alleviation, and sparing natural habitat. These technologies have played a massive role in the dramatic rise in crop yields seen in countries like Sri Lanka, India, China, and Bangladesh. See the chart below.\nA valid concern, then, is that taking these pesticides away would reduce crop yields and food production. That would not only be bad for food security, nutrition, and incomes, but possibly for suicide rates, too. If a large share of suicides is the result of impulsive acts due to acute stress, there is a reasonable argument that the repeated stress of crop failures, loss of income, and rural poverty could increase rates of suicide attempts.\nAfter reviewing the scientific literature and data, I don’t think these bans have had significant negative impacts on productivity. What’s crucial is that there was no total ban on all pesticides; it was aimed at highly toxic ones, for which there were often less toxic substitutes or other ways to reduce pests.\nSome of these replacements were direct chemical substitutes, which could perform just as well. In other cases, changes to farming practices have also been crucial, such as incorporating integrated pest management (which involves changes in crop rotations and resistant crop varieties). There is little evidence that, on aggregate, these bans have hindered crop yields. But that does not mean the substitution is always easy: in some cases, the substitute pesticide is more expensive; in others, it requires training and adaptations from farmers to use pesticides more efficiently.\nSri Lanka provides a good case study of both interventions; while they introduced a series of bans on specific HHPs over several decades, in 2021, the government also introduced an immediate ban on all fertilizer and pesticide imports.\n17\nIt gave several reasons for the ban. Sri Lanka was facing a severe foreign currency shortage and wanted to reduce the country’s spending on chemical imports. It also tried positioning itself as the first “organic nation” to tackle environmental pollution and health concerns about overusing these agricultural inputs. These health concerns were not specifically related to suicides from pesticides.\nLet’s look at Sri Lanka’s rice yields since the 1960s. This is shown in the chart below. I’ve added lines flagging the dates of particular pesticide bans and the complete ban in 2021. Although there is always year-to-year variability in yields due to factors such as weather, there has been a fairly gradual increase in yields, particularly since the 1970s. There were no sudden drops in yields following any of the specific HHP bans. There was, however, a huge drop in yields over the 2021–2022 season, following the complete prohibition of fertilizer and pesticide imports (which made up most of the country’s supply).\nThis was even bigger than the 2016–2017 drop resulting from\nsevere drought\n.\nThis data\nmatches the experiences\nof Sri Lankan farmers, many of whom experienced large declines in yields — sometimes more than 50% — as they were effectively forced to go organic overnight, without time to transition and develop new crop management strategies. The ban was so damaging and received so much backlash that the government reversed its decision in November 2021, just seven months after it was announced.\nThere is also evidence that banning less toxic pesticides\ndoes not\nhelp to reduce suicides from self-poisoning; therefore, blanket bans would not help to save lives from suicide and would hurt agricultural productivity in the process.\n18\nAs the study puts it:\n“These findings support the restriction of acutely toxic pesticides in resource-poor countries to help reduce hospitalization for and deaths from deliberate self-poisonings and caution against arbitrary bans of less toxic pesticides while more toxic pesticides remain available.”\nStudies in other countries have found similar results. When toxic pesticides were banned in Bangladesh, pesticide deaths went down, but there was no clear impact on crop yields or food production.\n19\nThe same is true for India, which\nhas had\na steady, fairly linear yield increase for over 50 years.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nTargeting the most toxic pesticides could be a cost-effective way to save lives in countries where this suicide method is common\nTo be clear: banning toxic pesticides is no substitute for addressing some of the underlying circumstances that drive people towards suicide, nor does it negate the need for investments in support for mental health. These bans are not going to stop self-poisonings completely. Despite a huge reduction in suicide rates in Sri Lanka, self-poisoning is still the leading method used.\nA ban on highly hazardous pesticides in just 14 countries could save 28,000 lives each year and would cost only $30 million\nBut the research is convincing that banning selective HHPs has played a role in reducing the fatality of suicide attempts, saving thousands of lives each year in the process.\nSome studies — although very few have modeled this — also suggest that it’s a relatively cost-effective intervention. This means it could save many lives, even in resource-constrained countries that don’t have large budgets to spend (which often means that mental health and suicide do not get attention at all).\nIn a study published in\nThe Lancet\n, researchers estimate that a ban on HPPs in just 14 countries could save 28,000 lives each year from suicide, and would cost just $30 million in total.\n20\nThe intervention appears to be so cheap because very little sustained “delivery” is involved: it’s not a medical treatment that implies drug manufacturing, delivery, and healthcare staff. Nor does it involve a psychological intervention, delivered by trained personnel or an online resource. The main costs were the upfront effort of enacting national legislation and some ongoing resources for enforcing these bans. Cost-effectiveness, as we might expect, was far higher in countries where self-poisoning from pesticides was a leading method of suicide; this means focusing on these high-impact countries would save more lives.\nIf these bans\ndid\nresult in reductions in crop yields, this would need to be factored into cost-effectiveness calculations. These studies assume zero impact, citing a lack of evidence that yields are affected.\nIn a\nseparate analysis\n, the charity evaluator GiveWell suggested that one of the leading research organizations working on this problem —\nThe Centre for Pesticide Suicide Prevention\n— was potentially among some of its most cost-effective interventions. However, it did stress that these analyses had to rely on a range of assumptions that make the conclusions less certain than the evidence base for other programmes.\nAgain, we should be clear that these interventions are only part of the solution. They are not a silver bullet to a varied and complex problem. But the evidence suggests that when carefully implemented, they have already saved many lives from suicide, and if implemented more broadly, could potentially save tens of thousands more, every year.\nAcknowledgments\nMany thanks to Edouard Mathieu, Saloni Dattani, and Simon van Teutem for feedback and suggestions on this article.\nContinue reading on Our World in Data\nHow do global statistics on suicide differ between sources?\nTo better monitor and prevent suicides globally, it's crucial to understand how they are measured and estimated by different sources.\nDepression is complicated — this is how our understanding of the condition has evolved over time\nOur understanding of depression has evolved over time, with wider screening for depression, new questionnaires, and better statistical tools.\nEndnotes\nThe global suicide\nrate\nhas fallen\n, though.\nIn 2000, the World Health Organization estimated that approximately 7,440 people died from suicide in Sri Lanka. In 2021, this was 3,175. That’s a reduction of around 57%.\nSnowdon, J. (2019). Indian suicide data: What do they mean?. Indian journal of medical research, 150(4), 315-320.\nThe\nWorld Health Organization\n(WHO) estimates that 15% to 20% of suicides globally are the result of pesticide poisoning. That’s around 140,000 deaths each year.\nA review across 108 countries estimated that around 14% of suicides from 2010 to 2014 were the result of pesticides. When accounting for underreporting, it puts this estimate at 20%. Suicide records from many countries — in particular, low-income countries in Sub-Saharan Africa — are often of poor quality, so these estimates are very uncertain.\nMew, E. J., Padmanathan, P., Konradsen, F., Eddleston, M., Chang, S. S., Phillips, M. R., & Gunnell, D. (2017). The global burden of fatal self-poisoning with pesticides 2006-15: systematic review. Journal of affective disorders, 219, 93-104.\nIt’s estimated that approximately\n720,000 people die from suicide\neach year, but these figures are uncertain, and possibly an undercount.\nAnother factor, which we might\nexpect\nto play a role, is that some more recent pesticides include ingredients called “emetics” which are designed to make people vomit. The idea is that people would then expel some toxic ingredients, reducing the risk of severe poisoning. The data on the effectiveness of these additions is limited. There are a few reported reasons why they’re less effective than expected. The first is that the person attempting suicide might ingest large amounts of pesticide, too much for emetics to counter. The second is that it can take some time for emetics to work and induce vomiting. By then, much of the active ingredient can already have been absorbed.\nEddleston, M. (2022). Evidence for the efficacy of the emetic PP796 in paraquat SL20 formulations–a narrative review of published and unpublished evidence. Clinical Toxicology, 60(10), 1163-1175.\nDeuster, E., Tomenson, J. A., Mohamed, F., Gawarammana, I., Buckley, N. A., Wilks, M. F., & Eddleston, M. (2023). Dose ingested, vomiting, and outcome in patients ingesting a standard paraquat 20SL formulation. Clinical Toxicology, 61(1), 29-38.\nKnipe, D. W., Metcalfe, C., Fernando, R., Pearson, M., Konradsen, F., Eddleston, M., & Gunnell, D. (2014). Suicide in Sri Lanka 1975–2012: age, period and cohort analysis of police and hospital data. BMC Public Health, 14, 1-13.\nKnipe, D. W., Chang, S. S., Dawson, A., Eddleston, M., Konradsen, F., Metcalfe, C., & Gunnell, D. (2017). Suicide prevention through means restriction: Impact of the 2008-2011 pesticide restrictions on suicide in Sri Lanka. PloS one, 12(3), e0172893.\nCha, E. S., Chang, S. S., Choi, Y., & Lee, W. J. (2020). Trends in pesticide suicide in South Korea, 1983–2014. Epidemiology and psychiatric sciences, 29, e25.\nCha, E. S., Chang, S. S., Gunnell, D., Eddleston, M., Khang, Y. H., & Lee, W. J. (2016). Impact of paraquat regulation on suicide in South Korea. International journal of epidemiology, 45(2), 470-479.\nGunnell, D., Knipe, D., Chang, S. S., Pearson, M., Konradsen, F., Lee, W. J., & Eddleston, M. (2017). Prevention of suicide with regulations aimed at restricting access to highly hazardous pesticides: a systematic review of the international evidence. The Lancet global health, 5(10), e1026-e1037.\nBonvoisin, T., Utyasheva, L., Knipe, D., Gunnell, D., & Eddleston, M. (2020). Suicide by pesticide poisoning in India: a review of pesticide regulations and their impact on suicide trends. BMC public health, 20, 1-16.\nCha, E. S., Chang, S. S., Gunnell, D., Eddleston, M., Khang, Y. H., & Lee, W. J. (2016). Impact of paraquat regulation on suicide in South Korea. International journal of epidemiology, 45(2), 470-479.\nChowdhury, F. R., Dewan, G., Verma, V. R., Knipe, D. W., Isha, I. T., Faiz, M. A., ... & Eddleston, M. (2018). Bans of WHO class I pesticides in Bangladesh—suicide prevention without hampering agricultural output. International journal of epidemiology, 47(1), 175-184.\nYan, Y., Jiang, Y., Liu, R., Eddleston, M., Tao, C., Page, A., ... & Liu, S. (2023). Impact of pesticide regulations on mortality from suicide by pesticide in China: an interrupted time series analysis. Frontiers in psychiatry, 14, 1189923.\nUtyasheva, L., Sharma, D., Ghimire, R., Karunarathne, A., Robertson, G., & Eddleston, M. (2021). Suicide by pesticide ingestion in Nepal and the impact of pesticide regulation. BMC public health, 21, 1-11.\nMoebus, S., & Boedeker, W. (2021). Case Fatality as an Indicator for the Human Toxicity of Pesticides — A Systematic Scoping Review on the Availability and Variability of Severity Indicators of Pesticide Poisoning. International journal of environmental research and public health, 18(16), 8307.\nKnipe, D. W., Chang, S. S., Dawson, A., Eddleston, M., Konradsen, F., Metcalfe, C., & Gunnell, D. (2017). Suicide prevention through means restriction: Impact of the 2008-2011 pesticide restrictions on suicide in Sri Lanka. PloS one, 12(3), e0172893.\nBonvoisin, T., Utyasheva, L., Knipe, D., Gunnell, D., & Eddleston, M. (2020). Suicide by pesticide poisoning in India: a review of pesticide regulations and their impact on suicide trends. BMC public health, 20, 1-16.\nThese rates were slightly lower in Asia, where most pesticide-related deaths still happen today.\nCarroll, R., Metcalfe, C., & Gunnell, D. (2014). Hospital presenting self-harm and risk of fatal and non-fatal repetition: systematic review and meta-analysis. PloS one, 9(2), e89944.\nThis can be 10 minutes or less in many patients.\nDeisenhammer, E. A., Ing, C. M., Strauss, R., Kemmler, G., Hinterhuber, H., & Weiss, E. M. (2009). The duration of the suicidal process: how much time is left for intervention between consideration and accomplishment of a suicide attempt?. Journal of Clinical Psychiatry, 70(1), 19.\nConner, K. R., Phillips, M. R., Meldrum, S., Knox, K. L., Zhang, Y., & Yang, G. (2005). Low-planned suicides in China. Psychological medicine, 35(8), 1197-1204.\nEddleston, M., Karunaratne, A., Weerakoon, M., Kumarasinghe, S., Rajapakshe, M., Rezvi Sheriff, M. H., ... & Gunnell, D. (2006). Choice of poison for intentional self-poisoning in rural Sri Lanka. Clinical Toxicology, 44(3), 283-286.\nDrechsel, P., Madhuwanthi, P., Nisansala, D., Ramamoorthi, D., & Bandara, T. (2025). On the feasibility of an agricultural revolution: Sri Lanka’s ban of chemical fertilizers in 2021. Food Security, 1-18.\nNoghrehchi, F., Dawson, A. H., Raubenheimer, J., Mohamed, F., Gawarammana, I. B., Eddleston, M., & Buckley, N. A. (2024). Restrictions on pesticides and deliberate self-poisoning in Sri Lanka. JAMA network open, 7(8), e2426209-e2426209.\nChowdhury, F. R., Dewan, G., Verma, V. R., Knipe, D. W., Isha, I. T., Faiz, M. A., ... & Eddleston, M. (2018). Bans of WHO class I pesticides in Bangladesh—suicide prevention without hampering agricultural output. International journal of epidemiology, 47(1), 175-184.\nLee, Y. Y., Chisholm, D., Eddleston, M., Gunnell, D., Fleischmann, A., Konradsen, F., ... & Van Ommeren, M. (2021). The cost-effectiveness of banning highly hazardous pesticides to prevent suicides due to pesticide self-ingestion across 14 countries: an economic modelling study. The Lancet global health, 9(3), e291-e300.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2025) - “Bans on highly toxic pesticides could be an effective way to save lives from suicide” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260619-185743/pesticide-bans-suicide-prevention.html' [Online Resource] (archived on June 19, 2026).\nBibTeX citation\n@article{owid-pesticide-bans-suicide-prevention,\nauthor = {Hannah Ritchie},\ntitle = {Bans on highly toxic pesticides could be an effective way to save lives from suicide},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260619-185743/pesticide-bans-suicide-prevention.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "pesticide-bans-suicide-prevention", "source_url": "https://ourworldindata.org/pesticide-bans-suicide-prevention", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Pesticide poisoning is a common method of suicide in many low- to middle-income countries. Banning highly toxic pesticides and substituting them with less fatal ones can save lives.", "numeric_mentions": ["1,", "2025", "1", "14%", "20%", "100,000", "150,000", "2", "3", "4", "5", "30 years", "6", "25 years", "1975", "2012", "7", "1980", "1990", "40", "10", "1995", "1998", "2010", "8", "2000", "9", "50%", "11", "7%", "80%", "12", "2011,", "2014,", "48%", "13", "10%", "177", "1.6%", "3.6%", "14", "96%", "15", "85%", "16", "2021,", "17", "1960", "2021", "1970", "2022", "2016", "2017", "18", "19", "50 years", "28,000", "30 million", "20", "2000,", "7,440", "3,175", "57%", "2019", "150", "315", "320", "15%", "140,000", "108", "2014", "2006", "219,", "93", "104", "720,000", "60", "1163", "1175", "2023"], "numeric_evidence": [{"title": "Number of suicides", "source_url": "https://ourworldindata.org/grapher/number-of-deaths-from-suicide-ghe.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Total deaths from self-harm among both sexes"], "row_count_total": 4422, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Total deaths from self-harm among both sexes": "858.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Total deaths from self-harm among both sexes": "870.15"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Total deaths from self-harm among both sexes": "900.94"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Total deaths from self-harm among both sexes": "967.23"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Total deaths from self-harm among both sexes": "1003.87"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Total deaths from self-harm among both sexes": "1024.83"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Total deaths from self-harm among both sexes": "1036.46"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Total deaths from self-harm among both sexes": "1024.1"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Total deaths from self-harm among both sexes": "1011.52"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Total deaths from self-harm among both sexes": "996.22"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Total deaths from self-harm among both sexes": "1004.02"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Total deaths from self-harm among both sexes": "1074.26"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Total deaths from self-harm among both sexes": "1113.15"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Total deaths from self-harm among both sexes": "1146.46"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Total deaths from self-harm among both sexes": "1169.49"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Total deaths from self-harm among both sexes": "1179.32"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Total deaths from self-harm among both sexes": "1208.2"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Total deaths from self-harm among both sexes": "1274.29"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Total deaths from self-harm among both sexes": "1312.21"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Total deaths from self-harm among both sexes": "1340.15"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Total deaths from self-harm among both sexes": "1372.49"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Total deaths from self-harm among both sexes": "1454.07"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Total deaths from self-harm among both sexes": "50729.52"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Total deaths from self-harm among both sexes": "51712.15"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Total deaths from self-harm among both sexes": "53226.96"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Total deaths from self-harm among both sexes": "54746.93"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Total deaths from self-harm among both sexes": "55766.68"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Total deaths from self-harm among both sexes": "56180.95"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Total deaths from self-harm among both sexes": "57754.49"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Total deaths from self-harm among both sexes": "59006.57"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Total deaths from self-harm among both sexes": "60830.258"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Total deaths from self-harm among both sexes": "62400.78"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Total deaths from self-harm among both sexes": "65805.57"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Total deaths from self-harm among both sexes": "66499.44"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Total deaths from self-harm among both sexes": "67693.96"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Total deaths from self-harm among both sexes": "69257.516"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Total deaths from self-harm among both sexes": "71360.96"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Total deaths from self-harm among both sexes": "73441.43"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Total deaths from self-harm among both sexes": "74913.94"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Total deaths from self-harm among both sexes": "76301.51"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Total deaths from self-harm among both sexes": "80148.516"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Total deaths from self-harm among both sexes": "82436.516"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Total deaths from self-harm among both sexes": "83064.89"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2021", "Total deaths from self-harm among both sexes": "89125.22"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Total deaths from self-harm among both sexes": "154.82"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Total deaths from self-harm among both sexes": "134.19"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Total deaths from self-harm among both sexes": "136.13"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Total deaths from self-harm among both sexes": "141.17"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Total deaths from self-harm among both sexes": "139.02"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Total deaths from self-harm among both sexes": "207.84"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Total deaths from self-harm among both sexes": "208.73"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Total deaths from self-harm among both sexes": "211.57"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Total deaths from self-harm among both sexes": "211.96"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Total deaths from self-harm among both sexes": "208.27"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Total deaths from self-harm among both sexes": "199.52"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Total deaths from self-harm among both sexes": "201.96"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Total deaths from self-harm among both sexes": "133.17"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Total deaths from self-harm among both sexes": "133.14"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Total deaths from self-harm among both sexes": "126.18"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Total deaths from self-harm among both sexes": "122.98"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Total deaths from self-harm among both sexes": "120.2"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Total deaths from self-harm among both sexes": "117.19"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Total deaths from self-harm among both sexes": "114.84"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Total deaths from self-harm among both sexes": "113.6"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Total deaths from self-harm among both sexes": "100.67"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Total deaths from self-harm among both sexes": "81.35"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Total deaths from self-harm among both sexes": "1281.47"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Total deaths from self-harm among both sexes": "1230.65"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Total deaths from self-harm among both sexes": "1228.9"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Total deaths from self-harm among both sexes": "1153.84"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Total deaths from self-harm among both sexes": "1109.23"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Total deaths from self-harm among both sexes": "1091.28"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Total deaths from self-harm among both sexes": "1059.58"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Total deaths from self-harm among both sexes": "1028.46"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Total deaths from self-harm among both sexes": "997.56"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Total deaths from self-harm among both sexes": "959.21"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Total deaths from self-harm among both sexes": "933.26"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Total deaths from self-harm among both sexes": "925.62"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Total deaths from self-harm among both sexes": "875.96"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Total deaths from self-harm among both sexes": "874.04"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Total deaths from self-harm among both sexes": "872.78"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Total deaths from self-harm among both sexes": "855.5"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Total deaths from self-harm among both sexes": "829.6"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Total deaths from self-harm among both sexes": "839.61"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Total deaths from self-harm among both sexes": "865.99"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Total deaths from self-harm among both sexes": "875.07"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Total deaths from self-harm among both sexes": "744.31"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Total deaths from self-harm among both sexes": "965.62"}, {"Entity": "Andorra", "Code": "AND", "Year": "2000", "Total deaths from self-harm among both sexes": "8.02"}, {"Entity": "Andorra", "Code": "AND", "Year": "2001", "Total deaths from self-harm among both sexes": "8.82"}, {"Entity": "Andorra", "Code": "AND", "Year": "2002", "Total deaths from self-harm among both sexes": "9.05"}, {"Entity": "Andorra", "Code": "AND", "Year": "2003", "Total deaths from self-harm among both sexes": "8.9"}, {"Entity": "Andorra", "Code": "AND", "Year": "2004", "Total deaths from self-harm among both sexes": "10.69"}, {"Entity": "Andorra", "Code": "AND", "Year": "2005", "Total deaths from self-harm among both sexes": "9.88"}, {"Entity": "Andorra", "Code": "AND", "Year": "2006", "Total deaths from self-harm among both sexes": "10.12"}, {"Entity": "Andorra", "Code": "AND", "Year": "2007", "Total deaths from self-harm among both sexes": "9.49"}, {"Entity": "Andorra", "Code": "AND", "Year": "2008", "Total deaths from self-harm among both sexes": "9.01"}, {"Entity": "Andorra", "Code": "AND", "Year": "2009", "Total deaths from self-harm among both sexes": "8.59"}, {"Entity": "Andorra", "Code": "AND", "Year": "2010", "Total deaths from self-harm among both sexes": "8.12"}, {"Entity": "Andorra", "Code": "AND", "Year": "2011", "Total deaths from self-harm among both sexes": "6.83"}, {"Entity": "Andorra", "Code": "AND", "Year": "2012", "Total deaths from self-harm among both sexes": "7.04"}, {"Entity": "Andorra", "Code": "AND", "Year": "2013", "Total deaths from self-harm among both sexes": "7.91"}, {"Entity": "Andorra", "Code": "AND", "Year": "2014", "Total deaths from self-harm among both sexes": "7.95"}, {"Entity": "Andorra", "Code": "AND", "Year": "2015", "Total deaths from self-harm among both sexes": "7.92"}, {"Entity": "Andorra", "Code": "AND", "Year": "2016", "Total deaths from self-harm among both sexes": "8.35"}, {"Entity": "Andorra", "Code": "AND", "Year": "2017", "Total deaths from self-harm among both sexes": "7.62"}, {"Entity": "Andorra", "Code": "AND", "Year": "2018", "Total deaths from self-harm among both sexes": "7.34"}, {"Entity": "Andorra", "Code": "AND", "Year": "2019", "Total deaths from self-harm among both sexes": "8.12"}, {"Entity": "Andorra", "Code": "AND", "Year": "2020", "Total deaths from self-harm among both sexes": "10.67"}, {"Entity": "Andorra", "Code": "AND", "Year": "2021", "Total deaths from self-harm among both sexes": "5.62"}, {"Entity": "Angola", "Code": "AGO", "Year": "2000", "Total deaths from self-harm among both sexes": "1431.65"}, {"Entity": "Angola", "Code": "AGO", "Year": "2001", "Total deaths from self-harm among both sexes": "1374.2"}, {"Entity": "Angola", "Code": "AGO", "Year": "2002", "Total deaths from self-harm among both sexes": "1379.64"}, {"Entity": "Angola", "Code": "AGO", "Year": "2003", "Total deaths from self-harm among both sexes": "1550.06"}, {"Entity": "Angola", "Code": "AGO", "Year": "2004", "Total deaths from self-harm among both sexes": "1614.49"}, {"Entity": "Angola", "Code": "AGO", "Year": "2005", "Total deaths from self-harm among both sexes": "1562.46"}, {"Entity": "Angola", "Code": "AGO", "Year": "2006", "Total deaths from self-harm among both sexes": "1673.52"}, {"Entity": "Angola", "Code": "AGO", "Year": "2007", "Total deaths from self-harm among both sexes": "1616.98"}, {"Entity": "Angola", "Code": "AGO", "Year": "2008", "Total deaths from self-harm among both sexes": "1574.36"}, {"Entity": "Angola", "Code": "AGO", "Year": "2009", "Total deaths from self-harm among both sexes": "1577.42"}], "rows_tail": [{"Entity": "Venezuela", "Code": "VEN", "Year": "2012", "Total deaths from self-harm among both sexes": "1816.19"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2013", "Total deaths from self-harm among both sexes": "1755.02"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2014", "Total deaths from self-harm among both sexes": "1705.4"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2015", "Total deaths from self-harm among both sexes": "1860.97"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2016", "Total deaths from self-harm among both sexes": "3217.92"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2017", "Total deaths from self-harm among both sexes": "2455.42"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2018", "Total deaths from self-harm among both sexes": "2207.86"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2019", "Total deaths from self-harm among both sexes": "2061.01"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2020", "Total deaths from self-harm among both sexes": "2062.97"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2021", "Total deaths from self-harm among both sexes": "2402.9"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2000", "Total deaths from self-harm among both sexes": "4657.99"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2001", "Total deaths from self-harm among both sexes": "4755.94"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2002", "Total deaths from self-harm among both sexes": "4857.26"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2003", "Total deaths from self-harm among both sexes": "4911.36"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2004", "Total deaths from self-harm among both sexes": "5048.67"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2005", "Total deaths from self-harm among both sexes": "5191.53"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2006", "Total deaths from self-harm among both sexes": "5330.42"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2007", "Total deaths from self-harm among both sexes": "5579.26"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2008", "Total deaths from self-harm among both sexes": "5846.05"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2009", "Total deaths from self-harm among both sexes": "6078.57"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2010", "Total deaths from self-harm among both sexes": "6329.69"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2011", "Total deaths from self-harm among both sexes": "6469.46"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2012", "Total deaths from self-harm among both sexes": "6643.03"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2013", "Total deaths from self-harm among both sexes": "6811.55"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2014", "Total deaths from self-harm among both sexes": "7036.54"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2015", "Total deaths from self-harm among both sexes": "7263.59"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2016", "Total deaths from self-harm among both sexes": "7309.96"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2017", "Total deaths from self-harm among both sexes": "7269.36"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2018", "Total deaths from self-harm among both sexes": "7347.25"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2019", "Total deaths from self-harm among both sexes": "7478.58"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "Total deaths from self-harm among both sexes": "6859.54"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2021", "Total deaths from self-harm among both sexes": "7368.87"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Total deaths from self-harm among both sexes": "764804.8"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Total deaths from self-harm among both sexes": "753919.94"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Total deaths from self-harm among both sexes": "760218.44"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Total deaths from self-harm among both sexes": "766595.44"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Total deaths from self-harm among both sexes": "770383.9"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Total deaths from self-harm among both sexes": "765525.1"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Total deaths from self-harm among both sexes": "747391.75"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Total deaths from self-harm among both sexes": "741475.7"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Total deaths from self-harm among both sexes": "742390.75"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Total deaths from self-harm among both sexes": "742800.94"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Total deaths from self-harm among both sexes": "742398.44"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Total deaths from self-harm among both sexes": "738499.4"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Total deaths from self-harm among both sexes": "732749.9"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Total deaths from self-harm among both sexes": "729132.9"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Total deaths from self-harm among both sexes": "726280.44"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Total deaths from self-harm among both sexes": "720666"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Total deaths from self-harm among both sexes": "719981.3"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Total deaths from self-harm among both sexes": "726442.25"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Total deaths from self-harm among both sexes": "729890.3"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Total deaths from self-harm among both sexes": "727975.44"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Total deaths from self-harm among both sexes": "706667.3"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Total deaths from self-harm among both sexes": "718032.6"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Total deaths from self-harm among both sexes": "1076.56"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Total deaths from self-harm among both sexes": "1102.79"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Total deaths from self-harm among both sexes": "1116.44"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Total deaths from self-harm among both sexes": "1092.47"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Total deaths from self-harm among both sexes": "1058.42"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Total deaths from self-harm among both sexes": "1121.68"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Total deaths from self-harm among both sexes": "1094.24"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Total deaths from self-harm among both sexes": "1234.47"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Total deaths from self-harm among both sexes": "1271.33"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Total deaths from self-harm among both sexes": "1273.65"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Total deaths from self-harm among both sexes": "1274.71"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Total deaths from self-harm among both sexes": "1262.98"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Total deaths from self-harm among both sexes": "1276"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Total deaths from self-harm among both sexes": "1300.11"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Total deaths from self-harm among both sexes": "1343.65"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Total deaths from self-harm among both sexes": "1344.04"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Total deaths from self-harm among both sexes": "1430.39"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Total deaths from self-harm among both sexes": "1431.24"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Total deaths from self-harm among both sexes": "1424.48"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Total deaths from self-harm among both sexes": "1384.9"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Total deaths from self-harm among both sexes": "1292.06"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Total deaths from self-harm among both sexes": "1420.79"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Total deaths from self-harm among both sexes": "656.42"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Total deaths from self-harm among both sexes": "655.01"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Total deaths from self-harm among both sexes": "686.99"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Total deaths from self-harm among both sexes": "629.6"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Total deaths from self-harm among both sexes": "678"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Total deaths from self-harm among both sexes": "672.05"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Total deaths from self-harm among both sexes": "737.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Total deaths from self-harm among both sexes": "748.87"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Total deaths from self-harm among both sexes": "763.31"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Total deaths from self-harm among both sexes": "758.6"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Total deaths from self-harm among both sexes": "805.58"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Total deaths from self-harm among both sexes": "818.25"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Total deaths from self-harm among both sexes": "827.39"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Total deaths from self-harm among both sexes": "863.94"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Total deaths from self-harm among both sexes": "870.16"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Total deaths from self-harm among both sexes": "908.75"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Total deaths from self-harm among both sexes": "908.16"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Total deaths from self-harm among both sexes": "955.63"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Total deaths from self-harm among both sexes": "964.89"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Total deaths from self-harm among both sexes": "1037.96"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Total deaths from self-harm among both sexes": "1112.17"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Total deaths from self-harm among both sexes": "1358.68"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Total deaths from self-harm among both sexes": "1437.08"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Total deaths from self-harm among both sexes": "1543.81"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Total deaths from self-harm among both sexes": "1649.92"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Total deaths from self-harm among both sexes": "1725.76"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Total deaths from self-harm among both sexes": "1726.93"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Total deaths from self-harm among both sexes": "1664.56"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Total deaths from self-harm among both sexes": "1662.19"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Total deaths from self-harm among both sexes": "1626.25"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Total deaths from self-harm among both sexes": "1688.66"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Total deaths from self-harm among both sexes": "1567.24"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Total deaths from self-harm among both sexes": "1748.29"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Total deaths from self-harm among both sexes": "1862.07"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Total deaths from self-harm among both sexes": "1897.43"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Total deaths from self-harm among both sexes": "1952.17"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Total deaths from self-harm among both sexes": "1966.7"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Total deaths from self-harm among both sexes": "2071.91"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Total deaths from self-harm among both sexes": "2204.7"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Total deaths from self-harm among both sexes": "2250.7"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Total deaths from self-harm among both sexes": "2445.58"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Total deaths from self-harm among both sexes": "2448.38"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Total deaths from self-harm among both sexes": "2439.14"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Total deaths from self-harm among both sexes": "2775.73"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "number-of-deaths-from-suicide-ghe", "metadata_url": "https://ourworldindata.org/grapher/number-of-deaths-from-suicide-ghe.metadata.json", "chart_title": "Number of suicides", "chart_subtitle": "Estimated annual number of deaths from suicide. Suicide deaths are underreported in many countries due to social stigma and cultural or legal concerns. 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Reuse should follow the source and license information in this chart metadata."}, {"grapher_slug": "death-rate-from-suicides", "source_url": "https://ourworldindata.org/grapher/death-rate-from-suicides", "parse_error": "Failed to fetch https://ourworldindata.org/grapher/death-rate-from-suicides.csv"}, {"title": "Suicide rate", "source_url": "https://ourworldindata.org/grapher/death-rate-from-suicides-gho.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Age-standardized death rate from self-harm among both sexes"], "row_count_total": 4422, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Age-standardized death rate from self-harm among both sexes": "7.04678"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Age-standardized death rate from self-harm among both sexes": "7.1279664"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Age-standardized death rate from self-harm among both sexes": "7.057435"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Age-standardized death rate from self-harm among both sexes": "7.085413"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Age-standardized death rate from self-harm among both sexes": "7.0883346"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Age-standardized death rate from self-harm among both sexes": "6.989182"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Age-standardized death rate from self-harm among both sexes": "6.7106414"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Age-standardized death rate from self-harm among both sexes": "6.49032"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Age-standardized death rate from self-harm among both sexes": "6.251322"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Age-standardized death rate from self-harm among both sexes": "5.857906"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Age-standardized death rate from self-harm among both sexes": "5.688911"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Age-standardized death rate from self-harm among both sexes": "5.8711257"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Age-standardized death rate from self-harm among both sexes": "5.7921"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Age-standardized death rate from self-harm among both sexes": "5.7145348"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Age-standardized death rate from self-harm among both sexes": "5.5813885"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Age-standardized death rate from self-harm among both sexes": "5.363797"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Age-standardized death rate from self-harm among both sexes": "5.3510666"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Age-standardized death rate from self-harm among both sexes": "5.499149"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Age-standardized death rate from self-harm among both sexes": "5.451466"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Age-standardized death rate from self-harm among both sexes": "5.39273"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Age-standardized death rate from self-harm among both sexes": "5.3621764"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Age-standardized death rate from self-harm among both sexes": "5.388687"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Age-standardized death rate from self-harm among both sexes": "9.970473"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Age-standardized death rate from self-harm among both sexes": "9.891504"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Age-standardized death rate from self-harm among both sexes": "9.89545"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Age-standardized death rate from self-harm among both sexes": "9.886266"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Age-standardized death rate from self-harm among both sexes": "9.790439"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Age-standardized death rate from self-harm among both sexes": "9.608829"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Age-standardized death rate from self-harm among both sexes": "9.592428"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Age-standardized death rate from self-harm among both sexes": "9.529765"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Age-standardized death rate from self-harm among both sexes": "9.525382"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Age-standardized death rate from self-harm among both sexes": "9.485207"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Age-standardized death rate from self-harm among both sexes": "9.689575"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Age-standardized death rate from self-harm among both sexes": "9.519722"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Age-standardized death rate from self-harm among both sexes": "9.393394"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Age-standardized death rate from self-harm among both sexes": "9.300334"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Age-standardized death rate from self-harm among both sexes": "9.275375"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Age-standardized death rate from self-harm among both sexes": "9.262834"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Age-standardized death rate from self-harm among both sexes": "9.173538"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Age-standardized death rate from self-harm among both sexes": "9.088877"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Age-standardized death rate from self-harm among both sexes": "9.22262"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Age-standardized death rate from self-harm among both sexes": "9.195812"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Age-standardized death rate from self-harm among both sexes": "9.210741"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2021", "Age-standardized death rate from self-harm among both sexes": "9.400316"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Age-standardized death rate from self-harm among both sexes": "5.208214"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Age-standardized death rate from self-harm among both sexes": "4.4798536"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Age-standardized death rate from self-harm among both sexes": "4.5293174"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Age-standardized death rate from self-harm among both sexes": "4.6417174"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Age-standardized death rate from self-harm among both sexes": "4.524418"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Age-standardized death rate from self-harm among both sexes": "6.8274097"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Age-standardized death rate from self-harm among both sexes": "6.7791924"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Age-standardized death rate from self-harm among both sexes": "6.8636017"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Age-standardized death rate from self-harm among both sexes": "6.884736"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Age-standardized death rate from self-harm among both sexes": "6.7752595"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Age-standardized death rate from self-harm among both sexes": "6.4947042"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Age-standardized death rate from self-harm among both sexes": "6.560598"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Age-standardized death rate from self-harm among both sexes": "4.116106"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Age-standardized death rate from self-harm among both sexes": "4.072404"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Age-standardized death rate from self-harm among both sexes": "3.8214164"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Age-standardized death rate from self-harm among both sexes": "3.6173608"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Age-standardized death rate from self-harm among both sexes": "3.487594"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Age-standardized death rate from self-harm among both sexes": "3.3308504"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Age-standardized death rate from self-harm among both sexes": "3.1883323"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Age-standardized death rate from self-harm among both sexes": "3.0821269"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Age-standardized death rate from self-harm among both sexes": "2.5677998"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Age-standardized death rate from self-harm among both sexes": "2.1897862"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Age-standardized death rate from self-harm among both sexes": "4.1094484"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Age-standardized death rate from self-harm among both sexes": "3.890561"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Age-standardized death rate from self-harm among both sexes": "3.7752733"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Age-standardized death rate from self-harm among both sexes": "3.4956343"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Age-standardized death rate from self-harm among both sexes": "3.3033764"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Age-standardized death rate from self-harm among both sexes": "3.1913438"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Age-standardized death rate from self-harm among both sexes": "3.0525823"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Age-standardized death rate from self-harm among both sexes": "2.917755"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Age-standardized death rate from self-harm among both sexes": "2.7877064"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Age-standardized death rate from self-harm among both sexes": "2.655292"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Age-standardized death rate from self-harm among both sexes": "2.552516"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Age-standardized death rate from self-harm among both sexes": "2.4951704"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Age-standardized death rate from self-harm among both sexes": "2.335973"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Age-standardized death rate from self-harm among both sexes": "2.2985022"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Age-standardized death rate from self-harm among both sexes": "2.2689452"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Age-standardized death rate from self-harm among both sexes": "2.2004573"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Age-standardized death rate from self-harm among both sexes": "2.1119583"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Age-standardized death rate from self-harm among both sexes": "2.1128728"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Age-standardized death rate from self-harm among both sexes": "2.148624"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Age-standardized death rate from self-harm among both sexes": "2.1487212"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Age-standardized death rate from self-harm among both sexes": "1.8345944"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Age-standardized death rate from self-harm among both sexes": "2.3034966"}, {"Entity": "Andorra", "Code": "AND", "Year": "2000", "Age-standardized death rate from self-harm among both sexes": "9.820946"}, {"Entity": "Andorra", "Code": "AND", "Year": "2001", "Age-standardized death rate from self-harm among both sexes": "11.114499"}, {"Entity": "Andorra", "Code": "AND", "Year": "2002", "Age-standardized death rate from self-harm among both sexes": "11.495886"}, {"Entity": "Andorra", "Code": "AND", "Year": "2003", "Age-standardized death rate from self-harm among both sexes": "10.657868"}, {"Entity": "Andorra", "Code": "AND", "Year": "2004", "Age-standardized death rate from self-harm among both sexes": "12.308897"}, {"Entity": "Andorra", "Code": "AND", "Year": "2005", "Age-standardized death rate from self-harm among both sexes": "10.724412"}, {"Entity": "Andorra", "Code": "AND", "Year": "2006", "Age-standardized death rate from self-harm among both sexes": "10.743566"}, {"Entity": "Andorra", "Code": "AND", "Year": "2007", "Age-standardized death rate from self-harm among both sexes": "9.7930355"}, {"Entity": "Andorra", "Code": "AND", "Year": "2008", "Age-standardized death rate from self-harm among both sexes": "9.010325"}, {"Entity": "Andorra", "Code": "AND", "Year": "2009", "Age-standardized death rate from self-harm among both sexes": "8.517546"}, {"Entity": "Andorra", "Code": "AND", "Year": "2010", "Age-standardized death rate from self-harm among both sexes": "8.380428"}, {"Entity": "Andorra", "Code": "AND", "Year": "2011", "Age-standardized death rate from self-harm among both sexes": "6.989669"}, {"Entity": "Andorra", "Code": "AND", "Year": "2012", "Age-standardized death rate from self-harm among both sexes": "7.4388313"}, {"Entity": "Andorra", "Code": "AND", "Year": "2013", "Age-standardized death rate from self-harm among both sexes": "8.837888"}, {"Entity": 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{"Entity": "Venezuela", "Code": "VEN", "Year": "2017", "Age-standardized death rate from self-harm among both sexes": "8.140386"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2018", "Age-standardized death rate from self-harm among both sexes": "7.5947895"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2019", "Age-standardized death rate from self-harm among both sexes": "7.370604"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2020", "Age-standardized death rate from self-harm among both sexes": "7.4971905"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2021", "Age-standardized death rate from self-harm among both sexes": "8.821515"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2000", "Age-standardized death rate from self-harm among both sexes": "7.21806"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2001", "Age-standardized death rate from self-harm among both sexes": "7.220595"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2002", "Age-standardized death rate from 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"2010", "Age-standardized death rate from self-harm among both sexes": "7.7608857"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2011", "Age-standardized death rate from self-harm among both sexes": "7.748663"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2012", "Age-standardized death rate from self-harm among both sexes": "7.7745724"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2013", "Age-standardized death rate from self-harm among both sexes": "7.7849536"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2014", "Age-standardized death rate from self-harm among both sexes": "7.858194"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2015", "Age-standardized death rate from self-harm among both sexes": "7.9344234"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2016", "Age-standardized death rate from self-harm among both sexes": "7.832501"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2017", "Age-standardized death rate from self-harm among both sexes": "7.6666117"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2018", "Age-standardized death rate from self-harm among both sexes": "7.6212244"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2019", "Age-standardized death rate from self-harm among both sexes": "7.6184196"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "Age-standardized death rate from self-harm among both sexes": "6.885008"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2021", "Age-standardized death rate from self-harm among both sexes": "7.2323165"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Age-standardized death rate from self-harm among both sexes": "13.626343"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Age-standardized death rate from self-harm among both sexes": "13.183542"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Age-standardized death rate from self-harm among both sexes": "13.036289"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Age-standardized death rate from self-harm among both sexes": "12.891957"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Age-standardized death rate from self-harm among both sexes": "12.689718"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Age-standardized death rate from self-harm among both sexes": "12.353321"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Age-standardized death rate from self-harm among both sexes": "11.81718"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Age-standardized death rate from self-harm among both sexes": "11.497938"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Age-standardized death rate from self-harm among both sexes": "11.297209"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Age-standardized death rate from self-harm among both sexes": "11.101283"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Age-standardized death rate from self-harm among both sexes": "10.894027"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Age-standardized death rate from self-harm among both sexes": "10.6465"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Age-standardized death rate from self-harm among both sexes": "10.384171"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Age-standardized death rate from self-harm among both sexes": "10.162778"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Age-standardized death rate from self-harm among both sexes": "9.958967"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Age-standardized death rate from self-harm among both sexes": "9.722083"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Age-standardized death rate from self-harm among both sexes": "9.559817"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Age-standardized death rate from self-harm among both sexes": "9.497429"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Age-standardized death rate from self-harm among both sexes": "9.389958"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Age-standardized death rate from self-harm among both sexes": "9.218698"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Age-standardized death rate from self-harm among both sexes": "8.797667"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Age-standardized death rate from self-harm among both sexes": "8.819593"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Age-standardized death rate from self-harm among both sexes": "7.422038"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Age-standardized death rate from self-harm among both sexes": "7.2751284"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Age-standardized death rate from self-harm among both sexes": "7.0851107"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Age-standardized death rate from self-harm among both sexes": "6.7707424"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Age-standardized death rate from self-harm among both sexes": "6.4712467"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Age-standardized death rate from self-harm among both sexes": "6.4023128"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Age-standardized death rate from self-harm among both sexes": "6.06685"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Age-standardized death rate from self-harm among both sexes": "6.33183"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Age-standardized death rate from self-harm among both sexes": "6.241684"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Age-standardized death rate from self-harm among both sexes": "6.023427"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Age-standardized death rate from self-harm among both sexes": "5.794762"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Age-standardized death rate from self-harm among both sexes": "5.5573096"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Age-standardized death rate from self-harm among both sexes": "5.415994"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Age-standardized death rate from self-harm among both sexes": "5.3236647"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Age-standardized death rate from self-harm among both sexes": "5.2849336"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Age-standardized death rate from self-harm among both sexes": "5.124882"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Age-standardized death rate from self-harm among both sexes": "5.251394"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Age-standardized death rate from self-harm among both sexes": "5.132993"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Age-standardized death rate from self-harm among both sexes": "5.0023313"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Age-standardized death rate from self-harm among both sexes": "4.79554"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Age-standardized death rate from self-harm among both sexes": "4.4780016"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Age-standardized death rate from self-harm among both sexes": "4.7128134"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Age-standardized death rate from self-harm among both sexes": "13.110368"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Age-standardized death rate from self-harm among both sexes": "12.906497"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Age-standardized death rate from self-harm among both sexes": "12.932304"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Age-standardized death rate from self-harm among both sexes": "12.06194"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Age-standardized death rate from self-harm among both sexes": "12.309802"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Age-standardized death rate from self-harm 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"Age-standardized death rate from self-harm among both sexes": "10.907879"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Age-standardized death rate from self-harm among both sexes": "10.66896"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Age-standardized death rate from self-harm among both sexes": "10.652452"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Age-standardized death rate from self-harm among both sexes": "10.367569"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Age-standardized death rate from self-harm among both sexes": "10.37211"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Age-standardized death rate from self-harm among both sexes": "10.233345"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Age-standardized death rate from self-harm among both sexes": "10.626126"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Age-standardized death rate from self-harm among both sexes": "11.511997"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Age-standardized death rate from self-harm among both sexes": "12.812294"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Age-standardized death rate from self-harm among both sexes": "17.78956"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Age-standardized death rate from self-harm among both sexes": "18.824253"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Age-standardized death rate from self-harm among both sexes": "19.858788"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Age-standardized death rate from self-harm among both sexes": "20.48987"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Age-standardized death rate from self-harm among both sexes": "20.65118"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Age-standardized death rate from self-harm among both sexes": "20.233013"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Age-standardized death rate from self-harm among both sexes": "19.91352"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Age-standardized death rate from self-harm among both sexes": "19.408457"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Age-standardized death rate from self-harm among both sexes": "19.98595"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Age-standardized death rate from self-harm among both sexes": "18.757444"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Age-standardized death rate from self-harm among both sexes": "20.728355"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Age-standardized death rate from self-harm among both sexes": "21.68553"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Age-standardized death rate from self-harm among both sexes": "21.7135"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Age-standardized death rate from self-harm among both sexes": "22.096712"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Age-standardized death rate from self-harm among both sexes": "22.171911"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Age-standardized death rate from self-harm among both sexes": "22.920153"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Age-standardized death rate from self-harm among both sexes": "23.850416"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Age-standardized death rate from self-harm among both sexes": "23.684412"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Age-standardized death rate from self-harm among both sexes": "24.847534"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Age-standardized death rate from self-harm among both sexes": "23.951984"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Age-standardized death rate from self-harm among both sexes": "23.8313"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Age-standardized death rate from self-harm among both sexes": "25.762676"}], 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World Health Organization, “Global Health Estimates” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/970944.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "ed4611fb7dbc2ccb0fbc"},
{"raw_link": "https://ourworldindata.org/global-inequality-opportunity-to-give", "title": "Global inequality is huge — but so is the opportunity for people in high-income countries to support poor people", "context": "Home\nEconomic Inequality\nGlobal inequality is huge — but so is the opportunity for people in high-income countries to support poor people\nPeople in high-income countries could dramatically improve lives worldwide with minimal financial commitment, yet few do.\nBy\nSimon van Teutem\nand\nJoe Hasell\nAugust 25, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nFew things are more surprising than data about income inequality around the world. Our intuitions about this can be wildly off the mark.\nIn the Netherlands, the average person’s income over\ntwo weeks\nis more than the entire yearly income of the average person in Malawi.\n1\nGlobal income inequality is staggering.\nBut we can also flip this insight around. Given vast inequality, those of us living in rich countries — even on an average income — find ourselves in an\nextraordinary\nposition to do good.\nIn this article, we explain the data that shows the scale of this opportunity and explore how those in rich countries might seize it.\nLess than 2% of the income of the top 10% global earners equals the\nentire\nannual income of the poorest 10%\nHere's the question that shifted our perspective on global inequality: how much would it take from the richest 10% of people in the world to match that of the poorest 10%?\nOur colleague Pablo Arriagada calculated the answer using data from the World Bank's Poverty and Inequality Platform.\n2\nAs the chart below shows, it takes less than 2 cents out of every dollar. That's less than\none-fiftieth\nof their income, or one week of income in a year.\nDownload\nFor a one-person household, an annual post-tax income of $20,000 already puts you in the richest tenth of the world\nThe second revelation that shifted our perspective on global inequality is that we are in the richest 10%. When we think of global inequality, it's easy to picture “the rich” as someone else — those with garages full of sports cars or taking private trips to space. But the richest tenth of the world includes us and likely many others who don’t intuitively think of themselves as “the rich”.\nFor a one-person household, an annual post-tax income of $20,000 puts you in this category. That income bracket captures\nmore than half the population\nin rich countries, like the Netherlands, the US, or Germany.\nRealizing this is both sobering and empowering. It means we're not just observers of global inequality — we're participants with big levers to act on it. We can reduce it. We’ll explore three avenues through which we can use this opportunity to support others.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nThree ways those in rich countries can realize the extraordinary opportunity to help those in poor countries\n1. Support global redistribution through government foreign aid\nForeign aid still\ndwarfs\nprivate philanthropy and is one of the biggest channels of global redistribution. Some of its accomplishments have been remarkable:\nGavi, the Vaccine Alliance\n, has vaccinated hundreds of millions of children against diseases and has been estimated to have saved 1.5 million lives.\n3\nBetween 2004 and 2023\n, the United States dedicated just under 0.2% of its national income to foreign aid programs. Still, its program combating the HIV epidemic, primarily in\nlow-income countries\n, has saved an estimated 19 million lives. And that represents just one program among many.\nA recent study in The Lancet estimates that USAID programs helped prevent over 91 million deaths between 2001 and 2021, including 30 million child deaths.\n4\nForeign aid is far from perfect, and money can easily be wasted on ineffective programs.\n5\nBut from where we stand, its challenges and inefficiencies are arguments for improving efficiency rather than cuts.\nPeople may disagree on how useful foreign aid is. But rich countries have seen that it can work, and they’ve committed to continuing to help the poorest countries.\nIn 1981, the major foreign aid donor countries made a promise at the UN: at least 0.15% of their national income would go to the world's least-developed countries. That’s far less than the 2% mentioned at the start of the article. These least-developed countries, such as Malawi and Mozambique, are home to populations largely among the poorest 10% of the global income distribution.\nOver the years, the major donor countries repeated their pledge. However, according to the most recent data (from 2022), most countries failed to honor this promise. The chart shows the 20 OECD countries giving the\nmost\naid (as a percentage of their national income) to the world’s poorest nations. Only three countries met the target in 2022: Luxembourg, Sweden, and Norway.\nDownload\nGiven the extreme global inequality outlined earlier, increasing aid to the least developed countries represents a modest step toward addressing a profound imbalance — one that rich nations have repeatedly promised but failed to take.\nMany countries, including the United States and the United Kingdom, have proposed aid cuts instead. Projections indicate that the American reductions alone could lead to 14 million additional deaths by 2030.\n6\nIt can feel like there's not much one person can do about government cuts. But this isn't a reason to despair. People in rich countries can not only demand that their governments do better; each of them also has the power to make a difference through direct donations that reach those in need.\n2. Address global inequality through charitable giving\nReaching the world's poorest was once extremely complicated for individuals. But today, reliable channels exist that handle the logistics for you.\nFor evidence-based giving,\nGiveWell\nresearches and ranks charitable organizations by their cost-effectiveness, helping donors find where their contributions will have the greatest measurable impact. Their research shows that many of us can\nsave a child’s life\nthrough simple interventions, like providing\nvitamin A supplements\nto children in low-income countries or distributing bednets to protect against malaria.\nWhen it comes to addressing global inequality, one charity that stands out is\nGiveDirectly\n. While charities often face criticism for high administrative costs, this cash transfer organization gets donations directly to people with limited overhead. For every $10 donated, $8 goes directly to those in need.\n7\nThese direct cash transfers reach families in countries with particularly high poverty rates, like Malawi, Mozambique, Rwanda, Uganda, and Kenya.\n8\nGiving money directly to poor households really does help. A meta-analysis of randomized controlled trials on such cash transfers to low- and middle-income countries shows a clear pattern: when poor people receive cash, they use it to improve their lives.\n9\nThey spend it on essentials like food, school fees, and healthcare. This improves their well-being, reduces stress, and leads to healthier and taller children.\nThe chart below shows the share of money reaching beneficiaries via GiveDirectly and the statistically significant effects identified in the meta-analysis. Additional studies have also shown that cash transfers increase recipients' use of health services.\n10\nThere’s also extensive research examining potential negative consequences, and common concerns appear to lack solid empirical support.\n11\nDownload\n3 – Raise awareness of global inequality\nLet’s return to where we began: the gap between our expectations and the reality of global inequality. To this end, we want to share an insight from a paper by political economist Gautam Nair.\n12\nTake a moment to consider: what do you think is the global median income? Make your best guess before checking the footnote.\n13\nWhen people learned the actual global median income, their willingness to donate to international charities rose by 55%\nIn his paper, Nair asked a large, representative sample of Americans about the global median income. He discovered that they think the median income is much higher than it is. On average, they overestimated this figure by a factor of 10.\nBecause Americans think people globally earn far more than they really do, they underestimate their own\nrelative\nincome. In other words, they imagine they are only a little richer than the typical person worldwide, when they are vastly richer. American citizens are ten times richer than they believe, compared to the global median. This reminded us of seeing the 2% figure for the first time, challenging how we thought about global inequality.\nThis gap between perception and reality matters when it comes to foreign aid and charitable giving. Nair’s research revealed that correcting false beliefs can directly impact a person’s willingness to support people in poorer countries.\nWhen participants learned the\nactual\nglobal median income, their generosity increased considerably. They not only expressed support for higher foreign aid spending, but also changed their behavior: their willingness to donate to international charities rose by 55% compared to those who remained unaware of the true global income disparity.\n14\nOnce they become aware of their relative position in the income distribution, many people want to help the poorest people more. If understanding these facts can spark generosity, even talking about the statistics in this article with someone could be a small but meaningful first step. Perhaps some people decide to act on this and use their opportunity to help those at the other end of the income distribution.\nAcknowledgments\nMany thanks to Ryan Briggs, Charles Kenny, Tyler Hall, Pablo Arriagada, Hannah Ritchie, Max Roser, Edouard Mathieu, Bastian Herre, and Saloni Dattani for their insights, feedback, and comments on this article.\nContinue reading on Our World in Data\nFor many of us, it doesn’t cost much to improve someone’s life, and we can do much more of it\nMost countries spend less than 1% of their national income on foreign aid; even small increases could make a big difference.\nThe history of global economic inequality\nThe 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?\nGlobal economic inequality: what matters most for your living conditions is not who you are, but where you are\nHow much does it matter to be born into a productive, industrialized economy?\nEndnotes\nIn Malawi, the mean daily income or consumption in 2019 was $2.59 (international dollars in 2021 prices, which account for differences in living costs between countries) based on consumption surveys, compared to $75.50 in the Netherlands in 2021 based on income surveys. This means the average person in the Netherlands earns in two weeks (2 × 7 × $75.50 = $1057) what the average person in Malawi earns in an entire year (365 × $2.59 = $945.35).\nThese figures involve methodological differences (consumption vs. income surveys and a two-year gap). The World Bank takes various steps to harmonize its data where possible, but comparability issues remain.\nOur\npage on data collection in inequality and poverty\ncan be useful for further details, and the PIP\nMethodology Handbook\nprovides a good summary of the comparability and data quality issues affecting this data and how it tries to address them.\nStill, the 30:1 ratio in daily figures illustrates the magnitude of income disparity between these economies.\nThese calculations use data from the World Bank's Poverty and Inequality Platform (PIP). The PIP provides a\nhigh-resolution global income distribution\ndivided into 1,000 income groups (\"bins\").\nAccording to this data, the top 10% of global earners receive 47.29% of global income, while the bottom 10% receive 0.91%. Therefore, to double the income of the bottom 10%, an additional 0.91% of global income would be needed, representing 1.92% of the top 10%'s current income (calculated as 0.91 / 47.29 × 100). You can read more about PIP’s methodology\nhere\n.\nIt's important to note that income surveys typically underrepresent the full incomes of the richest individuals due to complex income sources that aren't fully captured. As a result, the true share held by the top income group may be higher than reported in PIP data, potentially making the income gap even larger than these figures suggest.\nShastry, G. K., & Tortorice, D. L. (2025).\nEffective Health Aid: Evidence from Gavi’s Vaccine Program\n. American Economic Journal: Economic Policy, 17(1), 540-574.\nCavalcanti, D., Sales, L. D. O. F. D., Silva, A. F., Landin, E., Pena, D. A., Monti, C., ... & Rasella, D. (2025) —\nEvaluating the Comprehensive Impact of Two Decades of USAID Interventions and Forecasting the Effects of Defunding on Mortality Up to 2030.\nDuring his tenure as Britain’s development minister, Rory Stewart discovered alarming inefficiencies; some £40,000 projects\ndelivering\nmerely £2,000 of actual value, a 95% loss rate. Aid programs face challenges beyond bureaucratic costs, such as corruption (though research suggests it's\nless pervasive than commonly believed\n) and\nineffective program designs\n.\nCavalcanti, D., Sales, L. D. O. F. D., Silva, A. F., Landin, E., Pena, D. A., Monti, C., ... & Rasella, D.\nEvaluating the Comprehensive Impact of Two Decades of USAID Interventions and Forecasting the Effects of Defunding on Mortality Up to 2030.\nSource: https://www.givedirectly.org/financials/\nWithin these countries, they specifically target the poorest regions. Source:\nhttps://www.givedirectly.org/rwanda/\nCrosta et al. (2024)\nconducted a meta-analysis on 114 studies covering 72 unconditional cash transfer randomized controlled trials.\nBastagli et al. (2018)\nreviewed 165 studies on 35 indicators in low- and middle-income countries. They found that “The evidence consistently shows that cash transfers lead to increases in use of health facilities.”\nBastagli, F., Hagen-Zanker, J., Harman, L., Barca, V., Sturge, G., & Schmidt, T. (2019). The impact of cash transfers: a review of the evidence from low-and middle-income countries. Journal of Social Policy, 48(3), 569-594.”\nA common concern is whether cash transfers reduce work effort. However, rigorous studies suggest otherwise: labor force participation and household income tend to increase. For instance, one review found “no evidence of ‘dependency’ theories that cash transfers demotivate income-generating activity on average” (\nCrosta et al. 2024\n, p.1). Another concluded that transfers were used to improve income-generating activities, with no observed reduction in labor supply (\nDaidone et al. 2019\n, p.1426).\nAnother concern is that recipients might spend cash on temptation goods such as alcohol or tobacco. The evidence does not support this either: “almost without exception, studies find either no significant impact or a significant negative impact” on such expenditures (\nEvans & Popova, 2014\n).\nOn broader economic effects—like inflation or community-level inequality—evidence from large-scale interventions indicates little price inflation and even positive spillovers for non-recipients: increased firm revenues and consumption, with no significant harms across metrics like education, health, or public goods (\nEgger et al. 2022\n)\nWhile large-scale transfers could, in principle, produce different outcomes, current evidence suggests that within the scale of existing programs, the benefits tend to outweigh the risks.\nSources:\nCrosta, T., Karlan, D., Ong, F., Rüschenpöhler, J., & Udry, C. R. (2024). Unconditional cash transfers: A Bayesian meta-analysis of randomized evaluations in low and middle income countries (No. w32779). National Bureau of Economic Research.\nDaidone, S., Davis, B., Handa, S., & Winters, P. (2019). The household and individual-level productive impacts of cash transfer programs in Sub-Saharan Africa. American journal of agricultural economics, 101(5), 1401-1431.\nEvans, D., & Popova, A. (2014). Cash transfers and temptation goods: a review of global evidence. World Bank Policy Research Working Paper, (6886).\nEgger, D., Haushofer, J., Miguel, E., Niehaus, P., & Walker, M. (2022). General equilibrium effects of cash transfers: experimental evidence from Kenya. Econometrica, 90(6), 2603-2643.\nNair, G. (2018).\nMisperceptions of relative affluence and support for international redistribution\n. The Journal of Politics, 80(3), 815-830.\nAs of 2025, the global median income was approximately 3,400 international dollars (in 2021 prices), based on the\nlatest data\nfrom the World Bank’s Poverty and Inequality Platform.\n(Nair's 2018 paper estimated it at $2,100, while the World Bank's estimate for that period was roughly\n$2,400\n. This difference is due to different base years in the conversion to international dollars: the World Bank uses 2021\ninternational dollars\n, and Nair uses 2005 international dollars.)\nIn the study, participants allocated a $20 amount (either actual or hypothetical) between themselves and charities. Ten percent of participants received the $20 bonus, while others made hypothetical choices. When informed about global income distribution, participants donated 15.7% to international charities versus 10.1% in the control group—a 55% increase. The information effect translated into real behavior, with total actual donations to international charities increasing from $122 in the control group to $196 in the information group—a 61% increase.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSimon van Teutem and Joe Hasell (2025) - “Global inequality is huge — but so is the opportunity for people in high-income countries to support poor people” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20251125-173858/global-inequality-opportunity-to-give.html' [Online Resource] (archived on November 25, 2025).\nBibTeX citation\n@article{owid-global-inequality-opportunity-to-give,\nauthor = {Simon van Teutem and Joe Hasell},\ntitle = {Global inequality is huge — but so is the opportunity for people in high-income countries to support poor people},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20251125-173858/global-inequality-opportunity-to-give.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "global-inequality-opportunity-to-give", "source_url": "https://ourworldindata.org/global-inequality-opportunity-to-give", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "People in high-income countries could dramatically improve lives worldwide with minimal financial commitment, yet few do.", "numeric_mentions": ["25,", "2025", "1", "2%", "10%", "2", "20,000", "1.5 million", "3", "2004", "2023", "0.2%", "19 million", "91 million", "2001", "2021,", "30 million", "4", "5", "1981,", "0.15%", "2022", "20", "14 million", "2030", "6", "10", "8", "7", "9", "11", "12", "13", "55%", "14", "1%", "2019", "2.59", "2021", "75.50", "1057", "365", "945.35", "30", "1,000", "47.29%", "0.91%", "1.92%", "0.91", "47.29", "100", "17", "540", "574", "40,000", "2,000", "95%", "2024", "114", "72", "2018", "165", "35", "48", "569", "594", "1426", "2014", "101", "1401", "1431", "6886", "90", "2603", "2643", "80", "815", "830", "2025,", "3,400"], "numeric_evidence": [{"title": "Foreign aid given as a share of national income", "source_url": "https://ourworldindata.org/grapher/foreign-aid-given-as-a-share-of-national-income.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "ODA by donor (% of GNI) - Official estimate", "ODA by donor (% of GNI) - Official estimate (Annotations)"], "row_count_total": 2070, "rows_head": [{"Entity": "Australia", "Code": "AUS", "Year": "1960", "ODA by donor (% of GNI) - Official estimate": "0.37020743", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1961", "ODA by donor (% of GNI) - Official estimate": "0.43858564", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1962", "ODA by donor (% of GNI) - Official estimate": "0.42609704", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1963", "ODA by donor (% of GNI) - Official estimate": "0.50767606", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1964", "ODA by donor (% of GNI) - Official estimate": "0.48169553", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1965", "ODA by donor (% of GNI) - 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Official estimate": "0.41944996", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1978", "ODA by donor (% of GNI) - Official estimate": "0.54610854", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1979", "ODA by donor (% of GNI) - Official estimate": "0.52791214", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1980", "ODA by donor (% of GNI) - Official estimate": "0.48352084", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1981", "ODA by donor (% of GNI) - Official estimate": "0.4109337", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1982", "ODA by donor (% of GNI) - Official estimate": "0.5610167", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1983", "ODA by donor (% of GNI) - Official estimate": "0.4898376", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1984", "ODA by donor (% of GNI) - Official estimate": "0.45016804", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1985", "ODA by donor (% of GNI) - Official estimate": "0.4829205", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1986", "ODA by donor (% of GNI) - Official estimate": "0.46579176", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1987", "ODA by donor (% of GNI) - Official estimate": "0.33528033", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1988", "ODA by donor (% of GNI) - Official estimate": "0.4608848", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1989", "ODA by donor (% of GNI) - Official estimate": "0.37607244", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1990", "ODA by donor (% of GNI) - Official estimate": "0.34014806", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1991", "ODA by donor (% of GNI) - Official estimate": "0.3752784", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1992", "ODA by donor (% of GNI) - Official estimate": "0.3660945", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1993", "ODA by donor (% of GNI) - Official estimate": "0.3470794", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1994", "ODA by donor (% of GNI) - Official estimate": "0.3415484", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1995", "ODA by donor (% of GNI) - Official estimate": "0.34253293", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1996", "ODA by donor (% of GNI) - Official estimate": "0.2737466", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1997", "ODA by donor (% of GNI) - Official estimate": "0.27040526", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1998", "ODA by donor (% of GNI) - Official estimate": "0.2712992", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1999", "ODA by donor (% of GNI) - Official estimate": "0.25704035", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2000", "ODA by donor (% of GNI) - Official estimate": "0.2666439", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2001", "ODA by donor (% of GNI) - Official estimate": "0.25189736", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2002", "ODA by donor (% of GNI) - Official estimate": "0.25560388", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2003", "ODA by donor (% of GNI) - Official estimate": "0.24719958", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2004", "ODA by donor (% of GNI) - Official estimate": "0.24514054", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2005", "ODA by donor (% of GNI) - Official estimate": "0.24757351", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2006", "ODA by donor (% of GNI) - Official estimate": "0.29507467", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2007", "ODA by donor (% of GNI) - Official estimate": "0.322592", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2008", "ODA by donor (% of GNI) - Official estimate": "0.31600815", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2009", "ODA by donor (% of GNI) - Official estimate": "0.29362306", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2010", "ODA by donor (% of GNI) - Official estimate": "0.32272726", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2011", "ODA by donor (% of GNI) - Official estimate": "0.34356582", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2012", "ODA by donor (% of GNI) - Official estimate": "0.3609295", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2013", "ODA by donor (% of GNI) - Official estimate": "0.3308984", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2014", "ODA by donor (% of GNI) - Official estimate": "0.31431124", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2015", "ODA by donor (% of GNI) - Official estimate": "0.29111192", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2016", "ODA by donor (% of GNI) - Official estimate": "0.26582664", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2017", "ODA by donor (% of GNI) - Official estimate": "0.23199669", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2018", "ODA by donor (% of GNI) - Official estimate": "0.22956939", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2019", "ODA by donor (% of GNI) - Official estimate": "0.21544313", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2020", "ODA by donor (% of GNI) - Official estimate": "0.21403845", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2021", "ODA by donor (% of GNI) - Official estimate": "0.22144523", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2022", "ODA by donor (% of GNI) - Official estimate": "0.18739358", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2023", "ODA by donor (% of GNI) - Official estimate": "0.19369817", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2024", "ODA by donor (% of GNI) - Official estimate": "0.19459914", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1960", "ODA by donor (% of GNI) - Official estimate": "0.0015923562", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1961", "ODA by donor (% of GNI) - Official estimate": "0.041726615", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1962", "ODA by donor (% of GNI) - Official estimate": "0.03108108", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1963", "ODA by donor (% of GNI) - Official estimate": "0.052697614", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1964", "ODA by donor (% of GNI) - Official estimate": "0.07551488", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1965", "ODA by donor (% of GNI) - Official estimate": "0.1142857", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1966", "ODA by donor (% of GNI) - Official estimate": "0.12225461", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1967", "ODA by donor (% of GNI) - Official estimate": "0.13875115", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1968", "ODA by donor (% of GNI) - Official estimate": "0.14149484", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1969", "ODA by donor (% of GNI) - Official estimate": "0.11268598", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1970", "ODA by donor (% of GNI) - Official estimate": "0.07334946", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1971", "ODA by donor (% of GNI) - Official estimate": "0.073436186", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1972", "ODA by donor (% of GNI) - Official estimate": "0.08620605", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1973", "ODA by donor (% of GNI) - Official estimate": "0.14588848", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1974", "ODA by donor (% of GNI) - Official estimate": "0.18122476", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1975", "ODA by donor (% of GNI) - Official estimate": "0.21141577", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1976", "ODA by donor (% of GNI) - Official estimate": "0.12416183", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1977", "ODA by donor (% of GNI) - Official estimate": "0.22684343", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1978", "ODA by donor (% of GNI) - Official estimate": "0.26837295", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1979", "ODA by donor (% of GNI) - Official estimate": "0.19287403", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1980", "ODA by donor (% of GNI) - Official estimate": "0.23333551", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1981", "ODA by donor (% of GNI) - Official estimate": "0.3340882", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1982", "ODA by donor (% of GNI) - Official estimate": "0.35568878", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1983", "ODA by donor (% of GNI) - Official estimate": "0.23649268", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1984", "ODA by donor (% of GNI) - Official estimate": "0.28199258", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1985", "ODA by donor (% of GNI) - Official estimate": "0.37689227", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1986", "ODA by donor (% of GNI) - Official estimate": "0.21110755", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1987", "ODA by donor (% of GNI) - Official estimate": "0.17233665", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1988", "ODA by donor (% of GNI) - Official estimate": "0.23960537", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1989", "ODA by donor (% of GNI) - Official estimate": "0.2251015", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1990", "ODA by donor (% of GNI) - Official estimate": "0.10642488", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1991", "ODA by donor (% of GNI) - Official estimate": "0.18111844", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1992", "ODA by donor (% of GNI) - Official estimate": "0.11148866", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1993", "ODA by donor (% of GNI) - Official estimate": "0.1136086", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1994", "ODA by donor (% of GNI) - Official estimate": "0.16507548", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1995", "ODA by donor (% of GNI) - Official estimate": "0.2676029", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1996", "ODA by donor (% of GNI) - Official estimate": "0.22903492", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1997", "ODA by donor (% of GNI) - Official estimate": "0.24296936", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1998", "ODA by donor (% of GNI) - Official estimate": "0.21897286", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1999", "ODA by donor (% of GNI) - Official estimate": "0.23813443", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2000", "ODA by donor (% of GNI) - Official estimate": "0.23443575", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2001", "ODA by donor (% of GNI) - Official estimate": "0.34156108", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2002", "ODA by donor (% of GNI) - Official estimate": "0.25512367", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2003", "ODA by donor (% of GNI) - Official estimate": "0.20161313", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2004", "ODA by donor (% of GNI) - Official estimate": "0.23290817", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2005", "ODA by donor (% of GNI) - Official estimate": "0.5217811", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2006", "ODA by donor (% of GNI) - Official estimate": "0.4688662", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2007", "ODA by donor (% of GNI) - Official estimate": "0.49805856", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2008", "ODA by donor (% of GNI) - Official estimate": "0.4282754", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2009", "ODA by donor (% of GNI) - Official estimate": "0.30203867", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2010", "ODA by donor (% of GNI) - Official estimate": "0.32242185", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2011", "ODA by donor (% of GNI) - Official estimate": "0.26712698", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2012", "ODA by donor (% of GNI) - Official estimate": "0.28021595", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2013", "ODA by donor (% of GNI) - Official estimate": "0.2735214", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2014", "ODA by donor (% of GNI) - Official estimate": "0.2843688", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}], "rows_tail": [{"Entity": "United Kingdom", "Code": "GBR", "Year": "1970", "ODA by donor (% of GNI) - Official estimate": "0.3890956", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1971", "ODA by donor (% of GNI) - Official estimate": "0.4413156", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1972", "ODA by donor (% of GNI) - Official estimate": "0.41915938", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1973", "ODA by donor (% of GNI) - Official estimate": "0.3550665", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1974", "ODA by donor (% of GNI) - Official estimate": "0.39747143", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1975", "ODA by donor (% of GNI) - Official estimate": "0.38410473", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1976", "ODA by donor (% of GNI) - Official estimate": "0.3875637", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1977", "ODA by donor (% of GNI) - Official estimate": "0.43956423", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1978", "ODA by donor (% of GNI) - Official estimate": "0.4553064", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1979", "ODA by donor (% of GNI) - Official estimate": "0.5146521", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1980", "ODA by donor (% of GNI) - Official estimate": "0.3468437", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1981", "ODA by donor (% of GNI) - Official estimate": "0.42564455", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1982", "ODA by donor (% of GNI) - Official estimate": "0.37146866", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1983", "ODA by donor (% of GNI) - Official estimate": "0.35226646", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1984", "ODA by donor (% of GNI) - Official estimate": "0.33395243", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1985", "ODA by donor (% of GNI) - Official estimate": "0.33393067", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1986", "ODA by donor (% of GNI) - Official estimate": "0.3127541", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1987", "ODA by donor (% of GNI) - Official estimate": "0.2754007", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1988", "ODA by donor (% of GNI) - Official estimate": "0.32062286", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1989", "ODA by donor (% of GNI) - Official estimate": "0.31200248", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1990", "ODA by donor (% of GNI) - Official estimate": "0.2737446", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1991", "ODA by donor (% of GNI) - Official estimate": "0.31923422", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1992", "ODA by donor (% of GNI) - Official estimate": "0.3117397", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1993", "ODA by donor (% of GNI) - Official estimate": "0.312221", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1994", "ODA by donor (% of GNI) - Official estimate": "0.30745938", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1995", "ODA by donor (% of GNI) - Official estimate": "0.28588504", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1996", "ODA by donor (% of GNI) - Official estimate": "0.27418944", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1997", "ODA by donor (% of GNI) - Official estimate": "0.2624891", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1998", "ODA by donor (% of GNI) - Official estimate": "0.27438694", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1999", "ODA by donor (% of GNI) - Official estimate": "0.23616421", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2000", "ODA by donor (% of GNI) - Official estimate": "0.31748632", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2001", "ODA by donor (% of GNI) - Official estimate": "0.31901875", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2002", "ODA by donor (% of GNI) - Official estimate": "0.30902418", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2003", "ODA by donor (% of GNI) - Official estimate": "0.342276", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2004", "ODA by donor (% of GNI) - Official estimate": "0.36267442", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2005", "ODA by donor (% of GNI) - Official estimate": "0.47269437", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2006", "ODA by donor (% of GNI) - Official estimate": "0.51408184", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2007", "ODA by donor (% of GNI) - Official estimate": "0.35530916", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2008", "ODA by donor (% of GNI) - Official estimate": "0.43036878", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2009", "ODA by donor (% of GNI) - Official estimate": "0.50755006", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2010", "ODA by donor (% of GNI) - Official estimate": "0.57260185", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2011", "ODA by donor (% of GNI) - Official estimate": "0.5624857", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2012", "ODA by donor (% of GNI) - Official estimate": "0.56192076", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2013", "ODA by donor (% of GNI) - Official estimate": "0.7046706", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2014", "ODA by donor (% of GNI) - Official estimate": "0.7007015", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2015", "ODA by donor (% of GNI) - Official estimate": "0.7047585", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2016", "ODA by donor (% of GNI) - Official estimate": "0.70011365", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2017", "ODA by donor (% of GNI) - Official estimate": "0.6984873", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2018", "ODA by donor (% of GNI) - Official estimate": "0.69543564", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2019", "ODA by donor (% of GNI) - Official estimate": "0.70329565", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2020", "ODA by donor (% of GNI) - Official estimate": "0.6980933", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2021", "ODA by donor (% of GNI) - Official estimate": "0.50391716", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2022", "ODA by donor (% of GNI) - 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Official estimate": "0.59701514", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1964", "ODA by donor (% of GNI) - Official estimate": "0.5602949", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1965", "ODA by donor (% of GNI) - Official estimate": "0.57776666", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1966", "ODA by donor (% of GNI) - Official estimate": "0.5008128", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1967", "ODA by donor (% of GNI) - Official estimate": "0.4078602", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1968", "ODA by donor (% of GNI) - Official estimate": "0.43632105", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1969", "ODA by donor (% of GNI) - Official estimate": "0.35654312", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1970", "ODA by donor (% of GNI) - Official estimate": "0.31630525", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1971", "ODA by donor (% of GNI) - Official estimate": "0.28727373", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1972", "ODA by donor (% of GNI) - Official estimate": "0.33203456", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1973", "ODA by donor (% of GNI) - Official estimate": "0.19918656", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1974", "ODA by donor (% of GNI) - Official estimate": "0.2545451", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1975", "ODA by donor (% of GNI) - Official estimate": "0.26681194", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1976", "ODA by donor (% of GNI) - Official estimate": "0.25197932", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1977", "ODA by donor (% of GNI) - Official estimate": "0.24247591", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1978", "ODA by donor (% of GNI) - Official estimate": "0.2603549", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1979", "ODA by donor (% of GNI) - Official estimate": "0.19267997", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1980", "ODA by donor (% of GNI) - Official estimate": "0.26916197", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1981", "ODA by donor (% of GNI) - Official estimate": "0.19369075", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1982", "ODA by donor (% of GNI) - Official estimate": "0.2651593", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1983", "ODA by donor (% of GNI) - Official estimate": "0.24310885", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1984", "ODA by donor (% of GNI) - Official estimate": "0.23684068", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1985", "ODA by donor (% of GNI) - Official estimate": "0.23578236", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1986", "ODA by donor (% of GNI) - Official estimate": "0.2266888", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1987", "ODA by donor (% of GNI) - Official estimate": "0.20244762", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1988", "ODA by donor (% of GNI) - Official estimate": "0.20904538", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1989", "ODA by donor (% of GNI) - Official estimate": "0.14959082", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1990", "ODA by donor (% of GNI) - Official estimate": "0.20941389", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1991", "ODA by donor (% of GNI) - Official estimate": "0.19976941", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1992", "ODA by donor (% of GNI) - Official estimate": "0.19610436", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1993", "ODA by donor (% of GNI) - Official estimate": "0.15431404", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1994", "ODA by donor (% of GNI) - Official estimate": "0.14340402", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1995", "ODA by donor (% of GNI) - Official estimate": "0.10178929", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1996", "ODA by donor (% of GNI) - Official estimate": "0.12277257", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1997", "ODA by donor (% of GNI) - Official estimate": "0.08533393", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1998", "ODA by donor (% of GNI) - Official estimate": "0.10041119", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1999", "ODA by donor (% of GNI) - Official estimate": "0.098344594", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "2000", "ODA by donor (% of GNI) - Official estimate": "0.1002658", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "2001", "ODA by donor (% of GNI) - Official estimate": "0.11250579", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "2002", "ODA by donor (% of GNI) - Official estimate": "0.12669034", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "2003", "ODA by donor (% of GNI) - Official estimate": "0.14861788", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "2004", "ODA by donor (% of GNI) - Official estimate": "0.1690522", "ODA by donor (% of GNI) - Official estimate (Annotations)": ""}, {"Entity": "United States", "Code": "USA", "Year": "2005", "ODA by donor (% of GNI) - 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From 2018, the official reporting method switched from net to grant-equivalent amounts.", "chart_note": "The United Nations' target is for developed countries to devote 0.7% of their national income to ODA.", "chart_citation": "OECD (2025)", "original_chart_url": "https://ourworldindata.org/grapher/foreign-aid-given-as-a-share-of-national-income", "owid_column_metadata": {"ODA by donor (% of GNI) - Official estimate": {"titleShort": "ODA by donor (% of GNI) - Official estimate", "titleLong": "ODA by donor (% of GNI) - Official estimate", "descriptionShort": "Official development assistance (ODA) divided by gross national income, expressed as a percentage. This is the official estimate that combines net disbursements with grant equivalents from 2018.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. 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{"raw_link": "https://ourworldindata.org/new-international-poverty-line-3-dollars-per-day", "title": "$3 a day: A new poverty line has shifted the World Bank’s data on extreme poverty. What changed, and why?", "context": "Home\nPoverty\n$3 a day: A new poverty line has shifted the World Bank’s data on extreme poverty. What changed, and why?\nIn June 2025, the World Bank increased its extreme poverty estimates by 125 million people. This doesn’t mean the world has gotten poorer: it reflects a new, higher International Poverty Line of $3 a day, up from $2.15.\nBy\nJoe Hasell\n,\nBertha Rohenkohl\n,\nand\nPablo Arriagada\nAugust 11, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nTo track progress towards\nending extreme poverty by 2030\n, the United Nations relies on the World Bank to estimate the share of people living below a certain threshold, called the International Poverty Line (IPL).\nIn June 2025, the World Bank announced a major change to this line, raising it significantly, from $2.15 to $3 per day.\nThis increase partly reflects inflation — a consequence of the World Bank using\ninternational dollars\nat 2021 prices, updated from 2017 prices.\nHowever, the IPL has also increased substantially, even after inflation adjustments. The poverty line has increased in\nreal\nterms. And with it, so have the World Bank’s estimates of extreme poverty. 125 million people who would not have been counted as extremely poor before June are now included.\n1\nThis recent rise in the IPL is due to an aspect of the World Bank’s approach that some users of the data may not be aware of. Although it’s an\ninternational\nthreshold, it is set to reflect\nnational\ndefinitions of poverty typical among low-income countries. Several low-income countries recently raised their own poverty lines, pushing up the IPL.\nThe higher estimates of extreme poverty reflect a higher poverty threshold, not that the world is poorer. The updated data on global incomes accompanying the new IPL shows that incomes among the world’s poorest are\nhigher\nthan previously estimated.\nHigher incomes but higher extreme poverty — this apparent paradox is unpacked in this article. We’ll explain in more detail how the International Poverty Line is defined, what has changed in the latest World Bank data, and what this means for our understanding of global poverty.\nSee the latest World Bank data\nYou can find all the updated World Bank data on our Poverty topic page.\nHow the World Bank measures global extreme poverty\nTo understand the new data and what it tells us about global poverty, we must understand how the International Poverty Line is set and used.\n2\nEstimating global extreme poverty involves two parts, illustrated in this figure and discussed in more detail below.\nFirst, the World Bank estimates the global distribution of income (shown on the left) — that’s an estimate of how many people are living at each income level, summed across all countries.\nSecond, it defines an extreme poverty threshold, the International Poverty Line (shown on the right).\nTogether, these yield the World Bank’s estimates of extreme poverty: the\nshare of the world’s population\nwith incomes falling below the International Poverty Line.\nThinking separately about these two halves helps explain the paradox of the June 2025 update. As we’ll see,\nboth\nsides of the calculation have changed simultaneously.\nDownload\nHow the World Bank estimates the global income distribution\nFirst, we’ll look at the left side of the calculation. You can think of the World Bank’s approach to estimating global incomes as being broken into three steps:\n1) Collecting the raw ingredient: data from household surveys\nThe main way statisticians find out about people’s incomes is by asking them. Almost all countries run household surveys to gather this information. This is what the World Bank’s global data relies on.\nHousehold surveys are run nationally, and their results can’t be directly compared at first. Countries ask different questions, capturing somewhat different definitions of income — as discussed in this footnote.\n3\nCoverage also varies, with some countries conducting surveys annually, and others at intervals of several years. The survey data are also measured in different local currencies: households in India report in rupees, while households in Argentina report in pesos.\n2) Estimating comparable national distributions\nTo get a\nglobal\nperspective on incomes, the World Bank takes a series of steps to make this national survey data as comparable as possible.\n4\nOne important step is converting the local currency data into\ninternational dollars\n. This hypothetical currency adjusts for differences in the cost of living between countries.\nYou can read more about this step in our dedicated article:\nWhat are international dollars?\nInternational dollars are used to compare incomes and purchasing power across countries and over time. Here, we explain how they’re calculated and why they’re used.\nWhat is important to know here is that this conversion is based on “purchasing power parity” rates (PPPs) — a measure of how much local currency you need in each country to buy the same value of goods and services. PPPs are based on detailed price data collected in “rounds” every few years. The World Bank updated to a new round — the\n2021\nPPPs — which triggered them to revise the International Poverty Line.\nWith this harmonized survey data, the World Bank can estimate national distributions that allow incomes to be compared within and between countries worldwide.\n3) Lining up the data to a common year\nThe available country data refers to a mix of different years and, in some cases, can be\nseveral years\nout of date. To account for how incomes may have changed, the World Bank “lines up” each country’s distribution to a reference year using growth rates observed in national accounts data.\n5\nThe lined-up, comparable national distributions can then be summed up to calculate the global income distribution for a given year.\nDownload\nHow the World Bank sets the International Poverty Line\nThe other half of the World Bank’s approach to measuring extreme poverty is setting an International Poverty Line. Again, we can split this into three steps:\n1) Collecting a comparable set of national poverty lines\nThe World Bank begins by collecting a large set of\nnational\npoverty lines — the lines used by individual countries to estimate official poverty rates among their populations.\nLike the income data, national poverty definitions can’t be meaningfully compared at first. The World Bank has developed an approach to align these, which you can learn more about in this footnote.\n6\nThe outcome is a comparable set of national poverty lines, all measured in international dollars.\n2) Selecting the poverty lines in\nlow-income\ncountries\nMaking national poverty lines comparable shows us a clear pattern:\nricher countries generally set higher poverty lines\n.\nThe world’s poorest countries set very low national poverty lines — sometimes, as low as $1.50 per day. Among the world’s richest countries, poverty lines are much higher, at $30 or $40 per day.\n7\nThe International Poverty Line aims to be an\nextremely\nlow income threshold, focusing the world’s attention on the situation of the poorest. To do this, the World Bank anchors the IPL to the national poverty lines adopted by\nlow-income\ncountries.\nCountries are classified as “low-income” according to a technical classification system used across the World Bank’s work. We explain this in more detail in a dedicated article:\n8\nHow does the World Bank classify countries by income?\nThe World Bank classifies countries into four income groups based on average income per person. This article explains how these groups are defined.\n3) Setting the IPL to the\nmedian\npoverty line among low-income countries\nThe IPL aims to reflect the\ntypical\ndefinition of poverty adopted among low-income countries. For this, the World Bank uses the\nmedian\nvalue.\nNational poverty lines among low-income countries range\nfrom around $1.50 to $5 per day\n. When setting the IPL, the World Bank found the median poverty line among the 23 countries with available data to be $3.04, which was rounded to give $3 — the new value of the International Poverty Line.\n9\nDownload\nOnce set, the International Poverty Line is applied to the global income distribution to estimate the share of people living in extreme poverty.\nDownload\nIn the updated World Bank data, extreme poverty is higher, but so are global incomes\nThe summary of the World Bank’s approach above helps us understand the June 2025 update. Three statements summarize what has changed in the new data.\n1) Inflation explains part of the rise\nEven if nothing else had changed, the switch from 2017 to 2021 international-$ would have led to higher values — for both global incomes and the International Poverty Line.\nAs we explain in our\narticle on international dollars\n, the value, or “purchasing power” of one international-$ is benchmarked to what one US dollar can buy in the United States. Prices in the US rose on average between 2017 and 2021 — about 11% in total.\n10\nThis means that one 2021 international dollar (the World Bank’s new units) is worth less than one 2017 international dollar (the old units). You need more 2021 international-$ to buy the same amount of goods and services.\nWe’ll look at how incomes and the IPL have changed before and after the June 2025 update. But here, we first plot this inflation as a backdrop to put those changes in context. To have stayed the same and kept the same purchasing power, global incomes and the IPL would have had to increase by 11% in nominal terms. That is shown in green in the chart. An increase beyond this level, falling in the beige area above, indicates a rise in\nreal\nterms (an increase in the quantity or quality of goods and services it could buy).\nDownload\n2) The new data shows the world’s poorest are slightly better off\nAs part of the update, the World Bank has changed its estimates of global incomes. In the new data, incomes among the world’s poorest are higher.\nThis can be seen in the chart. The three arrows show how estimated incomes have changed before and after the update, across richer and poorer households. They show the percentage change in the “nominal” figures — without any inflation adjustment to account for the data having shifted from being measured in international-$ in\n2017 prices\nto international-$ in\n2021 prices\n.\nThe arrow on the left shows the change in the income level that places someone in the world’s poorest tenth in 2024. This rose by 28% in nominal terms, from $2.34 per day (measured in 2017 international-$) to $3.00 per day (in 2021 international-$).\nComparing this change to the green background, we see that incomes among the world’s poorest are higher not just because of inflation, but beyond inflation. They are higher in\nreal\nterms. According to the new data, those at the bottom 10% of the global distribution can buy and consume more — 16% more — than the old data showed.\n11\nThe two other arrows show the nominal change at the global median and the top 10%. In contrast with incomes at the bottom 10%, these rose roughly in line with inflation: the data before and after the update agree about what households positioned here in the global distribution can afford.\n12\nYou can compare the data before and after the update in\nthis chart\n.\nThis increase in incomes among the world’s poorest is due to two factors: new survey data and new price data.\nWith the update, the World Bank has added several new household surveys to its dataset. In particular, new data for India plays a big role. An improvement in the survey methodology used in India has shown incomes to be substantially higher than data based on earlier methods.\n13\nIncomes at the bottom of the global distribution are also higher due to the change from 2017 to 2021 international dollars. As mentioned, these units use cross-country price data to adjust for differences in the cost of living. The new 2021 price data revises our picture of living standards in low-income countries, showing, on average, lower costs of living and consequently higher incomes.\n14\nDownload\n3) The International Poverty Line increased even more\nThe International Poverty Line rose by 40%, from $2.15 to $3, far beyond inflation and income growth.\nHad the IPL only risen in line with inflation — an 11% increase from $2.15 to $2.38 — the World Bank’s new data on global incomes would show roughly 540 million people living in extreme poverty in 2024. That’s around a fifth\nlower\nthan the World Bank’s estimates before the update — the effect of the higher incomes just discussed.\nWith the revised IPL, extreme poverty estimates for 2024 stand at\n817 million\n— around 50% higher than had the IPL maintained its purchasing power and only risen in line with inflation. The calculations behind these numbers are given in the footnote here.\n15\nThis large real-terms rise in the IPL is the joint outcome of the many steps involved in setting the IPL, described above. In part, it reflects the use of the new 2021 PPPs and some changes in the group of countries classified as “low-income”.\n16\nHowever,\nmost\nof the increase is explained by changes in the raw ingredient: the national poverty lines on which the IPL is based.\nSince the last revision of the International Poverty Line, several low-income countries have increased their poverty line substantially. This includes several countries with poverty lines falling close to the median value to which the IPL is anchored.\n17\nAs we saw, it’s common for countries to raise their poverty line\nas incomes increase\n. In the case of this group of countries, however, the step change is mostly a consequence of their adoption of an improved household survey methodology. For many of these countries, this resulted in higher recorded incomes, and national poverty lines were raised in line with this shift.\n18\nDownload\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nPutting the updated International Poverty Line in context\nAlthough it’s the result of a consistently applied methodology, the June 2025 update has seen a significant, real-terms jump in the International Poverty Line. This change has shifted the goalposts on the first, and best-known of the UN’s\nSustainable Development Goals\n: the goal to eradicate extreme poverty by 2030. Around 125 million more people are now counted as living in extreme poverty under this new, higher threshold.\nThis increase does not mean that the world is poorer, though. According to the World Bank’s latest data, incomes among the world’s poorest are somewhat higher.\nThese revisions — to the IPL, the data on global incomes, and the estimates of extreme poverty they produce — are substantial. However, they need to be placed in context. This context is provided by the chart here, which shows the share of people living below a range of poverty thresholds since 1990.\nFirstly, we must consider the recent revisions in the context of income changes over the past generation. The prevalence of extreme poverty decreased massively over this time, from one in every two or three people to one in ten. This is a huge change for our world. The revisions discussed in this article are small in the context of this large change over time.\nThey also need to be put in the context of the huge range of incomes we see globally today — the great inequality between rich and poor countries. Whether measured at $2.15 or $3, the International Poverty Line is an\nextremely\nlow benchmark. In our home country, the United Kingdom, national poverty is measured using a threshold\nten times\nhigher than the IPL,\nclose to $30 per day\n. This is similar to many\nother rich countries\n. As shown in the chart, if we apply this threshold globally, the vast majority — 80% — of the world’s population would be counted as poor.\nPoverty, very clearly, does not end at the IPL. Although nine out of ten people today fall above this extreme definition of poverty, many of these people face living conditions that are barely imaginable for most people in rich countries: lacking\naccess to clean water\nclose to home; having to cook on solid fuel fires that create\ndangerous levels of indoor pollution\n; being unable to afford a diet that provides\nsufficient nutrients\nfor themselves or their children. As global incomes grow, it’s right that we\nlift our ambitions\nabout what should count as a morally acceptable minimum standard of living. From that perspective, the rise in the IPL can be seen as a positive step.\nBut at the same time, we\nalso\nneed to focus on the world’s poorest. This is crucial because of the huge number of people stuck on such extremely low incomes — perhaps the most important thing shown in the chart above. The progress seen in reducing extreme poverty over the past decades has ended. Incomes are stagnating in\nmany countries\nwhere the world’s poorest live. Because of this, we are\nvery clearly on track to fail\nin eradicating extreme poverty by 2030. That was clear in the data before the June 2025 update, and remains clear now.\nAmong the technical discussions needed to interpret the World Bank’s data correctly, we mustn’t lose sight of what this data shows: should current trends continue, hundreds of millions will remain in dire poverty for decades. That is surely one of the most important insights we could know about our world.\nAcknowledgments\nWe thank Marwa Boukarim for her work designing and producing the article's visualizations and Max Roser, Edouard Mathieu, and Esteban Ortiz-Ospina for their valuable comments and feedback.\nContinue reading on Our World in Data\nWhat are international dollars?\nInternational dollars are used to compare incomes and purchasing power across countries and over time. Here, we explain how they’re calculated and why they’re used.\nHow does the World Bank classify countries by income?\nThe World Bank classifies countries into four income groups based on average income per person. This article explains how these groups are defined.\nBeyond income: understanding poverty through the Multidimensional Poverty Index\nThe experience of poverty goes beyond a very low income. What is the Multidimensional Poverty Index, and how does it capture the diverse ways people experience deprivation?\nEndnotes\nAccording to the June 2025 update, 817 million people lived in extreme poverty in 2024 under the new $3 a day line. This is 125 million more than the previous estimate based on the old $2.15 definition.\nThis chart\ncompares the September 2024 data with the latest World Bank data.\nThis article discusses the World Bank’s current methodology. Although the general approach has remained relatively constant, the specifics have changed over time. For more background on the approach and its history, see the paper by World Bank researchers Dean Jolliffe and Espen Beer Prydz (2016).\nEstimating International Poverty Lines from Comparable National Thresholds\n. Policy Research Working Paper 7606. World Bank, Washington, D.C.\nA particularly important difference concerns the use of income and consumption surveys. While the data for high-income countries measures households’ post-tax\nincome\n, most low and middle-income countries measure the value of households’\nconsumption\nof goods and services. For convenience, in this article, we’ll use “income” to refer to what is, in reality, a mix of income and consumption data.\nAlthough the World Bank takes many steps to harmonize the national survey data, this is one important difference it cannot adjust for.\nIncome and consumption are closely related but not the same: households' consumption equals their income minus any savings.\nOne important difference is that, while zero consumption is not a feasible value — people with zero consumption would starve — zero income is a possible value. This means that income and consumption can give quite different pictures of a person’s economic situation at the bottom end of the distribution. For instance, a person spending their savings in retirement may have a very low, or even zero, income, but have a high level of consumption nevertheless.\nTherefore, income-based estimates of extreme poverty are generally higher than consumption-based estimates. Indeed, we see this in the data for several Eastern European countries that\ninclude data points for both\nmeasures.\nThis mixing of concepts is a limitation of the global “incomes” data more generally, but not a problem for the global estimates of extreme poverty, since almost all the world’s extreme poor live in countries with consumption surveys.\nSee the World Bank’s Poverty and Inequality Platform’s\nmethodology documentation\n, Chapters 2 and 3, for more details.\nNational accounts are a set of statistics, produced by almost every country in the world, that aim to measure incomes, consumption, and production across the whole economy. The best known national accounts series is Gross Domestic Product (GDP) — a measure of the value of goods and services produced in a country (and the income this generates). However, the data also includes other series that capture other concepts. This includes Household Final Consumption Expenditure (HFCE) — the total value of goods and services purchased by households.\nTo line up the survey data to a common year, the World Bank uses the growth rates seen in either GDP or HFCE. It assumes that every person’s income in a country has increased (or decreased) at the same rate — i.e., that inequality stays the same as found in the most recent survey data.\nFor more information, see the World Bank’s\nmethodology documentation\n, especially Chapter 5, available on the Poverty and Inequality Platform website.\nAs well as using different local currencies, national poverty definitions are often made up of a schedule of varying poverty lines — tailored to geographic regions or to account for various household sizes. The particular approach differs widely across countries, making direct comparisons impossible.\nThe World Bank relies on the harmonized income distribution data discussed above to create a comparable set of national poverty lines. For each country, it finds the income threshold that gives the same poverty rate as reported in official national estimates when applied to its distribution.\nFor example, the poverty line for Ethiopia included in\nthe World Bank’s harmonized dataset\nis based on a 2015 household survey. The official national poverty rate in that year was 23.5%, based on a national poverty definition expressed in the local currency, Birr.\nYou can read more about that survey and how the poverty line was set in the national report analyzing the survey’s results, the 2016 Poverty Interim Report (available on\nScribd\nand at the\nInternational Household Survey Network\n). This is summarized in the World Bank’s October 2024 ‘Poverty and Equity Brief’\nfor Ethiopia\n.\nAccording to the World Bank’s harmonized “incomes” data, 23.5% of people consumed $2.59 per day or less in 2015 (measured in 2021 international dollars). Therefore, $2.59 per day is the comparable national poverty line used in the World Bank’s calculation of the IPL.\nYou can read more about the method in Foster et al. (2025) — the World Bank paper accompanying the June 2025 update to the IPL. An older paper, Jolliffe and Beer Prydz (2016), initially set out the methodology and provides a good background.\nFoster, E.M., Jolliffe, D., Ibarra, G.L., Lakner, C., Tetteh Baah, S.K. (2025).\nGlobal Poverty Revisited Using 2021 PPPs and New Data on Consumption\n. Policy Research Working Paper 11137. World Bank, Washington, D.C.\nJolliffe, D., & Prydz, E. B. (2016).\nEstimating International Poverty Lines from Comparable National Thresholds\n. Policy Research Working Paper 7606. World Bank, Washington, D.C.\nHere are some examples. Democratic Republic of Congo: $1.48; Uganda: $1.52; Madagascar: $1.77; France: $31.98; UK: $32.45; Germany: $39.23; Norway: $43.16 (all measured in 2021 international-$). You can see the harmonized national poverty lines for each country plotted against GDP per capita in this chart\nhere\n.\nIt’s worth noting that this income group classification uses a different measure of income than the one we’ve just seen based on household survey data. It uses the “Atlas method”, which is based on National Accounts data without adjusting to account for differences in the cost of living across countries. Because of this, the rankings of countries differ somewhat between the two measures. Due to the harmonized survey data on which the World Bank’s poverty estimates are based, several countries defined as “low-income” actually have higher average incomes than some “lower-middle” income countries.\nFor more details of the calculation, see Foster et al. (2025). There, they also conduct a range of robustness checks showing that different aggregation methods lead to similar IPL values. However, they note that the poverty lines are more “bimodal” than at the last update, “with some low-income countries clustering at a lower poverty line of around $2.00 or a higher poverty line of around $3.40”.\nFoster, E.M., Jolliffe, D., Ibarra, G.L., Lakner, C., Tetteh Baah, S.K. (2025).\nGlobal Poverty Revisited Using 2021 PPPs and New Data on Consumption\n. Policy Research Working Paper 11137. World Bank, Washington, D.C.\nThis figure for US inflation is taken from the World Bank paper accompanying the new IPL and measures the consumer price index.\nFoster, E.M., Jolliffe, D., Ibarra, G.L., Lakner, C., Tetteh Baah, S.K. (2025).\nGlobal Poverty Revisited Using 2021 PPPs and New Data on Consumption\n. Policy Research Working Paper 11137. World Bank, Washington, D.C.\nFigures refer to the data for 2024 before and after the June 2025 update. The nominal increase is $3.00 / $2.34 – 1 = 28%.\nUpdating the old value only for the 11% inflation seen in the US, in line with the value of the international-$ — yields a P10 of $2.60 ($2.34 x 1.11). The real terms increase in the global P10 is $3.00 / ($2.34 x 1.11) – 1 = 16%.\nThe median before the update was $8.25 (2017 int.-$) and $9.20 after (2021 int.-$). $9.20 / $8.25 – 1 = 12%. The threshold to the top 10% was $48.40 before and $53.70 after. $53.70 / $48.40 – 1 = 11%.\nThe survey data for India measures household\nconsumption\n. (As discussed more in footnote 3, in this article, we are using “income” as a convenient shorthand for the mix of income and consumption survey data that goes into the World Bank’s estimates.)\nIndia recently published the 2022–23 household survey — the first in over a decade. This survey uses a different recall period when asking people about their consumption of various goods. A shorter recall period is used for food and other frequently consumed items; a longer recall period is used for items purchased more infrequently. Previous surveys used a fixed recall period across all consumption categories. Studies have shown that the newer, “mixed reference period” approach captures a higher consumption. People can remember their consumption more accurately. The 2022–23 India survey also changed in other ways, such as the questionnaire, the number of visits, and the sample design.\nThe new survey data has increased the incomes for India shown in the World Bank not just for 2022-23 but for the entire series. To avoid a break in the series for such a populous country, the World Bank has adjusted the earlier data to be more comparable with the new data.\nYou can read more in the following World Bank papers:\nMahler, D.G., Foster, E., Tetteh-Baah, S., 2024.\nHow Improved Household Surveys Influence National and International Poverty Rates\n. World Bank, Washington, D.C.\nFoster, E.M., Jolliffe, D., Ibarra, G.L., Lakner, C., Tetteh Baah, S.K. (2025).\nGlobal Poverty Revisited Using 2021 PPPs and New Data on Consumption\n. Policy Research Working Paper 11137. World Bank, Washington, D.C.\nWorld Bank (2025).\nIndia: Trends in Poverty, 2011-12 to 2022-23\n. Methodology Note. World Bank, Washington, D.C.\nAs shown in\nthis chart\n, with the change from 2017 to 2021 international-$, there has generally been a larger increase in nominal incomes among low-income countries than in high-income countries — 21% compared to 11% on average (Foster et al. (2025), Table C1).\nA nominal increase of 11% — in line with US inflation in this period — means that the income of high-income countries is, on average, the same in terms of purchasing power measured in either unit. There has been a greater increase among low-income countries, so they are, on average, slightly better off when incomes are calculated in the new 2021 international-$.\nFoster, E.M., Jolliffe, D., Ibarra, G.L., Lakner, C., Tetteh Baah, S.K. (2025).\nGlobal Poverty Revisited Using 2021 PPPs and New Data on Consumption\n. Policy Research Working Paper 11137. World Bank, Washington, D.C.\nAs discussed in this article, the IPL would have had to increase in line with US inflation of 11% to maintain its purchasing power.\n$2.15 * 1.11 = $2.386 (3.d.p.). This rounds to $2.39. In\nFoster et al. (2025)\n, this inflation-adjusted IPL value is reported as $2.3\n8\n. We assume the <$0.01 discrepancy is due to rounding of the US inflation figure.\nThe World Bank’s\nPoverty and Inequality Platform\nallows you to query the global distribution at $0.10 intervals. As of August 1, 2025, the data shows that 545 million people lived below $2.40 (2021 int-$) in 2024. The number living below $2.38 falls slightly below that, approximately 540 million.\nPrior to the June 2025 update, the World Bank estimated that\n692 million people\nwould be living in extreme poverty in 2024. This estimate is based on older survey data, the 2017 PPPs, and the $2.15-a-day poverty line.\n692 million minus 540 million is 152 million, or approximately 150 million. 152 / 692 = 22%, or roughly a fifth fewer people in extreme poverty.\nBased on the new data, new PPPs, and $3 a day IPL, the estimate for 2024 is $817 million. 817 / 540 – 1 = 51%, or roughly half as many more people in extreme poverty.\nFoster, E.M., Jolliffe, D., Ibarra, G.L., Lakner, C., Tetteh Baah, S.K. (2025).\nGlobal Poverty Revisited Using 2021 PPPs and New Data on Consumption\n. Policy Research Working Paper 11137. World Bank, Washington, D.C.\nAdjusting only for inflation in the US (11%) would have taken the IPL to $2.38.\nFoster et al. (2025)\nshow that accounting for both inflation and the change in relative prices brought in the PPP revision — but with no other changes — would have led to an IPL of $2.46 (a 14% increase).\nSome countries, including Azerbaijan, Benin, Nepal, Uzbekistan, and Zimbabwe, have moved out of the “low-income” group since the last update to their poverty lines. However, because their old poverty lines were spread above and below the median, their exclusion had little impact on the final IPL value.\nNote that the IPL is calculated using the World Bank’s\nincome classification for each country\nin the year their poverty line was defined. So, although (for example) Azerbaijan was already classified as a lower-middle-income country when the previous $2.15 a day IPL was set, its poverty line dated back to 2001, when it was a low-income country. As such, it was included in the set from which the median is calculated to give the IPL.\nFoster, E.M., Jolliffe, D., Ibarra, G.L., Lakner, C., Tetteh Baah, S.K. (2025).\nGlobal Poverty Revisited Using 2021 PPPs and New Data on Consumption\n. Policy Research Working Paper 11137. World Bank, Washington, D.C.\nFor example, Burkina Faso. When the World Bank set the $2.15 a day IPL, the national poverty line for the country was $2.16. In setting the $3-a-day IPL, the national poverty line moved to $3.04, a 41% increase in nominal terms (the former being measured in 2017 international-$, the latter 2021 international-$).\nOther relevant countries include Central African Republic ($2.16 → $2.82; 31% increase), Chad ($2.66 → $3.42; 29% increase), Guinea-Bissau ($2.28 → $3.47; 52% increase), Mali ($1.95 → $3.41; 75% increase), Niger ($1.87 → $2.39; 28% increase), and Togo ($2.17 → $3.51; 62% increase).\nAgain, the surveys in question measure household consumption (see footnote 3 for a discussion of consumption vs income surveys). Through more detailed, comprehensive questionnaires, these surveys now do a better job of capturing what people consume, and have shown household consumption to be higher than previously found.\nLike most low-income countries, the national poverty lines in these countries are set to reflect the cost of basic needs, incorporating both a food and a non-food component. These are set based on consumption patterns observed in household survey data: the non-food allowance reflects how households balance food and non-food needs. The revised survey methodology increased observed non-food consumption in a number of countries, which led them to set a higher non-food allowance within their cost-of-basic-needs poverty lines.\nYou can read more about this in this\nWorld Bank blog\nor the paper by\nDaniel Mahler and colleagues (2024)\n.\nMahler, D.G., Foster, E., Tetteh-Baah, S., 2024.\nHow Improved Household Surveys Influence National and International Poverty Rates\n. World Bank, Washington, D.C.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nJoe Hasell, Bertha Rohenkohl, and Pablo Arriagada (2025) - “$3 a day: A new poverty line has shifted the World Bank’s data on extreme poverty. What changed, and why?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-093348/new-international-poverty-line-3-dollars-per-day.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-new-international-poverty-line-3-dollars-per-day,\nauthor = {Joe Hasell and Bertha Rohenkohl and Pablo Arriagada},\ntitle = {$3 a day: A new poverty line has shifted the World Bank’s data on extreme poverty. What changed, and why?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-093348/new-international-poverty-line-3-dollars-per-day.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "new-international-poverty-line-3-dollars-per-day", "source_url": "https://ourworldindata.org/new-international-poverty-line-3-dollars-per-day", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "In June 2025, the World Bank increased its extreme poverty estimates by 125 million people. 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"World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1770", "Share of population in poverty ($3 a day)": "", "Population": "2931397", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1780", "Share of population in poverty ($3 a day)": "", "Population": "2998019", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1790", "Share of population in poverty ($3 a day)": "", "Population": "3066156", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1800", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1801", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1802", "Share of population in 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{"Entity": "Afghanistan", "Code": "AFG", "Year": "1854", "Share of population in poverty ($3 a day)": "", "Population": "3818038", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1855", "Share of population in poverty ($3 a day)": "", "Population": "3835192", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1856", "Share of population in poverty ($3 a day)": "", "Population": "3852417", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1857", "Share of population in poverty ($3 a day)": "", "Population": "3869714", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1858", "Share of population in poverty ($3 a day)": "", "Population": "3887081", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1859", "Share of population in poverty ($3 a day)": "", "Population": "3904521", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1860", "Share of population in poverty ($3 a day)": "", "Population": "3922032", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1861", "Share of population in poverty ($3 a day)": "", "Population": "3939616", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1862", "Share of population in poverty ($3 a day)": "", "Population": "3957271", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1863", "Share of population in poverty ($3 a day)": "", "Population": "3975000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1864", "Share of population in poverty ($3 a day)": "", "Population": "3992801", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1865", "Share of population in 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"1726171", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1927", "Share of population in poverty ($3 a day)": "", "Population": "1762449", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1928", "Share of population in poverty ($3 a day)": "", "Population": "1799490", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1929", "Share of population in poverty ($3 a day)": "", "Population": "1836997", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1930", "Share of population in poverty ($3 a day)": "", "Population": "1874974", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1931", "Share of population in poverty ($3 a day)": "", "Population": "1913425", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1932", "Share of population in poverty ($3 a day)": "", "Population": "1952352", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1933", "Share of population in poverty ($3 a day)": "", "Population": "1991759", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1934", "Share of population in poverty ($3 a day)": "", "Population": "2031962", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1935", "Share of population in poverty ($3 a day)": "", "Population": "2072976", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1936", "Share of population in poverty ($3 a day)": "", "Population": "2114819", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1937", "Share of population in poverty ($3 a day)": "", "Population": "2157505", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1938", "Share of population in poverty ($3 a day)": "", "Population": "2201053", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1939", "Share of population in poverty ($3 a day)": "", "Population": "2245025", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1940", "Share of population in poverty ($3 a day)": "", "Population": "2289421", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1941", "Share of population in poverty ($3 a day)": "", "Population": "2334240", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1942", "Share of population in poverty ($3 a day)": "", "Population": "2379481", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1943", "Share of population in poverty ($3 a day)": "", "Population": "2425144", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1944", "Share of population in poverty ($3 a day)": "", "Population": "2471683", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1945", "Share of population in poverty ($3 a day)": "", "Population": "2519116", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1946", "Share of population in poverty ($3 a day)": "", "Population": "2567459", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1947", "Share of population in poverty ($3 a day)": "", "Population": "2616729", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1948", "Share of population in poverty ($3 a day)": "", "Population": "2666945", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1949", "Share of population in poverty ($3 a day)": "", "Population": "2725021", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1950", "Share of population in poverty ($3 a day)": "", "Population": "2790908", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1951", "Share of population in poverty ($3 a day)": "", "Population": "2880901", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1952", "Share of population in poverty ($3 a day)": "", "Population": "2972913", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1953", "Share of population in poverty ($3 a day)": "", "Population": "3067172", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1954", "Share of population in poverty ($3 a day)": "", "Population": "3163884", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1955", "Share of population in poverty ($3 a day)": "", "Population": "3263221", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1956", "Share of population in poverty ($3 a day)": "", "Population": "3365533", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1957", "Share of population in poverty ($3 a day)": "", "Population": "3471158", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1958", "Share of population in poverty ($3 a day)": "", "Population": "3580185", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1959", "Share of population in poverty ($3 a day)": "", "Population": "3692810", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1960", "Share of population in poverty ($3 a day)": "", "Population": "3809392", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1961", "Share of population in poverty ($3 a day)": "", "Population": "3930402", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1962", "Share of population in poverty ($3 a day)": "", "Population": "4055968", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1963", "Share of population in poverty ($3 a day)": "", "Population": "4185875", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1964", "Share of population in poverty ($3 a day)": "", "Population": "4320004", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1965", "Share of population in poverty ($3 a day)": "", "Population": "4458457", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "Share of population in poverty ($3 a day)": "", "Population": "4601223", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "Share of population in poverty ($3 a day)": "", "Population": "4748309", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "Share of population in poverty ($3 a day)": "", "Population": "4900440", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "Share of population in poverty ($3 a day)": "", "Population": "5058185", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "Share of population in poverty ($3 a day)": "", "Population": "5215919", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "Share of population in poverty ($3 a day)": "", "Population": "5374719", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "Share of population in poverty ($3 a day)": "", "Population": "5542444", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "Share of population in poverty ($3 a day)": "", "Population": "5720413", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "Share of population in poverty ($3 a day)": "", "Population": "5908337", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Share of population in poverty ($3 a day)": "", "Population": "6098653", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Share of population in poverty ($3 a day)": "", "Population": "6287107", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Share of population in poverty ($3 a day)": "", "Population": "6449517", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Share of population in poverty ($3 a day)": "", "Population": "6543573", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Share of population in poverty ($3 a day)": "", "Population": "6647557", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Share of population in poverty ($3 a day)": "", "Population": "7041304", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Share of population in poverty ($3 a day)": "", "Population": "7498641", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Share of population in poverty ($3 a day)": "", "Population": "7796503", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Share of population in poverty ($3 a day)": "", "Population": "8098408", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Share of population in poverty ($3 a day)": "", "Population": "8391494", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Share of population in poverty ($3 a day)": "", "Population": "8686815", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Share of population in poverty ($3 a day)": "", "Population": "8982579", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Share of population in poverty ($3 a day)": "", "Population": "9284649", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Share of population in poverty ($3 a day)": "", "Population": "9583099", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Share of population in poverty ($3 a day)": "", "Population": "9864797", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Share of population in poverty ($3 a day)": "", "Population": "10137287", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Share of population in poverty ($3 a day)": "", "Population": "10404820", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Share of population in poverty ($3 a day)": "", "Population": "10702697", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Share of population in poverty ($3 a day)": "", "Population": "10860285", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Share of population in poverty ($3 a day)": "", "Population": "10873146", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Share of population in poverty ($3 a day)": "", "Population": "10974607", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Share of population in poverty ($3 a day)": "", "Population": "11158364", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Share of population in poverty ($3 a day)": "", "Population": "11369833", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Share of population in poverty ($3 a day)": "", "Population": "11594299", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Share of population in poverty ($3 a day)": "", "Population": "11783454", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Share of population in poverty ($3 a day)": "", "Population": "11892055", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Share of population in poverty ($3 a day)": "", "Population": "11971904", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Share of population in poverty ($3 a day)": "", "Population": "12087661", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Share of population in poverty ($3 a day)": "", "Population": "12232324", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Share of population in poverty ($3 a day)": "", "Population": "12365901", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Share of population in poverty ($3 a day)": "", "Population": "12483433", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Share of population in poverty ($3 a day)": "", "Population": "12636442", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Share of population in poverty ($3 a day)": "", "Population": "12804062", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Share of population in poverty ($3 a day)": "", "Population": "12959154", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Share of population in poverty ($3 a day)": "", "Population": "13142791", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Share of population in poverty ($3 a day)": "", "Population": "13356551", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Share of population in poverty ($3 a day)": "35.71699857711792", "Population": "13595421", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Share of population in poverty ($3 a day)": "", "Population": "13817887", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Share of population in poverty ($3 a day)": "", "Population": "14013811", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Share of population in poverty ($3 a day)": "", "Population": "14207367", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Share of population in poverty ($3 a day)": "", "Population": "14399008", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Share of population in poverty ($3 a day)": "", "Population": "14600297", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Share of population in poverty ($3 a day)": "44.65687274932861", "Population": "14812484", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Share of population in poverty ($3 a day)": "", "Population": "15034457", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Share of population in poverty ($3 a day)": "49.21989440917969", "Population": "15271377", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Share of population in poverty ($3 a day)": "", "Population": "15526887", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Share of population in poverty ($3 a day)": "", "Population": "15797220", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Share of population in poverty ($3 a day)": "", "Population": "16069061", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Share of population in poverty ($3 a day)": "", "Population": "16340829", "World region according to OWID": "Africa"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "share-of-population-in-extreme-poverty", "metadata_url": "https://ourworldindata.org/grapher/share-of-population-in-extreme-poverty.metadata.json", "chart_title": "Share of population living in extreme poverty", "chart_subtitle": "Extreme poverty is defined as living below the International Poverty Line of $3 per day. This data is adjusted for inflation and differences in living costs between countries.", "chart_note": "This data is expressed in international-$ at 2021 prices. Depending on the country and year, it relates to income (measured after taxes and benefits) or to consumption, per capita.", "chart_citation": "World Bank Poverty and Inequality Platform (2026)", "original_chart_url": "https://ourworldindata.org/grapher/share-of-population-in-extreme-poverty", "owid_column_metadata": {"Share of population in poverty ($3 a day, 2021 prices)": {"titleShort": "Share of population in poverty ($3 a day)", "titleLong": "Share of population in poverty ($3 a day)", "descriptionShort": "Percentage of population living in households with an income or consumption below $3 per day.", "descriptionKey": ["The World Bank defines extreme poverty as living on less than $3 per day. This threshold, known as the \"International Poverty Line\", is set so that poverty can be compared across countries. This indicator plays an important and successful role in focusing the world's attention on the very poorest people. The UN uses this indicator to track progress towards [ending extreme poverty by 2030](https://ourworldindata.org/sdgs/no-poverty).", "Two centuries ago, most of the world's population was extremely poor. Many believed that widespread poverty was inevitable. But this turned out to be wrong. Economic growth is possible, and poverty can decline. With this poverty line, we can track whether countries are leaving the worst poverty behind.", "This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "Many people, today and in the past, have no formal monetary income. This data accounts for that by including the estimated value of non-market income, such as food grown by subsistence farmers for their own use.", "The data comes from the World Bank's Poverty and Inequality Platform (PIP), which draws on national household surveys. Regional and global estimates are extrapolated to the year of the data release using GDP growth estimates and forecasts. For more details about the methodology, please refer to the [World Bank PIP documentation](https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#nowcasts).", "Depending on the country and year, the data refers either to income (after taxes and benefits) or to consumption, per capita. These are not perfectly comparable — consumption tends to be more evenly distributed than income. For most countries, we have only one option available. But when there is a mix of consumption and income data points, we process the data to keep one observation per country and year."], "descriptionProcessing": "For most countries in the dataset, estimates relate to disposable income or consumption, for all available years. Several countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\n\nIn most of our charts, we present the data with some data points dropped to present a single series for each country. This allows us to make readable visualizations that combine multiple countries. In choosing which data points to keep, we strike a balance between maintaining comparability over time and showing as long a time series as possible. As such, the exact approach varies across countries.", "shortUnit": "%", "unit": "%", "timespan": "1963-2026", "type": "Numeric", "owidVariableId": 1220228, "shortName": "headcount_ratio__ppp_version_2021__poverty_line_300__welfare_type_income_or_consumption__table_income_or_consumption_consolidated__survey_comparability_no_spells", "lastUpdated": "2026-03-24", "nextUpdate": "2026-09-20", "citationShort": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data", "citationLong": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data. “Share of population in poverty ($3 a day) – World Bank” [dataset]. World Bank Poverty and Inequality Platform, “World Bank Poverty and Inequality Platform (PIP) 20260324_2021, 20260324_2017” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1220228.metadata.json"}, "Population (historical)": {"titleShort": "Population", "titleLong": "Population", "descriptionShort": "Population by country, available from 10,000 BCE to 2023, based on data and estimates from different sources.", "descriptionKey": ["Population is the most commonly used metric throughout Our World in Data. It is used directly to understand population growth over time, and indirectly to calculate per-capita indicators, making it easier to compare countries of different sizes.", "We construct this indicator by combining multiple sources covering different periods.\n  - HYDE v3.3 (2023): historical estimates from 10,000 BCE to 1799.\n  - Gapminder v7 (2022): for 1800-1949.\n  - UN World Population Prospects (2024): for 1950 onwards, including 2100 projections.\n  - Gapminder Systema Globalis (2023): additional source for former countries (Yugoslavia, USSR, etc.)", "Breaks in the data may occur at the boundaries between sources due to their methodological differences.", "You can read more about the sources and methodology in our [dedicated article](https://ourworldindata.org/population-sources). We also provide a table of sources showing the source we use for each country-year.", "We calculate geographical aggregates (continents, income groups, etc.) by summing individual country populations. For years before 1800, we rely directly on HYDE's values for continents to ensure historical consistency."], "descriptionProcessing": "### Combination of different sources\nWe construct our long-run population data by combining multiple sources:\n\n- 10,000 BCE–1799: historical estimates by HYDE (v3.3).\n\n- 1800–1949: historical estimates by Gapminder (v7).\n\n- 1950–2023: population records from the United Nations World Population Prospects (2024 revision).\n\n**Geographical aggregates**\n\n- For most years, we calculate aggregates by summing the population of member countries.\n- We do this based on [our definition of continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and the [World Bank’s income groups](https://ourworldindata.org/grapher/world-bank-income-groups).\n- The only exception is before 1800, where we use HYDE's estimates for continents (but not income groups).\n\nFor most of the years, we've estimated regional aggregates by summing the population of countries in each region. We've relied on [our continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and [World Bank income group definitions](https://ourworldindata.org/grapher/world-bank-income-groups). The only exception is before 1800, where we've used HYDE's estimates on continents (but not income groups).\n\n**World**\n- Before 1800: we use data from HYDE.\n- 1800-1950: we estimate the global population by summing all available countries in the dataset.\n- After 1950, we rely on estimates from the United Nations World Population Prospects.", "shortUnit": "", "unit": "people", "timespan": "-10000-2023", "type": "Integer", "owidVariableId": 953903, "shortName": "population_historical", "lastUpdated": "2024-07-15", "nextUpdate": "2026-07-15", "citationShort": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data", "citationLong": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data. “Population – HYDE, Gapminder, UN – Long-run data” [dataset]. PBL Netherlands Environmental Assessment Agency, “History Database of the Global Environment 3.3”; Gapminder, “Population v7”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update”; Gapminder, “Systema Globalis” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/953903.metadata.json"}, "World region according to OWID": {"titleShort": "World region according to OWID", "titleLong": "World region according to OWID", "descriptionShort": "Regions defined by Our World in Data, which are used in OWID charts and maps.", "unit": "", "timespan": "2023-2023", "type": "Continent", "owidVariableId": 900801, "shortName": "owid_region", "lastUpdated": "2023-01-01", "citationShort": "Our World in Data – processed by Our World in Data", "citationLong": "Our World in Data – processed by Our World in Data. “World region according to OWID” [dataset]. Our World in Data, “Regions” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/900801.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Number of income/consumption surveys in the past decade available via the World Bank", "source_url": "https://ourworldindata.org/grapher/data-deprivation-poverty-surveys-per-decade.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Number of surveys in the past decade"], "row_count_total": 11529, "rows_head": [{"Entity": "Albania", "Code": "ALB", "Year": "1963", "Number of surveys in the past decade": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1964", "Number of surveys in the past decade": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1965", "Number of surveys in the past decade": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1966", "Number of surveys in the past decade": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1967", "Number of surveys in the past decade": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1968", "Number of surveys in the past 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in the past decade": "2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Number of surveys in the past decade": "2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2025", "Number of surveys in the past decade": "2"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "data-deprivation-poverty-surveys-per-decade", "metadata_url": "https://ourworldindata.org/grapher/data-deprivation-poverty-surveys-per-decade.metadata.json", "chart_title": "Number of income/consumption surveys in the past decade available via the World Bank", "chart_subtitle": "", "chart_note": "Each decade comprises the reference year and the nine years before.", "chart_citation": "World Bank Poverty and Inequality Platform (2026)", "original_chart_url": "https://ourworldindata.org/grapher/data-deprivation-poverty-surveys-per-decade", "owid_column_metadata": {"Number of surveys in the past decade": {"titleShort": "Number of surveys in the past decade", "titleLong": "Number of surveys in the past decade", "descriptionShort": "The number of income or consumption surveys available in the past decade. Each decade comprises the current year and the nine years before.", "descriptionKey": ["Many people, today and in the past, have no formal monetary income. This data accounts for that by including the estimated value of non-market income, such as food grown by subsistence farmers for their own use."], "descriptionProcessing": "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 one of the estimates, to strike a balance between maintaining comparability over time and showing as long a time series as possible. This means the indicator is estimating the number of years at least one survey was conducted in the past decade, rather than the number of surveys.", "shortUnit": "", "unit": "surveys", "timespan": "1963-2025", "type": "Numeric", "owidVariableId": 1227320, "shortName": "surveys_past_decade", "lastUpdated": "2026-03-24", "nextUpdate": "2026-09-20", "citationShort": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data", "citationLong": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data. “Number of surveys in the past decade – World Bank” [dataset]. World Bank Poverty and Inequality Platform, “World Bank Poverty and Inequality Platform (PIP) 20260324_2021, 20260324_2017” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1227320.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "National poverty line vs. mean daily income or consumption", "source_url": "https://ourworldindata.org/grapher/mean-daily-expenditure-per-capita-vs-national-poverty-line.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Harmonized national poverty line", "Mean", "Population", "World region according to OWID"], "row_count_total": 2788, "rows_head": [{"Entity": "Albania", "Code": "ALB", "Year": "1996", "Harmonized national poverty line": "", "Mean": "8.442031860351562", "Population": "3245680", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Harmonized national poverty line": "", "Mean": "8.62833309173584", "Population": "3134095", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Harmonized national poverty line": "", "Mean": "9.753928184509277", "Population": "3076156", "World 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"Population": "15835743", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "1989", "Harmonized national poverty line": "", "Mean": "44.298553466796875", "Population": "16869362", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "1995", "Harmonized national poverty line": "", "Mean": "40.79104995727539", "Population": "18095340", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "2001", "Harmonized national poverty line": "", "Mean": "46.99628448486328", "Population": "19365438", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "2003", "Harmonized national poverty line": "", "Mean": "48.466163635253906", "Population": "19819449", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "2004", "Harmonized national poverty line": "", "Mean": "52.74925994873047", "Population": "20046679", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "2008", "Harmonized national poverty line": "", "Mean": "66.69456481933594", "Population": "21370595", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "2010", "Harmonized national poverty line": "", "Mean": "66.45447540283203", "Population": "22141583", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "2014", "Harmonized national poverty line": "", "Mean": "71.04176330566406", "Population": "23595599", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "2016", "Harmonized national poverty line": "", "Mean": "69.4010009765625", "Population": "24329963", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "2018", "Harmonized national poverty line": "36.54909", "Mean": "70.29949188232422", "Population": "25119700", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "2020", "Harmonized national poverty line": "", "Mean": "72.71121978759766", "Population": "25743787", "World region according to OWID": "Oceania"}, {"Entity": "Austria", "Code": "AUT", "Year": "1994", "Harmonized national poverty line": "", "Mean": "62.79384231567383", "Population": "7938157", "World region according to OWID": "Europe"}, {"Entity": "Austria", "Code": "AUT", "Year": "1995", "Harmonized national poverty line": "", "Mean": "61.458187103271484", "Population": "7950322", "World region according to OWID": "Europe"}, {"Entity": "Austria", "Code": "AUT", "Year": "1996", "Harmonized national poverty line": "", "Mean": "58.96555709838867", "Population": "7960917", "World region according to OWID": "Europe"}, {"Entity": "Austria", "Code": "AUT", "Year": "1997", "Harmonized national poverty line": "", "Mean": "58.90663146972656", "Population": "7969804", "World region according to OWID": 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"Code": "YEM", "Year": "1998", "Harmonized national poverty line": "", "Mean": "7.159192085266113", "Population": "18446015", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Harmonized national poverty line": "", "Mean": "5.662644386291504", "Population": "22790082", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Harmonized national poverty line": "3.7196462", "Mean": "4.9619035720825195", "Population": "30226311", "World region according to OWID": "Asia"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Harmonized national poverty line": "", "Mean": "3.527235269546509", "Population": "7981653", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Harmonized national poverty line": "", "Mean": "3.165553092956543", "Population": "8373920", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Harmonized national poverty line": "", "Mean": "4.092196941375732", "Population": "9004059", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Harmonized national poverty line": "", "Mean": "4.05233097076416", "Population": "9482414", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Harmonized national poverty line": "", "Mean": "3.0897467136383057", "Population": "10647955", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Harmonized national poverty line": "", "Mean": "3.2914113998413086", "Population": "11338201", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Harmonized national poverty line": "", "Mean": "3.030801773071289", "Population": "12129560", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Harmonized national poverty line": "", "Mean": "3.030522346496582", "Population": "13965592", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Harmonized national poverty line": "", "Mean": "3.365255117416382", "Population": "16399092", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Harmonized national poverty line": "2.1690578", "Mean": "2.858142375946045", "Population": "20152935", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Harmonized national poverty line": "", "Mean": "5.650390148162842", "Population": "13595421", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Harmonized national poverty line": "", "Mean": "5.061988353729248", "Population": "14812484", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Harmonized national poverty line": "2.3983622", "Mean": "5.166445732116699", "Population": "15271377", "World region according to OWID": "Africa"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "mean-daily-expenditure-per-capita-vs-national-poverty-line", "metadata_url": "https://ourworldindata.org/grapher/mean-daily-expenditure-per-capita-vs-national-poverty-line.metadata.json", "chart_title": "National poverty line vs. mean daily income or consumption", "chart_subtitle": "This data is adjusted for inflation and differences in living costs between countries.", "chart_note": "This data is expressed in international-$ at 2021 prices.", "chart_citation": "Foster et al. (2025); World Bank Poverty and Inequality Platform (2026)", "original_chart_url": "https://ourworldindata.org/grapher/mean-daily-expenditure-per-capita-vs-national-poverty-line", "owid_column_metadata": {"Harmonized national poverty line": {"titleShort": "Harmonized national poverty line", "titleLong": "Harmonized national poverty line", "descriptionShort": "National poverty line used to construct global poverty lines representing low-income and middle-income countries. This data is adjusted for inflation and differences in living costs between countries.", "descriptionKey": ["This data is constructed from data available in World Bank datasets, and also from the European Statistical Office (Eurostat), the OECD, and some national statistical offices.", "The original data comes usually as national poverty _rates_, and the authors select the closest poverty line available for this rate in the World Bank Poverty and Inequality Platform. For more details on the methodology, please refer to [the original paper](https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099503206032533226).", "The resulting national poverty lines are used to construct global poverty lines in international dollars. The International Poverty Line, used to measure extreme poverty, is the median value among the harmonized national poverty lines in low-income countries. Similar calculations are made for lower- and upper-middle-income countries.", "The data is measured in international-$ at 2021 prices – this adjusts for inflation and for differences in living costs between countries."], "shortUnit": "$", "unit": "international-$ in 2021 prices", "timespan": "2003-2023", "type": "Numeric", "owidVariableId": 1077932, "shortName": "harmonized_national_poverty_line", "lastUpdated": "2025-06-11", "nextUpdate": "2027-06-11", "citationShort": "Foster et al. (2025) – with minor processing by Our World in Data", "citationLong": "Foster et al. (2025) – with minor processing by Our World in Data. “Harmonized national poverty line – World Bank” [dataset]. Foster et al., “Harmonized national poverty lines” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077932.metadata.json"}, "Mean (per day, 2021 prices)": {"titleShort": "Mean", "titleLong": "Mean", "descriptionShort": "Average income or consumption of the population per day.", "descriptionKey": ["This data shows the mean (average) income or consumption per person. Because incomes are unevenly distributed, with a small number of people on very high incomes, the mean is typically higher than what most people have. To see the income of a typical person, you can look at the median income instead. We discuss how incomes are distributed in more detail on our page on [economic inequality](https://ourworldindata.org/economic-inequality).", "This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "Depending on the country and year, the data refers either to income (after taxes and benefits) or to consumption, per capita. These are not perfectly comparable — consumption tends to be more evenly distributed than income. For most countries, we have only one option available. But when there is a mix of consumption and income data points, we process the data to keep one observation per country and year.", "Many people, today and in the past, have no formal monetary income. This data accounts for that by including the estimated value of non-market income, such as food grown by subsistence farmers for their own use.", "The data comes from the World Bank's Poverty and Inequality Platform (PIP), which draws on national household surveys. Regional and global estimates are extrapolated to the year of the data release using GDP growth estimates and forecasts. For more details about the methodology, please refer to the [World Bank PIP documentation](https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#nowcasts)."], "descriptionProcessing": "For most countries in the dataset, estimates relate to disposable income or consumption, for all available years. Several countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\n\nIn most of our charts, we present the data with some data points dropped to present a single series for each country. This allows us to make readable visualizations that combine multiple countries. In choosing which data points to keep, we strike a balance between maintaining comparability over time and showing as long a time series as possible. As such, the exact approach varies across countries.", "shortUnit": "$", "unit": "international-$ in 2021 prices", "timespan": "1963-2026", "type": "Numeric", "owidVariableId": 1214753, "shortName": "mean__ppp_version_2021__welfare_type_income_or_consumption__period_day__table_income_or_consumption_consolidated__survey_comparability_no_spells", "lastUpdated": "2026-03-24", "nextUpdate": "2026-09-20", "citationShort": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data", "citationLong": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data. “Mean – World Bank” [dataset]. World Bank Poverty and Inequality Platform, “World Bank Poverty and Inequality Platform (PIP) 20260324_2021, 20260324_2017” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1214753.metadata.json"}, "Population (historical)": {"titleShort": "Population", "titleLong": "Population", "descriptionShort": "Population by country, available from 10,000 BCE to 2023, based on data and estimates from different sources.", "descriptionKey": ["Population is the most commonly used metric throughout Our World in Data. It is used directly to understand population growth over time, and indirectly to calculate per-capita indicators, making it easier to compare countries of different sizes.", "We construct this indicator by combining multiple sources covering different periods.\n  - HYDE v3.3 (2023): historical estimates from 10,000 BCE to 1799.\n  - Gapminder v7 (2022): for 1800-1949.\n  - UN World Population Prospects (2024): for 1950 onwards, including 2100 projections.\n  - Gapminder Systema Globalis (2023): additional source for former countries (Yugoslavia, USSR, etc.)", "Breaks in the data may occur at the boundaries between sources due to their methodological differences.", "You can read more about the sources and methodology in our [dedicated article](https://ourworldindata.org/population-sources). We also provide a table of sources showing the source we use for each country-year.", "We calculate geographical aggregates (continents, income groups, etc.) by summing individual country populations. For years before 1800, we rely directly on HYDE's values for continents to ensure historical consistency."], "descriptionProcessing": "### Combination of different sources\nWe construct our long-run population data by combining multiple sources:\n\n- 10,000 BCE–1799: historical estimates by HYDE (v3.3).\n\n- 1800–1949: historical estimates by Gapminder (v7).\n\n- 1950–2023: population records from the United Nations World Population Prospects (2024 revision).\n\n**Geographical aggregates**\n\n- For most years, we calculate aggregates by summing the population of member countries.\n- We do this based on [our definition of continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and the [World Bank’s income groups](https://ourworldindata.org/grapher/world-bank-income-groups).\n- The only exception is before 1800, where we use HYDE's estimates for continents (but not income groups).\n\nFor most of the years, we've estimated regional aggregates by summing the population of countries in each region. We've relied on [our continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and [World Bank income group definitions](https://ourworldindata.org/grapher/world-bank-income-groups). The only exception is before 1800, where we've used HYDE's estimates on continents (but not income groups).\n\n**World**\n- Before 1800: we use data from HYDE.\n- 1800-1950: we estimate the global population by summing all available countries in the dataset.\n- After 1950, we rely on estimates from the United Nations World Population Prospects.", "shortUnit": "", "unit": "people", "timespan": "-10000-2023", "type": "Integer", "owidVariableId": 953903, "shortName": "population_historical", "lastUpdated": "2024-07-15", "nextUpdate": "2026-07-15", "citationShort": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data", "citationLong": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data. “Population – HYDE, Gapminder, UN – Long-run data” [dataset]. PBL Netherlands Environmental Assessment Agency, “History Database of the Global Environment 3.3”; Gapminder, “Population v7”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update”; Gapminder, “Systema Globalis” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/953903.metadata.json"}, "World region according to OWID": {"titleShort": "World region according to OWID", "titleLong": "World region according to OWID", "descriptionShort": "Regions defined by Our World in Data, which are used in OWID charts and maps.", "unit": "", "timespan": "2023-2023", "type": "Continent", "owidVariableId": 900801, "shortName": "owid_region", "lastUpdated": "2023-01-01", "citationShort": "Our World in Data – processed by Our World in Data", "citationLong": "Our World in Data – processed by Our World in Data. “World region according to OWID” [dataset]. Our World in Data, “Regions” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/900801.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "db35edba32946461ae71"},
{"raw_link": "https://ourworldindata.org/cardiovascular-deaths-decline", "title": "Death rates from cardiovascular disease have fallen dramatically — what were the breakthroughs behind this?", "context": "Home\nCardiovascular Diseases\nDeath rates from cardiovascular disease have fallen dramatically — what were the breakthroughs behind this?\nOver a century of progress in surgery, drugs, prevention, and emergency response has driven down death rates from heart disease and stroke.\nBy\nSaloni Dattani\nAugust 4, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nFor much of history, heart disease was a mystery. Middle-aged adults often collapsed without warning, and doctors usually blamed “dropsy”, “apoplexy”, or simply “old age.”\n1\nIn 1945 — at just 63 years old — President Franklin D. Roosevelt was sitting for a portrait when he raised a hand to his head and whispered, “I have a terrific pain in the back of my head.” Minutes later, he lost consciousness and died from a massive brain hemorrhage — a consequence of uncontrolled high blood pressure and heart disease, which doctors at the time couldn’t treat.\n2\nRoosevelt wasn’t alone. Mid-twentieth-century medicine, even for some of the world's most powerful people, often lacked the tools to treat or sometimes even diagnose specific cardiovascular diseases.\nThere was no routine blood pressure screening. There were only basic diagnostic tools — no\nCT\n,\nMRI\n, or\nechocardiography\nto spot clots or artery damage. Even if someone was diagnosed, few effective medicines or surgeries were available to treat them.\n3\nToday, pills could have driven down Roosevelt’s blood pressure within weeks. The hypertension that struck him and many others without warning, often known as “the silent killer”, is routinely diagnosed and treated.\nCardiovascular diseases — the broad term for conditions that affect the heart and blood vessels — are still the\nleading cause of death\nworldwide. But the story reflects a remarkable and often overlooked fact: the risk of dying from cardiovascular diseases has fallen dramatically in recent decades.\nIn the United States alone, the\nage-standardized\ndeath rate from cardiovascular disease has fallen by three-quarters since 1950. This means that for people of the same age, the annual risk of dying from cardiovascular disease is now just one-quarter what it was in 1950.\nThis progress was built on decades of biomedical research, surgical advances, public health efforts, and lifestyle changes, which means that far fewer people die from sudden strokes or heart attacks. If they do, it happens much later, after they’ve lived longer and healthier lives.\nIn this article, I’ll look at how and why deaths from cardiovascular disease have declined. I’ll focus on data from the United States, but as we’ll see, many other countries have followed a similar path.\nCardiovascular mortality rates have declined greatly\nThe annual risk of dying from cardiovascular disease has fallen dramatically.\nIn the chart below, you can see this decline across age groups. Across all ages, the risk of death from cardiovascular disease has fallen.\nNote that the vertical scales vary between age groups; the risks are much higher at older ages.\nFor example, women over 85 are about two-thirds less likely to die from cardiovascular disease today than women over 85 in 1950. This pattern is visible for both men and women, from young adulthood through old age.\nThese rates can be combined into the\nage-standardized\ndeath rate from cardiovascular disease. This lets us understand how the risk has changed for people of the same age.\nIt’s shown in the chart below. In the United States, that risk has fallen dramatically. In the 1950s, over 500 out of every 100,000 people died of cardiovascular disease each year. Today, that figure is below 150 — a decline of around three-quarters.\nThis metric also lets us see if this improvement is unique to the United States or has happened elsewhere. As you can see, there have also been large declines in many other countries, including Australia, France, Canada, Germany, and Brazil.\nThese gains haven’t simply shifted health risks to other causes. Instead, the drop in cardiovascular mortality is a major reason why overall death rates have fallen and\nlife expectancy has risen globally\n. Fewer people dying early from heart attacks and strokes means many more years lived in better health.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nPublic health efforts and advances in medicine, surgery, and emergency care have all contributed to the decline in cardiovascular deaths\nWhat made this progress possible?\nThere wasn’t just one discovery or intervention that did it all. Medical breakthroughs — in detection, treatment, surgery, and emergency care — have made\nsurviving\ncardiovascular disease much more likely. In addition, public health measures and lifestyle changes have stopped many people from\ndeveloping\nit in the first place.\nThe twentieth century was a very exciting time to be a cardiovascular doctor or surgeon. Many breakthroughs have made surviving cardiovascular disease far more likely. I’ve shown many of them in the chart below, and will go through some of them in more detail.\nDownload\nDrugs to prevent and treat heart disease\nOne major area of progress has been the development of drugs that can help people manage risks and treat heart conditions when they appear. This includes:\nStatins\n, which were first used widely in the 1980s, help millions keep their arteries cleaner by lowering LDL (“bad”) cholesterol levels and stabilizing plaque that can clog blood vessels.\n4\nNewer drugs, like\nPCSK9 inhibitors\nintroduced in 2015, help people lower LDL cholesterol when statins aren’t enough.\n5\nBlood pressure medications\n, like\nbeta blockers\n,\nACE inhibitors\n,\nARBs\n, and\ndiuretics\n, help keep high blood pressure under control, reducing the risk of strokes, heart attacks, and heart failure.\n6\nClot-busting medicines\nare used to break up blockages and quickly restore blood flow, and dramatically improve survival rates for heart attack and stroke patients.\n7\nTreatments like these are routine today and have substantially lowered the chances that people with high\ncholesterol\n,\nhypertension\n, or previous heart problems will go on to develop life-threatening complications.\nDevices, diagnostics, and surgeries\nBeyond drugs, medical devices and surgical techniques have revolutionized cardiovascular care. Just over a century ago, the heart was considered untouchable. Surgeons feared, often rightly at the time, that opening the chest would lead to massive bleeding or deadly infections.\nPositive pressure ventilation\nis a way of mechanically pushing air into the patient’s lungs to keep them breathing. This is essential in open-chest surgery because opening the chest cavity can collapse the lungs, so they can’t inflate without assistance.\nA\nheart-lung machine\ntemporarily takes over the work of the heart and lungs by adding oxygen to the blood and pumping it around the body. This is crucial when the heart needs to be stopped for surgery.\nHeart surgery became possible through advances in anesthesia, antiseptics, blood transfusions, positive pressure ventilation, and the invention of the heart-lung machine. This opened the door to an organ that was previously seen as untouchable.\nHere are some other key advances over the past century:\nStabilising the heart’s rhythm\n. In the late 1950s, doctors implanted the first\npacemaker\n, which sends tiny electrical signals to keep the heart beating at a healthy rhythm when it slows down too much.\n8\nBy the 1980s, implantable defibrillators were added to detect dangerous heart rhythms and deliver a shock to restore a normal heartbeat — helping prevent sudden death in people at high risk.\n9\nLooking inside the heart.\nDoctors first used\nechocardiography\nin the early 1950s to watch the heart’s real-time movement.\n10\nCT scans in the 1970s and MRIs in the 1980s brought clearer views of blockages and damage, without the need for surgery.\n11\nTreating blocked arteries\nhas transformed as well. In 1974, doctors first used\nangioplasty\nto thread a balloon into clogged arteries and restore blood flow. Doctors soon applied this to the coronary arteries, opening them up without needing open-heart surgery.\n12\nStents\n— tiny mesh tubes that keep arteries open — followed in the 1980s, and in the 2000s, drug-coated versions (“drug-eluted stents”) were introduced to prevent re-narrowing.\n13\nBypassing severe blockages.\nWhen stents weren’t enough, surgeons could reroute blood around the blockage using a healthy vessel from another part of the body. This bypass surgery became a routine and life-saving option by the late 1960s.\n14\nRepairing and replacing heart valves\n. In 1960, the first mechanical heart valve was implanted.\n15\nLater, in the 2000s,\ntranscatheter valve replacement\nallowed damaged valves to be replaced through a thin tube instead of open surgery, offering a safer alternative for many patients.\n16\nReplacing the whole heart\n. For people with severe heart failure, the only solution might be a transplant. The first successful human heart transplant took place in 1967.\n17\nPrecision through robotics\n. Robotic assistance in surgery began in 1985, and precision was improved with tools like robotic arms or biopsy needles. Over time, more devices were added, and eventually they were combined in the first “da Vinci” surgical robot, which let surgeons perform complex operations through a small incision, with precise wrist-like instruments, all controlled from a console displaying a 3D view.\n18\nEmergency care\nSaving lives from heart attacks and strokes hasn’t depended only on hospitals and doctors, but also on what happens in the first critical minutes.\nIn the 1930s, London introduced the world’s first “999” emergency phone line, allowing anyone to call for help quickly. The United States followed decades later, with the first “911” call in the 1960s.\n19\nAs emergency phone systems expanded, emergency care and ambulances became more widely used. Alongside them, new tools and techniques were developed that allowed both professionals and bystanders to act quickly.\nOne of the most important came in the 1950s, when cardiologist Paul Zoll developed the first external defibrillator. This device could deliver an electric shock to restart a stopped heart. This technology became portable, safer, and easier to use in the following years. Eventually,\nautomated external defibrillators (AEDs)\nwere designed so ordinary people could follow voice instructions, apply pads, and deliver the life-saving shock within minutes, even before emergency services arrived.\n20\nAround the same time, researchers developed cardiopulmonary resuscitation (CPR), another tool that would become central to emergency care. By the late 1950s, CPR — a combination of chest compressions and mouth-to-mouth breathing — gave bystanders a way to keep blood and oxygen temporarily flowing until emergency care arrives.\nAs these interventions became available, getting the wider public to recognize emergency signs became more valuable so they could respond quickly and effectively. Many large public awareness campaigns were launched. The American Heart Association led efforts in the US to teach people how to recognize heart attack symptoms — like chest pain, pressure, or pain in the arm — and to seek help immediately.\n21\nMore recently, the UK’s “Act FAST” campaign helped people spot strokes by remembering four key signs: “Face drooping, Arm weakness, Speech trouble, and Time to call 999.”\n22\nTogether, these developments have prevented many heart attacks and strokes from becoming fatal. Many more people get treated within minutes, not hours, and that speed has saved many lives.\n23\nPublic health efforts and lifestyle changes have reduced cardiovascular risks\nIn the chart below, I’ve compiled some of the biggest risk factors of cardiovascular disease — obesity,\nuncontrolled blood pressure\n,\ncholesterol\n, and cigarette smoking. Data on all four risk factors has been available from national surveys since 1999, so the chart focuses on the most recent decades.\nYou can see that, over decades, obesity has worsened, but the other risk factors (uncontrolled blood pressure, cigarette smoking, and high cholesterol) have improved. Public policy opened the door, but millions also changed their daily habits. This led to a change in the risk factors for cardiovascular disease.\nDownload\nOne important reason for progress is the decline in smoking, thanks to tobacco control efforts. Cigarettes carry a mixture of carcinogens and harmful chemicals that injure blood vessels and fuel inflammation, which means fewer people smoking leads to fewer people developing clogged arteries and heart disease. In the 1960s, about 40% of US adults smoked cigarettes. Today, it’s less than 15%.\n24\nAnother improvement has been with cholesterol. High levels of LDL cholesterol — sometimes called “bad cholesterol” — contribute to fatty deposits in arteries, raising the risk of heart attacks and strokes. Average cholesterol levels have fallen since the 2000s. This is likely the result of factors such as national screening programs to detect high cholesterol levels early,\n25\ndietary guidelines that encouraged cutting trans and saturated fats\n26\n, and due to\na wider use of statins\n— drugs that lower cholesterol levels and help prevent dangerous buildups in arteries; they are one of the most prescribed drug classes today and play a key role in preventing heart attacks and strokes.\n27\nUncontrolled high blood pressure is another major risk factor. High blood pressure can develop when blood vessels become stiffer or narrower — but it can also drive further damage by putting extra pressure on artery walls, making them more likely to weaken, clog, or rupture, leading to strokes, heart attacks, and heart failure.\n28\nThe share of adults with high blood pressure has fallen since the late 1990s, likely due to better detection, better medication, and more routine monitoring.\nUnfortunately, not all trends have been positive. Obesity has increased steadily and remains a major risk factor for heart disease — partly by raising blood pressure and cholesterol (which treatments have helped control), but also through other pathways like insulin resistance, inflammation, and extra strain on the heart. New treatments and weight loss medicines could help reverse this trend, but they’d need to be used widely to make a difference.\n29\nAnother factor often missed is vaccination for infections like\ninfluenza\nand pneumococcal disease, which have also helped protect people from these infections that can trigger heart attacks.\n30\nLooking back, it’s surprising to realize how many lifesaving tools we have today that didn’t exist until quite recently in history. Antiseptics to prevent infections, CPR to restart the heart, pacemakers to keep it beating, simple blood pressure cuffs to warn of hypertension: these tools are each so common we now rarely think about them or what life was like before them.\nThe dramatic decline in cardiovascular deaths shows just how much can change with science, policy, and everyday habits.\nHeart disease is still the leading cause of death worldwide, killing\naround 20 million\npeople globally each year, and this tells us there’s still more to do. And some risks, like obesity and diabetes, have risen in many countries. They remind us that progress can stall or even reverse without more effort.\nBut new advances are pushing the boundaries of what’s possible even further. 3D heart reconstructions help surgeons plan complex operations more precisely; new valve replacement techniques mean patients can recover faster without surgery; and newly developed weight loss drugs offer hope for tackling obesity across the population.\nThe fight against cardiovascular disease isn’t over. Understanding what brought us this far, and seeing continued progress today, tells us that even more is possible. The next chapters in this story are up to us.\nContinue reading on Our World in Data\nWhat are the different types of cardiovascular diseases, and how many deaths do they cause?\nCardiovascular diseases are a range of related health conditions that develop in the heart and blood vessels. What are the different diseases, and what is their impact worldwide?\nHow does the risk of death change as we age – and how has this changed over time?\nDeath rates decline rapidly after birth but rise again in adolescence. From adulthood onwards, they rise exponentially.\nHow many people die from the flu?\nThe risk of death from influenza has declined over time, but globally, hundreds of thousands of people still die from the disease each year.\nEndnotes\nDropsy is an old term for swelling in the body due to fluid buildup, now called edema. Apoplexy is an old term for sudden loss of consciousness or paralysis, usually because of a stroke or brain bleed.\nEngelhardt, E. (2017). Apoplexy, cerebrovascular disease, and stroke: Historical evolution of terms and definitions. Dementia & Neuropsychologia, 11(4), 449–453.\nhttps://doi.org/10.1590/1980-57642016dn11-040016\nVentura, H. O., & Mehra, M. R. (2005). Bloodletting as a Cure For Dropsy: Heart Failure Down the Ages. Journal of Cardiac Failure, 11(4), 247–252.\nhttps://doi.org/10.1016/j.cardfail.2004.10.003\nBerciano, J. (2024). Neurological illnesses of Franklin Delano Roosevelt: from middle-age acute ascending paralysis to final cerebrovascular stroke.\nAvailable online\n.\nTesler, U. F. (2020). A history of cardiac surgery: An adventurous voyage from antiquity to the artificial heart. Cambridge Scholars Publishing.\nFournier, J., Barret, L., Khouri, C., Naudet, F., Boussageon, R., & Roustit, M. (2024). The evidence base of the 10 most prescribed drugs in England, France, and the United States: A scoping review. Journal of Clinical Epidemiology, 174, 111478.\nhttps://doi.org/10.1016/j.jclinepi.2024.111478\nFournier, J., Barret, L., Khouri, C., Naudet, F., Boussageon, R., & Roustit, M. (2024). The evidence base of the 10 most prescribed drugs in England, France, and the United States: A scoping review. Journal of Clinical Epidemiology, 174, 111478.\nhttps://doi.org/10.1016/j.jclinepi.2024.111478\nWarden, B. A., Fazio, S., & Shapiro, M. D. (2020). The PCSK9 revolution: Current status, controversies, and future directions. Trends in Cardiovascular Medicine, 30(3), 179–185.\nhttps://doi.org/10.1016/j.tcm.2019.05.007\nEttehad, D., Emdin, C. A., Kiran, A., Anderson, S. G., Callender, T., Emberson, J., Chalmers, J., Rodgers, A., & Rahimi, K. (2016). Blood pressure lowering for prevention of cardiovascular disease and death: A systematic review and meta-analysis. The Lancet, 387(10022), 957–967.\nhttps://doi.org/10.1016/s0140-6736(15)01225-8\nChatterjee, S., Chakraborty, A., Weinberg, I., Kadakia, M., Wilensky, R. L., Sardar, P., Kumbhani, D. J., Mukherjee, D., Jaff, M. R., & Giri, J. (2014). Thrombolysis for Pulmonary Embolism and Risk of All-Cause Mortality, Major Bleeding, and Intracranial Hemorrhage: A Meta-analysis. JAMA, 311(23), 2414.\nhttps://doi.org/10.1001/jama.2014.5990\nWard, C., Henderson, S., & Metcalfe, N. H. (2013). A short history on pacemakers. International Journal of Cardiology, 169(4), 244–248.\nhttps://doi.org/10.1016/j.ijcard.2013.08.093\nMaron, B. J., Estes, N. A. M., Rowin, E. J., Maron, M. S., & Reynolds, M. R. (2023). Development of the Implantable Cardioverter-Defibrillator. Journal of the American College of Cardiology, 82(4), 353–373.\nhttps://doi.org/10.1016/j.jacc.2023.04.056\nHurlock, G. S., Higashino, H., & Mochizuki, T. (2009). History of cardiac computed tomography: Single to 320-detector row multislice computed tomography. The International Journal of Cardiovascular Imaging, 25(S1), 31–42.\nhttps://doi.org/10.1007/s10554-008-9408-z\nPennell, D. J., & Mohiaddin, R. H. (2024). Cardiovascular Magnetic Resonance: Past, Present, and Future. Circulation: Cardiovascular Imaging, 17(8).\nhttps://doi.org/10.1161/circimaging.124.016523\nTurina, M. (2017). The first PTCAs in Zurich, in 1977.\nEuropean Heart Journal\n,\n38\n(28), 2166–2167.\nhttps://doi.org/10.1093/eurheartj/ehx336\nTomberli, B., Mattesini, A., Baldereschi, G. I., & Di Mario, C. (2018). A Brief History of Coronary Artery Stents.\nRevista Española de Cardiología (English Edition)\n,\n71\n(5), 312–319.\nhttps://doi.org/10.1016/j.rec.2017.11.022\nLee, D.-H., & de la Torre Hernandez, J. M. (2018). The Newest Generation of Drug-eluting Stents and Beyond. European Cardiology, 13(1), 54–59.\nhttps://doi.org/10.15420/ecr.2018:8:2\nBakaeen, F. G., Blackstone, E. H., Pettersson, G. B., Gillinov, A. M., & Svensson, L. G. (2018). The father of coronary artery bypass grafting: René Favaloro and the 50th anniversary of coronary artery bypass grafting.\nThe Journal of Thoracic and Cardiovascular Surgery\n,\n155\n(6), 2324–2328.\nhttps://doi.org/10.1016/j.jtcvs.2017.09.167\nGott, V. L., Alejo, D. E., & Cameron, D. E. (2003). Mechanical heart valves: 50 years of evolution. The Annals of Thoracic Surgery, 76(6), S2230–S2239.\nhttps://doi.org/10.1016/j.athoracsur.2003.09.002\nFigulla, H. R., Franz, M., & Lauten, A. (2020). The History of Transcatheter Aortic Valve Implantation (TAVI)—A Personal View Over 25 Years of development. Cardiovascular Revascularization Medicine, 21(3), 398–403.\nhttps://doi.org/10.1016/j.carrev.2019.05.024\nBBC News (2017) The operation that took medicine into the media age. Available\nonline\n.\nMorrell, A. L. G., Morrell-Junior, A. C., Morrell, A. G., Mendes, J. M. F., Tustumi, F., De-Oliveira-E-Silva, L. G., & Morrell, A. (2021). The history of robotic surgery and its evolution: When illusion becomes reality. Revista Do Colégio Brasileiro de Cirurgiões, 48.\nhttps://doi.org/10.1590/0100-6991e-20202798\nGreater London Authority (2025). 999 celebrates its 77th birthday. City Hall blog. Available\nonline\n.\nThe City of Haleyville (). The First 9-1-1 Call. Available\nonline\n.\nEMS Museum (n.d.) Heart-Aid Defibrillator. Available\nonline\n.\nAmerican Heart Association (n.d.) History of the American Heart Association. Available\nonline\n.\nDepartment of Health and Social Care (2012). Acting FAST proves it can save hundreds of lives. Press release. Available\nonline\n.\nSee examples of research on the expansion of Automated External Defibrillators in public spaces, and the improved survival rates for people with heart attacks -\nKitamura, T., Iwami, T., Kawamura, T., Nagao, K., Tanaka, H., & Hiraide, A. (2010). Nationwide Public-Access Defibrillation in Japan.\nNew England Journal of Medicine\n,\n362\n(11), 994–1004.\nhttps://doi.org/10.1056/nejmoa0906644\nKitamura, T., Kiyohara, K., Sakai, T., Matsuyama, T., Hatakeyama, T., Shimamoto, T., Izawa, J., Fujii, T., Nishiyama, C., Kawamura, T., & Iwami, T. (2016). Public-Access Defibrillation and Out-of-Hospital Cardiac Arrest in Japan.\nNew England Journal of Medicine\n,\n375\n(17), 1649–1659.\nhttps://doi.org/10.1056/nejmsa1600011\nAmerican Lung Association (2024) Overall Smoking Trends. Available\nonline\n.\nWhelton, P. K. (2019). Evolution of Blood Pressure Clinical Practice Guidelines: A Personal Perspective. The Canadian Journal of Cardiology, 35(5), 570–581.\nhttps://doi.org/10.1016/j.cjca.2019.02.019\nJahns, L., Davis-Shaw, W., Lichtenstein, A. H., Murphy, S. P., Conrad, Z., & Nielsen, F. (2018). The History and Future of Dietary Guidance in America.\nAdvances in Nutrition\n,\n9\n(2), 136–147.\nhttps://doi.org/10.1093/advances/nmx025\nInstitute of Medicine (US) Committee on Examination of Front-of-Package Nutrition Rating Systems and Symbols (2010) History of Nutrition Labeling.. Front-of-Package Nutrition Rating Systems and Symbols: Phase I Report.\nhttps://www.ncbi.nlm.nih.gov/books/NBK209859/\nMills, E. J., Wu, P., Chong, G., Ghement, I., Singh, S., Akl, E. A., Eyawo, O., Guyatt, G., Berwanger, O., & Briel, M. (2011). Efficacy and safety of statin treatment for cardiovascular disease: A network meta-analysis of 170 255 patients from 76 randomized trials. QJM, 104(2), 109–124.\nhttps://doi.org/10.1093/qjmed/hcq165\nFournier, J., Barret, L., Khouri, C., Naudet, F., Boussageon, R., & Roustit, M. (2024). The evidence base of the 10 most prescribed drugs in England, France, and the United States: A scoping review. Journal of Clinical Epidemiology, 174, 111478.\nhttps://doi.org/10.1016/j.jclinepi.2024.111478\nDrazner, M. H. (2011). The Progression of Hypertensive Heart Disease. Circulation, 123(3), 327–334.\nhttps://doi.org/10.1161/CIRCULATIONAHA.108.845792\nDrucker, D. J. (2025). GLP-1-based therapies for diabetes, obesity and beyond. Nature Reviews Drug Discovery. https://doi.org/10.1038/s41573-025-01183-8\nMüller, T. D., Blüher, M., Tschöp, M. H., & DiMarchi, R. D. (2022). Anti-obesity drug discovery: Advances and challenges. Nature Reviews Drug Discovery, 21(3), 201–223.\nhttps://doi.org/10.1038/s41573-021-00337-8\nBehrouzi, 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.\nhttps://doi.org/10.1001/jamanetworkopen.2022.8873\nBarberis, I., Myles, P., Ault, S. K., Bragazzi, N. L., & Martini, M. (2016). History and evolution of influenza control through vaccination: From the first monovalent vaccine to universal vaccines. Journal of Preventive Medicine and Hygiene, 57(3), E115–E120.\nhttps://pmc.ncbi.nlm.nih.gov/articles/PMC5139605/\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSaloni Dattani (2025) - “Death rates from cardiovascular disease have fallen dramatically — what were the breakthroughs behind this?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260622-080904/cardiovascular-deaths-decline.html' [Online Resource] (archived on June 22, 2026).\nBibTeX citation\n@article{owid-cardiovascular-deaths-decline,\nauthor = {Saloni Dattani},\ntitle = {Death rates from cardiovascular disease have fallen dramatically — what were the breakthroughs behind this?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260622-080904/cardiovascular-deaths-decline.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "cardiovascular-deaths-decline", "source_url": "https://ourworldindata.org/cardiovascular-deaths-decline", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Over a century of progress in surgery, drugs, prevention, and emergency response has driven down death rates from heart disease and stroke.", "numeric_mentions": ["4,", "2025", "1", "1945", "63 years", "2", "3", "1950", "85", "500", "100,000", "150", "1980", "4", "2015,", "5", "6", "7", "8", "9", "10", "1970", "11", "1974,", "12", "2000", "13", "1960", "14", "1960,", "15", "16", "1967", "17", "1985,", "18", "1930", "999", "911", "19", "20", "21", "22", "23", "1999,", "40%", "15%", "24", "25", "26", "27", "28", "1990", "29", "30", "20 million", "2017", "449", "453", "10.1590", "57642016", "040016", "2005", "247", "252", "10.1016", "2004.10", "003", "2024", "2020", "174,", "111478", "2024.111478", "179", "185", "2019.05", "007", "2016", "387", "10022"], "numeric_evidence": [{"title": "Death rate from cardiovascular diseases by age group", 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{"Entity": "Uzbekistan", "Code": "UZB", "Year": "1999", "20–24 years": "12.0635195", "30–34 years": "33.223625", "40–44 years": "102.67677", "50–54 years": "407.5564", "60–64 years": "1303.2734", "70–74 years": "3537.705", "80–84 years": "8758.381", "≥85 years": "15623.172", "All ages": "271.82043"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2000", "20–24 years": "9.39925", "30–34 years": "35.638", "40–44 years": "109.64967", "50–54 years": "441.8616", "60–64 years": "1339.7812", "70–74 years": "3954.3892", "80–84 years": "9583.4795", "≥85 years": "16909.148", "All ages": "289.59756"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2001", "20–24 years": "8.489947", "30–34 years": "30.57917", "40–44 years": "100.26659", "50–54 years": "436.08823", "60–64 years": "1345.14", "70–74 years": "4050.6719", "80–84 years": "9548.95", "≥85 years": "15464.277", "All ages": "283.3471"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2002", "20–24 years": "8.380293", "30–34 years": 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"251.81598", "60–64 years": "791.37933", "70–74 years": "1890.2078", "80–84 years": "3576.6423", "≥85 years": "6231.441", "All ages": "111.38833"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1970", "20–24 years": "7.456504", "30–34 years": "19.94468", "40–44 years": "70.84529", "50–54 years": "238.54132", "60–64 years": "731.6667", "70–74 years": "1915.3516", "80–84 years": "3549.2957", "≥85 years": "6161.017", "All ages": "105.97276"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1971", "20–24 years": "7.834004", "30–34 years": "24.783361", "40–44 years": "87.75334", "50–54 years": "265.31293", "60–64 years": "777.1277", "70–74 years": "1830.8572", "80–84 years": "3660.3174", "≥85 years": "5163.8228", "All ages": "110.90693"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1972", "20–24 years": "7.3937154", "30–34 years": "25.218561", "40–44 years": "79.27212", "50–54 years": "270.99484", "60–64 years": "805.98553", "70–74 years": "1981.1321", "80–84 years": "3787.6924", "≥85 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"20–24 years": "8.067904", "30–34 years": "22.988506", "40–44 years": "74.32047", "50–54 years": "283.06656", "60–64 years": "815.35565", "70–74 years": "1987.6161", "80–84 years": "4253.6875", "≥85 years": "6461.783", "All ages": "123.589325"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1977", "20–24 years": "10.649455", "30–34 years": "27.362837", "40–44 years": "82.010086", "50–54 years": "258.69977", "60–64 years": "720.9619", "70–74 years": "1796.0975", "80–84 years": "4206.3955", "≥85 years": "6293.75", "All ages": "119.88191"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1978", "20–24 years": "9.162099", "30–34 years": "27.235773", "40–44 years": "76.142136", "50–54 years": "273.4416", "60–64 years": "754.7336", "70–74 years": "1800", "80–84 years": "4206.215", "≥85 years": "6484.8486", "All ages": "121.76498"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1979", "20–24 years": "7.3221755", "30–34 years": "20.847652", "40–44 years": "89.3039", "50–54 years": "248.97025", "60–64 years": "653.4351", "70–74 years": "1660.4839", "80–84 years": "4066.6667", "≥85 years": "6283.3335", "All ages": "114.961174"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1980", "20–24 years": "7.1677103", "30–34 years": "21.505377", "40–44 years": "82.899025", "50–54 years": "245.77777", "60–64 years": "668.9139", "70–74 years": "1615.2174", "80–84 years": "5996.875", "≥85 years": "8170", "All ages": "117.89971"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1981", "20–24 years": "6.7794333", "30–34 years": "19.892174", "40–44 years": "70.747215", "50–54 years": "230.04292", "60–64 years": "684.11554", "70–74 years": "1616.7832", "80–84 years": "4965", "≥85 years": "9459.259", "All ages": "114.562744"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1982", "20–24 years": "9.5306015", "30–34 years": "20.655272", "40–44 years": "74.36369", "50–54 years": "232.40317", "60–64 years": "687.8472", "70–74 years": "1668.7075", "80–84 years": "5216.6665", "≥85 years": "9489.286", "All ages": "116.9697"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1983", "20–24 years": "9.922131", "30–34 years": "25.038588", "40–44 years": "84.13955", "50–54 years": "262.29175", "60–64 years": "702.3026", "70–74 years": "1918.2433", "80–84 years": "5923.7666", "≥85 years": "10574.83", "All ages": "131.9735"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1985", "20–24 years": "6.3720818", "30–34 years": "17.82846", "40–44 years": "62.105263", "50–54 years": "199.80658", "60–64 years": "557.79816", "70–74 years": "1625.1533", "80–84 years": "4569.697", "≥85 years": "8265.672", "All ages": "108.16211"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1986", "20–24 years": "6.3184743", "30–34 years": "17.510944", "40–44 years": "54.30328", "50–54 years": "179.78163", "60–64 years": "515.4762", "70–74 years": "1481.194", "80–84 years": "4049.0195", "≥85 years": "8020.468", "All ages": "101.308586"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1987", "20–24 years": "6.09584", "30–34 years": "19.334703", "40–44 years": "61.611374", "50–54 years": "203.7037", "60–64 years": "566.6667", "70–74 years": "1499.7091", "80–84 years": "4398.855", "≥85 years": "8923.296", "All ages": "110.54187"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1988", "20–24 years": "6.5072303", "30–34 years": "16.456635", "40–44 years": "55.87583", "50–54 years": "214.46655", "60–64 years": "584.90564", "70–74 years": "1531.1476", "80–84 years": "4508.772", "≥85 years": "8732.5", "All ages": "118.52173"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1989", "20–24 years": "5.786026", "30–34 years": "19.264069", "40–44 years": "55.974842", "50–54 years": "206.16197", "60–64 years": "552.7273", "70–74 years": "1447.1204", "80–84 years": "4300.1685", "≥85 years": "8127.098", "All ages": "115.04659"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1990", "20–24 years": "8.083203", "30–34 years": "22.843359", "40–44 years": "71.30593", "50–54 years": "239.6809", "60–64 years": "626.1172", "70–74 years": "1732.8358", "80–84 years": "6037.77", "≥85 years": "12294.595", "All ages": "138.0488"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1992", "20–24 years": "7.1250067", "30–34 years": "22.854141", "40–44 years": "74.55084", "50–54 years": "232.6472", "60–64 years": "647.9436", "70–74 years": "1755.2996", "80–84 years": "6263.5024", "≥85 years": "11452.089", "All ages": "142.84108"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1993", "20–24 years": "8.66487", "30–34 years": "23.600681", "40–44 years": "75.10022", "50–54 years": "227.02374", "60–64 years": "651.1735", "70–74 years": "1619.6809", "80–84 years": "6160.9375", "≥85 years": "11140.845", "All ages": "140.97243"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1994", "20–24 years": "7.9460506", "30–34 years": "21.489012", "40–44 years": "70.42738", "50–54 years": "218.78165", "60–64 years": "639.2962", "70–74 years": "1648.9587", "80–84 years": "6342.772", "≥85 years": "11420.582", "All ages": "144.70816"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1996", "20–24 years": "7.3968997", "30–34 years": "20.284636", "40–44 years": "69.617584", "50–54 years": "200.44986", "60–64 years": "550.2293", "70–74 years": "1509.6997", "80–84 years": "4420.6553", "≥85 years": "9145.063", "All ages": "138.90149"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1997", "20–24 years": "8.18864", "30–34 years": "16.008093", "40–44 years": "72.22506", "50–54 years": "196.05595", "60–64 years": "513.85156", "70–74 years": "1466.3342", "80–84 years": "4157.4014", "≥85 years": "8325.404", "All ages": "133.20055"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1998", "20–24 years": "6.000123", "30–34 years": "16.943249", "40–44 years": "72.066025", "50–54 years": "200.31532", "60–64 years": "514.21014", "70–74 years": "1493.4182", "80–84 years": "3904.1545", "≥85 years": "7728.4897", "All ages": "134.35779"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1999", "20–24 years": "5.958809", "30–34 years": "16.900087", "40–44 years": "66.320885", "50–54 years": "192.80371", "60–64 years": "528.8511", "70–74 years": "1455.515", "80–84 years": "3917.7397", "≥85 years": "8017.1924", "All ages": "134.963"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2000", "20–24 years": "7.0894475", "30–34 years": "19.48305", "40–44 years": "70.631256", "50–54 years": "205.11397", "60–64 years": "499.96274", "70–74 years": "1417.238", "80–84 years": "3849.1816", "≥85 years": "7579.56", "All ages": "132.42976"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2001", "20–24 years": "5.6082373", "30–34 years": "18.49483", "40–44 years": "68.60707", "50–54 years": "198.18796", "60–64 years": "479.39835", "70–74 years": "1433.6329", "80–84 years": "3870.2864", "≥85 years": "7747.38", "All ages": "136.65242"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2002", "20–24 years": "5.5677485", "30–34 years": "15.694443", "40–44 years": "60.234936", "50–54 years": "198.1702", "60–64 years": "423.5814", "70–74 years": "1302.1378", "80–84 years": "3523.0557", "≥85 years": "7025.083", "All ages": "129.67917"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2003", "20–24 years": "5.704468", "30–34 years": "15.823024", "40–44 years": "59.212315", "50–54 years": "189.95372", "60–64 years": "455.14313", "70–74 years": "1363.4164", "80–84 years": "3892.5562", "≥85 years": "7563.701", "All ages": "139.06747"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2004", "20–24 years": "4.9512305", "30–34 years": "15.403986", "40–44 years": "53.345516", "50–54 years": "193.99553", "60–64 years": "425.16165", "70–74 years": "1264.3611", "80–84 years": "3636.4756", "≥85 years": "7317.4795", "All ages": "134.34068"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2005", "20–24 years": "5.8210797", "30–34 years": "15.905134", "40–44 years": "55.826218", "50–54 years": "180.08289", "60–64 years": "424.82233", "70–74 years": "1183.6187", "80–84 years": "3553.7888", "≥85 years": "7295.3286", "All ages": "134.11546"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2006", "20–24 years": "5.2363415", "30–34 years": "16.04663", "40–44 years": "54.3503", "50–54 years": "186.10023", "60–64 years": "413.58926", "70–74 years": "1205.5645", "80–84 years": "3559.9788", "≥85 years": "6958.705", "All ages": "136.945"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2007", "20–24 years": "4.7442036", "30–34 years": "17.01203", "40–44 years": "53.617577", "50–54 years": "182.01247", "60–64 years": "416.67426", "70–74 years": "1161.3606", "80–84 years": "3518.4058", "≥85 years": "6949.9023", "All ages": "138.50389"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2008", "20–24 years": "6.5419517", "30–34 years": "16.17883", "40–44 years": "52.68973", "50–54 years": "177.99622", "60–64 years": "430.72543", "70–74 years": "1165.6697", "80–84 years": "3657.9739", "≥85 years": "7165.3716", "All ages": "145.01825"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2009", "20–24 years": "5.403311", "30–34 years": "16.704264", "40–44 years": "55.469227", "50–54 years": "177.35522", "60–64 years": "418.28937", "70–74 years": "1107.7797", "80–84 years": "3591.4854", "≥85 years": "6707.7534", "All ages": "143.07726"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2010", "20–24 years": "5.878753", "30–34 years": "15.901978", "40–44 years": "49.740963", "50–54 years": "171.59529", "60–64 years": "449.61987", "70–74 years": "1133.0934", "80–84 years": "3740.4631", "≥85 years": "7421.133", "All ages": "149.19159"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2011", "20–24 years": "5.6382155", "30–34 years": "16.719057", "40–44 years": "52.94249", "50–54 years": "168.30397", "60–64 years": "452.65308", "70–74 years": "1100.6709", "80–84 years": "3767.577", "≥85 years": "7448.2188", "All ages": "153.96771"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2012", "20–24 years": "5.4205437", "30–34 years": "16.512062", "40–44 years": "51.038994", "50–54 years": "163.48811", "60–64 years": "452.51508", "70–74 years": "1059.8617", "80–84 years": "3550.2993", "≥85 years": "7325.327", "All ages": "152.6966"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2013", "20–24 years": "5.868463", "30–34 years": "14.986917", "40–44 years": "51.14136", "50–54 years": "161.89188", "60–64 years": "441.74884", "70–74 years": "1044.3975", "80–84 years": "3483.181", "≥85 years": "6981.15", "All ages": "151.79121"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2014", "20–24 years": "6.3869195", "30–34 years": "16.218218", "40–44 years": "54.58798", "50–54 years": "164.0188", "60–64 years": "466.49445", "70–74 years": "1086.1106", "80–84 years": "3745.1838", "≥85 years": "8129.9697", "All ages": "168.16248"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2015", "20–24 years": "5.534689", "30–34 years": "14.616706", "40–44 years": "51.069912", "50–54 years": "152.50854", "60–64 years": "444.71957", "70–74 years": "1061.2399", "80–84 years": "3601.6448", "≥85 years": "8210.552", "All ages": "168.0915"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2016", "20–24 years": "8.712392", "30–34 years": "17.472652", "40–44 years": "57.579876", "50–54 years": "162.8186", "60–64 years": "478.41547", "70–74 years": "1180.6783", "80–84 years": "3914.8074", "≥85 years": "8776.411", "All ages": "189.38232"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "death-rate-from-cardiovascular-diseases-age-group-who-mdb", "metadata_url": "https://ourworldindata.org/grapher/death-rate-from-cardiovascular-diseases-age-group-who-mdb.metadata.json", "chart_title": "Death rate from cardiovascular diseases by age group", "chart_subtitle": "The reported annual number of deaths from cardiovascular diseases per 100,000 people in each age group. This is based on the underlying cause listed on death certificates.", "chart_note": "", "chart_citation": "WHO Mortality Database (2025)", "original_chart_url": "https://ourworldindata.org/grapher/death-rate-from-cardiovascular-diseases-age-group-who-mdb", "owid_column_metadata": {"Deaths from cardiovascular diseases per 100,000 people in both sexes aged 20-24 years": {"titleShort": "20–24 years", "titleLong": "20–24 years", "descriptionShort": "Reported deaths from cardiovascular diseases per 100,000 people in both sexes aged 20-24 years.", "descriptionKey": ["The International Classification of Diseases (Version 10) codes that define cardiovascular diseases are I00-I99."], "unit": "deaths per 100,000 people", "timespan": "1950-2023", "type": "Numeric", "owidVariableId": 1088532, "shortName": "death_rate_per_100_000_population__sex_both_sexes__age_group_20_24_years__cause_cardiovascular_diseases__icd10_codes_i00_i99", "lastUpdated": "2025-04-17", "nextUpdate": "2026-07-22", "citationShort": "WHO Mortality Database (2025) – with minor processing by Our World in Data", "citationLong": "WHO Mortality Database (2025) – with minor processing by Our World in Data. “20–24 years – WHO” [dataset]. 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Reuse should follow the source and license information in this chart metadata."}, {"title": "Death rate from cardiovascular diseases", "source_url": "https://ourworldindata.org/grapher/cardiovascular-disease-death-rate-who-mdb.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages"], "row_count_total": 4904, "rows_head": [{"Entity": "Albania", "Code": "ALB", "Year": "1987", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "331.37842"}, {"Entity": "Albania", "Code": "ALB", "Year": "1988", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "333.6215"}, {"Entity": "Albania", "Code": "ALB", "Year": "1989", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "339.1301"}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "289.6585"}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "264.37045"}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "236.3284"}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "290.53366"}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "314.9575"}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "281.10385"}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "314.32132"}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "288.9499"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "349.8244"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "277.15262"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "298.93408"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "328.60852"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "312.56067"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "294.53677"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "281.12512"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "239.36775"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "279.01453"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "264.95486"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "184.45703"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1961", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "378.4812"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1962", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "381.81592"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1963", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "401.75586"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1964", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "357.03195"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1966", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "470.8961"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1969", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "318.32715"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1970", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "554.5336"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1971", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "565.9357"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1972", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "597.7632"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1973", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "579.50885"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1974", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "628.1369"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1975", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "587.698"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1976", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "617.3869"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1977", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "573.5276"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1978", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "408.92725"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1983", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "269.01956"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1985", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "337.97183"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1986", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "308.56305"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1987", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "334.39246"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1988", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "362.64005"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1989", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "264.45505"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1990", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "302.57874"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1991", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "265.8481"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1992", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "306.7622"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1993", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "298.78052"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1994", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "266.757"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1995", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "268.94742"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1998", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "282.2647"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1999", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "294.9562"}, {"Entity": "Antigua and 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"Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "255.59813"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2006", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "221.31737"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2007", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "199.60213"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2008", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "221.87457"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2009", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "157.05225"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2010", "Age-standardized deaths that are from 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people, in both sexes aged all ages": "181.09926"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2016", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "176.05775"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2017", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "192.16563"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2018", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "187.48418"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2019", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "216.84155"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2020", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": 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"Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "379.20407"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1988", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "353.71768"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1989", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "341.54858"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1990", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "347.03247"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1991", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "331.42654"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1992", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "336.09177"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1993", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "328.75882"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1994", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "295.6187"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1995", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "305.76575"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1996", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "287.45767"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1997", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "234.82921"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1998", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "235.62079"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1999", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "237.77393"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2000", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "216.99747"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2001", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "215.14868"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2002", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "212.10309"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2003", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "207.15826"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2004", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "194.51419"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2005", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "188.04074"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2006", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "182.73055"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2007", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "187.65993"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2008", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "174.23799"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2009", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "169.95134"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2010", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "179.86206"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2011", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "173.71593"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2012", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "168.57495"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2013", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "161.67715"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2014", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "155.36261"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2015", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "159.68933"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2016", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "165.11478"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2017", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "155.17032"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2018", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "150.7534"}], "rows_tail": [{"Entity": "Uruguay", "Code": "URY", "Year": "1999", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "210.35536"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2000", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "196.2997"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2001", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "195.16841"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2002", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "187.07153"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2003", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "195.43385"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2004", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "187.66515"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2005", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "180.0007"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2006", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "165.96814"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2007", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "173.63098"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2008", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "155.80576"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2009", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "146.86967"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2010", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "151.77582"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2012", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "141.84737"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2013", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "133.36638"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2014", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "127.17292"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2015", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "133.26936"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2016", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "132.5821"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2017", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "123.697075"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2018", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "120.95905"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2019", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "116.10632"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2020", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "106.46751"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2021", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "113.280594"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2022", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "107.78839"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1981", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "427.49948"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1982", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "444.64856"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1985", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "447.44305"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1986", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "434.04773"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1987", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "430.65952"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1988", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "457.0909"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1989", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "449.84366"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1990", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "452.51248"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1991", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "480.50836"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1992", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "505.5781"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1993", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "558.98517"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1994", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "595.4329"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1995", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "587.1021"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1996", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "581.9719"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1997", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "550.1595"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1998", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "587.0876"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1999", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "543.78925"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2000", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "578.819"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2001", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "562.49927"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2002", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "580.9394"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2003", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "560.19635"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2004", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "528.3416"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2005", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "568.2264"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2009", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "513.3439"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2010", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "529.81573"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2011", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "536.1273"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2012", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "528.57666"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2013", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "507.85742"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2014", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "506.98273"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2015", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "487.6102"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2016", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "484.2495"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2017", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "483.45926"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2018", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "456.01877"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2019", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "441.96405"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2020", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "667.87646"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2021", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "524.6609"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2022", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "564.03345"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2023", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "594.05206"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1955", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "220.51321"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1956", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "233.86784"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1957", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "241.36119"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1958", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "239.57635"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1959", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "244.75569"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1960", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "238.46555"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1961", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "232.88805"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1962", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "239.82538"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1963", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "245.61946"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1964", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "251.63261"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1965", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "250.58118"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1966", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "249.61572"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1967", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "263.10034"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1968", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "286.15848"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1969", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "285.36688"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1970", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "271.92053"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1971", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "265.73242"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1972", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "277.04257"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1973", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "272.77307"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1974", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "281.9253"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1975", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "291.65042"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1976", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "304.0038"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1977", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "294.18048"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1978", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "299.33224"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1979", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "281.30106"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1980", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "292.81915"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1981", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "281.18713"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1982", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "284.76086"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1983", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "317.93726"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1985", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "253.39572"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1986", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "237.62524"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1987", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "259.34503"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1988", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "262.46536"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1989", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "250.11455"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1990", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "316.0102"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1992", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "315.00735"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1993", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "304.55466"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1994", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "308.47415"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1996", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "264.6826"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1997", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "247.59302"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1998", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "243.20886"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1999", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "240.75316"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2000", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "231.7668"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2001", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "233.36794"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2002", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "216.02838"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2003", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "227.60887"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2004", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "215.55257"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2005", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "210.79869"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2006", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "209.23705"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2007", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "206.16945"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2008", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "210.36505"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2009", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "203.0272"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2010", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "208.44048"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2011", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "209.00166"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2012", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "201.83017"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2013", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "196.01802"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2014", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "212.63083"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2015", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "208.01509"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2016", "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages": "224.29936"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "cardiovascular-disease-death-rate-who-mdb", "metadata_url": "https://ourworldindata.org/grapher/cardiovascular-disease-death-rate-who-mdb.metadata.json", "chart_title": "Death rate from cardiovascular diseases", "chart_subtitle": "Reported annual death rate from cardiovascular diseases per 100,000 people, based on the underlying cause listed on death certificates.", "chart_note": "To allow for comparisons between countries and over time, this metric is age-standardized. All deaths in a country may not have been registered with a cause of death.", "chart_citation": "WHO Mortality Database (2025)", "original_chart_url": "https://ourworldindata.org/grapher/cardiovascular-disease-death-rate-who-mdb", "owid_column_metadata": {"Age-standardized deaths from cardiovascular diseases in both sexes in those aged all ages per 100,000 people": {"titleShort": "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages", "titleLong": "Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages", "descriptionShort": "Reported deaths from cardiovascular diseases in both sexes in those aged all ages per 100,000 people.", "descriptionKey": ["The International Classification of Diseases (Version 10) codes that define cardiovascular diseases are I00-I99."], "unit": "deaths per 100,000 people", "timespan": "1950-2023", "type": "Numeric", "owidVariableId": 1088376, "shortName": "age_standardized_death_rate_per_100_000_standard_population__sex_both_sexes__age_group_all_ages__cause_cardiovascular_diseases__icd10_codes_i00_i99", "lastUpdated": "2025-04-17", "nextUpdate": "2026-07-22", "citationShort": "WHO Mortality Database (2025) – with minor processing by Our World in Data", "citationLong": "WHO Mortality Database (2025) – with minor processing by Our World in Data. “Age-standardized deaths that are from cardiovascular diseases per 100,000 people, in both sexes aged all ages – WHO” [dataset]. WHO Mortality Database, “WHO Mortality Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1088376.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Number of deaths from cardiovascular diseases", "source_url": "https://ourworldindata.org/grapher/deaths-from-cardiovascular-disease-ghe.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Total deaths from cardiovascular diseases among both sexes"], "row_count_total": 4422, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Total deaths from cardiovascular diseases among both sexes": "42742.87"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Total deaths from cardiovascular diseases among both sexes": "43331.7"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Total deaths from cardiovascular diseases among both sexes": "45327.83"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Total deaths from cardiovascular diseases among both sexes": "48414.6"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Total deaths from cardiovascular diseases among both sexes": "49727.8"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Total deaths from cardiovascular diseases among both sexes": "50470.97"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Total deaths from cardiovascular diseases among both sexes": "51277.46"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Total deaths from cardiovascular diseases among both sexes": "51079.69"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Total deaths from cardiovascular diseases among both sexes": "50387.95"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Total deaths from cardiovascular diseases among both sexes": "50047.83"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Total deaths from cardiovascular diseases among both sexes": "50411.43"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Total deaths from cardiovascular diseases among both sexes": "52265.36"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Total deaths from cardiovascular diseases among both sexes": "53421.72"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Total deaths from cardiovascular diseases among both sexes": "54917.25"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Total deaths from cardiovascular diseases among both sexes": "55947.87"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Total deaths from cardiovascular diseases among both sexes": "56755.12"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Total deaths from cardiovascular diseases among both sexes": "58235.6"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Total deaths from cardiovascular diseases among both sexes": "60394.39"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Total deaths from cardiovascular diseases among both sexes": "61871.65"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Total deaths from cardiovascular diseases among both sexes": "64082.85"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Total deaths from cardiovascular diseases among both sexes": "68299.81"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Total deaths from cardiovascular diseases among both sexes": "64374.38"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Total deaths from cardiovascular diseases among both sexes": "1016362.4"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Total deaths from cardiovascular diseases among both sexes": "1047458.06"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Total deaths from cardiovascular diseases among both sexes": "1075286.1"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Total deaths from cardiovascular diseases among both sexes": "1105869.5"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Total deaths from cardiovascular diseases among both sexes": "1120118.6"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Total deaths from cardiovascular diseases among both sexes": "1128471.4"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Total deaths from cardiovascular diseases among both sexes": "1159391.8"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Total deaths from cardiovascular diseases among both sexes": "1186258.1"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Total deaths from cardiovascular diseases among both sexes": "1218620.5"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Total deaths from cardiovascular diseases among both sexes": "1249885.4"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Total deaths from cardiovascular diseases among both sexes": "1288649"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Total deaths from cardiovascular diseases among both sexes": "1311821.9"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Total deaths from cardiovascular diseases among both sexes": "1344600.8"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Total deaths from cardiovascular diseases among both sexes": "1359509.8"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Total deaths from cardiovascular diseases among both sexes": "1396874.1"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Total deaths from cardiovascular diseases among both sexes": "1459415.4"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Total deaths from cardiovascular diseases among both sexes": "1478670.2"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Total deaths from cardiovascular diseases among both sexes": "1505100.4"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Total deaths from cardiovascular diseases among both sexes": "1562862"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Total deaths from cardiovascular diseases among both sexes": "1605135.2"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Total deaths from cardiovascular diseases among both sexes": "1699255"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2021", "Total deaths from cardiovascular diseases among both sexes": "1701207.6"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Total deaths from cardiovascular diseases among both sexes": "11822.55"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Total deaths from cardiovascular diseases among both sexes": "8531.45"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Total deaths from cardiovascular diseases among both sexes": "9853.88"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Total deaths from cardiovascular diseases among both sexes": "11494.35"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Total deaths from cardiovascular diseases among both sexes": "11373.51"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Total deaths from cardiovascular diseases among both sexes": "12084.3"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Total deaths from cardiovascular diseases among both sexes": "12128.16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Total deaths from cardiovascular diseases among both sexes": "12109.98"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Total deaths from cardiovascular diseases among both sexes": "12039.12"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Total deaths from cardiovascular diseases among both sexes": "11842.11"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Total deaths from cardiovascular diseases among both sexes": "11511.12"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Total deaths from cardiovascular diseases among both sexes": "11424.77"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Total deaths from cardiovascular diseases among both sexes": "13678.31"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Total deaths from cardiovascular diseases among both sexes": "13549.32"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Total deaths from cardiovascular diseases among both sexes": "14067.32"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Total deaths from cardiovascular diseases among both sexes": "15743.61"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Total deaths from cardiovascular diseases among both sexes": "17237.65"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Total deaths from cardiovascular diseases among both sexes": "19024.13"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Total deaths from cardiovascular diseases among both sexes": "20948.68"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Total deaths from cardiovascular diseases among both sexes": "23092.83"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Total deaths from cardiovascular diseases among both sexes": "25199.39"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Total deaths from cardiovascular diseases among both sexes": "15050.28"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Total deaths from cardiovascular diseases among both sexes": "49867.24"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Total deaths from cardiovascular diseases among both sexes": "50813.58"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Total deaths from cardiovascular diseases among both sexes": "51497.81"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Total deaths from cardiovascular diseases among both sexes": "52487.16"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Total deaths from cardiovascular diseases among both sexes": "53215.38"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Total deaths from cardiovascular diseases among both sexes": "53775.5"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Total deaths from cardiovascular diseases among both sexes": "54658.43"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Total deaths from cardiovascular diseases among both sexes": "55567.14"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Total deaths from cardiovascular diseases among both sexes": "56802.48"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Total deaths from cardiovascular diseases among both sexes": "57839.64"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Total deaths from cardiovascular diseases among both sexes": "60070.93"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Total deaths from cardiovascular diseases among both sexes": "62082.51"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Total deaths from cardiovascular diseases among both sexes": "63041.99"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Total deaths from cardiovascular diseases among both sexes": "65220.43"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Total deaths from cardiovascular diseases among both sexes": "67449.7"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Total deaths from cardiovascular diseases among both sexes": "70170.86"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Total deaths from cardiovascular diseases among both sexes": "71861.01"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Total deaths from cardiovascular diseases among both sexes": "74531.18"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Total deaths from cardiovascular diseases among both sexes": "78723.67"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Total deaths from cardiovascular diseases among both sexes": "82336.94"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Total deaths from cardiovascular diseases among both sexes": "87924.52"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Total deaths from cardiovascular diseases among both sexes": "92984.32"}, {"Entity": "Andorra", "Code": "AND", "Year": "2000", "Total deaths from cardiovascular diseases among both sexes": "148.28"}, {"Entity": "Andorra", "Code": "AND", "Year": "2001", "Total deaths from cardiovascular diseases among both sexes": "148.64"}, {"Entity": "Andorra", "Code": "AND", "Year": "2002", "Total deaths from cardiovascular diseases among both sexes": "147.89"}, {"Entity": "Andorra", "Code": "AND", "Year": "2003", "Total deaths from cardiovascular diseases among both sexes": "150.11"}, {"Entity": "Andorra", "Code": "AND", "Year": "2004", "Total deaths from cardiovascular diseases among both sexes": "156.39"}, {"Entity": "Andorra", "Code": "AND", "Year": "2005", "Total deaths from cardiovascular diseases among both sexes": "157.27"}, {"Entity": "Andorra", "Code": "AND", "Year": "2006", "Total deaths from cardiovascular diseases among both sexes": "155.96"}, {"Entity": "Andorra", "Code": "AND", "Year": "2007", "Total deaths from cardiovascular diseases among both sexes": "145.7"}, {"Entity": "Andorra", "Code": "AND", "Year": "2008", "Total deaths from cardiovascular diseases among both sexes": "140.12"}, {"Entity": "Andorra", "Code": "AND", "Year": "2009", "Total deaths from cardiovascular diseases among both sexes": "131.5"}, {"Entity": "Andorra", "Code": "AND", "Year": "2010", "Total deaths from cardiovascular diseases among both sexes": "120.75"}, {"Entity": "Andorra", "Code": "AND", "Year": "2011", "Total deaths from cardiovascular diseases among both sexes": "114.85"}, {"Entity": "Andorra", "Code": "AND", "Year": "2012", "Total deaths from cardiovascular diseases among both sexes": "119.05"}, {"Entity": "Andorra", "Code": "AND", "Year": "2013", "Total deaths from cardiovascular diseases among both sexes": "126.61"}, {"Entity": "Andorra", "Code": "AND", "Year": "2014", "Total deaths from cardiovascular diseases among both sexes": "132.53"}, {"Entity": "Andorra", "Code": "AND", "Year": "2015", "Total deaths from cardiovascular diseases among both sexes": "138.17"}, {"Entity": "Andorra", "Code": "AND", "Year": "2016", "Total deaths from cardiovascular diseases among both sexes": "143.1"}, {"Entity": "Andorra", "Code": "AND", "Year": "2017", "Total deaths from cardiovascular diseases among both sexes": "144.33"}, {"Entity": "Andorra", "Code": "AND", "Year": "2018", "Total deaths from cardiovascular diseases among both sexes": "145.75"}, {"Entity": "Andorra", "Code": "AND", "Year": "2019", "Total deaths from cardiovascular diseases among both sexes": "145.44"}, {"Entity": "Andorra", "Code": "AND", "Year": "2020", "Total deaths from cardiovascular diseases among both sexes": "112.82"}, {"Entity": "Andorra", "Code": "AND", "Year": "2021", "Total deaths from cardiovascular diseases among both sexes": "123.94"}, {"Entity": "Angola", "Code": "AGO", "Year": "2000", "Total deaths from cardiovascular diseases among both sexes": "21290.77"}, {"Entity": "Angola", "Code": "AGO", "Year": "2001", "Total deaths from cardiovascular diseases among both sexes": "20941.67"}, {"Entity": "Angola", "Code": "AGO", "Year": "2002", "Total deaths from cardiovascular diseases among both sexes": "21582.97"}, {"Entity": "Angola", "Code": "AGO", "Year": "2003", "Total deaths from cardiovascular diseases among both sexes": "23093.62"}, {"Entity": "Angola", "Code": "AGO", "Year": "2004", "Total deaths from cardiovascular diseases among both sexes": "23737.79"}, {"Entity": "Angola", "Code": "AGO", "Year": "2005", "Total deaths from cardiovascular diseases among both sexes": "23343.5"}, {"Entity": "Angola", "Code": "AGO", "Year": "2006", "Total deaths from cardiovascular diseases among both sexes": "24496.07"}, {"Entity": "Angola", "Code": "AGO", "Year": "2007", "Total deaths from cardiovascular diseases among both sexes": "24076.61"}, {"Entity": "Angola", "Code": "AGO", "Year": "2008", "Total deaths from cardiovascular diseases among both sexes": "24253.95"}, {"Entity": "Angola", "Code": "AGO", "Year": "2009", "Total deaths from cardiovascular diseases among both sexes": "24419.42"}], "rows_tail": [{"Entity": "Venezuela", "Code": "VEN", "Year": "2012", "Total deaths from cardiovascular diseases among both sexes": "37488.87"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2013", "Total deaths from cardiovascular diseases among both sexes": "37364.59"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2014", "Total deaths from cardiovascular diseases among both sexes": "41560.57"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2015", "Total deaths from cardiovascular diseases among both sexes": "44018.54"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2016", "Total deaths from cardiovascular diseases among both sexes": "50861.3"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2017", "Total deaths from cardiovascular diseases among both sexes": "49522.18"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2018", "Total deaths from cardiovascular diseases among both sexes": "50307.72"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2019", "Total deaths from cardiovascular diseases among both sexes": "52119.52"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2020", "Total deaths from cardiovascular diseases among both sexes": "61929.13"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2021", "Total deaths from cardiovascular diseases among both sexes": "64649.36"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2000", "Total deaths from cardiovascular diseases among both sexes": "149847.86"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2001", "Total deaths from cardiovascular diseases among both sexes": "155845.64"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2002", "Total deaths from cardiovascular diseases among both sexes": "161559.73"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2003", "Total deaths from cardiovascular diseases among both sexes": "168134.61"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2004", "Total deaths from cardiovascular diseases among both sexes": "176698.05"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2005", "Total deaths from cardiovascular diseases among both sexes": "183965.73"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2006", "Total deaths from cardiovascular diseases among both sexes": "191849.31"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2007", "Total deaths from cardiovascular diseases among both sexes": "201284.39"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2008", "Total deaths from cardiovascular diseases among both sexes": "209832.42"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2009", "Total deaths from cardiovascular diseases among both sexes": "217247.02"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2010", "Total deaths from cardiovascular diseases among both sexes": "224386.77"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2011", "Total deaths from cardiovascular diseases among both sexes": "228825.55"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2012", "Total deaths from cardiovascular diseases among both sexes": "232865.23"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2013", "Total deaths from cardiovascular diseases among both sexes": "238245.4"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2014", "Total deaths from cardiovascular diseases among both sexes": "243558.4"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2015", "Total deaths from cardiovascular diseases among both sexes": "250283.7"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2016", "Total deaths from cardiovascular diseases among both sexes": "254779.05"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2017", "Total deaths from cardiovascular diseases among both sexes": "257766.62"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2018", "Total deaths from cardiovascular diseases among both sexes": "262692.06"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2019", "Total deaths from cardiovascular diseases among both sexes": "268629.9"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "Total deaths from cardiovascular diseases among both sexes": "251540.38"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2021", "Total deaths from cardiovascular diseases among both sexes": "275877.25"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Total deaths from cardiovascular diseases among both sexes": "14122606"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Total deaths from cardiovascular diseases among both sexes": "14343015"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Total deaths from cardiovascular diseases among both sexes": "14737166"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Total deaths from cardiovascular diseases among both sexes": "15206531"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Total deaths from cardiovascular diseases among both sexes": "15297544"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Total deaths from cardiovascular diseases among both sexes": "15565461"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Total deaths from cardiovascular diseases among both sexes": "15412245"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Total deaths from cardiovascular diseases among both sexes": "15568867"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Total deaths from cardiovascular diseases among both sexes": "15887055"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Total deaths from cardiovascular diseases among both sexes": "16065573"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Total deaths from cardiovascular diseases among both sexes": "16444195"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Total deaths from cardiovascular diseases among both sexes": "16523324"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Total deaths from cardiovascular diseases among both sexes": "16725561"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Total deaths from cardiovascular diseases among both sexes": "16954008"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Total deaths from cardiovascular diseases among both sexes": "17242772"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Total deaths from cardiovascular diseases among both sexes": "17541646"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Total deaths from cardiovascular diseases among both sexes": "17771084"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Total deaths from cardiovascular diseases among both sexes": "18043746"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Total deaths from cardiovascular diseases among both sexes": "18377766"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Total deaths from cardiovascular diseases among both sexes": "18638998"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Total deaths from cardiovascular diseases among both sexes": "18993384"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Total deaths from cardiovascular diseases among both sexes": "19211318"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Total deaths from cardiovascular diseases among both sexes": "34321.7"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Total deaths from cardiovascular diseases among both sexes": "34637.12"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Total deaths from cardiovascular diseases among both sexes": "34757.43"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Total deaths from cardiovascular diseases among both sexes": "34741.82"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Total deaths from cardiovascular diseases among both sexes": "35004.31"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Total deaths from cardiovascular diseases among both sexes": "35492.92"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Total deaths from cardiovascular diseases among both sexes": "35760.96"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Total deaths from cardiovascular diseases among both sexes": "36920.89"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Total deaths from cardiovascular diseases among both sexes": "37738.07"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Total deaths from cardiovascular diseases among both sexes": "38373.53"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Total deaths from cardiovascular diseases among both sexes": "38874.53"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Total deaths from cardiovascular diseases among both sexes": "39632.61"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Total deaths from cardiovascular diseases among both sexes": "40690.38"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Total deaths from cardiovascular diseases among both sexes": "41738.21"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Total deaths from cardiovascular diseases among both sexes": "42489.28"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Total deaths from cardiovascular diseases among both sexes": "43533.76"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Total deaths from cardiovascular diseases among both sexes": "45170.84"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Total deaths from cardiovascular diseases among both sexes": "46681.47"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Total deaths from cardiovascular diseases among both sexes": "48099.12"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Total deaths from cardiovascular diseases among both sexes": "49949.88"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Total deaths from cardiovascular diseases among both sexes": "52650.88"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Total deaths from cardiovascular diseases among both sexes": "51514.51"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Total deaths from cardiovascular diseases among both sexes": "7489.46"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Total deaths from cardiovascular diseases among both sexes": "7499.05"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Total deaths from cardiovascular diseases among both sexes": "7505.87"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Total deaths from cardiovascular diseases among both sexes": "7333.14"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Total deaths from cardiovascular diseases among both sexes": "7345.02"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Total deaths from cardiovascular diseases among both sexes": "7156.84"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Total deaths from cardiovascular diseases among both sexes": "7560.46"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Total deaths from cardiovascular diseases among both sexes": "7515.18"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Total deaths from cardiovascular diseases among both sexes": "7717.66"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Total deaths from cardiovascular diseases among both sexes": "7704.23"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Total deaths from cardiovascular diseases among both sexes": "7893.75"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Total deaths from cardiovascular diseases among both sexes": "8487.31"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Total deaths from cardiovascular diseases among both sexes": "8660.1"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Total deaths from cardiovascular diseases among both sexes": "8811.01"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Total deaths from cardiovascular diseases among both sexes": "9120.15"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Total deaths from cardiovascular diseases among both sexes": "9492.01"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Total deaths from cardiovascular diseases among both sexes": "9798.09"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Total deaths from cardiovascular diseases among both sexes": "9907.87"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Total deaths from cardiovascular diseases among both sexes": "10328.15"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Total deaths from cardiovascular diseases among both sexes": "11072.98"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Total deaths from cardiovascular diseases among both sexes": "12916.47"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Total deaths from cardiovascular diseases among both sexes": "14751.13"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Total deaths from cardiovascular diseases among both sexes": "11292.18"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Total deaths from cardiovascular diseases among both sexes": "11876.79"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Total deaths from cardiovascular diseases among both sexes": "12611.92"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Total deaths from cardiovascular diseases among both sexes": "13395.33"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Total deaths from cardiovascular diseases among both sexes": "14171.31"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Total deaths from cardiovascular diseases among both sexes": "14360.68"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Total deaths from cardiovascular diseases among both sexes": "14351.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Total deaths from cardiovascular diseases among both sexes": "14605.08"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Total deaths from cardiovascular diseases among both sexes": "15451.39"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Total deaths from cardiovascular diseases among both sexes": "15756.57"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Total deaths from cardiovascular diseases among both sexes": "18168.31"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Total deaths from cardiovascular diseases among both sexes": "18562.99"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Total deaths from cardiovascular diseases among both sexes": "18532.61"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Total deaths from cardiovascular diseases among both sexes": "19127.64"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Total deaths from cardiovascular diseases among both sexes": "19490.85"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Total deaths from cardiovascular diseases among both sexes": "20181.88"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Total deaths from cardiovascular diseases among both sexes": "20866.73"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Total deaths from cardiovascular diseases among both sexes": "21168.46"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Total deaths from cardiovascular diseases among both sexes": 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{"raw_link": "https://ourworldindata.org/ending-tuberculosis-save-millions", "title": "The world left its fight against tuberculosis unfinished — how can we complete the job?", "context": "Home\nTuberculosis\nThe world left its fight against tuberculosis unfinished — how can we complete the job?\nIf we get it right, the world could save more than 1.2 million lives every year.\nBy\nHannah Ritchie\nand\nFiona Spooner\nJuly 28, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nIn the United Kingdom, tuberculosis (TB) is a disease you only hear about in history class. It is taught alongside lessons on the large cholera epidemics in the 19th century, the Black Death, or the Spanish flu pandemic. Across the rich world, TB was once a huge killer, but it is a disease that is largely consigned to the past. Death rates are very low in high-income countries.\nHowever, TB is still very common in large parts of the world. It kills\n1.2 million people\nevery year, more than any other infectious disease.\n1\nSomeone in Lesotho, the Central African Republic, or Gabon is at least 3,000 times more likely to die from TB than someone in the United States or Denmark. You can see these huge differences in death rates in the chart below.\nThis inequality is unacceptable. We know what causes tuberculosis and how it spreads. We’ve known how to test and screen for TB for over a century. And we’ve had effective antibiotics to treat it for over 70 years. We could make fast progress against this disease at a relatively low cost. And there’s a lot at stake: as we explained in one of our\nprevious articles\n, closing the gap between countries like the United States and elsewhere would save over one million lives a year.\nIn this article, we look at why there are such large differences in TB death rates, and what can be done to consign tuberculosis to the history books,\neverywhere\n.\nWhy are death rates so high in some countries?\nLet’s start with the question of why someone in Lesotho is thousands of times more likely to die from tuberculosis than an American of the same age.\nThere are two elements to this: the risk of\ngetting\nan active tuberculosis infection, and then the risk of dying from it. The average person from Lesotho is at a much higher risk of both.\nThe left map below shows the huge differences in the rates of new TB cases. Someone in central Africa is hundreds of times more likely to be infected than in North America or Europe.\nBut after infection, someone with an active TB infection in a poorer country also has lower odds of surviving. Getting tested and diagnosed is typically slower (when people are diagnosed at all) because of weaker healthcare systems. That means the disease is treated late. And while TB can be cured with antibiotics, many people can’t afford the full course of treatment or struggle to complete it, since it can last more than a year.\nOn the right map, you can see the share of people who are diagnosed with TB and die from it (the case-fatality rate). In most rich countries, less than 10% do. In poorer countries, this can be more than three times higher.\nTB is caused by\nMycobacterium tuberculosis\n, a bacterium that spreads from person to person through water droplets. Having access to clean water, sanitation, and hygiene products greatly reduces people’s exposure. That is one reason why rates tend to be higher in lower-income countries.\nMany people have “latent tuberculosis”, which means they have been infected with the bacterium but it’s inactive in their system: they don’t have any symptoms, and they can’t spread the disease.\n2\nIn this case, the bacteria are effectively surrounded by granulomas — small clusters of immune cells — that stop them from multiplying and keep them suppressed. But when someone’s immune system weakens or is compromised, the bacteria can break out of these granulomas and multiply. That person then has “active tuberculosis”, which means they are contagious and develop TB symptoms.\nVarious\nrisk factors\nincrease the chance of developing an\nactive\nTB infection. Malnutrition is the biggest problem, and it’s much\nmore common\namong lower-income individuals. Having HIV/AIDS is another issue, and HIV is much\nmore prevalent\nacross Sub-Saharan Africa.\nIn the rest of this article, we’ll focus on what can be done regarding detection, medical prevention, and treatment. But it should go without saying that improving living standards overall — tackling malnutrition, and improving access to water and hygiene — would also make a big difference in reducing rates of tuberculosis. It would have many positive spillovers for preventing other diseases, too. It was these general improvements in living conditions that led to the\nmassive drop\nin tuberculosis deaths in Britain and the United States, even before effective treatments had arrived.\nAll newborns should have access to vaccination\nLet’s start with one of the earliest interventions that can protect a child from developing tuberculosis: a vaccine.\nAs early as 1921, decades before the development of antibiotic treatments, the scientists Albert Calmette and Camille Guérin had developed the “BCG vaccine”, which can protect infants and young children from severe tuberculosis.\n3\nOver the next fifty years, its usage grew slowly and was mostly consigned to rich countries. However, by the 1980s, international efforts led by the World Health Organization focused on making the vaccine accessible everywhere. The chart below shows the rapid uptake of the BCG vaccine across regions, particularly in the 1980s and 1990s. It’s now one of the most widely used vaccines in the world.\nBut many infants are still left behind: the global share of newborns vaccinated against TB has stagnated at around 88% for fifteen years.\nThe BCG vaccine is not effective for adults. In children, it typically reduces the risk of severe tuberculosis\nby around 70% to 80%\n, but it tends to be less effective in countries close to the equator, which is a problem because this is where children are at the highest risk. This is likely due to several factors, such as the commonality of different strains of the TB bacterium in different parts of the world, interactions with other bacteria in the environment, and differences in immune response.\n4\nDespite being less effective in some countries, the BCG vaccine still protects children and is a cheap way to do so. The average dose\ntypically costs\nless than $0.20.\n5\nOnce we factor in distribution and delivery costs, the costs in low-income countries are around $2.\n6\nThat’s still extremely cheap for a potentially life-saving vaccine, and as we’ll see later, far more affordable than an extensive course of treatment if someone has tuberculosis.\nEnsuring the vaccine is available to every child who would benefit is a first step towards beating the disease.\nTesting and prevention programs are the most cost-effective way to save lives\nThe chart below shows that\npreventing\nTB is much cheaper than treating it.\nPreventive treatment is given to people with an inactive tuberculosis infection to stop it from becoming active, or to those at high risk of being infected. These measures typically have an effectiveness of 60% to 90% in preventing an active infection. These treatments are not given to everyone, and are targeted at those living with people who have tuberculosis, or have compromised immune systems, due to factors like HIV/AIDS.\n7\nThese preventive treatments involve a\n3, 6, or 12-month course\nof antibiotics that must be taken daily (or for some drugs, weekly).\nThe symptoms, pain, and risk of severe TB are the main reasons to prevent someone from developing an active infection. But it also makes sense economically: treating active tuberculosis is more expensive, especially if it becomes drug-resistant. The chart shows that treating drug-resistant TB can cost many hundreds or over a thousand dollars.\nTo accurately target preventive interventions, people need to know whether they are at risk. A key part of that is screening and testing. If you’re living with people who have active tuberculosis, you can get treatments that dramatically reduce the risk of catching it. If you\ndon’t\nknow they have TB — or realize too late — then the opportunity to prevent it is gone.\nMass and community screening programs were vital tools that high-income countries — like the United States and many in Europe — used in their 20th-century battles against TB. These programs were often extremely effective in finding unknown cases so that patients could start their treatments, and others could be given preventive treatments early to stop the spread.\nAs one example, in 1957, the city of Glasgow in Scotland implemented the largest mass chest x-ray screening program to date, to identify hidden cases of TB.\n8\nBefore the program, TB infections had fallen at around 2.3% per year. After the intervention, the rate of decline more than doubled to over 5%.\nWe might not be able to roll out screening programs at this scale everywhere, but it shows how important testing is to reducing the spread of TB.\nAs you can see in the map below, the detection rate of cases — the estimated share of all cases that are diagnosed and treated — is below 60% in many low-income countries. This dramatically reduces their ability to stop the spread.\nTo be clear: the point here is not that we put all of our resources into cheap preventive treatments and forget about treating those who already have a TB infection. Both are crucial.\nThe cost of treating drug-resistant TB has fallen, but it is still very high\nMost people who develop an active infection can be treated and cured with first-line drugs. Their strain of\nMycobacterium tuberculosis\ncan be killed or controlled with standard antibiotic drugs that we’ve had for over 50 years.\n9\nThese treatment regimes can often take 6 to 12 months, but are effective. As we saw from the chart earlier, they cost between $30 and $150 per person. This is the reality for\nmore than nine out of ten\npeople with tuberculosis.\nBut standard drugs don't work well for around 700,000 patients worldwide. Their strain has developed a resistance to the antibiotics that are effective for others. This can come in two forms. Those with “\nmultidrug-resistant TB\n” do not respond to treatment from the two main first-line drugs: rifampicin and isoniazid. Those with “\nextensively-resistant TB\n” are also resistant to second-line drugs.\nTreating drug-resistant TB takes longer and is much more expensive. Rather than costing tens to a hundred dollars, it usually costs over a thousand. This is still prohibitively expensive for many people or governments.\nTo put the total costs in context, there\nare around\n450,000 new cases of drug-resistant TB a year. If each costs $1,200 on average to treat, that’s $540 million globally.\nBut as this next chart shows, even treatments against extensively drug-resistant TB have gotten much cheaper in the last five to ten years. In some countries, like India, Pakistan, or Zimbabwe, treating TB would have cost as much as $12,000 per person. Since then, costs have dropped to hundreds or a few thousand dollars.\nThis has happened for several reasons. First,\nalternative treatments\nhave been developed; they replace injections with oral tablets, making them cheaper and cutting treatment time down to six months.\nInternational health agencies, such as the WHO and the Stop TB Partnership, have also developed global procurement systems that have reduced costs for many countries.\nFinally, some of the patents for these TB drugs expired, meaning other generic manufacturers can now produce them at a lower, more competitive cost.\n10\nThese changes in patents — and the cheaper drugs that resulted — have made these life-saving treatments much more accessible to some of the poorest people in the world. They will have saved lives. But the cost of battling and treating TB is still a huge barrier for many people in the world. Without further progress, many are at risk of being left behind.\nExpensive treatments can cripple many families\nImagine you are a parent living in the Central African Republic, and your child has tuberculosis. The public health system is stretched, and inadequate funding for proper testing means they are diagnosed late and have developed a strain that is resistant to standard antibiotics. Without treatment, your child will die within six months.\nMulti-drug-resistant treatment will cost around $1,000 (in US dollars). In the Central African Republic, the average amount\nspent on healthcare\nper person per year is just $42, and\nhalf of that\ncomes from “out-of-pocket” sources — families paying for treatment themselves. As an average person, you live on around $500 a year, so all of your money would be needed to cover TB treatment for your child.\n11\nThis is a truly terrible position for a parent to be in.\nSome parents might even face a worse situation: not only does one of their kids have tuberculosis, but they have the disease too. That doesn’t just add hundreds to thousands of dollars in treatment costs, but also means they can’t work for at least six months. The major source of income is gone.\nDownload\nWe’ve picked some of the most extreme circumstances to illustrate this: the Central African Republic is one of the poorest countries in the world. But the reality is that millions of people across low- and middle-income countries face crippling costs of TB treatment every year.\n12\nResearchers looked at national survey data across 22 countries and found the average total cost of an episode of TB to be around $1,250.\n13\nWhile we often focus on the cost of the medicine and procedures, they cost “just” $200, on average. Around $500 came from the indirect costs of travel, food, and accommodation — remember, these courses of treatment can last well over a year. Finally, another $500 came from the indirect costs of lost income from being out of work.\nThree-quarters of the poorest households experienced “catastrophic” costs, defined as spending more than 20% of the household’s income on TB. Even among the richest families, one-quarter had to spend more than that.\nFor the small share who have drug-resistant tuberculosis, the costs are even more prohibitive. The result is that most of a household’s income is spent on medical treatment. Or even worse: they do not have enough money, and have to watch their family members die as a result.\nYou might say that this pressure shouldn’t be on\nindividuals\n. Governments should pay. However, the reality is that governments in many countries do not have the resources to cover these costs. In many countries, a\nlarge share\nof health spending is paid “out-of-pocket”. This is also true specifically for tuberculosis.\n14\nThat is why international funding is so crucial if we want to beat tuberculosis. External aid already plays an important role today. If we look at budgets across low-income countries that have high levels of TB, less than 20% of their funding comes from domestic sources.\n15\nThe remainder comes from the\nGlobal Fund\n(a program that provides funding to beat TB, HIV, and malaria) and other international donors. While money from richer donor countries makes up a large share of these budgets, the absolute sums are small. What is a tiny contribution from countries like the UK or the US is a life-saving intervention for someone in Ethiopia, Uganda, or Mozambique.\nDespite this support, there are still large gaps in the budget. Those gaps represent people with TB potentially dying from the disease because they do not have enough money to pay for treatment.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nTuberculosis is a disease that the world can beat\nHumans have been at war with tuberculosis for thousands of years.\n16\nFor most of that time, we didn’t know what it was, how it spread, and we had no way of treating it. It was one of our biggest killers for long periods.\nWhile no country has eradicated TB, many have made the disease extremely rare. There is no reason that the rest of the world cannot follow the same path.\nIn our\nfirst article\nin this series, we looked at TB deaths in some of the poorest countries compared to historical rates in Britain. England and Wales used to be in a far worse position than some of the worst-hit countries today.\nDriving these rates down quickly — especially with improved antibiotics and international support — is possible elsewhere.\nDeveloping an effective TB vaccine for adults is one way that we could drive disease rates even lower. Scientists\nare working\non that.\nAcknowledgments\nWe thank Max Roser, Saloni Dattani, Edouard Mathieu, and Simon van Teutem for valuable comments and feedback on this article.\nThis is the third article in our three-part series on tuberculosis:\nOnce a leading killer, tuberculosis is now rare in rich countries — here’s how it happened\nAs much as one quarter of deaths in Europe and the United States were once from tuberculosis.\nThe end of tuberculosis that wasn’t\nIn the 1980s, many thought tuberculosis was on the path to elimination. In reality, more were dying from the disease than ever.\nThe world left its fight against tuberculosis unfinished — how can we complete the job?\nIf we get it right, the world could save more than 1.2 million lives every year.\nEndnotes\nExcept for COVID-19 in the last few years.\nEsmail, H., Barry, C. E., Young, D. B., & Wilkinson, R. J. (2014). The ongoing challenge of latent tuberculosis. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1645), 20130437. https://doi.org/10.1098/rstb.2013.0437.\nFatima, S., Kumari, A., Das, G., & Dwivedi, V. P. (2020). Tuberculosis vaccine: A journey from BCG to present. Life sciences, 252, 117594.\nKuan, R., Muskat, K., Peters, B., & Lindestam Arlehamn, C. S. (2020). Is mapping the BCG vaccine‐induced immune responses the key to improving the efficacy against tuberculosis?. Journal of Internal Medicine, 288(6), 651-660.\nIn high-income countries, it can be up to $2 per dose.\nThysen, S. M., Byberg, S., Martins, J. S., Kallestrup, P., Griffiths, U. K., & Fisker, A. B. (2019). Household costs of seeking BCG vaccination in rural Guinea-Bissau. Vaccine, 37(37), 5505-5508.\nThe WHO provides updated guidelines on which sub-groups should receive preventive treatments. It stresses that the benefits of taking treatments must be weighed against the cost and potential side effects, which is why they are not given to everyone.\nWHO consolidated guidelines on tuberculosis. Module 1: prevention – tuberculosis preventive treatment, second edition. Geneva: World Health Organization; 2024. Licence: CC BY-NC-SA 3.0 IGO.\nMacPherson, P., Stagg, H. R., Schwalb, A., Henderson, H., Taylor, A. E., Burke, R. M., ... & Corbett, E. L. (2024). Impact of active case finding for tuberculosis with mass chest X-ray screening in Glasgow, Scotland, 1950–1963: An epidemiological analysis of historical data. PLoS medicine, 21(11), e1004448.\nThe main four antibiotics used in these treatments are Isoniazid, Rifampicin, Pyrazinamide, and Ethambutol.\nThe drug “Linezolid” was patented and produced by Pfizer until 2017, when its patent expired. Johnson & Johnson produced “Bedaquiline”, but its patent expired in 2023. It filed for a secondary patent through 2029, but under pressure from advocacy groups, it withdrew this patent in many low- and middle-income countries, meaning it can be produced more cheaply by other manufacturers.\nIf you want to learn more about this challenge to Johnson & Johnson’s patent strategy, John Green\nmade a video\nabout it. Shortly after this highly-watched video went online, a decision was made to withdraw these patents.\nThe average income per person in the Central African Republic was around\n$1004 in 2017 international dollars\n[2.27 * 365]. When converted to 2021 US$, this was roughly $500.\nYoungkong, S., Kamolwat, P., Wongrot, P., Thavorncharoensap, M., Chaikledkaew, U., Nateniyom, S., ... & Yamanaka, T. (2024). Catastrophic costs incurred by tuberculosis-affected households from Thailand’s first national tuberculosis patient cost survey. Scientific reports, 14(1), 11205.\nPanda, A., Behera, B. K., & Mishra, A. (2024). Financial hardship of tuberculosis patients registered under National Tuberculosis Elimination Programme (NTEP) in rural India: A longitudinal study. Indian Journal of Tuberculosis, 71, S229-S236.\nI’ve rounded the figures here to make them easier to follow.\nPortnoy, A., Yamanaka, T., Nguhiu, P., Nishikiori, N., Baena, I. G., Floyd, K., & Menzies, N. A. (2023). Costs incurred by people receiving tuberculosis treatment in low-income and middle-income countries: a meta-regression analysis. The Lancet Global Health, 11(10), e1640-e1647.\nSu, Y., Baena, I. G., Harle, A. C., Crosby, S. W., Micah, A. E., Siroka, A., ... & Dieleman, J. L. (2020). Tracking total spending on tuberculosis by source and function in 135 low-income and middle-income countries, 2000–17: a financial modelling study. The Lancet Infectious Diseases, 20(8), 929-942.\nThis data comes from the WHO’s\n2023 Tuberculosis Report\n.\nHershkovitz, I., Donoghue, H. D., Minnikin, D. E., Besra, G. S., Lee, O. Y., Gernaey, A. M., ... & Spigelman, M. (2008). Detection and molecular characterization of 9000-year-old Mycobacterium tuberculosis from a Neolithic settlement in the Eastern Mediterranean. PloS one, 3(10), e3426.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Fiona Spooner (2025) - “The world left its fight against tuberculosis unfinished — how can we complete the job?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-095641/ending-tuberculosis-save-millions.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-ending-tuberculosis-save-millions,\nauthor = {Hannah Ritchie and Fiona Spooner},\ntitle = {The world left its fight against tuberculosis unfinished — how can we complete the job?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-095641/ending-tuberculosis-save-millions.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "ending-tuberculosis-save-millions", "source_url": "https://ourworldindata.org/ending-tuberculosis-save-millions", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "If we get it right, the world could save more than 1.2 million lives every year.", "numeric_mentions": ["1.2 million", "28,", "2025", "19", "1", "3,000", "70 years", "10%", "2", "1921,", "3", "1980", "1990", "88%", "70%", "80%", "4", "0.20", "5", "6", "60%", "90%", "7", "3,", "6,", "12", "20", "1957,", "8", "2.3%", "5%", "50 years", "9", "12 months", "30", "150", "700,000", "450,000", "1,200", "540 million", "12,000", "10", "1,000", "42,", "500", "11", "22", "1,250", "13", "200,", "20%", "14", "15", "16", "2014", "369", "1645", "20130437", "10.1098", "2013.0437", "2020", "252,", "117594", "288", "651", "660", "2019", "37", "5505", "5508", "2024", "3.0", "1950", "1963", "21", "2017,", "2023", "2029,", "1004", "2017"], "numeric_evidence": [{"title": "Tuberculosis deaths", "source_url": "https://ourworldindata.org/grapher/tuberculosis-deaths-who.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Estimated number of deaths from all forms of tuberculosis"], "row_count_total": 5597, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Estimated number of deaths from all forms of tuberculosis": "10000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Estimated number of deaths from all forms of tuberculosis": "11000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Estimated number of deaths from all forms of tuberculosis": "13000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Estimated number of deaths from all forms of tuberculosis": "15000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Estimated number of deaths from all forms of tuberculosis": "16000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Estimated number of deaths from all forms of tuberculosis": "16000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Estimated number of deaths from all forms of tuberculosis": "16000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Estimated number of deaths from all forms of tuberculosis": "15000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Estimated number of deaths from all forms of tuberculosis": "15000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Estimated number of deaths from all forms of tuberculosis": "17000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Estimated number of deaths from all forms of tuberculosis": "15000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Estimated number of deaths from all forms of tuberculosis": "15000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Estimated number of deaths from all forms of tuberculosis": "14000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Estimated number of deaths from all forms of tuberculosis": "14000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Estimated number of deaths from all forms of tuberculosis": "15000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Estimated number of deaths from all forms of tuberculosis": "15000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Estimated number of deaths from all forms of tuberculosis": "14000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Estimated number of deaths from all forms of tuberculosis": "13000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Estimated number of deaths from all forms of tuberculosis": "14000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Estimated number of deaths from all forms of tuberculosis": "14000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Estimated number of deaths from all forms of tuberculosis": "14000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Estimated number of 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"Year": "2004", "Estimated number of deaths from all forms of tuberculosis": "1011810"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Estimated number of deaths from all forms of tuberculosis": "1050260"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Estimated number of deaths from all forms of tuberculosis": "1059265"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Estimated number of deaths from all forms of tuberculosis": "1038543"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Estimated number of deaths from all forms of tuberculosis": "1012630"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Estimated number of deaths from all forms of tuberculosis": "1002838"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Estimated number of deaths from all forms of tuberculosis": "913607"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Estimated number of deaths from all forms of tuberculosis": "913630"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Estimated number of deaths from all forms of tuberculosis": "872799"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Estimated number of deaths from all forms of tuberculosis": "838384"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Estimated number of deaths from all forms of tuberculosis": "830157"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Estimated number of deaths from all forms of tuberculosis": "802774"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Estimated number of deaths from all forms of tuberculosis": "768366"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Estimated number of deaths from all forms of tuberculosis": "726079"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Estimated number of deaths from all forms of tuberculosis": "678028"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Estimated number of deaths from all forms of tuberculosis": "644730"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Estimated number of deaths from all forms of tuberculosis": "630281"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2021", "Estimated number of deaths from all forms of tuberculosis": "576964"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2022", "Estimated number of deaths from all forms of tuberculosis": "492917"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2023", "Estimated number of deaths from all forms of tuberculosis": "456134"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2024", "Estimated number of deaths from all forms of tuberculosis": "436775"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Estimated number of deaths from all forms of tuberculosis": "23"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Estimated number of deaths from all forms of tuberculosis": "21"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Estimated number of deaths from all forms of tuberculosis": "20"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Estimated number of deaths from all forms of tuberculosis": "19"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Estimated number of deaths from all forms of tuberculosis": "26"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Estimated number of deaths from all forms of tuberculosis": "12"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Estimated number of deaths from all forms of tuberculosis": "11"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Estimated number of deaths from all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Estimated number of deaths from all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Estimated number of deaths from all forms of tuberculosis": "5"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Estimated number of deaths from all forms of tuberculosis": "9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Estimated number of deaths from all forms of tuberculosis": "9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Estimated number of deaths from all forms of tuberculosis": "9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Estimated number of deaths from all forms of tuberculosis": "9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Estimated number of deaths from all forms of tuberculosis": "9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Estimated number of deaths from all forms of tuberculosis": "9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Estimated number of deaths from all forms of tuberculosis": "9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Estimated number of deaths from all forms of tuberculosis": "9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Estimated number of deaths from all forms of tuberculosis": "9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Estimated number of deaths from all forms of tuberculosis": "10"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Estimated number of deaths from all forms of tuberculosis": "10"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Estimated number of deaths from all forms of tuberculosis": "10"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "Estimated number of deaths from all forms of tuberculosis": "11"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Estimated number of deaths from all forms of tuberculosis": "10"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "Estimated number of deaths from all forms of tuberculosis": "9"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Estimated number of deaths from all forms of tuberculosis": "8200"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Estimated number of deaths from all forms of tuberculosis": "7700"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Estimated number of deaths from all forms of tuberculosis": "7700"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Estimated number of deaths from all forms of tuberculosis": "7600"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Estimated number of deaths from all forms of tuberculosis": "7400"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Estimated number of deaths from all forms of tuberculosis": "7500"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Estimated number of deaths from all forms of tuberculosis": "7000"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Estimated number of deaths from all forms of tuberculosis": "6700"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Estimated number of deaths from all forms of tuberculosis": "6100"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Estimated number of deaths from all forms of tuberculosis": "6000"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Estimated number of deaths from all forms of tuberculosis": "5800"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Estimated number of deaths from all forms of tuberculosis": "5200"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Estimated number of deaths from all forms of tuberculosis": "5000"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Estimated number of deaths from all forms of tuberculosis": "4400"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Estimated number of deaths from all forms of tuberculosis": "4500"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Estimated number of deaths from all forms of tuberculosis": "4400"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Estimated number of deaths from all forms of tuberculosis": "4200"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Estimated number of deaths from all forms of tuberculosis": "4200"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Estimated number of deaths from all forms of tuberculosis": "4200"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Estimated number of deaths from all forms of tuberculosis": "3700"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Estimated number of deaths from all forms of tuberculosis": "3100"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Estimated number of deaths from all forms of tuberculosis": "3400"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2022", "Estimated number of deaths from all forms of tuberculosis": "3400"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "Estimated number of deaths from all forms of tuberculosis": "3400"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2024", "Estimated number of deaths from all forms of tuberculosis": "3300"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2000", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2001", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2002", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2003", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2004", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2005", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2006", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2007", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2008", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2009", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2010", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2011", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2012", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2013", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2014", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2015", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2016", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2017", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2018", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2019", "Estimated number of deaths from all forms of tuberculosis": "0"}], "rows_tail": [{"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2005", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2006", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2007", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2008", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2009", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2010", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2011", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2012", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2013", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2014", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2015", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2016", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2017", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2018", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2019", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2020", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2021", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2022", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2023", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2024", "Estimated number of deaths from all forms of tuberculosis": "0"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Estimated number of deaths from all forms of tuberculosis": "2942711"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Estimated number of deaths from all forms of tuberculosis": "2839459"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Estimated number of deaths from all forms of tuberculosis": "2727612"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Estimated number of deaths from all forms of tuberculosis": "2643104"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Estimated number of deaths from all forms of tuberculosis": "2528146"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Estimated number of deaths from all forms of tuberculosis": "2520963"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Estimated number of deaths from all forms of tuberculosis": "2470895"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Estimated number of deaths from all forms of tuberculosis": "2384146"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Estimated number of deaths from all forms of tuberculosis": "2330601"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Estimated number of deaths from all forms of tuberculosis": "2260114"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Estimated number of deaths from all forms of tuberculosis": "2108135"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Estimated number of deaths from all forms of tuberculosis": "2064326"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Estimated number of deaths from all forms of tuberculosis": "1967047"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Estimated number of deaths from all forms of tuberculosis": "1880447"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Estimated number of deaths from all forms of tuberculosis": "1808203"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Estimated number of deaths from all forms of tuberculosis": "1731706"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Estimated number of deaths from all forms of tuberculosis": "1649151"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Estimated number of deaths from all forms of tuberculosis": "1566162"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Estimated number of deaths from all forms of tuberculosis": "1478186"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Estimated number of deaths from all forms of tuberculosis": "1397183"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Estimated number of deaths from all forms of tuberculosis": "1389311"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Estimated number of deaths from all forms of tuberculosis": "1402435"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Estimated number of deaths from all forms of tuberculosis": "1327167"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Estimated number of deaths from all forms of tuberculosis": "1241137"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Estimated number of deaths from all forms of tuberculosis": "1198800"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Estimated number of deaths from all forms of tuberculosis": "3700"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Estimated number of deaths from all forms of tuberculosis": "3700"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Estimated number of deaths from all forms of tuberculosis": "3900"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Estimated number of deaths from all forms of tuberculosis": "4000"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Estimated number of deaths from all forms of tuberculosis": "3800"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Estimated number of deaths from all forms of tuberculosis": "3700"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Estimated number of deaths from all forms of tuberculosis": "3500"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Estimated number of deaths from all forms of tuberculosis": "3100"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Estimated number of deaths from all forms of tuberculosis": "2700"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Estimated number of deaths from all forms of tuberculosis": "2200"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Estimated number of deaths from all forms of tuberculosis": "1900"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Estimated number of deaths from all forms of tuberculosis": "1900"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Estimated number of deaths from all forms of tuberculosis": "1300"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Estimated number of deaths from all forms of tuberculosis": "1200"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Estimated number of deaths from all forms of tuberculosis": "1700"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Estimated number of deaths from all forms of tuberculosis": "2800"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Estimated number of deaths from all forms of tuberculosis": "2200"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Estimated number of deaths from all forms of tuberculosis": "2200"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Estimated number of deaths from all forms of tuberculosis": "2400"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Estimated number of deaths from all forms of tuberculosis": "2300"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Estimated number of deaths from all forms of tuberculosis": "3900"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Estimated number of deaths from all forms of tuberculosis": "3900"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2022", "Estimated number of deaths from all forms of tuberculosis": "3700"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2023", "Estimated number of deaths from all forms of tuberculosis": "3700"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2024", "Estimated number of deaths from all forms of tuberculosis": "2200"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Estimated number of deaths from all forms of tuberculosis": "24000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Estimated number of deaths from all forms of tuberculosis": "25000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Estimated number of deaths from all forms of tuberculosis": "18000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Estimated number of deaths from all forms of tuberculosis": "15000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Estimated number of deaths from all forms of tuberculosis": "16000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Estimated number of deaths from all forms of tuberculosis": "18000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Estimated number of deaths from all forms of tuberculosis": "19000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Estimated number of deaths from all forms of tuberculosis": "20000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Estimated number of deaths from all forms of tuberculosis": "20000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Estimated number of deaths from all forms of tuberculosis": "19000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Estimated number of deaths from all forms of tuberculosis": "19000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Estimated number of deaths from all forms of tuberculosis": "19000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Estimated number of deaths from all forms of tuberculosis": "20000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Estimated number of deaths from all forms of tuberculosis": "20000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Estimated number of deaths from all forms of tuberculosis": "19000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Estimated number of deaths from all forms of tuberculosis": "20000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Estimated number of deaths from all forms of tuberculosis": "18000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Estimated number of deaths from all forms of tuberculosis": "19000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Estimated number of deaths from all forms of tuberculosis": "19000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Estimated number of deaths from all forms of tuberculosis": "17000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Estimated number of deaths from all forms of tuberculosis": "14000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Estimated number of deaths from all forms of tuberculosis": "8000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Estimated number of deaths from all forms of tuberculosis": "5500"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Estimated number of deaths from all forms of tuberculosis": "5000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Estimated number of deaths from all forms of tuberculosis": "8200"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Estimated number of deaths from all forms of tuberculosis": "19000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Estimated number of deaths from all forms of tuberculosis": "17000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Estimated number of deaths from all forms of tuberculosis": "15000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Estimated number of deaths from all forms of tuberculosis": "17000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Estimated number of deaths from all forms of tuberculosis": "17000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Estimated number of deaths from all forms of tuberculosis": "20000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Estimated number of deaths from all forms of tuberculosis": "22000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Estimated number of deaths from all forms of tuberculosis": "22000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Estimated number of deaths from all forms of tuberculosis": "21000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Estimated number of deaths from all forms of tuberculosis": "15000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Estimated number of deaths from all forms of tuberculosis": "11000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Estimated number of deaths from all forms of tuberculosis": "12000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Estimated number of deaths from all forms of tuberculosis": "12000"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Estimated number of deaths from all forms of tuberculosis": "8800"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Estimated number of deaths from all forms of tuberculosis": "8500"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Estimated number of deaths from all forms of tuberculosis": "7100"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Estimated number of deaths from all forms of tuberculosis": "6500"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Estimated number of deaths from all forms of tuberculosis": "6100"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Estimated number of deaths from all forms of tuberculosis": "5600"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Estimated number of deaths from all forms of tuberculosis": "7200"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Estimated number of deaths from all forms of tuberculosis": "7600"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", 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Reuse should follow the source and license information in this chart metadata."}, {"title": "Tuberculosis death rate", "source_url": "https://ourworldindata.org/grapher/tuberculosis-death-rate-age-standardized.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Age-standardized death rate from tuberculosis among both sexes"], "row_count_total": 4422, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Age-standardized death rate from tuberculosis among both sexes": "134.88753"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Age-standardized death rate from tuberculosis among both sexes": "123.845276"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Age-standardized death rate from tuberculosis among both sexes": "114.704575"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Age-standardized death rate from tuberculosis among both sexes": "115.15049"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Age-standardized death rate from tuberculosis among both sexes": "101.41524"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Age-standardized death rate from tuberculosis among both sexes": "94.16339"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Age-standardized death rate from tuberculosis among both sexes": "86.06287"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Age-standardized death rate from tuberculosis among both sexes": "77.412964"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Age-standardized death rate from tuberculosis among both sexes": "81.37906"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Age-standardized death rate from tuberculosis among both sexes": "90.997826"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Age-standardized death rate from tuberculosis among both sexes": "87.7192"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Age-standardized death rate from tuberculosis 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"Afghanistan", "Code": "AFG", "Year": "2019", "Age-standardized death rate from tuberculosis among both sexes": "53.15034"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Age-standardized death rate from tuberculosis among both sexes": "52.734604"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Age-standardized death rate from tuberculosis among both sexes": "51.99995"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Age-standardized death rate from tuberculosis among both sexes": "168.54243"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Age-standardized death rate from tuberculosis among both sexes": "165.11057"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Age-standardized death rate from tuberculosis among both sexes": "157.61801"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Age-standardized death rate from tuberculosis among both sexes": "157.6001"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Age-standardized death rate from tuberculosis among both sexes": "153.13455"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Age-standardized death rate from tuberculosis among both sexes": "154.2705"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Age-standardized death rate from tuberculosis among both sexes": "150.32431"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Age-standardized death rate from tuberculosis among both sexes": "145.26277"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Age-standardized death rate from tuberculosis among both sexes": "139.37456"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Age-standardized death rate from tuberculosis among both sexes": "134.51001"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Age-standardized death rate from tuberculosis among both sexes": "120.67522"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Age-standardized death rate from tuberculosis among both sexes": "116.974945"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Age-standardized death rate from tuberculosis among both sexes": "111.24114"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Age-standardized death rate from tuberculosis among both sexes": "104.572815"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Age-standardized death rate from tuberculosis among both sexes": "101.01129"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Age-standardized death rate from tuberculosis among both sexes": "95.00688"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Age-standardized death rate from tuberculosis among both sexes": "89.70099"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Age-standardized death rate from tuberculosis among both sexes": "83.12532"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Age-standardized death rate from tuberculosis among both sexes": "75.482864"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Age-standardized death rate from tuberculosis among both sexes": "69.90261"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Age-standardized death rate from tuberculosis among both sexes": "67.98886"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2021", "Age-standardized death rate from tuberculosis among both sexes": "59.433792"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Age-standardized death rate from tuberculosis among both sexes": "0.76379913"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Age-standardized death rate from tuberculosis among both sexes": "0.6606519"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Age-standardized death rate from tuberculosis among both sexes": "0.636089"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Age-standardized death rate from tuberculosis among both sexes": "0.6003252"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Age-standardized death rate from tuberculosis among both sexes": "0.81246597"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Age-standardized death rate from tuberculosis among both sexes": "0.3834587"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Age-standardized death rate from tuberculosis among both sexes": "0.34178913"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Age-standardized death rate from tuberculosis among both sexes": "0.49372798"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Age-standardized death rate from tuberculosis among both sexes": "0.4939978"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Age-standardized death rate from tuberculosis among both sexes": "0.18289438"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Age-standardized death rate from tuberculosis among both sexes": "0.29629612"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Age-standardized death rate from tuberculosis among both sexes": "0.29532635"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Age-standardized death rate from tuberculosis among both sexes": "0.2791843"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Age-standardized death rate from tuberculosis among both sexes": "0.2878654"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Age-standardized death rate from tuberculosis among both sexes": "0.2746091"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Age-standardized death rate from tuberculosis among both sexes": "0.2625561"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Age-standardized death rate from tuberculosis among both sexes": "0.25607443"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Age-standardized death rate from tuberculosis among both sexes": "0.2613615"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Age-standardized death rate from tuberculosis among both sexes": "0.24235001"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Age-standardized death rate from tuberculosis among both sexes": "0.22600892"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Age-standardized death rate from tuberculosis among both sexes": "0.21098804"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Age-standardized death rate from tuberculosis among both sexes": "0.22143587"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Age-standardized death rate from tuberculosis among both sexes": "13.190009"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Age-standardized death rate from tuberculosis among both sexes": "12.66393"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Age-standardized death rate from tuberculosis among both sexes": "12.847421"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Age-standardized death rate from tuberculosis among both sexes": "13.086613"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Age-standardized death rate from tuberculosis among both sexes": "12.888542"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Age-standardized death rate from tuberculosis among both sexes": "13.47566"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Age-standardized death rate from tuberculosis among both sexes": "13.019011"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Age-standardized death rate from tuberculosis among both sexes": "12.826071"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Age-standardized death rate from tuberculosis among both sexes": "12.021109"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Age-standardized death rate from tuberculosis among both sexes": "12.343269"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Age-standardized death rate from tuberculosis among both sexes": "12.344729"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Age-standardized death rate from tuberculosis among both sexes": "11.497146"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Age-standardized death rate from tuberculosis among both sexes": "11.406232"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Age-standardized death rate from tuberculosis among both sexes": "10.423429"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Age-standardized death rate from tuberculosis among both sexes": "10.967255"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Age-standardized death rate from tuberculosis among both sexes": "11.197344"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Age-standardized death rate from tuberculosis among both sexes": "10.419439"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Age-standardized death rate from tuberculosis among both sexes": "10.224158"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Age-standardized death rate from tuberculosis among both sexes": "10.075777"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Age-standardized death rate from tuberculosis among both sexes": "8.674639"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Age-standardized death rate from tuberculosis among both sexes": "12.358189"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Age-standardized death rate from tuberculosis among both sexes": "7.8516245"}, {"Entity": "Andorra", "Code": "AND", "Year": "2000", "Age-standardized death rate from tuberculosis among both sexes": "1.1358612"}, {"Entity": "Andorra", "Code": "AND", "Year": "2001", "Age-standardized death rate from tuberculosis among both sexes": "0.9116177"}, {"Entity": "Andorra", "Code": "AND", "Year": "2002", "Age-standardized death rate from tuberculosis among both sexes": "0.44414034"}, {"Entity": "Andorra", "Code": "AND", "Year": "2003", "Age-standardized death rate from tuberculosis among both sexes": "0.91425425"}, {"Entity": "Andorra", "Code": "AND", "Year": "2004", "Age-standardized death rate from tuberculosis among both sexes": "0.555567"}, {"Entity": "Andorra", "Code": "AND", "Year": "2005", "Age-standardized death rate from tuberculosis among both sexes": "0.7567627"}, {"Entity": "Andorra", "Code": "AND", "Year": "2006", "Age-standardized death rate from tuberculosis among both sexes": "0.8604801"}, {"Entity": "Andorra", "Code": "AND", "Year": "2007", "Age-standardized death rate from tuberculosis among both sexes": "0.3294411"}, {"Entity": "Andorra", "Code": "AND", "Year": "2008", "Age-standardized death rate from tuberculosis among both sexes": "0.24102883"}, {"Entity": "Andorra", "Code": "AND", "Year": "2009", "Age-standardized death rate from tuberculosis among both sexes": "0.468549"}, {"Entity": "Andorra", "Code": "AND", "Year": "2010", "Age-standardized death rate from tuberculosis among both sexes": "0.40638575"}, {"Entity": "Andorra", "Code": "AND", "Year": "2011", "Age-standardized death rate from tuberculosis among both sexes": "0.1812092"}, {"Entity": "Andorra", "Code": "AND", "Year": "2012", "Age-standardized death rate from tuberculosis among both sexes": "0.55069315"}, {"Entity": "Andorra", "Code": "AND", "Year": "2013", "Age-standardized death rate from tuberculosis among both sexes": "0.32013902"}, {"Entity": "Andorra", "Code": "AND", "Year": "2014", "Age-standardized death rate from tuberculosis among both sexes": "0.40598765"}, {"Entity": "Andorra", "Code": "AND", "Year": "2015", "Age-standardized death rate from tuberculosis among both sexes": "0.27393168"}, {"Entity": "Andorra", "Code": "AND", "Year": "2016", "Age-standardized death rate from tuberculosis among both sexes": "0.27342469"}, {"Entity": "Andorra", "Code": "AND", "Year": "2017", "Age-standardized death rate from tuberculosis among both sexes": "0.06733304"}, {"Entity": "Andorra", "Code": "AND", "Year": "2018", "Age-standardized death rate from tuberculosis among both sexes": "0.12012042"}, {"Entity": "Andorra", "Code": "AND", "Year": "2019", "Age-standardized death rate from tuberculosis among both sexes": "0.32626554"}, {"Entity": "Andorra", "Code": 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"36.999466"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2008", "Age-standardized death rate from tuberculosis among both sexes": "33.26322"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2009", "Age-standardized death rate from tuberculosis among both sexes": "32.000473"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2010", "Age-standardized death rate from tuberculosis among both sexes": "29.182379"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2011", "Age-standardized death rate from tuberculosis among both sexes": "26.680838"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2012", "Age-standardized death rate from tuberculosis among both sexes": "26.40761"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2013", "Age-standardized death rate from tuberculosis among both sexes": "24.952738"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2014", "Age-standardized death rate from tuberculosis among both sexes": "22.439468"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2015", 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{"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Age-standardized death rate from tuberculosis among both sexes": "241.41335"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Age-standardized death rate from tuberculosis among both sexes": "239.96634"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Age-standardized death rate from tuberculosis among both sexes": "199.63182"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Age-standardized death rate from tuberculosis among both sexes": "167.09424"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Age-standardized death rate from tuberculosis among both sexes": "86.18448"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Age-standardized death rate from tuberculosis among both sexes": "211.79341"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Age-standardized death rate from tuberculosis among both sexes": "190.5275"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Age-standardized 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{"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Age-standardized death rate from tuberculosis among both sexes": "130.77951"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Age-standardized death rate from tuberculosis among both sexes": "140.72028"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Age-standardized death rate from tuberculosis among both sexes": "134.63182"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Age-standardized death rate from tuberculosis among both sexes": "95.80694"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Age-standardized death rate from tuberculosis among both sexes": "93.44939"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Age-standardized death rate from tuberculosis among both sexes": "76.260605"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Age-standardized death rate from tuberculosis among both sexes": "67.90076"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", 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"Estimated incidence of all forms of tuberculosis": "206"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "204"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "203"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "307.7052"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "312.2114"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "317.5772"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "324.17758"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "326.75827"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "326.7312"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "324.492"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "319.32596"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "309.5995"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "303.956"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "295.28622"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "287.67355"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "278.2931"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "267.15274"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "258.88785"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "247.69968"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "232.53922"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "223.07628"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "214.67708"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "206.3748"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "198.72528"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "194.18031"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "190.03506"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "185.18423"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "180.91634"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "21"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "20"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "21"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "20"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "20"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "19"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "17"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "17"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "17"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "18"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "20"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "17"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "15"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "15"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "15"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "15"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "15"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "118"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "111"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "112"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "112"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "109"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "113"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "107"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "104"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "95"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "96"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "95"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "87"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "85"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "77"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "80"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "81"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "76"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "75"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "74"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "65"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "52"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "57"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "55"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "55"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "54"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "5.3"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "5.3"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "3.5"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "5.3"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "8.8"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "11"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "7.1"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "5.3"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "5.4"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "7.2"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "7.2"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "5.5"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "6.2"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "6.1"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "5.9"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "7.6"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "5.6"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "9.7"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "2"}], "rows_tail": [{"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "48"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "42"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "14"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "40"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "67"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "36"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "15"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "18"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "1.3"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "8.6"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "1.4"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "1"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "0.68"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "0"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "187.37431"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "186.89374"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "186.47076"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "186.29141"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "184.6343"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "182.96432"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "180.31314"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "176.89838"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "173.4296"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "170.39687"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "166.18083"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "163.26082"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "159.16869"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "154.6183"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "151.21239"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "147.45613"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "142.34831"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "138.7778"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "135.2258"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "131.95148"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "128.99347"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "129.7268"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "132.14767"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "131.66058"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "129.30185"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "94"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "87"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "76"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "66"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "61"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "54"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "49"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "47"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "45"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "45"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "45"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "42"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "47"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "48"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "43"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "33"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "40"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "40"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "39"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "39"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "33"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "34"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "36"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "38"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "40"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "759"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "728"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "695"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "662"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "631"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "602"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "577"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "554"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "534"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "514"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "495"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "475"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "456"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "437"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "406"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "391"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "376"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "361"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "346"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "333"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "319"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "307"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "295"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "283"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "272"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "605"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "617"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "617"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "617"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "607"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "588"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "561"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "527"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "487"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "450"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "416"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "384"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "355"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "304"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "278"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "242"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "233"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "221"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "210"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "199"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "189"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Estimated incidence of all forms of tuberculosis": 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{"raw_link": "https://ourworldindata.org/demographic-health-surveys-risk", "title": "The Demographic and Health Surveys brought crucial data for more than 90 countries — without them, we risk darkness", "context": "Home\nPopulation Growth\nThe Demographic and Health Surveys brought crucial data for more than 90 countries — without them, we risk darkness\nCuts to US aid could end the Demographic and Health Surveys. This would leave a massive gap in our understanding of global health, mortality, and development.\nBy\nSaloni Dattani\nJuly 21, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nIf you were to die tomorrow, the details about your death — what you died from, your age, and demographic information — would very likely be recorded officially. Most countries have statistical offices that collect and report this data. This helps researchers understand causes of death, track trends over time, uncover risk factors, and identify emerging problems.\nBut every year, millions of people worldwide die without having these details recorded because of a lack of doctors, nurses, or functional\nvital registries\n. We therefore struggle to understand the burden of particular diseases, which interventions might save lives, and where to direct resources.\nThis lack of data directly affects low- and middle-income countries, but also limits the ability of other governments, researchers, and organizations to plan effective global health programs, assess the impact of aid, and ensure cost-effectiveness.\nOne key source that has filled this gap is the\nDemographic and Health Surveys (DHS)\n. This is a program where large, nationally representative surveys have been conducted in over 90 lower- and middle-income countries\n1\n, roughly every five years, focusing on maternal and child health and mortality, but also collecting essential data on births, income and education, water and sanitation, and in recent years, diseases such as HIV and malaria.\nThe DHS program filled critical data gaps, provided an independent check on official records, and made estimates more reliable and transparent.\nIt was primarily funded by the United States Agency for International Development (USAID) and run by the American company ICF International. But earlier this year, the US government announced that it had\nterminated funding\nfor the program. This threatens global health and development efforts and will affect many of the estimates we provide on Our World in Data.\nGovernments and international organizations will be in a much worse position without these surveys if they want to tackle some of the world’s biggest problems. However, it doesn’t have to end this way — there’s now\nan effort to rescue the DHS program\n, and new funders could help secure its future.\nMany births, deaths, and their causes go uncounted across the world\nWhen it comes to basic, official data on populations — how many people are born, when they die, and what they die from — much of the world remains in the dark. Many countries still lack complete vital statistics, as shown in the two maps below.\nIn many parts of sub-Saharan Africa and Asia, many deaths aren’t officially recorded. And even when a death is registered, we often don’t know the cause. In much of Africa, for example,\nmost deaths happen without being registered at all\n. And among the ones that are registered, many don’t have a medical certificate or an official cause of death. In India, most deaths are registered, but most still don’t have a cause of death listed.\nIn a previous article, I wrote about why data on causes of death is missing in many parts of the world:\nHow are causes of death registered around the world?\nIn many countries, when people die, the cause of their death is officially registered in their country’s national system. How is this determined?\nThis lack of data reduces our understanding of global health. The chart below shows one example: during the COVID-19 pandemic, the global death toll — measured by excess deaths — was estimated to be three to five times higher than the number of confirmed COVID-19 deaths reported by countries.\nExcess deaths\nare the number of deaths during a crisis beyond what we would expect under normal conditions. This measure captures both direct and indirect impacts, including unreported COVID-19 deaths and those caused by broader disruptions.\nThis estimate is especially uncertain in countries without mortality reporting. Globally, the margin of error is around ten million deaths in either direction. But even the most conservative estimates suggest the true toll was at least three times higher than the confirmed count.\nOne reason estimates like these are so uncertain is that statistical institutions are a\nrelatively recent development\nin global history. Civil registries, population censuses, and national statistical agencies only became widespread in the last century, and they remain weak in many lower-income countries. Collecting rigorous data requires resources, systems, and staff that many poor countries today cannot support.\nSo, how do we know about health and mortality in these countries? For four decades, a major answer has been the Demographic and Health Surveys.\nThe Demographic and Health Surveys have filled critical data gaps across health, population sizes, and more\nThe DHS program was launched in 1984, originally to track fertility, but was expanded to cover more countries and a wide range of metrics.\n2\nIt surveys many countries where data from vital registration systems is incomplete. You can see this in the map below: most DHS surveys have been conducted in Africa, Latin America, and South and South-East Asia, the same regions where data was missing in the maps above.\nIn many ways, the DHS program has been an exceptional success. What sets it apart is its rigor and coverage. It has conducted over 400 nationally representative surveys across more than 90 countries, with large sample sizes — typically 5,000 to 30,000 households per country.\n3\nIt uses detailed, standardized training manuals, calibrated questionnaires, and strategies that reduce or track interviewers' and respondents' biases. The results are publicly available, making the DHS a consistent and trusted source of cross-country data.\nIn addition, the surveys have high participation rates, and since they include geographical data, researchers and analysts can track patterns at the local level.\n3\nThe DHS program collected core demographic data and focused on these crucial areas:\n3\nMaternal and child health\nNutrition and early childhood development\nFertility and family planning\nHIV and malaria\nDomestic and sexual violence\nFemale genital cutting, child labor, and disability\nService availability, urban living conditions, household structure, energy access, and water/sanitation\nSome chronic diseases, injuries, mental health conditions, and out-of-pocket healthcare costs\nEducation and literacy\nCauses of maternal and child deaths in some countries, using\nverbal autopsies\nIn many countries, the DHS program has been the only source of data\nIn many low-income countries, the Demographic and Health Surveys have been the\nonly\nsource of national data for some indicators.\nThe DHS program was, for example, the only underlying source on maternal mortality for 24 countries in the United Nations’ global estimates of maternal mortality. These countries are shown in the map below.\n4\nThese countries lack birth or death registry data, so the surveys have filled the gap. Without this data, researchers, public health experts, doctors, and policymakers would have very little idea of how many women are dying in childbirth, and why. It’s incredibly hard to make informed decisions in this darkness.\nDHS data is used for a wide range of indicators, including by us at Our World in Data\nThe importance of the DHS extends far beyond the countries where it is conducted. Many researchers and institutions use it as a vital data source, and it feeds into nearly every major global estimate of development, health, and well-being.\n5\nIt’s also widely used to estimate countries’ population sizes and birth rates, which are used as denominators for many per-capita metrics. Researchers also use the data to study other key factors, such as age distributions, household sizes, and urban-rural breakdowns.\n5\nOn Our World in Data, much of our coverage of patterns and trends in lower- and middle-income countries relies on DHS data as a foundational source. Here are some indicators for which the statistical institutions we rely on use the DHS as a key data source. I’ve also included links to our work on each of them.\nTopic\nInstitutions using the DHS to make international estimates\nMaternal mortality\nUN MMEIG\nChild mortality\nUN Inter-agency Group for Child Mortality Estimation\nLife expectancy\n,\nfertility rates\n, and\ncauses of death\nIHME Global Burden of Disease, WHO Global Health Estimates\nPopulation sizes\nUN World Population Prospects\nContraceptive use\nUN Development Programme, WHO\nHIV prevalence\nJoint United Nations Programme on HIV/AIDS\nWater, sanitation, and hygiene\nWHO/UNICEF Joint Monitoring Programme\nPoverty\nUN Development Programme\nUrbanization\nUN-Habitat\nEnergy access\nWorld Bank, International Energy Agency\nMigration\nUN Migration, UN High Commissioner for Refugees\nGender equality\nUN Women\nEducation\nUNESCO\nHuman Development Index\nUN Development Programme\nLabour force participation\nInternational Labour Organization, UNICEF\nAs a result, the DHS program allows our team to build better and more accurate visualizations of global patterns and trends. This would be very challenging with inconsistent or missing government records.\nThe US government terminated funding for the DHS program, cutting off 23 ongoing surveys\nIn 2025, the US government shut down the DHS program alongside other international health data initiatives.\n6\nWhile this reduced US government spending by around $47 million per year, that’s a very small amount in relative terms: less than 0.1% of the total US aid budget, and 0.0007% of the total federal budget.\n7\nBut this decision has broad, long-term consequences. When the US cut its funding, surveys were set up or were already collecting data in 23 countries, which you can see in the map below.\n8\nThe data had already been collected in ten countries, but the analysis and reports were incomplete. This includes large countries like Nigeria, Indonesia, and the Democratic Republic of Congo. All the resources that went into setting up, conducting those surveys, and compiling the data will be wasted if the program doesn’t continue.\nThis comes on top of the disruptions that resulted from the COVID-19 pandemic. You can see this in the chart below: the number of surveys dropped sharply during the pandemic, and recovery had just begun. Now, that pipeline is at the risk of being broken entirely.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nReinstating data collection is critical\nEach uncounted life is a missed opportunity to better understand the world, to track progress, spot emerging problems, answer empirical questions, and direct resources efficiently.\nTo build a world where fewer people suffer or die unseen, we must invest resources in seeing more clearly. That means recognizing data collection as an intentional and vital act to improve the world.\nThe Demographic and Health Surveys showed that rigorous, large-scale data collection was possible even in the poorest parts of the world. Its loss shouldn’t be seen as inevitable. Reinstating the DHS program, or building something better in its place, is both possible and critical. If we care about saving lives, we should care about counting them.\nThe first step is to resume funding for the surveys that were underway. This would prevent the waste of years of planning, coordination, and investment.\nThe second is restoring the program for the long term. Sustaining DHS operations has cost about $47 million per year.\n9\nThat’s a tiny fraction of most national or international budgets, less than many single aid programs. But it’s likely still a large burden for one philanthropic funder alone.\nThis is a solvable problem, and many organizations have already started\nan effort to save it\n. More governments, foundations, and donors can join them and help secure this vital data source for the years to come.\nContinue reading on Our World in Data\nThe limits of our personal experience and the value of statistics\nThe world is huge; to get a clear idea of what our world is like, we have to rely on carefully collected, well-documented statistics.\nFor many of us, it doesn’t cost much to improve someone’s life, and we can do much more of it\nMost countries spend less than 1% of their national income on foreign aid; even small increases could make a big difference.\nHow are causes of death registered around the world?\nIn many countries, when people die, the cause of their death is officially registered in their country’s national system. How is this determined?\nEndnotes\nThis refers to the\ncountry’s income status\nat the time of the survey; a few countries have moved between categories since then.\nFisher, A. A., & Way, A. A. (1988). The demographic and health surveys program: An overview. International Family Planning Perspectives, 15-19.\nCorsi, D. J., Neuman, M., Finlay, J. E., & Subramanian, S. (2012). Demographic and health surveys: A profile. International Journal of Epidemiology, 41(6), 1602–1613.\nhttps://doi.org/10.1093/ije/dys184\nCorsi, D. J., Neuman, M., Finlay, J. E., & Subramanian, S. (2012). Demographic and health surveys: A profile. International Journal of Epidemiology, 41(6), 1602–1613.\nhttps://doi.org/10.1093/ije/dys184\nThis is based on metadata from the UN MMEIG report.\nTrends in maternal mortality 2000 to 2023: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. Geneva: World Health Organization; 2025. Available\nonline\n.\nShort Fabic, M., Choi, Y., & Bird, S. (2012). A systematic review of Demographic and Health Surveys: Data availability and utilization for research. Bulletin of the World Health Organization, 90(8), 604–612.\nhttps://doi.org/10.2471/BLT.11.095513\nMandavilli, A. (2025). Trump Administration Ends Global Health Research Program. New York Times.\nhttps://www.nytimes.com/2025/02/26/health/usaid-global-health-surveys.html\nFigures for FY 2024.\nUSASpending.gov\n(2025). Agency Profile: Agency for International Development (USAID). Available\nonline\n.\nFiscalData.Treasury.gov\n(2025) Federal Spending. Available\nonline\n.\nKhaki, J. J., Molenaar, J., Karki, S., Olal, E., Straneo, M., Mosuse, M. A., Fouogue, J. T., Hensen, B., Baguiya, A., Musau Nkola, A., Wong, K. L. M., Ba, O. A., Kikula, A., Grovogui, F. M., Semaan, A., Asefa, A., Macharia, P. M., Chikwari, C. D., Ouédraogo, M. O., … Beňová, L. (2025). When health data go dark: The importance of the DHS Program and imagining its future. BMC Medicine, 23(1), 241.\nhttps://doi.org/10.1186/s12916-025-04062-6\nThis is an annual figure based on the most recent\nrenewal in 2024\n, where the survey program was renewed for five years for $236 million.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSaloni Dattani (2025) - “The Demographic and Health Surveys brought crucial data for more than 90 countries — without them, we risk darkness” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260622-064358/demographic-health-surveys-risk.html' [Online Resource] (archived on June 22, 2026).\nBibTeX citation\n@article{owid-demographic-health-surveys-risk,\nauthor = {Saloni Dattani},\ntitle = {The Demographic and Health Surveys brought crucial data for more than 90 countries — without them, we risk darkness},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260622-064358/demographic-health-surveys-risk.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "demographic-health-surveys-risk", "source_url": "https://ourworldindata.org/demographic-health-surveys-risk", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Cuts to US aid could end the Demographic and Health Surveys. 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{"Entity": "Albania", "Code": "ALB", "Year": "2019", "Share of deaths that are registered": "84.12", "World region according to OWID": "Europe"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Share of deaths that are registered": "91.06", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Share of deaths that are registered": "91.94", "World region according to OWID": "Africa"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2015", "Share of deaths that are registered": "97.52", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2016", "Share of deaths that are registered": "84.85", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2017", "Share of deaths that are registered": "92.81", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2018", "Share of deaths that are registered": "88.17", "World region according to OWID": "Oceania"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2019", "Share of deaths that are registered": "80.35", "World region according to OWID": "Oceania"}, {"Entity": "Andorra", "Code": "AND", "Year": "2015", "Share of deaths that are registered": "57.2", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2016", "Share of deaths that are registered": "61.02", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2017", "Share of deaths that are registered": "61.76", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2018", "Share of deaths that are registered": "62.5", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2019", "Share of deaths that are registered": "54.73", "World region according to OWID": "Europe"}, {"Entity": "Angola", "Code": "AGO", "Year": "2017", "Share of deaths that are registered": "11.09", "World region according to OWID": "Africa"}, {"Entity": "Angola", "Code": "AGO", "Year": "2018", "Share of deaths that are registered": "9.94", "World region according to OWID": "Africa"}, {"Entity": "Angola", "Code": "AGO", "Year": "2019", "Share of deaths that are registered": "8.13", "World region according to OWID": "Africa"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2015", "Share of deaths that are registered": "95.52", "World region according to OWID": "North America"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2016", "Share of deaths that are registered": "96.31", "World region according to OWID": "North America"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2017", "Share of deaths that are registered": "98.79", "World region according to OWID": "North America"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2018", "Share of deaths that are registered": "98.64", "World region according to OWID": "North America"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2019", "Share of deaths that are registered": "100", "World region according to OWID": "North America"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "South America"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2016", "Share of deaths that are registered": "100", "World region according to OWID": "South America"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2017", "Share of deaths that are registered": "99.91", "World region according to OWID": "South America"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2018", "Share of deaths that are registered": "98.97", "World region according to OWID": "South America"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2019", "Share of deaths that are registered": "98.59", "World region according to OWID": "South America"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2015", "Share of deaths that are registered": "99.84", "World region according to OWID": "Asia"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2016", "Share of deaths that are registered": "100", "World region according to OWID": "Asia"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2017", "Share of deaths that are registered": "99.05", "World region according to OWID": "Asia"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2018", "Share of deaths that are registered": "94.14", "World region according to OWID": "Asia"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2019", "Share of deaths that are registered": "97.53", "World region according to OWID": "Asia"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2015", "Share of deaths that are registered": "81.81", "World region according to OWID": "North America"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2016", "Share of deaths that are registered": "89.36", "World region according to OWID": "North America"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2017", "Share of deaths that are registered": "79.71", "World region according to OWID": "North America"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2018", "Share of deaths that are registered": "78.88", "World region according to OWID": "North America"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2019", "Share of deaths that are registered": "71.35", "World region according to OWID": "North America"}, {"Entity": "Australia", "Code": "AUS", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "2016", "Share of deaths that are registered": "100", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "2017", "Share of deaths that are registered": "100", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "2018", "Share of deaths that are registered": "98.44", "World region according to OWID": "Oceania"}, {"Entity": "Australia", "Code": "AUS", "Year": "2019", "Share of deaths that are registered": "98.18", "World region according to OWID": "Oceania"}, {"Entity": "Austria", "Code": "AUT", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "Europe"}, {"Entity": "Austria", "Code": "AUT", "Year": "2016", "Share of deaths that are registered": "96.87", "World region according to OWID": "Europe"}, {"Entity": "Austria", "Code": "AUT", "Year": "2017", "Share of deaths that are registered": "97.82", "World region according to OWID": "Europe"}, {"Entity": "Austria", "Code": "AUT", "Year": "2018", "Share of deaths that are registered": "97.87", "World region according to OWID": "Europe"}, {"Entity": "Austria", "Code": "AUT", "Year": "2019", "Share of deaths that are registered": "97.4", "World region according to OWID": "Europe"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2015", "Share of deaths that are registered": "74.3", "World region according to OWID": "Asia"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2016", "Share of deaths that are registered": "76.27", "World region according to OWID": "Asia"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2017", "Share of deaths that are registered": "75.76", "World region according to OWID": "Asia"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2018", "Share of deaths that are registered": "77.1", "World region according to OWID": "Asia"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2019", "Share of deaths that are registered": "75.88", "World region according to OWID": "Asia"}, {"Entity": "Bahamas", "Code": "BHS", "Year": "2015", "Share of deaths that are registered": "90.78", "World region according to OWID": "North America"}, {"Entity": "Bahamas", "Code": "BHS", "Year": "2016", "Share of deaths that are registered": "91.13", "World region according to OWID": "North America"}, {"Entity": "Bahamas", "Code": "BHS", "Year": "2017", "Share of deaths that are registered": "94.52", "World region according to OWID": "North America"}, {"Entity": "Bahamas", "Code": "BHS", "Year": "2018", "Share of deaths that are registered": "97", "World region according to OWID": "North America"}, {"Entity": "Bahamas", "Code": "BHS", "Year": "2019", "Share of deaths that are registered": "91.98", "World region according to OWID": "North America"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2015", "Share of deaths that are registered": "85.67", "World region according to OWID": "Asia"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2016", "Share of deaths that are registered": "84.36", "World region according to OWID": "Asia"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2017", "Share of deaths that are registered": "80.99", "World region according to OWID": "Asia"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2018", "Share of deaths that are registered": "80.93", "World region according to OWID": "Asia"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2019", "Share of deaths that are registered": "75.91", "World region according to OWID": "Asia"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2015", "Share of deaths that are registered": "9.72", "World region according to OWID": "Asia"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2016", "Share of deaths that are registered": "14.47", "World region according to OWID": "Asia"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2017", "Share of deaths that are registered": "20.23", "World region according to OWID": "Asia"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2018", "Share of deaths that are registered": "23.83", "World region according to OWID": "Asia"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2015", "Share of deaths that are registered": "93.32", "World region according to OWID": "North America"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2016", "Share of deaths that are registered": "92.42", "World region according to OWID": "North America"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2017", "Share of deaths that are registered": "92.58", "World region according to OWID": "North America"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2018", "Share of deaths that are registered": "87.07", "World region according to OWID": "North America"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2019", "Share of deaths that are registered": "96.4", "World region according to OWID": "North America"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2015", "Share of deaths that are registered": "98", "World region according to OWID": "Europe"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2016", "Share of deaths that are registered": "97.22", "World region according to OWID": "Europe"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2017", "Share of deaths that are registered": "96.97", "World region according to OWID": "Europe"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2018", "Share of deaths that are registered": "97.43", "World region according to OWID": "Europe"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2019", "Share of deaths that are registered": "97.46", "World region according to OWID": "Europe"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "Europe"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2016", "Share of deaths that are registered": "98.84", "World region according to OWID": "Europe"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2017", "Share of deaths that are registered": "98.42", "World region according to OWID": "Europe"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2018", "Share of deaths that are registered": "98.3", "World region according to OWID": "Europe"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2019", "Share of deaths that are registered": "96.69", "World region according to OWID": "Europe"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "North America"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2016", "Share of deaths that are registered": "100", "World region according to OWID": "North America"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2017", "Share of deaths that are registered": "100", "World region according to OWID": "North America"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2018", "Share of deaths that are registered": "100", "World region according to OWID": "North America"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2019", "Share of deaths that are registered": "100", "World region according to OWID": "North America"}, {"Entity": "Benin", "Code": "BEN", "Year": "2018", "Share of deaths that are registered": "0.98", "World region according to OWID": "Africa"}, {"Entity": "Benin", "Code": "BEN", "Year": "2019", "Share of deaths that are registered": "2.55", "World region according to OWID": "Africa"}, {"Entity": "Bermuda", "Code": "BMU", "Year": "2015", "Share of deaths that are registered": "93.18", "World region according to OWID": "North America"}, {"Entity": "Bermuda", "Code": "BMU", "Year": "2016", "Share of deaths that are registered": "92.31", "World region according to OWID": "North America"}, {"Entity": "Bermuda", "Code": "BMU", "Year": "2017", "Share of deaths that are registered": "95.06", "World region according to OWID": "North America"}, {"Entity": "Bermuda", "Code": "BMU", "Year": "2018", "Share of deaths that are registered": "93.67", "World region according to OWID": "North America"}, {"Entity": "Bermuda", "Code": "BMU", "Year": "2019", "Share of deaths that are registered": "92.24", "World region according to OWID": "North America"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2019", "Share of deaths that are registered": "69.68", "World region according to OWID": "Asia"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2015", "Share of deaths that are registered": "66.14", "World region according to OWID": "South America"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2016", "Share of deaths that are registered": "67.05", "World region according to OWID": "South America"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2017", "Share of deaths that are registered": "64.45", "World region according to OWID": "South America"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2018", "Share of deaths that are registered": "65.06", "World region according to OWID": "South America"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2019", "Share of deaths that are registered": "64.79", "World region according to OWID": "South America"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2015", "Share of deaths that are registered": "98.95", "World region according to OWID": "Europe"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2016", "Share of deaths that are registered": "96.53", "World region according to OWID": "Europe"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2017", "Share of deaths that are registered": "95.32", "World region according to OWID": "Europe"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2018", "Share of deaths that are registered": "94.17", "World region according to OWID": "Europe"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2019", "Share of deaths that are registered": "95.53", "World region according to OWID": "Europe"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2015", "Share of deaths that are registered": "68.25", "World region according to OWID": "Africa"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2016", "Share of deaths that are registered": "68.48", "World region according to OWID": "Africa"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2017", "Share of deaths that are registered": "66.28", "World region according to OWID": "Africa"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2018", "Share of deaths that are registered": "64.69", "World region according to OWID": "Africa"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2019", "Share of deaths that are registered": "66.76", "World region according to OWID": "Africa"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2015", "Share of deaths that are registered": "97.06", "World region according to OWID": "South America"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2016", "Share of deaths that are registered": "97.57", "World region according to OWID": "South America"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2017", "Share of deaths that are registered": "97.75", "World region according to OWID": "South America"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2018", "Share of deaths that are registered": "96.33", "World region according to OWID": "South America"}], "rows_tail": [{"Entity": "Spain", "Code": "ESP", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "Europe"}, {"Entity": "Spain", "Code": "ESP", "Year": "2016", "Share of deaths that are registered": "99.69", "World region according to OWID": "Europe"}, {"Entity": "Spain", "Code": "ESP", "Year": "2017", "Share of deaths that are registered": "99.94", "World region according to OWID": "Europe"}, {"Entity": "Spain", "Code": "ESP", "Year": "2018", "Share of deaths that are registered": "100", "World region according to OWID": "Europe"}, {"Entity": "Spain", "Code": "ESP", "Year": "2019", "Share of deaths that are registered": "98.49", "World region according to OWID": "Europe"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2015", "Share of deaths that are registered": "96.54", "World region according to OWID": "Asia"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2016", "Share of deaths that are registered": "94.78", "World region according to OWID": "Asia"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2017", "Share of deaths that are registered": "99.09", "World region according to OWID": "Asia"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2018", "Share of deaths that are registered": "98.02", "World region according to OWID": "Asia"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2019", "Share of deaths that are registered": "100", "World region according to OWID": "Asia"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2015", "Share of deaths that are registered": "93.32", "World region according to OWID": "South America"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2016", "Share of deaths that are registered": "90.07", "World region according to OWID": "South America"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2017", "Share of deaths that are registered": "86.51", "World region according to OWID": "South America"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2018", "Share of deaths that are registered": "89.34", "World region according to OWID": "South America"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2019", "Share of deaths that are registered": "90.59", "World region according to OWID": "South America"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "Europe"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2016", "Share of deaths that are registered": "99.12", "World region according to OWID": "Europe"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2017", "Share of deaths that are registered": "99.23", "World region according to OWID": "Europe"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2018", "Share of deaths that are registered": "99.02", "World region according to OWID": "Europe"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2019", "Share of deaths that are registered": "96.69", "World region according to OWID": "Europe"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "Europe"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2016", "Share of deaths that are registered": "98.45", "World region according to OWID": "Europe"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2017", "Share of deaths that are registered": "98.71", "World region according to OWID": "Europe"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2018", "Share of deaths that are registered": "97.85", "World region according to OWID": "Europe"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2019", "Share of deaths that are registered": "97.91", "World region according to OWID": "Europe"}, {"Entity": "Syria", "Code": "SYR", "Year": "2015", "Share of deaths that are registered": "32.47", "World region according to OWID": "Asia"}, {"Entity": "Syria", "Code": "SYR", "Year": "2016", "Share of deaths that are registered": "33.12", "World region according to OWID": "Asia"}, {"Entity": "Syria", "Code": "SYR", "Year": "2018", "Share of deaths that are registered": "59.27", "World region according to OWID": "Asia"}, {"Entity": "Syria", "Code": "SYR", "Year": "2019", "Share of deaths that are registered": "65.54", "World region according to OWID": "Asia"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "Asia"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2016", "Share of deaths that are registered": "100", "World region according to OWID": "Asia"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2017", "Share of deaths that are registered": "99.55", "World region according to OWID": "Asia"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2018", "Share of deaths that are registered": "99.7", "World region according to OWID": "Asia"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2019", "Share of deaths that are registered": "99.2", "World region according to OWID": "Asia"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2015", "Share of deaths that are registered": "70.13", "World region according to OWID": "Asia"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2016", "Share of deaths that are registered": "69.87", "World region according to OWID": "Asia"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2017", "Share of deaths that are registered": "65.34", "World region according to OWID": "Asia"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2018", "Share of deaths that are registered": "67.74", "World region according to OWID": "Asia"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2019", "Share of deaths that are registered": "69.9", "World region according to OWID": "Asia"}, {"Entity": "Thailand", "Code": "THA", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "Asia"}, {"Entity": "Thailand", "Code": "THA", "Year": "2016", "Share of deaths that are registered": "100", "World region according to OWID": "Asia"}, {"Entity": "Thailand", "Code": "THA", "Year": "2017", "Share of deaths that are registered": "98.21", "World region according to OWID": "Asia"}, {"Entity": "Thailand", "Code": "THA", "Year": "2018", "Share of deaths that are registered": "96.61", "World region according to OWID": "Asia"}, {"Entity": "Thailand", "Code": "THA", "Year": "2019", "Share of deaths that are registered": "100", "World region according to OWID": "Asia"}, {"Entity": "Togo", "Code": "TGO", "Year": "2015", "Share of deaths that are registered": "11.57", "World region according to OWID": "Africa"}, {"Entity": "Togo", "Code": "TGO", "Year": "2016", "Share of deaths that are registered": "11.84", "World region according to OWID": "Africa"}, {"Entity": "Togo", "Code": "TGO", "Year": "2017", "Share of deaths that are registered": "12.03", "World region according to OWID": "Africa"}, {"Entity": "Togo", "Code": "TGO", "Year": "2018", "Share of deaths that are registered": "13.87", "World region according to OWID": "Africa"}, {"Entity": "Togo", "Code": "TGO", "Year": "2019", "Share of deaths that are registered": "15.13", "World region according to OWID": "Africa"}, {"Entity": "Tonga", "Code": "TON", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "Oceania"}, {"Entity": "Tonga", "Code": "TON", "Year": "2016", "Share of deaths that are registered": "100", "World region according to OWID": "Oceania"}, {"Entity": "Tonga", "Code": "TON", "Year": "2017", "Share of deaths that are registered": "100", "World region according to OWID": "Oceania"}, {"Entity": "Tonga", "Code": "TON", "Year": "2018", "Share of deaths that are registered": "100", "World region according to OWID": "Oceania"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "North America"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2016", "Share of deaths that are registered": "100", "World region according to OWID": "North America"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2017", "Share of deaths that are registered": "100", "World region according to OWID": "North America"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2018", "Share of deaths that are registered": "100", "World region according to OWID": "North America"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2019", "Share of deaths that are registered": "100", "World region according to OWID": "North America"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "Africa"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2016", "Share of deaths that are registered": "100", "World region according to OWID": "Africa"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2017", "Share of deaths that are registered": "100", "World region according to OWID": "Africa"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2018", "Share of deaths that are registered": "100", "World region according to OWID": "Africa"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2019", "Share of deaths that are registered": "100", "World region according to OWID": "Africa"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2015", "Share of deaths that are registered": "98.69", "World region according to OWID": "Asia"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2016", "Share of deaths that are registered": "99.83", "World region according to OWID": "Asia"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2017", "Share of deaths that are registered": "99.59", "World region according to OWID": "Asia"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2018", "Share of deaths that are registered": "98.99", "World region according to OWID": "Asia"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2019", "Share of deaths that are registered": "99.9", "World region according to OWID": "Asia"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2015", "Share of deaths that are registered": "91.36", "World region according to OWID": "Asia"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2019", "Share of deaths that are registered": "0.91", "World region according to OWID": "Africa"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2015", "Share of deaths that are registered": "92.07", "World region according to OWID": "Europe"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2016", "Share of deaths that are registered": "92.8", "World region according to OWID": "Europe"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2017", "Share of deaths that are registered": "93.25", "World region according to OWID": "Europe"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2018", "Share of deaths that are registered": "93.17", "World region according to OWID": "Europe"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2019", "Share of deaths that are registered": "92.75", "World region according to OWID": "Europe"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2018", "Share of deaths that are registered": "44.26", "World region according to OWID": "Asia"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2019", "Share of deaths that are registered": "42.48", "World region according to OWID": "Asia"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "Europe"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2016", "Share of deaths that are registered": "99.36", "World region according to OWID": "Europe"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2017", "Share of deaths that are registered": "100", "World region according to OWID": "Europe"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2018", "Share of deaths that are registered": "99.94", "World region according to OWID": "Europe"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2019", "Share of deaths that are registered": "98.9", "World region according to OWID": "Europe"}, {"Entity": "United States", "Code": "USA", "Year": "2015", "Share of deaths that are registered": "99.77", "World region according to OWID": "North America"}, {"Entity": "United States", "Code": "USA", "Year": "2016", "Share of deaths that are registered": "99.56", "World region according to OWID": "North America"}, {"Entity": "United States", "Code": "USA", "Year": "2017", "Share of deaths that are registered": "100", "World region according to OWID": "North America"}, {"Entity": "United States", "Code": "USA", "Year": "2018", "Share of deaths that are registered": "99.49", "World region according to OWID": "North America"}, {"Entity": "United States", "Code": "USA", "Year": "2019", "Share of deaths that are registered": "98.42", "World region according to OWID": "North America"}, {"Entity": "United States Virgin Islands", "Code": "VIR", "Year": "2015", "Share of deaths that are registered": "53.25", "World region according to OWID": "North America"}, {"Entity": "United States Virgin Islands", "Code": "VIR", "Year": "2016", "Share of deaths that are registered": "51.13", "World region according to OWID": "North America"}, {"Entity": "United States Virgin Islands", "Code": "VIR", "Year": "2017", "Share of deaths that are registered": "55.93", "World region according to OWID": "North America"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2015", "Share of deaths that are registered": "100", "World region according to OWID": "South America"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2016", "Share of deaths that are registered": "100", "World region according to OWID": "South America"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2017", "Share of deaths that are registered": "99.76", "World region according to OWID": "South America"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2018", "Share of deaths that are registered": "100", "World region according to OWID": "South America"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2019", "Share of deaths that are registered": "100", "World region according to OWID": "South America"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2015", "Share of deaths that are registered": "80.68", "World region according to OWID": "Asia"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2016", "Share of deaths that are registered": "81.77", "World region according to OWID": "Asia"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2017", "Share of deaths that are registered": "84.62", "World region according to OWID": "Asia"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2018", "Share of deaths that are registered": "81.05", "World region according to OWID": "Asia"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2019", "Share of deaths that are registered": "80.74", "World region according to OWID": "Asia"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2015", "Share of deaths that are registered": "14.04", "World region according to OWID": "Oceania"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2016", "Share of deaths that are registered": "10.63", "World region according to OWID": "Oceania"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2017", "Share of deaths that are registered": "7.79", "World region according to OWID": "Oceania"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2018", "Share of deaths that are registered": "10.01", "World region according to OWID": "Oceania"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2015", "Share of deaths that are registered": "97.17", "World region according to OWID": "South America"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2016", "Share of deaths that are registered": "100", "World region according to OWID": "South America"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2017", "Share of deaths that are registered": "100", "World region according to OWID": "South America"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2018", "Share of deaths that are registered": "95.56", "World region according to OWID": "South America"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2019", "Share of deaths that are registered": "70.57", "World region according to OWID": "South America"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2018", "Share of deaths that are registered": "86.57", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2019", "Share of deaths that are registered": "83.32", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Share of deaths that are registered": "15.53", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Share of deaths that are registered": "18.55", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Share of deaths that are registered": "21.84", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Share of deaths that are registered": "43.48", "World region according to OWID": "Asia"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Share of deaths that are registered": "33.97", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Share of deaths that are registered": "32.32", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Share of deaths that are registered": "23.87", "World region according to OWID": "Africa"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Share of deaths that are registered": "26.34", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Share of deaths that are registered": "32.95", "World region according to OWID": "Africa"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "share-of-deaths-registered", "metadata_url": "https://ourworldindata.org/grapher/share-of-deaths-registered.metadata.json", "chart_title": "Share of deaths that are registered", "chart_subtitle": 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Expected deaths are taken as the average of estimates from three international sources: the UN, WHO, and IHME.", "descriptionKey": ["The most common way of knowing how many deaths occur in a country is to rely on death certificates registered in national Vital Registry systems. In many countries, however, a large share of deaths are not registered. This is due to factors such as a lack of doctors and nurses to fill in death certificates, or a poorly functioning Vital Registry system.", "This indicator estimates the extent of under-registering, given as the share of deaths that were registered, out of the total deaths expected for that year.", "The number of expected deaths is estimated by taking the average number of deaths from three data sources: the UN's World Population Prospects, WHO's Global Health Estimates and IHME's Global Burden of Disease study. These three sources themselves estimate the number of deaths from models based on data from censuses and household surveys. For many countries, the estimates of the three sources are very similar. However, for others, where vital registration systems are lacking or not functional, they tend to differ."], "shortUnit": "%", "unit": "%", "timespan": "2015-2019", "type": "Numeric", "owidVariableId": 1230073, "shortName": "death_comp", "lastUpdated": "2026-06-01", "citationShort": "Ariel Karlinsky (2024) – processed by Our World in Data", "citationLong": "Ariel Karlinsky (2024) – processed by Our World in Data. “Share of deaths that are registered – Ariel Karlinsky (2024)” [dataset]. Ariel Karlinsky, “International Completeness of Death Registration 2015-2019” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1230073.metadata.json"}, "World region according to OWID": {"titleShort": "World region according to OWID", "titleLong": "World region according to OWID", "descriptionShort": "Regions defined by Our World in Data, which are used in OWID charts and maps.", "unit": "", "timespan": "2023-2023", "type": "Continent", "owidVariableId": 900801, "shortName": "owid_region", "lastUpdated": "2023-01-01", "citationShort": "Our World in Data – processed by Our World in Data", "citationLong": "Our World in Data – processed by Our World in Data. “World region according to OWID” [dataset]. Our World in Data, “Regions” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/900801.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Share of deaths for which the cause is registered", "source_url": "https://ourworldindata.org/grapher/share-of-deaths-cause-is-registered.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Completeness of cause-of-death data (%)"], "row_count_total": 125, "rows_head": [{"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2017", "Completeness of cause-of-death data (%)": "5.6312"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Completeness of cause-of-death data (%)": "53.25743"}, {"Entity": "Americas (WHO)", "Code": "WHO_AMR", "Year": "2017", "Completeness of cause-of-death data (%)": "93.66158"}, {"Entity": "Andorra", "Code": "AND", "Year": "2015", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2016", "Completeness of cause-of-death data (%)": "83.18349"}, {"Entity": "Argentina", "Code": "ARG", "Year": 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{"Entity": "Seychelles", "Code": "SYC", "Year": "2015", "Completeness of cause-of-death data (%)": "90.58611"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2016", "Completeness of cause-of-death data (%)": "66.11993"}, {"Entity": "Slovakia", "Code": "SVK", "Year": "2014", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2015", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2015", "Completeness of cause-of-death data (%)": "92.46873"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "South-East Asia (WHO)", "Code": "WHO_SEAR", "Year": "2017", "Completeness of cause-of-death data (%)": "10.34986"}, {"Entity": "Spain", "Code": "ESP", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2014", "Completeness of cause-of-death data (%)": "79.61775"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Syria", "Code": "SYR", "Year": "2010", "Completeness of cause-of-death data (%)": "82.65034"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2016", "Completeness of cause-of-death data (%)": "87.35947"}, {"Entity": "Thailand", "Code": "THA", "Year": "2016", "Completeness of cause-of-death data (%)": "87.23092"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2012", "Completeness of cause-of-death data (%)": "83.27411"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2013", "Completeness of cause-of-death data (%)": "29.14845"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2016", "Completeness of cause-of-death data (%)": "91.60294"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2015", "Completeness of cause-of-death data (%)": "85.1591"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2017", "Completeness of cause-of-death data (%)": "92.33643"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2010", "Completeness of cause-of-death data (%)": "59.02764"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "United States", "Code": "USA", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}], "rows_tail": [{"Entity": "Argentina", "Code": "ARG", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Australia", "Code": "AUS", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Austria", "Code": "AUT", "Year": "2017", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Bahamas", "Code": "BHS", "Year": "2014", "Completeness of cause-of-death data (%)": "89.00755"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2014", "Completeness of cause-of-death data (%)": "96.39304"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2013", "Completeness of cause-of-death data (%)": "79.04472"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2014", "Completeness of cause-of-death data (%)": "99.902"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2016", "Completeness of cause-of-death data (%)": "88.82793"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2016", "Completeness of cause-of-death data (%)": "95.02706"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2016", "Completeness of cause-of-death data (%)": "99.01759"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2016", "Completeness of cause-of-death data (%)": "99.66735"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2015", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Canada", "Code": "CAN", "Year": "2015", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Cape Verde", "Code": "CPV", "Year": "2012", "Completeness of cause-of-death data (%)": "92.54958"}, {"Entity": "Chile", "Code": "CHL", "Year": "2016", "Completeness of cause-of-death data (%)": "94.89843"}, {"Entity": "China", "Code": "CHN", "Year": "2015", "Completeness of cause-of-death data (%)": "61.51"}, {"Entity": "Colombia", "Code": "COL", "Year": "2015", "Completeness of cause-of-death data (%)": "79.75487"}, {"Entity": "Cook Islands", "Code": "COK", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Costa Rica", "Code": "CRI", "Year": "2014", "Completeness of cause-of-death data (%)": "87.43953"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Cuba", "Code": "CUB", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2016", "Completeness of cause-of-death data (%)": "67.53566"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2017", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2015", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Dominica", "Code": "DMA", "Year": "2015", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2013", "Completeness of cause-of-death data (%)": "58.36623"}, {"Entity": "Eastern Mediterranean (WHO)", "Code": "WHO_EMR", "Year": "2017", "Completeness of cause-of-death data (%)": "31.94384"}, {"Entity": "Ecuador", "Code": "ECU", "Year": "2016", "Completeness of cause-of-death data (%)": "82.44621"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2015", "Completeness of cause-of-death data (%)": "93.99346"}, {"Entity": "El Salvador", "Code": "SLV", "Year": "2014", "Completeness of cause-of-death data (%)": "92.65285"}, {"Entity": "Estonia", "Code": "EST", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Europe (WHO)", "Code": "WHO_EUR", "Year": "2017", "Completeness of cause-of-death data (%)": "97.43075"}, {"Entity": "Fiji", "Code": "FJI", "Year": "2012", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Finland", "Code": "FIN", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "France", "Code": "FRA", "Year": "2015", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Georgia", "Code": "GEO", "Year": "2015", "Completeness of cause-of-death data (%)": "90.27994"}, {"Entity": "Germany", "Code": "DEU", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Greece", "Code": "GRC", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Grenada", "Code": "GRD", "Year": "2017", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Guatemala", "Code": "GTM", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Guyana", "Code": "GUY", "Year": "2014", "Completeness of cause-of-death data (%)": "90.09867"}, {"Entity": "Honduras", "Code": "HND", "Year": "2013", "Completeness of cause-of-death data (%)": "13.60325"}, {"Entity": "Hungary", "Code": "HUN", "Year": "2017", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Iceland", "Code": "ISL", "Year": "2017", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "India", "Code": "IND", "Year": "2011", "Completeness of cause-of-death data (%)": "10"}, {"Entity": "Iran", "Code": "IRN", "Year": "2016", "Completeness of cause-of-death data (%)": "90.13338"}, {"Entity": "Iraq", "Code": "IRQ", "Year": "2016", "Completeness of cause-of-death data (%)": "64.51578"}, {"Entity": "Ireland", "Code": "IRL", "Year": "2015", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Israel", "Code": "ISR", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Italy", "Code": "ITA", "Year": "2015", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Jamaica", "Code": "JAM", "Year": "2014", "Completeness of cause-of-death data (%)": "93.61326"}, {"Entity": "Japan", "Code": "JPN", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Jordan", "Code": "JOR", "Year": "2015", "Completeness of cause-of-death data (%)": "56.27327"}, {"Entity": "Kazakhstan", "Code": "KAZ", "Year": "2017", "Completeness of cause-of-death data (%)": "86.99246"}, {"Entity": "Kuwait", "Code": "KWT", "Year": "2014", "Completeness of cause-of-death data (%)": "59.12189"}, {"Entity": "Kyrgyzstan", "Code": "KGZ", "Year": "2016", "Completeness of cause-of-death data (%)": "91.00684"}, {"Entity": "Latvia", "Code": "LVA", "Year": "2015", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Lithuania", "Code": "LTU", "Year": "2017", "Completeness of cause-of-death data (%)": "99.19991"}, {"Entity": "Luxembourg", "Code": "LUX", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Malaysia", "Code": "MYS", "Year": "2014", "Completeness of cause-of-death data (%)": "51.81053"}, {"Entity": "Maldives", "Code": "MDV", "Year": "2015", "Completeness of cause-of-death data (%)": "91.47556"}, {"Entity": "Malta", "Code": "MLT", "Year": "2015", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Mauritius", "Code": "MUS", "Year": "2017", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Mexico", "Code": "MEX", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Moldova", "Code": "MDA", "Year": "2017", "Completeness of cause-of-death data (%)": "79.62334"}, {"Entity": "Monaco", "Code": "MCO", "Year": "2012", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Mongolia", "Code": "MNG", "Year": "2016", "Completeness of cause-of-death data (%)": "83.68059"}, {"Entity": "Montenegro", "Code": "MNE", "Year": "2009", "Completeness of cause-of-death data (%)": "93.78612"}, {"Entity": "Morocco", "Code": "MAR", "Year": "2014", "Completeness of cause-of-death data (%)": "29.11381"}, {"Entity": "Netherlands", "Code": "NLD", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "New Zealand", "Code": "NZL", "Year": "2014", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Nicaragua", "Code": "NIC", "Year": "2017", "Completeness of cause-of-death data (%)": "79.38277"}, {"Entity": "North Macedonia", "Code": "MKD", "Year": "2013", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Norway", "Code": "NOR", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Oman", "Code": "OMN", "Year": "2014", "Completeness of cause-of-death data (%)": "73.56889"}, {"Entity": "Palau", "Code": "PLW", "Year": "2013", "Completeness of cause-of-death data (%)": "95"}, {"Entity": "Panama", "Code": "PAN", "Year": "2016", "Completeness of cause-of-death data (%)": "92.28472"}, {"Entity": "Paraguay", "Code": "PRY", "Year": "2016", "Completeness of cause-of-death data (%)": "88.16917"}, {"Entity": "Peru", "Code": "PER", "Year": "2015", "Completeness of cause-of-death data (%)": "57.17635"}, {"Entity": "Philippines", "Code": "PHL", "Year": "2011", "Completeness of cause-of-death data (%)": "88.94887"}, {"Entity": "Poland", "Code": "POL", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2017", "Completeness of cause-of-death data (%)": "50.37641"}, {"Entity": "Romania", "Code": "ROU", "Year": "2017", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Russia", "Code": "RUS", "Year": "2015", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Saint Kitts and Nevis", "Code": "KNA", "Year": "2013", "Completeness of cause-of-death data (%)": "87.55"}, {"Entity": "Saint Lucia", "Code": "LCA", "Year": "2014", "Completeness of cause-of-death data (%)": "97.17564"}, {"Entity": "Saint Vincent and the Grenadines", "Code": "VCT", "Year": "2016", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "San Marino", "Code": "SMR", "Year": "2015", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2012", "Completeness of cause-of-death data (%)": "41.71169"}, {"Entity": "Serbia", "Code": "SRB", "Year": "2016", "Completeness of cause-of-death data (%)": "94.05333"}, {"Entity": "Seychelles", "Code": "SYC", "Year": "2015", "Completeness of cause-of-death data (%)": "90.58611"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2016", "Completeness of cause-of-death data (%)": "66.11993"}, {"Entity": "Slovakia", "Code": "SVK", "Year": "2014", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2015", "Completeness of cause-of-death data (%)": "100"}, {"Entity": "South Africa", "Code": 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{"raw_link": "https://ourworldindata.org/world-bank-income-groups-explained", "title": "How does the World Bank classify countries by income?", "context": "Home\nEconomic Growth\nHow does the World Bank classify countries by income?\nThe World Bank’s income groups are widely used in global data. This article explains how they are defined and updated.\nBy\nBertha Rohenkohl\nand\nPablo Arriagada\nJuly 14, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nWhen people talk about countries as “rich” or “poor”, they can mean many different things. But for researchers and policymakers, it helps to have a way to compare countries by income using clear criteria.\nOne widely used approach is the World Bank’s income classification system, which places countries into four groups: low, lower-middle, upper-middle, and high-income countries. These groups are primarily intended for analytical and statistical purposes.\n1\nThese income groups are used across many international datasets, like the World Bank’s World Development Indicators — which include information on topics from access to\nenergy\nand\neducation\nto\nglobal trade\n— and are often cited in research and the media.\nThis article explains how countries are assigned to income groups, how the thresholds between groups are set and updated, some of the limitations of this classification, and how we use these groups at Our World in Data.\nHow are countries assigned to income groups?\nEvery year, the World Bank assigns each country to an income group based on its\ngross national income (GNI)\nper capita. GNI per capita is a measure of the average income of a country’s residents, including income that is earned abroad.\n2\nIt’s calculated by dividing a country’s total GNI by its population.\nThe World Bank uses GNI per capita because it’s available for most countries. It’s not a perfect measure of development, but it usually lines up with other indicators of living standards, like\nchild mortality\n,\nlife expectancy\n, and access to basics such as\nsafe drinking water\n.\n3\nSince countries report GNI in their local currencies, the World Bank converts these figures into US dollars using exchange rates. The conversion uses the\nAtlas Method\n, which averages exchange rates over the current and past two years, adjusted for inflation, to reduce short-term currency fluctuations.\nWe might expect the World Bank to adjust for what people can actually buy with their income — that is, to adjust for differences in purchasing power between countries — but this isn’t part of the current method. We come back to this in the limitations section below.\nOnce GNI per capita is expressed in US dollars, countries are put into one of four income groups based on specific thresholds. The classification covers most of the world: 189 World Bank member countries and many others with populations over 30,000.\nEvery year in July, the World Bank updates the classification using the most recent GNI per capita estimates.\n4\nThe map below shows how countries are classified by income. Using the timeline, you can explore how the groups have changed annually since 1987.\nHow are the income thresholds determined?\nThe income thresholds that separate groups were\nfirst set in the late 1980s\nwhen this classification system was introduced. At that time, these were aligned with the World Bank’s policies for lending money to countries. The Bank used average incomes to determine which countries were eligible for concessional loans (loans with low interest rates and long repayment periods), which were reserved for the world’s poorest countries.\nThis threshold for receiving such loans became the boundary between low-income and middle-income countries. The Bank then added two more thresholds to allow for further distinctions among the remaining countries. These were chosen based on the distribution of country incomes at the time, rather than on lending rules.\nToday, the thresholds are no longer linked to the Bank’s operations, but they have been updated yearly to account for inflation. This adjustment is based on a measure of global inflation.\n5\nThis means that the classification is\nabsolute\n. Countries are put into groups according to predetermined thresholds, and a country’s placement depends only on its GNI per capita, not on how it stacks up relative to other countries.\nThe thresholds for the latest income groups are (in US dollars):\n6\nLow income: $1,135 or less\nLower-middle income: $1,136 to $4,495\nUpper-middle income: $4,496 to $13,935\nHigh income: More than $13,935\nIf a country’s GNI per capita crosses a threshold, it moves into a new income group in the following update.\nBecause GNI per capita changes over time, and thresholds are revised annually, countries can move between income groups over time. These movements may reflect real changes in income, shifts in exchange rates, or updates to population data.\nIn the long run, most countries have moved up the income ladder as their economies have grown. However, countries can also move down — and some have, particularly in periods of war and economic crisis. Two examples are Syria and Yemen, which went from low-middle income to low income in 2017. The\nnumber of countries in each income group\nhas changed considerably since 1987.\nWhen we hear that there are four income groups, we might imagine that the world’s population is evenly divided across them, with around 25% of people living in each. But this isn’t the case. Again, these groups are defined based on\nabsolute\nthresholds, not relative cut-offs that change based on other countries’ progress.\nIn 1987, more than half of the population lived in low-income countries. Today, that share has fallen to less than 10%. Only a small share of the world’s population lives in low-income countries, while most live in middle-income countries. You can see this change over time in the chart below.\nBig shifts here often reflect changes in the classification of populous countries. For example, India moved to the lower-middle-income group in 2007, and China moved to the upper-middle-income group in 2010.\nWhat are the limitations of the World Bank’s method?\nThe World Bank’s income groups offer a useful way to compare countries by income level, but like any classification system, they have limitations.\nOne limitation is that the placement into a group is based only on GNI per capita, which reflects the average income in a country. As an average, it doesn’t show how income is\ndistributed\nwithin the country. If income is distributed very unequally, a country classified as high-income could have large numbers of people living in poverty or high levels of income inequality. Today, many of the world’s poorest people live in countries labeled “middle-income”.\nIn addition, GNI per capita may underestimate average incomes in countries where much of the economy happens informally or isn’t recorded. In particular, in lower-income countries, many people work outside the formal sector. GNI figures may not fully capture what people earn and live on in such cases.\n7\nThe thresholds that separate income groups also have limitations. They were originally tied to the Bank’s lending rules in the 1980s, and since then, they have remained mostly unchanged, apart from adjustments for inflation. There are no natural breaks in income levels at these thresholds, and moving from just below to just above a threshold doesn’t mean people’s living standards have changed meaningfully. Some researchers have questioned whether the current thresholds capture meaningful differences in development and have called for updated approaches.\n8\nFinally, this classification relies on market exchange rates to convert GNI per capita from local currencies into US dollars. With the\nAtlas Method\n, the World Bank uses rates averaged over three years to smooth fluctuations, but this approach doesn’t account for differences in purchasing power between countries — that is, what people can buy with their income in their own country.\n9\nUsing purchasing power parity (PPP) adjusted figures,\nsuch as international dollars\n, is one option to factor these price differences in. The World Bank has acknowledged this issue and notes that using PPPs\nis under consideration for future updates\n.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nHow we use income groups on Our World in Data\nOn Our World in Data, we use the World Bank’s income groups in many of our charts, for example, to compare trends across low- and high-income countries. In many charts, you will find an option to group countries by income level.\nWhen this option is shown, we always classify countries based on their income group in the\nlatest\nWorld Bank classification at the time of creating or updating the chart. That grouping is then applied consistently across all years. This helps readers compare how indicators have changed over time for the same fixed set of countries.\nYou can see how this works in the chart below. For example, the data points for low-income countries refer to the same set of countries in 2023, 2000, 1950, and even 1750. The same applies to the other groups, which were all based on how countries were classified when we last updated this chart.\nAcknowledgments\nThanks to Hannah Ritchie, Pablo Rosado, Marcel Gerber, and Edouard Mathieu for their comments and feedback on this article.\nContinue reading on Our World in Data\nWhat are international dollars?\nInternational dollars are used to compare incomes and purchasing power across countries and over time. Here, we explain how they’re calculated and why they’re used.\nMeasuring inequality: what is the Gini coefficient?\nThe Gini coefficient is the most common way of measuring inequality. But what does it actually measure? And how does it differ from other measures of inequality?\nBeyond income: understanding poverty through the Multidimensional Poverty Index\nThe experience of poverty goes beyond a very low income. What is the Multidimensional Poverty Index, and how does it capture the diverse ways people experience deprivation?\nEndnotes\nA common belief is that these income groups determine which countries the World Bank lends to. However, the Bank uses a separate classification system for its financing and operational activities.\nThis differs from Gross Domestic Product (GDP), which includes income generated within a country’s borders.\nBoth measures are correlated\n.\nSee the\nWorld Bank’s explanation\nfor using GNI per capita.\nThe income groups remain fixed for one year until next July, even if revised GNI data becomes available earlier than that.\nThe income thresholds are updated yearly in line with inflation observed in the\nSpecial Drawing Rights (SDR) deflator\n. The International Monetary Fund developed this indicator to track inflation across a basket of major currencies. It’s a weighted average of inflation in China, Japan, the United Kingdom, the United States, and the Euro Area.\nThe 2025 classification is based on GNI per capita estimates for 2024. The World Bank refers to it as\nincome classifications for the financial year 2026\n, starting on the 1st July 2025.\nCountries differ in how they measure and include informal activity or, more broadly, the “non-observed” economy in their national accounts. Estimates vary depending on the data and methods available. You can read more about it in this 2022 UNECE review:\nIn-depth review of measuring the non-observed/informal economy\n.\nKenny, C. (2011).\nWhat Does It Mean to Be Low Income?\nKenny, C. (2023).\nPast Time for a More Rational Approach to Global Income Classifications\nRavallion, M. (2013).\nWhy $12,616?\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nBertha Rohenkohl and Pablo Arriagada (2025) - “How does the World Bank classify countries by income?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260608-233547/world-bank-income-groups-explained.html' [Online Resource] (archived on June 8, 2026).\nBibTeX citation\n@article{owid-world-bank-income-groups-explained,\nauthor = {Bertha Rohenkohl and Pablo Arriagada},\ntitle = {How does the World Bank classify countries by income?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260608-233547/world-bank-income-groups-explained.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "world-bank-income-groups-explained", "source_url": "https://ourworldindata.org/world-bank-income-groups-explained", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "The World Bank classifies countries into four income groups based on average income per person. This article explains how these groups are defined.", "numeric_mentions": ["14,", "2025", "1", "2", "3", "189", "30,000", "4", "1987", "1980", "5", "6", "1,135", "1,136", "4,495", "4,496", "13,935", "2017", "25%", "1987,", "10%", "2007,", "2010", "7", "8", "9", "2023,", "2000,", "1950,", "1750", "2024", "2026", "2022", "2011", "2023", "2013", "12,616", "20260608", "233547", "8,"], "numeric_evidence": [{"title": "Number of people without access to electricity", "source_url": "https://ourworldindata.org/grapher/people-without-electricity-country.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Number of people without access to electricity"], "row_count_total": 6912, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Number of people without access to electricity": "19244592"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Number of people without access to electricity": "18397866"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", 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electricity": "9901946"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Number of people without access to electricity": "10893163"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Number of people without access to electricity": "10351899"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Number of people without access to electricity": "10558849"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Number of people without access to electricity": "10763899"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Number of people without access to electricity": "11460521"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Number of people without access to electricity": "11298972"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Number of people without access to electricity": "10926717"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Number of people without access to electricity": "10412468"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Number of people without access to electricity": "10748194"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Number of people without access to electricity": "10552888"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Number of people without access to electricity": "10558905"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Number of people without access to electricity": "10448722"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Number of people without access to electricity": "10519834"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Number of people without access to electricity": "10134019"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Number of people without access to electricity": "7684539"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Number of people without access to electricity": "7475824"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Number of people without access to electricity": "7817794"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Number of people without access to electricity": "7468370.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Number of people without access to electricity": "7550048"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Number of people without access to electricity": "7649323"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Number of people without access to electricity": "7756138"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Number of people without access to electricity": "7258610"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Number of people without access to electricity": "7884433"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Number of people without access to electricity": "7877511.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Number of people without access to electricity": "7953676.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Number of people without access to electricity": "7938777.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Number of people without access to electricity": "7963637"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Number of people without access to electricity": "7976913"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Number of people without access to electricity": "7935687.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Number of people without access to electricity": "8040948.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Number of people without access to electricity": "8073549.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Number of people without access to electricity": "7438819"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Number of people without access to electricity": "8160851"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Number of people without access to electricity": "8578712"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Number of people without access to electricity": "7738017"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Number of people without access to electricity": "8324202.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Number of people without access to electricity": "9618382"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Number of people without access to electricity": "9546546"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Number of people without access to electricity": "8395169"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Number of people without access to electricity": "8294990"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Number of people without access to electricity": "8208811"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Number of people without access to electricity": "8139639"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Number of people without access to electricity": "7344218"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Number of people without access to electricity": "8056577"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Number of people without access to electricity": "8018459"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Number of people without access to electricity": "6209512.5"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "people-without-electricity-country", "metadata_url": "https://ourworldindata.org/grapher/people-without-electricity-country.metadata.json", "chart_title": "Number of people without access to electricity", "chart_subtitle": "Having access to electricity is defined in international statistics as having an electricity source that can provide very basic lighting, and charge a phone or power a radio for 4 hours per day.", "chart_note": null, "chart_citation": "Data compiled from multiple sources by the World Bank", "original_chart_url": "https://ourworldindata.org/grapher/people-without-electricity-country", "owid_column_metadata": {"Access to electricity (number of people without access)": {"titleShort": "Number of people without access to electricity", "titleLong": "Number of people without access to electricity - World Bank", "descriptionShort": "Access to electricity means having an electricity source that can provide very basic lighting, and charge a phone or power a radio for 4 hours per day.", "descriptionProcessing": "We calculated the number of people without access to electricity by multiplying the fraction of the population without access by the total population.", "shortUnit": "", "unit": "people", "timespan": "1990-2023", "type": "Numeric", "owidVariableId": 1205625, "shortName": "eg_elc_accs_zs_without_number", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "Data compiled from multiple sources by the World Bank – with major processing by Our World in Data", "citationLong": "Data compiled from multiple sources by the World Bank – with major processing by Our World in Data. “Number of people without access to electricity – World Bank” [dataset]. SDG 7.1.1 Electrification Dataset, World Bank, via World Bank, “World Development Indicators 125”; United Nations Population Division, national statistical offices, and Eurostat, via World Bank, “World Development Indicators 125” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205625.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Literacy rates among adults", "source_url": "https://ourworldindata.org/grapher/literacy-rate-adults.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Literacy rate among adults"], "row_count_total": 1833, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Literacy rate among adults": "18.16"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Literacy rate among adults": "31.45"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Literacy rate among adults": "33.75"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Literacy rate among adults": "36.014965"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Literacy rate among adults": "37.27"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Literacy rate among adults": "98.71"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Literacy rate among adults": "95.94"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Literacy rate among adults": "96.85"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Literacy rate among adults": "97.25"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Literacy rate among adults": "98.81"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Literacy rate among adults": "97.68"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1987", "Literacy rate among adults": "49.63"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Literacy rate among adults": "69.87"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Literacy rate among adults": "72.65"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Literacy rate among adults": "75.14"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "1980", "Literacy rate among adults": "97.34"}, {"Entity": "Angola", "Code": "AGO", "Year": "2001", "Literacy rate among adults": "67.41"}, {"Entity": "Angola", "Code": "AGO", "Year": "2014", "Literacy rate among adults": "66.03"}, {"Entity": "Angola", "Code": "AGO", "Year": "2015", "Literacy rate among adults": "66.24"}, {"Entity": "Angola", "Code": "AGO", "Year": "2023", "Literacy rate among adults": "68.18"}, {"Entity": "Anguilla", "Code": "AIA", "Year": "1984", "Literacy rate among adults": "95.41"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2001", "Literacy rate among adults": "98.95"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1980", "Literacy rate among adults": "93.91"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1991", "Literacy rate among adults": "96.04"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2001", "Literacy rate among adults": "97.19"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2020", "Literacy rate among adults": "99.14"}, {"Entity": "Armenia", "Code": "ARM", "Year": "1989", "Literacy rate among adults": "98.75"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2001", "Literacy rate among adults": "99.4"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2011", "Literacy rate among adults": "99.74"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2016", "Literacy rate among adults": "99.74"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2017", "Literacy rate among adults": "99.74"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2018", "Literacy rate among adults": "100"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2019", "Literacy rate among adults": "100"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2020", "Literacy rate among adults": "99.79"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2022", "Literacy rate among adults": "99.82"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2023", "Literacy rate among adults": "99.84"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2000", "Literacy rate among adults": "97.29"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2010", "Literacy rate among adults": "96.82"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "1999", "Literacy rate among adults": "98.79"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2007", "Literacy rate among adults": "99.59"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2009", "Literacy rate among adults": "99.76"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2010", "Literacy rate among adults": "99.77"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2011", "Literacy rate among adults": "99.78"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2012", "Literacy rate among adults": "99.78"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2013", "Literacy rate among adults": "99.79"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2014", "Literacy rate among adults": "99.79"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2015", "Literacy rate among adults": "99.79"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2016", "Literacy rate among adults": "99.79"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2017", "Literacy rate among adults": "99.79"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2019", "Literacy rate among adults": "99.8"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2023", "Literacy rate among adults": "99.78"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "1981", "Literacy rate among adults": "69.75"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "1991", "Literacy rate among adults": "84.01"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2001", "Literacy rate among adults": "86.55"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2022", "Literacy rate among adults": "97.87"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2023", "Literacy rate among adults": "97.85"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2024", "Literacy rate among adults": "97.82"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "1981", "Literacy rate among adults": "29.23"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "1991", "Literacy rate among adults": "35.32"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2001", "Literacy rate among adults": "47.49"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2011", "Literacy rate among adults": "58.77"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2012", "Literacy rate among adults": "57.86"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2013", "Literacy rate among adults": "61.02"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2014", "Literacy rate among adults": "61.09"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2015", "Literacy rate among adults": "65.14"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2016", "Literacy rate among adults": "72.76"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2017", "Literacy rate among adults": "72.89"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2018", "Literacy rate among adults": "73.91"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2019", "Literacy rate among adults": "74.68"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2020", "Literacy rate among adults": "74.91"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2021", "Literacy rate among adults": "76.36"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2022", "Literacy rate among adults": "79"}, {"Entity": "Barbados", "Code": "BRB", "Year": "1970", "Literacy rate among adults": "99.27"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1989", "Literacy rate among adults": "97.88"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1999", "Literacy rate among adults": "99.59"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2009", "Literacy rate among adults": "99.62"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2019", "Literacy rate among adults": "99.87"}, {"Entity": "Belize", "Code": "BLZ", "Year": "1991", "Literacy rate among adults": "70.3"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2000", "Literacy rate among adults": "76.9"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2015", "Literacy rate among adults": "90.92"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2022", "Literacy rate among adults": "87.88"}, {"Entity": "Benin", "Code": "BEN", "Year": "1979", "Literacy rate among adults": "16.48"}, {"Entity": "Benin", "Code": "BEN", "Year": "1992", "Literacy rate among adults": "27.25"}, {"Entity": "Benin", "Code": "BEN", "Year": "2002", "Literacy rate among adults": "34.66"}, {"Entity": "Benin", "Code": "BEN", "Year": "2011", "Literacy rate among adults": "44.68812"}, {"Entity": "Benin", "Code": "BEN", "Year": "2013", "Literacy rate among adults": "45.08"}, {"Entity": "Benin", "Code": "BEN", "Year": "2017", "Literacy rate among adults": "38.87"}, {"Entity": "Benin", "Code": "BEN", "Year": "2019", "Literacy rate among adults": "49"}, {"Entity": "Benin", "Code": "BEN", "Year": "2021", "Literacy rate among adults": "43.83"}, {"Entity": "Benin", "Code": "BEN", "Year": "2022", "Literacy rate among adults": "51.38"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2005", "Literacy rate among adults": "52.81"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2012", "Literacy rate among adults": "55.32"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2017", "Literacy rate among adults": "66.56"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2022", "Literacy rate among adults": "64.91"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "1976", "Literacy rate among adults": "63.21"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "1992", "Literacy rate among adults": "79.99"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2001", "Literacy rate among adults": "86.72"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2007", "Literacy rate among adults": "90.29"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2008", "Literacy rate among adults": "90.7"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2009", "Literacy rate among adults": "91.17"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2011", "Literacy rate among adults": "92.23"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2012", "Literacy rate among adults": "94.46"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2015", "Literacy rate among adults": "92.46"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2020", "Literacy rate among adults": "93.85"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2023", "Literacy rate among adults": "95.55"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2024", "Literacy rate among adults": "96.73"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "1991", "Literacy rate among adults": "89.06"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2000", "Literacy rate among adults": "96.66"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2013", "Literacy rate among adults": "96.99"}, {"Entity": "Botswana", "Code": "BWA", "Year": "1991", "Literacy rate among adults": "68.58"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2003", "Literacy rate among adults": "81.19"}, {"Entity": "Brazil", "Code": "BRA", "Year": "1980", "Literacy rate among adults": "74.59"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2000", "Literacy rate among adults": "86.37"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2004", "Literacy rate among adults": "88.62"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2006", "Literacy rate among adults": "89.62"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2007", "Literacy rate among adults": "90.01"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2008", "Literacy rate among adults": "90.04"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2009", "Literacy rate among adults": "90.3"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2010", "Literacy rate among adults": "90.38"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2011", "Literacy rate among adults": "91.41"}], "rows_tail": [{"Entity": "Uruguay", "Code": "URY", "Year": "2006", "Literacy rate among adults": "97.79"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2007", "Literacy rate among adults": "97.86"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2008", "Literacy rate among adults": "98.16"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2009", "Literacy rate among adults": "98.26"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2010", "Literacy rate among adults": "98.07"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2011", "Literacy rate among adults": "98.34"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2012", "Literacy rate among adults": "98.4"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2013", "Literacy rate among adults": "98.36"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2014", "Literacy rate among adults": "98.44"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2015", "Literacy rate among adults": "98.52"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2016", "Literacy rate among adults": "98.56"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2017", "Literacy rate among adults": "98.62"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2018", "Literacy rate among adults": "98.7"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2019", "Literacy rate among adults": "98.77"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2020", "Literacy rate among adults": "98.95962"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2022", "Literacy rate among adults": "98.85"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2023", "Literacy rate among adults": "98.82549"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2024", "Literacy rate among adults": "98.91"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2000", "Literacy rate among adults": "98.64"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2013", "Literacy rate among adults": "99.99"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2014", "Literacy rate among adults": "99.98"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2015", "Literacy rate among adults": "99.98"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2016", "Literacy rate among adults": "99.99"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2018", "Literacy rate among adults": "99.99"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2019", "Literacy rate among adults": "100"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2021", "Literacy rate among adults": "100"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2022", "Literacy rate among adults": "100"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "1979", "Literacy rate among adults": "52.87"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "1999", "Literacy rate among adults": "74"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2019", "Literacy rate among adults": "92.47445"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2020", "Literacy rate among adults": "89.7931"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2023", "Literacy rate among adults": "87.96"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1981", "Literacy rate among adults": "84.73"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1990", "Literacy rate among adults": "89.83"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2001", "Literacy rate among adults": "92.98"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2005", "Literacy rate among adults": "94.370705"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2006", "Literacy rate among adults": "94.66207"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2007", "Literacy rate among adults": "95.15"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2008", "Literacy rate among adults": "95.3117"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2009", "Literacy rate among adults": "95.51"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2010", "Literacy rate among adults": "95.39242"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2011", "Literacy rate among adults": "94.77"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2012", "Literacy rate among adults": "95.98719"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2015", "Literacy rate among adults": "96.61"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2016", "Literacy rate among adults": "97.13"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2017", "Literacy rate among adults": "97.183716"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "1979", "Literacy rate among adults": "83.83"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "1989", "Literacy rate among adults": "87.6"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "1999", "Literacy rate among adults": "90.28"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2000", "Literacy rate among adults": "90.16"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2009", "Literacy rate among adults": "93.52"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2019", "Literacy rate among adults": "95.75"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2022", "Literacy rate among adults": "96.13"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2018", "Literacy rate among adults": "99.320526"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2019", "Literacy rate among adults": "99.313416"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2020", "Literacy rate among adults": "96.55"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2023", "Literacy rate among adults": "99.43576"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1975", "Literacy rate among adults": "65.42"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1976", "Literacy rate among adults": "65.57"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1977", "Literacy rate among adults": "65.88"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1978", "Literacy rate among adults": "66.5"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1979", "Literacy rate among adults": "67.15"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1980", "Literacy rate among adults": "67.76"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1981", "Literacy rate among adults": "68.35"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1982", "Literacy rate among adults": "68.93"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1983", "Literacy rate among adults": "69.49"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1984", "Literacy rate among adults": "70.15"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1985", "Literacy rate among adults": "70.73"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1986", "Literacy rate among adults": "71.27"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1987", "Literacy rate among adults": "71.94"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1988", "Literacy rate among adults": "73.55"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1989", "Literacy rate among adults": "74.07"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1990", "Literacy rate among adults": "74.63"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1991", "Literacy rate among adults": "75.06"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1992", "Literacy rate among adults": "75.51"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1993", "Literacy rate among adults": "75.97"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1994", "Literacy rate among adults": "76.42"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1995", "Literacy rate among adults": "76.87"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1996", "Literacy rate among adults": "77.3"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1997", "Literacy rate among adults": "79.3"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1998", "Literacy rate among adults": "80.52"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1999", "Literacy rate among adults": "80.9"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Literacy rate among adults": "81.12"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Literacy rate among adults": "81.37"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Literacy rate among adults": "81.74"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Literacy rate among adults": "82.22"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Literacy rate among adults": "82.56"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Literacy rate among adults": "82.57"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Literacy rate among adults": "82.53"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Literacy rate among adults": "83.12"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Literacy rate among adults": "83.48"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Literacy rate among adults": "83.8"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Literacy rate among adults": "84.37"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Literacy rate among adults": "84.71"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Literacy rate among adults": "84.91"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Literacy rate among adults": "85.21"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Literacy rate among adults": "85.56"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Literacy rate among adults": "85.96"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Literacy rate among adults": "86.28"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Literacy rate among adults": "86.47"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Literacy rate among adults": "86.76"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Literacy rate among adults": "86.79"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Literacy rate among adults": "86.98"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Literacy rate among adults": "87.17"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Literacy rate among adults": "87.39"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Literacy rate among adults": "87.58"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Literacy rate among adults": "87.74"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1994", "Literacy rate among adults": "37.09"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Literacy rate among adults": "65"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Literacy rate among adults": "68"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Literacy rate among adults": "69.15"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Literacy rate among adults": "83.14"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Literacy rate among adults": "71.13"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Literacy rate among adults": "81.753006"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Literacy rate among adults": "79.94946"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Literacy rate among adults": "79.984146"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Literacy rate among adults": "71.77"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Literacy rate among adults": "77.79"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Literacy rate among adults": "83.51"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Literacy rate among adults": "93.23"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "literacy-rate-adults", "metadata_url": "https://ourworldindata.org/grapher/literacy-rate-adults.metadata.json", "chart_title": "Literacy rates among adults", "chart_subtitle": "Share of adults aged 15 years and older who can read and write a simple sentence about their daily life.", "chart_note": null, "chart_citation": "UNESCO Institute for Statistics (2026)", "original_chart_url": "https://ourworldindata.org/grapher/literacy?age_group=adult&sex=both", "owid_column_metadata": {"Literacy rate among adults": {"titleShort": "Literacy rate among adults aged 15+", "titleLong": "Literacy rate among adults aged 15+", "descriptionShort": "Share of the population aged 15 years and older who can read and write.", "descriptionKey": ["Literacy is a foundational skill. Children need to learn to read so that they can read to learn. When we fail to teach this foundational skill, people have fewer opportunities to lead the rich and interesting lives that a good education offers.", "This indicator measures the percentage of people who can read and write a simple sentence about their daily life. It’s calculated as the number of people in a given age group who report being able to do so, divided by the total number in that group. UNESCO tracks this across different generations – including youth, adults, and older people – to show how literacy is changing over time.", "Most of the data comes from national surveys. In some countries, people are asked directly whether they can read and write; in others, they take a short test.", "In many high–income countries, literacy rates reached near–universal levels by the late 20th century. As a result, regular tracking has been scaled back, since changes are small and less relevant for education policy.", "This data tells us whether someone can read and write at a very basic level – for example, reading simple sentences or writing their name. But it doesn’t tell us whether they can use reading and writing in everyday life, like filling out a job application or reading health instructions. Those kinds of skills take more years of schooling and are much harder to measure, especially when comparing across countries and over time."], "shortUnit": "%", "unit": "%", "timespan": "1970-2024", "type": "Numeric", "owidVariableId": 1271634, "shortName": "adult_literacy_rate__population_15plus_years__both_sexes__pct__lr_ag15t99", "lastUpdated": "2026-05-12", "nextUpdate": "2027-05-12", "citationShort": "UNESCO Institute for Statistics (2026) – with minor processing by Our World in Data", "citationLong": "UNESCO Institute for Statistics (2026) – with minor processing by Our World in Data. “Literacy rate among adults aged 15+” [dataset]. UNESCO Institute for Statistics, “UNESCO Institute for Statistics (UIS) - Education” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1271634.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Trade as a share of GDP", "source_url": "https://ourworldindata.org/grapher/trade-as-share-of-gdp.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Trade (% of GDP)"], "row_count_total": 9519, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Trade (% of GDP)": "46.709896"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Trade (% of GDP)": "51.411716"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Trade (% of GDP)": "72.88547"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Trade (% of GDP)": "67.58466"}, {"Entity": "Albania", "Code": "ALB", "Year": "1980", "Trade (% of GDP)": "47.49409"}, {"Entity": "Albania", "Code": "ALB", "Year": "1981", "Trade (% of GDP)": "46.100468"}, {"Entity": "Albania", "Code": "ALB", "Year": "1982", "Trade (% of GDP)": "44.810562"}, {"Entity": "Albania", "Code": "ALB", "Year": "1983", "Trade (% of GDP)": "40.410667"}, {"Entity": "Albania", "Code": "ALB", "Year": "1984", "Trade (% of GDP)": "38.115685"}, {"Entity": "Albania", "Code": "ALB", "Year": "1985", "Trade (% of GDP)": "35.934822"}, {"Entity": "Albania", "Code": "ALB", "Year": "1986", "Trade (% of GDP)": "31.637304"}, {"Entity": "Albania", "Code": "ALB", "Year": "1987", "Trade (% of GDP)": "32.159565"}, {"Entity": "Albania", "Code": "ALB", "Year": "1988", "Trade (% of GDP)": "36.89787"}, {"Entity": "Albania", "Code": "ALB", "Year": "1989", "Trade (% of GDP)": "40.066086"}, {"Entity": "Albania", "Code": "ALB", "Year": "1990", "Trade (% of GDP)": "39.436962"}, {"Entity": "Albania", "Code": "ALB", "Year": "1991", "Trade (% of GDP)": "36.07052"}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "Trade (% of GDP)": "108.78547"}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "Trade (% of GDP)": "80.51833"}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "Trade (% of GDP)": "53.102585"}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "Trade (% of GDP)": "40.1387"}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "Trade (% of GDP)": "44.41178"}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "Trade (% of GDP)": "44.729378"}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "Trade (% of GDP)": "47.130627"}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "Trade (% of GDP)": "49.89542"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Trade (% of GDP)": "61.60926"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Trade (% of GDP)": "64.247444"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Trade (% of GDP)": "65.99146"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Trade (% of GDP)": "64.82321"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Trade (% of GDP)": "65.03794"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Trade (% of GDP)": "69.116295"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Trade (% of GDP)": "72.20191"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Trade (% of GDP)": "79.9119"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Trade (% of GDP)": "75.24855"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Trade (% of GDP)": "73.32136"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Trade (% of GDP)": "75.53253"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Trade (% of GDP)": "80.699"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Trade (% of GDP)": "76.96836"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Trade (% of GDP)": "75.75062"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Trade (% of GDP)": "75.0212"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Trade (% of GDP)": "71.27945"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Trade (% of GDP)": "74.01445"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Trade (% of GDP)": "76.787384"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Trade (% of GDP)": "75.69408"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Trade (% of GDP)": "75.382126"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Trade (% of GDP)": "59.5207"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Trade (% of GDP)": "75.5904"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "Trade (% of GDP)": "84.698044"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Trade (% of GDP)": "82.35249"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "Trade (% of GDP)": "79.454636"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1960", "Trade (% of GDP)": "106.186806"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1961", "Trade (% of GDP)": "113.74833"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1962", "Trade (% of GDP)": "40.61252"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1963", "Trade (% of GDP)": "61.510197"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1964", "Trade (% of GDP)": "54.523823"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1965", "Trade (% of GDP)": "48.437023"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1966", "Trade (% of GDP)": "50.63977"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1967", "Trade (% of GDP)": "45.066185"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1968", "Trade (% of GDP)": "47.322887"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1969", "Trade (% of GDP)": "51.859535"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1970", "Trade (% of GDP)": "51.225403"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1971", "Trade (% of GDP)": "46.106297"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1972", "Trade (% of GDP)": "46.17643"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1973", "Trade (% of GDP)": "57.093426"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1974", "Trade (% of GDP)": "74.23882"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1975", "Trade (% of GDP)": "76.65453"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1976", "Trade (% of GDP)": "70.173256"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1977", "Trade (% of GDP)": "72.32688"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1978", "Trade (% of GDP)": "65.70457"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1979", "Trade (% of GDP)": "64.014046"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1980", "Trade (% of GDP)": "64.676926"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1981", "Trade (% of GDP)": "65.465"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1982", "Trade (% of GDP)": "59.92293"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1983", "Trade (% of GDP)": "53.744118"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1984", "Trade (% of GDP)": "53.176384"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1985", "Trade (% of GDP)": "50.326122"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1986", "Trade (% of GDP)": "36.02671"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1987", "Trade (% of GDP)": "32.684586"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1988", "Trade (% of GDP)": "38.111588"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1989", "Trade (% of GDP)": "47.15332"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1990", "Trade (% of GDP)": "48.380714"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1991", "Trade (% of GDP)": "52.717587"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1992", "Trade (% of GDP)": "49.189083"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1993", "Trade (% of GDP)": "44.922813"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1994", "Trade (% of GDP)": "48.58444"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1995", "Trade (% of GDP)": "55.191006"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1996", "Trade (% of GDP)": "53.705147"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1997", "Trade (% of GDP)": "52.24391"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1998", "Trade (% of GDP)": "45.09445"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1999", "Trade (% of GDP)": "50.929108"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Trade (% of GDP)": "62.858345"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Trade (% of GDP)": "54.529648"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Trade (% of GDP)": "56.589603"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Trade (% of GDP)": "57.769135"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Trade (% of GDP)": "61.357998"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Trade (% of GDP)": "66.84046"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Trade (% of GDP)": "65.91529"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Trade (% of GDP)": "67.54547"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Trade (% of GDP)": "71.028496"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Trade (% of GDP)": "64.28475"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Trade (% of GDP)": "63.491215"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Trade (% of GDP)": "62.22009"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Trade (% of GDP)": "60.75764"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Trade (% of GDP)": "59.15101"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Trade (% of GDP)": "56.923542"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Trade (% of GDP)": "53.204624"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Trade (% of GDP)": "49.760933"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Trade (% of GDP)": "49.820816"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Trade (% of GDP)": "52.43617"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Trade (% of GDP)": "46.506844"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Trade (% of GDP)": "40.389755"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Trade (% of GDP)": "46.83706"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2022", "Trade (% of GDP)": "51.19009"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "Trade (% of GDP)": "43.7398"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2024", "Trade (% of GDP)": "40.134575"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2002", "Trade (% of GDP)": "210.9375"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2003", "Trade (% of GDP)": "207.44275"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2004", "Trade (% of GDP)": "202.35756"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2005", "Trade (% of GDP)": "223.2"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2006", "Trade (% of GDP)": "229.81744"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2007", "Trade (% of GDP)": "218.14671"}], "rows_tail": [{"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Trade (% of GDP)": "55.734985"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Trade (% of GDP)": "53.911423"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Trade (% of GDP)": "55.588245"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Trade (% of GDP)": "57.12412"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Trade (% of GDP)": "55.90911"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Trade (% of GDP)": "51.973362"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Trade (% of GDP)": "56.71048"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Trade (% of GDP)": "62.421665"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Trade (% of GDP)": "58.198174"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Trade (% of GDP)": "56.758144"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1990", "Trade (% of GDP)": "27.627666"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1991", "Trade (% of GDP)": "47.92136"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1992", "Trade (% of GDP)": "46.150352"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1993", "Trade (% of GDP)": "58.140415"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1994", "Trade (% of GDP)": "68.96404"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1995", "Trade (% of GDP)": "87.444695"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1996", "Trade (% of GDP)": "85.93911"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1997", "Trade (% of GDP)": "80.14599"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1998", "Trade (% of GDP)": "72.661995"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1999", "Trade (% of GDP)": "72.00393"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Trade (% of GDP)": "75.438835"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Trade (% of GDP)": "70.892395"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Trade (% of GDP)": "74.73045"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Trade (% of GDP)": "74.382614"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Trade (% of GDP)": "71.84667"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Trade (% of GDP)": "76.773285"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Trade (% of GDP)": "82.076164"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Trade (% of GDP)": "79.12342"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Trade (% of GDP)": "81.24472"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Trade (% of GDP)": "68.07139"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Trade (% of GDP)": "64.385635"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Trade (% of GDP)": "63.05946"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Trade (% of GDP)": "61.361637"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Trade (% of GDP)": "52.51188"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Trade (% of GDP)": "48.243042"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Trade (% of GDP)": "30.367659"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Trade (% of GDP)": "31.081545"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Trade (% of GDP)": "42.969906"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Trade (% of GDP)": "56.077824"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Trade (% of GDP)": "66.6506"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Trade (% of GDP)": "69.226776"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Trade (% of GDP)": "63.821484"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Trade (% of GDP)": "58.27286"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Trade (% of GDP)": "56.250305"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Trade (% of GDP)": "59.767532"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Trade (% of GDP)": "60.38747"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Trade (% of GDP)": "64.66828"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Trade (% of GDP)": "64.9165"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Trade (% of GDP)": "62.312843"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Trade (% of GDP)": "70.81307"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Trade (% of GDP)": "62.200283"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Trade (% of GDP)": "57.85682"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Trade (% of GDP)": "65.77146"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Trade (% of GDP)": "59.454887"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Trade (% of GDP)": "56.12138"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Trade (% of GDP)": "65.0147"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Trade (% of GDP)": "72.79543"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Trade (% of GDP)": "75.36771"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Trade (% of GDP)": "80.45624"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Trade (% of GDP)": "76.1935"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Trade (% of GDP)": "79.86527"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Trade (% of GDP)": "73.958565"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Trade (% of GDP)": "71.58569"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Trade (% of GDP)": "74.88837"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Trade (% of GDP)": "68.79121"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Trade (% of GDP)": "79.20685"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Trade (% of GDP)": "86.20851"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Trade (% of GDP)": "69.29732"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Trade (% of GDP)": "78.24189"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Trade (% of GDP)": "62.53489"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Trade (% of GDP)": "47.19861"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Trade (% of GDP)": "41.557346"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Trade (% of GDP)": "41.61263"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Trade (% of GDP)": "42.007145"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Trade (% of GDP)": "44.461136"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Trade (% of GDP)": "49.890415"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Trade (% of GDP)": "45.330643"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Trade (% of GDP)": "39.14529"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Trade (% of GDP)": "35.916855"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Trade (% of GDP)": "41.366135"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Trade (% of GDP)": "44.213676"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Trade (% of GDP)": "45.570354"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Trade (% of GDP)": "45.290596"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Trade (% of 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"Zimbabwe", "Code": "ZWE", "Year": "2000", "Trade (% of GDP)": "74.07089"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Trade (% of GDP)": "67.898094"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Trade (% of GDP)": "66.80982"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Trade (% of GDP)": "70.451965"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Trade (% of GDP)": "76.03943"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Trade (% of GDP)": "76.04307"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Trade (% of GDP)": "82.82003"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Trade (% of GDP)": "84.16828"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Trade (% of GDP)": "109.52354"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Trade (% of GDP)": "61.77649"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Trade (% of GDP)": "83.123405"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Trade (% of GDP)": "89.46594"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Trade (% of GDP)": "74.1627"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Trade (% of GDP)": "58.656075"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Trade (% of GDP)": "54.67176"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Trade (% of GDP)": "56.749653"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Trade (% of GDP)": "51.219036"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Trade (% of GDP)": "55.028316"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Trade (% of GDP)": "54.550316"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Trade (% of GDP)": "46.106544"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Trade (% of GDP)": "40.178406"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Trade (% of GDP)": "44.11456"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Trade (% of GDP)": "55.067352"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Trade (% of GDP)": "40.29126"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Trade (% of GDP)": "41.48653"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "trade-as-share-of-gdp", "metadata_url": "https://ourworldindata.org/grapher/trade-as-share-of-gdp.metadata.json", "chart_title": "Trade as a share of GDP", "chart_subtitle": "Sum of exports and imports of goods and services, divided by gross domestic product, expressed as a percentage. This is also known as the \"trade openness index\".", "chart_note": null, "chart_citation": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates (2026)", "original_chart_url": "https://ourworldindata.org/grapher/trade-as-share-of-gdp", "owid_column_metadata": {"Trade (% of GDP)": {"titleShort": "Trade (% of GDP)", "titleLong": "Trade (% of GDP)", "descriptionShort": "Trade in goods and services as a share of GDP, shown as a percentage.", "descriptionKey": ["This indicator measures a country’s total trade (exports and imports) relative to the size of its economy.", "Exports of goods and services represent the value of all goods and other market services provided to the rest of the world. Imports represent the value of all goods and other market services received from the rest of the world. In both cases, they include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services."], "shortUnit": "%", "unit": "% of GDP", "timespan": "1960-2024", "type": "Numeric", "owidVariableId": 1204733, "shortName": "ne_trd_gnfs_zs", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates (2026) – processed by Our World in Data", "citationLong": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates (2026) – processed by Our World in Data. “Trade (% of GDP)” [dataset]. National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates, “World Development Indicators 125” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1204733.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "0bb8386ff3fd7a9b98bb"},
{"raw_link": "https://ourworldindata.org/new-feature-embed-archived-charts", "title": "New feature: embed archived versions of our interactive charts in your website", "context": "New feature: embed archived versions of our interactive charts in your website\nLearn more about different options for embedding our interactive charts.\nBy\nMarcel Gerber\nand\nCharlie Giattino\nJuly 9, 2025\nBrowse past versions\nReuse our work freely\nWe’re very excited to announce that you can now embed archived versions of our interactive charts in your website.\nIt’s already been possible to embed\nlive\nversions that get updated whenever we update the chart on our site; for example, when we add new data.\nThe new archived versions, in contrast, will never change or update, offering you more control over how our charts appear on your site.\nMany of our users are happy that their embedded charts stay updated with the latest data. But we’ve heard from others that they’d prefer to have an option where the chart didn’t change; for instance, when referring to specific data points in an article.\nNow you can choose one or the other depending on what’s best for you.\nIn this article, we describe how to embed both archived and live versions of our charts and how to choose between the two options.\nHow to embed our charts\nTo embed one of our interactive charts, simply:\nclick the “Share” button at the bottom of the chart\nclick “Embed”\ncopy the iframe code for either “Chart with data updates” or “Archived chart without data updates”\n1\npaste it into any HTML page\nFor example, to embed\nour map on the universal right to vote\n, the iframe code for the archived embed is:\n<iframe src=\"https://archive.ourworldindata.org/20250626-192628/grapher/universal-suffrage-lexical.html\" loading=\"lazy\" style=\"width: 100%; height: 600px; border: 0px none;\" allow=\"web-share; clipboard-write\"></iframe>\nThe iframe code (and URL) will adapt to your selections. For example, if you configure the map to zoom in on Europe in 1925, the iframe code becomes:\n<iframe src=\"https://archive.ourworldindata.org/20250626-192628/grapher/universal-suffrage-lexical.html?time=1925&region=Europe&globe=1&globeRotation=55%2C10&globeZoom=2.95\" loading=\"lazy\" style=\"width: 100%; height: 600px; border: 0px none;\" allow=\"web-share; clipboard-write\"></iframe>\nThis applies to other selections you might make, such as looking at the line chart instead of the map view.\nHere are a few examples of websites that embed our charts so you can see what this looks like:\nLuxembourg Income Study (LIS) Cross-national Data Center\n80,000 Hours page on risks from artificial intelligence\nEarth.org page on air pollution\nCORE Econ open-access economics textbooks\nHow to choose between live or archived embeds\nHere are some points to consider when choosing between embedding a “chart with data updates” and an “archived chart without data updates”.\nChart with data updates\nChoose this option if you always want to show the latest data for a chart; for example, for dashboards on a given topic. Keep in mind you’ll be embedding a chart that:\nwill likely change in the future; for example, when we update to the latest available data from the source, or make small changes to the text to reflect changes in the data or to improve clarity\nmay change significantly; for example, we might feel it’s best to change the dataset to a different source, or remove historical data points\nin rare cases may be deleted; for example, if we decide that the data quality is no longer up to our standards and there isn’t an alternative data source.\nArchived chart without data updates\nChoose this option if you want to refer to specific data points or trends in a stable way; for example, in the context of an article or online exam. Keep in mind you’ll be embedding a chart that:\nwill always show the exact data, text, and other content as it does on the day of embedding; it is frozen in time\nwill not be deleted\nmay contain outdated data that has since been updated in the live version of the chart, or data that has since been found to contain quality issues\nMore stable citations with archived charts\nOur citation instructions now point to archived versions of charts, to ensure that any data points you are referring to in the cited chart remain stable and unchanged.\nIn the citation instructions there is also a link to view and browse the archived versions of a chart on our site, as you can see in this video.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nBuilt in response to user feedback\nWe built this feature in response to feedback from users like you.\nWhat do you think about our new archived charts and embeds?\nLet us know by filling out\nour feedback form\nor emailing us at\ninfo@ourworldindata.org\n. We love to hear feedback and always consider it when deciding the direction of our work.\nEndnotes\nAn iframe (inline frame) is used to display a website within another website; w3schools has\nmore information on iframes\n.\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "new-feature-embed-archived-charts", "source_url": "https://ourworldindata.org/new-feature-embed-archived-charts", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Learn more about different options for embedding our interactive charts.", "numeric_mentions": ["9,", "2025", "1", "20250626", "192628", "100%", "600", "0", "1925,", "1925", "55%", "2", "2.95", "80,000"], "numeric_evidence": [{"title": "Universal right to vote", "source_url": "https://ourworldindata.org/grapher/universal-suffrage-lexical.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Universal right to vote"], "row_count_total": 35624, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1789", "Universal right to vote": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1790", "Universal right to vote": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1791", "Universal right to vote": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1792", "Universal right to vote": "0"}, {"Entity": "Afghanistan", 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"grapher_slug": "universal-suffrage-lexical", "metadata_url": "https://ourworldindata.org/grapher/universal-suffrage-lexical.metadata.json", "chart_title": "Universal right to vote", "chart_subtitle": "This does not consider elections not being held, informal restrictions, or legal restrictions based on age, criminal conviction, disability, and local residency.", "chart_note": "", "chart_citation": "Lexical Index (2026)", "original_chart_url": "https://ourworldindata.org/grapher/universal-suffrage-lexical", "owid_column_metadata": {"Universal suffrage": {"titleShort": "Universal right to vote", "titleLong": "Universal right to vote", "descriptionShort": "Indicates whether virtually all men and women that are citizens are allowed to vote in national elections (score 2), whether it is only men (score 1), or there is no universal rights to vote for either men or women (score 0). 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{"raw_link": "https://ourworldindata.org/mobile-money-why-it-matters", "title": "There are now more than half a billion mobile money accounts in the world, mostly in Africa — here's why this matters", "context": "Home\nTechnological Change\nThere are now more than half a billion mobile money accounts in the world, mostly in Africa — here's why this matters\nMobile money allows people without banks to securely transfer funds via text message, and its adoption is growing rapidly.\nBy\nSimon van Teutem\nJuly 7, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nBy the end of today, you'll probably have used your bank account — maybe to buy groceries, pay rent, or send money to a friend. Even better, to receive your salary. It's something many of us take for granted.\nHowever, for more than a billion people globally, transactions only happen with cash.\n1\nThat means carrying around physical notes and coins, traveling long distances just to send or receive money, and facing the constant risk of losing it or having it stolen. The absence of formal banking services adds yet another hurdle for people trying to escape poverty.\nBut in recent years, “mobile money” has transformed how many people access financial services. Mobile money differs from traditional bank accounts; you don’t need a physical bank branch or even an Internet connection. Instead, you use text messages for services like deposits, transfers, and payments via a mobile phone.\n2\nIn this sense, it’s\nnot\nthe same as standard Internet banking, which many of us now use for most transactions.\nMany people might be unfamiliar with how mobile money works, so let me briefly explain. You dial a short code for the mobile money provider, choose “send money”, and enter the recipient’s phone number (which serves as their account number). Next, type the amount and your secure PIN.\n3\nThat’s it — both the sender and recipient get an SMS confirmation within seconds. If you need to add funds to your mobile money account or retrieve your PIN, you can visit a local mobile money agent, often found in small shops or kiosks, which can be easier to reach than traditional banks.\nIn this article, I'll look at how mobile money opens up new opportunities for bank account ownership in developing countries and why this matters.\nMore than half of mobile money accounts are in Sub-Saharan Africa\nIn 2010, there were just 13 million mobile money accounts in the world, fewer than the population of my home country, the Netherlands. By 2023, this had reached more than 640 million. That’s more than twice the total number of Netflix subscriptions worldwide.\n4\nYou can see this growth in the chart below.\nWhat’s immediately obvious is how much of this growth has come from Sub-Saharan Africa; it’s home to more than half of the world’s accounts. In 2023, there were over 330 million active mobile money accounts in the region; more than one mobile money account for every four people.\n5\nWhat’s changed? One of the obvious drivers of this growth has been the widespread adoption of mobile phones, not just in the richest countries but across the globe. Mobile subscriptions have surged\nin nearly every region\n.\nBut the total\nnumber\nof mobile money accounts doesn’t tell us what\npercentage\nof people use mobile money. A small portion of people could each have many accounts. So instead of examining absolute numbers, let's look at the share of people with mobile money accounts in Sub-Saharan Africa.\nAs the chart below illustrates, the percentage of people in Sub-Saharan Africa with a mobile money account grew rapidly, from 12% in 2014 to 33% by 2021.\nThe chart also highlights trends in a few specific countries: in Ghana and Uganda, mobile money has become the norm, with most people now using it. Adoption in Nigeria and Mauritius has been slower but continues to follow an upward trajectory. I'll explore the reasons behind these differences in adoption later.\nThere’s good reason to focus on Sub-Saharan Africa here, both in terms of how quickly mobile money has grown there (it hosts more than half of all accounts) and the financial benefits it could bring (the region is home to over\ntwo-thirds\nof the global population living in extreme poverty).\nMobile money boosts bank account ownership in Sub-Saharan Africa\nThere are two main types of overall bank accounts: those at traditional financial institutions, like banks, and mobile money accounts. Some individuals exclusively use institutional accounts, others rely solely on mobile money, while many use both. How has this surge of mobile money across Sub-Saharan Africa affected overall bank account ownership?\nThe chart below illustrates how mobile money has contributed to the growth of\ntotal\naccount ownership in Sub-Saharan Africa and across three countries.\nIn 2014, one-third of adults in Sub-Saharan Africa had a bank account. By 2021, this had increased to more than half. But as you can see in the chart, the share that\nonly\nhad an account at a financial institution did not change over that period — the line is flat, at 22%. That means that almost all of the growth in this share has come from those getting a mobile money account (either on its own or alongside one from a financial institution).\nIn Malawi, a\nlow-income country\nin Sub-Saharan Africa, the percentage of people with any kind of bank account more than doubled between 2014 and 2021, largely because of a rapid increase in mobile money accounts. For every person with a mobile money account in 2014, more than eight people had one by 2021.\nIn Togo, the share of individuals with financial institution accounts, including those combined with mobile money, even declined between 2017 and 2021, while mobile money usage more than doubled. In other words, mobile money doesn’t just help people access financial services for the first time; it also offers a preferred alternative for some people already using traditional accounts.\nIn Sub-Saharan Africa, more people now receive wages through their mobile phones than through traditional bank accounts.\n6\nHaving a bank account might seem basic, but rising ownership is changing employment, safety nets, and aid systems\nWhy does this rapid development matter? Most of us probably think of our bank accounts as boring services — possibly even a chore to manage — but the ability to easily deposit and transfer money via a mobile phone unlocks life-changing opportunities for many.\nBank accounts are gateways to the formal financial system. They provide two essential things everyone needs: a secure place to store money and a simple way to send or receive payments. Here are three ways this technology makes a difference.\nFlexibility: more freedom to work or study where you want\nWhen sending and receiving money becomes easier, people can make different decisions about where and how they earn a living.\n7\nIf you exclusively handle money in the form of cash, you need to be close to your family if you want to give them money often and easily. You can’t move to a town or city far from your home village.\nThis geographical constraint disappears if you know you can send money home cheaply and reliably.\n8\nMobile money reduces the cost and difficulty of doing that, resulting in more people making the move.\nIn rural Mozambique, researchers ran an experiment to understand the impact of mobile money on employment. With access to mobile money services, more people started moving from rural villages to cities, with a higher probability of finding a job with higher wages than farming.\n9\nThese results have been replicated in other studies.\n10\nNew job opportunities don’t just provide employment; they allow people to escape poverty and purchase small comforts beyond mere necessities. A 2016 study in\nScience\nfound that Kenya's M-PESA mobile money system increased household consumption levels and lifted 194,000 households (about 2% of all Kenyan households) out of extreme poverty.\n11\nAccess to bank accounts helps people support loved ones within countries\nand\nacross continents and oceans. As my colleague Tuna Acisu and I showed in\nanother article\n, the money migrants send back to their home countries is more than three times the amount that comes from international aid. Most of these funds flow from wealthier to poorer nations, and the costs of sending this money continue to drop.\nMobile money doesn’t just help people manage the income they already\nhave\n; it also opens up better ways to earn and distribute it.\nSafety net: risk spreading against unexpected income shocks\nI’ve discussed how mobile money helps people study or work elsewhere while continuing to support their families through regular transfers. But there's another, perhaps less visible way mobile money protects people in developing countries: as a shield against unexpected income shocks.\nLife throws curveballs. Your business crumbles overnight. The job you counted on vanishes. The crops you carefully nurtured yield half what you expected. These unexpected income shocks are severe disruptions for those living in poverty; small financial shifts can determine whether schooling or healthcare is affordable.\nWhen limited to cash, only those physically close can help during unexpected crises. They must physically hand you money when you need it most. Mobile money changes this dynamic: with a simple message, you can reach family or friends miles away, borrowing just enough to maintain stability when times are tough.\nA 2014 study in a top economic journal found that households without access to mobile accounts typically cut their spending by around 7% when hit with income shocks. In families\nwith\nmobile accounts, consumption remained steady, protected by their ability to quickly receive support.\n12\nOther studies found similar results with sudden health expenses.\n13\nMobile money is a practical safety net, helping families weather financial uncertainties.\nAid: support from people in rich countries is now easier and cheaper\nSo far, I’ve focused on transfers within lower-income countries themselves. However, mobile banking is also changing how people in wealthy countries can support people in the poorest ones. With a few taps on a phone, aid money travels directly to those who need it most, bypassing expensive middlemen and complicated logistics.\nForeign aid\nhas saved millions of lives. The USAID’s PEPFAR program alone is estimated to have prevented 25 million deaths from HIV\n14\n, and foreign aid has helped the world close in on the\neradication of polio\n— a disease that used to paralyze hundreds of thousands of children each year.\nBut aid delivery hasn’t always been efficient.\n15\nIt can be hampered by expensive administration,\nflawed designs\n, and occasionally corruption, though the latter is\nless widespread than commonly believed\n.\nMobile money offers a simpler path. Organizations like\nGiveDirectly\nuse mobile money to send cash directly to recipients' phones. For every $100 donated, $89 reaches families in need.\nSome aid organizations combine these direct cash transfers using mobile money with basic support services — a productive asset (like livestock or a tool) and some training. These so-called \"big push programs\" produce lasting changes in people's lives.\n16\nWhile mobile money creates powerful changes within countries — opening job opportunities, connecting families, and protecting against unexpected hardship — it also improves global development efforts.\n17\nWithout phones or IDs, many can't access mobile money\nThe adoption of mobile money has grown quickly in some countries (like Ghana) while progress has been much slower in others (like Nigeria). To help more people gain access to financial services, it’s important to understand the reasons behind these differences.\nOne major barrier to using mobile money is still not having a mobile phone. The Global Findex 2021 survey asked adults without bank accounts why they don’t use mobile money. A lack of money was the top reason. But the next most common was the absence of a mobile phone (probably related to a lack of money). While three-quarters of Sub-Saharan Africans in the survey now own phones, many countries still have low ownership rates.\n18\nThe chart below shows the share of adults with a mobile money account against the share of adults with a mobile phone for countries in Sub-Saharan Africa. The two are positively correlated: unsurprisingly, people in countries with more phones are more likely to use mobile money.\nAccess to affordable phones is key to improving financial inclusion in low-income countries. However, there’s another surprising challenge: the lack of necessary documentation.\nIn Sub-Saharan Africa, 13% of adults without bank accounts said they didn’t use mobile money because they didn’t have the required documents.\n18\nThis was a bigger barrier than the distance to mobile money agents, or even the lack of a mobile phone in some countries.\nMany people in Sub-Saharan Africa still lack formal identification. The World Bank found that in seven Sub-Saharan African countries, fewer than 60% of adults have an ID. In Togo, only 40% of adults have one.\nReasons for this include not having a birth certificate, high fees for obtaining IDs, or the difficulty of traveling to registration offices. Making it easier and cheaper to get an ID could boost access to mobile money and help people find jobs, receive medical care, and vote.\nAlternatively, mobile money providers and network providers could explore tiered verification systems, allowing basic mobile money accounts with lower transaction limits using minimal documentation, while requiring full identification only for higher-value services.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nJust over a decade of progress with mobile money, and still much to do\nIn just over a decade, mobile money has achieved what traditional banking couldn't do in centuries: give bank accounts to billions of people in rural areas in developing countries. This technology changes how people manage their money, pursue better opportunities, and support each other.\nStill, more than one billion people rely exclusively on cash. Many of them live in the poorest regions and in remote areas, where they could benefit most from financial inclusion. If the evidence presented in this article shows anything, it's that there remains huge untapped potential for improving lives when these people gain access to basic financial services.\nAcknowledgments\nThanks to Hannah Ritchie, Saloni Dattani, and Edouard Mathieu for their feedback and comments on this article, and to the World Bank for publishing\nthis insightful article\n, which sparked my interest.\nContinue reading on Our World in Data\nTwo billion people don’t have safe drinking water: what does this really mean for them?\nFor billions, it can mean hours spent collecting water. For almost a million, it means dying from disease.\nThe number of people without electricity more than halved over the last 20 years\nThe world has made significant progress on improving electricity access.\nWhat is economic growth? And why is it so important?\nThe goods and services that we all need are not just there; they need to be produced. Growth means that their quality and quantity increase.\nEndnotes\n”Over 80% of the world's 1.4 billion adults without financial accounts reside in places at risk from climate, intensifying their susceptibility to economic and environmental shocks.”\nWorld Bank\n”Similar to prepaid credit, mobile money enables convenient electronic money transfers. The mobile phone number is linked to the bank account. This also means that the ability to make financial transactions is not reliant on Internet access. A mobile phone signal is all that’s required.”\nNevis\nWise.com,\nMTN Mobile Money international transfer guide\n.\nReuters,\nNetflix signals confidence with upbeat revenue outlook\n.\nThe estimated population of Sub-Saharan Africa in 2023 was 1.26 billion, according to the\nWorld Bank\n.\nWorld Bank,\nFinancial Inclusion Overview\n.\nIn 2021,\nmore than half\nof all mobile money users in Sub-Saharan Africa used their accounts to send or receive money from family members elsewhere in their country.\n”To explain the negative impact of mobile money on agricultural activity and investment, we conjectured this effect may be due to an increase in migration out of rural areas. This may be the result of the substantial decrease in the transaction costs associated with sending migrant remittances to rural areas, leading not only to an increase in the value of migrant remittances received by treated rural households, as learnt from our empirical analysis, but also to increased incentives to move away from rural to urban areas – where there is a higher probability of finding a more productive occupation.” Batista, C., & Vicente, P. C. (2023). Is mobile money changing rural Africa? Evidence from a field experiment. Review of Economics and Statistics, 1-29.\nBatista, C., & Vicente, P. C. (2023). Is mobile money changing rural Africa? Evidence from a field experiment. Review of Economics and Statistics, 1-29.\nA similar pattern showed up in a randomized control trial in Uganda. In rural regions far away from bank branches, people who gained access to mobile money were more likely to get a job outside of farming. The share of people in non-farm self-employment almost doubled — from 3.4% to 6.4%. At the same time, severe food insecurity fell: the share of households with very low food security dropped from 63% to 47%.\nIn Kenya, researchers tracked what happened as the mobile money system M-PESA expanded across the country. In places with better access to mobile money agents, women began shifting away from traditional farming. Between 2008 and 2014, an estimated 185,000 women moved into small-scale retail.\nSuri, T., & Jack, W. (2016).\nThe long-run poverty and gender impacts of mobile money\n. Science, 354(6317), 1288-1292.\nSuri, T., & Jack, W. (2016).\nThe long-run poverty and gender impacts of mobile money\n. Science, 354(6317), 1288-1292.\nJack, W., & Suri, T. (2014). Risk sharing and transactions costs: Evidence from Kenya's mobile money revolution. American Economic Review, 104(1), 183-223.\nResearch shows that people who experienced negative health shocks\nwithout\nM-PESA often had to make painful sacrifices – cutting non-food expenses and even withdrawing their children from school to cover these costs. Those with mobile money accounts, however, could spend more. Similar results have been documented in randomized control trials in\nMozambique\nand\nUganda\n. Sources: -\nThe Abdul Latif Jameel Poverty Action Lab\n- Suri, T., Jack, W., & Stoker, T. M. (2012). D\nocumenting the birth of a financial economy\n. Proceedings of the National Academy of Sciences, 109(26), 10257-10262. - Jack, W., & Suri, T. (2014). Risk sharing and transactions costs: Evidence from Kenya's mobile money revolution. American Economic Review, 104(1), 183-223.\nHIV.gov,\nWhat is PEPFAR?\nWhen Rory Stewart served as Britain’s development minister, he uncovered alarming inefficiencies — like projects costing £40,000 that\ndelivered\njust £2,000 in real impact. That’s a 95% loss in value.\nRandomized control trials across several countries found that participant households experienced considerably more food consumption, higher income, increased work hours, improved mental health, and greater political involvement, with all these benefits persisting after two years. What’s especially compelling is that these programs often generate more value than they cost. Abhijit Banerjee et al., A multifaceted program causes lasting progress for the very poor: Evidence from six countries. Science 348, 1260799 (2015). DOI: 10.1126/science.1260799\nYou might wonder whether these programs truly contribute to development in a broader sense. Consider this: helping people move beyond mere survival is often necessary for broader development to take root. The evidence clearly shows these programs lift people out of extreme poverty and dramatically improve the lives of the poorest, creating a foundation for further progress.\nWorld Bank,\nData From the Global Findex 2021: The Impact of Mobile Money in Sub-Saharan Africa\n.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSimon van Teutem (2025) - “There are now more than half a billion mobile money accounts in the world, mostly in Africa — here's why this matters” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-090244/mobile-money-why-it-matters.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-mobile-money-why-it-matters,\nauthor = {Simon van Teutem},\ntitle = {There are now more than half a billion mobile money accounts in the world, mostly in Africa — here's why this matters},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-090244/mobile-money-why-it-matters.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "mobile-money-why-it-matters", "source_url": "https://ourworldindata.org/mobile-money-why-it-matters", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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{"Entity": "South Asia", "Year": "2018", "Active mobile money accounts": "87211100"}, {"Entity": "South Asia", "Year": "2019", "Active mobile money accounts": "82927000"}, {"Entity": "South Asia", "Year": "2020", "Active mobile money accounts": "94404830"}, {"Entity": "South Asia", "Year": "2021", "Active mobile money accounts": "101637760"}, {"Entity": "South Asia", "Year": "2022", "Active mobile money accounts": "116046140"}, {"Entity": "South Asia", "Year": "2023", "Active mobile money accounts": "124917784"}, {"Entity": "South Asia", "Year": "2024", "Active mobile money accounts": "139414600"}, {"Entity": "Sub-Saharan Africa", "Year": "2010", "Active mobile money accounts": "10431262"}, {"Entity": "Sub-Saharan Africa", "Year": "2011", "Active mobile money accounts": "16535769"}, {"Entity": "Sub-Saharan Africa", "Year": "2012", "Active mobile money accounts": "27475952"}, {"Entity": "Sub-Saharan Africa", "Year": "2013", "Active mobile money accounts": "41734932"}, {"Entity": "Sub-Saharan Africa", "Year": "2014", "Active mobile money accounts": "62225136"}, {"Entity": "Sub-Saharan Africa", "Year": "2015", "Active mobile money accounts": "83782600"}, {"Entity": "Sub-Saharan Africa", "Year": "2016", "Active mobile money accounts": "103285130"}, {"Entity": "Sub-Saharan Africa", "Year": "2017", "Active mobile money accounts": "129925890"}, {"Entity": "Sub-Saharan Africa", "Year": "2018", "Active mobile money accounts": "160472940"}, {"Entity": "Sub-Saharan Africa", "Year": "2019", "Active mobile money accounts": "190961090"}, {"Entity": "Sub-Saharan Africa", "Year": "2020", "Active mobile money accounts": "227375280"}, {"Entity": "Sub-Saharan Africa", "Year": "2021", "Active mobile money accounts": "274293980"}, {"Entity": "Sub-Saharan Africa", "Year": "2022", "Active mobile money accounts": "308724770"}, {"Entity": "Sub-Saharan Africa", "Year": "2023", "Active mobile money accounts": "370489950"}, {"Entity": "Sub-Saharan Africa", "Year": "2024", "Active mobile money accounts": "430034750"}], "rows_tail": [], "sampling_note": "Stored first 90 rows and last 90 rows when the table is larger.", "grapher_slug": "active-mobile-money-accounts", "metadata_url": "https://ourworldindata.org/grapher/active-mobile-money-accounts.metadata.json", "chart_title": "Active mobile money accounts", "chart_subtitle": "Mobile money accounts are financial accounts managed via mobile devices. They offer services like deposits, transfers, and payments, mainly in regions with limited banking access.", "chart_note": "Accounts are considered active when they have been used to perform at least one mobile money payment during the last 90 days of each year. North America is not shown because mobile money accounts are not used across this region.", "chart_citation": "GSM Association (2025)", "original_chart_url": "https://ourworldindata.org/grapher/active-mobile-money-accounts", "owid_column_metadata": {"Active mobile money accounts": {"titleShort": "Active mobile money accounts", "titleLong": "Active mobile money accounts", "descriptionShort": "Mobile money accounts used to perform at least one mobile money payment during the last 90 days of each year.", "descriptionKey": ["Mobile money accounts are financial accounts managed via mobile devices. They offer services like deposits, transfers, and payments, mainly in regions with limited banking access.", "In this data, accounts are considered active when they have been used to perform at least one mobile money payment during the last 90 days of each year.", "North America is not shown because mobile money accounts are not used across this region."], "shortUnit": "", "unit": "active accounts", "timespan": "2010-2024", "type": "Numeric", "owidVariableId": 1209706, "shortName": "active_accounts_90d", "lastUpdated": "2026-03-09", "nextUpdate": "2027-03-09", "citationShort": "GSM Association (2025) – with minor processing by Our World in Data", "citationLong": "GSM Association (2025) – with minor processing by Our World in Data. “Active mobile money accounts” [dataset]. GSM Association, “Global Mobile Money Dataset” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1209706.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "World Bank income groups", "source_url": "https://ourworldindata.org/grapher/world-bank-income-groups.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "World Bank's income classification"], "row_count_total": 7953, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "World Bank's income classification": "Low-income countries"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "World Bank's income classification": "Low-income countries"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "World Bank's income classification": "Low-income countries"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "World Bank's income classification": "Low-income countries"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "World Bank's income classification": "Low-income countries"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "World 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{"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "World Bank's income classification": "Lower-middle-income countries"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "world-bank-income-groups", "metadata_url": "https://ourworldindata.org/grapher/world-bank-income-groups.metadata.json", "chart_title": "World Bank income groups", "chart_subtitle": "The World Bank's income classification divides countries into four categories based on their gross national income (GNI) per capita. Thresholds between income groups have changed over time.", "chart_note": "Countries are grouped based on the income classification for each respective year. This means that group membership can change over time. Venezuela and Ethiopia are currently unclassified.", "chart_citation": "World Bank (2025)", "original_chart_url": "https://ourworldindata.org/grapher/world-bank-income-groups", "owid_column_metadata": {"World Bank's income classification": {"titleShort": "World Bank's income classification", "titleLong": "World Bank's income classification", "descriptionShort": "Income classification based on the country's income each year.", "descriptionKey": ["The World Bank creates a yearly classification of countries by income, for all countries with population over 30,000.", "This classification stays the same throughout the World Bank's fiscal year (from July 1 to June 30) even if the income data for a country changes.", "Low-income countries are those with a gross national income (GNI) per capita of $1,135 or less in 2024.", "Lower-middle-income countries are those with a GNI per capita between $1,136 and $4,495 in 2024.", "Upper-middle-income countries are those with a GNI per capita between $4,496 and $13,935 in 2024.", "High-income countries are those with a GNI per capita of more than $13,935 in 2024.", "Venezuela, classified as an upper-middle income country until the fiscal year 2021, has been unclassified since then due to the unavailability of data. Ethiopia is currently in a temporary status of unclassification."], "unit": "", "timespan": "1987-2024", "type": "Ordinal", "owidVariableId": 1077017, "shortName": "classification", "lastUpdated": "2025-07-01", "nextUpdate": "2026-07-01", "citationShort": "World Bank (2025) – with major processing by Our World in Data", "citationLong": "World Bank (2025) – with major processing by Our World in Data. “World Bank's income classification” [dataset]. World Bank, “Income Classifications” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077017.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Share of people using mobile money accounts", "source_url": "https://ourworldindata.org/grapher/mobile-money-account-usage.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Share of people aged 15+ using a mobile money account"], "row_count_total": 264, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.3044038"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Share of people aged 15+ using a mobile money account": "0.91381615"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Share of people aged 15+ using a mobile money account": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Share of people aged 15+ using a mobile money account": "2.377762"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.43231958"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2017", "Share of people aged 15+ using a mobile money account": "2.4161675"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2021", "Share of people aged 15+ using a mobile money account": "35.084137"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.6578374"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2017", "Share of people aged 15+ using a mobile money account": "9.755306"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2021", "Share of people aged 15+ using a mobile money account": "16.742744"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2014", "Share of people aged 15+ using a mobile money account": "2.6917143"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2017", "Share of people aged 15+ using a mobile money account": "21.24596"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2021", "Share of people aged 15+ using a mobile money account": "29.005793"}, {"Entity": "Benin", "Code": "BEN", "Year": "2014", "Share of people aged 15+ using a mobile money account": "2.022727"}, {"Entity": "Benin", "Code": "BEN", "Year": "2017", "Share of people aged 15+ using a mobile money account": "18.093838"}, {"Entity": "Benin", "Code": "BEN", "Year": "2021", "Share of people aged 15+ using a mobile money account": "36.683716"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2014", "Share of people aged 15+ using a mobile money account": "2.7771435"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2017", "Share of people aged 15+ using a mobile money account": "7.1193895"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2021", "Share of people aged 15+ using a mobile money account": "12.954222"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2014", "Share of people aged 15+ using a mobile money account": "20.751524"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2017", "Share of people aged 15+ using a mobile money account": "24.378307"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2022", "Share of people aged 15+ using a mobile money account": "36.56813"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.8577456"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2017", "Share of people aged 15+ using a mobile money account": "4.8365307"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2021", "Share of people aged 15+ using a mobile money account": "26.960125"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2014", "Share of people aged 15+ using a mobile money account": "3.0809696"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2017", "Share of people aged 15+ using a mobile money account": "33.022797"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2021", "Share of people aged 15+ using a mobile money account": "24.665169"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.7463846"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2014", "Share of people aged 15+ using a mobile money account": "13.294556"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2017", "Share of people aged 15+ using a mobile money account": "5.6595087"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2021", "Share of people aged 15+ using a mobile money account": "6.598233"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2014", "Share of people aged 15+ using a mobile money account": "1.7966049"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2017", "Share of people aged 15+ using a mobile money account": "15.137831"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2021", "Share of people aged 15+ using a mobile money account": "42.433025"}, {"Entity": "Chad", "Code": "TCD", "Year": "2014", "Share of people aged 15+ using a mobile money account": "5.750107"}, {"Entity": "Chad", "Code": "TCD", "Year": "2017", "Share of people aged 15+ using a mobile money account": "15.23341"}, {"Entity": "Chad", "Code": "TCD", "Year": "2022", "Share of people aged 15+ using a mobile money account": "12.086765"}, {"Entity": "Chile", "Code": "CHL", "Year": "2014", "Share of people aged 15+ using a mobile money account": "3.7898507"}, {"Entity": "Chile", "Code": "CHL", "Year": "2017", "Share of people aged 15+ using a mobile money account": "18.670815"}, {"Entity": "Colombia", "Code": "COL", "Year": "2014", "Share of people aged 15+ using a mobile money account": "2.212442"}, {"Entity": "Colombia", "Code": "COL", "Year": "2017", "Share of people aged 15+ using a mobile money account": "4.737451"}, {"Entity": "Colombia", "Code": "COL", "Year": "2021", "Share of people aged 15+ using a mobile money account": "21.766266"}, {"Entity": "Comoros", "Code": "COM", "Year": "2022", "Share of people aged 15+ using a mobile money account": "7.7259064"}, {"Entity": "Congo", "Code": "COG", "Year": "2014", "Share of people aged 15+ using a mobile money account": "1.985446"}, {"Entity": "Congo", "Code": "COG", "Year": "2017", "Share of people aged 15+ using a mobile money account": "6.238439"}, {"Entity": "Congo", "Code": "COG", "Year": "2021", "Share of people aged 15+ using a mobile money account": "36.77744"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2014", "Share of people aged 15+ using a mobile money account": "24.261375"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2017", "Share of people aged 15+ using a mobile money account": "34.053173"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2021", "Share of people aged 15+ using a mobile money account": "40.395943"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2014", "Share of people aged 15+ using a mobile money account": "9.208664"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2017", "Share of people aged 15+ using a mobile money account": "16.09634"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2022", "Share of people aged 15+ using a mobile money account": "22.676184"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2014", "Share of people aged 15+ using a mobile money account": "2.3180556"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2017", "Share of people aged 15+ using a mobile money account": "3.8946795"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2021", "Share of people aged 15+ using a mobile money account": "7.5716877"}, {"Entity": "East Asia and Pacific (excluding high income) (WB)", "Code": "", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.40373924"}, {"Entity": "East Asia and Pacific (excluding high income) (WB)", "Code": "", "Year": "2017", "Share of people aged 15+ using a mobile money account": "1.2205565"}, {"Entity": "East Asia and Pacific (excluding high income) (WB)", "Code": "", "Year": "2021", "Share of people aged 15+ using a mobile money account": "5.808365"}, {"Entity": "Ecuador", "Code": "ECU", "Year": "2017", "Share of people aged 15+ using a mobile money account": "2.9369748"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2014", "Share of people aged 15+ using a mobile money account": "1.1449442"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2017", "Share of people aged 15+ using a mobile money account": "1.7920163"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2021", "Share of people aged 15+ using a mobile money account": "2.915438"}, {"Entity": "El Salvador", "Code": "SLV", "Year": "2014", "Share of people aged 15+ using a mobile money account": "4.556788"}, {"Entity": "El Salvador", "Code": "SLV", "Year": "2017", "Share of people aged 15+ using a mobile money account": "3.549615"}, {"Entity": "El Salvador", "Code": "SLV", "Year": "2021", "Share of people aged 15+ using a mobile money account": "10.918221"}, {"Entity": "Eswatini", "Code": "SWZ", "Year": "2022", "Share of people aged 15+ using a mobile money account": "56.744396"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.027894864"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2017", "Share of people aged 15+ using a mobile money account": "0.3159289"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2022", "Share of people aged 15+ using a mobile money account": "4.638402"}, {"Entity": "Europe and Central Asia (excluding high income) (WB)", "Code": "", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.17085493"}, {"Entity": "Europe and Central Asia (excluding high income) (WB)", "Code": "", "Year": "2017", "Share of people aged 15+ using a mobile money account": "3.290283"}, {"Entity": "Europe and Central Asia (excluding high income) (WB)", "Code": "", "Year": "2021", "Share of people aged 15+ using a mobile money account": "16.652103"}, {"Entity": "Gabon", "Code": "GAB", "Year": "2014", "Share of people aged 15+ using a mobile money account": "6.648425"}, {"Entity": "Gabon", "Code": "GAB", "Year": "2017", "Share of people aged 15+ using a mobile money account": "43.577988"}, {"Entity": "Gabon", "Code": "GAB", "Year": "2021", "Share of people aged 15+ using a mobile money account": "57.47777"}, {"Entity": "Gambia", "Code": "GMB", "Year": "2022", "Share of people aged 15+ using a mobile money account": "3.7456253"}, {"Entity": "Georgia", "Code": "GEO", "Year": "2017", "Share of people aged 15+ using a mobile money account": "2.2010217"}, {"Entity": "Georgia", "Code": "GEO", "Year": "2021", "Share of people aged 15+ using a mobile money account": "8.231201"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2014", "Share of people aged 15+ using a mobile money account": "13.00655"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2017", "Share of people aged 15+ using a mobile money account": "38.94778"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2021", "Share of people aged 15+ using a mobile money account": "59.68793"}, {"Entity": "Guatemala", "Code": "GTM", "Year": "2014", "Share of people aged 15+ using a mobile money account": "1.7702768"}, {"Entity": "Guatemala", "Code": "GTM", "Year": "2017", "Share of people aged 15+ using a mobile money account": "2.126406"}, {"Entity": "Guatemala", "Code": "GTM", "Year": "2022", "Share of people aged 15+ using a mobile money account": "5.2358036"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2014", "Share of people aged 15+ using a mobile money account": "1.4732857"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2017", "Share of people aged 15+ using a mobile money account": "13.780469"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2021", "Share of people aged 15+ using a mobile money account": "21.510021"}, {"Entity": "Haiti", "Code": "HTI", "Year": "2014", "Share of people aged 15+ using a mobile money account": "3.7826896"}, {"Entity": "Haiti", "Code": "HTI", "Year": "2017", "Share of people aged 15+ using a mobile money account": "13.517189"}, {"Entity": "Honduras", "Code": "HND", "Year": "2014", "Share of people aged 15+ using a mobile money account": "3.408701"}, {"Entity": "Honduras", "Code": "HND", "Year": "2017", "Share of people aged 15+ using a mobile money account": "6.1960125"}, {"Entity": "Honduras", "Code": "HND", "Year": "2021", "Share of people aged 15+ using a mobile money account": "8.868231"}, {"Entity": "India", "Code": "IND", "Year": "2014", "Share of people aged 15+ using a mobile money account": "2.35221"}, {"Entity": "India", "Code": "IND", "Year": "2017", "Share of people aged 15+ using a mobile money account": "1.9945432"}, {"Entity": "India", "Code": "IND", "Year": "2021", "Share of people aged 15+ using a mobile money account": "10.439687"}, {"Entity": "Indonesia", "Code": "IDN", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.44963145"}, {"Entity": "Indonesia", "Code": "IDN", "Year": "2017", "Share of people aged 15+ using a mobile money account": "3.1239157"}, {"Entity": "Indonesia", "Code": "IDN", "Year": "2021", "Share of people aged 15+ using a mobile money account": "9.292318"}, {"Entity": "Iran", "Code": "IRN", "Year": "2014", "Share of people aged 15+ using a mobile money account": "4.4956913"}, {"Entity": "Iran", "Code": "IRN", "Year": "2017", "Share of people aged 15+ using a mobile money account": "26.29661"}, {"Entity": "Iran", "Code": "IRN", "Year": "2021", "Share of people aged 15+ using a mobile money account": "12.386351"}, {"Entity": "Iraq", "Code": "IRQ", "Year": "2017", "Share of people aged 15+ using a mobile money account": "4.2147555"}, {"Entity": "Iraq", "Code": "IRQ", "Year": "2021", "Share of people aged 15+ using a mobile money account": "4.805505"}, {"Entity": "Jamaica", "Code": "JAM", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.9165031"}, {"Entity": "Jamaica", "Code": "JAM", "Year": "2021", "Share of people aged 15+ using a mobile money account": "12.6601305"}, {"Entity": "Jordan", "Code": "JOR", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.46866584"}, {"Entity": "Jordan", "Code": "JOR", "Year": "2017", "Share of people aged 15+ using a mobile money account": "1.0609684"}, {"Entity": "Jordan", "Code": "JOR", "Year": "2021", "Share of people aged 15+ using a mobile money account": "10.659783"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2014", "Share of people aged 15+ using a mobile money account": "58.39263"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2017", "Share of people aged 15+ using a mobile money account": "72.93169"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2021", "Share of people aged 15+ using a mobile money account": "68.65869"}, {"Entity": "Kyrgyzstan", "Code": "KGZ", "Year": "2017", "Share of people aged 15+ using a mobile money account": "3.1068764"}, {"Entity": "Kyrgyzstan", "Code": "KGZ", "Year": "2021", "Share of people aged 15+ using a mobile money account": "11.429437"}, {"Entity": "Laos", "Code": "LAO", "Year": "2021", "Share of people aged 15+ using a mobile money account": "5.479982"}, {"Entity": "Latin America and Caribbean (excluding high income) (WB)", "Code": "", "Year": "2014", "Share of people aged 15+ using a mobile money account": "1.7115467"}, {"Entity": "Latin America and Caribbean (excluding high income) (WB)", "Code": "", "Year": "2017", "Share of people aged 15+ using a mobile money account": "5.2414107"}, {"Entity": "Latin America and Caribbean (excluding high income) (WB)", "Code": "", "Year": "2021", "Share of people aged 15+ using a mobile money account": "23.360256"}, {"Entity": "Lebanon", "Code": "LBN", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.6707376"}, {"Entity": "Lesotho", "Code": "LSO", "Year": "2017", "Share of people aged 15+ using a mobile money account": "27.641842"}], "rows_tail": [{"Entity": "Mauritania", "Code": "MRT", "Year": "2022", "Share of people aged 15+ using a mobile money account": "10.161957"}, {"Entity": "Mauritius", "Code": "MUS", "Year": "2014", "Share of people aged 15+ using a mobile 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{"Entity": "Middle-income countries", "Code": "", "Year": "2014", "Share of people aged 15+ using a mobile money account": "2.2172124"}, {"Entity": "Middle-income countries", "Code": "", "Year": "2017", "Share of people aged 15+ using a mobile money account": "4.5841193"}, {"Entity": "Middle-income countries", "Code": "", "Year": "2021", "Share of people aged 15+ using a mobile money account": "12.066295"}, {"Entity": "Mongolia", "Code": "MNG", "Year": "2014", "Share of people aged 15+ using a mobile money account": "4.9979873"}, {"Entity": "Mongolia", "Code": "MNG", "Year": "2017", "Share of people aged 15+ using a mobile money account": "21.897148"}, {"Entity": "Mongolia", "Code": "MNG", "Year": "2021", "Share of people aged 15+ using a mobile money account": "58.812492"}, {"Entity": "Morocco", "Code": "MAR", "Year": "2017", "Share of people aged 15+ using a mobile money account": "0.6392115"}, {"Entity": "Morocco", "Code": "MAR", "Year": "2021", "Share of people aged 15+ using a mobile money account": "6.2719727"}, {"Entity": "Mozambique", "Code": "MOZ", "Year": "2017", "Share of people aged 15+ using a mobile money account": "21.866821"}, {"Entity": "Mozambique", "Code": "MOZ", "Year": "2021", "Share of people aged 15+ using a mobile money account": "29.376596"}, {"Entity": "Myanmar", "Code": "MMR", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.16379206"}, {"Entity": "Myanmar", "Code": "MMR", "Year": "2017", "Share of people aged 15+ using a mobile money account": "0.6904999"}, {"Entity": "Myanmar", "Code": "MMR", "Year": "2021", "Share of people aged 15+ using a mobile money account": "29.030704"}, {"Entity": "Namibia", "Code": "NAM", "Year": "2014", "Share of people aged 15+ using a mobile money account": "10.359095"}, {"Entity": "Namibia", "Code": "NAM", "Year": "2017", "Share of people aged 15+ using a mobile money account": "43.42947"}, {"Entity": "Namibia", "Code": "NAM", "Year": "2021", "Share of people aged 15+ using a mobile money account": "42.624683"}, {"Entity": "Nepal", "Code": "NPL", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.3360386"}, {"Entity": "Nepal", "Code": "NPL", "Year": "2021", "Share of people aged 15+ using a mobile money account": "6.0919075"}, {"Entity": "Nicaragua", "Code": "NIC", "Year": "2014", "Share of people aged 15+ using a mobile money account": "1.0537744"}, {"Entity": "Nicaragua", "Code": "NIC", "Year": "2017", "Share of people aged 15+ using a mobile money account": "3.9274168"}, {"Entity": "Nicaragua", "Code": "NIC", "Year": "2021", "Share of people aged 15+ using a mobile money account": "6.558079"}, {"Entity": "Niger", "Code": "NER", "Year": "2014", "Share of people aged 15+ using a mobile money account": "3.8526654"}, {"Entity": "Niger", "Code": "NER", "Year": "2017", "Share of people aged 15+ using a mobile money account": "8.717238"}, {"Entity": "Niger", "Code": "NER", "Year": "2022", "Share of people aged 15+ using a mobile money account": "5.3773766"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2014", "Share of people aged 15+ using a mobile money account": "2.2949615"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2017", "Share of people aged 15+ using a mobile money account": "5.606165"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2021", "Share of people aged 15+ using a mobile money account": "8.650209"}, {"Entity": "Pakistan", "Code": "PAK", "Year": "2014", "Share of people aged 15+ using a mobile money account": "5.7997994"}, {"Entity": "Pakistan", "Code": "PAK", "Year": "2017", "Share of people aged 15+ using a mobile money account": "6.8855577"}, {"Entity": "Pakistan", "Code": "PAK", "Year": "2021", "Share of people aged 15+ using a mobile money account": "8.537407"}, {"Entity": "Palestine", "Code": "PSE", "Year": "2021", "Share of people aged 15+ using a mobile money account": "2.3281279"}, {"Entity": "Panama", "Code": "PAN", "Year": "2014", "Share of people aged 15+ using a mobile money account": "1.5579504"}, {"Entity": "Panama", "Code": "PAN", "Year": "2017", "Share of people aged 15+ using a mobile money account": "3.5031688"}, {"Entity": "Paraguay", "Code": "PRY", "Year": "2017", "Share of people aged 15+ using a mobile money account": "28.881767"}, {"Entity": "Paraguay", "Code": "PRY", "Year": "2021", "Share of people aged 15+ using a mobile money account": "37.70081"}, {"Entity": "Peru", "Code": "PER", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0"}, {"Entity": "Peru", "Code": "PER", "Year": "2017", "Share of people aged 15+ using a mobile money account": "2.6341662"}, {"Entity": "Peru", "Code": "PER", "Year": "2021", "Share of people aged 15+ using a mobile money account": "14.344785"}, {"Entity": "Philippines", "Code": "PHL", "Year": "2014", "Share of people aged 15+ using a mobile money account": "4.225874"}, {"Entity": "Philippines", "Code": "PHL", "Year": "2017", "Share of people aged 15+ using a mobile money account": "4.521299"}, {"Entity": "Philippines", "Code": "PHL", "Year": "2021", "Share of people aged 15+ using a mobile money account": "21.738909"}, {"Entity": "Romania", "Code": "ROU", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.46293402"}, {"Entity": "Romania", "Code": "ROU", "Year": "2017", "Share of people aged 15+ using a mobile money account": "2.9901695"}, {"Entity": "Russia", "Code": "RUS", "Year": "2021", "Share of people aged 15+ using a mobile money account": "32.938335"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2014", "Share of people aged 15+ using a mobile money account": "18.097685"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2017", "Share of people aged 15+ using a mobile money account": "31.114641"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2014", "Share of people aged 15+ using a mobile money account": "6.188361"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2017", "Share of people aged 15+ using a mobile money account": "31.798092"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2021", "Share of people aged 15+ using a mobile money account": "44.889874"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2014", "Share of people aged 15+ using a mobile money account": "4.452388"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2017", "Share of people aged 15+ using a mobile money account": "11.018614"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2021", "Share of people aged 15+ using a mobile money account": "19.040215"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2014", "Share of people aged 15+ using a mobile money account": "6.14946"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2017", "Share of people aged 15+ using a mobile money account": "9.548008"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2021", "Share of people aged 15+ using a mobile money account": "30.604267"}, {"Entity": "Somalia", "Code": "SOM", "Year": "2014", "Share of people aged 15+ using a mobile money account": "37.065914"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2014", "Share of people aged 15+ using a mobile money account": "14.428181"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2017", "Share of people aged 15+ using a mobile money account": "19.016846"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2021", "Share of people aged 15+ using a mobile money account": "36.565308"}, {"Entity": "South Asia (WB)", "Code": "WB_SA", "Year": "2014", "Share of people aged 15+ using a mobile money account": "2.6457062"}, {"Entity": "South Asia (WB)", "Code": "WB_SA", "Year": "2017", "Share of people aged 15+ using a mobile money account": "4.2011795"}, {"Entity": "South Asia (WB)", "Code": "WB_SA", "Year": "2021", "Share of people aged 15+ using a mobile money account": "11.568461"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2021", "Share of people aged 15+ using a mobile money account": "0.849857"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.07663684"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2017", "Share of people aged 15+ using a mobile money account": "2.4207602"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2021", "Share of people aged 15+ using a mobile money account": "3.126422"}, {"Entity": "Sub-Saharan Africa (WB)", "Code": "WB_SSA", "Year": "2014", "Share of people aged 15+ using a mobile money account": "11.567172"}, {"Entity": "Sub-Saharan Africa (WB)", "Code": "WB_SSA", "Year": "2017", "Share of people aged 15+ using a mobile money account": "20.772936"}, {"Entity": "Sub-Saharan Africa (WB)", "Code": "WB_SSA", "Year": "2021", "Share of people aged 15+ using a mobile money account": "33.190426"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2021", "Share of people aged 15+ using a mobile money account": "2.0897527"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2014", "Share of people aged 15+ using a mobile money account": "32.358696"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2017", "Share of people aged 15+ using a mobile money account": "38.53803"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2021", "Share of people aged 15+ using a mobile money account": "44.579487"}, {"Entity": "Thailand", "Code": "THA", "Year": "2014", "Share of people aged 15+ using a mobile money account": "1.3012658"}, {"Entity": "Thailand", "Code": "THA", "Year": "2017", "Share of people aged 15+ using a mobile money account": "8.25686"}, {"Entity": "Thailand", "Code": "THA", "Year": "2021", "Share of people aged 15+ using a mobile money account": "59.99989"}, {"Entity": "Togo", "Code": "TGO", "Year": "2014", "Share of people aged 15+ using a mobile money account": "1.4100387"}, {"Entity": "Togo", "Code": "TGO", "Year": "2017", "Share of people aged 15+ using a mobile money account": "21.457376"}, {"Entity": "Togo", "Code": "TGO", "Year": "2021", "Share of people aged 15+ using a mobile money account": "36.48081"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.6187012"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2017", "Share of people aged 15+ using a mobile money account": "2.0371897"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2021", "Share of people aged 15+ using a mobile money account": "3.7122092"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.80819976"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2017", "Share of people aged 15+ using a mobile money account": "16.411953"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2021", "Share of people aged 15+ using a mobile money account": "18.284203"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2014", "Share of people aged 15+ using a mobile money account": "35.10038"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2017", "Share of people aged 15+ using a mobile money account": "50.58124"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2021", "Share of people aged 15+ using a mobile money account": "53.796238"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2014", "Share of people aged 15+ using a mobile money account": "11.490987"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2017", "Share of people aged 15+ using a mobile money account": "21.331797"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2021", "Share of people aged 15+ using a mobile money account": "13.25789"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.66960806"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2017", "Share of people aged 15+ using a mobile money account": "2.3262851"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2021", "Share of people aged 15+ using a mobile money account": "9.909851"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2014", "Share of people aged 15+ using a mobile money account": "1.2334332"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2014", "Share of people aged 15+ using a mobile money account": "2.9790692"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2017", "Share of people aged 15+ using a mobile money account": "10.970383"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2021", "Share of people aged 15+ using a mobile money account": "30.310907"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2014", "Share of people aged 15+ using a mobile money account": "0.49650693"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2017", "Share of people aged 15+ using a mobile money account": "3.4995008"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2022", "Share of people aged 15+ using a mobile money account": "16.465946"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Share of people aged 15+ using a mobile money account": "2.06456"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Share of people aged 15+ using a mobile money account": "4.3467984"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Share of people aged 15+ using a mobile money account": "10.243359"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Share of people aged 15+ using a mobile money account": "12.114307"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Share of people aged 15+ using a mobile money account": "27.84239"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Share of people aged 15+ using a mobile money account": "41.631283"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Share of people aged 15+ using a mobile money account": "21.595522"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Share of people aged 15+ using a mobile money account": "48.581635"}, {"Entity": "Zimbabwe", "Code": "ZWE", 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{"raw_link": "https://ourworldindata.org/the-end-of-tuberculosis-that-wasnt", "title": "The end of tuberculosis that wasn’t", "context": "Home\nTuberculosis\nThe end of tuberculosis that wasn’t\nIn the 1980s, many thought tuberculosis was on the path to elimination. In reality, more were dying from the disease than ever.\nBy\nHannah Ritchie\nand\nFiona Spooner\nJune 30, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nIn the late 1980s, many thought the fight against tuberculosis (TB) had been won. A disease that had plagued humans for at least 9000 years was on the path to being eliminated.\n1\nThe world knew what caused it, how to screen for it, and finally had effective antibiotics to treat it.\nBy the mid-20th century, tuberculosis in the United States and Europe had already declined thanks to improved nutrition and living conditions. Once treatments arrived in the 1950s, deaths tumbled: by the late 1980s, they had fallen by over 90% in the United States.\n2\nThe United States was so confident that tuberculosis would gradually disappear that the US Congress stopped direct government funding for TB programs in 1972.\nYou have to know the history of tuberculosis to fully appreciate what a victory that would be. The disease, which spreads from person to person via water droplets, was once one of the biggest killers in many parts of the world. Without treatment, getting it was practically a death sentence.\n3\nIt was responsible for as much as one-quarter of deaths in the United States and Europe during parts of the 19th and early 20th centuries. Go back to 1750s London, and 1% of the population were dying from tuberculosis\nevery year\n.\nYou can explore the history of TB in rich countries in our\nfirst article\nof this series.\nFast-forward to the 1980s, and just when TB seemed to be on its way out in the United States, there was a bump in the number of cases and the\nnumber of deaths\n. You can see this rise in the second half of the 1980s in the chart below.\nAs a story in the\nLos Angeles Times\nput it\n:\n“We really thought (tuberculosis) was going the way of polio. Up until 1988, the TB rate was going down every year. And then all of a sudden, the rate started increasing and everyone was caught off guard.”\nThe\nNew York Times\nran the\nfollowing headline\nin 1985.\nDownload\nIn this article, we cover the reality of tuberculosis in the 1980s and 1990s at two different levels. First, we explain why there was a temporary reversal in the United States. Second, we zoom out to understand the world’s re-evaluation of the scale of the TB problem at a\nglobal\nlevel.\nTuberculosis made a temporary comeback in the United States and other countries\nThree factors raised concerns about the resurgence of tuberculosis in the United States and rich countries in Europe.\nThe emergence of the HIV/AIDS epidemic\nThe first was the\nHIV/AIDS epidemic\n, which began in the 1980s and continued to grow throughout the 1990s. In the chart below, you can see the steep rise in the number of Americans dying from HIV/AIDS over these decades.\nWhat did this have to do with tuberculosis? Well, scientists and health experts started to see that\nrates\nof TB cases and deaths were higher in those with HIV than in the general population. This is because those with HIV have a weakened immune system, which means tuberculosis bacteria can thrive, and turn an “inactive”\nlatent tuberculosis\ninfection into an “active” one.\n4\nAfter malnutrition, having HIV is the\nleading risk factor\nfor developing tuberculosis. This introduced a new driver of infection that Americans had not faced in the 1950s, 60s, or 70s when deaths were falling steeply.\nThroughout the 1980s and 1990s, the share of Americans with tuberculosis who also had HIV rose steeply. HIV-positive patients weren’t just more likely to develop active tuberculosis — they were far more likely to die from it. In 1993, among TB patients with known HIV status in the US,\nnearly half\nwere HIV-positive, but they accounted for 82% of TB\ndeaths\n.\nAs late as the year 2000, almost as many Americans dying from tuberculosis had HIV as didn’t. You can see this in the chart below. That fact is staggering, given\nthat just 0.5%\nof Americans had HIV at the time. In other words, 0.5% of the population accounted for half of tuberculosis deaths, with the other half coming from the remaining 99.5%.\nBut as you can also see in the chart, even more aggressive controls on TB and HIV meant that this share has fallen a lot over the last decades.\nUnfortunately, I couldn’t find consistent time-series data on HIV status going back to the 1980s.\nThe rise of drug-resistant tuberculosis\nThe second factor causing alarm was the rise of\ndrug-resistant tuberculosis\n. In the 1950s, scientists had discovered a combination of antibiotics that were extremely effective in treating patients with TB. However, over time, it became clear that some individuals were not responding as positively to treatment. This was because of the development of tuberculosis infections that were drug-resistant.\n5\nThese cases are\nmuch more expensive to treat\nand have a much lower success rate. This is still the case today, as you can see in the chart below, and the odds of a successful treatment were likely even lower in the 1990s.\nHigher rates of TB in foreign-born populations\nThe third, which will lead us on to the global part of the narrative change, was the\nhigher rates of TB in the foreign-born population\n.\nIn the US, TB rates among immigrants were almost four times higher than among native-born residents in the 1980s.\n6\nMost of these cases were diagnosed within five years of arriving in the US, which suggests that many had moved with an existing infection.\n7\nOf course, people were migrating to the US before the 1980s, while TB rates were still falling. However, a few things changed before and during that period and could have had an impact. First is the\n1965 Immigration and Nationality Act\n, which opened immigration opportunities to migrants from other parts of the world. Before 1965, most immigrants to the US came from Europe, where TB rates had already dropped dramatically. Second, rates of immigration\nincreased substantially\nfrom the 1970s to the 1990s. Not only were more people moving to the US, but they were often moving from countries where tuberculosis rates were high.\nThis was also true in many European countries. Half of TB cases in the Netherlands and Scandinavia were among foreign-born residents (despite immigrants making up a much smaller share of the population).\n8\nThis shouldn’t have been that surprising. Richer countries had invested significant amounts into screening and treating the disease, which many other countries didn’t have the resources for, or detailed data to understand the scale of the problem. Having people move from high-burden TB countries to lower-burden ones would naturally introduce new cases into the population.\n9\nInfectious diseases like tuberculosis do not respect borders. As long as a disease is common amongst the global population, individual countries will always be at risk of a resurgence. If countries were to eliminate tuberculosis once and for all, it had to be a global effort, not just a national one.\nA key issue is that in the 1980s, there was a severe lack of data about the TB burden in low- and many middle-income countries. But, of course, not having data measuring a problem does not mean it doesn’t exist. Still, inadequate measurement meant that tuberculosis was underestimated and neglected. Only when better estimates became available did people wake up to the true scale and focus more on doing something about it.\nFrom a shrinking problem to a global health emergency\nDespite having easy ways to screen for TB and highly effective treatments, the world was losing its battle against tuberculosis in the 1990s. The WHO\npronounced that\nmore people died from TB in 1995 than in any year in history.\n10\nWhile the US and some countries in Europe had decades of detailed records of cases, deaths, and risk factors, much of the rest of the world was blind to the scale of the problem. It wasn’t until the early 1990s that the World Health Organization (WHO) carried out the first comprehensive estimate of the global burden of TB. Of course, this involved much more than simply counting up known cases of TB; it also required modelling and expert judgement on the true spread and mortality of the disease.\nIn 1990, there were\nan estimated\n8 million\nnew\ncases of active TB, and nearly 3 million deaths. That was more than double the number of cases that had been recorded and\nreported\nto the WHO. Tuberculosis was not a problem on the way out; it often went unseen, leading to a huge underestimation of its true size. One of history’s biggest killers was killing more people than ever.\nLatest figures from another source — the Global Burden of Disease —\nestimate that\n2 to 2.2 million people were dying from TB in the 1990s.\nAs Dr. Hiroshi Nakajima, the WHO Director-General then, put it:\n\"Not only has TB returned, it has upstaged its own horrible legacy\"\n.\nIn 1993, the WHO\ndeclared\ntuberculosis a “global health emergency”.\nThe world has made progress in reducing the burden of TB since then. In 2000, around 2.6 million people were still dying from tuberculosis each year. That has more than halved to 1.3 million. You can see this in the chart below.\nBut with over a million deaths each year, tuberculosis is still a huge killer. The fact that so many still die from this preventable disease, particularly in lower-income countries, is still a gross failure to me.\nRobert Koch, who discovered the tubercle bacillus, would be disappointed by our inability to finally overcome this disease. As early as 1905, he was confident that the war against it would be won:\n11\n“The struggle [against tuberculosis] has caught hold along the whole line and enthusiasm for the lofty aim runs so high that a slackening is no longer to be feared. If the work goes on in this powerful way, then the victory must be won.”\n120 years on, and we’re still not close, despite having the tools we need to do so.\nIn the final article in this three-part series, we will examine today’s burden of TB in more detail and what is needed to achieve what the US and Europe have done everywhere.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nWe need good data to understand the scale of global problems and how we tackle them effectively\nThis wake-up to the tuberculosis crisis was a perfect example of two points that are core to our work at\nOur World in Data\n.\nThe first is about how crucial\ngood\ndata is for understanding the scale, distribution, and directional change of the big problems the world faces. You cannot understand the situation of tuberculosis from personal situations or anecdotes. You also can’t understand the\nglobal\nproblem by extrapolating progress in the United States and Europe to the rest of the world.\nIndeed, a lot of progress had been made in high-income countries, but tuberculosis was still a devastating problem elsewhere. It still is.\nIt wasn’t until health experts provided new and better estimates of the\nglobal\nburden of tuberculosis that it was put back on the agenda. Without this data, it might not be. Tuberculosis would still be spreading rapidly, and killing many millions every year; the world would just be blind to the real cause.\nThis renewed understanding of the scale of the problem had obvious knock-on impacts for policymaking and priority-setting. It can be partly attributed to\nthe decline\nin deaths we’ve seen since then. If you know where tuberculosis is spreading and its risk factors, you can invest money in the right places, ensuring that communities have the testing technologies and treatments they need.\nThe second point is complacency. At Our World in Data, we often use historical data to show that progress on many problems is possible. But we also try to make clear that progress is in no way inevitable. Even on a good path, there is always the risk of backsliding. This was the case with tuberculosis in the late 1980s: many expected cases and deaths to keep falling, but they didn’t. Progress stalled, then temporarily reversed.\nWhat’s crucial, though — and this links back to my first point — is that close monitoring and transparent data can often alert people to these reversals early. High-quality monitoring of TB cases and deaths in the US meant that the rise was quickly detected, action could be taken, and the backslide was only temporary. What’s more: good data on\nwho\nwas dying from tuberculosis helped to identify the reasons why trends had turned: it was clear that there was a link to HIV, and that treatments were not working for some people, those who had a drug-resistant infection. Without this detailed data, it would have taken the US far longer to notice a reversal in the trend and identify why this was happening.\nThese lessons apply to so many issues, not just tuberculosis. Without good data, we are often blind to the scale of the world’s problems. But when we fail to\nact\non that data, we effectively close our eyes and turn the other way.\nAcknowledgments\nWe thank Saloni Dattani, Edouard Mathieu, and Simon van Teutem for valuable comments and feedback on this article.\nThis is the second article in our three-part series on tuberculosis:\nOnce a leading killer, tuberculosis is now rare in rich countries — here’s how it happened\nAs much as one quarter of deaths in Europe and the United States were once from tuberculosis.\nThe end of tuberculosis that wasn’t\nIn the 1980s, many thought tuberculosis was on the path to elimination. In reality, more were dying from the disease than ever.\nThe world left its fight against tuberculosis unfinished — how can we complete the job?\nIf we get it right, the world could save more than 1.2 million lives every year.\nEndnotes\nWe know our relationship with tuberculosis goes back at least 9000 years because there is archaeological evidence of tuberculosis lesions in bone samples from the Middle East. Hershkovitz, I., Donoghue, H. D., Minnikin, D. E., Besra, G. S., Lee, O. Y., Gernaey, A. M., ... & Spigelman, M. (2008).\nDetection and molecular characterization of 9000-year-old Mycobacterium tuberculosis from a Neolithic settlement in the Eastern Mediterranean\n. PloS one, 3(10), e3426. Gibbons (2021).\nHow tuberculosis reshaped our immune systems\n. Science.\nIn 1953, 19,707 people in the US died from tuberculosis. By 1987, this had fallen to 1755. That’s a reduction of around 91% [(19,707 - 1755) / 19,707 * 100 = 91%].\nThis data comes from the\nUS Centers for Disease Control and Prevention\n.\nHere I’m talking about\nactive\ncases of tuberculosis. Many more people have\nlatent\n(or “inactive”) TB and will never suffer any symptoms.\nBell, L. C. K., & Noursadeghi, M. (2018). Pathogenesis of HIV-1 and Mycobacterium tuberculosis co-infection. Nature Reviews Microbiology, 16(2), 80–90. https://doi.org/10.1038/nrmicro.2017.128\nBruchfeld, J., Correia-Neves, M., & Källenius, G. (2015). Tuberculosis and HIV Coinfection. Cold Spring Harbor Perspectives in Medicine, 5(7), a017871. https://doi.org/10.1101/cshperspect.a017871 Many people in the world have been infected with latent or inactive tuberculosis. This will usually never develop into an active infection that causes severe symptoms.\nFarhat, M., Cox, H., Ghanem, M., Denkinger, C. M., Rodrigues, C., Abd El Aziz, M. S., ... & Pai, M. (2024). Drug-resistant tuberculosis: a persistent global health concern. Nature Reviews Microbiology, 22(10), 617-635.\nMcKenna, M. T., McCray, E., & Onorato, I. (1995). The epidemiology of tuberculosis among foreign-born persons in the United States, 1986 to 1993. New England Journal of Medicine, 332(16), 1071-1076. It’s still the case today that TB rates are higher in those born outside of the US: Menzies, N. A., Hill, A. N., Cohen, T., & Salomon, J. A. (2018). The impact of migration on tuberculosis in the United States. The International Journal of Tuberculosis and Lung Disease, 22(12), 1392-1403.\nThis was exacerbated by the fact that poverty is a key risk factor for TB and many immigrants had lower incomes and lived in communities with higher levels of poverty.\nRaviglione, M. C. (2003). The TB epidemic from 1992 to 2002. Tuberculosis, 83(1-3), 4-14.\nThis is potentially less of a risk today. Some high-income countries\nprovide TB screening\nto immigrants from high-burden TB countries who are entering the country. If diagnosed with TB, they are then put on treatment. This reduces the risk of introducing untreated active cases into the general population.\nThe WHO estimated that in the year 1900, two to three million people were dying from tuberculosis globally.\nThis quote was part of Robert Koch’s Nobel Lecture in 1905.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Fiona Spooner (2025) - “The end of tuberculosis that wasn’t” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260604-074426/the-end-of-tuberculosis-that-wasnt.html' [Online Resource] (archived on June 4, 2026).\nBibTeX citation\n@article{owid-the-end-of-tuberculosis-that-wasnt,\nauthor = {Hannah Ritchie and Fiona Spooner},\ntitle = {The end of tuberculosis that wasn’t},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260604-074426/the-end-of-tuberculosis-that-wasnt.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "the-end-of-tuberculosis-that-wasnt", "source_url": "https://ourworldindata.org/the-end-of-tuberculosis-that-wasnt", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "In the 1980s, many thought tuberculosis was on the path to elimination. 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""}, {"Entity": "United States", "Code": "USA", "Year": "1986", "Tuberculosis cases (post-1975)": "22768", "Tuberculosis cases (pre-1975)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1987", "Tuberculosis cases (post-1975)": "22517", "Tuberculosis cases (pre-1975)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1988", "Tuberculosis cases (post-1975)": "22436", "Tuberculosis cases (pre-1975)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1989", "Tuberculosis cases (post-1975)": "23495", "Tuberculosis cases (pre-1975)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1990", "Tuberculosis cases (post-1975)": "25701", "Tuberculosis cases (pre-1975)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1991", "Tuberculosis cases (post-1975)": "26283", "Tuberculosis cases (pre-1975)": ""}, {"Entity": "United States", "Code": "USA", "Year": "1992", "Tuberculosis cases (post-1975)": "26673", "Tuberculosis cases (pre-1975)": ""}, {"Entity": 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"Code": "USA", "Year": "2000", "Tuberculosis cases (post-1975)": "16305", "Tuberculosis cases (pre-1975)": ""}, {"Entity": "United States", "Code": "USA", "Year": "2001", "Tuberculosis cases (post-1975)": "15937", "Tuberculosis cases (pre-1975)": ""}, {"Entity": "United States", "Code": "USA", "Year": "2002", "Tuberculosis cases (post-1975)": "15048", "Tuberculosis cases (pre-1975)": ""}, {"Entity": "United States", "Code": "USA", "Year": "2003", "Tuberculosis cases (post-1975)": "14828", "Tuberculosis cases (pre-1975)": ""}, {"Entity": "United States", "Code": "USA", "Year": "2004", "Tuberculosis cases (post-1975)": "14494", "Tuberculosis cases (pre-1975)": ""}, {"Entity": "United States", "Code": "USA", "Year": "2005", "Tuberculosis cases (post-1975)": "14052", "Tuberculosis cases (pre-1975)": ""}, {"Entity": "United States", "Code": "USA", "Year": "2006", "Tuberculosis cases (post-1975)": "13716", "Tuberculosis cases (pre-1975)": ""}, {"Entity": "United States", "Code": "USA", 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{"raw_link": "https://ourworldindata.org/what-no-safe-water-means", "title": "Two billion people don’t have safe drinking water: what does this really mean for them?", "context": "Home\nClean Water & Sanitation\nClean Water\nTwo billion people don’t have safe drinking water: what does this really mean for them?\nFor billions, it can mean hours spent collecting water. For almost a million, it means dying from disease.\nBy\nHannah Ritchie\nJune 23, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nIn the time it would take me to write the next sentence, I could get up, walk to the kitchen, and pour myself a glass of clean water. I’ve never had to worry about whether that water would make me sick.\nAlmost six billion other people in the world share this reality. They have safe drinking water in their homes, ready whenever needed.\nThat still leaves two billion people without. That’s the most important number in this article:\ntwo billion people who don’t have safe water to drink\n.\nBut these large numbers can seem abstract. It’s not clear what this means in practice. If people don’t have safe water, what\nare\nthey drinking? What does it mean for their daily lives?\nHere, I want to answer these questions and bring a more human perspective that sometimes gets lost when we’re talking about millions or billions of people.\nBefore we get into some of the personal stories and accounts, it’s important to understand how levels of drinking water services are defined and how many people fall on each “rung” of the ladder. I’ve summarized this in the diagram below based on data from the WHO and UNICEF.\n1\nDownload\nFor someone to have “safe drinking water”, their water source needs to meet three criteria: it needs to be free from contamination, located at home, and available whenever needed. Again, this is the reality for almost six billion people.\nWhat are the other two billion drinking? If you’d asked me in the past, I might have guessed that most people without safe drinking water were collecting water from streams or lakes. The world was binary: you either had safe piped water or were collecting it from a river. But that’s not the reality: people collecting surface water are in the minority. Only around 156 million people get their water this way. That’s\n1.4% of the global population\n.\nThe vast majority of the two billion — around three-quarters of them —\ndo\nhave access to a piped water source or protected well that is\nprobably\nsafe to drink. But it’s either not located in their home, is not always available, or there’s no guarantee it’s completely contamination-free. That usually means they must travel to get there; whether that round-trip takes less or more than 30 minutes determines whether they fall into the “basic source” or “limited source” category. This is summarized in the diagram below.\nDownload\nThe reality for people without access to safe drinking water\nWe’ll now look at the realities for families in each of the categories above. To do this, I’ve relied heavily on the\nDollar Street project\n, built by Anna Rosling Rönnlund at Gapminder.\n2\nIt lets us see the realities of daily life for more than 250 families across 50 countries — from the very poorest to some of the richest.\nAlthough families on different rungs of the “drinking water ladder” can have very different experiences, the costs of not having safe drinking water are the same. There are two main ones (which I’ll expand on later).\nThe first is the most obvious. Drinking contaminated water can\nspread disease, make us sick, and in many cases, can be fatal\n.\nThe second thing we think about less is\nthe time it takes to collect this water\n. For many families, it can be tens of hours every week. That’s extremely far from my reality, where walking to the kitchen tap takes seconds.\nSurface water from rivers, lakes, ponds, and canals\nThat’s 7% of people without safe drinking water\nMeet J and D, and their six children. The couple\nlives in\nBurundi, and they earn around $41 per month each, which means they live in\nextreme poverty\n.\n3\nThey farm on their own land, which provides most of their food. They built their three-bedroom house themselves.\nThey do not have safe drinking water. Instead, D and her children spend 14 hours a week — two hours a day — fetching water from a small stream nearby.\nDownload\nSource: Johan Eriksson for Dollar Street 2015\nIn the video, you can see them using a small plastic jerry can to collect the water from the river. In the next photo, one of the children is drinking from it.\nThere’s no guarantee that the stream water is safe to drink. It could contain chemicals, fertilizers, or pesticides washing off from farms upstream; bacteria from animal waste; or viruses and parasites from biological sources. Everyone in the family is at risk of developing diarrheal or bacterial diseases, which can be particularly damaging for people living in poverty because they are often\nalready malnourished\nor have limited access to healthcare and medicines.\nThe time spent collecting the water is also a problem. Those 14 hours a week could be spent on work to increase the family’s income. For the children, it could mean the opportunity to go to school, read, play, or improve their skills in other areas. D and her kids spend another 21 hours a week collecting firewood. That’s almost a full-time job spent collecting water and wood.\nAnother 156 million people are in the same situation as J and D. That’s still a lot, equal in size to the population of Russia. But it’s important to note that this is not the reality for\nmost\npeople who don’t have access to safe drinking water. Even in low-income countries,\njust 5% of people\nget their water from streams, rivers, or ponds.\nSource: Johan Eriksson for Dollar Street 2015 (Free to use under CC BY 4.0)\nDownload\nSource: Johan Eriksson for Dollar Street 2015\nWater from unprotected wells or springs\nThat’s 14% of people without safe drinking water\nOn the next rung of the water ladder, you’ll find B. She\nlives with\nher husband and four children in Burkina Faso. In the photograph, you can see her cycling to collect water for the family.\nBoth adults in the family earn the equivalent of around $53 per month, which again, means they live in extreme poverty.\nThe nearest well is a two-hour walk away. Since D cycles, the journey is a bit faster, but she still spends 40 hours every week collecting water and firewood.\n4\nWith six family members to provide for and limited space to carry water on the back, she probably has to make several daily trips.\nDownload\nSource: Zoriah Miller for Dollar Street 2015\nThe video shows her filling water cans with a hose. This hose is connected to an\nunprotected\nwell, which means the water is unsafe to drink.\nWhile D’s situation differs from that of the previous family because she isn’t fetching water from streams or lakes, the problems are similar: collecting water is almost a full-time job, and the family is at risk of getting sick.\nSome families don’t have to travel quite as far: this\nfamily in Kenya\ndoes not have access to a clean water source, but the water they collect is “only” 30 minutes — rather than 2 hours — away.\nD is among the almost 300 million people (a population the size of Indonesia) with an “unimproved” water source.\n5\nIt’s from a well or a spring, but that water source is not protected from pollution.\nCombined with those who rely on surface water,\naround 5%\nof the world's population relies on water from an “unimproved” source. The other 95% have a piped system, protected spring, well, borehole, or safe bottled water. While it’s not guaranteed, most will have clean water that is much less likely to make them ill. We’ll look at their realities now.\nZoriah Miller for Dollar Street 2015\nWater from a protected pipe, well, or spring that’s more than 30 minutes from home\nThat’s 13% of people without safe drinking water\nMany people in this category probably\ndo\nhave safe drinking water — at least at its initial source point — but it’s either hard to verify as contamination-free, or it’s far from home and takes a long time to collect.\nT and her husband\nlive with\ntheir five children and sister in Haiti.\nThe house has no running water, but safe water can be collected from public piped systems. You can see T filling buckets in the photograph. The problem is that the family spends\n70 hours\na week fetching it, the equivalent of two full-time jobs.\nThey might be free from the worry of disease and infection, but it’s a huge time investment with a clear opportunity cost for other forms of work, education, and free time.\nThis is a common experience. This\nmother and her daughters\nin the Philippines spend 7 hours a week collecting safe water, a 60-minute trip every time.\nDownload\nSource: Zoriah Miller for Dollar Street 2015\nWater from a protected pipe, well, or spring that’s less than 30 minutes from home\nThat’s 66% of people without safe drinking water\n1.5 billion people are in the same situation as those families above, but collecting water doesn’t take them as long. These are families with access to an improved water source — which means it’s\nprobably\nsafe to drink, but not guaranteed — but it’s not in their own home or available all the time.\nThis\nfamily in India\nhas safe drinking water, but it takes around 20 minutes to collect. As shown in the video, someone — often one of the women — uses a public water pump to retrieve it.\nSource: Zoriah Miller for Dollar Street 2015\nAcross the Indian Ocean in Sub-Saharan Africa, this\nfamily from Ghana\n— shown in the photograph — does the same using a public source around 10 minutes from home.\nThat saves a lot more time compared to the families that spend tens of hours every week fetching water. But it’s still a relatively foreign concept for people living in conditions where water is available at home, whenever they need it.\nDownload\nSource: Zoriah Miller for Dollar Street 2015\nSafe water that’s free from contamination, reliable, and at home\nThat brings us to the final group: most people worldwide have safe water to drink at home (inside or on the premises).\nMost people in high-income countries live this reality, but many people in middle and low-income countries experience this, too.\nThis\nfamily in Togo\n— who spend most of their income on food and still rely on wood for cooking — has a safe water supply at home. In the video, you can see them pouring water from the tap.\nDownload\nSource: Global Exploration for Dollar Street 2018\nSource: Global Exploration for Dollar Street 2018\nWhile income is a\ngood predictor\nof who will and won’t have safe drinking water, location and circumstances matter too. A family could have a moderate income, but if they’re in an extremely remote area without piped networks and accessible wells, it’s difficult to get clean water easily. The opposite is also true: some low-income families could find themselves close to safe water sources that are easy to tap into.\nOur next family is a clear example: in the photograph, you can see J, R, and their 8 children. They\nlive in\nthe Philippines. Each adult earns around $94 per month, which, shared among the family, means they live in extreme poverty.\nBut they\ndo\nhave a clean water source in their yard. Even though it’s not\nin\nthe house, they would still meet the criteria for having safe drinking water. Of course, this category — the top rung of the drinking water ladder — extends to much richer families. This\nfamily in South Africa\nnot only has safe water from the tap in their house, but also a fancy refrigerator to keep it cool.\nOther parts of daily life for the families from Togo, the Philippines, and South Africa are very different. However, when it comes to drinking water, their experiences are similar. It doesn’t take them long to pour it, and they can drink it with the confidence that neither they nor their children will get sick. That’s ultimately what people want and need.\nDownload\nSource: Luc Forsyth for Dollar Street 2015\n95% of the world uses an “improved” water source — they now need one in their home, and verification that it’s free from contamination\n“Safe drinking water” only became the main indicator of progress on clean water in 2017. Before that, the focus was on the number of people who had access to an “improved water source.”\nAn improved water source can potentially deliver safe water: it’s a protected pipe, spring, borehole, or other system that\nprobably\ndelivers safe water.\nThe problem is that it doesn’t guarantee the water is safe at the point of consumption. Imagine you collect a bucket of water from a pipe an hour from home. It might be safe when you collect it, but once you’ve trekked an hour back and left it sitting unrefrigerated in the heat for the rest of the day, there’s no guarantee that it’s free of pathogens when you drink it the next morning.\nThat’s the key point here. 95% of the world uses an improved water supply. As the map below shows, the majority in every country does, even in the poorest countries.\n6\nMany countries have rapidly increased this share in the last few decades. In the slope chart, you can see the change in the share of the population with improved water for a selection of countries that still had pretty poor coverage in 2000. In Ethiopia, for example, access has tripled from just 26% to 80%.\nCountries\ncan\nquickly increase access to a (probably) clean piped, spring, or borehole source. The biggest challenge is getting those pipes into each individual household\nand\nmaking sure that the source is completely contamination-free. This aspect is harder, and often means expanding a single community-shared pipe into a whole water network. This is even more difficult in rural settings where infrastructure is less concentrated.\nBut to get universal access to safe drinking water, this is what the world will need to do.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nThe costs of not having safe drinking water\nResearching this article has helped me understand these abstract definitions and numbers. I hope they’ve clarified some of this for you, too.\nI want to close by highlighting, again, the costs of not having safe drinking water.\nThe one I had underappreciated was the amount of time that some families spend collecting water. This is often women or children. We saw that this could take 70 hours a week in the most extreme examples. For most of us, that seems unfathomable: imagine that collecting water was a full-time job for not only you but another member of your family.\nThe second is more obvious, but still a huge global problem. Unsafe water\nleads to\nmore than 800,000 deaths every year.\n7\nThis is because it can lead to the spread of\ndiarrheal diseases\n, such as cholera or dysentery, and other diseases, including polio and hepatitis. It can also lead to malnutrition, which\ncontributes to\nhalf of all childhood deaths.\n8\nThese deaths tend to be concentrated in lower-income countries where fewer people have safe water to drink. As you can see on the map, in some of the worst-off countries, more than 5% of all deaths are attributed to unsafe water.\nAgain, a number as large as 800,000 can seem abstract. But that’s 800,000 families that have to endure the loss of a loved one, often their own child. What’s perhaps even more painful is that they died from one of the few things that humans need to stay alive.\nVery few people in the world know this better than\nHaja, a young mother\nfrom Sierra Leone. She lost three children in just three years; two of them were small babies when they died from diarrheal diseases as a result of unsafe water.\nAs she puts it, “I have given birth to six children, but I only have three alive.”\nThat’s the reality of not having safe water to drink. Every day, you have no choice but to give your children, siblings, parents, or grandparents the very thing that could kill them.\nAcknowledgments\nI relied heavily on\nDollar Street\n— a project published by Gapminder and Anna Rosling Rönnlund — for the stories and visuals used in this article. Many thanks to them for this work and for making it available for reuse. I highly recommend it if you want to explore more of these experiences and living conditions across the world.\nI would also like to thank Ike Saunders, Daniel Bachler, Esteban Ortiz-Ospina, Edouard Mathieu, Joe Hasell, Fiona Spooner, Saloni Dattani, and Simon van Teutem for comments and feedback on this work.\nContinue reading on Our World in Data\nThe world is making progress on clean water and sanitation, but is far behind its target to ensure universal access by 2030\nAll countries pledged to provide safe water and sanitation for everyone by 2030. How far are we from reaching these targets?\nEnergy poverty and indoor air pollution: a problem as old as humanity that we can end within our lifetime\nAbout three billion people in the world do not have access to modern energy sources for cooking. Millions die from indoor air pollution every year.\nThe number of people without electricity more than halved over the last 20 years\nThe world has made significant progress on improving electricity access.\nEndnotes\nTogether, these organizations publish global data from their Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP).\nThe motivation for the project was very similar to what I’m trying to do here, as the project states: “Dollar Street was invented by Anna Rosling Rönnlund at Gapminder. For 15 years, she spent her workdays making global public data easier to understand and use. Over time her frustration grew: carefully selecting data to present it in colorful and moving charts made overall global trends and patterns easier to understand. But it did not make everyday life on different income levels understandable. Especially not in places far from home.”\nThe international poverty line — the definition of extreme poverty — is $3 per person per day. This would be equivalent to around $90 per month. This family was surveyed in 2015; in 2021 international dollars, used to define the international poverty line, their income would be slightly higher, but not enough to move them out of extreme poverty.\nUnfortunately, I don’t know how much she spends on each.\nThe population of Indonesia is only\nslightly lower\n, at around 280 million.\nIn 2022, no country had a share below 50%. Papua New Guinea was the lowest with 53%.\nThe Institute for Health Metrics and Evaluation’s Global Burden of Disease study estimated around 802,000 deaths in 2021.\nEven if people don’t\ndie\nfrom unsafe water, getting sick from it can impact both physical and mental development. This is especially true for children. When kids are repeatedly ill, they often struggle to retain nutrients. This can lead to malnutrition, which hinders their growth and development. “\nStunting\n” — where children are too short for their age — is common in lower-income countries and is\nassociated with\nrisk factors such as unsafe water, sanitation, and hygiene.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2025) - “Two billion people don’t have safe drinking water: what does this really mean for them?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-090244/what-no-safe-water-means.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-what-no-safe-water-means,\nauthor = {Hannah Ritchie},\ntitle = {Two billion people don’t have safe drinking water: what does this really mean for them?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-090244/what-no-safe-water-means.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "what-no-safe-water-means", "source_url": "https://ourworldindata.org/what-no-safe-water-means", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "For billions, it can mean hours spent collecting water. For almost a million, it means dying from disease.", "numeric_mentions": ["23,", "2025", "1", "156 million", "1.4%", "30", "2", "250", "50", "7%", "41", "3", "14", "2015", "21", "5%", "4.0", "14%", "53", "40", "4", "300 million", "5", "95%", "13%", "70", "7", "60", "66%", "1.5 billion", "20", "10", "2018", "8", "94", "2017", "6", "2000", "26%", "80%", "800,000", "2030", "20 years", "15 years", "90", "2021", "280 million", "2022,", "50%", "53%", "802,000", "20260518", "090244", "18,", "2026"], "numeric_evidence": [{"title": "Drinking water service usage", "source_url": "https://ourworldindata.org/grapher/access-drinking-water-stacked.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Safely managed", "Basic", "Limited", "Unimproved", "No access (surface water only)"], "row_count_total": 6061, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Safely managed": "11.483052", "Basic": "18.247913", "Limited": "2.7366688", "Unimproved": "43.16326", "No access (surface water only)": "24.369104"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Safely managed": "11.495201", "Basic": "18.267725", "Limited": "2.7370446", "Unimproved": "43.149384", "No access (surface water only)": "24.350647"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Safely managed": "12.2981", "Basic": "19.537977", "Limited": "2.956518", "Unimproved": "41.647217", "No access (surface water only)": "23.560188"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Safely managed": "13.10075", "Basic": "20.807878", "Limited": "3.1756995", "Unimproved": "40.145107", "No access (surface water only)": "22.770565"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Safely managed": "13.910267", "Basic": "22.089088", "Limited": "3.394554", "Unimproved": "38.63472", "No access (surface water only)": "21.97137"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Safely managed": "14.726554", "Basic": "23.3815", "Limited": "3.6128173", "Unimproved": "37.11596", "No access (surface water only)": "21.16317"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Safely managed": "15.542417", "Basic": "24.673346", "Limited": "3.8304324", "Unimproved": "35.597168", "No access (surface water only)": "20.356638"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Safely managed": "16.65025", "Basic": "25.779245", "Limited": "3.9406974", "Unimproved": "34.0782", "No access (surface water only)": "19.551605"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Safely managed": "17.554295", "Basic": "27.099499", "Limited": "4.038717", "Unimproved": "32.559216", "No access (surface water only)": "18.748274"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Safely managed": "18.389793", "Basic": "28.49879", "Limited": "4.1245484", "Unimproved": "31.040209", "No access (surface water only)": "17.94666"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Safely managed": "19.224722", "Basic": "29.909073", "Limited": "4.1982517", "Unimproved": "29.521183", "No access (surface water only)": "17.14677"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Safely managed": "20.059193", "Basic": "31.330479", "Limited": "4.259877", "Unimproved": "28.001984", "No access (surface water only)": "16.348467"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Safely managed": "20.89308", "Basic": "32.76273", "Limited": "4.3094916", "Unimproved": "26.48277", "No access (surface water only)": "15.551928"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Safely managed": "21.72638", "Basic": "34.205753", "Limited": "4.347157", "Unimproved": "24.963541", "No access (surface water only)": "14.757167"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Safely managed": "22.55909", "Basic": "35.659485", "Limited": "4.372934", "Unimproved": "23.444294", "No access (surface water only)": "13.964198"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Safely managed": "23.391308", "Basic": "37.12404", "Limited": "4.3868737", "Unimproved": "21.924885", "No access (surface water only)": "13.1728945"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Safely managed": "24.222923", "Basic": "38.599148", "Limited": "4.3890495", "Unimproved": "20.40546", "No access (surface water only)": "12.38342"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Safely managed": "25.055178", "Basic": "40.08696", "Limited": "4.3793836", "Unimproved": "18.884254", "No access (surface water only)": "11.594225"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Safely managed": "25.888214", "Basic": "41.587658", "Limited": "4.3579125", "Unimproved": "17.360981", "No access (surface water only)": "10.805237"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Safely managed": "26.721855", "Basic": "43.10084", "Limited": "4.3247147", "Unimproved": "15.835808", "No access (surface water only)": "10.01678"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Safely managed": "27.555935", "Basic": "44.626137", "Limited": "4.2798777", "Unimproved": "14.308889", "No access (surface water only)": "9.229162"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Safely managed": "28.39068", "Basic": "46.163845", "Limited": "4.22345", "Unimproved": "12.779799", "No access (surface water only)": "8.4422245"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Safely managed": "29.225822", "Basic": "47.713387", "Limited": "4.1555495", "Unimproved": "11.1886635", "No access (surface water only)": "7.716579"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Safely managed": "29.907448", "Basic": "49.019115", "Limited": "4.062646", "Unimproved": "9.965378", "No access (surface water only)": "7.045412"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2024", "Safely managed": "30.554722", "Basic": "50.277714", "Limited": "3.9564328", "Unimproved": "8.832665", "No access (surface water only)": "6.3784637"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2000", "Safely managed": "19.846087", "Basic": "27.731165", "Limited": "8.707156", "Unimproved": "26.263779", "No access (surface water only)": "17.451813"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2001", "Safely managed": "20.093462", "Basic": "28.229921", "Limited": "8.984085", "Unimproved": "25.605541", "No access (surface water only)": "17.086992"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2002", "Safely managed": "20.49743", "Basic": "28.652384", "Limited": "9.216421", "Unimproved": "25.067661", "No access (surface water only)": "16.566103"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2003", "Safely managed": "20.909882", "Basic": "29.097439", "Limited": "9.439755", "Unimproved": "24.530802", "No access (surface water only)": "16.022123"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2004", "Safely managed": "21.375198", "Basic": "29.50216", "Limited": "9.654407", "Unimproved": "23.9927", "No access (surface water only)": "15.4755335"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2005", "Safely managed": "21.844574", "Basic": "29.912775", "Limited": "9.862604", "Unimproved": "23.449556", "No access (surface water only)": "14.9304905"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2006", "Safely managed": "22.346731", "Basic": "30.310411", "Limited": "10.058106", "Unimproved": "22.893717", "No access (surface water only)": "14.391035"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2007", "Safely managed": "22.889801", "Basic": "30.662504", "Limited": "10.250028", "Unimproved": "22.344606", "No access (surface water only)": "13.853061"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2008", "Safely managed": "23.466745", "Basic": "31.002356", "Limited": "10.435985", "Unimproved": "21.78323", "No access (surface water only)": "13.311685"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2009", "Safely managed": "24.066639", "Basic": "31.325136", "Limited": "10.615736", "Unimproved": "21.221865", "No access (surface water only)": "12.770624"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2010", "Safely managed": "24.688044", "Basic": "31.627975", "Limited": "10.788951", "Unimproved": "20.659039", "No access (surface water only)": "12.235991"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2011", "Safely managed": "25.060333", "Basic": "31.992205", "Limited": "11.099038", "Unimproved": "20.04173", "No access (surface water only)": "11.806693"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2012", "Safely managed": "25.728968", "Basic": "32.24335", "Limited": "11.266967", "Unimproved": "19.484352", "No access (surface water only)": "11.276363"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2013", "Safely managed": "26.422888", "Basic": "32.472687", "Limited": "11.427803", "Unimproved": "18.932085", "No access (surface water only)": "10.744536"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2014", "Safely managed": "27.148869", "Basic": "32.680214", "Limited": "11.576384", "Unimproved": "18.380499", "No access (surface water only)": "10.214036"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2015", "Safely managed": "27.912064", "Basic": "32.827435", "Limited": "11.747834", "Unimproved": "17.827124", "No access (surface water only)": "9.685543"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2016", "Safely managed": "28.6639", "Basic": "32.96124", "Limited": "11.919096", "Unimproved": "17.2894", "No access (surface water only)": "9.166364"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2017", "Safely managed": "29.407", "Basic": "33.10607", "Limited": "12.078267", "Unimproved": "16.7715", "No access (surface water only)": "8.637161"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2018", "Safely managed": "30.213354", "Basic": "33.17029", "Limited": "12.258199", "Unimproved": "16.224285", "No access (surface water only)": "8.133871"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2019", "Safely managed": "31.01558", "Basic": "33.231544", "Limited": "12.44457", "Unimproved": "15.67372", "No access (surface water only)": "7.6345873"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2020", "Safely managed": "31.636965", "Basic": "33.465527", "Limited": "12.62954", "Unimproved": "15.1205", "No access (surface water only)": "7.1474686"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2021", "Safely managed": "32.1418", "Basic": "33.814453", "Limited": "12.807114", "Unimproved": "14.540016", "No access (surface water only)": "6.6966186"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2022", "Safely managed": "32.596992", "Basic": "34.07826", "Limited": "13.014832", "Unimproved": "14.048718", "No access (surface water only)": "6.261197"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2023", "Safely managed": "33.042763", "Basic": "34.507214", "Limited": "13.08847", "Unimproved": "13.525816", "No access (surface water only)": "5.835739"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2024", "Safely managed": "33.474796", "Basic": "34.86368", "Limited": "13.231759", "Unimproved": "12.968756", "No access (surface water only)": "5.4610057"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Safely managed": "49.13832", "Basic": "37.258724", "Limited": "10.139525", "Unimproved": "2.5397263", "No access (surface water only)": "0.9237058"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Safely managed": "49.0812", "Basic": "37.822872", "Limited": "9.678688", "Unimproved": "2.5696044", "No access (surface water only)": "0.8476376"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Safely managed": "48.984764", "Basic": "38.46687", "Limited": "9.19203", "Unimproved": "2.5882528", "No access (surface water only)": "0.76808107"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Safely managed": "48.88544", "Basic": "39.10175", "Limited": "8.713076", "Unimproved": "2.6088738", "No access (surface water only)": "0.6908594"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Safely managed": "50.717678", "Basic": "37.792904", "Limited": "8.241953", "Unimproved": "2.6314719", "No access (surface water only)": "0.61599314"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Safely managed": "52.578335", "Basic": "36.442856", "Limited": "7.77907", "Unimproved": "2.6561968", "No access (surface water only)": "0.54354334"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Safely managed": "54.467083", "Basic": "35.05205", "Limited": "7.3243704", "Unimproved": "2.6829963", "No access (surface water only)": "0.4734987"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Safely managed": "56.383842", "Basic": "33.620346", "Limited": "6.8780193", "Unimproved": "2.7119102", "No access (surface water only)": "0.40588066"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Safely managed": "58.32848", "Basic": "32.14767", "Limited": "6.440165", "Unimproved": "2.7429764", "No access (surface water only)": "0.3407062"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Safely managed": "60.300907", "Basic": "30.633505", "Limited": "6.0112095", "Unimproved": "2.7763612", "No access (surface water only)": "0.2780162"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Safely managed": "62.30063", "Basic": "29.07885", "Limited": "5.5908566", "Unimproved": "2.8118942", "No access (surface water only)": "0.2177699"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Safely managed": "64.3274", "Basic": "27.483484", "Limited": "5.179411", "Unimproved": "2.849714", "No access (surface water only)": "0.15999106"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Safely managed": "66.38085", "Basic": "25.847927", "Limited": "4.7768054", "Unimproved": "2.88975", "No access (surface water only)": "0.10466496"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Safely managed": "68.45983", "Basic": "24.171762", "Limited": "4.384006", "Unimproved": "2.9325821", "No access (surface water only)": "0.05181725"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Safely managed": "70.563255", "Basic": "22.456797", "Limited": "4.000567", "Unimproved": "2.9780202", "No access (surface water only)": "0.0013596156"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Safely managed": "70.59197", "Basic": "22.802355", "Limited": "3.6263838", "Unimproved": "2.9792883", "No access (surface water only)": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Safely managed": "70.61651", "Basic": "23.13837", "Limited": "3.2611578", "Unimproved": "2.9839659", "No access (surface water only)": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Safely managed": "70.63694", "Basic": "23.465155", "Limited": "2.9046466", "Unimproved": "2.9932582", "No 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"Code": "OWID_WRL", "Year": "2010", "Safely managed": "64.84133", "Basic": "21.305384", "Limited": "3.018857", "Unimproved": "7.956735", "No access (surface water only)": "2.8776925"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Safely managed": "65.350914", "Basic": "21.153252", "Limited": "3.0955775", "Unimproved": "7.624417", "No access (surface water only)": "2.7758377"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Safely managed": "65.91863", "Basic": "21.000034", "Limited": "3.143975", "Unimproved": "7.28868", "No access (surface water only)": "2.6486802"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Safely managed": "66.5065", "Basic": "20.8245", "Limited": "3.18919", "Unimproved": "6.9622345", "No access (surface water only)": "2.5175776"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Safely managed": "67.09997", "Basic": "20.640284", "Limited": "3.2342055", "Unimproved": "6.6368856", "No access (surface water only)": 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"Unimproved": "3.706478", "No access (surface water only)": "1.3038616"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Safely managed": "", "Basic": "", "Limited": "20.30507", "Unimproved": "33.586224", "No access (surface water only)": "6.009825"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Safely managed": "", "Basic": "", "Limited": "20.315298", "Unimproved": "33.38541", "No access (surface water only)": "5.9765005"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Safely managed": "", "Basic": "", "Limited": "20.325682", "Unimproved": "33.181507", "No access (surface water only)": "5.942663"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Safely managed": "", "Basic": "", "Limited": "20.845749", "Unimproved": "31.855711", "No access (surface water only)": "5.7503533"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Safely managed": "", "Basic": "", "Limited": "21.35909", "Unimproved": "30.541607", "No access (surface water only)": "5.559005"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Safely managed": "", "Basic": "", "Limited": "21.350746", "Unimproved": "29.240166", "No access (surface water only)": "5.3687644"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Safely managed": "", "Basic": "", "Limited": "21.312016", "Unimproved": "27.95058", "No access (surface water only)": "5.179476"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Safely managed": "", "Basic": "", "Limited": "21.243177", "Unimproved": "26.673498", "No access (surface water only)": "4.991227"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Safely managed": "", "Basic": "", "Limited": "21.144531", "Unimproved": "25.409197", "No access (surface water only)": "4.804046"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Safely managed": "", "Basic": "", "Limited": "21.01639", "Unimproved": "24.157953", "No access (surface water only)": "4.61796"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Safely managed": "", "Basic": "", "Limited": "20.859072", "Unimproved": "22.92005", "No access (surface water only)": "4.4329967"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Safely managed": "", "Basic": "", "Limited": "20.67295", "Unimproved": "21.69603", "No access (surface water only)": "4.249235"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Safely managed": "", "Basic": "", "Limited": "20.458393", "Unimproved": "20.48613", "No access (surface water only)": "4.066699"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Safely managed": "", "Basic": "", "Limited": "20.215712", "Unimproved": "19.290361", "No access (surface water only)": "3.8853621"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Safely managed": "", "Basic": "", "Limited": "19.94539", "Unimproved": "18.109446", "No access (surface water only)": "3.7053487"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Safely managed": "", "Basic": "", "Limited": "19.647774", "Unimproved": "16.943367", "No access (surface water 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"No access (surface water only)": "18.051119"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Safely managed": "", "Basic": "", "Limited": "4.067262", "Unimproved": "29.922329", "No access (surface water only)": "17.443478"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Safely managed": "", "Basic": "", "Limited": "4.2167478", "Unimproved": "29.147177", "No access (surface water only)": "16.841408"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Safely managed": "", "Basic": "", "Limited": "4.366046", "Unimproved": "28.376663", "No access (surface water only)": "16.244967"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Safely managed": "", "Basic": "", "Limited": "4.515099", "Unimproved": "27.611105", "No access (surface water only)": "15.654437"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Safely managed": "", "Basic": "", "Limited": "4.6638236", "Unimproved": "26.850273", "No access (surface water only)": "15.069642"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Safely managed": "", "Basic": "", "Limited": "4.8121667", "Unimproved": "26.094475", "No access (surface water only)": "14.490851"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Safely managed": "", "Basic": "", "Limited": "4.9600153", "Unimproved": "25.343246", "No access (surface water only)": "13.9177"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Safely managed": "", "Basic": "", "Limited": "5.1073885", "Unimproved": "24.597631", "No access (surface water only)": "13.351048"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Safely managed": "", "Basic": "", "Limited": "5.2541223", "Unimproved": "23.856674", "No access (surface water only)": "12.790144"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Safely managed": "", "Basic": "", "Limited": "5.4001937", "Unimproved": "23.120916", "No access (surface water only)": "12.235427"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Safely managed": "", "Basic": "", "Limited": "5.545368", "Unimproved": "22.38925", "No access (surface water only)": "11.686065"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Safely managed": "", "Basic": "", "Limited": "5.6894984", "Unimproved": "21.661585", "No access (surface water only)": "11.142023"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Safely managed": "", "Basic": "", "Limited": "5.832543", "Unimproved": "20.938477", "No access (surface water only)": "10.603789"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Safely managed": "", "Basic": "", "Limited": "5.974313", "Unimproved": "20.219618", "No access (surface water only)": "10.071171"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Safely managed": "", "Basic": "", "Limited": "6.114774", "Unimproved": "19.505547", "No access (surface water only)": "9.544624"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Safely managed": "", "Basic": "", "Limited": "6.253772", "Unimproved": "18.796177", "No access (surface water only)": "9.024125"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Safely managed": "", "Basic": "", "Limited": "6.3911877", "Unimproved": "18.091625", "No access (surface water only)": "8.509815"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Safely managed": "", "Basic": "", "Limited": "6.526956", "Unimproved": "17.392197", "No access (surface water only)": "8.001962"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Safely managed": "", "Basic": "", "Limited": "6.6609654", "Unimproved": "16.697992", "No access (surface water only)": "7.5006847"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Safely managed": "", "Basic": "", "Limited": "6.761943", "Unimproved": "16.009125", "No access (surface water only)": "7.0061016"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Safely managed": "", "Basic": "", "Limited": "6.860095", "Unimproved": "15.325683", "No access (surface water only)": "6.518334"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Safely managed": "", "Basic": "", "Limited": "6.9554443", "Unimproved": "14.647948", "No access (surface water only)": "6.0376024"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Safely managed": "", "Basic": "", "Limited": "7.047959", "Unimproved": "13.975996", "No access (surface water only)": "5.5640073"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Safely managed": "29.39526", "Basic": "38.52648", "Limited": "8.229726", "Unimproved": "17.896162", "No access (surface water only)": "5.952369"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Safely managed": "29.545853", "Basic": "38.542877", "Limited": "8.19708", "Unimproved": "17.796482", "No access (surface water only)": "5.917708"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Safely managed": "29.760683", "Basic": "38.50517", "Limited": "8.396393", "Unimproved": "17.4073", "No access (surface water only)": "5.9304547"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Safely managed": "29.818277", "Basic": "38.411743", "Limited": "8.63956", "Unimproved": "17.143173", "No access (surface water only)": "5.987248"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Safely managed": "29.847073", "Basic": "38.313488", "Limited": "8.892241", "Unimproved": "16.896055", "No access (surface water only)": "6.0511436"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Safely managed": "29.627148", "Basic": "38.462673", "Limited": "9.147878", "Unimproved": "16.646975", "No access (surface water only)": "6.1153264"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Safely managed": "29.40307", "Basic": "38.61431", "Limited": "9.406574", "Unimproved": "16.39616", "No access (surface water only)": "6.1798835"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Safely managed": "29.175194", "Basic": "38.76843", "Limited": "9.66825", "Unimproved": "16.143404", "No access (surface water only)": "6.2447248"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Safely managed": "28.943127", "Basic": "38.924995", "Limited": "9.933012", "Unimproved": "15.888928", "No access (surface water only)": "6.309937"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Safely managed": "28.70723", "Basic": "39.084038", "Limited": "10.200773", "Unimproved": "15.632531", "No access (surface water only)": "6.3754287"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Safely managed": "28.467104", "Basic": "39.245537", "Limited": "10.471648", "Unimproved": "15.374421", "No access (surface water only)": "6.4412904"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Safely managed": "28.223124", "Basic": "39.409504", "Limited": "10.7455435", "Unimproved": "15.114402", "No access (surface water only)": "6.5074277"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Safely managed": "27.974873", "Basic": "39.57593", "Limited": "11.022579", "Unimproved": "14.852685", "No access (surface water only)": "6.5739326"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Safely managed": "27.722734", "Basic": "39.744823", "Limited": "11.302656", "Unimproved": "14.589078", "No access (surface water only)": "6.6407094"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Safely managed": "27.478418", "Basic": "39.91594", "Limited": "11.582354", "Unimproved": "14.318359", "No access (surface water only)": "6.704931"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Safely managed": "27.242687", "Basic": "40.08886", "Limited": "11.86128", "Unimproved": "14.040763", "No access (surface water only)": "6.766409"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Safely managed": "27.015501", "Basic": "40.263172", "Limited": "12.139273", "Unimproved": "13.7569065", "No access (surface water only)": "6.8251476"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Safely managed": "26.797232", "Basic": "40.43845", "Limited": "12.416046", "Unimproved": "13.467219", "No access (surface water only)": "6.8810554"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Safely managed": "26.588678", "Basic": "40.614254", "Limited": "12.691183", "Unimproved": "13.171946", "No access (surface water only)": "6.93394"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Safely managed": "26.389387", "Basic": "40.790207", "Limited": "12.964648", "Unimproved": "12.871848", "No access (surface water only)": "6.983911"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Safely managed": "26.200596", "Basic": "40.965828", "Limited": "13.235877", "Unimproved": "12.567024", "No access (surface water only)": "7.030675"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Safely managed": "26.02185", "Basic": "41.140755", "Limited": "13.504835", "Unimproved": "12.258216", "No access (surface water only)": "7.0743437"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Safely managed": "25.823425", "Basic": "41.34548", "Limited": "13.770935", "Unimproved": "11.94066", "No access (surface water only)": "7.1194963"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Safely managed": "25.63607", "Basic": "41.54885", "Limited": "14.033998", "Unimproved": "11.618565", "No access (surface water only)": "7.1625185"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Safely managed": "25.460564", "Basic": "41.750458", "Limited": "14.293552", "Unimproved": "11.293405", "No access (surface water only)": "7.2020226"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "access-drinking-water-stacked", "metadata_url": "https://ourworldindata.org/grapher/access-drinking-water-stacked.metadata.json", "chart_title": "Drinking water service usage", "chart_subtitle": "Share of population using five different levels of drinking water services: safely managed, basic,  limited, unimproved and surface water.", "chart_note": null, "chart_citation": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025)", "original_chart_url": "https://ourworldindata.org/grapher/access-drinking-water-stacked", "owid_column_metadata": {"Share of the population using safely managed drinking water": {"titleShort": "Safely managed", "titleLong": "Safely managed", "descriptionShort": "Proportion of people using an improved drinking water source that is located on premises, available when needed and free from faecal and priority chemical contamination.", "descriptionKey": ["Easy and reliable access to drinking water is fundamental to human health and well-being. Improved drinking water sources reduce the risk of contamination and the spread of waterborne diseases.\n", "Having access to water on premises and when needed reduces the time and effort spent collecting water, which frees up time for education, work and participation in community life.\n", "Safely managed drinking water services are defined as an improved drinking water source that is located on premises, available when needed and free from faecal and priority chemical contamination.\n", "This is the highest level of drinking water service and indicates reliable access to safe drinking water.", "This data is provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). They compile data from nationally representative household surveys and censuses, administrative data and service provider data. To learn more, see the [JMP Methodology](https://washdata.org/topics/methods/data-sources).\n", "This data reflects actual service use, which is directly linked to health outcomes and can be consistently measured across countries using household surveys. It is possible to theoretically have access to some kind of water and sanitation infrastructure, but not use them for daily needs. Therefore we refer to \"use\" or \"using\" rather than \"access\" to better reflect the underlying data.\n"], "shortUnit": "%", "unit": "%", "timespan": "2000-2024", "type": "Numeric", "owidVariableId": 1132755, "shortName": "wat_sm__residence_total", "lastUpdated": "2025-12-08", "nextUpdate": "2027-12-08", "citationShort": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data", "citationLong": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data. “Safely managed” [dataset]. World Health Organization/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene, “WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) 2000-2024 report” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132755.metadata.json"}, "Share of the population using a basic drinking water source": {"titleShort": "Basic", "titleLong": "Basic", "descriptionShort": "Proportion of people who use an improved drinking water source, where collection takes no more than 30 minutes for a roundtrip.", "descriptionKey": ["Easy and reliable access to drinking water is fundamental to human health and well-being. Improved drinking water sources reduce the risk of contamination and the spread of waterborne diseases.\n", "Having to collect water from sources outside the home can be time-consuming and physically demanding. Spending hours each day collecting water can limit opportunities for education, employment, and community participation.\n", "Basic drinking water services are defined as an improved drinking water source, provided collection time is not more than 30 minutes for a roundtrip including queuing.\n", "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.\n", "This data is provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). They compile data from nationally representative household surveys and censuses, administrative data and service provider data. To learn more, see the [JMP Methodology](https://washdata.org/topics/methods/data-sources).\n", "This data reflects actual service use, which is directly linked to health outcomes and can be consistently measured across countries using household surveys. It is possible to theoretically have access to some kind of water and sanitation infrastructure, but not use them for daily needs. Therefore we refer to \"use\" or \"using\" rather than \"access\" to better reflect the underlying data.\n"], "shortUnit": "%", "unit": "%", "timespan": "2000-2024", "type": "Numeric", "owidVariableId": 1132726, "shortName": "wat_bas__residence_total", "lastUpdated": "2025-12-08", "nextUpdate": "2027-12-08", "citationShort": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data", "citationLong": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data. “Basic” [dataset]. World Health Organization/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene, “WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) 2000-2024 report” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132726.metadata.json"}, "Share of the population using a limited drinking water source": {"titleShort": "Limited", "titleLong": "Limited", "descriptionShort": "Proportion of people using an improved drinking water source for which collection time exceeds 30 minutes for a roundtrip.", "descriptionKey": ["Easy and reliable access to drinking water is fundamental to human health and well-being. Improved drinking water sources reduce the risk of contamination and the spread of waterborne diseases.\n", "Having to collect water from sources outside the home can be time-consuming and physically demanding. Spending hours each day collecting water can limit opportunities for education, employment, and community participation.\n", "Limited drinking water services are defined as drinking water from an improved source for which collection time exceeds 30 minutes for a roundtrip including queuing.\n", "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.\n", "This data is provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). They compile data from nationally representative household surveys and censuses, administrative data and service provider data. To learn more, see the [JMP Methodology](https://washdata.org/topics/methods/data-sources).\n", "This data reflects actual service use, which is directly linked to health outcomes and can be consistently measured across countries using household surveys. It is possible to theoretically have access to some kind of water and sanitation infrastructure, but not use them for daily needs. Therefore we refer to \"use\" or \"using\" rather than \"access\" to better reflect the underlying data.\n"], "shortUnit": "%", "unit": "%", "timespan": "2000-2024", "type": "Numeric", "owidVariableId": 1132746, "shortName": "wat_lim__residence_total", "lastUpdated": "2025-12-08", "nextUpdate": "2027-12-08", "citationShort": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data", "citationLong": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data. “Limited” [dataset]. World Health Organization/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene, “WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) 2000-2024 report” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132746.metadata.json"}, "Share of the population using unimproved drinking water": {"titleShort": "Unimproved", "titleLong": "Unimproved", "descriptionShort": "Proportion of people using an unimproved drinking water source.", "descriptionKey": ["Easy and reliable access to drinking water is fundamental to human health and well-being. Improved drinking water sources reduce the risk of contamination and the spread of waterborne diseases.\n", "Unimproved drinking water services are defined as drinking water from an unprotected dug well or unprotected spring.\n", "Unimproved drinking water sources are more likely to be contaminated and pose a higher risk to health.", "This data is provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). They compile data from nationally representative household surveys and censuses, administrative data and service provider data. To learn more, see the [JMP Methodology](https://washdata.org/topics/methods/data-sources).\n", "This data reflects actual service use, which is directly linked to health outcomes and can be consistently measured across countries using household surveys. It is possible to theoretically have access to some kind of water and sanitation infrastructure, but not use them for daily needs. Therefore we refer to \"use\" or \"using\" rather than \"access\" to better reflect the underlying data.\n"], "shortUnit": "%", "unit": "%", "timespan": "2000-2024", "type": "Numeric", "owidVariableId": 1132758, "shortName": "wat_unimp__residence_total", "lastUpdated": "2025-12-08", "nextUpdate": "2027-12-08", "citationShort": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data", "citationLong": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data. “Unimproved” [dataset]. World Health Organization/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene, “WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) 2000-2024 report” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132758.metadata.json"}, "Share of the population using surface water for drinking": {"titleShort": "No access (surface water only)", "titleLong": "No access (surface water only)", "descriptionShort": "Proportion of people using drinking water directly collected from surface waters.", "descriptionKey": ["Easy and reliable access to drinking water is fundamental to human health and well-being. Improved drinking water sources reduce the risk of contamination and the spread of waterborne diseases.\n", "Surface water includes rivers, streams, ponds, lakes, dams, canals and irrigation channels.\n", "This is the lowest level of drinking water service and indicates a lack of access to safe drinking water.", "This data is provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). They compile data from nationally representative household surveys and censuses, administrative data and service provider data. To learn more, see the [JMP Methodology](https://washdata.org/topics/methods/data-sources).\n", "This data reflects actual service use, which is directly linked to health outcomes and can be consistently measured across countries using household surveys. It is possible to theoretically have access to some kind of water and sanitation infrastructure, but not use them for daily needs. Therefore we refer to \"use\" or \"using\" rather than \"access\" to better reflect the underlying data.\n"], "shortUnit": "%", "unit": "%", "timespan": "2000-2024", "type": "Numeric", "owidVariableId": 1132749, "shortName": "wat_ns__residence_total", "lastUpdated": "2025-12-08", "nextUpdate": "2027-12-08", "citationShort": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data", "citationLong": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data. “No access (surface water only)” [dataset]. World Health Organization/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene, “WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) 2000-2024 report” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132749.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Share of population living in extreme poverty", "source_url": "https://ourworldindata.org/grapher/share-of-population-in-extreme-poverty.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Share of population in poverty ($3 a day)", "Population", "World region according to OWID"], "row_count_total": 59371, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "-10000", "Share of population in poverty ($3 a day)": "", "Population": "14737", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "-9000", "Share of population in poverty ($3 a day)": "", "Population": "20405", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "-8000", "Share of population in poverty ($3 a day)": "", "Population": "28253", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "-7000", "Share of population in poverty ($3 a day)": "", "Population": "39120", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "-6000", "Share of population in poverty ($3 a day)": "", "Population": "54166", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "-5000", "Share of population in poverty ($3 a day)": "", "Population": "75000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "-4000", "Share of population in poverty ($3 a day)": "", "Population": "306250", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "-3000", "Share of population in poverty ($3 a day)": "", "Population": "537500", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "-2000", "Share of population in poverty ($3 a day)": "", "Population": "768750", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "-1000", "Share of population in poverty ($3 a day)": "", "Population": "1000000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "0", "Share of population in poverty ($3 a day)": "", "Population": "2000000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "100", "Share of population in poverty ($3 a day)": "", "Population": "2250000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "200", "Share of population in poverty ($3 a day)": "", "Population": "2500000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "300", "Share of population in poverty ($3 a day)": "", "Population": "2500000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "400", "Share of population in poverty ($3 a day)": "", "Population": "2500000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "500", "Share of population in poverty ($3 a day)": "", "Population": "2500000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "600", "Share of population in poverty ($3 a day)": "", "Population": "2500000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "700", "Share of population in poverty ($3 a day)": "", "Population": "2425000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "800", "Share of population in poverty ($3 a day)": "", "Population": "2350000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "900", "Share of population in poverty ($3 a day)": "", "Population": "2300000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1000", "Share of population in poverty ($3 a day)": "", "Population": "2250000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1100", "Share of population in poverty ($3 a day)": "", "Population": "2375000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1200", "Share of population in poverty ($3 a day)": "", "Population": "2500000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1300", "Share of population in poverty ($3 a day)": "", "Population": "1750000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1400", "Share of population in poverty ($3 a day)": "", "Population": "1875000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1500", "Share of population in poverty ($3 a day)": "", "Population": "2000000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1600", "Share of population in poverty ($3 a day)": "", "Population": "2500000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1700", "Share of population in poverty ($3 a day)": "", "Population": "2500000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1710", "Share of population in poverty ($3 a day)": "", "Population": "2561625", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1720", "Share of population in poverty ($3 a day)": "", "Population": "2619844", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1730", "Share of population in poverty ($3 a day)": "", "Population": "2679386", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1740", "Share of population in poverty ($3 a day)": "", "Population": "2740281", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1750", "Share of population in poverty ($3 a day)": "", "Population": "2802560", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1760", "Share of population in poverty ($3 a day)": "", "Population": "2866255", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1770", "Share of population in poverty ($3 a day)": "", "Population": "2931397", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1780", "Share of population in poverty ($3 a day)": "", "Population": "2998019", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1790", "Share of population in poverty ($3 a day)": "", "Population": "3066156", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1800", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1801", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1802", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1803", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1804", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1805", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1806", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1807", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1808", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1809", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1810", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1811", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1812", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1813", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1814", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1815", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1816", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1817", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1818", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1819", "Share of population in poverty ($3 a day)": "", "Population": "3282935", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1820", "Share of population in poverty ($3 a day)": "", "Population": "3288817", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1821", "Share of population in poverty ($3 a day)": "", "Population": "3297661", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1822", "Share of population in poverty ($3 a day)": "", "Population": "3309479", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1823", "Share of population in poverty ($3 a day)": "", "Population": "3324285", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1824", "Share of population in poverty ($3 a day)": "", "Population": "3339157", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1825", "Share of population in poverty ($3 a day)": "", "Population": "3354095", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1826", "Share of population in poverty ($3 a day)": "", "Population": "3369101", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1827", "Share of population in poverty ($3 a day)": "", "Population": "3384173", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1828", "Share of population in poverty ($3 a day)": "", "Population": "3399313", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1829", "Share of population in poverty ($3 a day)": "", "Population": "3414520", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1830", "Share of population in poverty ($3 a day)": "", "Population": "3429796", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1831", "Share of population in poverty ($3 a day)": "", "Population": "3445140", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1832", "Share of population in poverty ($3 a day)": "", "Population": "3460553", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1833", "Share of population in poverty ($3 a day)": "", "Population": "3476034", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1834", "Share of population in poverty ($3 a day)": "", "Population": "3491585", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1835", "Share of population in poverty ($3 a day)": "", "Population": "3507205", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1836", "Share of population in poverty ($3 a day)": "", "Population": "3522896", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1837", "Share of population in poverty ($3 a day)": "", "Population": "3538656", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1838", "Share of population in poverty ($3 a day)": "", "Population": "3554487", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1839", "Share of population in poverty ($3 a day)": "", "Population": "3570389", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1840", "Share of population in poverty ($3 a day)": "", "Population": "3586362", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1841", "Share of population in poverty ($3 a day)": "", "Population": "3602406", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1842", "Share of population in poverty ($3 a day)": "", "Population": "3618522", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1843", "Share of population in poverty ($3 a day)": "", "Population": "3634711", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1844", "Share of population in poverty ($3 a day)": "", "Population": "3650971", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1845", "Share of population in poverty ($3 a day)": "", "Population": "3667305", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1846", "Share of population in poverty ($3 a day)": "", "Population": "3683711", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1847", "Share of population in poverty ($3 a day)": "", "Population": "3700191", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1848", "Share of population in poverty ($3 a day)": "", "Population": "3716745", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1849", "Share of population in poverty ($3 a day)": "", "Population": "3734342", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1850", "Share of population in poverty ($3 a day)": "", "Population": "3752993", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1851", "Share of population in poverty ($3 a day)": "", "Population": "3767956", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1852", "Share of population in poverty ($3 a day)": "", "Population": "3783940", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1853", "Share of population in poverty ($3 a day)": "", "Population": "3800954", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1854", "Share of population in poverty ($3 a day)": "", "Population": "3818038", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1855", "Share of population in poverty ($3 a day)": "", "Population": "3835192", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1856", "Share of population in poverty ($3 a day)": "", "Population": "3852417", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1857", "Share of population in poverty ($3 a day)": "", "Population": "3869714", "World region according to OWID": "Asia"}, 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"Share of population in poverty ($3 a day)": "", "Population": "4458457", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "Share of population in poverty ($3 a day)": "", "Population": "4601223", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "Share of population in poverty ($3 a day)": "", "Population": "4748309", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "Share of population in poverty ($3 a day)": "", "Population": "4900440", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "Share of population in poverty ($3 a day)": "", "Population": "5058185", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "Share of population in poverty ($3 a day)": "", "Population": "5215919", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "Share of population in poverty ($3 a day)": "", "Population": "5374719", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "Share of population in poverty ($3 a day)": "", "Population": "5542444", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "Share of population in poverty ($3 a day)": "", "Population": "5720413", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "Share of population in poverty ($3 a day)": "", "Population": "5908337", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Share of population in poverty ($3 a day)": "", "Population": "6098653", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Share of population in poverty ($3 a day)": "", "Population": "6287107", "World region 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"Population": "7796503", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Share of population in poverty ($3 a day)": "", "Population": "8098408", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Share of population in poverty ($3 a day)": "", "Population": "8391494", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Share of population in poverty ($3 a day)": "", "Population": "8686815", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Share of population in poverty ($3 a day)": "", "Population": "8982579", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Share of population in poverty ($3 a day)": "", "Population": "9284649", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Share of population in poverty ($3 a day)": "", "Population": "9583099", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Share of population in poverty ($3 a day)": "", "Population": "9864797", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Share of population in poverty ($3 a day)": "", "Population": "10137287", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Share of population in poverty ($3 a day)": "", "Population": "10404820", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Share of population in poverty ($3 a day)": "", "Population": "10702697", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Share of population in poverty ($3 a day)": "", "Population": "10860285", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Share of population in poverty ($3 a day)": "", "Population": "10873146", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Share of population in poverty ($3 a day)": "", "Population": "10974607", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Share of population in poverty ($3 a day)": "", "Population": "11158364", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Share of population in poverty ($3 a day)": "", "Population": "11369833", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Share of population in poverty ($3 a day)": "", "Population": "11594299", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Share of population in poverty ($3 a day)": "", "Population": "11783454", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Share of population in poverty ($3 a day)": "", "Population": "11892055", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Share of population in poverty ($3 a day)": "", "Population": "11971904", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Share of population in poverty ($3 a day)": "", "Population": "12087661", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Share of population in poverty ($3 a day)": "", "Population": "12232324", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Share of population in poverty ($3 a day)": "", "Population": "12365901", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Share of population in poverty ($3 a day)": "", "Population": "12483433", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Share of population in poverty ($3 a day)": "", "Population": "12636442", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Share of population in poverty ($3 a day)": "", "Population": "12804062", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Share of population in poverty ($3 a day)": "", "Population": "12959154", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Share of population in poverty ($3 a day)": "", "Population": "13142791", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Share of population in poverty ($3 a day)": "", "Population": "13356551", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Share of population in poverty ($3 a day)": "35.71699857711792", "Population": "13595421", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Share of population in poverty ($3 a day)": "", "Population": "13817887", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Share of population in poverty ($3 a day)": "", "Population": "14013811", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Share of population in poverty ($3 a day)": "", "Population": "14207367", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Share of population in poverty ($3 a day)": "", "Population": "14399008", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Share of population in poverty ($3 a day)": "", "Population": "14600297", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Share of population in poverty ($3 a day)": "44.65687274932861", "Population": "14812484", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Share of population in poverty ($3 a day)": "", "Population": "15034457", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Share of population in poverty ($3 a day)": "49.21989440917969", "Population": "15271377", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Share of population in poverty ($3 a day)": "", "Population": "15526887", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Share of population in poverty ($3 a day)": "", "Population": "15797220", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Share of population in poverty ($3 a day)": "", "Population": "16069061", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Share of population in poverty ($3 a day)": "", "Population": "16340829", "World region according to OWID": "Africa"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "share-of-population-in-extreme-poverty", "metadata_url": "https://ourworldindata.org/grapher/share-of-population-in-extreme-poverty.metadata.json", "chart_title": "Share of population living in extreme poverty", "chart_subtitle": "Extreme poverty is defined as living below the International Poverty Line of $3 per day. This data is adjusted for inflation and differences in living costs between countries.", "chart_note": "This data is expressed in international-$ at 2021 prices. Depending on the country and year, it relates to income (measured after taxes and benefits) or to consumption, per capita.", "chart_citation": "World Bank Poverty and Inequality Platform (2026)", "original_chart_url": "https://ourworldindata.org/grapher/share-of-population-in-extreme-poverty", "owid_column_metadata": {"Share of population in poverty ($3 a day, 2021 prices)": {"titleShort": "Share of population in poverty ($3 a day)", "titleLong": "Share of population in poverty ($3 a day)", "descriptionShort": "Percentage of population living in households with an income or consumption below $3 per day.", "descriptionKey": ["The World Bank defines extreme poverty as living on less than $3 per day. This threshold, known as the \"International Poverty Line\", is set so that poverty can be compared across countries. This indicator plays an important and successful role in focusing the world's attention on the very poorest people. The UN uses this indicator to track progress towards [ending extreme poverty by 2030](https://ourworldindata.org/sdgs/no-poverty).", "Two centuries ago, most of the world's population was extremely poor. Many believed that widespread poverty was inevitable. But this turned out to be wrong. Economic growth is possible, and poverty can decline. With this poverty line, we can track whether countries are leaving the worst poverty behind.", "This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "Many people, today and in the past, have no formal monetary income. This data accounts for that by including the estimated value of non-market income, such as food grown by subsistence farmers for their own use.", "The data comes from the World Bank's Poverty and Inequality Platform (PIP), which draws on national household surveys. Regional and global estimates are extrapolated to the year of the data release using GDP growth estimates and forecasts. For more details about the methodology, please refer to the [World Bank PIP documentation](https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#nowcasts).", "Depending on the country and year, the data refers either to income (after taxes and benefits) or to consumption, per capita. These are not perfectly comparable — consumption tends to be more evenly distributed than income. For most countries, we have only one option available. But when there is a mix of consumption and income data points, we process the data to keep one observation per country and year."], "descriptionProcessing": "For most countries in the dataset, estimates relate to disposable income or consumption, for all available years. Several countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\n\nIn most of our charts, we present the data with some data points dropped to present a single series for each country. This allows us to make readable visualizations that combine multiple countries. In choosing which data points to keep, we strike a balance between maintaining comparability over time and showing as long a time series as possible. As such, the exact approach varies across countries.", "shortUnit": "%", "unit": "%", "timespan": "1963-2026", "type": "Numeric", "owidVariableId": 1220228, "shortName": "headcount_ratio__ppp_version_2021__poverty_line_300__welfare_type_income_or_consumption__table_income_or_consumption_consolidated__survey_comparability_no_spells", "lastUpdated": "2026-03-24", "nextUpdate": "2026-09-20", "citationShort": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data", "citationLong": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data. “Share of population in poverty ($3 a day) – World Bank” [dataset]. World Bank Poverty and Inequality Platform, “World Bank Poverty and Inequality Platform (PIP) 20260324_2021, 20260324_2017” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1220228.metadata.json"}, "Population (historical)": {"titleShort": "Population", "titleLong": "Population", "descriptionShort": "Population by country, available from 10,000 BCE to 2023, based on data and estimates from different sources.", "descriptionKey": ["Population is the most commonly used metric throughout Our World in Data. It is used directly to understand population growth over time, and indirectly to calculate per-capita indicators, making it easier to compare countries of different sizes.", "We construct this indicator by combining multiple sources covering different periods.\n  - HYDE v3.3 (2023): historical estimates from 10,000 BCE to 1799.\n  - Gapminder v7 (2022): for 1800-1949.\n  - UN World Population Prospects (2024): for 1950 onwards, including 2100 projections.\n  - Gapminder Systema Globalis (2023): additional source for former countries (Yugoslavia, USSR, etc.)", "Breaks in the data may occur at the boundaries between sources due to their methodological differences.", "You can read more about the sources and methodology in our [dedicated article](https://ourworldindata.org/population-sources). We also provide a table of sources showing the source we use for each country-year.", "We calculate geographical aggregates (continents, income groups, etc.) by summing individual country populations. For years before 1800, we rely directly on HYDE's values for continents to ensure historical consistency."], "descriptionProcessing": "### Combination of different sources\nWe construct our long-run population data by combining multiple sources:\n\n- 10,000 BCE–1799: historical estimates by HYDE (v3.3).\n\n- 1800–1949: historical estimates by Gapminder (v7).\n\n- 1950–2023: population records from the United Nations World Population Prospects (2024 revision).\n\n**Geographical aggregates**\n\n- For most years, we calculate aggregates by summing the population of member countries.\n- We do this based on [our definition of continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and the [World Bank’s income groups](https://ourworldindata.org/grapher/world-bank-income-groups).\n- The only exception is before 1800, where we use HYDE's estimates for continents (but not income groups).\n\nFor most of the years, we've estimated regional aggregates by summing the population of countries in each region. We've relied on [our continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and [World Bank income group definitions](https://ourworldindata.org/grapher/world-bank-income-groups). The only exception is before 1800, where we've used HYDE's estimates on continents (but not income groups).\n\n**World**\n- Before 1800: we use data from HYDE.\n- 1800-1950: we estimate the global population by summing all available countries in the dataset.\n- After 1950, we rely on estimates from the United Nations World Population Prospects.", "shortUnit": "", "unit": "people", "timespan": "-10000-2023", "type": "Integer", "owidVariableId": 953903, "shortName": "population_historical", "lastUpdated": "2024-07-15", "nextUpdate": "2026-07-15", "citationShort": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data", "citationLong": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data. “Population – HYDE, Gapminder, UN – Long-run data” [dataset]. PBL Netherlands Environmental Assessment Agency, “History Database of the Global Environment 3.3”; Gapminder, “Population v7”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update”; Gapminder, “Systema Globalis” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/953903.metadata.json"}, "World region according to OWID": {"titleShort": "World region according to OWID", "titleLong": "World region according to OWID", "descriptionShort": "Regions defined by Our World in Data, which are used in OWID charts and maps.", "unit": "", "timespan": "2023-2023", "type": "Continent", "owidVariableId": 900801, "shortName": "owid_region", "lastUpdated": "2023-01-01", "citationShort": "Our World in Data – processed by Our World in Data", "citationLong": "Our World in Data – processed by Our World in Data. “World region according to OWID” [dataset]. Our World in Data, “Regions” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/900801.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Drinking water services", "source_url": "https://ourworldindata.org/grapher/drinking-water-service-coverage.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Surface water", "Unimproved", "Limited", "Basic", "Safely managed"], "row_count_total": 6061, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Surface water": "4905580", "Unimproved": "8688905", "Limited": "550900.4", "Basic": "3673364.5", "Safely managed": "2311575.8"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Surface water": "4939360", "Unimproved": "8752553", "Limited": "555190.5", "Basic": "3705481.2", "Safely managed": "2331721.8"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Surface water": "5036724.5", "Unimproved": "8903391", "Limited": "632047.9", "Basic": "4176851.8", "Safely managed": "2629102.2"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Surface water": "5176443.5", "Unimproved": "9126207", "Limited": "721933.3", "Basic": "4730265", "Safely managed": "2978200"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Surface water": "5176598.5", "Unimproved": "9102593", "Limited": "799779.1", "Basic": "5204334", "Safely managed": "3277350"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Surface water": "5164780", "Unimproved": "9057989", "Limited": "881692.4", "Basic": "5706154", "Safely managed": "3593951.8"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Surface water": "5175490.5", "Unimproved": "9050257", "Limited": "973852.75", "Basic": "6272974.5", "Safely managed": "3951518.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Surface water": "5065792", "Unimproved": "8829611", "Limited": "1021028.9", "Basic": "6679364", "Safely managed": "4314055"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Surface water": "4965034.5", "Unimproved": "8622534", "Limited": "1069558.1", "Basic": "7176657.5", "Safely managed": "4648837.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Surface water": "4929247.5", "Unimproved": "8525535", "Limited": "1132852.6", "Basic": "7827507", "Safely managed": "5050959.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Surface water": "4849808", "Unimproved": "8349798", "Limited": "1187437.2", "Basic": "8459509", "Safely managed": "5437537.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Surface water": "4797900", "Unimproved": "8217940", "Limited": "1250176.2", "Basic": "9194777", "Safely managed": "5886913"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Surface water": "4752674", "Unimproved": "8093143.5", "Limited": "1316982.1", "Basic": "10012301", "Safely managed": "6384932"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Surface water": "4666615", "Unimproved": "7894146.5", "Limited": "1374688.5", "Basic": "10816784", "Safely managed": "6870468.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Surface water": "4579213", "Unimproved": "7687975.5", "Limited": "1433995.4", "Basic": "11693645", "Safely managed": "7397695"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Surface water": "4456623", "Unimproved": "7417575.5", "Limited": "1484156.9", "Basic": "12559718", "Safely managed": "7913692.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Surface water": "4297122.5", "Unimproved": "7080820", "Limited": "1523027.1", "Basic": "13394141", "Safely managed": "8405503"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Surface water": "4137855.2", "Unimproved": "6739589", "Limited": "1562955.2", "Basic": "14306608", "Safely managed": "8941926"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Surface water": "3970172.5", "Unimproved": "6378952", "Limited": "1601229.5", "Basic": "15280569", "Safely managed": "9512117"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Surface water": "3791964.2", "Unimproved": "5994822.5", "Limited": "1637169.2", "Basic": "16316307", "Safely managed": "10115858"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Surface water": "3605739.5", "Unimproved": "5590337", "Limited": "1672104.6", "Basic": "17434976", "Safely managed": "10765823"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Surface water": "3376924.5", "Unimproved": "5111972", "Limited": "1689397.4", "Basic": "18465728", "Safely managed": "11356389"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Surface water": "3131298.5", "Unimproved": "4540230.5", "Limited": "1686274", "Basic": "19361540", "Safely managed": "11859501"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Surface water": "2920658.8", "Unimproved": "4131123.5", "Limited": "1684160.1", "Basic": "20320758", "Safely managed": "12398061"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2024", "Surface water": "2720254.8", "Unimproved": "3766910.2", "Limited": "1687319.4", "Basic": "21442184", "Safely managed": "13030823"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2000", "Surface water": "117026760", "Unimproved": "176117230", "Limited": "58387650", "Basic": "185957100", "Safely managed": "133082060"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2001", "Surface water": "117585016", "Unimproved": "176205840", "Limited": "61824440", "Basic": "194265650", "Safely managed": "138274190"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2002", "Surface water": "117010140", "Unimproved": "177058580", "Limited": "65097670", "Basic": "202378290", "Safely managed": "144778000"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2003", "Surface water": "116175830", "Unimproved": "177871950", "Limited": "68447330", "Basic": "210984480", "Safely managed": "151616800"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2004", "Surface water": "115222790", "Unimproved": "178637200", "Limited": 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"Safely managed": "267856110"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2015", "Surface water": "98122760", "Unimproved": "180603870", "Limited": "119015520", "Basic": "332569730", "Safely managed": "282772860"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2016", "Surface water": "95305736", "Unimproved": "179763650", "Limited": "123926810", "Basic": "342709000", "Safely managed": "298028130"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2017", "Surface water": "92131270", "Unimproved": "178899000", "Limited": "128837016", "Basic": "353137380", "Safely managed": "313679940"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2018", "Surface water": "89026710", "Unimproved": "177577780", "Limited": "134168240", "Basic": "363054940", "Safely managed": "330690720"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2019", "Surface water": "85717140", "Unimproved": "175976300", "Limited": "139721090", "Basic": "373106340", "Safely managed": "348226660"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2020", "Surface water": "82292860", "Unimproved": "174090900", "Limited": "145411070", "Basic": "385307600", "Safely managed": "364254340"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2021", "Surface water": "79033380", "Unimproved": "171601020", "Limited": "151149330", "Basic": "399077600", "Safely managed": "379336960"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2022", "Surface water": "75714830", "Unimproved": "169887060", "Limited": "157384560", "Basic": "412098460", "Safely managed": "394185950"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2023", "Surface water": "72331750", "Unimproved": "167647310", "Limited": "162226600", "Basic": "427703700", "Safely managed": "409552420"}, {"Entity": "Africa (WHO)", "Code": "WHO_AFR", "Year": "2024", "Surface water": "69367690", "Unimproved": "164733870", "Limited": "168074640", "Basic": "442851200", "Safely managed": "425209100"}, 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{"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Surface water": "123805840", "Unimproved": "354771420", "Limited": "278831070", "Basic": "1544159600", "Safely managed": "5651138600"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Surface water": "117235110", "Unimproved": "334827460", "Limited": "280028860", "Basic": "1547676900", "Safely managed": "5739921000"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Surface water": "111518720", "Unimproved": "316515650", "Limited": "281276130", "Basic": "1551330000", "Safely managed": "5829394400"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Surface water": "106398860", "Unimproved": "302459300", "Limited": "287348450", "Basic": "1447078500", "Safely managed": "6017002500"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Surface water": "1179376.5", "Unimproved": "6591008", "Limited": "3984695.8", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Surface water": "1208815.8", "Unimproved": "6752582", "Limited": "4109002", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Surface water": "1238159", "Unimproved": "6913395.5", "Limited": "4234873", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Surface water": "1233817.6", "Unimproved": "6835082", "Limited": "4472743", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Surface water": "1228271.5", "Unimproved": "6748219.5", "Limited": "4719327", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Surface water": "1223546", "Unimproved": "6663859", "Limited": "4865853.5", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Surface water": "1219408.2", "Unimproved": "6580428", "Limited": "5017505.5", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Surface water": "1213770.8", "Unimproved": "6486483", "Limited": "5165933.5", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Surface water": "1206389.2", "Unimproved": "6380742.5", "Limited": "5309802.5", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Surface water": "1197332", "Unimproved": "6263607.5", "Limited": "5449071.5", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Surface water": "1186021.1", "Unimproved": "6132118.5", "Limited": "5580716.5", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Surface water": "1172062.1", "Unimproved": "5984393.5", "Limited": "5702198.5", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Surface water": "1156310.9", "Unimproved": "5824953.5", "Limited": "5817067", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Surface water": "1138914.2", "Unimproved": "5654574", "Limited": "5925821.5", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Surface water": "1119990.1", "Unimproved": "5473817", "Limited": "6028755.5", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Surface water": "1098876.4", "Unimproved": "5279448", "Limited": "6122124.5", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Surface water": "1075420.1", "Unimproved": "5070840.5", "Limited": "6204529.5", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Surface water": "1050077.9", "Unimproved": "4850223.5", "Limited": "6278169", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Surface water": "1022143.3", "Unimproved": "4614386", "Limited": "6338392", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Surface water": "992140.2", "Unimproved": "4365822.5", "Limited": "6387984", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Surface water": "959046", "Unimproved": "4100148.5", "Limited": "6419796.5", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Surface water": "922560.56", "Unimproved": "3816485.8", "Limited": "6430650.5", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2022", "Surface water": "885020.5", "Unimproved": "3524707", "Limited": "6436261", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2023", "Surface water": "855969.44", "Unimproved": "3214085.8", "Limited": "6436351.5", "Basic": "", "Safely managed": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2024", "Surface water": "825460.4", "Unimproved": "2886482.8", "Limited": "6419334.5", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Surface water": "1862822.4", "Unimproved": "3146193.2", "Limited": "377384.34", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Surface water": "1863811.4", "Unimproved": "3170044.5", "Limited": "404505.8", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Surface water": "1857372.6", "Unimproved": "3186114.5", "Limited": "433080", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Surface water": "1849792", "Unimproved": "3201407.8", "Limited": "463150.5", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Surface water": "1841886.5", "Unimproved": "3217402.2", "Limited": "495030.94", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Surface water": "1834515.2", "Unimproved": "3235695.5", "Limited": "529116.3", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Surface water": "1827880.1", "Unimproved": "3256818", "Limited": "565700.94", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Surface water": "1820787.8", "Unimproved": "3278793", "Limited": "604652.8", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Surface water": "1812268.9", "Unimproved": "3300026.2", "Limited": "645859.7", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Surface water": "1801108.4", "Unimproved": "3318316", "Limited": "689006.56", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Surface water": "1786219.5", "Unimproved": "3331726.2", "Limited": "733769.4", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Surface water": "1766526", "Unimproved": "3338150.8", "Limited": "779668.94", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Surface water": "1742816.4", "Unimproved": "3339049.8", "Limited": "827015.6", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Surface water": "1715759.8", "Unimproved": "3335666.8", "Limited": "876125.7", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Surface water": "1685505.8", "Unimproved": "3328237", "Limited": "927101.06", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Surface water": "1651580.4", "Unimproved": "3315833.2", "Limited": "979732.94", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Surface water": "1614418.2", "Unimproved": "3299250.8", "Limited": "1034278.8", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Surface water": "1573926.6", "Unimproved": "3278301.5", "Limited": "1090740.4", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Surface water": "1529517.5", "Unimproved": "3251710.8", "Limited": "1148724.5", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Surface water": "1481470.4", "Unimproved": "3219963.5", "Limited": "1208390.2", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Surface water": "1429585.1", "Unimproved": "3182536.2", "Limited": "1269539.6", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Surface water": "1373448.6", "Unimproved": "3138366", "Limited": "1325584.8", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Surface water": "1313635.8", "Unimproved": "3088575.2", "Limited": "1382510.6", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Surface water": "1251230.6", "Unimproved": "3035635.8", "Limited": "1441443.9", "Basic": "", "Safely managed": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Surface water": "1185965.6", "Unimproved": "2978977.2", "Limited": "1502269.2", "Basic": "", "Safely managed": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Surface water": "707859", "Unimproved": "2128221.5", "Limited": "978683.5", "Basic": "4581590.5", "Safely managed": "3495700.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Surface water": "708462.1", "Unimproved": "2130577.2", "Limited": "981346.3", "Basic": "4614315", "Safely managed": "3537200.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Surface water": "716852.8", "Unimproved": "2104134.2", "Limited": "1014926.9", "Basic": "4654371.5", "Safely managed": "3597368.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Surface water": "732379.5", "Unimproved": "2097008.4", "Limited": "1056818.9", "Basic": "4698648.5", "Safely managed": "3647468"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Surface water": "748278.2", "Unimproved": "2089348.8", "Limited": "1099605.2", "Basic": "4737806.5", "Safely managed": "3690858"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Surface water": "763402.56", "Unimproved": "2078113.8", "Limited": "1141969", "Basic": "4801461.5", "Safely managed": "3698484.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Surface water": "780917.6", "Unimproved": "2071891.8", "Limited": "1188656.6", "Basic": "4879476.5", "Safely managed": "3715503"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Surface water": "799578.1", "Unimproved": "2067010.8", "Limited": "1237928.2", "Basic": "4963932", "Safely managed": "3735608.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Surface water": "817714.2", "Unimproved": "2059070", "Limited": "1287233.9", "Basic": "5044348.5", "Safely managed": "3750783"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Surface water": "837909.2", "Unimproved": "2054550.8", "Limited": "1340666.2", "Basic": "5136733", "Safely managed": "3772931"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Surface water": "860334.06", "Unimproved": "2053491.9", "Limited": "1398650.8", "Basic": "5241849", "Safely managed": "3802222.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Surface water": "884712.4", "Unimproved": "2054867", "Limited": "1460902.1", "Basic": "5357889", "Safely managed": "3837053.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Surface water": "908378.56", "Unimproved": "2052327.1", "Limited": "1523087.5", "Basic": "5468557.5", "Safely managed": "3865536.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Surface water": "930616.25", "Unimproved": "2044485.4", "Limited": "1583932.5", "Basic": "5569763", "Safely managed": "3885010.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Surface water": "952593.6", "Unimproved": "2034260.8", "Limited": "1645546.6", "Basic": "5671001", "Safely managed": "3903957.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Surface water": "974296.06", "Unimproved": "2021731.2", "Limited": "1707907.2", "Basic": "5772400", "Safely managed": "3922678"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Surface water": "996491.6", "Unimproved": "2008548.8", "Limited": "1772369.5", "Basic": "5878541.5", "Safely managed": "3944342.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Surface water": "1019255.1", "Unimproved": "1994829.5", "Limited": "1839124.6", "Basic": "5989938.5", "Safely managed": "3969335.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Surface water": "1042479.9", "Unimproved": "1980329.9", "Limited": "1908049.9", "Basic": "6106130.5", "Safely managed": "3997462"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Surface water": "1066538.8", "Unimproved": "1965707.4", "Limited": "1979879.1", "Basic": "6229222.5", "Safely managed": "4030020.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Surface water": "1091645", "Unimproved": "1951267.8", "Limited": "2055119.8", "Basic": "6360718", "Safely managed": "4068137"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Surface water": "1117548.9", "Unimproved": "1936456.1", "Limited": "2133387.2", "Basic": "6499091.5", "Safely managed": "4110726.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Surface water": "1144035.9", "Unimproved": "1918751.4", "Limited": "2212859.2", "Basic": "6643828.5", "Safely managed": "4149580.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Surface water": "1170414.4", "Unimproved": "1898569", "Limited": "2293270.5", "Basic": "6789424", "Safely managed": "4189144.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Surface water": "1198011.2", "Unimproved": "1878587", "Limited": "2377642.8", "Basic": "6944927", "Safely managed": "4235205"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "drinking-water-service-coverage", "metadata_url": "https://ourworldindata.org/grapher/drinking-water-service-coverage.metadata.json", "chart_title": "Drinking water services", "chart_subtitle": "Total population using the five different levels of drinking water services: surface water, unimproved, limited, basic and safely managed water.", "chart_note": null, "chart_citation": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025)", "original_chart_url": "https://ourworldindata.org/grapher/drinking-water-service-coverage", "owid_column_metadata": {"Number of people using surface water for drinking": {"titleShort": "Surface water", "titleLong": "Surface water", "descriptionShort": "Number of people using drinking water directly collected from surface waters.", "descriptionKey": ["Easy and reliable access to drinking water is fundamental to human health and well-being. Improved drinking water sources reduce the risk of contamination and the spread of waterborne diseases.\n", "Surface water includes rivers, streams, ponds, lakes, dams, canals and irrigation channels.\n", "This is the lowest level of drinking water service and indicates a lack of access to safe drinking water.", "This data is provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). They compile data from nationally representative household surveys and censuses, administrative data and service provider data. To learn more, see the [JMP Methodology](https://washdata.org/topics/methods/data-sources).\n", "This data reflects actual service use, which is directly linked to health outcomes and can be consistently measured across countries using household surveys. It is possible to theoretically have access to some kind of water and sanitation infrastructure, but not use them for daily needs. Therefore we refer to \"use\" or \"using\" rather than \"access\" to better reflect the underlying data.\n"], "shortUnit": "", "unit": "people", "timespan": "2000-2024", "type": "Numeric", "owidVariableId": 1132872, "shortName": "wat_ns_pop__residence_total", "lastUpdated": "2025-12-08", "nextUpdate": "2027-12-08", "citationShort": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data", "citationLong": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data. “Surface water” [dataset]. World Health Organization/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene, “WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) 2000-2024 report” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132872.metadata.json"}, "Number of people using unimproved drinking water sources": {"titleShort": "Unimproved", "titleLong": "Unimproved", "descriptionShort": "Number of people using only unimproved drinking water sources.", "descriptionKey": ["Easy and reliable access to drinking water is fundamental to human health and well-being. Improved drinking water sources reduce the risk of contamination and the spread of waterborne diseases.\n", "Unimproved drinking water services are defined as drinking water from an unprotected dug well or unprotected spring.\n", "Unimproved drinking water sources are more likely to be contaminated and pose a higher risk to health.", "This data is provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). They compile data from nationally representative household surveys and censuses, administrative data and service provider data. To learn more, see the [JMP Methodology](https://washdata.org/topics/methods/data-sources).\n", "This data reflects actual service use, which is directly linked to health outcomes and can be consistently measured across countries using household surveys. It is possible to theoretically have access to some kind of water and sanitation infrastructure, but not use them for daily needs. Therefore we refer to \"use\" or \"using\" rather than \"access\" to better reflect the underlying data.\n"], "shortUnit": "", "unit": "people", "timespan": "2000-2024", "type": "Numeric", "owidVariableId": 1132881, "shortName": "wat_unimp_pop__residence_total", "lastUpdated": "2025-12-08", "nextUpdate": "2027-12-08", "citationShort": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data", "citationLong": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data. “Unimproved” [dataset]. World Health Organization/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene, “WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) 2000-2024 report” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132881.metadata.json"}, "Number of people using only a limited drinking water source": {"titleShort": "Limited", "titleLong": "Limited", "descriptionShort": "Number of people using an improved drinking water source for which collection time exceeds 30 minutes for a roundtrip.", "descriptionKey": ["Easy and reliable access to drinking water is fundamental to human health and well-being. Improved drinking water sources reduce the risk of contamination and the spread of waterborne diseases.\n", "Having to collect water from sources outside the home can be time-consuming and physically demanding. Spending hours each day collecting water can limit opportunities for education, employment, and community participation.\n", "Limited drinking water services are defined as drinking water from an improved source for which collection time exceeds 30 minutes for a roundtrip including queuing.\n", "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.\n", "This data is provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). They compile data from nationally representative household surveys and censuses, administrative data and service provider data. To learn more, see the [JMP Methodology](https://washdata.org/topics/methods/data-sources).\n", "This data reflects actual service use, which is directly linked to health outcomes and can be consistently measured across countries using household surveys. It is possible to theoretically have access to some kind of water and sanitation infrastructure, but not use them for daily needs. Therefore we refer to \"use\" or \"using\" rather than \"access\" to better reflect the underlying data.\n"], "shortUnit": "", "unit": "people", "timespan": "2000-2024", "type": "Numeric", "owidVariableId": 1132871, "shortName": "wat_lim_pop__residence_total", "lastUpdated": "2025-12-08", "nextUpdate": "2027-12-08", "citationShort": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data", "citationLong": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data. “Limited” [dataset]. World Health Organization/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene, “WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) 2000-2024 report” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132871.metadata.json"}, "Number of people using only a basic drinking water source": {"titleShort": "Basic", "titleLong": "Basic", "descriptionShort": "Number of people who do not have an improved water source at home, but use an improved drinking water source where collection takes no more than 30 minutes for a roundtrip.", "descriptionKey": ["Easy and reliable access to drinking water is fundamental to human health and well-being. Improved drinking water sources reduce the risk of contamination and the spread of waterborne diseases.\n", "Having to collect water from sources outside the home can be time-consuming and physically demanding. Spending hours each day collecting water can limit opportunities for education, employment, and community participation.\n", "Basic drinking water services are defined as an improved drinking water source, provided collection time is not more than 30 minutes for a roundtrip including queuing.\n", "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.\n", "This data is provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). They compile data from nationally representative household surveys and censuses, administrative data and service provider data. To learn more, see the [JMP Methodology](https://washdata.org/topics/methods/data-sources).\n", "This data reflects actual service use, which is directly linked to health outcomes and can be consistently measured across countries using household surveys. It is possible to theoretically have access to some kind of water and sanitation infrastructure, but not use them for daily needs. Therefore we refer to \"use\" or \"using\" rather than \"access\" to better reflect the underlying data.\n"], "shortUnit": "", "unit": "people", "timespan": "2000-2024", "type": "Numeric", "owidVariableId": 1132848, "shortName": "wat_bas_pop__residence_total", "lastUpdated": "2025-12-08", "nextUpdate": "2027-12-08", "citationShort": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data", "citationLong": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data. “Basic” [dataset]. World Health Organization/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene, “WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) 2000-2024 report” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132848.metadata.json"}, "Number of people using safely managed drinking water": {"titleShort": "Safely managed", "titleLong": "Safely managed", "descriptionShort": "Number of people using an improved drinking water source that is located on premises, available when needed and free from faecal and priority chemical contamination.", "descriptionKey": ["Easy and reliable access to drinking water is fundamental to human health and well-being. Improved drinking water sources reduce the risk of contamination and the spread of waterborne diseases.\n", "Safely managed drinking water services are defined as an improved drinking water source that is located on premises, available when needed and free from faecal and priority chemical contamination.\n", "This is the highest level of drinking water service and indicates reliable access to safe drinking water.", "This data is provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). They compile data from nationally representative household surveys and censuses, administrative data and service provider data. To learn more, see the [JMP Methodology](https://washdata.org/topics/methods/data-sources).\n", "This data reflects actual service use, which is directly linked to health outcomes and can be consistently measured across countries using household surveys. It is possible to theoretically have access to some kind of water and sanitation infrastructure, but not use them for daily needs. Therefore we refer to \"use\" or \"using\" rather than \"access\" to better reflect the underlying data.\n"], "shortUnit": "", "unit": "people", "timespan": "2000-2024", "type": "Numeric", "owidVariableId": 1132878, "shortName": "wat_sm_pop__residence_total", "lastUpdated": "2025-12-08", "nextUpdate": "2027-12-08", "citationShort": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data", "citationLong": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2025) – processed by Our World in Data. “Safely managed” [dataset]. World Health Organization/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene, “WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) 2000-2024 report” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132878.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "9f35fbf674626763f369"},
{"raw_link": "https://ourworldindata.org/measles-increases-disease-risk", "title": "Measles leaves children vulnerable to other diseases for years", "context": "Home\nVaccination\nMeasles leaves children vulnerable to other diseases for years\nMeasles causes more than an acute illness: it suppresses immune memory and increases the risk of complications for years.\nBy\nSaloni Dattani\nJune 16, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nThe author Roald Dahl wrote a\npublic letter\ndescribing his daughter’s measles infection in 1962, the year before vaccination became available.\nOlivia, my eldest daughter, caught measles when she was seven years old. As the illness took its usual course I can remember reading to her often in bed and not feeling particularly alarmed about it. Then one morning, when she was well on the road to recovery, I was sitting on her bed showing her how to fashion little animals out of coloured pipe-cleaners, and when it came to her turn to make one herself, I noticed that her fingers and her mind were not working together and she couldn’t do anything.\n“Are you feeling all right?” I asked her.\n“I feel all sleepy,” she said.\nIn an hour, she was unconscious. In twelve hours she was dead.\nThe measles had turned into a terrible thing called measles encephalitis and there was nothing the doctors could do to save her. That was twenty-four years ago in 1962, but even now, if a child with measles happens to develop the same deadly reaction from measles as Olivia did, there would still be nothing the doctors could do to help her.\nDahl’s story shows how measles can strike suddenly and unpredictably. Itʼs also an important reminder that we need to understand not just its immediate dangers but also the lasting effects it can have.\nEven today, measles-caused encephalitis (a dangerous inflammation of the brain) is difficult to treat. Although three-quarters of those who develop it survive the condition, out of those who survive, around one-third will sustain lifelong brain damage.\n1\nMeasles is often seen as a routine childhood illness — a fever, a rash, and recovery — but complications are common. Even when it doesn’t kill, measles can cause lasting damage. It weakens the immune system, making people vulnerable to other infections for months or years. That means children who seem to recover may still face serious health risks long after the illness is gone.\nIn some countries, measles has\nre-emerged\nin recent years, leading to outbreaks that many thought to be a thing of the past. At the same time, the case for vaccination has come under renewed scrutiny. If measles deaths are rare in high-income countries, why worry?\nBut evaluating the harm caused by measles isn’t just about the number of deaths. It’s also about what the disease does to the immune system and the chain of complications it can set off. Preventing measles matters — not only to stop the virus but to protect children from subsequent infections.\nIn this article, I explain how measles spreads and damages the body’s defenses, and why preventing it is still critical.\nIn the United States, deaths fell before vaccines, but measles remained dangerous\nThe chart below shows the number of measles cases and deaths in the United States since 1919. You can see that the number of deaths from measles began to fall several decades before vaccines were introduced in 1963.\nDownload\nThe decline likely resulted from better treatment of secondary infections, improved sanitation and hygiene that limited their spread, and better childhood nutrition that lowered the risk of severe illness.\n2\nHowever, we shouldn’t think this meant measles was no longer a public health issue. Although deaths had fallen, measles was still far from mild: before vaccines arrived, there were about 50,000 hospitalizations and hundreds of deaths each year in the United States alone.\n3\nLarge outbreaks also continued because measles remained extremely contagious until vaccination rates rose. The time series for cases in the chart shows that while there were often annual fluctuations, cases didn’t decline in a sustained way until the 1960s.\nBecause measles is airborne, clean water and sanitation weren’t enough to stop its spread. That’s because measles is also one of the most contagious diseases. On average, each person infected with measles would infect 12 to 18 other people in a population without immunity, which means it could spread very rapidly across the population.\n4\nSo, without vaccines, measles deaths couldn’t be eliminated, and we couldn’t stop cases either — leaving many people vulnerable to harmful and long-lasting complications of the disease.\nIn the next section, I’ll discuss what those measles cases meant and the complications children faced.\nMeasles spreads through the air and can cause complications across the body\nThe measles virus spreads through the air and can be inhaled into people’s lungs as they breathe. It infects immune cells in their airways, where it hitches a ride to their lymph nodes, which coordinate their immune responses.\nThere, it finds its main targets — memory T and B cells, which help the immune system recognize past infections. But, instead of fighting the virus, these cells become its transport and carry it deeper into the bloodstream; measles turns the body’s defense system against itself. Now, the virus can spread into the thymus, spleen, bone marrow, gastrointestinal tract, kidneys, liver, and skin.\n5\nHowever, visible signs of infection only appear after one or two weeks. Fever, cough, runny nose, and red, inflamed eyes (conjunctivitis) are common. These symptoms worsen over days, before tiny, blueish-white dots (known as “Koplik’s spots”) appear on the inside of the cheeks.\n1\nBlood vessels in the skin swell and leak, resulting in characteristic red patches called the “measles rash”, which start on the face and neck. Over the next few days, the rash spreads from the chest to the back, arms, and legs. Individual spots merge into large, inflamed patches, fever spikes, and the body struggles to control the virus.\n1\nBy multiplying rapidly and spreading across the body, the virus can leave children vulnerable to many complications and additional infections for years.\nAs measles infects immune cells, it depletes important cells that provide the body with memory of past infections and help protect against them.\nThe loss of immune memory caused by measles — often called “immune amnesia” — leaves a gap for other infections to take hold.\nThis can result in ear infections, pneumonia, diarrhea, dehydration, malnourishment, blindness, and brain swelling.\n5\nIn the diagram, I’ve illustrated the many ways that measles can lead to complications across the body.\nDownload\nEstimates from Perry and Halsey (2004)\n1\n; Wendorf et al. (2017)\n6\nMeasles can also cause several rare complications. One is “noma”, a condition where mouth ulcers develop and eat away at soft tissue, resulting in facial disfigurement.\nFor one or two children in a thousand, the brain is affected as well: “post-infectious encephalomyelitis” can develop days after the rash fades and causes seizures, confusion, and paralysis. Of those who develop this condition, one in four die, and one in three survive with lifelong brain damage.\n1\nThe virus can also resurface as “subacute sclerosing panencephalitis” years later, which affects around 1 in 2,000 children.\n6\nThis is a condition where children initially appear irritable, screaming, and crying; their ability to think, make decisions, and control their body is gradually reduced until they’re in a vegetative state.\n7\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nMeasles infections cause lasting immune damage\nEven in a typical case of measles, children who survive the infection recover slowly.\nThe rash fades and peels away, but the immune amnesia means they remain vulnerable for the next few years to many other diseases that would normally be mild or harmless.\n5\nEvidence of this is shown in the chart below: children infected with measles use medical care more often for several years after their infection.\nDownload\nEstimates from Gadroen et al. (2018)\n8\nComplications from measles are most severe in infants and malnourished children, who have the highest fatality rates from the disease, as well as in pregnant women.\n9\nIn high-income countries today, many people have never seen a case of measles. But it was a feared and familiar part of childhood before widespread vaccination. The virus could sweep through communities, hospitalize a quarter of children, and leave some blind, with lasting breathing difficulties, permanent brain injury, or dead.\nThe suffering caused by measles was part of everyday life. Now, in places where vaccination rates have fallen, that past is starting to return.\nMeasles is one of the most contagious diseases, and it doesn’t just cause rashes. It infects and destroys important white blood cells, which are critical for the body’s defenses against infections.\nBy targeting memory T and B cells, measles weakens the immune system by erasing its memory of past infections. As a result, children remain vulnerable to other diseases for years.\nThe good news is that measles is preventable. With widespread vaccination, we can stop its spread, protect our immune systems, and prevent needless suffering.\nWhen we prevent measles, we’re not just avoiding one illness. We’re also preserving the immune system’s knowledge of previous infections. That’s because vaccination doesn’t just stop measles; it also protects the body from the lasting damage the disease leaves behind.\nContinue reading on Our World in Data\nMeasles vaccines save millions of lives each year\nMeasles once killed millions every year. Vaccines changed this, preventing disease, long-term immune damage, and deadly outbreaks.\nHow effective and safe are measles vaccines?\nData from large meta-analyses show that measles vaccination is highly effective and safe, giving a 95% reduction in the risk of measles.\nVaccines have saved 150 million children over the last 50 years\nEvery ten seconds, one child is saved by a vaccine against a fatal disease.\nEndnotes\nPerry, R. T., & Halsey, N. A. (2004). The Clinical Significance of Measles: A Review. The Journal of Infectious Diseases, 189(Supplement_1), S4–S16.\nhttps://doi.org/10.1086/377712\nSchneider, E. B. (2023). The effect of nutritional status on historical infectious disease morbidity: Evidence from the London Foundling Hospital, 1892-1919. The History of the Family, 28(2), 198–228.\nhttps://doi.org/10.1080/1081602X.2021.2007499\nShanks, G. D., Hu, Z., Waller, M., Lee, S. -e., Terfa, D., Howard, A., Van Heyningen, E., & Brundage, J. F. (2014). Measles Epidemics of Variable Lethality in the Early 20th Century. American Journal of Epidemiology, 179(4), 413–422.\nhttps://doi.org/10.1093/aje/kwt282\nThe CDC reports that in the years before vaccines, measles caused an estimated 3 to 4 million cases, with around 500,000 cases reported annually, along with 48,000 hospitalizations, 1,000 cases with encephalitis (brain swelling), and 400 to 500 deaths. Centers for Disease Control and Prevention (2019). Measles Data and Statistics. Available\nonline\n.\nGuerra, F. M., Bolotin, S., Lim, G., Heffernan, J., Deeks, S. L., Li, Y., & Crowcroft, N. S. (2017). The basic reproduction number (R 0 ) of measles: A systematic review. The Lancet Infectious Diseases, 17(12), e420–e428.\nhttps://doi.org/10.1016/S1473-3099(17)30307-9\nDe Vries, R. D., Mesman, A. W., Geijtenbeek, T. B., Duprex, W. P., & De Swart, R. L. (2012). The pathogenesis of measles. Current Opinion in Virology, 2(3), 248–255.\nhttps://doi.org/10.1016/j.coviro.2012.03.005\nWendorf, K. A., Winter, K., Zipprich, J., Schechter, R., Hacker, J. K., Preas, C., Cherry, J. D., Glaser, C., & Harriman, K. (2017). Subacute Sclerosing Panencephalitis: The Devastating Measles Complication That Might Be More Common Than Previously Estimated. Clinical Infectious Diseases, 65(2), 226–232.\nhttps://doi.org/10.1093/cid/cix302\nMiller, D. L. (1964). Frequency of Complications of Measles, 1963. BMJ, 2(5401), 75–78.\nhttps://doi.org/10.1136/bmj.2.5401.75\nWendorf, K. A., Winter, K., Zipprich, J., Schechter, R., Hacker, J. K., Preas, C., Cherry, J. D., Glaser, C., & Harriman, K. (2017). Subacute Sclerosing Panencephalitis: The Devastating Measles Complication That Might Be More Common Than Previously Estimated. Clinical Infectious Diseases, 65(2), 226–232.\nhttps://doi.org/10.1093/cid/cix302\nPerry, R. T., & Halsey, N. A. (2004). The Clinical Significance of Measles: A Review. The Journal of Infectious Diseases, 189(Supplement_1), S4–S16.\nhttps://doi.org/10.1086/377712\nGadroen, K., Dodd, C. N., Masclee, G. M. C., De Ridder, M. A. J., Weibel, D., Mina, M. J., Grenfell, B. T., Sturkenboom, M. C. J. M., Van De Vijver, D. A. M. C., & De Swart, R. L. (2018). Impact and longevity of measles-associated immune suppression: A matched cohort study using data from the THIN general practice database in the UK. BMJ Open, 8(11), e021465.\nhttps://doi.org/10.1136/bmjopen-2017-021465\nSchneider, E. B. (2023). The effect of nutritional status on historical infectious disease morbidity: Evidence from the London Foundling Hospital, 1892-1919. The History of the Family, 28(2), 198–228.\nhttps://doi.org/10.1080/1081602X.2021.2007499\nPortnoy, A., Jit, M., Ferrari, M., Hanson, M., Brenzel, L., & Verguet, S. (2019). Estimates of case-fatality ratios of measles in low-income and middle-income countries: A systematic review and modelling analysis. The Lancet Global Health, 7(4), e472–e481.\nhttps://doi.org/10.1016/S2214-109X(18)30537-0\nWolfson, L. J., Grais, R. F., Luquero, F. J., Birmingham, M. E., & Strebel, P. M. (2009). Estimates of measles case fatality ratios: A comprehensive review of community-based studies. International Journal of Epidemiology, 38(1), 192–205.\nhttps://doi.org/10.1093/ije/dyn224\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSaloni Dattani (2025) - “Measles leaves children vulnerable to other diseases for years” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20251125-173858/measles-increases-disease-risk.html' [Online Resource] (archived on November 25, 2025).\nBibTeX citation\n@article{owid-measles-increases-disease-risk,\nauthor = {Saloni Dattani},\ntitle = {Measles leaves children vulnerable to other diseases for years},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20251125-173858/measles-increases-disease-risk.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "measles-increases-disease-risk", "source_url": "https://ourworldindata.org/measles-increases-disease-risk", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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"Code": "USA", "Year": "1956", "Number of measles cases": "611936", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1957", "Number of measles cases": "486799", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1958", "Number of measles cases": "763094", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1959", "Number of measles cases": "406162", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1960", "Number of measles cases": "441703", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1961", "Number of measles cases": "423919", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1962", "Number of measles cases": "481530", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1963", "Number of measles cases": "385156", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1964", "Number of measles cases": "458083", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1965", "Number of measles cases": "261904", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1966", "Number of measles cases": "204136", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1967", "Number of measles cases": "62705", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1968", "Number of measles cases": "22231", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1969", "Number of measles cases": "25826", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1970", "Number of measles cases": "47351", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1971", "Number of measles cases": "75290", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1972", "Number of measles cases": "32275", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1973", "Number of measles cases": "26690", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1974", "Number of measles cases": "22094", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1975", "Number of measles cases": "24374", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1976", "Number of measles cases": "41126", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1977", "Number of measles cases": "57345", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1978", "Number of measles cases": "26871", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1979", "Number of measles cases": "13597", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1980", "Number of measles cases": "13506", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1981", "Number of measles cases": "3124", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1982", "Number of measles cases": "1714", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1983", "Number of measles cases": "1497", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1984", "Number of measles cases": "2587", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1985", "Number of measles cases": "2822", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1986", "Number of measles cases": "6255", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1987", "Number of measles cases": "3652", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1988", "Number of measles cases": "3410", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1989", "Number of measles cases": "17914", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1990", "Number of measles cases": "27808", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1991", "Number of measles cases": "9643", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1992", "Number of measles cases": "2126", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1993", "Number of measles cases": "312", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1994", "Number of measles cases": "899", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1995", "Number of measles cases": "308", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1996", "Number of measles cases": "492", "Number of measles cases (Annotations)": "Data for 2026 is incomplete"}, {"Entity": "United States", "Code": "USA", "Year": "1997", "Number of measles cases": "141", "Number of measles 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Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "a459769bb192cc477ca9"},
{"raw_link": "https://ourworldindata.org/new-features-better-maps", "title": "Announcing new features: better interactive maps", "context": "Announcing new features: better interactive maps\nLearn what’s new and try it out yourself.\nBy\nSophia Mersmann\nand\nCharlie Giattino\nJune 10, 2025\nBrowse past versions\nReuse our work freely\nWe’re very excited to announce several new features that make exploring global data with our interactive maps even better.\nIn this short post, we walk through the new features and give you a chance to try them out yourself with\none of our maps\n.\nWhat’s new\nEnhanced country/region selection with several sorting options and a bar chart preview\nSelecting a country highlights it and shows its value on the map\nZooming to your selection brings up a 3D globe view\nExplore the globe and zoom in on different continents\nSwitch between 3D and 2D views\nFilter to see continents and income groups\nOn mobile, we have a simplified version that focuses on selecting and zooming to individual countries.\nAnd as always with our interactive charts, you can:\nChoose different years and “play” the map over time\nSwitch to a line, bar, or slope chart view with country selection maintained\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nTry it out and let us know what you think\nDo you like our new maps? Let us know what you think by filling out\nour feedback form\nor emailing\ninfo@ourworldindata.org\n. We love to hear feedback!\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "new-features-better-maps", "source_url": "https://ourworldindata.org/new-features-better-maps", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Learn what’s new and try it out yourself.", "numeric_mentions": ["10,", "2025", "3", "2"], "numeric_evidence": [{"title": "CO₂ emissions per capita", "source_url": "https://ourworldindata.org/grapher/co-emissions-per-capita.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "CO₂ emissions per capita"], "row_count_total": 26509, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1949", "CO₂ emissions per capita": "0.0019921463"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1950", "CO₂ emissions per capita": "0.010837197"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1951", "CO₂ emissions per capita": "0.011625335"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1952", "CO₂ emissions per capita": "0.011467511"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1953", "CO₂ emissions per capita": "0.013123366"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1954", "CO₂ emissions per capita": "0.012945492"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1955", "CO₂ emissions per capita": "0.018480647"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1956", "CO₂ emissions per capita": "0.021669438"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1957", "CO₂ emissions per capita": "0.034129996"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1958", "CO₂ emissions per capita": "0.037801724"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1959", "CO₂ emissions per capita": "0.04335994"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1960", "CO₂ emissions per capita": "0.045808833"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "CO₂ emissions per capita": "0.05326608"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "CO₂ emissions per capita": "0.07322033"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "CO₂ emissions per 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"CO₂ emissions per capita": "0.817324"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1920", "CO₂ emissions per capita": "0.9085351"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1921", "CO₂ emissions per capita": "0.8893564"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1922", "CO₂ emissions per capita": "0.7819926"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1923", "CO₂ emissions per capita": "0.9150087"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1924", "CO₂ emissions per capita": "0.9470679"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1925", "CO₂ emissions per capita": "1.0814468"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1926", "CO₂ emissions per capita": "1.3457392"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1927", "CO₂ emissions per capita": "1.3679329"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1928", "CO₂ emissions per capita": "1.614653"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1929", "CO₂ emissions per capita": "1.4979143"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1930", "CO₂ emissions per capita": "1.328829"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1931", "CO₂ emissions per capita": "0.8138286"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1932", "CO₂ emissions per capita": "0.5949173"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1933", "CO₂ emissions per capita": "0.643853"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1934", "CO₂ emissions per capita": "0.84028345"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1935", "CO₂ emissions per capita": "0.88905615"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1936", "CO₂ emissions per capita": "0.8835933"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1937", "CO₂ emissions per capita": "1.2669004"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1938", "CO₂ emissions per capita": "1.2584813"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1939", "CO₂ emissions per capita": "1.321963"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1940", "CO₂ emissions per capita": "1.4963783"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1941", "CO₂ emissions per capita": "1.6042087"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1942", "CO₂ emissions per capita": "1.7400095"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1943", "CO₂ emissions per capita": "1.9459594"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1944", "CO₂ emissions per capita": "1.9404495"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1945", "CO₂ emissions per capita": "1.7714489"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1946", "CO₂ emissions per capita": "1.6795832"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1947", "CO₂ emissions per capita": "1.5429304"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1948", "CO₂ emissions per capita": "1.7007232"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1949", "CO₂ emissions per capita": "1.8822875"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1950", "CO₂ emissions per 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"Zimbabwe", "Code": "ZWE", "Year": "1961", "CO₂ emissions per capita": "1.2877215"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1962", "CO₂ emissions per capita": "1.2053741"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1963", "CO₂ emissions per capita": "1.1229138"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1964", "CO₂ emissions per capita": "1.0345057"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1965", "CO₂ emissions per capita": "1.1683859"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "CO₂ emissions per capita": "1.3128958"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "CO₂ emissions per capita": "1.1150243"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "CO₂ emissions per capita": "1.3014458"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "CO₂ emissions per capita": "1.3332607"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "CO₂ emissions per capita": "1.5633165"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "CO₂ emissions per capita": "1.6247768"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "CO₂ emissions per capita": "1.4823471"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "CO₂ emissions per capita": "1.6206609"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "CO₂ emissions per capita": "1.5312294"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "CO₂ emissions per capita": "1.3627455"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "CO₂ emissions per capita": "1.7265382"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "CO₂ emissions per capita": "1.4395514"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "CO₂ emissions per capita": "1.4181368"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "CO₂ emissions per capita": "1.4188153"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "CO₂ emissions per capita": "1.3653653"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "CO₂ emissions per capita": "1.2542789"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "CO₂ emissions per capita": "1.1261276"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "CO₂ emissions per capita": "1.2873019"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "CO₂ emissions per capita": "1.1773397"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "CO₂ emissions per capita": "1.1757715"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "CO₂ emissions per capita": "1.4548186"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "CO₂ emissions per capita": "1.6338912"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "CO₂ emissions per capita": "1.6726803"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "CO₂ emissions per capita": "1.63345"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "CO₂ emissions per capita": "1.5326599"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "CO₂ emissions per capita": "1.4887861"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "CO₂ emissions per capita": "1.5605763"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "CO₂ emissions per capita": "1.4762003"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "CO₂ emissions per capita": "1.5986724"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "CO₂ emissions per capita": "1.368377"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "CO₂ emissions per capita": "1.3348055"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "CO₂ emissions per capita": "1.2226206"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "CO₂ emissions per capita": "1.2184846"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "CO₂ emissions per capita": "1.3344223"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "CO₂ emissions per capita": "1.1616611"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "CO₂ emissions per capita": "1.0448399"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "CO₂ emissions per capita": "0.98413074"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "CO₂ emissions per capita": "0.8673399"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "CO₂ emissions per capita": "0.7624711"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "CO₂ emissions per capita": "0.85701495"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "CO₂ emissions per capita": "0.8202832"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "CO₂ emissions per capita": "0.7681282"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "CO₂ emissions per capita": "0.5957252"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "CO₂ emissions per capita": "0.62770295"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "CO₂ emissions per capita": "0.65540963"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "CO₂ emissions per capita": "0.76225656"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "CO₂ emissions per capita": "0.8147"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "CO₂ emissions per capita": "0.83284795"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "CO₂ emissions per capita": "0.84084177"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "CO₂ emissions per capita": "0.83455706"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "CO₂ emissions per capita": "0.7252073"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "CO₂ emissions per capita": "0.6636679"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "CO₂ emissions per capita": "0.7455699"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "CO₂ emissions per capita": "0.6722901"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "CO₂ emissions per capita": "0.54684746"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "CO₂ emissions per capita": "0.6471251"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "CO₂ emissions per capita": "0.7612047"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "CO₂ emissions per capita": "0.8226813"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "CO₂ emissions per capita": "0.82366556"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "co-emissions-per-capita", "metadata_url": "https://ourworldindata.org/grapher/co-emissions-per-capita.metadata.json", "chart_title": "CO₂ emissions per capita", "chart_subtitle": null, "chart_note": null, "chart_citation": "Global Carbon Budget (2025); Population based on various sources (2024)", "original_chart_url": "https://ourworldindata.org/grapher/co-emissions-per-capita", "owid_column_metadata": {"Annual CO₂ emissions (per capita)": {"titleShort": "CO₂ emissions per capita", "titleLong": "CO₂ emissions per capita", "descriptionShort": "Carbon dioxide (CO₂) emissions from burning fossil fuels and industrial processes. This includes emissions from transport, electricity generation, and heating, but not land-use change.", "descriptionKey": ["Carbon dioxide (CO₂) is the primary greenhouse gas causing climate change.", "Global CO₂ emissions have stayed just below five tonnes per person for over a decade. But across countries, emissions vary widely, rising in some, falling in others.", "Fossil fuel burning is the main source of CO₂ emissions. This data includes fossil CO₂ emissions from activities such as transport, electricity generation, and heating.", "These figures don't include CO₂ emissions from changes in land use, like deforestation or reforestation.", "Emissions from international aviation and shipping are not included in the data for any individual country or region. They are only counted in the global total.", "This data is based on territorial emissions, meaning the emissions produced within a country's borders, but not those from imported goods. For example, emissions from imported steel are counted in the country where the steel is produced. To learn more and look at emissions adjusted for trade, read our article: [How do CO₂ emissions compare when we adjust for trade?](https://ourworldindata.org/consumption-based-co2)", "The data comes from the Global Carbon Budget. Fossil CO₂ emissions are estimated using national statistics on energy use — such as coal, oil, and gas consumption — and industrial production, particularly cement. These figures are converted into CO₂ emissions using standardized emission factors. For more details, read [the Global Carbon Budget paper](https://doi.org/10.5194/essd-15-5301-2023).", "CO₂ emissions per capita are calculated by dividing emissions by population. They represent the average emissions per person in a country or region. To learn more about how different metrics capture the distribution of CO₂ emissions, read our article: [Per capita, national, historical: how do countries compare on CO2 metrics?](https://ourworldindata.org/co2-emissions-metrics)"], "descriptionProcessing": "- Global emissions are converted from tonnes of carbon to tonnes of carbon dioxide (CO₂) using a factor of 3.664. This is the conversion factor [recommended by the Global Carbon Project](https://globalcarbonbudgetdata.org/downloads/jGJH0-data/Global+Carbon+Budget+v2024+Dataset+Descriptions.pdf). It reflects that one tonne of carbon, when fully oxidized, forms 3.664 tonnes of CO₂, based on the relative molecular weights of carbon and oxygen in CO₂.\n- Emissions from the 1991 Kuwaiti oil fires are included in Kuwait's emissions for that year.- To calculate CO₂ emissions per capita, we divide the original data by a country's estimated population. These estimates come from our population dataset based on [multiple sources](https://ourworldindata.org/population-sources).", "shortUnit": "t/person", "unit": "tonnes per person", "timespan": "1750-2024", "type": "Numeric", "owidVariableId": 1119914, "shortName": "emissions_total_per_capita", "lastUpdated": "2025-11-13", "nextUpdate": "2026-11-13", "citationShort": "Global Carbon Budget (2025); Population based on various sources (2024) – with major processing by Our World in Data", "citationLong": "Global Carbon Budget (2025); Population based on various sources (2024) – with major processing by Our World in Data. “CO₂ emissions per capita” [dataset]. Global Carbon Project, “Global Carbon Budget v15”; Various sources, “Population” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1119914.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "8ffec1529857787ed6b4"},
{"raw_link": "https://ourworldindata.org/childhood-leukemia-treatment-history", "title": "Childhood leukemia: how a deadly cancer became treatable", "context": "Home\nCancer\nChildhood leukemia: how a deadly cancer became treatable\nBefore the 1970s, most children affected by leukemia would quickly die from it. Now, most children in rich countries are cured.\nBy\nSaloni Dattani\nJune 9, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nIn the past, when I’d hear the words childhood leukemia, I’d picture a young child who suddenly became seriously ill, and whose parents were told their child had only a few years to live.\nI’d wonder how a child might grasp the idea of limited time, or how painful it must have been to face the possibility of missing out on growing up, discovering who they are, and forming deep friendships. It would also be a tragic experience for their family, friends, and classmates, who might struggle to understand what’s happening.\nThis picture, reflected in films, books, and television, depicts what used to be a grim reality. Childhood leukemia was fatal for the vast majority of children who developed it in the past. Before the 1970s, fewer than 10% of children diagnosed with the disease survived five years after diagnosis.\nBut since then, this outlook has improved dramatically. In North America and Europe, around 85% now survive that long.\n1\nWhat made this dramatic change possible? In this article, I’ll describe the progress achieved and some concrete reasons behind it.\nThis article focuses on data from North America and Europe; death rates from childhood cancers have also\ndeclined in other world regions\n, but remain higher.\nLeukemia is the most common childhood cancer, and its mortality rates have been reduced substantially\nMany different types of cancer can affect children. This article focuses on leukemia, the most common type of childhood cancer.\nIn the chart below, you can see the reduction in cancer death rates in the United States among children of different ages. The decline has been particularly large for leukemia, with a 14-fold drop, but we’ve also made much progress on\nother, less common childhood cancers\n.\nDownload\nExplore the data for other countries in\nan interactive chart\n.\nLeukemia is a cancer of the blood and bone marrow — the tissue that produces blood cells. It develops when immature white blood cells grow out of control and crowd out healthy ones, leading to symptoms like fatigue, infections, easy bruising or bleeding, and pale skin.\nWhile leukemia becomes more common with age, it’s the most frequent cancer in children, making up about a quarter of all childhood cancer cases in the United States.\n2\nOne reason may be that blood-forming tissues are especially active during childhood, when the body grows quickly and needs a steady supply of new blood cells to carry oxygen, fight infections, and form clots. Many of these cells also have short lifespans, and this stage of life is a crucial period for immune system development.\n3\nThis is very demanding for the bone marrow, which must constantly produce new cells. Each time a cell divides, there’s a small chance of a DNA error, and the more divisions that occur, the greater the chance that some of these errors will lead to cancer.\nThere are two main types of leukemia in children. The most common is\nacute lymphoblastic leukemia (ALL)\n, which starts in early lymphoid cells. The other is\nacute myeloid leukemia (AML)\n, which begins in other blood-forming cells.\n4\nMost childhood leukemia cases result from genetic mutations that develop spontaneously during this rapid cell division, often occurring before birth, while others are due to inherited genetic mutations, which are less common.\n5\nWhile several environmental exposures have been investigated, there hasnʼt been consistent evidence for any environmental causes.\n6\nChildhood leukemia has become much more treatable\nSurvival rates for children with leukemia have risen dramatically in the last 50 years.\nThe chart below plots overall survival rates — the share of children still alive after a given time since their diagnosis. The data comes from trials by the Children’s Oncology Group, which has enrolled tens of thousands of children since the 1960s, and now includes over half of all children with leukemia in the US.\n7\nThe top panel displays this for acute lymphoblastic leukemia (ALL) while the bottom panel shows acute myeloid leukemia (AML).\nIn the top panel, you can see that in the 1960s, only around 14% of children with acute lymphoblastic leukemia survived at least five years. Despite initially improving upon treatment, most relapsed and died soon after.\nBy the 2010s, the chances of survival had increased dramatically: 94% of children survived at least five years.\nSome might wonder whether treatment only delays death rather than being a cure. However, researchers have also analyzed survival rates over the long term and found large improvements: the chart shows that most children are still alive ten years after diagnosis. After the initial years of treatment, their long-term survival is much more stable.\nDownload\nThe bottom panel of the chart shows the improvement for acute myeloid leukemia. This subtype, which makes up about 25% of childhood leukemia cases, is more challenging to treat than acute lymphoblastic leukemia. Survival rates for this form have also improved, though not quite as dramatically.\nIn the 1970s, just 14% of children with acute myeloid leukemia survived at least five years. Now, over 60% do.\nThis improvement in survival reflects the impact of intensive treatment regimens. These treatments usually still involve years of intensive chemotherapy, which is often physically and mentally challenging and can cause long-term side effects. However, chronic health problems after treatment have become less common, and the long-term health of these children has improved significantly.\n8\nThis dramatic improvement in survival rates is reflected in the large declines in death rates we saw in the previous section.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nCoordination, drug development, and molecular research have driven progress in treating leukemia\nYou may notice from the chart above that much of this progress has been continuous. Progress in treating leukemia hasn’t come from a single breakthrough but from a series of advances building on one another.\nBefore the 1940s, children with leukemia usually died within a few weeks of diagnosis, and only comfort care was available. The first chemotherapy agents — aminopterin and, later, 6-mercaptopurine — could briefly eliminate leukemia cells. They gave families hope, but the cancer almost always returned.\n9\nChemotherapy is better tailored to each child\nResearchers identified more drugs during the 1950s and early 1960s. Then they took the next logical step: giving several medicines together as a combination and using cranial radiation on spinal fluid — that is, targeting the brain and spine with radiation to kill hidden cancer cells. This helped eliminate leukemia cells that remained in the central nervous system, and doctors began to notice that a small number of patients were being cured.\nThe 1960s and 1970s saw another breakthrough. Researchers tried “multi-phase” chemotherapy regimens, where treatment was given in four key stages — induction, consolidation, delayed intensification, and maintenance — typically over two to three years. Each phase uses combinations of chemotherapy drugs to eliminate leukemia cells and prevent relapse.\n10\nIn clinical trials, these regimens were successful, seeing survival rates of more than 50%, and hospitals in both North America and Europe ran similar studies and adopted the regimen.\n11\nIn the 1980s and early 1990s, researchers found that intensive chemotherapy directed at the spinal fluid could protect the brain just as well as cranial radiation, but with far fewer long-term side effects.\n11\nBy the mid-1990s, it also became clear that a single regimen didn’t fit everyone. Large trials sorted children into risk groups (this is called “risk stratification”) based on their age, white blood cell counts, and early genetic findings. Researchers found that lighter chemotherapy could spare the low-risk group from side effects, while stronger chemotherapy could save many in the high-risk group.\n12\nLaboratory science then gave doctors even sharper diagnostic tools. For example, tests of “measurable residual disease”, which became widely used in the early 2000s, can spot a single leukemia cell among 10,000 normal cells and help guide further treatment — such as whether to dial treatment back or trigger extra therapy.\n13\nAll of this showed that large improvements came not just from inventing new drugs, but from optimizing how existing drugs were used. This included finding the right combinations and dosages for different risk groups, adjusting treatment timing and duration to minimize toxic side effects, and identifying subgroups that could safely receive less intensive chemotherapy.\nBut how did researchers learn which treatment strategies work best?\nA key reason was widespread collaboration in clinical trials and research networks.\nLarge collaborations made better research possible\nChildhood leukemia is rare, so it’s very unlikely that a single hospital will see enough cases to draw strong conclusions on its own. To overcome this, researchers formed large collaborative groups and enrolled thousands of children in research studies and clinical trials. These trials helped test which regimens were safer and more effective.\nSince then, research groups merged into even larger collaborations to run bigger clinical trials, like the Children’s Oncology Group in North America and the International BFM Study Group in Europe.\n14\nOver 50% of children with leukemia in the US are enrolled in clinical trials.\n7\nThis coordination has been crucial in increasing survival rates.\nIt has helped increase “statistical power” — the ability to detect differences between treatments. It also helped address another challenge: before these clinical trials, treatment was highly variable, with different doctors and hospitals using protocols that were often suboptimal, and survival rates varied widely.\nThe result was better treatment standards, refined chemotherapy regimens, and reduced harmful practices. One example I mentioned earlier was cranial irradiation, which was once commonly used to prevent leukemia from spreading to the brain. Although it was effective, it carried serious long-term risks, including cognitive impairment and growth problems. Based on the results of these trials, it has now been largely replaced by less toxic chemotherapy-based strategies.\n15\nBreakthroughs in genetic and molecular research led to further progress\nAdvances in genetic and molecular research have also transformed the treatment of childhood leukemia. By uncovering which genetic mutations drove different subtypes of the disease, researchers could better identify which children were likely to benefit from standard therapy, and which needed more intensive chemotherapy or other types of treatment, as part of the “risk stratification” approach mentioned earlier.\nThe research also led to targeted drugs to block some children's specific cancer mutations. One prominent example was imatinib (“Gleevec”), a drug initially developed for chronic myeloid leukemia in adults. It blocks a mutant protein that triggers leukemia cells to multiply rapidly. Although only a small percentage of children with leukemia have this mutation, their survival used to be very poor, and they often needed bone marrow transplants. When imatinib was added to chemotherapy regimens in the 2000s, their survival improved dramatically, and many no longer needed a transplant.\n15\nMore recently, new immunotherapies have reshaped treatment, including CAR-T cell therapy and antibody therapies.\n16\nBetter supportive care helped children\nWhile chemotherapy, targeted therapy, and immunotherapy get much of the attention, better supportive care has also been critical in treating childhood leukemia. This is important because chemotherapy can harm vital organs and suppress the immune system, so children need protection from infections, bleeding, and complications.\nOver the last few decades, new treatments and vaccines have helped protect children against these other complications, including:\nRoutine platelet transfusions.\nBefore the 1970s, low platelet counts caused fatal bleeds in the brain or gut during intensive chemotherapy. Once blood banks learned to collect and store platelet concentrates at room temperature, daily blood transfusions became practical and deaths fell.\n17\nAntibiotics, antifungals, and antivirals\nto prevent and treat infections, which are a major cause of early deaths during chemotherapy. Recently, more treatments have been approved and added to standard care for leukemia.\n18\nExpanded use of vaccines\nto protect children with weakened immune systems. Over the past few decades, new vaccines — for example, against pneumococcal disease, chickenpox\n(varicella), and rotavirus — have helped prevent common but potentially serious infections in children with cancer. Some vaccines are recommended for patients themselves, while others are for family members and caregivers to reduce the risk of passing infections.\nStem cell transplantation\nis still reserved for the hardest cases, but how it’s done has changed. Years ago, doctors performed transplants with full-body radiation and often reinfused the child’s cells — both practices raised long-term risks and could risk cancer cells sneaking back in. Today, they usually give high-dose chemotherapy instead of radiation, and use stem cells from another donor, which can also help hunt down remaining leukemia cells.\n19\nFor families today, a diagnosis of childhood leukemia is no longer the terrifying death sentence it once was. Most children now survive, complete treatment, return to school, and grow up being able to look forward to longer, healthier lives.\nThe experience is still incredibly difficult. Hospital visits, harsh side effects, and long periods of uncertainty take a serious emotional toll. Not every child is cured, and there are long-term risks from treatment, though these have been reduced as well.\nThe impact of this progress is undeniable. Children now live longer and healthier, and their families can hope for and plan for the future in ways that weren’t possible just a few decades ago.\nThe story of childhood leukemia shows how science, collaboration, and persistence can turn a deadly disease into a largely treatable one. In just decades, it has gone from one of the most feared childhood illnesses to one of the most treatable cancers, and it’s a model for what medical research can achieve.\nThe next big challenge is ensuring that these advances reach children everywhere. This article has focused on progress in high-income countries, but access to timely diagnosis and treatment is still limited in many parts of the world. Expanding access globally is essential so that every child, no matter where they live, has the chance to live a long life.\nContinue reading on Our World in Data\nHPV vaccination: How the world can eliminate cervical cancer\nHPV vaccines offer a rare opportunity to effectively eliminate one type of cancer. By taking this opportunity, it’s possible to save hundreds of thousands of women each year.\nMeasles vaccines save millions of lives each year\nMeasles once killed millions every year. Vaccines changed this, preventing disease, long-term immune damage, and deadly outbreaks.\nThe world is awful. The world is much better. The world can be much better.\nIt is wrong to think these three statements contradict each other. We need to see that they are all true to see that a better world is possible.\nEndnotes\nSultan, I., Alfaar, A. S., Sultan, Y., Salman, Z., & Qaddoumi, I. (2025). Trends in childhood cancer: Incidence and survival analysis over 45 years of SEER data.\nPLOS ONE\n,\n20\n(1), e0314592.\nhttps://doi.org/10.1371/journal.pone.0314592\nBased on data from 2018–2022.\nNCI SEER (2023). Cancer Stat Facts: Childhood Leukemia (Ages 0–19). Available\nonline\n.\nRubin, P., Williams, J. P., Devesa, S. S., Travis, L. B., & Constine, L. S. (2010). Cancer Genesis Across the Age Spectrum: Associations With Tissue Development, Maintenance, and Senescence. Seminars in Radiation Oncology, 20(1), 3–11.\nhttps://doi.org/10.1016/j.semradonc.2009.08.001\nCreutzig, U., Kutny, M. A., Barr, R., Schlenk, R. F., & Ribeiro, R. C. (2018). Acute myelogenous leukemia in adolescents and young adults. Pediatric Blood & Cancer, 65(9), e27089.\nhttps://doi.org/10.1002/pbc.27089\nKuhlen, M., Taeubner, J., Brozou, T., Wieczorek, D., Siebert, R., & Borkhardt, A. (2019). Family-based germline sequencing in children with cancer. Oncogene, 38(9), 1367–1380.\nhttps://doi.org/10.1038/s41388-018-0520-9\nPlon, S. E., & Lupo, P. J. (2019). Genetic Predisposition to Childhood Cancer in the Genomic Era. Annual Review of Genomics and Human Genetics, 20, 241–263.\nhttps://doi.org/10.1146/annurev-genom-083118-015415\nSchüz, J., & Erdmann, F. (2016). Environmental Exposure and Risk of Childhood Leukemia: An Overview. Archives of Medical Research, 47(8), 607–614.\nhttps://doi.org/10.1016/j.arcmed.2016.11.017\nBased on data from 2008 to 2015.\nBrown, A. L., Sok, P., Scheurer, M. E., Rabin, K. R., Marcotte, E. L., Hawkins, D. S., Spector, L. G., & Lupo, P. J. (2022). An updated assessment of 43,110 patients enrolled in the Childhood Cancer Research Network: A Children’s Oncology Group report. Cancer, 128(14), 2760–2767.\nhttps://doi.org/10.1002/cncr.34248\nTurcotte, L. M., Whitton, J. A., Leisenring, W. M., Howell, R. M., Neglia, J. P., Phelan, R., Oeffinger, K. C., Ness, K. K., Woods, W. G., Kolb, E. A., Robison, L. L., Armstrong, G. T., & Chow, E. J. (2023). Chronic conditions, late mortality, and health status after childhood AML: A Childhood Cancer Survivor Study report. Blood, 141(1), 90–101.\nhttps://doi.org/10.1182/blood.2022016487\nArmstrong, G. T., Chen, Y., Yasui, Y., Leisenring, W., Gibson, T. M., Mertens, A. C., Stovall, M., Oeffinger, K. C., Bhatia, S., Krull, K. R., Nathan, P. C., Neglia, J. P., Green, D. M., Hudson, M. M., & Robison, L. L. (2016). Reduction in Late Mortality among 5-Year Survivors of Childhood Cancer. New England Journal of Medicine, 374(9), 833–842.\nhttps://doi.org/10.1056/NEJMoa1510795\nYeh, J. M., Ward, Z. J., Chaudhry, A., Liu, Q., Yasui, Y., Armstrong, G. T., Gibson, T. M., Howell, R., Hudson, M. M., Krull, K. R., Leisenring, W. M., Oeffinger, K. C., & Diller, L. (2020). Life Expectancy of Adult Survivors of Childhood Cancer Over 3 Decades. JAMA Oncology, 6(3), 350.\nhttps://doi.org/10.1001/jamaoncol.2019.5582\nIzraeli, S. (2022). The first achievement of complete remission in childhood leukemia by treatment with the folic acid antagonist aminopterin. Haematologica, 107(4), 782.\nhttps://doi.org/10.3324/haematol.2022.280670\nKohn, K. W. (2022). Drugs Against Cancer: Stories of Discovery and the Quest for a Cure. Chapter 7: The 6-mercaptopurine (6MP) story. United States: National Cancer Institute. Available\nonline\n.\nThe 1960s and 1970s refer to research by St Jude’s Hospital in the late 1960s and the Berlin-Frankfurt-Münster (BFM) protocol in the 1970s.\nSchrappe, M., Reiter, A., Zimmermann, M., Harbott, J., Ludwig, W.-D., Henze, G., Gadner, H., Odenwald, E., & Riehm, H. (2000). Long-term results of four consecutive trials in childhood ALL performed by the ALL-BFM study group from 1981 to 1995. Leukemia, 14(12), 2205–2222.\nhttps://doi.org/10.1038/sj.leu.2401973\nHunger, S. P., & Mullighan, C. G. (2015). Acute Lymphoblastic Leukemia in Children. New England Journal of Medicine, 373(16), 1541–1552.\nhttps://doi.org/10.1056/NEJMra1400972\nPui, C.-H., & Evans, W. E. (2013). A 50-Year Journey to Cure Childhood Acute Lymphoblastic Leukemia. Seminars in Hematology, 50(3), 185–196.\nhttps://doi.org/10.1053/j.seminhematol.2013.06.007\nPui, C.-H., & Evans, W. E. (2013). A 50-Year Journey to Cure Childhood Acute Lymphoblastic Leukemia. Seminars in Hematology, 50(3), 185–196.\nhttps://doi.org/10.1053/j.seminhematol.2013.06.007\nHunger, S. P., & Mullighan, C. G. (2015). Acute Lymphoblastic Leukemia in Children. New England Journal of Medicine, 373(16), 1541–1552.\nhttps://doi.org/10.1056/NEJMra1400972\nButler, E., Ludwig, K., Pacenta, H. L., Klesse, L. J., Watt, T. C., & Laetsch, T. W. (2021). Recent progress in the treatment of cancer in children. CA: A Cancer Journal for Clinicians, 71(4), 315–332.\nhttps://doi.org/10.3322/caac.21665\nPagliaro, L., Chen, S.-J., Herranz, D., Mecucci, C., Harrison, C. J., Mullighan, C. G., Zhang, M., Chen, Z., Boissel, N., Winter, S. S., & Roti, G. (2024). Acute lymphoblastic leukaemia. Nature Reviews Disease Primers, 10(1), 41.\nhttps://doi.org/10.1038/s41572-024-00525-x\nCooper, T. M., Alonzo, T. A., Tasian, S. K., Kutny, M. A., Hitzler, J., Pollard, J. A., Aplenc, R., Meshinchi, S., & Kolb, E. A. (2023). Children’s Oncology Group’s 2023 blueprint for research: Myeloid neoplasms. Pediatric Blood & Cancer, 70(S6), e30584.\nhttps://doi.org/10.1002/pbc.30584\nRaetz, E. A., Bhojwani, D., Devidas, M., Gore, L., Rabin, K. R., Tasian, S. K., Teachey, D. T., & Loh, M. L. (2023). Children’s Oncology Group blueprint for research: Acute lymphoblastic leukemia. Pediatric Blood & Cancer, 70(S6), e30585.\nhttps://doi.org/10.1002/pbc.30585\nTomizawa, D. (2015). Recent progress in the treatment of infant acute lymphoblastic leukemia. Pediatrics International, 57(5), 811–819.\nhttps://doi.org/10.1111/ped.12758\nRasche, M., Zimmermann, M., Borschel, L., Bourquin, J.-P., Dworzak, M., Klingebiel, T., Lehrnbecher, T., Creutzig, U., Klusmann, J.-H., & Reinhardt, D. (2018). Successes and challenges in the treatment of pediatric acute myeloid leukemia: A retrospective analysis of the AML-BFM trials from 1987 to 2012. Leukemia, 32(10), 2167–2177.\nhttps://doi.org/10.1038/s41375-018-0071-7\nHunger, S. P., & Mullighan, C. G. (2015). Acute Lymphoblastic Leukemia in Children. New England Journal of Medicine, 373(16), 1541–1552.\nhttps://doi.org/10.1056/NEJMra1400972\nButler, E., Ludwig, K., Pacenta, H. L., Klesse, L. J., Watt, T. C., & Laetsch, T. W. (2021). Recent progress in the treatment of cancer in children. CA: A Cancer Journal for Clinicians, 71(4), 315–332.\nhttps://doi.org/10.3322/caac.21665\nBlajchman, M. A. (2008). Platelet Transfusions: An Historical Perspective. Hematology, 2008(1), 197–197.\nhttps://doi.org/10.1182/asheducation-2008.1.197\nTiberghien, P., Folléa, G., & Muller, J.-Y. (2016). Platelet Transfusions in Acute Leukemia. New England Journal of Medicine, 375(1), 96–97.\nhttps://doi.org/10.1056/NEJMc1515066\nThis includes broad-spectrum beta-lactam antibiotics and carbapenems (for bacterial infections), caspofungin and liposomal amphotericin (for fungal infections), and aciclovir (for viral infections like herpes). Rasche, M., Zimmermann, M., Borschel, L., Bourquin, J.-P., Dworzak, M., Klingebiel, T., Lehrnbecher, T., Creutzig, U., Klusmann, J.-H., & Reinhardt, D. (2018). Successes and challenges in the treatment of pediatric acute myeloid leukemia: A retrospective analysis of the AML-BFM trials from 1987 to 2012.\nLeukemia\n,\n32\n(10), 2167–2177.\nhttps://doi.org/10.1038/s41375-018-0071-7\nRasche, M., Zimmermann, M., Borschel, L., Bourquin, J.-P., Dworzak, M., Klingebiel, T., Lehrnbecher, T., Creutzig, U., Klusmann, J.-H., & Reinhardt, D. (2018). Successes and challenges in the treatment of pediatric acute myeloid leukemia: A retrospective analysis of the AML-BFM trials from 1987 to 2012.\nLeukemia\n,\n32\n(10), 2167–2177.\nhttps://doi.org/10.1038/s41375-018-0071-7\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSaloni Dattani (2025) - “Childhood leukemia: how a deadly cancer became treatable” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260415-083518/childhood-leukemia-treatment-history.html' [Online Resource] (archived on April 15, 2026).\nBibTeX citation\n@article{owid-childhood-leukemia-treatment-history,\nauthor = {Saloni Dattani},\ntitle = {Childhood leukemia: how a deadly cancer became treatable},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260415-083518/childhood-leukemia-treatment-history.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "childhood-leukemia-treatment-history", "source_url": "https://ourworldindata.org/childhood-leukemia-treatment-history", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Before the 1970s, most children affected by leukemia would quickly die from it. Now, most children in rich countries are cured.", "numeric_mentions": ["1970", "9,", "2025", "10%", "85%", "1", "14", "2", "3", "4", "5", "6", "50 years", "1960", "7", "14%", "2010", "94%", "25%", "60%", "8", "1940", "9", "1950", "10", "50%", "11", "1980", "1990", "12", "2000", "10,000", "13", "15", "16", "17", "18", "19", "45 years", "20", "10.1371", "0314592", "2018", "2022", "2023", "0", "10.1016", "2009.08", "001", "65", "10.1002", "27089", "2019", "38", "1367", "1380", "10.1038", "018", "0520", "20,", "241", "263", "10.1146", "083118", "015415", "2016", "47", "607", "614", "2016.11", "017", "2008", "2015", "43,110", "128", "2760", "2767", "34248", "141", "90"], "numeric_evidence": [{"grapher_slug": "cancer-death-rate-in-children-under-5-years-old-by-type", "source_url": "https://ourworldindata.org/grapher/cancer-death-rate-in-children-under-5-years-old-by-type", "parse_error": "Failed to fetch 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{"Entity": "Argentina", "Code": "ARG", "Year": "1978", "All malignant cancers combined": "4.0824575", "Brain and nervous system cancers": "", "Leukemia": "2.9693666", "Lymphomas and multiple myeloma": "0.6201842"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1979", "All malignant cancers combined": "4.615997", "Brain and nervous system cancers": "", "Leukemia": "3.2550397", "Lymphomas and multiple myeloma": "0.850749"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1980", "All malignant cancers combined": "4.8448825", "Brain and nervous system cancers": "", "Leukemia": "3.1370857", "Lymphomas and multiple myeloma": "0.7137285"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1981", "All malignant cancers combined": "6.311846", "Brain and nervous system cancers": "", "Leukemia": "3.8010821", "Lymphomas and multiple myeloma": "0.84652925"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1982", "All malignant cancers combined": "5.129209", "Brain and nervous system cancers": "", 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"0.42550722"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1992", "All malignant cancers combined": "5.12625", "Brain and nervous system cancers": "", "Leukemia": "1.980833", "Lymphomas and multiple myeloma": "0.77346814"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1993", "All malignant cancers combined": "5.135056", "Brain and nervous system cancers": "", "Leukemia": "2.1840174", "Lymphomas and multiple myeloma": "0.48533723"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1994", "All malignant cancers combined": "5.102461", "Brain and nervous system cancers": "", "Leukemia": "2.423368", "Lymphomas and multiple myeloma": "0.5977286"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1996", "All malignant cancers combined": "4.308082", "Brain and nervous system cancers": "0.5892498", "Leukemia": "2.7457035", "Lymphomas and multiple myeloma": "0.60405475"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1997", "All malignant cancers combined": "3.9900515", "Brain and nervous system cancers": "0.4922368", "Leukemia": "2.7164178", "Lymphomas and multiple myeloma": "0.4922368"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1998", "All malignant cancers combined": "4.18205", "Brain and nervous system cancers": "0.4847262", "Leukemia": "2.0163016", "Lymphomas and multiple myeloma": "0.40393847"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1999", "All malignant cancers combined": "3.771332", "Brain and nervous system cancers": "0.60496676", "Leukemia": "2.3006191", "Lymphomas and multiple myeloma": "0.3441871"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2000", "All malignant cancers combined": "3.7600324", "Brain and nervous system cancers": "0.6867666", "Leukemia": "2.2771735", "Lymphomas and multiple myeloma": "0.45182016"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2001", "All malignant cancers combined": "4.096727", "Brain and nervous system cancers": "0.5578341", "Leukemia": "2.303315", "Lymphomas and multiple myeloma": "0.52154744"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2002", "All malignant cancers combined": "4.883942", "Brain and nervous system cancers": "0.4121857", "Leukemia": "2.0430074", "Lymphomas and multiple myeloma": "0.3942646"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2003", "All malignant cancers combined": "4.213448", "Brain and nervous system cancers": "0.49764267", "Leukemia": "1.7674026", "Lymphomas and multiple myeloma": "0.5175484"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2004", "All malignant cancers combined": "4.611383", "Brain and nervous system cancers": "0.41645026", "Leukemia": "2.3479724", "Lymphomas and multiple myeloma": "0.5156051"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2005", "All malignant cancers combined": "3.7281096", "Brain and nervous system cancers": "0.6205285", "Leukemia": "1.7020211", "Lymphomas and multiple myeloma": "0.3161939"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2006", "All malignant cancers combined": "4.418584", "Brain and nervous system cancers": "0.7236107", "Leukemia": "2.117885", "Lymphomas and multiple myeloma": "0.41297668"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2007", "All malignant cancers combined": "4.7063975", "Brain and nervous system cancers": "0.43971848", "Leukemia": "1.6357527", "Lymphomas and multiple myeloma": "0.26383108"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2008", "All malignant cancers combined": "4.445737", "Brain and nervous system cancers": "0.578649", "Leukemia": "1.9463649", "Lymphomas and multiple myeloma": "0.29276833"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2009", "All malignant cancers combined": "4.00063", "Brain and nervous system cancers": "0.61153114", "Leukemia": "2.0966783", "Lymphomas and multiple myeloma": "0.2527147"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2010", "All malignant cancers combined": "3.661564", "Brain and nervous system cancers": "0.6263825", "Leukemia": "2.1227407", "Lymphomas and multiple myeloma": "0.34836724"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2011", "All malignant cancers combined": "5.84161", "Brain and nervous system cancers": "0.6578572", "Leukemia": "1.748515", "Lymphomas and multiple myeloma": "0.32755527"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2012", "All malignant cancers combined": "4.475593", "Brain and nervous system cancers": "0.7563021", "Leukemia": "2.337661", "Lymphomas and multiple myeloma": "0.4297171"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2013", "All malignant cancers combined": "4.447981", "Brain and nervous system cancers": "0.6479303", "Leukemia": "1.8469971", "Lymphomas and multiple myeloma": "0.29073104"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2014", "All malignant cancers combined": "4.2623053", "Brain and nervous system cancers": "0.84081054", "Leukemia": "1.8188963", "Lymphomas and multiple myeloma": "0.24023159"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2015", "All malignant cancers combined": "4.068485", "Brain and nervous system cancers": "0.47076735", "Leukemia": "2.2666576", "Lymphomas and multiple myeloma": "0.3874441"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2016", "All malignant cancers combined": "4.4063745", "Brain and nervous system cancers": "0.7630389", "Leukemia": "1.5739794", "Lymphomas and multiple myeloma": "0.4067587"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "cancer-death-rates-in-children-by-type", "metadata_url": "https://ourworldindata.org/grapher/cancer-death-rates-in-children-by-type.metadata.json", "chart_title": "Cancer death rates in children under 10 years old", "chart_subtitle": "Reported annual death rate from childhood cancers, per 100,000 children aged under ten, based on the underlying cause listed on death certificates. This is shown for all malignant cancers combined, brain and nervous system cancers, leukemia, lymphomas and multiple myeloma.", "chart_note": "Figures may fluctuate greatly due to low numbers, especially in countries with smaller populations. Comparisons may be affected by differences in measurement.", "chart_citation": "WHO Mortality Database (2025)", "original_chart_url": "https://ourworldindata.org/grapher/cancer-death-rates-in-children-under-10-years-old", "owid_column_metadata": {"Deaths from malignant neoplasms per 100,000 people in both sexes aged 10-14 years": {"titleShort": "All malignant cancers combined", "titleLong": "All malignant cancers combined", "descriptionShort": "Reported deaths from malignant neoplasms per 100,000 people in both sexes aged 10-14 years.", "descriptionKey": ["The International Classification of Diseases (Version 10) codes that define malignant neoplasms are C00-C97."], "unit": "deaths per 100,000 people", "timespan": "1950-2023", "type": "Numeric", "owidVariableId": 1088496, "shortName": "death_rate_per_100_000_population__sex_both_sexes__age_group_10_14_years__cause_malignant_neoplasms__icd10_codes_c00_c97", "lastUpdated": "2025-04-17", "nextUpdate": "2026-07-22", "citationShort": "WHO Mortality Database (2025) – with minor processing by Our World in Data", "citationLong": "WHO Mortality Database (2025) – with minor processing by Our World in Data. “All malignant cancers combined – WHO” [dataset]. WHO Mortality Database, “WHO Mortality Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1088496.metadata.json"}, "Deaths from brain and nervous system cancers per 100,000 people in both sexes aged <10 years": {"titleShort": "Brain and nervous system cancers", "titleLong": "Brain and nervous system cancers", "descriptionShort": "Reported deaths from brain and nervous system cancers per 100,000 people in both sexes aged <10 years.", "descriptionKey": ["The International Classification of Diseases (Version 10) codes that define brain and nervous system cancers are C70-C72."], "unit": "deaths per 100,000 people", "timespan": "1979-2023", "type": "Numeric", "owidVariableId": 1094396, "shortName": "death_rate_per_100_000_population__sex_both_sexes__age_group__lt_10_years__cause_brain_and_nervous_system_cancers__icd10_codes_c70_c72", "lastUpdated": "2025-08-05", "nextUpdate": "2026-08-05", "citationShort": "WHO Mortality Database (2025) – with minor processing by Our World in Data", "citationLong": "WHO Mortality Database (2025) – with minor processing by Our World in Data. “Brain and nervous system cancers” [dataset]. WHO Mortality Database, “WHO Mortality Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1094396.metadata.json"}, "Deaths from leukaemia per 100,000 people in both sexes aged <10 years": {"titleShort": "Leukemia", "titleLong": "Leukemia", "descriptionShort": "Reported deaths from leukaemia per 100,000 people in both sexes aged <10 years.", "descriptionKey": ["The International Classification of Diseases (Version 10) codes that define leukaemia are C91-C95."], "unit": "deaths per 100,000 people", "timespan": "1950-2023", "type": "Numeric", "owidVariableId": 1094402, "shortName": "death_rate_per_100_000_population__sex_both_sexes__age_group__lt_10_years__cause_leukaemia__icd10_codes_c91_c95", "lastUpdated": "2025-08-05", "nextUpdate": "2026-08-05", "citationShort": "WHO Mortality Database (2025) – with minor processing by Our World in Data", "citationLong": "WHO Mortality Database (2025) – with minor processing by Our World in Data. “Leukemia” [dataset]. WHO Mortality Database, “WHO Mortality Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1094402.metadata.json"}, "Deaths from lymphomas, multiple myeloma per 100,000 people in both sexes aged <10 years": {"titleShort": "Lymphomas and multiple myeloma", "titleLong": "Lymphomas and multiple myeloma", "descriptionShort": "Reported deaths from lymphomas, multiple myeloma per 100,000 people in both sexes aged <10 years.", "descriptionKey": ["The International Classification of Diseases (Version 10) codes that define lymphomas, multiple myeloma are C81-C90, C96."], "unit": "deaths per 100,000 people", "timespan": "1950-2023", "type": "Numeric", "owidVariableId": 1094403, "shortName": "death_rate_per_100_000_population__sex_both_sexes__age_group__lt_10_years__cause_lymphomas__multiple_myeloma__icd10_codes_c81_c90__c96", "lastUpdated": "2025-08-05", "nextUpdate": "2026-08-05", "citationShort": "WHO Mortality Database (2025) – with minor processing by Our World in Data", "citationLong": "WHO Mortality Database (2025) – with minor processing by Our World in Data. “Lymphomas and multiple myeloma” [dataset]. WHO Mortality Database, “WHO Mortality Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1094403.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Cancer death rates in children under 10 years old", "source_url": "https://ourworldindata.org/grapher/cancer-death-rates-in-children-under-10-years-old.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "All malignant cancers combined", "Brain and nervous system cancers", "Leukemia", "Lymphomas and multiple myeloma"], "row_count_total": 5004, "rows_head": [{"Entity": "Albania", "Code": "ALB", "Year": "1987", "All malignant cancers combined": "3.1635559", "Brain and nervous system cancers": "0.96914876", "Leukemia": "2.261347", "Lymphomas and multiple myeloma": "0.48457438"}, {"Entity": "Albania", "Code": "ALB", "Year": "1988", "All malignant cancers combined": "7.133995", "Brain and nervous system cancers": "0.9501188", "Leukemia": "2.2169437", "Lymphomas and multiple myeloma": "1.1084719"}, {"Entity": "Albania", "Code": "ALB", "Year": "1989", "All malignant cancers combined": "4.2813454", "Brain and nervous system cancers": "1.0928962", "Leukemia": "1.7174083", "Lymphomas and multiple myeloma": "0.7806401"}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "All malignant cancers combined": "3.8472922", "Brain and nervous system cancers": "0.7627765", "Leukemia": "0.9153318", "Lymphomas and multiple myeloma": "0.7627765"}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "All malignant cancers combined": "3.2276995", "Brain and nervous system cancers": "0.8333333", "Leukemia": "2.20234", "Lymphomas and multiple myeloma": "0.8258775"}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "All malignant cancers combined": "3.194888", "Brain and nervous system cancers": "0.6901311", "Leukemia": "2.2084196", "Lymphomas and multiple myeloma": "0.3057169"}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "All malignant cancers combined": "2.586207", "Brain and nervous system cancers": "0.79533404", "Leukemia": "0.91982216", "Lymphomas and multiple myeloma": "0.3066074"}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "All malignant cancers combined": "4.255319", "Brain and nervous system cancers": "0.45392647", "Leukemia": "1.2417219", "Lymphomas and multiple myeloma": "0.5235602"}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "All malignant cancers combined": "2.2427812", "Brain and nervous system cancers": "1.2269939", "Leukemia": "1.2269939", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "All malignant cancers combined": "2.7708507", "Brain and nervous system cancers": "0.44629574", "Leukemia": "3.0013642", "Lymphomas and multiple myeloma": "0.35599858"}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "All malignant cancers combined": "3.581267", "Brain and nervous system cancers": "0.94953877", "Leukemia": "3.2555616", "Lymphomas and multiple myeloma": "0.295858"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "All malignant cancers combined": "2.085195", "Brain and nervous system cancers": "0.27480078", "Leukemia": "1.3206162", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "All malignant cancers combined": "3.689481", "Brain and nervous system cancers": "0.76701677", "Leukemia": "1.7434086", "Lymphomas and multiple myeloma": "0.6586249"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "All malignant cancers combined": "1.8411007", "Brain and nervous system cancers": "0.5864392", "Leukemia": "0.8869305", "Lymphomas and multiple myeloma": "0.7819189"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "All malignant cancers combined": "6.2361245", "Brain and nervous system cancers": "1.091298", "Leukemia": "2.182596", "Lymphomas and multiple myeloma": "1.1590395"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "All malignant cancers combined": "4.852045", "Brain and nervous system cancers": "1.6550708", "Leukemia": "2.2757225", "Lymphomas and multiple myeloma": "2.0552027"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "All malignant cancers combined": "2.3425944", "Brain and nervous system cancers": "1.3144808", "Leukemia": "0.8763205", "Lymphomas and multiple myeloma": "0.3715925"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "All malignant cancers combined": "3.1001236", "Brain and nervous system cancers": "1.694707", "Leukemia": "0.7701698", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "All malignant cancers combined": "2.1345172", "Brain and nervous system cancers": "1.208649", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "All malignant cancers combined": "2.2030232", "Brain and nervous system cancers": "0.93751466", "Leukemia": "0.93751466", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "All malignant cancers combined": "1.5176694", "Brain and nervous system cancers": "1.6127515", "Leukemia": "1.6127515", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "All malignant cancers combined": "0.78963995", "Brain and nervous system cancers": "0.94505453", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1961", "All malignant cancers combined": "", "Brain and nervous system cancers": "", "Leukemia": "14.705882", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1962", "All malignant cancers combined": "", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1963", "All malignant cancers combined": "", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1964", "All malignant cancers combined": "", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1966", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1969", "All malignant cancers combined": "12.195122", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "10.869565"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1970", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1971", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1972", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1973", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1974", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1975", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1976", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1977", "All malignant cancers combined": "25.24615", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1978", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1983", "All malignant cancers combined": "", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1985", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1986", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "17.755682", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1987", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1988", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1989", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1990", "All malignant cancers combined": "16.18385", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1991", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1992", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1993", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1994", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1995", "All malignant cancers combined": "0", "Brain and nervous system cancers": "", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1998", "All malignant cancers combined": "0", "Brain and nervous system cancers": "0", "Leukemia": "0", "Lymphomas and multiple myeloma": "0"}, {"Entity": 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"Uzbekistan", "Code": "UZB", "Year": "2001", "All malignant cancers combined": "3.5591388", "Brain and nervous system cancers": "", "Leukemia": "1.5910512", "Lymphomas and multiple myeloma": "0.50243723"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2002", "All malignant cancers combined": "4.3621864", "Brain and nervous system cancers": "", "Leukemia": "1.4655974", "Lymphomas and multiple myeloma": "0.71918195"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2003", "All malignant cancers combined": "3.8799865", "Brain and nervous system cancers": "", "Leukemia": "1.7545066", "Lymphomas and multiple myeloma": "0.51394635"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2004", "All malignant cancers combined": "4.24475", "Brain and nervous system cancers": "0.58060294", "Leukemia": "1.5603703", "Lymphomas and multiple myeloma": "0.5206627"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2005", "All malignant cancers combined": "4.311283", "Brain and nervous system 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{"Entity": "Uzbekistan", "Code": "UZB", "Year": "2013", "All malignant cancers combined": "3.1724844", "Brain and nervous system cancers": "0.65737927", "Leukemia": "1.4158939", "Lymphomas and multiple myeloma": "0.4341663"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2014", "All malignant cancers combined": "3.8202426", "Brain and nervous system cancers": "0.8686531", "Leukemia": "1.0981086", "Lymphomas and multiple myeloma": "0.4425214"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2015", "All malignant cancers combined": "3.7749684", "Brain and nervous system cancers": "1.0343015", "Leukemia": "1.5275838", "Lymphomas and multiple myeloma": "0.33415896"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2016", "All malignant cancers combined": "4.2356567", "Brain and nervous system cancers": "0.9128009", "Leukemia": "1.3769369", "Lymphomas and multiple myeloma": "0.35583764"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2017", "All malignant cancers combined": 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combined": "2.4164317", "Brain and nervous system cancers": "", "Leukemia": "2.3517637", "Lymphomas and multiple myeloma": "0.96590906"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1958", "All malignant cancers combined": "3.7864907", "Brain and nervous system cancers": "", "Leukemia": "2.040462", "Lymphomas and multiple myeloma": "0.47755492"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1959", "All malignant cancers combined": "3.8850954", "Brain and nervous system cancers": "", "Leukemia": "2.594685", "Lymphomas and multiple myeloma": "0.5021971"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1960", "All malignant cancers combined": "5.0974174", "Brain and nervous system cancers": "", "Leukemia": "2.8586383", "Lymphomas and multiple myeloma": "0.7787805"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1961", "All malignant cancers combined": "2.5158608", "Brain and nervous system cancers": "", "Leukemia": "2.7215116", "Lymphomas and multiple myeloma": "0.34990865"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1962", "All malignant cancers combined": "5.0526314", "Brain and nervous system cancers": "", "Leukemia": "2.5440533", "Lymphomas and multiple myeloma": "0.8604886"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1963", "All malignant cancers combined": "5.3752537", "Brain and nervous system cancers": "", "Leukemia": "3.3166301", "Lymphomas and multiple myeloma": "0.61285555"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1964", "All malignant cancers combined": "4.6975923", "Brain and nervous system cancers": "", "Leukemia": "2.330597", "Lymphomas and multiple myeloma": "0.7304856"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1965", "All malignant cancers combined": "6.081916", "Brain and nervous system cancers": "", "Leukemia": "3.2061226", "Lymphomas and multiple myeloma": "0.51711655"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1966", "All malignant cancers combined": "5.146586", "Brain and nervous system cancers": "", "Leukemia": "2.4340637", "Lymphomas and multiple myeloma": "0.9336134"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1967", "All malignant cancers combined": "4.07332", "Brain and nervous system cancers": "", "Leukemia": "2.6740491", "Lymphomas and multiple myeloma": "0.8591988"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1968", "All malignant cancers combined": "5.3777432", "Brain and nervous system cancers": "", "Leukemia": "3.2353485", "Lymphomas and multiple myeloma": "0.83194673"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1969", "All malignant cancers combined": "3.9446077", "Brain and nervous system cancers": "", "Leukemia": "2.684884", "Lymphomas and multiple myeloma": "1.0739536"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1970", "All malignant cancers combined": "5.0312424", "Brain and nervous system cancers": "", "Leukemia": "2.3652253", "Lymphomas and multiple myeloma": "1.1158516"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1971", "All malignant cancers combined": "4.025262", "Brain and nervous system cancers": "", "Leukemia": "2.474067", "Lymphomas and multiple myeloma": "0.9836652"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1972", "All malignant cancers combined": "4.241282", "Brain and nervous system cancers": "", "Leukemia": "2.6312745", "Lymphomas and multiple myeloma": "0.9831136"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1973", "All malignant cancers combined": "5.0927134", "Brain and nervous system cancers": "", "Leukemia": "2.916433", "Lymphomas and multiple myeloma": "0.7851935"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1974", "All malignant cancers combined": "5.008669", "Brain and nervous system cancers": "", "Leukemia": "2.6934516", "Lymphomas and multiple myeloma": "0.7623646"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1975", "All malignant cancers combined": "4.7840867", "Brain and nervous system cancers": "", "Leukemia": "2.6906815", "Lymphomas and multiple myeloma": "0.9884136"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1976", "All malignant cancers combined": "4.6387925", "Brain and nervous system cancers": "", "Leukemia": "3.0329056", "Lymphomas and multiple myeloma": "0.88571584"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1977", "All malignant cancers combined": "4.4591045", "Brain and nervous system cancers": "", "Leukemia": "2.6959822", "Lymphomas and multiple myeloma": "0.94228506"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1978", "All malignant cancers combined": "5.514052", "Brain and nervous system cancers": "", "Leukemia": "2.9724855", "Lymphomas and multiple myeloma": "0.6859582"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1979", "All malignant cancers combined": "4.2205963", "Brain and nervous system cancers": "", "Leukemia": "2.3389132", "Lymphomas and multiple myeloma": "0.94015133"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1980", "All malignant cancers combined": "4.16222", "Brain and nervous system cancers": "", "Leukemia": "2.3970213", "Lymphomas and multiple myeloma": "0.5231494"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1981", "All malignant cancers combined": "5.464771", "Brain and nervous system cancers": "", "Leukemia": "2.1754894", "Lymphomas and multiple myeloma": "0.9517766"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1982", "All malignant cancers combined": "4.196831", "Brain and nervous system cancers": "", "Leukemia": "2.0772562", "Lymphomas and multiple myeloma": "0.70715106"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1983", "All malignant cancers combined": "5.00671", "Brain and nervous system cancers": "", "Leukemia": "2.3934793", "Lymphomas and multiple myeloma": "0.711575"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1985", "All malignant cancers combined": "4.5834994", "Brain and nervous system cancers": "", "Leukemia": "2.4832382", "Lymphomas and multiple myeloma": "0.60179615"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1986", "All malignant cancers combined": "4.3646946", "Brain and nervous system cancers": "", "Leukemia": "2.4574478", "Lymphomas and multiple myeloma": "0.58414745"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1987", "All malignant cancers combined": "4.816319", "Brain and nervous system cancers": "", "Leukemia": "2.255521", "Lymphomas and multiple myeloma": "0.5491704"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1988", "All malignant cancers combined": "4.345816", "Brain and nervous system cancers": "", "Leukemia": "2.3730912", "Lymphomas and multiple myeloma": "0.72224516"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1989", "All malignant cancers combined": "4.012624", "Brain and nervous system cancers": "", "Leukemia": "2.7652602", "Lymphomas and multiple myeloma": "0.6892907"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1990", "All malignant cancers combined": "6.4726343", "Brain and nervous system cancers": "", "Leukemia": "2.514361", "Lymphomas and multiple myeloma": "0.42550722"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1992", "All malignant cancers combined": "5.12625", "Brain and nervous system cancers": "", "Leukemia": "1.980833", "Lymphomas and multiple myeloma": "0.77346814"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1993", "All malignant cancers combined": "5.135056", "Brain and nervous system cancers": "", "Leukemia": "2.1840174", "Lymphomas and multiple myeloma": "0.48533723"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1994", "All malignant cancers combined": "5.102461", "Brain and nervous system cancers": "", "Leukemia": "2.423368", "Lymphomas and multiple myeloma": "0.5977286"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1996", "All malignant cancers combined": "4.308082", "Brain and nervous system cancers": "0.5892498", "Leukemia": "2.7457035", "Lymphomas and multiple myeloma": "0.60405475"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1997", "All malignant cancers combined": "3.9900515", "Brain and nervous system cancers": "0.4922368", "Leukemia": "2.7164178", "Lymphomas and multiple myeloma": "0.4922368"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1998", "All malignant cancers combined": "4.18205", "Brain and nervous system cancers": "0.4847262", "Leukemia": "2.0163016", "Lymphomas and multiple myeloma": "0.40393847"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1999", "All malignant cancers combined": "3.771332", "Brain and nervous system cancers": "0.60496676", "Leukemia": "2.3006191", "Lymphomas and multiple myeloma": "0.3441871"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2000", "All malignant cancers combined": "3.7600324", "Brain and nervous system cancers": "0.6867666", "Leukemia": "2.2771735", "Lymphomas and multiple myeloma": "0.45182016"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2001", "All malignant cancers combined": "4.096727", "Brain and nervous system cancers": "0.5578341", "Leukemia": "2.303315", "Lymphomas and multiple myeloma": "0.52154744"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2002", "All malignant cancers combined": "4.883942", "Brain and nervous system cancers": "0.4121857", "Leukemia": "2.0430074", "Lymphomas and multiple myeloma": "0.3942646"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2003", "All malignant cancers combined": "4.213448", "Brain and nervous system cancers": "0.49764267", "Leukemia": "1.7674026", "Lymphomas and multiple myeloma": "0.5175484"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2004", "All malignant cancers combined": "4.611383", "Brain and nervous system cancers": "0.41645026", "Leukemia": "2.3479724", "Lymphomas and multiple myeloma": "0.5156051"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2005", "All malignant cancers combined": "3.7281096", "Brain and nervous system cancers": "0.6205285", "Leukemia": "1.7020211", "Lymphomas and multiple myeloma": "0.3161939"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2006", "All malignant cancers combined": "4.418584", "Brain and nervous system cancers": "0.7236107", "Leukemia": "2.117885", "Lymphomas and multiple myeloma": "0.41297668"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2007", "All malignant cancers combined": "4.7063975", "Brain and nervous system cancers": "0.43971848", "Leukemia": "1.6357527", "Lymphomas and multiple myeloma": "0.26383108"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2008", "All malignant cancers combined": "4.445737", "Brain and nervous system cancers": "0.578649", "Leukemia": "1.9463649", "Lymphomas and multiple myeloma": "0.29276833"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2009", "All malignant cancers combined": "4.00063", "Brain and nervous system cancers": "0.61153114", "Leukemia": "2.0966783", "Lymphomas and multiple myeloma": "0.2527147"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2010", "All malignant cancers combined": "3.661564", "Brain and nervous system cancers": "0.6263825", "Leukemia": "2.1227407", "Lymphomas and multiple myeloma": "0.34836724"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2011", "All malignant cancers combined": "5.84161", "Brain and nervous system cancers": "0.6578572", "Leukemia": "1.748515", "Lymphomas and multiple myeloma": "0.32755527"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2012", "All malignant cancers combined": "4.475593", "Brain and nervous system cancers": "0.7563021", "Leukemia": "2.337661", "Lymphomas and multiple myeloma": "0.4297171"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2013", "All malignant cancers combined": "4.447981", "Brain and nervous system cancers": "0.6479303", "Leukemia": "1.8469971", "Lymphomas and multiple myeloma": "0.29073104"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2014", "All malignant cancers combined": "4.2623053", "Brain and nervous system cancers": "0.84081054", "Leukemia": "1.8188963", "Lymphomas and multiple myeloma": "0.24023159"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2015", "All malignant cancers combined": "4.068485", "Brain and nervous system cancers": "0.47076735", "Leukemia": "2.2666576", "Lymphomas and multiple myeloma": "0.3874441"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2016", "All malignant cancers combined": "4.4063745", "Brain and nervous system cancers": "0.7630389", "Leukemia": "1.5739794", "Lymphomas and multiple myeloma": "0.4067587"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "cancer-death-rates-in-children-under-10-years-old", "metadata_url": "https://ourworldindata.org/grapher/cancer-death-rates-in-children-under-10-years-old.metadata.json", "chart_title": "Cancer death rates in children under 10 years old", "chart_subtitle": "Reported annual death rate from childhood cancers, per 100,000 children aged under ten, based on the underlying cause listed on death certificates. This is shown for all malignant cancers combined, brain and nervous system cancers, leukemia, lymphomas and multiple myeloma.", "chart_note": "Figures may fluctuate greatly due to low numbers, especially in countries with smaller populations. Comparisons may be affected by differences in measurement.", "chart_citation": "WHO Mortality Database (2025)", "original_chart_url": "https://ourworldindata.org/grapher/cancer-death-rates-in-children-under-10-years-old", "owid_column_metadata": {"Deaths from malignant neoplasms per 100,000 people in both sexes aged 10-14 years": {"titleShort": "All malignant cancers combined", "titleLong": "All malignant cancers combined", "descriptionShort": "Reported deaths from malignant neoplasms per 100,000 people in both sexes aged 10-14 years.", "descriptionKey": ["The International Classification of Diseases (Version 10) codes that define malignant neoplasms are C00-C97."], "unit": "deaths per 100,000 people", "timespan": "1950-2023", "type": "Numeric", "owidVariableId": 1088496, "shortName": "death_rate_per_100_000_population__sex_both_sexes__age_group_10_14_years__cause_malignant_neoplasms__icd10_codes_c00_c97", "lastUpdated": "2025-04-17", "nextUpdate": "2026-07-22", "citationShort": "WHO Mortality Database (2025) – with minor processing by Our World in Data", "citationLong": "WHO Mortality Database (2025) – with minor processing by Our World in Data. “All malignant cancers combined – WHO” [dataset]. WHO Mortality Database, “WHO Mortality Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1088496.metadata.json"}, "Deaths from brain and nervous system cancers per 100,000 people in both sexes aged <10 years": {"titleShort": "Brain and nervous system cancers", "titleLong": "Brain and nervous system cancers", "descriptionShort": "Reported deaths from brain and nervous system cancers per 100,000 people in both sexes aged <10 years.", "descriptionKey": ["The International Classification of Diseases (Version 10) codes that define brain and nervous system cancers are C70-C72."], "unit": "deaths per 100,000 people", "timespan": "1979-2023", "type": "Numeric", "owidVariableId": 1094396, "shortName": "death_rate_per_100_000_population__sex_both_sexes__age_group__lt_10_years__cause_brain_and_nervous_system_cancers__icd10_codes_c70_c72", "lastUpdated": "2025-08-05", "nextUpdate": "2026-08-05", "citationShort": "WHO Mortality Database (2025) – with minor processing by Our World in Data", "citationLong": "WHO Mortality Database (2025) – with minor processing by Our World in Data. “Brain and nervous system cancers” [dataset]. 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{"raw_link": "https://ourworldindata.org/tuberculosis-history-decline", "title": "Once a leading killer, tuberculosis is now rare in rich countries — here’s how it happened", "context": "Home\nTuberculosis\nOnce a leading killer, tuberculosis is now rare in rich countries — here’s how it happened\nAs much as one quarter of deaths in Europe and the United States were once from tuberculosis.\nBy\nHannah Ritchie\nand\nFiona Spooner\nJune 2, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nPeople often romanticize what’s rare and look down on what’s common. This was not the case for tuberculosis. It was everywhere, but still carried a strange sense of allure.\nBy the middle of the 18th century, around 1% of London's population was dying from tuberculosis (TB)\nevery year\n. You can see this in the chart below, which shows modeled estimates of TB death rates in London.\n1\nLet’s pause on that. Every year, 1 in 100 people died from TB. That means that if you lived in London, every five years, 1 in 20 people you knew might have died from it. That’s one person for every three or four households.\n2\nIf London were to experience that scale of infection and death today, tuberculosis would kill around 90,000 people every year.\n3\nThat’s almost double the number who currently die in London from\nall\ncauses — cancer, heart disease, the flu, COVID-19, dementia, road injuries, homicides, and many others.\n4\nOur history with tuberculosis dates back much further, with evidence that it has plagued humans for\nat least 9000 years\n.\nDownload\nTo understand how bad things were back then, it’s also worth comparing these rates to the hardest-hit countries today. Very few people now die of tuberculosis in rich countries. But many low- and middle-income countries are still battling this terrible disease. Lesotho has the\nhighest death rate\nin the world, at 165 deaths per 100,000 people. This is shown as the beige line at the bottom of the chart. In 1750s London, the death rate was more than five times higher.\nTuberculosis was not just a problem in the big cities. The disease was responsible for as many as one-quarter of all deaths in the United States and Europe during parts of the 18th and 19th centuries.\n5\nFor context, all cancers make up\naround one-quarter\nof deaths in the UK today.\nTuberculosis: the mysterious and dignified way to die\nDespite being so widespread, tuberculosis was, for a long time, a disease cloaked in mystery. Before Robert Koch identified the bacteria\nMycobacterium tuberculosis\nas the cause of TB in 1882, there were many theories about where it came from.\n6\nOne of the most common was that it was a genetic condition passed from generation to generation. This seemed like a reasonable explanation for why many people in the same family would get the disease. Another was that it was caused by damp, cold weather. There was even\nthe theory\nin New England that vampires caused it; the first person in the family to die of TB disease came back (as a vampire) and infected everyone else. In reality, it’s because TB spreads from person to person through water droplets, and families were spreading the disease at home.\nTuberculosis was not just seen as a mysterious way to die, but also a noble one. As the poet Lord Byron put it:\n“How pale I look! – I should like, I think, to die of consumption … because then the women would all say, ‘see that poor Byron – how interesting he looks in dying!’”\n“Consumption” was a common name for tuberculosis.\nTuberculosis often had a positive stigma attached, both in terms of intellectual creativity and aesthetic beauty.\nMany famous writers and artists we still admire today\ndied from TB\n: John Keats, the sisters Emily and Charlotte Brontë, Robert Louis Stevenson, George Orwell, Frédéric Chopin, Edgar Allen Poe, the list goes on.\nMany concluded that TB was a crucial ingredient of their success, with fever and confusion giving them an artistic and creative edge that others lacked. Of course, when you run the numbers on just how many people were dying from tuberculosis, it shouldn’t surprise us that some famous people, including writers and artists, were on the list. It was a statistical coincidence, not statistically significant. These examples also make clear that while wealth and status might have reduced the risks of some infectious diseases, they did not offer the protection that today’s high living standards do, especially when there were no treatments.\n7\nThis romanticization turned into negative stigma when it was clear that this was an infectious disease, and TB patients were carriers.\nPeople suffering from tuberculosis were seen as physically beautiful, too. This is especially true for women. TB sufferers were often extremely pale, and as a result, many people called it the “white plague”. People would lose a lot of weight, giving it another name — “consumption” — as it seemed TB was literally eating away at the body. This pale and slim aesthetic, with flushed cheeks from fever, was idolized in North America and Europe, solidifying beauty standards for the time. Previously, women had to go on restrictive diets and wear extremely tight corsets to achieve this look; now, tuberculosis did it for them.\nThis is what Charlotte Brontë\nwrote\nwhile watching her sister Anne die from it:\n“Consumption, I am aware, is a flattering malady.”\nYou can see this depiction of a woman dying from tuberculosis in the image below, from the mid-1800s.\nDownload\nA young woman with tuberculosis being cared for. A watercolor by Robert Humphrey Giles in the 1800s. Source:\nWellcome Collection\n.\nFor most people, getting tuberculosis was a death sentence\nOf course, a slim waistline and pale complexion came at a cost. For most people, it was death.\nWhile TB was called “consumption” because it led to a loss of appetite, weight loss, and fatigue — an “eating away” of the body — most people died from the destruction of lung tissue. Dead lung tissue can form cavities or holes, leading to coughing and breathing problems. Have you ever watched an old film or TV programme, and seen a character coughing up blood into a handkerchief? That was often tuberculosis, and a silent way of telling you that the character was doomed.\nThe famous poet John Keats, after coughing blood into a handkerchief,\nremarked\n:\n\"I cannot be deceived in that colour – that drop of blood is my death-warrant – I must die\".\nWithout treatment, most people who had an active tuberculosis infection would die.\nData from the early 1900s in the United Kingdom, Sweden, and Denmark\nshows that\none-third of patients diagnosed with active tuberculosis had died within one year, and two-thirds within five years.\n8\nBy year 10, as many as 80% of people had died. This is shown in the chart below.\nDownload\nThis left around one-quarter of people who spontaneously recovered from the disease. It’s not entirely clear why they responded more positively than most others.\nWhile TB was incredibly deadly, it spread and developed more slowly than other infectious diseases. This added to its positive stigma: while diseases like cholera and the bubonic plague would rapidly tear through entire households and communities, tuberculosis appeared to evolve more slowly: symptoms often didn’t appear until years after infection, and then death would take several years more. It was a slower, and therefore more dignified, way to go.\nThanks to improved living standards, deaths started falling even before treatments became available\nTuberculosis was already in decline before effective medical treatments arrived. To understand why, we must understand the conditions that led to such large outbreaks in cities like London, Hamburg, New York, and Stockholm.\nTuberculosis is a bacterial disease that spreads from person to person in the air through water droplets. That means they can spread when a person with an active infection speaks, coughs, sneezes, or spits.\nMany people have what we call “latent tuberculosis”, which means they have been infected with\nMycobacterium tuberculosis\n, but it lies dormant in their system: they show no symptoms (including all of the ones we discussed above) and cannot spread it.\n9\nThis is\nstill true today\n. In this case, the bacteria are effectively surrounded by granulomas — small clusters of immune cells — that stop them from multiplying and keep them suppressed. But when someone’s immune system weakens or is compromised, the bacteria can break out of these granulomas and multiply. That person then has “active tuberculosis”, which means they are contagious and develop TB symptoms.\nDensely populated areas with poor ventilation and poor sanitation standards are hotspots for the spread of TB.\n7\nYou can probably now guess why a city like London saw such large and devastating outbreaks at the dawn of the Industrial Revolution. Housing was densely packed, water supplies were poorly managed, and many people had started working in factories and sweatshops.\nConditions were not just perfect for the spread of TB, but people were also highly vulnerable to contracting an active infection. Malnourishment is the\nbiggest risk factor\nfor developing an active infection, and at the time, many Brits suffered from\npoor nutrition\n.\nThis combination of factors led to the extremely high death rates that we saw earlier.\nBut over the next two centuries, rates started to decline thanks to improvements in all of these factors. Clean water and sanitation became more readily available, living and work conditions improved, and nutrition improved. You can see this drop in the chart below, which shows death rates from tuberculosis in England and Wales since the mid-1800s.\n10\nRates declined dramatically for both men and women; one suggested reason for the higher rates in men is that they were more likely to smoke (which is\na risk factor\nfor TB).\nPublic health interventions also played a crucial role. After Robert Koch discovered the real cause of tuberculosis in the late 1800s, public health programs were developed to raise awareness of how TB spread and how families and communities could prevent it.\nBelow, you can see several posters that were used for public messaging. These focused on the fact that TB spreads through droplets (and limiting that spread would reduce the risks of infecting others).\nDownload\nLeft:\nA poster about TB transmission to children in New York. Source:\nLibrary of Congress\n.\nRight:\nA public health poster from London in 1910. Source:\nEconomic History Society\n.\nA better understanding of the cause and its risk factors also led to the opening of specialized hospitals, called “tuberculosis sanatoriums”. These sanatoriums were often set up in rural areas (sometimes in the mountains at higher altitudes) with the belief that a good cure for TB was exposure to sunlight and “good air”.\n11\nBed rest and a good diet were crucial to the treatment regimen. Since poor nutrition is a key risk factor for developing tuberculosis, weight gain and an improved diet made sense when caring for TB patients. In the pictures below, you can see two sanatoriums: one for women and another for children.\nDownload\nFemale TB patients at a tuberculosis sanatorium, outside in “good air” on bed rest.\nDownload\nChildren with tuberculosis at a sanatorium, on bed rest outside in “good air”.\nWhile people from various socioeconomic backgrounds went to these sanatoriums, the quality of care varied greatly. People from richer families often went to private ones that could keep them longer and provide more specialized care. Governments in countries like the US did set up publicly-funded sanatoriums, which were more accessible for poorer households, but this often meant longer wait times for admission and shorter stays.\nThese hospitals didn’t cure tuberculosis, but rest and improved nutrition did help some patients go into remission (although for many, the disease would return later).\nAntibiotic treatments became available in the 1950s, and TB rates dropped dramatically\nAntibiotics were the breakthrough that the world was waiting for.\nIn 1944, the first anti-TB treatment — streptomycin — was discovered.\n12\nAlmost simultaneously, the Swedish chemist Jörgen Lehmann discovered that para-aminosalicylic acid was also effective in treating tuberculosis. Later that decade, the UK Medical Research Council found that combining these two drugs was more effective than either alone. And by 1951, another antibiotic — isoniazid — was added to the mix, creating the first triple therapy for an infectious disease.\n13\nThe treatment plan for TB was long, usually taking 18 to 24 months.\n14\nDuring that time, patients needed to consistently take the triple antibiotic therapy. But around 90% of those who did recover fully.\n15\nThis led to a dramatic decline in tuberculosis deaths in countries that could afford these treatments and made them widely available, mostly in North America and Europe.\nIn 1952, almost 20,000 people\nwere dying\nfrom tuberculosis in the US every year. A decade later, this had more than halved. And by the 1980s, deaths had dropped below 2,000. You can see this decline in the chart.\nBy the late 1980s, the path to beating TB hit a roadblock (which I’ll cover in a separate article). Despite those setbacks, deaths in the US continued to decline and now fluctuate between 500 and 600 per year.\nOnce a huge killer, it is a disease that is mostly forgotten.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nThe story of tuberculosis might be mostly over in the rich world, but it’s not in the rest of the world\nTuberculosis is not mentioned much in the rich world anymore, but the fight continues in other parts of the world. The world waged war on the disease but left it half-finished.\nTuberculosis still kills almost\n1.3 million people\nevery year. That makes it the world’s deadliest infectious disease.\n16\nMost of these deaths occur in low- and middle-income countries, where a combination of factors makes it more likely that TB spreads, people develop an active infection,\nand\nreceive worse treatment.\nYou might wonder whether this progress in the US and Europe can be replicated elsewhere. We think it can, for a few reasons.\nFirst, low-income countries have\nalready made\nprogress\n; TB death rates have fallen in recent decades. There’s no reason this has to stop.\nSecond, death rates in the US and the UK were far higher in the past than they are in some of the worst-off countries today. Look at the chart below, which shows the long historical rates from England and Wales we saw earlier, alongside rates in Ghana, Sierra Leone, Côte d’Ivoire, and Ethiopia in the last few decades. Death rates in the latter are similar to those in Britain in the 1950s and 1960s. There’s little reason why these countries can’t replicate what Britain did over the next 30 to 40 years.\nDownload\nDespite progress in many countries, there are still\nhuge\ndifferences in death rates across the world. People in the hardest-hit countries — such as Lesotho and the Central African Republic — are around 800 times\nmore likely\nto die from TB than an American.\n17\nTo understand what’s at stake, let’s assume every country could control and treat TB like the United States. In a separate article in this series, we’ll explore what would be needed to achieve this. Rather than 1.28 million people dying, this figure would be “only” 16,000.\n18\nWe’d save over 1.2 million lives every year.\nIt’s only by looking at the US or Europe’s history with tuberculosis that we know this change is possible. In the early 1950s, the death rate in the United States was 12.4 per 100,000 people. That’s not much less than the global average of 16 per 100,000 today.\nGoing further back in time, we saw that the human toll of the disease was far worse in historical London or New York than you’ll find almost anywhere in the world today. The fact that we either forget or are unaware of this means that this tragedy is not a given.\nDownload\nAcknowledgments\nWe thank Saloni Dattani, Edouard Mathieu, and Simon van Teutem for valuable comments and feedback on this article.\nFor this work, we relied heavily on academic research and detailed long-run datasets on disease prevalence and mortality. We also found John Green’s book\nEverything is Tuberculosis\nto be a useful and accessible account of the disease's history.\nThis is the first article in our three-part series on tuberculosis:\nOnce a leading killer, tuberculosis is now rare in rich countries — here’s how it happened\nAs much as one quarter of deaths in Europe and the United States were once from tuberculosis.\nThe end of tuberculosis that wasn’t\nIn the 1980s, many thought tuberculosis was on the path to elimination. In reality, more were dying from the disease than ever.\nThe world left its fight against tuberculosis unfinished — how can we complete the job?\nIf we get it right, the world could save more than 1.2 million lives every year.\nEndnotes\nThese estimates come from the work of Hans L. Rieder (1999).\nEpidemiologic Basis of Tuberculosis Control\n.\nThis was also republished in: Lönnroth, K., Jaramillo, E., Williams, B. G., Dye, C., & Raviglione, M. (2009).\nDrivers of tuberculosis epidemics: the role of risk factors and social determinants\n. Social science & medicine, 68(12), 2240-2246.\nThe average size of a household then was likely around 6 or 7.\nThe population of London is approximately 9 million. 1% of 9 million is 90,000.\nThere\nare around\n56,000 deaths in London (which includes Inner London and outer boroughs) a year.\nBloom, B. R. (1994). Tuberculosis: pathogenesis, protection, and control. ASM Press.\nKoch’s identification of the bacteria that caused tuberculosis was viewed as the first conclusive proof of “germ theory”, which had a much broader impact on society’s understanding of disease and how it spreads.\nZürcher, K., Ballif, M., Zwahlen, M., Rieder, H. L., Egger, M., & Fenner, L. (2016). Tuberculosis mortality and living conditions in Bern, Switzerland, 1856-1950. PLoS One, 11(2), e0149195.\nWhile testing methods back then were as accurate and sophisticated as they are today, we have had ways to test for TB since the late 19th century. In the 1880s, ways to detect tuberculosis under a microscope were developed. By the early 1900s, the Tuberculin Skin Test — where purified protein derivative is injected into the skin to measure immune response — was available. And by the 1920s, there were X-ray tests. Hans L. Rieder (1999). Epidemiologic Basis of Tuberculosis Control. International Union Against Tuberculosis and Lung Disease.\nEsmail, H., Barry, C. E., Young, D. B., & Wilkinson, R. J. (2014). The ongoing challenge of latent tuberculosis. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1645), 20130437. https://doi.org/10.1098/rstb.2013.0437\nThis data comes from the\nRoutledge History of Death Since 1800\n. The raw data was received by personal communication with Dr Romola Davenport, the author of this chapter.\nPatriarca, C., Bello, G. L., Zannella, S., & Agati, S. A. (2022). Tuberculosis: the sanatorium season in the early 20th century.\nPathologica\n, 114(4), 342.\nThis discovery is typically attributed to Selman Abraham Waksman, who won the\n1952 Nobel Prize in Medicine\nfor it. However, the role his PhD students and colleagues, particularly Albert Schatz and Elizabeth Bugie, played in the discovery is a point of contention. Many claim it was a co-discovery by all three scientists, but Waksman took the credit.\nThere have been changes to this mixture since then; streptomycin and para-aminosalicylic acid were later replaced by more effective antibiotics, and a fourth antibiotic — rifampicin — was added to treatments in the 1970s.\nThis long treatment time probably meant that cure rates were higher for richer people, who had more resources to have a consistent supply of medicines and be able to take them daily for one to two years.\nEstimates of recovery rates range from around 85% to 95%. Iseman, M. D. (2002). Tuberculosis therapy: past, present and future. European Respiratory Journal, 20(36 suppl), 87S-94s.\nIn recent years, it has been second to COVID-19, but is likely to retake the top spot.\nThe\ndeath rate\nin the United States is 0.2 deaths per 100,000 people compared to 165 in Lesotho, and 156 in the Central African Republic.\n1.28 million people die from tuberculosis globally. The global death rate is 16 deaths per 100,000 people. In the US, this rate is 0.2 per 100,000. To get this hypothetical figure, we calculate: [1.28 million / 16 * 0.2 = 16,000].\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Fiona Spooner (2025) - “Once a leading killer, tuberculosis is now rare in rich countries — here’s how it happened” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-095641/tuberculosis-history-decline.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-tuberculosis-history-decline,\nauthor = {Hannah Ritchie and Fiona Spooner},\ntitle = {Once a leading killer, tuberculosis is now rare in rich countries — here’s how it happened},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-095641/tuberculosis-history-decline.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "tuberculosis-history-decline", "source_url": "https://ourworldindata.org/tuberculosis-history-decline", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "As much as one quarter of deaths in Europe and the United States were once from tuberculosis.", "numeric_mentions": ["2,", "2025", "18", "1%", "1", "100", "20", "2", "90,000", "3", "19,", "4", "9000 years", "165", "100,000", "1750", "19", "5", "1882,", "6", "7", "1800", "1900", "8", "10,", "80%", "9", "10", "1910", "11", "1950", "1944,", "12", "1951,", "13", "24 months", "14", "90%", "15", "1952,", "20,000", "1980", "2,000", "500", "600", "1.3 million", "16", "1960", "30", "40 years", "800", "17", "1.28 million", "16,000", "1.2 million", "12.4", "1999", "2009", "68", "2240", "2246", "9 million", "56,000", "1994", "2016", "1856", "1880", "1920", "2014", "369", "1645", "20130437", "10.1098", "2013.0437", "2022", "114", "342", "1952", "1970", "85%"], "numeric_evidence": [{"title": "Tuberculosis death rate", "source_url": 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mortality from all forms of tuberculosis per 100,000 population": "36"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Estimated mortality from all forms of tuberculosis per 100,000 population": "37"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Estimated mortality from all forms of tuberculosis per 100,000 population": "36"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Estimated mortality from all forms of tuberculosis per 100,000 population": "34"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Estimated mortality from all forms of tuberculosis per 100,000 population": "34"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2024", "Estimated mortality from all forms of tuberculosis per 100,000 population": "34"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Estimated mortality from all forms of tuberculosis per 100,000 population": "123.37549"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Estimated mortality from all forms of tuberculosis per 100,000 population": "119.66141"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Estimated mortality from all forms of tuberculosis per 100,000 population": "114.25867"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Estimated mortality from all forms of tuberculosis per 100,000 population": "113.14764"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Estimated mortality from all forms of tuberculosis per 100,000 population": "110.175354"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Estimated mortality from all forms of tuberculosis per 100,000 population": "111.47894"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Estimated mortality from all forms of tuberculosis per 100,000 population": "109.599014"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Estimated mortality from all forms of tuberculosis per 100,000 population": "104.73238"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Estimated mortality from all forms of tuberculosis per 100,000 population": "99.508385"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Estimated mortality from all forms of tuberculosis per 100,000 population": "96.02446"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Estimated mortality from all forms of tuberculosis per 100,000 population": "85.23965"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Estimated mortality from all forms of tuberculosis per 100,000 population": "83.07744"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Estimated mortality from all forms of tuberculosis per 100,000 population": "77.348595"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Estimated mortality from all forms of tuberculosis per 100,000 population": "72.380936"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Estimated mortality from all forms of tuberculosis per 100,000 population": "69.83822"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Estimated mortality from all forms of tuberculosis per 100,000 population": "65.82899"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Estimated mortality from all forms of tuberculosis per 100,000 population": "61.442184"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Estimated mortality from all forms of tuberculosis per 100,000 population": "56.628937"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Estimated mortality from all forms of tuberculosis per 100,000 population": "51.576385"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Estimated mortality from all forms of tuberculosis per 100,000 population": "47.858368"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Estimated mortality from all forms of tuberculosis per 100,000 population": "45.674534"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2021", "Estimated mortality from all forms of tuberculosis per 100,000 population": "40.838017"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2022", "Estimated mortality from all forms of tuberculosis per 100,000 population": "34.09155"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2023", "Estimated mortality from all forms of tuberculosis per 100,000 population": "30.826317"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2024", "Estimated mortality from all forms of tuberculosis per 100,000 population": "28.84846"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.74"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.66"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.64"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.61"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.85"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.4"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.36"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.53"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.54"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.18"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.31"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.31"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.32"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.32"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.32"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.32"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.32"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.32"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.32"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.35"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.36"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.37"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.37"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.34"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.32"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Estimated mortality from all forms of tuberculosis per 100,000 population": "27"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Estimated mortality from all forms of tuberculosis per 100,000 population": "25"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Estimated mortality from all forms of tuberculosis per 100,000 population": "24"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Estimated mortality from all forms of tuberculosis per 100,000 population": "24"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Estimated mortality from all forms of tuberculosis per 100,000 population": "23"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Estimated mortality from all forms of tuberculosis per 100,000 population": "23"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Estimated mortality from all forms of tuberculosis per 100,000 population": "21"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Estimated mortality from all forms of tuberculosis per 100,000 population": "20"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Estimated mortality from all forms of tuberculosis per 100,000 population": "17"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Estimated mortality from all forms of tuberculosis per 100,000 population": "17"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Estimated mortality from all forms of tuberculosis per 100,000 population": "16"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Estimated mortality from all forms of tuberculosis per 100,000 population": "14"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Estimated mortality from all forms of tuberculosis per 100,000 population": "13"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Estimated mortality from all forms of tuberculosis per 100,000 population": "12"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Estimated mortality from all forms of tuberculosis per 100,000 population": "11"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Estimated mortality from all forms of tuberculosis per 100,000 population": "11"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Estimated mortality from all forms of tuberculosis per 100,000 population": "10"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Estimated mortality from all forms of tuberculosis per 100,000 population": "10"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Estimated mortality from all forms of tuberculosis per 100,000 population": "10"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Estimated mortality from all forms of tuberculosis per 100,000 population": "8.6"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Estimated mortality from all forms of tuberculosis per 100,000 population": "7"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Estimated mortality from all forms of tuberculosis per 100,000 population": "7.6"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2022", "Estimated mortality from all forms of tuberculosis per 100,000 population": "7.4"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "Estimated mortality from all forms of tuberculosis per 100,000 population": "7.3"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2024", "Estimated mortality from all forms of tuberculosis per 100,000 population": "7.1"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2000", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.16"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2001", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.16"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2002", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.11"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2003", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.16"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2004", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.26"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2005", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.32"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2006", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.21"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2007", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.16"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2008", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.16"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2009", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.22"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2010", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.22"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2011", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.16"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2012", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.19"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2013", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.18"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2014", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.18"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2015", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.23"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2016", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.17"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2017", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.29"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2018", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2019", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.06"}], "rows_tail": [{"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2005", "Estimated mortality from all forms of tuberculosis per 100,000 population": "1.4"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2006", "Estimated mortality from all forms of tuberculosis per 100,000 population": "1.3"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2007", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.43"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2008", "Estimated mortality from all forms of tuberculosis per 100,000 population": "1.2"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2009", "Estimated mortality from all forms of tuberculosis per 100,000 population": "2"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2010", "Estimated mortality from all forms of tuberculosis per 100,000 population": "1.1"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2011", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.46"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2012", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.53"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2013", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.48"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2014", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2015", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2016", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2017", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.04"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2018", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2019", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.26"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2020", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2021", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.04"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2022", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.03"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2023", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0.02"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2024", "Estimated mortality from all forms of tuberculosis per 100,000 population": "0"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Estimated mortality from all forms of tuberculosis per 100,000 population": "47.6807"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Estimated mortality from all forms of tuberculosis per 100,000 population": "45.39549"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Estimated mortality from all forms of tuberculosis per 100,000 population": "43.037678"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Estimated mortality from all forms of tuberculosis per 100,000 population": "41.167526"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Estimated mortality from all forms of tuberculosis per 100,000 population": "38.87435"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Estimated mortality from all forms of tuberculosis per 100,000 population": "38.271965"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Estimated mortality from all forms of tuberculosis per 100,000 population": "37.03684"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Estimated mortality from all forms of tuberculosis per 100,000 population": "35.28248"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Estimated mortality from all forms of tuberculosis per 100,000 population": "34.050922"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Estimated mortality from all forms of tuberculosis per 100,000 population": "32.600464"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Estimated mortality from all forms of tuberculosis per 100,000 population": "30.023005"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Estimated mortality from all forms of tuberculosis per 100,000 population": "29.03035"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Estimated mortality from all forms of tuberculosis per 100,000 population": "27.315535"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Estimated mortality from all forms of tuberculosis per 100,000 population": "25.788538"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Estimated mortality from all forms of tuberculosis per 100,000 population": "24.49603"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Estimated mortality from all forms of tuberculosis per 100,000 population": "23.180616"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Estimated mortality from all forms of tuberculosis per 100,000 population": "21.818338"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Estimated mortality from all forms of tuberculosis per 100,000 population": "20.48444"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Estimated mortality from all forms of tuberculosis per 100,000 population": "19.122957"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Estimated mortality from all forms of tuberculosis per 100,000 population": "17.886703"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Estimated mortality from all forms of tuberculosis per 100,000 population": "17.6152"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Estimated mortality from all forms of tuberculosis per 100,000 population": "17.630827"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Estimated mortality from all forms of tuberculosis per 100,000 population": "16.545315"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Estimated mortality from all forms of tuberculosis per 100,000 population": "15.33833"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Estimated mortality from all forms of tuberculosis per 100,000 population": "14.687626"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Estimated mortality from all forms of tuberculosis per 100,000 population": "19"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Estimated mortality from all forms of tuberculosis per 100,000 population": "18"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Estimated mortality from all forms of tuberculosis per 100,000 population": "19"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Estimated mortality from all forms of tuberculosis per 100,000 population": "19"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Estimated mortality from all forms of tuberculosis per 100,000 population": "17"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Estimated mortality from all forms of tuberculosis per 100,000 population": "16"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Estimated mortality from all forms of tuberculosis per 100,000 population": "15"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Estimated mortality from all forms of tuberculosis per 100,000 population": "13"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Estimated mortality from all forms of tuberculosis per 100,000 population": "11"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Estimated mortality from all forms of tuberculosis per 100,000 population": "8.7"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Estimated mortality from all forms of tuberculosis per 100,000 population": "6.9"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Estimated mortality from all forms of tuberculosis per 100,000 population": "6.8"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Estimated mortality from all forms of tuberculosis per 100,000 population": "4.7"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Estimated mortality from all forms of tuberculosis per 100,000 population": "4.2"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Estimated mortality from all forms of tuberculosis per 100,000 population": "5.7"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Estimated mortality from all forms of tuberculosis per 100,000 population": "9"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Estimated mortality from all forms of tuberculosis per 100,000 population": "6.8"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Estimated mortality from all forms of tuberculosis per 100,000 population": "6.7"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Estimated mortality from all forms of tuberculosis per 100,000 population": "6.9"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Estimated mortality from all forms of tuberculosis per 100,000 population": "6.6"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Estimated mortality from all forms of tuberculosis per 100,000 population": "11"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Estimated mortality from all forms of tuberculosis per 100,000 population": "10"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2022", "Estimated mortality from all forms of tuberculosis per 100,000 population": "9.8"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2023", "Estimated mortality from all forms of tuberculosis per 100,000 population": "9.4"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2024", "Estimated mortality from all forms of tuberculosis per 100,000 population": "5.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Estimated mortality from all forms of tuberculosis per 100,000 population": "236"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Estimated mortality from all forms of tuberculosis per 100,000 population": "239"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Estimated mortality from all forms of tuberculosis per 100,000 population": "173"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Estimated mortality from all forms of tuberculosis per 100,000 population": "138"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Estimated mortality from all forms of tuberculosis per 100,000 population": "143"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Estimated mortality from all forms of tuberculosis per 100,000 population": "156"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Estimated mortality from all forms of tuberculosis per 100,000 population": "156"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Estimated mortality from all forms of tuberculosis per 100,000 population": "156"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Estimated mortality from all forms of tuberculosis per 100,000 population": "156"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Estimated mortality from all forms of tuberculosis per 100,000 population": "143"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Estimated mortality from all forms of tuberculosis per 100,000 population": "138"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Estimated mortality from all forms of tuberculosis per 100,000 population": "133"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Estimated mortality from all forms of tuberculosis per 100,000 population": "135"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Estimated mortality from all forms of tuberculosis per 100,000 population": "128"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Estimated mortality from all forms of tuberculosis per 100,000 population": "123"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Estimated mortality from all forms of tuberculosis per 100,000 population": "120"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Estimated mortality from all forms of tuberculosis per 100,000 population": "108"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Estimated mortality from all forms of tuberculosis per 100,000 population": "110"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Estimated mortality from all forms of tuberculosis per 100,000 population": "108"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Estimated mortality from all forms of tuberculosis per 100,000 population": "91"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Estimated mortality from all forms of tuberculosis per 100,000 population": "73"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Estimated mortality from all forms of tuberculosis per 100,000 population": "41"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Estimated mortality from all forms of tuberculosis per 100,000 population": "27"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Estimated mortality from all forms of tuberculosis per 100,000 population": "24"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Estimated mortality from all forms of tuberculosis per 100,000 population": "39"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Estimated mortality from all forms of tuberculosis per 100,000 population": "159"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": 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{"Entity": "Uzbekistan", "Code": "UZB", "Year": "2004", "Digestive diseases": "6.421047", "Infectious and parasitic diseases": "3.2691736", "Respiratory diseases": "2.486918", "Diabetes, blood and endocrine disorders": "2.5988865", "Intentional injuries": "1.479202", "Perinatal conditions": "1.9910578", "Respiratory infections": "6.3524947", "Dementias and neuropsychiatric disorders": "1.6642927", "Cancers": "7.21549", "Cardiovascular disease": "54.67411", "Ill-defined injuries": "0.9505892", "Musculoskeletal diseases": "0.25364277", "Congenital anomalies": "0.5468935", "Maternal conditions": "0.11882365", "Nutritional deficiencies": "0.1294873", "Non-malignant cancers": "0.07083717", "Skin diseases": "0.02361239", "Unintentional injuries": "4.981453", "Ill-defined diseases": "2.9294598", "Genitourinary diseases": "1.8120606"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2005", "Digestive diseases": "6.2467546", "Infectious and parasitic diseases": "3.084255", "Respiratory 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"Cardiovascular disease": "29.608736", "Ill-defined injuries": "5.4582257", "Musculoskeletal diseases": "0.4016978", "Congenital anomalies": "1.7331064", "Maternal conditions": "0.26047593", "Nutritional deficiencies": "0.5342895", "Non-malignant cancers": "1.2435372", "Skin diseases": "0.01176849", "Unintentional injuries": "8.282663", "Ill-defined diseases": "0.5743023", "Genitourinary diseases": "1.514997"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2008", "Digestive diseases": "4.1032243", "Infectious and parasitic diseases": "4.4788294", "Respiratory diseases": "3.3445315", "Diabetes, blood and endocrine disorders": "6.718992", "Intentional injuries": "8.619464", "Perinatal conditions": "3.456016", "Respiratory infections": "3.0819073", "Dementias and neuropsychiatric disorders": "1.3153661", "Cancers": "14.812459", "Cardiovascular disease": "29.986307", "Ill-defined injuries": "5.6026516", "Musculoskeletal diseases": "0.413016", "Congenital anomalies": "1.6146531", "Maternal conditions": "0.28207794", "Nutritional deficiencies": "0.48933417", "Non-malignant cancers": "1.2824446", "Skin diseases": "0.01346791", "Unintentional injuries": "8.199714", "Ill-defined diseases": "0.4945717", "Genitourinary diseases": "1.673014"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2009", "Digestive diseases": "4.073241", "Infectious and parasitic diseases": "4.351569", "Respiratory diseases": "3.2605226", "Diabetes, blood and endocrine disorders": "7.022036", "Intentional injuries": "7.8199105", "Perinatal conditions": "3.6152985", "Respiratory infections": "3.208568", "Dementias and neuropsychiatric disorders": "1.3344911", "Cancers": "15.057929", "Cardiovascular disease": "29.767021", "Ill-defined injuries": "6.3829947", "Musculoskeletal diseases": "0.3510647", "Congenital anomalies": "1.5920376", "Maternal conditions": "0.32211855", "Nutritional deficiencies": "0.4200901", "Non-malignant cancers": "1.3433977", "Skin diseases": "0.01039092", "Unintentional injuries": "7.597248", "Ill-defined diseases": "0.5388435", "Genitourinary diseases": "1.9104451"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2010", "Digestive diseases": "3.9543984", "Infectious and parasitic diseases": "4.4504128", "Respiratory diseases": "3.3609138", "Diabetes, blood and endocrine disorders": "7.399064", "Intentional injuries": "7.228672", "Perinatal conditions": "3.5905101", "Respiratory infections": "3.419396", "Dementias and neuropsychiatric disorders": "1.3718015", "Cancers": "15.295588", "Cardiovascular disease": "30.634495", "Ill-defined injuries": "5.8770866", "Musculoskeletal diseases": "0.39854446", "Congenital anomalies": "1.727026", "Maternal conditions": "0.29746434", "Nutritional deficiencies": "0.4599145", "Non-malignant cancers": "1.4302835", "Skin diseases": "0.00722001", "Unintentional injuries": "6.6850057", "Ill-defined diseases": "0.48879454", "Genitourinary diseases": "1.9104141"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2011", "Digestive diseases": "4.0434904", "Infectious and parasitic diseases": "4.453223", "Respiratory diseases": "3.590407", "Diabetes, blood and endocrine disorders": "7.3863797", "Intentional injuries": "6.8270173", "Perinatal conditions": "3.5407636", "Respiratory infections": "3.4876242", "Dementias and neuropsychiatric disorders": "1.3949099", "Cancers": "15.611103", "Cardiovascular disease": "31.099148", "Ill-defined injuries": "5.88589", "Musculoskeletal diseases": "0.3628863", "Congenital anomalies": "1.6088659", "Maternal conditions": "0.30485246", "Nutritional deficiencies": "0.4356034", "Non-malignant cancers": "1.42987", "Skin diseases": "0.00699203", "Unintentional injuries": "6.3529577", "Ill-defined diseases": "0.4565795", "Genitourinary diseases": "1.7025591"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2012", "Digestive diseases": "3.9483547", "Infectious and parasitic diseases": "4.238049", "Respiratory diseases": "3.532383", "Diabetes, blood and endocrine disorders": "7.6110663", "Intentional injuries": "7.29166", "Perinatal conditions": "3.352759", "Respiratory infections": "3.4290655", "Dementias and neuropsychiatric disorders": "1.3910742", "Cancers": "15.407835", "Cardiovascular disease": "30.27477", "Ill-defined injuries": "6.3827343", "Musculoskeletal diseases": "0.33831465", "Congenital anomalies": "1.669289", "Maternal conditions": "0.28091595", "Nutritional deficiencies": "0.3484438", "Non-malignant cancers": "1.5477388", "Skin diseases": "0.00810334", "Unintentional injuries": "6.769669", "Ill-defined diseases": "0.4828243", "Genitourinary diseases": "1.6719902"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2013", "Digestive diseases": "4.1354184", "Infectious and parasitic diseases": "4.172783", "Respiratory diseases": "3.6183245", "Diabetes, blood and endocrine disorders": "8.179428", "Intentional injuries": "6.4159703", "Perinatal conditions": "3.252689", "Respiratory infections": "3.855187", "Dementias and neuropsychiatric 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"1.5565382", "Skin diseases": "0.01129218", "Unintentional injuries": "4.862176", "Ill-defined diseases": "1.8513236", "Genitourinary diseases": "1.7568258"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2016", "Digestive diseases": "4.090322", "Infectious and parasitic diseases": "5.20176", "Respiratory diseases": "3.5894663", "Diabetes, blood and endocrine disorders": "7.471886", "Intentional injuries": "7.9743166", "Perinatal conditions": "3.562166", "Respiratory infections": "4.430001", "Dementias and neuropsychiatric disorders": "2.0123482", "Cancers": "13.564056", "Cardiovascular disease": "29.680166", "Ill-defined injuries": "5.561389", "Musculoskeletal diseases": "0.35227904", "Congenital anomalies": "1.561368", "Maternal conditions": "0.51765597", "Nutritional deficiencies": "0.78488404", "Non-malignant cancers": "1.2369143", "Skin diseases": "0.0084001", "Unintentional injuries": "4.3775005", "Ill-defined diseases": "2.1719499", "Genitourinary diseases": "1.8411962"}], 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WHO Mortality Database, “WHO Mortality Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1086884.metadata.json"}, "Share of total deaths in both sexes in those aged all ages that are from maternal conditions": {"titleShort": "Maternal conditions", "titleLong": "Maternal conditions", "descriptionShort": "Share of total reported deaths in both sexes in those aged all ages that are from maternal conditions.", "descriptionKey": ["The International Classification of Diseases (Version 10) codes that define maternal conditions are O00-O99."], "shortUnit": "%", "unit": "%", "timespan": "1950-2023", "type": "Numeric", "owidVariableId": 1086892, "shortName": "percentage_of_cause_specific_deaths_out_of_total_deaths__sex_both_sexes__age_group_all_ages__cause_maternal_conditions__icd10_codes_o00_o99", "lastUpdated": "2025-04-17", "nextUpdate": "2026-07-22", "citationShort": "WHO Mortality Database (2025) – with minor processing by Our World in Data", "citationLong": "WHO Mortality Database (2025) – with minor processing by Our World in Data. “Maternal conditions – WHO” [dataset]. 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WHO Mortality Database, “WHO Mortality Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1086895.metadata.json"}, "Share of total deaths in both sexes in those aged all ages that are from other neoplasms": {"titleShort": "Non-malignant cancers", "titleLong": "Non-malignant cancers", "descriptionShort": "Share of total reported deaths in both sexes in those aged all ages that are from other neoplasms.", "descriptionKey": ["The International Classification of Diseases (Version 10) codes that define other neoplasms are D00-D48."], "shortUnit": "%", "unit": "%", "timespan": "1950-2023", "type": "Numeric", "owidVariableId": 1086897, "shortName": "percentage_of_cause_specific_deaths_out_of_total_deaths__sex_both_sexes__age_group_all_ages__cause_other_neoplasms__icd10_codes_d00_d48", "lastUpdated": "2025-04-17", "nextUpdate": "2026-07-22", "citationShort": "WHO Mortality Database (2025) – with minor processing by Our World in Data", "citationLong": "WHO Mortality Database (2025) – with minor processing by Our World in Data. “Non-malignant cancers – WHO” [dataset]. WHO Mortality Database, “WHO Mortality Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1086897.metadata.json"}, "Share of total deaths in both sexes in those aged all ages that are from skin diseases": {"titleShort": "Skin diseases", "titleLong": "Skin diseases", "descriptionShort": "Share of total reported deaths in both sexes in those aged all ages that are from skin diseases.", "descriptionKey": ["The International Classification of Diseases (Version 10) codes that define skin diseases are L00-L98."], "shortUnit": "%", "unit": "%", "timespan": "1950-2023", "type": "Numeric", "owidVariableId": 1086902, "shortName": "percentage_of_cause_specific_deaths_out_of_total_deaths__sex_both_sexes__age_group_all_ages__cause_skin_diseases__icd10_codes_l00_l98", "lastUpdated": "2025-04-17", "nextUpdate": "2026-07-22", "citationShort": "WHO Mortality Database (2025) – with minor processing by Our World in Data", "citationLong": "WHO Mortality Database (2025) – with minor processing by Our World in Data. “Skin diseases – WHO” [dataset]. WHO Mortality Database, “WHO Mortality Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1086902.metadata.json"}, "Share of total deaths in both sexes in those aged all ages that are from unintentional injuries": {"titleShort": "Unintentional injuries", "titleLong": "Unintentional injuries", "descriptionShort": "Share of total reported deaths in both sexes in those aged all ages that are from unintentional injuries.", "descriptionKey": ["The International Classification of Diseases (Version 10) codes that define unintentional injuries are V01-X59, Y40-Y86, Y88, Y89 (minus X41-X42, X44-X45), U12.9."], "shortUnit": "%", "unit": "%", "timespan": "1950-2023", "type": "Numeric", "owidVariableId": 1086903, "shortName": "percentage_of_cause_specific_deaths_out_of_total_deaths__sex_both_sexes__age_group_all_ages__cause_unintentional_injuries__icd10_codes_v01_x59__y40_y86__y88__y89__minus_x41_x42__x44_x45__u12_9", "lastUpdated": "2025-04-17", "nextUpdate": "2026-07-22", "citationShort": "WHO Mortality Database (2025) – with minor processing by Our World in Data", "citationLong": "WHO Mortality Database (2025) – with minor processing by Our World in Data. “Unintentional injuries – WHO” [dataset]. WHO Mortality Database, “WHO Mortality Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1086903.metadata.json"}, "Share of total deaths in both sexes in those aged all ages that are from ill-defined diseases": {"titleShort": "Ill-defined diseases", "titleLong": "Ill-defined diseases", "descriptionShort": "Share of total reported deaths in both sexes in those aged all ages that are from ill-defined diseases.", "descriptionKey": ["The International Classification of Diseases (Version 10) codes that define ill-defined diseases are R00-R94, R96-R99."], "shortUnit": "%", "unit": "%", "timespan": "1950-2023", "type": "Numeric", "owidVariableId": 1086887, "shortName": "percentage_of_cause_specific_deaths_out_of_total_deaths__sex_both_sexes__age_group_all_ages__cause_ill_defined_diseases__icd10_codes_r00_r94__r96_r99", "lastUpdated": "2025-04-17", "nextUpdate": "2026-07-22", "citationShort": "WHO Mortality Database (2025) – with minor processing by Our World in Data", "citationLong": "WHO Mortality Database (2025) – with minor processing by Our World in Data. “Ill-defined diseases – WHO” [dataset]. 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{"raw_link": "https://ourworldindata.org/international-dollars", "title": "What are international dollars?", "context": "Home\nEconomic Growth\nWhat are international dollars?\nInternational dollars are used to compare incomes and purchasing power across countries and over time. Here, we explain how they’re calculated and why they’re used.\nBy\nBertha Rohenkohl\n,\nJoe Hasell\n,\nPablo Arriagada\n,\nand\nEsteban Ortiz-Ospina\nMay 26, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nMuch of the economic data we use to understand the world, such as the incomes people receive or the goods and services firms produce and people buy, is recorded in the local currencies of each country. For example, in 2023,\nGross Domestic Product (GDP)\nper capita — a common measure of the average income of people in a year — was around 205,000 rupees in India, 89,000 yuan in China, and 83,000 dollars in the United States.\n1\nThese numbers in local currencies can provide useful context within each country. But on their own, they tell us nothing about how these figures\ncompare\n. How rich or poor is someone with 205,000 rupees in India compared to 89,000 yuan in China or 83,000 dollars in the United States?\nInternational dollars are a hypothetical currency that helps us answer such questions by equating different currencies with\nwhat they can buy\n. They adjust for the fact that the cost of living is much higher in some countries than in others, allowing us to compare data denominated in different currencies in terms of their local “purchasing power”.\nIn this article, we explain what international dollars are, how they are calculated, and the crucial perspective they offer for understanding living standards worldwide.\nInternational dollars: a hypothetical currency for comparing economic data across time and place\nOne obvious approach for comparing figures in rupees, yuan, and US dollars would be to convert them into a common currency using\ncurrency exchange rates — the kind you would see when changing money at a bank or airport kiosk. These are known as “market exchange rates”.\nThis can be a good approach in some cases, but it doesn’t give us the comparison we often look for: what this money\ncan actually buy\nin different countries. This is because the cost of living varies a lot across countries. Such price differences are very clear when you travel abroad: A cup of coffee that costs $5 in the United States may cost $1 (about 85 rupees in market exchange rate terms) in India. And it's not just coffee — your meals, the hotel you're staying in, the taxi you take to get there, the clothes you buy in the shop — are often cheaper too. Your money has more purchasing power in India than in the US because goods and services generally cost less.\nConverting currencies using market exchange rates is not enough to compare what different amounts of money can buy in various places; we also need to adjust for differences in the cost of living. (The appendix below explains more about why market exchange rates fail to capture such price differences.)\nA similar issue arises when we compare monetary values\nover time\n. The same amount of money may buy more in one year than another, depending on inflation. Knowing that the GDP per capita in India was 6,600 rupees in 1990 and 205,000 rupees in 2023 tells us little about how incomes changed, because prices also changed during that time.\n2\nTo compare these values across time meaningfully, we need to account for the changes in price levels by adjusting for inflation.\nInternational dollars do both: they adjust for differences in prices across countries and inflation over time.\nAn\ninternational dollar\n(sometimes written as international-$ or int-$) is a hypothetical currency that aims to have the same purchasing power everywhere. It is adjusted for differences in living costs across countries\nand\nprice changes over time.\n3\nOne international dollar is intended to buy the same quantity and quality of goods and services, no matter\nwhere\nor\nwhen\nit is “spent.” This makes it easier to compare what money can buy across different countries and over time, regardless of differences in local currencies or price levels.\nMany of the economic indicators on Our World in Data — including measures of\npoverty\nand\neconomic growth\n— are expressed in international dollars to facilitate comparisons.\nHow are international dollars calculated?\nInternational dollars are calculated by making these two adjustments to local currency data: (i) adjusting for differences in the cost of living across countries and (ii) adjusting for inflation — changes in prices over time.\nAdjusting for differences in the cost of living across countries\nTo account for the differences in the cost of living, economic indicators are adjusted using\npurchasing power parity (PPP)\nrates. PPP rates tell us how much of a country’s local currency is needed to buy the same basket of goods and services (of comparable quality) in another country.\nThis requires very detailed price data. The\nInternational Comparison Program (ICP)\n, led by the World Bank and guided by the United Nations Statistical Commission, calculates the most widely used PPP rates.\n4\nThe ICP collects local price data for thousands of goods and services — from food and clothing to education and transport — in over 170 countries.\n5\nThis work is complex and expensive, so the ICP does not collect data yearly but works with a system of “rounds.” The most recent round was in 2021, with previous ones in 2017 and 2011.\nOnce local price data is collected, the ICP calculates the cost of each country's “standardized” basket of goods and services in its local currency. This basket represents what people typically consume, considering cultural differences while comparing similar items. As you can imagine, designing this basket is a complex task because goods and services popular in one country may only be consumed rarely, if at all, in another. We discuss these challenges further in the appendix.\nPPP rates are then determined by comparing the cost of these baskets across countries. This is done by dividing the basket's price in each country (in local currency) by what was found for a chosen benchmark country. In theory, any country can be used as the benchmark. In practice, the United States is almost always used.\nUsing the US dollar as the benchmark currency means that PPP rates tell us how much local currency is needed to match the purchasing power of one US dollar spent in the United States.\n6\nDividing local currency amounts by these PPP rates converts them into a common unit — international dollars — which allows for direct comparisons of purchasing power across countries.\nWherever it is “spent,” one international dollar buys the same goods and services as one US dollar would in the United States.\nAdjusting for changes in prices over time\nAs mentioned earlier, the purchasing power of money depends not only on\nwhere\nit is spent but also\nwhen\n.\nPrices change over time. The products you see at the supermarket today don’t have the same price tag as they did some years ago. Many readers will be used to seeing prices generally increasing over time — inflation — although periods of falling prices (deflation) also occur. Here, we’ll use “inflation” to describe price changes in both directions.\nEconomic data is initially recorded in “current” prices, meaning the actual prices observed at the time of measurement. But if we want to compare figures across different years, we must adjust for inflation.\nOverall, the process is similar to how adjustments for differences in the cost of living between countries are made using PPPs. The goal is to make values comparable in terms of what people can actually buy, by benchmarking them to the changing price of a representative basket of goods and services.\nDifferent baskets of goods are used depending on which part of the economy is being measured. The\nConsumer Price Index\n(CPI) — a very common measure of inflation — tracks the prices consumers pay for consumer goods and services.\n7\nEach country has a basket tailored to the products most relevant to its economy.\n8\nThe idea is that by comparing the cost of this basket at different times, we can measure how much prices have risen or fallen.\nOnce we know how much prices have changed, we can “deflate” figures, turning current price figures into “constant” prices. Constant prices express values in terms of the purchasing power in a chosen base year. This allows us to compare figures over time in terms of what they can buy.\nYou can read more here:\nHow are incomes adjusted for inflation?\nAdjusting incomes for inflation is crucial if we want to learn how standards of living are changing. How is this adjustment done?\nBy combining these two adjustments — for differences in living costs across countries and inflation over time — we can express data in\ninternational dollars at constant prices\n.\nThis allows us to compare economic values in a way that better reflects their real purchasing power across both time and place.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nWhy international dollars matter: how they change our understanding of global inequality\nOne of the main reasons we adjust economic data into international dollars is to make incomes comparable across countries. This matters especially for our understanding of global inequality.\nThe chart below compares GDP per capita for the United States and four selected countries in 2023 using two measures: international dollars (adjusted for differences in local prices using PPP rates) and market dollars (local currencies converted to US dollars using market exchange rates).\nBoth series are adjusted for inflation, with 2021 as the base year.\nSince the US is the benchmark country for calculating PPP rates, its GDP per capita is the same in both measures. For the other countries shown, GDP per capita is considerably higher when measured in international dollars.\nDownload\nThis illustrates why international dollars are so important: they help us see what people can buy at different income levels.\nUsing market dollars to compare incomes across countries understates people's real income in countries where local prices are often much lower than in the US. This can make these countries appear poorer than they actually are, based on what people can afford locally.\nBecause prices are generally lower in poorer countries, focusing on market dollars exaggerates income disparities between poorer and richer countries. For some countries, the difference is particularly large.\nTake India, for example. Its GDP per capita is almost 30 times lower than that of the US in market dollars. But when we adjust for differences in living costs, the gap shrinks to about eight times lower. For Burkina Faso, the difference is even starker. Its GDP per capita is 83 times lower than that of the US in market dollars, and 30 times lower after adjusting for differences in living costs.\nWhile measuring GDP per capita in international dollars reduces the income gaps we see, it does not eliminate them. Global inequality remains large, even after accounting for what people can buy locally.\nMuch of what we know about global poverty, inequality, and progress depends on our ability to compare economic data across countries and over time. International dollars make those comparisons possible. They are a fundamental tool for measuring how everyday reality differs for people around the world, and how this is changing.\nAppendix\nThe “rounds” of the International Comparison Program\nThe International Comparison Program (ICP) doesn’t collect global price data yearly. Instead, it conducts the work in “rounds.” The most recent round was in 2021, with previous ones in 2017 and 2011.\nWhen converting economic data into international dollars, the choice of ICP round to adjust for differences in living costs is, in principle, separate from selecting the base year for inflation adjustments. But in practice, the same year is usually chosen for both.\nThe 2021 and 2017 rounds share the same core methods, but there are differences in the number of participating countries, how the standard basket of goods is defined, and the addition of new data sources. These updates can lead to more accurate purchasing power parity (PPP) estimates, but they also mean that PPPs from different rounds are not always directly comparable.\nPart of the challenge of comparing PPPs across rounds is that two factors are operating at the same time. First, changes in PPPs can come from improvements in data collection and methods used by the ICP, such as new price surveys and better data quality. Second, the change in PPPs reflects actual changes in relative price levels between countries. Inflation happens at different rates in different countries. In part, such changes are accounted for by the inflation adjustment made when converting to international-$, as discussed above. However, the way PPP rates are calculated means that the change in PPPs doesn’t exactly track the various countries' inflation rates.\nFor more details on methodology changes across ICP rounds, visit the frequently asked questions on\nthe World Bank ICP website\n.\nChallenges and limitations of PPPs\nPPP adjustments are very useful for comparing purchasing power globally, but they have important limitations. Calculating PPPs is a huge statistical undertaking; the challenges are well-documented in the academic literature.\n9\nA central challenge is that PPPs rely on gathering accurate price data for a wide range of goods and services across many locations. However, many countries, particularly low-income ones, lack the resources and infrastructure to collect high-quality price data. To fill gaps, the International Comparison Program estimates missing values by extrapolating from regional averages or by relying on data from large urban areas. However, this can introduce bias, as prices in cities tend to be higher than in rural areas.\nEven when price data is available, identifying a single “representative” price for each item in every country is difficult. Prices can vary widely within the same country, depending on availability and seasonal fluctuations.\nA related issue is that it is difficult to define a comparable basket of goods and services, as consumption patterns vary across countries. Agreeing on broad categories like “food” might be straightforward, but specifying the exact items to include is much harder. Consumption habits, product availability, and quality standards vary widely. In practice, the items that should be included in the basket of goods produced and consumed in Sweden would look very different from those in Saudi Arabia.\nBeyond these technical challenges, there is a more fundamental issue. PPPs are calculated at the country and regional levels, but the goods and services consumed are not necessarily the same in each place. Comparing prices globally means including a broad range of items, even for things rarely consumed in some countries. Economists\nAngus Deaton and Alan Heston\ndescribe an extreme case: a rural worker in Ethiopia might consume\nteff\n, while a worker in Thailand eats rice. But it’s hard to find rice in Ethiopia and teff in Thailand, making direct price comparisons impossible. Similarly, many items consumed in the US or other rich countries might not exist or be hard to find in poorer countries. Although PPPs are designed to reflect local living costs, they are still influenced by global price structures, sometimes in ways that don’t fully capture real differences between economies.\nThe ICP continues to refine its methods with each new round, but many of these challenges remain. Because of these limitations, it is often useful to complement PPP-adjusted data with other indicators that are not reliant on these. For example,\nmultidimensional poverty estimates\noffer valuable insights alongside monetary poverty estimates, helping capture different aspects of deprivation.\nWhy not just use exchange rates?\nAt first glance, converting local currencies into a common currency (say, US dollars) using exchange rates might seem like a straightforward way to compare incomes and purchasing power across countries. After all, exchange rates tell us how much of one currency can be traded for another.\nThere are good reasons to consider this approach in some cases. Exchange rate data is widely available, and conversions are easy to implement. In fact, exchange rates are the appropriate choice for many types of international comparisons, such as when comparing investment flows,\nforeign aid\n,\nremittances\n, or international trade data like\nthe value of exports and imports\n, where global market values and financial transactions are central.\nBut exchange rates are not the right tool when the goal is to compare what money can buy in countries with very different economies and income levels.\nIn general, richer countries tend to have higher prices, and poorer countries often have lower prices — a phenomenon known as the\nPenn or Balassa-Samuelson effect\n.\n10\nOne main reason is the cost of the so-called “nontradable” goods and services — things that must be consumed where they are produced and can’t be traded in international markets. These include, for example, housing, construction services, a hotel stay, and public health care, which are typically more expensive in wealthier countries.\nSince exchange rates do not account for differences in local price levels, they fail to capture these variations in purchasing power. In addition, exchange rates can fluctuate for many other reasons unrelated to living standards, such as trade policies, speculation, or capital flows. This can create a disconnect between currency conversion rates and what people can buy in different countries.\nIn contrast, international dollars are specifically designed to reflect the differences in local living costs, making them a better tool for global comparisons.\nAcknowledgments\nThis article is based on a previous article by Esteban Ortiz-Ospina and Marco Molteni. We thank Bastian Herre, Edouard Mathieu, and Hannah Ritchie for providing feedback.\nContinue reading on Our World in Data\nWhat is economic growth? And why is it so important?\nThe goods and services that we all need are not just there; they need to be produced. Growth means that their quality and quantity increase.\nMeasuring inequality: what is the Gini coefficient?\nThe Gini coefficient is the most common way of measuring inequality. But what does it actually measure? And how does it differ from other measures of inequality?\nBeyond income: understanding poverty through the Multidimensional Poverty Index\nThe experience of poverty goes beyond a very low income. What is the Multidimensional Poverty Index, and how does it capture the diverse ways people experience deprivation?\nEndnotes\nGDP per capita is a country's total economic output divided by its population. This is different, though\nclosely related\n, to individuals' personal income, for example, what a person receives in their paycheck.\nThese figures come from the\nWorld Bank\nand represent GDP per capita in current local currency units (LCU).\nIndia’s GDP per capita\nin current local currency units (LCU) from the World Bank.\nIt is also possible to refer to “current” international dollars, which reflect the prices of a specific year without adjusting for inflation. But most comparisons use “constant” (inflation-adjusted) international dollars to capture changes in purchasing power over time.\nThe ICP is the primary source of global price data for PPP calculations. However, other teams, such as those working on the World Bank’s World Development Indicators and the\nPenn World Table\n, also use ICP price data to create their own\nPPP series\n, sometimes with modifications. Eurostat and the OECD also\ncalculate PPPs\nfor selected countries.\nTo ensure accurate comparisons, the ICP identifies comparable products and services across countries, accounting for differences in quality and availability. They collaborate with national statistical offices and regional organizations, which help collect and validate the data.\nIt is also possible to use other currencies as benchmarks. For example, the\nWorld Inequality Database\ncalculates PPP rates for conversions into PPP-adjusted dollars, euros, or yuan. Eurostat uses the euro as the benchmark to create a similar hypothetical currency unit, the\nPurchasing Power Standard (PPS)\n.\nAnother commonly used price index is the GDP deflator, which tracks changes in prices of all goods and services\nproduced\nin a country. A similar distinction can also be made for purchasing power adjustments across countries, depending on whether you compare consumer prices or prices for the entire economy.\nThis basket can be updated over time to reflect changes in consumption patterns or the economy. Statisticians track the prices of all the products in the basket and calculate a weighted average, giving more importance to items on which people spend a large share of their income. This average price level is then expressed relative to the price level in a chosen base year.\nFor example:\nDeaton, A., & Heston, A. (2010). Understanding PPPs and PPP-based national accounts.\nAmerican Economic Journal: Macroeconomics\n, 2(4), 1-35.\nhttps://doi.org/10.1257/mac.2.4.1\nDeaton, A., & Schreyer, P. (2022). GDP, wellbeing, and health: thoughts on the 2017 round of the International Comparison Program.\nReview of Income and Wealth\n, 68(1), 1-15.\nhttps://doi.org/10.1111/roiw.12520\n.\nYou can see how general price levels compare across countries in this chart of\nGDP price levels relative to the US\n. A value below one means that the same US dollar has a higher purchasing power in that country than in the US for the same year.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nBertha Rohenkohl, Joe Hasell, Pablo Arriagada, and Esteban Ortiz-Ospina (2025) - “What are international dollars?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20251125-173858/international-dollars.html' [Online Resource] (archived on November 25, 2025).\nBibTeX citation\n@article{owid-international-dollars,\nauthor = {Bertha Rohenkohl and Joe Hasell and Pablo Arriagada and Esteban Ortiz-Ospina},\ntitle = {What are international dollars?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20251125-173858/international-dollars.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "international-dollars", "source_url": "https://ourworldindata.org/international-dollars", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "International dollars are used to compare incomes and purchasing power across countries and over time. Here, we explain how they’re calculated and why they’re used.", "numeric_mentions": ["26,", "2025", "2023,", "205,000", "89,000", "83,000", "1", "5", "85", "6,600", "1990", "2023", "2", "3", "4", "170", "2021,", "2017", "2011", "6", "7", "8", "2021", "30", "83", "9", "10", "2010", "35", "10.1257", "2.4", "2022", "68", "15", "10.1111", "12520", "20251125", "173858", "25,"], "numeric_evidence": [{"title": "Share of population living in extreme poverty", "source_url": "https://ourworldindata.org/grapher/share-of-population-in-extreme-poverty.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Share of population in poverty ($3 a day)", "Population", "World region according to OWID"], "row_count_total": 59371, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "-10000", "Share of population in poverty ($3 a day)": "", "Population": "14737", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "-9000", "Share of population in 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"World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "900", "Share of population in poverty ($3 a day)": "", "Population": "2300000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1000", "Share of population in poverty ($3 a day)": "", "Population": "2250000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1100", "Share of population in poverty ($3 a day)": "", "Population": "2375000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1200", "Share of population in poverty ($3 a day)": "", "Population": "2500000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1300", "Share of population in poverty ($3 a day)": "", "Population": "1750000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1400", "Share of population in poverty 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"Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1790", "Share of population in poverty ($3 a day)": "", "Population": "3066156", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1800", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1801", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1802", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1803", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1804", "Share of population in poverty ($3 a day)": "", "Population": 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"Afghanistan", "Code": "AFG", "Year": "1816", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1817", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1818", "Share of population in poverty ($3 a day)": "", "Population": "3280000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1819", "Share of population in poverty ($3 a day)": "", "Population": "3282935", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1820", "Share of population in poverty ($3 a day)": "", "Population": "3288817", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1821", "Share of population in poverty ($3 a day)": "", "Population": "3297661", "World 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day)": "", "Population": "3384173", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1828", "Share of population in poverty ($3 a day)": "", "Population": "3399313", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1829", "Share of population in poverty ($3 a day)": "", "Population": "3414520", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1830", "Share of population in poverty ($3 a day)": "", "Population": "3429796", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1831", "Share of population in poverty ($3 a day)": "", "Population": "3445140", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1832", "Share of population in poverty ($3 a day)": "", "Population": "3460553", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1833", "Share of population in poverty ($3 a day)": "", "Population": "3476034", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1834", "Share of population in poverty ($3 a day)": "", "Population": "3491585", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1835", "Share of population in poverty ($3 a day)": "", "Population": "3507205", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1836", "Share of population in poverty ($3 a day)": "", "Population": "3522896", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1837", "Share of population in poverty ($3 a day)": "", "Population": "3538656", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1838", "Share of population in poverty ($3 a day)": "", "Population": "3554487", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1839", "Share of population in poverty ($3 a day)": "", "Population": "3570389", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1840", "Share of population in poverty ($3 a day)": "", "Population": "3586362", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1841", "Share of population in poverty ($3 a day)": "", "Population": "3602406", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1842", "Share of population in poverty ($3 a day)": "", "Population": "3618522", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1843", "Share of population in poverty ($3 a day)": "", "Population": "3634711", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1844", "Share of population in poverty ($3 a day)": "", "Population": "3650971", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1845", "Share of population in poverty ($3 a day)": "", "Population": "3667305", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1846", "Share of population in poverty ($3 a day)": "", "Population": "3683711", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1847", "Share of population in poverty ($3 a day)": "", "Population": "3700191", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1848", "Share of population in poverty ($3 a day)": "", "Population": "3716745", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1849", "Share of population in poverty ($3 a day)": "", "Population": "3734342", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1850", "Share of population in poverty ($3 a day)": "", "Population": "3752993", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1851", "Share of population in poverty ($3 a day)": "", "Population": "3767956", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1852", "Share of population in poverty ($3 a day)": "", "Population": "3783940", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1853", "Share of population in poverty ($3 a day)": "", "Population": "3800954", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1854", "Share of population in poverty ($3 a day)": "", "Population": "3818038", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1855", "Share of population in poverty ($3 a day)": "", "Population": "3835192", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1856", "Share of population in poverty ($3 a day)": "", "Population": "3852417", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1857", "Share of population in poverty ($3 a day)": "", "Population": "3869714", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1858", "Share of population in poverty ($3 a day)": "", "Population": "3887081", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1859", "Share of population in poverty ($3 a day)": "", "Population": "3904521", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1860", "Share of population in poverty ($3 a day)": "", "Population": "3922032", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1861", "Share of population in poverty ($3 a day)": "", "Population": "3939616", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1862", "Share of population in poverty ($3 a day)": "", "Population": "3957271", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1863", "Share of population in poverty ($3 a day)": "", "Population": "3975000", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1864", "Share of population in poverty ($3 a day)": "", "Population": "3992801", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1865", "Share of population in poverty ($3 a day)": "", "Population": "4010675", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1866", "Share of population in poverty ($3 a day)": "", "Population": "4028623", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1867", "Share of population in poverty ($3 a day)": "", "Population": "4046645", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1868", "Share of population in poverty ($3 a day)": "", "Population": "4064740", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1869", "Share of population in poverty ($3 a day)": "", "Population": "4082915", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1870", "Share of population in poverty ($3 a day)": "", "Population": "4101170", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1871", "Share of population in poverty ($3 a day)": "", "Population": "4119504", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1872", "Share of population in poverty ($3 a day)": "", "Population": "4137920", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1873", "Share of population in poverty ($3 a day)": "", "Population": "4156416", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1874", "Share of population in poverty ($3 a day)": "", "Population": "4174988", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1875", "Share of population in poverty ($3 a day)": "", "Population": "4193635", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1876", "Share of population in poverty ($3 a day)": "", "Population": "4212359", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1877", "Share of population in poverty ($3 a day)": "", "Population": "4231159", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1878", "Share of population in poverty ($3 a day)": "", "Population": "4250036", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1879", "Share of population in poverty ($3 a day)": "", "Population": "4268990", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1880", "Share of population in poverty ($3 a day)": "", "Population": "4288021", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1881", "Share of population in poverty ($3 a day)": "", "Population": "4307129", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1882", "Share of population in poverty ($3 a day)": "", "Population": "4326316", "World region according to OWID": "Asia"}], "rows_tail": [{"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1904", "Share of population in poverty ($3 a day)": "", "Population": "1182536", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1905", "Share of population in poverty ($3 a day)": "", "Population": "1202129", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1906", "Share of population in poverty ($3 a day)": "", "Population": "1222046", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1907", "Share of population in poverty ($3 a day)": "", "Population": "1242294", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1908", "Share of population in poverty ($3 a day)": "", "Population": "1262877", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1909", "Share of population in poverty ($3 a day)": "", "Population": "1283555", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1910", "Share of population in poverty ($3 a day)": "", "Population": "1304325", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1911", "Share of population in poverty ($3 a day)": "", "Population": "1325186", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1912", "Share of population in poverty ($3 a day)": "", "Population": "1346136", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1913", "Share of population in poverty ($3 a day)": "", "Population": "1367170", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1914", "Share of population in poverty ($3 a day)": "", "Population": "1388533", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1915", "Share of population in poverty ($3 a day)": "", "Population": "1410230", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1916", "Share of population in poverty ($3 a day)": "", "Population": "1432265", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1917", "Share of population in poverty ($3 a day)": "", "Population": "1454646", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1918", "Share of population in poverty ($3 a day)": "", "Population": "1477198", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1919", "Share of population in poverty ($3 a day)": "", "Population": "1501779", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1920", "Share of population in poverty ($3 a day)": "", "Population": "1528456", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1921", "Share of population in poverty ($3 a day)": "", "Population": "1557297", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1922", "Share of population in poverty ($3 a day)": "", "Population": "1588373", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1923", "Share of population in poverty ($3 a day)": "", "Population": "1621755", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1924", "Share of population in poverty ($3 a day)": "", "Population": "1655839", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1925", "Share of population in poverty ($3 a day)": "", "Population": "1690639", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1926", "Share of population in poverty ($3 a day)": "", "Population": "1726171", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1927", "Share of population in poverty ($3 a day)": "", "Population": "1762449", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1928", "Share of population in poverty ($3 a day)": "", "Population": "1799490", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1929", "Share of population in poverty ($3 a day)": "", "Population": "1836997", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1930", "Share of population in poverty ($3 a day)": "", "Population": "1874974", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1931", "Share of population in poverty ($3 a day)": "", "Population": "1913425", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1932", "Share of population in poverty ($3 a day)": "", "Population": "1952352", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1933", "Share of population in poverty ($3 a day)": "", "Population": "1991759", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1934", "Share of population in poverty ($3 a day)": "", "Population": "2031962", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1935", "Share of population in poverty ($3 a day)": "", "Population": "2072976", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1936", "Share of population in poverty ($3 a day)": "", "Population": "2114819", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1937", "Share of population in poverty ($3 a day)": "", "Population": "2157505", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1938", "Share of population in poverty ($3 a day)": "", "Population": "2201053", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1939", "Share of population in poverty ($3 a day)": "", "Population": "2245025", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1940", "Share of population in poverty ($3 a day)": "", "Population": "2289421", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1941", "Share of population in poverty ($3 a day)": "", "Population": "2334240", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1942", "Share of population in poverty ($3 a day)": "", "Population": "2379481", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1943", "Share of population in poverty ($3 a day)": "", "Population": "2425144", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1944", "Share of population in poverty ($3 a day)": "", "Population": "2471683", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1945", "Share of population in poverty ($3 a day)": "", "Population": "2519116", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1946", "Share of population in poverty ($3 a day)": "", "Population": "2567459", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1947", "Share of population in poverty ($3 a day)": "", "Population": "2616729", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1948", "Share of population in poverty ($3 a day)": "", "Population": "2666945", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1949", "Share of population in poverty ($3 a day)": "", "Population": "2725021", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1950", "Share of population in poverty ($3 a day)": "", "Population": "2790908", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1951", "Share of population in poverty ($3 a day)": "", "Population": "2880901", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1952", "Share of population in poverty ($3 a day)": "", "Population": "2972913", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1953", "Share of population in poverty ($3 a day)": "", "Population": "3067172", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1954", "Share of population in poverty ($3 a day)": "", "Population": "3163884", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1955", "Share of population in poverty ($3 a day)": "", "Population": "3263221", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1956", "Share of population in poverty ($3 a day)": "", "Population": "3365533", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1957", "Share of population in poverty ($3 a day)": "", "Population": "3471158", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1958", "Share of population in poverty ($3 a day)": "", "Population": "3580185", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1959", "Share of population in poverty ($3 a day)": "", "Population": "3692810", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1960", "Share of population in poverty ($3 a day)": "", "Population": "3809392", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1961", "Share of population in poverty ($3 a day)": "", "Population": "3930402", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1962", "Share of population in poverty ($3 a day)": "", "Population": "4055968", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1963", "Share of population in poverty ($3 a day)": "", "Population": "4185875", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1964", "Share of population in poverty ($3 a day)": "", "Population": "4320004", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1965", "Share of population in poverty ($3 a day)": "", "Population": "4458457", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "Share of population in poverty ($3 a day)": "", "Population": "4601223", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "Share of population in poverty ($3 a day)": "", "Population": "4748309", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "Share of population in poverty ($3 a day)": "", "Population": "4900440", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "Share of population in poverty ($3 a day)": "", "Population": "5058185", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "Share of population in poverty ($3 a day)": "", "Population": "5215919", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "Share of population in poverty ($3 a day)": "", "Population": "5374719", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "Share of population in poverty ($3 a day)": "", "Population": "5542444", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "Share of population in poverty ($3 a day)": "", "Population": "5720413", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "Share of population in poverty ($3 a day)": "", "Population": "5908337", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Share of population in poverty ($3 a day)": "", "Population": "6098653", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Share of population in poverty ($3 a day)": "", "Population": "6287107", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Share of population in poverty ($3 a day)": "", "Population": "6449517", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Share of population in poverty ($3 a day)": "", "Population": "6543573", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Share of population in poverty ($3 a day)": "", "Population": "6647557", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Share of population in poverty ($3 a day)": "", "Population": "7041304", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Share of population in poverty ($3 a day)": "", "Population": "7498641", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Share of population in poverty ($3 a day)": "", "Population": "7796503", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Share of population in poverty ($3 a day)": "", "Population": "8098408", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Share of population in poverty ($3 a day)": "", "Population": "8391494", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Share of population in poverty ($3 a day)": "", "Population": "8686815", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Share of population in poverty ($3 a day)": "", "Population": "8982579", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Share of population in poverty ($3 a day)": "", "Population": "9284649", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Share of population in poverty ($3 a day)": "", "Population": "9583099", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Share of population in poverty ($3 a day)": "", "Population": "9864797", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Share of population in poverty ($3 a day)": "", "Population": "10137287", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Share of population in poverty ($3 a day)": "", "Population": "10404820", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Share of population in poverty ($3 a day)": "", "Population": "10702697", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Share of population in poverty ($3 a day)": "", "Population": "10860285", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Share of population in poverty ($3 a day)": "", "Population": "10873146", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Share of population in poverty ($3 a day)": "", "Population": "10974607", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Share of population in poverty ($3 a day)": "", "Population": "11158364", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Share of population in poverty ($3 a day)": "", "Population": "11369833", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Share of population in poverty ($3 a day)": "", "Population": "11594299", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Share of population in poverty ($3 a day)": "", "Population": "11783454", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Share of population in poverty ($3 a day)": "", "Population": "11892055", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Share of population in poverty ($3 a day)": "", "Population": "11971904", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Share of population in poverty ($3 a day)": "", "Population": "12087661", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Share of population in poverty ($3 a day)": "", "Population": "12232324", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Share of population in poverty ($3 a day)": "", "Population": "12365901", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Share of population in poverty ($3 a day)": "", "Population": "12483433", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Share of population in poverty ($3 a day)": "", "Population": "12636442", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Share of population in poverty ($3 a day)": "", "Population": "12804062", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Share of population in poverty ($3 a day)": "", "Population": "12959154", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Share of population in poverty ($3 a day)": "", "Population": "13142791", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Share of population in poverty ($3 a day)": "", "Population": "13356551", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Share of population in poverty ($3 a day)": "35.71699857711792", "Population": "13595421", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Share of population in poverty ($3 a day)": "", "Population": "13817887", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Share of population in poverty ($3 a day)": "", "Population": "14013811", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Share of population in poverty ($3 a day)": "", "Population": "14207367", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Share of population in poverty ($3 a day)": "", "Population": "14399008", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Share of population in poverty ($3 a day)": "", "Population": "14600297", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Share of population in poverty ($3 a day)": "44.65687274932861", "Population": "14812484", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Share of population in poverty ($3 a day)": "", "Population": "15034457", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Share of population in poverty ($3 a day)": "49.21989440917969", "Population": "15271377", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Share of population in poverty ($3 a day)": "", "Population": "15526887", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Share of population in poverty ($3 a day)": "", "Population": "15797220", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Share of population in poverty ($3 a day)": "", "Population": "16069061", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Share of population in poverty ($3 a day)": "", "Population": "16340829", "World region according to OWID": "Africa"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "share-of-population-in-extreme-poverty", "metadata_url": "https://ourworldindata.org/grapher/share-of-population-in-extreme-poverty.metadata.json", "chart_title": "Share of population living in extreme poverty", "chart_subtitle": "Extreme poverty is defined as living below the International Poverty Line of $3 per day. This data is adjusted for inflation and differences in living costs between countries.", "chart_note": "This data is expressed in international-$ at 2021 prices. Depending on the country and year, it relates to income (measured after taxes and benefits) or to consumption, per capita.", "chart_citation": "World Bank Poverty and Inequality Platform (2026)", "original_chart_url": "https://ourworldindata.org/grapher/share-of-population-in-extreme-poverty", "owid_column_metadata": {"Share of population in poverty ($3 a day, 2021 prices)": {"titleShort": "Share of population in poverty ($3 a day)", "titleLong": "Share of population in poverty ($3 a day)", "descriptionShort": "Percentage of population living in households with an income or consumption below $3 per day.", "descriptionKey": ["The World Bank defines extreme poverty as living on less than $3 per day. This threshold, known as the \"International Poverty Line\", is set so that poverty can be compared across countries. This indicator plays an important and successful role in focusing the world's attention on the very poorest people. The UN uses this indicator to track progress towards [ending extreme poverty by 2030](https://ourworldindata.org/sdgs/no-poverty).", "Two centuries ago, most of the world's population was extremely poor. Many believed that widespread poverty was inevitable. But this turned out to be wrong. Economic growth is possible, and poverty can decline. With this poverty line, we can track whether countries are leaving the worst poverty behind.", "This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "Many people, today and in the past, have no formal monetary income. This data accounts for that by including the estimated value of non-market income, such as food grown by subsistence farmers for their own use.", "The data comes from the World Bank's Poverty and Inequality Platform (PIP), which draws on national household surveys. Regional and global estimates are extrapolated to the year of the data release using GDP growth estimates and forecasts. For more details about the methodology, please refer to the [World Bank PIP documentation](https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#nowcasts).", "Depending on the country and year, the data refers either to income (after taxes and benefits) or to consumption, per capita. These are not perfectly comparable — consumption tends to be more evenly distributed than income. For most countries, we have only one option available. But when there is a mix of consumption and income data points, we process the data to keep one observation per country and year."], "descriptionProcessing": "For most countries in the dataset, estimates relate to disposable income or consumption, for all available years. Several countries, however, have a mix of income and consumption data points, with both data types sometimes available for particular years.\n\nIn most of our charts, we present the data with some data points dropped to present a single series for each country. This allows us to make readable visualizations that combine multiple countries. In choosing which data points to keep, we strike a balance between maintaining comparability over time and showing as long a time series as possible. As such, the exact approach varies across countries.", "shortUnit": "%", "unit": "%", "timespan": "1963-2026", "type": "Numeric", "owidVariableId": 1220228, "shortName": "headcount_ratio__ppp_version_2021__poverty_line_300__welfare_type_income_or_consumption__table_income_or_consumption_consolidated__survey_comparability_no_spells", "lastUpdated": "2026-03-24", "nextUpdate": "2026-09-20", "citationShort": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data", "citationLong": "World Bank Poverty and Inequality Platform (2026) – with major processing by Our World in Data. “Share of population in poverty ($3 a day) – World Bank” [dataset]. World Bank Poverty and Inequality Platform, “World Bank Poverty and Inequality Platform (PIP) 20260324_2021, 20260324_2017” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1220228.metadata.json"}, "Population (historical)": {"titleShort": "Population", "titleLong": "Population", "descriptionShort": "Population by country, available from 10,000 BCE to 2023, based on data and estimates from different sources.", "descriptionKey": ["Population is the most commonly used metric throughout Our World in Data. It is used directly to understand population growth over time, and indirectly to calculate per-capita indicators, making it easier to compare countries of different sizes.", "We construct this indicator by combining multiple sources covering different periods.\n  - HYDE v3.3 (2023): historical estimates from 10,000 BCE to 1799.\n  - Gapminder v7 (2022): for 1800-1949.\n  - UN World Population Prospects (2024): for 1950 onwards, including 2100 projections.\n  - Gapminder Systema Globalis (2023): additional source for former countries (Yugoslavia, USSR, etc.)", "Breaks in the data may occur at the boundaries between sources due to their methodological differences.", "You can read more about the sources and methodology in our [dedicated article](https://ourworldindata.org/population-sources). We also provide a table of sources showing the source we use for each country-year.", "We calculate geographical aggregates (continents, income groups, etc.) by summing individual country populations. For years before 1800, we rely directly on HYDE's values for continents to ensure historical consistency."], "descriptionProcessing": "### Combination of different sources\nWe construct our long-run population data by combining multiple sources:\n\n- 10,000 BCE–1799: historical estimates by HYDE (v3.3).\n\n- 1800–1949: historical estimates by Gapminder (v7).\n\n- 1950–2023: population records from the United Nations World Population Prospects (2024 revision).\n\n**Geographical aggregates**\n\n- For most years, we calculate aggregates by summing the population of member countries.\n- We do this based on [our definition of continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and the [World Bank’s income groups](https://ourworldindata.org/grapher/world-bank-income-groups).\n- The only exception is before 1800, where we use HYDE's estimates for continents (but not income groups).\n\nFor most of the years, we've estimated regional aggregates by summing the population of countries in each region. We've relied on [our continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and [World Bank income group definitions](https://ourworldindata.org/grapher/world-bank-income-groups). The only exception is before 1800, where we've used HYDE's estimates on continents (but not income groups).\n\n**World**\n- Before 1800: we use data from HYDE.\n- 1800-1950: we estimate the global population by summing all available countries in the dataset.\n- After 1950, we rely on estimates from the United Nations World Population Prospects.", "shortUnit": "", "unit": "people", "timespan": "-10000-2023", "type": "Integer", "owidVariableId": 953903, "shortName": "population_historical", "lastUpdated": "2024-07-15", "nextUpdate": "2026-07-15", "citationShort": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data", "citationLong": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data. “Population – HYDE, Gapminder, UN – Long-run data” [dataset]. PBL Netherlands Environmental Assessment Agency, “History Database of the Global Environment 3.3”; Gapminder, “Population v7”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update”; Gapminder, “Systema Globalis” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/953903.metadata.json"}, "World region according to OWID": {"titleShort": "World region according to OWID", "titleLong": "World region according to OWID", "descriptionShort": "Regions defined by Our World in Data, which are used in OWID charts and maps.", "unit": "", "timespan": "2023-2023", "type": "Continent", "owidVariableId": 900801, "shortName": "owid_region", "lastUpdated": "2023-01-01", "citationShort": "Our World in Data – processed by Our World in Data", "citationLong": "Our World in Data – processed by Our World in Data. “World region according to OWID” [dataset]. Our World in Data, “Regions” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/900801.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "GDP per capita", "source_url": "https://ourworldindata.org/grapher/gdp-per-capita-worldbank.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "GDP per capita", "World region according to OWID"], "row_count_total": 7240, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "GDP per capita": "1617.8264", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "GDP per capita": "1454.1108", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "GDP per capita": "1774.3087", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "GDP per capita": "1815.9282", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "GDP per capita": "1776.9182", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "GDP per capita": "1908.1147", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "GDP per capita": "1929.7239", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "GDP per capita": "2155.353", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "GDP per capita": "2191.5044", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "GDP per capita": "2565.022", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "GDP per capita": "2848.5862", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "GDP per capita": "2757.0525", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "GDP per capita": "2985.319", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "GDP per capita": "3046.5798", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "GDP per capita": "3017.9426", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "GDP per capita": "2967.6921", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "GDP per capita": "2958.7854", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "GDP per capita": "2952.999", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "GDP per capita": "2902.392", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "GDP per capita": "2927.245", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "GDP per capita": "2769.6858", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "GDP per capita": "2144.1665", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "GDP per capita": "1981.7102", "World region according to OWID": "Asia"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "GDP per capita": "1983.8126", "World region according to OWID": "Asia"}, {"Entity": "Albania", "Code": "ALB", "Year": "1990", "GDP per capita": "5560.857", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1991", "GDP per capita": "4027.9055", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "GDP per capita": "3761.1555", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "GDP per capita": "4145.92", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "GDP per capita": "4517.799", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "GDP per capita": "5151.3975", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "GDP per capita": "5563.793", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "GDP per capita": "4943.004", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "GDP per capita": "5387.5684", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "GDP per capita": "6086.009", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "GDP per capita": "6582.0166", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "GDP per capita": "7232.9907", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "GDP per capita": "7590.49", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "GDP per capita": "8025.2812", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "GDP per capita": "8483.294", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "GDP per capita": "8964.318", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "GDP per capita": "9564.029", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "GDP per capita": "10262.967", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "GDP per capita": "11056.352", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "GDP per capita": "11430.622", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "GDP per capita": "11829.054", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "GDP per capita": "12153.114", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "GDP per capita": "12463.57", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "GDP per capita": "12873.483", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "GDP per capita": "13366.56", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "GDP per capita": "13876.819", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "GDP per capita": "14643.489", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "GDP per capita": "15359.461", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "GDP per capita": "16170.99", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "GDP per capita": "16761.193", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "GDP per capita": "16457.787", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "GDP per capita": "18212.871", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "GDP per capita": "19388.873", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "GDP per capita": "20481.035", "World region according to OWID": "Europe"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "GDP per capita": "21641.074", "World region according to OWID": "Europe"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1990", "GDP per capita": "11728.546", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1991", "GDP per capita": "11314.864", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1992", "GDP per capita": "11241.415", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1993", "GDP per capita": "10743.706", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1994", "GDP per capita": "10414.035", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1995", "GDP per capita": "10588.443", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1996", "GDP per capita": "10808.879", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1997", "GDP per capita": "10725.968", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1998", "GDP per capita": "11094.888", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1999", "GDP per capita": "11292.037", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "GDP per capita": "11558.221", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "GDP per capita": "11742.595", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "GDP per capita": "12213.126", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "GDP per capita": "12835.182", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "GDP per capita": "13226.765", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "GDP per capita": "13738.496", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "GDP per capita": "13920.694", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "GDP per capita": "14114.675", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "GDP per capita": "14206.776", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "GDP per capita": "14104.428", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "GDP per capita": "14496.421", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "GDP per capita": "14641.964", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "GDP per capita": "14697.54", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "GDP per capita": "14778.191", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "GDP per capita": "15073.763", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "GDP per capita": "15239.518", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "GDP per capita": "15511.686", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "GDP per capita": "15427.664", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "GDP per capita": "15343.426", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "GDP per capita": "15199.199", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "GDP per capita": "14194.155", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "GDP per capita": "14496.865", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2022", "GDP per capita": "14782.2", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "GDP per capita": "15159.324", "World region according to OWID": "Africa"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2024", "GDP per capita": "15501.92", "World region according to OWID": "Africa"}, {"Entity": "Andorra", "Code": "AND", "Year": "1990", "GDP per capita": "50036.3", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1991", "GDP per capita": "47624.97", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1992", "GDP per capita": "45246.53", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1993", "GDP per capita": "42605.66", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1994", "GDP per capita": "42716.367", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1995", "GDP per capita": "44375.023", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1996", "GDP per capita": "46386.09", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1997", "GDP per capita": "50032.35", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1998", "GDP per capita": "51092.223", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "1999", "GDP per capita": "52921.047", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2000", "GDP per capita": "54809.145", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2001", "GDP per capita": "59109.016", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2002", "GDP per capita": "61188.637", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2003", "GDP per capita": "63656.195", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2004", "GDP per capita": "64353.484", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2005", "GDP per capita": "65114.805", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2006", "GDP per capita": "66390.3", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2007", "GDP per capita": "65534.117", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2008", "GDP per capita": "60691.605", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2009", "GDP per capita": "57203.973", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2010", "GDP per capita": "58285.06", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2011", "GDP per capita": "60470.465", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2012", "GDP per capita": "58172.133", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2013", "GDP per capita": "57332.16", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2014", "GDP per capita": "59929.246", "World region according to OWID": "Europe"}, {"Entity": "Andorra", "Code": "AND", "Year": "2015", "GDP per capita": "62105.152", "World region according to OWID": "Europe"}], "rows_tail": [{"Entity": "Vietnam", "Code": "VNM", "Year": "2010", "GDP per capita": "7274.733", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2011", "GDP per capita": "7652.619", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2012", "GDP per capita": "7979.868", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2013", "GDP per capita": "8324.198", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2014", "GDP per capita": "8751.883", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2015", "GDP per capita": "9248.024", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2016", "GDP per capita": "9743.188", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2017", "GDP per capita": "10290.549", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2018", "GDP per capita": "10936.894", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2019", "GDP per capita": "11628.614", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "GDP per capita": "11851.396", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2021", "GDP per capita": "12048.902", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2022", "GDP per capita": "12979.764", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2023", "GDP per capita": "13545.935", "World region according to OWID": "Asia"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2024", "GDP per capita": "14415.216", "World region according to OWID": "Asia"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1990", "GDP per capita": "11257.437", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1991", "GDP per capita": 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capita": "12351.548", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "GDP per capita": "12769.831", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "GDP per capita": "12904.862", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "GDP per capita": "13093.065", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "GDP per capita": "13402.992", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "GDP per capita": "13914.812", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "GDP per capita": "14370.885", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "GDP per capita": "14927.549", "World region according to OWID": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", 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This data is adjusted for inflation and differences in living costs between countries.", "chart_note": "This data is expressed in international-$ at 2021 prices.", "chart_citation": "Eurostat, OECD, IMF, and World Bank (2026)", "original_chart_url": "https://ourworldindata.org/grapher/gdp-per-capita-worldbank", "owid_column_metadata": {"GDP per capita, PPP (constant 2021 international $)": {"titleShort": "GDP per capita", "titleLong": "GDP per capita - World Bank – In constant international-$", "descriptionShort": "Average economic output per person in a country or region per year. This data is adjusted for inflation and differences in living costs between countries.", "descriptionKey": ["GDP per capita is a comprehensive measure of people's average income. It helps compare income levels across countries and track how they change over time. It is especially useful for understanding trends in economic growth and living standards.", "GDP per capita is calculated as the value of all final goods and services produced each year in a country (the gross domestic product), divided by the population. It represents the average economic output per person.", "This indicator shows the large inequality between people in different countries. In the poorest countries, average incomes are below $1,000 per year; in rich countries, they are more than 50 times higher.", "This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "This data comes from the World Bank and starts in 1990. For estimates going back several centuries, explore our chart of GDP per capita from the [Maddison Project Database](https://ourworldindata.org/grapher/gdp-per-capita-maddison-project-database)."], "shortUnit": "$", "unit": "international-$ in 2021 prices", "timespan": "1990-2024", "type": "Numeric", "owidVariableId": 1204826, "shortName": "ny_gdp_pcap_pp_kd", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "Eurostat, OECD, IMF, and World Bank (2026) – with minor processing by Our World in Data", "citationLong": "Eurostat, OECD, IMF, and World Bank (2026) – with minor processing by Our World in Data. “GDP per capita – World Bank – In constant international-$” [dataset]. Eurostat, OECD, IMF, and World Bank, “World Development Indicators 125” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1204826.metadata.json"}, "World region according to OWID": {"titleShort": "World region according to OWID", "titleLong": "World region according to OWID", "descriptionShort": "Regions defined by Our World in Data, which are used in OWID charts and maps.", "unit": "", "timespan": "2023-2023", "type": "Continent", "owidVariableId": 900801, "shortName": "owid_region", "lastUpdated": "2023-01-01", "citationShort": "Our World in Data – processed by Our World in Data", "citationLong": "Our World in Data – processed by Our World in Data. “World region according to OWID” [dataset]. Our World in Data, “Regions” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/900801.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Consumer price index", "source_url": "https://ourworldindata.org/grapher/consumer-price-index.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Consumer price index (2010 = 100)"], "row_count_total": 9103, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Consumer price index (2010 = 100)": "63.523396"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Consumer price index (2010 = 100)": "71.582146"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Consumer price index (2010 = 100)": "76.438705"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Consumer price index (2010 = 100)": "83.07402"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Consumer price index (2010 = 100)": "105.021065"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Consumer price index (2010 = 100)": "97.86791"}, {"Entity": "Afghanistan", 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This is expressed relative to a base year – in this case 2010. For example, a CPI of 120 means that prices were 20% higher than in 2010.", "chart_note": null, "chart_citation": "International Monetary Fund (IMF) International Financial Statistics, via World Bank (2026)", "original_chart_url": "https://ourworldindata.org/grapher/consumer-price-index", "owid_column_metadata": {"Consumer price index (2010 = 100)": {"titleShort": "Consumer price index (2010 = 100)", "titleLong": "Consumer price index (2010 = 100)", "unit": "2010 = 100", "timespan": "1960-2024", "type": "Numeric", "owidVariableId": 1204431, "shortName": "fp_cpi_totl", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "International Monetary Fund (IMF) International Financial Statistics, via World Bank (2026) – processed by Our World in Data", "citationLong": "International Monetary Fund (IMF) International Financial Statistics, via World Bank (2026) – processed by Our World in Data. “Consumer price index (2010 = 100)” [dataset]. International Monetary Fund (IMF) International Financial Statistics, via World Bank, “World Development Indicators 125” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1204431.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "59e611b420701ad2152b"},
{"raw_link": "https://ourworldindata.org/measles-vaccines-save-lives", "title": "Measles vaccines save millions of lives each year", "context": "Home\nVaccination\nMeasles vaccines save millions of lives each year\nMeasles once killed millions every year. Vaccines changed this, preventing disease, long-term immune damage, and deadly outbreaks.\nBy\nSaloni Dattani\nand\nFiona Spooner\nMay 19, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nMeasles used to be an extremely common disease. Just sixty years ago, over 90% of children would have been infected by it, and of those who developed symptoms, around a quarter would be hospitalized.\n1\nThe United States alone had around three to four million cases annually, leading to tens of thousands of hospitalizations and hundreds of deaths each year.\n2\nHowever, in 1963, John Enders developed the first effective measles vaccine. Vaccination efforts ramped up rapidly in richer countries, and in the 1970s and 1980s, they were scaled up worldwide.\nIn just the last fifty years, it’s estimated that measles vaccinations have prevented over ninety million deaths worldwide. Two to three million people would die from measles every year without them.\n3\nThis means these vaccines are likely the most life-saving ones currently in use, as you can see in the chart.\n4\nIn this article, I explain how measles vaccines were developed and their impact on saving lives.\nThe introduction of the measles vaccine led to a substantial decline in cases\nJohn Enders and his colleagues developed the first measles vaccine. Enders had previously played a key role in research for the development of polio vaccines\n5\nand in 1954, his team turned its attention to measles.\nThey collected samples from throat swabs and blood from infected school children during an outbreak. The researchers successfully isolated the measles virus from one sample, naming it the “Edmonston strain” after the young boy it came from.\n6\nThe next challenge was weakening or “attenuating” the virus so it could trigger immunity without causing disease. Enders and his colleagues experimented with growing the virus in human kidney cells and then in fertilized chicken eggs, forcing it to adapt to different environments. By repeating this process many times, the virus lost its ability to cause severe disease in humans.\n7\nThe first human trials in 1960 were successful: school children given the vaccine developed strong immunity and were protected against the virus. However, the vaccine still caused fever and rash in many children. To reduce these side effects, doctors gave children “measles immune globulin” alongside the vaccine to blunt its impact.\n7\nMeasles immune globulin is a concentrated preparation of antibodies against measles taken from donated blood, used to provide short-term passive immunity or soften the body’s reaction to the vaccine.\nMaurice Hilleman, a microbiologist who developed over 40 vaccines in his lifetime, improved the measles vaccine. In the 1960s, he further weakened the measles virus by growing it in chick embryo cells at lower temperatures, creating an even safer version. It was licensed in 1968 and became the standard measles vaccine. A few years later, in 1971, he developed the measles-mumps-rubella (MMR) combination.\n8\nThe impact of measles vaccination was rapid and substantial. Analysis of over a hundred studies shows that the vaccines reduce the chances of developing measles twenty-fold.\n9\nYou can see the impact of vaccination in the United States in the heatmap below. Each row is one state, and the colors represent the number of reported cases over time. In the first half of the 20th century, measles rates were high, and the years are shown in dark blue, but the number of cases rapidly dropped due to vaccination.\nDownload image or data\nThis chart was inspired by Tynan DeBold and Dov Friedman’s\ndata visualizations\nin the Wall Street Journal. Explore the data in\nan interactive chart\n. Scripts to recreate this chart can be found\non GitHub\n.\nDeaths remained common in poorer countries until vaccines became widely available\nOther factors have also played a role in the reduction in measles deaths. In the US, deaths had already been falling in the decades before — probably due to better treatment for the secondary infectious diseases that people with measles become vulnerable to, improved sanitation and hygiene that limited the spread of those infections, and better childhood nutrition, which lowered the risk of severe illness.\n10\nHowever, these improvements were not effective in reducing measles\ncases\n. The measles virus is airborne, so improvements in hygiene, such as clean water or better sewage systems, do not reduce its spread. Since measles is so contagious, nearly every child still got measles before vaccines were available\n11\n, and the number of cases only began to decline after the vaccines arrived.\nYet this doesn’t mean these cases were mild: before vaccines were available, there were still around 50,000 hospitalizations and hundreds of deaths in just the United States each year.\n2\nIn contrast, measles\ndeaths\ncontinued to be common in poorer countries until vaccines became widely available. In the chart below, you can see that hundreds of thousands of people died from measles annually in Africa and South-East Asia between the 1980s and 2000s.\nThat’s because, in low- and middle-income countries, the\ncase fatality rate\nfrom measles has been much higher than in richer countries. It’s estimated that in the 1980s, 5 to 10% of children with the disease then died from it. This would have been particularly severe when widespread measles outbreaks infected most children in the population.\n12\nThe number of measles deaths dropped dramatically in the 2000s, particularly in Africa, as vaccination efforts scaled up, as we’ll see in the following section.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nMeasles vaccination saves millions of lives each year\nThe global rollout of measles vaccines has been one of history’s most successful public health efforts. Each year, they save millions of lives.\nThis is especially true in low-income countries where children face the highest risk of dying from measles because of poorer overall health, nutrition, and living standards.\n10\nThis chart shows how far the world has come. It shows the share of one-year-olds who have received their first dose of the measles vaccine.\nIn the 1980s, coverage was very low in many parts of the world, especially in Africa, Southeast Asia, and the Eastern Mediterranean. In some countries, like Yemen, only 2% of children received vaccines; in Spain, only 8%.\nBut since then, vaccination rates have increased rapidly.\nOne reason is the scale-up of the\nExpanded Programme on Immunization\nby the World Health Assembly from the 1970s, which aimed to vaccinate children against the deadliest infectious diseases, including measles. Vaccination efforts reached more than 90 million children — or 60% of all infants — by the early 2000s.\nBut millions of children were still left behind, particularly in poorer countries. In response, the Gavi Vaccine Alliance was established in 2000 to close these gaps and ensure that life-saving vaccines reached the most vulnerable children.\nNow, over a hundred million infants receive vaccinations for measles, which is\nover 80% of them\n.\nThese efforts have transformed global health, dramatically reducing child mortality.\nThis next chart shows estimates of the cumulative number of lives saved by measles vaccinations over time.\n13\nFifty years since the start of measles vaccination programs, we can see that their impact has been substantial: researchers estimate that 94 million lives have been saved from measles vaccines. That means, on average, nearly two million measles deaths prevented every year.\n13\nThe impact has been greatest in Africa, with 29 million lives saved, and Southeast Asia, with 20 million lives saved. These are regions where measles was a\nleading cause of death in children\nuntil recently.\nThis means measles vaccines rank as the most life-saving childhood vaccines currently in use.\n4\nMeasles was once an unavoidable disease, infecting nearly every child and causing millions of deaths each year. This was not because of poor hygiene or healthcare, but because of how contagious measles is. Without immunity, each infected person would typically pass it to more than a dozen others\n14\n, making sanitation and hygiene measures alone insufficient to control its spread.\nThe breakthrough came when researchers isolated and weakened the virus, creating a safe and effective vaccine. Over the following decades, measles vaccination was scaled up globally, reaching hundreds of millions of children and preventing an estimated 90 million deaths in the last fifty years — more than any other childhood vaccine used today.\nMeasles vaccination does more than prevent the disease. It preserves a child’s broader immunity and protects those most at risk, including infants, pregnant women, and people with weakened immune systems from health conditions or undergoing cancer treatments.\nIt also prevents the virus from spreading. When most people are immune, the virus runs into dead ends. Chains of transmission are broken before they can grow. As a result, outbreaks have become smaller, easier to contain, and increasingly rare. But keeping it this way requires high vaccination rates: the best way to stop a measles outbreak is to prevent it from ever starting.\nContinue reading on Our World in Data\nHow effective and safe are measles vaccines?\nData from large meta-analyses show that measles vaccination is highly effective and safe, giving a 95% reduction in the risk of measles.\nVaccines have saved 150 million children over the last 50 years\nEvery ten seconds, one child is saved by a vaccine against a fatal disease.\nOur history is a battle against the microbes: we lost terribly before science, public health, and vaccines allowed us to protect ourselves\nFor most of history, we were losing the battle against microbes. Vaccines were one of the breakthroughs that turned it around.\nEndnotes\nCenters for Disease Control and Prevention. (2021). Measles (14th ed.) Available\nonline\n.\nPerry, R. T., & Halsey, N. A. (2004). The Clinical Significance of Measles: A Review. The Journal of Infectious Diseases, 189(Supplement_1), S4–S16.\nhttps://doi.org/10.1086/377712\nThe CDC reports that in the years before vaccines, measles caused an estimated 3 to 4 million cases, with around 500,000 cases reported annually, along with 48,000 hospitalizations, 1,000 cases with encephalitis (brain swelling), and 400 to 500 deaths.\nCenters for Disease Control and Prevention (2019). Measles Data and Statistics. Available\nonline\n.\nProgress Toward Measles Elimination — Worldwide, 2000–2023. Available\nonline\n.\nThis doesn’t include smallpox vaccines, which are no longer in use, since the disease was eradicated worldwide in 1980.\nIn 1949, Enders, along with Thomas Weller and Frederick Robbins, successfully grew poliovirus in human tissue cultures, which earned them the 1954 Nobel Prize in Physiology or Medicine. This breakthrough enabled the production of large amounts of poliovirus without relying on live animals. It paved the way for Jonas Salk’s inactivated polio vaccine and Albert Sabin’s oral polio vaccine.\nNobel Prize Outreach (2025) John F. Enders – Facts. Available\nonline\n.\nThe 13-year-old boy from whom the measles virus was derived was named David Edmonston.\nKatz, S. L. (2011). The History of Measles Virus and the Development and Utilization of Measles Virus Vaccines. In S. A. Plotkin (Ed.), History of Vaccine Development (pp. 199–206). Springer New York.\nhttps://doi.org/10.1007/978-1-4419-1339-5_22\nKatz, S. L. (2011). The History of Measles Virus and the Development and Utilization of Measles Virus Vaccines. In S. A. Plotkin (Ed.), History of Vaccine Development (pp. 199–206). Springer New York.\nhttps://doi.org/10.1007/978-1-4419-1339-5_22\nHilleman, M. R. (2011). The Development of Live Attenuated Mumps Virus Vaccine in Historic Perspective and Its Role in the Evolution of Combined Measles–Mumps–Rubella. In S. A. Plotkin (Ed.), History of Vaccine Development (pp. 207–218). Springer New York.\nhttps://doi.org/10.1007/978-1-4419-1339-5_23\nDi Pietrantonj, C., Rivetti, A., Marchione, P., Debalini, M. G., & Demicheli, V. (2021). Vaccines for measles, mumps, rubella, and varicella in children. Cochrane Database of Systematic Reviews, 2021(11).\nhttps://doi.org/10.1002/14651858.CD004407.pub5\nSchneider, E. B. (2023). The effect of nutritional status on historical infectious disease morbidity: Evidence from the London Foundling Hospital, 1892-1919. The History of the Family, 28(2), 198–228.\nhttps://doi.org/10.1080/1081602X.2021.2007499\nCenters for Disease Control and Prevention (2024). History of measles. Available\nonline\n.\nGuerra, F. M., Bolotin, S., Lim, G., Heffernan, J., Deeks, S. L., Li, Y., & Crowcroft, N. S. (2017). The basic reproduction number (R 0 ) of measles: A systematic review. The Lancet Infectious Diseases, 17(12), e420–e428.\nhttps://doi.org/10.1016/S1473-3099(17)30307-9\nPortnoy, A., Jit, M., Ferrari, M., Hanson, M., Brenzel, L., & Verguet, S. (2019). Estimates of case-fatality ratios of measles in low-income and middle-income countries: A systematic review and modelling analysis. The Lancet Global Health, 7(4), e472–e481.\nhttps://doi.org/10.1016/S2214-109X(18)30537-0\nShattock, A. J., Johnson, H. C., Sim, S. Y., Carter, A., Lambach, P., Hutubessy, R. C. W., Thompson, K. M., Badizadegan, K., Lambert, B., Ferrari, M. J., Jit, M., Fu, H., Silal, S. P., Hounsell, R. A., White, R. G., Mosser, J. F., Gaythorpe, K. A. M., Trotter, C. L., Lindstrand, A., … Bar-Zeev, N. (2024). Contribution of vaccination to improved survival and health: Modelling 50 years of the Expanded Programme on Immunization. The Lancet, 403(10441), 2307–2316.\nhttps://doi.org/10.1016/S0140-6736(24)00850-X\nGuerra, F. M., Bolotin, S., Lim, G., Heffernan, J., Deeks, S. L., Li, Y., & Crowcroft, N. S. (2017). The basic reproduction number (R 0 ) of measles: A systematic review. The Lancet Infectious Diseases, 17(12), e420–e428.\nhttps://doi.org/10.1016/S1473-3099(17)30307-9\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSaloni Dattani and Fiona Spooner (2025) - “Measles vaccines save millions of lives each year” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260611-092127/measles-vaccines-save-lives.html' [Online Resource] (archived on June 11, 2026).\nBibTeX citation\n@article{owid-measles-vaccines-save-lives,\nauthor = {Saloni Dattani and Fiona Spooner},\ntitle = {Measles vaccines save millions of lives each year},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260611-092127/measles-vaccines-save-lives.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "measles-vaccines-save-lives", "source_url": "https://ourworldindata.org/measles-vaccines-save-lives", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Measles once killed millions every year. Vaccines changed this, preventing disease, long-term immune damage, and deadly outbreaks.", "numeric_mentions": ["19,", "2025", "90%", "1", "2", "1963,", "1970", "1980", "3", "4", "5", "1954,", "6", "7", "1960", "40", "1968", "1971,", "8", "9", "20", "10", "11", "50,000", "2000", "10%", "12", "2%", "8%", "90 million", "60%", "80%", "13", "94 million", "29 million", "20 million", "14", "95%", "150 million", "50 years", "2021", "2004", "189", "10.1086", "377712", "4 million", "500,000", "48,000", "1,000", "400", "500", "2019", "2023", "1949,", "1954", "2011", "199", "206", "10.1007", "978", "4419", "1339", "22", "207", "218", "23", "10.1002", "14651858", "1892", "1919", "28", "198", "228", "10.1080", "1081602", "2021.2007499", "2024", "2017", "0", "17"], "numeric_evidence": [{"title": "Rate of measles cases by state in the United States", "source_url": "https://ourworldindata.org/grapher/reported-measles-case-rate-by-state-in-the-united-states.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Measles case rate per 100,000 population"], "row_count_total": 5678, "rows_head": [{"Entity": "Alabama", "Code": "", "Year": "1900", "Measles case rate per 100,000 population": "0.109180436"}, {"Entity": "Alabama", "Code": "", "Year": "1904", "Measles case rate per 100,000 population": "0.101011224"}, {"Entity": "Alabama", "Code": "", "Year": "1907", "Measles case rate per 100,000 population": "0.14562696"}, {"Entity": "Alabama", "Code": "", "Year": "1908", "Measles case rate per 100,000 population": "0.19304366"}, {"Entity": "Alabama", "Code": "", "Year": "1909", "Measles case rate per 100,000 population": "0.047390938"}, {"Entity": "Alabama", "Code": "", "Year": "1910", "Measles case rate per 100,000 population": "28.90133"}, {"Entity": "Alabama", "Code": "", "Year": "1911", "Measles case rate per 100,000 population": "27.158197"}, {"Entity": "Alabama", "Code": "", "Year": "1912", "Measles case rate per 100,000 population": "5.0017643"}, {"Entity": "Alabama", "Code": "", "Year": "1913", "Measles case rate per 100,000 population": "0.30684543"}, {"Entity": "Alabama", "Code": "", "Year": "1914", "Measles case rate per 100,000 population": "0.17106181"}, {"Entity": "Alabama", "Code": "", "Year": "1915", "Measles case rate per 100,000 population": "0.08520264"}, {"Entity": "Alabama", "Code": "", "Year": "1916", "Measles case rate per 100,000 population": "2.7950008"}, {"Entity": "Alabama", "Code": "", "Year": "1917", "Measles case rate per 100,000 population": "220.99883"}, {"Entity": "Alabama", "Code": "", "Year": "1918", "Measles case rate per 100,000 population": "71.67395"}, {"Entity": "Alabama", "Code": "", "Year": "1919", "Measles case rate per 100,000 population": "15.944689"}, {"Entity": "Alabama", "Code": "", "Year": "1920", "Measles case rate per 100,000 population": "12.069321"}, {"Entity": "Alabama", "Code": "", "Year": "1921", "Measles case rate per 100,000 population": "20.171335"}, {"Entity": "Alabama", "Code": "", "Year": "1922", "Measles case rate per 100,000 population": "4.843144"}, {"Entity": "Alabama", "Code": "", "Year": "1923", "Measles case rate per 100,000 population": "202.67882"}, {"Entity": "Alabama", "Code": "", "Year": "1924", "Measles case rate per 100,000 population": "64.58955"}, {"Entity": "Alabama", "Code": "", "Year": "1925", "Measles case rate per 100,000 population": "2.0894654"}, {"Entity": "Alabama", "Code": "", "Year": "1926", "Measles case rate per 100,000 population": "69.93396"}, {"Entity": "Alabama", "Code": "", "Year": "1927", "Measles case rate per 100,000 population": "70.56952"}, {"Entity": "Alabama", "Code": "", "Year": "1928", "Measles case rate per 100,000 population": "390.4429"}, {"Entity": "Alabama", "Code": "", "Year": "1929", "Measles case rate per 100,000 population": "116.78941"}, {"Entity": "Alabama", "Code": "", "Year": "1930", "Measles case rate per 100,000 population": "229.04938"}, {"Entity": "Alabama", "Code": "", "Year": "1931", "Measles case rate per 100,000 population": "401.63687"}, {"Entity": "Alabama", "Code": "", "Year": "1932", "Measles case rate per 100,000 population": "11.673212"}, {"Entity": "Alabama", "Code": "", "Year": "1933", "Measles case rate per 100,000 population": "83.49411"}, {"Entity": "Alabama", "Code": "", "Year": "1934", "Measles case rate per 100,000 population": "679.84155"}, {"Entity": "Alabama", "Code": "", "Year": "1935", "Measles case rate per 100,000 population": "294.483"}, {"Entity": "Alabama", "Code": "", "Year": "1936", "Measles case rate per 100,000 population": "21.414968"}, {"Entity": "Alabama", "Code": "", "Year": "1937", "Measles case rate per 100,000 population": "32.552532"}, {"Entity": "Alabama", "Code": "", "Year": "1938", "Measles case rate per 100,000 population": "600.26086"}, {"Entity": "Alabama", "Code": "", "Year": "1939", "Measles case rate per 100,000 population": "163.76659"}, {"Entity": "Alabama", "Code": "", "Year": "1940", "Measles case rate per 100,000 population": "126.41138"}, {"Entity": "Alabama", "Code": "", "Year": "1941", "Measles case rate per 100,000 population": "348.65204"}, {"Entity": "Alabama", "Code": "", "Year": "1942", "Measles case rate per 100,000 population": "127.10853"}, {"Entity": "Alabama", "Code": "", "Year": "1943", "Measles case rate per 100,000 population": "144.37665"}, {"Entity": "Alabama", "Code": "", "Year": "1944", "Measles case rate per 100,000 population": "273.35263"}, {"Entity": "Alabama", "Code": "", "Year": "1945", "Measles case rate per 100,000 population": "13.284014"}, {"Entity": "Alabama", "Code": "", "Year": "1946", "Measles case rate per 100,000 population": "146.81299"}, {"Entity": "Alabama", "Code": "", "Year": "1947", "Measles case rate per 100,000 population": "148.89937"}, {"Entity": "Alabama", "Code": "", "Year": "1948", "Measles case rate per 100,000 population": "70.996704"}, {"Entity": "Alabama", "Code": "", "Year": "1949", "Measles case rate per 100,000 population": "368.49817"}, {"Entity": "Alabama", "Code": "", "Year": "1950", "Measles case rate per 100,000 population": "53.34757"}, {"Entity": "Alabama", "Code": "", "Year": "1951", "Measles case rate per 100,000 population": "107.77062"}, {"Entity": "Alabama", "Code": "", "Year": "1952", "Measles case rate per 100,000 population": "521.93567"}, {"Entity": "Alabama", "Code": "", "Year": "1953", "Measles case rate per 100,000 population": "97.74045"}, {"Entity": "Alabama", "Code": "", "Year": "1954", "Measles case rate per 100,000 population": "280.1114"}, {"Entity": "Alabama", "Code": "", "Year": "1955", "Measles case rate per 100,000 population": "69.60253"}, {"Entity": "Alabama", "Code": "", "Year": "1956", "Measles case rate per 100,000 population": "238.25084"}, {"Entity": "Alabama", "Code": "", "Year": "1957", "Measles case rate per 100,000 population": "293.2738"}, {"Entity": "Alabama", "Code": "", "Year": "1958", "Measles case rate per 100,000 population": "241.61737"}, {"Entity": "Alabama", "Code": "", "Year": "1959", "Measles case rate per 100,000 population": "108.10039"}, {"Entity": "Alabama", "Code": "", "Year": "1960", "Measles case rate per 100,000 population": "63.314816"}, {"Entity": "Alabama", "Code": "", "Year": "1961", "Measles case rate per 100,000 population": "82.0651"}, {"Entity": "Alabama", "Code": "", "Year": "1962", "Measles case rate per 100,000 population": "67.76251"}, {"Entity": "Alabama", "Code": "", "Year": "1963", "Measles case rate per 100,000 population": "34.71811"}, {"Entity": "Alabama", "Code": "", "Year": "1964", "Measles case rate per 100,000 population": "532.75146"}, {"Entity": "Alabama", "Code": "", "Year": "1965", "Measles case rate per 100,000 population": "68.07018"}, {"Entity": "Alabama", "Code": "", "Year": "1966", "Measles case rate per 100,000 population": "52.286053"}, {"Entity": "Alabama", "Code": "", "Year": "1967", "Measles case rate per 100,000 population": "38.85646"}, {"Entity": "Alabama", "Code": "", "Year": "1968", "Measles case rate per 100,000 population": "4.580446"}, {"Entity": "Alabama", "Code": "", "Year": "1969", "Measles case rate per 100,000 population": "0.34848872"}, {"Entity": "Alabama", "Code": "", "Year": "1970", "Measles case rate per 100,000 population": "14.095953"}, {"Entity": "Alabama", "Code": "", "Year": "1971", "Measles case rate per 100,000 population": "55.648033"}, {"Entity": "Alabama", "Code": "", "Year": "1972", "Measles case rate per 100,000 population": "4.007972"}, {"Entity": "Alabama", "Code": "", "Year": "1973", "Measles case rate per 100,000 population": "0.41860166"}, {"Entity": "Alabama", "Code": "", "Year": "1974", "Measles case rate per 100,000 population": "0.57849246"}, {"Entity": "Alabama", "Code": "", "Year": "1975", "Measles case rate per 100,000 population": "0.13577762"}, {"Entity": "Alabama", "Code": "", "Year": "1976", "Measles case rate per 100,000 population": "0"}, {"Entity": "Alabama", "Code": "", "Year": "1977", "Measles case rate per 100,000 population": "2.087637"}, {"Entity": "Alabama", "Code": "", "Year": "1978", "Measles case rate per 100,000 population": "1.5642651"}, {"Entity": "Alabama", "Code": "", "Year": "1979", "Measles case rate per 100,000 population": "2.428869"}, {"Entity": "Alabama", "Code": "", "Year": "1980", "Measles case rate per 100,000 population": "0.5634859"}, {"Entity": "Alabama", "Code": "", "Year": "1981", "Measles case rate per 100,000 population": "0.050988555"}, {"Entity": "Alabama", "Code": "", "Year": "1982", "Measles case rate per 100,000 population": "0.050901063"}, {"Entity": "Alabama", "Code": "", "Year": "1983", "Measles case rate per 100,000 population": "0.12696685"}, {"Entity": "Alabama", "Code": "", "Year": "1984", "Measles case rate per 100,000 population": "0.075838566"}, {"Entity": "Alabama", "Code": "", "Year": "1985", "Measles case rate per 100,000 population": "0"}, {"Entity": "Alabama", "Code": "", "Year": "1986", "Measles case rate per 100,000 population": "0.050055563"}, {"Entity": "Alabama", "Code": "", "Year": "1987", "Measles case rate per 100,000 population": "0.09952034"}, {"Entity": "Alabama", "Code": "", "Year": "1988", "Measles case rate per 100,000 population": "0.024827037"}, {"Entity": "Alabama", "Code": "", "Year": "1989", "Measles case rate per 100,000 population": "1.5120523"}, {"Entity": "Alabama", "Code": "", "Year": "1990", "Measles case rate per 100,000 population": "0.69092196"}, {"Entity": "Alabama", "Code": "", "Year": "1991", "Measles case rate per 100,000 population": "0.048838664"}, {"Entity": "Alabama", "Code": "", "Year": "1992", "Measles case rate per 100,000 population": "0"}, {"Entity": "Alabama", "Code": "", "Year": "1993", "Measles case rate per 100,000 population": "0.0238248"}, {"Entity": "Alabama", "Code": "", "Year": "1994", "Measles case rate per 100,000 population": "0"}, {"Entity": "Alabama", "Code": "", "Year": "1995", "Measles case rate per 100,000 population": "0"}, {"Entity": "Alabama", "Code": "", "Year": "1996", "Measles case rate 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"Code": "", "Year": "2016", "Measles case rate per 100,000 population": "0.020526757"}, {"Entity": "Alabama", "Code": "", "Year": "2017", "Measles case rate per 100,000 population": "0"}, {"Entity": "Alabama", "Code": "", "Year": "2018", "Measles case rate per 100,000 population": "0"}, {"Entity": "Alabama", "Code": "", "Year": "2019", "Measles case rate per 100,000 population": "0"}, {"Entity": "Alabama", "Code": "", "Year": "2020", "Measles case rate per 100,000 population": "0"}, {"Entity": "Alabama", "Code": "", "Year": "2021", "Measles case rate per 100,000 population": "0"}, {"Entity": "Alabama", "Code": "", "Year": "2022", "Measles case rate per 100,000 population": "0"}, {"Entity": "Alaska", "Code": "", "Year": "1954", "Measles case rate per 100,000 population": "690.93695"}, {"Entity": "Alaska", "Code": "", "Year": "1955", "Measles case rate per 100,000 population": "241.65024"}], "rows_tail": [{"Entity": "Wisconsin", "Code": "", "Year": "2007", "Measles case rate per 100,000 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{"Entity": "Wyoming", "Code": "", "Year": "1932", "Measles case rate per 100,000 population": "241.93198"}, {"Entity": "Wyoming", "Code": "", "Year": "1933", "Measles case rate per 100,000 population": "268.42722"}, {"Entity": "Wyoming", "Code": "", "Year": "1934", "Measles case rate per 100,000 population": "950.12286"}, {"Entity": "Wyoming", "Code": "", "Year": "1935", "Measles case rate per 100,000 population": "932.40094"}, {"Entity": "Wyoming", "Code": "", "Year": "1936", "Measles case rate per 100,000 population": "57.858807"}, {"Entity": "Wyoming", "Code": "", "Year": "1937", "Measles case rate per 100,000 population": "69.88896"}, {"Entity": "Wyoming", "Code": "", "Year": "1938", "Measles case rate per 100,000 population": "248.93805"}, {"Entity": "Wyoming", "Code": "", "Year": "1939", "Measles case rate per 100,000 population": "794.7698"}, {"Entity": "Wyoming", "Code": "", "Year": "1940", "Measles case rate per 100,000 population": "257.34265"}, {"Entity": "Wyoming", "Code": "", "Year": "1941", "Measles case rate per 100,000 population": "329.62988"}, {"Entity": "Wyoming", "Code": "", "Year": "1942", "Measles case rate per 100,000 population": "652.3357"}, {"Entity": "Wyoming", "Code": "", "Year": "1943", "Measles case rate per 100,000 population": "1142.1776"}, {"Entity": "Wyoming", "Code": "", "Year": "1944", "Measles case rate per 100,000 population": "717.87714"}, {"Entity": "Wyoming", "Code": "", "Year": "1945", "Measles case rate per 100,000 population": "107.84195"}, {"Entity": "Wyoming", "Code": "", "Year": "1946", "Measles case rate per 100,000 population": "419.26578"}, {"Entity": "Wyoming", "Code": "", "Year": "1947", "Measles case rate per 100,000 population": "171.9211"}, {"Entity": "Wyoming", "Code": "", "Year": "1948", "Measles case rate per 100,000 population": "806.6283"}, {"Entity": "Wyoming", "Code": "", "Year": "1949", "Measles case rate per 100,000 population": "201.2428"}, {"Entity": "Wyoming", "Code": "", "Year": "1950", "Measles 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population": "280.96902"}, {"Entity": "Wyoming", "Code": "", "Year": "1960", "Measles case rate per 100,000 population": "209.45822"}, {"Entity": "Wyoming", "Code": "", "Year": "1961", "Measles case rate per 100,000 population": "153.85208"}, {"Entity": "Wyoming", "Code": "", "Year": "1962", "Measles case rate per 100,000 population": "132.30013"}, {"Entity": "Wyoming", "Code": "", "Year": "1963", "Measles case rate per 100,000 population": "97.81885"}, {"Entity": "Wyoming", "Code": "", "Year": "1964", "Measles case rate per 100,000 population": "84.2815"}, {"Entity": "Wyoming", "Code": "", "Year": "1965", "Measles case rate per 100,000 population": "225.97884"}, {"Entity": "Wyoming", "Code": "", "Year": "1966", "Measles case rate per 100,000 population": "69.58985"}, {"Entity": "Wyoming", "Code": "", "Year": "1967", "Measles case rate per 100,000 population": "35.36836"}, {"Entity": "Wyoming", "Code": "", "Year": "1968", "Measles case rate per 100,000 population": "14.8000145"}, {"Entity": "Wyoming", "Code": "", "Year": "1969", "Measles case rate per 100,000 population": "2.4291818"}, {"Entity": "Wyoming", "Code": "", "Year": "1970", "Measles case rate per 100,000 population": "3.305805"}, {"Entity": "Wyoming", "Code": "", "Year": "1971", "Measles case rate per 100,000 population": "24.954128"}, {"Entity": "Wyoming", "Code": "", "Year": "1972", "Measles case rate per 100,000 population": "14.668155"}, {"Entity": "Wyoming", "Code": "", "Year": "1973", "Measles case rate per 100,000 population": "18.622236"}, {"Entity": "Wyoming", "Code": "", "Year": "1974", "Measles case rate per 100,000 population": "4.64651"}, {"Entity": "Wyoming", "Code": "", "Year": "1975", "Measles case rate per 100,000 population": "0.7851841"}, {"Entity": "Wyoming", "Code": "", "Year": "1976", "Measles case rate per 100,000 population": "1.0066743"}, {"Entity": "Wyoming", "Code": "", "Year": "1977", "Measles case rate per 100,000 population": "4.591956"}, {"Entity": "Wyoming", "Code": 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Project Tycho, “Project Tycho - Measles cases”; Centers for Disease Prevention and Control, “CDC Summary of Notifiable Infectious Diseases - Measles”; Centers for Disease Control and Prevention, “Reported measles cases - CDC”; Centers for Disease Control and Prevention, “National Notifiable Diseases Surveillance System - Measles”; U.S. Census Bureau, “Annual Estimates of the Population for each of the States in the USA” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1013902.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"grapher_slug": "deaths-due-to-measles-by-region", "source_url": "https://ourworldindata.org/grapher/deaths-due-to-measles-by-region", "parse_error": "Failed to fetch https://ourworldindata.org/grapher/deaths-due-to-measles-by-region.csv"}, {"title": "Share of one-year-olds vaccinated against measles", "source_url": "https://ourworldindata.org/grapher/share-of-children-vaccinated-against-measles.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Measles, first dose (MCV1)"], "row_count_total": 9048, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Measles, first dose (MCV1)": "11"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Measles, first dose (MCV1)": "8"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Measles, first dose (MCV1)": "9"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Measles, first dose (MCV1)": "14"}, {"Entity": 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"Year": "2001", "Measles, first dose (MCV1)": "73"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Measles, first dose (MCV1)": "70"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Measles, first dose (MCV1)": "68"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Measles, first dose (MCV1)": "66"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Measles, first dose (MCV1)": "67"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Measles, first dose (MCV1)": "68"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Measles, first dose (MCV1)": "69"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Measles, first dose (MCV1)": "70"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Measles, first dose (MCV1)": "76"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Measles, first dose (MCV1)": "90"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Measles, first dose (MCV1)": "92"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Measles, first dose (MCV1)": "97"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Measles, first dose (MCV1)": "93"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Measles, first dose (MCV1)": "92"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Measles, first dose (MCV1)": "86"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Measles, first dose (MCV1)": "95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Measles, first dose (MCV1)": "90"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Measles, first dose (MCV1)": "88"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Measles, first dose (MCV1)": "85"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Measles, first dose (MCV1)": "85"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Measles, first dose (MCV1)": "88"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Measles, first dose (MCV1)": "90"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Measles, first dose (MCV1)": "90"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Measles, first dose (MCV1)": "90"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "share-of-children-vaccinated-against-measles", "metadata_url": "https://ourworldindata.org/grapher/share-of-children-vaccinated-against-measles.metadata.json", "chart_title": "Share of one-year-olds vaccinated against measles", "chart_subtitle": "Share of one-year-olds who have received the first dose of the measles vaccine (MCV1).", "chart_note": "Measles is a highly contagious viral disease, most common in young children. Its effects include blindness, inflammation of the brain, severe diarrhea, and severe respiratory infections such as pneumonia.", "chart_citation": "WHO & UNICEF (2025); UN, World Population Prospects (2024)", "original_chart_url": "https://ourworldindata.org/grapher/share-of-children-vaccinated-against-measles", "owid_column_metadata": {"Share of one-year-olds who have had one dose of the measles vaccine": {"titleShort": "Share of one-year-olds who have had one dose of the measles vaccine", "titleLong": "Share of one-year-olds who have had one dose of the measles vaccine", "descriptionShort": "Share of one-year-olds who have had the first dose of the measles vaccine in a given year.", "descriptionKey": ["Measles is one of the most contagious diseases. The first dose of measles vaccine is critical for building immunity. In countries where the national schedule recommends the first dose at 12 months or later based on local epidemiology, these estimates reflect the percentage of children who received their first dose as recommended.", "This chart shows official estimates of national immunization coverage published by the WHO and UNICEF. The estimates include all WHO member states, even those that did not report 2023 data. For non-reporting countries, WHO uses statistical methods to extrapolate from previously reported data, ensuring global coverage can be assessed.", "Global and regional vaccination coverage is calculated using population-weighted averages. In 2023, approximately 5% of countries did not report data, requiring extrapolation from their 2022 data to maintain complete global estimates.", "These estimates combine several sources: official administrative data from health facilities, coverage surveys that meet WHO quality standards, and other relevant information like vaccine supply issues or schedule changes. The accuracy of these estimates depends on how complete and reliable each country’s reporting systems are."], "shortUnit": "%", "unit": "%", "timespan": "1980-2024", "type": "Numeric", "owidVariableId": 1077440, "shortName": "coverage__antigen_mcv1", "lastUpdated": "2025-07-15", "nextUpdate": "2026-07-15", "citationShort": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "WHO & UNICEF (2025); UN, World Population Prospects (2024) – processed by Our World in Data. “Share of one-year-olds who have had one dose of the measles vaccine” [dataset]. WHO & UNICEF, “WHO Immunization Data - Vaccination coverage”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077440.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "05647cca2b6100a56c9b"},
{"raw_link": "https://ourworldindata.org/where-are-babies-at-lowest-risk-of-dying", "title": "Where in the world are babies at the lowest risk of dying?", "context": "Home\nChild & Infant Mortality\nWhere in the world are babies at the lowest risk of dying?\nIt’s difficult to compare countries because they don’t always measure infant mortality in the same way.\nBy\nHannah Ritchie\nMay 12, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nWhich country is the safest for a baby to be born? Answering this question might seem easy: divide the number of infants who die by the total number of infants born; make a map of these rates and find the lowest number.\nBut while these comparisons are very helpful in identifying the huge differences across countries, things get a bit more complicated when it comes to the small differences between the countries with the\nlowest\nmortality rates. This is because countries measure infant deaths slightly differently, specifically the number of live births that are recorded.\nTwo main factors can affect which babies are included as live births.\nThe first is deciding whether a baby has shown “signs of life”. The World Health Organization\nhas clear\n(and deliberately broad) criteria for this: live births should include\nany\nsign of life, even if it is assisted or very brief.\n1\nThis can include breathing, a heartbeat, a pulsating umbilical cord, or clear movement of muscles.\n2\nThese are only\nrecommended\ncriteria, but most rich countries now follow them.\n3\nWhile these protocols might sound simple to follow, not every case will be straightforward in practice, and decisions will often rely on clinical judgment from health professionals. That can introduce some variability not only between countries but also between hospitals\nwithin\ncountries. But overall, I expect this effect to be the much smaller of the two factors and don’t think it will have a notable impact on our country comparisons later.\nThe second factor, which has a\nmuch\nlarger impact on the rankings, is whether extremely premature babies should be included in these statistics. Most of us are taught that a pregnancy lasts 40 weeks. But many babies are born much earlier: at 35 weeks, 30 weeks, and even some younger than 22 weeks. Should\nall\nof these newborns be included, even if they’re born so prematurely that there’s almost no chance of them surviving? Countries' statistical officials answer this question differently, and this can affect the infant mortality rate they record and report.\nTo take an extreme example, let’s imagine Country A includes all infant deaths, regardless of when they’re born: 20-week babies are treated the same as those born after 40 weeks. Country B decides it will exclude all\npre-term births\n, which means only babies born after 37 weeks are included. If everything else is equal, country B would have a much lower\nreported\ninfant mortality rate because they’ve excluded many infants who are at a higher risk of dying.\nTo understand how small differences in this “inclusion threshold” can affect infant mortality rates, see the chart below. It shows the share of newborns born at different gestational ages who died within the first year of life in England and Wales.\n4\nOn the left, you can see that almost all infants born before 22 weeks of gestation died. Just 1% survived. At 22 weeks, 86% died. But look at how rapidly survival rates improve after that: a baby born at 23 weeks had a two-thirds chance of dying. By 24 weeks — just one week later — the odds have reversed, and it has a two-thirds chance of surviving. By 27 weeks, more than 90% of babies survived, and from there, the odds continued to improve.\nDownload\nThe fact that survival rates change so dramatically within only a few weeks of gestation, especially from 22 to 27 weeks, means that where the threshold is set for reporting births and deaths matters a lot for the reported mortality rate.\nFew comparisons are as extreme as my “Country A vs. Country B” thought experiment earlier. Most countries include some pre-term infants in their statistics. But there are still important differences. Some countries — like the United States, the United Kingdom, and Japan — include\nall\nlive births, even if they’re born before 22 weeks. Others do use a threshold, such as 22, 24, or 26 weeks.\nIn this article, I’ll examine how countries with low mortality rates compare when adjusting for these reporting differences. This will help us answer the question, “Where are infants most likely to survive?” and, in turn, help us see how we can save more children elsewhere.\nNewborn mortality rates can change a lot, depending on age thresholds\nLet’s first look at mortality in the\nfirst month\nof life. This is called neonatal mortality.\nThe OECD\npublishes data\nthat helps us address this question; the data I use here is for 2021.\n5\nIt provides\nreported\nmortality rates, based on each country’s national definition and methodology for what live births are and aren’t included. These are shown as the red dots in the chart below.\nHowever, it also publishes rates that set a “gestational age” threshold of 22 weeks. This means only newborns born from 22 weeks onward are included; extremely preterm births earlier than 22 weeks are excluded. This gives us a consistent measure across countries. These rates are shown as the blue dots.\nDownload\nThe precise birth data needed to produce these comparable rates is not available for all countries, so only a selection of OECD countries is shown. I’ve also based this on countries with more than 20,000 births per year for reasons I explain in the footnote.\n6\nThe first thing to notice is the cluster of countries at the bottom of the chart: Japan, Sweden, South Korea, and Finland had the lowest neonatal mortality rates. This is true even when the consistent 22-week threshold is used.\nHowever, you can also see that this threshold adjustment makes a big difference for some countries. Denmark has one of the lowest neonatal mortality rates when we use this consistent comparison (the blue dots); we might miss this by looking at the raw reported mortality rate, which is closer to the middle of the pack.\nMortality rates in the United States also change a lot when we apply the 22-week threshold. Its mortality rate drops from 0.35% to 0.27%, indicating that many of the deaths recorded in the US are extremely pre-term. This is also true for Switzerland and Austria.\nWhat about the share of newborns who die in the first\nyear\nof life?\nOf course, we don’t just care about protecting newborns in the first\nmonth\nof life. They’re also at relatively high risk throughout the first\nyear\n. This is called infant mortality.\nThe data is in the chart below. It’s in the same format as before: red dots for reported rates and blue for adjusted 22-week rates.\nDownload\nThe patterns don’t change dramatically. Countries like Japan and the Nordics — Finland, Norway, Sweden, Denmark — are all among the best. One of the biggest differences is South Korea. It was the second-best performer in neonatal mortality, but falls in the rankings when the next 11 months are included. To be clear, it still has very low infant mortality rates, but not as low as expected from its neonatal rates. (More on this later.)\nAgain, the 22-week threshold matters greatly for some countries, namely Denmark, Austria, Switzerland, and the United States. This suggests that their national statistics include a substantial number of deaths of infants born under 22 weeks.\nEven when we use the consistent definition — the blue dots — it’s clear that outcomes in the United States are worse than in most European countries (and rich countries in Asia). An infant is around three times more likely to die in the US than in Finland, Japan, or Sweden. This is not just because of differences in measurement; this is a real difference in health outcomes.\nAnother way to understand the differences in these rates is to look at the breakdown of neonatal rates (deaths in the first month of life) and\npost-\nneonatal rates (deaths in the next 11 months). The chart below shows this; I’m using the 22-week threshold for all countries to make it consistent.\nDownload\nTypically, countries with the highest neonatal mortality rates also have high post-neonatal rates. In other words, no country has a high infant mortality rate\nonly\nbecause it has a high neonatal mortality rate. If a country is good at preventing deaths in the first month, it’s generally good at avoiding them in the next 11 months.\nBut there are some clear differences. Why does South Korea have such low neonatal rates, then fairly poor post-neonatal rates compared to other best performers? A baby in South Korea is three times more likely to die from 1 to 11 months than in Finland, and twice as likely as those in Norway.\nSwitzerland stands out in the opposite direction: it has moderate neonatal rates but incredibly low post-neonatal ones.\nWhat explains these differences across countries?\nUntangling these differences is difficult, but it’s useful to consider\nwhat\ninfants are dying from in these different stages of life. Neonatal mortality is strongly influenced by conditions around the time of birth (or before it). Being born prematurely drastically increases the risk of dying, which means factors like maternal health and midwife visits during pregnancy matter a lot. What’s also crucial is the level of specialized care for premature newborns. Countries with widely accessible and high-quality neonatal intensive care units tend to perform very well. The expansion of these units in South Korea has been a large part of its success in lowering neonatal mortality in the last few decades.\n7\nCountries like Japan, South Korea, and the Nordics have invested heavily in both maternal care during pregnancy and intensive care units after birth. That explains why their neonatal mortality rates are so low.\nPost-neonatal mortality is more strongly driven by environmental and social factors. After one month, infants tend to die from infections, accidents, injuries, or sleep-related deaths, or longer-term complications from premature births. The accessibility of healthcare can make a big difference here, which means that countries with larger inequalities in healthcare access and proximity are more likely to have higher rates of infant mortality.\n8\nStudies in South Korea, for example, suggest that there are still large differences in infant mortality rates across regions, partly due to differences in the quality and availability of healthcare (with the city of Seoul performing best).\n7\nGood policies and norms around parental leave can also make a difference: they not only reduce pressure on parents, leading to a faster recovery after childbirth, but can also improve the quality of care. Parents spend more time with their children, pay closer attention to their baby’s health and nutrition, and attend more medical appointments. On paper, countries like South Korea have relatively generous policies for parental leave, but uptake is very low. If we look at the\namount of public money\nspent on maternity and paternity leave per live birth, South Korea is almost at the bottom of the list. Japan is also close to the bottom, which might partly explain why its post-neonatal mortality rates are slightly higher than in other top-performing countries.\nFinally, there can be trade-offs between saving very premature babies in the first few days of life and losing them later. Complications from extreme prematurity or congenital abnormalities can increase the risk of dying in the following 11 months. So if a country has very low neonatal mortality rates — because they invest heavily in saving\nany\nnewborn — they could have slightly higher post-neonatal rates, having simply “delayed” these deaths.\nNone of these issues alone explains these different patterns across countries: it’s usually a complicated mix of genetic, environmental, and behavioral factors. But these areas could explain some differences.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nSo, where is the safest place for a baby to be born?\nDifferences in measurement matter when trying to answer this question accurately. We want to know which countries perform best so we can learn from them. But if we highlight those who are only doing well because of nuances in reporting, we might copy practices or policies that aren’t helpful.\nLet’s return to the initial question: where are infant mortality rates the lowest?\nJapan, Sweden, and Finland are at the very bottom. They’re consistently among the best, even when we adjust for reporting inconsistencies. The other Nordic countries — Denmark and Norway — also have very low mortality rates.\nOther countries, such as South Korea, perform extremely well when we look at deaths in the first\nmonth\nof life, but tend to fall behind the top performers when deaths in the following 11 months are also included.\nIt’s worth highlighting that a few smaller countries weren’t included because they didn’t meet our threshold of 20,000 births per year, but some also do very well. Estonia and Slovenia have made\nrapid progress\nin reducing infant mortality rates. They can often have rates as low as the top performers, but with large annual changes because they have so few births.\nThe differences between the top performers and many other high-income countries are not small. A baby in the UK, France, or the US can be two to three times more likely to die than one in Japan or Finland. But it doesn’t have to be this way: these countries once had rates as high as America or Britain, and we know there are things we can do to save more lives — even in the richest countries.\nAcknowledgments\nMany thanks to Max Roser and Edouard Mathieu for their comments and feedback on this article.\nContinue reading on Our World in Data\nChildren in rich countries are much less likely to die than a few decades ago, but we rarely hear about this progress\nIn most rich countries, child mortality has more than halved in the last thirty years; we know we can go further.\nChild mortality: an everyday tragedy of enormous scale that we can make progress against\nWe live in a world in which ten children die every minute.\nMortality in the past: every second child died\nThe chances that a newborn survives childhood have increased from 50% to 96% globally. How do we know about the mortality of children in the past? And what can we learn from it for our future?\nEndnotes\nAs it states: “Live birth is the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of pregnancy, which after such separation, breathes or shows any other evidence of life, such as beating of the heart, pulsation of the umbilical cord or definite movement of voluntary muscles, whether or not the umbilical cord has been cut or the placenta is attached; each product of such a birth is considered liveborn.”\nThis is also included in the latest edition of the International Statistical Classification of Diseases (ICD-10), which is the standardized international methodology used to code health and medical diagnoses.\nIn the past, some countries had stricter criteria, so far more babies were classified as stillbirths rather than live births, even if there were some brief signs of life.\nThis was based on data from 2022 from the\nOffice for National Statistics\n(ONS).\nResearchers also carried out this analysis using OECD, which was published in Pediatrics (but for 2020, rather than 2021).\nLiang, F. W., Chang, Y. S., Chou, C. S., Jeng, M. J., Kawachi, I., & Lu, T. H. (2024).\nNeonatal Mortality Rates With and Without a Minimum Threshold\n. JAMA Network Open, 7(11), e2447487-e2447487.\nOne challenge with infant mortality data, especially where rates are relatively low, is that they can be noisy from year to year in small countries. Take the example of Estonia: it has\nfewer than 20\nneonatal deaths per year. Even small differences in this figure can substantially change the neonatal mortality rate, meaning its ranking is often dictated by a very small number of individual cases.\nI have only included countries with more than 20,000 births per year for this analysis. If we take the average infant mortality rate of around 0.5% across the EU, this would result in around 100 deaths per year. Any fewer than 20,000 births at this level of mortality rate would be vulnerable to large variability from year to year. In this case, Estonia, Latvia, and Slovenia were not included since\nthey have fewer\nthan 20,000 births per year.\nWoo, H., & Kim, J. S. (2023).\nRegional Disparities in the Infant Mortality Rate in Korea Between 2001 and 2021\n. Journal of Korean Medical Science, 38(44).\nAmiri, A., Vehviläinen-Julkunen, K., Solankallio-Vahteri, T., & Tuomi, S. (2020). Impact of nurse staffing on reducing infant, neonatal and perinatal mortality rates: evidence from panel data analysis in 35 OECD countries. International journal of nursing sciences, 7(2), 161-169.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2025) - “Where in the world are babies at the lowest risk of dying?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20251229-155238/where-are-babies-at-lowest-risk-of-dying.html' [Online Resource] (archived on December 29, 2025).\nBibTeX citation\n@article{owid-where-are-babies-at-lowest-risk-of-dying,\nauthor = {Hannah Ritchie},\ntitle = {Where in the world are babies at the lowest risk of dying?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20251229-155238/where-are-babies-at-lowest-risk-of-dying.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "where-are-babies-at-lowest-risk-of-dying", "source_url": "https://ourworldindata.org/where-are-babies-at-lowest-risk-of-dying", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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"Year": "2019", "Infant mortality rate": "4.5962663"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Infant mortality rate": "4.4898086"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Infant mortality rate": "4.362161"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Infant mortality rate": "4.222358"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Infant mortality rate": "4.057281"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "infant-mortality", "metadata_url": "https://ourworldindata.org/grapher/infant-mortality.metadata.json", "chart_title": "Infant mortality rate", "chart_subtitle": "Estimated share of newborns who die before they are one year old.", "chart_note": null, "chart_citation": "United Nations Inter-agency Group for Child Mortality Estimation (2025)", "original_chart_url": "https://ourworldindata.org/grapher/infant-mortality", "owid_column_metadata": {"Infant mortality rate of babies aged under one year, per 100 live births": {"titleShort": "Infant mortality rate", "titleLong": "Infant mortality rate", "descriptionShort": "The estimated number of deaths of children aged under one year, per 100 live births .", "shortUnit": "%", "unit": "deaths per 100 live births", "timespan": "1932-2023", "type": "Numeric", "owidVariableId": 1027774, "shortName": "observation_value__indicator_infant_mortality_rate__sex_total__wealth_quintile_total__unit_of_measure_deaths_per_100_live_births", "lastUpdated": "2025-03-25", "citationShort": "United Nations Inter-agency Group for Child Mortality Estimation (2025) – with minor processing by Our World in Data", "citationLong": "United Nations Inter-agency Group for Child Mortality Estimation (2025) – with minor processing by Our World in Data. “Infant mortality rate” [dataset]. United Nations Inter-agency Group for Child Mortality Estimation, “United Nations Inter-agency Group for Child Mortality Estimation”; Various sources, “Population” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1027774.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Number of neonatal deaths", "source_url": "https://ourworldindata.org/grapher/number-of-neonatal-deaths-igme.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Neonatal deaths"], "row_count_total": 10491, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Neonatal deaths": "47026"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Neonatal deaths": "47174"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Neonatal deaths": "48018"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Neonatal deaths": "47288"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Neonatal deaths": "48325"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Neonatal deaths": "53705"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Neonatal deaths": "59013"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Neonatal 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{"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Neonatal deaths": "53121"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Neonatal deaths": "52740"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Neonatal deaths": "52292"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Neonatal deaths": "51406"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Neonatal deaths": "50351"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1978", "Neonatal deaths": "943675"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1979", "Neonatal deaths": "951892"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1980", "Neonatal deaths": "957873"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1981", "Neonatal deaths": "999330"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1982", "Neonatal deaths": "1007516"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1983", "Neonatal deaths": "1019655"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1984", "Neonatal deaths": "1033326"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Neonatal deaths": "1096486"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Neonatal deaths": "1096149"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Neonatal deaths": "1095635"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Neonatal deaths": "1102270"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Neonatal deaths": "1105880"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Neonatal deaths": "1170464"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Neonatal deaths": "1179094"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Neonatal deaths": "1187491"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Neonatal deaths": "1194166"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Neonatal deaths": "1199089"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Neonatal 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"Africa", "Code": "OWID_AFR", "Year": "2007", "Neonatal deaths": "1206492"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Neonatal deaths": "1209013"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Neonatal deaths": "1209654"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Neonatal deaths": "1209134"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Neonatal deaths": "1209842"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Neonatal deaths": "1205339"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Neonatal deaths": "1201581"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Neonatal deaths": "1202689"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Neonatal deaths": "1199009"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Neonatal deaths": "1192876"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Neonatal deaths": "1189147"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Neonatal deaths": "1179219"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Neonatal deaths": "1170857"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Neonatal deaths": "1161905"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2021", "Neonatal deaths": "1157702"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2022", "Neonatal deaths": "1153024"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2023", "Neonatal deaths": "1145027"}, {"Entity": "Albania", "Code": "ALB", "Year": "1980", "Neonatal deaths": "1561"}, {"Entity": "Albania", "Code": "ALB", "Year": "1981", "Neonatal deaths": "1463"}, {"Entity": "Albania", "Code": "ALB", "Year": "1982", "Neonatal deaths": "1395"}, {"Entity": "Albania", "Code": "ALB", "Year": "1983", "Neonatal deaths": "1317"}, {"Entity": "Albania", "Code": "ALB", "Year": "1984", "Neonatal deaths": "1268"}, {"Entity": "Albania", "Code": "ALB", "Year": "1985", "Neonatal deaths": "1222"}, {"Entity": "Albania", "Code": "ALB", "Year": "1986", "Neonatal deaths": "1183"}, {"Entity": "Albania", "Code": "ALB", "Year": "1987", "Neonatal deaths": "1157"}, {"Entity": "Albania", "Code": "ALB", "Year": "1988", "Neonatal deaths": "1135"}, {"Entity": "Albania", "Code": "ALB", "Year": "1989", "Neonatal deaths": "1108"}, {"Entity": "Albania", "Code": "ALB", "Year": "1990", "Neonatal deaths": "1080"}, {"Entity": "Albania", "Code": "ALB", "Year": "1991", "Neonatal deaths": "1048"}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "Neonatal deaths": "1014"}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "Neonatal deaths": "980"}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "Neonatal deaths": "963"}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "Neonatal deaths": "941"}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "Neonatal deaths": "897"}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "Neonatal deaths": "836"}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "Neonatal deaths": "784"}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "Neonatal deaths": "728"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Neonatal deaths": "674"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Neonatal deaths": "619"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Neonatal deaths": "563"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Neonatal deaths": "546"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Neonatal deaths": "499"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Neonatal deaths": "450"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Neonatal deaths": "397"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Neonatal deaths": "348"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Neonatal deaths": "304"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Neonatal deaths": "268"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Neonatal deaths": "241"}, {"Entity": 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"Code": "ZMB", "Year": "1992", "Neonatal deaths": "13523"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Neonatal deaths": "13729"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Neonatal deaths": "13995"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Neonatal deaths": "14311"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Neonatal deaths": "14620"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Neonatal deaths": "14905"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Neonatal deaths": "15199"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Neonatal deaths": "15405"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Neonatal deaths": "15437"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Neonatal deaths": "15232"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Neonatal deaths": "14961"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Neonatal deaths": "14660"}, {"Entity": "Zambia", "Code": "ZMB", "Year": 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{"raw_link": "https://ourworldindata.org/child-mortality-rich-countries-decline", "title": "Children in rich countries are much less likely to die than a few decades ago, but we rarely hear about this progress", "context": "Home\nChild & Infant Mortality\nChildren in rich countries are much less likely to die than a few decades ago, but we rarely hear about this progress\nIn most rich countries, child mortality has more than halved in the last thirty years; we know we can go further.\nBy\nHannah Ritchie\nMay 5, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nAs recently as 1990, one in five newborns in Ethiopia would die before the age of five. This was the norm across many poorer countries. Since most couples would have\nmore than five children\n, many parents had to see one of their children die. If you were born into one of these families — and among those that survived — there’s a high chance that you would have grieved the loss of a brother or sister.\nBut in the last 30 years, child mortality rates have plummeted in low-income countries. In Ethiopia, they’ve dropped from 20% to 5%, as shown in the chart below. The Gambia and Afghanistan are just two more examples of countries with dramatic declines.\nI’ve also shown the change in rich countries on the chart. From this way of looking at the data, it might seem that child mortality is no longer an issue in rich countries. Their rates are very low and barely visible compared to many other countries. It also looks like almost no progress has been made in the last 30 years: mortality was low and is still low.\nBut I think both of these conclusions are wrong. Countries in the European Union, Japan, South Korea, the United Kingdom — the list goes on — have made childhood much safer in my own 30-year lifetime.\n1\nIt’s just something we rarely hear about. I also don’t think that this is a “solved problem”; it is still too common for parents to see their children die, and there’s a lot more that we can do to save their lives.\nWe have this perception because we compare countries by their\nabsolute\nreduction in child mortality. Many low- and middle-income countries have reduced these rates by 5, 10, or 20 percentage points over the last 30 years. Of course, that would be impossible for many richer countries: the child mortality rate in the European Union (EU) was around 1% in 1990, so the maximum reduction it could achieve in absolute terms would be one percentage point.\nIt’s only when we look at the\nrelative\nreduction in child mortality that we see that rich countries have also made impressive progress.\nThe chart below shows these same countries — or groups of countries — plotted as the\nchange\nin mortality rates since 1990. All of them have halved child mortality rates or more.\nIn the previous chart, progress in the EU looked a little underwhelming. But, in fact, rates have fallen by 69%. Even in Japan, one of\nthe safest\ncountries to be born in, child mortality rates have dropped by almost two-thirds. Those are not small reductions. Children are much less likely to die than they were in 1990.\nThe small increase for Japan in 2011 was due to the Tōhoku earthquake and tsunami.\nBefore studying this data, I probably\nwouldn’t\nhave guessed that if I had a baby today they’d have less than half the risk of dying in childhood than I did.\n2\nIt’s progress that we almost never see on the news.\nI think it’s important to highlight this point for two reasons.\nFirst, the idea that progress on health has stalled (or even regressed) in rich countries is, I think, a common one. I’ve previously held that view myself. But it’s not true: improved treatments and vaccinations developed by scientists, dedicated care from doctors, midwives, and nurses, health policies developed by governments, and parents' choices have made things much safer for children even in the world’s richest countries. These efforts were not for nothing: they’ve given kids a future and spared many families the pain of losing a child.\nSecond, child mortality in rich countries is not a “solved problem”.\n23,000 children still die\nin the United States every year. That’s around 50 times more than the number who die from natural disasters.\n3\nAnd more than the total number of homicides.\n4\nNo one would say that murders in the US are a “solved problem”.\nSo it would have been wrong of us to accept or be happy with where we were in 1990, with around 1% of children still dying. I also think it would be wrong of us to assume that 0.5% is the level we should accept today; we know that we can save more lives.\n5\nAcknowledgments\nMany thanks to Max Roser and Edouard Mathieu for feedback and suggestions on this article.\nContinue reading on Our World in Data\nWhere in the world are babies at the lowest risk of dying?\nIt’s difficult to compare countries because they don’t always measure infant mortality in the same way.\nChild mortality: an everyday tragedy of enormous scale that we can make progress against\nWe live in a world in which ten children die every minute.\nMortality in the past: every second child died\nThe chances that a newborn survives childhood have increased from 50% to 96% globally. How do we know about the mortality of children in the past? And what can we learn from it for our future?\nEndnotes\nI was born in 1993.\nI was born and still live in the United Kingdom, where child mortality rates have fallen by over 50% since the early 1990s.\nThe\nannual average\nof 436 disaster deaths in this decade is similar to previous decades, too.\n23,000 / 475 = 52.8 times.\nIn 2021, there\nwere around\n18,800 homicide deaths in the United States.\n0.4% is the average in the European Union, and 0.6%\nis the rate\nin the United States.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2025) - “Children in rich countries are much less likely to die than a few decades ago, but we rarely hear about this progress” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-090244/child-mortality-rich-countries-decline.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-child-mortality-rich-countries-decline,\nauthor = {Hannah Ritchie},\ntitle = {Children in rich countries are much less likely to die than a few decades ago, but we rarely hear about this progress},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-090244/child-mortality-rich-countries-decline.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "child-mortality-rich-countries-decline", "source_url": "https://ourworldindata.org/child-mortality-rich-countries-decline", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "In most rich countries, child mortality has more than halved in the last thirty years; we know we can go further.", "numeric_mentions": ["5,", "2025", "1990,", "30 years", "20%", "5%", "30", "1", "10,", "20 percentage points", "1%", "1990", "69%", "2011", "2", "23,000", "50", "3", "4", "0.5%", "5", "50%", "96%", "1993", "436", "475", "52.8", "2021,", "18,800", "0.4%", "0.6%", "20260518", "090244", "18,", "2026"], "numeric_evidence": [{"title": "Fertility rate: births per woman", "source_url": "https://ourworldindata.org/grapher/children-born-per-woman.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Total fertility rate"], "row_count_total": 19402, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1950", "Total fertility rate": "7.248"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1951", "Total fertility rate": "7.26"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1952", "Total fertility rate": "7.26"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1953", "Total fertility rate": "7.266"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1954", "Total fertility rate": "7.254"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1955", "Total fertility rate": "7.262"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1956", "Total fertility rate": "7.269"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1957", "Total fertility rate": "7.264"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1958", "Total fertility rate": "7.269"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1959", "Total fertility rate": "7.276"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1960", "Total fertility rate": "7.282"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Total fertility rate": "7.284"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Total fertility rate": "7.292"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Total fertility rate": "7.302"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Total fertility rate": "7.304"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Total fertility rate": "7.305"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Total fertility rate": "7.32"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Total fertility rate": "7.339"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Total fertility rate": "7.363"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1969", "Total fertility rate": "7.389"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1970", "Total fertility rate": "7.4"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1971", "Total fertility rate": "7.432"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1972", "Total fertility rate": "7.453"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Total fertility rate": "7.487"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Total fertility rate": "7.526"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Total fertility rate": "7.542"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Total fertility rate": "7.561"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Total fertility rate": "7.591"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Total fertility rate": "7.599"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Total fertility rate": "7.612"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Total fertility rate": "7.643"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Total fertility rate": "7.617"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Total fertility rate": "7.6"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Total fertility rate": "7.57"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Total fertility rate": "7.554"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Total fertility rate": "7.55"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Total fertility rate": "7.553"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Total fertility rate": "7.548"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Total fertility rate": "7.551"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Total fertility rate": "7.559"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Total fertility rate": "7.576"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Total fertility rate": "7.631"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Total fertility rate": "7.703"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Total fertility rate": "7.761"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Total fertility rate": "7.767"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Total fertility 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"Year": "1986", "Total fertility rate": "6.1838737"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Total fertility rate": "6.1125216"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Total fertility rate": "6.0366774"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Total fertility rate": "5.9492836"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Total fertility rate": "5.8539023"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Total fertility rate": "5.774524"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Total fertility rate": "5.6971273"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Total fertility rate": "5.625158"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Total fertility rate": "5.526201"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Total fertility rate": "5.463184"}], "rows_tail": [{"Entity": "Zambia", "Code": "ZMB", "Year": "1978", "Total fertility rate": 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"Total fertility rate": "7.231"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1963", "Total fertility rate": "7.23"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1964", "Total fertility rate": "7.23"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1965", "Total fertility rate": "7.231"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "Total fertility rate": "7.196"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "Total fertility rate": "7.157"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "Total fertility rate": "7.114"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "Total fertility rate": "7.036"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "Total fertility rate": "6.982"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "Total fertility rate": "6.939"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "Total fertility rate": "6.942"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "Total fertility rate": "6.942"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "Total fertility rate": "6.879"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Total fertility rate": "6.832"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Total fertility rate": "6.771"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Total fertility rate": "6.713"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Total fertility rate": "6.658"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Total fertility rate": "6.59"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Total fertility rate": "6.517"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Total fertility rate": "6.445"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Total fertility rate": "6.333"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Total fertility rate": "6.197"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Total fertility rate": "6.044"}, {"Entity": "Zimbabwe", "Code": 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fertility rate": "4.11"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Total fertility rate": "4.075"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Total fertility rate": "4.067"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Total fertility rate": "4.056"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Total fertility rate": "4.009"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Total fertility rate": "3.983"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Total fertility rate": "3.925"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Total fertility rate": "3.862"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Total fertility rate": "3.776"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Total fertility rate": "3.693"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Total fertility rate": "3.635"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Total fertility rate": "3.677"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Total fertility rate": "3.783"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Total fertility rate": "3.95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Total fertility rate": "4.04"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Total fertility rate": "4.126"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Total fertility rate": "4.134"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Total fertility rate": "4.111"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Total fertility rate": "4.011"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Total fertility rate": "3.911"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Total fertility rate": "3.828"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Total fertility rate": "3.768"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Total fertility rate": "3.744"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Total fertility rate": "3.748"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Total fertility rate": "3.754"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Total fertility rate": "3.765"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Total fertility rate": "3.767"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Total fertility rate": "3.724"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "children-born-per-woman", "metadata_url": "https://ourworldindata.org/grapher/children-born-per-woman.metadata.json", "chart_title": "Fertility rate: births per woman", "chart_subtitle": "The total fertility rate summarizes the total number of births a woman would have, if she experienced the birth rates seen in women of each age group in one particular year across her childbearing years.", "chart_note": "", "chart_citation": "Human Fertility Database (2025); UN, World Population Prospects (2024)", "original_chart_url": "https://ourworldindata.org/grapher/children-born-per-woman", "owid_column_metadata": {"Fertility rate (period), historical": {"titleShort": "Fertility rate: births per woman", "titleLong": "Fertility rate: births per woman - HFD, UN WPP – period tables", "descriptionShort": "The average number of live births a hypothetical cohort of women would have at the end of their reproductive period if they were subject during their whole lives to the fertility rates of a given period and if they were not subject to mortality.", "descriptionKey": ["Assumes current age-specific fertility rates remain constant throughout a woman's lifetime.", "Does not account for potential changes in social, economic, or health conditions that could affect fertility rates."], "descriptionProcessing": "The fertility data is constructed by combining data from multiple sources:\n\n- Before 1950: Historical estimates by Human Fertility Database (2025).\n\n- 1950-2023: Population records by the UN World Population Prospects (2024 revision).", "unit": "live births per woman", "timespan": "1891-2023", "type": "Numeric", "owidVariableId": 1118640, "shortName": "fertility_rate_hist", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Fertility Database (2025); UN, World Population Prospects (2024) – with major processing by Our World in Data", "citationLong": "Human Fertility Database (2025); UN, World Population Prospects (2024) – with major processing by Our World in Data. “Fertility rate: births per woman – HFD, UN WPP – period tables” [dataset]. Human Fertility Database, “Human Fertility Database”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118640.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Child mortality rate", "source_url": "https://ourworldindata.org/grapher/child-mortality.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Under-five mortality rate (selected)"], "row_count_total": 16835, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1957", "Under-five mortality rate (selected)": "37.13"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1958", "Under-five mortality rate (selected)": "36.52"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1959", "Under-five mortality rate (selected)": "35.95"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1960", "Under-five mortality rate (selected)": "35.32"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Under-five mortality rate (selected)": "34.76"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Under-five mortality rate (selected)": "34.23"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Under-five mortality rate (selected)": "33.68"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Under-five mortality rate (selected)": "33.17"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Under-five mortality rate (selected)": "32.65"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Under-five mortality rate (selected)": "32.15"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Under-five mortality rate (selected)": "31.66"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Under-five mortality rate (selected)": "31.16"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1969", "Under-five mortality rate (selected)": "30.64"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1970", "Under-five mortality rate (selected)": "30.16"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1971", "Under-five mortality rate (selected)": "29.65"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1972", "Under-five mortality rate (selected)": "29.14"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Under-five mortality rate (selected)": "28.59"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Under-five mortality rate (selected)": "28.06"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Under-five mortality rate (selected)": "27.52"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Under-five mortality rate (selected)": "26.97"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Under-five mortality rate (selected)": "26.38"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Under-five mortality rate (selected)": "25.79"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Under-five mortality rate (selected)": "25.19"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Under-five mortality rate (selected)": "24.57"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Under-five mortality rate (selected)": "23.94"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Under-five mortality rate (selected)": "27.99"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Under-five mortality rate (selected)": "27.44"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Under-five mortality rate (selected)": "30.91"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Under-five mortality rate (selected)": "30.09"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Under-five mortality rate (selected)": "25.31"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Under-five mortality rate (selected)": "24.72"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Under-five mortality rate (selected)": "21.84"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Under-five mortality rate (selected)": "18.71"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Under-five mortality rate (selected)": "18.07"}, {"Entity": 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(selected)": "9.22"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Under-five mortality rate (selected)": "8.83"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Under-five mortality rate (selected)": "8.46"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Under-five mortality rate (selected)": "8.12"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Under-five mortality rate (selected)": "7.8"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Under-five mortality rate (selected)": "7.51"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Under-five mortality rate (selected)": "7.24"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Under-five mortality rate (selected)": "7"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Under-five mortality rate (selected)": "6.76"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Under-five mortality rate (selected)": "6.54"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Under-five mortality rate (selected)": "6.33"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Under-five mortality rate (selected)": "6.13"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Under-five mortality rate (selected)": "5.93"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Under-five mortality rate (selected)": "5.74"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Under-five mortality rate (selected)": "5.55"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1966", "Under-five mortality rate (selected)": "25.95"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1967", "Under-five mortality rate (selected)": "25.34"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1968", "Under-five mortality rate (selected)": "24.73"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1969", "Under-five mortality rate (selected)": "24.39"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1970", "Under-five mortality rate (selected)": "24"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1971", "Under-five mortality rate (selected)": "23.61"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1972", "Under-five mortality rate (selected)": "23.27"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1973", "Under-five mortality rate (selected)": "22.76"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1974", "Under-five mortality rate (selected)": "22.02"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1975", "Under-five mortality rate (selected)": "21.51"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1976", "Under-five mortality rate (selected)": "20.7"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1977", "Under-five mortality rate (selected)": "20.19"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1978", "Under-five mortality rate (selected)": "19.68"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1979", "Under-five mortality rate (selected)": "19.18"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1980", "Under-five mortality rate (selected)": "18.78"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1981", "Under-five mortality rate (selected)": "18.3"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1982", "Under-five mortality rate (selected)": "17.89"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1983", "Under-five mortality rate (selected)": "18.27"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1984", "Under-five mortality rate (selected)": "17.86"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Under-five mortality rate (selected)": "17.45"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Under-five mortality rate (selected)": "16.98"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Under-five mortality rate (selected)": "16.18"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Under-five mortality rate (selected)": "16.24"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Under-five mortality rate (selected)": "15.64"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Under-five mortality rate (selected)": "15.42"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Under-five mortality rate (selected)": "15.5"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Under-five mortality rate (selected)": "15.35"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Under-five mortality rate (selected)": "14.91"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Under-five mortality rate (selected)": "14.8"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Under-five mortality rate (selected)": "14.43"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1996", "Under-five mortality rate (selected)": "14.18"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1997", "Under-five mortality rate (selected)": "13.89"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1998", "Under-five mortality rate (selected)": "13.69"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1999", "Under-five mortality rate (selected)": "13.16"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Under-five mortality rate (selected)": "12.75"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Under-five mortality rate (selected)": "12.32"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Under-five mortality rate (selected)": "11.86"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Under-five mortality rate (selected)": "11.41"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Under-five mortality rate (selected)": "11.02"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Under-five mortality rate (selected)": "10.59"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Under-five mortality rate (selected)": "10.12"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Under-five mortality rate (selected)": "9.71"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Under-five mortality rate (selected)": "9.3"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Under-five mortality rate (selected)": "9.08"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Under-five mortality rate (selected)": "8.64"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Under-five mortality rate (selected)": "8.53"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Under-five mortality rate (selected)": "8.07"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Under-five mortality rate (selected)": "7.79"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Under-five mortality rate (selected)": "7.68"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Under-five mortality rate (selected)": "7.49"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Under-five mortality rate (selected)": "7.31"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Under-five mortality rate (selected)": "7.17"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Under-five mortality rate (selected)": "6.83"}], "rows_tail": [{"Entity": "Zambia", "Code": "ZMB", "Year": "1975", "Under-five mortality rate (selected)": "15.54"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1976", "Under-five mortality rate (selected)": "15.36"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1977", "Under-five mortality rate (selected)": "15.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1978", "Under-five mortality rate (selected)": "15.32"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1979", "Under-five mortality rate (selected)": "15.39"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1980", "Under-five mortality rate (selected)": "15.49"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1981", "Under-five mortality rate (selected)": "15.56"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1982", "Under-five mortality rate (selected)": "15.66"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1983", "Under-five mortality rate (selected)": "15.87"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1984", "Under-five mortality rate (selected)": "16.18"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Under-five mortality rate (selected)": "16.57"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Under-five mortality rate (selected)": "16.97"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Under-five mortality rate (selected)": "17.34"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Under-five mortality rate (selected)": "17.66"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Under-five mortality rate (selected)": "17.89"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Under-five mortality rate (selected)": "18.06"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Under-five mortality rate (selected)": "18.14"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Under-five mortality rate (selected)": "18.11"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Under-five mortality rate (selected)": "17.94"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Under-five mortality rate (selected)": "17.67"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Under-five mortality rate (selected)": "17.34"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Under-five mortality rate (selected)": "17"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Under-five mortality rate (selected)": "16.66"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Under-five mortality rate (selected)": "16.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Under-five mortality rate (selected)": "15.87"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Under-five mortality rate (selected)": "15.25"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Under-five mortality rate (selected)": "14.38"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Under-five mortality rate (selected)": "13.37"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Under-five mortality rate (selected)": "12.36"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Under-five mortality rate (selected)": "11.37"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Under-five mortality rate (selected)": "10.48"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Under-five mortality rate (selected)": "9.71"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Under-five mortality rate (selected)": "9.14"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Under-five mortality rate (selected)": "8.62"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Under-five mortality rate (selected)": "8"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Under-five mortality rate (selected)": "7.57"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Under-five mortality rate (selected)": "7.31"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Under-five mortality rate (selected)": "7.04"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Under-five mortality rate (selected)": "6.72"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Under-five mortality rate (selected)": "6.38"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Under-five mortality rate (selected)": "6.15"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Under-five mortality rate (selected)": "5.9"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Under-five mortality rate (selected)": "5.61"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Under-five mortality rate (selected)": "5.52"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Under-five mortality rate (selected)": "5.41"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Under-five mortality rate (selected)": "5.24"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Under-five mortality rate (selected)": "4.92"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Under-five mortality rate (selected)": "4.67"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Under-five mortality rate (selected)": "4.47"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1953", "Under-five mortality rate (selected)": "18.06"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1954", "Under-five mortality rate (selected)": "17.09"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1955", "Under-five mortality rate (selected)": "16.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1956", "Under-five mortality rate (selected)": "16.46"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1957", "Under-five mortality rate (selected)": "16.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1958", "Under-five mortality rate (selected)": "15.89"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1959", "Under-five mortality rate (selected)": "15.54"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1960", "Under-five mortality rate (selected)": "15.18"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1961", "Under-five mortality rate (selected)": "14.77"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1962", "Under-five mortality rate (selected)": "14.37"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1963", "Under-five mortality rate (selected)": "13.95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1964", "Under-five mortality rate (selected)": "13.52"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1965", "Under-five mortality rate (selected)": "13.11"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "Under-five mortality rate (selected)": "12.72"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "Under-five mortality rate (selected)": "12.37"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "Under-five mortality rate (selected)": "12.07"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "Under-five mortality rate (selected)": "11.81"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "Under-five mortality rate (selected)": "11.65"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "Under-five mortality rate (selected)": "11.53"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "Under-five mortality rate (selected)": "11.46"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "Under-five mortality rate (selected)": "11.42"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "Under-five mortality rate (selected)": "11.41"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Under-five mortality rate (selected)": "11.44"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Under-five mortality rate (selected)": "11.47"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Under-five mortality rate (selected)": "11.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Under-five mortality rate (selected)": "11.47"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Under-five mortality rate (selected)": "11.34"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Under-five mortality rate (selected)": "11.1"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Under-five mortality rate (selected)": "10.68"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Under-five mortality rate (selected)": "10.12"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Under-five mortality rate (selected)": "9.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Under-five mortality rate (selected)": "8.87"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Under-five mortality rate (selected)": "8.31"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Under-five mortality rate (selected)": "7.86"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Under-five mortality rate (selected)": "7.61"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Under-five mortality rate (selected)": "7.55"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Under-five mortality rate (selected)": "7.69"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Under-five mortality rate (selected)": "7.97"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Under-five mortality rate (selected)": "8.33"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Under-five mortality rate (selected)": "8.73"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Under-five mortality rate (selected)": "9.16"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Under-five mortality rate (selected)": "9.52"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Under-five mortality rate (selected)": "9.83"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Under-five mortality rate (selected)": "10.02"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Under-five mortality rate (selected)": "10.08"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Under-five mortality rate (selected)": "10.04"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Under-five mortality rate (selected)": "9.92"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Under-five mortality rate (selected)": "9.83"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Under-five mortality rate (selected)": "9.79"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Under-five mortality rate (selected)": "8.48"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Under-five mortality rate (selected)": "8.85"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Under-five mortality rate (selected)": "9.17"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Under-five mortality rate (selected)": "9.23"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Under-five mortality rate (selected)": "9.4"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Under-five mortality rate (selected)": "9.47"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Under-five mortality rate (selected)": "9.34"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Under-five mortality rate (selected)": "9.03"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Under-five mortality rate (selected)": "8.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Under-five mortality rate (selected)": "7.88"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Under-five mortality rate (selected)": "7.14"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Under-five mortality rate (selected)": "6.55"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Under-five mortality rate (selected)": "6.18"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Under-five mortality rate (selected)": "5.98"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Under-five mortality rate (selected)": "5.69"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Under-five mortality rate (selected)": "5.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Under-five mortality rate (selected)": "5.23"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Under-five mortality rate (selected)": "5.11"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Under-five mortality rate (selected)": "5.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Under-five mortality rate (selected)": "4.76"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Under-five mortality rate (selected)": "4.6"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Under-five mortality rate (selected)": "4.42"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "child-mortality", "metadata_url": "https://ourworldindata.org/grapher/child-mortality.metadata.json", "chart_title": "Child mortality rate", "chart_subtitle": "Estimated share of newborns who die before age 5.", "chart_note": null, "chart_citation": "Gapminder (2015); UN Inter-agency Group for Child Mortality Estimation (2025)", "original_chart_url": "https://ourworldindata.org/grapher/child-mortality", "owid_column_metadata": {"Child mortality rate": {"titleShort": "Child mortality rate", "titleLong": "Child mortality rate - Gapminder; UN IGME – Long-run data", "descriptionShort": "The long-run estimated share of newborns who die before reaching the age of five.", "descriptionKey": ["Child mortality, the death of children under the age of five, is still extremely common in our world today. The historical data makes clear that it doesn’t have to be this way: societies can protect their children and reduce child mortality to very low rates. For child mortality to reach low levels, many things have to go right at the same time: good healthcare, good nutrition, clean water and sanitation, maternal health, and high living standards. We can, therefore, think of child mortality as a proxy indicator of a country’s living conditions.", "The chart shows our long-run data on child mortality, which allows you to see how child mortality has changed in countries around the world. It combines data from two sources: Gapminder and the UN Inter-agency Group for Child Mortality Estimation (UN IGME).", "[Gapminder](https://www.gapminder.org/data/documentation/gd005/) provides estimates of child mortality rates from 1800 to 2015. The full list of sources used can be found in [their documentation](https://www.gapminder.org/data/documentation/gd005/).", "[UN IGME](https://childmortality.org/all-cause-mortality/data) provides estimates of child mortality rates for some countries from 1932 onward.", "For years where data from both sources is available, we prioritize the UN IGME data. See [this page](https://docs.google.com/spreadsheets/d/1n-WO7yEbi6sXPpeWrorSEVu8w_Yu5dM0n97q1h16L0g/edit?gid=0#gid=0) for more details on which source is used for each data point.", "This indicator is calculated as the number of children under the age of five who died in a given year, divided by the number of newborns in that year."], "descriptionProcessing": "This indicator is a combination of data from two sources:\n  - Gapminder, which provides estimates of child mortality rates for the years 1800 to 2015.\n  - The UN Inter-agency Group for Child Mortality Estimation (UN IGME) provides estimates of child mortality rates, for some countries from 1932 onward.\n\nFor years where data from both sources is available, we prioritize the UN IGME data. See [this page](https://docs.google.com/spreadsheets/d/1n-WO7yEbi6sXPpeWrorSEVu8w_Yu5dM0n97q1h16L0g/edit?gid=0#gid=0) for more details on which source is used for each data point.\n\nIn the Gapminder dataset we remove rows where the source is labelled as \"Guesstimate\" or \"Model based on Life Expectancy\" to try and ensure we use the best available data.\n\nWe remove data for Austria before 1830 from the Gapminder dataset, as there is a jump in 1830 that is likely an error.", "shortUnit": "%", "unit": "deaths per 100 live births", "timespan": "1751-2023", "type": "Numeric", "owidVariableId": 1027766, "shortName": "child_mortality_rate", "lastUpdated": "2025-04-25", "citationShort": "Gapminder (2015); UN Inter-agency Group for Child Mortality Estimation (2025) – processed by Our World in Data", "citationLong": "Gapminder (2015); UN Inter-agency Group for Child Mortality Estimation (2025) – processed by Our World in Data. “Child mortality rate – Gapminder; UN IGME – Long-run data” [dataset]. United Nations Inter-agency Group for Child Mortality Estimation, “United Nations Inter-agency Group for Child Mortality Estimation”; Gapminder, “Child mortality rate under age five v7”; Gapminder based on UN IGME & UN WPP, “Under-five Mortality v11”; Various sources, “Population” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1027766.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Number of child deaths", "source_url": "https://ourworldindata.org/grapher/number-of-child-deaths-igme.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Under-five deaths"], "row_count_total": 12398, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Under-five deaths": "154218"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Under-five deaths": "155189"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Under-five deaths": "156392"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Under-five deaths": "157570"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Under-five deaths": "158960"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Under-five deaths": "160469"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Under-five deaths": "161983"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1969", "Under-five deaths": "163423"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1970", "Under-five deaths": "164862"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1971", "Under-five deaths": "166221"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1972", "Under-five deaths": "167452"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Under-five deaths": "168465"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Under-five deaths": "169818"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Under-five deaths": "170851"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Under-five deaths": "171447"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Under-five deaths": "171562"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Under-five deaths": "171336"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Under-five deaths": "170739"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Under-five 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{"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Under-five deaths": "106698"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Under-five deaths": "111673"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Under-five deaths": "120537"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Under-five deaths": "126608"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Under-five deaths": "129372"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Under-five deaths": "131067"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Under-five deaths": "131775"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Under-five deaths": "131813"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Under-five deaths": "131295"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Under-five deaths": "127247"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Under-five deaths": "122364"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Under-five deaths": "121395"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Under-five deaths": "120817"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Under-five deaths": "117491"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Under-five deaths": "114112"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Under-five deaths": "111365"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Under-five deaths": "107118"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Under-five deaths": "103657"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Under-five deaths": "101519"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Under-five deaths": "99136"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Under-five deaths": "97575"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Under-five deaths": "96435"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Under-five deaths": "95003"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Under-five deaths": "93740"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Under-five deaths": "92166"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Under-five deaths": "90260"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Under-five deaths": "88626"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Under-five deaths": "87176"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Under-five deaths": "85846"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Under-five deaths": "84498"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Under-five deaths": "82705"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Under-five deaths": "80507"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1971", "Under-five deaths": "3248093"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1972", 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"Under-five deaths": "4130908"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1984", "Under-five deaths": "4133289"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Under-five deaths": "4230710"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Under-five deaths": "4211806"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Under-five deaths": "4087276"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Under-five deaths": "4343358"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Under-five deaths": "4190428"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Under-five deaths": "4234646"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Under-five deaths": "4380499"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Under-five deaths": "4424615"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Under-five deaths": "4350136"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Under-five deaths": "4410422"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Under-five deaths": "4368486"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1996", "Under-five deaths": "4379487"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1997", "Under-five deaths": "4369616"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1998", "Under-five deaths": "4401847"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1999", "Under-five deaths": "4294005"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Under-five deaths": "4246039"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Under-five deaths": "4184569"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Under-five deaths": "4105127"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Under-five deaths": "4029541"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Under-five deaths": "3969608"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Under-five deaths": "3895213"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Under-five deaths": "3806036"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Under-five deaths": "3731190"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Under-five deaths": "3658593"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Under-five deaths": "3671857"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Under-five deaths": "3550919"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Under-five deaths": "3609269"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Under-five deaths": "3441895"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Under-five deaths": "3374806"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Under-five deaths": "3379708"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Under-five deaths": "3345435"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Under-five deaths": "3302192"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Under-five deaths": "3275993"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Under-five deaths": "3140067"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Under-five deaths": "3091068"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Under-five deaths": "3003771"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2021", "Under-five deaths": "2960064"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2022", "Under-five deaths": "2995109"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2023", "Under-five deaths": "2844963"}, {"Entity": "Albania", "Code": "ALB", "Year": "1983", "Under-five deaths": "4770"}, {"Entity": "Albania", "Code": "ALB", "Year": "1984", "Under-five deaths": "4489"}, {"Entity": "Albania", "Code": "ALB", "Year": "1985", "Under-five deaths": "4247"}, {"Entity": "Albania", "Code": "ALB", "Year": "1986", "Under-five deaths": "4034"}, 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{"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Under-five deaths": "37472"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Under-five deaths": "38628"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Under-five deaths": "39804"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Under-five deaths": "40892"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Under-five deaths": "36255"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Under-five deaths": "38590"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Under-five deaths": "40502"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Under-five deaths": "40899"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Under-five deaths": "41596"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Under-five deaths": "42289"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Under-five deaths": "42658"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Under-five 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{"raw_link": "https://ourworldindata.org/what-is-foreign-aid", "title": "What is foreign aid? How “Official Development Assistance” is measured", "context": "Home\nForeign Aid\nWhat is foreign aid? How “Official Development Assistance” is measured\nForeign aid measurement is complicated — what exactly counts as Official Development Assistance, what doesn’t, and how much is actually spent abroad?\nBy\nSimon van Teutem\nand\nPablo Arriagada\nApril 14, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nIn 2003, over\n1.5 million people\nworldwide lost their lives to HIV/AIDS. That same year, the United States launched the President’s Emergency Plan for AIDS Relief (PEPFAR), a program to combat the epidemic. Twenty years later, more than 20 million individuals were receiving life-saving antiretroviral therapy (ART) through\nPEPFAR\n.\nThis is just one example of the benefits that\neffective foreign aid programs\ncan bring. To understand foreign aid — and to make it work better — we need to understand its key definitions and how it is measured.\nThe\nOrganization for Economic Co-operation and Development (OECD)\nsets the standard for measuring and reporting foreign aid. This article presents an overview of how the OECD measures and standardizes foreign aid, explores some surprising components counted as aid in this framework, and discusses the areas where measurement practices occasionally diverge between countries.\nLet's start with one simple number: £223. This is how much foreign aid the United Kingdom gave per UK resident in 2023.\n1\nThe chart below shows that this figure represents less than 0.6% of the average income of people in the United Kingdom, measured as Gross National Income (GNI) per capita.\n2\nDownload\nWhat’s included in that £223? What categories does the OECD use to break down that £223? And how much of this money is actually spent abroad?\nWhat is Official Development Assistance (ODA) and what does it count?\nThe technical term that researchers use for foreign aid is \"Official Development Assistance\" (ODA). For money to be counted as ODA by the OECD, it must be administered “with the promotion of economic development and welfare of developing countries as its main objective”.\n3\nThe major donor countries are the so-called “\nDAC countries\n”, comprising 31 individual countries plus the European Union as an institution. DAC stands for Development Assistance Committee; this group of countries was responsible for\n94% of ODA funds\nin the world in 2022.\nA few non-DAC countries, such as Turkey, Saudi Arabia, and Qatar, also voluntarily report statistics on their development finance to the OECD. To keep things simple, we’ll refer to all reporting countries together as “donor countries” in this article.\nThe chart here shows foreign aid from various donor countries, measured in US dollars (adjusted for inflation). While the total amount of aid has increased, DAC countries now contribute a\nsmaller share\nof their national income to foreign aid than in 1960.\nThe OECD's standardized reporting framework helps us understand not only how much aid countries provide but also how that money is used. The chart below shows how donor countries allocate their aid budgets across different sectors, such as social infrastructure, economic infrastructure, production sectors, and humanitarian assistance.\nThanks to these consistent reporting standards, we see clear patterns. For example, DAC countries, on the whole, have allocated approximately a third of their aid budgets to social infrastructure and services since 2000. This category focuses on developing the human resource potential of developing countries and includes areas like education, healthcare, and access to clean water and sanitation.\nStill, many questions come up when looking at foreign aid figures closely. Are donor countries giving money away or making loans at reduced interest rates? Are they helping countries develop over decades, or are they responding with help against particular disasters or crises? Does military aid count toward ODA? And — perhaps in conflict with the term\nforeign\naid — how much of this money actually crosses borders and how much is spent within the donor countries?\nGrants or grant-equivalents of concessional loans?\nMost aid comes as grants; that is, money given to countries and organizations with specific rules about how it can be spent.\nThis is true\nfor all donor countries. In 2022,\nover 90% of aid\nfrom DAC countries worked this way.\nMost of these grants\nare a form of\nbilateral aid\n, given directly to other countries, rather than\nmultilateral aid\n, going to multilateral organizations.\n4\nUsually, giving grants means providing things people in\nlower-income countries\nneed. An example is the aid provided via\nGavi, the Vaccine Alliance\n, which has vaccinated\nhundreds of millions\nof children against diseases and has been estimated to save\n1.5 million lives\n.\nBut sometimes, donor countries make cheap (“concessional”) loans instead of giving grants. Suppose a poor country normally pays 10% interest but gets a loan at 5%. In that case, the money they save by paying a lower interest rate is counted as aid.\n5\nThere are some challenges associated with this method. The first challenge comes in calculating how much the concessional loans are worth. Some experts argue current methods make aid look bigger than it is and that they encourage lending to middle-income countries at near-market rates rather than focusing on low-income countries or providing genuinely favorable loans.\n6\nSecondly, loans to the poorest countries must be\nmore\ngenerous than loans to middle-income countries for it to count as aid.\n7\nThis may sound sensible — countries may want to help the poorest most — but this rule makes it less likely for the poorest countries to receive aid. If you're a rich country thinking about making a loan, you\nmight avoid\nthe poorest countries because the loan has to be so generous to count as aid.\n8\nThe OECD reporting standards on concessional loans are subject to methodological debates and criticism. However, concessional loans represent only\na small fraction\nof most donor countries' aid portfolios. In the decade leading up to 2023, concessional loans accounted for merely 7% of donor countries' total aid disbursements. This means these methodological questions only apply to a relatively small part of the total ODA.\n9\nAid for longer-term development or emergency help?\nForeign aid mostly funds long-term development, but it also provides emergency aid when disasters occur or wars break out. This is often referred to as “humanitarian aid”.\nIn 2023, humanitarian aid represented\n12% of rich countries' aid budgets\n. This\nincludes\nhumanitarian aid for Ukraine, Gaza, and the West Bank.\n10\nMeanwhile, the 2024 Global Humanitarian Overview reports a\n$36 billion\ngap between needs and actual funding. If total aid budgets were larger, donors could address humanitarian crises without compromising funding for other projects, such as clean water infrastructure and vaccines.\nCountries or private donors?\nMost aid comes from government budgets, whether delivered directly or through multilateral organizations. Contributions from private donors, such as the Gates Foundation and the LEGO Foundation, do\nnot\ncount as ODA. These are called “philanthropic contributions”. While not all foundations report their contributions as philanthropic contributions,\nmany of the largest ones do\n.\nIn 2022, aid from private donors in the OECD was more than\n20 times\nsmaller than government-funded ODA. This means that voters in rich democracies play\na crucial role\nin determining how much aid donor countries spend, as citizens influence policy and taxpayers want their money to be used effectively. They hold agency through the governments they elect and the priorities they demand from them.\nCross-border donations from individuals or\nmoney sent back home by migrants\ndon’t count as ODA.\n11\nMilitary aid is not included in ODA\nOne of the most common questions we’re asked is whether military aid is included in ODA statistics. The answer is no. This ensures consistency across all donor countries, as none can classify their military assistance as official development aid.\nThis distinction makes a big difference during periods of conflict. Take, for example, US support for Ukraine during the war with Russia. From the Russian attack in February 2022 to January 2025, the US provided about\n$23 billion per year\nin military aid to Ukraine.\nFor context, the US spent\n$64 billion on foreign aid in 2023\n, but none of the military assistance is included in these figures.\nThere's one exception: countries can count 15% of what they spend on “peacekeeping” as foreign aid.\n12\nThis works differently because it’s possible to identify how much funding in peacekeeping is going toward developmental objectives, and the money goes through the UN rather than directly to other countries. The UN framework helps avoid some of the contentious problems you get with countries giving military aid directly to each other.\nHow much aid is spent abroad?\nSomething that surprises people is that donor countries can count some money they spend domestically as foreign aid. In this article, we call this “aid money spent at home”. The OECD reporting framework allows us to see how this practice changes over time and differs across nations. The chart below shows aid money spent at home for all donor countries, broken down by category.\nThe biggest in-donor cost in 2023 is refugee support. According to the OECD framework, countries can count what they spend on housing, feeding, and administrating refugees for their first twelve months. Including refugee costs might seem odd at first, but there's a logic: countries spend significant money hosting refugees, and while this doesn't directly benefit their home countries, it does address the urgent humanitarian needs of foreign citizens who’ve been forcibly displaced.\nAnother element that may be surprising is student scholarships — these can count as aid when they align with the host country's development plans.\n13\nFor most countries, this contribution is very small. But there are a few exceptions. Hungary is the most extreme case: it uses\n41% of its aid budget\nfor foreign students. In addition to refugee support and scholarships, aid money spent domestically includes funding for development awareness programs and administrative costs not already incorporated into specific aid initiatives.\nLet's now look at how individual donor countries differ in their domestic spending of foreign aid budgets. The chart below shows how much foreign aid was spent in DAC countries in 2023, broken down by category.\nThe arrival of Ukrainian refugees has reshaped aid spending patterns. Thirteen donor countries now spend 20% or more of their aid budget at home — in Ireland, it's 55%. The UK spends less domestically: 33% of its aid budget, or £74 out of £223 per citizen, mostly on refugee support. These numbers may drop soon. By 2024, most Ukrainian refugees will have been in their host countries for more than a year. After that first year, their support no longer counts as foreign aid.\nWhile a few countries allocate large portions of aid domestically, the broader reality is that most donor nations channel over three-quarters of their aid budgets overseas. We examine the implications and trends of aid money spent at home in greater detail in another article:\nHow much foreign aid is spent domestically rather than overseas?\nIn many countries, a significant share of aid is spent domestically on hosting refugees, offering student scholarships, and administrative costs.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nWhat do we know about foreign aid from countries that don’t report to the OECD?\nThere are limitations to measuring foreign aid with Official Development Assistance.\nRight now, only rich countries and a few others report it, following rules they set themselves through the OECD.\n14\nGiven that rich OECD countries give the most, this covers most aid. However, it leaves out important donors like China, the\nworld’s largest economy\n— when accounting for differences in living costs.\nA few years ago, researchers at the Center for Global Development proposed a new solution: a measure called Finance for International Development (FID). FID allows us to compare traditional donors, like the US, with newer ones that don’t report ODA, such as China.\nFID also addresses some of ODA’s most debated aspects — for example, it excludes money spent on refugees in donor countries.\n15\nThe available data only extends up to 2019, but at that time, China ranked among the top 10 donors, contributing about one-fifth of what the US did. This measure helps us understand how China's aid compares to others.\nHowever, ODA is just one type of money that flows from governments in one country to people in others. There's another category called \"Other Official Flows\" — the OECD uses this term for financial flows that don’t fully count as ODA\n16\n, such as commercially motivated grants to build ports using the donor country's contractors or concessional loans that aren’t generous enough to count as aid.\nThis distinction matters. Between 2014 and 2021, China gave out huge loans through its\nBelt and Road Initiative\n. The chart below shows data from AidData comparing ODA and OOF from China and the\nG7 countries\nduring this period. While their estimate of China's ODA was much smaller than US aid, its total spending — including these other flows — was more than double. These loans are much less generous than ODA\n17\n, but looking only at aid numbers may not capture this bigger picture of how governments fund projects in other countries.\nDownload\nOverall, foreign aid is measured in a way that enables reliable analysis of aid flows. While we've highlighted some reporting challenges and variations in this article, these represent exceptions affecting a small portion of total aid.\nMost ODA reporting follows consistent standards, providing a solid foundation for meaningful analysis. But understanding the differences and controversies is needed to interpret aid data accurately.\nAcknowledgments\nMany thanks to Euan Ritchie, Ammar Malik, Harsh Desai, Charles Kenny, Hannah Ritchie, Max Roser, Edouard Mathieu, Bastian Herre, and Saloni Dattani for their insights, feedback and comments on this article.\nContinue reading on Our World in Data\nMany of us can save a child’s life, if we rely on the best data\nThere are many ways to improve the world, but their cost-effectiveness varies immensely. You can achieve a lot more if you rely on the best data on where to donate.\nFor many of us, it doesn’t cost much to improve someone’s life, and we can do much more of it\nMost countries spend less than 1% of their national income on foreign aid; even small increases could make a big difference.\nHow much foreign aid is spent domestically rather than overseas?\nIn many countries, a significant share of aid is spent domestically on hosting refugees, offering student scholarships, and administrative costs.\nEndnotes\nThe United Kingdom spent\n£15,344 million\non Official Development Assistance in 2023, which, with a population of\n68.68 million people\n, translates to £223 per capita\nIn 2023, the United Kingdom was estimated to have a Gross National Income (GNI) of\n£2,652 billion\n, and it spent\n£15,344 million\non Official Development Assistance. Dividing both numbers by the population of\n68.68 million people\ngives a GNI per capita of £38,619 and foreign aid per capita of £223. So, foreign aid per capita was 0.58% of GNI per capita. In other words, for every £173 in GNI per capita, £1 went to foreign aid.\nRead the full definition of ODA\nhere\n.\nDisbursements from international organizations often originate from countries. So if you see a graph with foreign aid given that includes both countries and international organizations, such as the World Bank, these numbers shouldn’t be summed up as they will partially refer to the same sums of money.\nIn international statistics, this is typically considered the “grant equivalent” of loans. For example, if a poor country saves $1 million by taking a cheap $10 million loan instead of a regular one, that $1 million is counted as ODA. Importantly, the $1 million here is not a flow of money; it’s an estimate of the aid effort that the donor is making by providing this concessional load. Also, the grant equivalent is\nnot\nbased on market rates. It starts with a standard 5% rate set by the IMF and then adjusts it based on the recipient country's income level. Lower-income countries are considered higher risk for lenders, so the calculation accounts for this additional risk donors take. The detailed conditions of a concessional loan can be found\nhere\n.\nIn 2020, Euan Ritchie from the Center for Global Development\nestimated\nthat DAC countries overstated these costs at $5 billion, or 3% of all aid from wealthy countries. One year later, Ritchie also\nargued\nthat ODA loan rules do not incentivize lending to poorer countries but, if anything, incentivize lending to middle-income countries at near-market rates. David Roodman has also\nexplained\nhow thresholds and discount rates interact to favor low-middle-income countries over low-income countries.\nFor loans to qualify, the grant element — that is, what the donor is giving divided by the total loan — must exceed 45% for low-income countries, 15% for lower-middle-income countries, and 10% for upper-middle-income countries.\nA loan with a grant element of 10% would boost a donor’s ODA for an upper-middle-income recipient but not for a low-income one.\nWe see this also in\nthe data\n: net and grant-equivalent amounts of foreign aid given are very similar for most donors.\nHowever, most of Ukraine’s aid targets development.\nFor example, in 2023, the British public donated an average of\n£203 per person\n. This is similar to the UK’s ODA, but it includes donations to charities operating in the UK or other wealthy countries, not developing countries.\nOECD (2016),\nThe ODA Coefficient for UN Peacekeeping Operations Explained\n.\nOECD (2024).\nConverged Statistical Reporting Directives for the Creditor Reporting System (CRS) and the Annual DAC Questionnaire\n, page 33, section 5.\nOECD (2024).\nConverged Statistical Reporting Directives for the Creditor Reporting System (CRS) and the Annual DAC Questionnaire\n.\nMitchell, I., Ritchie, E. and Rogerson, A. (2020).\nFinance for International Development (FID): A New Measure to Compare Traditional and Emerging Provider Countries’ Official Development Finance Efforts, and Some Provisional Results\nOECD (2024).\nConverged Statistical Reporting Directives for the Creditor Reporting System (CRS) and the Annual DAC Questionnaire\n, p.10.\nCenter for Global Development (2020).\nThe Problem Isn’t that Chinese Lending Is Too Big, It’s that the US and Europe’s Is Too Small\n..\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSimon van Teutem and Pablo Arriagada (2025) - “What is foreign aid? How “Official Development Assistance” is measured” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-083815/what-is-foreign-aid.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-what-is-foreign-aid,\nauthor = {Simon van Teutem and Pablo Arriagada},\ntitle = {What is foreign aid? How “Official Development Assistance” is measured},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-083815/what-is-foreign-aid.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "what-is-foreign-aid", "source_url": "https://ourworldindata.org/what-is-foreign-aid", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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Net disbursements": "143065500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank", "Code": "", "Year": "2015", "Official development assistance (ODA) and private grants by donor - Net disbursements": "142531780", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank", "Code": "", "Year": "2016", "Official development assistance (ODA) and private grants by donor - Net disbursements": "186638910", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank", "Code": "", "Year": "2017", "Official development assistance (ODA) and private grants by donor - Net disbursements": "136605100", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank", "Code": "", "Year": "2018", "Official development assistance (ODA) and private grants by donor - Net disbursements": "107708560", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank", "Code": "", "Year": "2019", "Official development assistance (ODA) and private grants by donor - Net disbursements": "14650066", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank", "Code": "", "Year": "2020", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7415864", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "9150927", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "5938928", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "8951578", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "9001417", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1965", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-462570", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1966", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-286197800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1967", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-280386880", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1975", "Official development assistance (ODA) and private grants by donor - Net disbursements": "15584397", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1976", "Official development assistance (ODA) and private grants by donor - Net disbursements": "41498000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1977", "Official development assistance (ODA) and private grants by donor - Net disbursements": "94324760", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1978", "Official development assistance (ODA) and private grants by donor - Net disbursements": "123265896", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1979", "Official development assistance (ODA) and private grants by donor - Net disbursements": "151057170", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1980", "Official development assistance (ODA) and private grants by donor - Net disbursements": "242434240", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1981", "Official development assistance (ODA) and private grants by donor - Net disbursements": "235031710", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1982", "Official development assistance (ODA) and private grants by donor - Net disbursements": "325283800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1983", "Official development assistance (ODA) and private grants by donor - Net disbursements": "418141000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1984", "Official development assistance (ODA) and private grants by donor - Net disbursements": "300206140", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1985", "Official development assistance (ODA) and private grants by donor - Net disbursements": "565934500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1986", "Official development assistance (ODA) and private grants by donor - Net disbursements": "600827650", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1987", "Official development assistance (ODA) and private grants by donor - Net disbursements": "712223000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1988", "Official development assistance (ODA) and private grants by donor - Net disbursements": "622248260", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1989", "Official development assistance (ODA) and private grants by donor - Net disbursements": "866800600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1990", "Official development assistance (ODA) and private grants by donor - Net disbursements": "967712060", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1991", "Official development assistance (ODA) and private grants by donor - Net disbursements": "965823100", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1992", "Official development assistance (ODA) and private grants by donor - Net disbursements": "992398400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1993", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1023375300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1994", "Official development assistance (ODA) and private grants by donor - Net disbursements": "840810750", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1995", "Official development assistance (ODA) and private grants by donor - Net disbursements": "724712100", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1996", "Official development assistance (ODA) and private grants by donor - Net disbursements": "805027260", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1997", "Official development assistance (ODA) and private grants by donor - Net disbursements": "851511940", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1998", "Official development assistance (ODA) and private grants by donor - Net disbursements": "845641700", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1999", "Official development assistance (ODA) and private grants by donor - Net disbursements": "663676300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2000", "Official development assistance (ODA) and private grants by donor - Net disbursements": "450897820", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2001", "Official development assistance (ODA) and private grants by donor - Net disbursements": "778864300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2002", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1101986400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2003", "Official development assistance (ODA) and private grants by donor - Net disbursements": "779187600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2004", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1358514200", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2005", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1257855700", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2006", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2607972600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2007", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1586556500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2008", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1917850000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2009", "Official development assistance (ODA) and private grants by donor - Net disbursements": "3033084700", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2010", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1997672100", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2011", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2340652000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2012", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2588419000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2013", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2418605000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2014", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2125332000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2015", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2513949400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2016", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2548231700", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2017", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2903135000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2018", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2271122000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2019", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1835591600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2020", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2612188200", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2013029500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "931325400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1272137500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1183876900", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1965", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-462570", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1966", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-286197800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1967", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-280386880", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1975", "Official development assistance (ODA) and private grants by donor - Net disbursements": "15584397", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1976", "Official development assistance (ODA) and private grants by donor - Net disbursements": "41498000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1977", "Official development assistance (ODA) and private grants by donor - Net disbursements": "94324760", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1978", "Official development assistance (ODA) and private grants by donor - Net disbursements": "123265896", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1979", "Official development assistance (ODA) and private grants by donor - Net disbursements": "151057170", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1980", "Official development assistance (ODA) and private grants by donor - Net disbursements": "242434240", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1981", "Official development assistance (ODA) and private grants by donor - Net disbursements": "235031710", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1982", "Official development assistance (ODA) and private grants by donor - Net disbursements": "325283800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1983", "Official development assistance (ODA) and private grants by donor - Net disbursements": "418141000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1984", "Official development assistance (ODA) and private grants by donor - Net disbursements": "300206140", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1985", "Official development assistance (ODA) and private grants by donor - Net disbursements": "565934500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1986", "Official development assistance (ODA) and private grants by donor - Net disbursements": "600827650", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1987", "Official development assistance (ODA) and private grants by donor - Net disbursements": "712223000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1988", "Official development assistance (ODA) and private grants by donor - Net disbursements": "622248260", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1989", "Official development assistance (ODA) and private grants by donor - Net disbursements": "866800600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1990", "Official development assistance (ODA) and private grants by donor - Net disbursements": "967712060", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1991", "Official development assistance (ODA) and private grants by donor - Net disbursements": "965823100", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1992", "Official development assistance (ODA) and private grants by donor - Net disbursements": "992398400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1993", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1023375300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1994", "Official development assistance (ODA) and private grants by donor - Net disbursements": "840810750", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1995", "Official development assistance (ODA) and private grants by donor - Net disbursements": "724712100", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1996", "Official development assistance (ODA) and private grants by donor - Net disbursements": "805027260", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1997", "Official development assistance (ODA) and private grants by donor - Net disbursements": "851511940", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1998", "Official development assistance (ODA) and private grants by donor - Net disbursements": "845641700", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1999", "Official development assistance (ODA) and private grants by donor - Net disbursements": "663676300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "2000", "Official development assistance (ODA) and private grants by donor - Net disbursements": "450897820", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}], "rows_tail": [{"Entity": "WTO - International Trade Centre", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "53225052", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2017", "Official development assistance (ODA) and private grants by donor - Net disbursements": "303621150", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2018", "Official development assistance (ODA) and private grants by donor - Net disbursements": "311397920", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2019", "Official development assistance (ODA) and private grants by donor - Net disbursements": "378434530", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2020", "Official development assistance (ODA) and private grants by donor - Net disbursements": "583376830", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "521239460", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "428908380", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "887722000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "533045730", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2017", "Official development assistance (ODA) and private grants by donor - Net disbursements": "226401000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2018", "Official development assistance (ODA) and private grants by donor - Net disbursements": "249855780", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2019", "Official development assistance (ODA) and private grants by donor - Net disbursements": "145049970", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2020", "Official development assistance (ODA) and private grants by donor - Net disbursements": "186200900", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "204096900", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "204646450", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "181459170", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "149252850", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1960", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-240355020", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1961", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-349699260", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1962", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-266635380", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1963", "Official development assistance (ODA) and private grants by donor - Net disbursements": "305909730", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1964", "Official development assistance (ODA) and private grants by donor - Net disbursements": "776646300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1965", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2081566500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1966", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2036855700", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1967", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2700777500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1968", "Official development assistance (ODA) and private grants by donor - Net disbursements": "931653100", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1969", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1717884500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1970", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1114339800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1971", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1844204500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1972", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1724460400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1973", "Official development assistance (ODA) and private grants by donor - Net disbursements": "3112191500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1974", "Official development assistance (ODA) and private grants by donor - Net disbursements": "4113869000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1975", "Official development assistance (ODA) and private grants by donor - Net disbursements": "4373215000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1976", "Official development assistance (ODA) and private grants by donor - Net disbursements": "5168001500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1977", "Official development assistance (ODA) and private grants by donor - Net disbursements": "4199820800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1978", "Official development assistance (ODA) and private grants by donor - Net disbursements": "3396855300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1979", "Official development assistance (ODA) and private grants by donor - Net disbursements": "3801276200", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1980", "Official development assistance (ODA) and private grants by donor - Net disbursements": "4174089500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1981", "Official development assistance (ODA) and private grants by donor - Net disbursements": "5208264000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1982", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6441008000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1983", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6314644000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1984", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6831449600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1985", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7092460500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1986", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7365242000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1987", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6732262400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1988", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6327317000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1989", "Official development assistance (ODA) and private grants by donor - Net disbursements": "5747814000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1990", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6277988000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1991", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6656654300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1992", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7056147500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1993", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6698970000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1994", "Official development assistance (ODA) and private grants by donor - Net disbursements": "8001275000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1995", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6269400600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1996", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7790538000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1997", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7589403600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1998", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7083127000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1999", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6524090400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2000", "Official development assistance (ODA) and private grants by donor - Net disbursements": "5876879400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2001", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7872358000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2002", "Official development assistance (ODA) and private grants by donor - Net disbursements": "9505157000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2003", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7558426600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2004", "Official development assistance (ODA) and private grants by donor - Net disbursements": "9595815000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2005", "Official development assistance (ODA) and private grants by donor - Net disbursements": "8408566300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2006", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7527171000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2007", "Official development assistance (ODA) and private grants by donor - Net disbursements": "9316659000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2008", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6711488500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2009", "Official development assistance (ODA) and private grants by donor - Net disbursements": "10199555000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2011", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7262974500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2012", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7301221000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2013", "Official development assistance (ODA) and private grants by donor - Net disbursements": "8506603500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2014", "Official development assistance (ODA) and private grants by donor - Net disbursements": "10681953000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2015", "Official development assistance (ODA) and private grants by donor - Net disbursements": "11582767000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2016", "Official development assistance (ODA) and private grants by donor - Net disbursements": "9432559000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2017", "Official development assistance (ODA) and private grants by donor - Net disbursements": "10842584000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2018", "Official development assistance (ODA) and private grants by donor - Net disbursements": "11917293000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2019", "Official development assistance (ODA) and private grants by donor - Net disbursements": "14092230000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2020", "Official development assistance (ODA) and private grants by donor - Net disbursements": "16726742000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "13799344000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "17104168000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "17987367000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "18080442000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2016", "Official development assistance (ODA) and private grants by donor - Net disbursements": "14919065", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2017", "Official development assistance (ODA) and private grants by donor - Net disbursements": "15830571", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2018", "Official development assistance (ODA) and private grants by donor - Net disbursements": "11587826", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2019", "Official development assistance (ODA) and private grants by donor - Net disbursements": "17908538", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2020", "Official development assistance (ODA) and private grants by donor - Net disbursements": "26020810", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "25230998", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "10501443", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "20145702", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "18947476", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2009", "Official development assistance (ODA) and private grants by donor - Net disbursements": "481835500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2010", "Official development assistance (ODA) and private grants by donor - Net disbursements": "399848830", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2011", "Official development assistance (ODA) and private grants by donor - Net disbursements": "465938080", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2012", "Official development assistance (ODA) and private grants by donor - Net disbursements": "418841950", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2013", "Official development assistance (ODA) and private grants by donor - Net disbursements": "493100480", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2014", "Official development assistance (ODA) and private grants by donor - Net disbursements": "490242750", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2015", "Official development assistance (ODA) and private grants by donor - Net disbursements": "764845300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2016", "Official development assistance (ODA) and private grants by donor - Net disbursements": "621864600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2017", "Official development assistance (ODA) and private grants by donor - Net disbursements": "604474400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2018", "Official development assistance (ODA) and private grants by donor - Net disbursements": "567882200", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2019", "Official development assistance (ODA) and private grants by donor - Net disbursements": "609279600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2020", "Official development assistance (ODA) and private grants by donor - Net disbursements": "296893380", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "620551800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "488090240", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "633541900", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "567149800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Organization for Animal Health", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "11636835", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Organization for Animal Health", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - 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This data is expressed in US dollars and adjusted for inflation.", "chart_note": "This data is expressed in constant 2023 US$. From 2018, the official reporting method switched from net to grant-equivalent amounts.", "chart_citation": "OECD (2025)", "original_chart_url": "https://ourworldindata.org/grapher/foreign-aid-given-net", "owid_column_metadata": {"Official development assistance (ODA) and private grants by donor - Net disbursements": {"titleShort": "Official development assistance (ODA) and private grants by donor - Net disbursements", "titleLong": "Official development assistance (ODA) and private grants by donor - Net disbursements", "descriptionShort": "Official development assistance (ODA) is defined as government aid designed to promote the economic development and welfare of developing countries. Grants by private voluntary agencies and non-government organizations (NGOs) are defined as transfers for development made by private voluntary agencies and NGOs in cash, goods or services for which no payment is required. Monetary aid is estimated as net disbursements. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "The private sector comprises private corporations, households and non-profit institutions serving households. Development funding from the private sector is becoming more significant. This includes civil society organizations, which play an increasing role in funding development and in finding innovative ways to promote it; non-government organizations; and the for-profit private sector.", "The data is reported as net disbursements. This refers to aid ultimately given and is different from commitments, which is only aid that has been pledged. These are net amounts because any money coming in (like loan repayments or interest) has been subtracted from money going out (like new grants or loans).", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We have combined net disbursements aid data from the [DAC1: Flows by donor (ODA+OOF+Private) dataset](https://data-explorer.oecd.org/vis?fs[0]=Topic%2C1%7CDevelopment%23DEV%23%7COfficial%20Development%20Assistance%20%28ODA%29%23DEV_ODA%23&pg=0&fc=Topic&bp=true&snb=20&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_DAC1%40DF_DAC1&df[ag]=OECD.DCD.FSD&df[vs]=1.2&dq=DAC...1140%2B1160..Q.&lom=LASTNPERIODS&lo=10&to[TIME_PERIOD]=false) with the [DAC2A: Aid (ODA) disbursements to countries and regions dataset](https://data-explorer.oecd.org/vis?fs[0]=Topic%2C1%7CDevelopment%23DEV%23%7COfficial%20Development%20Assistance%20%28ODA%29%23DEV_ODA%23&pg=0&fc=Topic&bp=true&snb=20&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_DAC2%40DF_DAC2A&df[ag]=OECD.DCD.FSD&df[vs]=1.1&dq=.DPGC.206.USD.Q&lom=LASTNPERIODS&lo=5&to[TIME_PERIOD]=false) to add aid given by multilateral organizations and grants given by civil society organizations.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1960-2024", "type": "Numeric", "owidVariableId": 1132327, "shortName": "i_oda_net_disbursements_multilaterals_private_grants", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Official development assistance (ODA) and private grants by donor - Net disbursements – Net disbursements” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC1: Flows by provider (ODA+OOF+Private)”; OECD, “OECD Official Development Assistance (ODA) - DAC2A: Aid (ODA) disbursements to countries and regions” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132327.metadata.json"}, "1132327-annotations": {"titleShort": "1132327-annotations", "titleLong": "1132327-annotations", "type": "SeriesAnnotation", "citationShort": " – processed by Our World in Data", "citationLong": " – processed by Our World in Data. “1132327-annotations” [dataset].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/undefined.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Foreign aid given by sector", "source_url": "https://ourworldindata.org/grapher/foreign-aid-given-by-sector.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Unspecified aid", "Humanitarian aid", "Aid related to debt", "Commodity aid", "Multi-sector aid", "Aid for production sectors", "Aid for economic infrastructure and services", "Aid for social infrastructure and services", "Unspecified aid (Annotations)", "Humanitarian aid (Annotations)", "Aid related to debt (Annotations)", "Commodity aid (Annotations)", "Multi-sector aid (Annotations)", "Aid for production sectors (Annotations)", "Aid for economic infrastructure and services (Annotations)", "Aid for social infrastructure and services (Annotations)"], "row_count_total": 3296, "rows_head": [{"Entity": "Adaptation Fund", "Code": "", "Year": "2010", "Unspecified aid": "", "Humanitarian aid": "", "Aid related to debt": "", 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This data is expressed in US dollars and adjusted for inflation.", "chart_note": "This data is expressed in constant 2023 US$.", "chart_citation": "OECD (2025)", "original_chart_url": "https://ourworldindata.org/grapher/foreign-aid-given-by-sector", "owid_column_metadata": {"ODA by donor and sector (Unallocated / Unspecified)": {"titleShort": "Unspecified aid", "titleLong": "Unspecified aid", "descriptionShort": "Official development assistance given that is unallocated / unspecified. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1967-2024", "type": "Numeric", "owidVariableId": 1132412, "shortName": "oda_by_sector__sector_unallocated__unspecified", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Unspecified aid” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC5: Aid (ODA) by sector and provider” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132412.metadata.json"}, "ODA by donor and sector (Humanitarian aid)": {"titleShort": "Humanitarian aid", "titleLong": "Humanitarian aid", "descriptionShort": "Official development assistance given that is humanitarian aid. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Humanitarian aid is assistance designed to save lives, alleviate suffering and maintain and protect human dignity during and in the aftermath of emergencies. To be classified as humanitarian, aid should be consistent with the humanitarian principles of humanity, impartiality, neutrality and independence. It broadly includes aid given for emergencies, such as natural disasters and wars, reconstruction in their aftermath, but also prevention and preparation for future emergencies.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1971-2024", "type": "Numeric", "owidVariableId": 1132389, "shortName": "oda_by_sector__sector_humanitarian_aid", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Humanitarian aid” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC5: Aid (ODA) by sector and provider” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132389.metadata.json"}, "ODA by donor and sector (Action relating to debt)": {"titleShort": "Aid related to debt", "titleLong": "Aid related to debt", "descriptionShort": "Official development assistance given that is action relating to debt. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1967-2024", "type": "Numeric", "owidVariableId": 1132405, "shortName": "oda_by_sector__sector_action_relating_to_debt", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Aid related to debt” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC5: Aid (ODA) by sector and provider” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132405.metadata.json"}, "ODA by donor and sector (Commodity aid / General programme assistance)": {"titleShort": "Commodity aid", "titleLong": "Commodity aid", "descriptionShort": "Official development assistance given that is commodity aid / general programme assistance. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Commodity aid / General programme assistance aid includes general budget support, development food assistance and other commodity assistance.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1967-2024", "type": "Numeric", "owidVariableId": 1132385, "shortName": "oda_by_sector__sector_commodity_aid__general_programme_assistance", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Commodity aid” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC5: Aid (ODA) by sector and provider” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132385.metadata.json"}, "ODA by donor and sector (Multi-sector / Cross-cutting)": {"titleShort": "Multi-sector aid", "titleLong": "Multi-sector aid", "descriptionShort": "Official development assistance given that is multi-sector / cross-cutting. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Multi-sector / Cross-cutting aid includes aid that is not sector-specific, such as aid for general environmental protection not allocable by sector.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1968-2024", "type": "Numeric", "owidVariableId": 1132378, "shortName": "oda_by_sector__sector_multi_sector__cross_cutting", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Multi-sector aid” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC5: Aid (ODA) by sector and provider” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132378.metadata.json"}, "ODA by donor and sector (Production sectors)": {"titleShort": "Aid for production sectors", "titleLong": "Aid for production sectors", "descriptionShort": "Official development assistance given that is production sectors. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Production sectors aid groups contributions to all directly productive sectors. Includes agriculture, forestry, fishing, industry, mineral resources and mining, construction, trade and tourism.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1967-2024", "type": "Numeric", "owidVariableId": 1132391, "shortName": "oda_by_sector__sector_production_sectors", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Aid for production sectors” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC5: Aid (ODA) by sector and provider” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132391.metadata.json"}, "ODA by donor and sector (Economic infrastructure and services)": {"titleShort": "Aid for economic infrastructure and services", "titleLong": "Aid for economic infrastructure and services", "descriptionShort": "Official development assistance given that is economic infrastructure and services. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Economic infrastructure and services aid groups assistance for networks, utilities and services that facilitate economic activity. Includes transport and storage, communications, energy, banking and financial services, and business and other services.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1967-2024", "type": "Numeric", "owidVariableId": 1132393, "shortName": "oda_by_sector__sector_economic_infrastructure_and_services", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Aid for economic infrastructure and services” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC5: Aid (ODA) by sector and provider” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132393.metadata.json"}, "ODA by donor and sector (Social infrastructure and services)": {"titleShort": "Aid for social infrastructure and services", "titleLong": "Aid for social infrastructure and services", "descriptionShort": "Official development assistance given that is social infrastructure and services. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Social infrastructure and services aid relates essentially to efforts to develop the human resource potential of developing countries. Includes education, health, population and reproductive health policies, water supply and sanitation, government and sanitation and other social services.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1967-2024", "type": "Numeric", "owidVariableId": 1132381, "shortName": "oda_by_sector__sector_social_infrastructure_and_services", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Aid for social infrastructure and services” [dataset]. 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{"raw_link": "https://ourworldindata.org/air-pollution-sources", "title": "Air pollution kills millions every year — where does it come from?", "context": "Home\nAir Pollution\nAir pollution kills millions every year — where does it come from?\nA breakdown of the sources of many air pollutants that damage our health and ecosystems.\nBy\nHannah Ritchie\nand\nPablo Rosado\nMarch 31, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nMillions of people\ndie prematurely\nfrom air pollution every year.\nThis problem has existed\nsince humans started burning materials for fuel — first wood and biomass, then fossil fuels.\nBut it’s an environmental and public health problem that we can make progress on. We know this because the world\nhas already\nbeen successful\nin reducing air pollutants, and many countries that used to be highly polluted now have much cleaner air than they used to.\nTo tackle air pollution effectively — to focus our efforts on the interventions that will have the biggest impact — we need to understand where it’s coming from.\nThat’s why we wrote this article.\n1\nA note on data and definitions\nThe main data source we rely on is the Community Emissions Data System (CEDS). There are a couple of reasons why we think it’s an incredibly valuable resource:\nIt has long-term global and national data extending back to the 18th century and is frequently updated with the latest estimates for 2022.\nIt’s published with an open-access license and transparent methodology and inputs, which you can find\non GitHub\n. The peer-reviewed paper describing the methodology is\nhere\n.\n2\nThis online data resource is open to user comments and feedback, so errors or issues can be easily reported.\nCEDS provides clear documentation of data improvements and detailed comparisons of recent updates against previous versions.\nTo be clear, CEDS does not have high-quality\nmeasurements\nof emissions of air pollutants — certainly not dating back to the 18th century. These figures are calculated and modeled based on inputs, such as the quantity of different fuels that were burned, technological advancements and pollution controls, fertilizer use, and agricultural production. You can, for example, estimate the amount of sulfur dioxide produced from burning one tonne of coal in a power plant (with or without pollution filters).\nOf course, this means the data comes with some uncertainty, especially in earlier periods. However, it gives us a reasonable and consistent global dataset to understand how trends in emissions of air pollutants have changed over time.\nThis article will focus on the breakdown of pollutants by their source. For this, we’ll use a categorization based on CEDS’s classification. In the table below, we summarize what is included in each category.\nCategory\nSub-categories\nAgriculture\nEnteric fermentation\nFuel use in agriculture, forestry, and fishing\nIndirect N₂O emissions\nManure management\nRice cultivation\nSoil emissions\nOther agricultural emissions\nBuildings\nCommercial and institutional buildings\nResidential buildings\nDomestic aviation\nDomestic aviation\nEnergy\nElectricity production (autoproducer)\nElectricity production (public)\nFossil fuel fires\nFugitive emissions from natural gas distribution\nFugitive emissions from natural gas production\nFugitive emissions from other energy sources\nFugitive emissions from petroleum\nFugitive emissions from solid fuels\nHeat production\nOther energy transformation\nOther fuel use (unspecified)\nIndustry\nAdipic acid production\nAluminum production\nCement production\nChemical industry\nIndustrial combustion (chemicals)\nIndustrial combustion (construction)\nIndustrial combustion (food and tobacco)\nIndustrial combustion (iron and steel)\nIndustrial combustion (machinery)\nIndustrial combustion (mining and quarrying)\nIndustrial combustion (non-ferrous metals)\nIndustrial combustion (non-metallic minerals)\nIndustrial combustion (other)\nIndustrial combustion (pulp and paper)\nIndustrial combustion (textile and leather)\nIndustrial combustion (transport equipment)\nIndustrial combustion (wood products)\nIron and steel alloy production\nLime production\nNitric acid production\nOther mineral production\nOther non-ferrous metal production\nPulp and paper, food, beverage, and wood processing\nInternational aviation\nInternational aviation\nInternational shipping\nInternational shipping\nOil tanker loading\nSolvents\nChemical products manufacture and processing\nDegreasing and cleaning\nOther product use\nPaint application\nTransport\nDomestic navigation\nRail transportation\nRoad transportation\nOther transport\nWaste\nSolid waste disposal\nWaste combustion\nWastewater handling\nOther waste handling\nOther waste sources\nUnspecified waste sources\nShow more\nHow air pollution damages our health\nBefore we discuss the sources of some of the key air pollutants, we should briefly explain how air pollution affects human health and how each of these pollutants contributes to this.\nThere are three key pathways by which a pollutant can cause harm\n3\n:\nDirect exposure:\nSome gases are toxic and can have an acute effect on health. These acute impacts are more common for people with existing respiratory conditions such as asthma or chronic obstructive pulmonary disease (COPD). This direct exposure does cost lives, but the total number is relatively small compared to the millions that die from chronic exposure to air pollution.\nFormation of particulate matter:\nMany of the pollutants we’ll look at contribute to health impacts\nindirectly\nby breaking down to form secondary smaller particles. These particles are called “particulate matter”. Typically, the smaller the particles are, the worse they can be for human health because they can enter our lungs and airways — and, in some cases, the bloodstream. Particulate matter can cause respiratory and cardiovascular problems, including cancer, strokes, and heart attacks.\nFormation of ozone:\nAnother\nindirect\nway these pollutants can affect our health is by forming a gas called ozone (O\n3\n). Ozone can cause breathing problems and worsen acute conditions like asthma and COPD\n4\n. However, it also affects our health through chronic exposure by causing inflammation of the lungs, increasing the risk of respiratory diseases, and reducing our cardiovascular health.\nAs we discuss each pollutant, we’ll briefly explain how it affects health through one or several pathways. While it’s difficult to pinpoint exactly how many deaths each pollutant causes, wherever possible, we’ll also try to give a rough order of magnitude estimate.\nTo give some sense of scale,\nhere\nis the Global Burden of Disease’s breakdown of global deaths from air pollution.\n5\nIn 2021, this totaled around\n8 million deaths.\nNote that there are also natural sources of particulate matter, so not\nall\nof these pollution deaths resulted from human emissions. But most did. For more on this, see our colleague Max Roser’s\narticle\n, which looks at estimates from various sources.\n3.1 million\ncame from\nhousehold\nair pollution, a combination of direct toxicity and particulate matter.\n4.7 million\ncame from outdoor particulate matter, and another\nhalf a million\nfrom outdoor ozone pollution.\n6\nBreaking down the sources of different air pollutants\nSulfur dioxide: the source of acid rain\nSulfur dioxide (SO\n2\n) is the main pollutant that causes\nacid rain\n. This has been a major environmental problem because acid rain can change the chemistry of rivers and lakes, affecting fish populations, soils, and the extent and quality of forests. You can also see the effects of acid rain on older limestone and marble buildings and statues, where the acidity dissolves parts of the structure.\nThere are two ways that SO\n2\ncan threaten\nhuman health. First, direct inhalation of SO\n2\ncan exacerbate respiratory problems such as asthma and bronchitis. But its main contribution is by breaking down to form small particulate matter. While an exact figure is hard to pin down, given that sulfur dioxide is a substantial contributor to particulate matter and that at least 4 million deaths are linked to these small particles yearly, we would estimate that\nhundreds of thousands\nof deaths per year are linked to SO\n2\n.\nSO\n2\nis formed when we burn fuels that contain sulfur.\nThe charts below show where global emissions come from and how these sectors have changed over time. In 2022, energy production was the biggest contributor by far. This is predominantly due to power from coal, which has sulfur impurities that are released when it’s burned.\nThe main contribution of the industry is the metal smelting process.\n7\nThis is because many of the ores that are used to produce metals – such as pyrite – contain large amounts of sulfur, which is released when they are roasted at high temperatures.\nOil also contains sulfur, which is why road transport, shipping, and aviation all contribute. Shipping emissions have received a lot of attention in the last few years because they\ndropped by more than 70%\nin 2020 after the introduction of tight regulations on maritime fuels.\nYou’ll notice that global emissions of SO\n2\npeaked in 1979 and have almost halved since then, thanks to the introduction of pollution controls, particularly in Europe, North America, and China. SO\n2\ncan be removed from smokestacks in coal plants using technologies that filter or “scrub” the sulfur away before it’s emitted into the atmosphere. This, combined with a\nmove away from coal\nin Europe and North America, has led to a rapid reduction in emissions.\nNitrogen oxides (NOₓ): the reason car exhaust fumes are so damaging\nNitrogen oxides (NOₓ) are a group of gases, mostly made up of nitric oxide (NO) and nitrogen dioxide (NO\n2\n). They are formed when fossil fuels are burned, causing nitrogen in the air — and, to a lesser extent, in the fuel itself — to react with oxygen.\nLike sulfur dioxide, which we just looked at, NOₓ can cause acid rain, threatening wildlife and ecosystems. NOₓ has a particularly large impact on human health because it acts through all three mechanisms we looked at earlier. It can be acutely toxic, inflaming the lungs. It reacts with other gases to form particulate matter, and it also forms ozone. NOₓ, therefore, causes smog and the thick haze you often see in highly polluted cities.\nAgain, we don’t have exact estimates for how many deaths it contributes to. But, given that it’s a main source of ozone (which kills around half a million) and a substantial fraction of particulate matter (which kills several million), it’s reasonable to expect that NOₓ is linked to\nover a million deaths yearly\n.\nSince coal, oil, and gas all contain nitrogen, NOₓ is produced in various sectors, as the chart below shows. The biggest source is transport — mostly from road vehicles — where NOₓ is emitted from the exhaust of cars and trucks. This is almost matched by the burning of coal and gas for electricity production, shown as “energy” in the chart. Like road transport, burning fuel for shipping emits significant amounts of NOₓ, making it a leading source, too.\nAnd industrial processes such as metal smelting, cement production, and petroleum refining contribute a lot.\nA smaller but still important source is agriculture. When nitrogen is applied to crops as synthetic fertilizer or manure, some of this nitrogen is converted to nitrogen oxides (and ammonia, which we’ll come on to later) in the soil.\n8\nWhile emissions of NOₓ from processes such as transport and electricity production have declined a lot globally, progress on agriculture has been much slower: emissions have flatlined but have not fallen much.\nSome countries have been successful in drastically reducing emissions of NOₓ – with huge benefits for human health. Moving away from fossil fuels – particularly coal in electricity production – has led to a large decline in “energy” emissions (have a look at\nthe United Kingdom\nas an example). Setting pollution control standards for automakers has also played a crucial role in reducing emissions from road transport. NOₓ emissions from exhausts can be dramatically reduced through the use of\ncatalytic converters\n, which are devices that split the NOₓ compounds into nitrogen and oxygen before they are released into the atmosphere.\nBlack carbon: the soot that fills our skies and lungs\nBlack carbon (BC) are the small particles that many of us know as “soot”.\nAs most of us know from experience — such as lighting a bonfire — soot is formed when we burn materials such as wood and biomass or fossil fuels like coal. It’s the\nincomplete\ncombustion of these materials that leads to the formation of these BC particles.\nThese particles are black because they absorb light, and this absorption of sunlight contributes to climate change. However, when it comes to health, it contributes through its direct toxicity and the formation of small particulates.\nBlack carbon can be a major issue for household and outdoor particulate matter and\nprobably contributes to\nseveral million deaths per year\n.\nIn the charts below, you can see the sources of black carbon globally.\nBuildings are a major source because many households in low- and middle-income countries still cook and heat with wood, charcoal, and other solid fuels. Even in richer countries, some households continue to burn wood for heating.\nRoad transport is another major contributor, since BC is formed from diesel engines and exhausts.\nEnergy production’s contribution mostly comes from coal and biomass burned for electricity and heat.\nThe open burning of waste plays a surprisingly large role, particularly in low-to-middle-income countries, where this is often used for waste disposal.\nSome countries — particularly richer ones — have seen\na huge drop\nin black carbon emissions over the last 50 years due to moving away from biomass and coal burning and introducing cleaner cars.\nMethane: burping cows, rice paddies, and gas leaks\nMethane (CH\n4\n) is a greenhouse gas, so it’s\nmostly discussed\nregarding contributions to climate change. However, methane can also affect health when it breaks down to form ozone, a gas that’s hazardous to human health. In fact, methane is the biggest precursor to ozone in many places.\nIt’s\nestimated that\nmethane can lead to up to\nhalf a million premature deaths\na year.\n9\nThe charts below show where it comes from.\nAgriculture, specifically livestock and rice production, is the biggest source of methane. Ruminant livestock — mostly cows — produce methane in their digestive systems and release it into the atmosphere by burping. That’s why beef and lamb\ntend to have\na high carbon footprint.\n10\nRice also produces methane because it’s often grown in flooded paddy fields with low oxygen levels. This means methane is produced rather than carbon dioxide. Eating less beef, lamb, and dairy could\ndramatically reduce\nemissions from agriculture. Finding innovative ways to reduce the amount of methane produced per cow by changing their diets could also help.\nEnergy generation is another large source of methane. Most of it comes from leaks — which we call “fugitive emissions” — from oil and gas wells. If these are not properly managed, some methane escapes into the atmosphere. Another key source is coal mining. Monitoring oil and gas wells for methane leaks and enforcing regulations to ensure that limits are not breached can reduce these emissions. New drone and satellite technologies are\nalready being developed\nto provide a global map of where these leaks are coming from.\nThe third sector that contributes a lot is waste. Methane is produced when organic material — like food waste or paper — rots in conditions without much oxygen (like in a landfill). Securely sealing landfills or capturing this methane for energy can effectively reduce these emissions. Methane from waste has been falling in many richer countries — like\nthe United Kingdom\n— that have implemented these strategies.\nAmmonia (NH\n3\n): it’s all about farming\nAs the chart below shows, nearly all human emissions of ammonia (NH\n3\n) come from agriculture. When we add nitrogen to crops as synthetic fertilizers or manure, some of this nitrogen is converted into NH\n3\nin the soil. Other smaller sources include decomposing organic waste in landfills and energy production.\nAlthough NH\n3\ndoesn’t stay in the atmosphere for long — typically hours to days — it can react with other gases to form small particulates that harm human health.\nSome studies suggest ammonia could drive\nseveral hundred thousand (up to 385,000) premature deaths\nfrom particulate matter.\n11\nIn the charts below, you can see that unlike most other air pollutants, where emissions have peaked globally, emissions of NH\n3\nhave continued to rise as\nlivestock production\nand the use of synthetic\nfertilizers\nhave increased.\nSome countries — particularly\nthose in Europe\n— have achieved small reductions in emissions because they\nuse less fertilizers\nthan a few decades ago.\nNon-methane volatile organic compounds (NMVOCs)\nNon-methane volatile organic compounds (NMVOCs) can threaten human health through all three of the pathways we looked at earlier: they can be directly toxic in high concentrations and mix with other gases to form ozone and small particulates.\nYou can see the global sources of NMVOCs in the chart below.\nNMVOCs are produced by traditional pollution sources like burning fossil fuels and car exhausts. However, unlike most other pollutants, solvents such as paints, cleaning products, and chemical plants are also major sources.\nIn addition to switching to low-carbon energy and phasing out gasoline cars, we also need to reduce the use of volatile organic compounds (VOCs) in personal care products and solvents. Setting emission limits on the chemicals industry will also be key to lowering our exposure to non-methane VOCs (NMVOCs).\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nWhile pollution sources are diverse, the solutions are often not\nGoing through so many pollutants, one by one — as we just did — can seem overwhelming. We don’t need to just tackle one or two; we need to tackle more than six.\n12\nThe good news is that the solutions we need often cut across several gases at the same time.\nBurning stuff for energy — whether that’s fossil fuels or biomass — is the root source for many of these gases. Moving to clean energy — deploying renewable or nuclear electricity,\nelectrifying our cars\n, our industry, and home heating — and ensuring that people worldwide\nhave access\nto\nmodern\nenergy sources would simultaneously cut many of these pollutants.\nReducing meat production and consumption\nby shifting to\nmore plant-based diets would reduce methane and ammonia emissions at the same time, too.\nThese transitions come with large health benefits, just from reducing air pollution alone.\nAnd we know that it can be done. Many countries\nhave\ndramatically reduced levels of air pollution, and as you can see in the chart below, they’ve prevented hundreds of thousands of early deaths as a result.\nThe total number of deaths has declined in these countries despite much larger older populations. The\ndecline in\ndeath\nrates\nhas been even larger.\nAcknowledgments\nMany thanks to Max Roser and Edouard Mathieu for their feedback and comments on this article.\nContinue reading on Our World in Data\nIn many countries, people breathe the cleanest air in centuries. What can the rest of the world learn from this?\nAir pollution tends to get worse before it gets better, but how can we accelerate this transition?\nAir Pollution\nExplore historical emissions of air pollutants across the world.\nData review: how many people die from air pollution?\nThis data review presents published estimates of the global death toll from air pollution and provides the context that makes them understandable.\nEndnotes\nHere, we’re not talking about greenhouse gases that drive climate change — which we cover in great detail elsewhere — although we will include a few greenhouse gases, such as methane, which can also act as a precursor to local air pollutants.\nHoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., ... & Zhang, Q. (2018).\nHistorical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS)\n. Geoscientific Model Development, 11(1), 369-408.\nWorld Health Organization (2021).\nWHO global air quality guidelines: particulate matter (‎PM2.5 and PM10)‎, ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide\n.\nNote that we’re talking about ground-level, or tropospheric, ozone in the lower atmosphere. At this level, it’s considered a pollutant. This differs from\nstratospheric\nozone, which is high in the atmosphere and crucial for protecting us from ultraviolet radiation. We cover this in our work on the\nOzone Layer\n. Mar, K. A., Unger, C., Walderdorff, L., & Butler, T. (2022). Beyond CO2 equivalence: The impacts of methane on climate, ecosystems, and health. Environmental science & policy.\nThe Global Burden of Disease is published by the Institute for Health Metrics and Evaluation (IHME).\nNote that when we add all of these\nindividual\nrisk factors — indoor particulates, outdoor particulates, and outdoor ozone — the total comes to 8.3 million, which is higher than the Global Burden of Disease reports on aggregate. This is because different risk factors can combine to increase health problems and the risk of premature death. In\nthis article\n, our colleague, Saloni Dattani, examines how risk factors are estimated and why they can’t be summed up to give the total number of premature deaths.\nFioletov, V. E., McLinden, C. A., Krotkov, N., Li, C., Joiner, J., Theys, N., ... & Moran, M. D. (2016).\nA global catalogue of large SO2 sources and emissions derived from the Ozone Monitoring Instrument\n. Atmospheric Chemistry and Physics, 16(18), 11497-11519.\nPan, S. Y., He, K. H., Lin, K. T., Fan, C., & Chang, C. T. (2022).\nAddressing nitrogenous gases from croplands toward low-emission agriculture\n. Npj Climate and Atmospheric Science.\nThis estimate comes from the UN Environment Programme and Climate and Clean Air Coalition:\nUNEP and Climate and Clean Air Coalition (2021)\nGlobal Methane Assessment: Benefits and Costs of Mitigating Methane Emissions\n.\nAlthough this is not the only reason they have a high carbon footprint, even\nwhen we ignore methane\n, they still emit much carbon through land use and manure.\nWyer, K. E., Kelleghan, D. B., Blanes-Vidal, V., Schauberger, G., & Curran, T. P. (2022). Ammonia emissions from agriculture and their contribution to fine particulate matter: A review of implications for human health. Journal of Environmental Management, 323, 116285.\nWe included six of the big ones in this article, but it’s not a complete list.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Pablo Rosado (2025) - “Air pollution kills millions every year — where does it come from?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260603-104356/air-pollution-sources.html' [Online Resource] (archived on June 3, 2026).\nBibTeX citation\n@article{owid-air-pollution-sources,\nauthor = {Hannah Ritchie and Pablo Rosado},\ntitle = {Air pollution kills millions every year — where does it come from?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260603-104356/air-pollution-sources.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "air-pollution-sources", "source_url": "https://ourworldindata.org/air-pollution-sources", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "A breakdown of the sources of many air pollutants that damage our health and ecosystems.", "numeric_mentions": ["31,", "2025", "1", "18", "2022", "2", "3", "4", "5", "2021,", "8 million", "3.1 million", "4.7 million", "6", "4 million", "2022,", "7", "70%", "2020", "1979", "8", "50 years", "9", "10", "385,000", "11", "12", "2018", "1750", "2014", "369", "408", "2021", "8.3 million", "2016", "16", "11497", "11519", "323,", "116285", "20260603", "104356", "3,", "2026"], "numeric_evidence": [{"grapher_slug": "deaths-from-household-and-outdoor-air-pollution", "source_url": "https://ourworldindata.org/grapher/deaths-from-household-and-outdoor-air-pollution", "parse_error": "Failed to fetch https://ourworldindata.org/grapher/deaths-from-household-and-outdoor-air-pollution.csv"}, {"grapher_slug": "death-rate-from-air-pollution-per-100000", "source_url": "https://ourworldindata.org/grapher/death-rate-from-air-pollution-per-100000", "parse_error": "Failed to fetch https://ourworldindata.org/grapher/death-rate-from-air-pollution-per-100000.csv"}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "89f229983871e07b229a"},
{"raw_link": "https://ourworldindata.org/foreign-aid-domestic-overseas", "title": "How much foreign aid is spent domestically rather than overseas?", "context": "Home\nForeign Aid\nHow much foreign aid is spent domestically rather than overseas?\nIn many countries, a substantial share of aid is spent domestically on hosting refugees, offering student scholarships, and administrative costs.\nBy\nSimon van Teutem\n,\nHannah Ritchie\n,\nand\nPablo Arriagada\nMarch 24, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nMuch of foreign aid is spent on goods that are shipped overseas: food supplies, medicines, or humanitarian assistance in emergency situations.\n1\nBut a surprising amount of what’s reported as foreign aid is not sent abroad; it’s spent domestically. Foreign aid budgets in rich countries can include the costs of hosting refugees, some scholarships to foreign students, and some administrative costs that are spent domestically.\n2\nThese domestic expenses are reported by countries to the OECD, which tracks and measures foreign aid allocations, so they are included in the widely quoted aid figures you’ll typically see. We'll refer to these combined costs as \"aid money spent at home\".\nIn 2023, 22% of total foreign aid for all\nDevelopment Assistance Committee (DAC)\ncountries was spent at home. The DAC countries are a group of 32 high-income countries; from this point onwards, we’ll refer to them as “rich donor countries”.\n3\nIn this article, we’ll look at how aid money spent at home varies across countries and categories, how this has changed over time, and what this means for the amount of money available for support overseas.\nMore foreign aid is spent domestically, mostly to host refugees\nSo, in 2023, 22% of foreign aid was spent domestically in rich donor countries. That was a record year, both in absolute and relative terms. Domestic spending has more than tripled from $14 billion to $48 billion since 2010. As a share of total aid, it has\nincreased from\n10% to 22%.\nWhat was this spending used for? The chart below shows the breakdown. You can see that the largest cost in the last few years has been for hosting refugees. This is mostly because of the increase in refugees from Ukraine following Russia’s invasion. There was also a significant increase in 2015 and 2016 as a result of the Syrian civil war.\nWhile it might seem odd to include domestic spending in a\nforeign\naid budget, there are reasonable arguments for doing so. Hosting refugees, for example, addresses urgent humanitarian needs of foreign citizens who’ve been forcibly displaced, even if the money is being spent at home.\nAs we explain later, our point is that not all aid money spent at home is “wasted” or “misspent”, but the size of these costs does have consequences for other parts of the aid budget.\nHow much foreign aid do different countries spend domestically?\nWhile 22% of foreign aid was spent domestically across all rich donor countries in 2023, spending patterns differed greatly. The chart below shows what share of each country’s total aid was spent domestically on different activities that year.\nYou can add and remove countries on the interactive chart.\nThree countries spent\nmore than\nhalf\nof their foreign aid domestically. Nearly all of this was spent on hosting refugees, predominantly from Ukraine.\nWhile most other countries spent more overseas than they do at home — which we might expect from “foreign aid” — their domestic share was still substantial. It was more than one-fifth in many countries across Europe and North America. In our home countries, the United Kingdom and the Netherlands, it was one-third and one-quarter, respectively.\nIn nearly all countries, the costs of hosting refugees — and, to a lesser extent, administrative costs — dominate. But there are a few exceptions. Hungary is a clear outlier, with 41% of its aid spent providing scholarships to overseas students.\nWhile most countries include the costs of hosting refugees in their foreign aid reporting, a few countries don't: Australia and Luxembourg exclude these costs, arguing they don't help poor countries develop.\nHow has domestic aid spending changed over time?\nThe number of refugees looking for protection — and, of course, the number able to make that journey — ebbs and flows depending on geopolitical and humanitarian crises. We might expect 2023 to be an outlier because of the number of Ukrainian refugees seeking safety from the war.\nSo, was 2023 an anomaly? How has aid money spent at home changed over time?\nIn the second chart in this article, we saw the\nabsolute\namount spent at home over time, including for refugees. This has increased, but so has\nthe total amount\nspent on aid.\nSo, let’s look at the change in the share of aid spent domestically. The chart below shows this for a selection of countries.\nAs mentioned earlier, rich donor countries now spend over 20% of their aid money domestically, but the patterns vary by country. France’s domestic and overseas aid spending has remained fairly stable, unlike Germany, which has seen strong shifts, particularly during the 2010s and the Syrian civil war. Italy’s spending aligns closely with Germany’s.\nDomestic spending has increased in the United States in recent years and even more dramatically in the United Kingdom. Japan shows the opposite trend.\nAs we consider this spending in 2024 and future years, an essential point is that refugee hosting costs only count as foreign aid for the first year. Since most Ukrainians will have been in their host countries for over a year by 2024, these figures could drop sharply.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nWhat does aid money spent at home mean for overseas aid spending?\nTo be clear, the point here is not that all aid money spent at home is “bad” or “wasteful” and all money sent overseas is “good”. We can think about this in the context of providing aid to a country that has just experienced a humanitarian crisis. There are several ways to help: sending money and resources to the country itself, or spending money on protecting refugees who had to flee those conditions. Both are valuable and worth doing.\nHowever, changes to aid money spent at home affect the amount available for\nvital and effective programs\noverseas.\nIf, for example, the total amount of foreign aid remained constant, an increase in aid money spent at home would inevitably mean reductions elsewhere. If the total aid budget was\nalso\nincreasing — partly to compensate for higher domestic costs — overseas spending could be maintained or even increased simultaneously.\nThe chart below shows the total amount of aid\nnot\nspent domestically but sent to recipient countries instead.\n4\nThis is both a reflection of each country’s total aid budget\nand\nhow it’s allocated. For rich donor countries as a whole, the budget has increased by around one-quarter since 2010.\n5\nAfter a large increase in the overseas budget between 2019 and 2022, it remained the same in 2023.\n6\nWhere\nthat money was spent also matters: low-income countries\nhave received\nslightly less aid in recent years than before the COVID-19 pandemic.\nIn many countries, the amount spent overseas dropped in 2023: Germany, France, and the United States are examples. The United Kingdom stands out: overseas spending in 2023 was 60% lower than in 2019.\nAs mentioned earlier, aid money spent at home will likely drop in many countries as refugees are only counted as ODA costs in the first year. This might have increased the amount of spending available overseas in 2024. However, that is unlikely to last with proposed cuts to total aid budgets from leading donors such as the\nUnited States\n,\nUnited Kingdom\n, and\nGermany\n.\nIn a world that’s more generous, aid activities like hosting refugees do not have to come at the cost of supporting lifesaving programs overseas. But in a world with a shrinking aid budget, this tension will only increase.\nAcknowledgments\nThanks to Pablo Arriagada, Bastian Herre, Max Roser, and Edouard Mathieu for their feedback and comments on this article.\nContinue reading on Our World in Data\nForeign Aid\nWho gives and receives foreign aid? Which forms does it take? What are examples of when it was (un-)successful?\nFor many of us, it doesn’t cost much to improve someone’s life, and we can do much more of it\nMost countries spend less than 1% of their national income on foreign aid; even small increases could make a big difference.\nThe great global redistributor we never hear about: money sent or brought back by migrants\nMigrants send or bring back over three times the amount of global foreign aid. Cutting transaction fees could make this support even more effective in reducing poverty.\nEndnotes\nWhen researchers talk about foreign aid, they’re usually talking about \"Official Development Assistance\" (ODA). This is money that flows from people in major donor countries to people in poorer countries. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms. This article treats these terms as synonymous.\nAdministrative costs represent only expenses not already included as integral parts of other ODA delivery. While some costs (like diplomatic staff) are spent within recipient countries, they don't reach the intended aid beneficiaries.\nIt’s worth noting that some non-DAC countries also provide foreign aid; they represent approximately 10% of the global total.\nNote that this does not necessarily mean that\nall\nof this money is received by locals in that country. Some of this money is also spent on foreign workers who are there to set up or implement programs or international non-profits.\nThe amount spent in 2010 was $120 billion. By 2023, this had increased to $168 billion.\nIn 2020-21, it rose partly due to\npandemic\nspending, while 2022 saw a sharp increase from\nUkraine\naid.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSimon van Teutem, Hannah Ritchie, and Pablo Arriagada (2025) - “How much foreign aid is spent domestically rather than overseas?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-083815/foreign-aid-domestic-overseas.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-foreign-aid-domestic-overseas,\nauthor = {Simon van Teutem and Hannah Ritchie and Pablo Arriagada},\ntitle = {How much foreign aid is spent domestically rather than overseas?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-083815/foreign-aid-domestic-overseas.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "foreign-aid-domestic-overseas", "source_url": "https://ourworldindata.org/foreign-aid-domestic-overseas", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "In many countries, a significant share of aid is spent domestically on hosting refugees, offering student scholarships, and administrative costs.", "numeric_mentions": ["24,", "2025", "1", "2", "2023,", "22%", "32", "3", "14 billion", "48 billion", "2010", "10%", "2015", "2016", "41%", "2023", "20%", "2024", "2024,", "4", "5", "2019", "2022,", "6", "19", "60%", "1%", "120 billion", "168 billion", "2020", "21,", "2022", "20260518", "083815", "18,", "2026"], "numeric_evidence": [{"title": "Share of foreign aid spent within donor countries", "source_url": "https://ourworldindata.org/grapher/share-of-foreign-aid-spent-within-donor-countries.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "ODA by donor - In-donor aid (% of ODA)", "ODA by donor - In-donor aid (% of ODA) (Annotations)"], "row_count_total": 805, "rows_head": [{"Entity": "Australia", "Code": "AUS", "Year": "2010", "ODA by donor - In-donor aid (% of ODA)": "10.61002", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2011", "ODA by donor - In-donor aid (% of ODA)": "12.023897", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2012", "ODA by donor - In-donor aid (% of ODA)": "14.765951", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2013", "ODA by donor - In-donor aid (% of ODA)": "20.83582", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2014", "ODA by donor - In-donor aid (% of ODA)": "13.280335", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2015", "ODA by donor - In-donor aid (% of ODA)": "12.684873", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2016", "ODA by donor - In-donor aid (% of ODA)": "11.737839", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2017", "ODA by donor - In-donor aid (% of ODA)": "11.280123", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2018", "ODA by donor - In-donor aid (% of ODA)": "11.009844", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2019", "ODA by donor - In-donor aid (% of ODA)": "9.317864", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2020", "ODA by donor - In-donor aid (% of ODA)": "10.532774", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2021", "ODA by donor - In-donor aid (% of ODA)": "5.8154087", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2022", "ODA by donor - In-donor aid (% of ODA)": "7.70595", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2023", "ODA by donor - In-donor aid (% of ODA)": "9.561269", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2024", "ODA by donor - In-donor aid (% of ODA)": "8.542373", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2010", "ODA by donor - In-donor aid (% of ODA)": "15.356417", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2011", "ODA by donor - In-donor aid (% of ODA)": "17.561207", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2012", "ODA by donor - In-donor aid (% of ODA)": "20.246891", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2013", "ODA by donor - 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In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2016", "ODA by donor - In-donor aid (% of ODA)": "12.518476", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2017", "ODA by donor - In-donor aid (% of ODA)": "6.7652254", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2018", "ODA by donor - In-donor aid (% of ODA)": "6.715328", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2019", "ODA by donor - In-donor aid (% of ODA)": "6.575929", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2020", "ODA by donor - In-donor aid (% of ODA)": "5.6229587", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2021", "ODA by donor - In-donor aid (% of ODA)": "7.2697363", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2022", "ODA by donor - In-donor aid (% of ODA)": "48.945045", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2023", "ODA by donor - In-donor aid (% of ODA)": "14.195464", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2024", "ODA by donor - In-donor aid (% of ODA)": "2.2305713", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2010", "ODA by donor - In-donor aid (% of ODA)": "16.567513", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2011", "ODA by donor - In-donor aid (% of ODA)": "16.166534", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2012", "ODA by donor - In-donor aid (% of ODA)": "13.093556", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2013", "ODA by donor - In-donor aid (% of ODA)": "12.958944", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2014", "ODA by donor - In-donor aid (% of ODA)": "15.140425", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2015", "ODA by donor - In-donor aid (% of ODA)": "12.9322", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2016", "ODA by donor - In-donor aid (% of ODA)": "16.711617", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2017", "ODA by donor - In-donor aid (% of ODA)": "17.798597", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2018", "ODA by donor - In-donor aid (% of ODA)": "17.501308", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2019", "ODA by donor - In-donor aid (% of ODA)": "17.519863", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2020", "ODA by donor - In-donor aid (% of ODA)": "19.1367", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2021", "ODA by donor - In-donor aid (% of ODA)": "12.675283", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2022", "ODA by donor - In-donor aid (% of ODA)": "13.645686", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2023", "ODA by donor - In-donor aid (% of ODA)": "22.587309", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2024", "ODA by donor - In-donor aid (% of ODA)": "24.903328", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Croatia", "Code": "HRV", "Year": "2012", "ODA by donor - In-donor aid (% of ODA)": "1.6853948", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Croatia", "Code": "HRV", "Year": "2013", "ODA by donor - In-donor aid (% of ODA)": "0.9538598", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Croatia", "Code": "HRV", "Year": "2014", "ODA by donor - In-donor aid (% of ODA)": "5.8676662", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Croatia", "Code": "HRV", "Year": "2015", "ODA by donor - In-donor aid (% of ODA)": "3.1316857", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Croatia", "Code": "HRV", "Year": "2016", "ODA by donor - In-donor aid (% of ODA)": "0.71375865", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Croatia", "Code": "HRV", "Year": "2017", "ODA by donor - In-donor aid (% of ODA)": "0.22379714", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Croatia", "Code": "HRV", "Year": "2018", "ODA by donor - In-donor aid (% of ODA)": "0.8999873", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Croatia", "Code": "HRV", "Year": "2019", "ODA by donor - In-donor aid (% of ODA)": "6.1818705", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Croatia", "Code": "HRV", "Year": "2020", "ODA by donor - In-donor aid (% of ODA)": "3.5103087", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Croatia", "Code": "HRV", "Year": "2021", "ODA by donor - In-donor aid (% of ODA)": "2.6660595", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Croatia", "Code": "HRV", "Year": "2022", "ODA by donor - In-donor aid (% of ODA)": "27.292606", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "Croatia", "Code": "HRV", "Year": "2023", "ODA by donor - In-donor aid (% of ODA)": "19.295002", "ODA by donor - 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In-donor aid (% of ODA)": "2.33073", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2018", "ODA by donor - In-donor aid (% of ODA)": "0.17834492", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2019", "ODA by donor - In-donor aid (% of ODA)": "2.6286724", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2020", "ODA by donor - In-donor aid (% of ODA)": "4.066014", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2021", "ODA by donor - In-donor aid (% of ODA)": "2.6445937", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2022", "ODA by donor - In-donor aid (% of ODA)": "3.5688932", "ODA by donor - In-donor aid (% of ODA) (Annotations)": ""}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2023", "ODA by donor - 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This includes administrative costs, hosting refugees, student scholarships, and development awareness.", "chart_note": null, "chart_citation": "OECD (2025)", "original_chart_url": "https://ourworldindata.org/grapher/share-of-foreign-aid-spent-within-donor-countries", "owid_column_metadata": {"ODA by donor - In-donor aid (% of ODA) - Net disbursements": {"titleShort": "ODA by donor - In-donor aid (% of ODA)", "titleLong": "ODA by donor - In-donor aid (% of ODA)", "descriptionShort": "Official development assistance component that represents in-donor aid, expressed as a percentage of ODA. Monetary aid is estimated as net disbursements.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "In-donor aid considers these subcategories of ODA: scholarships and student costs in donor countries, administrative costs not included elsewhere, development awareness, and refugees in donor countries", "The data is reported as net disbursements. This refers to aid ultimately given and is different from commitments, which is only aid that has been pledged. These are net amounts because any money coming in (like loan repayments or interest) has been subtracted from money going out (like new grants or loans)."], "descriptionProcessing": "We calculated this indicator by summing the subcomponents of ODA that are considered in-donor (scholarships and student costs in donor countries, administrative costs not included elsewhere, development awareness, and refugees in donor countries). In the case of shares, we divided this sum by total ODA by donor, expressed as a percentage.", "shortUnit": "%", "unit": "%", "timespan": "1960-2024", "type": "Numeric", "owidVariableId": 1132315, "shortName": "oda_indonor_net_disbursements_share_oda", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with minor processing by Our World in Data", "citationLong": "OECD (2025) – with minor processing by Our World in Data. “ODA by donor - In-donor aid (% of ODA) – Net disbursements” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC1: Flows by provider (ODA+OOF+Private)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132315.metadata.json"}, "1132315-annotations": {"titleShort": "1132315-annotations", "titleLong": "1132315-annotations", "type": "SeriesAnnotation", "citationShort": " – processed by Our World in Data", "citationLong": " – processed by Our World in Data. “1132315-annotations” [dataset].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/undefined.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Share of foreign aid spent domestically within donor countries", "source_url": "https://ourworldindata.org/grapher/foreign-aid-given-to-donor-countries-share.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Refugees in donor countries", "Administrative costs", "Scholarships and student costs", "Development awareness", "Refugees in donor countries (Annotations)", "Administrative costs (Annotations)", "Scholarships and student costs (Annotations)", "Development awareness (Annotations)"], "row_count_total": 686, "rows_head": [{"Entity": "Australia", "Code": "AUS", "Year": "2010", "Refugees in donor countries": "0.14453359", "Administrative costs": "4.2322474", "Scholarships and student costs": "6.13732", "Development awareness": "0.09592014", "Refugees in donor countries (Annotations)": "", "Administrative costs (Annotations)": "", "Scholarships and student costs (Annotations)": "", "Development awareness (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2011", "Refugees in donor countries": "0.00040136537", "Administrative costs": "6.0524874", "Scholarships and student costs": "5.9067893", "Development awareness": "0.0642195", "Refugees in donor countries (Annotations)": "", "Administrative costs (Annotations)": "", "Scholarships and student costs (Annotations)": "", "Development awareness (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2012", "Refugees in donor countries": "2.8454292", "Administrative costs": "6.1983824", "Scholarships and student costs": "5.663094", "Development awareness": "0.059044547", "Refugees in donor countries (Annotations)": "", "Administrative costs (Annotations)": "", "Scholarships and student costs (Annotations)": "", "Development awareness (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2013", "Refugees in donor countries": "7.0695796", "Administrative costs": "6.6403193", "Scholarships and student costs": "7.085882", "Development awareness": "0.040036734", "Refugees in donor countries (Annotations)": "", "Administrative costs (Annotations)": "", "Scholarships and student costs (Annotations)": "", "Development awareness (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2014", "Refugees in donor countries": "", "Administrative costs": "6.2084427", "Scholarships and student costs": "7.049758", "Development awareness": "0.022133898", "Refugees in donor countries (Annotations)": "", "Administrative costs (Annotations)": "", "Scholarships and student costs (Annotations)": "", "Development awareness (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2015", "Refugees in donor countries": "", "Administrative costs": "5.189188", "Scholarships and student costs": "7.494827", "Development awareness": "0.0008587037", "Refugees in donor countries (Annotations)": "", "Administrative costs (Annotations)": "", "Scholarships and student costs (Annotations)": "", "Development awareness (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2016", "Refugees in donor countries": "", "Administrative costs": "5.876089", "Scholarships and student costs": "5.859308", "Development awareness": "0.0024408812", "Refugees in donor countries (Annotations)": "", "Administrative costs (Annotations)": "", "Scholarships and student costs (Annotations)": "", "Development awareness (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2017", "Refugees in donor countries": "", "Administrative costs": "6.6328278", "Scholarships and student costs": "4.6430125", "Development awareness": "0.004282001", "Refugees in donor countries (Annotations)": "", "Administrative costs (Annotations)": "", "Scholarships and student costs (Annotations)": "", "Development awareness (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "2018", "Refugees in donor countries": "", 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"Scholarships and student costs (Annotations)": "", "Development awareness (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2016", "Refugees in donor countries": "36.449852", "Administrative costs": "1.8899652", "Scholarships and student costs": "7.3923244", "Development awareness": "0.5258395", "Refugees in donor countries (Annotations)": "", "Administrative costs (Annotations)": "", "Scholarships and student costs (Annotations)": "", "Development awareness (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2017", "Refugees in donor countries": "12.249954", "Administrative costs": "2.6317265", "Scholarships and student costs": "10.319116", "Development awareness": "0.73045784", "Refugees in donor countries (Annotations)": "", "Administrative costs (Annotations)": "", "Scholarships and student costs (Annotations)": "", "Development awareness (Annotations)": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "2018", "Refugees in donor countries": 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Net disbursements": "242434240", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1981", "Official development assistance (ODA) and private grants by donor - Net disbursements": "235031710", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1982", "Official development assistance (ODA) and private grants by donor - Net disbursements": "325283800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1983", "Official development assistance (ODA) and private grants by donor - Net disbursements": "418141000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1984", "Official development assistance (ODA) and private grants by donor - Net disbursements": "300206140", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1985", "Official development assistance (ODA) and private grants by donor - Net disbursements": "565934500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1986", "Official development assistance (ODA) and private grants by donor - Net disbursements": "600827650", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1987", "Official development assistance (ODA) and private grants by donor - Net disbursements": "712223000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1988", "Official development assistance (ODA) and private grants by donor - Net disbursements": "622248260", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1989", "Official development assistance (ODA) and private grants by donor - Net disbursements": "866800600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1990", "Official development assistance (ODA) and private grants by donor - Net disbursements": "967712060", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1991", "Official development assistance (ODA) and private grants by donor - Net disbursements": "965823100", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1992", "Official development assistance (ODA) and private grants by donor - Net disbursements": "992398400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1993", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1023375300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1994", "Official development assistance (ODA) and private grants by donor - Net disbursements": "840810750", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1995", "Official development assistance (ODA) and private grants by donor - Net disbursements": "724712100", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1996", "Official development assistance (ODA) and private grants by donor - Net disbursements": "805027260", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1997", "Official development assistance (ODA) and private grants by donor - Net disbursements": "851511940", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1998", "Official development assistance (ODA) and private grants by donor - Net disbursements": "845641700", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "1999", "Official development assistance (ODA) and private grants by donor - Net disbursements": "663676300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2000", "Official development assistance (ODA) and private grants by donor - Net disbursements": "450897820", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2001", "Official development assistance (ODA) and private grants by donor - Net disbursements": "778864300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2002", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1101986400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2003", "Official development assistance (ODA) and private grants by donor - Net disbursements": "779187600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2004", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1358514200", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2005", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1257855700", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2006", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2607972600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2007", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1586556500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2008", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1917850000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2009", "Official development assistance (ODA) and private grants by donor - Net disbursements": "3033084700", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2010", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1997672100", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2011", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2340652000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2012", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2588419000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2013", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2418605000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2014", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2125332000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2015", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2513949400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2016", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2548231700", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2017", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2903135000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2018", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2271122000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2019", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1835591600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2020", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2612188200", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2013029500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "931325400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1272137500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Bank, Total", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1183876900", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1965", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-462570", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1966", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-286197800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1967", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-280386880", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1975", "Official development assistance (ODA) and private grants by donor - Net disbursements": "15584397", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1976", "Official development assistance (ODA) and private grants by donor - Net disbursements": "41498000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1977", "Official development assistance (ODA) and private grants by donor - Net disbursements": "94324760", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1978", "Official development assistance (ODA) and private grants by donor - Net disbursements": "123265896", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1979", "Official development assistance (ODA) and private grants by donor - Net disbursements": "151057170", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1980", "Official development assistance (ODA) and private grants by donor - Net disbursements": "242434240", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1981", "Official development assistance (ODA) and private grants by donor - Net disbursements": "235031710", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1982", "Official development assistance (ODA) and private grants by donor - Net disbursements": "325283800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1983", "Official development assistance (ODA) and private grants by donor - Net disbursements": "418141000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1984", "Official development assistance (ODA) and private grants by donor - Net disbursements": "300206140", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1985", "Official development assistance (ODA) and private grants by donor - Net disbursements": "565934500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1986", "Official development assistance (ODA) and private grants by donor - Net disbursements": "600827650", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1987", "Official development assistance (ODA) and private grants by donor - Net disbursements": "712223000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1988", "Official development assistance (ODA) and private grants by donor - Net disbursements": "622248260", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1989", "Official development assistance (ODA) and private grants by donor - Net disbursements": "866800600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1990", "Official development assistance (ODA) and private grants by donor - Net disbursements": "967712060", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1991", "Official development assistance (ODA) and private grants by donor - Net disbursements": "965823100", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1992", "Official development assistance (ODA) and private grants by donor - Net disbursements": "992398400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1993", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1023375300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1994", "Official development assistance (ODA) and private grants by donor - Net disbursements": "840810750", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1995", "Official development assistance (ODA) and private grants by donor - Net disbursements": "724712100", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1996", "Official development assistance (ODA) and private grants by donor - Net disbursements": "805027260", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1997", "Official development assistance (ODA) and private grants by donor - Net disbursements": "851511940", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1998", "Official development assistance (ODA) and private grants by donor - Net disbursements": "845641700", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "1999", "Official development assistance (ODA) and private grants by donor - Net disbursements": "663676300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "African Development Fund", "Code": "", "Year": "2000", "Official development assistance (ODA) and private grants by donor - Net disbursements": "450897820", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}], "rows_tail": [{"Entity": "WTO - International Trade Centre", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "53225052", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2017", "Official development assistance (ODA) and private grants by donor - Net disbursements": "303621150", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2018", "Official development assistance (ODA) and private grants by donor - Net disbursements": "311397920", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2019", "Official development assistance (ODA) and private grants by donor - Net disbursements": "378434530", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2020", "Official development assistance (ODA) and private grants by donor - Net disbursements": "583376830", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "521239460", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "428908380", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "887722000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "Wellcome Trust", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "533045730", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2017", "Official development assistance (ODA) and private grants by donor - Net disbursements": "226401000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2018", "Official development assistance (ODA) and private grants by donor - Net disbursements": "249855780", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2019", "Official development assistance (ODA) and private grants by donor - Net disbursements": "145049970", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2020", "Official development assistance (ODA) and private grants by donor - Net disbursements": "186200900", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "204096900", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "204646450", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "181459170", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "William and Flora Hewlett Foundation", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "149252850", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1960", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-240355020", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1961", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-349699260", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1962", "Official development assistance (ODA) and private grants by donor - Net disbursements": "-266635380", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1963", "Official development assistance (ODA) and private grants by donor - Net disbursements": "305909730", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1964", "Official development assistance (ODA) and private grants by donor - Net disbursements": "776646300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1965", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2081566500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1966", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2036855700", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1967", "Official development assistance (ODA) and private grants by donor - Net disbursements": "2700777500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1968", "Official development assistance (ODA) and private grants by donor - Net disbursements": "931653100", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1969", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1717884500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1970", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1114339800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1971", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1844204500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1972", "Official development assistance (ODA) and private grants by donor - Net disbursements": "1724460400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1973", "Official development assistance (ODA) and private grants by donor - Net disbursements": "3112191500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1974", "Official development assistance (ODA) and private grants by donor - Net disbursements": "4113869000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1975", "Official development assistance (ODA) and private grants by donor - Net disbursements": "4373215000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1976", "Official development assistance (ODA) and private grants by donor - Net disbursements": "5168001500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1977", "Official development assistance (ODA) and private grants by donor - Net disbursements": "4199820800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1978", "Official development assistance (ODA) and private grants by donor - Net disbursements": "3396855300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1979", "Official development assistance (ODA) and private grants by donor - Net disbursements": "3801276200", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1980", "Official development assistance (ODA) and private grants by donor - Net disbursements": "4174089500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1981", "Official development assistance (ODA) and private grants by donor - Net disbursements": "5208264000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1982", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6441008000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1983", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6314644000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1984", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6831449600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1985", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7092460500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1986", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7365242000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1987", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6732262400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1988", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6327317000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1989", "Official development assistance (ODA) and private grants by donor - Net disbursements": "5747814000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1990", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6277988000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1991", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6656654300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1992", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7056147500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1993", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6698970000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1994", "Official development assistance (ODA) and private grants by donor - Net disbursements": "8001275000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1995", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6269400600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1996", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7790538000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1997", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7589403600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1998", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7083127000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "1999", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6524090400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2000", "Official development assistance (ODA) and private grants by donor - Net disbursements": "5876879400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2001", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7872358000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2002", "Official development assistance (ODA) and private grants by donor - Net disbursements": "9505157000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2003", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7558426600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2004", "Official development assistance (ODA) and private grants by donor - Net disbursements": "9595815000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2005", "Official development assistance (ODA) and private grants by donor - Net disbursements": "8408566300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2006", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7527171000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2007", "Official development assistance (ODA) and private grants by donor - Net disbursements": "9316659000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2008", "Official development assistance (ODA) and private grants by donor - Net disbursements": "6711488500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2009", "Official development assistance (ODA) and private grants by donor - Net disbursements": "10199555000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2011", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7262974500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2012", "Official development assistance (ODA) and private grants by donor - Net disbursements": "7301221000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2013", "Official development assistance (ODA) and private grants by donor - Net disbursements": "8506603500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2014", "Official development assistance (ODA) and private grants by donor - Net disbursements": "10681953000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2015", "Official development assistance (ODA) and private grants by donor - Net disbursements": "11582767000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2016", "Official development assistance (ODA) and private grants by donor - Net disbursements": "9432559000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2017", "Official development assistance (ODA) and private grants by donor - Net disbursements": "10842584000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2018", "Official development assistance (ODA) and private grants by donor - Net disbursements": "11917293000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2019", "Official development assistance (ODA) and private grants by donor - Net disbursements": "14092230000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2020", "Official development assistance (ODA) and private grants by donor - Net disbursements": "16726742000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "13799344000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "17104168000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "17987367000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Bank Group", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "18080442000", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2016", "Official development assistance (ODA) and private grants by donor - Net disbursements": "14919065", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2017", "Official development assistance (ODA) and private grants by donor - Net disbursements": "15830571", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2018", "Official development assistance (ODA) and private grants by donor - Net disbursements": "11587826", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2019", "Official development assistance (ODA) and private grants by donor - Net disbursements": "17908538", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2020", "Official development assistance (ODA) and private grants by donor - Net disbursements": "26020810", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "25230998", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "10501443", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "20145702", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Diabetes Foundation", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "18947476", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2009", "Official development assistance (ODA) and private grants by donor - Net disbursements": "481835500", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2010", "Official development assistance (ODA) and private grants by donor - Net disbursements": "399848830", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2011", "Official development assistance (ODA) and private grants by donor - Net disbursements": "465938080", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2012", "Official development assistance (ODA) and private grants by donor - Net disbursements": "418841950", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2013", "Official development assistance (ODA) and private grants by donor - Net disbursements": "493100480", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2014", "Official development assistance (ODA) and private grants by donor - Net disbursements": "490242750", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2015", "Official development assistance (ODA) and private grants by donor - Net disbursements": "764845300", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2016", "Official development assistance (ODA) and private grants by donor - Net disbursements": "621864600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2017", "Official development assistance (ODA) and private grants by donor - Net disbursements": "604474400", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2018", "Official development assistance (ODA) and private grants by donor - Net disbursements": "567882200", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2019", "Official development assistance (ODA) and private grants by donor - Net disbursements": "609279600", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2020", "Official development assistance (ODA) and private grants by donor - Net disbursements": "296893380", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "620551800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "488090240", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "633541900", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Health Organization (WHO)", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "567149800", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Organization for Animal Health", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "11636835", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Organization for Animal Health", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "11758403", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Organization for Animal Health", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "16132326", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Organization for Animal Health", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "13363301", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Tourism Organization (UNWTO)", "Code": "", "Year": "2016", "Official development assistance (ODA) and private grants by donor - Net disbursements": "15130459", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Tourism Organization (UNWTO)", "Code": "", "Year": "2017", "Official development assistance (ODA) and private grants by donor - Net disbursements": "14473286", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Tourism Organization (UNWTO)", "Code": "", "Year": "2018", "Official development assistance (ODA) and private grants by donor - Net disbursements": "10668394", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Tourism Organization (UNWTO)", "Code": "", "Year": "2019", "Official development assistance (ODA) and private grants by donor - Net disbursements": "9243262", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Tourism Organization (UNWTO)", "Code": "", "Year": "2020", "Official development assistance (ODA) and private grants by donor - Net disbursements": "8668026", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Tourism Organization (UNWTO)", "Code": "", "Year": "2021", "Official development assistance (ODA) and private grants by donor - Net disbursements": "13593015", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Tourism Organization (UNWTO)", "Code": "", "Year": "2022", "Official development assistance (ODA) and private grants by donor - Net disbursements": "13094965", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Tourism Organization (UNWTO)", "Code": "", "Year": "2023", "Official development assistance (ODA) and private grants by donor - Net disbursements": "14131678", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Tourism Organization (UNWTO)", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "12373210", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}, {"Entity": "World Trade Organization (WTO)", "Code": "", "Year": "2024", "Official development assistance (ODA) and private grants by donor - Net disbursements": "9946475", "Official development assistance (ODA) and private grants by donor - Net disbursements (Annotations)": ""}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "foreign-aid-given-net", "metadata_url": "https://ourworldindata.org/grapher/foreign-aid-given-net.metadata.json", "chart_title": "Foreign aid given", "chart_subtitle": "Net official development assistance (ODA) from governments and multilateral organizations, grants from civil society organizations. This data is expressed in US dollars and adjusted for inflation.", "chart_note": "This data is expressed in constant 2023 US$. From 2018, the official reporting method switched from net to grant-equivalent amounts.", "chart_citation": "OECD (2025)", "original_chart_url": "https://ourworldindata.org/grapher/foreign-aid-given-net", "owid_column_metadata": {"Official development assistance (ODA) and private grants by donor - Net disbursements": {"titleShort": "Official development assistance (ODA) and private grants by donor - Net disbursements", "titleLong": "Official development assistance (ODA) and private grants by donor - Net disbursements", "descriptionShort": "Official development assistance (ODA) is defined as government aid designed to promote the economic development and welfare of developing countries. Grants by private voluntary agencies and non-government organizations (NGOs) are defined as transfers for development made by private voluntary agencies and NGOs in cash, goods or services for which no payment is required. Monetary aid is estimated as net disbursements. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "The private sector comprises private corporations, households and non-profit institutions serving households. Development funding from the private sector is becoming more significant. This includes civil society organizations, which play an increasing role in funding development and in finding innovative ways to promote it; non-government organizations; and the for-profit private sector.", "The data is reported as net disbursements. This refers to aid ultimately given and is different from commitments, which is only aid that has been pledged. These are net amounts because any money coming in (like loan repayments or interest) has been subtracted from money going out (like new grants or loans).", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We have combined net disbursements aid data from the [DAC1: Flows by donor (ODA+OOF+Private) dataset](https://data-explorer.oecd.org/vis?fs[0]=Topic%2C1%7CDevelopment%23DEV%23%7COfficial%20Development%20Assistance%20%28ODA%29%23DEV_ODA%23&pg=0&fc=Topic&bp=true&snb=20&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_DAC1%40DF_DAC1&df[ag]=OECD.DCD.FSD&df[vs]=1.2&dq=DAC...1140%2B1160..Q.&lom=LASTNPERIODS&lo=10&to[TIME_PERIOD]=false) with the [DAC2A: Aid (ODA) disbursements to countries and regions dataset](https://data-explorer.oecd.org/vis?fs[0]=Topic%2C1%7CDevelopment%23DEV%23%7COfficial%20Development%20Assistance%20%28ODA%29%23DEV_ODA%23&pg=0&fc=Topic&bp=true&snb=20&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_DAC2%40DF_DAC2A&df[ag]=OECD.DCD.FSD&df[vs]=1.1&dq=.DPGC.206.USD.Q&lom=LASTNPERIODS&lo=5&to[TIME_PERIOD]=false) to add aid given by multilateral organizations and grants given by civil society organizations.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1960-2024", "type": "Numeric", "owidVariableId": 1132327, "shortName": "i_oda_net_disbursements_multilaterals_private_grants", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Official development assistance (ODA) and private grants by donor - Net disbursements – Net disbursements” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC1: Flows by provider (ODA+OOF+Private)”; OECD, “OECD Official Development Assistance (ODA) - DAC2A: Aid (ODA) disbursements to countries and regions” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132327.metadata.json"}, "1132327-annotations": {"titleShort": "1132327-annotations", "titleLong": "1132327-annotations", "type": "SeriesAnnotation", "citationShort": " – processed by Our World in Data", "citationLong": " – processed by Our World in Data. “1132327-annotations” [dataset].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/undefined.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "98d1cb842f8093d626ce"},
{"raw_link": "https://ourworldindata.org/foreign-aid-donations-increase", "title": "For many of us, it doesn’t cost much to improve someone’s life, and we can do much more of it", "context": "Home\nForeign Aid\nFor many of us, it doesn’t cost much to improve someone’s life, and we can do much more of it\nMost countries spend less than 1% of their national income on foreign aid; even small increases could make a big difference.\nBy\nHannah Ritchie\nMarch 10th 2025\nBrowse past versions\nCite this article\nReuse our work freely\nIn the early 1980s, almost half a million people were paralyzed by polio every year. Most of them were children. Polio is\nan awful disease\nthat can cause paralysis within hours and even lead to suffocation and death.\nBut look at the progress the world has made in the chart below. The number of cases has fallen by more than 99% from its peak.\n1\nIn all of 2023, there were the same number of cases as just\ntwo days\nin 1981. Once endemic in\nalmost every country\nin the world, wild polio is now only endemic in two: Afghanistan and Pakistan.\nThis has changed the life trajectory of millions of children.\nForeign aid programs have played a crucial role in the fight against polio. In 1998, the\nGlobal Polio Eradication Initiative\nwas launched to ensure that all children had access to the polio vaccine.\nEven as early as the 1950s, some funding for research into a polio vaccine came from grassroots campaigns and individual Americans giving small donations to find a cure (alongside larger organizations such as the\nMarch of Dimes\n).\n2\nBy the late 1980s, several G7 governments and larger philanthropic donors had stepped in to scale up these efforts. The chart below shows the sources of polio eradication funding over time; contributions from foreign governments are shown in red. Note that some of the funding coming from the “multilateral sector” is also sourced from donor countries.\nWhile private donors have made the largest contributions in recent years, governments have played a crucial role over the last few decades.\n3\nIn the late 1990s and early 2000s, in particular, donor countries were funding more than 80% of these efforts.\nThat means that if you live and pay taxes in any of these countries, you have contributed to the amazing progress shown in the chart above.\nWhat’s true for polio is also true for other diseases and essential resources like food. The PEPFAR program — launched by the US in 2003 under George Bush’s administration — is estimated to\nhave saved\nover 25 million lives from HIV. Donations for bednets and antimalarial treatments have\nhelped reduce\nthe number of people catching and dying from malaria. The Global Fund and USAID\nhave reduced deaths\nfrom tuberculosis. Emergency aid has kept people alive during famine and acute food shortages. The list goes on.\nThese successes have been achieved with a relatively small amount of money. In 2023, the world gave around $240 billion in foreign aid. It’s a very small percentage of most rich countries’ economies. Take the OECD countries combined, and it was\njust 0.37%\nof their gross national income (GNI). As you can see in the chart below, Norway is the only country that spends more than 1% of its GNI on aid.\n4\nDownload\nAs a UK citizen, I’m extremely happy for my taxes to be spent this way. I can’t think of anything I’d rather contribute to.\nOne reason small amounts of funding can\nhave a big impact\nis that a dollar goes much further in the poorest countries than in the richest. Treatments that many of us might take for granted — like the polio vaccine — often cost\nas little as a few dollars\n. For the cost of a takeaway coffee, we could vaccinate several children from potentially fatal diseases.\nHow can the world achieve more of this?\nI feel like I’m in a good position — arguably one of the best positions in history — to help with this in some way. The\nbiggest factor\nin someone’s opportunities and outcomes is where and when they were born. That is a random lottery. And I happened to get a lucky draw for two reasons.\nFirst, I was born in a rich country, the United Kingdom. Second, I was born at a time and in circumstances that have given me some disposable income and the freedom to choose how to spend it.\nThat gives me two ways to contribute. First, I can advocate for and put pressure on my government to increase its spending on foreign aid. Second, I can make a personal contribution to some of the most cost-effective charities in lower-income countries.\nIf you’re in a similar position to me — or have at least\none\nof those options — then there’s something we can do to have a positive impact.\nMost aid comes from governments, not private philanthropic donors\nOne question you might have is whether most of the world’s aid comes from governments or private donors, which are dominated by billionaire-funded philanthropies. If it’s the former, citizens can have\nsome\ninfluence on the global aid budget. If it’s the latter, it’s completely out of our hands.\nAs the chart below shows, more than 95% of foreign aid came from national governments in 2023. Just under $11 billion — or 4.5% of the total — came from private grants.\n5\nNote that private donors, here, only include contributions submitted to the OECD that meet its criteria for development grants. This is mostly from large philanthropic foundations. It’s not to be confused with total\nprivate flows for development\n, which can include foreign investment, money sent home by migrants, and other forms of private money transfer.\nDownload\nThat means two things.\nFirst, a drop in support for aid can have huge consequences for the global total. Let me illustrate this point with some back-of-the-envelope calculations.\nThe United States\ngave $62 billion\nin aid in 2023. If it had cut its aid budget by just 20%, its contributions would have been around $13 billion lower. That would be the same as eliminating\nall\nprivate philanthropic donations worldwide.\n6\nEven reductions in funding from much smaller nations — like my home country, the UK — could have a large impact. “Reform UK”, one of the UK’s right-wing parties,\npledged to halve\nforeign aid in its last manifesto. A halving of foreign aid would have reduced the UK’s contributions by $8.7 billion.\n7\nAgain, that’s not far — in relative terms — from the $10.8 billion given by all private donors combined.\nEven more recently, the UK prime minister\ndid\nannounce\na $6 billion cut\nto foreign aid by 2027. That’s equal to more than half of all private philanthropic grants.\nThe second implication is that if we want to see an increase in global foreign aid, building\npublic\nsupport for more generous aid budgets from our governments matters a lot. In the next section, I’ll run some numbers to prove this.\nSmall increases in foreign aid could go a long way\nWe can illustrate this point by focusing on the UN’s target for developed countries to give 0.7% of their GNI to foreign aid.\n8\nOnly five countries — Norway, Luxembourg, Sweden, Germany, and Denmark —\nmet this target\nin 2023.\nLet’s imagine that the public in developed countries pressured their governments to step up and meet this target. If\nall\ndeveloped countries achieved this, we’d add an extra $216 billion to the pot, meaning the global official development assistance budget would almost double.\n9\nThis is illustrated in the chart below. Remember: private donors collectively gave $11 billion in 2023, so this increase would equate to around 20 times as much.\nDownload\nIf we take this further and assume that all donor countries were as generous as the\nmost\ngenerous country — which, as we saw earlier, is Norway at 1.1% of GNI — the global aid budget would increase to around\n$700 billion\n. That means we could deliver up to three times as many polio vaccines, antimalarial bednets, HIV treatments and food packages as we currently do.\nAgain, it’s important to highlight that these are still relatively small amounts of money for developed economies, just 0.7% or 1.1% of their national income.\nInterestingly, this is far less than most people\nthink\ntheir countries currently give to foreign aid.\nVery recent data is hard to find, but in\na 2015 survey\n, American citizens were asked to guess how much US federal spending goes to foreign aid. The correct answer was just under 1%. Only 3% of respondents got the answer right. The\naverage\nguess was a whopping 31%. An\nearlier survey\nfound similar results, with the average guess being 25%.\nThese questions are about federal spending, which differs from GNI. The US spends about 0.25% of its GNI on foreign aid, but this is around 1% of its federal budget.\nWhat’s also interesting is that when asked how much federal spending\nshould\nbe going to foreign aid, the average answer was 10%. That’s ten times more than what is currently spent.\nNot all surveys find such a large discrepancy between public perception and reality, but the gaps are still big. In\na 2012 survey\nin the Netherlands, the average guess was that 5.6% of GDP was spent on foreign aid. The correct answer was 0.7%.\nSo, while “foreign aid” is often the\nmost common answer\nto questions like, “What sector is the government spending too much on?” some of this might be explained by the fact that most people think we spend far more on foreign aid than we actually do.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nYour personal donations can also make a difference, especially if you focus on where they can go the furthest\nAs I said earlier, pressuring governments in rich countries to increase foreign aid — or voting for political parties that would — is just one way that I can help move money from the top to the bottom of the global income distribution.\nI also donate some of my money directly to the most effective causes. Several years ago, I took the\nGiving What We Can\npledge, committing to give at least 10% of my income to charity. I specifically focus on programs working in some of the lowest-income countries and on causes where small amounts of money can do a lot. The charity evaluator\nGiveWell\nis a good resource to find some of the most cost-effective causes to support.\nThis point on cost-effectiveness is particularly important to me in terms of personal donations, but it also matters when it comes to foreign aid. At the beginning of this article, I highlighted a number of successful aid programs: efforts to eradicate polio, reduce deaths from HIV and malaria, and provide food supplies during famine. It would be great if all aid money were as well-spent as this, but that’s not the reality. Some foreign aid\nis\nspent on programs and activities that make little difference. More than half of Brits\nthink that\naid is a complete waste due to corruption, and it’s these examples that can often make people sceptical that their dollars are being put to good use.\nWhile I advocate for an increase in the foreign aid budget (and personal donations) overall, I’d also like to see a renewed focus on making sure that we’re spending this money in the best possible way. This would not only improve the\noutcomes\nfor people who need this support but could also increase support for higher foreign aid budgets in rich countries.\nWhen aid programs work well, they can transform the lives of millions. These programs are not designed to last forever; they’re there to support lower-income countries and kickstart self-fulfilling progress. Unfortunately, these stories of progress are not well-known. If we want to see a resurgence in support for foreign aid, we need to talk about them much more.\nAcknowledgments\nMany thanks to Max Roser, Edouard Mathieu, Bastian Herre, Simon van Teutem, and Saloni Dattani for their feedback and comments on this article.\nContinue reading on Our World in Data\nThe global fight against polio — how far have we come?\nA generation ago, polio paralyzed hundreds of thousands of children every year. Many countries have eliminated the disease, and our generation has the chance to eradicate it.\nGlobal Inequality of Opportunity\nToday’s global inequality of opportunity means that the good or bad luck of where you were born matters most for your living conditions. We look at how this chance factor is the strongest determinant of your standard of living, whether in life expectancy, income, or education.\nForeign Aid\nWho gives and receives foreign aid? Which forms does it take? What are examples of when it was (un-)successful?\nEndnotes\nIn 1981, there were an estimated 460,000 cases. By 2023, this had fallen to 3800. That’s a reduction of just over 99% [(460,000 - 3800) / 460,000 * 100 = 99.2%].\nPage 188 in Oshinsky, D. (2005). Polio: An American Story (1st ed.). New York: Oxford University Press. Parts are available on Google Books.\nThe\nGates Foundation\nand\nRotary International\nhave been the largest donors in recent years.\nFunding from most rich countries\nhas been consistently\nbetween 0.2% and 1% since the 1960s.\nNot that grant data for some private donors is for 2022, rather than 2023. However, the change from year-to-year is usually small, so it’s unlikely that updated data would be significantly different from $11 billion.\nPrivate donors gave $10.8 billion collectively in 2023.\nIn 2023, the UK\ngave $17.3 billion\nin official development assistance.\nThis target\nwas adopted\nby countries at the UN General Assembly in 1970. Most countries accepted the target, except for the United States and Switzerland.\nI’ve calculated this by assuming that all current national donors with contributions below 0.7% increased their aid to that level. The five countries already at or above this maintained their contributions at the same level.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2025) - “For many of us, it doesn’t cost much to improve someone’s life, and we can do much more of it” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-095641/foreign-aid-donations-increase.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-foreign-aid-donations-increase,\nauthor = {Hannah Ritchie},\ntitle = {For many of us, it doesn’t cost much to improve someone’s life, and we can do much more of it},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-095641/foreign-aid-donations-increase.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "foreign-aid-donations-increase", "source_url": "https://ourworldindata.org/foreign-aid-donations-increase", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Most countries spend less than 1% of their national income on foreign aid; even small increases could make a big difference.", "numeric_mentions": ["1%", "10", "2025", "1980", "99%", "1", "2023,", "1981", "1998,", "1950", "2", "3", "1990", "2000", "80%", "2003", "25 million", "240 billion", "0.37%", "4", "95%", "2023", "11 billion", "4.5%", "5", "62 billion", "20%", "13 billion", "6", "8.7 billion", "7", "10.8 billion", "6 billion", "2027", "0.7%", "8", "216 billion", "9", "20", "1.1%", "700 billion", "2015", "3%", "31%", "25%", "0.25%", "10%", "2012", "5.6%", "1981,", "460,000", "3800", "100", "99.2%", "188", "2005", "0.2%", "1960", "2022,", "17.3 billion", "1970", "20260518", "095641", "18,", "2026"], "numeric_evidence": [{"title": "The decade of the last recorded wild paralytic polio case", "source_url": "https://ourworldindata.org/grapher/the-decade-of-the-last-recorded-case-of-paralytic-polio-by-country.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Latest year of wild polio case"], "row_count_total": 208, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Latest year of wild polio case": "4000"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Latest year of wild polio case": "1978"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "Latest year of wild polio case": "1996"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2023", "Latest year of wild polio case": "1950"}, {"Entity": "Andorra", "Code": "AND", "Year": "2023", "Latest year of wild polio case": "1959"}, {"Entity": "Angola", "Code": "AGO", "Year": "2023", "Latest year of wild polio case": "2001"}, {"Entity": "Anguilla", "Code": "AIA", "Year": "2023", "Latest year of wild polio case": "1962"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2023", "Latest year 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{"Entity": "Belgium", "Code": "BEL", "Year": "2023", "Latest year of wild polio case": "1979"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2023", "Latest year of wild polio case": "1981"}, {"Entity": "Benin", "Code": "BEN", "Year": "2023", "Latest year of wild polio case": "2000"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2023", "Latest year of wild polio case": "1986"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2023", "Latest year of wild polio case": "1989"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2023", "Latest year of wild polio case": "1993"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2023", "Latest year of wild polio case": "1989"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2023", "Latest year of wild polio case": "1989"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2023", "Latest year of wild polio case": "1978"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2023", "Latest year of wild polio case": "1982"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2023", "Latest year of wild polio case": "2000"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2023", "Latest year of wild polio case": "1997"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Canada", "Code": "CAN", "Year": "2023", "Latest year of wild polio case": "1979"}, {"Entity": "Cape Verde", "Code": "CPV", "Year": "2023", "Latest year of wild polio case": "1988"}, {"Entity": "Cayman Islands", "Code": "CYM", "Year": "2023", "Latest year of wild polio case": "1958"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2023", "Latest year of wild polio case": "2000"}, {"Entity": "Chad", "Code": "TCD", "Year": "2023", "Latest year of wild polio case": "2000"}, {"Entity": "Chile", "Code": "CHL", "Year": "2023", "Latest year of wild polio case": "1975"}, {"Entity": "China", "Code": "CHN", "Year": "2023", "Latest year of wild polio case": "1994"}, {"Entity": "Colombia", "Code": "COL", "Year": "2023", "Latest year of wild polio case": "1991"}, {"Entity": "Comoros", "Code": "COM", "Year": "2023", "Latest year of wild polio case": "1983"}, {"Entity": "Congo", "Code": "COG", "Year": "2023", "Latest year of wild polio case": "2000"}, {"Entity": "Cook Islands", "Code": "COK", "Year": "2023", "Latest year of wild polio case": "1959"}, {"Entity": "Costa Rica", "Code": "CRI", "Year": "2023", "Latest year of wild polio case": "1972"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2023", "Latest year of wild polio case": "2000"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2023", "Latest year of wild polio case": "1990"}, {"Entity": "Cuba", "Code": "CUB", "Year": "2023", "Latest year of wild polio case": "1962"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2023", "Latest year of wild polio case": "1995"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2023", "Latest year of wild polio case": "1960"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2023", "Latest year of wild polio case": "2000"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2023", "Latest year of wild polio case": "1976"}, {"Entity": "Djibouti", "Code": "DJI", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Dominica", "Code": "DMA", "Year": "2023", "Latest year of wild polio case": "1980"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2023", "Latest year of wild polio case": "1985"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2023", "Latest year of wild polio case": "1984"}, {"Entity": "Ecuador", "Code": "ECU", "Year": "2023", "Latest year of wild polio case": "1990"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2023", "Latest year of wild polio case": "2004"}, {"Entity": "El Salvador", "Code": "SLV", "Year": "2023", "Latest year of wild polio case": "1987"}, {"Entity": "Equatorial Guinea", "Code": "GNQ", "Year": "2023", "Latest year of wild polio case": "1992"}, {"Entity": "Eritrea", "Code": "ERI", "Year": "2023", "Latest year of wild polio case": "1992"}, {"Entity": "Estonia", "Code": "EST", "Year": "2023", "Latest year of wild polio case": "1961"}, {"Entity": "Eswatini", "Code": "SWZ", "Year": "2023", "Latest year of wild polio case": "1989"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2023", "Latest year of wild polio case": "2001"}, {"Entity": "Fiji", "Code": "FJI", "Year": "2023", "Latest year of wild polio case": "1962"}, {"Entity": "Finland", "Code": "FIN", "Year": "2023", "Latest year of wild polio case": "1963"}, {"Entity": "France", "Code": "FRA", "Year": "2023", "Latest year of wild polio case": "1989"}, {"Entity": "French Guiana", "Code": "GUF", "Year": "2023", "Latest year of wild polio case": "1983"}, {"Entity": "French Polynesia", "Code": "PYF", "Year": "2023", "Latest year of wild polio case": "1982"}, {"Entity": "Gabon", "Code": "GAB", "Year": "2023", "Latest year of wild polio case": "1996"}, {"Entity": "Gambia", "Code": "GMB", "Year": "2023", "Latest year of wild polio case": "1997"}, {"Entity": "Georgia", "Code": "GEO", "Year": "2023", "Latest year of wild polio case": "1991"}, {"Entity": "Germany", "Code": "DEU", "Year": "2023", "Latest year of wild polio case": "1990"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2023", "Latest year of wild polio case": "2000"}, {"Entity": "Greece", "Code": "GRC", "Year": "2023", "Latest year of wild polio case": "1982"}, {"Entity": "Grenada", "Code": "GRD", "Year": "2023", "Latest year of wild polio case": "1970"}, {"Entity": "Guam", "Code": "GUM", "Year": "2023", "Latest year of wild polio case": "1964"}, {"Entity": "Guatemala", "Code": "GTM", "Year": "2023", "Latest year of wild polio case": "1990"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Guinea-Bissau", "Code": "GNB", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Guyana", "Code": "GUY", "Year": "2023", "Latest year of wild polio case": "1962"}, {"Entity": "Haiti", "Code": "HTI", "Year": "2023", "Latest year of wild polio case": "1989"}, {"Entity": "Honduras", "Code": "HND", "Year": "2023", "Latest year of wild polio case": "1989"}, {"Entity": "Hong Kong", "Code": "HKG", "Year": "2023", "Latest year of wild polio case": "1983"}, {"Entity": "Hungary", "Code": "HUN", "Year": "2023", "Latest year of wild polio case": "1969"}, {"Entity": "Iceland", "Code": "ISL", "Year": "2023", "Latest year of wild polio case": "1960"}, {"Entity": "India", "Code": "IND", "Year": "2023", "Latest year of wild polio case": "2011"}, {"Entity": "Indonesia", "Code": "IDN", "Year": "2023", "Latest year of wild polio case": "1995"}, {"Entity": "Iran", "Code": "IRN", "Year": "2023", "Latest year of wild polio case": "1997"}, {"Entity": "Iraq", "Code": "IRQ", "Year": "2023", "Latest year of wild polio case": "2000"}, {"Entity": "Ireland", "Code": "IRL", "Year": "2023", "Latest year of wild polio case": "1965"}, {"Entity": "Israel", "Code": "ISR", "Year": "2023", "Latest year of wild polio case": "1988"}, {"Entity": "Italy", "Code": "ITA", "Year": "2023", "Latest year of wild polio case": "1982"}, {"Entity": "Jamaica", "Code": "JAM", "Year": "2023", "Latest year of wild polio case": "1982"}, {"Entity": "Japan", "Code": "JPN", "Year": "2023", "Latest year of wild polio case": "1980"}, {"Entity": "Jordan", "Code": "JOR", "Year": "2023", "Latest year of wild polio case": "1988"}, {"Entity": "Kazakhstan", "Code": "KAZ", "Year": "2023", "Latest year of wild polio case": "1995"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2023", "Latest year of wild polio case": "1988"}, {"Entity": "Kuwait", "Code": "KWT", "Year": "2023", "Latest year of wild polio case": "1985"}, {"Entity": "Kyrgyzstan", "Code": "KGZ", "Year": "2023", "Latest year of wild polio case": "1992"}, {"Entity": "Laos", "Code": "LAO", "Year": "2023", "Latest year of wild polio case": "1996"}, {"Entity": "Latvia", "Code": "LVA", "Year": "2023", "Latest year of wild polio case": "1992"}, {"Entity": "Lebanon", "Code": "LBN", "Year": "2023", "Latest year of wild polio case": "1994"}, {"Entity": "Lesotho", "Code": "LSO", "Year": "2023", "Latest year of wild polio case": "1987"}, {"Entity": "Liberia", "Code": "LBR", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Libya", "Code": "LBY", "Year": "2023", "Latest year of wild polio case": "1991"}, {"Entity": "Lithuania", "Code": "LTU", "Year": "2023", "Latest year of wild polio case": "1971"}, {"Entity": "Luxembourg", "Code": "LUX", "Year": "2023", "Latest year of wild polio case": "1963"}, {"Entity": "Macao", "Code": "MAC", "Year": "2023", "Latest year of wild polio case": "1975"}, {"Entity": "Madagascar", "Code": "MDG", "Year": "2023", "Latest year of wild polio case": "1997"}, {"Entity": "Malawi", "Code": "MWI", "Year": "2023", "Latest year of wild polio case": "3000"}, {"Entity": "Malaysia", "Code": "MYS", "Year": "2023", "Latest year of wild polio case": "1986"}, {"Entity": "Maldives", "Code": "MDV", "Year": "2023", "Latest year of wild polio case": "1980"}, {"Entity": "Mali", "Code": "MLI", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Malta", "Code": "MLT", "Year": "2023", "Latest year of wild polio case": "1964"}, {"Entity": "Marshall Islands", "Code": "MHL", "Year": "2023", "Latest year of wild polio case": "1976"}, {"Entity": "Mauritania", "Code": "MRT", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Mauritius", "Code": "MUS", "Year": "2023", "Latest year of wild polio case": "1980"}, {"Entity": "Mexico", "Code": "MEX", "Year": "2023", "Latest year of wild polio case": "1990"}, {"Entity": "Micronesia (country)", "Code": "FSM", "Year": "2023", "Latest year of wild polio case": "1970"}, {"Entity": "Moldova", "Code": "MDA", "Year": "2023", "Latest year of wild polio case": "1996"}], "rows_tail": [{"Entity": "Iraq", "Code": "IRQ", "Year": "2023", "Latest year of wild polio case": "2000"}, {"Entity": "Ireland", "Code": "IRL", "Year": "2023", "Latest year of wild polio case": "1965"}, {"Entity": "Israel", "Code": "ISR", "Year": "2023", "Latest year of wild polio case": "1988"}, {"Entity": "Italy", "Code": "ITA", "Year": "2023", "Latest year of wild polio case": "1982"}, {"Entity": "Jamaica", "Code": "JAM", "Year": "2023", "Latest year of wild polio case": "1982"}, {"Entity": "Japan", "Code": "JPN", "Year": "2023", "Latest year of wild polio case": "1980"}, {"Entity": "Jordan", "Code": "JOR", "Year": "2023", "Latest year of wild polio case": "1988"}, {"Entity": "Kazakhstan", "Code": "KAZ", "Year": "2023", "Latest year of wild polio case": "1995"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2023", "Latest year of wild polio case": "1988"}, {"Entity": "Kuwait", "Code": "KWT", "Year": "2023", "Latest year of wild polio case": "1985"}, {"Entity": "Kyrgyzstan", "Code": "KGZ", "Year": "2023", "Latest year of wild polio case": "1992"}, {"Entity": "Laos", "Code": "LAO", "Year": "2023", "Latest year of wild polio case": "1996"}, {"Entity": "Latvia", "Code": "LVA", "Year": "2023", "Latest year of wild polio case": "1992"}, {"Entity": "Lebanon", "Code": "LBN", "Year": "2023", "Latest year of wild polio case": "1994"}, {"Entity": "Lesotho", "Code": "LSO", "Year": "2023", "Latest year of wild polio case": "1987"}, {"Entity": "Liberia", "Code": "LBR", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Libya", "Code": "LBY", "Year": "2023", "Latest year of wild polio case": "1991"}, {"Entity": "Lithuania", "Code": "LTU", "Year": "2023", "Latest year of wild polio case": "1971"}, {"Entity": "Luxembourg", "Code": "LUX", "Year": "2023", "Latest year of wild polio case": "1963"}, {"Entity": "Macao", "Code": "MAC", "Year": "2023", "Latest year of wild polio case": "1975"}, {"Entity": "Madagascar", "Code": "MDG", "Year": "2023", "Latest year of wild polio case": "1997"}, {"Entity": "Malawi", "Code": "MWI", "Year": "2023", "Latest year of wild polio case": "3000"}, {"Entity": "Malaysia", "Code": "MYS", "Year": "2023", "Latest year of wild polio case": "1986"}, {"Entity": "Maldives", "Code": "MDV", "Year": "2023", "Latest year of wild polio case": "1980"}, {"Entity": "Mali", "Code": "MLI", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Malta", "Code": "MLT", "Year": "2023", "Latest year of wild polio case": "1964"}, {"Entity": "Marshall Islands", "Code": "MHL", "Year": "2023", "Latest year of wild polio case": "1976"}, {"Entity": "Mauritania", "Code": "MRT", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Mauritius", "Code": "MUS", "Year": "2023", "Latest year of wild polio case": "1980"}, {"Entity": "Mexico", "Code": "MEX", "Year": "2023", "Latest year of wild polio case": "1990"}, {"Entity": "Micronesia (country)", "Code": "FSM", "Year": "2023", "Latest year of wild polio case": "1970"}, {"Entity": "Moldova", "Code": "MDA", "Year": "2023", "Latest year of wild polio case": "1996"}, {"Entity": "Monaco", "Code": "MCO", "Year": "2023", "Latest year of wild polio case": "1964"}, {"Entity": "Mongolia", "Code": "MNG", "Year": "2023", "Latest year of wild polio case": "1993"}, {"Entity": "Montserrat", "Code": "MSR", "Year": "2023", "Latest year of wild polio case": "1976"}, {"Entity": "Morocco", "Code": "MAR", "Year": "2023", "Latest year of wild polio case": "1989"}, {"Entity": "Mozambique", "Code": "MOZ", "Year": "2023", "Latest year of wild polio case": "3000"}, {"Entity": "Myanmar", "Code": "MMR", "Year": "2023", "Latest year of wild polio case": "1996"}, {"Entity": "Namibia", "Code": "NAM", "Year": "2023", "Latest year of wild polio case": "1995"}, {"Entity": "Nauru", "Code": "NRU", "Year": "2023", "Latest year of wild polio case": "1910"}, {"Entity": "Nepal", "Code": "NPL", "Year": "2023", "Latest year of wild polio case": "2000"}, {"Entity": "Netherlands", "Code": "NLD", "Year": "2023", "Latest year of wild polio case": "1960"}, {"Entity": "New Caledonia", "Code": "NCL", "Year": "2023", "Latest year of wild polio case": "1982"}, {"Entity": "New Zealand", "Code": "NZL", "Year": "2023", "Latest year of wild polio case": "1962"}, {"Entity": "Nicaragua", "Code": "NIC", "Year": "2023", "Latest year of wild polio case": "1981"}, {"Entity": "Niger", "Code": "NER", "Year": "2023", "Latest year of wild polio case": "2006"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2023", "Latest year of wild polio case": "2016"}, {"Entity": "Niue", "Code": "NIU", "Year": "2023", "Latest year of wild polio case": "1950"}, {"Entity": "North Korea", "Code": "PRK", "Year": "2023", "Latest year of wild polio case": "1996"}, {"Entity": "North Macedonia", "Code": "MKD", "Year": "2023", "Latest year of wild polio case": "1993"}, {"Entity": "Northern Mariana Islands", "Code": "MNP", "Year": "2023", "Latest year of wild polio case": "1960"}, {"Entity": "Norway", "Code": "NOR", "Year": "2023", "Latest year of wild polio case": "1969"}, {"Entity": "Oman", "Code": "OMN", "Year": "2023", "Latest year of wild polio case": "1993"}, {"Entity": "Pakistan", "Code": "PAK", "Year": "2023", "Latest year of wild polio case": "4000"}, {"Entity": "Palau", "Code": "PLW", "Year": "2023", "Latest year of wild polio case": "1940"}, {"Entity": "Palestine", "Code": "PSE", "Year": "2023", "Latest year of wild polio case": "1988"}, {"Entity": "Panama", "Code": "PAN", "Year": "2023", "Latest year of wild polio case": "1972"}, {"Entity": "Papua New Guinea", "Code": "PNG", "Year": "2023", "Latest year of wild polio case": "1996"}, {"Entity": "Paraguay", "Code": "PRY", "Year": "2023", "Latest year of wild polio case": "1985"}, {"Entity": "Peru", "Code": "PER", "Year": "2023", "Latest year of wild polio case": "1991"}, {"Entity": "Philippines", "Code": "PHL", "Year": "2023", "Latest year of wild polio case": "1993"}, {"Entity": "Poland", "Code": "POL", "Year": "2023", "Latest year of wild polio case": "1984"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2023", "Latest year of wild polio case": "1986"}, {"Entity": "Puerto Rico", "Code": "PRI", "Year": "2023", "Latest year of wild polio case": "1974"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2023", "Latest year of wild polio case": "1990"}, {"Entity": "Reunion", "Code": "REU", "Year": "2023", "Latest year of wild polio case": "1979"}, {"Entity": "Romania", "Code": "ROU", "Year": "2023", "Latest year of wild polio case": "1992"}, {"Entity": "Russia", "Code": "RUS", "Year": "2023", "Latest year of wild polio case": "1996"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Saint Kitts and Nevis", "Code": "KNA", "Year": "2023", "Latest year of wild polio case": "1969"}, {"Entity": "Saint Lucia", "Code": "LCA", "Year": "2023", "Latest year of wild polio case": "1970"}, {"Entity": "Saint Vincent and the Grenadines", "Code": "VCT", "Year": "2023", "Latest year of wild polio case": "1977"}, {"Entity": "Samoa", "Code": "WSM", "Year": "2023", "Latest year of wild polio case": "1950"}, {"Entity": "San Marino", "Code": "SMR", "Year": "2023", "Latest year of wild polio case": "1982"}, {"Entity": "Sao Tome and Principe", "Code": "STP", "Year": "2023", "Latest year of wild polio case": "1983"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2023", "Latest year of wild polio case": "1995"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2023", "Latest year of wild polio case": "1998"}, {"Entity": "Seychelles", "Code": "SYC", "Year": "2023", "Latest year of wild polio case": "1980"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2023", "Latest year of wild polio case": "1978"}, {"Entity": "Slovakia", "Code": "SVK", "Year": "2023", "Latest year of wild polio case": "1960"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2023", "Latest year of wild polio case": "1978"}, {"Entity": "Solomon Islands", "Code": "SLB", "Year": "2023", "Latest year of wild polio case": "1972"}, {"Entity": "Somalia", "Code": "SOM", "Year": "2023", "Latest year of wild polio case": "2002"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2023", "Latest year of wild polio case": "1989"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2023", "Latest year of wild polio case": "1983"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2023", "Latest year of wild polio case": "2004"}, {"Entity": "Spain", "Code": "ESP", "Year": "2023", "Latest year of wild polio case": "1988"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2023", "Latest year of wild polio case": "1993"}, {"Entity": "Sudan", "Code": "SDN", "Year": "2023", "Latest year of wild polio case": "2004"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2023", "Latest year of wild polio case": "1982"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2023", "Latest year of wild polio case": "1977"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2023", "Latest year of wild polio case": "1982"}, {"Entity": "Syria", "Code": "SYR", "Year": "2023", "Latest year of wild polio case": "1998"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2023", "Latest year of wild polio case": "1997"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2023", "Latest year of wild polio case": "1996"}, {"Entity": "Thailand", "Code": "THA", "Year": "2023", "Latest year of wild polio case": "1997"}, {"Entity": "Togo", "Code": "TGO", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Tokelau", "Code": "TKL", "Year": "2023", "Latest year of wild polio case": "1950"}, {"Entity": "Tonga", "Code": "TON", "Year": "2023", "Latest year of wild polio case": "1982"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2023", "Latest year of wild polio case": "1972"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2023", "Latest year of wild polio case": "1994"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2023", "Latest year of wild polio case": "1998"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2023", "Latest year of wild polio case": "1996"}, {"Entity": "Turks and Caicos Islands", "Code": "TCA", "Year": "2023", "Latest year of wild polio case": "1977"}, {"Entity": "Tuvalu", "Code": "TUV", "Year": "2023", "Latest year of wild polio case": "1936"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2023", "Latest year of wild polio case": "1996"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2023", "Latest year of wild polio case": "1996"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2023", "Latest year of wild polio case": "1992"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2023", "Latest year of wild polio case": "1982"}, {"Entity": "United States", "Code": "USA", "Year": "2023", "Latest year of wild polio case": "1979"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2023", "Latest year of wild polio case": "1978"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2023", "Latest year of wild polio case": "1995"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2023", "Latest year of wild polio case": "1989"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2023", "Latest year of wild polio case": "1989"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2023", "Latest year of wild polio case": "1997"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2023", "Latest year of wild polio case": "1972"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2023", "Latest year of wild polio case": "1999"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Latest year of wild polio case": "1995"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Latest year of wild polio case": "1991"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "the-decade-of-the-last-recorded-case-of-paralytic-polio-by-country", "metadata_url": "https://ourworldindata.org/grapher/the-decade-of-the-last-recorded-case-of-paralytic-polio-by-country.metadata.json", "chart_title": "The decade of the last recorded wild paralytic polio case", "chart_subtitle": "Countries are considered endemic if they have indigenous cases of polio from wild polioviruses.", "chart_note": "The following countries eradicated polio before 1960 but are hard to see in the map at the moment: Nauru (1910), Tuvalu (1936), Palau (1940), American Samoa, Niue and Tokelau (1950), Cayman Islands (1958), and Andorra and Cook Islands (1959).", "chart_citation": "Global Polio Eradication Initiative (2023)", "original_chart_url": "https://ourworldindata.org/grapher/the-decade-of-the-last-recorded-case-of-paralytic-polio-by-country", "owid_column_metadata": {"Latest year of wild polio case": {"titleShort": "Latest year of wild polio case", "titleLong": "Latest year of wild polio case", "descriptionShort": "The most recent year in which a case of wild poliovirus was detected in a country.", "shortUnit": "", "unit": "", "timespan": "2023-2023", "type": "Integer", "owidVariableId": 899515, "shortName": "latest_year_wild_polio_case", "lastUpdated": "2024-04-12", "nextUpdate": "2026-07-22", "citationShort": "Global Polio Eradication Initiative (2023) – with minor processing by Our World in Data", "citationLong": "Global Polio Eradication Initiative (2023) – with minor processing by Our World in Data. “Latest year of wild polio case” [dataset]. Global Polio Eradication Initiative, “Polio-Free Countries” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/899515.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Paralytic polio: estimated cases by world region", "source_url": "https://ourworldindata.org/grapher/number-of-estimated-paralytic-polio-cases-by-world-region.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Estimated polio cases"], "row_count_total": 7919, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Estimated polio cases": "6160"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Estimated polio cases": "5859"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Estimated polio cases": "9730"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Estimated polio cases": "13937"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Estimated polio cases": "3864"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Estimated polio cases": "13867"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Estimated polio cases": "12901"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Estimated polio cases": "4396"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Estimated polio cases": "2149"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Estimated polio cases": "385"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Estimated polio cases": "336"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Estimated polio cases": "14"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Estimated polio cases": "0"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Estimated polio cases": "133"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Estimated polio cases": "413"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Estimated polio cases": "1050"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Estimated polio cases": "840"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Estimated polio cases": "22"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Estimated polio cases": "11.1"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Estimated polio cases": "8.88"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Estimated polio cases": "4.44"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Estimated polio cases": "9.99"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Estimated polio cases": "34.41"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Estimated polio cases": "18.87"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Estimated polio cases": "34.41"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Estimated polio cases": "42.18"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Estimated polio cases": "33.3"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Estimated polio cases": "89.91"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Estimated polio cases": "51.06"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Estimated polio cases": "18.87"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Estimated polio cases": "31.08"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Estimated polio cases": "22.2"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Estimated polio cases": "14.43"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Estimated polio cases": "15.54"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Estimated polio cases": "23.31"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Estimated polio cases": "32.19"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Estimated polio cases": "404.04"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Estimated polio cases": "49.95"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Estimated polio cases": "2.22"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Estimated polio cases": "6.66"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1980", "Estimated polio cases": "79660"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1981", "Estimated polio cases": "42266"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1982", "Estimated polio cases": "45094"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1983", "Estimated polio cases": "30422"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1984", "Estimated polio cases": "28084"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Estimated polio cases": "32984"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Estimated polio cases": "28364"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Estimated polio cases": "25368"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Estimated polio cases": "36785"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Estimated polio cases": "26544"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Estimated polio cases": "33663"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Estimated polio cases": "16723"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Estimated polio cases": "15092"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Estimated polio cases": "16919"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Estimated polio cases": "12173"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Estimated polio cases": "16037"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1996", "Estimated polio cases": "14700"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1997", "Estimated polio cases": "7952"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1998", "Estimated polio cases": "7616"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1999", "Estimated polio cases": "20650"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Estimated polio cases": "14294"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Estimated polio cases": "581"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Estimated polio cases": "1554"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Estimated polio cases": "3129"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Estimated polio cases": "7434"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Estimated polio cases": "7665"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Estimated polio cases": "8729"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Estimated polio cases": "3108"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Estimated polio cases": "7161"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Estimated polio cases": "6328"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Estimated polio cases": "4970"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Estimated polio cases": "2842"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Estimated polio cases": "1183"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Estimated polio cases": "2009"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Estimated polio cases": "392"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Estimated polio cases": "126"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Estimated polio cases": "35"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Estimated polio cases": "154"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Estimated polio cases": "546"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Estimated polio cases": "2317"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Estimated polio cases": "4452"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2021", "Estimated polio cases": "3808"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2022", "Estimated polio cases": "5075"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2023", "Estimated polio cases": "3563"}, {"Entity": "Albania", "Code": "ALB", "Year": "1980", "Estimated polio cases": "7"}, {"Entity": "Albania", "Code": "ALB", "Year": "1981", "Estimated polio cases": "7"}, {"Entity": "Albania", "Code": "ALB", "Year": "1982", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1983", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1984", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1985", "Estimated polio cases": "7"}, {"Entity": "Albania", "Code": "ALB", "Year": "1986", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1987", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1988", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1989", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1990", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1991", "Estimated polio cases": "7"}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "Estimated polio cases": "7"}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "Estimated polio cases": "966"}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Estimated polio cases": "0"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Estimated polio cases": "0"}], "rows_tail": [{"Entity": "Yemen", "Code": "YEM", "Year": "1991", "Estimated polio cases": "189"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1992", "Estimated polio cases": "315"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1993", "Estimated polio cases": "406"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1994", "Estimated polio cases": "1211"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1995", "Estimated polio cases": "315"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1996", "Estimated polio cases": "49"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1997", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1998", "Estimated polio cases": "112"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1999", "Estimated polio cases": "175"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Estimated polio cases": "956"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Estimated polio cases": "1.11"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Estimated polio cases": "9.99"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Estimated polio cases": "2.22"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Estimated polio cases": "1.11"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Estimated polio cases": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Estimated polio cases": "1.11"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Estimated polio cases": "34.41"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Estimated polio cases": "65.49"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2022", "Estimated polio cases": "177.6"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2023", "Estimated polio cases": "8.88"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1980", "Estimated polio cases": "1932"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1981", "Estimated polio cases": "3003"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1982", "Estimated polio cases": "1701"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1983", "Estimated polio cases": "1274"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1984", "Estimated polio cases": "1239"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Estimated polio cases": "896"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Estimated polio cases": "938"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Estimated polio cases": "483"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Estimated polio cases": "595"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Estimated polio cases": "329"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Estimated polio cases": "553"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Estimated polio cases": "35"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Estimated polio cases": "2590"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Estimated polio cases": "98"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Estimated polio cases": "42"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Estimated polio cases": "35"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Estimated polio cases": "42"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Estimated polio cases": "203"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Estimated polio cases": "6"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Estimated polio cases": "4"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Estimated polio cases": "2.22"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Estimated polio cases": "0"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Estimated polio cases": "1.11"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Estimated polio cases": "224"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Estimated polio cases": "196"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Estimated polio cases": "63"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Estimated polio cases": "21"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Estimated polio cases": "28"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Estimated polio cases": "483"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Estimated polio cases": "644"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Estimated polio cases": "35"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Estimated polio cases": "28"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Estimated polio cases": "7"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Estimated polio cases": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Estimated polio cases": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Estimated polio cases": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Estimated polio cases": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Estimated polio cases": "35"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Estimated polio cases": "7"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Estimated polio cases": "7"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Estimated polio cases": "21"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Estimated polio cases": "119"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Estimated polio cases": "14"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Estimated polio cases": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Estimated polio cases": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Estimated polio cases": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Estimated polio cases": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Estimated polio cases": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Estimated polio cases": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Estimated polio cases": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Estimated polio cases": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Estimated polio cases": "0"}, {"Entity": 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{"raw_link": "https://ourworldindata.org/global-temperatures-el-nino-la-nina", "title": "“Cool” years are now hotter than the “warm” years of the past: tracking global temperatures through El Niño and La Niña", "context": "Home\nClimate Change\n“Cool” years are now hotter than the “warm” years of the past: tracking global temperatures through El Niño and La Niña\nThe world is warming despite natural fluctuations from the El Niño cycle.\nBy\nVeronika Samborska\nand\nHannah Ritchie\nMarch 3, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nIn 2024, the world was around 1.5°C warmer than it was in pre-industrial times.\n1\nYou can see this in the chart below, which shows average warming relative to average temperatures from 1861 to 1890.\n2\nTemperatures, as defined by “climate”, are based on temperatures over longer periods of time — typically 20-to-30-year averages — rather than single-year data points. But even when based on longer-term averages, the world has still warmed by around 1.3°C.\n3\nBut you’ll also notice, in the chart, that temperatures haven’t increased linearly. There are spikes and dips along the long-run trend.\nMany of these short-term fluctuations are caused by “ENSO” — the\nEl Niño-Southern Oscillation\n— a natural climate cycle caused by changes in wind patterns and sea surface temperatures in the Pacific Ocean.\nWhile it’s caused by patterns in the Pacific Ocean and most strongly affects countries in the tropics, it also impacts\nglobal\ntemperatures and climate.\nThere are two key phases of this cycle: the La Niña phase, which tends to cause cooler global temperatures, and the El Niño phase, which brings hotter conditions. The world cycles between El Niño and La Niña phases every two to seven years.\n4\nThere are also “neutral” periods between these phases where the world is not in either extreme.\nThe zig-zag trend of global temperatures becomes understandable when you are taking the phases of the ENSO cycles into account. In the chart below, we see the data on global temperatures\n5\n, but the line is now colored by the ENSO phase at that time.\n6\nThe El Niño (warm phase) is shown in orange and red, and the La Niña (cold phase) is shown in blue.\nYou can see that temperatures often reach a short-term peak during warm El Niño years before falling back slightly as the world moves into La Niña years, shown in blue.\nWe will update this chart monthly\nwith the latest global temperature anomaly and ENSO phase.\nWhat’s striking is that global temperatures during recent La Niña years were\nwarmer\nthan El Niño years just a few decades before. “Cold years” today are hotter than “hot years” not too long ago.\n7\nContinue reading on Our World in Data\nClimate Change\nHow are global temperatures changing, and what are the impacts on sea level rise, sea ice, and ice sheets?\nHow much have temperatures risen in countries across the world?\nExplore country-by-country data on monthly temperature anomalies.\nMore people care about climate change than you think\nThe majority of people in every country support action on climate, but the public consistently underestimates this share.\nEndnotes\nThis was based on the annual average temperature anomaly, measured relative to 1850 to 1900 temperatures.\nNote that the original dataset, HadCRUT5 — published by the\nMet Office Hadley Centre\nused average temperatures from 1960 to 1990 as the baseline. To make the total warming relative to pre-industrial times clearer, we have adapted this to make the baseline the average from 1861 to 1890.\nFor more context on how much the world has warmed — and how this relates to the 1.5°C target, see this article by Richard Betts and colleagues.\nBetts, R. A., Belcher, S. E., Hermanson, L., Klein Tank, A., Lowe, J. A., Jones, C. D., ... & Stott, P. A. (2023).\nApproaching 1.5° C: how will we know we’ve reached this crucial warming mark?\n. Nature.\nThe exact length and intensity of these cycles are not always consistent but tend to be in that range.\nTo allow people to track monthly temperature changes, we use temperature anomalies from the European Centre for Medium-Range Weather Forecasts (ECMWF)\nERA5 project\n. Since satellite data are available only from the 1950s, constructing an 1861–1890 baseline isn’t feasible. Instead, we use a 1991–2020 baseline, which is the standard reference period employed by our data source. Despite this change, the chart still shows a clear warming trend.\nWe’ve categorized this based on the Oceanic Niño Index (ONI),\npublished by\nthe National Oceanic and Atmospheric Administration (NOAA). The ONI is used to monitor and track the presence and intensity of El Niño and La Niña events. It measures deviations in sea surface temperatures (SSTs) in a specific area of the Pacific Ocean, known as the Niño 3.4 region. We’ve used a classification based on the 3-month running mean of sea temperature anomalies.\nFor those keeping track of temperature changes on a monthly basis, we provide a chart that details this data by month. Using the same color categories as before,\nthis chart\nshows the global temperature anomalies for each individual month.\nYou can see that typically, months in La Niña conditions are cooler than the same month in the preceding year during El Niño conditions. The red dots (indicating El Niño) usually sit above the preceding blue ones (La Niña). Nonetheless, January 2025 was the hottest January on record, surpassing January 2024’s El Niño temperatures even as the world transitioned into a colder La Niña phase.\nAgain, we will keep this updated monthly so you can track these temperature changes over time.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nVeronika Samborska and Hannah Ritchie (2025) - ““Cool” years are now hotter than the “warm” years of the past: tracking global temperatures through El Niño and La Niña” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260619-192121/global-temperatures-el-nino-la-nina.html' [Online Resource] (archived on June 19, 2026).\nBibTeX citation\n@article{owid-global-temperatures-el-nino-la-nina,\nauthor = {Veronika Samborska and Hannah Ritchie},\ntitle = {“Cool” years are now hotter than the “warm” years of the past: tracking global temperatures through El Niño and La Niña},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260619-192121/global-temperatures-el-nino-la-nina.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "global-temperatures-el-nino-la-nina", "source_url": "https://ourworldindata.org/global-temperatures-el-nino-la-nina", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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"0.74655515", "Upper bound": "0.8115073"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1998", "Average": "0.93592083", "Lower bound": "0.9028746", "Upper bound": "0.968967"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1999", "Average": "0.68199766", "Lower bound": "0.64938253", "Upper bound": "0.7146128"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Average": "0.6894687", "Lower bound": "0.6567287", "Upper bound": "0.7222087"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Average": "0.8474103", "Lower bound": "0.8136172", "Upper bound": "0.88120335"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Average": "0.90111876", "Lower bound": "0.87089485", "Upper bound": "0.93134266"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Average": "0.9022393", "Lower bound": "0.8687722", "Upper bound": "0.9357065"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Average": "0.826385", "Lower bound": "0.79601645", "Upper bound": "0.8567535"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Average": "0.9662671", "Lower bound": "0.9350287", "Upper bound": "0.99750555"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Average": "0.93028426", "Lower bound": "0.898975", "Upper bound": "0.9615935"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Average": "0.949474", "Lower bound": "0.9175324", "Upper bound": "0.9814156"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Average": "0.82314956", "Lower bound": "0.7906812", "Upper bound": "0.85561794"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Average": "0.9539675", "Lower bound": "0.9231017", "Upper bound": "0.9848333"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Average": "1.0376073", "Lower bound": "1.0069752", "Upper bound": "1.0682396"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Average": "0.8965769", "Lower bound": "0.8656566", "Upper bound": "0.9274972"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Average": "0.93547153", "Lower bound": "0.9037727", "Upper bound": "0.9671704"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Average": "0.98260313", "Lower bound": "0.94711965", "Upper bound": "1.0180867"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Average": "1.031819", "Lower bound": "0.9993273", "Upper bound": "1.0643106"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Average": "1.1819104", "Lower bound": "1.148253", "Upper bound": "1.2155677"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Average": "1.2887791", "Lower bound": "1.2575042", "Upper bound": "1.3200539"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Average": "1.2004167", "Lower bound": "1.1683854", "Upper bound": "1.2324479"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Average": "1.1171584", "Lower bound": "1.0857053", "Upper bound": "1.1486115"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Average": "1.2513031", "Lower bound": "1.2179996", "Upper bound": "1.2846066"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Average": "1.2802037", "Lower bound": "1.2460152", "Upper bound": "1.3143922"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Average": "1.1312726", "Lower bound": "1.0963446", "Upper bound": "1.1662005"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Average": "1.1658032", "Lower bound": "1.1287576", "Upper bound": "1.2028489"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Average": "1.4737644", "Lower bound": "1.4368042", "Upper bound": "1.5107247"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Average": "1.5334455", "Lower bound": "1.4942731", "Upper bound": "1.5726179"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2025", "Average": "1.4137621", "Lower bound": "1.3743255", "Upper bound": "1.4531987"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2026", "Average": "1.3962804", "Lower bound": "1.2677863", "Upper bound": "1.5247746"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "temperature-anomaly", "metadata_url": "https://ourworldindata.org/grapher/temperature-anomaly.metadata.json", "chart_title": "Temperature change relative to the pre-industrial period", "chart_subtitle": "Temperature anomaly, measured as the difference between the average land-sea surface temperature in a given year and the 1861-1890 mean, in degrees Celsius.", "chart_note": "The period 1861–1890 is used as the baseline to measure temperature changes relative to pre-industrial times, [as recommended by the source](https://www.metoffice.gov.uk/hadobs/indicators/index.html#:~:text=For%20global%20average%20temperatures%2C%20an%201861%2D1890%20period%20is%20sometimes%20used%20to%20show%20the%20warming%20since%20the%20%22pre%2Dindustrial%22%20period.).", "chart_citation": "Met Office Hadley Centre - HadCRUT5 (2026)", "original_chart_url": "https://ourworldindata.org/grapher/temperature-anomaly", "owid_column_metadata": {"Global average temperature anomaly relative to 1861-1890": {"titleShort": "Global average temperature anomaly relative to 1861-1890", "titleLong": "Global average temperature anomaly relative to 1861-1890", "descriptionShort": "The difference in average land-sea surface temperature compared to the 1861-1890 mean, in degrees Celsius.", "descriptionKey": ["Temperature anomalies show how many degrees Celsius temperatures have changed compared to the 1861-1890 period. This baseline period is commonly used to highlight the changes in temperature since pre-industrial times, prior to major human impacts.", "Temperature averages and anomalies are calculated over all land and ocean surfaces.", "The data includes separate measurements for the Northern and Southern Hemispheres, which helps researchers analyze regional differences.", "The global temperature anomaly is the average of both hemisphere measurements.", "This data is based on the HadCRUT5 method. This method averages temperature measurements onto a fixed grid. If no data is available for a grid cell, it remains empty and adds extra uncertainty when calculating averages like the global mean.", "Despite different approaches, HadCRUT5 and other methods show similar global temperature trends."], "descriptionProcessing": "- We switch from using 1961-1990 to using 1861-1890 as our baseline to better show how temperatures have changed since pre-industrial times.\n- For each region, we calculate the mean temperature anomalies for 1961-1990 and for 1861-1890. The difference between these two means serves as the adjustment factor.\n- This factor is applied uniformly to both the temperature anomalies and the confidence intervals to ensure that both the central values and the associated uncertainty bounds are correctly shifted relative to the new 1861-1890 baseline.", "shortUnit": "°C", "unit": "degrees Celsius", "timespan": "1850-2026", "type": "Numeric", "owidVariableId": 1271207, "shortName": "near_surface_temperature_anomaly", "lastUpdated": "2026-06-19", "nextUpdate": "2026-08-18", "citationShort": "Met Office Hadley Centre - HadCRUT5 (2026) – with major processing by Our World in Data", "citationLong": "Met Office Hadley Centre - HadCRUT5 (2026) – with major processing by Our World in Data. “Global average temperature anomaly relative to 1861-1890” [dataset]. Met Office Hadley Centre, “HadCRUT5 HadCRUT.5.1.0.0” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1271207.metadata.json"}, "Lower bound of the annual temperature anomaly (95% confidence interval)": {"titleShort": "Lower bound of the annual temperature anomaly (95% confidence interval)", "titleLong": "Lower bound of the annual temperature anomaly (95% confidence interval)", "descriptionKey": ["Temperature anomalies show how many degrees Celsius temperatures have changed compared to the 1861-1890 period. This baseline period is commonly used to highlight the changes in temperature since pre-industrial times, prior to major human impacts.", "Temperature averages and anomalies are calculated over all land and ocean surfaces.", "The data includes separate measurements for the Northern and Southern Hemispheres, which helps researchers analyze regional differences.", "The global temperature anomaly is the average of both hemisphere measurements.", "This data is based on the HadCRUT5 method. This method averages temperature measurements onto a fixed grid. If no data is available for a grid cell, it remains empty and adds extra uncertainty when calculating averages like the global mean.", "Despite different approaches, HadCRUT5 and other methods show similar global temperature trends."], "descriptionProcessing": "- We switch from using 1961-1990 to using 1861-1890 as our baseline to better show how temperatures have changed since pre-industrial times.\n- For each region, we calculate the mean temperature anomalies for 1961-1990 and for 1861-1890. The difference between these two means serves as the adjustment factor.\n- This factor is applied uniformly to both the temperature anomalies and the confidence intervals to ensure that both the central values and the associated uncertainty bounds are correctly shifted relative to the new 1861-1890 baseline.", "shortUnit": "°C", "unit": "degrees Celsius", "timespan": "1850-2026", "type": "Numeric", "owidVariableId": 1271208, "shortName": "near_surface_temperature_anomaly_lower", "lastUpdated": "2026-06-19", "nextUpdate": "2026-08-18", "citationShort": "Met Office Hadley Centre - HadCRUT5 (2026) – with major processing by Our World in Data", "citationLong": "Met Office Hadley Centre - HadCRUT5 (2026) – with major processing by Our World in Data. “Lower bound of the annual temperature anomaly (95% confidence interval)” [dataset]. Met Office Hadley Centre, “HadCRUT5 HadCRUT.5.1.0.0” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1271208.metadata.json"}, "Upper bound of the annual temperature anomaly (95% confidence interval)": {"titleShort": "Upper bound of the annual temperature anomaly (95% confidence interval)", "titleLong": "Upper bound of the annual temperature anomaly (95% confidence interval)", "descriptionKey": ["Temperature anomalies show how many degrees Celsius temperatures have changed compared to the 1861-1890 period. This baseline period is commonly used to highlight the changes in temperature since pre-industrial times, prior to major human impacts.", "Temperature averages and anomalies are calculated over all land and ocean surfaces.", "The data includes separate measurements for the Northern and Southern Hemispheres, which helps researchers analyze regional differences.", "The global temperature anomaly is the average of both hemisphere measurements.", "This data is based on the HadCRUT5 method. This method averages temperature measurements onto a fixed grid. If no data is available for a grid cell, it remains empty and adds extra uncertainty when calculating averages like the global mean.", "Despite different approaches, HadCRUT5 and other methods show similar global temperature trends."], "descriptionProcessing": "- We switch from using 1961-1990 to using 1861-1890 as our baseline to better show how temperatures have changed since pre-industrial times.\n- For each region, we calculate the mean temperature anomalies for 1961-1990 and for 1861-1890. The difference between these two means serves as the adjustment factor.\n- This factor is applied uniformly to both the temperature anomalies and the confidence intervals to ensure that both the central values and the associated uncertainty bounds are correctly shifted relative to the new 1861-1890 baseline.", "shortUnit": "°C", "unit": "degrees Celsius", "timespan": "1850-2026", "type": "Numeric", "owidVariableId": 1271209, "shortName": "near_surface_temperature_anomaly_upper", "lastUpdated": "2026-06-19", "nextUpdate": "2026-08-18", "citationShort": "Met Office Hadley Centre - HadCRUT5 (2026) – with major processing by Our World in Data", "citationLong": "Met Office Hadley Centre - HadCRUT5 (2026) – with major processing by Our World in Data. “Upper bound of the annual temperature anomaly (95% confidence interval)” [dataset]. Met Office Hadley Centre, “HadCRUT5 HadCRUT.5.1.0.0” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1271209.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Global temperature anomalies by El Niño and La Niña conditions", "source_url": "https://ourworldindata.org/grapher/global-temperature-anomalies-by-el-nino-la-nina.csv", "file_type": "csv", "columns": ["Entity", "Code", "Day", "Temperature anomaly", "Oceanic Niño Index (ONI) anomaly"], "row_count_total": 195108, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Day": "1950-02-15", "Temperature anomaly": "-4.8566694", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Day": "1950-03-15", "Temperature anomaly": "-3.0694683", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Day": "1950-04-15", "Temperature anomaly": "-4.7350464", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Day": "1950-05-15", "Temperature anomaly": "-1.4261665", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": 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"Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2020-07-15", "Temperature anomaly": "-0.2044468", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2020-08-15", "Temperature anomaly": "0.5589733", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2020-09-15", "Temperature anomaly": "-0.19061089", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2020-10-15", "Temperature anomaly": "-0.34319115", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2020-11-15", "Temperature anomaly": "1.1854057", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2020-12-15", "Temperature anomaly": "-0.7526474", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2021-01-15", "Temperature anomaly": "-0.62034225", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2021-02-15", "Temperature anomaly": "-0.60834503", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2021-03-15", "Temperature anomaly": "-0.5274334", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2021-04-15", "Temperature anomaly": "0.04137802", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2021-05-15", "Temperature anomaly": "-0.92041016", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2021-06-15", "Temperature anomaly": "0.13837814", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2021-07-15", "Temperature anomaly": "-0.3395815", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2021-08-15", "Temperature anomaly": "0.4699173", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", 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"Code": "ZWE", "Day": "2023-07-15", "Temperature anomaly": "0.47984505", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2023-08-15", "Temperature anomaly": "0.41130638", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2023-09-15", "Temperature anomaly": "1.604187", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2023-10-15", "Temperature anomaly": "0.85302925", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2023-11-15", "Temperature anomaly": "1.3471203", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2023-12-15", "Temperature anomaly": "1.1598339", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2024-01-15", "Temperature anomaly": "-0.05359459", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2024-02-15", "Temperature anomaly": "2.1130066", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2024-03-15", "Temperature anomaly": "2.4165096", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2024-04-15", "Temperature anomaly": "1.2365685", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2024-05-15", "Temperature anomaly": "1.0779591", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2024-06-15", "Temperature anomaly": "0.86564827", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2024-07-15", "Temperature anomaly": "2.6941433", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2024-08-15", "Temperature anomaly": "1.5672932", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2024-09-15", "Temperature anomaly": "1.1273327", "Oceanic 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"Zimbabwe", "Code": "ZWE", "Day": "2025-05-15", "Temperature anomaly": "-0.14381981", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2025-06-15", "Temperature anomaly": "0.5639343", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2025-07-15", "Temperature anomaly": "0.68423843", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2025-08-15", "Temperature anomaly": "0.53972244", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2025-09-15", "Temperature anomaly": "1.6774807", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2025-10-15", "Temperature anomaly": "0.5061703", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2025-11-15", "Temperature anomaly": "-1.6850433", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2025-12-15", "Temperature anomaly": "-0.74077034", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2026-01-15", "Temperature anomaly": "-0.58859444", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2026-02-15", "Temperature anomaly": "0.10715294", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2026-03-15", "Temperature anomaly": "-0.19028282", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2026-04-15", "Temperature anomaly": "0.8228378", "Oceanic Niño Index (ONI) anomaly": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2026-05-15", "Temperature anomaly": "1.1682186", "Oceanic Niño Index (ONI) anomaly": ""}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "global-temperature-anomalies-by-el-nino-la-nina", "metadata_url": "https://ourworldindata.org/grapher/global-temperature-anomalies-by-el-nino-la-nina.metadata.json", "chart_title": "Global temperature anomalies by El Niño and La Niña conditions", "chart_subtitle": "The difference between a month's average land-sea surface temperature and the 1991–2020 average of the same month, measured in degrees Celsius. It is classified as  El Niño or La Niña based on the Oceanic Niño Index, which tracks warming or cooling patterns in the central Pacific Ocean.", "chart_note": null, "chart_citation": "Contains modified Copernicus Climate Change Service information (2026); NOAA National Centers for Environmental Information (2026)", "original_chart_url": "https://ourworldindata.org/grapher/global-temperature-anomalies-by-el-nino-la-nina", "owid_column_metadata": {"Temperature anomaly": {"titleShort": "Temperature anomaly", "titleLong": "Temperature anomaly", "descriptionShort": "The difference of a specific month's average surface temperature from the 1991-2020 mean, in degrees Celsius.", "descriptionProcessing": "- Temperature measured in kelvin was converted to degrees Celsius (°C) by subtracting 273.15.\n\n- Initially, the temperature dataset is provided with specific coordinates in terms of longitude and latitude. To tailor this data to each country, we utilize geographical boundaries as defined by the World Bank. The method involves trimming the global temperature dataset to match the exact geographical shape of each country. To correct for potential distortions caused by the Earth's curvature on a flat map, we apply a latitude-based weighting. This step is essential for maintaining accuracy, especially in high-latitude regions where distortion is more pronounced. The result of this process is a latitude-weighted average temperature for each nation.\n\n- It's important to note, however, that due to the resolution constraints of the Copernicus dataset, this methodology might not be as effective for countries with very small landmasses. In these cases, the process may not yield reliable data.\n\n- The derived 2-meter temperature readings for each country are calculated based on administrative borders, encompassing all land surface types within these defined areas. As a result, temperatures over oceans and seas are not included in these averages, focusing the data primarily on terrestrial environments.\n\n- Global temperature averages and anomalies are calculated over all land and ocean surfaces.\n- The temperature anomaly is calculated by comparing the average surface temperature of a specific time period (e.g., a particular year or month) to the mean surface temperature of the same period from 1991 to 2020.\n\n- When calculating anomalies for each country, the average surface temperature of a given year or month is compared to the 1991-2020 mean temperature for that specific country.\n\n- The reason for using the 1991-2020 period as the reference mean is that it is the standard reference period used by our data source, the Copernicus Climate Change Service. This period is also adopted by the UK Met Office. This approach ensures consistency in identifying climate variations over time.", "shortUnit": "°C", "unit": "°C", "timespan": "", "type": "Numeric", "owidVariableId": 1271571, "shortName": "temperature_anomaly", "lastUpdated": "2026-06-19", "citationShort": "Contains modified Copernicus Climate Change Service information (2026) – with major processing by Our World in Data", "citationLong": "Contains modified Copernicus Climate Change Service information (2026) – with major processing by Our World in Data. “Temperature anomaly” [dataset]. Contains modified Copernicus Climate Change Service information, “ERA5 monthly averaged data on single levels from 1940 to present 2” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1271571.metadata.json"}, "Oceanic Niño Index (ONI) anomaly by month": {"titleShort": "Oceanic Niño Index (ONI) anomaly", "titleLong": "Oceanic Niño Index (ONI) anomaly", "descriptionShort": "The Oceanic Niño Index (ONI) anomaly is a measure of the sea surface temperature anomalies in the east-central tropical Pacific Ocean.", "descriptionKey": ["The Oceanic Niño Index (ONI) is a tool used by the National Oceanic and Atmospheric Administration (NOAA) to monitor and track the presence and intensity of El Niño and La Niña events.", "These events are part of the broader El Niño-Southern Oscillation (ENSO), a natural climate pattern that affects global weather patterns, including rainfall, droughts, and hurricane activity.", "The ONI measures deviations in sea surface temperatures (SSTs) in a specific area of the Pacific Ocean, known as the Niño 3.4 region. This region spans from 120°W to 170°W longitude, along the equator, in the east-central tropical Pacific.", "NOAA calculates the ONI by taking a 3-month running mean of SST anomalies. An anomaly is the difference between observed SSTs and the 30-year climatological average for the same period. NOAA periodically updates the baseline period to ensure consistency with long-term climate trends. For example, the 1991–2020 average is often used.", "El Niño (ONI ≥ +0.5°C) occurs when sea surface temperatures in the Niño 3.4 region are warmer than usual, often bringing drier conditions to Asia and Australia, wetter weather to the southern United States, and weakened trade winds.", "El Niño can lead to weaker Atlantic hurricane seasons but stronger and more frequent Pacific hurricanes.", "Neutral (−0.5°C < ONI < +0.5°C) means sea surface temperatures are near average, with no significant ENSO event.", "La Niña (ONI ≤ −0.5°C) happens when sea surface temperatures are cooler than usual, often causing drier conditions in South America, increased rainfall in Indonesia and northern Australia, and stronger trade winds.", "La Niña tends to cause more hurricanes in the Atlantic and drought conditions in the southern U.S."], "unit": "", "timespan": "", "type": "Numeric", "owidVariableId": 1271165, "shortName": "oni_anomaly", "lastUpdated": "2026-06-19", "nextUpdate": "2026-07-20", "citationShort": "NOAA National Centers for Environmental Information (2026) – with minor processing by Our World in Data", "citationLong": "NOAA National Centers for Environmental Information (2026) – with minor processing by Our World in Data. “Oceanic Niño Index (ONI) anomaly” [dataset]. NOAA National Centers for Environmental Information, “Equatorial Pacific Sea Surface Temperatures (SST) data” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1271165.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Global temperature anomalies by El Niño and La Niña and month", "source_url": "https://ourworldindata.org/grapher/global-temperature-anomalies-by-el-nino-la-nina-and-month.csv", "file_type": "csv", "columns": ["Entity", "Year", "Temperature anomaly", "Oceanic Niño Index (ONI) anomaly"], "row_count_total": 917, "rows_head": [{"Entity": "April", "Year": "1950", "Temperature anomaly": "-0.836648", "Oceanic Niño Index (ONI) anomaly": "-1.16"}, {"Entity": "April", "Year": "1951", "Temperature anomaly": "-0.71882915", "Oceanic Niño Index (ONI) anomaly": "-0.17"}, {"Entity": "April", "Year": "1952", "Temperature anomaly": "-0.7059593", "Oceanic Niño Index (ONI) anomaly": "0.34"}, {"Entity": "April", "Year": "1953", "Temperature anomaly": "-0.50220203", "Oceanic Niño Index (ONI) anomaly": "0.63"}, {"Entity": "April", "Year": "1954", "Temperature anomaly": "-0.77287674", "Oceanic Niño Index (ONI) anomaly": "-0.05"}, {"Entity": "April", "Year": "1955", "Temperature anomaly": "-0.84453773", "Oceanic Niño Index (ONI) anomaly": "-0.69"}, {"Entity": "April", "Year": "1956", "Temperature anomaly": "-0.8796396", "Oceanic Niño Index (ONI) anomaly": "-0.63"}, {"Entity": "April", "Year": "1957", "Temperature anomaly": "-0.66009617", "Oceanic Niño Index (ONI) anomaly": "0.41"}, {"Entity": "April", "Year": "1958", "Temperature anomaly": "-0.53918743", "Oceanic Niño Index (ONI) anomaly": "1.27"}, {"Entity": "April", "Year": "1959", "Temperature anomaly": "-0.46754837", "Oceanic Niño Index (ONI) anomaly": "0.52"}, {"Entity": "April", "Year": "1960", "Temperature anomaly": "-0.7444658", "Oceanic Niño Index (ONI) anomaly": "-0.07"}, {"Entity": "April", "Year": "1961", "Temperature anomaly": "-0.5174122", "Oceanic Niño Index (ONI) anomaly": "0.04"}, {"Entity": "April", "Year": "1962", "Temperature anomaly": "-0.59061337", "Oceanic Niño Index (ONI) anomaly": "-0.2"}, {"Entity": "April", "Year": "1963", "Temperature anomaly": "-0.7342434", "Oceanic Niño Index (ONI) anomaly": "0.15"}, {"Entity": "April", "Year": "1964", "Temperature anomaly": "-0.87139225", "Oceanic Niño Index (ONI) anomaly": "0.12"}, {"Entity": "April", "Year": "1965", "Temperature anomaly": "-0.8030367", "Oceanic Niño Index (ONI) anomaly": "-0.07"}, {"Entity": "April", "Year": "1966", "Temperature anomaly": "-0.69640446", "Oceanic Niño Index (ONI) anomaly": "0.98"}, {"Entity": "April", "Year": "1967", "Temperature anomaly": "-0.65558624", "Oceanic Niño Index (ONI) anomaly": "-0.53"}, {"Entity": "April", "Year": "1968", "Temperature anomaly": "-0.6871834", "Oceanic Niño Index (ONI) anomaly": "-0.62"}, {"Entity": "April", "Year": "1969", "Temperature anomaly": "-0.4611292", "Oceanic Niño Index (ONI) anomaly": "0.95"}, {"Entity": "April", "Year": "1970", "Temperature anomaly": "-0.4808731", "Oceanic Niño Index (ONI) anomaly": "0.29"}, {"Entity": "April", "Year": "1971", "Temperature anomaly": "-0.7092209", "Oceanic Niño Index (ONI) anomaly": "-1.12"}, {"Entity": "April", "Year": "1972", "Temperature anomaly": "-0.61412716", "Oceanic Niño Index (ONI) anomaly": "0.06"}, {"Entity": "April", "Year": "1973", "Temperature anomaly": "-0.41615963", "Oceanic Niño Index (ONI) anomaly": "0.54"}, {"Entity": "April", "Year": "1974", "Temperature anomaly": "-0.7592621", "Oceanic Niño Index (ONI) anomaly": "-1.23"}, {"Entity": "April", "Year": "1975", "Temperature anomaly": "-0.6411934", "Oceanic Niño Index (ONI) anomaly": "-0.65"}, {"Entity": "April", "Year": "1976", "Temperature anomaly": "-0.8217974", "Oceanic Niño Index (ONI) anomaly": "-0.73"}, {"Entity": "April", "Year": "1977", "Temperature anomaly": "-0.50898075", "Oceanic Niño Index (ONI) anomaly": "0.34"}, {"Entity": "April", "Year": "1978", "Temperature anomaly": "-0.5516424", "Oceanic Niño Index (ONI) anomaly": "0.06"}, {"Entity": "April", "Year": "1979", "Temperature anomaly": "-0.5085182", "Oceanic Niño Index (ONI) anomaly": "0.2"}, {"Entity": "April", "Year": "1980", "Temperature anomaly": "-0.18407059", "Oceanic Niño Index (ONI) anomaly": "0.34"}, {"Entity": "April", "Year": "1981", "Temperature anomaly": "-0.21317005", "Oceanic Niño Index (ONI) anomaly": "-0.47"}, {"Entity": "April", "Year": "1982", "Temperature anomaly": "-0.537735", "Oceanic Niño Index (ONI) anomaly": "0.19"}, {"Entity": "April", "Year": "1983", "Temperature anomaly": "-0.2767191", "Oceanic Niño Index (ONI) anomaly": "1.54"}, {"Entity": "April", "Year": "1984", "Temperature anomaly": "-0.46905327", "Oceanic Niño Index (ONI) anomaly": "-0.34"}, {"Entity": "April", "Year": "1985", "Temperature anomaly": "-0.47266388", "Oceanic Niño Index (ONI) anomaly": "-0.77"}, {"Entity": "April", "Year": "1986", "Temperature anomaly": "-0.2999115", "Oceanic Niño Index (ONI) anomaly": "-0.31"}, {"Entity": "April", "Year": "1987", "Temperature anomaly": "-0.33420658", "Oceanic Niño Index (ONI) anomaly": "1.06"}, {"Entity": "April", "Year": "1988", "Temperature 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anomaly": "-0.59"}, {"Entity": "April", "Year": "1997", "Temperature anomaly": "-0.3279724", "Oceanic Niño Index (ONI) anomaly": "-0.1"}, {"Entity": "April", "Year": "1998", "Temperature anomaly": "0.12081814", "Oceanic Niño Index (ONI) anomaly": "1.44"}, {"Entity": "April", "Year": "1999", "Temperature anomaly": "-0.30352116", "Oceanic Niño Index (ONI) anomaly": "-1.07"}, {"Entity": "April", "Year": "2000", "Temperature anomaly": "-0.08273029", "Oceanic Niño Index (ONI) anomaly": "-1.07"}, {"Entity": "April", "Year": "2001", "Temperature anomaly": "-0.15341854", "Oceanic Niño Index (ONI) anomaly": "-0.44"}, {"Entity": "April", "Year": "2002", "Temperature anomaly": "-0.063630104", "Oceanic Niño Index (ONI) anomaly": "0.09"}, {"Entity": "April", "Year": "2003", "Temperature anomaly": "-0.12009716", "Oceanic Niño Index (ONI) anomaly": "0.38"}, {"Entity": "April", "Year": "2004", "Temperature anomaly": "-0.014400482", "Oceanic Niño Index (ONI) anomaly": "0.23"}, {"Entity": "April", "Year": "2005", "Temperature anomaly": "0.07043171", "Oceanic Niño Index (ONI) anomaly": "0.45"}, {"Entity": "April", "Year": "2006", "Temperature anomaly": "-0.069927216", "Oceanic Niño Index (ONI) anomaly": "-0.57"}, {"Entity": "April", "Year": "2007", "Temperature anomaly": "0.17370224", "Oceanic Niño Index (ONI) anomaly": "-0.12"}, {"Entity": "April", "Year": "2008", "Temperature anomaly": "-0.126688", "Oceanic Niño Index (ONI) anomaly": "-1.29"}, {"Entity": "April", "Year": "2009", "Temperature anomaly": "-0.060088158", "Oceanic Niño Index (ONI) anomaly": "-0.61"}, {"Entity": "April", "Year": "2010", "Temperature anomaly": "0.25240517", "Oceanic Niño Index (ONI) anomaly": "0.84"}, {"Entity": "April", "Year": "2011", "Temperature anomaly": "0.0136089325", "Oceanic Niño Index (ONI) anomaly": "-0.8"}, {"Entity": "April", "Year": "2012", "Temperature anomaly": "0.054305077", "Oceanic Niño Index (ONI) anomaly": "-0.46"}, {"Entity": "April", "Year": "2013", "Temperature anomaly": "-0.036406517", "Oceanic Niño Index (ONI) anomaly": "-0.21"}, {"Entity": "April", "Year": "2014", "Temperature anomaly": "0.10949421", "Oceanic Niño Index (ONI) anomaly": "-0.14"}, {"Entity": "April", "Year": "2015", "Temperature anomaly": "0.056487083", "Oceanic Niño Index (ONI) anomaly": "0.65"}, {"Entity": "April", "Year": "2016", "Temperature anomaly": "0.5295439", "Oceanic Niño Index (ONI) anomaly": "1.71"}, {"Entity": "April", "Year": "2017", "Temperature anomaly": "0.32820606", "Oceanic Niño Index (ONI) anomaly": "0.18"}, {"Entity": "April", "Year": "2018", "Temperature anomaly": "0.3051052", "Oceanic Niño Index (ONI) anomaly": "-0.57"}, {"Entity": "April", "Year": "2019", "Temperature anomaly": "0.43720055", "Oceanic Niño Index (ONI) anomaly": "0.84"}, {"Entity": "April", "Year": "2020", "Temperature anomaly": "0.52027035", "Oceanic Niño Index (ONI) anomaly": "0.53"}, {"Entity": "April", "Year": "2021", "Temperature anomaly": "0.19053555", "Oceanic Niño Index (ONI) anomaly": "-0.71"}, {"Entity": "April", "Year": "2022", "Temperature anomaly": "0.2832718", "Oceanic Niño Index (ONI) anomaly": "-0.86"}, {"Entity": "April", "Year": "2023", "Temperature anomaly": "0.3205328", "Oceanic Niño Index (ONI) anomaly": "-0.02"}, {"Entity": "April", "Year": "2024", "Temperature anomaly": "0.67018795", "Oceanic Niño Index (ONI) anomaly": "1.26"}, {"Entity": "April", "Year": "2025", "Temperature anomaly": "0.6011734", "Oceanic Niño Index (ONI) anomaly": "-0.06"}, {"Entity": "April", "Year": "2026", "Temperature anomaly": "0.5236006", "Oceanic Niño Index (ONI) anomaly": "0.13"}, {"Entity": "August", "Year": "1950", "Temperature anomaly": "-0.8166046", "Oceanic Niño Index (ONI) anomaly": "-0.54"}, {"Entity": "August", "Year": "1951", "Temperature anomaly": "-0.48820114", "Oceanic Niño Index (ONI) anomaly": "0.7"}, {"Entity": "August", "Year": "1952", "Temperature anomaly": "-0.6869316", "Oceanic Niño Index (ONI) anomaly": "-0.08"}, {"Entity": "August", "Year": "1953", "Temperature anomaly": "-0.52975655", "Oceanic Niño Index (ONI) anomaly": "0.75"}, {"Entity": "August", "Year": "1954", "Temperature anomaly": "-0.80227757", "Oceanic Niño Index (ONI) anomaly": "-0.64"}, {"Entity": "August", "Year": "1955", "Temperature anomaly": "-0.5998764", "Oceanic Niño Index (ONI) anomaly": "-0.68"}, {"Entity": "August", "Year": "1956", "Temperature anomaly": "-0.88370514", "Oceanic Niño Index (ONI) anomaly": "-0.57"}, {"Entity": "August", "Year": "1957", "Temperature anomaly": "-0.5275841", "Oceanic Niño Index (ONI) anomaly": "1.25"}, {"Entity": "August", "Year": "1958", "Temperature anomaly": "-0.57756615", "Oceanic Niño Index (ONI) anomaly": "0.57"}, {"Entity": "August", "Year": "1959", "Temperature anomaly": "-0.59372425", "Oceanic Niño Index (ONI) anomaly": "-0.18"}, {"Entity": "August", "Year": "1960", "Temperature anomaly": "-0.53197956", "Oceanic Niño Index (ONI) anomaly": "0.13"}, {"Entity": "August", "Year": "1961", "Temperature anomaly": "-0.54902935", "Oceanic Niño Index (ONI) anomaly": "0.14"}, {"Entity": "August", "Year": "1962", "Temperature anomaly": "-0.60115623", "Oceanic Niño Index (ONI) anomaly": "-0.04"}, {"Entity": "August", "Year": "1963", "Temperature anomaly": "-0.3528309", "Oceanic Niño Index (ONI) anomaly": "0.86"}, {"Entity": "August", "Year": "1964", "Temperature anomaly": "-0.6946621", "Oceanic Niño Index (ONI) anomaly": "-0.6"}, {"Entity": "August", "Year": "1965", "Temperature anomaly": "-0.6724491", "Oceanic Niño Index (ONI) anomaly": "1.22"}, {"Entity": "August", "Year": "1966", "Temperature anomaly": "-0.5796337", "Oceanic Niño Index (ONI) anomaly": "0.24"}, {"Entity": "August", "Year": "1967", "Temperature anomaly": "-0.57115364", "Oceanic Niño Index (ONI) anomaly": "0.05"}, {"Entity": "August", "Year": "1968", "Temperature anomaly": "-0.652915", "Oceanic Niño Index (ONI) anomaly": "0.58"}, {"Entity": "August", "Year": "1969", "Temperature anomaly": "-0.6583824", "Oceanic Niño Index (ONI) anomaly": "0.36"}, {"Entity": "August", "Year": "1970", "Temperature anomaly": "-0.68392754", "Oceanic Niño Index (ONI) anomaly": "-0.63"}, {"Entity": "August", "Year": "1971", "Temperature anomaly": "-0.7044306", "Oceanic Niño Index (ONI) anomaly": "-0.8"}, {"Entity": "August", "Year": "1972", "Temperature anomaly": "-0.45670414", "Oceanic Niño Index (ONI) anomaly": "1.13"}, {"Entity": "August", "Year": "1973", "Temperature anomaly": "-0.6240673", "Oceanic Niño Index (ONI) anomaly": "-1.11"}, {"Entity": "August", "Year": "1974", "Temperature anomaly": "-0.62120056", "Oceanic Niño Index (ONI) anomaly": "-0.53"}, {"Entity": "August", "Year": "1975", "Temperature anomaly": "-0.83821774", "Oceanic Niño Index (ONI) anomaly": "-1.13"}, {"Entity": "August", "Year": "1976", "Temperature anomaly": "-0.75347805", "Oceanic Niño Index (ONI) anomaly": "0.18"}, {"Entity": "August", "Year": "1977", "Temperature anomaly": "-0.4668398", "Oceanic Niño Index (ONI) anomaly": "0.35"}, {"Entity": "August", "Year": "1978", "Temperature anomaly": "-0.72218895", "Oceanic Niño Index (ONI) anomaly": "-0.36"}, {"Entity": "August", "Year": "1979", "Temperature anomaly": "-0.40953922", "Oceanic Niño Index (ONI) anomaly": "0.04"}, {"Entity": "August", "Year": "1980", "Temperature anomaly": "-0.28450108", "Oceanic Niño Index (ONI) anomaly": "0.25"}, {"Entity": "August", "Year": "1981", "Temperature anomaly": "-0.23632145", "Oceanic Niño Index (ONI) anomaly": "-0.3"}, {"Entity": "August", "Year": "1982", "Temperature anomaly": "-0.45393944", "Oceanic Niño Index (ONI) anomaly": "0.79"}, {"Entity": "August", "Year": "1983", "Temperature anomaly": "-0.2498312", "Oceanic Niño Index (ONI) anomaly": "0.31"}, {"Entity": "August", "Year": "1984", "Temperature anomaly": "-0.4386263", "Oceanic Niño Index (ONI) anomaly": "-0.3"}, {"Entity": "August", "Year": "1985", "Temperature anomaly": "-0.39429283", "Oceanic Niño Index (ONI) anomaly": "-0.49"}, {"Entity": "August", "Year": "1986", "Temperature anomaly": "-0.4373436", "Oceanic Niño Index (ONI) anomaly": "0.22"}, {"Entity": "August", "Year": "1987", "Temperature anomaly": "-0.27093315", "Oceanic Niño Index (ONI) anomaly": "1.51"}, {"Entity": "August", "Year": "1988", "Temperature anomaly": "-0.1666317", "Oceanic Niño Index (ONI) anomaly": "-1.3"}, {"Entity": "August", "Year": "1989", "Temperature anomaly": "-0.32171345", "Oceanic Niño Index (ONI) anomaly": "-0.31"}, {"Entity": "August", "Year": "1990", "Temperature anomaly": "-0.16622066", "Oceanic Niño Index (ONI) anomaly": "0.33"}, {"Entity": "August", "Year": "1991", "Temperature anomaly": "-0.13983059", "Oceanic Niño Index (ONI) anomaly": "0.73"}, {"Entity": "August", "Year": "1992", "Temperature anomaly": "-0.48837852", "Oceanic Niño Index (ONI) anomaly": "0.37"}], "rows_tail": [{"Entity": "October", "Year": "1982", "Temperature anomaly": "-0.5201969", "Oceanic Niño Index (ONI) anomaly": "1.58"}, {"Entity": "October", "Year": "1983", "Temperature anomaly": "-0.47554874", "Oceanic Niño Index 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{"Entity": "October", "Year": "1992", "Temperature anomaly": "-0.6016693", "Oceanic Niño Index (ONI) anomaly": "-0.13"}, {"Entity": "October", "Year": "1993", "Temperature anomaly": "-0.43048668", "Oceanic Niño Index (ONI) anomaly": "0.15"}, {"Entity": "October", "Year": "1994", "Temperature anomaly": "-0.2922449", "Oceanic Niño Index (ONI) anomaly": "0.55"}, {"Entity": "October", "Year": "1995", "Temperature anomaly": "-0.19515514", "Oceanic Niño Index (ONI) anomaly": "-0.81"}, {"Entity": "October", "Year": "1996", "Temperature anomaly": "-0.4405651", "Oceanic Niño Index (ONI) anomaly": "-0.35"}, {"Entity": "October", "Year": "1997", "Temperature anomaly": "-0.053113937", "Oceanic Niño Index (ONI) anomaly": "2.14"}, {"Entity": "October", "Year": "1998", "Temperature anomaly": "-0.17791367", "Oceanic Niño Index (ONI) anomaly": "-1.31"}, {"Entity": "October", "Year": "1999", "Temperature anomaly": "-0.30163097", "Oceanic Niño Index (ONI) anomaly": "-1.16"}, {"Entity": "October", "Year": 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"-0.33603668", "Oceanic Niño Index (ONI) anomaly": "-0.25"}, {"Entity": "September", "Year": "1982", "Temperature anomaly": "-0.46557617", "Oceanic Niño Index (ONI) anomaly": "1.07"}, {"Entity": "September", "Year": "1983", "Temperature anomaly": "-0.19696712", "Oceanic Niño Index (ONI) anomaly": "-0.08"}, {"Entity": "September", "Year": "1984", "Temperature anomaly": "-0.48609352", "Oceanic Niño Index (ONI) anomaly": "-0.16"}, {"Entity": "September", "Year": "1985", "Temperature anomaly": "-0.5208702", "Oceanic Niño Index (ONI) anomaly": "-0.46"}, {"Entity": "September", "Year": "1986", "Temperature anomaly": "-0.52695084", "Oceanic Niño Index (ONI) anomaly": "0.44"}, {"Entity": "September", "Year": "1987", "Temperature anomaly": "-0.22409725", "Oceanic Niño Index (ONI) anomaly": "1.7"}, {"Entity": "September", "Year": "1988", "Temperature anomaly": "-0.1873064", "Oceanic Niño Index (ONI) anomaly": "-1.11"}, {"Entity": "September", "Year": "1989", "Temperature anomaly": "-0.28293705", "Oceanic Niño Index (ONI) anomaly": "-0.27"}, {"Entity": "September", "Year": "1990", "Temperature anomaly": "-0.23057461", "Oceanic Niño Index (ONI) anomaly": "0.38"}, {"Entity": "September", "Year": "1991", "Temperature anomaly": "-0.20186901", "Oceanic Niño Index (ONI) anomaly": "0.64"}, {"Entity": "September", "Year": "1992", "Temperature anomaly": "-0.6091318", "Oceanic Niño Index (ONI) anomaly": "0.09"}, {"Entity": "September", "Year": "1993", "Temperature anomaly": "-0.51664925", "Oceanic Niño Index (ONI) anomaly": "0.25"}, {"Entity": "September", "Year": "1994", "Temperature anomaly": "-0.23872185", "Oceanic Niño Index (ONI) anomaly": "0.43"}, {"Entity": "September", "Year": "1995", "Temperature anomaly": "-0.20063305", "Oceanic Niño Index (ONI) anomaly": "-0.54"}, {"Entity": "September", "Year": "1996", "Temperature anomaly": "-0.32755566", "Oceanic Niño Index (ONI) anomaly": "-0.32"}, {"Entity": "September", "Year": "1997", "Temperature anomaly": "-0.06467819", "Oceanic Niño Index (ONI) anomaly": "1.9"}, {"Entity": "September", "Year": "1998", "Temperature anomaly": "-0.05279255", "Oceanic Niño Index (ONI) anomaly": "-1.12"}, {"Entity": "September", "Year": "1999", "Temperature anomaly": "-0.20069408", "Oceanic Niño Index (ONI) anomaly": "-1.11"}, {"Entity": "September", "Year": "2000", "Temperature anomaly": "-0.23873329", "Oceanic Niño Index (ONI) anomaly": "-0.51"}, {"Entity": "September", "Year": "2001", "Temperature anomaly": "-0.07816315", "Oceanic Niño Index (ONI) anomaly": "-0.13"}, {"Entity": "September", "Year": "2002", "Temperature anomaly": "0.005221367", "Oceanic Niño Index (ONI) anomaly": "0.86"}, {"Entity": "September", "Year": "2003", "Temperature anomaly": "0.07381439", "Oceanic Niño Index (ONI) anomaly": "0.21"}, {"Entity": "September", "Year": "2004", "Temperature anomaly": "-0.040070534", "Oceanic Niño Index (ONI) anomaly": "0.64"}, {"Entity": "September", "Year": "2005", "Temperature anomaly": "0.12966347", "Oceanic Niño Index (ONI) anomaly": "-0.14"}, {"Entity": "September", "Year": "2006", "Temperature anomaly": "0.04808998", "Oceanic Niño Index (ONI) anomaly": "0.3"}, {"Entity": "September", "Year": "2007", "Temperature anomaly": "-0.089821815", "Oceanic Niño Index (ONI) anomaly": "-0.81"}, {"Entity": "September", "Year": "2008", "Temperature anomaly": "-0.030119896", "Oceanic Niño Index (ONI) anomaly": "-0.23"}, {"Entity": "September", "Year": "2009", "Temperature anomaly": "0.103710175", "Oceanic Niño Index (ONI) anomaly": "0.58"}, {"Entity": "September", "Year": "2010", "Temperature anomaly": "0.099030495", "Oceanic Niño Index (ONI) anomaly": "-1.35"}, {"Entity": "September", "Year": "2011", "Temperature anomaly": "0.047351837", "Oceanic Niño Index (ONI) anomaly": "-0.58"}, {"Entity": "September", "Year": "2012", "Temperature anomaly": "0.11289787", "Oceanic Niño Index (ONI) anomaly": "0.41"}, {"Entity": "September", "Year": "2013", "Temperature anomaly": "0.16967964", "Oceanic Niño Index (ONI) anomaly": "-0.28"}, {"Entity": "September", "Year": "2014", "Temperature anomaly": "0.18443108", "Oceanic Niño Index (ONI) anomaly": "0.11"}, {"Entity": "September", "Year": "2015", "Temperature anomaly": "0.26686192", "Oceanic Niño Index (ONI) anomaly": "1.91"}, {"Entity": "September", "Year": "2016", "Temperature anomaly": "0.35773182", "Oceanic Niño Index (ONI) anomaly": "-0.5"}, {"Entity": "September", "Year": "2017", "Temperature anomaly": "0.2795391", "Oceanic Niño Index (ONI) anomaly": "-0.07"}, {"Entity": "September", "Year": "2018", "Temperature anomaly": "0.1994009", "Oceanic Niño Index (ONI) anomaly": "0.27"}, {"Entity": "September", "Year": "2019", "Temperature anomaly": "0.3787241", "Oceanic Niño Index (ONI) anomaly": "0.19"}, {"Entity": "September", "Year": "2020", "Temperature anomaly": "0.4334898", "Oceanic Niño Index (ONI) anomaly": "-0.53"}, {"Entity": "September", "Year": "2021", "Temperature anomaly": "0.40136528", "Oceanic Niño Index (ONI) anomaly": "-0.45"}, {"Entity": "September", "Year": "2022", "Temperature anomaly": "0.3493948", "Oceanic Niño Index (ONI) anomaly": "-0.87"}, {"Entity": "September", "Year": "2023", "Temperature anomaly": "0.9306059", "Oceanic Niño Index (ONI) anomaly": "1.37"}, {"Entity": "September", "Year": "2024", "Temperature anomaly": "0.7279062", "Oceanic Niño Index (ONI) anomaly": "-0.07"}, {"Entity": "September", "Year": "2025", "Temperature anomaly": "0.6621523", "Oceanic Niño Index (ONI) anomaly": "-0.28"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "global-temperature-anomalies-by-el-nino-la-nina-and-month", "metadata_url": "https://ourworldindata.org/grapher/global-temperature-anomalies-by-el-nino-la-nina-and-month.metadata.json", "chart_title": "Global temperature anomalies by El Niño and La Niña and month", "chart_subtitle": "The difference between a month's average land-sea surface temperature and the 1991–2020 average of the same month, measured in degrees Celsius. It is classified as El Niño or La Niña based on the Oceanic Niño Index, which tracks warming or cooling patterns in the central Pacific Ocean.", "chart_note": null, "chart_citation": "Contains modified Copernicus Climate Change Service information (2026); NOAA National Centers for Environmental Information (2026)", "original_chart_url": "https://ourworldindata.org/grapher/global-temperature-anomalies-by-el-nino-la-nina-and-month", "owid_column_metadata": {"Temperature anomaly": {"titleShort": "Temperature anomaly", "titleLong": "Temperature anomaly", "descriptionShort": "The difference of a specific month's average surface temperature from the 1991-2020 mean, in degrees Celsius.", "descriptionProcessing": "- Temperature measured in kelvin was converted to degrees Celsius (°C) by subtracting 273.15.\n\n- Initially, the temperature dataset is provided with specific coordinates in terms of longitude and latitude. To tailor this data to each country, we utilize geographical boundaries as defined by the World Bank. The method involves trimming the global temperature dataset to match the exact geographical shape of each country. To correct for potential distortions caused by the Earth's curvature on a flat map, we apply a latitude-based weighting. This step is essential for maintaining accuracy, especially in high-latitude regions where distortion is more pronounced. The result of this process is a latitude-weighted average temperature for each nation.\n\n- It's important to note, however, that due to the resolution constraints of the Copernicus dataset, this methodology might not be as effective for countries with very small landmasses. In these cases, the process may not yield reliable data.\n\n- The derived 2-meter temperature readings for each country are calculated based on administrative borders, encompassing all land surface types within these defined areas. As a result, temperatures over oceans and seas are not included in these averages, focusing the data primarily on terrestrial environments.\n\n- Global temperature averages and anomalies are calculated over all land and ocean surfaces.\n- The temperature anomaly is calculated by comparing the average surface temperature of a specific time period (e.g., a particular year or month) to the mean surface temperature of the same period from 1991 to 2020.\n\n- When calculating anomalies for each country, the average surface temperature of a given year or month is compared to the 1991-2020 mean temperature for that specific country.\n\n- The reason for using the 1991-2020 period as the reference mean is that it is the standard reference period used by our data source, the Copernicus Climate Change Service. This period is also adopted by the UK Met Office. This approach ensures consistency in identifying climate variations over time.", "shortUnit": "°C", "unit": "°C", "timespan": "1940-2026", "type": "Numeric", "owidVariableId": 1271400, "shortName": "temperature_anomaly", "lastUpdated": "2026-06-19", "citationShort": "Contains modified Copernicus Climate Change Service information (2026) – with major processing by Our World in Data", "citationLong": "Contains modified Copernicus Climate Change Service information (2026) – with major processing by Our World in Data. “Temperature anomaly” [dataset]. Contains modified Copernicus Climate Change Service information, “ERA5 monthly averaged data on single levels from 1940 to present 2” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1271400.metadata.json"}, "Oceanic Niño Index (ONI) anomaly": {"titleShort": "Oceanic Niño Index (ONI) anomaly", "titleLong": "Oceanic Niño Index (ONI) anomaly", "descriptionShort": "The Oceanic Niño Index (ONI) anomaly is a measure of the sea surface temperature anomalies in the east-central tropical Pacific Ocean.", "descriptionKey": ["The Oceanic Niño Index (ONI) is a tool used by the National Oceanic and Atmospheric Administration (NOAA) to monitor and track the presence and intensity of El Niño and La Niña events.", "These events are part of the broader El Niño-Southern Oscillation (ENSO), a natural climate pattern that affects global weather patterns, including rainfall, droughts, and hurricane activity.", "The ONI measures deviations in sea surface temperatures (SSTs) in a specific area of the Pacific Ocean, known as the Niño 3.4 region. This region spans from 120°W to 170°W longitude, along the equator, in the east-central tropical Pacific.", "NOAA calculates the ONI by taking a 3-month running mean of SST anomalies. An anomaly is the difference between observed SSTs and the 30-year climatological average for the same period. NOAA periodically updates the baseline period to ensure consistency with long-term climate trends. For example, the 1991–2020 average is often used.", "El Niño (ONI ≥ +0.5°C) occurs when sea surface temperatures in the Niño 3.4 region are warmer than usual, often bringing drier conditions to Asia and Australia, wetter weather to the southern United States, and weakened trade winds.", "El Niño can lead to weaker Atlantic hurricane seasons but stronger and more frequent Pacific hurricanes.", "Neutral (−0.5°C < ONI < +0.5°C) means sea surface temperatures are near average, with no significant ENSO event.", "La Niña (ONI ≤ −0.5°C) happens when sea surface temperatures are cooler than usual, often causing drier conditions in South America, increased rainfall in Indonesia and northern Australia, and stronger trade winds.", "La Niña tends to cause more hurricanes in the Atlantic and drought conditions in the southern U.S."], "unit": "", "timespan": "1950-2026", "type": "Numeric", "owidVariableId": 1271163, "shortName": "oni_anomaly", "lastUpdated": "2026-06-19", "nextUpdate": "2026-07-20", "citationShort": "NOAA National Centers for Environmental Information (2026) – with minor processing by Our World in Data", "citationLong": "NOAA National Centers for Environmental Information (2026) – with minor processing by Our World in Data. “Oceanic Niño Index (ONI) anomaly” [dataset]. NOAA National Centers for Environmental Information, “Equatorial Pacific Sea Surface Temperatures (SST) data” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1271163.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "f9c1047853a62a58b82c"},
{"raw_link": "https://ourworldindata.org/baby-boom-seven-charts", "title": "The baby boom in seven charts", "context": "Home\nFertility Rate\nThe baby boom in seven charts\nThe baby boom reshaped family life and drove population growth in many countries. In this article, we explore the key patterns in seven charts.\nBy\nSaloni Dattani\nand\nLucas Rodés-Guirao\nFebruary 24, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nThe baby boom was a period that saw a surge in birth rates alongside a dramatic decline in death rates due to advances in medicine and public health.\nThis combination led to rapid population growth in many high-income countries, which influenced their societies for generations.\nHowever, many other aspects of the baby boom are less well-known, including when it began, how marriage rates changed, and how the ages of mothers at childbirth changed.\nWhat were the main patterns of the baby boom? In this article, we’ll explore key data on the baby boom in seven charts.\nBirth rates began to rise in the 1930s, before World War II\nThe baby boom is typically defined as the time period between 1946 and 1964. As an example, Brittanica’s\nentry\non the baby boom states that it describes “the increase in the birth rate between 1946 and 1964”. Similarly, the US Census Bureau\ndefines\nbaby boomers as “those born between 1946 and 1964”, with the common belief that the baby boom started immediately after World War II.\nBut as the chart below shows, the rise began earlier.\nBirth rates in the United States had been falling in the early twentieth century, and the decline began to slow down at the end of the 1920s. Then, in the late 1930s, they turned around and began to rise, and this continued during parts of World War II. At the end of the war, they surged, but this was part of a multi-decadal increase.\nDownload\nData from Sandra L. Colby and Jennifer M. Ortman (2014)\n1\n; Human Mortality Database (2024); UN World Population Prospects (2024).\nOne of the striking aspects of the baby boom is that it happened in many countries at the same time.\nMany high-income countries saw a rise in birth rates — it wasn’t just nations directly involved in World War II. Sweden and Switzerland did not actively participate in the war, but they also experienced significant increases in birth rates. You can see this in the chart below.\nSeveral countries — such as Australia, Canada, Denmark, Sweden, Norway, and the United States — began to see a turnaround in birth rates before the war. In many others, it occurred during the war.\nDownload\nYou can explore the data for each country in\nthis interactive chart\n.\nThis common trend across many countries suggests that the baby boom was driven by shared societal shifts rather than isolated national circumstances.\nThe causes of the baby boom are still widely debated by demographers. Various theories have been put forward, including the role of economic factors, such as rising wages and opportunities, and lower housing costs, as well as declining maternal mortality and societal changes.\n2\nThe baby boom was also surprising because it happened alongside rising levels of women’s education and workforce participation — changes that often coincide with falling birth rates.\n3\nIt’s likely that multiple factors played a role and that no single explanation fully accounts for the surge in births. Although we don’t know the full story, in the following sections, we’ll explore some of the pieces that shaped this remarkable demographic shift.\nIt wasn’t just that married couples had more children, but more people got married in the first place\nStarting in the 1930s, marriage became more common.\nThe chart below shows the large rise in marriage rates among women between the ages of 20 and 24.\nIn 1930, around 54% of young American women of this age were married.\n4\nBy 1960, this had increased to 72%.\nIn England and Wales, the share who were married more than doubled: from 26% to 58%.\nResearch suggests that the baby boom across countries was primarily driven by higher marriage rates and less by married couples having significantly more children overall.\n5\nDownload\nData from Jan Van Bavel and David S Reher\n6\nThe average age of women at childbirth declined as more younger women began having children\nDuring the baby boom, women not only had more children than the previous generation but also started their families earlier.\nThe chart here shows the average age of women at childbirth.\nIf you look at the red line, you see that in 1933, the average age of American women giving birth was 28. At the time, birth rates were slowly increasing, and until the early 1940s, the average age was gradually declining. However, during World War II, some women delayed having children, which caused the average age at childbirth to rise slightly.\nHowever, once World War II ended, women began having children earlier, especially for their first and second births, and the average age at childbirth dropped. This pattern of starting families earlier continued through the baby boom years and lasted until the mid-1970s. Since then, the age of mothers has risen gradually and continuously. In 2021, the average age was 30.\nAgain, this trend is similar in other high-income countries, as you can see by switching the chart to different countries.\nAmerican women had more children, and earlier in their lifetimes, than previous generations\nHow did the baby boom shape birth patterns for different generations of women over their lifetimes?\nThis chart can look complicated at first, but it helps us answer these questions by showing how the timing and number of births changed across cohorts of women in the United States.\nEach curve on the chart represents birth patterns among women born in a specific year. By following a curve from left to right, you can see when women born in a particular year had children.\nThe height of the curve represents the number of births per woman at that age. This means that birth cohorts with taller curves had higher birth rates at that particular age.\nBefore the baby boom, women had children at a broader range of ages, often spreading births from their twenties into their late thirties and early forties.\nWe see a clear shift for women born in the 1920s and 1930s — many of whom gave birth during the peak of the baby boom — as childbirth became more common in the twenties and early thirties.\nThe chart also shows a diagonal ridge, a striking surge in births, which corresponds to the year 1946, and shows women’s ages in that year. For example, there is a rise in fertility rates among 26-year-old women born in 1920 and 21-year-old women born in 1925 — reflecting the year 1946.\nBirth rates surged during the baby boom as more couples married and started families, creating a noticeable peak in the timing of childbirth for women during this era.\nWomen born in the 1950s and 1960s mostly had children in their twenties and early thirties, meaning the age range at childbirth narrowed.\nThen, for women born in later decades, the range grew again.\nRecent cohorts of women have children across a much broader range of ages, with many delaying childbirth into their late thirties and early forties. You can see this at the bottom of the chart.\nDownload\nScripts to recreate this plot can be found on\nGitHub\n.\nWomen living through the baby boom had more children in total\nWe can also look at the total number of births women had over their\nchildbearing years\nin each generation.\nThis is measured by the “\ncompleted cohort fertility rate\n”. It’s given by the woman’s birth year.\n7\nThe data ends in 1973 as recent generations of women have yet to complete their childbearing years.\nLet’s look at women born from the 1910s onwards who had children during the baby boom.\nAs you can see, the cohort fertility rate — the average number of births per woman by the end of her childbearing years — rose for these cohorts of women. It peaked at an average of more than 3.2 births per woman.\nThis shows us that the baby boom not only changed the timing of births but also raised the total number of children women had.\nTwo-fifths of women had four children or more, and fewer women had no children\nDid the average number of children increase because women were having more children than in the past? Or because fewer women had no children?\nThe data tells us that it was both.\nThis chart explores the data: it shows the share of women in each birth cohort who had zero, one, two, three, or four or more children during their lifetimes.\nAround two-fifths of women born in 1918 had had three or more births, while around a fifth had no children.\nAt the peak of the baby boom years — women born in the late 1920s or early 1930s — three or more births became the norm, and almost 40% had four or more.\nFor those born in the late 1930s, the trend began to reverse.\nThe share of women having three or more children started to decline. Two became the typical number of children in many high-income countries.\n8\nThe chart also shows that it became less common for women to have no children across their lifetimes.\nHaving no children was relatively common for women born in the early 1900s, with about 18% having no children.\nAs we move to the baby boom cohorts, born in the 1920s and 1930s, this figure sharply declined. For those born in the late 1930s, only about 6% of women had no children.\nThe trend reversed for women born after the late 1930s, and the share of women with no children steadily rose through the 1940s and 1950s cohorts. For women born in the late 1960s and early 1970s, it reversed again.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nConclusion\nWhen we think of the baby boom, we often imagine a surge in births in the United States starting at the end of World War II, as soldiers returned home and families began to grow.\nBut although this is part of the story, the trend is much broader, as we’ve seen in this article.\nBirth rates were already on the rise in the 1930s, setting the stage for an even bigger increase in the 40s and 50s. The baby boom wasn’t just a post-war phenomenon; it was part of a broader trend that spanned multiple decades.\nWomen born during the baby boom years had more children, and family sizes grew larger than in earlier or later generations.\nAfter the baby boom period, families started to shrink, and childbirth became concentrated in a narrower range of ages, primarily in women’s twenties and early thirties.\nOver time, however, this compressed pattern of family formation began to change again. Recent generations of women have had children across a much broader range of ages, from their twenties through their forties.\nThe data also show that cohort fertility rates — the total number of children born to women over their lifetimes — rose again among women born in the 1960s, reversing earlier declines.\nA combination of higher birth rates and lower death rates led to a period of rapid population growth, which profoundly shaped the economies, societies, and cultures of high-income countries.\nBy looking closer at how birth patterns shifted — when women had children, how many they had, and the broader societal changes that shaped these trends — we have a clearer picture of the baby boom and how it reshaped family life for the generations that followed.\nAcknowledgements\nMax Roser, Hannah Ritchie, and Edouard Mathieu provided valuable feedback that helped improve this article.\nEndnotes\nSandra L. Colby and Jennifer M. Ortman; U.S. Department of Commerce (2014). The Baby Boom Cohort in the United States: 2012 to 2060. Available\nonline\n.\nVan Bavel, J., Klesment, M., Beaujouan, E., Brzozowska, Z., and (in alphabetical order), Puur, A., Reher, D., Requena, M., Sandström, G., Sobotka, T., & Zeman, K. (2018). Seeding the gender revolution: Women’s education and cohort fertility among the baby boom generations. Population Studies, 72(3), 283–304.\nhttps://doi.org/10.1080/00324728.2018.1498223\nSánchez-Barricarte, J. J. (2018). Measuring and explaining the baby boom in the developed world in the mid-twentieth century. Demographic Research, 38, 1189–1240.\nhttps://doi.org/10.4054/DemRes.2018.38.40\nSánchez-Barricarte, J. J. (2018). Measuring and explaining the baby boom in the developed world in the mid-twentieth century. Demographic Research, 38, 1189–1240.\nhttps://doi.org/10.4054/DemRes.2018.38.40\nThe figure includes women aged 20 to 24 who were married at the time or had ever been married.\nSánchez-Barricarte, J. J. (2018). Measuring and explaining the baby boom in the developed world in the mid-twentieth century.\nDemographic Research\n,\n38\n, 1189–1240.\nhttps://doi.org/10.4054/DemRes.2018.38.40\nVan Bavel, J., & Reher, D. S. (2013). The Baby Boom and Its Causes: What We Know and What We Need to Know. Population and Development Review, 39(2), 257–288.\nhttps://doi.org/10.1111/j.1728-4457.2013.00591.x\nThe completed cohort fertility rate is different from the “total fertility rate”, which is a snapshot measure of births in a particular year.\nVan Bavel, J., Klesment, M., Beaujouan, E., Brzozowska, Z., and (in alphabetical order), Puur, A., Reher, D., Requena, M., Sandström, G., Sobotka, T., & Zeman, K. (2018). Seeding the gender revolution: Women’s education and cohort fertility among the baby boom generations. Population Studies, 72(3), 283–304.\nhttps://doi.org/10.1080/00324728.2018.1498223\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSaloni Dattani and Lucas Rodés-Guirao (2025) - “The baby boom in seven charts” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-090244/baby-boom-seven-charts.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-baby-boom-seven-charts,\nauthor = {Saloni Dattani and Lucas Rodés-Guirao},\ntitle = {The baby boom in seven charts},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-090244/baby-boom-seven-charts.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "baby-boom-seven-charts", "source_url": "https://ourworldindata.org/baby-boom-seven-charts", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "The baby boom reshaped family life and drove population growth in many countries. 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{"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1962", "Birth rate": "47.741"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1963", "Birth rate": "47.645"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1964", "Birth rate": "47.51"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1965", "Birth rate": "47.353"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1966", "Birth rate": "47.301"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1967", "Birth rate": "47.197"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1968", "Birth rate": "47.089"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1969", "Birth rate": "46.999"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1970", "Birth rate": "46.915"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1971", "Birth rate": "46.892"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1972", "Birth rate": "46.795"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1973", "Birth 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rate": "32.025"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Birth rate": "31.665"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Birth rate": "32.405"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Birth rate": "33.372"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Birth rate": "34.47"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Birth rate": "35.615"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Birth rate": "36.128"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Birth rate": "36.463"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Birth rate": "36.539"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Birth rate": "36.613"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Birth rate": "36.326"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Birth rate": "35.803"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Birth rate": "35.203"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Birth rate": "35.651"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Birth rate": "36.361"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Birth rate": "37.518"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Birth rate": "37.869"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Birth rate": "38.142"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Birth rate": "37.805"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Birth rate": "36.975"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Birth rate": "35.407"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Birth rate": "33.918"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Birth rate": "32.704"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Birth rate": "31.813"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Birth rate": "31.327"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Birth rate": "31.121"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Birth rate": "30.988"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Birth rate": "30.932"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Birth rate": "30.882"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Birth rate": "30.41"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "long-run-birth-rate", "metadata_url": "https://ourworldindata.org/grapher/long-run-birth-rate.metadata.json", "chart_title": "Birth rate", "chart_subtitle": "The total number of births per 1,000 people in a given year.", "chart_note": "The birth rate is not adjusted for the change in the population's age structure.", "chart_citation": "Human Mortality Database (2025); UN, World Population Prospects (2024)", "original_chart_url": "https://ourworldindata.org/grapher/long-run-birth-rate", "owid_column_metadata": {"Birth rate (historical)": {"titleShort": "Birth rate", "titleLong": "Birth rate - HMD, UN WPP – total", "descriptionShort": "The total number of births per 1,000 people in a given year.", "descriptionKey": ["This indicator has been constructed by combining data from two sources:\n  - Before 1950: Historical estimates by Human Mortality Database (2025).\n  - 1950-2023: Population records by the UN World Population Prospects (2024 revision)."], "descriptionProcessing": "The birth data is constructed by combining data from multiple sources:\n\n- Before 1950: Historical estimates by Human Mortality Database (2025).\n\n- 1950-2023: Population records by the UN World Population Prospects (2024 revision).", "unit": "births per 1,000 people", "timespan": "1751-2023", "type": "Numeric", "owidVariableId": 1118319, "shortName": "birth_rate_hist", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Mortality Database (2025); UN, World Population Prospects (2024) – processed by Our World in Data", "citationLong": "Human Mortality Database (2025); UN, World Population Prospects (2024) – processed by Our World in Data. “Birth rate – HMD, UN WPP – total” [dataset]. Human Mortality Database, “Human Mortality Database”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118319.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Average age of mothers at childbirth by birth order", "source_url": "https://ourworldindata.org/grapher/period-average-age-of-mothers-birth-order.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "1st birth", "2nd birth", "3rd birth", "4th birth", "5th birth or higher", "All"], "row_count_total": 2667, "rows_head": [{"Entity": "Austria", "Code": "AUT", "Year": "1951", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "28.05"}, {"Entity": "Austria", "Code": "AUT", "Year": "1952", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.98"}, {"Entity": "Austria", "Code": "AUT", "Year": "1953", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.89"}, {"Entity": "Austria", "Code": "AUT", "Year": "1954", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.87"}, {"Entity": "Austria", "Code": "AUT", "Year": "1955", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.85"}, {"Entity": "Austria", "Code": "AUT", "Year": "1956", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.83"}, {"Entity": "Austria", "Code": "AUT", "Year": "1957", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.8"}, {"Entity": "Austria", "Code": "AUT", "Year": "1958", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.69"}, {"Entity": "Austria", "Code": "AUT", "Year": "1959", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.63"}, {"Entity": "Austria", "Code": "AUT", "Year": "1960", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.58"}, {"Entity": "Austria", "Code": "AUT", "Year": "1961", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.52"}, {"Entity": "Austria", "Code": "AUT", "Year": "1962", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.47"}, {"Entity": "Austria", "Code": "AUT", "Year": "1963", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.41"}, {"Entity": "Austria", "Code": "AUT", "Year": "1964", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.38"}, {"Entity": "Austria", "Code": "AUT", "Year": "1965", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.26"}, {"Entity": "Austria", "Code": "AUT", "Year": "1966", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.11"}, {"Entity": "Austria", "Code": "AUT", "Year": "1967", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.97"}, {"Entity": "Austria", "Code": "AUT", "Year": "1968", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.85"}, {"Entity": "Austria", "Code": "AUT", "Year": "1969", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.76"}, {"Entity": "Austria", "Code": "AUT", "Year": "1970", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.67"}, {"Entity": "Austria", "Code": "AUT", "Year": "1971", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.67"}, {"Entity": "Austria", "Code": "AUT", "Year": "1972", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.53"}, {"Entity": "Austria", "Code": "AUT", "Year": "1973", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.41"}, {"Entity": "Austria", "Code": "AUT", "Year": "1974", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.31"}, {"Entity": "Austria", "Code": "AUT", "Year": "1975", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.27"}, {"Entity": "Austria", "Code": "AUT", "Year": "1976", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.24"}, {"Entity": "Austria", "Code": "AUT", "Year": "1977", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.27"}, {"Entity": "Austria", "Code": "AUT", "Year": "1978", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.25"}, {"Entity": "Austria", "Code": "AUT", "Year": "1979", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.27"}, {"Entity": "Austria", "Code": "AUT", "Year": "1980", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.27"}, {"Entity": "Austria", "Code": "AUT", "Year": "1981", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.33"}, {"Entity": "Austria", "Code": "AUT", "Year": "1982", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.35"}, {"Entity": "Austria", "Code": "AUT", "Year": "1983", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "26.47"}, {"Entity": "Austria", "Code": "AUT", "Year": "1984", "1st birth": "24.1", "2nd birth": "26.9", "3rd birth": "29.83", "4th birth": "32.15", "5th birth or higher": "35.02", "All": "26.56"}, {"Entity": "Austria", "Code": "AUT", "Year": "1985", "1st birth": "24.32", "2nd birth": "27.06", "3rd birth": "29.99", "4th birth": "32.16", "5th birth or higher": "34.93", "All": "26.68"}, {"Entity": "Austria", "Code": "AUT", "Year": "1986", "1st birth": "24.42", "2nd birth": "27.22", "3rd birth": "30.1", "4th birth": "32.37", "5th birth or higher": "35.04", "All": "26.77"}, {"Entity": "Austria", "Code": "AUT", "Year": "1987", "1st birth": "24.63", "2nd birth": "27.37", "3rd birth": "30.24", "4th birth": "32.46", "5th birth or higher": "35.16", "All": "26.91"}, {"Entity": "Austria", "Code": "AUT", "Year": "1988", "1st birth": "24.72", "2nd birth": "27.46", "3rd birth": "30.35", "4th birth": "32.54", "5th birth or higher": "35.01", "All": "26.95"}, {"Entity": "Austria", "Code": "AUT", "Year": "1989", "1st birth": "24.82", "2nd birth": "27.59", "3rd birth": "30.44", "4th birth": "32.62", "5th birth or higher": "34.89", "All": "27.07"}, {"Entity": "Austria", "Code": "AUT", "Year": "1990", "1st birth": "24.97", "2nd birth": "27.76", "3rd birth": "30.64", "4th birth": "32.63", "5th birth or higher": "35.05", "All": "27.21"}, {"Entity": "Austria", "Code": "AUT", "Year": "1991", "1st birth": "24.96", "2nd birth": "27.89", "3rd birth": "30.6", "4th birth": "32.66", "5th birth or higher": "35.05", "All": "27.22"}, {"Entity": "Austria", "Code": "AUT", "Year": "1992", "1st birth": "25.04", "2nd birth": "27.91", "3rd birth": "30.64", "4th birth": "32.79", "5th birth or higher": "34.95", "All": "27.26"}, {"Entity": "Austria", "Code": "AUT", "Year": "1993", "1st birth": "25.14", "2nd birth": "27.92", "3rd birth": "30.74", "4th birth": "32.73", "5th birth or higher": "34.98", "All": "27.31"}, {"Entity": "Austria", "Code": "AUT", "Year": "1994", "1st birth": "25.41", "2nd birth": "28.08", "3rd birth": "30.76", "4th birth": "32.73", "5th birth or higher": "34.89", "All": "27.5"}, {"Entity": "Austria", "Code": "AUT", "Year": "1995", "1st birth": "25.65", "2nd birth": "28.21", "3rd birth": "30.81", "4th birth": "32.9", "5th birth or higher": "35.06", "All": "27.66"}, {"Entity": "Austria", "Code": "AUT", "Year": "1996", "1st birth": "25.91", "2nd birth": "28.34", "3rd birth": "30.91", "4th birth": "32.93", "5th birth or higher": "34.82", "All": "27.81"}, {"Entity": "Austria", "Code": "AUT", "Year": "1997", "1st birth": "26", "2nd birth": "28.55", "3rd birth": "31.11", "4th birth": "33.15", "5th birth or higher": "35.23", "All": "27.93"}, {"Entity": "Austria", "Code": "AUT", "Year": "1998", "1st birth": "26.15", "2nd birth": "28.64", "3rd birth": "31.03", "4th birth": "32.93", "5th birth or higher": "35.22", "All": "28.02"}, {"Entity": "Austria", "Code": "AUT", "Year": "1999", "1st birth": "26.31", "2nd birth": "28.84", "3rd birth": "31.05", "4th birth": "33.01", "5th birth or higher": "35.24", "All": "28.15"}, {"Entity": "Austria", "Code": "AUT", "Year": "2000", "1st birth": "26.39", "2nd birth": "28.92", "3rd birth": "31.19", "4th birth": "32.96", "5th birth or higher": "35.09", "All": "28.22"}, {"Entity": "Austria", "Code": "AUT", "Year": "2001", "1st birth": "26.54", "2nd birth": "29.18", "3rd birth": "31.38", "4th birth": "33.03", "5th birth or higher": "35.06", "All": "28.39"}, {"Entity": "Austria", "Code": "AUT", "Year": "2002", "1st birth": "26.75", "2nd birth": "29.32", "3rd birth": "31.41", "4th birth": "33.02", "5th birth or higher": "34.84", "All": "28.57"}, {"Entity": "Austria", "Code": "AUT", "Year": "2003", "1st birth": "26.94", "2nd birth": "29.48", "3rd birth": "31.7", "4th birth": "33.23", "5th birth or higher": "35", "All": "28.77"}, {"Entity": "Austria", "Code": "AUT", "Year": "2004", "1st birth": "27.04", "2nd birth": "29.58", "3rd birth": "31.61", "4th birth": "33.2", "5th birth or higher": "35.09", "All": "28.84"}, {"Entity": "Austria", "Code": "AUT", "Year": "2005", "1st birth": "27.26", "2nd birth": "29.79", "3rd birth": "31.69", "4th birth": "33.24", "5th birth or higher": "34.98", "All": "29.03"}, {"Entity": "Austria", "Code": "AUT", "Year": "2006", "1st birth": "27.48", "2nd birth": "29.94", "3rd birth": "31.82", "4th birth": "33.3", "5th birth or higher": "35.27", "All": "29.21"}, {"Entity": "Austria", "Code": "AUT", "Year": "2007", "1st birth": "27.65", "2nd birth": "30.02", "3rd birth": "32.07", "4th birth": "33.54", "5th birth or higher": "34.88", "All": "29.37"}, {"Entity": "Austria", "Code": "AUT", "Year": "2008", "1st birth": "27.77", "2nd birth": "30.21", "3rd birth": "32.19", "4th birth": "33.54", "5th birth or higher": "35.21", "All": "29.49"}, {"Entity": "Austria", "Code": "AUT", "Year": "2009", "1st birth": "27.97", "2nd birth": "30.46", "3rd birth": "32.24", "4th birth": "33.52", "5th birth or higher": "35.13", "All": "29.68"}, {"Entity": "Austria", "Code": "AUT", "Year": "2010", "1st birth": "28.23", "2nd birth": "30.54", "3rd birth": "32.37", "4th birth": "33.66", "5th birth or higher": "35", "All": "29.83"}, {"Entity": "Austria", "Code": "AUT", "Year": "2011", "1st birth": "28.47", "2nd birth": "30.75", "3rd birth": "32.45", "4th birth": "33.82", "5th birth or higher": "35.29", "All": "30.03"}, {"Entity": "Austria", "Code": "AUT", "Year": "2012", "1st birth": "28.67", "2nd birth": "30.9", "3rd birth": "32.62", "4th birth": "34.04", "5th birth or higher": "35.35", "All": "30.19"}, {"Entity": "Austria", "Code": "AUT", "Year": "2013", "1st birth": "28.82", "2nd birth": "31.05", "3rd birth": "32.76", "4th birth": "33.98", "5th birth or higher": "35.42", "All": "30.32"}, {"Entity": "Austria", "Code": "AUT", "Year": "2014", "1st birth": "28.95", "2nd birth": "31.15", "3rd birth": "32.74", "4th birth": "33.81", "5th birth or higher": "35.33", "All": "30.42"}, {"Entity": "Austria", "Code": "AUT", "Year": "2015", "1st birth": "29.16", "2nd birth": "31.28", "3rd birth": "32.78", "4th birth": "33.9", "5th birth or higher": "35.45", "All": "30.57"}, {"Entity": "Austria", "Code": "AUT", "Year": "2016", "1st birth": "29.24", "2nd birth": "31.3", "3rd birth": "32.64", "4th birth": "33.82", "5th birth or higher": "35.15", "All": "30.63"}, {"Entity": "Austria", "Code": "AUT", "Year": "2017", "1st birth": "29.32", "2nd birth": "31.39", "3rd birth": "32.78", "4th birth": "33.89", "5th birth or higher": "35.25", "All": "30.72"}, {"Entity": "Austria", "Code": "AUT", "Year": "2018", "1st birth": "29.5", "2nd birth": "31.45", "3rd birth": "32.89", "4th birth": "33.95", "5th birth or higher": "35.67", "All": "30.86"}, {"Entity": "Austria", "Code": "AUT", "Year": "2019", "1st birth": "29.66", "2nd birth": "31.59", "3rd birth": "33.02", "4th birth": "34.25", "5th birth or higher": "35.51", "All": "31.01"}, {"Entity": "Austria", "Code": "AUT", "Year": "2020", "1st birth": "29.71", "2nd birth": "31.66", "3rd birth": "32.98", "4th birth": "34.13", "5th birth or higher": "35.68", "All": "31.02"}, {"Entity": "Austria", "Code": "AUT", "Year": "2021", "1st birth": "29.91", "2nd birth": "31.85", "3rd birth": "33.13", "4th birth": "34.27", "5th birth or higher": "35.43", "All": "31.2"}, {"Entity": "Austria", "Code": "AUT", "Year": "2022", "1st birth": "29.89", "2nd birth": "31.84", "3rd birth": "33.08", "4th birth": "34.04", "5th birth or higher": "35.38", "All": "31.15"}, {"Entity": "Austria", "Code": "AUT", "Year": "2023", "1st birth": "29.9", "2nd birth": "31.87", "3rd birth": "32.98", "4th birth": "33.96", "5th birth or higher": "35.4", "All": "31.17"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1964", "1st birth": "24.82", "2nd birth": "28.41", "3rd birth": "31.27", "4th birth": "33.3", "5th birth or higher": "36.78", "All": "28.4"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1965", "1st birth": "24.86", "2nd birth": "28.43", "3rd birth": "31.34", "4th birth": "33.43", "5th birth or higher": "36.61", "All": "28.33"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1966", "1st birth": "24.64", "2nd birth": "28.34", "3rd birth": "31.04", "4th birth": "33.45", "5th birth or higher": "36.47", "All": "28.22"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1967", "1st birth": "24.59", "2nd birth": "28.19", "3rd birth": "31.43", "4th birth": "33.41", "5th birth or higher": "36.45", "All": "27.98"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1968", "1st birth": "24.57", "2nd birth": "28.36", "3rd birth": "31.31", "4th birth": "33.35", "5th birth or higher": "36.38", "All": "27.92"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1969", "1st birth": "24.39", "2nd birth": "28.32", "3rd birth": "31.23", "4th birth": "33.22", "5th birth or higher": "36.27", "All": "27.74"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1970", "1st birth": "24.07", "2nd birth": "28.22", "3rd birth": "31.19", "4th birth": "33.13", "5th birth or higher": "35.99", "All": "27.46"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1971", "1st birth": "23.89", "2nd birth": "28.17", "3rd birth": "31.08", "4th birth": "33.01", "5th birth or higher": "35.96", "All": "27.27"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1972", "1st birth": "23.81", "2nd birth": "28.11", "3rd birth": "31.09", "4th birth": "32.99", "5th birth or higher": "35.75", "All": "27.16"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1973", "1st birth": "23.76", "2nd birth": "28.03", "3rd birth": "31.12", "4th birth": "33.08", "5th birth or higher": "35.87", "All": "27.08"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1974", "1st birth": "23.65", "2nd birth": "27.95", "3rd birth": "31.16", "4th birth": "33.02", "5th birth or higher": "35.66", "All": "26.94"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1975", "1st birth": "23.55", "2nd birth": "27.79", "3rd birth": "30.99", "4th birth": "33.03", "5th birth or higher": "35.72", "All": "26.79"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1976", "1st birth": "23.49", "2nd birth": "27.7", "3rd birth": "31", "4th birth": "32.91", "5th birth or higher": "35.53", "All": "26.61"}, {"Entity": "Belarus", "Code": "BLR", "Year": 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{"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1980", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.06"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1981", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.19"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1982", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.31"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1983", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.49"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1984", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th birth": "", "5th birth or higher": "", "All": "27.69"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1985", "1st birth": "", "2nd birth": "", "3rd birth": "", "4th 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Reuse should follow the source and license information in this chart metadata."}, {"title": "Completed cohort fertility rate: births per woman", "source_url": "https://ourworldindata.org/grapher/cohort-fertility-rate.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Completed cohort fertility rate"], "row_count_total": 1319, "rows_head": [{"Entity": "Austria", "Code": "AUT", "Year": "1936", "Completed cohort fertility rate": "2.436"}, {"Entity": "Austria", "Code": "AUT", "Year": "1937", "Completed cohort fertility rate": "2.41"}, {"Entity": "Austria", "Code": "AUT", "Year": "1938", "Completed cohort fertility rate": "2.339"}, {"Entity": "Austria", "Code": "AUT", "Year": "1939", "Completed cohort fertility rate": "2.202"}, {"Entity": "Austria", "Code": "AUT", "Year": "1940", "Completed cohort fertility rate": "2.132"}, {"Entity": "Austria", "Code": "AUT", "Year": "1941", "Completed cohort fertility rate": "2.065"}, {"Entity": "Austria", "Code": "AUT", "Year": "1942", 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fertility rate": "1.812"}, {"Entity": "Austria", "Code": "AUT", "Year": "1953", "Completed cohort fertility rate": "1.817"}, {"Entity": "Austria", "Code": "AUT", "Year": "1954", "Completed cohort fertility rate": "1.784"}, {"Entity": "Austria", "Code": "AUT", "Year": "1955", "Completed cohort fertility rate": "1.768"}, {"Entity": "Austria", "Code": "AUT", "Year": "1956", "Completed cohort fertility rate": "1.746"}, {"Entity": "Austria", "Code": "AUT", "Year": "1957", "Completed cohort fertility rate": "1.73"}, {"Entity": "Austria", "Code": "AUT", "Year": "1958", "Completed cohort fertility rate": "1.713"}, {"Entity": "Austria", "Code": "AUT", "Year": "1959", "Completed cohort fertility rate": "1.714"}, {"Entity": "Austria", "Code": "AUT", "Year": "1960", "Completed cohort fertility rate": "1.698"}, {"Entity": "Austria", "Code": "AUT", "Year": "1961", "Completed cohort fertility rate": "1.676"}, {"Entity": "Austria", "Code": "AUT", "Year": "1962", "Completed cohort fertility rate": 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"Austria", "Code": "AUT", "Year": "1973", "Completed cohort fertility rate": "1.661"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1949", "Completed cohort fertility rate": "1.968"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1950", "Completed cohort fertility rate": "1.938"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1951", "Completed cohort fertility rate": "1.964"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1952", "Completed cohort fertility rate": "1.928"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1953", "Completed cohort fertility rate": "1.886"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1954", "Completed cohort fertility rate": "1.915"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1955", "Completed cohort fertility rate": "1.931"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1956", "Completed cohort fertility rate": "1.926"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1957", "Completed cohort fertility rate": "1.924"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1958", "Completed cohort fertility rate": "1.921"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1959", "Completed cohort fertility rate": "1.891"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1960", "Completed cohort fertility rate": "1.873"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1961", "Completed cohort fertility rate": "1.857"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1962", "Completed cohort fertility rate": "1.815"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1963", "Completed cohort fertility rate": "1.736"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1964", "Completed cohort fertility rate": "1.684"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1965", "Completed cohort fertility rate": "1.689"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1966", "Completed cohort fertility rate": "1.7"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1967", "Completed cohort fertility rate": "1.713"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1968", "Completed cohort fertility rate": "1.709"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1932", "Completed cohort fertility rate": "2.292"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1933", "Completed cohort fertility rate": "2.279"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1934", "Completed cohort fertility rate": "2.278"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1935", "Completed cohort fertility rate": "2.274"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1936", "Completed cohort fertility rate": "2.235"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1937", "Completed cohort fertility rate": "2.22"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1938", "Completed cohort fertility rate": "2.186"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1939", "Completed cohort fertility rate": "2.149"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1940", "Completed cohort fertility rate": "2.183"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1941", "Completed cohort fertility rate": "2.141"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1942", "Completed cohort fertility rate": "2.088"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1943", "Completed cohort fertility rate": "2.034"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1944", "Completed cohort fertility rate": "1.989"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1945", "Completed cohort fertility rate": "1.942"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1946", "Completed cohort fertility rate": "1.899"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1947", "Completed cohort fertility rate": "1.88"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1948", "Completed cohort fertility rate": "1.87"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1949", "Completed cohort fertility rate": "1.849"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1950", "Completed cohort fertility rate": "1.867"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1951", "Completed cohort fertility rate": "1.834"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1952", "Completed cohort fertility rate": "1.844"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1953", "Completed cohort fertility rate": "1.834"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1954", "Completed cohort fertility rate": "1.836"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1955", "Completed cohort fertility rate": "1.835"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1956", "Completed cohort fertility rate": "1.855"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1957", "Completed cohort fertility rate": "1.837"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1958", "Completed cohort fertility rate": "1.848"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1959", "Completed cohort fertility rate": "1.849"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1960", "Completed cohort fertility rate": "1.872"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1961", "Completed cohort fertility rate": "1.847"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1962", "Completed cohort fertility rate": "1.851"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1963", "Completed cohort fertility rate": "1.828"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1964", "Completed cohort fertility rate": "1.825"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1965", "Completed cohort fertility rate": "1.821"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1966", "Completed cohort fertility rate": "1.815"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1967", "Completed cohort fertility rate": "1.823"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1968", "Completed cohort fertility rate": "1.824"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1969", "Completed cohort fertility rate": "1.827"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1970", "Completed cohort fertility rate": "1.824"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1971", "Completed cohort fertility rate": "1.822"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1972", "Completed cohort fertility rate": "1.84"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1932", "Completed cohort fertility rate": "2.045"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1933", "Completed cohort fertility rate": "2.049"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1934", "Completed cohort fertility rate": "2.041"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1935", "Completed cohort fertility rate": "2.032"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1936", "Completed cohort fertility rate": "2.036"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1937", "Completed cohort fertility rate": "2.039"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1938", "Completed cohort fertility rate": "2.038"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1939", "Completed cohort fertility rate": "2.052"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1940", "Completed cohort fertility rate": "2.085"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1941", "Completed cohort fertility rate": "2.094"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1942", "Completed cohort fertility rate": "2.091"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1943", "Completed cohort fertility rate": "2.078"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1944", "Completed cohort fertility rate": "2.077"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1945", "Completed cohort fertility rate": "2.072"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1946", "Completed cohort fertility rate": "2.053"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1947", "Completed cohort fertility rate": "2.051"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1948", "Completed cohort fertility rate": "2.065"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1949", "Completed cohort fertility rate": "2.076"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1950", "Completed cohort fertility rate": "2.066"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1951", "Completed cohort fertility rate": "2.043"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1952", "Completed cohort fertility rate": "2.042"}], "rows_tail": [{"Entity": "Taiwan", "Code": "TWN", "Year": "1971", "Completed cohort fertility rate": "1.68"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "1972", "Completed cohort fertility rate": "1.649"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "1973", "Completed cohort fertility rate": "1.617"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1944", "Completed cohort fertility rate": "1.839"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1945", "Completed cohort fertility rate": "1.831"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1946", "Completed cohort fertility rate": "1.856"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1947", "Completed cohort fertility rate": "1.867"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1948", "Completed cohort fertility rate": "1.886"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1949", "Completed cohort fertility rate": "1.916"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1950", "Completed cohort fertility rate": "1.901"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1951", "Completed cohort fertility rate": "1.911"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1952", "Completed cohort fertility rate": "1.883"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1953", "Completed cohort fertility rate": "1.848"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1954", "Completed cohort fertility rate": "1.839"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1955", "Completed cohort fertility rate": "1.831"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1956", "Completed cohort fertility rate": "1.824"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1957", "Completed cohort fertility rate": "1.842"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1958", "Completed cohort fertility rate": "1.862"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1959", "Completed cohort fertility rate": "1.857"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1960", "Completed cohort fertility rate": "1.834"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1961", "Completed cohort fertility rate": "1.806"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1962", "Completed cohort fertility rate": "1.774"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1963", "Completed cohort fertility rate": "1.739"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1959", "Completed cohort fertility rate": "1.983"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1960", "Completed cohort fertility rate": "1.98"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1961", "Completed cohort fertility rate": "1.952"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1962", "Completed cohort fertility rate": "1.933"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1963", "Completed cohort fertility rate": "1.929"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1964", "Completed cohort fertility rate": "1.925"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1965", "Completed cohort fertility rate": "1.917"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1966", "Completed cohort fertility rate": "1.905"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1967", "Completed cohort fertility rate": "1.906"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1968", "Completed cohort fertility rate": "1.911"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1969", "Completed cohort fertility rate": "1.905"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1970", "Completed cohort fertility rate": "1.903"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1971", "Completed cohort fertility rate": "1.895"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1972", "Completed cohort fertility rate": "1.882"}, {"Entity": "United States", "Code": "USA", "Year": "1918", "Completed cohort fertility rate": "2.507"}, {"Entity": "United States", "Code": "USA", "Year": "1919", "Completed cohort fertility rate": "2.595"}, {"Entity": "United States", "Code": "USA", "Year": "1920", "Completed cohort fertility rate": "2.708"}, {"Entity": "United States", "Code": "USA", "Year": "1921", "Completed cohort fertility rate": "2.749"}, {"Entity": "United States", "Code": "USA", "Year": "1922", "Completed cohort fertility rate": "2.784"}, {"Entity": "United States", "Code": "USA", "Year": "1923", "Completed cohort fertility rate": "2.852"}, {"Entity": "United States", "Code": "USA", "Year": "1924", "Completed cohort fertility rate": "2.942"}, {"Entity": "United States", "Code": "USA", "Year": "1925", "Completed cohort fertility rate": "2.986"}, {"Entity": "United States", "Code": "USA", "Year": "1926", "Completed cohort fertility rate": "3.025"}, {"Entity": "United States", "Code": "USA", "Year": "1927", "Completed cohort fertility rate": "3.092"}, {"Entity": "United States", "Code": "USA", "Year": "1928", "Completed cohort fertility rate": "3.109"}, {"Entity": "United States", "Code": "USA", "Year": "1929", "Completed cohort fertility rate": "3.171"}, {"Entity": "United States", "Code": "USA", "Year": "1930", "Completed cohort fertility rate": "3.239"}, {"Entity": "United States", "Code": "USA", "Year": "1931", "Completed cohort fertility rate": "3.241"}, {"Entity": "United States", "Code": "USA", "Year": "1932", "Completed cohort fertility rate": "3.253"}, {"Entity": "United States", "Code": "USA", "Year": "1933", "Completed cohort fertility rate": "3.256"}, {"Entity": "United States", "Code": "USA", "Year": "1934", "Completed cohort fertility rate": "3.254"}, {"Entity": "United States", "Code": "USA", "Year": "1935", "Completed cohort fertility rate": "3.214"}, {"Entity": "United States", "Code": "USA", "Year": "1936", "Completed cohort fertility rate": "3.131"}, {"Entity": "United States", "Code": "USA", "Year": "1937", "Completed cohort fertility rate": "3.059"}, {"Entity": "United States", "Code": "USA", "Year": "1938", "Completed cohort fertility rate": "2.983"}, {"Entity": "United States", "Code": "USA", "Year": "1939", "Completed cohort fertility rate": "2.892"}, {"Entity": "United States", "Code": "USA", "Year": "1940", "Completed cohort fertility rate": "2.795"}, {"Entity": "United States", "Code": "USA", "Year": "1941", "Completed cohort fertility rate": "2.68"}, {"Entity": "United States", "Code": "USA", "Year": "1942", "Completed cohort fertility rate": "2.561"}, {"Entity": "United States", "Code": "USA", "Year": "1943", "Completed cohort fertility rate": "2.464"}, {"Entity": "United States", "Code": "USA", "Year": "1944", "Completed cohort fertility rate": "2.365"}, {"Entity": "United States", "Code": "USA", "Year": "1945", "Completed cohort fertility rate": "2.292"}, {"Entity": "United States", "Code": "USA", "Year": "1946", "Completed cohort fertility rate": "2.215"}, {"Entity": "United States", "Code": "USA", "Year": "1947", "Completed cohort fertility rate": "2.15"}, {"Entity": "United States", "Code": "USA", "Year": "1948", "Completed cohort fertility rate": "2.104"}, {"Entity": "United States", "Code": "USA", "Year": "1949", "Completed cohort fertility rate": "2.057"}, {"Entity": "United States", "Code": "USA", "Year": "1950", "Completed cohort fertility rate": "2.022"}, {"Entity": "United States", "Code": "USA", "Year": "1951", "Completed cohort fertility rate": "1.998"}, {"Entity": "United States", "Code": "USA", "Year": "1952", "Completed cohort fertility rate": "1.982"}, {"Entity": "United States", "Code": "USA", "Year": "1953", "Completed cohort fertility rate": "1.972"}, {"Entity": "United States", "Code": "USA", "Year": "1954", "Completed cohort fertility rate": "1.974"}, {"Entity": "United States", "Code": "USA", "Year": "1955", "Completed cohort fertility rate": "1.981"}, {"Entity": "United States", "Code": "USA", "Year": "1956", "Completed cohort fertility rate": "1.985"}, {"Entity": "United States", "Code": "USA", "Year": "1957", "Completed cohort fertility rate": "1.987"}, {"Entity": "United States", "Code": "USA", "Year": "1958", "Completed cohort fertility rate": "1.991"}, {"Entity": "United States", "Code": "USA", "Year": "1959", "Completed cohort fertility rate": "2.012"}, {"Entity": "United States", "Code": "USA", "Year": "1960", "Completed cohort fertility rate": "2.024"}, {"Entity": "United States", "Code": "USA", "Year": "1961", "Completed cohort fertility rate": "2.024"}, {"Entity": "United States", "Code": "USA", "Year": "1962", "Completed cohort fertility rate": "2.034"}, {"Entity": "United States", "Code": "USA", "Year": "1963", "Completed cohort fertility rate": "2.051"}, {"Entity": "United States", "Code": "USA", "Year": "1964", "Completed cohort fertility rate": "2.066"}, {"Entity": "United States", "Code": "USA", "Year": "1965", "Completed cohort fertility rate": "2.075"}, {"Entity": "United States", "Code": "USA", "Year": "1966", "Completed cohort fertility rate": "2.089"}, {"Entity": "United States", "Code": "USA", "Year": "1967", "Completed cohort fertility rate": "2.109"}, {"Entity": "United States", "Code": "USA", "Year": "1968", "Completed cohort fertility rate": "2.111"}, {"Entity": "United States", "Code": "USA", "Year": "1969", "Completed cohort fertility rate": "2.114"}, {"Entity": "United States", "Code": "USA", "Year": "1970", "Completed cohort fertility rate": "2.137"}, {"Entity": "United States", "Code": "USA", "Year": "1971", "Completed cohort fertility rate": "2.161"}, {"Entity": "United States", "Code": "USA", "Year": "1972", "Completed cohort fertility rate": "2.183"}, {"Entity": "United States", "Code": "USA", "Year": "1973", "Completed cohort fertility rate": "2.203"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1941", "Completed cohort fertility rate": "1.902"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1942", "Completed cohort fertility rate": "1.85"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1943", "Completed cohort fertility rate": "1.809"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1944", "Completed cohort fertility rate": "1.777"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1945", "Completed cohort fertility rate": "1.774"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1946", "Completed cohort fertility rate": "1.778"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1947", "Completed cohort fertility rate": "1.75"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1948", "Completed cohort fertility rate": "1.728"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1949", "Completed cohort fertility rate": "1.714"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1950", "Completed cohort fertility rate": "1.7"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1951", "Completed cohort fertility rate": "1.658"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1952", "Completed cohort fertility rate": "1.647"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1953", "Completed cohort fertility rate": "1.63"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1954", "Completed cohort fertility rate": "1.607"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1955", "Completed cohort fertility rate": "1.623"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1956", "Completed cohort fertility rate": "1.621"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1957", "Completed cohort fertility rate": "1.605"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1958", "Completed cohort fertility rate": "1.608"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1959", "Completed cohort fertility rate": "1.606"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1960", "Completed cohort fertility rate": "1.609"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1961", "Completed cohort fertility rate": 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a woman by the end of her childbearing years, given by her birth year.", "chart_note": "", "chart_citation": "Human Fertility Database (2025)", "original_chart_url": "https://ourworldindata.org/grapher/cohort-fertility-rate", "owid_column_metadata": {"Completed cohort fertility rate - Total": {"titleShort": "Completed cohort fertility rate", "titleLong": "Completed cohort fertility rate", "descriptionShort": "The average number of children born to women in the current cohort over their lifetime.", "descriptionKey": ["Shows the total number of children that women born in the same year have by the end of their childbearing years.", "Helps compare how many children different generations of women have on average.", "Calculated using the actual number of births recorded at different ages throughout a woman's childbearing years."], "unit": "births per woman", "timespan": "1876-1974", "type": "Numeric", "owidVariableId": 1118712, "shortName": "ccf__birth_order_total", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Fertility Database (2025) – processed by Our World in Data", "citationLong": "Human Fertility Database (2025) – processed by Our World in Data. “Completed cohort fertility rate – HFD” [dataset]. Human Fertility Database, “Human Fertility Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118712.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "b2b020bd31cbf3724a4e"},
{"raw_link": "https://ourworldindata.org/total-fertility-rate-births-per-woman", "title": "Why the total fertility rate doesn’t necessarily tell us the number of births women eventually have", "context": "Home\nFertility Rate\nWhy the total fertility rate doesn’t necessarily tell us the number of births women eventually have\nThe total fertility rate is commonly confused with the eventual number of births per woman. This can result in misinterpreting the impact of policies and trends over time.\nBy\nSaloni Dattani\nand\nLucas Rodés-Guirao\nFebruary 24, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nWhen people talk about trends in fertility, they often use a single metric: the total fertility rate (TFR). It seems straightforward — after all, it’s often described as the average number of children per woman. But while TFR is a widely used metric, it’s not as simple as it sounds.\nA common mistake is to think of the TFR as a prediction of how many children women will have on average over their lifetimes. That’s not the case. In fact, the TFR can decline even when the eventual number of children per woman stays the same or even increases.\n1\nAnd the opposite can happen, too — the TFR can rise even when the eventual number of children stays the same.\n2\nIn this article, I explore why this can happen. I explain why the TFR doesn’t always mean what people assume and how to interpret it correctly. Understanding this can give us a clearer picture of the average number of births per woman, trends over time, and the impact of policies.\nThe total fertility rate can differ from the eventual number of births per woman\nIn the chart below, I’ve compared two metrics: the period\ntotal fertility rate\nand the\ncompleted cohort fertility rate\n, which is the eventual average number of births per woman in each birth cohort.\nI’ve shown the data from Sweden, which has the longest time series of data.\n3\nIn the chart, the completed cohort fertility rate is given by the woman’s birth year plus 30 years. This adjustment reflects when women born in a particular year would reach the age of 30 — roughly the average age of childbearing for Swedish women during this period.\n4\nAs the chart shows, fertility rates in Sweden fell in the late 19th and early 20th centuries, regardless of which metric we look at. However, the two metrics have diverged since the mid-20th century. While the total fertility rate has had several large rises and falls, the completed cohort fertility rate has been much more stable.\nDownload\nYou can explore this for other countries with available data in our\ninteractive version\nonline or see other examples in Frejka and Gietel-Basten (2016).\n5\nBut why can the two metrics differ so much? Why can the level of period total fertility rate be misleading about the eventual number of births? To answer these questions, I’ll introduce each of these metrics and how they are calculated.\nWhat is the total fertility rate, and how is it calculated?\nHere, I’ll explain the two different ways to measure fertility: the\ntotal fertility rate\nand the\ncompleted cohort fertility rate\n.\nI’ll start with the cohort measure of fertility, which is more straightforward.\nCompleted cohort fertility rate\nThe completed cohort fertility rate (or simply the “CCFR”) is the average number of children born to women in a specific birth cohort across all their childbearing years.\nA birth cohort includes all women born in the same year, and the completed cohort fertility rate is calculated after a cohort of women has completed their childbearing years.\n6\nA simplified version of this metric would be to calculate the total number of births divided by the total number of women, for women born in a particular year — in other words, the average number of births per woman.\nHowever, not all women survive until the end of their childbearing years, and the population size might change due to migration. This is why, in practice, the cohort fertility rate is calculated with annual data instead.\nFor example, if we consider women born in 1990, we would calculate their completed cohort fertility rate by calculating the number of births per woman each year among women aged 12 to 55 years old and then calculating the sum total.\n7\nIt’s not necessary to follow the calculations here step by step, but if you are interested, I’ve shown them in a diagram below. The diagram has one simplification to make it fit: I’ve compressed their childbearing years into only five years.\nDownload\nNow that we’ve looked at a hypothetical example, let’s look at the actual data.\nThe interactive chart below shows the cohort fertility rate for several countries. Note that the horizontal axis refers to the women’s birth year, not the year when they gave birth.\nYou can see the period of the “baby boom” in the United States, where there was a rise in the completed cohort fertility rate for women who were born between the late 1910s and early 1940s.\nYou can click the “Edit countries” button to explore the data for other countries.\nTotal fertility rate\nThe period total fertility rate, often simply called the total fertility rate or the “TFR”, is the most commonly used metric.\nIt doesn’t require waiting for the entire cohort to complete their childbearing years before it can be calculated.\nInstead, it is a snapshot of fertility rates in one particular year. It is the average number of children born to a 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.\nIf you want to see how it’s calculated, I’ve shown that in the diagram below.\nWhile the previous diagram followed one birth cohort over time, this diagram looks at multiple cohorts in one particular year.\nIn this hypothetical example, each cohort has the same number and timing of births as the previous cohort. For this reason, the total fertility rates stayed constant: in each year, the fertility rate was 2.0.\nDownload\nWhat does the data for the total fertility rate actually look like?\nThe interactive chart below shows it for the same countries we considered before.\nAs you can see, the trend line often fluctuates greatly. This is because the total fertility rate is affected by the timing of births in each birth cohort, such as delays in childbearing.\nAs a consequence, there are sharp drops or spikes in certain years triggered by particular events. In Spain, the chart shows large fluctuations during the Spanish Civil War and World War Two, when births were delayed in certain years.\nAnother example is Japan, which saw a sudden drop in fertility rates in 1966. This was because of cultural beliefs that children born that year under\nthe “fire horse” astrological sign\nwould bring bad fortune. Many parents opted to have children either a year earlier or later.\nThe total fertility rate can change while the completed cohort fertility rate stays constant\nWith data from Sweden, we’ve already seen how the total fertility rate can change while the completed cohort fertility rate stays constant. The same pattern is also seen in other countries, such as the United Kingdom and France.\n8\nBut why?\nHere, I’ll take you through a few examples to see why the volatility of the two metrics can be different.\nLet’s take an example. Following the previous examples, I’ve visualized this effect in diagram form as well.\nImagine that an event affects many women at a particular time. For example, a natural disaster, pandemic, or war might affect childbearing decisions for many women across age groups in a particular year.\n9\nIn the hypothetical example, half of the women who planned to have children in 2023 decided to delay those births to the following year — except for those in their final year of childbearing, who decided not to wait.\nIn this example, the total fertility rate dropped to 1.1 in 2023 because the women who were delaying children didn’t have births that year, and their birth rates suddenly dropped in that particular year.\nHowever, the women who decided to delay their births in 2023 compensated by having these births in the following year. Now, the fertility rate spiked to 2.9 in 2024.\nAlthough the total fertility rate suddenly dropped and then spiked, the completed cohort fertility rate remained constant: in the end, they had the same number of children.\nDownload\nLet’s take another example.\nWhat if there’s an event that affects people when they reach a particular age, regardless of their birth cohort?\nOne situation we can imagine is a change in the years of schooling. Say, if there was a sudden increase in the mandatory years of schooling.\nImagine that the education system changed for women born in 1994 or later: from then on, children had to stay in school for an additional year.\nThe diagram shows what might happen.\nIn this hypothetical example, half of the women delayed having their first births early on. They delayed those births until the following years.\nThe total fertility rate dropped slightly to 1.9 in 2023. The following year, it dropped a little further to 1.7 before climbing back up again to 1.9 and then 2.0.\nAgain, although the total fertility rate dropped and then returned, the completed cohort fertility rate remained constant: by the end, they had the same number of children.\nDownload\nChanges in the timing of childbirths, the total number of childbirths, or both can affect the total fertility rate\nThere are two ways that fertility rates can change over time. They’re described as “tempo” and “quantum” effects, and I’ve illustrated them in the diagram below.\nThe diagram shows the fertility rate by age.\nAs you can see, a tempo effect is a change in the timing of childbearing. For example, women may delay having children. Alternatively, they might have children sooner than previous generations, which would also be described as a tempo effect.\nMeanwhile, a quantum effect occurs when women have a different number of children. For example, women may have fewer children than in the past or more children (such as during the baby boom).\n10\nDownload\nA decline in the fertility rate at early ages of a particular birth cohort may not necessarily tell us about their eventual number of births because it can initially be unclear whether there is a tempo or quantum effect — in other words, whether births are being delayed or won’t occur at all.\nThe total fertility rate is calculated in such a way that tempo effects (changes in timing) can resemble quantum effects (changes in the number of births), if the total fertility rate is interpreted as the actual number of births per woman.\nOf course, in reality, both can occur together: the timing of childbearing can shift, and the number of children can also change.\nLet’s explore how these patterns look in real data.\nThis is shown in the interactive chart below.\nIt shows both\nwhen\nwomen give birth and\nhow many children\nthey have at each age. For example, a value of 0.1 at the age of 25 would mean that 1 in 10 women of that age gave birth that year.\nIn Sweden, for example, the curve has shifted to the right: younger generations of women are having children later than in the past, but this hasn’t led to a drop in the average number of children per woman.\nIn Spain, the curve has shifted and dropped. Women have children later than in the past and have fewer children at those ages.\nYou can explore the data for other countries by clicking the “Edit countries” button.\nIn the chart, the total area below each curve represents the total number of births that women of each generation had.\nThis is equivalent to the\ncompleted cohort fertility rate\nfor that birth cohort.\nWe’ll use another chart below to display this total more clearly.\nBelow, you can see the cumulative fertility rate by age: it displays the number of births women have had up to a given age. The final value at 55 years old is the completed cohort fertility rate.\nNow you can see that in Sweden, although the curves are pushed towards the right (meaning that childbirths have been delayed), eventually, each cohort reached a cumulative fertility rate of 2 births per woman. This reflects a tempo effect but not a quantum effect.\nThis looks different in other countries: in Spain, the curves have also been pushed towards the right, reflecting delayed childbirths, but they reached a lower point at the end, which reflects a tempo\nand\na quantum effect.\nJapan’s curves look similar to Spain’s — with a delay in childbearing\nand\na decline in the eventual number.\nThe United States saw a large increase in cumulative fertility rates, corresponding to the baby boom. Only after this did the curves start to shift rightwards as childbirths were delayed. These post-baby boom cohorts also experienced a large quantum fertility decrease to just 2 births per woman.\nAnother thing to note is that we can roughly predict the completed cohort fertility rate when the curve has leveled off.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nConclusion: what can we learn from fertility rates?\nAs we’ve seen, the period\ntotal fertility rate\ncan be quite different from the eventual number of births per woman.\nWe need to be careful when interpreting a short-term decline in fertility rates — it can initially be unclear whether childbirths are being delayed or if they won’t occur at all.\nSimilarly, a short-term rise in fertility rates could reflect the end of a delay in childbearing\n11\n, rather than a rise in the eventual number of births per woman.\nIn sum, the total fertility rate can decline because of changes in the timing of births in each cohort.\nAcknowledgements\nMax Roser, Hannah Ritchie, Edouard Mathieu, Julia Rohrer and Ilya Kashnitsky provided valuable feedback that helped improve this article.\nEndnotes\nBongaarts, J., & Feeney, G. (2010). When is a tempo effect a tempo distortion? Genus, 66(2), 1–15. Available\nonline\n.\nBongaarts, J., & Sobotka, T. (2012). A Demographic Explanation for the Recent Rise in European Fertility. Population and Development Review, 38(1), 83–120.\nhttps://doi.org/10.1111/j.1728-4457.2012.00473.x\nYou can see other examples here:\nFrejka, T., & Gietel-Basten, S. (2016). Fertility and Family Policies in Central and Eastern Europe after 1990. Comparative Population Studies, 41(1). https://doi.org/10.12765/CPoS-2016-03\nSobotka, T. (2017). POST-TRANSITIONAL FERTILITY: THE ROLE OF CHILDBEARING POSTPONEMENT IN FUELLING THE SHIFT TO LOW AND UNSTABLE FERTILITY LEVELS. Journal of Biosocial Science, 49(S1), S20–S45.\nhttps://doi.org/10.1017/S0021932017000323\nPison, G. (2017) . Fewer births in France in 2016. Population & Societies, No 542(3), 1-4.\nhttps://shs.cairn.info/journal-population-and-societies-2017-3-page-1?lang=en\n.\nThis average includes all childbirths, not just the firstborn.\nFrejka, T., & Gietel-Basten, S. (2016). Fertility and Family Policies in Central and Eastern Europe after 1990. Comparative Population Studies, 41(1).\nhttps://doi.org/10.12765/CPoS-2016-03\nDemographers may measure this across a very wide range of potential years that women could give birth (such as, from the age of 12 to 55).\nIt’s worth noting that, with real data, because we’d have to wait for women born in 1990 to turn 55, their results wouldn’t be available until 2045.\nSobotka, T. (2017). POST-TRANSITIONAL FERTILITY: THE ROLE OF CHILDBEARING POSTPONEMENT IN FUELLING THE SHIFT TO LOW AND UNSTABLE FERTILITY LEVELS. Journal of Biosocial Science, 49(S1), S20–S45.\nhttps://doi.org/10.1017/S0021932017000323\nPison, G. (2017) . Fewer births in France in 2016. Population & Societies, No 542(3), 1-4.\nhttps://shs.cairn.info/journal-population-and-societies-2017-3-page-1?lang=en\n.\nA real-life example is the impact of the World Wars on fertility rates, which led to a temporary delay in childbearing and, thus, a sudden drop in total fertility rates before they rebounded after the wars.\nToulemon, L., Pailhé, A., & Rossier, C. (2008). France: High and stable fertility. Demographic Research, 19, 503–556.\nhttps://doi.org/10.4054/DemRes.2008.19.16\nBongaarts, J., & Feeney, G. (2008). The quantum and tempo of life-cycle events. In E. Barbi, J. W. Vaupel, & J. Bongaarts (Eds.), How Long Do We Live? (pp. 29–65). Springer Berlin Heidelberg.\nhttps://doi.org/10.1007/978-3-540-78520-0_3\nIn demography research, this phenomenon is known as “recuperation”.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSaloni Dattani and Lucas Rodés-Guirao (2025) - “Why the total fertility rate doesn’t necessarily tell us the number of births women eventually have” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-093348/total-fertility-rate-births-per-woman.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-total-fertility-rate-births-per-woman,\nauthor = {Saloni Dattani and Lucas Rodés-Guirao},\ntitle = {Why the total fertility rate doesn’t necessarily tell us the number of births women eventually have},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-093348/total-fertility-rate-births-per-woman.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "total-fertility-rate-births-per-woman", "source_url": "https://ourworldindata.org/total-fertility-rate-births-per-woman", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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"Completed cohort fertility rate shifted +30 years": "2.436", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1967", "Total fertility rate": "2.621", "Completed cohort fertility rate shifted +30 years": "2.41", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1968", "Total fertility rate": "2.586", "Completed cohort fertility rate shifted +30 years": "2.339", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1969", "Total fertility rate": "2.488", "Completed cohort fertility rate shifted +30 years": "2.202", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1970", "Total fertility rate": "2.292", "Completed cohort fertility rate shifted +30 years": "2.132", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1971", "Total fertility rate": "2.2", "Completed cohort fertility rate shifted +30 years": "2.065", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1972", "Total fertility rate": "2.085", "Completed cohort fertility rate shifted +30 years": "2.037", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1973", "Total fertility rate": "1.938", "Completed cohort fertility rate shifted +30 years": "1.983", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1974", "Total fertility rate": "1.908", "Completed cohort fertility rate shifted +30 years": "1.953", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1975", "Total fertility rate": "1.827", "Completed cohort fertility rate shifted +30 years": "1.938", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1976", "Total fertility rate": "1.688", "Completed cohort fertility rate shifted +30 years": "1.986", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1977", "Total fertility rate": "1.631", "Completed cohort fertility rate shifted +30 years": "1.931", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1978", "Total fertility rate": "1.604", "Completed cohort fertility rate shifted +30 years": "1.916", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1979", "Total fertility rate": "1.598", "Completed cohort fertility rate shifted +30 years": "1.908", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1980", "Total fertility rate": "1.652", "Completed cohort fertility rate shifted +30 years": "1.863", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1981", "Total fertility rate": "1.674", "Completed cohort fertility rate shifted +30 years": "1.835", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1982", "Total fertility rate": "1.661", "Completed cohort fertility rate shifted +30 years": "1.812", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1983", "Total fertility rate": "1.559", "Completed cohort fertility rate shifted +30 years": "1.817", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1984", "Total fertility rate": "1.522", "Completed cohort fertility rate shifted +30 years": "1.784", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Austria", "Code": "AUT", "Year": "1985", "Total fertility rate": "1.474", "Completed cohort fertility rate shifted +30 years": "1.768", "Tempo-adjusted total fertility rate": "1.735"}, {"Entity": "Austria", "Code": "AUT", "Year": "1986", "Total fertility rate": "1.449", "Completed cohort fertility rate shifted +30 years": "1.746", "Tempo-adjusted total fertility rate": "1.705"}, {"Entity": "Austria", "Code": "AUT", "Year": "1987", "Total fertility rate": "1.432", "Completed cohort fertility rate shifted +30 years": "1.73", "Tempo-adjusted total fertility rate": "1.647"}, {"Entity": "Austria", "Code": "AUT", "Year": "1988", "Total fertility rate": "1.447", "Completed cohort fertility rate shifted +30 years": "1.713", "Tempo-adjusted total fertility rate": "1.6"}, {"Entity": "Austria", "Code": "AUT", "Year": "1989", "Total fertility rate": "1.446", "Completed cohort fertility rate shifted +30 years": "1.714", "Tempo-adjusted total fertility rate": "1.664"}, {"Entity": "Austria", "Code": "AUT", "Year": "1990", "Total fertility rate": "1.457", "Completed cohort fertility rate shifted +30 years": "1.698", "Tempo-adjusted total fertility rate": "1.614"}, {"Entity": "Austria", "Code": "AUT", "Year": "1991", "Total fertility rate": "1.507", "Completed cohort fertility rate shifted +30 years": "1.676", "Tempo-adjusted total fertility rate": "1.578"}, {"Entity": "Austria", "Code": "AUT", "Year": "1992", "Total fertility rate": "1.505", "Completed cohort fertility rate shifted +30 years": "1.675", "Tempo-adjusted total fertility rate": "1.6"}, {"Entity": "Austria", "Code": "AUT", "Year": "1993", "Total fertility rate": "1.501", "Completed cohort fertility rate shifted +30 years": "1.667", "Tempo-adjusted total fertility rate": "1.717"}, {"Entity": "Austria", "Code": "AUT", "Year": "1994", "Total fertility rate": "1.465", "Completed cohort fertility rate shifted +30 years": "1.652", "Tempo-adjusted total fertility rate": "1.801"}, {"Entity": "Austria", "Code": "AUT", "Year": "1995", "Total fertility rate": "1.423", "Completed cohort fertility rate shifted +30 years": "1.657", "Tempo-adjusted total fertility rate": "1.739"}, {"Entity": "Austria", "Code": "AUT", "Year": "1996", "Total fertility rate": "1.446", "Completed cohort fertility rate shifted +30 years": "1.64", "Tempo-adjusted total fertility rate": "1.731"}, {"Entity": "Austria", "Code": "AUT", "Year": "1997", "Total fertility rate": "1.393", "Completed cohort fertility rate shifted +30 years": "1.626", "Tempo-adjusted total fertility rate": "1.585"}, {"Entity": "Austria", "Code": "AUT", "Year": "1998", "Total fertility rate": "1.37", "Completed cohort fertility rate shifted +30 years": "1.61", "Tempo-adjusted total fertility rate": "1.563"}, {"Entity": "Austria", "Code": "AUT", "Year": "1999", "Total fertility rate": "1.34", "Completed cohort fertility rate shifted +30 years": "1.615", "Tempo-adjusted total fertility rate": "1.519"}, {"Entity": "Austria", "Code": "AUT", "Year": "2000", "Total fertility rate": "1.364", "Completed cohort fertility rate shifted +30 years": "1.624", "Tempo-adjusted total fertility rate": "1.583"}, {"Entity": "Austria", "Code": "AUT", "Year": "2001", "Total fertility rate": "1.331", "Completed cohort fertility rate shifted +30 years": "1.634", "Tempo-adjusted total fertility rate": "1.606"}, {"Entity": "Austria", "Code": "AUT", "Year": "2002", "Total fertility rate": "1.394", "Completed cohort fertility rate shifted +30 years": "1.651", "Tempo-adjusted total fertility rate": "1.681"}, {"Entity": "Austria", "Code": "AUT", "Year": "2003", "Total fertility rate": "1.376", "Completed cohort fertility rate shifted +30 years": "1.661", "Tempo-adjusted total fertility rate": "1.587"}, {"Entity": "Austria", "Code": "AUT", "Year": "2004", "Total fertility rate": "1.419", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.638"}, {"Entity": "Austria", "Code": "AUT", "Year": "2005", "Total fertility rate": "1.408", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.728"}, {"Entity": "Austria", "Code": "AUT", "Year": "2006", "Total fertility rate": "1.409", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.687"}, {"Entity": "Austria", "Code": "AUT", "Year": "2007", "Total fertility rate": "1.385", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.613"}, {"Entity": "Austria", "Code": "AUT", "Year": "2008", "Total fertility rate": "1.417", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.704"}, {"Entity": "Austria", "Code": "AUT", "Year": "2009", "Total fertility rate": "1.396", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.71"}, {"Entity": "Austria", "Code": "AUT", "Year": "2010", "Total fertility rate": "1.443", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.789"}, {"Entity": "Austria", "Code": "AUT", "Year": "2011", "Total fertility rate": "1.431", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.778"}, {"Entity": "Austria", "Code": "AUT", "Year": "2012", "Total fertility rate": "1.44", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.712"}, {"Entity": "Austria", "Code": "AUT", "Year": "2013", "Total fertility rate": "1.436", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.628"}, {"Entity": "Austria", "Code": "AUT", "Year": "2014", "Total fertility rate": "1.464", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.68"}, {"Entity": "Austria", "Code": "AUT", "Year": "2015", "Total fertility rate": "1.491", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.648"}, {"Entity": "Austria", "Code": "AUT", "Year": "2016", "Total fertility rate": "1.53", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.622"}, {"Entity": "Austria", "Code": "AUT", "Year": "2017", "Total fertility rate": "1.518", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.706"}, {"Entity": "Austria", "Code": "AUT", "Year": "2018", "Total fertility rate": "1.475", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.718"}, {"Entity": "Austria", "Code": "AUT", "Year": "2019", "Total fertility rate": "1.461", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.617"}, {"Entity": "Austria", "Code": "AUT", "Year": "2020", "Total fertility rate": "1.436", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.616"}, {"Entity": "Austria", "Code": "AUT", "Year": "2021", "Total fertility rate": "1.476", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.598"}, {"Entity": "Austria", "Code": "AUT", "Year": "2022", "Total fertility rate": "1.409", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.394"}, {"Entity": "Austria", "Code": "AUT", "Year": "2023", "Total fertility rate": "1.316", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Belarus", "Code": "BLR", "Year": "1964", "Total fertility rate": "2.282", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "Belarus", "Code": "BLR", "Year": "1965", "Total fertility rate": "2.241", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.097"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1966", "Total fertility rate": "2.309", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.129"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1967", "Total fertility rate": "2.286", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.301"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1968", "Total fertility rate": "2.316", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.221"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1969", "Total fertility rate": "2.29", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.998"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1970", "Total fertility rate": "2.354", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.047"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1971", "Total fertility rate": "2.36", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.166"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1972", "Total fertility rate": "2.311", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.206"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1973", "Total fertility rate": "2.227", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.106"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1974", "Total fertility rate": "2.214", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.011"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1975", "Total fertility rate": "2.157", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.969"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1976", "Total fertility rate": "2.12", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.988"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1977", "Total fertility rate": "2.087", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.911"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1978", "Total fertility rate": "2.065", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.898"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1979", "Total fertility rate": "2.028", "Completed cohort fertility rate shifted +30 years": "1.968", "Tempo-adjusted total fertility rate": "1.881"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1980", "Total fertility rate": "2.007", "Completed cohort fertility rate shifted +30 years": "1.938", "Tempo-adjusted total fertility rate": "1.942"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1981", "Total fertility rate": "2.008", "Completed cohort fertility rate shifted +30 years": "1.964", "Tempo-adjusted total fertility rate": "1.987"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1982", "Total fertility rate": "1.991", "Completed cohort fertility rate shifted +30 years": "1.928", "Tempo-adjusted total fertility rate": "1.927"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1983", "Total fertility rate": "2.154", "Completed cohort fertility rate shifted +30 years": "1.886", "Tempo-adjusted total fertility rate": "2.092"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1984", "Total fertility rate": "2.096", "Completed cohort fertility rate shifted +30 years": "1.915", "Tempo-adjusted total fertility rate": "1.974"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1985", "Total fertility rate": "2.055", "Completed cohort fertility rate shifted +30 years": "1.931", "Tempo-adjusted total fertility rate": "2.106"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1986", "Total fertility rate": "2.156", "Completed cohort fertility rate shifted +30 years": "1.926", "Tempo-adjusted total fertility rate": "2.106"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1987", "Total fertility rate": "2.086", "Completed cohort fertility rate shifted +30 years": "1.924", "Tempo-adjusted total fertility rate": "1.921"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1988", "Total fertility rate": "2.12", "Completed cohort fertility rate shifted +30 years": "1.921", "Tempo-adjusted total fertility rate": "2.023"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1989", "Total fertility rate": "2.028", "Completed cohort fertility rate shifted +30 years": "1.891", "Tempo-adjusted total fertility rate": "1.781"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1990", "Total fertility rate": "1.913", "Completed cohort fertility rate shifted +30 years": "1.873", "Tempo-adjusted total fertility rate": "1.715"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1991", "Total fertility rate": "1.806", "Completed cohort fertility rate shifted +30 years": "1.857", "Tempo-adjusted total fertility rate": "1.695"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1992", "Total fertility rate": "1.765", "Completed cohort fertility rate shifted +30 years": "1.815", "Tempo-adjusted total fertility rate": "1.706"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1993", "Total fertility rate": "1.624", "Completed cohort fertility rate shifted +30 years": "1.736", "Tempo-adjusted total fertility rate": "1.687"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1994", "Total fertility rate": "1.535", "Completed cohort fertility rate shifted +30 years": "1.684", "Tempo-adjusted total fertility rate": "1.662"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1995", "Total fertility rate": "1.407", "Completed cohort fertility rate shifted +30 years": "1.689", "Tempo-adjusted total fertility rate": "1.556"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1996", "Total fertility rate": "1.335", "Completed cohort fertility rate shifted +30 years": "1.7", "Tempo-adjusted total fertility rate": "1.422"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1997", "Total fertility rate": "1.25", "Completed cohort fertility rate shifted +30 years": "1.713", "Tempo-adjusted total fertility rate": "1.378"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1998", "Total fertility rate": "1.3", "Completed cohort fertility rate shifted +30 years": "1.709", "Tempo-adjusted total fertility rate": "1.505"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1999", "Total fertility rate": "1.311", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.583"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2000", "Total fertility rate": "1.318", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.572"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2001", "Total fertility rate": "1.287", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.521"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2002", "Total fertility rate": "1.241", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.501"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2003", "Total fertility rate": "1.232", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.465"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2004", "Total fertility rate": "1.234", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.5"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2005", "Total fertility rate": "1.253", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.458"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2006", "Total fertility rate": "1.335", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.549"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2007", "Total fertility rate": "1.43", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.675"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2008", "Total fertility rate": "1.488", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.739"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2009", "Total fertility rate": "1.509", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.788"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2010", "Total fertility rate": "1.495", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.684"}], "rows_tail": [{"Entity": "United States", "Code": "USA", "Year": "1966", "Total fertility rate": "2.714", "Completed cohort fertility rate shifted +30 years": "3.131", "Tempo-adjusted total fertility rate": "2.958"}, {"Entity": "United States", "Code": "USA", "Year": "1967", "Total fertility rate": "2.564", "Completed cohort fertility rate shifted +30 years": "3.059", "Tempo-adjusted total fertility rate": "2.822"}, {"Entity": "United States", "Code": "USA", "Year": "1968", "Total fertility rate": "2.467", "Completed cohort fertility rate shifted +30 years": "2.983", "Tempo-adjusted total fertility rate": "2.662"}, {"Entity": "United States", "Code": "USA", "Year": "1969", "Total fertility rate": "2.457", "Completed cohort fertility rate shifted +30 years": "2.892", "Tempo-adjusted total fertility rate": "2.507"}, {"Entity": "United States", "Code": "USA", "Year": "1970", "Total fertility rate": "2.461", "Completed cohort fertility rate shifted +30 years": "2.795", "Tempo-adjusted total fertility rate": "2.535"}, {"Entity": "United States", "Code": "USA", "Year": "1971", "Total fertility rate": "2.268", "Completed cohort fertility rate shifted +30 years": "2.68", "Tempo-adjusted total fertility rate": "2.46"}, {"Entity": "United States", "Code": "USA", "Year": "1972", "Total fertility rate": "2.008", "Completed cohort fertility rate shifted +30 years": "2.561", "Tempo-adjusted total fertility rate": "2.23"}, {"Entity": "United States", "Code": "USA", "Year": "1973", "Total fertility rate": "1.871", "Completed cohort fertility rate shifted +30 years": "2.464", "Tempo-adjusted total fertility rate": "2.065"}, {"Entity": "United States", "Code": "USA", "Year": "1974", "Total fertility rate": "1.827", "Completed cohort fertility rate shifted +30 years": "2.365", "Tempo-adjusted total fertility rate": "2.026"}, {"Entity": "United States", "Code": "USA", "Year": "1975", "Total fertility rate": "1.769", "Completed cohort fertility rate shifted +30 years": "2.292", "Tempo-adjusted total fertility rate": "1.995"}, {"Entity": "United States", "Code": "USA", "Year": "1976", "Total fertility rate": "1.739", "Completed cohort fertility rate shifted +30 years": "2.215", "Tempo-adjusted total fertility rate": "1.957"}, {"Entity": "United States", "Code": "USA", "Year": "1977", "Total fertility rate": "1.783", "Completed cohort fertility rate shifted +30 years": "2.15", "Tempo-adjusted total fertility rate": "1.967"}, {"Entity": "United States", "Code": "USA", "Year": "1978", "Total fertility rate": "1.747", "Completed cohort fertility rate shifted +30 years": "2.104", "Tempo-adjusted total fertility rate": "1.93"}, {"Entity": "United States", "Code": "USA", "Year": "1979", "Total fertility rate": "1.795", "Completed cohort fertility rate shifted +30 years": "2.057", "Tempo-adjusted total fertility rate": "1.939"}, {"Entity": "United States", "Code": "USA", "Year": "1980", "Total fertility rate": "1.821", "Completed cohort fertility rate shifted +30 years": "2.022", "Tempo-adjusted total fertility rate": "1.999"}, {"Entity": "United States", "Code": "USA", "Year": "1981", "Total fertility rate": "1.806", "Completed cohort fertility rate shifted +30 years": "1.998", "Tempo-adjusted total fertility rate": "2.009"}, {"Entity": "United States", "Code": "USA", "Year": "1982", "Total fertility rate": "1.815", "Completed cohort fertility rate shifted +30 years": "1.982", "Tempo-adjusted total fertility rate": "1.998"}, {"Entity": "United States", "Code": "USA", "Year": "1983", "Total fertility rate": "1.784", "Completed cohort fertility rate shifted +30 years": "1.972", "Tempo-adjusted total fertility rate": "1.991"}, {"Entity": "United States", "Code": "USA", "Year": "1984", "Total fertility rate": "1.793", "Completed cohort fertility rate shifted +30 years": "1.974", "Tempo-adjusted total fertility rate": "1.961"}, {"Entity": "United States", "Code": "USA", "Year": "1985", "Total fertility rate": "1.835", "Completed cohort fertility rate shifted +30 years": "1.981", "Tempo-adjusted total fertility rate": "1.97"}, {"Entity": "United States", "Code": "USA", "Year": "1986", "Total fertility rate": "1.835", "Completed cohort fertility rate shifted +30 years": "1.985", "Tempo-adjusted total fertility rate": "1.994"}, {"Entity": "United States", "Code": "USA", "Year": "1987", "Total fertility rate": "1.865", "Completed cohort fertility rate shifted +30 years": "1.987", "Tempo-adjusted total fertility rate": "1.989"}, {"Entity": "United States", "Code": "USA", "Year": "1988", "Total fertility rate": "1.922", "Completed cohort fertility rate shifted +30 years": "1.991", "Tempo-adjusted total fertility rate": "1.952"}, {"Entity": "United States", "Code": "USA", "Year": "1989", "Total fertility rate": "1.998", "Completed cohort fertility rate shifted +30 years": "2.012", "Tempo-adjusted total fertility rate": "1.998"}, {"Entity": "United States", "Code": "USA", "Year": "1990", "Total fertility rate": "2.068", "Completed cohort fertility rate shifted +30 years": "2.024", "Tempo-adjusted total fertility rate": "2.038"}, {"Entity": "United States", "Code": "USA", "Year": "1991", "Total fertility rate": "2.056", "Completed cohort fertility rate shifted +30 years": "2.024", "Tempo-adjusted total fertility rate": "2.067"}, {"Entity": "United States", "Code": "USA", "Year": "1992", "Total fertility rate": "2.042", "Completed cohort fertility rate shifted +30 years": "2.034", "Tempo-adjusted total fertility rate": "2.181"}, {"Entity": "United States", "Code": "USA", "Year": "1993", "Total fertility rate": "2.018", "Completed cohort fertility rate shifted +30 years": "2.051", "Tempo-adjusted total fertility rate": "2.224"}, {"Entity": "United States", "Code": "USA", "Year": "1994", "Total fertility rate": "2.001", "Completed cohort fertility rate shifted +30 years": "2.066", "Tempo-adjusted total fertility rate": "2.252"}, {"Entity": "United States", "Code": "USA", "Year": "1995", "Total fertility rate": "1.98", "Completed cohort fertility rate shifted +30 years": "2.075", "Tempo-adjusted total fertility rate": "2.215"}, {"Entity": "United States", "Code": "USA", "Year": "1996", "Total fertility rate": "1.978", "Completed cohort fertility rate shifted +30 years": "2.089", "Tempo-adjusted total fertility rate": "2.178"}, {"Entity": "United States", "Code": "USA", "Year": "1997", "Total fertility rate": "1.973", "Completed cohort fertility rate shifted +30 years": "2.109", "Tempo-adjusted total fertility rate": "2.141"}, {"Entity": "United States", "Code": "USA", "Year": "1998", "Total fertility rate": "2.002", "Completed cohort fertility rate shifted +30 years": "2.111", "Tempo-adjusted total fertility rate": "2.187"}, {"Entity": "United States", "Code": "USA", "Year": "1999", "Total fertility rate": "2.008", "Completed cohort fertility rate shifted +30 years": "2.114", "Tempo-adjusted total fertility rate": "2.26"}, {"Entity": "United States", "Code": "USA", "Year": "2000", "Total fertility rate": "2.053", "Completed cohort fertility rate shifted +30 years": "2.137", "Tempo-adjusted total fertility rate": "2.335"}, {"Entity": "United States", "Code": "USA", "Year": "2001", "Total fertility rate": "2.031", "Completed cohort fertility rate shifted +30 years": "2.161", "Tempo-adjusted total fertility rate": "2.328"}, {"Entity": "United States", "Code": "USA", "Year": "2002", "Total fertility rate": "2.024", "Completed cohort fertility rate shifted +30 years": "2.183", "Tempo-adjusted total fertility rate": "2.387"}, {"Entity": "United States", "Code": "USA", "Year": "2003", "Total fertility rate": "2.053", "Completed cohort fertility rate shifted +30 years": "2.203", "Tempo-adjusted total fertility rate": "2.309"}, {"Entity": "United States", "Code": "USA", "Year": "2004", "Total fertility rate": "2.058", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.148"}, {"Entity": "United States", "Code": "USA", "Year": "2005", "Total fertility rate": "2.061", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.065"}, {"Entity": "United States", "Code": "USA", "Year": "2006", "Total fertility rate": "2.112", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.119"}, {"Entity": "United States", "Code": "USA", "Year": "2007", "Total fertility rate": "2.122", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.242"}, {"Entity": "United States", "Code": "USA", "Year": "2008", "Total fertility rate": "2.074", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.295"}, {"Entity": "United States", "Code": "USA", "Year": "2009", "Total fertility rate": "2.002", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.355"}, {"Entity": "United States", "Code": "USA", "Year": "2010", "Total fertility rate": "1.925", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.335"}, {"Entity": "United States", "Code": "USA", "Year": "2011", "Total fertility rate": "1.889", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.209"}, {"Entity": "United States", "Code": "USA", "Year": "2012", "Total fertility rate": "1.874", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.173"}, {"Entity": "United States", "Code": "USA", "Year": "2013", "Total fertility rate": "1.851", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.197"}, {"Entity": "United States", "Code": "USA", "Year": "2014", "Total fertility rate": "1.863", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.166"}, {"Entity": "United States", "Code": "USA", "Year": "2015", "Total fertility rate": "1.845", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.141"}, {"Entity": "United States", "Code": "USA", "Year": "2016", "Total fertility rate": "1.817", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "2.078"}, {"Entity": "United States", "Code": "USA", "Year": "2017", "Total fertility rate": "1.765", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.99"}, {"Entity": "United States", "Code": "USA", "Year": "2018", "Total fertility rate": "1.727", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.914"}, {"Entity": "United 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"OWID_GFR", "Year": "1956", "Total fertility rate": "2.192", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1957", "Total fertility rate": "2.28", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1958", "Total fertility rate": "2.289", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1959", "Total fertility rate": "2.368", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1960", "Total fertility rate": "2.372", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1961", "Total fertility rate": "2.448", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1962", "Total fertility rate": "2.443", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1963", "Total fertility rate": "2.52", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1964", "Total fertility rate": "2.551", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1965", "Total fertility rate": "2.509", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1966", "Total fertility rate": "2.535", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1967", "Total fertility rate": "2.488", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1968", "Total fertility rate": "2.386", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1969", "Total fertility rate": "2.213", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1970", "Total fertility rate": "1.991", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1971", "Total fertility rate": "1.919", "Completed cohort fertility rate shifted +30 years": "1.902", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1972", "Total fertility rate": "1.716", "Completed cohort fertility rate shifted +30 years": "1.85", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1973", "Total fertility rate": "1.542", "Completed cohort fertility rate shifted +30 years": "1.809", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1974", "Total fertility rate": "1.511", "Completed cohort fertility rate shifted +30 years": "1.777", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1975", "Total fertility rate": "1.45", "Completed cohort fertility rate shifted +30 years": "1.774", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1976", "Total fertility rate": "1.457", "Completed cohort fertility rate shifted +30 years": "1.778", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1977", "Total fertility rate": "1.403", "Completed cohort fertility rate shifted +30 years": "1.75", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1978", "Total fertility rate": "1.379", "Completed cohort fertility rate shifted +30 years": "1.728", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1979", "Total fertility rate": "1.378", "Completed cohort fertility rate shifted +30 years": "1.714", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1980", "Total fertility rate": "1.448", "Completed cohort fertility rate shifted +30 years": "1.7", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1981", "Total fertility rate": "1.434", "Completed cohort fertility rate shifted +30 years": "1.658", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1982", "Total fertility rate": "1.407", "Completed cohort fertility rate shifted +30 years": "1.647", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1983", "Total fertility rate": "1.33", "Completed cohort fertility rate shifted +30 years": "1.63", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1984", "Total fertility rate": "1.294", "Completed cohort fertility rate shifted +30 years": "1.607", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1985", "Total fertility rate": "1.281", "Completed cohort fertility rate shifted +30 years": "1.623", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1986", "Total fertility rate": "1.345", "Completed cohort fertility rate shifted +30 years": "1.621", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1987", "Total fertility rate": "1.362", "Completed cohort fertility rate shifted +30 years": "1.605", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1988", "Total fertility rate": "1.416", "Completed cohort fertility rate shifted +30 years": "1.608", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1989", "Total fertility rate": "1.394", "Completed cohort fertility rate shifted +30 years": "1.606", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1990", "Total fertility rate": "1.459", "Completed cohort fertility rate shifted +30 years": "1.609", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1991", "Total fertility rate": "1.429", "Completed cohort fertility rate shifted +30 years": "1.587", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1992", "Total fertility rate": "1.414", "Completed cohort fertility rate shifted +30 years": "1.574", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1993", "Total fertility rate": "1.404", "Completed cohort fertility rate shifted +30 years": "1.554", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1994", "Total fertility rate": "1.358", "Completed cohort fertility rate shifted +30 years": "1.539", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1995", "Total fertility rate": "1.351", "Completed cohort fertility rate shifted +30 years": "1.53", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1996", "Total fertility rate": "1.413", "Completed cohort fertility rate shifted +30 years": "1.51", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1997", "Total fertility rate": "1.456", "Completed cohort fertility rate shifted +30 years": "1.489", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1998", "Total fertility rate": "1.429", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1999", "Total fertility rate": "1.422", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2000", "Total fertility rate": "1.436", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2001", "Total fertility rate": "1.399", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2002", "Total fertility rate": "1.389", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2003", "Total fertility rate": "1.383", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2004", "Total fertility rate": "1.396", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2005", "Total fertility rate": "1.376", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2006", "Total fertility rate": "1.363", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2007", "Total fertility rate": "1.4", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2008", "Total fertility rate": "1.403", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2009", "Total fertility rate": "1.376", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2010", "Total fertility rate": "1.408", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.671"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2011", "Total fertility rate": "1.381", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.584"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2012", "Total fertility rate": "1.4", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.59"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2013", "Total fertility rate": "1.413", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.624"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2014", "Total fertility rate": "1.471", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.585"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2015", "Total fertility rate": "1.503", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.484"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2016", "Total fertility rate": "1.602", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": "1.644"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "2017", "Total fertility rate": "1.578", "Completed cohort fertility rate shifted +30 years": "", "Tempo-adjusted total fertility rate": ""}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "total-vs-cohort-fertility-rate-plus-thirty-years", "metadata_url": "https://ourworldindata.org/grapher/total-vs-cohort-fertility-rate-plus-thirty-years.metadata.json", "chart_title": "Total fertility rate  vs cohort fertility rate vs tempo-adjusted total fertility rate", "chart_subtitle": "Three measures of fertility rate are compared: the total fertility rate (a summary metric based on birth rates across different age groups of women in one particular year); the completed cohort fertility rate (the average number of children born to a woman by the end of her childbearing years); and the tempo-adjusted total fertility rate (which aims to adjust for changes in the timing of childbearing).", "chart_note": "The completed cohort fertility rate is typically given by the woman's birth year. In this chart, it has been shifted +30 years to see how the two measures compare at roughly the average age of mothers at childbirth.", "chart_citation": "Human Fertility Database (2025)", "original_chart_url": "https://ourworldindata.org/grapher/total-fertility-rate-vs-cohort-fertility-rate-vs-tempo-adjusted-tfr", "owid_column_metadata": {"Period total fertility rate - Total": {"titleShort": "Fertility rate: births per woman", "titleLong": "Fertility rate: births per woman - HFD", "descriptionShort": "The average number of children a woman would have in her lifetime if she experienced the fertility rates of a specific year.", "descriptionKey": ["Assumes current age-specific fertility rates remain constant throughout a woman's lifetime.", "Does not account for potential changes in social, economic, or health conditions that could affect fertility rates."], "unit": "births per woman", "timespan": "1891-2024", "type": "Numeric", "owidVariableId": 1118905, "shortName": "tfr__birth_order_total", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Fertility Database (2025) – processed by Our World in Data", "citationLong": "Human Fertility Database (2025) – processed by Our World in Data. “Fertility rate: births per woman – HFD” [dataset]. Human Fertility Database, “Human Fertility Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118905.metadata.json"}, "Completed cohort fertility rate (adjusted +30 years) - Total": {"titleShort": "Completed cohort fertility rate shifted +30 years", "titleLong": "Completed cohort fertility rate shifted +30 years", "descriptionShort": "The average number of children born to women in the 30-years-ago cohort over their lifetime.", "descriptionKey": ["Shows the total number of children that women born in the same year have by the end of their childbearing years.", "Helps compare how many children different generations of women have on average.", "Calculated using the actual number of births recorded at different ages throughout a woman's childbearing years."], "unit": "births per woman", "timespan": "1906-2004", "type": "Numeric", "owidVariableId": 1118715, "shortName": "ccf_plus30y__birth_order_total", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Fertility Database (2025) – processed by Our World in Data", "citationLong": "Human Fertility Database (2025) – processed by Our World in Data. “Completed cohort fertility rate shifted +30 years – HFD” [dataset]. Human Fertility Database, “Human Fertility Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118715.metadata.json"}, "Tempo-adjusted total fertility rate - Total": {"titleShort": "Tempo-adjusted total fertility rate", "titleLong": "Tempo-adjusted total fertility rate", "descriptionShort": "The total fertility rate adjusted to account for delays or advances in childbearing.", "descriptionKey": ["The TFR has been adjusted using a method proposed by [Bongaarts-Feeney](https://en.wikipedia.org/wiki/Sub-replacement_fertility). It sums order-specific TFRs and adjusts for changes in the mean age of order-specific fertility schedule.", "The tempo-adjusted TFR adjusts for timing shifts in childbearing, such as postponement of births.", "The tempo-adjusted TFR helps to distinguish between changes in the number of children women have and changes in the timing of when they have them.", "The tempo-adjusted TFR often displays large year-to-year fluctuations ([Sobotka 2003](https://www.demographic-research.org/articles/volume/8/6)), which can make its use for specific years problematic. Therefore, three- or five-year moving averages are often used to smooth out fluctuations.", "Requires careful interpretation, as the adjustment is based on specific assumptions about timing effects."], "unit": "births per woman", "timespan": "1934-2023", "type": "Numeric", "owidVariableId": 1118900, "shortName": "adjtfr__birth_order_total", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Fertility Database (2025) – processed by Our World in Data", "citationLong": "Human Fertility Database (2025) – processed by Our World in Data. “Tempo-adjusted total fertility rate – HFD” [dataset]. Human Fertility Database, “Human Fertility Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118900.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Completed cohort fertility rate: births per woman", "source_url": "https://ourworldindata.org/grapher/cohort-fertility-rate.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Completed cohort fertility rate"], "row_count_total": 1319, "rows_head": [{"Entity": "Austria", "Code": "AUT", "Year": "1936", "Completed cohort fertility rate": "2.436"}, {"Entity": "Austria", "Code": "AUT", "Year": "1937", "Completed cohort fertility rate": "2.41"}, {"Entity": "Austria", "Code": "AUT", "Year": "1938", "Completed cohort fertility rate": "2.339"}, {"Entity": "Austria", "Code": "AUT", "Year": "1939", "Completed cohort fertility rate": "2.202"}, {"Entity": "Austria", "Code": "AUT", "Year": "1940", "Completed cohort fertility rate": "2.132"}, {"Entity": "Austria", "Code": "AUT", "Year": "1941", "Completed cohort fertility rate": "2.065"}, {"Entity": "Austria", "Code": "AUT", "Year": "1942", "Completed cohort fertility rate": "2.037"}, {"Entity": "Austria", "Code": "AUT", "Year": "1943", "Completed cohort fertility rate": "1.983"}, {"Entity": "Austria", "Code": "AUT", "Year": "1944", "Completed cohort fertility rate": "1.953"}, {"Entity": "Austria", "Code": "AUT", "Year": "1945", "Completed cohort fertility rate": "1.938"}, {"Entity": "Austria", "Code": "AUT", "Year": "1946", "Completed cohort fertility rate": "1.986"}, {"Entity": "Austria", "Code": "AUT", "Year": "1947", "Completed cohort fertility rate": "1.931"}, {"Entity": "Austria", "Code": "AUT", "Year": "1948", "Completed cohort fertility rate": "1.916"}, {"Entity": "Austria", "Code": "AUT", "Year": "1949", "Completed cohort fertility rate": "1.908"}, {"Entity": "Austria", "Code": "AUT", "Year": "1950", "Completed cohort fertility rate": "1.863"}, {"Entity": "Austria", "Code": "AUT", "Year": "1951", "Completed cohort fertility rate": "1.835"}, {"Entity": "Austria", "Code": "AUT", "Year": "1952", "Completed cohort fertility rate": "1.812"}, {"Entity": "Austria", "Code": "AUT", "Year": "1953", "Completed cohort fertility rate": "1.817"}, {"Entity": "Austria", "Code": "AUT", "Year": "1954", "Completed cohort fertility rate": "1.784"}, {"Entity": "Austria", "Code": "AUT", "Year": "1955", "Completed cohort fertility rate": "1.768"}, {"Entity": "Austria", "Code": "AUT", "Year": "1956", "Completed cohort fertility rate": "1.746"}, {"Entity": "Austria", "Code": "AUT", "Year": "1957", "Completed cohort fertility rate": "1.73"}, {"Entity": "Austria", "Code": "AUT", "Year": "1958", "Completed cohort fertility rate": "1.713"}, {"Entity": "Austria", "Code": "AUT", "Year": "1959", "Completed cohort fertility rate": "1.714"}, {"Entity": "Austria", "Code": "AUT", "Year": "1960", "Completed cohort fertility rate": "1.698"}, {"Entity": "Austria", "Code": "AUT", "Year": "1961", "Completed cohort fertility rate": "1.676"}, {"Entity": "Austria", "Code": "AUT", "Year": "1962", "Completed cohort fertility rate": "1.675"}, {"Entity": "Austria", "Code": "AUT", "Year": "1963", "Completed cohort fertility rate": "1.667"}, {"Entity": "Austria", "Code": "AUT", "Year": "1964", "Completed cohort fertility rate": "1.652"}, {"Entity": "Austria", "Code": "AUT", "Year": "1965", "Completed cohort fertility rate": "1.657"}, {"Entity": "Austria", "Code": "AUT", "Year": "1966", "Completed cohort fertility rate": "1.64"}, {"Entity": "Austria", "Code": "AUT", "Year": "1967", "Completed cohort fertility rate": "1.626"}, {"Entity": "Austria", "Code": "AUT", "Year": "1968", "Completed cohort fertility rate": "1.61"}, {"Entity": "Austria", "Code": "AUT", "Year": "1969", "Completed cohort fertility rate": "1.615"}, {"Entity": "Austria", "Code": "AUT", "Year": "1970", "Completed cohort fertility rate": "1.624"}, {"Entity": "Austria", "Code": "AUT", "Year": "1971", "Completed cohort fertility rate": "1.634"}, {"Entity": "Austria", "Code": "AUT", "Year": "1972", "Completed cohort fertility rate": "1.651"}, {"Entity": "Austria", "Code": "AUT", "Year": "1973", "Completed cohort fertility rate": "1.661"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1949", "Completed cohort fertility rate": "1.968"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1950", "Completed cohort fertility rate": "1.938"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1951", "Completed cohort fertility rate": "1.964"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1952", "Completed cohort fertility rate": "1.928"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1953", "Completed cohort fertility rate": "1.886"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1954", "Completed cohort fertility rate": "1.915"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1955", "Completed cohort fertility rate": "1.931"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1956", "Completed cohort fertility rate": "1.926"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1957", "Completed cohort fertility rate": "1.924"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1958", "Completed cohort fertility rate": "1.921"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1959", "Completed cohort fertility rate": "1.891"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1960", "Completed cohort fertility rate": "1.873"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1961", "Completed cohort fertility rate": "1.857"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1962", "Completed cohort fertility rate": "1.815"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1963", "Completed cohort fertility rate": "1.736"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1964", "Completed cohort fertility rate": "1.684"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1965", "Completed cohort fertility rate": "1.689"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1966", "Completed cohort fertility rate": "1.7"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1967", "Completed cohort fertility rate": "1.713"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1968", "Completed cohort fertility rate": "1.709"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1932", "Completed cohort fertility rate": "2.292"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1933", "Completed cohort fertility rate": "2.279"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1934", "Completed cohort fertility rate": "2.278"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1935", "Completed cohort fertility rate": "2.274"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1936", "Completed cohort fertility rate": "2.235"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1937", "Completed cohort fertility rate": "2.22"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1938", "Completed cohort fertility rate": "2.186"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1939", "Completed cohort fertility rate": "2.149"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1940", "Completed cohort fertility rate": "2.183"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1941", "Completed cohort fertility rate": "2.141"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1942", "Completed cohort fertility rate": "2.088"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1943", "Completed cohort fertility rate": "2.034"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1944", "Completed cohort fertility rate": "1.989"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1945", "Completed cohort fertility rate": "1.942"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1946", "Completed cohort fertility rate": "1.899"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1947", "Completed cohort fertility rate": "1.88"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1948", "Completed cohort fertility rate": "1.87"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1949", "Completed cohort fertility rate": "1.849"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1950", "Completed cohort fertility rate": "1.867"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1951", "Completed cohort fertility rate": "1.834"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1952", "Completed cohort fertility rate": "1.844"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1953", "Completed cohort fertility rate": "1.834"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1954", "Completed cohort fertility rate": "1.836"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1955", "Completed cohort fertility rate": "1.835"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1956", "Completed cohort fertility rate": "1.855"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1957", "Completed cohort fertility rate": "1.837"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1958", "Completed cohort fertility rate": "1.848"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1959", "Completed cohort fertility rate": "1.849"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1960", "Completed cohort fertility rate": "1.872"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1961", "Completed cohort fertility rate": "1.847"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1962", "Completed cohort fertility rate": "1.851"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1963", "Completed cohort fertility rate": "1.828"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1964", "Completed cohort fertility rate": "1.825"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1965", "Completed cohort fertility rate": "1.821"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1966", "Completed cohort fertility rate": "1.815"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1967", "Completed cohort fertility rate": "1.823"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1968", "Completed cohort fertility rate": "1.824"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1969", "Completed cohort fertility rate": "1.827"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1970", "Completed cohort fertility rate": "1.824"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1971", "Completed cohort fertility rate": "1.822"}, {"Entity": "Belgium", "Code": "BEL", "Year": "1972", "Completed cohort fertility rate": "1.84"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1932", "Completed cohort fertility rate": "2.045"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1933", "Completed cohort fertility rate": "2.049"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1934", "Completed cohort fertility rate": "2.041"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1935", "Completed cohort fertility rate": "2.032"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1936", "Completed cohort fertility rate": "2.036"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1937", "Completed cohort fertility rate": "2.039"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1938", "Completed cohort fertility rate": "2.038"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1939", "Completed cohort fertility rate": "2.052"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1940", "Completed cohort fertility rate": "2.085"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1941", "Completed cohort fertility rate": "2.094"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1942", "Completed cohort fertility rate": "2.091"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1943", "Completed cohort fertility rate": "2.078"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1944", "Completed cohort fertility rate": "2.077"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1945", "Completed cohort fertility rate": "2.072"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1946", "Completed cohort fertility rate": "2.053"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1947", "Completed cohort fertility rate": "2.051"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1948", "Completed cohort fertility rate": "2.065"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1949", "Completed cohort fertility rate": "2.076"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1950", "Completed cohort fertility rate": "2.066"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1951", "Completed cohort fertility rate": "2.043"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "1952", "Completed cohort fertility rate": "2.042"}], "rows_tail": [{"Entity": "Taiwan", "Code": "TWN", "Year": "1971", "Completed cohort fertility rate": "1.68"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "1972", "Completed cohort fertility rate": "1.649"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "1973", "Completed cohort fertility rate": "1.617"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1944", "Completed cohort fertility rate": "1.839"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1945", "Completed cohort fertility rate": "1.831"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1946", "Completed cohort fertility rate": "1.856"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1947", "Completed cohort fertility rate": "1.867"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1948", "Completed cohort fertility rate": "1.886"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1949", "Completed cohort fertility rate": "1.916"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1950", "Completed cohort fertility rate": "1.901"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1951", "Completed cohort fertility rate": "1.911"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1952", "Completed cohort fertility rate": "1.883"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1953", "Completed cohort fertility rate": "1.848"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1954", "Completed cohort fertility rate": "1.839"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1955", "Completed cohort fertility rate": "1.831"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1956", "Completed cohort fertility rate": "1.824"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1957", "Completed cohort fertility rate": "1.842"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1958", "Completed cohort fertility rate": "1.862"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1959", "Completed cohort fertility rate": "1.857"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1960", "Completed cohort fertility rate": "1.834"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1961", "Completed cohort fertility rate": "1.806"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1962", "Completed cohort fertility rate": "1.774"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "1963", "Completed cohort fertility rate": "1.739"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1959", "Completed cohort fertility rate": "1.983"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1960", "Completed cohort fertility rate": "1.98"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1961", "Completed cohort fertility rate": "1.952"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1962", "Completed cohort fertility rate": "1.933"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1963", "Completed cohort fertility rate": "1.929"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1964", "Completed cohort fertility rate": "1.925"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1965", "Completed cohort fertility rate": "1.917"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1966", "Completed cohort fertility rate": "1.905"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1967", "Completed cohort fertility rate": "1.906"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1968", "Completed cohort fertility rate": "1.911"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1969", "Completed cohort fertility rate": "1.905"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1970", "Completed cohort fertility rate": "1.903"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1971", "Completed cohort fertility rate": "1.895"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "1972", "Completed cohort fertility rate": "1.882"}, {"Entity": "United States", "Code": "USA", "Year": "1918", "Completed cohort fertility rate": "2.507"}, {"Entity": "United States", "Code": "USA", "Year": "1919", "Completed cohort fertility rate": "2.595"}, {"Entity": "United States", "Code": "USA", "Year": "1920", "Completed cohort fertility rate": "2.708"}, {"Entity": "United States", "Code": "USA", "Year": "1921", "Completed cohort fertility rate": "2.749"}, {"Entity": "United States", "Code": "USA", "Year": "1922", "Completed cohort fertility rate": "2.784"}, {"Entity": "United States", "Code": "USA", "Year": "1923", "Completed cohort fertility rate": "2.852"}, {"Entity": "United States", "Code": "USA", "Year": "1924", "Completed cohort fertility rate": "2.942"}, {"Entity": "United States", "Code": "USA", "Year": "1925", "Completed cohort fertility rate": "2.986"}, {"Entity": "United States", "Code": "USA", "Year": "1926", "Completed cohort fertility rate": "3.025"}, {"Entity": "United States", "Code": "USA", "Year": "1927", "Completed cohort fertility rate": "3.092"}, {"Entity": "United States", "Code": "USA", "Year": "1928", "Completed cohort fertility rate": "3.109"}, {"Entity": "United States", "Code": "USA", "Year": "1929", "Completed cohort fertility rate": "3.171"}, {"Entity": "United States", "Code": "USA", "Year": "1930", "Completed cohort fertility rate": "3.239"}, {"Entity": "United States", "Code": "USA", "Year": "1931", "Completed cohort fertility rate": "3.241"}, {"Entity": "United States", "Code": "USA", "Year": "1932", "Completed cohort fertility rate": "3.253"}, {"Entity": "United States", "Code": "USA", "Year": "1933", "Completed cohort fertility rate": "3.256"}, {"Entity": "United States", "Code": "USA", "Year": "1934", "Completed cohort fertility rate": "3.254"}, {"Entity": "United States", "Code": "USA", "Year": "1935", "Completed cohort fertility rate": "3.214"}, {"Entity": "United States", "Code": "USA", "Year": "1936", "Completed cohort fertility rate": "3.131"}, {"Entity": "United States", "Code": "USA", "Year": "1937", "Completed cohort fertility rate": "3.059"}, {"Entity": "United 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fertility rate": "2.15"}, {"Entity": "United States", "Code": "USA", "Year": "1948", "Completed cohort fertility rate": "2.104"}, {"Entity": "United States", "Code": "USA", "Year": "1949", "Completed cohort fertility rate": "2.057"}, {"Entity": "United States", "Code": "USA", "Year": "1950", "Completed cohort fertility rate": "2.022"}, {"Entity": "United States", "Code": "USA", "Year": "1951", "Completed cohort fertility rate": "1.998"}, {"Entity": "United States", "Code": "USA", "Year": "1952", "Completed cohort fertility rate": "1.982"}, {"Entity": "United States", "Code": "USA", "Year": "1953", "Completed cohort fertility rate": "1.972"}, {"Entity": "United States", "Code": "USA", "Year": "1954", "Completed cohort fertility rate": "1.974"}, {"Entity": "United States", "Code": "USA", "Year": "1955", "Completed cohort fertility rate": "1.981"}, {"Entity": "United States", "Code": "USA", "Year": "1956", "Completed cohort fertility rate": "1.985"}, {"Entity": "United States", "Code": "USA", "Year": "1957", "Completed cohort fertility rate": "1.987"}, {"Entity": "United States", "Code": "USA", "Year": "1958", "Completed cohort fertility rate": "1.991"}, {"Entity": "United States", "Code": "USA", "Year": "1959", "Completed cohort fertility rate": "2.012"}, {"Entity": "United States", "Code": "USA", "Year": "1960", "Completed cohort fertility rate": "2.024"}, {"Entity": "United States", "Code": "USA", "Year": "1961", "Completed cohort fertility rate": "2.024"}, {"Entity": "United States", "Code": "USA", "Year": "1962", "Completed cohort fertility rate": "2.034"}, {"Entity": "United States", "Code": "USA", "Year": "1963", "Completed cohort fertility rate": "2.051"}, {"Entity": "United States", "Code": "USA", "Year": "1964", "Completed cohort fertility rate": "2.066"}, {"Entity": "United States", "Code": "USA", "Year": "1965", "Completed cohort fertility rate": "2.075"}, {"Entity": "United States", "Code": "USA", "Year": "1966", "Completed cohort fertility rate": "2.089"}, {"Entity": "United States", "Code": "USA", "Year": "1967", "Completed cohort fertility rate": "2.109"}, {"Entity": "United States", "Code": "USA", "Year": "1968", "Completed cohort fertility rate": "2.111"}, {"Entity": "United States", "Code": "USA", "Year": "1969", "Completed cohort fertility rate": "2.114"}, {"Entity": "United States", "Code": "USA", "Year": "1970", "Completed cohort fertility rate": "2.137"}, {"Entity": "United States", "Code": "USA", "Year": "1971", "Completed cohort fertility rate": "2.161"}, {"Entity": "United States", "Code": "USA", "Year": "1972", "Completed cohort fertility rate": "2.183"}, {"Entity": "United States", "Code": "USA", "Year": "1973", "Completed cohort fertility rate": "2.203"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1941", "Completed cohort fertility rate": "1.902"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1942", "Completed cohort fertility rate": "1.85"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1943", "Completed cohort fertility rate": "1.809"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1944", "Completed cohort fertility rate": "1.777"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1945", "Completed cohort fertility rate": "1.774"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1946", "Completed cohort fertility rate": "1.778"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1947", "Completed cohort fertility rate": "1.75"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1948", "Completed cohort fertility rate": "1.728"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1949", "Completed cohort fertility rate": "1.714"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1950", "Completed cohort fertility rate": "1.7"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1951", "Completed cohort fertility rate": "1.658"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1952", "Completed cohort fertility rate": "1.647"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1953", "Completed cohort fertility rate": "1.63"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1954", "Completed cohort fertility rate": "1.607"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1955", "Completed cohort fertility rate": "1.623"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1956", "Completed cohort fertility rate": "1.621"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1957", "Completed cohort fertility rate": "1.605"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1958", "Completed cohort fertility rate": "1.608"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1959", "Completed cohort fertility rate": "1.606"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1960", "Completed cohort fertility rate": "1.609"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1961", "Completed cohort fertility rate": "1.587"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1962", "Completed cohort fertility rate": "1.574"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1963", "Completed cohort fertility rate": "1.554"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1964", "Completed cohort fertility rate": "1.539"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1965", "Completed cohort fertility rate": "1.53"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1966", "Completed cohort fertility rate": "1.51"}, {"Entity": "West Germany", "Code": "OWID_GFR", "Year": "1967", "Completed cohort fertility rate": "1.489"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "cohort-fertility-rate", "metadata_url": "https://ourworldindata.org/grapher/cohort-fertility-rate.metadata.json", "chart_title": "Completed cohort fertility rate: births per woman", "chart_subtitle": "The average number of children born to a woman by the end of her childbearing years, given by her birth year.", "chart_note": "", "chart_citation": "Human Fertility Database (2025)", "original_chart_url": "https://ourworldindata.org/grapher/cohort-fertility-rate", "owid_column_metadata": {"Completed cohort fertility rate - Total": {"titleShort": "Completed cohort fertility rate", "titleLong": "Completed cohort fertility rate", "descriptionShort": "The average number of children born to women in the current cohort over their lifetime.", "descriptionKey": ["Shows the total number of children that women born in the same year have by the end of their childbearing years.", "Helps compare how many children different generations of women have on average.", "Calculated using the actual number of births recorded at different ages throughout a woman's childbearing years."], "unit": "births per woman", "timespan": "1876-1974", "type": "Numeric", "owidVariableId": 1118712, "shortName": "ccf__birth_order_total", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Fertility Database (2025) – processed by Our World in Data", "citationLong": "Human Fertility Database (2025) – processed by Our World in Data. “Completed cohort fertility rate – HFD” [dataset]. Human Fertility Database, “Human Fertility Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118712.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Fertility rate: births per woman", "source_url": "https://ourworldindata.org/grapher/children-born-per-woman.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Total fertility rate"], "row_count_total": 19402, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1950", "Total fertility rate": "7.248"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1951", "Total fertility rate": "7.26"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1952", "Total fertility rate": "7.26"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1953", "Total fertility rate": "7.266"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1954", "Total fertility rate": "7.254"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1955", "Total fertility rate": "7.262"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1956", "Total fertility rate": "7.269"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1957", "Total fertility rate": "7.264"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1958", "Total fertility rate": "7.269"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1959", "Total fertility rate": "7.276"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1960", "Total fertility rate": "7.282"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Total fertility rate": "7.284"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Total fertility rate": "7.292"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Total fertility rate": "7.302"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Total fertility rate": "7.304"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Total fertility rate": "7.305"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Total fertility rate": "7.32"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Total fertility rate": "7.339"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Total fertility rate": "7.363"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1969", "Total fertility rate": "7.389"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1970", "Total fertility rate": "7.4"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1971", "Total fertility rate": "7.432"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1972", "Total fertility rate": "7.453"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Total fertility rate": "7.487"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Total fertility rate": "7.526"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Total fertility rate": "7.542"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Total fertility rate": "7.561"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Total fertility rate": "7.591"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Total fertility rate": "7.599"}, {"Entity": "Afghanistan", "Code": 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"AFG", "Year": "2001", "Total fertility rate": "7.453"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Total fertility rate": "7.32"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Total fertility rate": "7.174"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Total fertility rate": "7.018"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Total fertility rate": "6.858"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Total fertility rate": "6.686"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Total fertility rate": "6.508"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Total fertility rate": "6.392"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Total fertility rate": "6.295"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Total fertility rate": "6.195"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Total fertility rate": "6.094"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Total fertility rate": "5.985"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Total fertility rate": "5.879"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Total fertility rate": "5.77"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Total fertility rate": "5.652"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Total fertility rate": "5.542"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Total fertility rate": "5.433"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Total fertility rate": "5.327"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Total fertility rate": "5.238"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Total fertility rate": "5.145"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Total fertility rate": "5.039"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Total fertility rate": "4.932"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Total fertility rate": "4.84"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1950", "Total fertility rate": "6.4367776"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1951", "Total fertility rate": "6.451228"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1952", "Total fertility rate": "6.4636226"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1953", "Total fertility rate": "6.4829826"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1954", "Total fertility rate": "6.4931884"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1955", "Total fertility rate": "6.50552"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1956", "Total fertility rate": "6.5189023"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1957", "Total fertility rate": "6.530977"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1958", "Total fertility rate": "6.5437264"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1959", "Total fertility rate": "6.5507164"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1960", "Total fertility rate": "6.561704"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1961", "Total fertility rate": "6.5730667"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1962", "Total fertility rate": "6.5883465"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1963", "Total fertility rate": "6.596562"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1964", "Total fertility rate": "6.5999"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1965", "Total fertility rate": "6.597559"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1966", "Total fertility rate": "6.60687"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1967", "Total fertility rate": "6.60904"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1968", "Total fertility rate": "6.609567"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1969", "Total fertility rate": "6.605879"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1970", "Total fertility rate": "6.608199"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1971", "Total fertility rate": "6.6169066"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1972", "Total fertility rate": "6.616913"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1973", "Total fertility rate": "6.6099615"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1974", "Total fertility rate": "6.609396"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1975", "Total fertility rate": "6.6072907"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1976", "Total fertility rate": "6.603195"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1977", "Total fertility rate": "6.5917606"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1978", "Total fertility rate": "6.5663705"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1979", "Total fertility rate": "6.545587"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1980", "Total fertility rate": "6.515052"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1981", "Total fertility rate": "6.4736576"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1982", "Total fertility rate": "6.434558"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1983", "Total fertility rate": "6.393361"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1984", "Total fertility rate": "6.3252063"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Total fertility rate": "6.256401"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Total fertility rate": "6.1838737"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Total fertility rate": "6.1125216"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Total fertility rate": "6.0366774"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Total fertility rate": "5.9492836"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Total fertility rate": "5.8539023"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Total fertility rate": "5.774524"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Total fertility rate": "5.6971273"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Total fertility rate": "5.625158"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Total fertility rate": "5.526201"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Total fertility rate": "5.463184"}], "rows_tail": [{"Entity": "Zambia", "Code": "ZMB", "Year": "1978", "Total fertility rate": "7.294"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1979", "Total fertility rate": "7.222"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1980", "Total fertility rate": "7.139"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1981", "Total fertility rate": "7.073"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1982", "Total fertility rate": "7.039"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1983", "Total fertility rate": "7.007"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1984", "Total fertility rate": "6.974"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Total fertility rate": "6.928"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Total fertility rate": "6.868"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Total fertility rate": "6.798"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Total fertility rate": "6.703"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Total fertility rate": "6.635"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Total fertility rate": "6.567"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Total fertility rate": "6.458"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Total fertility rate": "6.349"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Total fertility rate": "6.298"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Total fertility rate": "6.26"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Total fertility rate": "6.232"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Total fertility rate": "6.182"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Total fertility rate": "6.124"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Total fertility rate": "6.067"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Total fertility rate": "5.998"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Total fertility rate": "5.921"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Total fertility rate": "5.848"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Total fertility rate": "5.774"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Total fertility rate": "5.71"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Total fertility rate": "5.698"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Total fertility rate": "5.697"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Total fertility rate": "5.679"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Total fertility rate": "5.613"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Total fertility rate": "5.53"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Total fertility rate": "5.453"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Total fertility rate": "5.359"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Total fertility rate": "5.256"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Total fertility rate": "5.136"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Total fertility rate": "5.014"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Total fertility rate": "4.89"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Total fertility rate": "4.775"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Total fertility rate": "4.669"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Total fertility rate": "4.567"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Total fertility rate": "4.492"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Total fertility rate": "4.418"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Total fertility rate": "4.323"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Total fertility rate": "4.246"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Total fertility rate": "4.175"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Total fertility rate": "4.101"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1950", "Total fertility rate": "7.041"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1951", "Total fertility rate": "7.06"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1952", "Total fertility rate": "7.069"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1953", "Total fertility rate": "7.081"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1954", "Total fertility rate": "7.091"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1955", "Total fertility rate": "7.099"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1956", "Total fertility rate": "7.121"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1957", "Total fertility rate": "7.142"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1958", "Total fertility rate": "7.159"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1959", "Total fertility rate": "7.173"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1960", "Total fertility rate": "7.195"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1961", "Total fertility rate": "7.212"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1962", "Total fertility rate": "7.231"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1963", "Total fertility rate": "7.23"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1964", "Total fertility rate": "7.23"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1965", "Total fertility rate": "7.231"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "Total fertility rate": "7.196"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "Total fertility rate": "7.157"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "Total fertility rate": "7.114"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "Total fertility rate": "7.036"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "Total fertility rate": "6.982"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "Total fertility rate": "6.939"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "Total fertility rate": "6.942"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "Total fertility rate": "6.942"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "Total fertility rate": "6.879"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Total fertility rate": "6.832"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Total fertility rate": "6.771"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Total fertility rate": "6.713"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Total fertility rate": "6.658"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Total fertility rate": "6.59"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Total fertility rate": "6.517"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Total fertility rate": "6.445"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Total fertility rate": "6.333"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Total fertility rate": "6.197"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Total fertility rate": "6.044"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Total fertility rate": "5.871"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Total fertility rate": "5.688"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Total fertility rate": "5.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Total fertility rate": "5.279"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Total fertility rate": "5.073"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Total fertility rate": "4.876"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Total fertility rate": "4.715"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Total fertility rate": "4.567"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Total fertility rate": "4.391"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Total fertility rate": "4.284"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Total fertility rate": "4.153"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Total fertility rate": "4.11"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Total fertility rate": "4.075"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Total fertility rate": "4.067"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Total fertility rate": "4.056"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Total fertility rate": "4.009"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Total fertility rate": "3.983"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Total fertility rate": "3.925"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Total fertility rate": "3.862"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Total fertility rate": "3.776"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Total fertility rate": "3.693"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Total fertility rate": "3.635"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Total fertility rate": "3.677"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Total fertility rate": "3.783"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Total fertility rate": "3.95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Total fertility rate": "4.04"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Total fertility rate": "4.126"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Total fertility rate": "4.134"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Total fertility rate": "4.111"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Total fertility rate": "4.011"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Total fertility rate": "3.911"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Total fertility rate": "3.828"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Total fertility rate": "3.768"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Total fertility rate": "3.744"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Total fertility rate": "3.748"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Total fertility rate": "3.754"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Total fertility rate": "3.765"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Total fertility rate": "3.767"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Total fertility rate": "3.724"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "children-born-per-woman", "metadata_url": "https://ourworldindata.org/grapher/children-born-per-woman.metadata.json", "chart_title": "Fertility rate: births per woman", "chart_subtitle": "The total fertility rate summarizes the total number of births a woman would have, if she experienced the birth rates seen in women of each age group in one particular year across her childbearing years.", "chart_note": "", "chart_citation": "Human Fertility Database (2025); UN, World Population Prospects (2024)", "original_chart_url": "https://ourworldindata.org/grapher/children-born-per-woman", "owid_column_metadata": {"Fertility rate (period), historical": {"titleShort": "Fertility rate: births per woman", "titleLong": "Fertility rate: births per woman - HFD, UN WPP – period tables", "descriptionShort": "The average number of live births a hypothetical cohort of women would have at the end of their reproductive period if they were subject during their whole lives to the fertility rates of a given period and if they were not subject to mortality.", "descriptionKey": ["Assumes current age-specific fertility rates remain constant throughout a woman's lifetime.", "Does not account for potential changes in social, economic, or health conditions that could affect fertility rates."], "descriptionProcessing": "The fertility data is constructed by combining data from multiple sources:\n\n- Before 1950: Historical estimates by Human Fertility Database (2025).\n\n- 1950-2023: Population records by the UN World Population Prospects (2024 revision).", "unit": "live births per woman", "timespan": "1891-2023", "type": "Numeric", "owidVariableId": 1118640, "shortName": "fertility_rate_hist", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Fertility Database (2025); UN, World Population Prospects (2024) – with major processing by Our World in Data", "citationLong": "Human Fertility Database (2025); UN, World Population Prospects (2024) – with major processing by Our World in Data. “Fertility rate: births per woman – HFD, UN WPP – period tables” [dataset]. Human Fertility Database, “Human Fertility Database”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118640.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "0c78b26990af38070361"},
{"raw_link": "https://ourworldindata.org/fertility-rate", "title": "Fertility Rate", "context": "Fertility Rate\nBy\nSaloni Dattani\n,\nLucas Rodés-Guirao\n,\nand\nMax Roser\nCite this work\nReuse this work\nHow many children people have, and how this changes over time, are important drivers of\npopulation growth\nand the\nage structure\nof populations. People’s decisions about when and how many children to have, in turn, reflect broader trends in societies and economies.\nGlobally, the\ntotal fertility rate\nwas 2.3 children per woman in 2023. This is much lower than in the past; in the 1950s, it was more than twice as high: 4.9.\nThe total fertility rate is the most commonly used metric to assess birth patterns: it captures the average number of births per woman, assuming she experiences the same age-specific fertility rates over her lifetime as the age-specific fertility rates in one particular year. It’s important to note that this does not predict how many children women will eventually have.\nOn this page, we present a wide range of different measures related to fertility. We cover parental ages at birth, teenage birth rates, twin birth rates, the use of reproductive technologies, and more.\nBy looking at these additional metrics, we can better understand when, why, and how parents have children — and what this means for individuals and societies. The following section highlights the key insights on this topic. Below are links to our in-depth articles, which explore several topics in more detail.\nRelated topics\nPopulation Growth\nAge Structure\nGender Ratio\nKey Insights on Fertility\nThe fertility rate has declined over history\nThe fertility rate varies greatly around the world\nTwo is the typical number of children among women in many high-income countries\nIn many countries, the average age of mothers at childbirth declined before rising again\nTeenage motherhood is still common in many parts of the world\nThe fertility rate has declined over history\nFertility rates have fallen around the world.\nFor example, total fertility rates in India have fallen from 5 to 2 births per woman since the 1970s.\nSouth Korea has seen a particularly rapid decline: from a total fertility rate of around 6 births per woman in the 1950s to less than 1 in 2023.\nAs you can see, this decline in fertility rates is not linear in many countries. The United States is an example of a visible rise between the 1940s and 1960s, referred to as the “baby boom”.\nIn this article, we cover this in more detail:\nThe global decline of the fertility rate\nThe total fertility rate has halved in sixty years — what are the causes of the decline?\nWhat you should know about this data\nThe total fertility rate is a metric that summarizes fertility rates across all age groups of women in one particular year.\nIt captures the average number of births per woman, assuming she experiences the same age-specific fertility rates over her lifetime as the fertility rates seen in each age group in one particular year.\nIt’s important to note that this is not a prediction of how many children women will eventually have. It includes all live births, not just children who survive early life.\nThis data is compiled from two sources: the Human Fertility Database and the United Nations’ World Population Prospects (UN WPP). For data points before 1950, we use Human Fertility Database data. From 1950 onwards, we use UN WPP data.\nThe HFD prioritizes uniformity in methods and is limited to specific countries and periods where high-quality fertility data is available nationally.\nThe UN WPP estimates ages at childbirth in various countries through various methods. In countries where birth registration data with mothers’ ages is often lacking, the underlying data frequently comes from national household surveys, which are then used to estimate fertility rates and ages at childbirth.\nThe fertility rate varies greatly around the world\nFertility rates vary greatly around the world.\nIn 2023, the total fertility rate was highest in countries in Africa and central Asia, where most countries had a total fertility rate between 3 to 7 births per woman, and lowest in countries in East Asia, where most countries had a total fertility rate below 1.\nHowever, a share of children die in infancy or early life, which means that the total fertility rate is higher than the number of surviving children per woman.\nThe map, therefore, shows the “effective fertility rate”, which considers how many newborns are expected to survive until their childbearing age. This data comes from estimates by economists Anup Malani and Ari Jacob.\nFor example, Niger had a total fertility rate of 6 in 2023, but because of high child mortality rates, 1 in 6 children in the country aren’t expected to survive until adulthood, and the effective fertility rate therefore is 5 children per woman.\nThe effective fertility rate also shows large differences around the world, but less so than the total fertility rate because high fertility is correlated with high child mortality.\nIn 2023, the effective fertility rate was between 1.5 and 2 children per woman in most regions. Sub-Saharan Africa, where it was substantially higher, had a rate of 2 to 6 children.\nWhat you should know about this data\nThe total fertility rate is a metric that summarizes fertility rates across all age groups of women in one particular year. It captures the average number of births per woman, assuming she experiences the same age-specific fertility rates over her lifetime as the fertility rates seen in each age group in one particular year when she reaches the same age. It’s important to note that this is not a prediction of how many children women will eventually have.\nThe effective fertility rate estimates what the total fertility rate would be if it only considered children who survived until their own childbearing age. It estimates the children per woman who would survive until their own childbearing years. It is calculated using historical estimates and projections of child survival rates. Here, we use calculations from economists Anup Malani and Ari Jacob.\nThe underlying data is compiled from the Human Fertility Database (HFD), Human Mortality Database (HMD), and the United Nations’ World Population Prospects (UN WPP). For data points before 1950, we use data from the HFD and HMD. From 1950 onwards, we use UN WPP data.\nThe HFD prioritizes uniformity in methods and is limited to specific countries and periods where high-quality fertility data is available nationally.\nThe UN WPP estimates ages at childbirth in various countries through various methods. In countries where birth registration data with mothers’ ages is often lacking, the underlying data frequently comes from national household surveys, which are then used to estimate fertility rates and ages at childbirth.\nTwo is the typical number of children among women in many high-income countries\nWhat share of women have one or two children? What share have none?\nTo better understand these questions, we can explore data on the total number of births per woman by the end of their\nchildbearing years\n. The data is given by women’s birth year. Unfortunately, this data is only available for some countries.\nWe’ll explore data from the United States. As you can see, having exactly two children is the most common family size. This is the case in many high-income countries, as you can see by clicking the “Change country” button.\nIn the US, for women born in 1971, around one in three have two children. One in five have one child, and another one in five have three children.\nWhat you should know about this data\nThis data is compiled from the Human Fertility Database (HFD).\nThe HFD prioritizes uniformity in methods and is limited to specific countries and periods where high-quality fertility data is available nationally.\nIn many countries, the average age of mothers at childbirth declined before rising again\nThe chart here shows the average age of women who have children in a given year by the number of births they’ve had.\nIn many countries, women's average age at childbirth declined slightly in the early twentieth century but has risen since then.\nThis pattern is visible in many countries worldwide. By clicking the “Edit countries” button, you explore this data for other countries with available data.\nFor countries without data broken down by birth order, you can explore the average age across all births in this related chart.\nAverage age of mothers at childbirth\nExplore the average age of mothers at childbirth across all births.\nWhat you should know about this data\nThis data is compiled from the Human Fertility Database (HFD).\nThe HFD prioritizes uniformity in methods and is limited to specific countries and periods where high-quality fertility data is available nationally.\nTeenage motherhood is still common in many parts of the world\nTeenage motherhood can be a health risk to both mothers and babies. It means a greater risk of complications during pregnancy and childbirth and challenges for the baby’s health and development.\n1\nThese are especially concerning in regions with limited healthcare.\nIn many parts of the world, teenage motherhood remains common. This is shown in the map.\nIn sub-Saharan Africa, around 100 in 1000 girls aged 15 to 19 give birth yearly. In Europe and East Asia, adolescent birth rates are much lower, with fewer than 20 in 1000 giving birth annually.\nOver time, teenage motherhood has declined gradually around the world.\nThis progress reflects better opportunities in education, reproductive healthcare, and contraception.\nWhat you should know about this data\nThis data comes from the United Nations World Population Prospects Database (UN WPP).\nThe UN WPP estimates ages at childbirth in various countries through various methods. In poorer countries, where birth registration data with mothers’ ages is often lacking, the underlying data frequently comes from national household surveys, which are then used to estimate fertility rates and ages at childbirth.\nResearch & Writing\nFebruary 20, 2014\nThe global decline of the fertility rate\nThe total fertility rate has halved in sixty years — what are the causes of the decline?\nMax Roser\nFebruary 24, 2025\nThe baby boom in seven charts\nThe baby boom reshaped family life and drove population growth in many countries. In this article, we explore the key patterns in seven charts.\nSaloni Dattani and Lucas Rodés-Guirao\nMore Articles on Fertility\nJune 2, 2023\nPopulation momentum: if the number of children per woman is falling, why is the population still increasing?\nMax Roser\nSeptember 3, 2019\nUntil the late 1960s, the total fertility rate was five — since then, it has halved\nMax Roser\nFebruary 24, 2025\nWhy the total fertility rate doesn’t necessarily tell us the number of births women eventually have\nSaloni Dattani and Lucas Rodés-Guirao\nNovember 27, 2023\nPeriod versus cohort measures: what’s the difference?\nSaloni Dattani\nKey Charts on Fertility Rate\nSee all charts on this topic\nAverage age of mothers at childbirth\nAverage age of mothers at childbirth by birth order\nAverage age of mothers at childbirth by mother's birth year\nby birth order\nBirth rate\nLong-run\nCompleted cohort fertility rate: births per woman\nFertility rate: births per woman\nLong-run\nShare of women who have had a given number of births\nAge distribution of mothers at childbirth by year\nAge distribution\nAge of mothers at childbirth by mother's birth year\nAge distribution\nBirth rate\nUN\nBirth rate among adolescent girls aged 15 to 19\nBirth rate among girls aged 10 to 14\nBirth rate by women's age group\nBirths, by age of mother\nCumulative fertility rate by women's birth year\nFertility rate vs. GDP per capita\nFertility rate: births per woman\nUN\nFertility rate: births per woman\n1950-2100, with UN projections\nModern contraception usage vs. average years of schooling for women\nNumber of births, by world region\nNumber of live births\nHFD\nPeak birth month\nShare of births by age of mother\nShare of births registered\nShare of births that are twins\nShare of women using modern contraceptive methods\nShare of women whose family planning needs are met\nStillbirth rate\nTotal fertility rate vs cohort fertility rate vs tempo-adjusted total fertility rate\nTotal fertility rate vs. contraceptive prevalence\nTotal fertility rate: births per woman\nHFD\nTotal number of births by birth order\nUnmet need for contraception among married women of reproductive age\nAnnual birth rate and peak birth month\nBirth seasonality\nBirths per day, on a monthly basis\nComparison of fertility rates that account for survival of children\nComparison\nFertility rate accounting for survival until childbearing age\nMap\nPeak birth month each year and the daily birth rate that month\nReplacement-level fertility rate\nTotal fertility rate vs. wanted fertility rate\nLines\nWanted fertility rate\nChart 1 of 42\nFeatured Data on\nFertility Rate\nEndnotes\nLeftwich, H. K., & Alves, M. V. O. (2017). Adolescent Pregnancy. Pediatric Clinics of North America, 64(2), 381–388.\nhttps://doi.org/10.1016/j.pcl.2016.11.007\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this topic page, please also cite the underlying data sources. This topic page can be cited as:\nSaloni Dattani, Lucas Rodés-Guirao, and Max Roser (2025) - “Fertility Rate” Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/fertility-rate' [Online Resource]\nBibTeX citation\n@article{owid-fertility-rate,\nauthor = {Saloni Dattani and Lucas Rodés-Guirao and Max Roser},\ntitle = {Fertility Rate},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://ourworldindata.org/fertility-rate}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "fertility-rate", "source_url": "https://ourworldindata.org/fertility-rate", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Explore changing patterns in fertility worldwide, from birth rates to parental ages, twinning rates, reproductive technologies, and more.", "numeric_mentions": ["2.3", "2023", "1950", "4.9", "5", "2", "1970", "6", "1", "1940", "1960", "1950,", "2023,", "3", "7", "1.5", "1971,", "100", "1000", "15", "19", "20", "20,", "2014", "24,", "2025", "2,", "3,", "2019", "27,", "10", "14", "2100,", "42", "2017", "64", "381", "388", "10.1016", "2016.11", "007"], "numeric_evidence": [{"title": "Fertility rate: births per woman", "source_url": "https://ourworldindata.org/grapher/children-born-per-woman.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Total fertility rate"], "row_count_total": 19402, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1950", "Total fertility rate": "7.248"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1951", "Total fertility rate": "7.26"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1952", "Total fertility rate": "7.26"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1953", "Total fertility rate": "7.266"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1954", "Total fertility rate": "7.254"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1955", "Total fertility rate": "7.262"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1956", "Total fertility rate": "7.269"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1957", "Total fertility rate": "7.264"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1958", "Total fertility rate": "7.269"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1959", "Total fertility rate": "7.276"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1960", "Total fertility rate": "7.282"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Total fertility rate": "7.284"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Total fertility rate": "7.292"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Total fertility rate": "7.302"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Total fertility rate": "7.304"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Total fertility rate": "7.305"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Total fertility rate": "7.32"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Total fertility rate": "7.339"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Total fertility rate": "7.363"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1969", "Total fertility rate": "7.389"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1970", "Total fertility rate": "7.4"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1971", "Total fertility rate": "7.432"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1972", "Total fertility rate": "7.453"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Total fertility rate": "7.487"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Total fertility rate": "7.526"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Total fertility rate": "7.542"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Total fertility rate": "7.561"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Total fertility rate": "7.591"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Total fertility rate": "7.599"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Total fertility rate": "7.612"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Total fertility rate": "7.643"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Total fertility rate": "7.617"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Total fertility rate": "7.6"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Total fertility rate": "7.57"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Total fertility rate": "7.554"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Total fertility rate": "7.55"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Total fertility rate": "7.553"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Total fertility rate": "7.548"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Total fertility rate": "7.551"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Total fertility rate": "7.559"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Total fertility rate": "7.576"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Total fertility rate": "7.631"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Total fertility rate": "7.703"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Total fertility rate": "7.761"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Total fertility rate": "7.767"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Total fertility rate": "7.767"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Total fertility rate": "7.757"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Total fertility rate": "7.732"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Total fertility rate": "7.693"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Total fertility rate": "7.641"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Total fertility rate": "7.566"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Total fertility rate": "7.453"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Total fertility rate": "7.32"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Total fertility rate": "7.174"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Total fertility rate": "7.018"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Total fertility rate": "6.858"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Total fertility rate": "6.686"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Total fertility rate": "6.508"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Total fertility rate": "6.392"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Total fertility rate": "6.295"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Total fertility rate": "6.195"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Total fertility rate": "6.094"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Total fertility rate": "5.985"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Total fertility rate": "5.879"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Total fertility rate": "5.77"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Total fertility rate": "5.652"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Total fertility rate": "5.542"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Total fertility rate": "5.433"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Total fertility rate": "5.327"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Total fertility rate": "5.238"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Total fertility rate": "5.145"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Total fertility rate": "5.039"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Total fertility rate": "4.932"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Total fertility rate": "4.84"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1950", "Total fertility rate": "6.4367776"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1951", "Total fertility rate": "6.451228"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1952", "Total fertility rate": "6.4636226"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1953", "Total fertility rate": "6.4829826"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1954", "Total fertility rate": "6.4931884"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1955", "Total fertility rate": "6.50552"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1956", "Total fertility rate": "6.5189023"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1957", "Total fertility rate": "6.530977"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1958", "Total fertility rate": "6.5437264"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1959", "Total fertility rate": "6.5507164"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1960", "Total fertility rate": "6.561704"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1961", "Total fertility rate": "6.5730667"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1962", "Total fertility rate": "6.5883465"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1963", "Total fertility rate": "6.596562"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1964", "Total fertility rate": "6.5999"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1965", "Total fertility rate": "6.597559"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1966", "Total fertility rate": "6.60687"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1967", "Total fertility rate": "6.60904"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1968", "Total fertility rate": "6.609567"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1969", "Total fertility rate": "6.605879"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1970", "Total fertility rate": "6.608199"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1971", "Total fertility rate": "6.6169066"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1972", "Total fertility rate": "6.616913"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1973", "Total fertility rate": "6.6099615"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1974", "Total fertility rate": "6.609396"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1975", "Total fertility rate": "6.6072907"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1976", "Total fertility rate": "6.603195"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1977", "Total fertility rate": "6.5917606"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1978", "Total fertility rate": "6.5663705"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1979", "Total fertility rate": "6.545587"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1980", "Total fertility rate": "6.515052"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1981", "Total fertility rate": "6.4736576"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1982", "Total fertility rate": "6.434558"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1983", "Total fertility rate": "6.393361"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1984", "Total fertility rate": "6.3252063"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Total fertility rate": "6.256401"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Total fertility rate": "6.1838737"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Total fertility rate": "6.1125216"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Total fertility rate": "6.0366774"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Total fertility rate": "5.9492836"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Total fertility rate": "5.8539023"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Total fertility rate": "5.774524"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Total fertility rate": "5.6971273"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Total fertility rate": "5.625158"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Total fertility rate": "5.526201"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Total fertility rate": "5.463184"}], "rows_tail": [{"Entity": "Zambia", "Code": "ZMB", "Year": "1978", "Total fertility rate": "7.294"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1979", "Total fertility rate": "7.222"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1980", "Total fertility rate": "7.139"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1981", "Total fertility rate": "7.073"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1982", "Total fertility rate": "7.039"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1983", "Total fertility rate": "7.007"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1984", "Total fertility rate": "6.974"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Total fertility rate": "6.928"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Total fertility rate": "6.868"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Total fertility rate": "6.798"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Total fertility rate": "6.703"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Total fertility rate": "6.635"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Total fertility rate": "6.567"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Total fertility rate": "6.458"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Total fertility rate": "6.349"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Total fertility rate": "6.298"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Total fertility rate": "6.26"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Total fertility rate": "6.232"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Total fertility rate": "6.182"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Total fertility rate": "6.124"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Total fertility rate": "6.067"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Total fertility rate": "5.998"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Total fertility rate": "5.921"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Total fertility rate": "5.848"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Total fertility rate": "5.774"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Total fertility rate": "5.71"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Total fertility rate": "5.698"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Total fertility rate": "5.697"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Total fertility rate": "5.679"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Total fertility rate": "5.613"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Total fertility rate": "5.53"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Total fertility rate": "5.453"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Total fertility rate": "5.359"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Total fertility rate": "5.256"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Total fertility rate": "5.136"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Total fertility rate": "5.014"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Total fertility rate": "4.89"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Total fertility rate": "4.775"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Total fertility rate": "4.669"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Total fertility rate": "4.567"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Total fertility rate": "4.492"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Total fertility rate": "4.418"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Total fertility rate": "4.323"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Total fertility rate": "4.246"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Total fertility rate": "4.175"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Total fertility rate": "4.101"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1950", "Total fertility 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"Zimbabwe", "Code": "ZWE", "Year": "1962", "Total fertility rate": "7.231"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1963", "Total fertility rate": "7.23"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1964", "Total fertility rate": "7.23"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1965", "Total fertility rate": "7.231"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "Total fertility rate": "7.196"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "Total fertility rate": "7.157"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "Total fertility rate": "7.114"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "Total fertility rate": "7.036"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "Total fertility rate": "6.982"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "Total fertility rate": "6.939"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "Total fertility rate": "6.942"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "Total fertility rate": "6.942"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "Total fertility rate": "6.879"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Total fertility rate": "6.832"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Total fertility rate": "6.771"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Total fertility rate": "6.713"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Total fertility rate": "6.658"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Total fertility rate": "6.59"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Total fertility rate": "6.517"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Total fertility rate": "6.445"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Total fertility rate": "6.333"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Total fertility rate": "6.197"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Total fertility rate": "6.044"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Total fertility rate": "5.871"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Total fertility rate": "5.688"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Total fertility rate": "5.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Total fertility rate": "5.279"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Total fertility rate": "5.073"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Total fertility rate": "4.876"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Total fertility rate": "4.715"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Total fertility rate": "4.567"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Total fertility rate": "4.391"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Total fertility rate": "4.284"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Total fertility rate": "4.153"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Total fertility rate": "4.11"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Total fertility rate": "4.075"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Total fertility rate": "4.067"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Total fertility rate": "4.056"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Total fertility rate": "4.009"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Total fertility rate": "3.983"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Total fertility rate": "3.925"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Total fertility rate": "3.862"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Total fertility rate": "3.776"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Total fertility rate": "3.693"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Total fertility rate": "3.635"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Total fertility rate": "3.677"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Total fertility rate": "3.783"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Total fertility rate": "3.95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Total fertility rate": "4.04"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Total fertility rate": "4.126"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Total fertility rate": "4.134"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Total fertility rate": "4.111"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Total fertility rate": "4.011"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Total fertility rate": "3.911"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Total fertility rate": "3.828"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Total fertility rate": "3.768"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Total fertility rate": "3.744"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Total fertility rate": "3.748"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Total fertility rate": "3.754"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Total fertility rate": "3.765"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Total fertility rate": "3.767"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Total fertility rate": "3.724"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "children-born-per-woman", "metadata_url": "https://ourworldindata.org/grapher/children-born-per-woman.metadata.json", "chart_title": "Fertility rate: births per woman", "chart_subtitle": "The total fertility rate summarizes the total number of births a woman would have, if she experienced the birth rates seen in women of each age group in one particular year across her childbearing years.", "chart_note": "", "chart_citation": "Human Fertility Database (2025); UN, World Population Prospects (2024)", "original_chart_url": "https://ourworldindata.org/grapher/children-born-per-woman", "owid_column_metadata": {"Fertility rate (period), historical": {"titleShort": "Fertility rate: births per woman", "titleLong": "Fertility rate: births per woman - HFD, UN WPP – period tables", "descriptionShort": "The average number of live births a hypothetical cohort of women would have at the end of their reproductive period if they were subject during their whole lives to the fertility rates of a given period and if they were not subject to mortality.", "descriptionKey": ["Assumes current age-specific fertility rates remain constant throughout a woman's lifetime.", "Does not account for potential changes in social, economic, or health conditions that could affect fertility rates."], "descriptionProcessing": "The fertility data is constructed by combining data from multiple sources:\n\n- Before 1950: Historical estimates by Human Fertility Database (2025).\n\n- 1950-2023: Population records by the UN World Population Prospects (2024 revision).", "unit": "live births per woman", "timespan": "1891-2023", "type": "Numeric", "owidVariableId": 1118640, "shortName": "fertility_rate_hist", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Fertility Database (2025); UN, World Population Prospects (2024) – with major processing by Our World in Data", "citationLong": "Human Fertility Database (2025); UN, World Population Prospects (2024) – with major processing by Our World in Data. “Fertility rate: births per woman – HFD, UN WPP – period tables” [dataset]. Human Fertility Database, “Human Fertility Database”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118640.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Fertility rate accounting for survival until childbearing age", "source_url": "https://ourworldindata.org/grapher/effective-fertility-rate-children-per-woman-who-are-expected-to-survive-until-childbearing-age.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Effective reproductive fertility rate"], "row_count_total": 20435, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1950", "Effective reproductive fertility rate": "3.3990664"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1951", "Effective reproductive fertility rate": "3.4518454"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1952", "Effective reproductive fertility rate": "3.4990385"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1953", "Effective reproductive fertility rate": "3.5494964"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1954", "Effective reproductive fertility rate": "3.5923235"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1955", "Effective reproductive fertility rate": "3.6482542"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1956", "Effective reproductive fertility rate": "3.7017775"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1957", "Effective reproductive fertility rate": "3.7497816"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1958", "Effective reproductive fertility rate": "3.8025942"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1959", "Effective reproductive fertility rate": "3.8567088"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1960", "Effective reproductive fertility rate": "3.9097517"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Effective reproductive fertility rate": "3.9604256"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Effective reproductive fertility rate": "4.013794"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Effective reproductive fertility 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"Effective reproductive fertility rate": "5.730008"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Effective reproductive fertility rate": "5.8487024"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Effective reproductive fertility rate": "5.995008"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Effective reproductive fertility rate": "6.125488"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Effective reproductive fertility rate": "6.1839905"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Effective reproductive fertility rate": "6.2311077"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Effective reproductive fertility rate": "6.269123"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Effective reproductive fertility rate": "6.2895412"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Effective reproductive fertility rate": "6.3015"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Effective reproductive fertility rate": "6.3107677"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Effective reproductive fertility rate": "6.2909145"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Effective reproductive fertility rate": "6.2372193"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Effective reproductive fertility rate": "6.16684"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Effective reproductive fertility rate": "6.086232"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Effective reproductive fertility rate": "5.990591"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Effective reproductive fertility rate": "5.8881407"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Effective reproductive fertility rate": "5.7741585"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Effective reproductive fertility rate": "5.653178"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": 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"Year": "2017", "Effective reproductive fertility rate": "4.9331117"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Effective reproductive fertility rate": "4.8503275"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Effective reproductive fertility rate": "4.7810216"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Effective reproductive fertility rate": "4.708063"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Effective reproductive fertility rate": "4.6240745"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Effective reproductive fertility rate": "4.5393896"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Effective reproductive fertility rate": "4.465598"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1950", "Effective reproductive fertility rate": "3.81193"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1951", "Effective reproductive fertility rate": "3.8582203"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1952", "Effective reproductive fertility rate": "3.9015832"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1953", "Effective reproductive fertility rate": "3.947401"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1954", "Effective reproductive fertility rate": "3.9885972"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1955", "Effective reproductive fertility rate": "4.0287232"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1956", "Effective reproductive fertility rate": "4.0640354"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1957", "Effective reproductive fertility rate": "4.0935464"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1958", "Effective reproductive fertility rate": "4.1394143"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1959", "Effective reproductive fertility rate": "4.191377"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1960", "Effective reproductive fertility rate": "4.2276998"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1961", "Effective reproductive fertility rate": "4.2637234"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1962", "Effective reproductive fertility rate": "4.3002415"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1963", "Effective reproductive fertility rate": "4.3300505"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1964", "Effective reproductive fertility rate": "4.3550577"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1965", "Effective reproductive fertility rate": "4.3738995"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1966", "Effective reproductive fertility rate": "4.4027224"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1967", "Effective reproductive fertility rate": "4.4345665"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1968", "Effective reproductive fertility rate": "4.459638"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1969", "Effective reproductive fertility rate": "4.481198"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1970", "Effective reproductive fertility rate": "4.507157"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1971", "Effective reproductive fertility rate": "4.535872"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1972", "Effective reproductive fertility rate": "4.5622"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1973", "Effective reproductive fertility rate": "4.588339"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1974", "Effective reproductive fertility rate": "4.614334"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1975", "Effective reproductive fertility rate": "4.6486883"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1976", "Effective reproductive fertility rate": "4.6810246"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1977", "Effective reproductive fertility rate": "4.705801"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1978", "Effective reproductive fertility rate": "4.72757"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1979", "Effective reproductive fertility rate": "4.741807"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1980", "Effective reproductive fertility rate": "4.750694"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1981", "Effective reproductive fertility rate": "4.7505364"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1982", "Effective reproductive fertility rate": "4.745309"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1983", "Effective reproductive fertility rate": "4.7211704"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1984", "Effective reproductive fertility rate": "4.707632"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1985", "Effective reproductive fertility rate": "4.691307"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1986", "Effective reproductive fertility rate": "4.6717157"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1987", "Effective reproductive fertility rate": "4.649939"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1988", "Effective reproductive fertility rate": "4.6061697"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1989", "Effective reproductive fertility rate": "4.570982"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1990", "Effective reproductive fertility rate": "4.512106"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1991", "Effective reproductive fertility rate": "4.4620185"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1992", "Effective reproductive fertility rate": "4.42328"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1993", "Effective reproductive fertility rate": "4.3838005"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1994", "Effective reproductive fertility rate": "4.3445787"}, {"Entity": "Africa (UN)", "Code": "UN_AFR", "Year": "1995", "Effective reproductive fertility rate": "4.3060675"}], "rows_tail": [{"Entity": "Zambia", "Code": "ZMB", "Year": "1978", "Effective reproductive fertility rate": "5.282513"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1979", "Effective reproductive fertility rate": "5.2515817"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1980", "Effective reproductive fertility rate": "5.2108774"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1981", "Effective reproductive fertility rate": "5.181034"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1982", "Effective reproductive fertility rate": "5.172865"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1983", "Effective reproductive fertility rate": "5.164747"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1984", "Effective reproductive fertility rate": "5.154447"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Effective reproductive fertility rate": "5.134409"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Effective reproductive fertility rate": "5.104699"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Effective reproductive fertility rate": "5.0690646"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Effective reproductive fertility rate": "5.0174494"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Effective reproductive fertility rate": "4.9890313"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Effective reproductive fertility rate": "4.9642906"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Effective reproductive fertility rate": "4.912217"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Effective reproductive fertility rate": "4.863768"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Effective reproductive fertility rate": "4.8624988"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Effective reproductive fertility rate": "4.8729076"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Effective reproductive fertility rate": "4.8927402"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Effective reproductive fertility rate": "4.897227"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Effective reproductive fertility rate": "4.8977456"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Effective reproductive fertility rate": "4.9007425"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Effective reproductive fertility rate": "4.8972573"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Effective reproductive fertility rate": "4.890225"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Effective reproductive fertility rate": "4.8860707"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Effective reproductive fertility rate": "4.8793974"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Effective reproductive fertility rate": "4.8781977"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Effective reproductive fertility rate": "4.917799"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Effective reproductive fertility rate": "4.9623327"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Effective reproductive fertility rate": "4.9869146"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Effective reproductive fertility rate": "4.963345"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Effective reproductive fertility rate": "4.9193015"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Effective reproductive fertility rate": "4.876064"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Effective reproductive fertility rate": "4.813259"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Effective reproductive fertility rate": "4.7387843"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Effective reproductive fertility rate": "4.646571"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Effective reproductive fertility rate": "4.550353"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Effective reproductive fertility rate": "4.4505177"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Effective reproductive fertility rate": "4.3568087"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Effective reproductive fertility rate": "4.27105"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Effective reproductive fertility rate": "4.1884184"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Effective reproductive fertility rate": "4.1303124"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Effective reproductive fertility rate": "4.0730805"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Effective reproductive fertility rate": "3.996937"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Effective reproductive fertility rate": "3.9385061"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Effective reproductive fertility rate": "3.8830512"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Effective reproductive fertility rate": "3.8210568"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1950", "Effective reproductive fertility rate": "5.2446074"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1951", "Effective reproductive fertility rate": "5.2713885"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1952", "Effective reproductive fertility rate": "5.287256"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1953", "Effective reproductive fertility rate": "5.3024383"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1954", "Effective reproductive fertility rate": "5.3129625"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1955", "Effective reproductive fertility rate": "5.318743"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1956", "Effective reproductive fertility rate": "5.3323126"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1957", "Effective reproductive fertility rate": "5.3432703"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1958", "Effective reproductive fertility rate": "5.349276"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1959", "Effective reproductive fertility rate": "5.351848"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1960", "Effective reproductive fertility rate": "5.3593335"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1961", "Effective reproductive fertility rate": "5.3629746"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1962", "Effective reproductive fertility rate": "5.3673143"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1963", "Effective reproductive fertility rate": "5.3570166"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1964", "Effective reproductive fertility rate": "5.347068"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1965", "Effective reproductive fertility rate": "5.3381157"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "Effective reproductive fertility rate": "5.3022823"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "Effective reproductive fertility rate": "5.264441"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "Effective reproductive fertility rate": "5.223981"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "Effective reproductive fertility rate": "5.1597033"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "Effective reproductive fertility rate": "5.1152124"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "Effective reproductive fertility rate": "5.0818095"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "Effective reproductive fertility rate": "5.0849676"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "Effective reproductive fertility rate": "5.090752"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "Effective reproductive fertility rate": "5.054741"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Effective reproductive fertility rate": "5.0360823"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Effective reproductive fertility rate": "5.013077"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Effective reproductive fertility rate": "4.9985723"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Effective reproductive fertility rate": "4.9929786"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Effective reproductive fertility rate": "4.9839916"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Effective reproductive fertility rate": "4.9784265"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Effective reproductive fertility rate": "4.9760942"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Effective reproductive fertility rate": "4.9444222"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Effective reproductive fertility rate": "4.8918905"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Effective reproductive fertility rate": "4.823656"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Effective reproductive fertility rate": "4.730991"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Effective reproductive fertility rate": "4.62216"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Effective reproductive fertility rate": "4.50035"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Effective reproductive fertility rate": "4.342878"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Effective reproductive fertility rate": "4.190098"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Effective reproductive fertility rate": "4.0387216"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Effective reproductive fertility rate": "3.9126651"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Effective reproductive fertility rate": "3.7949154"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Effective reproductive fertility rate": "3.6532445"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Effective reproductive fertility rate": "3.5700724"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Effective reproductive fertility rate": "3.4684236"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Effective reproductive fertility rate": "3.4419172"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Effective reproductive fertility rate": "3.4251351"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Effective reproductive fertility rate": "3.4316318"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Effective reproductive fertility rate": "3.4366186"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Effective reproductive fertility rate": "3.410447"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Effective reproductive fertility rate": "3.4007144"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Effective reproductive fertility rate": "3.361574"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Effective reproductive fertility rate": "3.3163018"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Effective reproductive fertility rate": "3.250003"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Effective reproductive fertility rate": "3.1858716"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Effective reproductive fertility rate": "3.1444535"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Effective reproductive fertility rate": "3.19193"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Effective reproductive fertility rate": "3.298476"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Effective reproductive fertility rate": "3.4650893"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Effective reproductive fertility rate": "3.5674708"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Effective reproductive fertility rate": "3.6662848"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Effective reproductive fertility rate": "3.695431"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Effective reproductive fertility rate": "3.6948915"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Effective reproductive fertility rate": "3.6227221"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Effective reproductive fertility rate": "3.546924"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Effective reproductive fertility rate": "3.48395"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Effective reproductive fertility rate": "3.439829"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Effective reproductive fertility rate": "3.427464"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Effective reproductive fertility rate": "3.4396763"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Effective reproductive fertility rate": "3.454165"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Effective reproductive fertility rate": "3.472272"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Effective reproductive fertility rate": "3.4818196"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Effective reproductive fertility rate": "3.4495897"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "effective-fertility-rate-children-per-woman-who-are-expected-to-survive-until-childbearing-age", "metadata_url": "https://ourworldindata.org/grapher/effective-fertility-rate-children-per-woman-who-are-expected-to-survive-until-childbearing-age.metadata.json", "chart_title": "Fertility rate accounting for survival until childbearing age", "chart_subtitle": "While the total fertility rate considers all births, this measure of the effective fertility rate considers how many children per woman are expected to survive to childbearing age, using historical data and projections of mortality rates.", "chart_note": "Childbearing age is defined by the authors as 15–49 years old, and the average survival across the age range is used.", "chart_citation": "Malani and Jacob (2024); UN, World Population Prospects (2024); Human Mortality Database (2025)", "original_chart_url": "https://ourworldindata.org/grapher/effective-fertility-rate-children-per-woman-who-are-expected-to-survive-until-childbearing-age", "owid_column_metadata": {"Reproductive Effective Fertility rate (scaled by sex ratio)": {"titleShort": "Effective reproductive fertility rate", "titleLong": "Effective reproductive fertility rate", "descriptionShort": "The number of children who live long enough to reproduce, per woman. This number is dependent on the survival of daughters to childbearing age (between 15 and 49 years old).", "descriptionProcessing": "For a given cohort year, we estimate the cumulative survival probability for a person to reach each age from 0 to 49. For example, the probability of a person born in 2000 reaching age 15, 16, 17, and so on up to 49. We have used HMD data for years before 1950, and UN's for years after 1950 (including).\n\nWe then estimate the Effective Fertility Rate (EFR) for each age group by multiplying the Total Fertility Rate (TFR) by the cumulative survival probability. The EFR for a given age gives us an approximation of the average number of children from a woman that will live long enough to reach that age.\n\nFor years before 1950, we have used HMD data, which does not provide TFR values. Instead, we have used an approximation of the TFR based on births and female population (in reproductive ages), as suggested by Jacob and Malani (2024).\n\nThe Reproductive Effective Fertility rate (EFR) is the average of the EFR over all reproductive ages (15-49).\n\nNote that the Reproductive Effective Fertility rate (EFR) is an approximation of the number of daughters, so it uses the total fertility rate of female children, or equivalently, the TFR weighted by the sex ratio at birth.\n\nSo we have that: EFR_repr = (TFR * mean(EFR)) / (1 + SRB), where SRB is the male-to-female ratio and the mean is taken over all reproductive ages (15-49).\n\nThis indicator is scaled by the sex ratio to allow easy comparability with the Total Fertility Rate (TFR) and the Labor Effective Fertility rate (EFR_labor).\n\nRead more details in the author's paper: https://www.nber.org/papers/w33175", "unit": "children per women", "timespan": "1751-2023", "type": "Numeric", "owidVariableId": 1118367, "shortName": "efr_repr", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Malani and Jacob (2024); UN, World Population Prospects (2024); Human Mortality Database (2025) – processed by Our World in Data", "citationLong": "Malani and Jacob (2024); UN, World Population Prospects (2024); Human Mortality Database (2025) – processed by Our World in Data. “Effective reproductive fertility rate” [dataset]. Malani and Jacob, “A New Measure of Surviving Children that Sheds Light on Long-term Trends in Fertility”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update”; Human Mortality Database, “Human Mortality Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118367.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Share of women who have had a given number of births", "source_url": "https://ourworldindata.org/grapher/share-of-women-having-births.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "No births", "1 birth", "2 births", "3 births", "4 births or more"], "row_count_total": 578, "rows_head": [{"Entity": "Austria", "Code": "AUT", "Year": "1969", "No births": "20.7", "1 birth": "22.600502", "2 births": "38.27216", "3 births": "13.378246", "4 births or more": "5.04909"}, {"Entity": "Austria", "Code": "AUT", "Year": "1970", "No births": "20.499998", "1 birth": "22.498499", "2 births": "38.24801", "3 births": "13.615037", "4 births or more": "5.1384573"}, {"Entity": "Austria", "Code": "AUT", "Year": "1971", "No births": "20.300001", "1 birth": "22.315998", "2 births": "38.504665", "3 births": "13.612001", "4 births or more": "5.267335"}, {"Entity": "Austria", "Code": "AUT", "Year": "1972", "No births": "20.200003", "1 birth": "22.1046", "2 births": "38.25205", "3 births": "13.843665", "4 births or more": "5.5996842"}, {"Entity": "Austria", "Code": "AUT", "Year": "1973", "No births": "19.800001", "1 birth": "21.8946", "2 births": "38.53987", "3 births": "14.231181", "4 births or more": "5.5343485"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1949", "No births": "5.699998", "1 birth": "20.840302", "2 births": "54.5071", "3 births": "13.4753", "4 births or more": "5.477302"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1950", "No births": "6.400001", "1 birth": "21.060001", "2 births": "54.405", "3 births": "12.91212", "4 births or more": "5.2228794"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1951", "No births": "4.6000004", "1 birth": "21.6558", "2 births": "55.82436", "3 births": "12.884366", "4 births or more": "5.035475"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1952", "No births": "5.800003", "1 birth": "21.571796", "2 births": "55.27006", "3 births": "12.671441", "4 births or more": "4.686698"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1953", "No births": "7.2000027", "1 birth": "21.900799", "2 births": "54.02519", "3 births": "12.520515", "4 births or more": "4.353494"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1954", "No births": "5.699998", "1 birth": "22.2548", "2 births": "55.042534", "3 births": "12.615979", "4 births or more": "4.386688"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1955", "No births": "4.799998", "1 birth": "22.0864", "2 births": "56.297474", "3 births": "12.477568", "4 births or more": "4.338561"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1956", "No births": "4.9000025", "1 birth": "21.777897", "2 births": "56.677982", "3 births": "12.433155", "4 births or more": "4.2109613"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1957", "No births": "4.500002", "1 birth": "21.965002", "2 births": "57.357296", "3 births": "12.133274", "4 births or more": "4.044425"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1958", "No births": "2.9999971", "1 birth": "24.347", "2 births": "57.10526", "3 births": "11.707451", "4 births or more": "3.8402925"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1959", "No births": "3.5000026", "1 birth": "25.283", "2 births": "56.403862", "3 births": "11.0654125", "4 births or more": "3.747723"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1960", "No births": "3.6000013", "1 birth": "25.931599", "2 births": "56.304253", "3 births": "10.48147", "4 births or more": "3.6826785"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1961", "No births": "3.7", "1 birth": "26.867699", "2 births": "55.615273", "3 births": "10.224601", "4 births or more": "3.5924273"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1962", "No births": "4.400003", "1 birth": "28.488796", "2 births": "53.957405", "3 births": "9.865346", "4 births or more": "3.2884488"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1963", "No births": "7.099998", "1 birth": "29.728", "2 births": "50.979805", "3 births": "9.119763", "4 births or more": "3.0724337"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1964", "No births": "8.999997", "1 birth": "30.303001", "2 births": "49.16457", "3 births": "8.522466", "4 births or more": "3.0099642"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1965", "No births": "8.600002", "1 birth": "30.8018", "2 births": "48.963345", "3 births": "8.551618", "4 births or more": "3.0832362"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1966", "No births": "7.800001", "1 birth": "31.347998", "2 births": "49.290123", "3 births": "8.347677", "4 births or more": "3.2142026"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1967", "No births": "5.000001", "1 birth": "35.435", "2 births": "48.128517", "3 births": "8.360066", "4 births or more": "3.076413"}, {"Entity": "Belarus", "Code": "BLR", "Year": "1968", "No births": "4.100001", "1 birth": "37.5928", "2 births": "46.878986", "3 births": "8.468304", "4 births or more": "2.9599066"}, 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"23.649963", "4 births or more": "22.1833"}, {"Entity": "United States", "Code": "USA", "Year": "1943", "No births": "9.299999", "1 birth": "14.512002", "2 births": "33.14178", "3 births": "23.072773", "4 births or more": "19.973444"}, {"Entity": "United States", "Code": "USA", "Year": "1944", "No births": "10.6", "1 birth": "15.2874", "2 births": "33.869457", "3 births": "22.093483", "4 births or more": "18.149656"}, {"Entity": "United States", "Code": "USA", "Year": "1945", "No births": "11.1", "1 birth": "16.002", "2 births": "34.91814", "3 births": "21.42064", "4 births or more": "16.559217"}, {"Entity": "United States", "Code": "USA", "Year": "1946", "No births": "11.199999", "1 birth": "17.138401", "2 births": "36.045784", "3 births": "20.906485", "4 births or more": "14.7093315"}, {"Entity": "United States", "Code": "USA", "Year": "1947", "No births": "11.799997", "1 birth": "17.904602", "2 births": "36.623905", "3 births": "20.303913", "4 births or more": "13.367585"}, {"Entity": "United States", "Code": "USA", "Year": "1948", "No births": "12.800002", "1 birth": "18.1376", "2 births": "36.534008", "3 births": "19.842318", "4 births or more": "12.686071"}, {"Entity": "United States", "Code": "USA", "Year": "1949", "No births": "14.200002", "1 birth": "18.103802", "2 births": "36.082073", "3 births": "19.4743", "4 births or more": "12.139824"}, {"Entity": "United States", "Code": "USA", "Year": "1950", "No births": "15.100002", "1 birth": "18.253498", "2 births": "35.722527", "3 births": "19.234713", "4 births or more": "11.689262"}, {"Entity": "United States", "Code": "USA", "Year": "1951", "No births": "15.600002", "1 birth": "18.483599", "2 births": "35.52894", "3 births": "19.083324", "4 births or more": "11.304135"}, {"Entity": "United States", "Code": "USA", "Year": "1952", "No births": "16.000002", "1 birth": "18.480001", "2 births": "35.44632", "3 births": "18.946417", "4 births or more": "11.127261"}, {"Entity": "United States", "Code": "USA", "Year": "1953", "No births": "16.500002", "1 birth": "18.370003", "2 births": "35.10507", "3 births": "18.94573", "4 births or more": "11.079198"}, {"Entity": "United States", "Code": "USA", "Year": "1954", "No births": "16.500002", "1 birth": "18.4535", "2 births": "34.86492", "3 births": "19.044573", "4 births or more": "11.137"}, {"Entity": "United States", "Code": "USA", "Year": "1955", "No births": "16.3", "1 birth": "18.497702", "2 births": "34.818027", "3 births": "19.172476", "4 births or more": "11.211796"}, {"Entity": "United States", "Code": "USA", "Year": "1956", "No births": "16.100002", "1 birth": "18.458002", "2 births": "34.880585", "3 births": "19.284252", "4 births or more": "11.277161"}, {"Entity": "United States", "Code": "USA", "Year": "1957", "No births": "16.2", "1 birth": "18.352198", "2 births": "34.621887", "3 births": "19.45115", "4 births or more": "11.374762"}, {"Entity": "United States", "Code": "USA", "Year": "1958", "No births": "16.2", "1 birth": "18.352198", "2 births": "34.490993", "3 births": "19.533747", "4 births or more": "11.423062"}, {"Entity": "United States", "Code": "USA", "Year": "1959", "No births": "15.499997", "1 birth": "18.421", "2 births": "34.691475", "3 births": "19.742754", "4 births or more": "11.644772"}, {"Entity": "United States", "Code": "USA", "Year": "1960", "No births": "15.200001", "1 birth": "18.4016", "2 births": "34.659966", "3 births": "19.899998", "4 births or more": "11.838435"}, {"Entity": "United States", "Code": "USA", "Year": "1961", "No births": "15.200001", "1 birth": "18.4016", "2 births": "34.593567", "3 births": "20.00524", "4 births or more": "11.799593"}, {"Entity": "United States", "Code": "USA", "Year": "1962", "No births": "14.899999", "1 birth": "18.5518", "2 births": "34.47197", "3 births": "20.111797", "4 births or more": "11.964435"}, {"Entity": "United States", "Code": "USA", "Year": "1963", "No births": "14.300001", "1 birth": "18.6826", "2 births": "34.647995", "3 births": "20.19851", "4 births or more": "12.170896"}, {"Entity": "United States", "Code": "USA", "Year": "1964", "No births": "13.9", "1 birth": "18.6837", "2 births": "34.651978", "3 births": "20.379408", "4 births or more": "12.384913"}, {"Entity": "United States", "Code": "USA", "Year": "1965", "No births": "13.999998", "1 birth": "18.576002", "2 births": "34.251392", "3 births": "20.533844", "4 births or more": "12.638764"}, {"Entity": "United States", "Code": "USA", "Year": "1966", "No births": "13.800001", "1 birth": "18.446798", "2 births": "34.147614", "3 births": "20.701042", "4 births or more": "12.904546"}, {"Entity": "United States", "Code": "USA", "Year": "1967", "No births": "13.200003", "1 birth": "18.4884", "2 births": "34.292423", "3 births": "20.853754", "4 births or more": "13.165421"}, {"Entity": "United States", "Code": "USA", "Year": "1968", "No births": "13.099998", "1 birth": "18.5097", "2 births": "34.331932", "3 births": "20.843721", "4 births or more": "13.214648"}, {"Entity": "United States", "Code": "USA", "Year": "1969", "No births": "12.800002", "1 birth": "18.835201", "2 births": "34.31913", "3 births": "20.835949", "4 births or more": "13.20972"}, {"Entity": "United States", "Code": "USA", "Year": "1970", "No births": "11.900002", "1 birth": "19.1177", "2 births": "34.56013", "3 births": "21.031944", "4 births or more": "13.3902235"}, {"Entity": "United States", "Code": "USA", "Year": "1971", "No births": "11.199999", "1 birth": "19.269602", "2 births": "34.62614", "3 births": "21.186886", "4 births or more": "13.717374"}, {"Entity": "United States", "Code": "USA", "Year": "1972", "No births": "10.799998", "1 birth": "19.177998", "2 births": "34.450825", "3 births": "21.378279", "4 births or more": "14.1929"}, {"Entity": "United States", "Code": "USA", "Year": "1973", "No births": "10.299998", "1 birth": "19.285498", "2 births": "34.362274", "3 births": "21.595285", "4 births or more": "14.456943"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "share-of-women-having-births", "metadata_url": "https://ourworldindata.org/grapher/share-of-women-having-births.metadata.json", "chart_title": "Share of women who have had a given number of births", "chart_subtitle": "The share of women who have had a given number of births by the end of their childbearing years.", "chart_note": null, "chart_citation": "Human Fertility Database (2025)", "original_chart_url": "https://ourworldindata.org/grapher/share-of-women-having-births", "owid_column_metadata": {"Share of women with 0 births": {"titleShort": "No births", "titleLong": "No births", "descriptionShort": "Share of women born in a given year that had 0 or more births.", "descriptionProcessing": "We have estimated the share of women with N births using the estimates on the cohort parity progression ratio (ppr). ppr(N) is an estimate on the probability of giving birth to an Nth child, conditioned on having had N-1 children before.\n\nConsidering all of this, we have estimated the share of women with N births as:\n\nshare of women with N births = ppr(1) * ppr(2) * ··· * ppr(N) * [1 - ppr(N+1)]\n\nNote that we need the [1 - ppr(N+1)] term to not count women that had more than N births.", "shortUnit": "%", "unit": "%", "timespan": "1918-1974", "type": "Numeric", "owidVariableId": 1118838, "shortName": "share_women__num_births_0", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Fertility Database (2025) – processed by Our World in Data", "citationLong": "Human Fertility Database (2025) – processed by Our World in Data. “No births – HFD” [dataset]. Human Fertility Database, “Human Fertility Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118838.metadata.json"}, "Share of women with 1 births": {"titleShort": "1 birth", "titleLong": "1 birth", "descriptionShort": "Share of women born in a given year that had 1 or more births.", "descriptionProcessing": "We have estimated the share of women with N births using the estimates on the cohort parity progression ratio (ppr). ppr(N) is an estimate on the probability of giving birth to an Nth child, conditioned on having had N-1 children before.\n\nConsidering all of this, we have estimated the share of women with N births as:\n\nshare of women with N births = ppr(1) * ppr(2) * ··· * ppr(N) * [1 - ppr(N+1)]\n\nNote that we need the [1 - ppr(N+1)] term to not count women that had more than N births.", "shortUnit": "%", "unit": "%", "timespan": "1918-1974", "type": "Numeric", "owidVariableId": 1118839, "shortName": "share_women__num_births_1", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Fertility Database (2025) – processed by Our World in Data", "citationLong": "Human Fertility Database (2025) – processed by Our World in Data. “1 birth – HFD” [dataset]. Human Fertility Database, “Human Fertility Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118839.metadata.json"}, "Share of women with 2 births": {"titleShort": "2 births", "titleLong": "2 births", "descriptionShort": "Share of women born in a given year that had 2 or more births.", "descriptionProcessing": "We have estimated the share of women with N births using the estimates on the cohort parity progression ratio (ppr). ppr(N) is an estimate on the probability of giving birth to an Nth child, conditioned on having had N-1 children before.\n\nConsidering all of this, we have estimated the share of women with N births as:\n\nshare of women with N births = ppr(1) * ppr(2) * ··· * ppr(N) * [1 - ppr(N+1)]\n\nNote that we need the [1 - ppr(N+1)] term to not count women that had more than N births.", "shortUnit": "%", "unit": "%", "timespan": "1918-1974", "type": "Numeric", "owidVariableId": 1118840, "shortName": "share_women__num_births_2", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Fertility Database (2025) – processed by Our World in Data", "citationLong": "Human Fertility Database (2025) – processed by Our World in Data. “2 births – HFD” [dataset]. Human Fertility Database, “Human Fertility Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118840.metadata.json"}, "Share of women with 3 births": {"titleShort": "3 births", "titleLong": "3 births", "descriptionShort": "Share of women born in a given year that had 3 or more births.", "descriptionProcessing": "We have estimated the share of women with N births using the estimates on the cohort parity progression ratio (ppr). ppr(N) is an estimate on the probability of giving birth to an Nth child, conditioned on having had N-1 children before.\n\nConsidering all of this, we have estimated the share of women with N births as:\n\nshare of women with N births = ppr(1) * ppr(2) * ··· * ppr(N) * [1 - ppr(N+1)]\n\nNote that we need the [1 - ppr(N+1)] term to not count women that had more than N births.", "shortUnit": "%", "unit": "%", "timespan": "1918-1974", "type": "Numeric", "owidVariableId": 1118841, "shortName": "share_women__num_births_3", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Fertility Database (2025) – processed by Our World in Data", "citationLong": "Human Fertility Database (2025) – processed by Our World in Data. “3 births – HFD” [dataset]. Human Fertility Database, “Human Fertility Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118841.metadata.json"}, "Share of women with 4 births": {"titleShort": "4 births or more", "titleLong": "4 births or more", "descriptionShort": "Share of women born in a given year that had 4 or more births.", "descriptionProcessing": "We have estimated the share of women with N births using the estimates on the cohort parity progression ratio (ppr). ppr(N) is an estimate on the probability of giving birth to an Nth child, conditioned on having had N-1 children before.\n\nConsidering all of this, we have estimated the share of women with N births as:\n\nshare of women with N births = ppr(1) * ppr(2) * ··· * ppr(N) * [1 - ppr(N+1)]\n\nNote that we need the [1 - ppr(N+1)] term to not count women that had more than N births.", "shortUnit": "%", "unit": "%", "timespan": "1918-1974", "type": "Numeric", "owidVariableId": 1118842, "shortName": "share_women__num_births_4", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Fertility Database (2025) – processed by Our World in Data", "citationLong": "Human Fertility Database (2025) – processed by Our World in Data. “4 births or more – HFD” [dataset]. Human Fertility Database, “Human Fertility Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118842.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "2f2a0fffff9abdcd4c4e"},
{"raw_link": "https://ourworldindata.org/cleanest-air-lessons", "title": "In many countries, people breathe the cleanest air in centuries. What can the rest of the world learn from this?", "context": "Home\nAir Pollution\nIn many countries, people breathe the cleanest air in centuries. What can the rest of the world learn from this?\nAir pollution tends to get worse before it gets better, but how can we accelerate this transition?\nBy\nHannah Ritchie\nFebruary 17, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nI’m lucky to have grown up with far cleaner air than my parents or grandparents did. In fact, air quality in the United Kingdom is now better than it was for several generations.\n1\nTake a look at the charts below, which show the centuries-long trajectory of two key pollutants in the UK.\nFor younger generations in many other countries, this is not the case. Those living in cities like Delhi, Dhaka, or Accra breathe in some of the most polluted air in their country’s history. This has a huge impact on people’s health: outdoor air pollution leads to\nmillions of premature deaths\nevery year.\nIf you look at it over time, you find that air pollution tends to follow a\nvery standard pattern\nas countries develop and get richer.\n2\nIt’s roughly as you see in the charts: an upside-down U. Outdoor pollution starts low, then climbs as countries burn more fossil fuels for energy and industrialize their economies. Eventually, this reaches a turning point, and emissions fall.\nSo, we could expect that\nall\ncountries will go through this transition naturally, just like the UK and many other rich countries did. The problem is that this process took a long time: centuries, in fact. If it takes the rest of the world just as long,\nbillions\nof people will be exposed to high levels of air pollution for most of their lives. Hundreds of millions will die prematurely due to air pollution.\nBut it doesn’t have to be this way. Today, we have better technologies and a much better understanding of how to tackle air pollution than we did 50 or 100 years ago. This means people around the world can accelerate this process if they put the right policies and interventions in place.\nIn this article, I want to focus on the two pollutants shown above: sulfur dioxide and nitrogen oxides. These are two of the four gaseous pollutants included in the World Health Organization’s\nAir Quality Guidelines\n.\n3\nThis is because they have not only direct impacts on human health but also form secondary pollutants, such as small particulates, which make respiratory and cardiovascular health problems even worse.\nBy looking at how countries like the UK cleaned up their air, we can better understand how others can do the same, but faster.\nHow countries reduced emissions of sulfur dioxide — and acid rain\nAcid rain was one of the biggest environmental problems of the late 20th century. Here’s the opening of\nan article\nin the New York Times in 1979:\n“The rapid rate at which rainfall is growing more acidic in more areas has led many scientists and governmental officials to conclude that acid rain is developing into one of the most serious worldwide environmental problems of the coming decades.”\nBut it’s a problem we hear very little about in Europe and North America today. That’s because it’s an environmental problem we’ve solved.\nAcid rain is mostly caused by emissions of a gas called sulfur dioxide (SO\n2\n).\n4\nSO\n2\ncan dissolve in water, making rainfall acidic, which damages ecosystems such as forests, soils, rivers, and lakes.\n5\nIt’s also the reason why you often see historical statues with faces that look\nlike they’ve melted\nor buildings with fine designs that look like they’ve been washed away. The faces of Kings, Queens, and Saints — made from marble or limestone — break down and dissolve.\nThe main source of SO\n2\nis coal burning, whether for electricity, heat, or industrial processes. Coal contains small amounts of sulfur, which is released into the atmosphere as sulfur dioxide when it's burned.\nTo stop acid rain, you need to stop this sulfur being emitted. There are two ways to do this:\n6\nBurn less coal\n.\nInstall technologies on coal plants which\nremove the sulfur\nfrom the smokestacks before it is emitted into the atmosphere: this is called “scrubbing” or “flue gas desulfurization”.\nThe United Kingdom did a mix of both. Its coal burning has fallen rapidly since the 1960s. Look at the two charts below: by 2022, coal consumption had fallen by 96% and sulfur dioxide emissions by more than 99%.\nThe same is true for other countries.\nWe can see this by looking more closely at the different\nrates\nat which these two measures declined. In the chart below, you can see the relative change in coal consumption and SO\n2\nemissions since 1980 in four countries. In the UK, you can see that both lines dropped, but the reduction in SO\n2\nhappened at a faster rate and continued to fall during periods where coal consumption was stagnant, such as the early 2000s. This is because the UK implemented “scrubbing” technologies.\nThis difference is even starker in other countries. In the United States, SO\n2\nemissions declined from the 1980s to the early 2000s while coal use\nincreased\n. In Italy, emissions had fallen by more than 90% before coal use started to fall significantly.\nIn these cases, too, countries installed “scrubbers” on their coal plants to capture the sulfur in the smokestack. The implementation of these technologies was initially driven by political action.\nIn 1990, the US included a cap-and-trade scheme on SO\n2\nas part of its Clean Air Act Amendments.\n7\nThe goal was to cut the country's emissions in half by 2010 compared to 1980. Each coal plant was given an “allowance” for how much SO\n2\nit could emit, forcing it to either implement technologies to reduce its emissions, trade credits with other plants, or pay a large fine for every tonne of extra sulfur it emitted. It was clearly successful and even exceeded its goal: as you can see in the chart, SO\n2\nemissions in the US had dropped by almost 80% by 2010.\nEuropean countries were under similar pressure to reduce emissions after targets were set within the region’s\nConvention on Long-range Transboundary Air Pollution\n, which was set in 1979. That’s why you see the rapid drops in the UK, Italy, Germany, and others throughout the 1980s, ‘90s, and 2000s.\nStep outside of Europe and North America, and we can see the approaches of different countries in tackling these emissions. In the chart below, I’ve shown the same data for the UK and the US, but China and India have been added for comparison.\nChina’s trajectory makes it very clear that reducing coal consumption is not the\nonly\nway to reduce SO\n2\nemissions. Emissions peaked in 2006 and have dropped rapidly in the last decade while coal consumption has continued to grow. There has been a very clear decoupling since the mid-2000s. Again, implementing “scrubber” technologies has allowed China to drastically reduce\nlocal\nair pollution while continuing to burn more fossil fuels (increasing its contribution to\nglobal\nclimate change).\nIn India, SO\n2\nemissions are still growing. While these emissions have grown more slowly than coal consumption in the last decade, they continue to increase. Just 8% of India’s coal plant capacity has desulfurization technologies installed.\n8\nIndia is not alone. Many low-to-middle-income countries are still on the upward part of the trajectory because installing pollution controls increases the cost of coal plants — often by around 10% to 30% — and providing cheap energy is usually a higher short-term priority.\n9\nMore than half of India’s coal plants have had a “bid awarded” to have desulfurization technologies installed, but increasing costs have led to huge delays.\n8\nWhat’s clear is that emissions\ncan\nfall very quickly once countries want to turn that corner. China provides one of the most stunning examples of this. Emissions have fallen by three-quarters since their peak in 2006 and by two-thirds in the last decade alone.\nI am more optimistic that low-to-middle-income countries can go through this transition much more quickly than the UK or the US because they can learn from the countries that have already done it. In the 1980s and 1990s, Europe and North America had to design commercial-scale technologies that could remove sulfur from coal plants. They had to bring these prices down. They had to design policies and trading systems from scratch. The countries that follow don’t face uncharted territory; they can take advantage of the successes and failures of the front-runners to do it cheaper and faster. Catching up is easier than leading the way.\nThe reduction in nitrogen oxides (NOx) — one of the most damaging air pollutants to human health\nNitrogen oxides (called “NOx”) are a group of local pollutants that have been a main target for public health experts and policymakers. NOx is also emitted when fossil fuels are burned — from power stations, industry, or transport fuels.\nMany countries have successfully reduced NOx emissions over the last few decades. Part of this has come from stricter pollution controls on power plants and industry. But here, I want to focus on reductions from transport, particularly road transport.\nRoad transport is one of the main sources of NOx, and for the huge number of people living in towns and cities — with high levels of congestion and traffic — it’s the\nlargest source\nof NOx that they’re exposed to.\nIn the chart below, you can see the dramatic reduction in NOx emissions from transport in the UK since 1950. It follows a very symmetrical pattern: climbing steeply through to the 1980s before peaking in 1990 and then falling. Emissions within the last few years have dipped lower than 1950 levels.\nWhat explains this?\nThe steep rise coincided with an increase in the number of miles driven on British roads. From 1952 to 1990, the number of miles driven by cars, buses, trucks and motorcycles increased about 300%.\nYou can see this in the chart below, which shows the relative change in road mileage (in blue) and NOx emissions from transport (in red) in the UK since 1952.\n10\nNOx emissions from transport were also climbing but faster, for several reasons. First, other transport sectors that emit NOx, such as aviation and rail, were growing. Second, the types and sizes of vehicles being driven on the roads were changing, too.\nHowever, NOx emissions peaked in 1990 and have fallen steeply while the number of miles driven has continued to increase. Brits have driven more but emitted much less.\nHow was this possible? Why did NOx emissions decline so steeply over the last three decades?\nEmissions limits for cars and technological advancements from automakers have made our cars much less polluting than they were 30 years ago.\nIn 1992, the first “Euro” standards for vehicles were put into force. These standards — which applied to all countries in the European Union, plus Norway, Iceland, and Liechtenstein — were designed to reduce emissions of harmful local air pollutants, including NOx.\n11\nAll new vehicles sold in these countries had to be below these emission limits, and these standards were tightened over time. “Euro 1” was launched in 1992, and we’re now on Euro 6 standards.\n12\nThis means that the cars on our roads have become steadily “cleaner” over time.\nTo comply with regulations, car manufacturers have had to innovate on technologies that can reduce the emissions of NOx and other pollutants from car exhausts. These technologies have included catalytic converters, filters for particulate matter, gas recirculation — which lowers the temperature of combustion and therefore produces less NOx from the exhaust — and “lean NOx traps” — which convert NOx into other less harmful nitrogen gases.\nThese have been extremely successful in reducing levels of harmful pollution in many cities.\nThe chart below shows the reduction in NOx emitted per kilometer by diesel and petrol cars in the UK since 1998. Diesel cars are still worse for local air pollution than similar petrol models, but both fuel types emit less than 15% of what they did in the late 1990s.\nDownload\nAs you can see in the next chart, low-to-middle-income countries are much further behind in this emissions curve. But some middle-income countries have passed their peak, including China, South Africa, and Brazil. And emissions\ngrowth\nis slowing down in lower-middle-income countries like India, Bangladesh, and Rwanda.\nThis is similar to the patterns that we saw for SO\n2\nearlier.\nLower-income countries\nshould\nbe able to experience a faster and shallower emissions curve. Many have adopted similar regulations to the “Euro” standards, and the use of technologies such as catalytic converters and NOx traps in exhausts has been established for a long time. These technologies have become mainstream for manufacturers, not just in Europe or North America but across most markets.\nElectric transport is another huge innovation that should allow many countries to scale up road transport without NOx emissions.\nElectric vehicles\nproduce no tailpipe emissions, eliminating NOx entirely. They were once considered a luxury product due to their high cost, but\nbattery prices\nhave fallen rapidly. As a result, many EVs now cost about the same as petrol cars and are cheaper to operate. This means they could soon be the most affordable option for consumers in developing markets.\nElectric motorcycles, which are being adopted across many lower- and middle-income countries, are also having a big impact on city pollution. Rwanda’s capital, Kigali, has\nalready banned\nthe registration of new gasoline motorcycles, accelerating the move to all-electric ones.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nInternational collaboration and lesson-sharing can save many lives from air pollution\nThe point of highlighting these successes in reducing air pollution is not to make us complacent. It’s to show what\ncan\nbe done and help us understand what technologies and policy decisions can and can’t be replicated elsewhere. Seeing what is possible should make us more ambitious.\nLow- and middle-income countries have the opportunity to follow a much cleaner development pathway than the UK or the US. They can increase energy use and improve living standards with a fraction of the environmental impact. But they can’t and won’t be able to do that if they ignore the innovations that made a difference.\nWe’re already seeing these cleaner pathways. Compare today's\nper capita\nsulfur dioxide emissions in India and China to the UK's trend over the past few centuries. You can see this in the chart below. China peaked at around one-fifth of the levels of the UK. And while India’s emissions are still rising, they’re a fraction of what the UK’s used to be. By implementing the best lessons from those that have gone before them, they can certainly peak at far lower levels.\nThis really matters. Billions of people are exposed to dangerous levels of air pollution. By accelerating the transition to cleaner energy technologies, we can save\nmillions of lives\nevery year.\n13\nAcknowledgments\nMany thanks to Max Roser and Edouard Mathieu for their feedback and suggestions on this article.\nContinue reading on Our World in Data\nData review: how many people die from air pollution?\nThis data review presents published estimates of the global death toll from air pollution and provides the context that makes them understandable.\nAir pollution: does it get worse before it gets better?\nAir pollution tends to get worse as incomes rise, then it turns and pollution levels decline.\nWhat are the safest and cleanest sources of energy?\nFossil fuels are the dirtiest and most dangerous energy sources, while nuclear and modern renewable energy sources are vastly safer and cleaner.\nEndnotes\nHere, I’m focusing on\noutdoor\nair pollution. Indoor air pollution — which is caused by cooking and heating from solid fuels like wood or charcoal — is\nstill a massive problem\n, too, and has been with us far longer than the Industrial Revolution, for as long as we discovered fire.\nThis is called the Environmental Kuznet’s Curve.\nThe other two gaseous pollutants are ozone (O\n3\n) and carbon monoxide.\nNitrogen oxides (NOx), which we’ll cover later, can also contribute to acid rain.\nGrodzińska-Jurczak, M., & Szarek-Łukaszewska, G. (1999). Evaluation of SO2 and NO2-related degradation of coniferous forest stands in Poland.\nScience of the Total Environment\n.\nDeHayes, D. H., Schaberg, P. G., Hawley, G. J., & Strimbeck, G. R. (1999). Acid rain impacts on calcium nutrition and forest health: alteration of membrane-associated calcium leads to membrane destabilization and foliar injury in red spruce. BioScience.\nLikens, G. E. (1989). Acid rain and its effects on sediments in lakes and streams. Hydrobiologia.\nThere is a third way to reduce SO2 emissions, but it’s less common and has played a much smaller role in the reductions we’ve seen so far. You could\nuse types of coal that have lower concentrations of sulfur\n. Coal deposits across the world have different chemical compositions — some with more sulfur than others. This affects the amount of sulfur that’s emitted when you burn them.\nSchmalensee, R., & Stavins, R. N. (2019). Policy evolution under the clean air act. Journal of Economic Perspectives.\nChan, G., Stavins, R., Stowe, R., & Sweeney, R. (2012). The SO₂ allowance-trading system and the Clean Air Act Amendments of 1990: Reflections on 20 years of policy innovation. National Tax Journal.\nManojkumar, M. (2024).\nIncreased SO₂ emissions from coal-fired power plants: FGD installation should not be delayed further\n. Centre for Research on Energy and Clean Air (CREA).\nThe exact figure will depend on the size of the plant, the sulfur content of the coal, and the technology installed. But, flue gas desulfurisation costs around $140,000 per megawatt of coal capacity. This figure comes from the following study, which assessed the cost-benefit of desulfurization in India in 2017. At the time, its estimated cost was around $110,000 per MW. In 2024 prices, that’s around $140,000.\nCropper, M. L., Guttikunda, S., Jawahar, P., Malik, K., & Partridge, I. (2018). Costs and benefits of installing flue-gas desulfurization units at coal-fired power plants in India.\nThis means that it adds around $70 million to the cost of a typical 500MW coal plant. That increases the capital cost of coal plants by around 10% to 30%.\nRoad mileage data comes from the UK’s\nDepartment for Transport\n. This data is for Great Britain, which is the UK minus Northern Ireland. However, road mileage for the entire UK will be very similar and is likely to follow a very similar rate of change.\nSeveral other countries adopt these standards too, including Switzerland, Turkey, Russia, and some countries in Asia.\nEuro 7 standards are expected to come into effect in 2025–2026.\nEuropean Commission. Emissions in the automotive sector. Available at:\nhttps://single-market-economy.ec.europa.eu/sectors/automotive-industry/environmental-protection/emissions-automotive-sector_en\nIn a\nprevious article\n, my colleague Max Roser looked at the range of estimates on how many people die from air pollution — and, specifically, pollution from fossil fuels. In all studies, it was in the order of millions.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2025) - “In many countries, people breathe the cleanest air in centuries. What can the rest of the world learn from this?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260603-104356/cleanest-air-lessons.html' [Online Resource] (archived on June 3, 2026).\nBibTeX citation\n@article{owid-cleanest-air-lessons,\nauthor = {Hannah Ritchie},\ntitle = {In many countries, people breathe the cleanest air in centuries. What can the rest of the world learn from this?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260603-104356/cleanest-air-lessons.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "cleanest-air-lessons", "source_url": "https://ourworldindata.org/cleanest-air-lessons", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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{"Entity": "World", "Code": "OWID_WRL", "Year": "1974", "Coal": "17696.432"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1975", "Coal": "18045.436"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1976", "Coal": "18699.785"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1977", "Coal": "19267.412"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1978", "Coal": "19467.492"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1979", "Coal": "20370.578"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1980", "Coal": "20877.783"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1981", "Coal": "21158.002"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1982", "Coal": "21395.428"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1983", "Coal": "22050.973"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1984", "Coal": "23008.44"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1985", "Coal": "23996.738"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1986", "Coal": 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Excludes coal converted to liquid or gaseous fuels, but includes coal consumed in transformation processes. Differences between the consumption figures and the world production statistics are accounted for by stock changes, and unavoidable disparities in the definition, measurement or conversion of coal supply and demand data."], "shortUnit": "TWh", "unit": "terawatt-hours", "timespan": "1965-2024", "type": "Numeric", "owidVariableId": 1077523, "shortName": "coal_consumption_twh", "lastUpdated": "2025-06-27", "nextUpdate": "2026-06-27", "citationShort": "Energy Institute - Statistical Review of World Energy (2025) – with major processing by Our World in Data", "citationLong": "Energy Institute - Statistical Review of World Energy (2025) – with major processing by Our World in Data. “Coal consumption” [dataset]. Energy Institute, “Statistical Review of World Energy” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077523.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Coal consumption and sulphur dioxide emissions", "source_url": "https://ourworldindata.org/grapher/sulphur-dioxide-and-coal.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Sulphur dioxide (SO₂) emissions", "Coal consumption"], "row_count_total": 65342, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1750", "Sulphur dioxide (SO₂) emissions": "174.87167", "Coal consumption": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1751", "Sulphur dioxide (SO₂) emissions": "175.58444", "Coal consumption": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1752", "Sulphur dioxide (SO₂) emissions": "176.29706", "Coal consumption": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1753", "Sulphur dioxide (SO₂) emissions": "177.00955", "Coal consumption": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1754", "Sulphur dioxide (SO₂) emissions": 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{"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Sulphur dioxide (SO₂) emissions": "68829.82", "Coal consumption": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Sulphur dioxide (SO₂) emissions": "69624.234", "Coal consumption": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Sulphur dioxide (SO₂) emissions": "67034.625", "Coal consumption": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Sulphur dioxide (SO₂) emissions": "60993.727", "Coal consumption": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Sulphur dioxide (SO₂) emissions": "53499.797", "Coal consumption": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Sulphur dioxide (SO₂) emissions": "56813.844", "Coal consumption": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Sulphur dioxide (SO₂) emissions": "53119.727", "Coal consumption": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Sulphur dioxide (SO₂) emissions": "43913.066", "Coal consumption": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Sulphur dioxide (SO₂) emissions": "45332.848", "Coal consumption": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Sulphur dioxide (SO₂) emissions": "51720.855", "Coal consumption": ""}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "sulphur-dioxide-and-coal", "metadata_url": "https://ourworldindata.org/grapher/sulphur-dioxide-and-coal.metadata.json", "chart_title": "Coal consumption and sulphur dioxide emissions", "chart_subtitle": "Sulphur dioxide (SO₂) is an air pollutant formed from the burning of fuels that contain sulphur, such as coal. Countries can reduce SO₂ emissions by burning less coal, or installing desulphurization technologies which remove sulphur from the smokestack.", "chart_note": null, "chart_citation": "Community Emissions Data System (CEDS) 2024; Energy Institute (2024)", "original_chart_url": "https://ourworldindata.org/grapher/sulphur-dioxide-and-coal", "owid_column_metadata": {"Sulfur dioxide emissions from all sectors": {"titleShort": "Sulfur dioxide emissions from all sectors", "titleLong": "Sulfur dioxide emissions from all sectors", "descriptionShort": "Sulfur dioxide (SO₂) is an air pollutant formed from the burning of fuels that contain sulfur, such as coal. SO₂ is one of the main chemicals that forms acid rain.", "descriptionKey": ["\"Agriculture\" includes air pollutant emissions from biological processes such as manure management, rice cultivation, soil processes, and livestock digestive processes. It also covers indirect N₂O emissions from non-agricultural nitrogen sources and fuel combustion in agricultural machinery, forestry, and fishing.", "\"Buildings\" includes air pollutant emissions from fuel combustion in residential, commercial, and institutional buildings. Key sources include heating, cooking, and other energy use in homes, offices, and public facilities.", "\"Domestic aviation\" includes air pollutant emissions from fuel combustion in aircraft operating on domestic routes.", "\"Energy\" includes air pollutant emissions from fuel combustion in electricity and heat production, as well as fugitive emissions from solid fuels, petroleum, and natural gas extraction. This sector also includes emissions from fossil fuel fires.", "\"Industry\" includes air pollutant emissions from fuel combustion and chemical processes in manufacturing sectors, such as iron and steel production, cement and lime manufacturing, mining, and non-metallic mineral processing.", "\"International aviation\" includes air pollutant emissions from fuel combustion in aircraft operating on international routes.", "\"International shipping\" includes air pollutant emissions from fuel combustion in ships engaged in international transport. It also covers emissions from oil tanker loading, the transfer of crude oil or petroleum products between storage facilities and transport systems such as tankers.", "\"Transport\" includes air pollutant emissions from fuel combustion in road, rail, and domestic navigation.", "\"Solvents\" includes air pollutant emissions from industrial and consumer applications such as degreasing, painting, and chemical manufacturing.", "\"Waste\" includes air pollutant emissions from solid waste disposal, waste combustion, wastewater handling, and other waste management processes.", "Emissions assigned to \"Other\" are those that are included in the global total, but cannot be allocated to any specific countries."], "descriptionProcessing": "Subsectors have been mapped into broader sectors as follows (using CEDS codes):\n* Agriculture: enteric fermentation (3E); fuel use in agriculture, forestry, and fishing (1A4c); indirect N₂O emissions (non-agricultural sources) (7BC); manure management (3B); other agricultural emissions (3I); rice cultivation (3D); soil emissions (3D).\n* Buildings: commercial and institutional buildings (1A4a); residential buildings (1A4b).\n* Domestic aviation: domestic aviation (1A3aii).\n* Energy: electricity production (autoproducer) (1A1a); electricity production (public) (1A1a); fossil fuel fires (7A); fugitive emissions from natural gas distribution (1B2b); fugitive emissions from natural gas production (1B2b); fugitive emissions from other energy sources (1B2d); fugitive emissions from petroleum (1B2); fugitive emissions from solid fuels (1B1); heat production (1A1a); other energy transformation (1A1bc); other fuel use (unspecified) (1A5).\n* Industry: adipic acid production (2B3); aluminum production (2C3); cement production (2A1); chemical industry (2B); industrial combustion (chemicals) (1A2c); industrial combustion (construction) (1A2g); industrial combustion (food and tobacco) (1A2e); industrial combustion (iron and steel) (1A2a); industrial combustion (machinery) (1A2g); industrial combustion (mining and quarrying) (1A2g); industrial combustion (non-ferrous metals) (1A2b); industrial combustion (non-metallic minerals) (1A2f); industrial combustion (other) (1A2g); industrial combustion (pulp and paper) (1A2d); industrial combustion (textile and leather) (1A2g); industrial combustion (transport equipment) (1A2g); industrial combustion (wood products) (1A2g); iron and steel alloy production (2C1); lime production (2A2); nitric acid production (2B2); other mineral production (2Ax); other non-ferrous metal production (2C4); pulp and paper, food, beverage, and wood processing (2H).\n* International aviation: international aviation (1A3ai).\n* International shipping: international shipping (1A3di); oil tanker loading (1A3di).\n* Solvents: chemical products manufacture and processing (2D); degreasing and cleaning (2D); other product use (2D); paint application (2D).\n* Transport: domestic navigation (1A3dii); other transport (1A3eii); rail transportation (1A3c); road transportation (1A3b).\n* Waste: other waste handling (5E); other waste sources (6A); solid waste disposal (5A); unspecified waste sources (6B); waste combustion (5C); wastewater handling (5D).", "shortUnit": "t", "unit": "tonnes", "timespan": "1750-2022", "type": "Numeric", "owidVariableId": 1013701, "shortName": "emissions__pollutant_so2__sector_all_sectors", "lastUpdated": "2025-02-12", "nextUpdate": "2026-07-22", "citationShort": "Hoesly et al. (2024) - Community Emissions Data System (CEDS) – with major processing by Our World in Data", "citationLong": "Hoesly et al. (2024) - Community Emissions Data System (CEDS) – with major processing by Our World in Data. “Sulfur dioxide emissions from all sectors” [dataset]. Hoesly et al., “Community Emissions Data System v_2024_07_08” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1013701.metadata.json"}, "Coal consumption - TWh": {"titleShort": "Coal consumption", "titleLong": "Coal consumption", "descriptionKey": ["Includes commercial solid fuels only, i.e. bituminous coal and anthracite (hard coal), and lignite and brown (sub-bituminous) coal, and other commercial solid fuels. Excludes coal converted to liquid or gaseous fuels, but includes coal consumed in transformation processes. Differences between the consumption figures and the world production statistics are accounted for by stock changes, and unavoidable disparities in the definition, measurement or conversion of coal supply and demand data."], "shortUnit": "TWh", "unit": "terawatt-hours", "timespan": "1965-2024", "type": "Numeric", "owidVariableId": 1077523, "shortName": "coal_consumption_twh", "lastUpdated": "2025-06-27", "nextUpdate": "2026-06-27", "citationShort": "Energy Institute - Statistical Review of World Energy (2025) – with major processing by Our World in Data", "citationLong": "Energy Institute - Statistical Review of World Energy (2025) – with major processing by Our World in Data. “Coal consumption” [dataset]. Energy Institute, “Statistical Review of World Energy” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077523.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Road miles and NOₓ emissions from transport in the United Kingdom", "source_url": "https://ourworldindata.org/grapher/road-miles-nox-emissions.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Road miles", "NOₓ emissions"], "row_count_total": 16543, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1952", "Road miles": "", "NOₓ emissions": "14546.945"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1953", "Road miles": "", "NOₓ emissions": "15441.879"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1954", "Road miles": "", "NOₓ emissions": "15479.301"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1955", "Road miles": "", "NOₓ emissions": "21548.482"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1956", "Road miles": "", "NOₓ emissions": "28505.955"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1957", "Road miles": "", "NOₓ emissions": "51070.023"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1958", "Road miles": "", "NOₓ emissions": "56372.527"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1959", "Road miles": "", "NOₓ emissions": "60819.152"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1960", "Road miles": "", "NOₓ emissions": "64403.664"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Road miles": "", "NOₓ emissions": "69735.32"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Road miles": "", "NOₓ emissions": "86423.33"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Road miles": "", "NOₓ emissions": "93532.34"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Road miles": "", "NOₓ emissions": "113776.65"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Road miles": "", "NOₓ emissions": "129674.02"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Road miles": "", "NOₓ emissions": "137179.64"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Road miles": "", "NOₓ emissions": "132621.06"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Road miles": "", "NOₓ emissions": "118654.984"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1969", "Road miles": "", "NOₓ emissions": "125806.68"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1970", "Road miles": "", "NOₓ emissions": "160330.64"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1971", "Road miles": "", "NOₓ emissions": "192249.9"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1972", "Road miles": "", "NOₓ emissions": "160045.89"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Road miles": "", "NOₓ emissions": "146178.7"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Road miles": "", "NOₓ emissions": "154032.89"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Road miles": "", "NOₓ emissions": "182113.9"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Road miles": "", "NOₓ emissions": "181372.94"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Road miles": "", "NOₓ emissions": "205508.6"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Road miles": "", "NOₓ emissions": "223525.61"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Road miles": "", "NOₓ emissions": "272922.97"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Road miles": "", "NOₓ emissions": "209090.8"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Road miles": "", "NOₓ emissions": "218601.4"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Road miles": "", "NOₓ emissions": "230255.2"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Road miles": "", "NOₓ emissions": "266897.78"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Road miles": "", "NOₓ emissions": "286194.7"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Road miles": "", "NOₓ emissions": "243561.86"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Road miles": "", "NOₓ emissions": "167154.39"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Road miles": "", "NOₓ emissions": "310102.25"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Road miles": "", "NOₓ emissions": "277561.34"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Road miles": "", "NOₓ emissions": "288219.12"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Road miles": "", "NOₓ emissions": "356151.9"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Road miles": "", "NOₓ emissions": "343770.25"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Road miles": "", "NOₓ emissions": "207267.19"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Road miles": "", "NOₓ emissions": "208619.47"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Road miles": "", "NOₓ emissions": "204915.62"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Road miles": "", "NOₓ emissions": "167656.06"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Road miles": "", "NOₓ emissions": "156476.94"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Road miles": "", "NOₓ emissions": "135224.2"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Road miles": "", "NOₓ emissions": "126104.06"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Road miles": "", "NOₓ emissions": "80816.2"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Road miles": "", "NOₓ emissions": "64938.492"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Road miles": "", "NOₓ emissions": "30889.955"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Road miles": "", "NOₓ emissions": "23304.938"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Road miles": "", "NOₓ emissions": "26495.652"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Road miles": "", "NOₓ emissions": "31108.049"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Road miles": "", "NOₓ emissions": "42019.184"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Road miles": "", "NOₓ emissions": "46790.832"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Road miles": "", "NOₓ emissions": "64000.4"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Road miles": "", "NOₓ emissions": "74252.51"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Road miles": "", "NOₓ emissions": "134200.81"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Road miles": "", "NOₓ emissions": "186808.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Road miles": "", "NOₓ emissions": "221469.14"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Road miles": "", "NOₓ emissions": "219818.25"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Road miles": "", "NOₓ emissions": "209161.77"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Road miles": "", "NOₓ emissions": "185215.27"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Road miles": "", "NOₓ emissions": "232702.34"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Road miles": "", "NOₓ emissions": "204353.6"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Road miles": "", "NOₓ emissions": "216882.73"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Road miles": "", "NOₓ emissions": "247661.6"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Road miles": "", "NOₓ emissions": "271686.94"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Road miles": "", "NOₓ emissions": "282395.22"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Road miles": "", "NOₓ emissions": "290097.12"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Road miles": "", "NOₓ emissions": "281383.72"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1952", "Road miles": "", "NOₓ emissions": "222514.88"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1953", "Road miles": "", "NOₓ emissions": "228353.2"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1954", "Road miles": "", "NOₓ emissions": "238640.97"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1955", "Road miles": "", "NOₓ emissions": "238749.4"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1956", "Road miles": "", "NOₓ emissions": "238804.25"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1957", "Road miles": "", "NOₓ emissions": "248259.75"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1958", "Road miles": "", "NOₓ emissions": "256989.44"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1959", "Road miles": "", "NOₓ emissions": "247473.33"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1960", "Road miles": "", "NOₓ emissions": "271494.47"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1961", "Road miles": "", "NOₓ emissions": "281492.28"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1962", "Road miles": "", "NOₓ emissions": "286515.6"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1963", "Road miles": "", "NOₓ emissions": "303496.34"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1964", "Road miles": "", "NOₓ emissions": "344576.03"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1965", "Road miles": "", "NOₓ emissions": "364715.56"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1966", "Road miles": "", "NOₓ emissions": "383601.7"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1967", "Road miles": "", "NOₓ emissions": "374364.66"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1968", "Road miles": "", "NOₓ emissions": "366880.38"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1969", "Road miles": "", "NOₓ emissions": "398225.62"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1970", "Road miles": "", "NOₓ emissions": "417842.44"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1971", "Road miles": "", "NOₓ emissions": "462431.9"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1972", "Road miles": "", "NOₓ emissions": "519010.06"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1973", "Road miles": "", "NOₓ emissions": "566123.06"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1974", "Road miles": "", "NOₓ emissions": "580796.75"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1975", "Road miles": "", "NOₓ emissions": "627418.8"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1976", "Road miles": "", "NOₓ emissions": "665748.06"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1977", "Road miles": "", "NOₓ emissions": "713887.25"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1978", "Road miles": "", "NOₓ emissions": "785514.2"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1979", "Road miles": "", "NOₓ emissions": "747971.94"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1980", "Road miles": "", "NOₓ emissions": "808299.75"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1981", "Road miles": "", "NOₓ emissions": "855579.7"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1982", "Road miles": "", "NOₓ emissions": "889296.3"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1983", "Road miles": "", "NOₓ emissions": "909091.56"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1984", "Road miles": "", "NOₓ emissions": "922720.56"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Road miles": "", "NOₓ emissions": "968869.5"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Road miles": "", "NOₓ emissions": "966395.94"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Road miles": "", "NOₓ emissions": "968883.6"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Road miles": "", "NOₓ emissions": "1041624.25"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Road miles": "", "NOₓ emissions": "1059917.1"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Road miles": "", "NOₓ emissions": "1107024.8"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Road miles": "", "NOₓ emissions": "1168335.2"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Road miles": "", "NOₓ emissions": "1245108"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Road miles": "", "NOₓ emissions": "1225497.4"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Road miles": "", "NOₓ emissions": "1232267.4"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Road miles": "", "NOₓ emissions": "1319213.6"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1996", "Road miles": "", "NOₓ emissions": "1344827.9"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1997", "Road miles": "", "NOₓ emissions": "1440160.5"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1998", "Road miles": "", "NOₓ emissions": "1423023.9"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1999", "Road miles": "", "NOₓ emissions": "1474120.2"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Road miles": "", "NOₓ emissions": "1522639.2"}], "rows_tail": [{"Entity": "Zambia", "Code": "ZMB", "Year": "1974", "Road miles": "", "NOₓ emissions": "6285.1055"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1975", "Road miles": "", "NOₓ emissions": "6729.5767"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1976", "Road miles": "", "NOₓ emissions": "7039.222"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1977", "Road miles": "", "NOₓ emissions": "6325.098"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1978", "Road miles": "", "NOₓ emissions": "5903.0977"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1979", "Road miles": "", "NOₓ emissions": "5290.498"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1980", "Road miles": "", "NOₓ emissions": "9358.053"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1981", "Road miles": "", "NOₓ emissions": "9019.565"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1982", "Road miles": "", "NOₓ emissions": "7880.241"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1983", "Road miles": "", "NOₓ emissions": "8094.8716"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1984", "Road miles": "", "NOₓ emissions": "8371.432"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Road miles": "", "NOₓ emissions": "7971.013"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Road miles": "", "NOₓ emissions": "7705.9565"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Road miles": "", "NOₓ emissions": "8151.146"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Road miles": "", "NOₓ emissions": "9323.909"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Road miles": "", "NOₓ emissions": "8152.569"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Road miles": "", "NOₓ emissions": "8474.07"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Road miles": "", "NOₓ emissions": "10577.159"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Road miles": "", "NOₓ emissions": "10201.723"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Road miles": "", "NOₓ emissions": "7287.126"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Road miles": "", "NOₓ emissions": "9094.025"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Road miles": "", "NOₓ emissions": "9157.207"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Road miles": "", "NOₓ emissions": "6901.2847"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Road miles": "", "NOₓ emissions": "7636.089"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Road miles": "", "NOₓ emissions": "7242.9336"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Road miles": "", "NOₓ emissions": "6920.086"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Road miles": "", "NOₓ emissions": "7487.5845"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Road miles": "", "NOₓ emissions": "7872.449"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Road miles": "", "NOₓ emissions": "8253.436"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Road miles": "", "NOₓ emissions": "8695.012"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Road miles": "", "NOₓ emissions": "9300.61"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Road miles": "", "NOₓ emissions": "10003.368"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Road miles": "", "NOₓ emissions": "10359.96"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Road miles": "", "NOₓ emissions": "4952.4243"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Road miles": "", "NOₓ emissions": "5430.852"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Road miles": "", "NOₓ emissions": "6472.8066"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Road miles": "", "NOₓ emissions": "7150.425"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Road miles": "", "NOₓ emissions": "7978.8643"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Road miles": "", "NOₓ emissions": "10015.141"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Road miles": "", "NOₓ emissions": "10700.175"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Road miles": "", "NOₓ emissions": "12154.643"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Road miles": "", "NOₓ emissions": "13063.818"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Road miles": "", "NOₓ emissions": "13082.941"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Road miles": "", "NOₓ emissions": "11186.992"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Road miles": "", "NOₓ emissions": "11212.394"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Road miles": "", "NOₓ emissions": "11007.458"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Road miles": "", "NOₓ emissions": "11463.835"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Road miles": "", "NOₓ emissions": "15038.164"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Road miles": "", "NOₓ emissions": "15113.95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1952", "Road miles": "", "NOₓ emissions": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1953", "Road miles": "", "NOₓ emissions": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1954", "Road miles": "", "NOₓ emissions": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1955", "Road miles": "", "NOₓ emissions": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1956", "Road miles": "", "NOₓ emissions": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1957", "Road miles": "", "NOₓ emissions": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1958", "Road miles": "", "NOₓ emissions": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1959", "Road miles": "", "NOₓ emissions": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1960", "Road miles": "", "NOₓ emissions": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1961", "Road miles": "", "NOₓ emissions": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1962", "Road miles": "", "NOₓ emissions": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1963", "Road miles": "", "NOₓ emissions": "0"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1964", "Road miles": "", "NOₓ emissions": "12145.752"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1965", "Road miles": "", "NOₓ emissions": "17048.709"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "Road miles": "", "NOₓ emissions": "10334.97"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "Road miles": "", "NOₓ emissions": "11622.381"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "Road miles": "", "NOₓ emissions": "12964.025"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "Road miles": "", "NOₓ emissions": "13895.501"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "Road miles": "", "NOₓ emissions": "14166.421"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "Road miles": "", "NOₓ emissions": "14762.108"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "Road miles": "", "NOₓ emissions": "17024.7"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "Road miles": "", "NOₓ emissions": "17867.26"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "Road miles": "", "NOₓ emissions": "16988.572"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Road miles": "", "NOₓ emissions": "19165.738"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Road miles": "", "NOₓ emissions": "16325.457"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Road miles": "", "NOₓ emissions": "16155.367"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Road miles": "", "NOₓ emissions": "16229.534"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Road miles": "", "NOₓ emissions": "14901.718"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Road miles": "", "NOₓ emissions": "17215.572"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Road miles": "", "NOₓ emissions": "20158.998"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Road miles": "", "NOₓ emissions": "16177.068"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Road miles": "", "NOₓ emissions": "20342.16"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Road miles": "", "NOₓ emissions": "19607.67"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Road miles": "", "NOₓ emissions": "20459.445"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Road miles": "", "NOₓ emissions": "20412.936"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Road miles": "", "NOₓ emissions": "20251.367"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Road miles": "", "NOₓ emissions": "22736.557"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Road miles": "", "NOₓ emissions": "22564.668"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Road miles": "", "NOₓ emissions": "21291.088"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Road miles": "", "NOₓ emissions": "12932.188"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Road miles": "", "NOₓ emissions": "22128.553"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Road miles": "", "NOₓ emissions": "19151.23"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Road miles": "", "NOₓ emissions": "19675.658"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Road miles": "", "NOₓ emissions": "22908.73"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Road miles": "", "NOₓ emissions": "21905.424"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Road miles": "", "NOₓ emissions": "20186.36"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Road miles": "", "NOₓ emissions": "19930.256"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Road miles": "", "NOₓ emissions": "31377.537"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Road miles": "", "NOₓ emissions": "19437.375"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Road miles": "", "NOₓ emissions": "18220.781"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Road miles": "", "NOₓ emissions": "16750.469"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Road miles": "", "NOₓ emissions": "13629.606"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Road miles": "", "NOₓ emissions": "12185.999"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Road miles": "", "NOₓ emissions": "12655.36"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Road miles": "", "NOₓ emissions": "12355.424"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Road miles": "", "NOₓ emissions": "11732.975"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Road miles": "", "NOₓ emissions": "10224.032"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Road miles": "", "NOₓ emissions": "11732.253"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Road miles": "", "NOₓ emissions": "14057.45"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Road miles": "", "NOₓ emissions": "24943.527"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Road miles": "", "NOₓ emissions": "28667.045"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Road miles": "", "NOₓ emissions": "30341.273"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Road miles": "", "NOₓ emissions": "27923.996"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Road miles": "", "NOₓ emissions": "25321.607"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Road miles": "", "NOₓ emissions": "20389.033"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Road miles": "", "NOₓ emissions": "22784.49"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Road miles": "", "NOₓ emissions": "27584.078"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Road miles": "", "NOₓ emissions": "26232.223"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Road miles": "", "NOₓ emissions": "18076.05"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Road miles": "", "NOₓ emissions": "17699.547"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Road miles": "", "NOₓ emissions": "17790.842"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "road-miles-nox-emissions", "metadata_url": "https://ourworldindata.org/grapher/road-miles-nox-emissions.metadata.json", "chart_title": "Road miles and NOₓ emissions from transport in the United Kingdom", "chart_subtitle": "Road miles are measured in passenger-kilometers, which is the number of miles traveled in cars, vans, buses and motorcycles. Nitrogen oxide (NOₓ) emissions are damaging to human health, and transport is a dominant source in towns and cities.", "chart_note": "Road miles are for Great Britain, which is the United Kingdom minus Northern Ireland.", "chart_citation": "UK Department for Transport (2024); Hoesly et al. (2024) - Community Emissions Data System (CEDS)", "original_chart_url": "https://ourworldindata.org/grapher/road-miles-nox-emissions", "owid_column_metadata": {"Road miles": {"titleShort": "Road miles", "titleLong": "Road miles", "shortUnit": "p-km", "unit": "passenger kilometers", "timespan": "1952-2022", "type": "Integer", "conversionFactor": 1000000000, "owidVariableId": 1009210, "shortName": "road_miles", "lastUpdated": "2025-02-03", "citationShort": "UK Department for Transport (2024) – processed by Our World in Data", "citationLong": "UK Department for Transport (2024) – processed by Our World in Data. “Road miles” [dataset]. UK Department for Transport, “road_miles_uk” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1009210.metadata.json"}, "Nitrogen oxides emissions from transport": {"titleShort": "Nitrogen oxides emissions from transport", "titleLong": "Nitrogen oxides emissions from transport", "descriptionShort": "Nitrogen oxides (NOₓ) are gases that are mainly formed during the burning of fossil fuels. Exposure to NOₓ gases can have negative impacts on respiratory health. NOₓ gases can also lead to the formation of ozone – another air pollutant.", "descriptionKey": ["\"Transport\" includes air pollutant emissions from fuel combustion in road, rail, and domestic navigation.", "Emissions assigned to \"Other\" are those that are included in the global total, but cannot be allocated to any specific countries."], "descriptionProcessing": "Subsectors have been mapped into broader sectors as follows (using CEDS codes):\n* Agriculture: enteric fermentation (3E); fuel use in agriculture, forestry, and fishing (1A4c); indirect N₂O emissions (non-agricultural sources) (7BC); manure management (3B); other agricultural emissions (3I); rice cultivation (3D); soil emissions (3D).\n* Buildings: commercial and institutional buildings (1A4a); residential buildings (1A4b).\n* Domestic aviation: domestic aviation (1A3aii).\n* Energy: electricity production (autoproducer) (1A1a); electricity production (public) (1A1a); fossil fuel fires (7A); fugitive emissions from natural gas distribution (1B2b); fugitive emissions from natural gas production (1B2b); fugitive emissions from other energy sources (1B2d); fugitive emissions from petroleum (1B2); fugitive emissions from solid fuels (1B1); heat production (1A1a); other energy transformation (1A1bc); other fuel use (unspecified) (1A5).\n* Industry: adipic acid production (2B3); aluminum production (2C3); cement production (2A1); chemical industry (2B); industrial combustion (chemicals) (1A2c); industrial combustion (construction) (1A2g); industrial combustion (food and tobacco) (1A2e); industrial combustion (iron and steel) (1A2a); industrial combustion (machinery) (1A2g); industrial combustion (mining and quarrying) (1A2g); industrial combustion (non-ferrous metals) (1A2b); industrial combustion (non-metallic minerals) (1A2f); industrial combustion (other) (1A2g); industrial combustion (pulp and paper) (1A2d); industrial combustion (textile and leather) (1A2g); industrial combustion (transport equipment) (1A2g); industrial combustion (wood products) (1A2g); iron and steel alloy production (2C1); lime production (2A2); nitric acid production (2B2); other mineral production (2Ax); other non-ferrous metal production (2C4); pulp and paper, food, beverage, and wood processing (2H).\n* International aviation: international aviation (1A3ai).\n* International shipping: international shipping (1A3di); oil tanker loading (1A3di).\n* Solvents: chemical products manufacture and processing (2D); degreasing and cleaning (2D); other product use (2D); paint application (2D).\n* Transport: domestic navigation (1A3dii); other transport (1A3eii); rail transportation (1A3c); road transportation (1A3b).\n* Waste: other waste handling (5E); other waste sources (6A); solid waste disposal (5A); unspecified waste sources (6B); waste combustion (5C); wastewater handling (5D).", "shortUnit": "t", "unit": "tonnes", "timespan": "1750-2022", "type": "Numeric", "owidVariableId": 1013688, "shortName": "emissions__pollutant_nox__sector_transport", "lastUpdated": "2025-02-12", "nextUpdate": "2026-07-22", "citationShort": "Hoesly et al. (2024) - Community Emissions Data System (CEDS) – with major processing by Our World in Data", "citationLong": "Hoesly et al. (2024) - Community Emissions Data System (CEDS) – with major processing by Our World in Data. “Nitrogen oxides emissions from transport” [dataset]. Hoesly et al., “Community Emissions Data System v_2024_07_08” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1013688.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "7d0346473682dee82082"},
{"raw_link": "https://ourworldindata.org/maternal-deaths-save-mothers", "title": "If we can make maternal deaths as rare as in the healthiest countries, we can save 275,000 mothers each year", "context": "Home\nMaternal Mortality\nIf we can make maternal deaths as rare as in the healthiest countries, we can save 275,000 mothers each year\nMaternal mortality was much more common in the past. It is much lower today, but global inequalities are still large.\nBy\nHannah Ritchie\nFebruary 3, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nFor most of human history, pregnancy and childbirth were very risky; mothers would die in at least 1 in 100 pregnancies.\n1\nSince the average woman would have\nat least\nfour or five children, the lifetime risk of dying from maternal causes would be at least 1 in 25.\n2\nThis was true everywhere.\nThankfully, that’s no longer the case. We’ve made\nhuge strides\nin not only protecting infants in childbirth and the early stages of their lives, but we’ve also made it much safer for women.\nBut we’re not done yet. There are still huge inequalities in the risks of pregnancy across the world. Pregnant women in countries like Sierra Leone and Kenya are around\n100 times more likely to die\nduring pregnancy or childbirth than those in countries like Norway, Sweden, or Germany.\n3\nBut it doesn’t have to be this way. We could save hundreds of thousands of lives a year by closing these gaps.\nI’ve compared three scenarios in the chart below to clarify these points.\nFirst, we can see that the situation today is awful.\n286,000 women\ndied from\nmaternal causes in 2020.\n4\nThat’s 784 deaths per day on average, or one mother dying every two minutes.\n5\nSecond, we can consider the very high maternal mortality rates of the past. Particularly good long-term data is available for Finland or Sweden, which\nshows\nthat in 1750, around 900 women died per 100,000 live births.\n6\nSince there were\n135 million births\nin 2020, I calculate that\n1.2 million women\nwould have died from maternal causes that year\nif\nthese rates hadn’t improved.\n7\nThings are much, much better than they used to be.\nFinally, things can still be much better. We know this because some countries have maternal mortality rates that are far lower than the global average. And they all used to be in a similar position to the worst-off countries today. In Europe, the maternal mortality rate was 8 deaths per 100,000 live births in 2020. That’s around\n25 times lower\nthan the global average.\n8\nIf all countries could achieve the same outcomes as Europe,\n11,000 women\nwould have died from maternal causes in 2020 — a small fraction of the 286,000 deaths that occurred.\n9\nProviding the best conditions for women everywhere would reduce the global death toll by 275,000 maternal deaths a year.\nDownload\nWhat’s crucial is that all three of these points are true at the same time. My colleague, Max Roser,\nwrote about this\nin more detail, using the example of child mortality.\nSeeing how bad conditions were in the past shows that it’s possible to make massive progress in improving health outcomes. While these improvements have saved hundreds of thousands of women a year, we shouldn’t be satisfied with where we are. We know we could save hundreds of thousands more.\nIt’s unacceptable that the risk of pregnancy for some women is a hundred times higher than for others, and this is almost entirely the result of\nthe lottery of where they were born\n.\nAcknowledgments\nMany thanks to Max Roser, Simon van Teutem, Saloni Dattani, and Edouard Mathieu for their comments and feedback on this article.\nContinue reading on Our World in Data\nMaternal Mortality\nWhat could be more tragic than a mother losing her life in the moment that she is giving birth to her newborn? Why are mothers dying and what can be done to prevent these deaths?\nMortality in the past: every second child died\nThe chances that a newborn survives childhood have increased from 50% to 96% globally. How do we know about the mortality of children in the past? And what can we learn from it for our future?\nThe world is awful. The world is much better. The world can be much better.\nIt is wrong to think these three statements contradict each other. We need to see that they are all true to see that a better world is possible.\nEndnotes\nWe only have\nlong-run estimates\nof maternal mortality for a few countries, such as Finland and Sweden. But even in these countries — which are some of the best-off ones today — maternal mortality rates were around 1000 per 100,000 live births back in the 18th century.\nIf 1% of pregnancies resulted in maternal death, then this risk would increase to 4% to 5% if women had four or five children. Note that these high rates are\nstill a reality\nfor women in some of the poorest countries today because maternal mortality is high in many such places, and women often have more than 5 or 6 children on average.\nThe maternal mortality rate in Norway is around 2 deaths per 100,000 live births. In Germany, it’s 4 deaths, and in Sweden it’s 5. In Sierra Leone, this rate was around 440 deaths per 100,000 in 2020; in Kenya, this rate was 530. That’s around 100 times higher. A handful of countries — including Nigeria, South Sudan, and Chad — have even higher rates, at more than 1,000 deaths per 100,000 births.\nThis data comes from the UN’s\nMaternal Mortality Estimation Inter-Agency Group\n(MMEIG). This global figure can vary slightly depending on the source. The WHO’s Global Health Observatory, for example,\nestimates that\n301,000 women died from maternal causes in 2020.\nWe get this daily figure by dividing 286,063 by 365; then, the number per hour by dividing that figure by 24. This comes out at 32 per hour, or around one every two minutes.\nIt’s unlikely that other countries would have lower rates than this.\nOf course, this is a hypothetical scenario. You could argue that we’d never have reached 135 million births per year without the improvements in global health and development that we’ve seen more broadly.\nThe global average rate was 212 deaths per 100,000 live births based on estimates from the UN MMEIG.\nSince there were 135 million births, a rate of 8 deaths per 100,000 results in 11,000 deaths [135 million / 100,000 * 8 = 10,778].\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2025) - “If we can make maternal deaths as rare as in the healthiest countries, we can save 275,000 mothers each year” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260105-154138/maternal-deaths-save-mothers.html' [Online Resource] (archived on January 5, 2026).\nBibTeX citation\n@article{owid-maternal-deaths-save-mothers,\nauthor = {Hannah Ritchie},\ntitle = {If we can make maternal deaths as rare as in the healthiest countries, we can save 275,000 mothers each year},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260105-154138/maternal-deaths-save-mothers.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "maternal-deaths-save-mothers", "source_url": "https://ourworldindata.org/maternal-deaths-save-mothers", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Maternal mortality was much more common in the past. It is much lower today, but global inequalities are still large.", "numeric_mentions": ["275,000", "3,", "2025", "1", "100", "25", "2", "3", "286,000", "2020", "4", "784", "5", "1750,", "900", "100,000", "6", "135 million", "2020,", "1.2 million", "7", "8", "11,000", "9", "50%", "96%", "1000", "18", "1%", "4%", "5%", "440", "530", "1,000", "301,000", "286,063", "365", "24", "32", "212", "10,778", "20260105", "154138", "5,", "2026"], "numeric_evidence": [{"title": "Fertility rate: births per woman", "source_url": "https://ourworldindata.org/grapher/children-born-per-woman.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Total fertility rate"], "row_count_total": 19402, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1950", "Total fertility rate": "7.248"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1951", "Total fertility rate": "7.26"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1952", "Total fertility rate": "7.26"}, {"Entity": 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{"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Total fertility rate": "4.067"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Total fertility rate": "4.056"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Total fertility rate": "4.009"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Total fertility rate": "3.983"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Total fertility rate": "3.925"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Total fertility rate": "3.862"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Total fertility rate": "3.776"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Total fertility rate": "3.693"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Total fertility rate": "3.635"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Total fertility rate": "3.677"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Total fertility rate": "3.783"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Total fertility rate": "3.95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Total fertility rate": "4.04"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Total fertility rate": "4.126"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Total fertility rate": "4.134"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Total fertility rate": "4.111"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Total fertility rate": "4.011"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Total fertility rate": "3.911"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Total fertility rate": "3.828"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Total fertility rate": "3.768"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Total fertility rate": "3.744"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Total fertility rate": "3.748"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Total fertility rate": "3.754"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Total fertility rate": "3.765"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Total fertility rate": "3.767"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Total fertility rate": "3.724"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "children-born-per-woman", "metadata_url": "https://ourworldindata.org/grapher/children-born-per-woman.metadata.json", "chart_title": "Fertility rate: births per woman", "chart_subtitle": "The total fertility rate summarizes the total number of births a woman would have, if she experienced the birth rates seen in women of each age group in one particular year across her childbearing years.", "chart_note": "", "chart_citation": "Human Fertility Database (2025); UN, World Population Prospects (2024)", "original_chart_url": "https://ourworldindata.org/grapher/children-born-per-woman", "owid_column_metadata": {"Fertility rate (period), historical": {"titleShort": "Fertility rate: births per woman", "titleLong": "Fertility rate: births per woman - HFD, UN WPP – period tables", "descriptionShort": "The average number of live births a hypothetical cohort of women would have at the end of their reproductive period if they were subject during their whole lives to the fertility rates of a given period and if they were not subject to mortality.", "descriptionKey": ["Assumes current age-specific fertility rates remain constant throughout a woman's lifetime.", "Does not account for potential changes in social, economic, or health conditions that could affect fertility rates."], "descriptionProcessing": "The fertility data is constructed by combining data from multiple sources:\n\n- Before 1950: Historical estimates by Human Fertility Database (2025).\n\n- 1950-2023: Population records by the UN World Population Prospects (2024 revision).", "unit": "live births per woman", "timespan": "1891-2023", "type": "Numeric", "owidVariableId": 1118640, "shortName": "fertility_rate_hist", "lastUpdated": "2025-10-22", "nextUpdate": "2026-10-22", "citationShort": "Human Fertility Database (2025); UN, World Population Prospects (2024) – with major processing by Our World in Data", "citationLong": "Human Fertility Database (2025); UN, World Population Prospects (2024) – with major processing by Our World in Data. “Fertility rate: births per woman – HFD, UN WPP – period tables” [dataset]. Human Fertility Database, “Human Fertility Database”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1118640.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Maternal mortality ratio", "source_url": "https://ourworldindata.org/grapher/maternal-mortality.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Maternal mortality ratio", "World region according to OWID", "Maternal mortality ratio (Annotations)"], "row_count_total": 9264, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Maternal mortality ratio": "1910.3416", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Maternal mortality ratio": "1602.9429", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Maternal mortality ratio": "1586.7214", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Maternal mortality ratio": "1413.9326", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Maternal mortality ratio": "1382.7299", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Maternal mortality ratio": "1377.8586", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Maternal mortality ratio": "1392.7859", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Maternal mortality ratio": "1451.594", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Maternal mortality ratio": "1368.8162", "World 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(Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Maternal mortality ratio": "1359.217", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Maternal mortality ratio": "1346.1442", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Maternal mortality ratio": "1273.4314", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Maternal mortality ratio": "1277.308", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Maternal mortality ratio": "1196.0907", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Maternal mortality ratio": "1114.8872", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Maternal mortality ratio": "1102.8097", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Maternal mortality ratio": "1044.4003", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Maternal mortality ratio": "1023.4955", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Maternal mortality ratio": "962.3215", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Maternal mortality ratio": "913.03577", "World 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"2020", "Maternal mortality ratio": "620.40753", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Maternal mortality ratio": "904.12787", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Maternal mortality ratio": "883.07526", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Maternal mortality ratio": "910.0019", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Maternal mortality ratio": "891.8916", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Maternal mortality ratio": "855.7746", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Maternal mortality ratio": "850.23047", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Maternal mortality ratio": "841.73444", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Maternal mortality ratio": "866.1556", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Maternal mortality ratio": "856.2383", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Maternal mortality ratio": "823.59766", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Maternal mortality ratio": "801.83624", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1996", "Maternal mortality ratio": "797.6708", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1997", "Maternal mortality ratio": "782.36926", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1998", "Maternal mortality ratio": "804.27374", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1999", "Maternal mortality ratio": "739.20233", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Maternal mortality ratio": "718.2771", "World region 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"Africa", "Code": "OWID_AFR", "Year": "2006", "Maternal mortality ratio": "619.63403", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Maternal mortality ratio": "611.9057", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Maternal mortality ratio": "607.67474", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Maternal mortality ratio": "595.9811", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Maternal mortality ratio": "583.5715", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Maternal mortality ratio": "559.11273", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Maternal mortality ratio": "544.01935", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Maternal mortality ratio": "535.4149", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Maternal mortality ratio": "527.16296", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Maternal mortality ratio": "511.22266", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Maternal mortality ratio": "504.3417", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Maternal mortality ratio": "496.15768", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Maternal mortality ratio": "487.91956", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Maternal mortality ratio": "478.50122", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Maternal mortality ratio": "464.0011", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1985", "Maternal mortality ratio": "47.698692", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1986", "Maternal mortality ratio": "41.96449", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1987", "Maternal mortality ratio": "39.203156", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1988", "Maternal mortality ratio": "36.728065", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1989", "Maternal mortality ratio": "34.94203", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1990", "Maternal mortality ratio": "32.874947", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1991", "Maternal mortality ratio": "29.07421", "World region according to OWID": "Europe", "Maternal mortality 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"1997", "Maternal mortality ratio": "18.388054", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "Maternal mortality ratio": "15.522337", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "Maternal mortality ratio": "14.890729", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Maternal mortality ratio": "14.326414", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Maternal mortality ratio": "12.525534", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Maternal mortality ratio": "12.407263", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Maternal mortality ratio": "12.1191635", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Maternal mortality ratio": "10.631596", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Maternal mortality ratio": "10.830499", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Maternal mortality ratio": "10.76677", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Maternal mortality ratio": "10.195825", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Maternal mortality ratio": "9.739404", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Maternal mortality ratio": "9.097263", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Maternal mortality ratio": "8.516676", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Maternal mortality ratio": "8.045227", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Maternal mortality ratio": "7.796703", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Maternal mortality ratio": "7.2153625", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Maternal mortality ratio": "7.0098605", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Maternal mortality ratio": "6.878285", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Maternal mortality ratio": "6.6560845", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Maternal mortality ratio": "6.689698", "World region according to OWID": "Europe", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Maternal mortality ratio": "5.4292927", "World region according to OWID": "Europe", "Maternal mortality 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"Maternal mortality ratio": "223.13094", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1989", "Maternal mortality ratio": "216.49147", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1990", "Maternal mortality ratio": "205.81978", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1991", "Maternal mortality ratio": "203.99115", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1992", "Maternal mortality ratio": "203.64143", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1993", "Maternal mortality ratio": "203.94144", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1994", "Maternal mortality ratio": "202.64119", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1995", "Maternal mortality ratio": "203.82845", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1996", "Maternal mortality ratio": "192.42587", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}], "rows_tail": [{"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Maternal mortality ratio": "258.04745", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Maternal mortality ratio": "248.80334", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Maternal mortality ratio": "241.46191", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Maternal mortality ratio": "231.28264", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Maternal mortality ratio": "228.95949", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Maternal mortality ratio": "223.14148", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Maternal mortality ratio": "219.4542", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Maternal mortality ratio": "215.49913", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Maternal mortality ratio": "213.16835", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Maternal mortality ratio": "215.09969", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Maternal mortality ratio": "214.92525", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Maternal mortality ratio": "212.23637", "World region according to OWID": "", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1985", "Maternal mortality ratio": "466.11166", "World region according to OWID": "Asia", "Maternal mortality ratio 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""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Maternal mortality ratio": "168.44948", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Maternal mortality ratio": "155.33047", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Maternal mortality ratio": "156.6496", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Maternal mortality ratio": "155.16437", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Maternal mortality ratio": "157.6561", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Maternal mortality ratio": "150.95755", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Maternal mortality ratio": "156.02693", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Maternal mortality ratio": "163.92967", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Maternal mortality ratio": "171.83197", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Maternal mortality ratio": "179.46648", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Maternal mortality ratio": "175.63083", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Maternal mortality ratio": "181.33215", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Maternal mortality ratio": "183.39972", "World region according to OWID": "Asia", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Maternal mortality ratio": "555.443", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Maternal mortality ratio": "573.41504", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Maternal mortality ratio": "560.2397", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Maternal mortality ratio": "580.4884", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Maternal mortality ratio": "588.8861", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Maternal mortality ratio": "602.9902", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Maternal mortality ratio": "607.2958", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Maternal mortality ratio": "599.1928", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Maternal mortality ratio": "576.1547", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Maternal mortality ratio": "543.74713", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Maternal mortality ratio": "523.1933", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Maternal mortality ratio": "508.71634", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Maternal mortality ratio": "500.6629", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Maternal mortality ratio": "491.66293", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Maternal 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"Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Maternal mortality ratio": "309.2504", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Maternal mortality ratio": "306.0923", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Maternal mortality ratio": "295.85287", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Maternal mortality ratio": "319.58072", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Maternal mortality ratio": "326.5901", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Maternal mortality ratio": "268.49405", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Maternal mortality ratio": "232.77356", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Maternal mortality ratio": "220.6309", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Maternal mortality ratio": "191.35355", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Maternal mortality ratio": "168.83693", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Maternal mortality ratio": "165.72624", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Maternal mortality ratio": "155.3573", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Maternal mortality ratio": "155.82332", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Maternal mortality ratio": "144.87115", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Maternal mortality ratio": "128.84006", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Maternal mortality ratio": "134.66542", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Maternal mortality ratio": "431.54065", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Maternal mortality ratio": "387.47693", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Maternal mortality ratio": "366.37717", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Maternal mortality ratio": "374.7746", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Maternal mortality ratio": "358.34622", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Maternal mortality ratio": "337.5501", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Maternal mortality ratio": "346.73132", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Maternal mortality ratio": "349.56744", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Maternal mortality ratio": "346.98022", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Maternal mortality ratio": "352.07843", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Maternal mortality ratio": "363.99518", "World region according to OWID": "Africa", "Maternal 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"Year": "2001", "Maternal mortality ratio": "589.1738", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Maternal mortality ratio": "442.7154", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Maternal mortality ratio": "533.8988", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Maternal mortality ratio": "488.08743", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Maternal mortality ratio": "533.07275", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Maternal mortality ratio": "558.164", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Maternal mortality ratio": "656.42145", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Maternal mortality ratio": "684.827", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Maternal mortality ratio": "669.8878", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Maternal mortality ratio": "618.3297", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Maternal mortality ratio": "562.12067", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Maternal mortality ratio": "527.61646", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Maternal mortality ratio": "495.25714", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Maternal mortality ratio": "440.85205", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Maternal mortality ratio": "408.11902", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Maternal mortality ratio": "399.7575", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Maternal mortality ratio": "366.381", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Maternal mortality ratio": "358.5035", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Maternal mortality ratio": "393.1763", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Maternal mortality ratio": "356.7589", "World region according to OWID": "Africa", "Maternal mortality ratio (Annotations)": ""}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "maternal-mortality", "metadata_url": "https://ourworldindata.org/grapher/maternal-mortality.metadata.json", "chart_title": "Maternal mortality ratio", "chart_subtitle": "Estimated number of women who die from maternal conditions per 100,000 live births, based on data from death certificates, large-scale surveys, and statistical modeling.", "chart_note": "Prior to 1985, only reported data are available, which are likely to underestimate the true maternal mortality rate. From 1985, estimates are shown, which aim to adjust for underreporting and misclassification.", "chart_citation": "UN MMEIG (2023) and other sources", "original_chart_url": "https://ourworldindata.org/grapher/maternal-mortality", "owid_column_metadata": {"Maternal mortality ratio": {"titleShort": "Maternal mortality ratio", "titleLong": "Maternal mortality ratio", "descriptionShort": "The estimated number of women who die from maternal conditions per 100,000 live births, based on data from death certificates, large-scale surveys, and statistical modeling.", "descriptionKey": ["Maternal deaths are defined as a death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and site of pregnancy,\nfrom any cause related or aggravated by the pregnancy or its management, but not from accidental or incidental causes."], "descriptionProcessing": "- The dataset combines three sources: WHO Mortality Database (before 1985), Gapminder (before 1985, if WHO Mortality Database data are unavailable), UN MMEIG (1985 onwards).\n  The WHO Mortality Database and Gapminder contain reported figures from countries, and are likely to underestimate the true maternal mortality figures. The UN MMEIG aims to estimates the true rate, by adjusting for underreporting and misclassification. Sudden jumps in mortality rate in 1985 are a consequence of switching data sources (from reported to estimated figures).\n- For the years between 1950 - 1985 we calculated the maternal mortality ratio and maternal mortality rate based\n  on the number of maternal deaths from the WHO mortality database and live births and female population of reproductive age from the UN WPP.\n- Where the reported maternal deaths in the WHO Mortality Database differed significantly from the estimated figures in the UN MMEIG data, we opted not to include them.\n- Where a data point is attached to a range of years in the Gapminder data set, we used the midpoint of the range.\n- The UN MMEIG data shown (post 1985) is the point estimate - this means there is a 50% chance that the true measure lies above this point,\n  and a 50% chance that the true value lies below this point.\n- We calculated regional aggregates by summing the maternal deaths and live births of all countries in the region and then calculating the MMR based on these figures.", "shortUnit": "", "unit": "deaths per 100,000 live births", "timespan": "1751-2020", "type": "Numeric", "owidVariableId": 959831, "shortName": "mmr", "lastUpdated": "2024-07-08", "nextUpdate": "2026-07-22", "citationShort": "UN MMEIG (2023) and other sources – with major processing by Our World in Data", "citationLong": "UN MMEIG (2023); WHO Mortality Database (2025); UN, World Population Prospects (2024); Gapminder (2010) – with major processing by Our World in Data. “Maternal mortality ratio” [dataset]. UN MMEIG (WHO, UNICEF, UNFPA, World Bank Group and UNDESA/ Population Division), “Trends in maternal mortality 2020”; WHO Mortality Database, “WHO Mortality Database”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update”; Gapminder, “Maternal mortality ratio V1” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/959831.metadata.json"}, "World region according to OWID": {"titleShort": "World region according to OWID", "titleLong": "World region according to OWID", "descriptionShort": "Regions defined by Our World in Data, which are used in OWID charts and maps.", "unit": "", "timespan": "2023-2023", "type": "Continent", "owidVariableId": 900801, "shortName": "owid_region", "lastUpdated": "2023-01-01", "citationShort": "Our World in Data – processed by Our World in Data", "citationLong": "Our World in Data – processed by Our World in Data. “World region according to OWID” [dataset]. Our World in Data, “Regions” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/900801.metadata.json"}, "959831-annotations": {"titleShort": "959831-annotations", "titleLong": "959831-annotations", "type": "SeriesAnnotation", "citationShort": " – processed by Our World in Data", "citationLong": " – processed by Our World in Data. “959831-annotations” [dataset].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/undefined.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Number of maternal deaths by region", "source_url": "https://ourworldindata.org/grapher/number-of-maternal-deaths-by-region.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Estimated maternal deaths", "Estimated maternal deaths (Annotations)"], "row_count_total": 7056, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Estimated maternal deaths": "10258.534", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Estimated maternal deaths": "8671.921", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Estimated maternal deaths": "8488.96", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Estimated maternal deaths": "7522.1216", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Estimated maternal deaths": "7549.705", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Estimated maternal deaths": "7812.4585", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Estimated maternal deaths": "7743.889", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Estimated maternal deaths": "8404.7295", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Estimated maternal deaths": "9554.337", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Estimated maternal deaths": "10849.466", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Estimated maternal deaths": "11949.192", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Estimated maternal deaths": "12200.793", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Estimated maternal deaths": "12478.709", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Estimated maternal deaths": "13717.248", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Estimated maternal deaths": "13157.221", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Estimated maternal deaths": "13407.596", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Estimated maternal deaths": "12339.55", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Estimated maternal deaths": "12517.618", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Estimated maternal deaths": "12714.444", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Estimated maternal deaths": "12230.3125", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Estimated maternal deaths": "12119.879", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Estimated maternal deaths": "11874.832", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Estimated maternal deaths": "11841.843", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Estimated maternal deaths": "10508.551", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Estimated maternal deaths": "10308.174", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Estimated maternal deaths": "10316.723", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Estimated maternal deaths": "10233.407", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Estimated maternal deaths": "10143.417", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Estimated maternal deaths": "10242.153", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Estimated maternal deaths": "10013.264", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Estimated maternal deaths": "10208.116", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Estimated maternal deaths": "9867.574", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Estimated maternal deaths": "9081.677", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Estimated maternal deaths": "8996.077", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Estimated maternal deaths": "8878.076", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Estimated maternal deaths": "8698.113", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Estimated maternal deaths": "222858.48", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Estimated maternal deaths": "221952.14", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Estimated maternal deaths": "233024.19", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Estimated maternal deaths": "231802.62", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Estimated maternal deaths": "225753.34", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Estimated maternal deaths": "227292.12", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Estimated maternal deaths": "228749.75", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Estimated maternal deaths": "239266.81", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Estimated maternal deaths": "240080.67", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Estimated maternal deaths": "234074.69", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Estimated maternal deaths": "231931.12", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1996", "Estimated maternal deaths": "234291.86", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1997", "Estimated maternal deaths": "232653.16", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1998", "Estimated maternal deaths": "242753.94", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1999", "Estimated maternal deaths": "227844.33", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Estimated maternal deaths": "225668.28", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Estimated maternal deaths": "226304.23", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Estimated maternal deaths": "226771.22", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Estimated maternal deaths": "223252.69", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Estimated maternal deaths": "222654.92", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Estimated maternal deaths": "222493.03", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Estimated maternal deaths": "221342.45", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Estimated maternal deaths": "223459.55", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Estimated maternal deaths": "227246.58", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Estimated maternal deaths": "227096.9", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Estimated maternal deaths": "227024.34", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Estimated maternal deaths": "221603.27", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Estimated maternal deaths": "218967.64", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Estimated maternal deaths": "218840.14", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Estimated maternal deaths": "218810.92", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Estimated maternal deaths": "215300.66", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Estimated maternal deaths": "214324.38", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Estimated maternal deaths": "213705.03", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Estimated maternal deaths": "213123.27", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Estimated maternal deaths": "211770.3", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Estimated maternal deaths": "207742.58", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1985", "Estimated maternal deaths": "40.066902", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1986", "Estimated maternal deaths": "35.25017", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1987", "Estimated maternal deaths": "32.930653", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1988", "Estimated maternal deaths": "30.484293", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1989", "Estimated maternal deaths": "28.652466", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1990", "Estimated maternal deaths": "26.628704", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1991", "Estimated maternal deaths": "22.968624", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "Estimated maternal deaths": "21.309374", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "Estimated maternal deaths": "19.88659", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "Estimated maternal deaths": "17.938005", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "Estimated maternal deaths": "15.86457", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "Estimated maternal deaths": "12.892809", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "Estimated maternal deaths": "11.952235", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "Estimated maternal deaths": "9.468626", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "Estimated maternal deaths": "8.636622", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Estimated maternal deaths": "7.879528", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Estimated maternal deaths": "6.388022", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Estimated maternal deaths": "5.955486", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Estimated maternal deaths": "5.5748153", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Estimated maternal deaths": "4.571586", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Estimated maternal deaths": "4.3321996", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Estimated maternal deaths": "4.0913725", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Estimated maternal deaths": "3.670497", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Estimated maternal deaths": "3.408791", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Estimated maternal deaths": "3.1840422", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Estimated maternal deaths": "2.9808366", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Estimated maternal deaths": "2.8158295", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Estimated maternal deaths": "2.728846", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Estimated maternal deaths": "2.5975306", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Estimated maternal deaths": "2.4534512", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Estimated maternal deaths": "2.3386168", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Estimated maternal deaths": "2.196508", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Estimated maternal deaths": "2.0738063", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Estimated maternal deaths": "1.6287878", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Estimated maternal deaths": "1.591507", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Estimated maternal deaths": "2.4829338", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1985", "Estimated maternal deaths": "2187.8162", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1986", "Estimated maternal deaths": "2024.1606", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1987", "Estimated maternal deaths": "1966.3341", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1988", "Estimated maternal deaths": "1822.9799", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1989", "Estimated maternal deaths": "1744.9213", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1990", "Estimated maternal deaths": "1615.6853", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1991", "Estimated maternal deaths": "1601.3304", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1992", "Estimated maternal deaths": "1608.7673", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1993", "Estimated maternal deaths": "1586.6643", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1994", "Estimated maternal deaths": "1540.073", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1995", "Estimated maternal deaths": "1436.9905", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Algeria", "Code": "DZA", "Year": "1996", "Estimated maternal deaths": "1268.0865", "Estimated maternal deaths (Annotations)": ""}], "rows_tail": [{"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Estimated maternal deaths": "363600.4", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Estimated maternal deaths": "351672.97", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Estimated maternal deaths": "342465.5", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Estimated maternal deaths": "332773.56", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Estimated maternal deaths": "327488.2", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Estimated maternal deaths": "319902.4", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Estimated maternal deaths": "312277.56", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Estimated maternal deaths": "307844.4", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Estimated maternal deaths": "303369.56", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Estimated maternal deaths": "299547.8", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Estimated maternal deaths": "295773.7", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Estimated maternal deaths": "286062.78", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1985", "Estimated maternal deaths": "2740.7366", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1986", "Estimated maternal deaths": "2854.6472", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1987", "Estimated maternal deaths": "2731.447", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1988", "Estimated maternal deaths": "2677.4636", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1989", "Estimated maternal deaths": "2687.9626", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1990", "Estimated maternal deaths": "2602.9065", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1991", "Estimated maternal deaths": "2570.742", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1992", "Estimated maternal deaths": "2626.8398", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1993", "Estimated maternal deaths": "2537.4624", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1994", "Estimated maternal deaths": "2641.6792", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1995", "Estimated maternal deaths": "2540.6257", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1996", "Estimated maternal deaths": "2420.8708", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1997", "Estimated maternal deaths": "2362.5579", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1998", "Estimated maternal deaths": "2306.6675", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "1999", "Estimated maternal deaths": "2224.8943", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Estimated maternal deaths": "2065.0767", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Estimated maternal deaths": "2004.595", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Estimated maternal deaths": "1833.0521", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Estimated maternal deaths": "1734.138", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Estimated maternal deaths": "1665.0138", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Estimated maternal deaths": "1591.47", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Estimated maternal deaths": "1521.3334", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Estimated maternal deaths": "1411.5365", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Estimated maternal deaths": "1443.612", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Estimated maternal deaths": "1352.9285", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Estimated maternal deaths": "1387.9154", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Estimated maternal deaths": "1404.2375", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Estimated maternal deaths": "1453.5892", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Estimated maternal deaths": "1417.4915", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Estimated maternal deaths": "1491.6174", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Estimated maternal deaths": "1586.8392", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Estimated maternal deaths": "1678.7983", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Estimated maternal deaths": "1764.1555", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Estimated maternal deaths": "1747.5267", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Estimated maternal deaths": "1818.7615", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Estimated maternal deaths": "1852.3372", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Estimated maternal deaths": "1782.9719", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Estimated maternal deaths": "1892.2695", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Estimated maternal deaths": "1893.6102", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Estimated maternal deaths": "2014.2947", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Estimated maternal deaths": "2096.4346", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Estimated maternal deaths": "2188.8542", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Estimated maternal deaths": "2259.1401", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Estimated maternal deaths": "2282.9246", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Estimated maternal deaths": "2252.765", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Estimated maternal deaths": "2180.426", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Estimated maternal deaths": "2145.0925", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Estimated maternal deaths": "2126.4343", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Estimated maternal deaths": "2127.8174", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Estimated maternal deaths": "2138.7336", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Estimated maternal deaths": "2084.4695", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Estimated maternal deaths": "1900.8717", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Estimated maternal deaths": "1668.0752", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Estimated maternal deaths": "1584.2882", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Estimated maternal deaths": "1482.4194", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Estimated maternal deaths": "1546.0079", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Estimated maternal deaths": "1580.2697", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Estimated maternal deaths": "1610.0454", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Estimated maternal deaths": "1591.6885", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Estimated maternal deaths": "1764.0856", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Estimated maternal deaths": "1841.9681", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Estimated maternal deaths": "1546.5258", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Estimated maternal deaths": "1366.3807", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Estimated maternal deaths": "1312.7539", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Estimated maternal deaths": "1151.9484", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Estimated maternal deaths": "1024.8402", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Estimated maternal deaths": "1017.55914", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Estimated maternal deaths": "969.42957", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Estimated maternal deaths": "986.3616", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Estimated maternal deaths": "931.5215", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Estimated maternal deaths": "840.0372", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Estimated maternal deaths": "891.4851", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Estimated maternal deaths": "1575.1234", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Estimated maternal deaths": "1410.416", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Estimated maternal deaths": "1329.9491", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Estimated maternal deaths": "1334.1976", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Estimated maternal deaths": "1254.2118", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Estimated maternal deaths": "1188.1764", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Estimated maternal deaths": "1237.8308", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Estimated maternal deaths": "1268.9298", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Estimated maternal deaths": "1252.5986", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Estimated maternal deaths": "1263.9615", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Estimated maternal deaths": "1306.7427", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Estimated maternal deaths": "1345.1625", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Estimated maternal deaths": "1245.2479", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Estimated maternal deaths": "1282.9302", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Estimated maternal deaths": "1595.9668", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Estimated maternal deaths": "1644.0614", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Estimated maternal deaths": "2539.339", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Estimated maternal deaths": "1916.9576", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Estimated maternal deaths": "2322.46", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Estimated maternal deaths": "2118.2996", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Estimated maternal deaths": "2292.213", "Estimated maternal deaths (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Estimated maternal deaths": "2383.3604", 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{"raw_link": "https://ourworldindata.org/how-much-subsidies-fossil-fuels", "title": "How much in subsidies do fossil fuels receive?", "context": "Home\nEnergy\nFossil Fuels\nHow much in subsidies do fossil fuels receive?\nEstimates range from less than $1 trillion to $7 trillion. Where do these numbers come from?\nBy\nHannah Ritchie\nJanuary 27, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nIf we want to tackle climate change, we need to move away from fossil fuels to low-carbon energy sources. This transition is a lot easier if these cleaner sources are cheaper than fossil fuels.\nHowever, fossil fuels are often subsidized, reducing the short-term economic incentives to switch.\nIn this article, I explain the scale of fossil fuel subsidies and where these numbers come from.\nBecause subsidies can be defined in different ways — production, consumption, explicit, implicit — people can quote very different numbers to this question, ranging from hundreds of billions of US dollars to as much as $7 trillion (ten times as much).\nAll estimates of subsidies in this article are expressed in US dollars.\nHow much does the world give to support fossil fuel production and consumption?\nLet’s start with\nexplicit subsidies\n. These follow the classic definition of a subsidy,\nwhich is\n“money that is paid by a government or an organization to reduce the costs of services or of producing goods so that their prices can be kept low.”\nIn other words, payments to make fossil fuels cheaper.\nThese payments can either go towards fossil fuel producers so that the extraction and refining cost is lower (called “production subsidies”) or to consumers so they can buy fossil fuels cheaper than the market price (called “consumption subsidies”). Consumption subsidies can include governments selling electricity or gas at more affordable rates, subsidizing gasoline so consumers pay less for gas at the pump, or supporting fuels like kerosene for cooking and heating, which is common in lower-income countries.\nHere, I’m going to focus on data for 2022. These are the numbers that are often quoted in discussions because subsidies increased significantly in that year for reasons I’ll explain soon.\nGlobal\nexplicit\nsubsidies for fossil fuels amounted to around $1.5 trillion in 2022. This is a vast sum. For context, that’s equivalent to around 1.5% of the global gross domestic product (GDP) or the entire GDP of countries like Russia or Australia.\n1\nMost of these explicit subsidies —\naround 80%\n— went to consumers. The rest went into fossil fuel production.\nGlobal subsidies ramped up in 2022 because the price of energy spiked due to Russia’s invasion of Ukraine. The chart below shows that consumption subsidies roughly doubled from 2021 to 2022 and fell back to their previous level in 2023.\nIn 2022, the price of gas\nincreased by\nas much as 400%. For many consumers, this meant a sudden and large jump in the cost of basic energy services such as heating and electricity. This hit the poorest households hardest, pushing many into “fuel poverty”.\nMany countries implemented mechanisms to support consumers, such as putting a price cap on gas and electricity — so that households paid a rate cheaper than the market price — with the government covering the remaining costs. This is what my government — in\nthe United Kingdom\n— did. Our energy system was particularly vulnerable because our electricity prices are set by gas\nmost of the time\n.\n2\nNow, this money\nwas\ngoing towards subsidizing fossil fuels. It’s clearly a fossil fuel subsidy. But it’s perhaps not quite as “explicit” a subsidy as the name might infer; the focus of governments was to make\nenergy\naffordable for households and businesses, not specifically fossil fuels. But in the context of the current energy system, it so happened that fossil fuels were driving high prices.\nThat’s not to undermine their significance — governments have made fossil fuels artificially cheap before and after the recent energy crunch — but rather to highlight the importance of cheap alternatives. Consumption subsidies make energy more affordable, which is especially important for low-income consumers. Taking these subsidies away\nwithout\ncheap and available alternative energy sources would push some households into fuel poverty.\nThe good news is that low-carbon energy sources, such as solar and wind, and electric vehicles for transport have\nbecome much cheaper\n, which should reduce this dilemma over time. My colleague Max Roser previously\nwrote about\nsome countries that have successfully reduced fossil fuel subsidies (even though this is often difficult and unpopular).\nIf anything, the huge spike in energy prices and fossil fuel subsidies in 2022 should give even greater motivation to transition away from fossil fuels to low-carbon sources. It’s the countries that were most reliant on fossil fuels — in particular, gas — that struggled the most. This is because fossil fuel plants rely on a constant supply of\nfuels\n, which can vary enormously in price depending on global markets. On the other hand, solar and wind have much lower\nrunning\ncosts because the fuels — the sun and wind — are essentially free. A faster transition to clean energy would have shielded countries from these price shocks.\nWhich countries give the highest subsidies to fossil fuels?\nSo far, we’ve focused on subsidies at a global level, but there are huge differences between countries.\nIn the map below, you can see the level of fossil fuel subsidies given\nper person.\nThis includes production and consumption subsidies, and it is shown for 2021, before the energy crisis.\nUnsurprisingly, the countries that give the largest subsidies are large fossil fuel producers. Major oil producers, such as Saudi Arabia, Turkmenistan, Libya, and Algeria, spend more than $500 per person (sometimes over $1,000) to support fossil fuel production. These subsidies can represent\nmore than 10% of GDP\n.\nCountries across Europe, North and South America, and East Asia typically give less than $100 per person, and in Africa and South Asia, it’s even less than $20 — and sometimes close to zero.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nThe $7 trillion figure includes the social and environmental costs of fossil fuels\nYou might have heard $7 trillion quoted as the amount of subsidies going towards fossil fuels. That’s more than four times as much as the $1.5 trillion I mentioned at the beginning.\nIt’s a number that has been widely covered in global media:\nFossil fuels being subsidised at rate of $13m a minute, says IMF: Oil, gas and coal benefited from $7tn in support in 2022 despite being primary cause of climate crisis\n— The Guardian\nU.N. chief calls for an end to $7 trillion in fossil fuel subsidies\n— CNBC\nThe world feeds the climate crisis with public money: Fossil fuel subsidies amounted to $7 trillion in 2022\n— El País\nThis $7 trillion figure comes from\na report by the International Monetary Fund\n(IMF). For context, $7 trillion is equivalent to around 7% of global GDP, a huge amount of money.\n3\nThis estimate is much higher than the figures we looked at earlier because it includes not only\nexplicit\nsubsidies (i.e., direct payments) but also\nimplicit\nsubsidies — the societal costs of burning fossil fuels. When we burn fossil fuels, we cause local air pollution that damages human health, and we drive climate change, which also results in environmental and social damage. The IMF also attributes to fossil fuels the social costs of road accidents and congestion. Economists usually refer to these indirect costs, which aren’t reflected in market prices, as “externalities” rather than “subsidies”.\nThe chart below breaks down the $7 trillion figure into its components. The explicit subsidies, shown in purple on the left, amount to the 1.3 trillion (or up to 1.7 trillion, depending on the source) production and consumption subsidies discussed above. But more than three-quarters come from damages — shown in orange and brown on the right — with 60% from air pollution and climate change alone.\nDownload\nThe IMF reports that fossil fuels received $7 trillion in subsidies in 2022. Where do these numbers come from?\n4\nThese are certainly huge costs: millions of people\ndie from air pollution\ncaused by fossil fuels alone, and there is a whole academic literature on the social cost of carbon. However, these costs are implicit and, in contrast to the explicit costs, need to be converted into monetary costs by the IMF. This conversion is helpful to arrive at a total size, but it is not straightforward. There is no universal figure for these costs, as they’re sensitive to assumptions about damages from pollution and climate change.\n5\nI think it’s important to understand the difference between these explicit and implicit subsidies.\nWhen people hear that global subsidies for fossil fuels come to $7 trillion, they might reasonably assume that all of these are, effectively, explicit payments. And, if governments are handing this money to fossil fuels, they could reallocate that pot to something else (such as low-carbon technologies). In theory, they could do this tomorrow, and we could transition to clean energy very quickly.\nThe problem is that there\nisn’t\na $7 trillion pot sitting there to be reallocated. There are annual payments of $1.2 to $1.5 trillion, given directly to fossil fuel production and consumption, which could be used elsewhere.\n6\nSimply removing these explicit subsidies wouldn’t be enough. To tackle the other $5.7 trillion would require various approaches.\nBelow, I’ve added a section to the chart we just looked at, giving some examples of interventions we might use for different components. The way to tackle externalities is to put a price on them (for example, a pollution or carbon tax). The aim of these taxes is to make sure that the price reflects the total cost of burning coal, gas, or oil. My colleague, Max Roser, wrote\nan article\non the argument for introducing a carbon price, and my colleague Pablo Rosado and I\nlooked at\nwhich countries have done so.\nDownload\nThe breakdown of fossil fuel subsidies and some examples of interventions that could reduce them.\n4\nThere is a strong argument for both reducing direct subsidies for fossil fuels\nand\nintroducing a pollution and carbon price.\nHowever, to see what interventions would help, we need to understand the differences between implicit and explicit subsidies. Bundling them together as a single “subsidy” figure makes it hard to get a clear picture of what’s needed. I hope my breakdown into explicit and implicit subsidies helps to put this into perspective.\nAcknowledgments\nMany thanks to Saloni Dattani, Simon van Teutem, Max Roser, and Edouard Mathieu for their feedback and comments on this article.\nContinue reading on Our World in Data\nThe argument for a carbon price\nWe are paying a price for fossil fuels, but that price is not paid by those who burn the fossil fuels — we need to change that.\nFossil fuel subsidies: If we want to reduce greenhouse gas emissions we should not pay people to burn fossil fuels\nRepealing subsidies is not easy, but it is possible – and the world is slowly making progress in this direction\nEndnotes\nGlobal GDP in 2022\nwas approximately\n$101 trillion. 1.5 trillion is around 1.5% of this figure [101 / 1.5 * 100 = 1.5%].\nIn the decade before 2022, these subsidies\nranged from\n0.6% to 1.2% of global GDP.\nTo put this in even more relatable terms, this was equivalent to around $187 per person. We get this figure by dividing 1.5 trillion by the\nglobal population\n— 8.02 billion. That comes to 187 [1,500,000,000,000 / 8,020,000,000 = 187].\nA paper by Behnam Zakeri, Iain Staffell, and colleagues looked at the role of gas in price-setting across a range of countries from 2015 to 2021. It found that natural gas set the price in the UK 98% of the time despite producing\njust 40%\nof its electricity.\nThis is because the UK — like many other countries — uses a marginal pricing system, where the price is set by the most expensive source of electricity (and in the case of the UK, this happened to be electricity from gas 98% of the time). Gas sets the price of electricity much more often than in most other countries, which puts a lot of pressure on electricity bills.\nZakeri, Behnam and Staffell, Iain and Dodds, Paul and Grubb, Michael and Ekins, Paul and Jääskeläinen, Jaakko and Cross, Samuel and Helin, Kristo and Castagneto-Gissey, Giorgio,\nEnergy Transitions in Europe – Role of Natural Gas in Electricity Prices\n(July 23, 2022).\nAgain, global GDP was approximately $101 trillion in 2022. 7 trillion is around 7% of that total.\nThe main figures in this chart come from the\nInternational Monetary Fund\n. I have additionally broken down explicit subsidies into production and consumption subsidies based on data from the\nInternational Energy Agency\n(IEA) and\nUN Environment Programme\n.\nSeveral studies estimate that pollution from fossil fuels leads to several million premature deaths every year. My colleague, Max Roser, provides an overview of the literature in\nthis article\n.\nLelieveld, 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. Proceedings of the National Academy of Sciences.\nVohra, K., Vodonos, A., Schwartz, J., Marais, E. A., Sulprizio, M. P., & Mickley, L. J. (2021) – Global mortality from outdoor fine particle pollution generated by fossil fuel combustion: Results from GEOS-Chem. In Environmental Research.\nEstimates of the explicit subsidies vary slightly between sources — ranging from $1.2 to $1.5 trillion, depending on how foregone VAT taxes are categorized.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2025) - “How much in subsidies do fossil fuels receive?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-093348/how-much-subsidies-fossil-fuels.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-how-much-subsidies-fossil-fuels,\nauthor = {Hannah Ritchie},\ntitle = {How much in subsidies do fossil fuels receive?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-093348/how-much-subsidies-fossil-fuels.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "how-much-subsidies-fossil-fuels", "source_url": "https://ourworldindata.org/how-much-subsidies-fossil-fuels", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Estimates range from less than $1 trillion to $7 trillion. Where do these numbers come from?", "numeric_mentions": ["1 trillion", "7 trillion", "27,", "2025", "2022", "1.5 trillion", "1.5%", "1", "80%", "2021", "2023", "2022,", "400%", "2", "2021,", "500", "1,000", "10%", "100", "20", "13", "7", "7%", "3", "1.3 trillion", "1.7 trillion", "60%", "4", "5", "1.2", "6", "5.7 trillion", "101 trillion", "101", "1.5", "0.6%", "1.2%", "187", "8.02 billion", "1,500,000,000,000", "8,020,000,000", "2015", "98%", "40%", "23,", "2019", "20260518", "093348", "18,", "2026"], "numeric_evidence": [{"title": "Explicit consumption subsidies for fossil fuels", "source_url": "https://ourworldindata.org/grapher/consumption-subsidies-for-fossil-fuels.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Total subsidies"], "row_count_total": 686, "rows_head": [{"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Total subsidies": "13795690429"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Total subsidies": "14688938476"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Total subsidies": "18020769531"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Total subsidies": "19348925781"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Total subsidies": "17871095703"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Total subsidies": "13836315429"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Total subsidies": "8150644042"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Total subsidies": "10808521484"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Total subsidies": "20029529296"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Total subsidies": "15864628906"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Total subsidies": "9888812500"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Total subsidies": "27323843750"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2022", "Total subsidies": "56935855468"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "Total subsidies": "26595935546"}, {"Entity": "Angola", "Code": "AGO", "Year": "2010", "Total subsidies": "1079592895"}, {"Entity": "Angola", "Code": "AGO", "Year": "2011", "Total subsidies": "9000000"}, {"Entity": "Angola", "Code": "AGO", "Year": "2012", "Total subsidies": "1339814941"}, {"Entity": "Angola", "Code": "AGO", "Year": "2013", "Total subsidies": "1838984497"}, {"Entity": "Angola", "Code": "AGO", "Year": "2014", "Total subsidies": "1587671508"}, {"Entity": "Angola", "Code": "AGO", "Year": "2015", "Total subsidies": "170779113"}, {"Entity": "Angola", "Code": "AGO", "Year": "2016", "Total subsidies": "497804229"}, {"Entity": "Angola", "Code": "AGO", "Year": "2017", "Total subsidies": "199061889"}, {"Entity": "Angola", "Code": "AGO", "Year": "2018", "Total subsidies": "1842561401"}, {"Entity": "Angola", "Code": "AGO", "Year": "2019", "Total subsidies": "1588916748"}, {"Entity": "Angola", "Code": "AGO", "Year": "2020", "Total subsidies": "2345600341"}, {"Entity": "Angola", "Code": "AGO", "Year": "2021", "Total subsidies": "4460127929"}, {"Entity": "Angola", "Code": "AGO", "Year": "2022", "Total subsidies": "4315597656"}, {"Entity": "Angola", "Code": "AGO", "Year": "2023", "Total subsidies": "4988079589"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2010", "Total subsidies": "10891997070"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2011", "Total subsidies": "13809314453"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2012", "Total subsidies": "12065802734"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2013", "Total subsidies": "14743704101"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2014", "Total subsidies": "14569007812"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2015", "Total subsidies": "9794788085"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2016", "Total subsidies": "5430131347"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2017", "Total subsidies": "4153864746"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2018", "Total subsidies": "6809922363"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2019", "Total subsidies": "5690266601"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2020", "Total subsidies": "3737594970"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2021", "Total subsidies": "17566943359"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2022", "Total subsidies": "21175974609"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2023", "Total subsidies": "9067964843"}, {"Entity": "Austria", "Code": "AUT", "Year": "2010", "Total subsidies": "0"}, {"Entity": "Austria", "Code": "AUT", "Year": "2011", "Total subsidies": "0"}, {"Entity": "Austria", "Code": "AUT", "Year": "2012", "Total subsidies": "0"}, {"Entity": "Austria", "Code": "AUT", "Year": "2013", "Total subsidies": "0"}, {"Entity": "Austria", "Code": "AUT", "Year": "2014", "Total subsidies": "0"}, {"Entity": "Austria", "Code": "AUT", "Year": "2015", "Total subsidies": "0"}, {"Entity": "Austria", "Code": "AUT", "Year": "2016", "Total subsidies": "0"}, {"Entity": "Austria", "Code": "AUT", "Year": "2017", "Total subsidies": "0"}, {"Entity": "Austria", "Code": "AUT", "Year": "2018", "Total subsidies": "0"}, {"Entity": "Austria", "Code": "AUT", "Year": "2019", "Total subsidies": "0"}, {"Entity": "Austria", "Code": "AUT", "Year": "2020", "Total subsidies": "0"}, {"Entity": "Austria", "Code": "AUT", "Year": "2021", "Total subsidies": "0"}, {"Entity": "Austria", "Code": "AUT", "Year": "2022", "Total subsidies": "1452264892"}, {"Entity": "Austria", "Code": "AUT", "Year": "2023", "Total subsidies": "0"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2010", "Total subsidies": "1297818969"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2011", "Total subsidies": "2072367431"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2012", "Total subsidies": "2269333496"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2013", "Total subsidies": "2592571289"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2014", "Total subsidies": "1498617553"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2015", "Total subsidies": "1687946044"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2016", "Total subsidies": "2302719238"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2017", "Total subsidies": "3152570800"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2018", "Total subsidies": "4542661621"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2019", "Total subsidies": "3014036865"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2020", "Total subsidies": "1368095703"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2021", "Total subsidies": "5878306152"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2022", "Total subsidies": "10613264648"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2023", "Total subsidies": "5073796386"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2010", "Total subsidies": "2349479980"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2011", "Total subsidies": "2547034423"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2012", "Total subsidies": "2822881347"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2013", "Total subsidies": "2735020019"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2014", "Total subsidies": "2573135009"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2015", "Total subsidies": "2030730224"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2016", "Total subsidies": "1338013671"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2017", "Total subsidies": "1534991699"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2018", "Total subsidies": "494974334"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2019", "Total subsidies": "852009582"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2020", "Total subsidies": "363954101"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2021", "Total subsidies": "2328466796"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2022", "Total subsidies": "4041481689"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2023", "Total subsidies": "2814375732"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2010", "Total subsidies": "6943885253"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2011", "Total subsidies": "8149622070"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2012", "Total subsidies": "10803259765"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2013", "Total subsidies": "6253714843"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2014", "Total subsidies": "3950052490"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2015", "Total subsidies": "1983857421"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2016", "Total subsidies": "1222582397"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2017", "Total subsidies": "1604518676"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2018", "Total subsidies": "3267578125"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2019", "Total subsidies": "1921892211"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2020", "Total subsidies": "1393152343"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2021", "Total subsidies": "7464931640"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2022", "Total subsidies": "24655154296"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2023", "Total subsidies": "7995154785"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2010", "Total subsidies": "0"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2011", "Total subsidies": "0"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2012", "Total subsidies": "1841378295"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2013", "Total subsidies": "2428722167"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2014", "Total subsidies": "2272464111"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2015", "Total subsidies": "750129516"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2016", "Total subsidies": "441065032"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2017", "Total subsidies": "653745971"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2018", "Total subsidies": "1275199218"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2019", "Total subsidies": "892421142"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2020", "Total subsidies": "338502471"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2021", "Total subsidies": "1530069213"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2022", "Total subsidies": "3582846191"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2023", "Total subsidies": "1379808105"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2010", "Total subsidies": "383572570"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2011", "Total subsidies": "432886077"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2012", "Total subsidies": "453484527"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2013", "Total subsidies": "368343414"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2014", "Total subsidies": "319292877"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2015", "Total subsidies": "198805007"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2016", "Total subsidies": "108326049"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2017", "Total subsidies": "188055618"}], "rows_tail": [{"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2016", "Total subsidies": "744856994"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2017", "Total subsidies": "781810058"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2018", "Total subsidies": "863104980"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2019", "Total subsidies": "700030029"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2020", "Total subsidies": "412915588"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2021", "Total subsidies": "985786437"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2022", "Total subsidies": "1207956176"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2023", "Total subsidies": "804600708"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2010", "Total subsidies": "11984283203"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2011", "Total subsidies": "12177917968"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2012", "Total subsidies": "12154153320"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2013", "Total subsidies": "11541567382"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2014", "Total subsidies": "9631636718"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2015", "Total subsidies": "7639526855"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2016", "Total subsidies": "5658813476"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2017", "Total subsidies": "5540478515"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2018", "Total subsidies": "5911472656"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2019", "Total subsidies": "3948979980"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2020", "Total subsidies": "2474283447"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2021", "Total subsidies": "7813882812"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2022", "Total subsidies": "19314183593"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2023", "Total subsidies": "6225659667"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2010", "Total subsidies": "18040113281"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2011", "Total subsidies": "20161509765"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2012", "Total subsidies": "17350812500"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2013", "Total subsidies": "14687720703"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2014", "Total subsidies": "15041877929"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2015", "Total subsidies": "12361455078"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2016", "Total subsidies": "6171800292"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2017", "Total subsidies": "4867748535"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2018", "Total subsidies": "7151774902"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2019", "Total subsidies": "3318334228"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2020", "Total subsidies": "2896629394"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2021", "Total subsidies": "11321931640"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2022", "Total subsidies": "18289914062"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2023", "Total subsidies": "5241892578"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2010", "Total subsidies": "20695623046"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2011", "Total subsidies": "19580361328"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2012", "Total subsidies": "22271914062"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2013", "Total subsidies": "21869941406"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2014", "Total subsidies": "16626923828"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2015", "Total subsidies": "12234280273"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2016", "Total subsidies": "9737168945"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2017", "Total subsidies": "8762303710"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2018", "Total subsidies": "8913044921"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2019", "Total subsidies": "6538283203"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2020", "Total subsidies": "5639861816"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2021", "Total subsidies": "18926113281"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2022", "Total subsidies": "35334367187"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2023", "Total subsidies": "14060748046"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2010", "Total subsidies": "0"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2011", "Total subsidies": "0"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2012", "Total subsidies": "0"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2013", "Total subsidies": "0"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2014", "Total subsidies": "0"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2015", "Total subsidies": "0"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2016", "Total subsidies": "0"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2017", "Total subsidies": "0"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2018", "Total subsidies": "0"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2019", "Total subsidies": "0"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2020", "Total subsidies": "0"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2021", "Total subsidies": "0"}, {"Entity": 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"465739531250"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Total subsidies": "537934250000"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Total subsidies": "596314937500"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Total subsidies": "547396062500"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Total subsidies": "489553781250"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Total subsidies": "375512875000"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Total subsidies": "328234125000"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Total subsidies": "367101125000"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Total subsidies": "512470656250"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Total subsidies": "423145906250"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Total subsidies": "262677156250"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Total 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This data is expressed in US dollars, adjusted for inflation.", "chart_note": "This data is expressed in constant 2023 US$.", "chart_citation": "International Energy Agency (2024)", "original_chart_url": "https://ourworldindata.org/grapher/consumption-subsidies-for-fossil-fuels", "owid_column_metadata": {"Total subsidies": {"titleShort": "Total subsidies", "titleLong": "Total subsidies", "descriptionShort": "This data is expressed in US dollars. It is adjusted for inflation but does not account for differences in living costs between countries.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "2010-2023", "type": "Integer", "owidVariableId": 1002456, "shortName": "total_subsidy", "lastUpdated": "2024-11-20", "nextUpdate": "2026-07-22", "citationShort": "International Energy Agency (2024) – with minor processing by Our World in Data", "citationLong": "International Energy Agency (2024) – with minor processing by Our World in Data. “Total subsidies” [dataset]. International Energy Agency, “Fossil Fuel Subsidies Database” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1002456.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Explicit fossil fuel subsidies as a share of GDP", "source_url": "https://ourworldindata.org/grapher/fossil-fuel-subsidies-gdp.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "12.c.1 - Fossil-fuel subsidies (consumption and production) as a proportion of total GDP (%) - ER_FFS_CMPT_GDP"], "row_count_total": 2284, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "12.c.1 - Fossil-fuel subsidies (consumption and production) as a proportion of total GDP (%) - ER_FFS_CMPT_GDP": "0.23"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "12.c.1 - Fossil-fuel subsidies (consumption and production) as a proportion of total GDP (%) - ER_FFS_CMPT_GDP": "0.13"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "12.c.1 - Fossil-fuel subsidies (consumption and production) as a proportion of total GDP (%) - ER_FFS_CMPT_GDP": "0.12"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "12.c.1 - 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ER_FFS_CMPT_PC_CD": "222.29"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "407.87"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "312.45"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "200.49"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "533.69"}, {"Entity": "Angola", "Code": "AGO", "Year": "2010", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "48.56"}, {"Entity": "Angola", "Code": "AGO", "Year": "2011", "12.c.1 - 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ER_FFS_CMPT_PC_CD": "16.82"}, {"Entity": "Angola", "Code": "AGO", "Year": "2017", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "8.24"}, {"Entity": "Angola", "Code": "AGO", "Year": "2018", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "61.81"}, {"Entity": "Angola", "Code": "AGO", "Year": "2019", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "43.29"}, {"Entity": "Angola", "Code": "AGO", "Year": "2020", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "54.42"}, {"Entity": "Angola", "Code": "AGO", "Year": "2021", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "94.68"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2010", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2011", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "105.34"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2012", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "110.54"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2013", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "107.84"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2014", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "108.89"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2015", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "115.38"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2016", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "105.42"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2017", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "177.5"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2018", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "442.02"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2019", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "388.27"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2020", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "130.76"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2010", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "122.5"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2011", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "189.32"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2012", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "221.08"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2013", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "282.76"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2014", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "283.04"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2015", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "298.69"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2016", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "275.43"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2017", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "203.69"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2018", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "140.45"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2019", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "108.48"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2020", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "115.81"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2021", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "225.23"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2010", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "9.57"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2011", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "12.78"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2012", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "14.21"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2013", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "14.37"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2014", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "14.4"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2015", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "10.96"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2016", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "7.64"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2017", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "7.74"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2018", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "1.54"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2019", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "1.56"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2020", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "4.14"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2021", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0.27"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2010", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2011", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2012", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2013", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2014", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2015", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2016", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2017", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2018", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2019", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0.19"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2020", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "424.89"}, {"Entity": "Asia (UN)", "Code": "UN_ASI", "Year": "2010", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "67.4"}, {"Entity": "Asia (UN)", "Code": "UN_ASI", "Year": "2011", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "81.35"}, {"Entity": "Asia (UN)", "Code": "UN_ASI", "Year": "2012", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "93.01"}, {"Entity": "Asia (UN)", "Code": "UN_ASI", "Year": "2013", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "87.58"}, {"Entity": "Asia (UN)", "Code": "UN_ASI", "Year": "2014", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "79.66"}, {"Entity": "Asia (UN)", "Code": "UN_ASI", "Year": "2015", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "53.87"}, {"Entity": "Asia (UN)", "Code": "UN_ASI", "Year": "2016", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "37.79"}, {"Entity": "Asia (UN)", "Code": "UN_ASI", "Year": "2017", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "46.12"}, {"Entity": "Asia (UN)", "Code": "UN_ASI", "Year": "2018", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "56.31"}, {"Entity": "Asia (UN)", "Code": "UN_ASI", "Year": "2019", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "52.92"}, {"Entity": "Asia (UN)", "Code": "UN_ASI", "Year": "2020", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "30.4"}, {"Entity": "Asia (UN)", "Code": "UN_ASI", "Year": "2021", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "55.1"}, {"Entity": "Australia", "Code": "AUS", "Year": "2010", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "241.65"}, {"Entity": "Australia", "Code": "AUS", "Year": "2011", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "296.62"}, {"Entity": "Australia", "Code": "AUS", "Year": "2012", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "252.31"}, {"Entity": "Australia", "Code": "AUS", "Year": "2013", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "236.49"}, {"Entity": "Australia", "Code": "AUS", "Year": "2014", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "219.61"}, {"Entity": "Australia", "Code": "AUS", "Year": "2015", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "183.71"}, {"Entity": "Australia", "Code": "AUS", "Year": "2016", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "294.63"}, {"Entity": "Australia", "Code": "AUS", "Year": "2017", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "308.32"}, {"Entity": "Australia", "Code": "AUS", "Year": "2018", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "296.72"}, {"Entity": "Australia", "Code": "AUS", "Year": "2019", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "288.48"}, {"Entity": "Australia", "Code": "AUS", "Year": "2020", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "284.06"}, {"Entity": "Australia", "Code": "AUS", "Year": "2021", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "359.5"}, {"Entity": "Australia and New Zealand (UN)", "Code": "", "Year": "2010", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "203.14"}, {"Entity": "Australia and New Zealand (UN)", "Code": "", "Year": "2011", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "249.29"}, {"Entity": "Australia and New Zealand (UN)", "Code": "", "Year": "2012", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "212.9"}], "rows_tail": [{"Entity": "United Kingdom", "Code": "GBR", "Year": "2016", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "50.22"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2017", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "39.51"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2018", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "33.96"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2019", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "43"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2020", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "38.04"}, {"Entity": "United States", "Code": "USA", "Year": "2010", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "48.53"}, {"Entity": "United States", "Code": "USA", "Year": "2011", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "34.31"}, {"Entity": "United States", "Code": "USA", "Year": "2012", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "36.03"}, {"Entity": "United States", "Code": "USA", "Year": "2013", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "35.07"}, {"Entity": "United States", "Code": "USA", "Year": "2014", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "31.88"}, {"Entity": "United States", "Code": "USA", "Year": "2015", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "26.84"}, {"Entity": "United States", "Code": "USA", "Year": "2016", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "20.26"}, {"Entity": "United States", "Code": "USA", "Year": "2017", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "20.23"}, {"Entity": "United States", "Code": "USA", "Year": "2018", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "28"}, {"Entity": "United States", "Code": "USA", "Year": "2019", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "31.32"}, {"Entity": "United States", "Code": "USA", "Year": "2020", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "26.18"}, {"Entity": "United States", "Code": "USA", "Year": "2021", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "28.16"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2010", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2011", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2012", "12.c.1 - 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ER_FFS_CMPT_PC_CD": "0.84"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2016", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "1.14"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2017", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "2.01"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2018", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "6.14"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2019", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "4.84"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "4.07"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2021", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "46.61"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "88.93"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "108.55"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "117.96"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "114.65"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "100.82"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "70.92"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "61.98"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "68.17"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "85.48"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "71.82"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "47.77"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "92.5"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "78.49"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "87.89"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "63.31"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "76.03"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "31.05"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "14.85"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "12.c.1 - Fossil-fuel subsidies (consumption and production) per capita (nominal United States dollars) - ER_FFS_CMPT_PC_CD": "0"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "12.c.1 - 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{"raw_link": "https://ourworldindata.org/scaling-up-ai", "title": "Scaling up: how increasing inputs has made artificial intelligence more capable", "context": "Home\nArtificial Intelligence\nScaling up: how increasing inputs has made artificial intelligence more capable\nThe path to recent advanced AI systems has been more about building larger systems than making scientific breakthroughs.\nBy\nVeronika Samborska\nJanuary 20, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nFor most of Artificial Intelligence’s (AI’s) history, many researchers expected that building truly capable systems would need a long series of scientific breakthroughs: revolutionary algorithms, deep insights into human cognition, or fundamental advances in our understanding of the brain. While scientific advances have played a role, recent AI progress has revealed an unexpected insight: a lot of the recent improvement in AI capabilities has come simply from scaling up existing AI systems.\n1\nHere, scaling means deploying more computational power, using larger datasets, and building bigger models. This approach has worked surprisingly well so far.\n2\nJust a few years ago, state-of-the-art AI systems struggled with basic tasks like counting.\n3\n4\nToday, they can\nsolve complex math problems,\nwrite software, create extremely realistic images and videos, and discuss academic topics.\nThis article will provide a brief overview of scaling in AI over the past years. The data comes from\nEpoch\n, an organization that analyzes trends in computing, data, and investments to understand where AI might be headed.\n5\nEpoch maintains the most extensive dataset on AI models and regularly publishes\nkey figures\non AI growth and change.\nWhat is scaling in AI models?\nLet’s briefly break down what scaling means in AI. Scaling is about increasing three main things during training, which typically need to grow together:\nThe amount of data used for training the AI;\nThe model’s size, measured in “parameters”;\nComputational resources, often called \"compute\" in AI.\nThe idea is simple but powerful: bigger AI systems, trained on more data and using more computational resources,\ntend to perform better\n. Even without substantial changes to the algorithms, this approach often leads to better performance across many tasks.\n6\nHere is another reason why this is important: as researchers scale up these AI systems, they not only\nimprove\nin the tasks they were trained on but can sometimes lead them to develop new abilities that they did not have on a smaller scale.\n7\nFor example, language models initially struggled with simple arithmetic tests like three-digit addition, but larger models could handle these easily once they reached a certain size.\n8\nThe transition wasn't a smooth, incremental improvement but a more abrupt leap in capabilities.\nThis abrupt jump in capability, rather than steady improvement, can be concerning. If, for example, models suddenly develop unexpected and potentially harmful behaviors simply as a result of getting bigger, it would be harder to anticipate and control.\nThis makes tracking these metrics important.\nWhat are the three components of scaling up AI models?\nData: scaling up the training data\nOne way to view today's AI models is by looking at them as very sophisticated pattern recognition systems. They work by identifying and learning from statistical regularities in the text, images, or other data on which they are trained. The more data the model has access to, the more it can learn about the nuances and complexities of the knowledge domain in which it’s designed to operate.\n9\nIn 1950, Claude Shannon built one of the earliest examples of “AI”: a robotic mouse named Theseus that could \"remember\" its path through a maze using simple relay circuits. Each wall Theseus bumped into became a data point, allowing it to learn the correct route. The total number of walls or data points was 40. You can find this data point in the chart; it is the first one.\nWhile Theseus stored simple binary states in relay circuits, modern AI systems utilize vast neural networks, which can learn much more complex patterns and relationships and thus process billions of data points.\nAll recent notable AI models — especially large, state-of-the-art ones — rely on vast amounts of training data. With the y-axis displayed on a logarithmic scale, the chart shows that the data used to train AI models has grown exponentially. From 40 data points for Theseus to trillions of data points for the largest modern systems in a little more than seven decades.\nSince 2010, the training data has doubled approximately every nine to ten months. You can see this rapid growth in the chart, shown by the purple line extending from the start of 2010 to October 2024, the latest data point as I write this article.\n10\nDatasets used for training large language models, in particular, have experienced an even faster growth rate,\ntripling in size each year since 2010\n. Large language models process text by breaking it into tokens — basic units the model can encode and understand. A token doesn't directly correspond to one word, but on average, three English words correspond to about four tokens.\nGPT-2, released in 2019, is estimated to have been trained on 4 billion tokens, roughly equivalent to 3 billion words. To put this in perspective, as of September 2024, the English Wikipedia contained around 4.6 billion words.\n11\nIn comparison, GPT-4, released in 2023, was trained on almost 13 trillion tokens, or about 9.75 trillion words.\n12\nThis means that GPT-4’s training data was equivalent to over 2000 times the amount of text of the entire English Wikipedia.\nAs we use more data to train AI systems, we might eventually run out of high-quality human-generated materials like books, articles, and research papers. Some researchers predict we could exhaust useful training materials within the next few decades\n13\n. While AI models themselves can generate vast amounts of data, training AI on machine-generated materials could create problems, making the models less accurate and more repetitive.\n14\nParameters: scaling up the model size\nIncreasing the amount of training data lets AI models learn from much more information than ever before. However, to pick up on the patterns in this data and learn effectively, models need what are called \"parameters\". Parameters are a bit like knobs that can be tweaked to improve how the model processes information and makes predictions. As the amount of training data grows, models need more capacity to capture all the details in the training data. This means larger datasets typically require the models to have more parameters to learn effectively.\nEarly neural networks had hundreds or thousands of parameters. With its simple maze-learning circuitry, Theseus was a model with just 40 parameters — equivalent to the number of walls it encountered. Recent large models, such as GPT-3, boast up to 175 billion parameters.\n15\nWhile the raw number may seem large, this roughly translates into 700 GB if stored on a disc, which is easily manageable by today’s computers.\nThe chart shows how the number of parameters in AI models has skyrocketed over time. Since 2010, the number of AI model parameters has approximately doubled every year. The highest estimated number of parameters recorded by Epoch is 1.6 trillion in the QMoE model.\nWhile bigger AI models can do more, they also face some problems. One major issue is called \"overfitting\". This happens when an AI becomes “too optimized” for processing the particular data it was trained on but struggles with new data. To combat this, researchers employ two strategies: implementing specialized techniques for more generalized learning and expanding the volume and diversity of training data.\nCompute: scaling up computational resources\nAs AI models grow in data and parameters, they require exponentially more computational resources. These resources, commonly referred to as “compute” in AI research, are typically measured in total floating-point operations (“FLOP”), where each FLOP represents a single arithmetic calculation like addition or multiplication.\nThe computational needs for AI training have changed dramatically over time. With their modest data and parameter counts, early models could be trained in hours on simple hardware. Today’s most advanced models require\nhundreds of days\nof continuous computations, even with tens of thousands of special-purpose computers.\nThe chart shows that the computation used to train each AI model — shown on the vertical axis — has consistently and exponentially increased over the last few decades. From 1950 to 2010, compute doubled roughly every two years. However, since 2010, this growth has accelerated dramatically, now doubling approximately every six months, with the most compute-intensive model reaching 50 billion petaFLOP as I write this article.\n16\nTo put this scale in perspective, a single high-end graphics card like the NVIDIA GeForce RTX 3090 — widely used in AI research — running at full capacity for an entire year would complete just\n1.1 million petaFLOP computations\n. 50 billion petaFLOP is approximately 45,455 times more than that.\nAchieving computations on this scale requires large energy and hardware investments. Training some of the latest models has been estimated to cost up to\n$40 million\n, making it accessible only to a few well-funded organizations.\nCompute, data, and parameters tend to scale at the same time\nCompute, data, and parameters are closely interconnected when it comes to scaling AI models. When AI models are trained on more data, there are more things to learn. To deal with the increasing complexity of the data, AI models, therefore, require more parameters to learn from the various features of the data. Adding more parameters to the model means that it needs more computational resources during training.\nThis interdependence means that data, parameters, and compute need to grow simultaneously. Today’s\nlargest public datasets\nare about ten times bigger than what most AI models currently use, some containing hundreds of trillions of words. But without enough compute and parameters, AI models can’t yet use these for training.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nWhat can we learn from these trends for the future of AI?\nCompanies are seeking large financial investments to develop and scale their AI models, with\na growing focus\non\ngenerative AI\ntechnologies. At the same time, the key hardware that is used for training — GPUs — is getting\nmuch cheaper\nand more powerful, with its computing speed doubling roughly every 2.5 years per dollar spent.\n17\nSome organizations are also now leveraging more computational resources not just in training AI models but also during inference — the phase when models generate responses — as illustrated by\nOpenAI's latest o1 model\n.\nThese developments could help create more sophisticated AI technologies faster and cheaper. As companies invest more money and the necessary hardware improves, we might see significant improvements in what AI can do, including potentially unexpected new capabilities.\nBecause these changes could have major effects on our society, it's important that we track and understand these developments early on. To support this, we will update key metrics — such as the growth in computational resources, training data volumes, and model parameters — on a monthly basis. These updates will help monitor the rapid evolution of AI technologies and provide valuable insights into their trajectory.\nAcknowledgments\nI’d like to thank Max Roser, Daniel Bachler, Charlie Giattino, and Edouard Mathieu for their helpful comments and ideas for this article and visualizations.\nEndnotes\nVaswani et al. (2017). Attention is all you need.\nAdvances in neural information processing systems\n,\n30.\nHestness et al. (2017). Deep learning scaling is predictable, empirically. arXiv preprint arXiv:1712.00409.\nAccording to some accounts, GPT-2, a state-of-the-art language model by OpenAI at the time, was unable to reliably count to ten.\nBengio et al. (2023). Managing AI risks in an era of rapid progress.\narXiv preprint arXiv:2310.17688.\nEpoch (2023), \"Key Trends and Figures in Machine Learning\". Published online at epochai.org. Retrieved from: 'https://epochai.org/trends' [online resource].\nHoffmann et al. (2022). Training compute-optimal large language models.\narXiv preprint arXiv:2203.15556\n.; Kaplan et al. (2020). Scaling laws for neural language models.\narXiv preprint arXiv:2001.08361\n.\nWei et al. (2022). Emergent abilities of large language models.\narXiv preprint arXiv:2206.07682\n.\nSome\nresearchers\nargue that identifying new skills in AI largely hinges on the metrics used for evaluation. As a result, unless the model shows outstanding performance in a specific task, its developing abilities may remain unrecognized before they are “perfect”, giving the impression that these skills suddenly emerged.\nFor instance, language models like GPT (Generative Pre-trained Transformer) are trained on datasets consisting of billions of words, enabling them to understand and generate human-like text.\nThe regression line for 2010 onward highlights the rapid growth driven largely by the success of deep learning methods—an approach where artificial neural networks learn and improve by analyzing vast amounts of data to identify patterns and make predictions.\nsee the\nWikipedia page of Wikipedia’s size\nYou can see the size of OpenAI's models in this\nchart\n.\nVillalobos et al. (2024). ‘Will we run out of data? Limits of LLM scaling based on human-generated data’. ArXiv [cs.LG]. arXiv. https://arxiv.org/abs/2211.04325\nShumailov et al. (2024). AI models collapse when trained on recursively generated data. Nature 631, 755–759. https://doi.org/10.1038/s41586-024-07566-y/\nBrown et al. (2020). Language models are few-shot learners.\nAdvances in neural information processing systems\n, 33, 1877-1901.\nOne petaFLOP is equal to 1,000,000,000,000,000 (one quadrillion) FLOP.\nHobbhahn and Besiroglu (2022). Trends in GPU Price-Performance.\nPublished online at epochai.org\n. Retrieved from: '\nhttps://epochai.org/blog/trends-in-gpu-price-performance\n' [online resource]\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nVeronika Samborska (2025) - “Scaling up: how increasing inputs has made artificial intelligence more capable” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260622-063240/scaling-up-ai.html' [Online Resource] (archived on June 22, 2026).\nBibTeX citation\n@article{owid-scaling-up-ai,\nauthor = {Veronika Samborska},\ntitle = {Scaling up: how increasing inputs has made artificial intelligence more capable},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260622-063240/scaling-up-ai.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "scaling-up-ai", "source_url": "https://ourworldindata.org/scaling-up-ai", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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"Performance on knowledge tests is measured with the MMLU benchmark, here with 5-shot learning, which gauges a model’s accuracy after receiving only five examples for each task. Training computation is measured in total petaFLOP, which is 10¹⁵ floating-point operations.", "chart_note": "The values for training computation are estimates and come with some uncertainty, especially for models for which only minimal information has been disclosed, such as GPT-4.", "chart_citation": "Epoch AI (2023)", "original_chart_url": "https://ourworldindata.org/grapher/ai-performance-knowledge-tests-vs-training-computation", "owid_column_metadata": {"MMLU avg": {"titleShort": "Performance on knowledge tests (MMLU)", "titleLong": "Performance on knowledge tests (MMLU)", "descriptionKey": ["The MMLU benchmark covers a wide range of 57 subjects, including STEM, humanities, social sciences, and more.", "It encompasses subjects of varying difficulty levels, spanning from elementary concepts to advanced professional topics. 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One FLOP is equivalent to one addition, subtraction, multiplication, or division of two decimal numbers, and one petaFLOP equals one quadrillion (10^15) FLOP.", "The AI systems shown here were built using machine learning and deep learning methods. These involve complex mathematical calculations that require significant computational resources. 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Each domain has a specific data point unit; for example, for vision it is images, for language it is words, and for games it is timesteps. This means systems can only be compared directly within the same domain.", "chart_note": "The regression lines show a sharp rise in data used to train AI systems since 2010, driven by the success of deep learning methods that leverage neural networks and massive datasets.", "chart_citation": "Epoch AI (2026)", "original_chart_url": "https://ourworldindata.org/grapher/exponential-growth-of-datapoints-used-to-train-notable-ai-systems", "owid_column_metadata": {"Training dataset size": {"titleShort": "Training dataset size", "titleLong": "Training dataset size", "descriptionShort": "The number of unique data points used to train the model. Each domain has a specific data point unit; for example, for vision it is images, for language it is words, and for games it is timesteps. This means systems can only be compared directly within the same domain.", "descriptionKey": ["Training data size measures the volume of unique examples used to train an AI model during its learning phase. It represents the total number of distinct data points the model learns from, counted only once regardless of how many times they're seen during training.", "To understand this concept, imagine teaching someone to identify different bird species. Each unique bird photo you show them is one piece of training data. If you show 100 different photos, your training data size is 100, even if you review those same photos multiple times.", "Since datasets vary by domain, there's no universal unit for measuring size. Text models might count tokens, image models count pictures, and video models count clips. Epoch AI typically uses the smallest unit that triggers a model update during training. For language models that predict the next word, this would be individual tokens.", "Training data size directly impacts model performance. Larger datasets enable deeper learning and more nuanced pattern recognition, allowing models to identify subtle distinctions and handle diverse real-world scenarios more effectively."], "unit": "unique datapoints", "timespan": "", "type": "Numeric", "owidVariableId": 1204041, "shortName": "training_dataset_size__total", "lastUpdated": "2025-03-12", "nextUpdate": "2026-07-22", "citationShort": "Epoch AI (2026) – with major processing by Our World in Data", "citationLong": "Epoch AI (2026) – with major processing by Our World in Data. “Training dataset size” [dataset]. 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{"raw_link": "https://ourworldindata.org/great-global-redistributor-money-sent-brought-back-migrants-remittances", "title": "The great global redistributor we never hear about: money sent or brought back by migrants", "context": "Home\nMigration\nThe great global redistributor we never hear about: money sent or brought back by migrants\nMigrants send or bring back over three times the amount of money provided by global foreign aid. Cutting transaction fees could make this support even more effective in reducing poverty.\nBy\nSimon van Teutem\nand\nTuna Acisu\nJanuary 13, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nEvery year, Carlos Hernández Mejía, a 35-year-old research scientist in Amsterdam, sends 3,000 to 4,000 euros to help maintain his mother’s house in Mexico. During the pandemic, he also helped his brother, sending 300 euros a month to cover rent while he was studying.\nCarlos is one of 200 million migrants who regularly send back money to support their families and communities.\n1\nThese cash transfers reach around 800 million people globally — more than the populations of the United States and the European Union combined.\nImagine a classroom of 30 students representing the world’s population; at least three would get money from remittances — one in ten people.\nThese payments have quietly become a major force in helping families pay school fees, make repairs to their homes, and cover medical bills.\nThe World Bank estimates that money sent back by migrants constitutes around two-thirds of what is called “remittances” in global statistics.\n2\nThe rest comes from border, seasonal, and other short-term jobs abroad or work with non-resident employers, such as embassies and international organizations.\nIn this article, we’ll ditch the jargon and refer to remittances as money\nsent back\nor\nbrought back\nby migrants. “Sent back” refers to personal transfers, and “brought back” refers to the compensation of employees.\nMigrants send and bring back much more money than the total global foreign aid\nThe amount of transfers sent or brought back by migrants adds up a massive amount. To see how much, let’s compare it to\nforeign aid\n.\nForeign aid\nis money transferred from one country to another, usually to support people in a lower-income country. It’s often seen as one of the largest efforts to redistribute wealth around the world.\nHowever, as the chart below shows, the amount of money sent or brought back by migrants was more than three times larger than foreign aid in 2021. We don’t often hear about these flows, but their scale is far bigger.\nDownload\nMost of this money flows from rich countries to poorer ones\nSo, the amount of money sent or brought back by migrants is larger than foreign aid. But, most foreign aid flows to\ncountries in need\n, such as low-income and middle-income countries. Is the same thing true for money sent or brought back by migrants?\nIf migrants were only sending money from Denmark to the Netherlands or from Switzerland to Monaco, the effects on global inequality would be minimal. But that’s not what’s happening.\nAs the chart below illustrates,\nhigh-income countries\nsend $680 billion but only receive $195 billion. In other words, people in these countries provide 87% of the funds while receiving just 25%.\n3\nEconomic resources from high-income countries are being redistributed abroad.\nDownload\nThe main beneficiaries are middle-income countries. Upper-middle-income countries send 7% but receive 30%, and lower-middle-income countries send only 4% but receive 44%.\nThis also means that very little money reaches the\npoorest\ncountries\n4\n, where people need it the most. Low-income countries receive just 1.7% of all money sent or brought back by migrants, despite being home to 9% of the global population.\nSmall sums from rich countries go a long way in poorer nations\nMoney sent or brought back by migrants has a big impact on recipient nations.\nAccording to the UN, migrant workers send back about 15% of their earnings on average.\n5\nBut even a little money from rich countries can make a big difference in poorer ones. For example, 15% of the average annual income in the United States is nearly twice the average annual income in Colombia.\n6\nThe chart below illustrates that many countries receive remittances in amounts that are large relative to their gross domestic product (GDP). In over thirty countries, remittances account for more than 10% of the value of their entire economies.\nConsider Central American countries, where many people have emigrated to high-income countries like the United States.\n7\nMoney sent or brought back by migrants accounts for up to 20% to 30% of GDP in Nicaragua, El Salvador, and Honduras.\n8\nHow money sent back by migrants improves living conditions around the world\nWhat happens when these funds reach families in poorer countries?\nIn low-income and lower-middle-income countries, an extra $100 from money sent or brought back by migrants can be the difference between a child going hungry and a child getting enough to eat. Research shows that money sent back by migrants reduces\nchild malnutrition\n, helping children grow healthier and stronger.\n9\nIt also covers healthcare where formal insurance systems fall short. Families can afford doctor visits, buy medicine, or pay for treatments that would otherwise be out of reach.\n10\nBeyond providing care, it also helps to prevent child deaths by improving access to better sanitation.\n11\nIt functions as a private safety net from families rather than governments.\nWith money sent from abroad, children can also stay in school longer. Thanks to Carlos, his brother in Mexico could afford rent and finish university. Data from Ghana shows that families receiving money from relatives abroad enroll their children in school at higher rates, from primary school to secondary education.\n12\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nThe more money lost to fees, the less families benefit from what migrants send back\nThere’s a big problem for people who move to new countries and want to help their families. When they send money, a significant share is lost to banks and money-transfer companies before their families receive it.\nThese fees\n, known as transaction costs, are one of the biggest hurdles stopping migrants from giving their families more support.\nTransaction costs reduce the money received in two ways. First, they directly bite into the amount of money migrants send. But they also have an indirect impact: research from the IMF shows that when fees are high, migrants often send less overall, knowing that a bigger chunk of what they send won’t make it to their families.\n13\nIf governments want to make it easier for migrants to help their families, lowering these transaction costs is key. The chart below shows that fees are moving in the right direction. The median fee for sending money has dropped from 8% in 2011 to 6% in 2020.\n14\nDownload\nThe United Nations wants to bring this global average below 3%, with no remittance route exceeding 5% by 2030, as part of its\nSustainable Development Goals\n.\nBut despite recent progress, we are still far from this target. For example, sending $100 from Uganda to the Democratic Republic of Congo can cost as much as $10 — more than double the UN’s recommended maximum.\n15\nThe global average of 6% is still twice as high as the target.\nGovernments can make it cheaper for migrants to send money back in at least three ways. First, they can require money transfer companies to clearly show all fees and exchange rates so migrants aren’t left guessing and can pick the cheapest option. This added transparency can also help drive prices down as companies compete to offer better deals.\nSecond, they can reduce the costs of money sent back by migrants by creating shared payment networks that connect banking systems across countries. For instance, when the US and Mexico linked their central banks’ payment systems, it slashed remittance fees to $0.67 per transaction with an exchange rate spread of only 0.21%.\n16\nExpanding this model to more countries could save migrants even more.\nFinally, governments could launch official remittance cost comparison websites, as Australia and New Zealand\nhave done\nfor sending money to the Pacific Islands.\nThe IMF estimates that meeting the UN’s remittance cost goals could lead to an extra $32 billion sent back each year.\n17\nThis would allow more families to pay for school, healthcare, and basic needs in places where every dollar counts.\nMigration doesn’t only result in redistribution through money sent or brought back by migrants. Since the percentage of migrants in high-income countries\nis growing faster\nthan in other regions, the direct impact of income increases can also be significant. For example, the bottom 5% of earners in the Netherlands earn more than the top 5% in Morocco.\n18\nIn the meantime, Carlos Hernández Mejía, like millions of other migrants, is making a big difference for his family — one transfer at a time.\nBreaking down remittances\nRemittances include both personal transfers (money sent back by migrants) and employee compensation (money brought back). Since these are different things, it would be better to analyze each category separately to better understand if increases are driven by transfers or employment.\nUnfortunately, the World Bank does not currently provide that level of detail in the data that it publishes. Access to this breakdown would greatly enhance our understanding of the actual trends and their impacts.\nContinue reading on Our World in Data\nMost international migrants don’t move very far from their home countries\nLong-distance moves are becoming more common, but they remain the exception. For most, international migration still means crossing a nearby border, not an ocean.\nHow do countries measure immigration, and how accurate is this data?\nCountries estimate how many people move in and out using censuses, surveys, and border records. How accurate are these numbers, and can they account for illegal migration?\nForeign Aid\nWho gives and receives foreign aid? Which forms does it take? What are examples of when it was (un-)successful?\nEndnotes\nUnited Nations (2019).\nRemittances matter: 8 facts you don’t know about the money migrants send back home\n.\nWorld Bank (2024).\nMigration and Development Brief 40\n, page 10.\nWhile 25% may still seem surprisingly large to some, it is likely more due to income from employment than personal transfers. Germany and France, for instance, are in the top 10 receiving countries globally in 2022, but this was largely due to the salaries of cross-border workers in Switzerland. Source: International Organization for Migration (2024).\nWorld Migration Report 2024\n, page 36.\nLow-income countries account for approximately 9% of the global population.\nUnited Nations (2019).\nRemittances matter: 8 facts you don’t know about the money migrants send back home\nFor this example, we refer to data on GDP per capita from\nthe World Bank\n. In 2023, the GDP per capita in the United States was $82,769.41. In Colombia, it was COL$30,053,963.64. The USD/COP average exchange rate was\n4,323.28\n. So, 15% of the U.S. average income is $12,415.41 (0.15 × 82,769.41). Converting this into Colombian pesos and comparing it to Colombia’s GDP per capita: (12,415.41 × 4,323.28)/ 30,053,963.64 = 1.8x the average income in Colombia. This is a rough back-of-the-envelope calculation. Of course, income differs from earnings, and some migrants don’t send the local currency, but it’s enough to give us a ballpark figure.\nWorld Bank (2024).\nA journey through borders: Understanding migration in Central America.\nSome economists worry that extreme reliance on remittances creates a dependency trap. If too many people rely on money sent from abroad, fewer may feel the need to join the workforce, which could hurt the local economy in the long run. It also leaves the country vulnerable to sudden shifts in exchange rates. But there is no clear agreement on where the tipping point is. International Organization for Migration (2024).\nWorld Migration Report 2024\n, page 36.\nTerrelonge (2014).\nFor Health, Strength, and Daily Food: The Dual Impact of Remittances and Public Health Expenditure on Household Health Spending and Child Health Outcomes.\nThis paper suggests that a 10% increase in remittance income leads to an increase in healthcare spending of between 2% and 4%. Chezum et al. (2018).\nAre Remittances Good for Your Health? Remittances and Nepal’s National Healthcare Policy\n.\nThis paper indicates that a 10% increase in remittance income results in a 0.8% decline in the under-five mortality rate from diarrheal diseases, while they do not change the under-five mortality rate from neonatal disorders or respiratory infections. Ramkissoon & Deonanan (2023).\nHow do remittances impact child mortality and are there preconditions?\nThis paper suggests that a 1% increase in real remittances per capita results in a 0.12% increase in the proportion of children enrolled in secondary school and an increase of 0.09% in the primary completion rate. Zhunio et al. (2011).\nThe influence of remittances on education and health outcomes: a cross country study\nHigher fees may also boost informal flows, such as people bringing cash themselves. Kpodar & Imam (2022).\nHow Do Transaction Costs Influence Remittances\nBeck et al. (2022).\nWhat Explains Remittance Fees? Panel Evidence\nBBC (2024).\nMigrants hit by high fees to send money home\nThe exchange rate spread is the small extra cost added to the exchange rate for currency conversion. World Bank (2006). Global Economic Prospects 2006. Chapter 6: Reducing Remittance Fees. p.148, Box 6.4\nKpodar & Imam (2022).\nHow Do Transaction Costs Influence Remittances\nFrederik (2023).\nEn dan nu het goede nieuws: het gaat steeds beter met Nederland\nChezum et al. (2018).\nAre Remittances Good for Your Health? Remittances and Nepal’s National Healthcare Policy\n.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSimon van Teutem and Tuna Acisu (2025) - “The great global redistributor we never hear about: money sent or brought back by migrants” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-093348/great-global-redistributor-money-sent-brought-back-migrants-remittances.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-great-global-redistributor-money-sent-brought-back-migrants-remittances,\nauthor = {Simon van Teutem and Tuna Acisu},\ntitle = {The great global redistributor we never hear about: money sent or brought back by migrants},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-093348/great-global-redistributor-money-sent-brought-back-migrants-remittances.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. 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We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "great-global-redistributor-money-sent-brought-back-migrants-remittances", "source_url": "https://ourworldindata.org/great-global-redistributor-money-sent-brought-back-migrants-remittances", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Migrants send or bring back over three times the amount of global foreign aid. 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"2003", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "World Bank's income classification": "Upper-middle-income countries"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "World Bank's income classification": "Upper-middle-income countries"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "World Bank's income classification": "Upper-middle-income countries"}, {"Entity": "Algeria", "Code": "DZA", "Year": 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"Year": "1989", "World Bank's income classification": "High-income countries"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "1990", "World Bank's income classification": "Upper-middle-income countries"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "1991", "World Bank's income classification": "Upper-middle-income countries"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "1992", "World Bank's income classification": "Upper-middle-income countries"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "1993", "World Bank's income classification": "Upper-middle-income countries"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "1994", "World Bank's income classification": "Upper-middle-income countries"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "1995", "World Bank's income classification": "Upper-middle-income countries"}], "rows_tail": [{"Entity": "Vietnam", "Code": "VNM", "Year": "2024", "World Bank's income classification": "Lower-middle-income 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{"Entity": "Yemen", "Code": "YEM", "Year": "2004", "World Bank's income classification": "Low-income countries"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "World Bank's income classification": "Low-income countries"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "World Bank's income classification": "Low-income countries"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "World Bank's income classification": "Low-income countries"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "World Bank's income classification": "Low-income countries"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "World 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"Code": "ZWE", "Year": "2015", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "World Bank's income classification": "Low-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "World Bank's income classification": "Lower-middle-income countries"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "World Bank's income classification": "Lower-middle-income countries"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "world-bank-income-groups", "metadata_url": "https://ourworldindata.org/grapher/world-bank-income-groups.metadata.json", "chart_title": "World Bank income groups", "chart_subtitle": "The World Bank's income classification divides countries into four categories based on their gross national income (GNI) per capita. Thresholds between income groups have changed over time.", "chart_note": "Countries are grouped based on the income classification for each respective year. This means that group membership can change over time. Venezuela and Ethiopia are currently unclassified.", "chart_citation": "World Bank (2025)", "original_chart_url": "https://ourworldindata.org/grapher/world-bank-income-groups", "owid_column_metadata": {"World Bank's income classification": {"titleShort": "World Bank's income classification", "titleLong": "World Bank's income classification", "descriptionShort": "Income classification based on the country's income each year.", "descriptionKey": ["The World Bank creates a yearly classification of countries by income, for all countries with population over 30,000.", "This classification stays the same throughout the World Bank's fiscal year (from July 1 to June 30) even if the income data for a country changes.", "Low-income countries are those with a gross national income (GNI) per capita of $1,135 or less in 2024.", "Lower-middle-income countries are those with a GNI per capita between $1,136 and $4,495 in 2024.", "Upper-middle-income countries are those with a GNI per capita between $4,496 and $13,935 in 2024.", "High-income countries are those with a GNI per capita of more than $13,935 in 2024.", "Venezuela, classified as an upper-middle income country until the fiscal year 2021, has been unclassified since then due to the unavailability of data. Ethiopia is currently in a temporary status of unclassification."], "unit": "", "timespan": "1987-2024", "type": "Ordinal", "owidVariableId": 1077017, "shortName": "classification", "lastUpdated": "2025-07-01", "nextUpdate": "2026-07-01", "citationShort": "World Bank (2025) – with major processing by Our World in Data", "citationLong": "World Bank (2025) – with major processing by Our World in Data. “World Bank's income classification” [dataset]. World Bank, “Income Classifications” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077017.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Money sent or brought back by migrants as a share of GDP", "source_url": "https://ourworldindata.org/grapher/money-sent-or-brought-back-by-migrants-as-a-share-of-gdp.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Personal remittances, received (% of GDP)"], "row_count_total": 7687, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Personal remittances, received (% of GDP)": "0.88572365"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Personal remittances, received (% of GDP)": "1.1331624"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Personal remittances, received (% of GDP)": "2.3851583"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Personal remittances, received (% of GDP)": "1.0059847"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Personal remittances, received (% of GDP)": "1.1021874"}, {"Entity": 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remittances, received (% of GDP)": "19.387339"}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "Personal remittances, received (% of GDP)": "12.399734"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Personal remittances, received (% of GDP)": "16.677034"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Personal remittances, received (% of GDP)": "17.22811"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Personal remittances, received (% of GDP)": "16.247387"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Personal remittances, received (% of GDP)": "15.318729"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Personal remittances, received (% of GDP)": "15.670685"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Personal remittances, received (% of GDP)": "15.620173"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Personal remittances, received (% of GDP)": "14.856709"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Personal remittances, received (% of GDP)": "13.2052555"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Personal remittances, received (% of GDP)": "14.0708275"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Personal remittances, received (% of GDP)": "13.924789"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Personal remittances, received (% of GDP)": "13.1267805"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Personal remittances, received (% of GDP)": "11.963217"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Personal remittances, received (% of GDP)": "11.593292"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Personal remittances, received (% of GDP)": "10.016797"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Personal remittances, received (% of GDP)": "10.687222"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Personal remittances, received (% of GDP)": "11.25409"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Personal remittances, received (% of GDP)": "10.893696"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Personal remittances, received (% of GDP)": "9.894372"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Personal remittances, received (% of GDP)": "9.481513"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Personal remittances, received (% of GDP)": "9.450123"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Personal remittances, received (% of GDP)": "9.618418"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Personal remittances, received (% of GDP)": "9.529486"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "Personal remittances, received (% of GDP)": "9.17717"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Personal remittances, received (% of GDP)": "8.66778"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "Personal remittances, received (% of GDP)": "8.409393"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1970", "Personal remittances, received (% of GDP)": "4.3384156"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1971", "Personal remittances, received (% of GDP)": "4.6876388"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1972", "Personal remittances, received (% of GDP)": "3.3546414"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1973", "Personal remittances, received (% of GDP)": "2.5838728"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1974", "Personal remittances, received (% of GDP)": "1.5291594"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1975", "Personal remittances, received (% of GDP)": "1.7097421"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1976", "Personal remittances, received (% of GDP)": "1.438383"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1977", "Personal remittances, received (% of GDP)": "1.6641146"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1978", "Personal remittances, received (% of GDP)": "1.4906414"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1979", "Personal remittances, received (% of GDP)": "1.2543727"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1980", "Personal remittances, received (% of GDP)": "0.9587721"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1981", "Personal remittances, received (% of GDP)": "1.0079238"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1982", "Personal remittances, received (% of GDP)": "1.1215036"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1983", "Personal remittances, received (% of GDP)": "0.80325615"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1984", "Personal remittances, received (% of GDP)": "0.6126795"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1985", "Personal remittances, received (% of GDP)": "0.5402339"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1986", "Personal remittances, received (% of GDP)": "0.56207997"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1987", "Personal remittances, received (% of GDP)": "0.72963375"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1988", "Personal remittances, received (% of GDP)": "0.64140105"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1989", "Personal remittances, received (% of GDP)": "0.62011635"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1990", "Personal remittances, received (% of GDP)": "0.5672981"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1991", "Personal remittances, received (% of GDP)": "2.8217893"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1992", "Personal remittances, received (% of GDP)": "2.8956444"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1993", "Personal remittances, received (% of GDP)": "2.282484"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1994", "Personal remittances, received (% of GDP)": "3.2790217"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1995", "Personal remittances, received (% of GDP)": "2.6817167"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1996", "Personal remittances, received (% of GDP)": "1.8746716"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1997", "Personal remittances, received (% of GDP)": "2.200192"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1998", "Personal remittances, received (% of GDP)": "2.1997278"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1999", "Personal remittances, received (% of GDP)": "1.6241552"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Personal remittances, received (% of GDP)": "1.4418584"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Personal remittances, received (% of GDP)": "1.1276917"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Personal remittances, received (% of GDP)": "1.7393819"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Personal remittances, received (% of GDP)": "2.381527"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Personal remittances, received (% of GDP)": "2.6764243"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Personal remittances, received (% of GDP)": "0.15880932"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Personal remittances, received (% of GDP)": "0.15355335"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Personal remittances, received (% of GDP)": "0.0694853"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Personal remittances, received (% of GDP)": "0.057450756"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Personal remittances, received (% of GDP)": "0.100013085"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Personal remittances, received (% of GDP)": "0.11057695"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Personal remittances, received (% of GDP)": "0.09291806"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Personal remittances, received (% of GDP)": "0.09458384"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Personal remittances, received (% of GDP)": "0.09124952"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Personal remittances, received (% of GDP)": "1.0263728"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Personal remittances, received (% of GDP)": "1.0653114"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Personal remittances, received (% of GDP)": "1.1003437"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Personal remittances, received (% of GDP)": "0.94369"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Personal remittances, received (% of GDP)": "1.0202789"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Personal remittances, received (% of GDP)": "0.9231065"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Personal remittances, received (% of GDP)": "1.0308568"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Personal remittances, received (% of GDP)": "0.9621893"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2022", "Personal remittances, received (% of GDP)": "0.7556946"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "Personal remittances, received (% of GDP)": "0.7532755"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2024", "Personal remittances, received (% of GDP)": "0.6679419"}, {"Entity": "Andorra", "Code": "AND", "Year": "2019", "Personal remittances, received (% of GDP)": "0.6691487"}, {"Entity": "Andorra", "Code": "AND", "Year": "2020", "Personal remittances, received (% of GDP)": "1.6401343"}, {"Entity": "Andorra", "Code": "AND", "Year": "2021", "Personal remittances, received (% of GDP)": "1.5941963"}, {"Entity": "Andorra", "Code": "AND", "Year": "2022", "Personal remittances, received (% of GDP)": "1.3186305"}, {"Entity": "Andorra", "Code": "AND", "Year": "2023", "Personal remittances, received (% of GDP)": "1.2754034"}, {"Entity": "Andorra", "Code": "AND", "Year": "2024", "Personal remittances, received (% of GDP)": "1.2954931"}, {"Entity": "Angola", "Code": "AGO", "Year": "1996", "Personal remittances, received (% of GDP)": "0.06831932"}, {"Entity": "Angola", "Code": "AGO", "Year": "2008", "Personal remittances, received (% of GDP)": "0.08308902"}, {"Entity": "Angola", "Code": "AGO", "Year": "2009", "Personal remittances, received (% of GDP)": "0.00019871263"}, {"Entity": "Angola", "Code": "AGO", "Year": "2010", "Personal remittances, received (% of GDP)": "0.018809646"}, {"Entity": "Angola", "Code": "AGO", "Year": "2011", "Personal remittances, received (% of GDP)": "0.00016308119"}, {"Entity": "Angola", "Code": "AGO", "Year": "2012", "Personal remittances, received (% of GDP)": "0.02810339"}, {"Entity": "Angola", "Code": "AGO", "Year": "2013", "Personal remittances, received (% of GDP)": "0.024614438"}, {"Entity": "Angola", "Code": "AGO", "Year": "2014", "Personal remittances, received (% of GDP)": "0.020183217"}, {"Entity": "Angola", "Code": "AGO", "Year": "2015", "Personal remittances, received (% of GDP)": "0.0108390665"}, {"Entity": "Angola", "Code": "AGO", "Year": "2016", "Personal remittances, received (% of GDP)": "0.006660262"}], "rows_tail": [{"Entity": "World", "Code": "OWID_WRL", "Year": "1990", "Personal remittances, received (% of GDP)": "0.40239358"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1991", "Personal remittances, received (% of GDP)": "0.339494"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1992", "Personal remittances, received (% of GDP)": "0.34870976"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1993", "Personal remittances, received (% of GDP)": "0.342237"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1994", "Personal remittances, received (% of GDP)": "0.3501043"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1995", "Personal remittances, received (% of GDP)": "0.3150405"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1996", "Personal remittances, received (% of GDP)": "0.3176336"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1997", "Personal remittances, received (% of GDP)": "0.36340475"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1998", "Personal remittances, received (% of GDP)": "0.36126426"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1999", "Personal remittances, received (% of GDP)": "0.3637147"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Personal remittances, received (% of GDP)": "0.37156573"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Personal remittances, received (% of GDP)": "0.40481234"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Personal remittances, received (% of GDP)": "0.45099694"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Personal remittances, received (% of GDP)": "0.49429"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Personal remittances, received (% of GDP)": "0.5029495"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Personal remittances, received (% of GDP)": "0.54849625"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Personal remittances, received (% of GDP)": "0.5872305"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Personal remittances, received (% of GDP)": "0.6239499"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Personal remittances, received (% of GDP)": "0.6605325"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Personal remittances, received (% of GDP)": "0.66520184"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Personal remittances, received (% of GDP)": "0.65104544"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Personal remittances, received (% of GDP)": "0.65647453"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Personal remittances, received (% of GDP)": "0.6725716"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Personal remittances, received (% of GDP)": "0.6969517"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Personal remittances, received (% of GDP)": "0.7250013"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Personal remittances, received (% of GDP)": "0.7517896"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Personal remittances, received (% of GDP)": "0.72822076"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Personal remittances, received (% of GDP)": "0.7461525"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Personal remittances, received (% of GDP)": "0.7529041"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Personal remittances, received (% of GDP)": "0.76105165"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Personal remittances, received (% of GDP)": "0.7855908"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Personal remittances, received (% of GDP)": "0.7709069"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Personal remittances, received (% of GDP)": "0.7944579"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Personal remittances, received (% of GDP)": "0.7871154"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Personal remittances, received (% of GDP)": "0.8201326"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1990", "Personal remittances, received (% of GDP)": "11.849265"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1991", "Personal remittances, received (% of GDP)": "6.8071575"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1992", "Personal remittances, received (% of GDP)": "5.669465"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1993", "Personal remittances, received (% of GDP)": "4.7780714"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1994", "Personal remittances, received (% of GDP)": "3.7805839"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1995", "Personal remittances, received (% of GDP)": "8.443817"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1996", "Personal remittances, received (% of GDP)": "17.464153"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1997", "Personal remittances, received (% of GDP)": "17.089046"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1998", "Personal remittances, received (% of GDP)": "19.01877"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1999", "Personal remittances, received (% of GDP)": "16.011885"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Personal remittances, received (% of GDP)": "13.306725"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Personal remittances, received (% of GDP)": "13.139285"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Personal remittances, received (% of GDP)": "12.100887"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Personal remittances, received (% of GDP)": "10.782394"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Personal remittances, received (% of GDP)": "9.248867"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Personal remittances, received (% of GDP)": "7.6657434"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Personal remittances, received (% of GDP)": "6.7281666"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Personal remittances, received (% of GDP)": "6.1038694"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Personal remittances, received (% of GDP)": "5.2414536"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Personal remittances, received (% of GDP)": "4.615946"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Personal remittances, received (% of GDP)": "4.937433"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Personal remittances, received (% of GDP)": "4.2898674"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Personal remittances, received (% of GDP)": "9.465746"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Personal remittances, received (% of GDP)": "8.270396"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Personal remittances, received (% of GDP)": "7.7506585"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Personal remittances, received (% of GDP)": "7.89384"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Personal remittances, received (% of GDP)": "12.039738"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Personal remittances, received (% of GDP)": "12.789612"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Personal remittances, received (% of GDP)": "15.889065"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Personal remittances, received (% of GDP)": "0.74053377"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Personal remittances, received (% of GDP)": "0.7779962"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Personal remittances, received (% of GDP)": "0.6345514"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Personal remittances, received (% of GDP)": "0.4521489"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Personal remittances, received (% of GDP)": "0.42185515"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Personal remittances, received (% of GDP)": "0.3807467"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Personal remittances, received (% of GDP)": "0.26920068"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Personal remittances, received (% of GDP)": "0.21542613"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Personal remittances, received (% of GDP)": "0.19726218"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Personal remittances, received (% of GDP)": "0.28570688"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Personal remittances, received (% of GDP)": "0.19253059"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Personal remittances, received (% of GDP)": "0.2148051"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Personal remittances, received (% of GDP)": "0.2213828"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Personal remittances, received (% of GDP)": "0.18352745"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Personal remittances, received (% of GDP)": "0.36192912"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Personal remittances, received (% of GDP)": "0.40653554"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Personal remittances, received (% of GDP)": "0.42155614"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Personal remittances, received (% of GDP)": "0.74355817"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Personal remittances, received (% of GDP)": "1.0848336"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Personal remittances, received (% of GDP)": "0.8348918"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Personal remittances, received (% of GDP)": "0.8908859"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Personal remittances, received (% of GDP)": "1.3212312"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Personal remittances, received (% of GDP)": "0.40409535"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Personal remittances, received (% of GDP)": "0.29611337"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Personal remittances, received (% of GDP)": "0.3744633"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Personal remittances, received (% of GDP)": "0.25278232"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Personal remittances, received (% of GDP)": "0.3530005"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Personal remittances, received (% of GDP)": "0.38500822"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Personal remittances, received (% of GDP)": "0.25175208"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Personal remittances, received (% of GDP)": "0.05989088"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Personal remittances, received (% of GDP)": "0.010986271"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Personal remittances, received (% of GDP)": "0.01262004"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Personal remittances, received (% of GDP)": "0.01694713"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Personal 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Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "8.71"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.07"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "13.04"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "13.41"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.35"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.33"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "8.77"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "10.68"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.55"}, {"Entity": "France", "Code": "FRA", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "11.63"}, {"Entity": "France", "Code": "FRA", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.56"}, {"Entity": "France", "Code": "FRA", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.57"}, {"Entity": "France", "Code": "FRA", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.53"}, {"Entity": "France", "Code": "FRA", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.83"}, {"Entity": "France", "Code": "FRA", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.3"}, {"Entity": "France", "Code": "FRA", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.21"}, {"Entity": "Germany", "Code": "DEU", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "12.64"}, {"Entity": "Germany", "Code": "DEU", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.32"}, {"Entity": "Germany", "Code": "DEU", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.57"}, {"Entity": "Germany", "Code": "DEU", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.31"}, {"Entity": "Germany", "Code": "DEU", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.64"}, {"Entity": "Germany", "Code": "DEU", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.47"}, {"Entity": "Germany", "Code": "DEU", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.37"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "15.78"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "8.66"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "16.4"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "8.16"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "8.08"}, {"Entity": "India", "Code": "IND", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.03"}, {"Entity": "India", "Code": "IND", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.95"}, {"Entity": "India", "Code": "IND", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.66"}, {"Entity": "Israel", "Code": "ISR", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "11.23"}, {"Entity": "Israel", "Code": "ISR", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "10.48"}, {"Entity": "Israel", "Code": "ISR", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "12.03"}, {"Entity": "Israel", "Code": "ISR", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "12.31"}, {"Entity": "Israel", "Code": "ISR", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "13.91"}, {"Entity": "Italy", "Code": "ITA", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "8.18"}, {"Entity": "Italy", "Code": "ITA", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.05"}, {"Entity": "Italy", "Code": "ITA", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.98"}, {"Entity": "Italy", "Code": "ITA", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.08"}, {"Entity": "Italy", "Code": "ITA", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.33"}, {"Entity": "Italy", "Code": "ITA", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.15"}, {"Entity": "Italy", "Code": "ITA", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.76"}, {"Entity": "Japan", "Code": "JPN", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "16.84"}, {"Entity": "Japan", "Code": "JPN", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "12.97"}, {"Entity": "Japan", "Code": "JPN", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "10.85"}, {"Entity": "Japan", "Code": "JPN", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "9.58"}], "rows_tail": [{"Entity": "Pakistan", "Code": "PAK", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "11.99"}, {"Entity": "Pakistan", "Code": "PAK", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "13.92"}, {"Entity": "Pakistan", "Code": "PAK", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "15.51"}, {"Entity": "Pakistan", "Code": "PAK", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.7"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.9"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.74"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.68"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.16"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.03"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.05"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.99"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.26"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.21"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.1"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.87"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.49"}, {"Entity": "Russia", "Code": "RUS", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "2.68"}, {"Entity": "Russia", "Code": "RUS", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "1.92"}, {"Entity": "Russia", "Code": "RUS", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "2.13"}, {"Entity": "Russia", "Code": "RUS", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "1.85"}, {"Entity": "Russia", "Code": "RUS", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "1.59"}, {"Entity": "Russia", "Code": "RUS", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "1.94"}, {"Entity": "Russia", "Code": "RUS", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "2.93"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.23"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.75"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.37"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "8.54"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.21"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.13"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.13"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.1"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.34"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.99"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.8"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.87"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.21"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.8"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.06"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.47"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.57"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.26"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.45"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.63"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.94"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.55"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.07"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.04"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.31"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.68"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "17.73"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "15.19"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "16.57"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "15.82"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "15.96"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "15.05"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "14.81"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.36"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.43"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.81"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.07"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.87"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.74"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.18"}, {"Entity": "Spain", "Code": "ESP", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.63"}, {"Entity": "Spain", "Code": "ESP", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.06"}, {"Entity": "Spain", "Code": "ESP", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.63"}, {"Entity": "Spain", "Code": "ESP", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.64"}, {"Entity": "Spain", "Code": "ESP", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.35"}, {"Entity": "Spain", "Code": "ESP", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.03"}, {"Entity": "Spain", "Code": "ESP", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "5.16"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "9.59"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "8.09"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.95"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.93"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.36"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "11.65"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "15.38"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "9.89"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "9.06"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "8.45"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "8.25"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "6.28"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "24.31"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "16.16"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "14.4"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "16.78"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "14.19"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "19.73"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "27.92"}, {"Entity": "Thailand", "Code": "THA", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "16.91"}, {"Entity": "Thailand", "Code": "THA", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "15.84"}, {"Entity": "Thailand", "Code": "THA", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "12.9"}, {"Entity": "Thailand", "Code": "THA", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "13.31"}, {"Entity": "Thailand", "Code": "THA", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "11.56"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "10.41"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "13.09"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "13.6"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "11.11"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "22.44"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.9"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2015", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.15"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2017", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.32"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2018", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.71"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2019", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.47"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2020", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "4.1"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2021", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "3.89"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2011", "10.c.1 - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted (%) - SI_RMT_COST_SND": "7.73"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2015", "10.c.1 - 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{"raw_link": "https://ourworldindata.org/simon-ehrlich-bet", "title": "Who would have won the Simon-Ehrlich bet over different decades, and what do long-term prices tell us about resource scarcity?", "context": "Home\nMetals & Minerals\nWho would have won the Simon-Ehrlich bet over different decades, and what do long-term prices tell us about resource scarcity?\nIn the 1980s, economist Julian Simon won his bet with biologist Paul Ehrlich on material prices. But what does the long-term data tell us about supply and demand for resources?\nBy\nHannah Ritchie\nJanuary 6, 2025\nBrowse past versions\nCite this article\nReuse our work freely\nIn 1980, the biologist Paul Ehrlich agreed to a bet with the economist Julian Simon on how the prices of five materials would change over the next decade.\nPaul Ehrlich had a clear expectation. He thought population growth would quickly deplete the planet’s resources.\n1\nAs a consequence, he expected that the cost of resources — including minerals — would rise steeply as they became more scarce.\n2\nThis claim got the attention of Julian Simon, who expected the opposite. Simon thought that human innovation and ingenuity would overcome resource shortages, and the price of resources would, therefore, not rise but fall. In the pages of the\nSocial Science Quarterly\n, he challenged Ehrlich to put money on the line.\nSimon offered Ehrlich the chance to choose any resource he wanted for the bet. Ehrlich chose five: chromium, copper, nickel, tin, and tungsten. The two bet $1,000 — $200 on each metal — on whether inflation-adjusted prices of these resources in September 1990 would be higher or lower than they were in September 1980.\n3\nIf prices had increased, Ehrlich would win. If they had fallen, victory would go to Simon.\nHere’s a more detailed explanation of how they determined the winner and payout:\n$200 was placed on each of the five metals individually. So, the total amount at stake was $1,000.\nThe 1980 price of each metal was taken as a baseline.\nThe change in the inflation-adjusted price of each metal was compared in 1990. For example, the price of copper fell by around 24% between 1980 and 1990.\nThey then multiplied the $200 for each metal by its respective price change. So, if the price of copper\nfell\nby 24%, Ehrlich owed $48; if it increased by 24%, Simon owed $48.\nThe loss or gain for each metal was then summed up to get the total. If the total basket of metals had increased in price, Ehrlich had to pay. If it decreased, Simon had to pay.\nThe $1,000 basket of metals in 1980 had dropped by $576, which is what Ehrlich owed Simon as the forfeit in the bet.\nShow more\nThe chart below shows us the change in the price of the five materials in 1990 compared to 1980. Note that this is based on the average prices that year, not necessarily the price in September of each year. But the final results of the bet are the same, regardless of whether you take annual average prices or those specifically in September.\nAll five had become cheaper, so Simon won the bet (and Ehrlich\ndid mail him\nthe check).\nThe cost of tungsten and tin was more than 60% lower. Copper was around 20% cheaper. Nickel and chromium were only slightly cheaper than a decade before. None of them had increased in price, contrary to Ehrlich’s prediction.\nThere has been lots of debate about whether Simon “got lucky” with this bet.\n4\nThe chart above shows that he got a bit lucky on nickel and chromium. If the bet had ended in 1989 rather than 1990, the price of these two materials would have been higher.\nBut a more general way to analyze this wager bet is to ask who would have won if it had occurred in any other decade of the 20th and early 21st centuries.\nI looked at inflation-adjusted price data — from the\nUnited States Geological Survey\n— for these five materials since 1900. I then calculated who would have won in each decade if they had made a bet on each of the five metals separately.\nIf the average annual price at the\nend\nof a given decade (for example, 1930) were lower than at the beginning (e.g., 1920), then Simon would win that decade. If the price was higher, Ehrlich did.\nHave a look at the chart to see the result. The price of all five elements varied quite a bit over time. Look at chromium at the very top, for example. In some decades, the price fell. In others, it went up. Whenever the price was higher at the end of the decade than at the beginning, Ehrlich would have won — those segments of the line I have colored red. Simon would have won when the price fell between the beginning and the end of a decade — the line segments colored blue.\nIt’s a pretty even split. Simon and Ehrlich would both have won around half the time. But as I’ll explain, I think the long-term data tells us a slightly different story: one that’s more in line with Simon’s worldview than Ehrlich’s.\nDownload\nWho would have won the Simon-Ehrlich wager in any decade since 1900?\n5\nThe Simon-Ehrlich bet was a poor test for their contrasting worldviews\nThis bet has come to represent a clash of worldviews: are we exhausting the planet of its resources, or can humans effectively respond to scarcity to ensure we don’t run out?\nUnderstanding which of these models of the world is more “correct” matters a lot for my work on sustainability. This motivated me to dig into the data on the Simon-Ehrlich wager in more detail.\nBut the more I reflected on it, the more it seemed that the original bet didn’t capture Simon and Ehrlich’s contrasting worldviews very well.\nLooking at mineral prices over just ten years doesn’t reflect either’s viewpoint.\nEhrlich’s core argument was that human demand would vastly outstrip the planet’s available resources.\n6\nHis argument is that prices will increase significantly over the long run as resources get increasingly scarce.\nSimon’s viewpoint was that this shouldn’t happen. As resources become scarcer — and prices increase to reflect this — human innovation and changes in supply should respond and bring prices back down.\nIn other words, both worldviews are about changes\nin the long run\n.\nSimon, in particular, was very specific about this in his\ninitial claim\nfor the wager: “to stake US$10,000... on my belief that the cost of non-government-controlled raw materials (including grain and oil) will not rise in the long run”. The emphasis is on “\nin the long run”\n.\nThe problem is that Simon and Ehrlich agreed to a bet about short-term changes.\nTheir decade-long wager was vulnerable to short-term fluctuations, which often had nothing to do with actual changes in physical supply and demand. Instead, they were influenced by geopolitical or temporary economic forces. That means either could have won depending on random year-to-year fluctuations that didn’t reflect their worldview.\nA more meaningful test for their arguments is the change in prices over the long run, which is a\nsignal\nof the trend rather than\nnoise\n.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nLong-term data brings me closer to Simon’s view of resource scarcity\nWith all that in mind, here’s why the long-run data makes me side more with Simon’s view than Ehrlich’s.\nLet’s look at the century-long price trends of the five materials again. They’re shown in the chart below.\nThe key takeaway for me is that, over the long run, prices didn’t change dramatically. A lot has changed since 1900, but the prices of the five metals are, surprisingly, not much different from what they were in 1900. Chromium is, perhaps, the one exception where average prices in the last few decades have been higher than they were in the early 20th century (although prices in 2020 were exactly the same as they were in 1900).\nOverall, material values have fluctuated up and down but around a reasonably consistent level. The time series are noisy, but the\nsignal\nis that prices have not changed much over more than a century.\nStudies stretching as far back as 1840 find that mineral prices — across many more than the five below — “have been basically trend-less” over the long term.\n7\nCrucially, this is despite the fact that the world produces\nmuch\nmore of these materials. The chart below shows the change in global production of each of the five materials since 1900.\nToday, the world produces 40 times as much copper annually and 250 times as much nickel as it did in 1900.\nThe fact that we produce far more materials than we did in the past, yet prices have barely changed, suggests that contrary to Ehrlich’s prediction, we’re not close to running out of these materials any time soon. That is what brings me closer to Simon’s worldview.\nAcknowledgments\nMany thanks to Max Roser, Simon van Teutem, Saloni Dattani, and Edouard Mathieu for their comments and feedback on this article.\nEndnotes\nThis is Paul R. Ehrlich, most famous for his 1968 book\nThe Population Bomb\n. It was not the Nobel-winning physician\nPaul Ehrlich\nwho died in 1915.\nEhrlich even went as far as suggesting that England would not exist in the year 2000. His exact\nquote was\n: “If I were a gambler, I would take even money that England will not exist in the year 2000.”\nThe bet was based on inflation-adjusted prices using the Consumer Price Index (CPI). In the following analysis, I use prices from the United States Geological Survey, which also uses the CPI to convert nominal prices into real prices.\nEmmett, R. B., & Grabowski, J. (2022). Better lucky than good: The Simon-Ehrlich bet through the lens of financial economics. Ecological Economics.\nA paper by Emmett and Grabowski gives an overview of the literature exploring who would have “won” the bet during different periods or with a different basket of goods.\nEmmett, R. B., & Grabowski, J. (2022). Better lucky than good: The Simon-Ehrlich bet through the lens of financial economics. Ecological Economics.\nOther analyses on this include:\nPooley, G., & Tupy, M. (2020). Luck or insight? The Simon–Ehrlich bet re‐examined. In Economic Affairs.\nKiel, K., Matheson, V., & Golembiewski, K. (2010). Luck or skill? An examination of the Ehrlich–Simon bet. In Ecological Economics.\nPerry, M. J. (2008). Would Julian Simon Have Won a Second Bet?. Available at:\nhttps://archive.vn/wip/AYSnC\nMcClintick, D., & Emmett, R. B. (2005). Betting on the Wealth of Nature. The Simon-Ehrlich wager. In PERC Reports.\nLiu, J., & Fitzpatrick, T. (2021). A 40th Anniversary Redux of the Simon and Ehrlich Bet. In Management.\nThis is based on the inflation-adjusted prices of the five metals in the Simon-Ehrlich bet. Price data comes from the\nUnited States Geological Survey\nand is adjusted for inflation using the Consumer Price Index (CPI). The CPI was the adjustment that was also used in the original wager.\nAnother key point is that much of Ehrlich’s work was focused on food supply. He predicted widespread food shortages and large-scale\nfamines\n(which did not come true; the\nfood supply per person\nhas increased since the 1980s). So it’s not obvious to me why he agreed to a bet on materials or why he chose those five ones in particular.\nStuermer, M. (2018). 150 years of boom and bust: what drives mineral commodity prices?. In Macroeconomic Dynamics.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2025) - “Who would have won the Simon-Ehrlich bet over different decades, and what do long-term prices tell us about resource scarcity?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-090244/simon-ehrlich-bet.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-simon-ehrlich-bet,\nauthor = {Hannah Ritchie},\ntitle = {Who would have won the Simon-Ehrlich bet over different decades, and what do long-term prices tell us about resource scarcity?},\njournal = {Our World in Data},\nyear = {2025},\nnote = {https://archive.ourworldindata.org/20260518-090244/simon-ehrlich-bet.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "simon-ehrlich-bet", "source_url": "https://ourworldindata.org/simon-ehrlich-bet", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "In the 1980s, economist Julian Simon won his bet with biologist Paul Ehrlich on mineral prices. 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"World", "Code": "OWID_WRL", "Year": "2011", "Chromium": "1970", "Copper": "6480", "Nickel": "16600", "Tungsten": "33900", "Tin": "25100"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Chromium": "1810", "Copper": "5750", "Nickel": "12400", "Tungsten": "40200", "Tin": "20100"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Chromium": "1570", "Copper": "5240", "Nickel": "10500", "Tungsten": "32600", "Tin": "16000"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Chromium": "1800", "Copper": "4830", "Nickel": "11600", "Tungsten": "31100", "Tin": "15500"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Chromium": "1470", "Copper": "3880", "Nickel": "8140", "Tungsten": "26000", "Tin": "11500"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Chromium": "1190", "Copper": "3370", "Nickel": "6520", "Tungsten": "17000", "Tin": "12600"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Chromium": "1680", "Copper": "4180", "Nickel": "6920", "Tungsten": "23300", "Tin": "13700"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Chromium": "1600", "Copper": "4280", "Nickel": "8510", "Tungsten": "29800", "Tin": "13400"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Chromium": "1280", "Copper": "3930", "Nickel": "7570", "Tungsten": "24700", "Tin": "12199.751"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Chromium": "1090", "Copper": "3980", "Nickel": "8670", "Tungsten": "", "Tin": "11100"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Chromium": "1440", "Copper": "", "Nickel": "", "Tungsten": "", "Tin": "21000"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Chromium": "2210", "Copper": "", "Nickel": "", "Tungsten": "", "Tin": ""}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "price-five-simon-ehrlich-materials", "metadata_url": "https://ourworldindata.org/grapher/price-five-simon-ehrlich-materials.metadata.json", "chart_title": "Prices of the five materials included in the Simon-Ehrlich wager", "chart_subtitle": "The change in the inflation-adjusted price of the five materials included in the wager between Julian Simon and Paul Ehrlich in the 1980s. Simon bet that prices would fall or stay constant between 1980 and 1990; Ehrlich bet that they would increase. Simon won the bet.", "chart_note": "Prices are measured per tonne in 1998 dollars, which adjusts for inflation using the Consumer Price Index (CPI).", "chart_citation": "USGS - Historical Statistics for Mineral and Material Commodities (2024)", "original_chart_url": "https://ourworldindata.org/grapher/price-five-simon-ehrlich-materials", "owid_column_metadata": {"unit value|Chromium|Mine|constant 1998 US$ per tonne": {"titleShort": "Chromium unit value", "titleLong": "Chromium unit value", "descriptionShort": "Value of 1 tonne of chromium, in constant 1998 US$ per tonne.", "descriptionProcessing": "- The majority of the data is sourced from USGS, supplemented by BGS data where available. Where both overlap, USGS data is prioritized.\n- As BGS does not provide global data, we calculated the world total by summing the data from individual countries, using this as a cross-check against USGS global figures.\n- Due to the inherent uncertainties in the data for certain minerals and countries, we allowed a maximum deviation of 10% between the global totals reported by USGS and the calculated ones for BGS. If the deviation exceeded this threshold, we excluded the BGS data.\n- The calculated global total from BGS data was used only on exceptional occasions, after ensuring that the resulting aggregate was sufficiently complete.\n- Both BGS and USGS datasets include numerous notes and footnotes. We have retained most of these, making only minor edits or deletions where necessary to maintain clarity.", "shortUnit": "$/t", "unit": "constant 1998 US$ per tonne", "timespan": "1900-2022", "type": "Integer", "owidVariableId": 1131231, "shortName": "unit_value_chromium_mine_constant_1998_usd_per_tonne", "lastUpdated": "2025-12-15", "nextUpdate": "2026-12-15", "citationShort": "USGS - Historical Statistics for Mineral and Material Commodities (2024) – with major processing by Our World in Data", "citationLong": "USGS - Historical Statistics for Mineral and Material Commodities (2024) – with major processing by Our World in Data. “Chromium unit value” [dataset]. United States Geological Survey, “Historical Statistics for Mineral and Material Commodities” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1131231.metadata.json"}, "unit value|Copper|Mine|constant 1998 US$ per tonne": {"titleShort": "Copper unit value", "titleLong": "Copper unit value", "descriptionShort": "Value of 1 tonne of copper, in constant 1998 US$ per tonne.", "descriptionKey": ["Unit value represents the price of a physical unit of apparent consumption (such as a metric ton) in dollars. Apparent consumption itself is determined by the formula: Apparent Consumption = Production + Imports - Exports ± (Stock Change).", "For commodities that exist in a single physical form, like copper, a straightforward price series can estimate the unit value.", "However, for commodities like chromium, which encompass multiple forms (e.g., metal, ferroalloys, ore), weighted averages are used, with each form's price weighted by its contribution to total consumption.", "When no direct price series is available, unit values are derived using export, import, and production data.", "To ensure consistency over time, these values are reported in 1998 constant dollars, adjusted for inflation."], "descriptionProcessing": "- The majority of the data is sourced from USGS, supplemented by BGS data where available. Where both overlap, USGS data is prioritized.\n- As BGS does not provide global data, we calculated the world total by summing the data from individual countries, using this as a cross-check against USGS global figures.\n- Due to the inherent uncertainties in the data for certain minerals and countries, we allowed a maximum deviation of 10% between the global totals reported by USGS and the calculated ones for BGS. If the deviation exceeded this threshold, we excluded the BGS data.\n- The calculated global total from BGS data was used only on exceptional occasions, after ensuring that the resulting aggregate was sufficiently complete.\n- Both BGS and USGS datasets include numerous notes and footnotes. We have retained most of these, making only minor edits or deletions where necessary to maintain clarity.", "shortUnit": "$/t", "unit": "constant 1998 US$ per tonne", "timespan": "1900-2020", "type": "Integer", "owidVariableId": 1131233, "shortName": "unit_value_copper_mine_constant_1998_usd_per_tonne", "lastUpdated": "2025-12-15", "nextUpdate": "2026-12-15", "citationShort": "USGS - Historical Statistics for Mineral and Material Commodities (2024) – with major processing by Our World in Data", "citationLong": "USGS - Historical Statistics for Mineral and Material Commodities (2024) – with major processing by Our World in Data. “Copper unit value” [dataset]. United States Geological Survey, “Historical Statistics for Mineral and Material Commodities” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1131233.metadata.json"}, "unit value|Nickel|Mine|constant 1998 US$ per tonne": {"titleShort": "Nickel unit value", "titleLong": "Nickel unit value", "descriptionShort": "Value of 1 tonne of nickel, in constant 1998 US$ per tonne.", "descriptionKey": ["Unit value represents the price of a physical unit of apparent consumption (such as a metric ton) in dollars. Apparent consumption itself is determined by the formula: Apparent Consumption = Production + Imports - Exports ± (Stock Change).", "For commodities that exist in a single physical form, like copper, a straightforward price series can estimate the unit value.", "However, for commodities like chromium, which encompass multiple forms (e.g., metal, ferroalloys, ore), weighted averages are used, with each form's price weighted by its contribution to total consumption.", "When no direct price series is available, unit values are derived using export, import, and production data.", "To ensure consistency over time, these values are reported in 1998 constant dollars, adjusted for inflation."], "descriptionProcessing": "- The majority of the data is sourced from USGS, supplemented by BGS data where available. Where both overlap, USGS data is prioritized.\n- As BGS does not provide global data, we calculated the world total by summing the data from individual countries, using this as a cross-check against USGS global figures.\n- Due to the inherent uncertainties in the data for certain minerals and countries, we allowed a maximum deviation of 10% between the global totals reported by USGS and the calculated ones for BGS. If the deviation exceeded this threshold, we excluded the BGS data.\n- The calculated global total from BGS data was used only on exceptional occasions, after ensuring that the resulting aggregate was sufficiently complete.\n- Both BGS and USGS datasets include numerous notes and footnotes. We have retained most of these, making only minor edits or deletions where necessary to maintain clarity.", "shortUnit": "$/t", "unit": "constant 1998 US$ per tonne", "timespan": "1900-2020", "type": "Integer", "owidVariableId": 1131258, "shortName": "unit_value_nickel_mine_constant_1998_usd_per_tonne", "lastUpdated": "2025-12-15", "nextUpdate": "2026-12-15", "citationShort": "USGS - Historical Statistics for Mineral and Material Commodities (2024) – with major processing by Our World in Data", "citationLong": "USGS - Historical Statistics for Mineral and Material Commodities (2024) – with major processing by Our World in Data. “Nickel unit value” [dataset]. United States Geological Survey, “Historical Statistics for Mineral and Material Commodities” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1131258.metadata.json"}, "unit value|Tungsten|Mine|constant 1998 US$ per tonne": {"titleShort": "Tungsten unit value", "titleLong": "Tungsten unit value", "descriptionShort": "Value of 1 tonne of tungsten, in constant 1998 US$ per tonne.", "descriptionKey": ["Unit value represents the price of a physical unit of apparent consumption (such as a metric ton) in dollars. Apparent consumption itself is determined by the formula: Apparent Consumption = Production + Imports - Exports ± (Stock Change).", "For commodities that exist in a single physical form, like copper, a straightforward price series can estimate the unit value.", "However, for commodities like chromium, which encompass multiple forms (e.g., metal, ferroalloys, ore), weighted averages are used, with each form's price weighted by its contribution to total consumption.", "When no direct price series is available, unit values are derived using export, import, and production data.", "To ensure consistency over time, these values are reported in 1998 constant dollars, adjusted for inflation."], "descriptionProcessing": "- The majority of the data is sourced from USGS, supplemented by BGS data where available. Where both overlap, USGS data is prioritized.\n- As BGS does not provide global data, we calculated the world total by summing the data from individual countries, using this as a cross-check against USGS global figures.\n- Due to the inherent uncertainties in the data for certain minerals and countries, we allowed a maximum deviation of 10% between the global totals reported by USGS and the calculated ones for BGS. If the deviation exceeded this threshold, we excluded the BGS data.\n- The calculated global total from BGS data was used only on exceptional occasions, after ensuring that the resulting aggregate was sufficiently complete.\n- Both BGS and USGS datasets include numerous notes and footnotes. We have retained most of these, making only minor edits or deletions where necessary to maintain clarity.", "shortUnit": "$/t", "unit": "constant 1998 US$ per tonne", "timespan": "1900-2019", "type": "Integer", "owidVariableId": 1131277, "shortName": "unit_value_tungsten_mine_constant_1998_usd_per_tonne", "lastUpdated": "2025-12-15", "nextUpdate": "2026-12-15", "citationShort": "USGS - Historical Statistics for Mineral and Material Commodities (2024) – with major processing by Our World in Data", "citationLong": "USGS - Historical Statistics for Mineral and Material Commodities (2024) – with major processing by Our World in Data. “Tungsten unit value” [dataset]. United States Geological Survey, “Historical Statistics for Mineral and Material Commodities” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1131277.metadata.json"}, "unit value|Tin|Mine|constant 1998 US$ per tonne": {"titleShort": "Tin unit value", "titleLong": "Tin unit value", "descriptionShort": "Value of 1 tonne of tin, in constant 1998 US$ per tonne.", "descriptionKey": ["Unit value represents the price of a physical unit of apparent consumption (such as a metric ton) in dollars. Apparent consumption itself is determined by the formula: Apparent Consumption = Production + Imports - Exports ± (Stock Change).", "For commodities that exist in a single physical form, like copper, a straightforward price series can estimate the unit value.", "However, for commodities like chromium, which encompass multiple forms (e.g., metal, ferroalloys, ore), weighted averages are used, with each form's price weighted by its contribution to total consumption.", "When no direct price series is available, unit values are derived using export, import, and production data.", "To ensure consistency over time, these values are reported in 1998 constant dollars, adjusted for inflation."], "descriptionProcessing": "- The majority of the data is sourced from USGS, supplemented by BGS data where available. Where both overlap, USGS data is prioritized.\n- As BGS does not provide global data, we calculated the world total by summing the data from individual countries, using this as a cross-check against USGS global figures.\n- Due to the inherent uncertainties in the data for certain minerals and countries, we allowed a maximum deviation of 10% between the global totals reported by USGS and the calculated ones for BGS. If the deviation exceeded this threshold, we excluded the BGS data.\n- The calculated global total from BGS data was used only on exceptional occasions, after ensuring that the resulting aggregate was sufficiently complete.\n- Both BGS and USGS datasets include numerous notes and footnotes. We have retained most of these, making only minor edits or deletions where necessary to maintain clarity.", "shortUnit": "$/t", "unit": "constant 1998 US$ per tonne", "timespan": "1900-2021", "type": "Numeric", "owidVariableId": 1131276, "shortName": "unit_value_tin_mine_constant_1998_usd_per_tonne", "lastUpdated": "2025-12-15", "nextUpdate": "2026-12-15", "citationShort": "USGS - Historical Statistics for Mineral and Material Commodities (2024) – with major processing by Our World in Data", "citationLong": "USGS - Historical Statistics for Mineral and Material Commodities (2024) – with major processing by Our World in Data. “Tin unit value” [dataset]. United States Geological Survey, “Historical Statistics for Mineral and Material Commodities” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1131276.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Daily supply of calories per person", "source_url": "https://ourworldindata.org/grapher/daily-per-capita-caloric-supply.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Daily calorie supply per person"], "row_count_total": 13265, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Daily calorie supply per person": "2914.3484"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Daily calorie supply per person": "2835.6074"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Daily calorie supply per person": "2623.7017"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Daily calorie supply per person": "2872.5679"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Daily calorie supply per person": "2876.179"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Daily calorie supply per person": "2663.3132"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Daily calorie supply per person": "2890.6982"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Daily calorie supply per person": "2839.0818"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1969", "Daily calorie supply per person": "2858.1667"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1970", "Daily calorie supply per person": "2472.4724"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1971", "Daily calorie supply per person": "2458.8677"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1972", "Daily calorie supply per person": "2610.9785"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Daily calorie supply per person": "2678.0298"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Daily calorie supply per person": "2670.5447"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Daily calorie supply per person": "2704.18"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Daily calorie supply per person": "2769.3225"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Daily calorie supply per person": "2431.7678"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Daily calorie supply per person": "2541.773"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Daily calorie supply per person": "2544.499"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Daily calorie supply per person": "2480.011"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Daily calorie supply per person": "2697.5786"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Daily calorie supply per person": "2850.6265"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Daily calorie supply per person": "2830.0835"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Daily calorie supply per person": "2597.447"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Daily calorie supply per person": "2424.4805"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Daily calorie supply per person": "2363.8213"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Daily calorie supply per person": "2649.216"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Daily calorie supply per person": "2406.4927"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Daily calorie supply per person": "2325.343"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Daily calorie supply per person": "2253.5522"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Daily calorie supply per person": "2106.3145"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Daily calorie supply per person": "1966.9045"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Daily calorie supply per person": "1939.6313"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Daily calorie supply per person": "1846.2291"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Daily calorie supply per person": "1900.2047"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Daily calorie supply per person": "1910.6196"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Daily calorie supply per person": "1931.7776"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Daily calorie supply per person": "1936.4718"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Daily calorie supply per person": "1861.2385"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Daily calorie supply per person": "1831.3184"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Daily calorie supply per person": "1828.0873"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Daily calorie supply per person": "1896.4564"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Daily calorie supply per person": "1923.8715"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Daily calorie supply per person": "2005.2656"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Daily calorie supply per person": "1984.4324"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Daily calorie supply per person": "1981.9991"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Daily calorie supply per person": "2080.6777"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Daily calorie supply per person": "2083.3396"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Daily calorie supply per person": "2099.3276"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Daily calorie supply per person": "2200.2102"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Daily calorie supply per person": "2171.8604"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Daily calorie supply per person": "2165.8794"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Daily calorie supply per person": "2205.3293"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Daily calorie supply per person": "2270.4495"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Daily calorie supply per person": "2251.2698"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Daily calorie supply per person": "2240.7097"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Daily calorie supply per person": "2307.2092"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Daily calorie supply per person": "2261.79"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Daily calorie supply per person": "2223.0999"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Daily calorie supply per person": "2259.9502"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Daily calorie supply per person": "2244.73"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Daily calorie supply per person": "2268.6294"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Daily calorie supply per person": "2314.63"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1961", "Daily calorie supply per person": "1995.521"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1962", "Daily calorie supply per person": "2028.8373"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1963", "Daily calorie supply per person": "2032.6653"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1964", "Daily calorie supply per person": "2043.507"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1965", "Daily calorie supply per person": "2049.4136"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1966", "Daily calorie supply per person": "2017.5956"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1967", "Daily calorie supply per person": "2050.2302"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1968", "Daily calorie supply per person": "2061.8005"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1969", "Daily calorie supply per person": "2094.1606"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1970", "Daily calorie supply per person": "2113.8118"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1971", "Daily calorie supply per person": "2095.787"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1972", "Daily calorie supply per person": "2057.6436"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1973", "Daily calorie supply per person": "2086.5698"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1974", "Daily calorie supply per person": "2124.3787"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1975", "Daily calorie supply per person": "2128.454"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1976", "Daily calorie supply per person": "2122.453"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1977", "Daily calorie supply per person": "2133.7434"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1978", "Daily calorie supply per person": "2157.3196"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1979", "Daily calorie supply per person": "2183.377"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1980", "Daily calorie supply per person": "2216.1858"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1981", "Daily calorie supply per person": "2220.0317"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1982", "Daily calorie supply per person": "2221.7766"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1983", "Daily calorie supply per person": "2184.5286"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1984", "Daily calorie supply per person": "2157.767"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Daily calorie supply per person": "2209.3574"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Daily calorie supply per person": "2243.4407"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Daily calorie supply per person": "2228.6338"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Daily calorie supply per person": "2247.1235"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Daily calorie supply per person": "2247.751"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Daily calorie supply per person": "2258.3152"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Daily calorie supply per person": "2283.9727"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Daily calorie supply per person": "2279.2983"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Daily calorie supply per person": "2288.8423"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Daily calorie supply per person": "2305.432"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Daily calorie supply per person": "2327.7705"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1996", "Daily calorie supply per person": "2338.3477"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1997", "Daily calorie supply per person": "2348.1433"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1998", "Daily calorie supply per person": "2366.1533"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1999", "Daily calorie supply per person": "2374.2822"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Daily calorie supply per person": "2372.997"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Daily calorie supply per person": "2393.8328"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Daily calorie supply per person": "2405.6643"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Daily calorie supply per person": "2409.091"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Daily calorie supply per person": "2418.1616"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Daily calorie supply per person": "2446.1987"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Daily calorie supply per person": "2463.3271"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Daily calorie supply per person": "2464.1948"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Daily calorie supply per person": "2477.2864"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Daily calorie supply per person": "2482.5527"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Daily calorie supply per person": "2537.1067"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Daily calorie supply per person": "2537.0596"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Daily calorie supply per person": "2550.78"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Daily calorie supply per person": "2548.8538"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Daily calorie supply per person": "2562.5964"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Daily calorie supply per person": "2558.8735"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Daily calorie supply per person": "2546.445"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Daily calorie supply per person": "2560.6023"}], "rows_tail": [{"Entity": "Zambia", "Code": "ZMB", "Year": "1967", "Daily calorie supply per person": "2129.2334"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1968", "Daily calorie supply per person": "2156.04"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1969", "Daily calorie supply per person": "2171.9858"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1970", "Daily calorie supply per person": "2170.3037"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1971", "Daily calorie supply per person": "2205.2563"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1972", "Daily calorie supply per person": "2223.1968"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1973", "Daily calorie supply per person": "2278.222"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1974", "Daily calorie supply per person": "2293.095"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1975", "Daily calorie supply per person": "2311.8506"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1976", "Daily calorie supply per person": "2334.6106"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1977", "Daily calorie supply per person": "2416.6938"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1978", "Daily calorie supply per person": "2293.1182"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1979", "Daily calorie supply per person": "2300.895"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1980", "Daily calorie supply per person": "2285.2593"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1981", "Daily calorie supply per person": "2256.816"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1982", "Daily calorie supply per person": "2157.503"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1983", "Daily calorie supply per person": "2153.0525"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1984", "Daily calorie supply per person": "2136.186"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Daily calorie supply per person": "2038.0023"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Daily calorie supply per person": "2059.9521"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Daily calorie supply per person": "2044.8807"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Daily calorie supply per person": "2078.2253"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Daily calorie supply per person": "2089.7886"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Daily calorie supply per person": "2095.7178"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Daily calorie supply per person": "2020.1615"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Daily calorie supply per person": "1995.6893"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Daily calorie supply per person": "1992.6107"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Daily calorie supply per person": "2033.4889"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Daily calorie supply per person": "2062.8875"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Daily calorie supply per person": "2049.5737"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Daily calorie supply per person": "1987.6879"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Daily calorie supply per person": "1949.2516"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Daily calorie supply per person": "1977.899"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Daily calorie supply per person": "1888.5872"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Daily calorie supply per person": "1856.5961"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Daily calorie supply per person": "1845.0139"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Daily calorie supply per person": "1884.6729"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Daily calorie supply per person": "1839.141"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Daily calorie supply per person": "1833.2317"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Daily calorie supply per person": "1785.334"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Daily calorie supply per person": "1716.4943"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Daily calorie supply per person": "1722.947"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Daily calorie supply per person": "1785.3702"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Daily calorie supply per person": "1925.2898"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Daily calorie supply per person": "1961.3605"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Daily calorie supply per person": "1990.8298"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Daily calorie supply per person": "2026.24"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Daily calorie supply per person": "2079.14"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Daily calorie supply per person": "2105.6099"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Daily calorie supply per person": "2166.629"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Daily calorie supply per person": "2214.599"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Daily calorie supply per person": "2244.1594"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Daily calorie supply per person": "2226"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Daily calorie supply per person": "2206.9006"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Daily calorie supply per person": "2183.79"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Daily calorie supply per person": "2119.1804"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Daily calorie supply per person": "2169.7705"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1961", "Daily calorie supply per person": "2086.2637"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1962", "Daily calorie supply per person": "2134.3772"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1963", "Daily calorie supply per person": "2124.9722"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1964", "Daily calorie supply per person": "2119.1924"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1965", "Daily calorie supply per person": "2128.4521"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "Daily calorie supply per person": "2134.476"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "Daily calorie supply per person": "2243.0686"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "Daily calorie supply per person": "2260.736"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "Daily calorie supply per person": "2213.25"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "Daily calorie supply per person": "2283.649"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "Daily calorie supply per person": "2315.4207"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "Daily calorie supply per person": "2345.862"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "Daily calorie supply per person": "2336.2747"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "Daily calorie supply per person": "2341.4075"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Daily calorie supply per person": "2290.486"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Daily calorie supply per person": "2322.6636"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Daily calorie supply per person": "2349.9502"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Daily calorie supply per person": "2372.4573"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Daily calorie supply per person": "2384.0146"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Daily calorie supply per person": "2375.7324"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Daily calorie supply per person": "2243.7378"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Daily calorie supply per person": "2246.9622"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Daily calorie supply per person": "2209.846"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Daily calorie supply per person": "2166.6816"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Daily calorie supply per person": "2128.6077"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Daily calorie supply per person": "2103.3682"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Daily calorie supply per person": "2068.6223"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Daily calorie supply per person": "2108.979"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Daily calorie supply per person": "2112.8008"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Daily calorie supply per person": "2077.4795"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Daily calorie supply per person": "2024.9153"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Daily calorie supply per person": "2005.836"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Daily calorie supply per person": "2005.746"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Daily calorie supply per person": "2029.7161"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Daily calorie supply per person": "2061.688"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Daily calorie supply per person": "2104.141"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Daily calorie supply per person": "2128.4758"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Daily calorie supply per person": "2123.3704"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Daily calorie supply per person": "2097.896"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Daily calorie supply per person": "2080.836"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Daily calorie supply per person": "2132.195"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Daily calorie supply per person": "2113.5156"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Daily calorie supply per person": "2082.4114"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Daily calorie supply per person": "2098.0671"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Daily calorie supply per person": "2061.9148"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Daily calorie supply per person": "2129.6548"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Daily calorie supply per person": "2098.4482"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Daily calorie supply per person": "2065.6978"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Daily calorie supply per person": "2105.5408"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Daily calorie supply per person": "2156.7297"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Daily calorie supply per person": "2154.4297"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Daily calorie supply per person": "2189.8005"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Daily calorie supply per person": "2176.4497"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Daily calorie supply per person": "2072.769"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Daily calorie supply per person": "2075.4702"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Daily calorie supply per person": "2140.749"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Daily calorie supply per person": "2180.8296"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Daily calorie supply per person": "2204.989"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Daily calorie supply per person": "2144.169"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Daily calorie supply per person": "2240.2004"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Daily calorie supply per person": "2310.1785"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Daily calorie supply per person": "2319.3997"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Daily calorie supply per person": "2393.3787"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "daily-per-capita-caloric-supply", "metadata_url": "https://ourworldindata.org/grapher/daily-per-capita-caloric-supply.metadata.json", "chart_title": "Daily supply of calories per person", "chart_subtitle": "Measured in kilocalories per person per day. This indicates the calories that are available for consumption, but does not necessarily measure the number of calories actually consumed, since it doesn't factor in consumer waste.", "chart_note": null, "chart_citation": "Food and Agriculture Organization of the United Nations (2025) and other sources", "original_chart_url": "https://ourworldindata.org/grapher/daily-per-capita-caloric-supply", "owid_column_metadata": {"Daily calorie supply per person": {"titleShort": "Daily calorie supply per person", "titleLong": "Daily calorie supply per person", "descriptionKey": ["This data shows per capita daily calorie supply, which is the amount of calories available to an average person, and does not necessarily correspond to the calories actually consumed by that person.", "Calorie supply is always larger than actual calorie consumption, since there may be waste at the household level.", "For historical data, daily calorie supply and consumption are sometimes used interchangeably, due to poor data availability.", "This data does not give a complete picture of nutrition - for a healthy diet [we need much more](https://ourworldindata.org/micronutrient-deficiency) than just energy. But as the most basic criteria of food security, getting enough calories is an important measure. It is used as input for the most important metrics used to assess global malnutrition: [undernourishment](https://ourworldindata.org/undernourishment-definition)."], "descriptionProcessing": "- For all countries, the data after 1960 is taken from FAOSTAT Food Balances datasets ([old](https://www.fao.org/faostat/en/#data/FBSH) and [new](https://www.fao.org/faostat/en/#data/FBS) methodologies combined).\n- For the UK: We load Appendix Table from [Harris et al. (2015)](https://www.emerald.com/insight/content/doi/10.1108/S0363-326820150000031003/full/html). From that table, we select values from [Broadberry et al. (2015)](https://www.cambridge.org/core/books/british-economic-growth-12701870/A270234C137117C8E0F1D1E7E6F0DA56) and the corrected values from [Floud et al (2011)](https://www.cambridge.org/core/books/changing-body/DE3BB0E3577205AC26823CF2120D8B7E) (taking the average value of Estimates (A) and (B)).\n- For the US: For years 1800-1900, we use Table 6.6 of [Floud et al. (2011)](https://www.cambridge.org/core/books/changing-body/DE3BB0E3577205AC26823CF2120D8B7E). For years 1900-1960, we use [the archived table of food supply from USDA](https://www.ers.usda.gov/webdocs/DataFiles/50472/nutrients.xls?v=6096.1).\n- For Iceland: We use Table 5 of [Jonsson (1994)](https://www.tandfonline.com/doi/abs/10.1080/03585522.1998.10414677).\n- For Finland, Germany, Italy, Norway: We use Table 1 from [Grigg (1995)](https://www.sciencedirect.com/science/article/abs/pii/S0305748885700187), which is a compilation of many sources.\n- For France: We use Table 1 from Grigg (1995).\n  - We include the two additional data points (1705 and 1785) from [Fogel (2004)](https://www.cambridge.org/core/books/escape-from-hunger-and-premature-death-17002100/384C6032DE4E73C90EF6C9D1E55009CA).\n- For Belgium and Netherlands: We use Table 5.5 of Floud et al. (2011).\n- For Uganda, Cambodia, China, India, Brazil, Mexico, and Peru for 1936 and 1947: We use Table 11 of [FAO (2000)](https://www.fao.org/4/x4400e/x4400e.pdf) (The State of Food and Agriculture).\n- For many countries (including some of the above) for 1947 and 1948: We use values from Table 15 from [FAO (1949)](https://www.fao.org/4/ap637e/ap637e.pdf).\n- Note that prior to 1961, data for the UK may correspond to England, or England and Wales; and Tanzania refers to Tanganyika.", "shortUnit": "kcal", "unit": "kilocalories per day", "timespan": "1274-2023", "type": "Numeric", "owidVariableId": 1205780, "shortName": "daily_calories", "lastUpdated": "2026-03-02", "nextUpdate": "2027-03-02", "citationShort": "Food and Agriculture Organization of the United Nations (2025) and other sources – with major processing by Our World in Data", "citationLong": "Food and Agriculture Organization of the United Nations (2025); Harris et al. (2015); Floud et al. (2011); Jonsson (1998); Grigg (1995); Fogel (2004); Food and Agriculture Organization of the United Nations (2000); Food and Agriculture Organization of the United Nations (1949); USDA Economic Research Service (ERS) (2015) – with major processing by Our World in Data. “Daily calorie supply per person” [dataset]. Food and Agriculture Organization of the United Nations, “Food Balances: Food Balances (-2013, old methodology and population)”; Food and Agriculture Organization of the United Nations, “Food Balances: Food Balances (2010-)”; Harris et al., “How Many Calories? Food Availability in England and Wales in the Eighteenth and Nineteenth Centuries”; Floud et al., “The Changing Body”; Jonsson, “Changes in food consumption in Iceland, 1770-1940”; Grigg, “The nutritional transition in Western Europe”; Fogel, “The Escape from Hunger and Premature Death”; Food and Agriculture Organization of the United Nations, “The State of Food and Agriculture 2000”; Food and Agriculture Organization of the United Nations, “The State of Food and Agriculture 1949”; USDA Economic Research Service (ERS), “U.S. food supply:  Nutrients and other food components, per capita per day” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205780.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Global mine production of minerals", "source_url": "https://ourworldindata.org/grapher/global-mine-production-minerals.csv", "file_type": "csv", "columns": ["Entity", "Year", "Global mine production of different minerals"], "row_count_total": 4659, "rows_head": [{"Entity": "Antimony", "Year": "1900", "Global mine production of different minerals": "7710"}, {"Entity": "Antimony", "Year": "1901", "Global mine production of different minerals": "7890"}, {"Entity": "Antimony", "Year": "1902", "Global mine production of different minerals": "8550"}, {"Entity": "Antimony", "Year": "1903", "Global mine production of different minerals": "8140"}, {"Entity": "Antimony", "Year": "1904", "Global mine production of different minerals": "8000"}, {"Entity": "Antimony", "Year": "1905", "Global mine production of different minerals": "8000"}, {"Entity": "Antimony", "Year": "1906", "Global mine production of different minerals": "14500"}, {"Entity": "Antimony", "Year": "1907", "Global mine production of different minerals": "15000"}, {"Entity": "Antimony", "Year": "1908", "Global mine production of different minerals": "16000"}, {"Entity": "Antimony", "Year": "1909", "Global mine production of different minerals": "15000"}, {"Entity": "Antimony", "Year": "1910", "Global mine production of different minerals": "15000"}, {"Entity": "Antimony", "Year": "1911", "Global mine production of different minerals": "15500"}, {"Entity": "Antimony", "Year": "1912", "Global mine production of different minerals": "24200"}, {"Entity": "Antimony", "Year": "1913", "Global mine production of different minerals": "24500"}, {"Entity": "Antimony", "Year": "1914", "Global mine production of different minerals": "23600"}, {"Entity": "Antimony", "Year": "1915", "Global mine production of different minerals": "43200"}, {"Entity": "Antimony", "Year": "1916", "Global mine production of different minerals": "81600"}, {"Entity": "Antimony", "Year": "1917", "Global mine production of different minerals": "57200"}, {"Entity": "Antimony", "Year": "1918", "Global mine production of different minerals": "30800"}, {"Entity": "Antimony", "Year": "1919", "Global mine production of different minerals": "11800"}, {"Entity": "Antimony", "Year": "1920", "Global mine production of different minerals": "29000"}, {"Entity": "Antimony", "Year": "1921", "Global mine production of different minerals": "18300"}, {"Entity": "Antimony", "Year": "1922", "Global mine production of different minerals": "18900"}, {"Entity": "Antimony", "Year": "1923", "Global mine production of different minerals": "17600"}, {"Entity": "Antimony", "Year": "1924", "Global mine production of different minerals": "17500"}, {"Entity": "Antimony", "Year": "1925", "Global mine production of different minerals": "25500"}, {"Entity": "Antimony", "Year": "1926", "Global mine production of different minerals": "29000"}, {"Entity": "Antimony", "Year": "1927", "Global mine production of different minerals": "28000"}, {"Entity": "Antimony", "Year": "1928", "Global mine production of different minerals": "28500"}, {"Entity": "Antimony", "Year": "1929", "Global mine production of different minerals": "31600"}, {"Entity": "Antimony", "Year": "1930", "Global mine production of different minerals": "23600"}, {"Entity": "Antimony", "Year": "1931", "Global mine production of different minerals": "15600"}, {"Entity": "Antimony", "Year": "1932", "Global mine production of different minerals": "17300"}, {"Entity": "Antimony", "Year": "1933", "Global mine production of different minerals": "20200"}, {"Entity": "Antimony", "Year": "1934", "Global mine production of different minerals": "22600"}, {"Entity": "Antimony", "Year": "1935", "Global mine production of different minerals": "29800"}, {"Entity": "Antimony", "Year": "1936", "Global mine production of different minerals": "35300"}, {"Entity": "Antimony", "Year": "1937", "Global mine production of different minerals": "38600"}, {"Entity": "Antimony", "Year": "1938", "Global mine production of different minerals": "33900"}, {"Entity": "Antimony", "Year": "1939", "Global mine production of different minerals": "38800"}, {"Entity": "Antimony", "Year": "1940", "Global mine production of different minerals": "46300"}, {"Entity": "Antimony", "Year": "1941", "Global mine production of different minerals": "49000"}, {"Entity": "Antimony", "Year": "1942", "Global mine production of different minerals": "51400"}, {"Entity": "Antimony", "Year": "1943", "Global mine production of different minerals": "53200"}, {"Entity": "Antimony", "Year": "1944", "Global mine production of different minerals": "36000"}, {"Entity": "Antimony", "Year": "1945", "Global mine production of different minerals": "27000"}, {"Entity": "Antimony", "Year": "1946", "Global mine production of different minerals": "26000"}, {"Entity": "Antimony", "Year": "1947", "Global mine production of different minerals": "38000"}, {"Entity": "Antimony", "Year": "1948", "Global mine production of different minerals": "45000"}, {"Entity": "Antimony", "Year": "1949", "Global mine production of different minerals": "37000"}, {"Entity": "Antimony", "Year": "1950", "Global mine production of different minerals": "50000"}, {"Entity": "Antimony", "Year": "1951", "Global mine production of different minerals": "65000"}, {"Entity": "Antimony", "Year": "1952", "Global mine production of different minerals": "44500"}, {"Entity": "Antimony", "Year": "1953", "Global mine production of different minerals": "33600"}, {"Entity": "Antimony", "Year": "1954", "Global mine production of different minerals": "39900"}, {"Entity": "Antimony", "Year": "1955", "Global mine production of different minerals": "46300"}, {"Entity": "Antimony", "Year": "1956", "Global mine production of different minerals": "53500"}, {"Entity": "Antimony", "Year": "1957", "Global mine production of different minerals": "50800"}, {"Entity": "Antimony", "Year": "1958", "Global mine production of different minerals": "46300"}, {"Entity": "Antimony", "Year": "1959", "Global mine production of different minerals": "53300"}, {"Entity": "Antimony", "Year": "1960", "Global mine production of different minerals": "53300"}, {"Entity": "Antimony", "Year": "1961", "Global mine production of different minerals": "51900"}, {"Entity": "Antimony", "Year": "1962", "Global mine production of different minerals": "53700"}, {"Entity": "Antimony", "Year": "1963", "Global mine production of different minerals": "58000"}, {"Entity": "Antimony", "Year": "1964", "Global mine production of different minerals": "63000"}, {"Entity": "Antimony", "Year": "1965", "Global mine production of different minerals": "63000"}, {"Entity": "Antimony", "Year": "1966", "Global mine production of different minerals": "61400"}, {"Entity": "Antimony", "Year": "1967", "Global mine production of different minerals": "58400"}, {"Entity": "Antimony", "Year": "1968", "Global mine production of different minerals": "61500"}, {"Entity": "Antimony", "Year": "1969", "Global mine production of different minerals": "66200"}, {"Entity": "Antimony", "Year": "1970", "Global mine production of different minerals": "70000"}, {"Entity": "Antimony", "Year": "1971", "Global mine production of different minerals": "64100"}, {"Entity": "Antimony", "Year": "1972", "Global mine production of different minerals": "68100"}, {"Entity": "Antimony", "Year": "1973", "Global mine production of different minerals": "69300"}, {"Entity": "Antimony", "Year": "1974", "Global mine production of different minerals": "70500"}, {"Entity": "Antimony", "Year": "1975", "Global mine production of different minerals": "67900"}, {"Entity": "Antimony", "Year": "1976", "Global mine production of different minerals": "69200"}, {"Entity": "Antimony", "Year": "1977", "Global mine production of different minerals": "72200"}, {"Entity": "Antimony", "Year": "1978", "Global mine production of different minerals": "68800"}, {"Entity": "Antimony", "Year": "1979", "Global mine production of different minerals": "71900"}, {"Entity": "Antimony", "Year": "1980", "Global mine production of different minerals": "67200"}, {"Entity": "Antimony", "Year": "1981", "Global mine production of different minerals": "59200"}, {"Entity": "Antimony", "Year": "1982", "Global mine production of different minerals": "53800"}, {"Entity": "Antimony", "Year": "1983", "Global mine production of different minerals": "48400"}, {"Entity": "Antimony", "Year": "1984", "Global mine production of different minerals": "53400"}, {"Entity": "Antimony", "Year": "1985", "Global mine production of different minerals": "55000"}, {"Entity": "Antimony", "Year": "1986", "Global mine production of different minerals": "59900"}, {"Entity": "Antimony", "Year": "1987", "Global mine production of different minerals": "56100"}, {"Entity": "Antimony", "Year": "1988", "Global mine production of different minerals": "64400"}, {"Entity": "Antimony", "Year": "1989", "Global mine production of different minerals": "68400"}, {"Entity": "Antimony", "Year": "1990", "Global mine production of different minerals": "60400"}, {"Entity": "Antimony", "Year": "1991", "Global mine production of different minerals": "64700"}, {"Entity": "Antimony", "Year": "1992", "Global mine production of different minerals": "76000"}, {"Entity": "Antimony", "Year": "1993", "Global mine production of different minerals": "73000"}, {"Entity": "Antimony", "Year": "1994", "Global mine production of different minerals": "106000"}, {"Entity": "Antimony", "Year": "1995", "Global mine production of different minerals": "103000"}, {"Entity": "Antimony", "Year": "1996", "Global mine production of different minerals": "156000"}, {"Entity": "Antimony", "Year": "1997", "Global mine production of different minerals": "155000"}, {"Entity": "Antimony", "Year": "1998", "Global mine production of different minerals": "117000"}, {"Entity": "Antimony", "Year": "1999", "Global mine production of different minerals": "108000"}, {"Entity": "Antimony", "Year": "2000", "Global mine production of different minerals": "118000"}, {"Entity": "Antimony", "Year": "2001", "Global mine production of different minerals": "157000"}, {"Entity": "Antimony", "Year": "2002", "Global mine production of different minerals": "118000"}, {"Entity": "Antimony", "Year": "2003", "Global mine production of different minerals": "116000"}, {"Entity": "Antimony", "Year": "2004", "Global mine production of different minerals": "142000"}, {"Entity": "Antimony", "Year": "2005", "Global mine production of different minerals": "172000"}, {"Entity": "Antimony", "Year": "2006", "Global mine production of different minerals": "173000"}, {"Entity": "Antimony", "Year": "2007", "Global mine production of different minerals": "180000"}, {"Entity": "Antimony", "Year": "2008", "Global mine production of different minerals": "185000"}, {"Entity": "Antimony", "Year": "2009", "Global mine production of different minerals": "158000"}, {"Entity": "Antimony", "Year": "2010", "Global mine production of different minerals": "182000"}, {"Entity": "Antimony", "Year": "2011", "Global mine production of different minerals": "187000"}, {"Entity": "Antimony", "Year": "2012", "Global mine production of different minerals": "181000"}, {"Entity": "Antimony", "Year": "2013", "Global mine production of different minerals": "193000"}, {"Entity": "Antimony", "Year": "2014", "Global mine production of different minerals": "175000"}, {"Entity": "Antimony", "Year": "2015", "Global mine production of different minerals": "150000"}, {"Entity": "Antimony", "Year": "2016", "Global mine production of different minerals": "148000"}, {"Entity": "Antimony", "Year": "2017", "Global mine production of different minerals": "144000"}, {"Entity": "Antimony", "Year": "2018", "Global mine production of different minerals": "147000"}, {"Entity": "Antimony", "Year": "2019", "Global mine production of different minerals": "162000"}], "rows_tail": [{"Entity": "Zinc", "Year": "1910", "Global mine production of different minerals": "810000"}, {"Entity": "Zinc", "Year": "1911", "Global mine production of different minerals": "895000"}, {"Entity": "Zinc", "Year": "1912", "Global mine production of different minerals": "971000"}, {"Entity": "Zinc", "Year": "1913", "Global mine production of different minerals": "939000"}, {"Entity": "Zinc", "Year": "1914", "Global mine production of different minerals": "795000"}, {"Entity": "Zinc", "Year": "1915", "Global mine production of different minerals": "760000"}, {"Entity": "Zinc", "Year": "1916", "Global mine production of different minerals": "882000"}, {"Entity": "Zinc", "Year": "1917", "Global mine production of different minerals": "901000"}, {"Entity": "Zinc", "Year": "1918", "Global mine production of different minerals": "849000"}, {"Entity": "Zinc", "Year": "1919", "Global mine production of different minerals": "719000"}, {"Entity": "Zinc", "Year": "1920", "Global mine production of different minerals": "682000"}, {"Entity": "Zinc", "Year": "1921", "Global mine production of different minerals": "464000"}, {"Entity": "Zinc", "Year": "1922", "Global mine production of different minerals": "730000"}, {"Entity": "Zinc", "Year": "1923", "Global mine production of different minerals": "889000"}, {"Entity": "Zinc", "Year": "1924", "Global mine production of different minerals": "986000"}, {"Entity": "Zinc", "Year": "1925", "Global mine production of different minerals": "1190000"}, {"Entity": "Zinc", "Year": "1926", "Global mine production of different minerals": "1410000"}, {"Entity": "Zinc", "Year": "1927", "Global mine production of different minerals": "1420000"}, {"Entity": "Zinc", "Year": "1928", "Global mine production of different minerals": "1360000"}, {"Entity": "Zinc", "Year": "1929", "Global mine production of different minerals": "1320000"}, {"Entity": "Zinc", "Year": "1930", "Global mine production of different minerals": "1260000"}, {"Entity": "Zinc", "Year": "1931", "Global mine production of different minerals": "904000"}, {"Entity": "Zinc", "Year": "1932", "Global mine production of different minerals": "709000"}, {"Entity": "Zinc", "Year": "1933", "Global mine production of different minerals": "892000"}, {"Entity": "Zinc", "Year": "1934", "Global mine production of different minerals": "1060000"}, {"Entity": "Zinc", "Year": "1935", "Global mine production of different minerals": "1210000"}, {"Entity": "Zinc", "Year": "1936", "Global mine production of different minerals": "1330000"}, {"Entity": "Zinc", "Year": "1937", "Global mine production of different minerals": "1470000"}, {"Entity": "Zinc", "Year": "1938", "Global mine production of different minerals": "1420000"}, {"Entity": "Zinc", "Year": "1939", "Global mine production of different minerals": "1500000"}, {"Entity": "Zinc", "Year": "1940", "Global mine production of different minerals": "1470000"}, {"Entity": "Zinc", "Year": "1941", "Global mine production of different minerals": "1590000"}, {"Entity": "Zinc", "Year": "1942", "Global mine production of different minerals": "1630000"}, {"Entity": "Zinc", "Year": "1943", "Global mine production of different minerals": "1830000"}, {"Entity": "Zinc", "Year": "1944", "Global mine production of different minerals": "1870000"}, {"Entity": "Zinc", "Year": "1945", "Global mine production of different minerals": "1470000"}, {"Entity": "Zinc", "Year": "1946", "Global mine production of different minerals": "1440000"}, {"Entity": "Zinc", "Year": "1947", "Global mine production of different minerals": "1600000"}, {"Entity": "Zinc", "Year": "1948", "Global mine production of different minerals": "1690000"}, {"Entity": "Zinc", "Year": "1949", "Global mine production of different minerals": "1730000"}, {"Entity": "Zinc", "Year": "1950", "Global mine production of different minerals": "2150000"}, {"Entity": "Zinc", "Year": "1951", "Global mine production of different minerals": "2360000"}, {"Entity": "Zinc", "Year": "1952", "Global mine production of different minerals": "2590000"}, {"Entity": "Zinc", "Year": "1953", "Global mine production of different minerals": "2670000"}, {"Entity": "Zinc", "Year": "1954", "Global mine production of different minerals": "2660000"}, {"Entity": "Zinc", "Year": "1955", "Global mine production of different minerals": "2900000"}, {"Entity": "Zinc", "Year": "1956", "Global mine production of different minerals": "3110000"}, {"Entity": "Zinc", "Year": "1957", "Global mine production of different minerals": "3150000"}, {"Entity": "Zinc", "Year": "1958", "Global mine production of different minerals": "2950000"}, {"Entity": "Zinc", "Year": "1959", "Global mine production of different minerals": "3020000"}, {"Entity": "Zinc", "Year": "1960", "Global mine production of different minerals": "3090000"}, {"Entity": "Zinc", "Year": "1961", "Global mine production of different minerals": "3490000"}, {"Entity": "Zinc", "Year": "1962", "Global mine production of different minerals": "3570000"}, {"Entity": "Zinc", "Year": "1963", "Global mine production of different minerals": "3660000"}, {"Entity": "Zinc", "Year": "1964", "Global mine production of different minerals": "4030000"}, {"Entity": "Zinc", "Year": "1965", "Global mine production of different minerals": "4310000"}, {"Entity": "Zinc", "Year": "1966", "Global mine production of different minerals": "4500000"}, {"Entity": "Zinc", "Year": "1967", "Global mine production of different minerals": "4840000"}, {"Entity": "Zinc", "Year": "1968", "Global mine production of different minerals": "4970000"}, {"Entity": "Zinc", "Year": "1969", "Global mine production of different minerals": "5340000"}, {"Entity": "Zinc", "Year": "1970", "Global mine production of different minerals": "5460000"}, {"Entity": "Zinc", "Year": "1971", "Global mine production of different minerals": "5520000"}, {"Entity": "Zinc", "Year": "1972", "Global mine production of different minerals": "5440000"}, {"Entity": "Zinc", "Year": "1973", "Global mine production of different minerals": "5710000"}, {"Entity": "Zinc", "Year": "1974", "Global mine production of different minerals": "5780000"}, {"Entity": "Zinc", "Year": "1975", "Global mine production of different minerals": "5850000"}, {"Entity": "Zinc", "Year": "1976", "Global mine production of different minerals": "5690000"}, {"Entity": "Zinc", "Year": "1977", "Global mine production of different minerals": "5920000"}, {"Entity": "Zinc", "Year": "1978", "Global mine production of different minerals": "5850000"}, {"Entity": "Zinc", "Year": "1979", "Global mine production of different minerals": "5990000"}, {"Entity": "Zinc", "Year": "1980", "Global mine production of different minerals": "5950000"}, {"Entity": "Zinc", "Year": "1981", "Global mine production of different minerals": "5950000"}, {"Entity": "Zinc", "Year": "1982", "Global mine production of different minerals": "6130000"}, {"Entity": "Zinc", "Year": "1983", "Global mine production of different minerals": "6280000"}, {"Entity": "Zinc", "Year": "1984", "Global mine production of different minerals": "6520000"}, {"Entity": "Zinc", "Year": "1985", "Global mine production of different minerals": "6760000"}, {"Entity": "Zinc", "Year": "1986", "Global mine production of different minerals": "6840000"}, {"Entity": "Zinc", "Year": "1987", "Global mine production of different minerals": "7190000"}, {"Entity": "Zinc", "Year": "1988", "Global mine production of different minerals": "6770000"}, {"Entity": "Zinc", "Year": "1989", "Global mine production of different minerals": "6820000"}, {"Entity": "Zinc", "Year": "1990", "Global mine production of different minerals": "7150000"}, {"Entity": "Zinc", "Year": "1991", "Global mine production of different minerals": "7270000"}, {"Entity": "Zinc", "Year": "1992", "Global mine production of different minerals": "7250000"}, {"Entity": "Zinc", "Year": "1993", "Global mine production of different minerals": "6910000"}, {"Entity": "Zinc", "Year": "1994", "Global mine production of different minerals": "7050000"}, {"Entity": "Zinc", "Year": "1995", "Global mine production of different minerals": "7280000"}, {"Entity": "Zinc", "Year": "1996", "Global mine production of different minerals": "7480000"}, {"Entity": "Zinc", "Year": "1997", "Global mine production of different minerals": "7540000"}, {"Entity": "Zinc", "Year": "1998", "Global mine production of different minerals": "7570000"}, {"Entity": "Zinc", "Year": "1999", "Global mine production of different minerals": "7960000"}, {"Entity": "Zinc", "Year": "2000", "Global mine production of different minerals": "8770000"}, {"Entity": "Zinc", "Year": "2001", "Global mine production of different minerals": "8910000"}, {"Entity": "Zinc", "Year": "2002", "Global mine production of different minerals": "8880000"}, {"Entity": "Zinc", "Year": "2003", "Global mine production of different minerals": "9520000"}, {"Entity": "Zinc", "Year": "2004", "Global mine production of different minerals": "9600000"}, {"Entity": "Zinc", "Year": "2005", "Global mine production of different minerals": "10000000"}, {"Entity": "Zinc", "Year": "2006", "Global mine production of different minerals": "10300000"}, {"Entity": "Zinc", "Year": "2007", "Global mine production of different minerals": "11100000"}, {"Entity": "Zinc", "Year": "2008", "Global mine production of different minerals": "11900000"}, {"Entity": "Zinc", "Year": "2009", "Global mine production of different minerals": "11600000"}, {"Entity": "Zinc", "Year": "2010", "Global mine production of different minerals": "12300000"}, {"Entity": "Zinc", "Year": "2011", "Global mine production of different minerals": "12500000"}, {"Entity": "Zinc", "Year": "2012", "Global mine production of different minerals": "13300000"}, {"Entity": "Zinc", "Year": "2013", "Global mine production of different minerals": "13200000"}, {"Entity": "Zinc", "Year": "2014", "Global mine production of different minerals": "13500000"}, {"Entity": "Zinc", "Year": "2015", "Global mine production of different minerals": "13300000"}, {"Entity": "Zinc", "Year": "2016", "Global mine production of different minerals": "13800000"}, {"Entity": "Zinc", "Year": "2017", "Global mine production of different minerals": "13700000"}, {"Entity": "Zinc", "Year": "2018", "Global mine production of different minerals": "13100000"}, {"Entity": "Zinc", "Year": "2019", "Global mine production of different minerals": "13600000"}, {"Entity": "Zinc", "Year": "2020", "Global mine production of different minerals": "12000000"}, {"Entity": "Zinc", "Year": "2021", "Global mine production of different minerals": "12700000"}, {"Entity": "Zinc", "Year": "2022", "Global mine production of different minerals": "12500000"}, {"Entity": "Zinc", "Year": "2023", "Global mine production of different minerals": "12100000"}, {"Entity": "Zinc", "Year": "2024", "Global mine production of different minerals": "12000000"}, {"Entity": "Zirconium and hafnium", "Year": "2020", "Global mine production of different minerals": "1200000"}, {"Entity": "Zirconium and hafnium", "Year": "2021", "Global mine production of different minerals": "1300000"}, {"Entity": "Zirconium and hafnium", "Year": "2022", "Global mine production of different minerals": "1440000"}, {"Entity": "Zirconium and hafnium", "Year": "2023", "Global mine production of different minerals": "1440000"}, {"Entity": "Zirconium and hafnium", "Year": "2024", "Global mine production of different minerals": "1500000"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "global-mine-production-minerals", "metadata_url": "https://ourworldindata.org/grapher/global-mine-production-minerals.metadata.json", "chart_title": "Global mine production of minerals", "chart_subtitle": null, "chart_note": null, "chart_citation": "USGS - Mineral Commodity Summaries (2025); USGS - Historical Statistics for Mineral and Material Commodities (2024); BGS - World Mineral Statistics (2025)", "original_chart_url": "https://ourworldindata.org/grapher/global-mine-production-minerals", "owid_column_metadata": {"Global mine production of different minerals": {"titleShort": "Global mine production of different minerals", "titleLong": "Global mine production of different minerals", "descriptionShort": "Measured in tonnes of mined, rather than refined production.", "descriptionKey": ["Antimony - Values are reported as tonnes of metal content.", "Asbestos - Values are reported as gross weight.", "Bismuth - Values are reported as tonnes of metal content.", "Chromium - Values are reported as tonnes of contained chromium.", "Gemstones - Values are reported as tonnes of gemstone-quality diamonds.", "Graphite - Values refer to natural graphite.", "Lithium - Values are reported as tonnes of lithium content.", "Platinum group metals (iridium) - Values are reported as tonnes of metal content.", "Platinum group metals (other) - Values are reported as tonnes of metal content.", "Platinum group metals (rhodium) - Values are reported as tonnes of metal content.", "Potash (chloride) - Values are reported as tonnes of potassium oxide content.", "Potash (polyhalite) - Values are reported as tonnes of potassium oxide content.", "Potash (potassic salts) - Values are reported as tonnes of potassium oxide content.", "Potash - Values are reported in tonnes of potassium oxide equivalent.", "Rare earths - Values are reported in tonnes of rare-earth-oxide equivalent.", "Titanium (ilmenite) - Values are reported as tonnes of titanium dioxide content.", "Uranium - Values are reported as tonnes of metal content."], "descriptionProcessing": "- The majority of the data is sourced from USGS, supplemented by BGS data where available. Where both overlap, USGS data is prioritized.\n- As BGS does not provide global data, we calculated the world total by summing the data from individual countries, using this as a cross-check against USGS global figures.\n- Due to the inherent uncertainties in the data for certain minerals and countries, we allowed a maximum deviation of 10% between the global totals reported by USGS and the calculated ones for BGS. If the deviation exceeded this threshold, we excluded the BGS data.\n- The calculated global total from BGS data was used only on exceptional occasions, after ensuring that the resulting aggregate was sufficiently complete.\n- Both BGS and USGS datasets include numerous notes and footnotes. We have retained most of these, making only minor edits or deletions where necessary to maintain clarity.", "shortUnit": "t", "unit": "tonnes", "timespan": "1900-2024", "type": "Numeric", "owidVariableId": 1131074, "shortName": "mine_production", "lastUpdated": "2025-12-15", "nextUpdate": "2026-12-15", "citationShort": "USGS - Mineral Commodity Summaries (2025); USGS - Historical Statistics for Mineral and Material Commodities (2024); BGS - World Mineral Statistics (2025) – with major processing by Our World in Data", "citationLong": "USGS - Mineral Commodity Summaries (2025); USGS - Historical Statistics for Mineral and Material Commodities (2024); BGS - World Mineral Statistics (2025) – with major processing by Our World in Data. “Global mine production of different minerals” [dataset]. United States Geological Survey, “Mineral Commodity Summaries”; United States Geological Survey, “Historical Statistics for Mineral and Material Commodities”; British Geological Survey, “World Mineral Statistics” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1131074.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "d8372d58b7b335b9d530"},
{"raw_link": "https://ourworldindata.org/antibiotics", "title": "Antibiotics and Antibiotic Resistance", "context": "Antibiotics and Antibiotic Resistance\nBy\nSaloni Dattani\n,\nFiona Spooner\n,\nHannah Ritchie\n,\nand\nMax Roser\nContents\nAntibiotics are one of the most important medical breakthroughs of the 20th century.\n1\nThey have revolutionized healthcare by making it possible to effectively treat bacterial infections. Before antibiotics, even minor infections could be life-threatening, and medical procedures and surgeries were much riskier due to the high chance of infection. As a consequence, antibiotics have saved countless lives.\nBut\nantibiotic resistance\nchallenges their effectiveness. The overuse and misuse of antibiotics have hastened this growing global threat that makes it harder and more costly to treat infections.\nThe use of antibiotics isn't limited to human medicine: antibiotics are widely used in livestock farming, often as a cheap substitute for better hygiene standards.\nThe world can combat resistance by using antibiotics more carefully, developing new drugs, regulating antibiotic usage in livestock, and ensuring better access to diagnostics and treatments.\nOn this page, we explore the history, impact, and future of antibiotics, and present global data and research on antibiotics and antibiotic resistance.\nResearch & Writing\nWhat was the Golden Age of Antibiotics, and how can we spark a new one?\nHow do antibiotics work, and how does antibiotic resistance evolve?\nLarge amounts of antibiotics are used in livestock, but several countries have shown this doesn’t have to be the case\nPublic data on antibiotic use in livestock is incomplete, making it difficult to track how much is used and where\nSee all interactive charts on antibiotics ↓\nThe Golden Age of Antibiotics was a period of rapid antibiotic discovery\nThe modern scientific journey of antibiotics began in the early 20th century with the microbiologist Paul Ehrlich. He searched for potential medicines that could target microbes without harming human cells. In 1910, after testing hundreds of compounds, he achieved a breakthrough with\nsalvarsan\n— the first effective treatment for\nsyphilis\nand the first synthetic antibiotic.\n2\nIn another article\n, we explain in more detail how different antibiotics work.\nAnother milestone came in 1928 when Alexander Fleming observed fungal mold on a contaminated Petri dish that killed bacteria. He had discovered\npenicillin\n.\nUnfortunately, scaling up its production took years.\n3\nIn the late 1930s and early 1940s, the U.S. War Production Board coordinated efforts to improve fermentation, organize trials, foster collaboration, and lift patent restrictions — which sped up development. By 1945, they had succeeded, making\npenicillin\nwidely available.\n4\nAnother breakthrough was also achieved during this time: scientists discovered the potential of\nactinomycetes\n, a group of soil-dwelling bacteria, which eventually became the source of many antibiotics like\nstreptomycin\n,\ntetracyclines\n, and\nerythromycin\n.\n5\nThe period between the 1940s and 1960s is known as “the golden age of antibiotics”, as intense research into natural and synthetic compounds led to the rapid discovery of many new antibiotics.\nDownload\nA timeline of antibiotic drug development. Data comes from Hutchings et al. (2019).\n2\nRecent antibiotic innovation can be tracked on\nAntibioticDB\n. Scripts to recreate this timeline can be found\non GitHub\n.\nAs the timeline shows, almost two-thirds of all antibiotic drug classes were developed and introduced during the golden age of antibiotics.\nBy the 1970s, however, the antibiotic pipeline slowed down. Pharmaceutical companies shifted focus to chronic disease treatments, which were more profitable, especially as bacterial resistance to antibiotics grew. In addition, efforts to spot new antibiotics by screening organisms for antibiotic activity often led to reidentifying the same compounds already discovered by others.\n2\nRead more in our article:\nWhat was the Golden Age of Antibiotics, and how can we spark a new one?\nMany antibiotics were developed during the “Golden Age of Antibiotics”. How did it happen, why has antibiotic development slowed down since then, and what can we do to reignite it?\nAntibiotics have greatly reduced infection death rates\nAntibiotics have been effective against a wide range of bacterial infections, and have also helped make childbirth, cancer treatments, and medical procedures — such as surgeries and organ transplants — much safer.\nPenicillin\n’s availability meant that\nstreptococcal infections\n,\nsyphilis\n, and\npneumonia\ncould be effectively treated.\n6\nErythromycin followed, treating respiratory infections like bronchitis and pneumonia, as well as skin and ear infections.\nTetracyclines\nhad an even broader range of uses, targeting proteins common in many bacteria and some parasites.\n7\nThe most evident example of their positive impact comes from\nsulfonamides\n(commonly known as “sulfa drugs”), the second class of antibiotics developed. They were made available in the United States in 1937, before many other new treatments, which makes it possible to see their impact on death rates from diseases. This is visualized in the chart below.\nSulfa antibiotics could effectively treat infections such as\nStreptococcal\npneumonia\n,\nscarlet fever\n, and urinary tract infections; they could also be used during C-sections to reduce the risks of infection and\nsepsis\n.\n8\nDownload\nSeema Jayachandran, Adriana Lleras-Muney, and Kimberly V. Smith (2010).\n9\nResearchers Seema Jayachandran, Adriana Lleras-Muney, and Kimberly V. Smith estimated their impact on death rates from various diseases, as shown in the chart above.\n9\nDeath rates from infectious diseases had already declined over the previous decades in the United States due to general improvements in hygiene, sanitation, and healthcare. But they dropped even more steeply after sulfa antibiotics became available.\nCompared to previous trends, the researchers estimate that sulfa antibiotics resulted in a 36% decline in death rates from\nmaternal conditions\n, a 24% decline from\ninfluenza\nand\npneumonia\n, and a 65% decline in scarlet fever.\n10\nBecause of these effects, it’s estimated that they led to a 3% decline in death rates overall, translating to a rise in the average\nlife expectancy\nof around half a year — a remarkable impact from a single group of antibiotics.\n9\nThe use of antibiotics could greatly lower child mortality and disease in poorer countries\nIn many poorer countries, diseases like pneumonia and diarrhea are the\nleading causes of child deaths\n. These illnesses are often caused by bacteria and are treatable with antibiotics but continue to claim lives because many families lack access to essential medicines and healthcare.\nOne example of a life-saving program has been the\nmass drug administration (MDA)\nof\nazithromycin\n, a\nbroad-spectrum antibiotic\nfor children in poorer regions.\n11\nIn several large\nrandomized trials\nconducted in different African countries — Niger, Burkina Faso, Tanzania, and Malawi — giving azithromycin tablets to children once or twice a year reduced child mortality rates by around 15%.\n12\nThat’s a very large reduction for a single antibiotic.\nAzithromycin’s effectiveness comes from its effect on a wide range of bacteria, its rapid spread to multiple organs, and the high prevalence of bacterial infections in these regions.\n13\nBeyond saving lives, antibiotics have helped reduce\ntrachoma\n, a painful bacterial eye infection that can lead to blindness. Large-scale efforts to distribute azithromycin tablets, provide clean water, and improve hygiene substantially reduced the prevalence of trachoma in many African regions, as shown in the map below.\nDownload\nData comes from the\nGlobal Trachoma Atlas\n, published in Kristen Renneker et al. (2022).\n14\nThis shows that targeted or high-impact uses of antibiotics can be very effective, although monitoring for antibiotic resistance is also essential to maintain high efficacy over time.\n15\nTrachoma: how a common cause of blindness can be prevented worldwide\nThe world has seen a large decline in trachoma, but millions are still at risk. How can we make more progress against it?\nAntibiotic usage varies greatly around the world\nThe map below shows the usage of antibiotics to treat children with respiratory infections. Data comes from large-scale surveys such as the\nDHS\nand\nUN MICS\n.\nThis counts all reported respiratory infections, which means it can also include antibiotics used to treat infections caused by viruses and other\npathogens\n, for which antibiotics are ineffective.\nAs you can see, in several countries in Eastern Europe and Central and South Asia, it’s common for children to receive antibiotics for respiratory infections. But it’s much less common in parts of Africa, where as few as one in four children receive them in some countries.\nWhat about antibiotic usage in the population as a whole?\nUnfortunately, the data for different countries relies on different sources, including insurance claims, import records, hospital prescriptions, and wholesale data, and may not be representative of the population. This can limit comparability.\nThe map below shows the data collected by the World Health Organization’s GLASS system, which tracks national antimicrobial use and\nresistance\nworldwide. It shows the average level of antibiotic consumption in each country, as measured by\n“defined daily doses” (DDDs)\nper 1,000 people.\nFor example, five DDDs correspond to the daily amount of antibiotics typically used to treat an infection in five people.\nAs you can see, the recorded usage of antibiotics varies widely, with high rates in parts of Asia and some countries in Africa and lower rates in parts of Europe. It can be difficult to make direct comparisons with the previous map, which showed antibiotic usage among children from large standardized surveys, while this data comes from a range of sources.\nThe map also shows data for many countries as missing, reflecting the limited national data collection and reporting on antibiotic usage.\nThere are likely multiple reasons for these differences. One is that rates of infectious diseases vary widely: for example,\ntuberculosis rates\nare around 10 times higher in sub-Saharan Africa than in Europe.\nIn addition, several countries, especially in Europe, use “\nantibiotic stewardship programs\n” to monitor and limit the overuse of antibiotics.\nAntibiotic resistance threatens our ability to treat common infections\nWhen bacteria evolve to evade antibiotics, common infections become much harder to treat, and life-saving medical procedures and surgeries can become much more dangerous. Resistant bacteria can also spread, leading to infections that are harder and more expensive to treat and often require medications with greater side effects.\nThe chart here shows estimates of the number of deaths caused by infectious syndromes, broken down by whether the deaths are attributed to\nantibiotic resistance\n— meaning they would have been prevented if the infection wasn’t resistant.\nAs you can see, deaths caused by antibiotic resistance are most common for bloodstream infections and lower-respiratory infections.\nDownload\nYou can view\nan interactive version of this chart\nonline.\nIn this related chart, you can see these estimates broken down by different\npathogens\n:\nGlobal deaths from pathogens attributable to antimicrobial resistance\nDeaths from pathogens, broken down by whether they could be prevented without antimicrobial resistance. Estimates are based on models of causes of death, infection types, pathogens, resistance levels, and their impact on infection duration and complications. Estimates are limited by poor data collection in many regions.\nA large share of antibiotics are used for livestock, often as a substitute for more hygienic conditions\nGlobally, it’s estimated that at least two-thirds of antibiotics are used for livestock.\n16\nBecause intensive farming keeps animals in cramped and unsanitary conditions, antibiotics are often used as a cheaper substitute for better living conditions and hygiene.\nHowever, overuse can lead to the evolution and spread of antibiotic-resistant bacteria, threatening both animal and human health. Resistant pathogens can spread through contaminated meat and dairy, making some diseases harder to treat.\nAntibiotic use varies widely between animals. Antibiotics are used more intensively for pigs and sheep than chickens and cattle\n17\n, partly due to their farming conditions and longer lifespans.\nUsage also varies around the world, as you can see in the map below. The intensity of antibiotic use is highest in Asia, Australasia, and the Americas. In contrast, antibiotic use is lower in Africa due to lower access, and in Europe, partly due to regulation.\nThe chart below shows that antibiotic sales for livestock have dropped significantly in several European countries.\nSeveral policies have contributed to this, such as requirements for veterinarian prescriptions, taxes on antibiotic sales, and prohibiting discounts. In addition, many farms have shifted to using slower-growing animal breeds without harming farm productivity.\nThere are several ways to reduce antibiotic overuse in livestock: improving sanitary conditions for animals, using antibiotics only when necessary, and reducing meat consumption.\nCutting meat consumption, even moderately, could dramatically lower antibiotic use while also benefiting the environment and public health.\nRead more in our article:\nLarge amounts of antibiotics are used in livestock, but several countries have shown this doesn’t have to be the case\nOveruse is a risk for antibiotic resistance, but there are ways to reduce it.\nWhat can we do to tackle antibiotic resistance?\nAntibiotic resistance is a growing challenge, but there are several ways to tackle it.\nThis includes public health measures, improved diagnostic technology and prescription practices,\nantibiotic stewardship programs\n, and economic incentives to develop new antibiotics.\nPublic health measures to reduce the spread of bacterial infections\nPreventing bacterial infections is one of the most effective ways to combat antibiotic resistance. Vaccination programs, access to clean water and sanitation, and better hygiene practices can significantly reduce infections.\nFor example, vaccines against\nStreptococcus pneumoniae\nand\nHaemophilus influenzae\ncan protect people from infectious diseases and reduce the need for antibiotics.\nImprovements in diagnosis, testing, and usage\nIt can often take several days to identify infections and to know whether they’re treatable with antibiotics. These delays frequently lead doctors to prescribe\nbroad-spectrum antibiotics\n, which can be ineffective and fuel resistance.\nBut testing for bacterial infections and resistance is limited in many countries. Without bacterial testing, it’s difficult to identify whether the disease is caused by a bacterium rather than another pathogen, such as a virus or parasite — for which antibiotics may be ineffective.\nWithout resistance testing, we can’t quickly identify whether antibiotics are less effective against an infection, help track the spread of antibiotic resistance, and determine whether we’re making progress against it.\nWith new and faster diagnostic tools, healthcare workers can quickly determine whether an infection is bacterial, what type of bacteria is causing it, and which antibiotics are effective.\nIn many wealthier countries,\nantibiotic stewardship programs\nguide appropriate antibiotic use. These programs help healthcare workers decide when and which antibiotics to prescribe while introducing regulations to reduce misuse.\nNew antibiotic drug development\nAs resistance grows, new antibiotics are crucial. However, developing them is difficult, partly due to economic challenges. Antibiotics are typically used for short periods, sold at low prices, and reserved for limited use to slow resistance — making them less profitable for manufacturers.\nGovernments are addressing this with innovative funding models. For example, the UK is piloting a subscription system where health services pay an annual fee for antibiotic access, encouraging innovation without overuse.\nCollaborative initiatives also fund projects to develop essential new drugs. These efforts could revitalize antibiotic discovery and ensure we have effective treatments in the future.\nRead more in our article:\nWhat was the Golden Age of Antibiotics, and how can we spark a new one?\nMany antibiotics were developed during the “Golden Age of Antibiotics”. How did it happen, why has antibiotic development slowed down since then, and what can we do to reignite it?\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nAntibiotics target different pathways, and bacteria can become resistant in different ways\nIn their natural environments, bacteria compete for resources like nutrients and space. Some bacteria produce antibiotics to suppress or kill competitors, giving them an advantage.\n18\nThey can target specific processes in bacterial cells that are critical for growth, reproduction, and stability, as the diagram below shows.\nDownload\nAdapted from Sanseverino et al. (2018)\n19\nand Hutchings, Truman, and Wilkinson (2019).\n20\nUnfortunately, bacteria can develop resistance to antibiotics. For example, they can produce enzymes called “beta-lactamases” that break down\nbeta-lactam antibiotics\n.\n6\nBacteria can also produce proteins that pump\ntetracycline antibiotics\nout of their cells.\n21\nThese mechanisms tend to evolve within individuals (between days to months) but take longer to spread across populations (between weeks to years).\nThey allow bacteria to adapt to new antibiotics and share resistance mechanisms, including between different species.\nResistance mechanisms can come with costs: for example, producing enzymes like beta-lactamase or maintaining protein pumps can use up energy or resources, slowing bacterial growth.\n22\nHowever, in the presence of antibiotics, the survival advantage can outweigh the trade-offs, allowing resistant bacteria to dominate over time.\nYou can read more in our article:\nHow do antibiotics work, and how does antibiotic resistance evolve?\nTo use antibiotics more effectively, it’s important to know how different antibiotics work and how antibiotic resistance can evolve and spread.\nKey Charts on Antibiotics & Antibiotic Resistance\nSee all charts on this topic\nAntibiotic use in livestock\nCountries enrolled in the WHO's program to monitor antimicrobial use and resistance\nGlobal deaths by infectious syndrome and antimicrobial resistance\nGlobal deaths from pathogens attributable to antimicrobial resistance\nShare of children with symptoms of a respiratory infection who received antibiotics\nSystems to monitor the spread of antimicrobial resistance in humans\nSystems to monitor the use of antimicrobials for human medicine\nAntibiotic consumption rate\nAntibiotic usage by surveillance category\nStacked\nAntibiotic use in livestock\nAntibiotic use in livestock in Europe per kilogram of meat\nAntibiotic use in livestock per kilogram of meat product\nConsumption of oral antibiotics by type\nCountries with laws or regulations on the sale of antimicrobials for livestock\nGlobal antibiotic use in livestock under reduction scenarios\nNeonatal deaths from bloodstream infections attributed to antimicrobial resistance\nNeonatal deaths from infections attributed to antimicrobial resistance\nNeonatal sepsis and infection death rate in infants\nNumber of countries that submit data on antibiotic use in livestock\nNumber of infections tested for antibiotic susceptibility\nSales of antibiotics for livestock\nShare of countries reporting confirmed bacterial infections\nAverage cost of tuberculosis treatment by type\nConfirmed tuberculosis cases that are drug-resistant versus drug-susceptible\nIncidence and prevalence of extensively drug-resistant tuberculosis\nMultidrug-resistant tuberculosis cases\nNumber of people with extensively drug resistant tuberculosis\nNumber of people with multidrug-resistant tuberculosis\nReported bloodstream infection rate by pathogen\nShare of countries testing bacterial infections for antibiotic susceptibility\nTuberculosis treatment success rate by type\nChart 1 of 31\nFeatured Data on\nAntibiotics & Antibiotic Resistance\nContinue reading on Our World in Data\nCauses of Death\nTo find ways to save lives, it’s essential to know what people are dying from. Explore global data and research on causes of death.\nVaccination\nVaccines have saved millions of lives and transformed global health. This page presents data and research on their history, impact, and future.\nChild and Infant Mortality\nChild mortality remains one of the world’s largest problems and is a painful reminder of work yet to be done. With global data on where, when, and how child deaths occur, we can accelerate efforts to prevent them.\nEndnotes\nThe term “antibiotics” may have different meanings in the literature.\nSometimes antibiotics refer to only naturally derived antibacterials.\nSometimes antibiotics refer to small molecules that target bacteria (which includes natural and synthetic molecules), and are distinct from a range of other types of antibacterial agents, such as antibodies, bacteriophages, etc. We use this second definition.\nHutchings, M. I., Truman, A. W., & Wilkinson, B. (2019). Antibiotics: Past, present and future. Current Opinion in Microbiology, 51, 72–80.\nhttps://doi.org/10.1016/j.mib.2019.10.008\nGaynes, R. (2017). The Discovery of Penicillin—New Insights After More Than 75 Years of Clinical Use.\nEmerging Infectious Diseases\n,\n23\n(5), 849–853.\nhttps://doi.org/10.3201/eid2305.161556\nSampat, B. N. (2023). Second World War and the Direction of Medical Innovation. SSRN Electronic Journal.\nhttps://doi.org/10.2139/ssrn.4422261\nGaynes, R. (2017). The Discovery of Penicillin—New Insights After More Than 75 Years of Clinical Use. Emerging Infectious Diseases, 23(5), 849–853.\nhttps://doi.org/10.3201/eid2305.161556\nWhy We Should Reexamine the “Golden Age” of Antibiotics in Social Context. (2024). AMA Journal of Ethics, 26(5), E418-428.\nhttps://doi.org/10.1001/amajethics.2024.418\nBaxter, J. P. (1946). Scientists Against Time (Vol. 1, Ch. 22). Little Brown. Available\nonline\n.\nWoodruff, H. B. (2014). Selman A. Waksman, Winner of the 1952 Nobel Prize for Physiology or Medicine. Applied and Environmental Microbiology, 80(1), 2–8.\nhttps://doi.org/10.1128/AEM.01143-13\nHutchings, M. I., Truman, A. W., & Wilkinson, B. (2019). Antibiotics: Past, present and future. Current Opinion in Microbiology, 51, 72–80.\nhttps://doi.org/10.1016/j.mib.2019.10.008\nBush, K., & Bradford, P. A. (2016). β-Lactams and β-Lactamase Inhibitors: An Overview. Cold Spring Harbor Perspectives in Medicine, 6(8), a025247.\nhttps://doi.org/10.1101/cshperspect.a025247\nGrossman, T. H. (2016). Tetracycline Antibiotics and Resistance.\nCold Spring Harbor Perspectives in Medicine\n,\n6\n(4), a025387.\nhttps://doi.org/10.1101/cshperspect.a025387\nHollingsworth, A., Karbownik, K., Thomasson, M. A., & Wray, A. (2022). The gift of a lifetime: The hospital, modern medicine, and mortality (No. w30663). National Bureau of Economic Research. Available\nonline\n.\nJayachandran, 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.\nhttps://doi.org/10.1257/app.2.2.118\nJayachandran, 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.\nhttps://doi.org/10.1257/app.2.2.118\nNote that influenza and pneumonia are broad categories that include several respiratory infections. Influenza is caused by a virus and is unaffected by the use of antibiotics, while pneumonia can be caused by some viral and bacterial pathogens.\nOther conditions, which are not treated by sulfa drugs, didn’t show a break in trend at this time point. This provides evidence that sulfa drugs were responsible for the steeper decline in infectious disease death rates.\nWebster, J. P., Molyneux, D. H., Hotez, P. J., & Fenwick, A. (2014). The contribution of mass drug administration to global health: Past, present and future.\nPhilosophical Transactions of the Royal Society B: Biological Sciences\n,\n369\n(1645), 20130434.\nhttps://doi.org/10.1098/rstb.2013.0434\nO’Brien, K. S., Arzika, A. M., Amza, A., Maliki, R., Aichatou, B., Bello, I. M., Beidi, D., Galo, N., Harouna, N., Karamba, A. M., Mahamadou, S., Abarchi, M., Ibrahim, A., Lebas, E., Peterson, B., Liu, Z., Le, V., Colby, E., Doan, T., … Lietman, T. M. (2024). Azithromycin to Reduce Mortality—An Adaptive Cluster-Randomized Trial.\nNew England Journal of Medicine\n,\n391\n(8), 699–709.\nhttps://doi.org/10.1056/NEJMoa2312093\nKeenan, J. D., Bailey, R. L., West, S. K., Arzika, A. M., Hart, J., Weaver, J., Kalua, K., Mrango, Z., Ray, K. J., Cook, C., Lebas, E., O’Brien, K. S., Emerson, P. M., Porco, T. C., & Lietman, T. M. (2018). Azithromycin to Reduce Childhood Mortality in Sub-Saharan Africa. New England Journal of Medicine, 378(17), 1583–1592.\nhttps://doi.org/10.1056/NEJMoa1715474\nOldenburg, C. E., Ouattara, M., Bountogo, M., Boudo, V., Ouedraogo, T., Compaoré, G., Dah, C., Zakane, A., Coulibaly, B., Bagagnan, C., Hu, H., O’Brien, K. S., Nyatigo, F., Keenan, J. D., Doan, T., Porco, T. C., Arnold, B. F., Lebas, E., Sié, A., & Lietman, T. M. (2024). Mass Azithromycin Distribution to Prevent Child Mortality in Burkina Faso: The CHAT Randomized Clinical Trial. JAMA, 331(6), 482.\nhttps://doi.org/10.1001/jama.2023.27393\nKeenan, J. D., Arzika, A. M., Maliki, R., Boubacar, N., Elh Adamou, S., Moussa Ali, M., Cook, C., Lebas, E., Lin, Y., Ray, K. J., O’Brien, K. S., Doan, T., Oldenburg, C. E., Callahan, E. K., Emerson, P. M., Porco, T. C., & Lietman, T. M. (2019). Longer-Term Assessment of Azithromycin for Reducing Childhood Mortality in Africa. New England Journal of Medicine, 380(23), 2207–2214.\nhttps://doi.org/10.1056/NEJMoa1817213\nOldenburg, C. E., Arzika, A. M., Amza, A., Gebre, T., Kalua, K., Mrango, Z., Cotter, S. Y., West, S. K., Bailey, R. L., Emerson, P. M., O’Brien, K. S., Porco, T. C., Keenan, J. D., & Lietman, T. M. (2019). Mass Azithromycin Distribution to Prevent Childhood Mortality: A Pooled Analysis of Cluster-Randomized Trials. The American Journal of Tropical Medicine and Hygiene, 100(3), 691–695.\nhttps://doi.org/10.4269/ajtmh.18-0846\nParnham, M. J., Haber, V. E., Giamarellos-Bourboulis, E. J., Perletti, G., Verleden, G. M., & Vos, R. (2014). Azithromycin: Mechanisms of action and their relevance for clinical applications.\nPharmacology & Therapeutics\n,\n143\n(2), 225–245.\nhttps://doi.org/10.1016/j.pharmthera.2014.03.003\nData and scripts to recreate these maps can be found on GitHub.\nRenneker, K. K., Abdala, M., Addy, J., Al-Khatib, T., Amer, K., Badiane, M. D., Batcho, W., Bella, L., Bougouma, C., Bucumi, V., Chisenga, T., Dat, T. M., Dézoumbé, D., Elshafie, B., Garae, M., Goepogui, A., Hammou, J., Kabona, G., Kadri, B., … Ngondi, J. M. (2022). Global progress toward the elimination of active trachoma: An analysis of 38 countries. The Lancet Global Health, 10(4), e491–e500.\nhttps://doi.org/10.1016/S2214-109X(22)00050-X\nKahn, R., Eyal, N., Sow, S. O., & Lipsitch, M. (2023). Mass drug administration of azithromycin: An analysis.\nClinical Microbiology and Infection\n,\n29\n(3), 326–331.\nhttps://doi.org/10.1016/j.cmi.2022.10.022\nGetting a definitive figure here is difficult because of data reporting and transparency issues, which I will describe later.\nSeveral of the most extensive studies on antimicrobial use in livestock cite 72% or 73%. This appears to come from a 2017 study by Thomas van Boeckel and colleagues.\nIt estimates that the\nintensity\nof antimicrobial use in humans is around 118 mg per kg. And 133 mg per kg in animals. When we multiply these figures by the estimated\nbiomass\n(i.e., the weight) of humans and livestock, we get a total estimate for humans of around 35,000 tonnes a year, compared to 85,000 tonnes in livestock. That would mean livestock accounted for around 72% of total antibiotic use.\nA recent study by Katie Tiseo and colleagues (2020) estimated that 66% of antimicrobials were used in livestock.\nVan Boeckel, T. P., Pires, J., Silvester, R., Zhao, C., Song, J., Criscuolo, N. G., ... & Laxminarayan, R. (2019). Global trends in antimicrobial resistance in animals in low-and middle-income countries. Science.\nVan Boeckel, T. P., Brower, C., Gilbert, M., Grenfell, B. T., Levin, S. A., Robinson, T. P., ... & Laxminarayan, R. (2017). Global trends in antimicrobial use in food animals. Proceedings of the National Academy of Sciences.\nMulchandani, R., Wang, Y., Gilbert, M., & Van Boeckel, T. P. (2023). Global trends in antimicrobial use in food-producing animals: 2020 to 2030. PLOS Global Public Health.\nTiseo, K., Huber, L., Gilbert, M., Robinson, T. P., & Van Boeckel, T. P. (2020). Global trends in antimicrobial use in food animals from 2017 to 2030. Antibiotics.\nAfter accounting for their larger size. The estimates are given per kilogram of meat, because larger animals tend to require higher volumes of antibiotics for treatment.\nAminov, R. I. (2009). The role of antibiotics and antibiotic resistance in nature. Environmental Microbiology, 11(12), 2970–2988.\nhttps://doi.org/10.1111/j.1462-2920.2009.01972.x\nNewman, D. J., & Cragg, G. M. (2016). Natural Products as Sources of New Drugs from 1981 to 2014. Journal of Natural Products, 79(3), 629–661.\nhttps://doi.org/10.1021/acs.jnatprod.5b01055\nVan Der Meij, A., Worsley, S. F., Hutchings, M. I., & Van Wezel, G. P. (2017). Chemical ecology of antibiotic production by actinomycetes. FEMS Microbiology Reviews, 41(3), 392–416.\nhttps://doi.org/10.1093/femsre/fux005\nEuropean Commission: Joint Research Centre, Sanseverino, I., Loos, R., Navarro Cuenca, A., Marinov, D. et al., State of the art on the contribution of water to antimicrobial resistance, Publications Office, 2018,\nhttps://data.europa.eu/doi/10.2760/771124\nHutchings, M. I., Truman, A. W., & Wilkinson, B. (2019). Antibiotics: Past, present and future.\nCurrent Opinion in Microbiology\n,\n51\n, 72–80.\nhttps://doi.org/10.1016/j.mib.2019.10.008\nGrossman, T. H. (2016). Tetracycline Antibiotics and Resistance. Cold Spring Harbor Perspectives in Medicine, 6(4), a025387.\nhttps://doi.org/10.1101/cshperspect.a025387\nSouque, C., González Ojeda, I., & Baym, M. (2024). From Petri Dishes to Patients to Populations: Scales and Evolutionary Mechanisms Driving Antibiotic Resistance. Annual Review of Microbiology, 78(1), 361–382.\nhttps://doi.org/10.1146/annurev-micro-041522-102707\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this topic page, please also cite the underlying data sources. This topic page can be cited as:\nSaloni Dattani, Fiona Spooner, Hannah Ritchie, and Max Roser (2024) - “Antibiotics and Antibiotic Resistance” Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/antibiotics' [Online Resource]\nBibTeX citation\n@article{owid-antibiotics,\nauthor = {Saloni Dattani and Fiona Spooner and Hannah Ritchie and Max Roser},\ntitle = {Antibiotics and Antibiotic Resistance},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://ourworldindata.org/antibiotics}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "antibiotics", "source_url": "https://ourworldindata.org/antibiotics", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Antibiotics revolutionized medicine, but their effectiveness is threatened by resistance. This page presents global data and research on antibiotics and antibiotic resistance.", "numeric_mentions": ["20", "1", "1910,", "2", "1928", "3", "1930", "1940", "1945,", "4", "5", "1960", "2019", "1970", "6", "7", "1937,", "8", "2010", "9", "36%", "24%", "65%", "10", "3%", "11", "15%", "12", "13", "2022", "14", "15", "1,000", "16", "17", "18", "2018", "19", "21", "22", "31", "51,", "72", "80", "10.1016", "2019.10", "008", "2017", "75 Years", "23", "849", "853", "10.3201", "161556", "2023", "10.2139", "4422261", "2024", "26", "428", "10.1001", "2024.418", "1946", "1,", "2014", "1952", "10.1128", "01143", "2016", "10.1101", "118", "146", "10.1257", "2.2", "369", "1645", "20130434", "10.1098", "2013.0434", "391"], "numeric_evidence": [{"title": "Share of children with symptoms of a respiratory infection who received antibiotics", "source_url": "https://ourworldindata.org/grapher/antibiotic-usage-in-children.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Antibiotic usage in children"], "row_count_total": 2527, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Antibiotic usage in children": "63.8"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Antibiotic usage in children": "63.8"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Antibiotic usage in children": "63.9"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Antibiotic usage in children": "65.4"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Antibiotic usage in children": "65.6"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Antibiotic usage in children": "66"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Antibiotic usage in children": "66.4"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Antibiotic usage in children": "67.2"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Antibiotic usage in children": "65.6"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Antibiotic usage in children": "67.4"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Antibiotic usage in children": "66.3"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Antibiotic usage in children": "65.7"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Antibiotic usage in children": "65.2"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Antibiotic usage in children": "64.8"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Antibiotic usage in children": "64.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Antibiotic usage in children": "64.2"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Antibiotic usage in children": "63.3"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Antibiotic usage in children": "64.4"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Antibiotic usage in children": "63.5"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Antibiotic usage in 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"Yemen", "Code": "YEM", "Year": "2014", "Antibiotic usage in children": "62.7"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Antibiotic usage in children": "67.1"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Antibiotic usage in children": "63.3"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Antibiotic usage in children": "64.7"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Antibiotic usage in children": "63.6"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Antibiotic usage in children": "36.4"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Antibiotic usage in children": "36.5"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Antibiotic usage in children": "36.2"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Antibiotic usage in children": "37.6"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Antibiotic usage in children": "37.9"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Antibiotic usage in children": "38.6"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Antibiotic usage in children": "38.4"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Antibiotic usage in children": "55.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Antibiotic usage in children": "56.8"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Antibiotic usage in children": "47.2"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Antibiotic usage in children": "40.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Antibiotic usage in children": "39.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Antibiotic usage in children": "35.7"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Antibiotic usage in children": "42.2"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Antibiotic usage in children": "42.7"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Antibiotic usage in children": "43.2"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Antibiotic usage in children": "43.7"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Antibiotic usage in children": "44.6"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Antibiotic usage in children": "44.6"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Antibiotic usage in children": "36.1"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Antibiotic usage in children": "35.9"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Antibiotic usage in children": "36.1"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Antibiotic usage in children": "34.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Antibiotic usage in children": "34.6"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Antibiotic usage in children": "33.4"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Antibiotic usage in children": "39.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Antibiotic usage in children": "40.7"}, {"Entity": 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(2021)", "original_chart_url": "https://ourworldindata.org/grapher/antibiotic-usage-in-children", "owid_column_metadata": {"Antibiotic usage in children": {"titleShort": "Antibiotic usage in children", "titleLong": "Antibiotic usage in children", "descriptionShort": "The caregiver reported share of children under five years old, with symptoms of lower respiratory tract infection, who received antibiotics for this illness.", "shortUnit": "%", "unit": "%", "timespan": "2000-2018", "type": "Numeric", "owidVariableId": 990526, "shortName": "antibiotic_usage__pct", "lastUpdated": "2024-10-09", "citationShort": "Browne AJ et al. (2021) – processed by Our World in Data", "citationLong": "Browne AJ et al. (2021) – processed by Our World in Data. “Antibiotic usage in children” [dataset]. 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Reuse should follow the source and license information in this chart metadata."}, {"title": "Antibiotic consumption rate", "source_url": "https://ourworldindata.org/grapher/antibiotic-consumption-rate.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day"], "row_count_total": 340, "rows_head": [{"Entity": "Armenia", "Code": "ARM", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "10.017727"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.9899645"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "13.337215"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.602548"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "19.28271"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "15.010103"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "10.155555"}, {"Entity": "Austria", "Code": "AUT", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "11.636425"}, {"Entity": "Austria", "Code": "AUT", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.155595"}, {"Entity": "Austria", "Code": "AUT", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "10.896752"}, {"Entity": "Austria", "Code": "AUT", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.217182"}, {"Entity": "Austria", "Code": "AUT", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "9.39604"}, {"Entity": "Austria", "Code": "AUT", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "9.465207"}, {"Entity": "Austria", "Code": "AUT", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "11.180478"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "23.843424"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "20.98845"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "19.797302"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "24.353333"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "24.902145"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "55.43198"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "48.947113"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2016", "Defined daily doses 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of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "16.910587"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "24.575663"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "23.107687"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "22.618177"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "21.711246"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "17.009113"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "17.75529"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "20.792952"}, {"Entity": "Benin", "Code": "BEN", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "22.111816"}, {"Entity": "Benin", "Code": "BEN", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "27.636494"}, {"Entity": "Benin", "Code": "BEN", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "23.263105"}, {"Entity": "Benin", "Code": "BEN", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "18.620092"}, {"Entity": "Benin", "Code": "BEN", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "15.7757435"}, {"Entity": "Benin", "Code": "BEN", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "17.458641"}, {"Entity": "Benin", "Code": "BEN", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "23.382936"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "11.781961"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "9.470717"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "9.575982"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.769004"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "19.51261"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "21.041296"}, {"Entity": "Canada", "Code": "CAN", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "14.790742"}, {"Entity": "Colombia", "Code": "COL", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "22.265644"}, {"Entity": "Colombia", "Code": "COL", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "19.812557"}, {"Entity": "Colombia", "Code": "COL", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "22.304657"}, {"Entity": "Colombia", "Code": "COL", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "26.115873"}, {"Entity": "Colombia", "Code": "COL", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "18.283798"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "16.531649"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "19.666134"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "19.460215"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "19.476248"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "19.852346"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "19.99893"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "16.818676"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "17.840157"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "19.899818"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "28.96844"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "29.49558"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "28.214714"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "30.565535"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "29.568851"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "25.782444"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "34.136124"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "17.567469"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "14.068926"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "14.178297"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "17.75234"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "17.329245"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "16.5549"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "15.865877"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "15.591172"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "14.463073"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "14.562188"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "15.294658"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "6.7746153"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "8.0168495"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "6.5744495"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "7.783921"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "30.457087"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "33.825657"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "49.820892"}, {"Entity": "Estonia", "Code": "EST", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.006966"}, {"Entity": "Estonia", "Code": "EST", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.107325"}, {"Entity": "Estonia", "Code": "EST", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.222214"}, {"Entity": "Estonia", "Code": "EST", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.051421"}, {"Entity": "Estonia", "Code": "EST", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "10.90743"}, {"Entity": "Estonia", "Code": "EST", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "10.310436"}, {"Entity": "Estonia", "Code": "EST", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.924988"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "10.509656"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.597177"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "11.317588"}, {"Entity": "Finland", "Code": "FIN", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "16.63527"}, {"Entity": "Finland", "Code": "FIN", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "15.121162"}, {"Entity": "Finland", "Code": "FIN", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "14.794987"}, {"Entity": "Finland", "Code": "FIN", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "14.117324"}, {"Entity": "Finland", "Code": "FIN", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "11.455109"}, {"Entity": "Finland", "Code": "FIN", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "10.932422"}, {"Entity": "Finland", "Code": "FIN", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.172713"}, {"Entity": "France", "Code": "FRA", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "26.515987"}, {"Entity": "France", "Code": "FRA", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "25.580854"}, {"Entity": "France", "Code": "FRA", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "26.125137"}, {"Entity": "France", "Code": "FRA", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "25.864227"}, {"Entity": "France", "Code": "FRA", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "21.033089"}, {"Entity": "France", "Code": "FRA", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "22.37986"}, {"Entity": "France", "Code": "FRA", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "25.253601"}, {"Entity": "Gabon", "Code": "GAB", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "16.961391"}, {"Entity": "Gabon", "Code": "GAB", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "24.650986"}, {"Entity": "Georgia", "Code": "GEO", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "22.418634"}, {"Entity": "Georgia", "Code": "GEO", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "25.12228"}, {"Entity": "Georgia", "Code": "GEO", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "20.780441"}, {"Entity": "Georgia", "Code": "GEO", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "16.467178"}, {"Entity": "Georgia", "Code": "GEO", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "14.507995"}, {"Entity": "Georgia", "Code": "GEO", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "25.148424"}], "rows_tail": [{"Entity": "Nepal", "Code": "NPL", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "56.3306"}, {"Entity": "Nepal", "Code": "NPL", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "49.546783"}, {"Entity": "Nepal", "Code": "NPL", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "78.11656"}, {"Entity": 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"Code": "PER", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.374885"}, {"Entity": "Peru", "Code": "PER", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "7.9186525"}, {"Entity": "Peru", "Code": "PER", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "8.629612"}, {"Entity": "Peru", "Code": "PER", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "10.115588"}, {"Entity": "Poland", "Code": "POL", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "22.011902"}, {"Entity": "Poland", "Code": "POL", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "25.57282"}, {"Entity": "Poland", "Code": "POL", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "24.722916"}, {"Entity": "Poland", "Code": "POL", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "24.013342"}, {"Entity": "Poland", "Code": "POL", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "18.908295"}, {"Entity": "Poland", "Code": "POL", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "20.754295"}, {"Entity": "Poland", "Code": "POL", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "24.453047"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "19.021236"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "18.41596"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "19.250631"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "19.47018"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "15.124077"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "15.35512"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "18.287363"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "3.2891738"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "7.2045927"}, {"Entity": "Romania", "Code": "ROU", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "26.258902"}, {"Entity": "Romania", "Code": "ROU", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "26.34417"}, {"Entity": "Romania", "Code": "ROU", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "26.840725"}, {"Entity": "Romania", "Code": "ROU", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "27.692993"}, {"Entity": "Romania", "Code": "ROU", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "26.779913"}, {"Entity": "Romania", "Code": "ROU", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "27.207792"}, {"Entity": "Romania", "Code": "ROU", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "28.673923"}, {"Entity": "Russia", "Code": "RUS", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "17.317398"}, {"Entity": "Russia", "Code": "RUS", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "18.376299"}, {"Entity": "Russia", "Code": "RUS", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "17.06708"}, {"Entity": "Russia", "Code": "RUS", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "17.280487"}, {"Entity": "Russia", "Code": "RUS", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "21.284986"}, {"Entity": "Russia", "Code": "RUS", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "18.207815"}, {"Entity": "Russia", "Code": "RUS", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "16.690329"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "8.681356"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "10.54433"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.989289"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "13.063693"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "13.101871"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "13.016865"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "10.355371"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "10.388976"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "12.671525"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "20.303118"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "44.14366"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "38.367184"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "40.582176"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 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antituberculosis drugs used per 1,000 inhabitants per day": "16.988468"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "20.014984"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "23.487333"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "30.764942"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "20.157782"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "17.240808"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2017", "Defined daily doses 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doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "27.98314"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "21.272968"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "23.38928"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2016", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "10.903477"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "11.540201"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "13.557756"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "20.983425"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "18.462305"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "13.468689"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "11.032576"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2017", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "18.994778"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2018", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "18.467142"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2019", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "18.203661"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2020", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "16.170284"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2021", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "16.013247"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2022", "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": "19.645208"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "antibiotic-consumption-rate", "metadata_url": "https://ourworldindata.org/grapher/antibiotic-consumption-rate.metadata.json", "chart_title": "Antibiotic consumption rate", "chart_subtitle": "Reported volume of antibiotics used per 1,000 people per day, measured in defined daily doses. Countries may report data from different sources, including insurance claims, import records, hospital prescriptions, and wholesale data.", "chart_note": "Only shown for countries reporting to the WHO's GLASS system to track antimicrobial usage and resistance.", "chart_citation": "WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) (2024)", "original_chart_url": "https://ourworldindata.org/grapher/antibiotic-consumption-rate", "owid_column_metadata": {"Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day": {"titleShort": "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day", "titleLong": "Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day", "descriptionShort": "Total defined daily doses of antibiotics and antituberculosis drugs used in a given year per 1,000 inhabitants per day.", "descriptionKey": ["For Kenya: data is incomplete since it's not collected from all sources.", "For Nepal: data is incomplete, not all antibiotics reported systematically.", "For Austria, Germany and Iceland: only antimicrobial consumption in the community is reported.", "For Bhutan, Burkina Faso and Sudan: for antibiotics, only antibiotics for systemic use (ATC code J01) are reported", "For Portugal: data are incomplete for antituberculosis medicines.", "For Kuwait, Oman, Papua New Guinea, Peru, Qatar, Rwanda, Saudi Arabia, South Africa and United Kingdom: only consumption in the public sector reported and this is estimated to represent less than 90% of total antimicrobial usage.", "For Bhutan, Cote d'Ivoire, Ethiopia, France, Gabon, Georgia, Laos, Malaysia, Maldives, Mali, Switzerland, Tunisia, Tanzania and Palestine: for antibiotics, only antibiotics for systemic use (ATC code J01) and nitroimidazole derivatives (ATC code P01AB) are reported."], "unit": "defined daily doses per 1,000 inhabitants per day", "timespan": "2016-2022", "type": "Numeric", "owidVariableId": 1000078, "shortName": "did_antibacterials_and_antituberculosis", "lastUpdated": "2024-11-12", "nextUpdate": "2026-07-22", "citationShort": "WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) (2024) – processed by Our World in Data", "citationLong": "WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) (2024) – processed by Our World in Data. “Defined daily doses of antibiotics and antituberculosis drugs used per 1,000 inhabitants per day” [dataset]. WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS), “WHO GLASS” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1000078.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Rate of new tuberculosis cases", "source_url": "https://ourworldindata.org/grapher/incidence-of-tuberculosis-sdgs.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Estimated incidence of all forms of tuberculosis"], "row_count_total": 5597, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "148"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "175"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "197"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "215"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "228"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "237"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "242"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "241"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "235"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "229"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "218"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "204"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "194"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "194"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "197"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "200"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "204"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "209"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "212"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "213"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "205"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "206"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "206"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "204"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "203"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "307.7052"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "312.2114"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "317.5772"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "324.17758"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "326.75827"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "326.7312"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "324.492"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "319.32596"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "309.5995"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "303.956"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "295.28622"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "287.67355"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "278.2931"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "267.15274"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "258.88785"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "247.69968"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "232.53922"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "223.07628"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "214.67708"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "206.3748"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "198.72528"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "194.18031"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "190.03506"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "185.18423"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "180.91634"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "21"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "20"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "21"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "20"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "20"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "19"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "17"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "17"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "17"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "18"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "20"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "17"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "15"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "15"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "15"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "15"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "15"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "118"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "111"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "112"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "112"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "109"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "113"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "107"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "104"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "95"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "96"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "95"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "87"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "85"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "77"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "80"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "81"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "76"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "75"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "74"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "65"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "52"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "57"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "55"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "55"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "54"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "5.3"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "5.3"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "3.5"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "5.3"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "8.8"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "11"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "7.1"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "5.3"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "5.4"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "7.2"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "7.2"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "5.5"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "6.2"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "6.1"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "5.9"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "7.6"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "5.6"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "9.7"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "0"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "2"}], "rows_tail": [{"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "48"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "42"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "14"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "40"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "67"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "36"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "15"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "18"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "16"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "1.3"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "8.6"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "0"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "1.4"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "1"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "0.68"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "0"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "187.37431"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "186.89374"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "186.47076"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "186.29141"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "184.6343"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "182.96432"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "180.31314"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "176.89838"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "173.4296"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "170.39687"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "166.18083"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "163.26082"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "159.16869"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "154.6183"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "151.21239"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "147.45613"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "142.34831"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "138.7778"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "135.2258"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "131.95148"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "128.99347"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "129.7268"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "132.14767"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "131.66058"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "129.30185"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "94"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "87"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "76"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "66"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "61"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "54"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "49"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "47"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "45"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "45"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "45"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "42"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "47"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "48"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "43"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "33"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "40"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "40"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "39"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "39"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "33"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "34"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "36"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "38"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "40"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "759"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "728"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "695"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "662"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "631"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "602"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "577"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "554"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "534"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "514"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "495"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "475"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "456"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Estimated incidence of all forms of tuberculosis": "437"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Estimated incidence of all forms of tuberculosis": "406"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Estimated incidence of all forms of tuberculosis": "391"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Estimated incidence of all forms of tuberculosis": "376"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Estimated incidence of all forms of tuberculosis": "361"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Estimated incidence of all forms of tuberculosis": "346"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Estimated incidence of all forms of tuberculosis": "333"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Estimated incidence of all forms of tuberculosis": "319"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Estimated incidence of all forms of tuberculosis": "307"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Estimated incidence of all forms of tuberculosis": "295"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Estimated incidence of all forms of tuberculosis": "283"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Estimated incidence of all forms of tuberculosis": "272"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Estimated incidence of all forms of tuberculosis": "605"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Estimated incidence of all forms of tuberculosis": "617"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Estimated incidence of all forms of tuberculosis": "617"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Estimated incidence of all forms of tuberculosis": "617"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Estimated incidence of all forms of tuberculosis": "607"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Estimated incidence of all forms of tuberculosis": "588"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Estimated incidence of all forms of tuberculosis": "561"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Estimated incidence of all forms of tuberculosis": "527"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Estimated incidence of all forms of tuberculosis": "487"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Estimated incidence of all forms of tuberculosis": "450"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Estimated incidence of all forms of tuberculosis": "416"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Estimated incidence of all forms of tuberculosis": "384"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Estimated incidence of all forms of tuberculosis": "355"}, {"Entity": "Zimbabwe", 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{"raw_link": "https://ourworldindata.org/multidimensional-poverty-index", "title": "Beyond income: understanding poverty through the Multidimensional Poverty Index", "context": "Home\nPoverty\nBeyond income: understanding poverty through the Multidimensional Poverty Index\nThe experience of poverty goes beyond a very low income. What is the Multidimensional Poverty Index, and how does it capture the diverse ways people experience deprivation?\nBy\nJoe Hasell\n,\nPablo Arriagada\n,\nand\nBertha Rohenkohl\nDecember 16, 2024\nBrowse past versions\nCite this article\nReuse our work freely\nIn monitoring poverty, the focus is typically on monetary poverty — the extent to which people have very low income or consumption levels. The definition of “extreme poverty” used by the UN and World Bank, living on\nless than $3 per day\n, is one prominent example of such a measure.\nMeasuring poverty in terms of people's income or consumption is valuable. It is a broader measure than many realize. For example, it includes the value of many non-market transactions — such as food grown by subsistence farmers for consumption or cash transfers and gifts in kind from friends and family.\n1\nIncome is correlated with other important aspects of well-being, including\nhealth\n,\nnutrition\n,\neducation\n,\nleisure time\n, and access to basic facilities like\nelectricity\nand\nclean drinking water\n. A person’s level of income gives us a good indication of the kind of opportunities and living conditions they experience.\nHowever, this correlation is not perfect. People at similar income levels may have very different experiences when it comes to having a basic education, accessing water and electricity, or getting nutritious and regular meals.\n2\nIncome alone offers us an incomplete picture of poverty.\nFor this reason, when trying to understand and monitor poverty, it is important to look beyond income and consumption to other important dimensions in people’s lives.\nThe Multidimensional Poverty Index: combining multiple indicators into a single measure\nDifferent well-being indicators can move at different speeds or even in different directions. When we consider multiple metrics, comparisons across countries or over time can become more difficult.\nThis is why researchers and policymakers construct aggregate indicators to combine various dimensions of well-being into a single, comprehensive measure.\nOne well-known measure is the global Multidimensional Poverty Index (MPI), published by the\nOxford Poverty & Human Development Initiative (OPHI)\nand the\nHuman Development Report Office of the United Nations Development Programme (UNDP)\n. The MPI measures how people experience poverty in key areas of well-being across more than 100 countries. This data focuses on low- and middle-income countries, where acute poverty is more severe and widespread, so it does not include richer countries.\nHow is multidimensional poverty defined in the MPI?\nThe MPI is made up of ten indicators grouped into three dimensions: health, education, and living standards.\nDownload\nDimensions and indicators of the Multidimensional Poverty Index. Adapted from HDRO and OPHI,\nGlobal Multidimensional Poverty Index 2024\n(page 4),\navailable online\n.\nSimilar to traditional measures of monetary poverty, the calculation of the MPI involves setting poverty thresholds and then counting the number of households that fall below these thresholds. This is measured using data drawn from household surveys.\n3\nHowever, while monetary poverty measures are based on just a single poverty threshold — for example, living on\nless than $3 per day\n— the MPI sets a poverty threshold for each of the ten indicators.\n4\nA household falling below the threshold set for the indicator is described as being “deprived” in that measure. For example, a household is deprived in\nYears of schooling\nif no member has completed at least six years of schooling. It is considered deprived in the\nCooking fuel\nindicator if the household relies on solid fuel, such as dung, crops, wood, charcoal, or coal, for cooking (since these fuels contribute to indoor air pollution that\nkills millions\nevery year).\nYou can see details on the specific thresholds for each indicator in the table below.\n5\nDimension\nIndicator\nDeprived if…\nHealth\nNutrition\nAny person under 70 years of age for whom there is nutritional information is undernourished\nChild mortality\nA child under 18 has died in the household in the five-year period preceding the survey\nEducation\nYears of schooling\nNo eligible household member has completed six years of schooling\nSchool attendance\nAny school-aged child is not attending school up to the age at which he/she would complete class 8\nLiving Standards\nCooking fuel\nA household cooks using solid fuel, such as dung, agricultural crop, shrubs, wood, charcoal, or coal\nSanitation\nThe household has unimproved or no sanitation facility or it is improved but shared with other households\nDrinking water\nThe household’s source of drinking water is not safe or safe drinking water is a 30-minute or longer walk from home, roundtrip.\nElectricity\nThe household has no electricity\nHousing\nThe household has inadequate housing materials in any of the three components: floor, roof, or walls\nAssets\nThe household does not own more than one of these assets: radio, TV, telephone, computer, animal cart, bicycle, motorbike, or refrigerator, and does not own a car or truck\nTo calculate the MPI, researchers first establish whether a household is deprived in each of the ten indicators.\nThese ten deprivations are then summed up using weights so that the three dimensions — health, education, and living standards — contribute equally to the final score.\n6\nIndividuals are classified as living in multidimensional poverty (“MPI-poor”) if their household is deprived in at least one-third of the weighted indicators.\n7\nWhat measures are available within the Multidimensional Poverty Index dataset?\nThe MPI dataset provides three different measures of multidimensional poverty.\nThe share of the population in multidimensional poverty\nThe most straightforward way to measure multidimensional poverty is by looking at the share of the population classified as MPI-poor.\nThis interactive map shows estimates based on the latest data available for each country. These estimates are based on household surveys, which are not always conducted yearly. You can hover over a country to see the specific year the estimate applies to.\nThe intensity of multidimensional poverty\nLooking solely at the share of the population living in poverty does not capture the\nintensity\nof poverty that individuals experience. Two countries might have the same share of people living in poverty, but in one country, those people might be poorer on average than in the other.\nThe intensity of monetary poverty can be gauged by observing how far below the poverty line people’s incomes fall, as shown in the\nincome gap ratio\n.\nThe MPI includes a measure of poverty intensity (sometimes called “breadth of poverty”), reflecting the average share of indicators in which people living in multidimensional poverty experience deprivation.\n8\nThe map shows this intensity — the share of indicators in which people living in multidimensional poverty are deprived on average.\nFor example, a country with a figure of 40% means that in this country, the people who live in multidimensional poverty are deprived, on average, in 40% of the ten indicators included in the MPI.\nThe measure of intensity can also be used to calculate different indicators, such as the share of the population in\nsevere multidimensional poverty\n. A person deprived in more than half of the indicators is considered to be in severe poverty, facing greater poverty intensity than someone deprived in one-third of the indicators.\nThe Multidimensional Poverty Index\nMany would agree that the share of people experiencing poverty and the intensity of that poverty are important factors to consider. Poverty becomes worse not only when someone crosses the threshold into poverty but also when a person already living in poverty faces even greater deprivation.\nIf poverty reduction initiatives focus only on people close to the poverty threshold, they risk overlooking the needs of the very poorest. Therefore, it is important to consider both factors in guiding policy-making.\nThe Multidimensional Poverty Index addresses this by combining information on the share of people in multidimensional poverty with the intensity of their poverty — by multiplying these two measures.\n9\nAs a result, MPI values range from 0 to 1, with higher values indicating higher multidimensional poverty.\nFor example, a country with a share of 65% and an intensity of 40% will have an MPI of 0.65 x 0.40 = 0.26.\nIn practice, because\nthe share of people in poverty and the intensity\nof their poverty are closely correlated, the\nMPI is closely correlated with the share of the population\nin multidimensional poverty.\nAdditional measures of multidimensional poverty\nMultidimensional poverty in urban and rural places\nThe Oxford Poverty & Human Development Initiative breaks down all the MPI measures into rural and urban areas.\nThe chart shows that the share of people living in multidimensional poverty is higher in rural areas than in urban areas, but the extent of this gap varies considerably across countries.\nShare of the population deprived by individual indicators of multidimensional poverty\nFor each country, it is possible to examine the share of people living in households classified as deprived according to the various well-being indicators included in the Multidimensional Poverty Index. The chart shows this for Burkina Faso, but you can switch the interactive chart to many other countries.\nBeing deprived in a single indicator does not indicate that a household is multidimensionally poor under the MPI definition, but it highlights an area where they face hardship.\nTracking changes in multidimensional poverty over time\nAll the measures shown above reflect the current state of multidimensional poverty based on each country's most recent available data.\nHousehold surveys evolve over time, with indicators being added or removed. Such changes can make successive “current” estimates based on different indicators difficult to compare across time.\nTo address this, OPHI provides “\nharmonized over time”\nmeasures of multidimensional poverty. These harmonized measures include only the indicators available in every survey, enabling more reliable comparisons over time within the same country.\nWhile the harmonized measures may not capture the whole picture of multidimensional poverty today, they offer a clear view of how it has changed over time.\nThe chart shows the harmonized-over-time estimates of the share of the population in multidimensional poverty.\nWe have also produced charts of the harmonized data for:\nThe intensity of multidimensional poverty\nThe Multidimensional Poverty Index\nDoes multidimensional poverty mirror monetary poverty?\nGiven the MPI's goal to capture the diverse ways people experience deprivation beyond monetary poverty, how much does the MPI align with traditional measures of income?\nTo look at this, we compare OPHI’s estimates of multidimensional poverty with the World Bank’s estimates of extreme poverty, defined as living on\nless than $3 per day\n.\nThe chart below displays estimates of the share of the population in MPI poverty and extreme poverty based on each country's latest survey data.\nIt shows a positive relationship: countries with more people living in extreme monetary poverty usually also have a large share of people in multidimensional poverty.\nYou can see the relationship more clearly for countries with lower poverty levels by selecting logarithmic axes in the chart settings or using a\nhigher monetary poverty line\n.\nThe data makes clear that the two poverty measures are far from identical. For example,\nLesotho and Chad\nhave very similar rates of extreme monetary poverty but very different rates of multidimensional poverty. The opposite happens in\nSenegal and Malawi\n. In both countries, around half of the population is in multidimensional poverty. Still, their estimates of monetary poverty paint a very different picture.\nThese differences come down to things like access to essential services, such as healthcare and education, or how well income translates into good outcomes, such as adequate nutrition.\n10\nWhen we look at\nchanges over time\n, we usually see that monetary and multidimensional poverty move in the same direction and have fallen in most countries in recent years.\n11\nBut there are exceptions. In some countries like\nZimbabwe and Gambia\n, the two measures show different trends over time — with falling multidimensional poverty but increasing extreme monetary poverty.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nWhat can we learn from multidimensional poverty measures?\nWe have seen that multidimensional poverty is correlated with the more commonly used monetary measure of extreme poverty, but not perfectly.\nThis relationship gives us important insights into the nature of poverty. Extremely low incomes go hand-in-hand with many other forms of deprivation: poorer health, less access to education, and fewer basic facilities. Tracking multidimensional poverty alongside monetary poverty can help us better understand the reality of life on an extremely low income.\nEstimates of monetary and multidimensional poverty can also broaden our understanding of poverty in many ways. Tracking both measures gives us a more complete picture of poverty, as the broader MPI definition captures people who might not fall below the extreme poverty line but who still experience severe deprivation in other areas.\n12\nExamining disparities between different poverty measures and looking at the subcomponents of multidimensional poverty can help uncover underlying causes and identify opportunities for more integrated and impactful poverty reduction programs.\n13\nMultidimensional poverty measures can also help governments monitor progress in essential services, such as healthcare or education, which may not be visible through measures of people’s income or consumption.\nFinally, the two types of poverty estimates can serve as a sense-check on each other. This is valuable given the many steps in producing these estimates from often imperfect underlying data. For example, the MPI is used by the World Bank as a benchmark to cross-check the adjustments it makes to account for differences in the cost of living across countries in its monetary poverty estimates.\n14\nOther sources of data on multidimensional poverty\nThe MPI data produced by OPHI is the best-known multidimensional poverty index. It is included in the UN’s annual\nHuman Development Report\n.\nThe principle of combining various well-being indicators into a single multidimensional measure of poverty is also used by other research teams.\nAnother global data source on multidimensional poverty is the “Multidimensional Poverty Measure” (MPM) produced by the World Bank. This measure is heavily influenced by the MPI produced by OPHI. However, unlike the MPI, the MPM includes the World Bank’s monetary definition of extreme poverty as one of its dimensions. You can find and explore this data on the\nWorld Bank’s website\n.\nMany countries have adopted\nnational multidimensional poverty measures\nas a regular part of their approach to monitoring poverty. The Multidimensional Poverty Peer Network provides a\nlist of these countries\nand the latest national estimates.\nAcknowledgments\nThanks to Edouard Mathieu, Max Roser, Hannah Ritchie, Nicolai Suppa, and Usha Kanagaratnam for their feedback on this article and help with the underlying data.\nContinue reading on Our World in Data\nPoverty\nIn order to make progress against poverty in the future, we need to understand poverty around the world today and how it has changed.\nEndnotes\nSuch household income or consumption data relies on national household surveys. Exactly what gets counted as income or consumption varies across surveys. The World Bank provides a detailed discussion of this issue on the\nmethodology page\naccompanying its poverty and inequality data.\nFor example, in this article on\nmaternal health\n, we identify some “exemplar” countries – places where fewer women die from pregnancy-related causes compared to countries with a similar income level.\nThe MPI is constructed from data obtained from the\nDemographic and Health Survey (DHS)\n, the\nMultiple Indicators Cluster Survey (MICS)\n, and national surveys where recent data is unavailable from those two sources.\nYou can read more about the methodology used in the MPI in the book\nMultidimensional poverty measurement and analysis\nby Alkire et al. (2015).\nYou can find more information about how each threshold is defined in the OPHI MPI methodological note by Alkire, Kanagaratnam, and Suppa (2024), page 3,\navailable online.\nWhen adding up the number of indicators in which a household is deprived, some count for more than others. Each indicator within ”health” and ”education” is given a weight of 1/6, while the indicators within “living standards” are given a weight of 1/18 each. This structure ensures that the three dimensions are equally weighted, and each indicator within a dimension is also equally weighted. In the book\nMultidimensional poverty measurement and analysis\n(chapter 6), the authors discuss the decisions behind indicators’ weights and the cutoff for multidimensional poverty in more detail.\nNote that the assessment of poverty is made at the\nhousehold\nlevel. MPI indicators, however, count\nindividuals\n(both adults and children of any age). Everyone in the same household is classified as having the same poverty status. This is also a common approach to measuring extreme monetary poverty, but it overlooks the different experiences that can exist within households, for example, between boys and girls. This is often difficult to measure, given the available survey data.\nThe calculation uses the same weights as those used to define if a household is MPI-poor.\nIn monetary poverty, a similar estimate is also calculated, known as the\npoverty gap index\n.\nA more detailed comparison of MPI and monetary poverty measures can be found in the paper by\nEvans et al. (2023)\n.\nEvans, M., Nogales, R., & Robson, M. (2024). Monetary and Multidimensional Poverty: Correlation, Mismatches, and a Combined Approach.\nThe Journal of Development Studies\n, 60(1), 147-170.\nFor an analysis of different poverty measures over time, see the working paper by\nAlkire et al. (2020)\n.\nAlkire, S., F. Kovesdi, M. Pinilla-Roncancio and S. Scharlin-Pettee. (2020). Changes over time in the global Multidimensional Poverty Index and other measures: Towards national poverty reports, OPHI Research in Progress 57a, Oxford Poverty and Human Development Initiative, University of Oxford.\nBecause the OPHI estimates of multidimensional poverty and World Bank estimates of extreme monetary poverty draw on different household survey data, it is not possible to directly compare whether individual households are classified as poor in each case.\nOther sources, however, do allow for such a household-level comparison. For example, the World Bank produces multidimensional poverty estimates that combine monetary and non-monetary dimensions from the same survey data. You can explore that data on\nthe World Bank’s website\n, including how monetary and non-monetary dimensions of poverty overlap.\nFor example, Bader et al. (2016) conducted such an investigation for one particular country, Laos, through a detailed analysis of how monetary and multidimensional poverty overlap among households. Another study by Alkire et al. (2021) has investigated a similar question for India. Roelen et al. (2012) use a similar approach to investigate the drivers of child poverty in Vietnam.\nBy focusing on specific dimensions included in the MPI, like health for example, researchers can identify which countries are most successful in protecting the health of their populations, study why they are successful, and present that information as clearly as possible so that others can learn what works, adapting to different circumstances. This is the idea behind the innovative research platform\nExemplars in Global Health\n.\nOur World in Data contributed to this research effort, including the post by our colleague Hannah Ritchie,\nWhich countries are most successful in preventing maternal deaths?\nBader, C., Bieri, S., Wiesmann, U., & Heinimann, A. (2016). Differences between monetary and multidimensional poverty in the Lao PDR: Implications for targeting of poverty reduction policies and interventions.\nPoverty & Public Policy\n, 8(2), 171-197.\nAlkire, S., Oldiges, C., & Kanagaratnam, U. (2021). Examining multidimensional poverty reduction in India 2005/6–2015/16: Insights and oversights of the headcount ratio.\nWorld Development\n, 142, 105454.\nRoelen, K., Gassmann, F., & de Neubourg, C. (2012). False positives or hidden dimensions: what can monetary and multidimensional measurement tell us about child poverty in Vietnam?.\nInternational Journal of Social Welfare\n, 21(4), 393-407.\nAs described in the paper by\nJoliffe et al. (2022)\n, introducing the World Bank’s updated International Poverty Line of $2.15 per day.\nJolliffe, D. M., Atamanov, A., Lakner, C., Mahler, D. G., & Baah, S. K. T. (2022). Assessing the impact of the 2017 PPPs on the international poverty line and global poverty. Washington, DC: World Bank.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nJoe Hasell, Pablo Arriagada, and Bertha Rohenkohl (2024) - “Beyond income: understanding poverty through the Multidimensional Poverty Index” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260605-103245/multidimensional-poverty-index.html' [Online Resource] (archived on June 5, 2026).\nBibTeX citation\n@article{owid-multidimensional-poverty-index,\nauthor = {Joe Hasell and Pablo Arriagada and Bertha Rohenkohl},\ntitle = {Beyond income: understanding poverty through the Multidimensional Poverty Index},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://archive.ourworldindata.org/20260605-103245/multidimensional-poverty-index.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "multidimensional-poverty-index", "source_url": "https://ourworldindata.org/multidimensional-poverty-index", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "The experience of poverty goes beyond a very low income. 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"16399092", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Global Hunger Index": "", "GDP per capita": "3598.1716", "Population": "16914428", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Global Hunger Index": "", "GDP per capita": "3612.5059", "Population": "17441328", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Global Hunger Index": "", "GDP per capita": "3646.9597", "Population": "17973574", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Global Hunger Index": "", "GDP per capita": "3591.5642", "Population": "18513839", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Global Hunger Index": "", "GDP per capita": "3391.5955", "Population": "19059394", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Global Hunger Index": "27.5", "GDP per capita": "3503.035", "Population": "19603610", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": "Value represents the mid-point of its group in the GHI severity scale"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Global Hunger Index": "", "GDP per capita": "3585.1238", "Population": "20152935", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Global Hunger Index": "", "GDP per capita": "3673.4841", "Population": "20723967", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Global Hunger Index": "", "GDP per capita": "3708.069", "Population": "20723967", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Global Hunger Index": "", "GDP per capita": "6082.8423", "Population": "10137287", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Global Hunger Index": "", "GDP per capita": "6254.275", "Population": "10404820", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Global Hunger Index": "", "GDP per capita": "5532.0376", "Population": "10702697", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Global Hunger Index": "", "GDP per capita": "5509.083", "Population": "10860285", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Global Hunger Index": "", "GDP per capita": "6010.742", "Population": "10873146", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Global Hunger Index": "", "GDP per capita": "5964.5894", "Population": "10974607", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Global Hunger Index": "", "GDP per capita": "6474.16", "Population": "11158364", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Global Hunger Index": "", "GDP per capita": "6524.0625", "Population": "11369833", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Global Hunger Index": "", "GDP per capita": "6582.3486", "Population": "11594299", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Global Hunger Index": "", "GDP per capita": "6423.709", "Population": "11783454", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Global Hunger Index": "", "GDP per capita": "6170.334", "Population": "11892055", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Global Hunger Index": "", "GDP per capita": "6217.4116", "Population": "11971904", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Global Hunger Index": "", "GDP per capita": "5610.1914", "Population": "12087661", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Global Hunger Index": "", "GDP per capita": "4601.6606", "Population": "12232324", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Global Hunger Index": "", "GDP per capita": "4287.598", "Population": "12365901", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Global Hunger Index": "", "GDP per capita": "4004.6646", "Population": "12483433", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Global Hunger Index": "", "GDP per capita": "3819.2334", "Population": "12636442", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Global Hunger Index": "", "GDP per capita": "3631.5376", "Population": "12804062", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Global Hunger Index": "", "GDP per capita": "2954.0994", "Population": "12959154", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Global Hunger Index": "", "GDP per capita": "3299.4138", "Population": "13142791", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Global Hunger Index": "", "GDP per capita": "3885.3938", "Population": "13356551", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Global Hunger Index": "", "GDP per capita": "4358.926", "Population": "13595421", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Global Hunger Index": "", "GDP per capita": "5003.4873", "Population": "13817887", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Global Hunger Index": "", "GDP per capita": "5031.6875", "Population": "14013811", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Global Hunger Index": "", "GDP per capita": "5081.1123", "Population": "14207367", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Global Hunger Index": "", "GDP per capita": "5102.7144", "Population": "14399008", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Global Hunger Index": "", "GDP per capita": "5070.4023", "Population": "14600297", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Global Hunger Index": "", "GDP per capita": "5234.384", "Population": "14812484", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Global Hunger Index": "", "GDP per capita": "5415.4697", "Population": "15034457", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Global Hunger Index": "", "GDP per capita": "4993.8438", "Population": "15271377", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Global Hunger Index": "", "GDP per capita": "4527.7197", "Population": "15526887", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Global Hunger Index": "27.5", "GDP per capita": "4827.089", "Population": "15797220", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": "Value represents the mid-point of its group in the GHI severity scale"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Global Hunger Index": "", "GDP per capita": "5036.761", "Population": "16069061", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Global Hunger Index": "", "GDP per capita": "5218.0225", "Population": "16340829", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Global Hunger Index": "", "GDP per capita": "5215.253", "Population": "16340829", "World region according to OWID": "Africa", "Global Hunger Index (Annotations)": ""}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "global-hunger-index-vs-gdp-per-capita", "metadata_url": "https://ourworldindata.org/grapher/global-hunger-index-vs-gdp-per-capita.metadata.json", "chart_title": "Global Hunger Index vs. GDP per capita", "chart_subtitle": "The Global Hunger Index is measured on a 100-point scale where 0 is the best (no hunger) and 100 the worst. GDP per capita is adjusted for inflation and differences in living costs between countries.", "chart_note": "GDP per capita is expressed in international-$ at 2021 prices.", "chart_citation": "Concern Worldwide and Welthungerhilfe (2021); Eurostat, OECD, IMF, and World Bank (2026)", "original_chart_url": "https://ourworldindata.org/grapher/global-hunger-index-vs-gdp-per-capita", "owid_column_metadata": {"Global Hunger Index (2021)": {"titleShort": "Global Hunger Index", "titleLong": "Global Hunger Index", "unit": "", "timespan": "2000-2021", "type": "Numeric", "owidVariableId": 1206526, "shortName": "global_hunger_index__2021", "lastUpdated": "2022-02-24", "citationShort": "Concern Worldwide and Welthungerhilfe (2021) – processed by Our World in Data", "citationLong": "Concern Worldwide and Welthungerhilfe (2021) – processed by Our World in Data. “Global Hunger Index” [dataset]. Concern Worldwide and Welthungerhilfe, “Global Hunger Index” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1206526.metadata.json"}, "GDP per capita, PPP (constant 2021 international $)": {"titleShort": "GDP per capita", "titleLong": "GDP per capita - World Bank – In constant international-$", "descriptionShort": "Average economic output per person in a country or region per year. This data is adjusted for inflation and differences in living costs between countries.", "descriptionKey": ["GDP per capita is a comprehensive measure of people's average income. It helps compare income levels across countries and track how they change over time. It is especially useful for understanding trends in economic growth and living standards.", "GDP per capita is calculated as the value of all final goods and services produced each year in a country (the gross domestic product), divided by the population. It represents the average economic output per person.", "This indicator shows the large inequality between people in different countries. In the poorest countries, average incomes are below $1,000 per year; in rich countries, they are more than 50 times higher.", "This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "This data comes from the World Bank and starts in 1990. For estimates going back several centuries, explore our chart of GDP per capita from the [Maddison Project Database](https://ourworldindata.org/grapher/gdp-per-capita-maddison-project-database)."], "shortUnit": "$", "unit": "international-$ in 2021 prices", "timespan": "1990-2024", "type": "Numeric", "owidVariableId": 1204826, "shortName": "ny_gdp_pcap_pp_kd", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "Eurostat, OECD, IMF, and World Bank (2026) – with minor processing by Our World in Data", "citationLong": "Eurostat, OECD, IMF, and World Bank (2026) – with minor processing by Our World in Data. “GDP per capita – World Bank – In constant international-$” [dataset]. Eurostat, OECD, IMF, and World Bank, “World Development Indicators 125” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1204826.metadata.json"}, "Population (historical)": {"titleShort": "Population", "titleLong": "Population", "descriptionShort": "Population by country, available from 10,000 BCE to 2023, based on data and estimates from different sources.", "descriptionKey": ["Population is the most commonly used metric throughout Our World in Data. It is used directly to understand population growth over time, and indirectly to calculate per-capita indicators, making it easier to compare countries of different sizes.", "We construct this indicator by combining multiple sources covering different periods.\n  - HYDE v3.3 (2023): historical estimates from 10,000 BCE to 1799.\n  - Gapminder v7 (2022): for 1800-1949.\n  - UN World Population Prospects (2024): for 1950 onwards, including 2100 projections.\n  - Gapminder Systema Globalis (2023): additional source for former countries (Yugoslavia, USSR, etc.)", "Breaks in the data may occur at the boundaries between sources due to their methodological differences.", "You can read more about the sources and methodology in our [dedicated article](https://ourworldindata.org/population-sources). We also provide a table of sources showing the source we use for each country-year.", "We calculate geographical aggregates (continents, income groups, etc.) by summing individual country populations. For years before 1800, we rely directly on HYDE's values for continents to ensure historical consistency."], "descriptionProcessing": "### Combination of different sources\nWe construct our long-run population data by combining multiple sources:\n\n- 10,000 BCE–1799: historical estimates by HYDE (v3.3).\n\n- 1800–1949: historical estimates by Gapminder (v7).\n\n- 1950–2023: population records from the United Nations World Population Prospects (2024 revision).\n\n**Geographical aggregates**\n\n- For most years, we calculate aggregates by summing the population of member countries.\n- We do this based on [our definition of continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and the [World Bank’s income groups](https://ourworldindata.org/grapher/world-bank-income-groups).\n- The only exception is before 1800, where we use HYDE's estimates for continents (but not income groups).\n\nFor most of the years, we've estimated regional aggregates by summing the population of countries in each region. We've relied on [our continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and [World Bank income group definitions](https://ourworldindata.org/grapher/world-bank-income-groups). The only exception is before 1800, where we've used HYDE's estimates on continents (but not income groups).\n\n**World**\n- Before 1800: we use data from HYDE.\n- 1800-1950: we estimate the global population by summing all available countries in the dataset.\n- After 1950, we rely on estimates from the United Nations World Population Prospects.", "shortUnit": "", "unit": "people", "timespan": "-10000-2023", "type": "Integer", "owidVariableId": 953903, "shortName": "population_historical", "lastUpdated": "2024-07-15", "nextUpdate": "2026-07-15", "citationShort": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data", "citationLong": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data. “Population – HYDE, Gapminder, UN – Long-run data” [dataset]. PBL Netherlands Environmental Assessment Agency, “History Database of the Global Environment 3.3”; Gapminder, “Population v7”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update”; Gapminder, “Systema Globalis” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/953903.metadata.json"}, "World region according to OWID": {"titleShort": "World region according to OWID", "titleLong": "World region according to OWID", "descriptionShort": "Regions defined by Our World in Data, which are used in OWID charts and maps.", "unit": "", "timespan": "2023-2023", "type": "Continent", "owidVariableId": 900801, "shortName": "owid_region", "lastUpdated": "2023-01-01", "citationShort": "Our World in Data – processed by Our World in Data", "citationLong": "Our World in Data – processed by Our World in Data. “World region according to OWID” [dataset]. Our World in Data, “Regions” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/900801.metadata.json"}, "1206526-annotations": {"titleShort": "1206526-annotations", "titleLong": "1206526-annotations", "type": "SeriesAnnotation", "citationShort": " – processed by Our World in Data", "citationLong": " – processed by Our World in Data. “1206526-annotations” [dataset].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/undefined.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Average learning outcomes vs. GDP per capita", "source_url": "https://ourworldindata.org/grapher/learning-outcomes-vs-gdp-per-capita.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Harmonized test scores among all students", "GDP per capita", "Population", "World regions according to WB"], "row_count_total": 7246, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Harmonized test scores among all students": "", "GDP per capita": "1617.8264", "Population": "20130334", "World regions according to WB": "Middle East, North Africa, Afghanistan and Pakistan (WB)"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Harmonized test scores among all students": "", "GDP per capita": "1454.1108", "Population": "20284303", "World regions according to WB": "Middle East, North Africa, Afghanistan and Pakistan (WB)"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Harmonized test scores among all students": "", "GDP per capita": "1774.3087", "Population": "21378123", "World regions according to WB": "Middle East, North Africa, Afghanistan and Pakistan (WB)"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Harmonized test scores among all students": "", "GDP per capita": "1815.9282", "Population": "22733053", "World regions according to WB": "Middle East, North Africa, Afghanistan and Pakistan (WB)"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Harmonized test scores among all students": "", "GDP per capita": "1776.9182", "Population": "23560656", "World regions according to WB": "Middle East, North Africa, Afghanistan and Pakistan (WB)"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Harmonized test scores among all students": "", "GDP per capita": "1908.1147", "Population": "24404575", "World regions according to WB": "Middle East, North Africa, Afghanistan and Pakistan (WB)"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Harmonized test scores among all students": "", "GDP per capita": "1929.7239", "Population": "25424100", "World regions according to WB": "Middle East, North Africa, Afghanistan and Pakistan (WB)"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Harmonized test scores among all students": "", "GDP per capita": "2155.353", "Population": "25909852", "World regions according to WB": "Middle East, North Africa, Afghanistan and Pakistan (WB)"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Harmonized test scores among all students": "", "GDP per capita": "2191.5044", "Population": "26482631", "World regions according to WB": "Middle East, North Africa, Afghanistan and Pakistan (WB)"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Harmonized test scores among all students": "", "GDP per capita": "2565.022", "Population": "27466102", "World regions according to WB": "Middle East, North Africa, Afghanistan and Pakistan (WB)"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Harmonized test scores among all students": "", "GDP per capita": "2848.5862", "Population": "28284088", "World regions according to WB": "Middle East, North Africa, Afghanistan and Pakistan (WB)"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Harmonized test scores among all students": "", "GDP per capita": "2757.0525", "Population": "29347708", "World regions according to WB": "Middle East, North Africa, Afghanistan and Pakistan (WB)"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Harmonized test scores among all students": "", "GDP per capita": "2985.319", "Population": "30560036", "World regions according to WB": "Middle East, North Africa, Afghanistan and Pakistan (WB)"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Harmonized test scores among all students": "", "GDP per capita": "3046.5798", "Population": "31622709", "World regions according to WB": "Middle East, 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{"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Harmonized test scores among all students": "", "GDP per capita": "5102.7144", "Population": "14399008", "World regions according to WB": "Sub-Saharan Africa (WB)"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Harmonized test scores among all students": "", "GDP per capita": "5070.4023", "Population": "14600297", "World regions according to WB": "Sub-Saharan Africa (WB)"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Harmonized test scores among all students": "396", "GDP per capita": "5234.384", "Population": "14812484", "World regions according to WB": "Sub-Saharan Africa (WB)"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Harmonized test scores among all students": "396.13882", "GDP per capita": "5415.4697", "Population": "15034457", "World regions according to WB": "Sub-Saharan Africa (WB)"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Harmonized test scores among all students": "", "GDP per capita": "4993.8438", "Population": "15271377", "World regions according to WB": "Sub-Saharan Africa (WB)"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Harmonized test scores among all students": "396.13882", "GDP per capita": "4527.7197", "Population": "15526887", "World regions according to WB": "Sub-Saharan Africa (WB)"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Harmonized test scores among all students": "", "GDP per capita": "4827.089", "Population": "15797220", "World regions according to WB": "Sub-Saharan Africa (WB)"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Harmonized test scores among all students": "", "GDP per capita": "5036.761", "Population": "16069061", "World regions according to WB": "Sub-Saharan Africa (WB)"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Harmonized test scores among all students": "", "GDP per capita": "5218.0225", "Population": "16340829", "World regions according to WB": "Sub-Saharan Africa (WB)"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Harmonized test scores among all students": "", "GDP per capita": "5215.253", "Population": "16340829", "World regions according to WB": "Sub-Saharan Africa (WB)"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "learning-outcomes-vs-gdp-per-capita", "metadata_url": "https://ourworldindata.org/grapher/learning-outcomes-vs-gdp-per-capita.metadata.json", "chart_title": "Average learning outcomes vs. GDP per capita", "chart_subtitle": "Average learning outcomes correspond to harmonized scores across standardized, psychometrically-robust international and regional student achievement tests. GDP data is adjusted for inflation and differences in living costs between countries.", "chart_note": "GDP per capita data is expressed in international-$ at 2021 prices.", "chart_citation": "World Bank (2024); World Bank; Eurostat, OECD, IMF, and World Bank (2026)", "original_chart_url": "https://ourworldindata.org/grapher/learning-outcomes-vs-gdp-per-capita", "owid_column_metadata": {"Harmonized test scores among all students": {"titleShort": "Harmonized test scores among all students", "titleLong": "Harmonized test scores among all students", "descriptionShort": "Average learning outcomes correspond to harmonized test scores across standardized, psychometrically-robust international and regional student achievement tests.", "descriptionKey": ["Harmonized learning outcomes combine student test results into scores that can be compared across countries.", "This data includes developing countries that are often missing from major international tests by incorporating regional assessments.", "The data combines well-known international tests like [TIMSS, PIRLS](https://timssandpirls.bc.edu/), and [PISA](https://www.oecd.org/en/about/programmes/pisa.html) with regional tests like [SACMEQ](https://healtheducationresources.unesco.org/organizations/southern-and-eastern-africa-consortium-monitoring-educational-quality-sacmeq).", "Test scores are adjusted using statistical methods so they can be compared fairly across different subjects, grade levels, and testing years.", "This creates a dataset where countries can be compared on the same scale, accounting for differences in when tests were taken and what grades were tested.", "The scoring system is based on TIMSS standards where 300 points represents basic skills and 625 points shows advanced performance.", "Higher scores mean students in that country typically perform better on these academic tests, though the tests don't cover all subjects or age groups."], "unit": "score", "timespan": "2010-2020", "type": "Numeric", "owidVariableId": 1104392, "shortName": "harmonized_test_scores__sex_all_students", "lastUpdated": "2025-08-20", "nextUpdate": "2026-08-20", "citationShort": "World Bank (2024) – processed by Our World in Data", "citationLong": "World Bank (2024) – processed by Our World in Data. “Harmonized test scores among all students” [dataset]. World Bank, “Human Capital Index - Harmonized Test Scores” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1104392.metadata.json"}, "GDP per capita, PPP (constant 2021 international $)": {"titleShort": "GDP per capita", "titleLong": "GDP per capita - World Bank – In constant international-$", "descriptionShort": "Average economic output per person in a country or region per year. This data is adjusted for inflation and differences in living costs between countries.", "descriptionKey": ["GDP per capita is a comprehensive measure of people's average income. It helps compare income levels across countries and track how they change over time. It is especially useful for understanding trends in economic growth and living standards.", "GDP per capita is calculated as the value of all final goods and services produced each year in a country (the gross domestic product), divided by the population. It represents the average economic output per person.", "This indicator shows the large inequality between people in different countries. In the poorest countries, average incomes are below $1,000 per year; in rich countries, they are more than 50 times higher.", "This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)", "This data comes from the World Bank and starts in 1990. For estimates going back several centuries, explore our chart of GDP per capita from the [Maddison Project Database](https://ourworldindata.org/grapher/gdp-per-capita-maddison-project-database)."], "shortUnit": "$", "unit": "international-$ in 2021 prices", "timespan": "1990-2024", "type": "Numeric", "owidVariableId": 1204826, "shortName": "ny_gdp_pcap_pp_kd", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "Eurostat, OECD, IMF, and World Bank (2026) – with minor processing by Our World in Data", "citationLong": "Eurostat, OECD, IMF, and World Bank (2026) – with minor processing by Our World in Data. “GDP per capita – World Bank – In constant international-$” [dataset]. Eurostat, OECD, IMF, and World Bank, “World Development Indicators 125” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1204826.metadata.json"}, "Population (historical)": {"titleShort": "Population", "titleLong": "Population", "descriptionShort": "Population by country, available from 10,000 BCE to 2023, based on data and estimates from different sources.", "descriptionKey": ["Population is the most commonly used metric throughout Our World in Data. It is used directly to understand population growth over time, and indirectly to calculate per-capita indicators, making it easier to compare countries of different sizes.", "We construct this indicator by combining multiple sources covering different periods.\n  - HYDE v3.3 (2023): historical estimates from 10,000 BCE to 1799.\n  - Gapminder v7 (2022): for 1800-1949.\n  - UN World Population Prospects (2024): for 1950 onwards, including 2100 projections.\n  - Gapminder Systema Globalis (2023): additional source for former countries (Yugoslavia, USSR, etc.)", "Breaks in the data may occur at the boundaries between sources due to their methodological differences.", "You can read more about the sources and methodology in our [dedicated article](https://ourworldindata.org/population-sources). We also provide a table of sources showing the source we use for each country-year.", "We calculate geographical aggregates (continents, income groups, etc.) by summing individual country populations. For years before 1800, we rely directly on HYDE's values for continents to ensure historical consistency."], "descriptionProcessing": "### Combination of different sources\nWe construct our long-run population data by combining multiple sources:\n\n- 10,000 BCE–1799: historical estimates by HYDE (v3.3).\n\n- 1800–1949: historical estimates by Gapminder (v7).\n\n- 1950–2023: population records from the United Nations World Population Prospects (2024 revision).\n\n**Geographical aggregates**\n\n- For most years, we calculate aggregates by summing the population of member countries.\n- We do this based on [our definition of continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and the [World Bank’s income groups](https://ourworldindata.org/grapher/world-bank-income-groups).\n- The only exception is before 1800, where we use HYDE's estimates for continents (but not income groups).\n\nFor most of the years, we've estimated regional aggregates by summing the population of countries in each region. We've relied on [our continents](https://ourworldindata.org/world-region-map-definitions#our-world-in-data) and [World Bank income group definitions](https://ourworldindata.org/grapher/world-bank-income-groups). The only exception is before 1800, where we've used HYDE's estimates on continents (but not income groups).\n\n**World**\n- Before 1800: we use data from HYDE.\n- 1800-1950: we estimate the global population by summing all available countries in the dataset.\n- After 1950, we rely on estimates from the United Nations World Population Prospects.", "shortUnit": "", "unit": "people", "timespan": "-10000-2023", "type": "Integer", "owidVariableId": 953903, "shortName": "population_historical", "lastUpdated": "2024-07-15", "nextUpdate": "2026-07-15", "citationShort": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data", "citationLong": "HYDE (2023); Gapminder (2022); UN WPP (2024) – with major processing by Our World in Data. “Population – HYDE, Gapminder, UN – Long-run data” [dataset]. PBL Netherlands Environmental Assessment Agency, “History Database of the Global Environment 3.3”; Gapminder, “Population v7”; United Nations, “World Population Prospects”; United Nations, “World Population Prospects - Interim Update”; Gapminder, “Systema Globalis” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/953903.metadata.json"}, "World regions according to WB": {"titleShort": "World regions according to WB", "titleLong": "World regions according to WB", "descriptionShort": "Regions as defined by the World Bank.", "unit": "", "timespan": "2023-2023", "type": "Ordinal", "owidVariableId": 954465, "shortName": "wb_region", "lastUpdated": "2023-01-01", "citationShort": "World Bank – processed by Our World in Data", "citationLong": "World Bank – processed by Our World in Data. “World regions according to WB” [dataset]. World Bank, “World Bank Country and Lending Groups” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/954465.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "1955f21799a355e71b69"},
{"raw_link": "https://ourworldindata.org/antibiotics-livestock", "title": "Large amounts of antibiotics are used in livestock, but several countries have shown this doesn’t have to be the case", "context": "Home\nAntibiotics & Antibiotic Resistance\nLarge amounts of antibiotics are used in livestock, but several countries have shown this doesn’t have to be the case\nOveruse is a risk for antibiotic resistance, but there are ways to reduce it.\nBy\nHannah Ritchie\nand\nFiona Spooner\nDecember 9, 2024\nBrowse past versions\nCite this article\nReuse our work freely\nFor humanity, antibiotics are a huge blessing. Antibiotics have saved millions of lives from bacterial infections. However, there is growing concern that these bacteria will become resistant to the drugs we use against them.\nWhen we think about antimicrobial resistance, we often focus on what drugs\nhumans\ntake. We might not even consider the use of antibiotics in livestock, but they also pose a threat.\nIn fact, much more antibiotics are given to livestock than to humans. Researchers previously estimated that, in the 2010s, around 70% of antibiotics used globally were given to farm animals..\n1\nWhile there hasn’t been an update of these figures in the last few years, it’s likely that more antibiotics are still used in livestock than humans.\nOverusing antibiotics in livestock increases the risk of disease in animals and humans in several ways. First, antibiotics are often used as a cheap substitute for basic animal welfare practices, such as giving animals enough space, keeping their living environments clean, and ensuring that barns are well-ventilated.\n2\nA failure to maintain hygienic conditions on farms increases the risk of disease for both livestock and humans.\nThe overuse of antibiotics can also increase the risk of bacteria that are resistant to treatment. That threatens the health of the animals but can also be a risk for humans for crossover diseases — diseases that also occur in humans, and we can treat with antibiotics. Finally, humans can be exposed to resistant pathogens by eating contaminated meat and dairy products.\nTo reduce the risks of antibiotic resistance, we don’t have to only consider the use of antibiotics in humans but also how to use them more effectively in the meat and dairy sector.\nWe know that this can be done: there are clear success stories of countries that have done so while maintaining healthy and productive meat and dairy industries.\nIn this article, we look at antibiotic use in livestock worldwide and what can be done to reduce it.\nPigs, chickens, and cattle use very different amounts of antibiotics\nOne of the key challenges in understanding the extent and risks of antibiotic resistance in livestock is the lack of transparent data sharing from countries. We go into more detail on this in a\nseparate article\n.\nWhile we don’t have high-quality data for many countries in the world, we can still understand the usage of different types of animals from countries that monitor and report their data transparently.\nIntensive livestock consumes four times the amount of antibiotics compared to livestock raised outdoors. However, the amount of antibiotics used in\ndifferent\nanimals varies a lot.\nOf course, comparing the\ntotal\namount of antibiotics given to cows, sheep, pigs, and chickens would be unfair. Cows are bigger than chickens, so we’d expect them to need more antibiotics for the same impact (just as adults tend to need larger doses of medicine than children).\nSo, researchers compare antibiotic use in units adjusted for the size of animals — usually as the number of milligrams used per kilogram of meat product.\nChickens tend to receive the least antibiotics. You can see this in the chart below: they receive about seven times less than sheep and five times less than pigs.\n3\nCows also receive less than pigs and sheep.\nOf course, the exact amount of antibiotics given varies\nacross\ncountries — as we’ll soon see. However, even\nwithin\ncountries, the rankings of animal antibiotic use remain the same.\nDownload\nOne of the reasons why antibiotics are used in lower quantities in chickens is that they are killed at a much younger age. Fast-growing breeds reach their “slaughter weight”\nat around 42 days\n, so they’re often slaughtered when they’re just 40 to 50 days old. Since their lifespan is shorter, they consume fewer antibiotics. Pigs are\nusually slaughtered\nat a slightly older age when they’re around five to six months.\nThe fact that intensive livestock get far more antibiotics than animals raised outdoors is one reason why cows tend to get less antibiotics than pigs. Although many cows\nare raised intensively\nin feedlots for\nsome\nportion of their life, they tend to spend more of their lives outdoors than pigs, in less intensive conditions.\nAntibiotic use differs hugely between countries\nThere are also massive gaps across countries.\nResearchers Ranya Mulchandani and colleagues estimated antibiotic use across the world based on the best available data, as well as extrapolations for those countries that don’t release data.\n3\nThe chart below shows their results; each bar represents a country. We have the most intense antibiotic users on the left, and on the right, we have the least intense. The figures are given in milligrams per kilogram of meat produced, which adjusts for the size of animals.\nThailand uses 80 times as much antibiotics for livestock as Norway.\nEach bar is colored by region. You can see that most of the bars on the left — the most intense users — tend to be in Asia, with a few in the Americas or Oceania. But most clearly, on the right, we see European and African countries. They tend to use far less than other regions.\nThe map below shows the same data. Asia, Oceania, and most of the Americas use a lot of antibiotics. Europe and Africa, in blue, tend to use less than 50 milligrams per kilogram.\nYou can also see this when we look at\nregional averages\n.\nIn the bar chart, you can see this given as a regional average. Again, the intensity of antibiotic use is highest in Asia, followed by the Americas, Africa, and then Europe.\nThere are a few reasons why these differences are so large.\nThe first one is affordability and access: farmers in Africa, for example, have less access, just like they have less access to other farming inputs, such as\nfertilizers\nor pesticides.\nAnother reason is the differences in regulatory and industry norms regarding antibiotic use. As I’ll discuss later, antibiotic use has dropped significantly in Europe, partly due to regulation.\nFinally, the most popular types of livestock make a difference. As we saw earlier, sheep and pigs tend to receive far more antibiotics than cattle or chickens, even after adjusting for their size.\nThat means countries that raise many pigs would tend to use more antibiotics. More than half of Thailand’s meat supply is in\nthe form of pigmeat\n. In China, it’s two-thirds. That’s more than the global average of one-third and more than most other countries.\nSome countries have reduced antibiotic use a lot\nAntibiotics can play an important role in preventing disease and illness in animals. This is no different from humans. So, removing them completely is not necessarily the best option.\nThe key is to use them more effectively: changing farming practices to reduce antibiotic use where it’s in excess, or there are alternative ways to prevent disease, and using antibiotics in smaller quantities when it is needed. Many antibiotics given today are not used to prevent disease but to promote growth and produce meat more efficiently.\n4\nWe know countries can reduce antibiotic use while maintaining healthy livestock sectors because some countries have already achieved rapid reductions.\nSeveral European countries have been particularly successful. You can see a big drop in antibiotic use in the chart below.\nBetween 2011 and 2022, sales of veterinary antibiotics — measured in tonnes — fell by more than half.\n5\nThe use of antibiotics considered critically important in human medicine also fell by half, with some specific drugs falling by 80% to 90%.\nAntibiotic use in livestock in Europe per kilogram of meat\nAntibiotic use is measured in milligrams of antibiotics per kilogram of food-producing animals. This is corrected for differences in livestock numbers and types, normalizing to a population-corrected unit (PCU).\nRegulation has played a crucial role.\n6\nIn many European countries, antibiotics today can only be administered with a prescription from a veterinarian.\n7\nVets are then given strict guidelines on how much and when they can be prescribed. Several countries — such as Denmark, Belgium, and France — have also imposed taxes or prohibited discounts on veterinary antibiotic sales, which reduces their incentives to prescribe them when they’re not essential.\n8\nOther changes in the livestock sector have probably also played a role. Reducing the\nintensity\nof animal production might have reduced the need for antibiotics. Countries such as the Netherlands have started to move away from fast-growing to\nslower-growing chicken breeds\n; this improves animal welfare and reduces the need for antibiotics.\n9\nAlthough animals tend to need fewer antibiotics when they have shorter lives (so that fast-growing breeds are “better” in this regard), the intensity of antibiotic use in slower-growing breeds is\nmuch\nlower and, therefore, results in a net reduction in the amount of antibiotics needed.\nThese countries have managed to decouple a healthy and productive livestock industry from antibiotic use\nFarmers don’t spend money on antibiotics for no reason. They do it because they believe that it keeps their animals healthy and improves the profitability of their farm.\nOne of their key concerns is that reducing antibiotic use would affect their income and bottom line. But the evidence suggests otherwise. The dramatic reduction in antibiotic use in Europe gives us insight into how these reductions affect farm productivity.\nA number of studies suggest that it did not affect economic performance. One study found that antibiotic use on pig farms in the Netherlands fell by 54% between 2004 and 2016, without negative impacts on animal welfare or economic results.\n10\nOther interventions in Europe have found similar results: antibiotic use has fallen while animal health and farmer profit remained stable or, in some cases, increased.\n11\nIf we look at the amount of\npigmeat produced\nper animal, we can see that it has been stable (or, in some cases, has continued increasing) in European countries. And most of these countries get higher yields than in China, which uses far more antibiotics. The same is true\nfor chicken\n. And\nbeef\n.\nDecoupling antibiotic use from productivity doesn’t happen on its own. Farmers need to implement other strategies to manage disease and improve the living conditions of animals on the farm. These interventions include things like vaccinations, providing animals with more space and ventilation, and keeping equipment and feeding spots clean and disinfected.\nAntibiotics have, in some sense, been a substitute for focusing on these alternative ways of managing animal health. But these exist and can be just as cost-effective.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nMore selective use of antibiotics, improved welfare practices,\nand\neating less meat could reduce the risks of antimicrobial resistance\nReducing the\nintensity\nof antibiotic use in livestock is the clearest path to reducing overall antibiotic use and the health risks of antimicrobial resistance.\nThomas van Boeckel and colleagues previously studied the impact of setting global guidelines to have antibiotic use below 50 milligrams per PCU.\n2\n. That’s around half the global average today. In the map below, you can see which countries fall below or above this suggested global guideline.\nMost countries are above it, but it’s not unachievable: 50 mg per PCU is far higher than the quantities used in many European countries, New Zealand, and the United States.\nIf the world achieved this, then antibiotic use in livestock would fall between half and two-thirds.\nImplementing other strategies to control and reduce disease in livestock can help with this. Basic\nwelfare practices\nsuch as making sure that feeding and watering troughs are emptied regularly; disinfecting farm equipment; changing bedding, straw, or other materials; providing adequate ventilation in indoor sheds or barns; and giving animals enough space can all reduce the risk of disease spread, and the need for antibiotics. It improves the lives of the animals, too.\nThe other way to reduce total antibiotic use is to reduce overall meat consumption. Fewer animals being farmed means less use of antibiotics. As I’ve written elsewhere, this would help across several other environmental problems, including\nclimate change\n,\nland use\n,\ndeforestation\n, and\nbiodiversity loss\n.\nIf meat consumption was reduced to 40 grams per person per day — equivalent to a thin beef patty — then antibiotic use could fall by two-thirds.\n12\nAmericans currently eat four to five times as much meat as this, so it would require pretty radical shifts in their diets.\nOf course, doing both — cutting the intensity of antibiotic use and reducing meat consumption — would lead to even more dramatic declines in antibiotic use.\nAcknowledgments\nMany thanks to Max Roser and Edouard Mathieu for their comments on this article, and to Thomas van Boeckel for discussion and feedback.\nEndnotes\nGetting a definitive figure here is difficult because of data reporting and transparency issues, which I will describe later.\nSeveral of the largest studies on antimicrobial use in livestock cite a figure of 72% or 73%. This appears to come from a study by Thomas van Boeckel and colleagues, published in 2017.\nIt estimates that the\nintensity\nof antimicrobial use in humans is around 118 mg per kg. And 133 mg per kg in animals. When we multiply these figures by the estimated\nbiomass\n(i.e., the weight) of humans and livestock, we get a total estimate for humans of around 35,000 tonnes a year, compared to 85,000 tonnes in livestock. That would mean livestock accounted for around 72% of total antibiotic use.\nA more recent study by Katie Tiseo and colleagues (2020) estimated that 66% of antimicrobials were used in livestock.\nVan Boeckel, T. P., Pires, J., Silvester, R., Zhao, C., Song, J., Criscuolo, N. G., ... & Laxminarayan, R. (2019). Global trends in antimicrobial resistance in animals in low-and middle-income countries. Science.\nVan Boeckel, T. P., Brower, C., Gilbert, M., Grenfell, B. T., Levin, S. A., Robinson, T. P., ... & Laxminarayan, R. (2017). Global trends in antimicrobial use in food animals. Proceedings of the National Academy of Sciences.\nMulchandani, R., Wang, Y., Gilbert, M., & Van Boeckel, T. P. (2023). Global trends in antimicrobial use in food-producing animals: 2020 to 2030. PLOS Global Public Health.\nTiseo, K., Huber, L., Gilbert, M., Robinson, T. P., & Van Boeckel, T. P. (2020). Global trends in antimicrobial use in food animals from 2017 to 2030. Antibiotics.\nVan Boeckel, T. P., Brower, C., Gilbert, M., Grenfell, B. T., Levin, S. A., Robinson, T. P., ... & Laxminarayan, R. (2015). Global trends in antimicrobial use in food animals. Proceedings of the National Academy of Sciences.\nMulchandani, R., Wang, Y., Gilbert, M., & Van Boeckel, T. P. (2023). Global trends in antimicrobial use in food-producing animals: 2020 to 2030. PLOS Global Public Health.\nScientists discovered that giving antibiotics to animals promoted growth as far back as the 1940s, but there’s still no consensus on\nwhy\n. There are a couple of key hypotheses: antibiotics might increase the absorption of nutrients by thinning the intestinal lining; nutrients are protected from being partially destroyed by bacteria; antibiotics prevent the formation of toxins by bacteria, which diverts less energy away from the animal itself; reductions in inflammation; and the prevention of subclinical infections, which would otherwise cost the animal energy to fight off.\nButaye, P., Devriese, L. A., & Haesebrouck, F. (2003). Antimicrobial growth promoters used in animal feed: effects of less well-known antibiotics on gram-positive bacteria. Clinical microbiology reviews.\nMiyakawa, M. E. F., Casanova, N. A., & Kogut, M. H. (2024). How did antibiotic growth promoters increase growth and feed efficiency in poultry?. Poultry Science.\nEuropean Medicines Agency, European Surveillance of Veterinary Antimicrobial Consumption, 2022. 'Sales of veterinary antimicrobial agents in 31 European countries in 2022' (EMA/299538/2023).\nSpeksnijder, D. C., Mevius, D. J., Bruschke, C. J. M., & Wagenaar, J. A. (2015). Reduction of veterinary antimicrobial use in the Netherlands. The Dutch success model. Zoonoses and public health.\nWaluszewski, A., Cinti, A., & Perna, A. (2021). Antibiotics in pig meat production: Restrictions as the odd case and overuse as normality? Experiences from Sweden and Italy. Humanities and Social Sciences Communications.\nThis can be requested for individual animals or a group of animals in certain cases. Schmerold, I., van Geijlswijk, I., & Gehring, R. (2023). European regulations on the use of antibiotics in veterinary medicine. European journal of pharmaceutical sciences.\nSternberg-Lewerin, S., Boqvist, S., Nørstebø, S. F., Grönthal, T., Heikinheimo, A., Johansson, V., ... & Wasteson, Y. (2022). Nordic Vets against AMR—An Initiative to Share and Promote Good Practices in the Nordic–Baltic Region. Antibiotics.\nFortané, N. (2019). Veterinarian ‘responsibility’: conflicts of definition and appropriation surrounding the public problem of antimicrobial resistance in France. Nature Humanities and Social Sciences Communications.\nStatens Serum Institute (2019). DANMAP 2018 - Use of antimicrobial agents and occurrence of antimicrobial resistance in bacteria from food animals, food and humans in Denmark.\nWageningen University and Research. Economics of antibiotic usage on Dutch farms (2019). The impact of antibiotic reduction on economic results of pig and broiler farms in the Netherlands.\nvan Asseldonk, M., de Lauwere, C., Bonestroo, J., Bondt, N., & Bergevoet, R. (2020). Antibiotics use versus profitability on sow farms in the Netherlands. Preventive veterinary medicine.\nCollineau, L., Rojo-Gimeno, C., Léger, A., Backhans, A., Loesken, S., Nielsen, E. O., ... & Krebs, S. (2017). Herd-specific interventions to reduce antimicrobial usage in pig production without jeopardising technical and economic performance. Preventive veterinary medicine. Postma, M., Vanderhaeghen, W., Sarrazin, S., Maes, D., & Dewulf, J. (2017). Reducing antimicrobial usage in pig production without jeopardizing production parameters. Zoonoses and public health. Rojo-Gimeno, C., Postma, M., Dewulf, J., Hogeveen, H., Lauwers, L., & Wauters, E. (2016). Farm-economic analysis of reducing antimicrobial use whilst adopting improved management strategies on farrow-to-finish pig farms. Preventive Veterinary Medicine.\nA Big Mac patty in McDonald’s is around 40 grams. Most burger patties are much thicker than this: often double or triple the weight.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Fiona Spooner (2024) - “Large amounts of antibiotics are used in livestock, but several countries have shown this doesn’t have to be the case” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-093348/antibiotics-livestock.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-antibiotics-livestock,\nauthor = {Hannah Ritchie and Fiona Spooner},\ntitle = {Large amounts of antibiotics are used in livestock, but several countries have shown this doesn’t have to be the case},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://archive.ourworldindata.org/20260518-093348/antibiotics-livestock.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "antibiotics-livestock", "source_url": "https://ourworldindata.org/antibiotics-livestock", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Overuse is a risk for antibiotic resistance, but there are ways to reduce it.", "numeric_mentions": ["9,", "2024", "2010", "70%", "1", "2", "3", "42 days", "40", "50 days", "80", "50", "4", "2011", "2022,", "5", "80%", "90%", "6", "7", "8", "9", "54%", "2004", "2016,", "10", "11", "12", "72%", "73%", "2017", "118", "133", "35,000", "85,000", "2020", "66%", "2019", "2023", "2030", "2015", "1940", "2003", "2022", "31", "299538", "2021", "2018", "2016", "20260518", "093348", "18,", "2026"], "numeric_evidence": [{"title": "Antibiotic use in livestock", "source_url": "https://ourworldindata.org/grapher/antibiotic-use-livestock-marimekko.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Antibiotic use in livestock", "World region according to OWID"], "row_count_total": 186, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Antibiotic use in livestock": "67.548805", "World region according to OWID": "Asia"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Antibiotic use in livestock": "98.16476", "World region according to OWID": "Europe"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Antibiotic use in livestock": "32.77327", "World region according to OWID": "Africa"}, {"Entity": "Angola", "Code": "AGO", "Year": "2020", "Antibiotic use in livestock": "21.049955", "World region according to OWID": "Africa"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2020", "Antibiotic use in livestock": "63.10585", "World region according to OWID": "South America"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2020", "Antibiotic use in livestock": "83.974335", "World region according to OWID": "Asia"}, {"Entity": "Australia", "Code": "AUS", "Year": "2020", "Antibiotic use in livestock": "165.06085", "World region according to OWID": "Oceania"}, {"Entity": "Austria", "Code": "AUT", "Year": "2020", "Antibiotic use in livestock": "17.489857", "World region according to OWID": "Europe"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2020", "Antibiotic use in livestock": "94.63444", "World region according to OWID": "Asia"}, {"Entity": "Bahamas", "Code": "BHS", "Year": "2020", "Antibiotic use in livestock": "49.14106", "World region according to OWID": "North America"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2020", "Antibiotic use in livestock": "87.99928", "World region according to OWID": "Asia"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2020", "Antibiotic use in livestock": "93.60142", "World region according to OWID": "Asia"}, {"Entity": "Barbados", "Code": "BRB", "Year": "2020", "Antibiotic use in livestock": "65.5506", "World region according to OWID": "North America"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2020", "Antibiotic use in livestock": "69.55221", "World region according to OWID": "Europe"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2020", "Antibiotic use in livestock": "40.137016", "World region according to OWID": "Europe"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2020", "Antibiotic use in livestock": "53.91637", "World region according to OWID": "North America"}, {"Entity": "Benin", "Code": "BEN", "Year": "2020", "Antibiotic use in livestock": "20.842548", "World region according to OWID": "Africa"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2020", "Antibiotic use in livestock": "109.80575", "World region according to OWID": "Asia"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2020", "Antibiotic use in livestock": "65.821785", "World region according to OWID": "South America"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2020", "Antibiotic use in livestock": "68.368324", "World region according to OWID": "Europe"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2020", "Antibiotic use in livestock": "20.603456", "World region according to OWID": "Africa"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2020", "Antibiotic use in livestock": "56.255173", "World region according to OWID": "South America"}, {"Entity": "Brunei", "Code": "BRN", "Year": "2020", "Antibiotic use in livestock": "57.93007", "World region according to OWID": "Asia"}, {"Entity": "Bulgaria", "Code": "BGR", "Year": "2020", "Antibiotic use in livestock": "74.94107", "World region according to OWID": "Europe"}, {"Entity": "Burkina Faso", "Code": "BFA", "Year": "2020", "Antibiotic use in livestock": "22.77397", "World region according to OWID": "Africa"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2020", "Antibiotic use in livestock": "19.864538", "World region according to OWID": "Africa"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2020", "Antibiotic use in livestock": "123.39872", "World region according to OWID": "Asia"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2020", "Antibiotic use in livestock": "31.499397", "World region according to OWID": "Africa"}, {"Entity": "Canada", "Code": "CAN", "Year": "2020", "Antibiotic use in livestock": "60.203117", "World region according to OWID": "North America"}, {"Entity": "Cape Verde", "Code": "CPV", "Year": "2020", "Antibiotic use in livestock": "48.2116", "World region according to OWID": "Africa"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2020", "Antibiotic use in livestock": "20.958248", "World region according to OWID": "Africa"}, {"Entity": "Chad", "Code": "TCD", "Year": "2020", "Antibiotic use in livestock": "27.874949", "World region according to OWID": "Africa"}, {"Entity": "Chile", "Code": "CHL", "Year": "2020", "Antibiotic use in livestock": "119.10854", "World region according to OWID": "South America"}, {"Entity": "China", "Code": "CHN", "Year": "2020", "Antibiotic use in livestock": "208.46768", "World region according to OWID": "Asia"}, {"Entity": "Colombia", "Code": "COL", "Year": "2020", "Antibiotic use in livestock": "55.51907", "World region according to OWID": "South America"}, {"Entity": "Comoros", "Code": "COM", "Year": "2020", "Antibiotic use in livestock": "72.72638", "World region according to OWID": "Africa"}, {"Entity": "Congo", "Code": "COG", "Year": "2020", "Antibiotic use in livestock": "19.53925", "World region according to OWID": "Africa"}, {"Entity": "Costa Rica", "Code": "CRI", "Year": "2020", "Antibiotic use in livestock": "64.80021", "World region according to OWID": "North America"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2020", "Antibiotic use in livestock": "17.894554", "World region according to OWID": "Africa"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2020", "Antibiotic use in livestock": "35.798412", "World region according to OWID": "Europe"}, {"Entity": "Cuba", "Code": "CUB", "Year": "2020", "Antibiotic use in livestock": "70.6292", "World region according to OWID": "North America"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2020", "Antibiotic use in livestock": "234.67392", "World region according to OWID": "Europe"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2020", "Antibiotic use in livestock": "25.411886", "World region according to OWID": "Europe"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2020", "Antibiotic use in livestock": "20.781662", "World region according to OWID": "Africa"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2020", "Antibiotic use in livestock": "20.45157", "World region according to OWID": "Europe"}, {"Entity": "Djibouti", "Code": "DJI", "Year": "2020", "Antibiotic use in livestock": "27.142885", "World region according to OWID": "Africa"}, {"Entity": "Dominica", "Code": "DMA", "Year": "2020", "Antibiotic use in livestock": "139.12346", "World region according to OWID": "North America"}, {"Entity": "Dominican Republic", "Code": "DOM", "Year": "2020", "Antibiotic use in livestock": "49.512768", "World region according to OWID": "North America"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2020", "Antibiotic use in livestock": "12.87329", "World region according to OWID": "Asia"}, {"Entity": "Ecuador", "Code": "ECU", "Year": "2020", "Antibiotic use in livestock": "61.98479", "World region according to OWID": "South America"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2020", "Antibiotic use in livestock": "14.747423", "World region according to OWID": "Africa"}, {"Entity": "El Salvador", "Code": "SLV", "Year": "2020", "Antibiotic use in livestock": "45.828823", "World region according to OWID": "North America"}, {"Entity": "Equatorial Guinea", "Code": "GNQ", "Year": "2020", "Antibiotic use in livestock": "253.98073", "World region according to OWID": "Africa"}, {"Entity": "Eritrea", "Code": "ERI", "Year": "2020", "Antibiotic use in livestock": "25.116394", "World region according to OWID": "Africa"}, {"Entity": "Estonia", "Code": "EST", "Year": "2020", "Antibiotic use in livestock": "23.348366", "World region according to OWID": "Europe"}, {"Entity": "Eswatini", "Code": "SWZ", "Year": "2020", "Antibiotic use in livestock": "19.692108", "World region according to OWID": "Africa"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2020", "Antibiotic use in livestock": "22.088852", "World region according to OWID": "Africa"}, {"Entity": "Faroe Islands", "Code": "FRO", "Year": "2020", "Antibiotic use in livestock": "313.00916", "World region according to OWID": "Europe"}, {"Entity": "Fiji", "Code": "FJI", "Year": "2020", "Antibiotic use in livestock": "77.76166", "World region according to OWID": "Oceania"}, {"Entity": "Finland", "Code": "FIN", "Year": "2020", "Antibiotic use in livestock": "7.416708", "World region according to OWID": "Europe"}, {"Entity": "France", "Code": "FRA", "Year": "2020", "Antibiotic use in livestock": "22.00374", "World region according to OWID": "Europe"}, {"Entity": "Gabon", "Code": "GAB", "Year": "2020", "Antibiotic use in livestock": "30.534264", "World region according to OWID": "Africa"}, {"Entity": "Gambia", "Code": "GMB", "Year": "2020", "Antibiotic use in livestock": "20.507132", "World region according to OWID": "Africa"}, {"Entity": "Georgia", "Code": "GEO", "Year": "2020", "Antibiotic use in livestock": "82.61754", "World region according to OWID": "Asia"}, {"Entity": "Germany", "Code": "DEU", "Year": "2020", "Antibiotic use in livestock": "36.395947", "World region according to OWID": "Europe"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2020", "Antibiotic use in livestock": "23.153818", "World region according to OWID": "Africa"}, {"Entity": "Greece", "Code": "GRC", "Year": "2020", "Antibiotic use in livestock": "78.13855", "World region according to OWID": "Europe"}, {"Entity": "Grenada", "Code": "GRD", "Year": "2020", "Antibiotic use in livestock": "220.3327", "World region according to OWID": "North America"}, {"Entity": "Guatemala", "Code": "GTM", "Year": "2020", "Antibiotic use in livestock": "60.38946", "World region according to OWID": "North America"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2020", "Antibiotic use in livestock": "19.622803", "World region according to OWID": "Africa"}, {"Entity": "Guinea-Bissau", "Code": "GNB", "Year": "2020", "Antibiotic use in livestock": "21.500631", "World region according to OWID": "Africa"}, {"Entity": "Guyana", "Code": "GUY", "Year": "2020", "Antibiotic use in livestock": "46.200123", "World region according to OWID": "South America"}, {"Entity": "Haiti", "Code": "HTI", "Year": "2020", "Antibiotic use in livestock": "61.217598", "World region according to OWID": "North America"}, {"Entity": "Honduras", "Code": "HND", "Year": "2020", "Antibiotic use in livestock": "46.59542", "World region according to OWID": "North America"}, {"Entity": "Hong Kong", "Code": "HKG", "Year": "2020", "Antibiotic use in livestock": "72.16804", "World region according to OWID": "Asia"}, {"Entity": "Hungary", "Code": "HUN", "Year": "2020", "Antibiotic use in livestock": "62.037323", "World region according to OWID": "Europe"}, {"Entity": "Iceland", "Code": "ISL", "Year": "2020", "Antibiotic use in livestock": "9.830115", "World region according to OWID": "Europe"}, {"Entity": "India", "Code": "IND", "Year": "2020", "Antibiotic use in livestock": "114.25339", "World region according to OWID": "Asia"}, {"Entity": "Indonesia", "Code": "IDN", "Year": "2020", "Antibiotic use in livestock": "87.04435", "World region according to OWID": "Asia"}, {"Entity": "Iran", "Code": "IRN", "Year": "2020", "Antibiotic use in livestock": "145.3703", "World region according to OWID": "Asia"}, {"Entity": "Iraq", "Code": "IRQ", "Year": "2020", "Antibiotic use in livestock": "56.085197", "World region according to OWID": "Asia"}, {"Entity": "Ireland", "Code": "IRL", "Year": "2020", "Antibiotic use in livestock": "20.455177", "World region according to OWID": "Europe"}, {"Entity": "Israel", "Code": "ISR", "Year": "2020", "Antibiotic use in livestock": "51.04705", "World region according to OWID": "Asia"}, {"Entity": "Italy", "Code": "ITA", "Year": "2020", "Antibiotic use in livestock": "72.59271", "World region according to OWID": "Europe"}, {"Entity": "Jamaica", "Code": "JAM", "Year": "2020", "Antibiotic use in livestock": "46.425358", "World region according to OWID": "North America"}, {"Entity": "Japan", "Code": "JPN", "Year": "2020", "Antibiotic use in livestock": "62.793568", "World region according to OWID": "Asia"}, {"Entity": "Jordan", "Code": "JOR", "Year": "2020", "Antibiotic use in livestock": "41.05746", "World region according to OWID": "Asia"}, {"Entity": "Kazakhstan", "Code": "KAZ", "Year": "2020", "Antibiotic use in livestock": "88.820786", "World region according to OWID": "Asia"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2020", "Antibiotic use in livestock": "25.196676", "World region according to OWID": "Africa"}, {"Entity": "Kuwait", "Code": "KWT", "Year": "2020", "Antibiotic use in livestock": "51.577732", "World region according to OWID": "Asia"}, {"Entity": "Kyrgyzstan", "Code": "KGZ", "Year": "2020", "Antibiotic use in livestock": "107.879776", "World region according to OWID": "Asia"}, {"Entity": "Laos", "Code": "LAO", "Year": "2020", "Antibiotic use in livestock": "107.583084", "World region according to OWID": "Asia"}, {"Entity": "Latvia", "Code": "LVA", "Year": "2020", "Antibiotic use in livestock": "16.158278", "World region according to OWID": "Europe"}, {"Entity": "Lebanon", "Code": "LBN", "Year": "2020", "Antibiotic use in livestock": "29.444818", "World region according to OWID": "Asia"}, {"Entity": "Lesotho", "Code": "LSO", "Year": "2020", "Antibiotic use in livestock": "27.820198", "World region according to OWID": "Africa"}, {"Entity": "Liberia", "Code": "LBR", "Year": "2020", "Antibiotic use in livestock": "12.843239", "World region according to OWID": "Africa"}, {"Entity": "Libya", "Code": "LBY", "Year": "2020", "Antibiotic use in livestock": "27.619755", "World region according to OWID": "Africa"}, {"Entity": "Lithuania", "Code": "LTU", "Year": "2020", "Antibiotic use in livestock": "8.52686", "World region according to OWID": "Europe"}, {"Entity": "Luxembourg", "Code": "LUX", "Year": "2020", "Antibiotic use in livestock": "12.2620325", "World region according to OWID": "Europe"}, {"Entity": "Madagascar", "Code": "MDG", "Year": "2020", "Antibiotic use in livestock": "21.192541", "World region according to OWID": "Africa"}, {"Entity": "Malawi", "Code": "MWI", "Year": "2020", "Antibiotic use in livestock": "16.09653", "World region according to OWID": "Africa"}, {"Entity": "Malaysia", "Code": "MYS", "Year": "2020", "Antibiotic use in livestock": "90.55596", "World region according to OWID": "Asia"}, {"Entity": "Mali", "Code": "MLI", "Year": "2020", "Antibiotic use in livestock": "25.480017", "World region according to OWID": "Africa"}, {"Entity": "Malta", "Code": "MLT", "Year": "2020", "Antibiotic use in livestock": "57.221275", "World region according to OWID": "Europe"}, {"Entity": "Mauritania", "Code": "MRT", "Year": "2020", "Antibiotic use in livestock": "35.61663", "World region according to OWID": "Africa"}, {"Entity": "Mauritius", "Code": "MUS", "Year": "2020", "Antibiotic use in livestock": "10.652156", "World region according to OWID": "Africa"}, {"Entity": "Mexico", "Code": "MEX", "Year": "2020", "Antibiotic use in livestock": "70.67738", "World region according to OWID": "North America"}, {"Entity": "Micronesia (country)", "Code": "FSM", "Year": "2020", "Antibiotic use in livestock": "143.53203", "World region according to OWID": "Oceania"}, {"Entity": "Moldova", "Code": "MDA", "Year": "2020", "Antibiotic use in livestock": "51.533665", "World region according to OWID": "Europe"}, {"Entity": "Mongolia", "Code": "MNG", "Year": "2020", "Antibiotic use in livestock": "252.78731", "World region according to OWID": "Asia"}, {"Entity": "Montenegro", "Code": "MNE", "Year": "2020", "Antibiotic use in livestock": "90.20159", "World region according to OWID": "Europe"}, {"Entity": "Morocco", "Code": "MAR", "Year": "2020", "Antibiotic use in livestock": "20.163773", "World region according to OWID": "Africa"}, {"Entity": "Mozambique", "Code": "MOZ", "Year": "2020", "Antibiotic use in livestock": "14.444178", "World region according to OWID": "Africa"}, {"Entity": "Myanmar", "Code": "MMR", "Year": "2020", "Antibiotic use in livestock": "91.840385", "World region according to OWID": "Asia"}, {"Entity": "Namibia", "Code": "NAM", "Year": "2020", "Antibiotic use in livestock": "22.401184", "World region according to OWID": "Africa"}, {"Entity": "Nepal", "Code": "NPL", "Year": "2020", "Antibiotic use in livestock": "102.81655", "World region according to OWID": "Asia"}, {"Entity": "Netherlands", "Code": "NLD", "Year": "2020", "Antibiotic use in livestock": "20.833656", "World region according to OWID": "Europe"}, {"Entity": "New Caledonia", "Code": "NCL", "Year": "2020", "Antibiotic use in livestock": "109.67122", "World region according to OWID": "Oceania"}, {"Entity": "New Zealand", "Code": "NZL", "Year": "2020", "Antibiotic use in livestock": "9.997013", "World region according to OWID": "Oceania"}, {"Entity": "Nicaragua", "Code": "NIC", "Year": "2020", "Antibiotic use in livestock": "49.126366", "World region according to OWID": "North America"}], "rows_tail": [{"Entity": "Greece", "Code": "GRC", "Year": "2020", "Antibiotic use in livestock": "78.13855", "World region according to OWID": "Europe"}, {"Entity": "Grenada", "Code": "GRD", "Year": "2020", "Antibiotic use in livestock": "220.3327", "World region according to OWID": "North America"}, {"Entity": "Guatemala", "Code": "GTM", "Year": "2020", "Antibiotic use in livestock": "60.38946", "World region according to OWID": "North America"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2020", "Antibiotic use in livestock": "19.622803", "World region according to OWID": "Africa"}, {"Entity": "Guinea-Bissau", "Code": "GNB", "Year": "2020", "Antibiotic use in livestock": "21.500631", "World region according to OWID": "Africa"}, {"Entity": "Guyana", "Code": "GUY", "Year": "2020", "Antibiotic use in livestock": "46.200123", "World region according to OWID": "South America"}, {"Entity": "Haiti", "Code": "HTI", "Year": "2020", "Antibiotic use in livestock": "61.217598", "World region according to OWID": "North America"}, {"Entity": "Honduras", "Code": "HND", "Year": "2020", "Antibiotic use in livestock": "46.59542", "World region according to OWID": "North America"}, {"Entity": "Hong Kong", "Code": "HKG", "Year": "2020", "Antibiotic use in livestock": "72.16804", "World region according to OWID": "Asia"}, {"Entity": "Hungary", "Code": "HUN", "Year": "2020", "Antibiotic use in livestock": "62.037323", "World region according to OWID": "Europe"}, {"Entity": "Iceland", "Code": "ISL", "Year": "2020", "Antibiotic use in livestock": "9.830115", "World region according to OWID": "Europe"}, {"Entity": "India", "Code": "IND", "Year": "2020", "Antibiotic use in livestock": "114.25339", "World region according to OWID": "Asia"}, {"Entity": "Indonesia", "Code": "IDN", "Year": "2020", "Antibiotic use in livestock": "87.04435", "World region according to OWID": "Asia"}, {"Entity": "Iran", "Code": "IRN", "Year": "2020", "Antibiotic use in livestock": "145.3703", "World region according to OWID": "Asia"}, {"Entity": "Iraq", "Code": "IRQ", "Year": "2020", "Antibiotic use in livestock": "56.085197", "World region according to OWID": "Asia"}, {"Entity": "Ireland", "Code": "IRL", "Year": "2020", "Antibiotic use in livestock": "20.455177", "World region according to OWID": "Europe"}, {"Entity": "Israel", "Code": "ISR", "Year": "2020", "Antibiotic use in livestock": "51.04705", "World region according to OWID": "Asia"}, {"Entity": "Italy", "Code": "ITA", "Year": "2020", "Antibiotic use in livestock": "72.59271", "World region according to OWID": "Europe"}, {"Entity": "Jamaica", "Code": "JAM", "Year": "2020", "Antibiotic use in livestock": "46.425358", "World region according to OWID": "North America"}, {"Entity": "Japan", "Code": "JPN", "Year": "2020", "Antibiotic use in livestock": "62.793568", "World region according to OWID": "Asia"}, {"Entity": "Jordan", "Code": "JOR", "Year": "2020", "Antibiotic use in livestock": "41.05746", "World region according to OWID": "Asia"}, {"Entity": "Kazakhstan", "Code": "KAZ", "Year": "2020", "Antibiotic use in livestock": "88.820786", "World region according to OWID": "Asia"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2020", "Antibiotic use in livestock": "25.196676", "World region according to OWID": "Africa"}, {"Entity": "Kuwait", "Code": "KWT", "Year": "2020", "Antibiotic use in livestock": "51.577732", "World region according to OWID": "Asia"}, {"Entity": "Kyrgyzstan", "Code": "KGZ", "Year": "2020", "Antibiotic use in livestock": "107.879776", "World region according to OWID": "Asia"}, {"Entity": "Laos", "Code": "LAO", "Year": "2020", "Antibiotic use in livestock": "107.583084", "World region according to OWID": "Asia"}, {"Entity": "Latvia", "Code": "LVA", "Year": "2020", "Antibiotic use in livestock": "16.158278", "World region according to OWID": "Europe"}, {"Entity": "Lebanon", "Code": "LBN", "Year": "2020", "Antibiotic use in livestock": "29.444818", "World region according to OWID": "Asia"}, {"Entity": "Lesotho", "Code": "LSO", "Year": "2020", "Antibiotic use in livestock": "27.820198", "World region according to OWID": "Africa"}, {"Entity": "Liberia", "Code": "LBR", "Year": "2020", "Antibiotic use in livestock": "12.843239", "World region according to OWID": "Africa"}, {"Entity": "Libya", "Code": "LBY", "Year": "2020", "Antibiotic use in livestock": "27.619755", "World region according to OWID": "Africa"}, {"Entity": "Lithuania", "Code": "LTU", "Year": "2020", "Antibiotic use in livestock": "8.52686", "World region according to OWID": "Europe"}, {"Entity": "Luxembourg", "Code": "LUX", "Year": "2020", "Antibiotic use in livestock": "12.2620325", "World region according to OWID": "Europe"}, {"Entity": "Madagascar", "Code": 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"Myanmar", "Code": "MMR", "Year": "2020", "Antibiotic use in livestock": "91.840385", "World region according to OWID": "Asia"}, {"Entity": "Namibia", "Code": "NAM", "Year": "2020", "Antibiotic use in livestock": "22.401184", "World region according to OWID": "Africa"}, {"Entity": "Nepal", "Code": "NPL", "Year": "2020", "Antibiotic use in livestock": "102.81655", "World region according to OWID": "Asia"}, {"Entity": "Netherlands", "Code": "NLD", "Year": "2020", "Antibiotic use in livestock": "20.833656", "World region according to OWID": "Europe"}, {"Entity": "New Caledonia", "Code": "NCL", "Year": "2020", "Antibiotic use in livestock": "109.67122", "World region according to OWID": "Oceania"}, {"Entity": "New Zealand", "Code": "NZL", "Year": "2020", "Antibiotic use in livestock": "9.997013", "World region according to OWID": "Oceania"}, {"Entity": "Nicaragua", "Code": "NIC", "Year": "2020", "Antibiotic use in livestock": "49.126366", "World region according to OWID": "North America"}, {"Entity": "Niger", "Code": "NER", "Year": "2020", "Antibiotic use in livestock": "24.47315", "World region according to OWID": "Africa"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2020", "Antibiotic use in livestock": "24.62592", "World region according to OWID": "Africa"}, {"Entity": "North Korea", "Code": "PRK", "Year": "2020", "Antibiotic use in livestock": "83.744736", "World region according to OWID": "Asia"}, {"Entity": "North Macedonia", "Code": "MKD", "Year": "2020", "Antibiotic use in livestock": "111.365364", "World region according to OWID": "Europe"}, {"Entity": "Norway", "Code": "NOR", "Year": "2020", "Antibiotic use in livestock": "4.339035", "World region according to OWID": "Europe"}, {"Entity": "Oman", "Code": "OMN", "Year": "2020", "Antibiotic use in livestock": "75.25929", "World region according to OWID": "Asia"}, {"Entity": "Pakistan", "Code": "PAK", "Year": "2020", "Antibiotic use in livestock": "101.17341", "World region according to OWID": "Asia"}, {"Entity": "Palestine", "Code": "PSE", "Year": "2020", "Antibiotic use in livestock": "94.70892", "World region according to OWID": "Asia"}, {"Entity": "Panama", "Code": "PAN", "Year": "2020", "Antibiotic use in livestock": "54.563137", "World region according to OWID": "North America"}, {"Entity": "Papua New Guinea", "Code": "PNG", "Year": "2020", "Antibiotic use in livestock": "141.55775", "World region according to OWID": "Oceania"}, {"Entity": "Paraguay", "Code": "PRY", "Year": "2020", "Antibiotic use in livestock": "54.19864", "World region according to OWID": "South America"}, {"Entity": "Peru", "Code": "PER", "Year": "2020", "Antibiotic use in livestock": "64.92041", "World region according to OWID": "South America"}, {"Entity": "Philippines", "Code": "PHL", "Year": "2020", "Antibiotic use in livestock": "116.32984", "World region according to OWID": "Asia"}, {"Entity": "Poland", "Code": "POL", "Year": "2020", "Antibiotic use in livestock": "68.81839", "World region according to 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{"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "Antibiotic use in livestock": "124.16831", "World region according to OWID": "Asia"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Antibiotic use in livestock": "52.33308", "World region according to OWID": "Asia"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Antibiotic use in livestock": "18.670872", "World region according to OWID": "Africa"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Antibiotic use in livestock": "21.346575", "World region according to OWID": "Africa"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "antibiotic-use-livestock-marimekko", "metadata_url": "https://ourworldindata.org/grapher/antibiotic-use-livestock-marimekko.metadata.json", "chart_title": "Antibiotic use in livestock", "chart_subtitle": "Milligrams of total antibiotic use per kilogram of livestock. 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"Libya", "Code": "LBY", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "27.619755"}, {"Entity": "Lithuania", "Code": "LTU", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "8.52686"}, {"Entity": "Luxembourg", "Code": "LUX", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "12.2620325"}, {"Entity": "Madagascar", "Code": "MDG", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "21.192541"}, {"Entity": "Malawi", "Code": "MWI", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "16.09653"}, {"Entity": "Malaysia", "Code": "MYS", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "90.55596"}, {"Entity": "Mali", "Code": "MLI", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "25.480017"}, {"Entity": "Malta", "Code": "MLT", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "57.221275"}, {"Entity": "Mauritania", "Code": "MRT", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "35.61663"}, {"Entity": "Mauritius", "Code": "MUS", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "10.652156"}, {"Entity": "Mexico", "Code": "MEX", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "70.67738"}, {"Entity": "Micronesia (country)", "Code": "FSM", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "143.53203"}, {"Entity": "Moldova", "Code": "MDA", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "51.533665"}, {"Entity": "Mongolia", "Code": "MNG", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "252.78731"}, {"Entity": "Montenegro", "Code": "MNE", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "90.20159"}, {"Entity": "Morocco", "Code": "MAR", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "20.163773"}, {"Entity": "Mozambique", "Code": "MOZ", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "14.444178"}, {"Entity": "Myanmar", "Code": "MMR", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "91.840385"}, {"Entity": "Namibia", "Code": "NAM", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "22.401184"}, {"Entity": "Nepal", "Code": "NPL", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "102.81655"}, {"Entity": "Netherlands", "Code": "NLD", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "20.833656"}, {"Entity": "New Caledonia", "Code": "NCL", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "109.67122"}, {"Entity": "New Zealand", "Code": "NZL", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "9.997013"}, {"Entity": "Nicaragua", "Code": "NIC", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "49.126366"}, {"Entity": "Niger", "Code": "NER", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "24.47315"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "24.62592"}, {"Entity": "Niue", "Code": "NIU", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "5347.2285"}, {"Entity": "North Korea", "Code": "PRK", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "83.744736"}, {"Entity": "North Macedonia", "Code": "MKD", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "111.365364"}, {"Entity": "Norway", "Code": "NOR", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "4.339035"}, {"Entity": "Oman", "Code": "OMN", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "75.25929"}, {"Entity": "Pakistan", "Code": "PAK", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "101.17341"}, {"Entity": "Palestine", "Code": "PSE", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "94.70892"}, {"Entity": "Panama", "Code": "PAN", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "54.563137"}, {"Entity": "Papua New Guinea", "Code": "PNG", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "141.55775"}, {"Entity": "Paraguay", "Code": "PRY", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "54.19864"}, {"Entity": "Peru", "Code": "PER", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "64.92041"}, {"Entity": "Philippines", "Code": "PHL", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "116.32984"}, {"Entity": "Poland", "Code": "POL", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "68.81839"}, {"Entity": "Portugal", "Code": "PRT", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "87.356"}, {"Entity": "Puerto Rico", "Code": "PRI", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "50.39188"}, {"Entity": "Qatar", "Code": "QAT", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "53.69376"}, {"Entity": "Romania", "Code": "ROU", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "59.422188"}, {"Entity": "Russia", "Code": "RUS", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "75.59826"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "20.638613"}, {"Entity": "Saint Kitts and Nevis", "Code": "KNA", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "368.54013"}, {"Entity": "Saint Lucia", "Code": "LCA", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "110.93447"}, {"Entity": "Saint Vincent and the Grenadines", "Code": "VCT", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "239.5675"}, {"Entity": "Samoa", "Code": "WSM", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "51.454216"}, {"Entity": "Sao Tome and Principe", "Code": "STP", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "164.74672"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "42.535236"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "19.640306"}, {"Entity": "Serbia", "Code": "SRB", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "52.55658"}, {"Entity": "Seychelles", "Code": "SYC", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "9496.926"}, {"Entity": "Sierra Leone", "Code": "SLE", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "17.483543"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "64.36674"}, {"Entity": "Slovakia", "Code": "SVK", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "21.549042"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "12.803318"}, {"Entity": "Solomon Islands", "Code": "SLB", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "118.34202"}, {"Entity": "Somalia", "Code": "SOM", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "30.64287"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "22.21957"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "121.5339"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "23.853924"}, {"Entity": "Spain", "Code": "ESP", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "78.777756"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "75.823845"}, {"Entity": "Sudan (former)", "Code": "OWID_SDN", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "27.021984"}, {"Entity": "Suriname", "Code": "SUR", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "51.811207"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "5.4230795"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "19.725359"}, {"Entity": "Syria", "Code": "SYR", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "88.994446"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "111.91895"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "89.30186"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "21.613647"}, {"Entity": "Thailand", "Code": "THA", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "337.77734"}, {"Entity": "Togo", "Code": "TGO", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "17.362648"}, {"Entity": "Tonga", "Code": "TON", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "147.62177"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "40.573902"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "22.305"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "68.738304"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "115.282005"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "20.967749"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "57.904045"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "52.438774"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "15.608991"}, {"Entity": "United States", "Code": "USA", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "30.603518"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "73.39216"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "83.21526"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "99.78377"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "54.22483"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "124.16831"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "52.33308"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "18.670872"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Antimicrobial usage in livestock (mg per population corrected units)": "21.346575"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "antibiotic-usage-in-livestock", "metadata_url": "https://ourworldindata.org/grapher/antibiotic-usage-in-livestock.metadata.json", "chart_title": "Antibiotic usage in livestock per kilogram of meat", "chart_subtitle": "Milligrams of antibiotic use per kilogram of livestock. This is adjusted for differences in livestock numbers and species by standardizing to a population-corrected unit (PCU).", "chart_note": "Researchers working on antimicrobial resistance have proposed a threshold of 50mg per PCU, which is shown as a threshold here.", "chart_citation": "Mulchandani et al. (2023)", "original_chart_url": "https://ourworldindata.org/grapher/antibiotic-usage-in-livestock", "owid_column_metadata": {"Antimicrobial usage in livestock (mg per population corrected units)": {"titleShort": "Antimicrobial usage in livestock (mg per population corrected units)", "titleLong": "Antimicrobial usage in livestock (mg per population corrected units)", "unit": "", "timespan": "2020-2020", "type": "Numeric", "owidVariableId": 772629, "shortName": "livestock_antimicrobial_usage_mgpcu", "lastUpdated": "2024-12-06", "citationShort": "Mulchandani et al. (2023) – processed by Our World in Data", "citationLong": "Mulchandani et al. (2023) – processed by Our World in Data. “Antimicrobial usage in livestock (mg per population corrected units)” [dataset]. Mulchandani et al., “Global trends in antimicrobial use in food-producing animals: 2020 to 2030” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/772629.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "62e066a37f1335312fb4"},
{"raw_link": "https://ourworldindata.org/antibiotics-livestock-data", "title": "Public data on antibiotic use in livestock is incomplete, making it difficult to track how much is used and where", "context": "Home\nAntibiotics & Antibiotic Resistance\nPublic data on antibiotic use in livestock is incomplete, making it difficult to track how much is used and where\nMany countries refuse to share their data, which is a risk for antimicrobial resistance.\nBy\nHannah Ritchie\nand\nFiona Spooner\nDecember 9, 2024\nBrowse past versions\nCite this article\nReuse our work freely\nIf we want to understand the risks of antimicrobial resistance, we need to know how antibiotics are being used, including their volumes, which types, and where they’re consumed.\nUnfortunately, there are significant data gaps in antibiotic use for livestock.\nIn 2016, the\nWorld Organisation for Animal Health\n(WOAH) started gathering and publishing data on the use of antibiotics in animals. Countries voluntarily submit their data to the WOAH; on a positive note, the number of countries contributing\nhas increased\nover time. But unfortunately, data is still limited.\nIn its first year, 130 countries participated in the WOAH network, with 82 submitting quantitative data that could be used to analyze trends and levels of antibiotic use.\n1\nThe other 48 countries provided some qualitative information, such as antibiotics being used and regulations they had in place. Still, they did not have — or refused to provide — quantitative estimates of the total amount of antibiotics being used nationally.\nIn\nits 2024 report\n, 152 countries participated, with 96 countries publishing data with sufficient detail for the WOAH to understand the types of antibiotics being used and in what quantities.\nVery few countries — just 39 — make their data publicly available online. WOAH cannot publicize national-level data in its reports, so it is limited to publishing just regional averages or totals. These are helpful but don’t give a precise picture of antibiotic use in each country.\nSome researchers do produce national estimates based on the data that is publicly available and extrapolated in models, but studies do not necessarily agree on how much antibiotics are used globally. Estimates range from around 76,000 tonnes to more than 100,000 tonnes.\n2\nOne of the most recent studies by Mulchandani and colleagues (which we cover in detail in our\nrelated article\n) estimates that the world uses around 99,500 tonnes of antibiotics in livestock each year but with a confidence interval stretching from 68,000 to 198,000 tonnes.\nThe world needs more transparent and precise data on antibiotic use. Without it, we do not have a global monitoring system to track the risks of antimicrobial resistance in livestock.\nIn our related article, we look at what the available data — and researchers’ estimates — tell us about antibiotic use and our ability to use them more effectively:\nLarge amounts of antibiotics are used in livestock, but several countries have shown this doesn’t have to be the case\nOveruse is a risk for antibiotic resistance, but there are ways to reduce it.\nEndnotes\nWorld Organisation for Human Health (WOAH), 2024. Annual Report on Antimicrobial Agents Intended for Use in Animals: 8th Report.\nArdakani, Z., Aragrande, M., & Canali, M. (2024). Global antimicrobial use in livestock farming: an estimate for cattle, chickens, and pigs. Animal.\nTiseo, K., Huber, L., Gilbert, M., Robinson, T. P., & Van Boeckel, T. P. (2020). Global trends in antimicrobial use in food animals from 2017 to 2030. Antibiotics.\nMulchandani, R., Wang, Y., Gilbert, M., & Van Boeckel, T. P. (2023). Global trends in antimicrobial use in food-producing animals: 2020 to 2030. PLOS Global Public Health.\nVan Boeckel, T. P., Brower, C., Gilbert, M., Grenfell, B. T., Levin, S. A., Robinson, T. P., ... & Laxminarayan, R. (2015). Global trends in antimicrobial use in food animals. Proceedings of the National Academy of Sciences.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Fiona Spooner (2024) - “Public data on antibiotic use in livestock is incomplete, making it difficult to track how much is used and where” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20251125-173858/antibiotics-livestock-data.html' [Online Resource] (archived on November 25, 2025).\nBibTeX citation\n@article{owid-antibiotics-livestock-data,\nauthor = {Hannah Ritchie and Fiona Spooner},\ntitle = {Public data on antibiotic use in livestock is incomplete, making it difficult to track how much is used and where},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://archive.ourworldindata.org/20251125-173858/antibiotics-livestock-data.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "antibiotics-livestock-data", "source_url": "https://ourworldindata.org/antibiotics-livestock-data", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Many countries refuse to share their data, which is a risk for antimicrobial resistance.", "numeric_mentions": ["9,", "2024", "2016,", "130", "82", "1", "48", "152", "96", "39", "76,000", "100,000", "2", "99,500", "68,000", "198,000", "8", "2020", "2017", "2030", "2023", "2015", "20251125", "173858", "25,", "2025"], "numeric_evidence": [{"title": "Number of countries that submit data on antibiotic use in livestock", "source_url": "https://ourworldindata.org/grapher/countries-data-antibiotic-livestock.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Number of countries contributing data"], "row_count_total": 8, "rows_head": [{"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Number of countries contributing data": "55"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Number of countries contributing data": "81"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Number of countries contributing data": "84"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Number of countries contributing data": "97"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Number of countries contributing data": "110"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Number of countries contributing data": "113"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Number of countries contributing data": "106"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Number of countries contributing data": "100"}], "rows_tail": [], "sampling_note": "Stored first 8 rows and last 8 rows when the table is larger.", "grapher_slug": "countries-data-antibiotic-livestock", "metadata_url": "https://ourworldindata.org/grapher/countries-data-antibiotic-livestock.metadata.json", "chart_title": "Number of countries that submit data on antibiotic use in livestock", "chart_subtitle": "The number of countries that submit data on the extent and quantity of antibiotic use in livestock to the World Organisation for Animal Health (WOAH). This helps to monitor the scale of antimicrobial use and risks of antimicrobial resistance.", "chart_note": null, "chart_citation": "World Organisation for Animal Health (2022)", "original_chart_url": "https://ourworldindata.org/grapher/countries-data-antibiotic-livestock", "owid_column_metadata": {"Number of countries contributing data": {"titleShort": "Number of countries contributing data", "titleLong": "Number of countries contributing data", "unit": "participants", "timespan": "2014-2021", "type": "Integer", "owidVariableId": 997273, "shortName": "participants", "lastUpdated": "2024-10-23", "nextUpdate": "2026-07-22", "citationShort": "World Organisation for Animal Health (2022) – processed by Our World in Data", "citationLong": "World Organisation for Animal Health (2022) – processed by Our World in Data. “Number of countries contributing data” [dataset]. World Organisation for Animal Health, “ANIMUSE” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/997273.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "c4c95835d81523c79511"},
{"raw_link": "https://ourworldindata.org/billions-people-suffer-anemia-cheap-ways-reduce", "title": "Billions of people suffer from anemia, but there are cheap ways to reduce this", "context": "Home\nMicronutrient Deficiency\nBillions of people suffer from anemia, but there are cheap ways to reduce this\nIn some countries, most children and pregnant women are anemic. There are low-cost ways to tackle this.\nBy\nHannah Ritchie\nNovember 25, 2024\nBrowse past versions\nCite this article\nReuse our work freely\nThere are few health problems that affect billions of people at any given time. There are even fewer that could be reduced substantially through pretty cheap interventions.\nAnemia\nis one of them. Estimates suggest that one in four people globally has anemia; that’s two billion people in total.\n1\nAlmost one in three women and\nalmost 40%\nof all children suffer from it.\nAnemia is a condition where someone has fewer red blood cells or lower hemoglobin levels in their blood cells.\nWhile anemia is much more common in poorer countries, it’s also a significant problem in rich ones. I have family members and friends who have struggled with it for a long time. And I probably know many more who have it but are undiagnosed.\nOne of the reasons why anemia is so overlooked is that its symptoms are often subtle: in most cases, it’s fatigue and weakness. These symptoms can be common for various reasons, making them harder to attribute to a specific condition. Even in children, when anemia can lead to delays in cognitive and physical development and poor concentration, the signs are not obvious or can’t be linked directly to micronutrient deficiencies.\nSevere anemia can lead to much more drastic outcomes, though. Anemia during pregnancy can significantly increase the risks of low birthweight babies and, therefore, the risk of infant mortality.\n2\nIt also increases the risk of\nmaternal mortality\n, especially if there is a lot of blood loss during childbirth.\n3\nAnd anemia in pregnant women is extremely common, especially in lower-income countries. You can see this in the map: in some of the poorest countries, more than half of pregnant women are anemic.\nWhile anemia and iron deficiency (which is its leading cause) don’t directly kill a large number of people, it makes up a large share of the world’s disease burden. The Global Burden of Disease study estimates that it accounts for about 2% of the world’s\ndisability-adjusted life years\n. This might not sound like a lot, but it’s more than other widely recognized health problems like\nHIV\n,\nbreast cancer\n, and Alzheimer’s disease.\nIron deficiency is the most common cause of anemia\nAround half to two-thirds of anemia cases globally are caused by nutritional deficiencies.\n1\nDeficiencies in vitamin A, B\n12\n,\nand folic acid can lead to anemia, but the most common one, by far, is iron deficiency.\nUnsurprisingly, people are at much higher risk of anemia when they have low dietary diversity.\n4\nThis tends to correlate very strongly with income — richer people\ncan afford\nmore varied diets — which is one reason why anemia is much more common in lower-income countries, as this chart shows.\nBut there are other causes of anemia too: infectious diseases such as malaria, HIV, and schistosomiasis; chronic diseases such as kidney disease; genetic disorders such as sickle cell disease; and finally, blood loss in women — such as heavy menstrual cycles or even hemorrhage during pregnancy.\nInfectious diseases, in particular,\nare more common\nin lower-income countries, which is another reason why rates of anemia are higher there.\nWomen and children are at a much higher risk of iron deficiency and anemia than men. This is because they often need more iron relative to the amount of calories they consume.\nNutrient-dense foods rich in minerals like iron are essential during childhood to maintain healthy growth. This is true for boys and girls.\nAfter puberty, iron deficiency is much more common in women. This is because they lose iron stores during menstruation, and iron requirements increase around threefold during pregnancy.\n5\nAmong people aged 15 to 49, the Global Burden of Disease study estimates that cases of anemia\nand\ncases of iron deficiency are three times as common in women than men.\n6\nOnly at older ages do the risks for men and women both rise to similar levels.\nThe maps below show the estimated share of children and women of reproductive age who have anemia. It’s much more common in Sub-Saharan Africa and South Asia, where the majority are anemic. But even in richer countries, less than a handful have rates below 10%.\nProgress on anemia has been slow, but some countries have shown it’s possible\nWe know that high rates of anemia are not inevitable: around 10% to 15% of women in rich countries are anemic, compared to more than 50% in poorer countries.\nHowever, progress in reducing rates of anemia has been incredibly slow in recent decades. Globally, the share of\npregnant women\nand women of reproductive age who are anemic\nhas not changed\nsince 2000.\nReductions among children have been slightly more promising: the share of under-fives that are anemic\ndropped from\n48% to 39% between 2000 and 2019.\nWhile progress has been slow globally, some countries have made much faster strides in the last few decades.\nIn the chart below, you can see the change in the share of children who are anemic between 2000 and 2019. Each line represents one country. I’ve highlighted a few countries that have reduced rates quite a lot: China, Nepal, Brazil, Bolivia, Uzbekistan, and Iran.\nThere are also examples of impressive reductions among women. See the chart below: rates have more than halved in the Philippines and fallen by two-thirds in Guatemala.\nWhile the stagnation at the global level is worrying, these positive developments show that it is a tractable problem that we can tackle with the right interventions.\nThe Exemplars in Global Health project did an\nin-depth review\nof what the Philippines has done to tackle anemia, and it has several insights that other countries can draw on. Increasing access to family planning — and making contraceptives available as essential medicines — improving rates of antenatal care and giving women access to iron-folic supplements before and during pregnancy have been key to its success.\nI’ll now go into some of these interventions in more detail.\nReducing nutritional deficiencies that cause anemia could be cheap\nNutritional deficiencies — mostly a lack of iron — cause around half of the global anemia cases.\nImproving iron supply is the easiest and quickest way to massively reduce the burden of anemia. It’s probably the cheapest, too.\nThere are a few ways to make sure people get enough iron.\nThe first and most obvious is to ensure they have a diverse diet and are getting enough through the food they eat: a balanced diet of cereals, fruit, vegetables, pulses, meat, dairy, or other iron-rich plant proteins.\n4\nIn an ideal world, this would be the solution. But the reality is that billions of people\ncan’t afford\na healthy diet. While we need to ensure they eventually can, this will not change overnight. This is the long-term solution, but it’s not going to solve the problem any time soon.\nA more direct way is delivering iron supplements to those who need them most. The World Health Organization\nstrongly recommends\ngiving pregnant women iron supplements combined with folic acid in settings where iron deficiency is widespread. This reduces rates of low-birth-weight children and improves other birth outcomes, especially when combined with other essential nutrient supplements.\n7\nOf course, iron supplements can also be given at other stages of life, and there is some evidence of improved cognitive development and concentration in children and adolescent girls, where the risk of anemia is also very high.\n8\nIron supplementation is cheap, costing just $1 to $2 per pregnancy.\nWhen anemia is severe and small doses of supplements are not sufficient, it can be treated through one-off iron injections. This tends to be more effective than supplements in the form of pills or capsules but is more expensive and requires medical infrastructure to set up.\n9\nOne option is to incorporate injections into prenatal appointments so that pregnant women are offered effective treatment during high-risk periods.\nFinally, micronutrient deficiencies can be tackled through food fortification. This is when vitamins and minerals are added to foods during the processing stage. This can be done extremely cheaply — and can reach a large number of people at once — but relies on people accessing their food through a more centralized system of producers. It’s much harder to reach people that buy from local markets or live in rural areas.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nHow to reduce anemia cases from other causes\nAs I wrote earlier, there are other causes of anemia beyond nutritional deficiencies.\nIt’s easier said than done, but preventing and treating infectious diseases such as malaria, HIV, and hookworm disease would have a big impact on reducing anemia cases, especially in lower-income countries.\n10\nThere are many other good reasons to prevent these fatal diseases, but it’s an additional benefit.\nTreated bednets\nand antimalarials to tackle malaria,\nantiretroviral therapy drugs\nto manage HIV, and deworming programs are all relatively cheap health interventions that we should be prioritizing to save lives in any case.\n11\nSince many cases of anemia are also associated with menstrual issues or pregnancy in women, improving access to prenatal and antenatal care and contraception can help. Anemia is more common in very young mothers — because nutritional demands are already higher during adolescence — so providing higher-quality care for teenage mothers before and after pregnancy can reduce some of these risks.\n12\nContraceptive pills might be an option for non-pregnant women who have heavy menstrual cycles, which makes anemia much more likely.\n13\nThis is a relatively low-cost intervention but is not readily available to women in many countries.\nOther genetic causes of anemia, such as\nsickle cell disease\n, tend to be harder and more expensive to manage. Since these disorders are often not curable, the main way of managing anemia is through blood transfusions. This is more expensive than other interventions, but it is the main way of managing anemia when sickle cells are the cause.\n14\nBut there are more than a billion people in the world who suffer from anemia that could be managed relatively cheaply. The effects might not be directly visible, but it would make a massive difference, giving people their energy back and giving children the opportunity to develop physically and mentally to their full potential.\nContinue reading on Our World in Data\nHalf of all child deaths are linked to malnutrition\nImproving the nutrition of mothers and children could save many lives at a relatively low cost.\nAlmost three billion people cannot afford a healthy diet\nA healthy, nutritious diet is much more expensive than a calorie sufficient one.\nEndnotes\nGardner, W. M., Razo, C., McHugh, T. A., Hagins, H., Vilchis-Tella, V. M., Hennessy, C., ... & Dongarwar, D. (2023). Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990–2021: findings from the Global Burden of Disease Study 2021. The Lancet Haematology.\nRahman, M. M., Abe, S. K., Rahman, M. S., Kanda, M., Narita, S., Bilano, V., ... & Shibuya, K. (2016). Maternal anemia and risk of adverse birth and health outcomes in low-and middle-income countries: systematic review and meta-analysis. The American Journal of Clinical Nutrition.\nYoung, M. F. (2018). Maternal anaemia and risk of mortality: a call for action. The Lancet Global Health.\nBeckert, R. H., Baer, R. J., Anderson, J. G., Jelliffe-Pawlowski, L. L., & Rogers, E. E. (2019). Maternal anemia and pregnancy outcomes: a population-based study. Journal of Perinatology.\nZerfu, T. A., Umeta, M., & Baye, K. (2016). Dietary diversity during pregnancy is associated with reduced risk of maternal anemia, preterm delivery, and low birth weight in a prospective cohort study in rural Ethiopia. The American journal of clinical nutrition.\nBothwell, T. H. (2000). Iron requirements in pregnancy and strategies to meet them. The American Journal of Clinical Nutrition.\nIn its\nlatest report\n, it estimates that in 2021, around 8% of men and 23% of women aged 15 to 49 years old had iron deficiency.\nSimilarly, 34% of women had anemia compared to 11% of men.\nGardner, W. M., Razo, C., McHugh, T. A., Hagins, H., Vilchis-Tella, V. M., Hennessy, C., ... & Dongarwar, D. (2023). Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990–2021: findings from the Global Burden of Disease Study 2021. The Lancet Haematology.\nKeats, E. C., Das, J. K., Salam, R. A., Lassi, Z. S., Imdad, A., Black, R. E., & Bhutta, Z. A. (2021). Effective interventions to address maternal and child malnutrition: an update of the evidence. The Lancet Child & Adolescent Health.\nHaider, B. A., & Bhutta, Z. A. (2017). Multiple‐micronutrient supplementation for women during pregnancy. Cochrane Database of Systematic Reviews.\nThe results of iron supplementation on cognitive development are often mixed. Numerous studies find positive impacts on intelligence. However, the impacts on concentration and school achievement are more mixed, with some studies showing a positive impact while others show no effect.\nFalkingham, M., Abdelhamid, A., Curtis, P., Fairweather-Tait, S., Dye, L., & Hooper, L. (2010). The effects of oral iron supplementation on cognition in older children and adults: a systematic review and meta-analysis. Nutrition Journal. Chen, Z., Yang, H., Wang, D., Sudfeld, C. R., Zhao, A., Xin, Y., ... & Li, Z. (2022). Effect of oral iron supplementation on cognitive function among children and adolescents in low-and middle-income countries: a systematic review and meta-analysis. Nutrients.\nQassim, A., Grivell, R. M., Henry, A., Kidson‐Gerber, G., Shand, A., & Grzeskowiak, L. E. (2019). Intravenous or oral iron for treating iron deficiency anaemia during pregnancy: systematic review and meta‐analysis. Medical Journal of Australia.\nWhite, N. J. (2018). Anaemia and malaria. Malaria Journal.\nGiveWell (2024). Mass Distribution of Insecticide-Treated Nets (ITNs). Available at:\nhttps://www.givewell.org/international/technical/programs/insecticide-treated-nets\nGiveWell (2023). Combination Deworming (Mass Drug Administration Targeting Both Schistosomiasis and Soil-Transmitted Helminths). Available at:\nhttps://www.givewell.org/international/technical/programs/deworming\nAmpiah, M. K., Kovey, J. J., Apprey, C., & Annan, R. A. (2019). Comparative analysis of trends and determinants of anaemia between adult and teenage pregnant women in two rural districts of Ghana. BMC public health.\nYefet, E., Yossef, A., & Nachum, Z. (2021). Prediction of anemia at delivery. Scientific Reports.\nBellizzi, S., & Ali, M. M. (2018). Effect of oral contraception on anemia in 12 low-and middle-income countries. Contraception.\nBathija, H., Lei, Z. W., Cheng, X. Q., Xie, L., Wang, Y., Rugpao, S., ... & Boukhris, R. (1998). Effects of contraceptives on hemoglobin and ferritin. Contraception.\nHan, H., Hensch, L., & Tubman, V. N. (2021). Indications for transfusion in the management of sickle cell disease. Hematology.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2024) - “Billions of people suffer from anemia, but there are cheap ways to reduce this” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-093348/billions-people-suffer-anemia-cheap-ways-reduce.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-billions-people-suffer-anemia-cheap-ways-reduce,\nauthor = {Hannah Ritchie},\ntitle = {Billions of people suffer from anemia, but there are cheap ways to reduce this},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://archive.ourworldindata.org/20260518-093348/billions-people-suffer-anemia-cheap-ways-reduce.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "billions-people-suffer-anemia-cheap-ways-reduce", "source_url": "https://ourworldindata.org/billions-people-suffer-anemia-cheap-ways-reduce", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "In some countries, most children and pregnant women are anemic. There are low-cost ways to tackle this.", "numeric_mentions": ["25,", "2024", "1", "40%", "2", "3", "2%", "12", "4", "5", "15", "49,", "6", "10%", "15%", "50%", "2000", "48%", "39%", "2019", "7", "8", "9", "10", "11", "13", "14", "2023", "1990", "2021", "2016", "2018", "2021,", "8%", "23%", "49 years", "34%", "11%", "2017", "2010", "2022", "1998", "20260518", "093348", "18,", "2026"], "numeric_evidence": [{"title": "Share of children who have anemia", "source_url": "https://ourworldindata.org/grapher/prevalence-of-anemia-in-children.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Prevalence of anemia among children (% of children ages 6-59 months)"], "row_count_total": 4060, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Prevalence of anemia among children (% of children ages 6-59 months)": "51.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Prevalence of anemia among children (% of children ages 6-59 months)": "49.8"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Prevalence of anemia among children (% of children ages 6-59 months)": "48.3"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Prevalence of anemia among children (% of children ages 6-59 months)": "47.1"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Prevalence of anemia among children (% of children ages 6-59 months)": "46.2"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Prevalence of anemia among children (% of children ages 6-59 months)": "45.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Prevalence of anemia among children (% of children ages 6-59 months)": "44.9"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Prevalence of anemia among children (% of children ages 6-59 months)": "44.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Prevalence of anemia among children (% of children ages 6-59 months)": "44.2"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Prevalence of anemia among children (% of children ages 6-59 months)": "43.9"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Prevalence of anemia among children (% of children ages 6-59 months)": "43.7"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Prevalence of anemia among children (% of children ages 6-59 months)": "43.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Prevalence of anemia among children (% of children ages 6-59 months)": "43.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Prevalence of anemia among children (% of children ages 6-59 months)": "43.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Prevalence of anemia among children (% of children ages 6-59 months)": "43.6"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Prevalence of anemia among children (% of children ages 6-59 months)": "43.7"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Prevalence of anemia among children (% of children ages 6-59 months)": "43.9"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Prevalence of anemia among children (% of children ages 6-59 months)": "44.2"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Prevalence of anemia among children (% of children ages 6-59 months)": "44.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Prevalence of anemia among children (% of children ages 6-59 months)": "44.9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Prevalence of anemia among children (% of children ages 6-59 months)": "34.9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Prevalence of anemia among children (% of children ages 6-59 months)": "33.4"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Prevalence of anemia among children (% of children ages 6-59 months)": "32.1"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Prevalence of anemia among children (% of children ages 6-59 months)": "30.9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Prevalence of anemia among children (% of children ages 6-59 months)": "29.6"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Prevalence of anemia among children (% of children ages 6-59 months)": "28.3"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Prevalence of anemia among children (% of children ages 6-59 months)": "27.1"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Prevalence of anemia among children (% of children ages 6-59 months)": "25.9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Prevalence of anemia among children (% of children ages 6-59 months)": "24.8"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Prevalence of anemia among children (% of children ages 6-59 months)": "23.8"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Prevalence of anemia among children (% of children ages 6-59 months)": "23"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Prevalence of anemia among children (% of children ages 6-59 months)": "22.9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Prevalence of anemia among children (% of children ages 6-59 months)": "23.1"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Prevalence of anemia among children (% of children ages 6-59 months)": "23.6"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Prevalence of anemia among children (% of children ages 6-59 months)": "24.4"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Prevalence of anemia among children (% of children ages 6-59 months)": "25.5"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Prevalence of anemia among children (% of children ages 6-59 months)": "26.9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Prevalence of anemia among children (% of children ages 6-59 months)": "28.4"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Prevalence of anemia among children (% of children ages 6-59 months)": "29.6"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Prevalence of anemia among children (% of children ages 6-59 months)": "30.9"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Prevalence of anemia among children (% of children ages 6-59 months)": "39.9"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "Prevalence of anemia among children (% of children ages 6-59 months)": "39.3"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "Prevalence of anemia among children (% of children ages 6-59 months)": "38.8"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "Prevalence of anemia among children (% of children ages 6-59 months)": "38.3"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "Prevalence of anemia among children (% of children ages 6-59 months)": "37.9"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Prevalence of anemia among children (% of children ages 6-59 months)": "37.4"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "Prevalence of anemia among children (% of children ages 6-59 months)": "36.9"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "Prevalence of anemia among children (% of children ages 6-59 months)": "36.3"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "Prevalence of anemia among children (% of children ages 6-59 months)": "35.8"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "Prevalence of anemia among children (% of children ages 6-59 months)": "35.2"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Prevalence of anemia among children (% of children ages 6-59 months)": "34.7"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "Prevalence of anemia among children (% of children ages 6-59 months)": "34.3"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "Prevalence of anemia among children (% of children ages 6-59 months)": "34.1"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "Prevalence of anemia among children (% of children ages 6-59 months)": "33.9"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "Prevalence of anemia among children (% of children ages 6-59 months)": "33.9"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Prevalence of anemia among children (% of children ages 6-59 months)": "33.9"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "Prevalence of anemia among children (% of children ages 6-59 months)": "34"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "Prevalence of anemia among children (% of children ages 6-59 months)": "34"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "Prevalence of anemia among children (% of children ages 6-59 months)": "34.1"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "Prevalence of anemia among children (% of children ages 6-59 months)": "34.3"}, {"Entity": "Andorra", "Code": "AND", "Year": "2000", "Prevalence of anemia among children (% of children ages 6-59 months)": "13.1"}, {"Entity": "Andorra", "Code": "AND", "Year": "2001", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.9"}, {"Entity": "Andorra", "Code": "AND", "Year": "2002", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.7"}, {"Entity": "Andorra", "Code": "AND", "Year": "2003", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.6"}, {"Entity": "Andorra", "Code": "AND", "Year": "2004", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.6"}, {"Entity": "Andorra", "Code": "AND", "Year": "2005", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.5"}, {"Entity": "Andorra", "Code": "AND", "Year": "2006", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.5"}, {"Entity": "Andorra", "Code": "AND", "Year": "2007", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.5"}, {"Entity": "Andorra", "Code": "AND", "Year": "2008", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.4"}, {"Entity": "Andorra", "Code": "AND", "Year": "2009", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.4"}, {"Entity": "Andorra", "Code": "AND", "Year": "2010", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.4"}, {"Entity": "Andorra", "Code": "AND", "Year": "2011", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.5"}, {"Entity": "Andorra", "Code": "AND", "Year": "2012", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.5"}, {"Entity": "Andorra", "Code": "AND", "Year": "2013", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.6"}, {"Entity": "Andorra", "Code": "AND", "Year": "2014", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.8"}, {"Entity": "Andorra", "Code": "AND", "Year": "2015", "Prevalence of anemia among children (% of children ages 6-59 months)": "12.9"}, {"Entity": "Andorra", "Code": "AND", "Year": "2016", "Prevalence of anemia among children (% of children ages 6-59 months)": "13.2"}, {"Entity": "Andorra", "Code": "AND", "Year": "2017", "Prevalence of anemia among children (% of children ages 6-59 months)": "13.4"}, {"Entity": "Andorra", "Code": "AND", "Year": "2018", "Prevalence of anemia among children (% of children ages 6-59 months)": "13.7"}, {"Entity": "Andorra", "Code": "AND", "Year": "2019", "Prevalence of anemia among children (% of children ages 6-59 months)": "14.1"}, {"Entity": "Angola", "Code": "AGO", "Year": "2000", "Prevalence of anemia among children (% of children ages 6-59 months)": "72.8"}, {"Entity": "Angola", "Code": "AGO", "Year": "2001", "Prevalence of anemia among children (% of children ages 6-59 months)": "71.5"}, {"Entity": "Angola", "Code": "AGO", "Year": "2002", "Prevalence of anemia among children (% of children ages 6-59 months)": "70.3"}, {"Entity": "Angola", "Code": "AGO", "Year": "2003", "Prevalence 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{"Entity": "Angola", "Code": "AGO", "Year": "2011", "Prevalence of anemia among children (% of children ages 6-59 months)": "60.6"}, {"Entity": "Angola", "Code": "AGO", "Year": "2012", "Prevalence of anemia among children (% of children ages 6-59 months)": "60.5"}, {"Entity": "Angola", "Code": "AGO", "Year": "2013", "Prevalence of anemia among children (% of children ages 6-59 months)": "60.7"}, {"Entity": "Angola", "Code": "AGO", "Year": "2014", "Prevalence of anemia among children (% of children ages 6-59 months)": "61.1"}, {"Entity": "Angola", "Code": "AGO", "Year": "2015", "Prevalence of anemia among children (% of children ages 6-59 months)": "61.4"}, {"Entity": "Angola", "Code": "AGO", "Year": "2016", "Prevalence of anemia among children (% of children ages 6-59 months)": "61.9"}, {"Entity": "Angola", "Code": "AGO", "Year": "2017", "Prevalence of anemia among children (% of children ages 6-59 months)": "62.1"}, {"Entity": "Angola", "Code": "AGO", "Year": "2018", "Prevalence of 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"ATG", "Year": "2012", "Prevalence of anemia among children (% of children ages 6-59 months)": "19.1"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2013", "Prevalence of anemia among children (% of children ages 6-59 months)": "18.6"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2014", "Prevalence of anemia among children (% of children ages 6-59 months)": "18.2"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2015", "Prevalence of anemia among children (% of children ages 6-59 months)": "17.9"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2016", "Prevalence of anemia among children (% of children ages 6-59 months)": "17.6"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2017", "Prevalence of anemia among children (% of children ages 6-59 months)": "17.4"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2018", "Prevalence of anemia among children (% of children ages 6-59 months)": "17.3"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2019", "Prevalence of anemia among children (% of children ages 6-59 months)": "17.2"}], "rows_tail": [{"Entity": "Venezuela", "Code": "VEN", "Year": "2000", "Prevalence of anemia among children (% of children ages 6-59 months)": "29.1"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2001", "Prevalence of anemia among children (% of children ages 6-59 months)": "28.8"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2002", "Prevalence of anemia among children (% of children ages 6-59 months)": "28.7"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2003", "Prevalence of anemia among children (% of children ages 6-59 months)": "28.7"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2004", "Prevalence of anemia among children (% of children ages 6-59 months)": "28.6"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2005", "Prevalence of anemia among children (% of children ages 6-59 months)": "28.2"}, {"Entity": "Venezuela", "Code": "VEN", 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{"Entity": "Vietnam", "Code": "VNM", "Year": "2001", "Prevalence of anemia among children (% of children ages 6-59 months)": "32.2"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2002", "Prevalence of anemia among children (% of children ages 6-59 months)": "30.8"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2003", "Prevalence of anemia among children (% of children ages 6-59 months)": "29.5"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2004", "Prevalence of anemia among children (% of children ages 6-59 months)": "28.2"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2005", "Prevalence of anemia among children (% of children ages 6-59 months)": "26.9"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2006", "Prevalence of anemia among children (% of children ages 6-59 months)": "25.7"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2007", "Prevalence of anemia among children (% of children ages 6-59 months)": "24.5"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2008", "Prevalence of anemia among children (% of children ages 6-59 months)": "23.4"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2009", "Prevalence of anemia among children (% of children ages 6-59 months)": "22.5"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2010", "Prevalence of anemia among children (% of children ages 6-59 months)": "21.9"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2011", "Prevalence of anemia among children (% of children ages 6-59 months)": "21.4"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2012", "Prevalence of anemia among children (% of children ages 6-59 months)": "21.2"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2013", "Prevalence of anemia among children (% of children ages 6-59 months)": "21.1"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2014", "Prevalence of anemia among children (% of children ages 6-59 months)": "21.2"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2015", "Prevalence of anemia among children (% of children ages 6-59 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"Code": "YEM", "Year": "2012", "Prevalence of anemia among children (% of children ages 6-59 months)": "79.9"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Prevalence of anemia among children (% of children ages 6-59 months)": "79.9"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Prevalence of anemia among children (% of children ages 6-59 months)": "79.9"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Prevalence of anemia among children (% of children ages 6-59 months)": "79.9"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Prevalence of anemia among children (% of children ages 6-59 months)": "79.9"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Prevalence of anemia among children (% of children ages 6-59 months)": "79.8"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Prevalence of anemia among children (% of children ages 6-59 months)": "79.7"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Prevalence of anemia among children (% of children ages 6-59 months)": "79.5"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Prevalence of anemia among children (% of children ages 6-59 months)": "65.6"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Prevalence of anemia among children (% of children ages 6-59 months)": "64.2"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Prevalence of anemia among children (% of children ages 6-59 months)": "62.6"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Prevalence of anemia among children (% of children ages 6-59 months)": "61.2"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Prevalence of anemia among children (% of children ages 6-59 months)": "59.9"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Prevalence of anemia among children (% of children ages 6-59 months)": "58.7"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Prevalence of anemia among children (% of children ages 6-59 months)": "57.6"}, {"Entity": "Zambia", "Code": 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"Year": "2002", "Prevalence of anemia among children (% of children ages 6-59 months)": "44.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Prevalence of anemia among children (% of children ages 6-59 months)": "45.9"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Prevalence of anemia among children (% of children ages 6-59 months)": "47.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Prevalence of anemia among children (% of children ages 6-59 months)": "48.9"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Prevalence of anemia among children (% of children ages 6-59 months)": "50.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Prevalence of anemia among children (% of children ages 6-59 months)": "51"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Prevalence of anemia among children (% of children ages 6-59 months)": "51.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Prevalence of anemia among children (% of children ages 6-59 months)": "50.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Prevalence of anemia among children (% of children ages 6-59 months)": "49.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Prevalence of anemia among children (% of children ages 6-59 months)": "48.3"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Prevalence of anemia among children (% of children ages 6-59 months)": "46.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Prevalence of anemia among children (% of children ages 6-59 months)": "44.3"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Prevalence of anemia among children (% of children ages 6-59 months)": "42.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Prevalence of anemia among children (% of children ages 6-59 months)": "41.7"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Prevalence of anemia among children (% of children ages 6-59 months)": "40.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Prevalence of anemia among children (% of children ages 6-59 months)": "39.6"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Prevalence of anemia among children (% of children ages 6-59 months)": "38.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Prevalence of anemia among children (% of children ages 6-59 months)": "37.8"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "prevalence-of-anemia-in-children", "metadata_url": "https://ourworldindata.org/grapher/prevalence-of-anemia-in-children.metadata.json", "chart_title": "Share of children who have anemia", "chart_subtitle": "Share of children under the age of five with anemia. Anemia is a condition that develops when your blood lacks enough healthy red blood cells or hemoglobin.", "chart_note": "Anemia is defined as hemoglobin levels lower than 110 grams per liter at sea level. Altitude can affect hemoglobin levels, requiring adjustments to the threshold for diagnosing anemia.", "chart_citation": "Global Health Observatory / World Health Statistics - World Health Organization (WHO), via World Bank (2026)", "original_chart_url": "https://ourworldindata.org/grapher/prevalence-of-anemia-in-children", "owid_column_metadata": {"Prevalence of anemia among children (% of children ages 6-59 months)": {"titleShort": "Prevalence of anemia among children (% of children ages 6-59 months)", "titleLong": "Prevalence of anemia among children (% of children ages 6-59 months)", "shortUnit": "%", "unit": "% of children under 5", "timespan": "2000-2019", "type": "Numeric", "owidVariableId": 1205060, "shortName": "sh_anm_chld_zs", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "Global Health Observatory / World Health Statistics - World Health Organization (WHO), via World Bank (2026) – processed by Our World in Data", "citationLong": "Global Health Observatory / World Health Statistics - World Health Organization (WHO), via World Bank (2026) – processed by Our World in Data. “Prevalence of anemia among children (% of children ages 6-59 months)” [dataset]. Global Health Observatory / World Health Statistics - World Health Organization (WHO), via World Bank, “World Development Indicators 125” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205060.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Share of pregnant women who have anemia", "source_url": "https://ourworldindata.org/grapher/prevalence-of-anemia-in-pregnant-women.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Prevalence of anemia among pregnant women (%)"], "row_count_total": 4920, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Prevalence of anemia among pregnant women (%)": "38.6"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Prevalence of anemia among pregnant women (%)": "38.2"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Prevalence of anemia among pregnant women (%)": "37.8"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Prevalence of anemia among pregnant women (%)": "37.4"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Prevalence of anemia among pregnant women (%)": "37"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": 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pregnant women (%)": "36.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Prevalence of anemia among pregnant women (%)": "36.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Prevalence of anemia among pregnant women (%)": "36.2"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Prevalence of anemia among pregnant women (%)": "36.2"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Prevalence of anemia among pregnant women (%)": "36.5"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Prevalence of anemia among pregnant women (%)": "36.7"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Prevalence of anemia among pregnant women (%)": "35.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Prevalence of anemia among pregnant women (%)": "34.9"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Prevalence of anemia among pregnant women (%)": "34.7"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Prevalence of anemia among 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among pregnant women (%)": "31.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Prevalence of anemia among pregnant women (%)": "30.9"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Prevalence of anemia among pregnant women (%)": "30.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Prevalence of anemia among pregnant women (%)": "30.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Prevalence of anemia among pregnant women (%)": "29.9"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Prevalence of anemia among pregnant women (%)": "29.6"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Prevalence of anemia among pregnant women (%)": "29.6"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Prevalence of anemia among pregnant women (%)": "29.7"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Prevalence of anemia among pregnant women (%)": "29.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Prevalence of 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Altitude can affect hemoglobin levels, requiring adjustments to the threshold for diagnosing anemia.", "chart_citation": "Global Health Observatory / World Health Statistics - World Health Organization (WHO), via World Bank (2026)", "original_chart_url": "https://ourworldindata.org/grapher/prevalence-of-anemia-in-pregnant-women", "owid_column_metadata": {"Prevalence of anemia among pregnant women (%)": {"titleShort": "Prevalence of anemia among pregnant women (%)", "titleLong": "Prevalence of anemia among pregnant women (%)", "shortUnit": "%", "unit": "%", "timespan": "2000-2023", "type": "Numeric", "owidVariableId": 1205120, "shortName": "sh_prg_anem", "lastUpdated": "2026-02-27", "nextUpdate": "2027-02-27", "citationShort": "Global Health Observatory / World Health Statistics - World Health Organization (WHO), via World Bank (2026) – processed by Our World in Data", "citationLong": "Global Health Observatory / World Health Statistics - World Health Organization (WHO), via World Bank (2026) – processed by Our World in Data. “Prevalence of anemia among pregnant women (%)” [dataset]. Global Health Observatory / World Health Statistics - World Health Organization (WHO), via World Bank, “World Development Indicators 125” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205120.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Share of the population with anemia in higher risk groups", "source_url": "https://ourworldindata.org/grapher/anemia-risk-groups.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Women of reproductive age", "Pregnant women", "Children"], "row_count_total": 4940, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Women of reproductive age": "31.6", "Pregnant women": "38.6", "Children": "51.5"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Women of reproductive age": "31.5", "Pregnant women": "38.2", "Children": "49.8"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Women of reproductive age": "31.4", "Pregnant women": "37.8", "Children": "48.3"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Women of reproductive age": "31.6", "Pregnant women": "37.4", "Children": "47.1"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": 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"Code": "AND", "Year": "2005", "Women of reproductive age": "12.1", "Pregnant women": "19.1", "Children": "12.5"}, {"Entity": "Andorra", "Code": "AND", "Year": "2006", "Women of reproductive age": "12.1", "Pregnant women": "18.8", "Children": "12.5"}, {"Entity": "Andorra", "Code": "AND", "Year": "2007", "Women of reproductive age": "12.2", "Pregnant women": "18.5", "Children": "12.5"}, {"Entity": "Andorra", "Code": "AND", "Year": "2008", "Women of reproductive age": "12.2", "Pregnant women": "18.3", "Children": "12.4"}, {"Entity": "Andorra", "Code": "AND", "Year": "2009", "Women of reproductive age": "12.3", "Pregnant women": "18", "Children": "12.4"}, {"Entity": "Andorra", "Code": "AND", "Year": "2010", "Women of reproductive age": "12.3", "Pregnant women": "17.8", "Children": "12.4"}, {"Entity": "Andorra", "Code": "AND", "Year": "2011", "Women of reproductive age": "12.4", "Pregnant women": "17.6", "Children": "12.5"}, {"Entity": "Andorra", "Code": "AND", "Year": "2012", "Women of reproductive age": "12.6", "Pregnant women": "17.4", "Children": "12.5"}, {"Entity": "Andorra", "Code": "AND", "Year": "2013", "Women of reproductive age": "12.7", "Pregnant women": "17.3", "Children": "12.6"}, {"Entity": "Andorra", "Code": "AND", "Year": "2014", "Women of reproductive age": "12.9", "Pregnant women": "17.2", "Children": "12.8"}, {"Entity": "Andorra", "Code": "AND", "Year": "2015", "Women of reproductive age": "13.1", "Pregnant women": "17", "Children": "12.9"}, {"Entity": "Andorra", "Code": "AND", "Year": "2016", "Women of reproductive age": "13.3", "Pregnant women": "16.9", "Children": "13.2"}, {"Entity": "Andorra", "Code": "AND", "Year": "2017", "Women of reproductive age": "13.6", "Pregnant women": "16.7", "Children": "13.4"}, {"Entity": "Andorra", "Code": "AND", "Year": "2018", "Women of reproductive age": "14", "Pregnant women": "16.6", "Children": "13.7"}, {"Entity": "Andorra", "Code": "AND", "Year": "2019", "Women of reproductive age": "14.3", "Pregnant women": "16.4", "Children": "14.1"}, {"Entity": "Andorra", "Code": "AND", "Year": "2020", "Women of reproductive age": "14.8", "Pregnant women": "16.3", "Children": ""}, {"Entity": "Andorra", "Code": "AND", "Year": "2021", "Women of reproductive age": "15.3", "Pregnant women": "16.3", "Children": ""}, {"Entity": "Andorra", "Code": "AND", "Year": "2022", "Women of reproductive age": "15.8", "Pregnant women": "16.3", "Children": ""}, {"Entity": "Andorra", "Code": "AND", "Year": "2023", "Women of reproductive age": "16.4", "Pregnant women": "16.3", "Children": ""}, {"Entity": "Angola", "Code": "AGO", "Year": "2000", "Women of reproductive age": "52.9", "Pregnant women": "52.3", "Children": "72.8"}, {"Entity": "Angola", "Code": "AGO", "Year": "2001", "Women of reproductive age": "52.4", "Pregnant women": "51.8", "Children": "71.5"}, {"Entity": "Angola", "Code": "AGO", "Year": "2002", "Women of reproductive age": "51.9", "Pregnant women": "51.3", "Children": "70.3"}, {"Entity": "Angola", "Code": 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Global Health Observatory / World Health Statistics - World Health Organization (WHO), via World Bank, “World Development Indicators 125” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1205060.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "f2264e8fd9ae3cddc191"},
{"raw_link": "https://ourworldindata.org/easier-to-reuse-our-data", "title": "We've made it much easier to reuse our data", "context": "We've made it much easier to reuse our data\nAn overview of our new features: enhanced data downloads and the Chart Data API.\nBy\nDaniel Bachler\n,\nCharlie Giattino\n,\nand\nMarcel Gerber\nNovember 21, 2024\nBrowse past versions\nReuse our work freely\nOur work at Our World in Data begins with one central premise: for research and data to make a difference, it has to be\naccessible\nand\nunderstandable\n.\nA wealth of crucial data on essential topics, from climate change to mental health, is already out there. It’s produced through the hard work of academic researchers, think tanks, and institutions like the UN, World Bank, WHO, and many others.\nBut this data is too often lost in inaccessible databases, locked behind paywalls, or buried under technical jargon in academic papers.\nAt Our World in Data, it's\nour mission\nto improve this situation: to make research and data on the world's biggest challenges easier for everyone to understand and use, to make progress against those challenges.\nOur work has already become a helpful and trusted resource for millions of people, from teachers and schoolchildren to journalists and policymakers worldwide.\nToday, we're excited to announce two significant improvements that make it even easier to access and reuse the data we have collated on our site: enhanced data download options and a new data API.\nUpdate: examples from users\nSince announcing these new data download features, users have written about how they use these features with both Python and R.\nAllen Downey’s “\nDownload the World in Data\n” (Python)\nR-bloggers “\nDownloading datasets from Our World in Data in R\n”\nChristoph Scheuch’s\n“owidapi” package for R\nTwo solutions for two different needs\nWe understand that our users have varying technical backgrounds and different ways of working with data. That's why we've developed two distinct solutions — both are accessible for every chart via the data download button in the lower right corner.\nEnhanced data downloads\nDownload\nFor those who prefer working with spreadsheets, we've created a new zip file download package that includes data in CSV format (which can be used with standard tools like Excel and Sheets), comprehensive metadata in JSON format, as well as a detailed README file explaining the data structure and content. The README and the metadata JSON files contain very similar content but are optimized for reading by humans or machines, respectively.\nWhen accessing data this way, you now have two options: download the complete data or only the currently displayed subset. This latter option can save you the time and effort of filtering and selecting that data yourself.\nWhile downloading the subset of displayed data is available for all of our chart types, the exact slice of data you get depends on the chart type: for example, with line charts, you’ll get data for the visible countries and time points; with maps, you’ll get data for all countries but only for the selected year.\nThe Chart Data API\nDownload\nFor users who work with automated workflows, computational notebooks, or custom applications, we now offer direct URLs to access data in CSV format and comprehensive metadata in JSON format. This is the same data and metadata included in the zip file download package described above.\nJust like with the download package, you can fetch the complete data or only the subset of the data currently displayed in the chart. You can also choose between longer column names that are easier to read for humans or shorter column names that are often more convenient to use in code.\nLearn more about the details of our Chart Data API and see examples of its use on our\ndocumentation page\n.\nThe benefits of accessing data from us\nWhen you use data from Our World in Data, you're benefiting from our rigorous data processing workflow:\nCareful data selection\n: We aim to identify and select the highest quality and most comprehensive data sources for each topic (\nread more about our process\n).\nUnified access\n: All our datasets on a wide variety of topics are available through the same API and download format, making it easier to work with multiple datasets.\nQuality control\n: Our team regularly identifies and investigates data anomalies, often working with original data providers to correct errors.\nData harmonization\n: We ensure data is as comparable as possible across countries and periods, enabling insights to be drawn across multiple data sources.\nRich metadata\n: We provide crucial context and information about each dataset in plain language.\nThe critical importance of metadata\nTo make a difference, research and data must be accessible and\nunderstandable\n.\nWhile often overlooked, accurate, clearly written metadata is an essential component of impactful data. Good metadata is like the legend on a map — it explains the symbols and markings so you can navigate with confidence. Without it, even the best map leaves you guessing and at risk of going off course or falling off a cliff.\nFor each dataset, we provide critical information such as:\nImportant methodological notes\n: For example, for GDP data, it’s crucial to know whether figures are adjusted for inflation or differences in prices between countries.\nKnown limitations or caveats\n: For instance, for greenhouse gas emissions data, land use is an important category, but it is often excluded since measuring it is more complex than measuring other categories.\nMeasurement units\n: For example, data on oil is often given in either barrels or kWh, and it’s important to make that choice clear.\nSource and last update information\n: Since most of the data we offer is republished from elsewhere, it is crucial to communicate where we got the data from and when it was last updated.\nOne example of the importance of metadata is the COVID-19 pandemic. During the pandemic, Our World in Data\nquickly became an essential global data source\n, especially for data about tests and vaccinations.\nIn both cases, metadata was vital: for example, making clear whether the number of tests reported by a country included PCR tests, antibody tests, or both. Collecting these essential details and making them available both in human- and machine-readable form is a crucial step in ensuring data is interpreted correctly.\nLooking ahead\nWe're not done yet. In the coming months, we plan to extend the Chart Data API to include data from our Data Explorers, such as our explorers on\nCO2 and greenhouse gas emissions\nand\nglobal health\n. This expansion will make even more of our datasets programmatically accessible.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nReaders like you make this work possible\nAll of the improvements described in this article were developed in direct response to feedback from readers and made possible thanks to the generous donations of\nthousands of supporters\n.\nIf you find Our World in Data valuable, please consider\nmaking a donation\n. As a nonprofit, your support is crucial for improving our platform and making important global data accessible to everyone.\nWe love feedback. If you have any feedback about this or any aspect of our work, you can write to us at\ninfo@ourworldindata.org\nor via the feedback button in the bottom right corner of our website. Your input helps us understand what features would be most valuable to develop next and how we can improve our work for you.\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "easier-to-reuse-our-data", "source_url": "https://ourworldindata.org/easier-to-reuse-our-data", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "An overview of our new features: enhanced data downloads and the Chart Data API.", "numeric_mentions": ["21,", "2024", "19"], "numeric_evidence": [{"title": "Child mortality rate", "source_url": "https://ourworldindata.org/grapher/child-mortality.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Under-five mortality rate (selected)"], "row_count_total": 16835, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1957", "Under-five mortality rate (selected)": "37.13"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1958", "Under-five mortality rate (selected)": "36.52"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1959", "Under-five mortality rate (selected)": "35.95"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1960", "Under-five mortality rate (selected)": "35.32"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Under-five mortality rate (selected)": "34.76"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Under-five mortality rate (selected)": "34.23"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Under-five mortality rate (selected)": "33.68"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Under-five mortality rate (selected)": "33.17"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Under-five mortality rate (selected)": "32.65"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Under-five mortality rate (selected)": "32.15"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Under-five mortality rate (selected)": "31.66"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Under-five mortality rate (selected)": "31.16"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1969", "Under-five mortality rate (selected)": "30.64"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1970", "Under-five mortality rate (selected)": "30.16"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1971", "Under-five mortality rate (selected)": "29.65"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1972", "Under-five mortality rate (selected)": "29.14"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Under-five mortality rate (selected)": "28.59"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Under-five mortality rate (selected)": "28.06"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Under-five mortality rate (selected)": "27.52"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Under-five mortality rate (selected)": "26.97"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Under-five mortality rate (selected)": "26.38"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Under-five mortality rate (selected)": "25.79"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Under-five mortality rate (selected)": "25.19"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Under-five mortality rate (selected)": "24.57"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Under-five mortality rate (selected)": "23.94"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Under-five mortality rate (selected)": "27.99"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Under-five mortality rate (selected)": "27.44"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Under-five mortality rate (selected)": "30.91"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Under-five mortality rate (selected)": "30.09"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Under-five mortality rate (selected)": "25.31"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Under-five mortality rate (selected)": "24.72"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Under-five mortality rate (selected)": "21.84"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Under-five mortality rate (selected)": "18.71"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Under-five mortality rate (selected)": "18.07"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Under-five mortality rate (selected)": "17.44"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Under-five mortality rate (selected)": "16.85"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Under-five mortality rate (selected)": "16.3"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Under-five mortality rate (selected)": "15.77"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Under-five mortality rate (selected)": "15.28"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Under-five mortality rate (selected)": "14.83"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Under-five mortality rate (selected)": "14.41"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Under-five mortality rate (selected)": "13.99"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Under-five mortality rate (selected)": "13.58"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Under-five mortality rate (selected)": "13.17"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Under-five mortality rate (selected)": "12.74"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Under-five mortality rate (selected)": "12.31"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Under-five mortality rate (selected)": "11.87"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Under-five mortality rate (selected)": "11.42"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Under-five mortality rate (selected)": "10.97"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Under-five mortality rate (selected)": "10.51"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Under-five mortality rate (selected)": "10.07"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Under-five mortality rate (selected)": "9.63"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Under-five mortality rate (selected)": "9.22"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Under-five mortality rate (selected)": "8.83"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Under-five mortality rate (selected)": "8.46"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Under-five mortality rate (selected)": "8.12"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Under-five mortality rate (selected)": "7.8"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Under-five mortality rate (selected)": "7.51"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Under-five mortality rate (selected)": "7.24"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Under-five mortality rate (selected)": "7"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": 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"Under-five mortality rate (selected)": "7.49"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Under-five mortality rate (selected)": "7.31"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Under-five mortality rate (selected)": "7.17"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Under-five mortality rate (selected)": "6.83"}], "rows_tail": [{"Entity": "Zambia", "Code": "ZMB", "Year": "1975", "Under-five mortality rate (selected)": "15.54"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1976", "Under-five mortality rate (selected)": "15.36"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1977", "Under-five mortality rate (selected)": "15.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1978", "Under-five mortality rate (selected)": "15.32"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1979", "Under-five mortality rate (selected)": "15.39"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1980", "Under-five mortality rate (selected)": "15.49"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1981", "Under-five mortality rate (selected)": "15.56"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1982", "Under-five mortality rate (selected)": "15.66"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1983", "Under-five mortality rate (selected)": "15.87"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1984", "Under-five mortality rate (selected)": "16.18"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Under-five mortality rate (selected)": "16.57"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Under-five mortality rate (selected)": "16.97"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Under-five mortality rate (selected)": "17.34"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Under-five mortality rate (selected)": "17.66"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Under-five mortality rate (selected)": "17.89"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Under-five mortality rate (selected)": "18.06"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Under-five mortality rate (selected)": "18.14"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Under-five mortality rate (selected)": "18.11"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Under-five mortality rate (selected)": "17.94"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Under-five mortality rate (selected)": "17.67"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Under-five mortality rate (selected)": "17.34"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Under-five mortality rate (selected)": "17"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Under-five mortality rate (selected)": "16.66"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Under-five mortality rate (selected)": "16.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Under-five mortality rate (selected)": "15.87"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Under-five mortality rate (selected)": "15.25"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Under-five mortality rate (selected)": "14.38"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Under-five mortality rate (selected)": "13.37"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Under-five mortality rate (selected)": "12.36"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Under-five mortality rate (selected)": "11.37"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Under-five mortality rate (selected)": "10.48"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Under-five mortality rate (selected)": "9.71"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Under-five mortality rate (selected)": "9.14"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Under-five mortality rate (selected)": "8.62"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Under-five mortality rate (selected)": "8"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Under-five mortality rate (selected)": "7.57"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Under-five mortality rate (selected)": "7.31"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Under-five mortality rate (selected)": "7.04"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Under-five mortality rate (selected)": "6.72"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Under-five mortality rate (selected)": "6.38"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Under-five mortality rate (selected)": "6.15"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Under-five mortality rate (selected)": "5.9"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Under-five mortality rate (selected)": "5.61"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Under-five mortality rate (selected)": "5.52"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Under-five mortality rate (selected)": "5.41"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Under-five mortality rate (selected)": "5.24"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Under-five mortality rate (selected)": "4.92"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Under-five mortality rate (selected)": "4.67"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Under-five mortality rate (selected)": "4.47"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1953", "Under-five mortality rate (selected)": "18.06"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1954", "Under-five mortality rate (selected)": "17.09"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1955", "Under-five mortality rate (selected)": "16.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1956", "Under-five mortality rate (selected)": "16.46"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1957", "Under-five mortality rate (selected)": "16.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1958", "Under-five mortality rate (selected)": "15.89"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1959", "Under-five mortality rate (selected)": "15.54"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1960", "Under-five mortality rate (selected)": "15.18"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1961", "Under-five mortality rate (selected)": "14.77"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1962", "Under-five mortality rate (selected)": "14.37"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1963", "Under-five mortality rate (selected)": "13.95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1964", "Under-five mortality rate (selected)": "13.52"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1965", "Under-five mortality rate (selected)": "13.11"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1966", "Under-five mortality rate (selected)": "12.72"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1967", "Under-five mortality rate (selected)": "12.37"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1968", "Under-five mortality rate (selected)": "12.07"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1969", "Under-five mortality rate (selected)": "11.81"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1970", "Under-five mortality rate (selected)": "11.65"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1971", "Under-five mortality rate (selected)": "11.53"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1972", "Under-five mortality rate (selected)": "11.46"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1973", "Under-five mortality rate (selected)": "11.42"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1974", "Under-five mortality rate (selected)": "11.41"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1975", "Under-five mortality rate (selected)": "11.44"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1976", "Under-five mortality rate (selected)": "11.47"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1977", "Under-five mortality rate (selected)": "11.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1978", "Under-five mortality rate (selected)": "11.47"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1979", "Under-five mortality rate (selected)": "11.34"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1980", "Under-five mortality rate (selected)": "11.1"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1981", "Under-five mortality rate (selected)": "10.68"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1982", "Under-five mortality rate (selected)": "10.12"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1983", "Under-five mortality rate (selected)": "9.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1984", "Under-five mortality rate (selected)": "8.87"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1985", "Under-five mortality rate (selected)": "8.31"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1986", "Under-five mortality rate (selected)": "7.86"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Under-five mortality rate (selected)": "7.61"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Under-five mortality rate (selected)": "7.55"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Under-five mortality rate (selected)": "7.69"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Under-five mortality rate (selected)": "7.97"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Under-five mortality rate (selected)": "8.33"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Under-five mortality rate (selected)": "8.73"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Under-five mortality rate (selected)": "9.16"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Under-five mortality rate (selected)": "9.52"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Under-five mortality rate (selected)": "9.83"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Under-five mortality rate (selected)": "10.02"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Under-five mortality rate (selected)": "10.08"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Under-five mortality rate (selected)": "10.04"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Under-five mortality rate (selected)": "9.92"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Under-five mortality rate (selected)": "9.83"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Under-five mortality rate (selected)": "9.79"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Under-five mortality rate (selected)": "8.48"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Under-five mortality rate (selected)": "8.85"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Under-five mortality rate (selected)": "9.17"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Under-five mortality rate (selected)": "9.23"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Under-five mortality rate (selected)": "9.4"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Under-five mortality rate (selected)": "9.47"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Under-five mortality rate (selected)": "9.34"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Under-five mortality rate (selected)": "9.03"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Under-five mortality rate (selected)": "8.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Under-five mortality rate (selected)": "7.88"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Under-five mortality rate (selected)": "7.14"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Under-five mortality rate (selected)": "6.55"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Under-five mortality rate (selected)": "6.18"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Under-five mortality rate (selected)": "5.98"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Under-five mortality rate (selected)": "5.69"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Under-five mortality rate (selected)": "5.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Under-five mortality rate (selected)": "5.23"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Under-five mortality rate (selected)": "5.11"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Under-five mortality rate (selected)": "5.01"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Under-five mortality rate (selected)": "4.76"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Under-five mortality rate (selected)": "4.6"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Under-five mortality rate (selected)": "4.42"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "child-mortality", "metadata_url": "https://ourworldindata.org/grapher/child-mortality.metadata.json", "chart_title": "Child mortality rate", "chart_subtitle": "Estimated share of newborns who die before age 5.", "chart_note": null, "chart_citation": "Gapminder (2015); UN Inter-agency Group for Child Mortality Estimation (2025)", "original_chart_url": "https://ourworldindata.org/grapher/child-mortality", "owid_column_metadata": {"Child mortality rate": {"titleShort": "Child mortality rate", "titleLong": "Child mortality rate - Gapminder; UN IGME – Long-run data", "descriptionShort": "The long-run estimated share of newborns who die before reaching the age of five.", "descriptionKey": ["Child mortality, the death of children under the age of five, is still extremely common in our world today. The historical data makes clear that it doesn’t have to be this way: societies can protect their children and reduce child mortality to very low rates. For child mortality to reach low levels, many things have to go right at the same time: good healthcare, good nutrition, clean water and sanitation, maternal health, and high living standards. We can, therefore, think of child mortality as a proxy indicator of a country’s living conditions.", "The chart shows our long-run data on child mortality, which allows you to see how child mortality has changed in countries around the world. It combines data from two sources: Gapminder and the UN Inter-agency Group for Child Mortality Estimation (UN IGME).", "[Gapminder](https://www.gapminder.org/data/documentation/gd005/) provides estimates of child mortality rates from 1800 to 2015. The full list of sources used can be found in [their documentation](https://www.gapminder.org/data/documentation/gd005/).", "[UN IGME](https://childmortality.org/all-cause-mortality/data) provides estimates of child mortality rates for some countries from 1932 onward.", "For years where data from both sources is available, we prioritize the UN IGME data. See [this page](https://docs.google.com/spreadsheets/d/1n-WO7yEbi6sXPpeWrorSEVu8w_Yu5dM0n97q1h16L0g/edit?gid=0#gid=0) for more details on which source is used for each data point.", "This indicator is calculated as the number of children under the age of five who died in a given year, divided by the number of newborns in that year."], "descriptionProcessing": "This indicator is a combination of data from two sources:\n  - Gapminder, which provides estimates of child mortality rates for the years 1800 to 2015.\n  - The UN Inter-agency Group for Child Mortality Estimation (UN IGME) provides estimates of child mortality rates, for some countries from 1932 onward.\n\nFor years where data from both sources is available, we prioritize the UN IGME data. See [this page](https://docs.google.com/spreadsheets/d/1n-WO7yEbi6sXPpeWrorSEVu8w_Yu5dM0n97q1h16L0g/edit?gid=0#gid=0) for more details on which source is used for each data point.\n\nIn the Gapminder dataset we remove rows where the source is labelled as \"Guesstimate\" or \"Model based on Life Expectancy\" to try and ensure we use the best available data.\n\nWe remove data for Austria before 1830 from the Gapminder dataset, as there is a jump in 1830 that is likely an error.", "shortUnit": "%", "unit": "deaths per 100 live births", "timespan": "1751-2023", "type": "Numeric", "owidVariableId": 1027766, "shortName": "child_mortality_rate", "lastUpdated": "2025-04-25", "citationShort": "Gapminder (2015); UN Inter-agency Group for Child Mortality Estimation (2025) – processed by Our World in Data", "citationLong": "Gapminder (2015); UN Inter-agency Group for Child Mortality Estimation (2025) – processed by Our World in Data. “Child mortality rate – Gapminder; UN IGME – Long-run data” [dataset]. United Nations Inter-agency Group for Child Mortality Estimation, “United Nations Inter-agency Group for Child Mortality Estimation”; Gapminder, “Child mortality rate under age five v7”; Gapminder based on UN IGME & UN WPP, “Under-five Mortality v11”; Various sources, “Population” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1027766.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "3a7b21a3056a642ead5e"},
{"raw_link": "https://ourworldindata.org/key-charts-understand-covid-pandemic", "title": "17 key charts to understand the COVID-19 pandemic", "context": "Home\nCOVID-19\n17 key charts to understand the COVID-19 pandemic\nThe pandemic has resulted in over twenty million deaths. In this article, we review the key insights from global data on COVID-19.\nBy\nSaloni Dattani\nand\nLucas Rodés-Guirao\nNovember 18, 2024\nBrowse past versions\nCite this article\nReuse our work freely\nAccording to the best estimates, the COVID-19 pandemic, which began spreading across the world in 2020, had caused around 27 million deaths by August 2024. It has caused grief and suffering over lost lives, put immense pressure on doctors and nurses, and continues to disrupt societies, economies, and lives.\nCountries implemented a range of policies to slow the spread of disease — from testing and contact tracing to social distancing and vaccination. The global response was slow and uneven, with significant disparities in healthcare and vaccination worldwide.\nSince February 2020, we at Our World in Data worked with data providers to build and maintain a global dataset of the most critical data on COVID.\nOur team made two major efforts: building the global dataset on\nvaccinations\nand the global dataset on\ntesting\n. We allowed people worldwide to explore and download this data through our site.\nOur goal was to enable everyone to study the data for themselves and help them make informed decisions on how to respond to the virus.\nIn this article, we will review some of the key insights on the COVID-19 pandemic.\nThe pandemic resulted in millions of deaths worldwide\nOverall, data compiled from many countries shows that in 2020, on average, around 1 in 140 people infected died from COVID-19.\n1\nThat’s 0.7% of the population infected.\nCOVID-19 can cause death through complications like severe respiratory disease, heart disease, and multi-organ failure. Many others experienced serious short-term or long-term consequences of the disease, or were admitted to hospitals or intensive care units.\nNumber of COVID-19 patients in intensive care units (ICU)\nSee how many patients were admitted to ICU over time.\nData shows that the mortality risks from COVID-19 rise exponentially with age.\n2\nThe risks are also influenced by other factors such as pre-existing health conditions, immune system risk factors, access to supplementary oxygen, treatment, healthcare, and the particular coronavirus strain.\n3\nIn the chart below, you can see how the total number of deaths in countries spiked during waves of the coronavirus compared to the trends in deaths in previous years.\nThe blue line shows the number of weekly deaths during the pandemic. Meanwhile, the gray line shows the estimated number of weekly deaths that would have been expected in a year without the pandemic. This estimated number of deaths is based on a projection of the average number of deaths in previous years.\nThe difference between reported and expected deaths is called “excess mortality” — in essence, it estimates the\nadditional\nnumber of deaths compared to a scenario where the pandemic had not occurred.\nThe estimates of expected deaths in the absence of the pandemic are made by projecting previous trends and seasonal patterns, using data from\nvital registries\n, hospital records, and other sources.\nExcess mortality during the Coronavirus pandemic (COVID-19)\nExplore data on COVID-19 excess mortality across the world.\nResearchers can estimate the pandemic’s total mortality by summing the number of excess deaths since the beginning of the pandemic.\nThis is shown in the chart below. Here, we show the range of estimates made by researchers at\nThe Economist\n, whose methods are transparently documented and regularly updated.\n4\nThe chart shows that 27 million people are estimated to have died from COVID-19 up to August 2024.\nThis includes around 5 million by the end of 2020 and another 10 million by the end of 2021.\nAlthough there are significant uncertainties in the total number of deaths, you can see that even the lower bound estimate is much higher than the number of confirmed COVID-19 deaths.\nIt is still frequent to hear the number of confirmed deaths being cited as the death toll of the pandemic, but this is incorrect, especially in countries with limited testing. The number of confirmed deaths represents only a share of total deaths because of factors such as limited testing and\ndeath registration\n.\nThese estimates make COVID-19\none of the deadliest pandemics\nof the last century, along with the 1918 “Spanish flu” pandemic and the HIV/AIDS pandemic.\nYou can read more in our article:\nWhat were the death tolls from pandemics in history?\nPandemics have killed millions of people throughout history. How many deaths were caused by different pandemics, and how have researchers estimated their death tolls?\nCountries were slow to recognize the rise of COVID-19 because of limited testing\nThe large number of deaths from COVID-19 was partly due to countries' slow recognition and response to rising outbreaks. This delay began at the start of the pandemic but continued in new waves later on, including epidemics caused by new variants.\nThe first cases of COVID-19 were reported in Wuhan, China, at the end of December 2019. A sudden outbreak emerged where patients had a severe new respiratory disease, which resembled the SARS-1 epidemic in 2003. Researchers identified its cause as a novel coronavirus, which appeared between October and December of 2019.\n5\nCases were soon being reported in other countries, and the outbreak spread globally.\nThe rise in cases in early 2020 is shown in the chart below.\nSince many of the cases identified weren’t known to be connected, it became clear that undiagnosed COVID-19 infections and asymptomatic infections also contributed to the spread of the disease.\n6\nThis reflects a major limitation of figures of confirmed cases — people who were infected but not tested were not counted. The figures only included cases\nconfirmed\nby testing, such as PCR testing of the saliva for viral RNA.\nHowever, many countries conducted limited testing, as shown in the map below. It shows which population groups could be tested for COVID-19.\nBy March 2020, many countries — shown in yellow — were only testing key groups (such as healthcare workers and recent travelers) who had symptoms but not the general public. Many other countries — shown in red — had no testing policy.\nThis meant the true number of infections was far higher than confirmed cases.\nWithout sufficient testing, countries were slow to recognize new outbreaks and respond to them, with delays in building up medical resources and hospital capacity, effectively tracing cases, and containing the spread of disease.\nBecause testing was limited, we at Our World in Data started building a testing database early on in the pandemic, to show the levels of testing over time and how they related to the number of cases detected. We published this database in the journal\nNature Scientific Data\n.\nThe Our World in Data COVID-19 Testing dataset has been published in the academic journal, Nature Scientific Data\nThe Our World in Data dataset which has tracked COVID-19 testing across the world since the start of the pandemic has been peer-reviewed and published in the academic journal, Nature Scientific Data.\nThe chart below compares the number of confirmed cases to different estimates of the true number of infections.\nDifferent research teams estimated the number of infections using epidemiological modeling, data on the number of cases and deaths, testing levels, and other information.\nAs you can see, these estimates vary greatly; there is considerable uncertainty in the real number of infections.\nBut all models agree that the true number of cases was much higher than the number of confirmed cases — this was especially true at the beginning of the outbreak when testing was minimal.\nThe chart shows an example: while the United States reported around 25,000 daily cases by April 2020, researchers estimate that the actual number of infections was around ten times higher — between 130,000 and 490,000 daily infections that month.\nIn our article, we explain these epidemiological models in more detail:\nHow epidemiological models of COVID-19 help us estimate the true number of infections\nWe know that confirmed COVID-19 cases are only a fraction of true infections. How small a fraction though?\nCountries implemented a range of anti-epidemic measures to reduce the spread of disease\nTo slow the spread of disease, countries implemented a range of policies: restrictions on international travel and mass gatherings, school and workplace closures, stay-at-home orders, contact tracing and testing, and requirements for face coverings and vaccination.\nOur charts here summarize these policies at a broad level — the data was compiled by our colleagues at the Blavatnik School of Government at the University of Oxford as part of the\nCOVID-19 Government Response Tracker\n.\nInternational travel restrictions were among the first policies implemented. As the chart shows, over half of countries had implemented a complete travel ban by April 2020.\nOver the next few months, many countries reduced these restrictions to partial bans or quarantines. More countries began screening arrivals: travelers could avoid quarantines or travel restrictions if they had a negative COVID-19 test result.\nBy 2022, few countries still had restrictions on international travel.\nRestrictions on public gatherings and requirements for wearing facial coverings were also common. You can see these in the two charts below.\nThe first chart looks at the number of countries with restrictions on public gatherings. It shows that by the spring of 2020, most countries implemented strict restrictions on public gatherings — with around half restricting gatherings even among groups of fewer than 10 people.\nThese restrictions were reduced during the summer but rose again during the winter and continued in 2021. By the spring of 2022, restrictions on public gatherings were relaxed in most countries around the world.\nThe second chart shows the number of countries that required facial coverings such as masks. These policies were implemented slightly later than international travel restrictions and public gatherings.\nBut by the summer of 2020, around half of countries had requirements to wear face coverings near other people or to always wear them outside the home.\nThe impact of policies varied between countries and depended on how they were implemented.\nHowever, since the coronavirus spreads between people through respiratory and airborne particles and human contact, policies led to a large reduction in contact between people and slowed down the spread of the virus.\n7\nThis impact is also visible in the spread of other respiratory infections, such as influenza, measles, pertussis, and common cold viruses, which are spread between people by similar mechanisms.\n8\nThe chart below shows this with data from the Seattle Flu study, which tested respiratory samples from the Seattle Area for many potential respiratory pathogens.\n9\nAs you can see, the share of positive tests for many respiratory pathogens declined during the pandemic. This decline was greatest from spring 2020 to 2021 when restrictions were strictest.\n10\nSARS-CoV-2, the virus that causes COVID-19, was more contagious than other pathogens and continued to spread. It became one of the most common respiratory pathogens circulating at the time but was also the deadliest and became one of\nthe leading causes of death\nin many countries.\n11\nEffective vaccines were rolled out from the end of 2020, but there were large disparities between countries\nSeveral COVID-19 vaccines became available within a year of the start of the pandemic after being developed and tested through all regular stages of large-scale clinical trials, which involved tens of thousands of study participants.\nThis rapid speed was possible for several reasons.\nOne is that there was a very high level of research and development funding compared to other diseases, as seen in the chart below. Many candidate vaccines were, therefore, developed and tested in clinical trials.\nAnother reason is that key research had already been conducted on coronaviruses and vaccines in the years before; this research suggested that people could clear the infection with immunity.\n12\nThere were also animal models to study the virus; coronavirus vaccines had already been developed for some animal coronavirus diseases.\n13\nThe clinical trial process also ran faster than usual. There was high interest in volunteering for clinical trials, and since outbreaks were widespread, it was faster to see the potential effect of vaccines.\n14\nIn addition, different stages of clinical trials ran in parallel — with phase 2 and 3 trials running simultaneously, for example — along with regulatory review, meaning that all stages could be completed in a shorter period.\n15\nDownload\nThis fast development was followed by a rapid rollout of vaccines in many high-income countries, while it lagged in poorer countries.\nWe at Our World in Data built and maintained the global vaccination database as soon as the first people were vaccinated to show the vaccination levels over time across the world. We published this dataset in the journal\nNature Human Behaviour\n.\nThe Our World in Data COVID-19 vaccination dataset has been published in the academic journal, Nature Human Behaviour\nOur free, open-access dataset tracking global COVID-19 vaccinations has been published in Nature Human Behaviour.\nBased on our data, the chart below shows the number of vaccine doses administered per 100 people in the population. Each dose is counted individually, meaning the figures include second doses and booster vaccinations.\nAs you can see, countries like the United States and the United Kingdom had administered more than 100 vaccine doses per 100 people by July 2021.\n16\nBut other countries, such as India, only reached this level by December 2021.\nEven by the end of 2022, our data revealed a wide disparity in vaccination rates worldwide.\nWe can also see this by looking at the share of people who completed at least the initial vaccination protocol, which doesn’t count boosters.\n17\nWhile more than 75% of people in many European countries and North America had received the initial vaccination protocol, that figure was less than 30% in most African countries.\nVaccination saved millions of lives but could have saved even more\nCOVID-19 vaccines have been highly effective in reducing the chances of severe disease, including hospitalizations and deaths.\nThis has been possible because the COVID-19 vaccines effectively present fragments of the coronavirus to our immune cells, which primes our immune system to recognize and respond to future infections by the virus. This is especially effective when the vaccine strains closely resemble those circulating in the population.\nThe impact of vaccines is also visible in data from the general population. We can see this by comparing overall death rates (from any cause) between vaccinated and unvaccinated people.\nThe chart below shows this with national data from the United States: death rates were much higher among unvaccinated people, especially during waves of the outbreak.\nDownload\nYou can read more about this in our article:\nHow do death rates from COVID-19 differ between people who are vaccinated and those who are not?\nTo understand how the pandemic is evolving, it’s crucial to know how death rates from COVID-19 are affected by vaccination status.\nResearchers have estimated that, in total, the COVID-19 vaccines saved around 20 million lives globally in just the first year of vaccine availability.\n18\nThese estimates are shown below, and come from statistical modeling using data on excess death rates, vaccine efficacy, and vaccination rates.\nDownload\nOliver J. Watson et al. (2022)\n18\nRecent research also demonstrates that the vaccines have continued to save many lives in the years since then.\n19\nThe coronavirus continues to evolve\nThe coronavirus evolves through random mutations, as well as through sudden recombination events, which can happen when a person is infected with two different strains of the coronavirus, and segments of the different virus genomes combine.\n20\nOver time, this leads to new coronavirus strains, varying in how easily they spread or how deadly they are among those infected.\nIn the chart below, which shows data for France, you can see how coronavirus strains evolved. All coronavirus sequences were classified as the “Wuhan-Hu-1” strain at the pandemic's start.\nIn November 2020, a new strain called “Alpha” was identified, which was more lethal than the initial strain. The virus has continued to evolve to evade our immunity.\n21\nAlthough genetically diverse strains can be better at evading the initial immune response, memory cells also persist and can respond to later stages of the disease.\n22\nAs a result, the virus’s impact, measured by hospitalizations and excess mortality, has generally declined as immunity from infections and vaccinations has grown.\nDespite this, COVID-19 continues to circulate in the population and causes waves of hospital admissions and severe disease. The chart below shows this for countries that have continued to report data.\nThis highlights the continued importance of booster vaccinations, especially among older people and vulnerable populations.\nConclusion\nThe COVID-19 pandemic has had a considerable global impact. It killed more than 25 million people, caused grief and suffering among their families and loved ones, impacted the health of people around the world, and disrupted work, economies, and lifestyles.\nFor our team at Our World in Data, it involved years of nonstop work through weekends under immense pressure since so many relied on our work. The same was true of many researchers, healthcare workers, and people worldwide who worked tirelessly to study the disease, create life-saving innovations, care for patients, and provide the crucial data that shaped public health responses.\nCOVID-19 continues to cause sickness and death, but the virus's impact on our lives has declined with a range of innovations, including rapid diagnostic testing, antiviral medicines, vaccinations, masks, social distancing, and public health efforts.\nWe should remember that pandemics happen repeatedly. To prevent suffering for years again, the world must respond more swiftly and effectively to pandemic risks. Our choices today determine whether we are prepared to face — and hopefully extinguish — the next pandemic.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nContinue reading on Our World in Data\nHow our team at Our World in Data became a global data source on COVID-19\nOur small team made COVID-19 data clear, reliable, and accessible to a global audience. This is how it happened.\nWhat were the death tolls from pandemics in history?\nPandemics have killed millions of people throughout history. How many deaths were caused by different pandemics, and how have researchers estimated their death tolls?\nEndnotes\nMeyerowitz-Katz, G., & Merone, L. (2020). A systematic review and meta-analysis of published research data on COVID-19 infection fatality rates. International Journal of Infectious Diseases, 101, 138-148. Available\nonline\n.\nLevin, A. T., Hanage, W. P., Owusu-Boaitey, N., Cochran, K. B., Walsh, S. P., & Meyerowitz-Katz, G. (2020). Assessing the age specificity of infection fatality rates for COVID-19: Systematic review, meta-analysis, and public policy implications. European Journal of Epidemiology, 35(12), 1123–1138.\nhttps://doi.org/10.1007/s10654-020-00698-1\nLevin, A. T., Hanage, W. P., Owusu-Boaitey, N., Cochran, K. B., Walsh, S. P., & Meyerowitz-Katz, G. (2020). Assessing the age specificity of infection fatality rates for COVID-19: Systematic review, meta-analysis, and public policy implications.\nEuropean Journal of Epidemiology\n,\n35\n(12), 1123–1138.\nhttps://doi.org/10.1007/s10654-020-00698-1\nVariation in the COVID-19 infection–fatality ratio by age, time, and geography during the pre-vaccine era: A systematic analysis. (2022). The Lancet, 399(10334), 1469–1488.\nhttps://doi.org/10.1016/S0140-6736(21)02867-1\nManry, J., Bastard, P., Gervais, A., Le Voyer, T., Rosain, J., Philippot, Q., Michailidis, E., Hoffmann, H.-H., Eto, S., Garcia-Prat, M., Bizien, L., Parra-Martínez, A., Yang, R., Haljasmägi, L., Migaud, M., Särekannu, K., Maslovskaja, J., De Prost, N., Tandjaoui-Lambiotte, Y., … Hamzeh-Cognasse, H. (2022). The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies. Proceedings of the National Academy of Sciences, 119(21), e2200413119.\nhttps://doi.org/10.1073/pnas.2200413119\nEstimates of excess mortality from other sources such as the World Health Organization (WHO) and Institute for Health Metrics and Evaluation (IHME) are only available for a limited part of the pandemic. However, their estimates for 2020 are similar to\nThe Economist\n’s estimates.\nHolmes, E. (2020). Novel 2019 Coronavirus genome. Virological.org. Available\nonline\n.\nPekar, J. E., Magee, A., Parker, E., Moshiri, N., Izhikevich, K., Havens, J. L., Gangavarapu, K., Malpica Serrano, L. M., Crits-Christoph, A., Matteson, N. L., Zeller, M., Levy, J. I., Wang, J. C., Hughes, S., Lee, J., Park, H., Park, M.-S., Ching Zi Yan, K., Lin, R. T. P., … Wertheim, J. O. (2022). The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2. Science, 377(6609), 960–966.\nhttps://doi.org/10.1126/science.abp8337\nWIRED (2020). How ProMED Crowdsourced the Arrival of COVID-19 and SARS. Available\nonline\n.\nKucharski, A. J., Russell, T. W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., Eggo, R. M., Sun, F., Jit, M., Munday, J. D., Davies, N., Gimma, A., Van Zandvoort, K., Gibbs, H., Hellewell, J., Jarvis, C. I., Clifford, S., Quilty, B. J., Bosse, N. I., … Flasche, S. (2020). Early dynamics of transmission and control of COVID-19: A mathematical modelling study. The Lancet Infectious Diseases, 20(5), 553–558.\nhttps://doi.org/10.1016/S1473-3099(20)30144-4\nPung, Rachael, Josh A. Firth, Lewis G. Spurgin, Singapore CruiseSafe working group, Annie Chang, Jade Kong, Jazzy Wong, et al. “Using High-Resolution Contact Networks to Evaluate SARS-CoV-2 Transmission and Control in Large-Scale Multi-Day Events.”\nNature Communications\n13, no. 1 (April 12, 2022): 1956.\nhttps://doi.org/10.1038/s41467-022-29522-y\nPung, Rachael, Hannah E. Clapham, Timothy W. Russell, CMMID COVID-19 Working Group, Vernon J. Lee, and Adam J. Kucharski. “Relative Role of Border Restrictions, Case Finding and Contact Tracing in Controlling SARS-CoV-2 in the Presence of Undetected Transmission: A Mathematical Modelling Study.” BMC Medicine 21, no. 1 (March 16, 2023): 97.\nhttps://doi.org/10.1186/s12916-023-02802-0\nMatczak, Soraya, Corinne Levy, Camille Fortas, Jérémie F Cohen, Stéphane Béchet, Fatima Aït El Belghiti, Sophie Guillot, et al. “Association between the COVID-19 Pandemic and Pertussis Derived from Multiple Nationwide Data Sources, France, 2013 to 2020.”\nEurosurveillance\n27, no. 25 (June 23, 2022).\nhttps://doi.org/10.2807/1560-7917.ES.2022.27.25.2100933\nTran, L. K., Huang, D. W., Li, N. K., Li, L. M., Palacios, J. A., & Chang, H. H. (2022). The impact of the COVID-19 preventive measures on influenza transmission: molecular and epidemiological evidence. International Journal of Infectious Diseases, 116, 11-13.\nhttps://doi.org/10.1016/j.ijid.2021.12.323\nChow, Eric J., Timothy M. Uyeki, and Helen Y. Chu. “The Effects of the COVID-19 Pandemic on Community Respiratory Virus Activity.” Nature Reviews Microbiology, October 17, 2022.\nhttps://doi.org/10.1038/s41579-022-00807-9\nHuh, Kyungmin, Jaehun Jung, Jinwook Hong, MinYoung Kim, Jong Gyun Ahn, Jong-Hun Kim, and Ji-Man Kang. “Impact of Nonpharmaceutical Interventions on the Incidence of Respiratory Infections During the Coronavirus Disease 2019 (COVID-19) Outbreak in Korea: A Nationwide Surveillance Study.” Clinical Infectious Diseases 72, no. 7 (April 8, 2021): e184–91.\nhttps://doi.org/10.1093/cid/ciaa1682\nSeattle Flu Alliance (2024). [Accessed 2nd September 2024]\nhttps://seattleflu.org\nChow, Eric J., Timothy M. Uyeki, and Helen Y. Chu. “The Effects of the COVID-19 Pandemic on Community Respiratory Virus Activity.” Nature Reviews Microbiology, October 17, 2022.\nhttps://doi.org/10.1038/s41579-022-00807-9\nBoon, H., Meinders, A., Van Hannen, E. J., Tersmette, M., & Schaftenaar, E. (2024). Comparative analysis of mortality in patients admitted with an infection with influenza A/B virus, respiratory syncytial virus, rhinovirus, metapneumovirus or SARS‐CoV‐2. Influenza and Other Respiratory Viruses, 18(1), e13237.\nhttps://doi.org/10.1111/irv.13237\nHedberg, P., Karlsson Valik, J., Van Der Werff, S., Tanushi, H., Requena Mendez, A., Granath, F., Bell, M., Mårtensson, J., Dyrdak, R., Hertting, O., Färnert, A., Ternhag, A., & Naucler, P. (2022). Clinical phenotypes and outcomes of SARS-CoV-2, influenza, RSV and seven other respiratory viruses: A retrospective study using complete hospital data. Thorax, 77(2), 1–10.\nhttps://doi.org/10.1136/thoraxjnl-2021-216949\nKirchdoerfer, Robert N., Christopher A. Cottrell, Nianshuang Wang, Jesper Pallesen, Hadi M. Yassine, Hannah L. Turner, Kizzmekia S. Corbett, Barney S. Graham, Jason S. McLellan, and Andrew B. Ward. “Pre-Fusion Structure of a Human Coronavirus Spike Protein.”\nNature\n531, no. 7592 (March 3, 2016): 118–21.\nhttps://doi.org/10.1038/nature17200\nWrapp, Daniel, Nianshuang Wang, Kizzmekia S. Corbett, Jory A. Goldsmith, Ching-Lin Hsieh, Olubukola Abiona, Barney S. Graham, and Jason S. McLellan. “Cryo-EM Structure of the 2019-NCoV Spike in the Prefusion Conformation.” Science 367, no. 6483 (March 13, 2020): 1260–63.\nhttps://doi.org/10.1126/science.abb2507\nTseng, Chien-Te, Elena Sbrana, Naoko Iwata-Yoshikawa, Patrick C. Newman, Tania Garron, Robert L. Atmar, Clarence J. Peters, and Robert B. Couch. “Immunization with SARS Coronavirus Vaccines Leads to Pulmonary Immunopathology on Challenge with the SARS Virus.” Edited by Stefan Poehlmann. PLoS ONE 7, no. 4 (April 20, 2012): e35421.\nhttps://doi.org/10.1371/journal.pone.0035421\nTizard, Ian R. “Vaccination against Coronaviruses in Domestic Animals.”\nVaccine\n38, no. 33 (July 2020): 5123–30.\nhttps://doi.org/10.1016/j.vaccine.2020.06.026\nZhao, Juanjuan, Quan Yuan, Haiyan Wang, Wei Liu, Xuejiao Liao, Yingying Su, Xin Wang, et al. “Antibody Responses to SARS-CoV-2 in Patients With Novel Coronavirus Disease 2019.” Clinical Infectious Diseases 71, no. 16 (November 19, 2020): 2027–34.\nhttps://doi.org/10.1093/cid/ciaa344\nDean, Natalie E., Pierre-Stéphane Gsell, Ron Brookmeyer, Victor De Gruttola, Christl A. Donnelly, M. Elizabeth Halloran, Momodou Jasseh, et al. “Design of Vaccine Efficacy Trials during Public Health Emergencies.”\nScience Translational Medicine\n11, no. 499 (July 3, 2019): eaat0360.\nhttps://doi.org/10.1126/scitranslmed.aat0360\nKalinke, Ulrich, Dan H. Barouch, Ruben Rizzi, Eleni Lagkadinou, Özlem Türeci, Shanti Pather, and Pieter Neels. “Clinical Development and Approval of COVID-19 Vaccines.”\nExpert Review of Vaccines\n21, no. 5 (May 4, 2022): 609–19.\nhttps://doi.org/10.1080/14760584.2022.2042257\nNational Centre for Immunisation Research and Surveillance Australia. “Phases of Clinical Trials,” (April 2023).\nhttps://ncirs.org.au/phases-clinical-trials\nEuropean Medicines Agency. “Fast-track procedures for treatments and vaccines for COVID-19.” (2020). Available\nonline\n.\nFigures above 100 doses per 100 people are possible because each dose is counted individually, meaning they include second doses and booster vaccinations.\nThis refers to the doses specified for each type of vaccine: some vaccines, such as the Pfizer/BioNTech, Moderna, and AstraZeneca/Oxford vaccines, had a two-dose initial protocol; others, such as the Novavax and Johnson & Johnson vaccines, had a one-dose initial protocol; some vaccines such as Sinopharm had a three-dose protocol for some groups.\nWatson, Oliver J, Gregory Barnsley, Jaspreet Toor, Alexandra B Hogan, Peter Winskill, and Azra C Ghani. “Global Impact of the First Year of COVID-19 Vaccination: A Mathematical Modelling Study.”\nThe Lancet Infectious Diseases\n22, no. 9 (September 2022): 1293–1302.\nhttps://doi.org/10.1016/S1473-3099(22)00320-6\nMeslé, M. M. I., Brown, J., Mook, P., Katz, M. A., Hagan, J., Pastore, R., Benka, B., Redlberger-Fritz, M., Bossuyt, N., Stouten, V., Vernemmen, C., Constantinou, E., Maly, M., Kynčl, J., Sanca, O., Krause, T. G., Vestergaard, L. S., Leino, T., Poukka, E., … Pebody, R. (2024). Estimated number of lives directly saved by COVID-19 vaccination programmes in the WHO European Region from December, 2020, to March, 2023: A retrospective surveillance study. The Lancet Respiratory Medicine, 12(9), 714–727.\nhttps://doi.org/10.1016/S2213-2600(24)00179-6\nMüller, Nicola F., Kathryn E. Kistler, and Trevor Bedford. “A Bayesian Approach to Infer Recombination Patterns in Coronaviruses.”\nNature Communications\n13, no. 1 (July 20, 2022): 4186.\nhttps://doi.org/10.1038/s41467-022-31749-8\n.\nTelenti, A., Hodcroft, E. B., & Robertson, D. L. (2022). The Evolution and Biology of SARS-CoV-2 Variants.\nCold Spring Harbor Perspectives in Medicine\n,\n12\n(5), a041390.\nhttps://doi.org/10.1101/cshperspect.a041390\nMarkov, P. V., Ghafari, M., Beer, M., Lythgoe, K., Simmonds, P., Stilianakis, N. I., & Katzourakis, A. (2023). The evolution of SARS-CoV-2. Nature Reviews Microbiology, 21(6), 361–379.\nhttps://doi.org/10.1038/s41579-023-00878-2\nSakharkar, M., Rappazzo, C. G., Wieland-Alter, W. F., Hsieh, C.-L., Wrapp, D., Esterman, E. S., Kaku, C. I., Wec, A. Z., Geoghegan, J. C., McLellan, J. S., Connor, R. I., Wright, P. F., & Walker, L. M. (2021). Prolonged evolution of the human B cell response to SARS-CoV-2 infection.\nScience Immunology\n,\n6\n(56), eabg6916.\nhttps://doi.org/10.1126/sciimmunol.abg6916\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSaloni Dattani and Lucas Rodés-Guirao (2024) - “17 key charts to understand the COVID-19 pandemic” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260622-064358/key-charts-understand-covid-pandemic.html' [Online Resource] (archived on June 22, 2026).\nBibTeX citation\n@article{owid-key-charts-understand-covid-pandemic,\nauthor = {Saloni Dattani and Lucas Rodés-Guirao},\ntitle = {17 key charts to understand the COVID-19 pandemic},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://archive.ourworldindata.org/20260622-064358/key-charts-understand-covid-pandemic.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "key-charts-understand-covid-pandemic", "source_url": "https://ourworldindata.org/key-charts-understand-covid-pandemic", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "The pandemic has resulted in over twenty million deaths. In this article, we review the key insights from global data on COVID-19.", "numeric_mentions": ["19", "17", "18,", "2024", "2020,", "27 million", "1", "140", "0.7%", "2", "3", "4", "5 million", "2020", "10 million", "2021", "1918", "2019", "2003", "5", "6", "25,000", "130,000", "490,000", "2022,", "10", "7", "8", "9", "2,", "19,", "11", "12", "13", "14", "15", "100", "16", "75%", "30%", "20 million", "18", "2022", "20", "21", "22", "25 million", "101,", "138", "148", "35", "1123", "1138", "10.1007", "020", "00698", "399", "10334", "1469", "1488", "10.1016", "6736", "02867", "119", "10.1073", "2200413119", "377", "6609", "960", "966", "10.1126", "553", "558", "3099", "30144", "13,", "12,", "1956", "10.1038", "022"], "numeric_evidence": [{"title": "Number of COVID-19 patients in intensive care (ICU)", "source_url": "https://ourworldindata.org/grapher/current-covid-patients-icu.csv", "file_type": "csv", "columns": ["Entity", "Code", "Day", "Current number of COVID-19 patients in ICU"], "row_count_total": 39116, "rows_head": [{"Entity": "Algeria", "Code": "DZA", "Day": "2020-07-17", "Current number of COVID-19 patients in ICU": "62"}, {"Entity": "Algeria", "Code": "DZA", "Day": "2020-07-18", "Current number of COVID-19 patients in ICU": "67"}, {"Entity": "Algeria", "Code": "DZA", "Day": "2020-07-20", "Current number of COVID-19 patients in ICU": "64"}, {"Entity": "Algeria", "Code": "DZA", "Day": "2020-07-21", "Current number of COVID-19 patients in ICU": "56"}, {"Entity": "Algeria", "Code": "DZA", "Day": "2020-07-22", "Current number of COVID-19 patients in ICU": "51"}, {"Entity": "Algeria", "Code": "DZA", "Day": "2020-07-23", "Current number of COVID-19 patients in ICU": "66"}, {"Entity": "Algeria", "Code": "DZA", "Day": "2020-07-25", "Current number of COVID-19 patients in ICU": "63"}, {"Entity": "Algeria", "Code": "DZA", "Day": "2020-07-26", "Current number of COVID-19 patients in ICU": "64"}, {"Entity": "Algeria", "Code": "DZA", "Day": "2020-07-27", "Current 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"Day": "2023-07-03", "Current number of COVID-19 patients in ICU": "0"}, {"Entity": "Wales", "Code": "", "Day": "2023-07-04", "Current number of COVID-19 patients in ICU": "0"}, {"Entity": "Wales", "Code": "", "Day": "2023-07-05", "Current number of COVID-19 patients in ICU": "3"}, {"Entity": "Wales", "Code": "", "Day": "2023-07-06", "Current number of COVID-19 patients in ICU": "0"}, {"Entity": "Wales", "Code": "", "Day": "2023-07-07", "Current number of COVID-19 patients in ICU": "0"}, {"Entity": "Wales", "Code": "", "Day": "2023-07-08", "Current number of COVID-19 patients in ICU": "0"}, {"Entity": "Wales", "Code": "", "Day": "2023-07-09", "Current number of COVID-19 patients in ICU": "0"}, {"Entity": "Wales", "Code": "", "Day": "2023-07-10", "Current number of COVID-19 patients in ICU": "0"}, {"Entity": "Wales", "Code": "", "Day": "2023-07-11", "Current number of COVID-19 patients in ICU": "0"}, {"Entity": "Wales", "Code": "", "Day": "2023-07-12", "Current number of COVID-19 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the closest metric (patients ventilated or in critical condition).", "Hospital and ICU data are sourced from official providers and collated by Our World in Data, but no new datapoints have been added since 13 August 2024.", "Gaps in more recent hospitalization trends may exist due to the cessation of regular updates after August 2024."], "unit": "patients in ICU", "timespan": "", "type": "Integer", "owidVariableId": 984240, "shortName": "daily_occupancy_icu", "lastUpdated": "2024-08-13", "citationShort": "Official data collated by Our World in Data (2024) – processed by Our World in Data", "citationLong": "Official data collated by Our World in Data (2024) – processed by Our World in Data. “Current number of COVID-19 patients in ICU” [dataset]. Official data collated by Our World in Data, “COVID-19, hospitalisations” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/984240.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. 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{"Entity": "Uruguay", "Code": "URY", "Day": "2021-01-31", "Baseline expected deaths": "2697.8", "Reported deaths": "3074"}, {"Entity": "Uruguay", "Code": "URY", "Day": "2021-02-28", "Baseline expected deaths": "2405", "Reported deaths": "2541"}, {"Entity": "Uruguay", "Code": "URY", "Day": "2021-03-31", "Baseline expected deaths": "2680.8", "Reported deaths": "3065"}, {"Entity": "Uruguay", "Code": "URY", "Day": "2021-04-30", "Baseline expected deaths": "2612.6", "Reported deaths": "4442"}, {"Entity": "Uruguay", "Code": "URY", "Day": "2021-05-31", "Baseline expected deaths": "3011.6", "Reported deaths": "4792"}, {"Entity": "Uruguay", "Code": "URY", "Day": "2021-06-30", "Baseline expected deaths": "3346.4", "Reported deaths": "4448"}, {"Entity": "Uruguay", "Code": "URY", "Day": "2021-07-31", "Baseline expected deaths": "3603.8", "Reported deaths": "3713"}, {"Entity": "Uruguay", "Code": "URY", "Day": "2021-08-31", "Baseline expected deaths": "3432", "Reported deaths": "3379"}, {"Entity": 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"UZB", "Day": "2020-05-31", "Baseline expected deaths": "13023.2", "Reported deaths": "11792"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2020-06-30", "Baseline expected deaths": "12291.8", "Reported deaths": "13787"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2020-07-31", "Baseline expected deaths": "13861.6", "Reported deaths": "17345"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2020-08-31", "Baseline expected deaths": "12382.6", "Reported deaths": "20706"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2020-09-30", "Baseline expected deaths": "10987", "Reported deaths": "13161"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2020-10-31", "Baseline expected deaths": "12047.6", "Reported deaths": "15629"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2020-11-30", "Baseline expected deaths": "12960.2", "Reported deaths": "14204"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2020-12-31", "Baseline expected deaths": "18866.2", "Reported deaths": "19938"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2021-01-31", "Baseline expected deaths": "10351.9", "Reported deaths": "11921"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2021-02-28", "Baseline expected deaths": "13539.5", "Reported deaths": "14702"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2021-03-31", "Baseline expected deaths": "13634.5", "Reported deaths": "13603"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2021-04-30", "Baseline expected deaths": "13191.1", "Reported deaths": "12432"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2021-05-31", "Baseline expected deaths": "13066.5", "Reported deaths": "12440"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2021-06-30", "Baseline expected deaths": "12335.1", "Reported deaths": "14629"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2021-07-31", "Baseline expected deaths": "13904.9", "Reported deaths": "17670"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2021-08-31", "Baseline expected deaths": 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"2022-04-30", "Baseline expected deaths": "13234.3", "Reported deaths": "12762"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2022-05-31", "Baseline expected deaths": "13109.7", "Reported deaths": "12847"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2022-06-30", "Baseline expected deaths": "12378.3", "Reported deaths": "14817"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2022-07-31", "Baseline expected deaths": "13948.1", "Reported deaths": "17269"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2022-08-31", "Baseline expected deaths": "12469.1", "Reported deaths": "18771"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2022-09-30", "Baseline expected deaths": "11073.5", "Reported deaths": "13351"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2022-10-31", "Baseline expected deaths": "12134.1", "Reported deaths": "13602"}, {"Entity": "Uzbekistan", "Code": "UZB", "Day": "2022-11-30", "Baseline expected deaths": "13046.7", "Reported deaths": "12850"}, {"Entity": 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compared to projection based on previous years", "chart_subtitle": "The reported number of weekly or monthly deaths in 2020–2023 and the projected number of deaths for the same period based on previous years.", "chart_note": "The reported number of deaths might not count all deaths that occurred due to incomplete coverage and delays in reporting.", "chart_citation": "Human Mortality Database (2023), World Mortality Dataset (2023)", "original_chart_url": "https://ourworldindata.org/grapher/excess-mortality-raw-death-count-single-series", "owid_column_metadata": {"projected_deaths_since_2020_all_ages": {"titleShort": "Baseline expected deaths", "titleLong": "Baseline expected deaths", "descriptionShort": "Projected number of weekly or monthly deaths from all causes for all ages for since 2020.", "descriptionKey": ["Excess deaths is estimated as _Excess deaths = Number of reported deaths - Number of expected deaths_. Excess mortality goes beyond confirmed COVID-19 fatalities by capturing all deaths above a projected baseline, including indirect deaths from pandemic-related disruptions.", "All-cause mortality data is from the Human Mortality Database (HMD) Short-term Mortality Fluctuations project and the World Mortality Dataset (WMD). Both sources are updated weekly.", "We use the baseline estimates by [Ariel Karlinsky and Dmitry Kobak (2021)](https://elifesciences.org/articles/69336) as part of their World Mortality Dataset (WMD).", "We do not use the data from some countries in WMD because they fail to meet the following data quality criteria: 1) at least three years of historical data; and 2) data published either weekly or monthly. The full list of excluded countries and reasons for exclusion can be found in [this spreadsheet](https://docs.google.com/spreadsheets/d/1JPMtzsx-smO3_K4ReK_HMeuVLEzVZ71qHghSuAfG788/edit?usp=sharing)."], "unit": "", "timespan": "", "type": "Numeric", "owidVariableId": 541435, "shortName": "projected_deaths_since_2020_all_ages", "lastUpdated": "2026-06-22", "citationShort": "Karlinsky and Kobak (2021) – processed by Our World in Data", "citationLong": "Karlinsky and Kobak (2021) – processed by Our World in Data. “Baseline expected deaths” [dataset]. Karlinsky and Kobak, “Excess mortality during the COVID-19 pandemic” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/541435.metadata.json"}, "deaths_since_2020_all_ages": {"titleShort": "Reported deaths", "titleLong": "Reported deaths", "descriptionShort": "Reported number of weekly or monthly deaths from all causes for all ages since 2020.", "descriptionKey": ["Excess deaths is estimated as _Excess deaths = Number of reported deaths - Number of expected deaths_. Excess mortality goes beyond confirmed COVID-19 fatalities by capturing all deaths above a projected baseline, including indirect deaths from pandemic-related disruptions.", "All-cause mortality data is from the Human Mortality Database (HMD) Short-term Mortality Fluctuations project and the World Mortality Dataset (WMD). Both sources are updated weekly.", "We use the baseline estimates by [Ariel Karlinsky and Dmitry Kobak (2021)](https://elifesciences.org/articles/69336) as part of their World Mortality Dataset (WMD).", "We do not use the data from some countries in WMD because they fail to meet the following data quality criteria: 1) at least three years of historical data; and 2) data published either weekly or monthly. The full list of excluded countries and reasons for exclusion can be found in [this spreadsheet](https://docs.google.com/spreadsheets/d/1JPMtzsx-smO3_K4ReK_HMeuVLEzVZ71qHghSuAfG788/edit?usp=sharing)."], "unit": "deaths", "timespan": "", "type": "Numeric", "owidVariableId": 541436, "shortName": "deaths_since_2020_all_ages", "lastUpdated": "2026-06-22", "citationShort": "Human Mortality Database (2026); Karlinsky and Kobak (2021) – processed by Our World in Data", "citationLong": "Human Mortality Database (2026); Karlinsky and Kobak (2021) – processed by Our World in Data. “Reported deaths” [dataset]. Human Mortality Database, “Short-term Mortality Fluctuations”; Karlinsky and Kobak, “World Mortality Dataset” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/541436.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"grapher_slug": "excess-deaths-cumulative-economist-single-entity", "source_url": "https://ourworldindata.org/grapher/excess-deaths-cumulative-economist-single-entity", "parse_error": "Failed to fetch https://ourworldindata.org/grapher/excess-deaths-cumulative-economist-single-entity.csv"}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "80e3e1ffca8a80371048"},
{"raw_link": "https://ourworldindata.org/owid-covid-history", "title": "How our team at Our World in Data became a global data source on COVID-19", "context": "Home\nCOVID-19\nHow our team at Our World in Data became a global data source on COVID-19\nOur small team made COVID-19 data clear, reliable, and accessible to a global audience. This is how it happened.\nBy\nSaloni Dattani\n,\nEdouard Mathieu\n,\nand\nLucas Rodés-Guirao\nNovember 18, 2024\nBrowse past versions\nCite this article\nReuse our work freely\nBefore COVID-19, Our World in Data (OWID) was a small team with large ambitions.\nWe made data and research on global issues like poverty, climate, and global health accessible to the general public. But our team took a gradual approach — we updated our charts manually, usually on an annual basis. We provided context on statistics, cleared common misconceptions, and communicated critical insights from the data.\nWhen COVID-19 spread across the world, our small team suddenly pivoted to compiling daily data that was of global importance. OWID became the primary global source for many COVID-19 indicators in a few short months. Our datasets powered the dashboards of large media organizations and became a crucial resource for journalists, governments, academic researchers, and the wider public.\nIn this retrospective article, we look back on how our small team faced the large, sudden demands of global COVID-19 data and how we adapted our processes to make this work part of our mission — to make data and research on the pandemic transparent and accessible to a wide audience.\nBefore the pandemic\nOur World in Data was launched in 2014 by Max Roser, who built it with a few part-time colleagues in his spare time.\nOver the years, more team members joined — including our colleagues Esteban Ortiz-Ospina, an economist and now co-director; Joe Hasell, who worked on global poverty and is now head of product and design; and Hannah Ritchie, who took charge of environmental research and is now deputy editor.\nThe authors of this article, Edouard Mathieu, Saloni Dattani, and Lucas Rodés-Guirao joined the team in March 2020, February 2021, and April 2021 respectively, and the team has grown much larger since the pandemic.\nDownload\nSide-by-side photographs of the Our World in Data team in February 2020 (left) vs. September 2024 (right).\n1\nHistory of Our World in Data\nThe history of how we got started and how we’ve grown as a team.\nOur World in Data aimed to cover a wide range of topics, but with a small team — only eight people at the start of 2020\n2\n— we placed more focus on areas like inequality, poverty, global health, climate change, and agriculture. We updated datasets yearly, which often relied on manual efforts.\nOur work centered on gathering and visualizing data from trusted research organizations and presenting it to the public in a digestible format. This small-scale approach changed drastically during the COVID-19 pandemic.\nThe drive to tackle COVID-19\nIn early 2020, as large outbreaks spread from China to Italy, the team saw a looming threat and a massive gap in available data to address it.\nFirst, the available data suggested that growth rates were high, and if no action was taken, the pandemic would only slow down on its own after around two-thirds of the population had been infected (the “herd immunity threshold”).\n3\nBy this time, many countries would see vast numbers of deaths and healthcare workers stretched beyond their limits.\nSecond, the world needed to track the spread of COVID-19 worldwide in real-time, in a centralized, accurate, and constantly updated fashion.\nBut official data sources were patchy at best. Early on, the World Health Organization (WHO) provided daily updates only in an online spreadsheet. These updates often had critical entry errors, such as global totals that didn’t match the sum of every country and cumulative deaths that were\nlower\nthan the previous day.\nOther data publication sites, like\nWorldometers\n, lacked transparency about their sources or contradicted official figures. There was nowhere to easily compare trends between countries or visualize the epidemic’s progression over time.\nWith this in mind, our team’s work shifted almost entirely to COVID-19 in late February 2020 — compiling official data sources, communicating the trends\nand\nlimitations of available data, and clearing up misconceptions.\nEarly work on COVID-19\nAs the pandemic took hold across countries, the initial work was very challenging.\nWe launched the first version of our COVID-19 page in early March 2020, initially embedding other dashboards published by Johns Hopkins University, the WHO, and the University of Oxford. At the time, the OWID\nGrapher\ntool, which powers our charts, could not handle daily data, which prevented us from visualizing it ourselves.\nDownload\nScreenshot of the earliest version of Our World in Data’s coronavirus page on March 7, 2020, which compiled data across sources and explained key indicators. The screenshot shows an embedded dashboard built by Johns Hopkins University, which shows recorded coronavirus cases. The archived page can be accessed\nonline\n.\nIt quickly became apparent that many of these alternative dashboards focused on the latest cumulative estimate, making it difficult to interpret trends over time.\nUnderstanding how the pandemic changed\nover time\nwas crucial. Realizing this, our software engineers Breck Yunits and Daniel Gavrilov overhauled the Grapher tool to handle daily updates so the team could match the pandemic’s fast pace.\nHannah Ritchie and Max Roser spent many early mornings manually transcribing numbers on the number of cases and tests conducted from WHO reports while outbreaks grew worldwide. They often faced mismatched totals, confusing formats, and outdated numbers.\nDownload\nScreenshot from March 25, 2020, of Our World in Data’s page comparing data sources on confirmed cases of COVID-19. The archived page can be accessed\nonline\n.\nThey also wrote pages to explain these indicators and how to interpret them and compare statistics across sources.\nThis was crucial because many misconceptions were common. For example, the number of cases was often misinterpreted as the number of infections, even though testing rates were limited and many infections had not been confirmed.\nSimilarly, early calculations of the case fatality rate (CFR) were often flawed. They underestimated the actual mortality risk because of the delays between cases and deaths, limited testing, and lack of death registration in some countries.\nTo address these, our team also compiled a global dataset on COVID-19 testing rates.\nBuilding the world’s datasets on COVID-19 testing and vaccination\nAlthough most of the data we present on Our World in Data is republished from other sources with credit rather than compiled from national sources by our team, there were two prominent exceptions during COVID-19: testing and vaccination data.\nEarly in the pandemic, it became clear to us that testing rates were essential to correctly interpret the number of cases. Without sufficient testing, the number of cases would give a very limited picture of how rapidly new outbreaks were growing and where.\nHowever, no global dataset on testing rates was available. So, in March 2020, our colleagues — starting with Joe Hasell and Esteban Ortiz-Ospina — began to build one, aiming to include as many countries as possible.\nThis was very challenging. Countries shared daily test counts in hard-to-process formats, including PDFs and HTML tables. Some countries counted “people tested”, while others counted “swabs tested”, creating inconsistent indicators across nations. This was complicated by the fact that many people would go on to be tested more than once.\nThis confusion also applied to the types of tests. Some countries only reported PCR tests, while others only reported antibody tests. More confusingly, some combined numbers from both types of tests.\nEdouard Mathieu, now our head of data and research, joined our team in March 2020 to manage this growing data pipeline. The rest of the team, especially former team members Cameron Appel and Daniel Gavrilov, helped him compile global testing data, eventually building a dataset of more than 130 countries and territories. Cameron became an all-around contributor, helping across multiple areas of COVID-19 data.\nThe chart below shows cumulative testing rates across countries and clarifies which indicators are used. Our team spent many months collecting this data, contacting national health organizations to clarify these differences, and updating it.\nWe published a peer-reviewed article presenting this database in the journal\nNature Scientific Data\n.\nThe Our World in Data COVID-19 Testing dataset has been published in the academic journal, Nature Scientific Data\nThe Our World in Data dataset which has tracked COVID-19 testing across the world since the start of the pandemic has been peer-reviewed and published in the academic journal, Nature Scientific Data.\nBy late 2020, after it became clear that vaccines would soon become available to the general public, our team contacted other international health institutions to understand whether they had plans to compile this data internationally. However, none of them planned on creating a global vaccination dataset.\nEdouard Mathieu persuaded the team that we should step up, as we had already improved our processes, and suggested that collecting the data ourselves would fill an essential data gap that the world needed.\nThe map below shows the first vaccination data point we added: on December 8, 2020, the first person was vaccinated outside of a clinical trial in the United Kingdom.\n4\nFinally, we could show a positive indicator, focusing on how we could handle the pandemic, and reduce the number of lives lost.\nYou can see how this evolved over time by clicking on the “Play timelapse” button, or from the line chart, which both show how more and more countries began vaccinating and reporting this data over time.\nAlthough the team was better prepared, collecting vaccination data was even more challenging. Data formats varied even more widely, from HTML tables and PDFs to press releases and even video announcements.\nWhile we were able to automate some parts of these data extraction procedures, our team also had to watch daily videos of press conferences to note down the number of daily vaccinations from some countries.\nOur vaccination dataset quickly became the only global source for COVID-19 vaccination statistics, including 210 countries and territories.\nIt was widely adopted by major organizations including the WHO, and served as the foundation for understanding vaccine distribution and equity worldwide.\nDownload\nThe New York Times’s page tracking coronavirus vaccinations worldwide, with data sourced from Our World in Data. The page can be viewed\nonline\n.\nWe published a peer-reviewed article presenting this database in the journal\nNature Human Behaviour\n.\nThe Our World in Data COVID-19 vaccination dataset has been published in the academic journal, Nature Human Behaviour\nOur free, open-access dataset tracking global COVID-19 vaccinations has been published in Nature Human Behaviour.\nThe COVID-19 Data Explorer\nAs data complexity grew, our team introduced the\nCOVID-19 Data Explorer\n, a powerful new chart tool that allowed users to easily switch between indicators and countries, and explore and track the progression of the pandemic in a much more accessible way.\nScreen-capture video of the earliest version of Our World in Data’s COVID-19 data explorer,\nlaunched\non May 15, 2020. The video runs through different indicators and features in the data explorer. The current version of the Explorer can be viewed\nonline\n.\nThis tool, with its daily updates and user-friendly design, became the go-to resource for millions of people worldwide to keep up with daily updates on COVID-19.\nIt expanded and allowed users to explore a wide range of indicators — cases, deaths, testing rates, hospitalizations, excess mortality, vaccination rates, mobility trends, and viral strains — and compare them side by side. You can explore the current version of the\nCOVID-19 Data Explorer\nonline.\nOur charts and data were widely used by news outlets such as The Guardian, BBC, The Financial Times, The Economist, The Spectator, Reuters, CNN, and The New York Times, academic researchers, health ministers, and political leaders of many countries, including both US Presidents Donald Trump and Joe Biden.\nDownload\nSide-by-side images of US Presidents Donald Trump and Joe Biden presenting data from our charts. Donald Trump held up our chart on global coronavirus data during an Oval Office meeting (credited to\nthe Washington Post\non May 6, 2020), while Joe Biden\ntweeted\nan animated chart with data sourced from Our World in Data on July 19, 2020.\nOpen source and public data provision\nOur datasets across topics have been downloadable and transparent for many years, but the open-source access we provided to our\nCOVID-19 data\nand\nGrapher tool\non GitHub became essential for maintaining global data on the pandemic.\nThis transparency was important because data needed to be compiled across many countries with different data collection and publishing procedures, and because those procedures occasionally changed. Web pages might be moved, data formats might be changed, and simple processing steps — such as what time the data was updated — weren’t explained.\nOur\nGitHub repository\nenabled users worldwide to contribute to COVID-19 data.\nThe first chart below shows the number of users who contributed to our repository each week. Contributions spiked at the beginning of 2021 when the vaccine rollout began across countries, and our dataset became the only source of international data on vaccination rates.\nWith our open source dataset, users could help identify data sources in regions where our team couldn’t access direct information, help to translate from official sources, flag changes, and potential data errors — which we passed on to other institutions — and suggest improvements to the data pipeline.\nThe second chart shows that more than 700 users worldwide contributed to our data repository, with many submitting issues,\npull requests\n, code reviews, or adding comments to help us improve our dataset. In total, they made\nmore than 7,000 contributions\n.\nEven now, anyone can contribute or\nbrowse each update\nwe made to the dataset, which has been updated more than 31,000 times since it launched.\nThis collaborative approach made our data more transparent, maintainable, up-to-date, and far less error-prone than if we had published it in static reports.\nSome contributors also joined our team. In April 2021, the team hired Lucas Rodés-Guirao, who had already contributed as a volunteer, to improve our processes on GitHub. By the end of the year, he was handling all our coronavirus data pipelines and updates.\nWith a growing team and user base, we were able to streamline processes and improve automation. The result was a faster, more accurate pipeline that allowed the team to focus on new work as the pandemic progressed.\nCommunication and public outreach\nWith our data powering dashboards and the team tracking trends daily, it became critical to communicate that information clearly. Our team spent many hours writing digestible explanations of how to properly interpret the figures and making these notes clear on the charts.\nWe also received questions and feedback from users, journalists, and officials, who used the data for policy decisions and public announcements, and we clarified indicators that could be easily misinterpreted.\nHannah Ritchie explained complex statistics in plain language on platforms like the\nBBC’s More or Less\nradio podcast and presented at the Royal Statistical Society’s\nevidence session\non the pandemic. Max Roser spoke at the\nUK Parliament’s Science & Technology Committee\nabout COVID-19 data and policies and the pandemic situation around the world. We communicated directly with the public and with those leading pandemic responses.\nDownload\nMax Roser speaking at the\nUK Parliament’s Science & Technology Committee\non October 21, 2020, televised by the BBC.\nEdouard Mathieu published a\ncommentary article in Nature\n, explaining how governments and international organizations could improve their data formats and publication processes. Charlie Giattino wrote about interpreting estimates of\nexcess mortality\nand\nthe number of infections\n. Edouard Mathieu and Max Roser wrote one of our most-viewed articles to explain visually\nhow death rates were higher among those unvaccinated\n.\nOur Twitter presence became a central avenue for rapid updates, with users flagging issues and officials from some countries directly contacting the team to clarify updates.\nOur colleagues, particularly Esteban Ortiz-Ospina, reviewed direct public feedback — through our site’s feedback form, email, GitHub, and Twitter — for hours each day, to help ensure our data was clear and transparent and could be quickly improved if there were any issues.\nConclusion\nOur work on COVID-19 data helped us see the importance of collaboration firsthand. People around the world helped us add new data, translate information, flag errors, and build a more accurate picture of the pandemic worldwide.\nIt underscored the need for open-source tools and automated workflows, which allowed us to respond quickly without sacrificing quality. By transitioning from largely manual processes to more streamlined systems, we made it possible to track crucial data more efficiently and reliably.\nUnrestricted funding was also essential to initiating this project. Although we received dedicated funding for COVID-19 work much later, we were able to pivot swiftly to COVID-19 data because we had support from unrestricted donations. It gave us the flexibility to address the pressing needs of the moment.\nUltimately, our unique collaboration of researchers, programmers, and data scientists made it possible for us to communicate research and pandemic trends to the public in a clear, accessible, and maintainable way. Our experience showed how impactful a small, adaptable team could be, providing clarity and transparency at a time when the world needed it most.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nContinue reading on Our World in Data\n17 key charts to understand the COVID-19 pandemic\nThe pandemic has resulted in over twenty million deaths. In this article, we review the key insights from global data on COVID-19.\nWhat were the death tolls from pandemics in history?\nPandemics have killed millions of people throughout history. How many deaths were caused by different pandemics, and how have researchers estimated their death tolls?\nEndnotes\nFrom left to right in the February 2020 photograph: Esteban Ortiz-Ospina, Max Roser, Hannah Ritchie, Joe Hasell, Matthieu Bergel, and Daniel Gavrilov. The photograph is missing Cameron Appel and Diana Beltekian.\nFrom left to right in each row from the top of the September 2024 photograph: Lars Yencken, Simon van Teutem, Marcel Gerber, Natalie Reynolds-Garcia, Max Roser, Valerie Rogers Muigai, Angela Wenham, and Sophia Mersmann; Daniel Bachler, Joe Hasell, Pablo Arriagada, Martin Račák, Bobbie Macdonald, Marwa Boukarim, Fiona Spooner, and Mojmir Vinkler; Charlie Giattino, Antoinette Finnegan, Ike Saunders, Veronika Samborska, Edouard Mathieu, and Bastian Herre; Pablo Rosado, Esteban Ortiz-Ospina, Saloni Dattani, Tuna Acisu, Hannah Ritchie and Lucas Rodés-Guirao. The photograph is missing Hassan Masum and Matthieu Bergel.\nThis includes Max Roser, Esteban Ortiz-Ospina, Hannah Ritchie, Joe Hasell, Daniel Gavrilov, Matthieu Bergel, Cameron Appel, and Diana Beltekian.\nMax cites Marc Lipsitch, quoted in this article in\nThe Atlantic\n, as one of the experts who flagged the high herd immunity threshold early on and the potential consequences of an unchecked pandemic.\nHamblin, J. (2020, February 24). You’re Likely to Get the Coronavirus. The Atlantic.\nhttps://archive.is/ArkIV\nPublished research also established this estimate:\nKwok, K. O., Lai, F., Wei, W. I., Wong, S. Y. S., & Tang, J. W. T. (2020). Herd immunity – estimating the level required to halt the COVID-19 epidemics in affected countries. Journal of Infection, 80(6), e32–e33.\nhttps://doi.org/10.1016/j.jinf.2020.03.027\nRandolph, H. E., & Barreiro, L. B. (2020). Herd Immunity: Understanding COVID-19. Immunity, 52(5), 737–741.\nhttps://doi.org/10.1016/j.immuni.2020.04.012\nNHS England news (2020). Landmark moment as first NHS patient receives COVID-19 vaccination. Available\nonline\n.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSaloni Dattani, Edouard Mathieu, and Lucas Rodés-Guirao (2024) - “How our team at Our World in Data became a global data source on COVID-19” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-090244/owid-covid-history.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-owid-covid-history,\nauthor = {Saloni Dattani and Edouard Mathieu and Lucas Rodés-Guirao},\ntitle = {How our team at Our World in Data became a global data source on COVID-19},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://archive.ourworldindata.org/20260518-090244/owid-covid-history.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "owid-covid-history", "source_url": "https://ourworldindata.org/owid-covid-history", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Our small team made COVID-19 data clear, reliable, and accessible to a global audience. This is how it happened.", "numeric_mentions": ["19", "18,", "2024", "19,", "2014", "2020,", "2021,", "2021", "2020", "1", "2", "3", "7,", "25,", "130", "8,", "4", "210", "15,", "6,", "700", "7,000", "31,000", "21,", "17", "24", "80", "6", "10.1016", "2020.03", "027", "52", "5", "737", "741", "2020.04", "012", "20260518", "090244", "2026"], "numeric_evidence": [{"title": "Total COVID-19 tests per 1,000 people", "source_url": "https://ourworldindata.org/grapher/full-list-cumulative-total-tests-per-thousand.csv", "file_type": "csv", "columns": ["Entity", "Code", "Day", "Cumulative total per 1,000 people", "Cumulative total per 1,000 people (Annotations)"], "row_count_total": 79387, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Day": "2022-01-29", "Cumulative total per 1,000 people": "21.272", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Afghanistan", "Code": "AFG", "Day": "2022-02-26", "Cumulative total per 1,000 people": "22.181", "Cumulative total per 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"Albania", "Code": "ALB", "Day": "2020-03-05", "Cumulative total per 1,000 people": "0.018", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-06", "Cumulative total per 1,000 people": "0.019", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-07", "Cumulative total per 1,000 people": "0.02", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-08", "Cumulative total per 1,000 people": "0.021", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-09", "Cumulative total per 1,000 people": "0.027", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-10", "Cumulative total per 1,000 people": "0.04", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-11", "Cumulative total per 1,000 people": "0.055", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-12", "Cumulative total per 1,000 people": "0.104", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-13", "Cumulative total per 1,000 people": "0.16", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-14", "Cumulative total per 1,000 people": "0.177", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-15", "Cumulative total per 1,000 people": "0.186", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-16", "Cumulative total per 1,000 people": "0.197", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-17", "Cumulative total per 1,000 people": "0.212", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-18", "Cumulative total per 1,000 people": "0.233", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-19", "Cumulative total per 1,000 people": "0.244", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-20", "Cumulative total per 1,000 people": "0.256", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-21", "Cumulative total per 1,000 people": "0.273", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-22", "Cumulative total per 1,000 people": "0.284", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-25", "Cumulative total per 1,000 people": "0.323", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-26", "Cumulative total per 1,000 people": "0.359", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-27", "Cumulative total per 1,000 people": "0.395", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-28", "Cumulative total per 1,000 people": "0.422", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-29", "Cumulative total per 1,000 people": "0.461", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-30", "Cumulative total per 1,000 people": "0.493", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-03-31", "Cumulative total per 1,000 people": "0.544", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-04-01", "Cumulative total per 1,000 people": "0.597", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-04-02", "Cumulative total per 1,000 people": "0.631", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-04-03", "Cumulative total per 1,000 people": "0.67", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-04-04", "Cumulative total per 1,000 people": "0.745", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-04-05", "Cumulative total per 1,000 people": "0.798", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-04-06", "Cumulative total per 1,000 people": "0.859", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-04-07", "Cumulative total per 1,000 people": "0.923", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-04-08", "Cumulative total per 1,000 people": "1.005", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-04-09", "Cumulative total per 1,000 people": "1.087", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-04-10", "Cumulative total per 1,000 people": "1.144", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-04-11", "Cumulative total per 1,000 people": "1.235", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-04-12", "Cumulative total per 1,000 people": "1.316", "Cumulative total per 1,000 people (Annotations)": ""}, {"Entity": "Albania", "Code": "ALB", "Day": "2020-04-13", "Cumulative total 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Type: vaccinations": "1"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2021-10-20", "Countries reporting data on COVID-19 vaccinations - Type: vaccinations": "1"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2021-10-21", "Countries reporting data on COVID-19 vaccinations - Type: vaccinations": "1"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Day": "2021-10-22", "Countries reporting data on COVID-19 vaccinations - Type: vaccinations": "1"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "country-reporting-data-on-covid-vaccinations", "metadata_url": "https://ourworldindata.org/grapher/country-reporting-data-on-covid-vaccinations.metadata.json", "chart_title": "Countries reporting data on COVID-19 vaccinations", "chart_subtitle": "Whether a country had started reporting data on COVID-19 vaccinations by a given date.", "chart_note": "Some countries reported data before vaccinations began; others started after.", "chart_citation": "Our World in Data (2024)", "original_chart_url": "https://ourworldindata.org/grapher/country-reporting-data-on-covid-vaccinations", "owid_column_metadata": {"Countries reporting data on COVID-19 vaccinations - 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{"raw_link": "https://ourworldindata.org/countries-measure-immigration-accurate-data", "title": "How do countries measure immigration, and how accurate is this data?", "context": "Home\nMigration\nHow do countries measure immigration, and how accurate is this data?\nCountries estimate how many people move in and out using censuses, surveys, and border records. How accurate are these numbers, and can they account for illegal migration?\nBy\nSimon van Teutem\nand\nTuna Acisu\nNovember 11, 2024\nBrowse past versions\nCite this article\nReuse our work freely\nDebates about migration are often in the news. People quote numbers about how many people are entering and leaving different countries. Governments need to plan and manage public resources based on how their own populations are changing.\nInformed discussions and effective policymaking rely on good migration data. But how much do we really know about migration, and where do estimates come from?\nIn this article, I look at how countries and international agencies define different forms of migration, how they estimate the number of people moving in and out of countries, and how accurate these estimates are.\nMigrants without legal status make up a small portion of the overall immigrant population. Most high-income countries and some middle-income ones have a solid understanding of how many immigrants live there. Tracking the exact flows of people moving in and out is trickier, but governments can reliably monitor long-term trends to understand the bigger picture.\nWho is considered an international migrant?\nIn the United Nations statistics, an international migrant is defined as “a person who moves to a country other than that of his or her usual residence for at least a year, so that the country of destination effectively becomes his or her new country of usual residence”.\n1\nFor example, an Argentinian person who spends nine months studying in the United States\nwouldn’t\ncount as a long-term immigrant in the US. But an Argentinian person who moves to the US for two years\nwould\n. Even if someone gains citizenship in their new country, they are still considered an immigrant in migration statistics.\nThe same applies in reverse for emigrants: someone leaving their home country for more than a year is considered a long-term emigrant for the country they’ve left. This does not change if they acquire citizenship in another country. Some national governments may have definitions that differ from the UN recommendations.\nWhat about illegal migration?\n“Illegal migration” refers to the movement of people outside the legal rules for entering or leaving a country. There isn’t a single agreed-upon definition, but it generally involves people who breach immigration laws. Some refer to this as irregular or unauthorized migration.\nThere are three types of migrants who don’t have a legal immigration status. First, those who cross borders without the right legal permissions. Second, those who enter a country legally but stay after their visa or permission expires. Third, some migrants have legal permission to stay but work in violation of employment restrictions — for example, students who work more hours than their visa allows.\nTracking illegal migration is difficult. In regions with free movement, like the European Union, it’s particularly challenging. For example, someone could move from Germany to France, live there without registering, and go uncounted in official migration records.\n2\nThe rise of remote work has made it easier for people to live in different countries without registering as employees or taxpayers.\nA large 2024 study examined how to measure irregular migration, compiling and cross-referencing data from 20 countries, including the US.\n3\nBecause of the challenges I mentioned, these estimates are imperfect. However, they are the best approximation of the share of immigrants who lack legal status in different countries.\nThe chart below shows that irregular migrants are estimated to be a small minority in most high-income nations. The US stands out, with an estimated 22% of its immigrant population lacking legal status. In contrast, in the UK the share is 7%, in Germany 4%, in France 3%, and in the Netherlands 2%. These percentages were calculated by dividing the estimates of irregular migrants by the total immigrant population based on UN DESA data.\nDownload\nMigration statistics aim to include everyone, including those without legal status. These numbers are factored into population estimates, though experts recognize the limitations in achieving this. In the US, researchers combine census data with studies on coverage gaps and surveys from countries of origin to estimate how many people might be missed. To adjust for undercounted groups, they typically add 5% to 15%, depending on factors like age or how recently someone arrived.\n4\nTo give some context on the broader scale of illegal migration in Europe, Pew Research estimated that, in 2017, there were between 4 and 5 million migrants without legal status in the European Union.\n5\nThat represents about 5% of all immigrants and less than 1% of the total population, with nearly half of them in Germany and the UK (when the UK was still part of the EU).\nThe difference between immigration flows and stocks\nBefore I get into how migration numbers are estimated, it’s worth clarifying a few key terms.\nFirst, statisticians talk about the\nstock\nof immigrants. This is the total number of immigrants living in a country at a given time. It’s a snapshot of how many foreign-born people have moved to a country.\nThe chart below shows the UN’s estimates of the stock of immigrants. For example, the US has about 50 million immigrants, which means 50 million people who live in the US were not born there.\nThe second term is about the\nflow\nof migrants, which refers to changes over time. If people are moving into a country, they’re counted as immigrants by that country. If people are leaving, they will be counted as emigrants. The difference between the two is called\nnet migration\n. This tells us how many more people have arrived minus how many have left during a period.\nFor instance, if 50,000 people arrive and 30,000 leave in one year, the net migration is 20,000.\nThe annual net migration rate is often expressed per 1,000 people in a country. So if the total population is 10 million, having a net migration of 20,000 gives an annual net migration rate of 2 migrants per 1,000 people. A negative net migration rate means that more people leave a country than enter it.\nThe chart below shows the net migration rate for 2023, indicating whether more people are moving into a country or leaving it. If a country is coloured blue, more people are moving in than out during that year. If it’s yellow or red, more people are leaving than arriving. For example, places like Canada and Australia have a lot more people moving in, while in some Balkan countries and South-East Asia, more people are moving out.\nHow are immigration\nstocks\nmeasured?\nMost countries count their population every ten years with a census.\nDuring a census, statisticians try to count people household by household, whether they are citizens or migrants with legal status or not.\n6\nIt’s a huge task, which is why it’s usually only done once a decade. This map shows which countries completed a population census in the past ten years as of 2023. Countries shaded in blue conducted a census, while those in orange did not.\nWhile censuses give a fairly complete picture of the national population, gaps still need to be filled. In the most recent census in the UK, 3% of households did not respond, and it is more likely that people without legal status don’t fill out a census. Therefore, the UK’s Office of National Statistics has to make additional estimates by using information from other records.\n7\nTo keep track of changes between censuses, some countries, like the US, use yearly surveys, such as the Current Population Survey, to estimate how many immigrants are in the country. These surveys are less detailed than a census, but they help provide an idea of population changes in between the bigger counts.\nNow, how do statisticians take all these national statistics and combine them to estimate global immigration numbers? The United Nations Department of Economic and Social Affairs collects information from countries up to 2020.\nFor most countries, the UN’s migration data comes from population censuses (about 70% of the time). In other cases, it comes from\npopulation registers\n(17%) or surveys (13%). Most countries count immigrants as people born in another country (~80%), but some use citizenship (~20%), which can be based on where their parents or family are from.\n8\nTo provide estimates between census years, the United Nations Population Division (UNPD) uses methods called interpolation (filling gaps in data) and extrapolation (predicting future trends).\nGetting reliable data is challenging in some places, especially in countries with limited resources or where conflicts are ongoing. The UN gathers data from 232 countries, territories, or regions. Most (87%) of these countries have provided at least some information about how many immigrants live there based on their last census. Many also share details about where immigrants come from (76%) and their age (71%).\n9\nCensus data is usually the best source for these numbers because it’s very detailed. However, since censuses are only conducted every ten years, it can be tricky for governments and media outlets to get up-to-date information. Additionally, they rarely address migration status or the type of visa the individual used to enter the country, making it even more challenging to estimate illegal immigration. Another challenge is that censuses usually only count immigrants, not emigrants (people leaving the country), and they don’t always record the exact year people moved or if they returned to their home country later.\n10\nHow are immigration\nflows\nmeasured?\nSince censuses are only carried out every decade or so, they can’t be used to monitor immigration flows in a single year or even shorter periods.\nHow do countries estimate immigration flows?\nThey collect this information using surveys and different registration systems. For example, they count how many immigrants arrived in a year using records like residence permits, visas, asylum applications, and other immigration documents. Yet, some registration systems rely on people reporting when they move, which isn’t always done reliably.\nSome countries, like the Netherlands or Japan, use population registers, which keep an updated record of who lives in the country by tracking changes such as births, deaths, and moves. When residents must register to live in a home or take up employment, tracking migration flows becomes much simpler and more accurate. However, only a minority of countries have these systems in place.\n11\nFor most, estimates must rely on piecing together data from various sources.\nHigh-income countries have reasonable estimates of the number of people entering each year, including those without legal status. However, the reliability of these numbers varies by region and source. Some data points can be treated as reasonably exact counts, especially those based on census data. Most others, however, are estimates. The reliability of these estimates isn’t a simple yes-or-no; it depends on the specific question you’re trying to answer. This article aims to explain the different methods used to make these estimates. Knowing where estimates come from can help you decide if the data quality is good enough for your needs.\nThe numbers are less reliable for shorter periods, like a month or a quarter, or very recent data, so any small changes shouldn’t be taken too confidently. However, these records give a clearer picture of migration trends over longer periods, like multiple years. This is because long-term data is anchored to the data from the censuses.\nTracking emigration is more complex than monitoring immigration because people rarely report when they leave. This makes it difficult to know how long emigrants stay or if they return to their home countries. Plus, most countries don’t share immigration data, so if someone registers as an immigrant in one country, the country they left may not know.\nAt the global level, measuring immigration flows is challenging because most countries do not provide detailed data on people entering and exiting. In the United Nations World Population Prospects (UNWPP) methodology, net migration can be calculated directly using detailed records on immigration and emigration in countries where high-quality data is available.\n12\nBut net migration needs to be indirectly estimated in many cases. The UNWPP estimates combine census data with information on fertility and mortality to calculate population changes that cannot be explained by natural factors alone. This method, known as residual estimation, assumes that any unexplained population growth or decline is due to migration after making adjustments to correct for net coverage errors or data quality problems.\nTo ensure consistency at the global level, the UNWPP methodology applies small adjustments to net migration estimates for countries where data is uncertain. This process, called net migration balancing, ensures that the total global net migration equals zero.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nOne example: the United Kingdom\nFinally, it might be worth looking at a country in more depth as an example. Here, I’ve taken the United Kingdom.\nRegarding stock data, the UK’s census is the most important source. It is conducted by the Office for National Statistics (ONS). The latest census (from 2021) shows that 16% of the UK’s population of 67 million was born abroad.\n13\nFor flow data, the ONS estimated net migration (immigrants minus emigrants) to be 685,000 people in 2023. They provided an uncertainty interval suggesting the actual figure is likely between 564,000 and 744,000.\n14\nThis means the actual number could vary by nearly 18% from the estimate or by more than a hundred thousand people. And it could be even more, because the uncertainty interval cannot quantify accuracy as well as confidence intervals.\nDownload\nEstimating migration flows to and from the UK is challenging, partly due to the absence of a population register — a point highlighted in the book\nBad Data\nby migration expert Georgina Sturge. In contrast, countries like Japan and Spain use population registers to track residents more accurately. The UK has to gather information from various government records without a single system or a way to quickly match individuals across these datasets. This is even harder for British nationals, whose estimates still rely on old survey methods, and foreign nationals with indefinite leave to remain, including EU nationals with settled status.\nRecently, the UK also changed its migration measurement methods to improve accuracy.\n15\nPreviously, estimates were based on a survey of people entering and leaving the country, which led to significant errors, such as overstating how many students overstayed their visas in the early 2010s.\n16\nBecause of the uncertainty, it’s hard to be certain about recent changes in yearly migration numbers. For example, the estimated\ndecrease\nin net migration from 2022 to 2023 was 79,000. But with these uncertainty intervals, the actual drop could easily be tens of thousands more or less. This shows how small shifts in estimates can lead to different interpretations of migration trends.\nOverall, not having a population register likely makes UK immigration statistics less accurate than in countries like the Netherlands. Still, the UK is a high-income country, an island, and has a well-developed statistical office. The margin of error for migration estimates could be significantly higher in countries with fewer resources, less developed systems, and land borders that are harder to control. This means migration data in lower-income countries is often less reliable and more uncertain.\nWhile migration data isn't perfect and can be imprecise in the short run, especially for specific groups or due to outdated methods, they still offer a solid foundation for understanding broader trends over time. Understanding how migration data is collected and its limitations is vital for anyone engaging with migration topics. Knowing how this data is gathered, along with its uncertainties, helps us assess how well it can inform decisions and track long-term trends.\nEndnotes\nUnited Nations (2020).\nHandbook on Measuring International Migration through Population Censuses\n, page 7, paragraph 23.\nFrance does not have a population register. (Sourced from\nUNECE\n).\nKierans, D. and Vargas-Silva, C. (2024). The Irregular Migrant Population of Europe.\nLink\nSourced from\nPew Research Center\nThis is about the European Union and the European Free Trade Association countries.\nSourced from\nUS Census\nSourced from\nONS\nSourced from\nUnited Nations Methodology Report 2020, p.4\nSourced from\nUnited Nations Methodology Report 2020, p.5\nSourced from\nInternational migration under the microscope, Willekens et al. 2016, Science, p.897, right-hand side column\nSourced from\nStatistics Norway\nSourced from\nUnited Nations World Population Prospects Methodology Report 2024\n, from p.25\nHowever, the ONS has indicated that it may not hold a census in 2031, leaving questions about how future estimates will be validated without this crucial dataset for accuracy checks.\nUK migration expert Georgina Sturge clarifies that the “uncertainty intervals” differ from confidence intervals. They are derived through simulation studies, where 95% of the simulated intervals will be expected to include the true value, provided the simulations have adequately captured the main sources of uncertainty. The challenge, however, lies in the potential for unknown variables affecting the outcome, as the model's reliability is assessed only by re-running it with small adjustments to a restricted set of assumptions. Georgina Sturge is a statistician on migration and justice for the House of Commons Library and the Author of Bad Data, How Governments, Politicians and the Rest of Us Get Misled by Numbers.\nSourced from\nCommons Library\nSourced from\nThe Guardian\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSimon van Teutem and Tuna Acisu (2024) - “How do countries measure immigration, and how accurate is this data?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260616-122048/countries-measure-immigration-accurate-data.html' [Online Resource] (archived on June 16, 2026).\nBibTeX citation\n@article{owid-countries-measure-immigration-accurate-data,\nauthor = {Simon van Teutem and Tuna Acisu},\ntitle = {How do countries measure immigration, and how accurate is this data?},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://archive.ourworldindata.org/20260616-122048/countries-measure-immigration-accurate-data.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "countries-measure-immigration-accurate-data", "source_url": "https://ourworldindata.org/countries-measure-immigration-accurate-data", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Countries estimate how many people move in and out using censuses, surveys, and border records. 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number of international immigrants": "16653698"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Total number of international immigrants": "15413140"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Total number of international immigrants": "16602802"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Total number of international immigrants": "17752216"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Total number of international immigrants": "22683929"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Total number of international immigrants": "25267356"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2024", "Total number of international immigrants": "29180440"}, {"Entity": "Albania", "Code": "ALB", "Year": "1990", "Total number of international immigrants": "66013"}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "Total number of international immigrants": "71354"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Total number of international immigrants": "76695"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Total number of international immigrants": "64739"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Total number of international immigrants": "52784"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Total number of international immigrants": "52031"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "Total number of international immigrants": "48810"}, {"Entity": "Albania", "Code": "ALB", "Year": "2024", "Total number of international immigrants": "46377"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1990", "Total number of international immigrants": "273954"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1995", "Total number of international immigrants": "262032"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2000", "Total number of international immigrants": "250110"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "Total number of international immigrants": "197728"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "Total number of international immigrants": "217268"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "Total number of international immigrants": "239473"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "Total number of international immigrants": "250378"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2024", "Total number of international immigrants": "259458"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "1990", "Total number of international immigrants": "21283"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "1995", "Total number of international immigrants": "23098"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2000", "Total number of international immigrants": "24912"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2005", "Total number of international immigrants": "24233"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2010", "Total number of international immigrants": "23555"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2015", "Total number of international immigrants": "23513"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2020", "Total number of international immigrants": "23608"}, {"Entity": "American Samoa", "Code": "ASM", "Year": "2024", "Total number of international immigrants": "23684"}, {"Entity": "Andorra", "Code": "AND", "Year": "1990", "Total number of international immigrants": "38891"}, {"Entity": "Andorra", "Code": "AND", "Year": "1995", "Total number of international immigrants": "44206"}, {"Entity": "Andorra", "Code": "AND", "Year": "2000", "Total number of international immigrants": "42147"}, {"Entity": "Andorra", "Code": "AND", "Year": "2005", "Total number of international immigrants": "50298"}, {"Entity": "Andorra", "Code": "AND", "Year": "2010", "Total number of international immigrants": "52053"}, {"Entity": "Andorra", "Code": "AND", "Year": "2015", "Total number of international immigrants": "42264"}, {"Entity": "Andorra", "Code": "AND", "Year": "2020", "Total number of international immigrants": "45574"}, {"Entity": "Andorra", "Code": "AND", "Year": "2024", "Total number of international immigrants": "48408"}, {"Entity": "Angola", "Code": "AGO", "Year": "1990", "Total number of international immigrants": "33517"}, {"Entity": "Angola", "Code": "AGO", "Year": "1995", "Total number of international immigrants": "39813"}, {"Entity": "Angola", "Code": "AGO", "Year": "2000", "Total number of international immigrants": "46108"}, {"Entity": "Angola", "Code": "AGO", "Year": "2005", "Total number of international immigrants": "62331"}, {"Entity": "Angola", "Code": "AGO", "Year": "2010", "Total number of international immigrants": "336367"}, {"Entity": "Angola", "Code": "AGO", "Year": "2015", "Total number of international immigrants": "632178"}, {"Entity": "Angola", "Code": "AGO", "Year": "2020", "Total number of international immigrants": "656434"}, {"Entity": "Angola", "Code": "AGO", "Year": "2024", "Total number of international immigrants": "676507"}, {"Entity": "Anguilla", "Code": "AIA", "Year": "1990", "Total number of international immigrants": "2570"}, {"Entity": "Anguilla", "Code": "AIA", "Year": "1995", "Total number of international immigrants": "3317"}, {"Entity": "Anguilla", "Code": "AIA", "Year": "2000", "Total number of international immigrants": "4063"}, {"Entity": "Anguilla", "Code": "AIA", "Year": "2005", "Total number of international immigrants": "4684"}, {"Entity": "Anguilla", "Code": "AIA", "Year": "2010", "Total number of international immigrants": "5103"}, {"Entity": "Anguilla", "Code": "AIA", "Year": "2015", "Total number of international immigrants": "5471"}, {"Entity": "Anguilla", "Code": "AIA", "Year": "2020", "Total number of international immigrants": "5715"}, {"Entity": "Anguilla", "Code": "AIA", "Year": "2024", "Total number of international immigrants": "5918"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1990", "Total number of international immigrants": "12029"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1995", "Total number of international immigrants": "17550"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2000", "Total number of international immigrants": "23071"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2005", "Total number of international immigrants": "24741"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2010", "Total number of international immigrants": "26412"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2015", "Total number of international immigrants": "28082"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2020", "Total number of international immigrants": "29386"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2024", "Total number of international immigrants": "30473"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1990", "Total number of international immigrants": "1647935"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1995", "Total number of international immigrants": "1589660"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2000", "Total number of international immigrants": "1543851"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2005", "Total number of international immigrants": "1641560"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2010", "Total number of international immigrants": "1799680"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2015", "Total number of international immigrants": "1856613"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2020", "Total number of international immigrants": "1912294"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2024", "Total number of international immigrants": "1958039"}, {"Entity": "Armenia", "Code": "ARM", "Year": "1990", "Total number of international immigrants": "433541"}, {"Entity": "Armenia", "Code": "ARM", "Year": "1995", "Total number of international immigrants": "622042"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2000", "Total number of international immigrants": "588242"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2005", "Total number of international immigrants": "476812"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2010", "Total number of international immigrants": "210873"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2015", "Total number of international immigrants": "190896"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2020", "Total number of international immigrants": "207139"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2024", "Total number of international immigrants": "274645"}, {"Entity": "Aruba", "Code": "ABW", "Year": "1990", "Total number of international immigrants": "14444"}, {"Entity": "Aruba", "Code": "ABW", "Year": "1995", "Total number of international immigrants": "22274"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2000", "Total number of international immigrants": "30104"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2005", "Total number of international immigrants": "32540"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2010", "Total number of international immigrants": "34328"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2015", "Total number of international immigrants": "36114"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2020", "Total number of international immigrants": "53593"}, {"Entity": "Aruba", "Code": "ABW", "Year": "2024", "Total number of international immigrants": "73494"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "1990", "Total number of international immigrants": "46657270"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "1995", "Total number of international immigrants": "44779342"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2000", "Total number of international immigrants": "47568162"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2005", "Total number of international immigrants": "51457579"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2010", "Total number of international immigrants": "64151786"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2015", "Total number of international immigrants": "78173152"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2020", "Total number of international immigrants": "84474238"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2024", "Total number of international immigrants": "91968240"}, {"Entity": "Australia", "Code": "AUS", "Year": "1990", "Total number of international immigrants": "3991501"}, {"Entity": "Australia", "Code": "AUS", "Year": "1995", "Total number of international immigrants": "4215646"}, {"Entity": "Australia", "Code": "AUS", "Year": "2000", "Total number of international immigrants": "4389847"}, {"Entity": "Australia", "Code": "AUS", "Year": "2005", "Total number of international immigrants": "4880921"}, {"Entity": "Australia", "Code": "AUS", "Year": "2010", "Total number of international immigrants": "5879802"}, {"Entity": "Australia", "Code": "AUS", "Year": "2015", "Total number of international immigrants": "6733056"}, {"Entity": "Australia", "Code": "AUS", "Year": "2020", "Total number of international immigrants": "7604850"}, {"Entity": "Australia", "Code": "AUS", "Year": "2024", "Total number of international immigrants": "8111404"}, {"Entity": "Austria", "Code": "AUT", "Year": "1990", "Total number of international immigrants": "633753"}, {"Entity": "Austria", "Code": "AUT", "Year": "1995", "Total number of international immigrants": "764758"}, {"Entity": "Austria", "Code": "AUT", "Year": "2000", "Total number of international immigrants": "920045"}, {"Entity": "Austria", "Code": "AUT", "Year": "2005", "Total number of international immigrants": "1174101"}, {"Entity": "Austria", "Code": "AUT", "Year": "2010", "Total number of international immigrants": "1285706"}, {"Entity": "Austria", "Code": "AUT", "Year": "2015", "Total number of international immigrants": "1540486"}, {"Entity": "Austria", "Code": "AUT", "Year": "2020", "Total number of international immigrants": "1781046"}, {"Entity": "Austria", "Code": "AUT", "Year": "2024", "Total number of international immigrants": "2327064"}], "rows_tail": [{"Entity": "Uruguay", "Code": "URY", "Year": "1990", "Total number of international immigrants": "98116"}, {"Entity": "Uruguay", "Code": "URY", "Year": "1995", "Total number of international immigrants": "93428"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2000", "Total number of international immigrants": "88874"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2005", "Total number of international immigrants": "82317"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2010", "Total number of international immigrants": "76303"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2015", "Total number of international immigrants": "78799"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2020", "Total number of international immigrants": "108267"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2024", "Total number of international immigrants": "160064"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1990", "Total number of international immigrants": "1653000"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1995", "Total number of international immigrants": "1512577"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2000", "Total number of international immigrants": "1406498"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2005", "Total number of international immigrants": "1329932"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2010", "Total number of international immigrants": "1220149"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2015", "Total number of international immigrants": "1170873"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2020", "Total number of international immigrants": "1162007"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2024", "Total number of international immigrants": "1154963"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "1990", "Total number of international immigrants": "2308"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "1995", "Total number of international immigrants": "2461"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2000", "Total number of international immigrants": "2626"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2005", "Total number of international immigrants": "2800"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2010", "Total number of international immigrants": "2991"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2015", "Total number of international immigrants": "3186"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2020", "Total number of international immigrants": "3257"}, {"Entity": "Vanuatu", "Code": "VUT", "Year": "2024", "Total number of international immigrants": "3315"}, {"Entity": "Vatican", "Code": "VAT", "Year": "1990", "Total number of international immigrants": "726"}, {"Entity": "Vatican", "Code": "VAT", "Year": "1995", "Total number of international immigrants": "705"}, {"Entity": "Vatican", "Code": "VAT", "Year": "2000", "Total number of international immigrants": "691"}, {"Entity": "Vatican", "Code": "VAT", "Year": "2005", "Total number of international immigrants": "659"}, {"Entity": "Vatican", "Code": "VAT", "Year": "2010", "Total number of international immigrants": "621"}, {"Entity": "Vatican", "Code": "VAT", "Year": "2015", "Total number of international immigrants": "572"}, {"Entity": "Vatican", "Code": "VAT", "Year": "2020", "Total number of international immigrants": "528"}, {"Entity": "Vatican", "Code": "VAT", "Year": "2024", "Total number of international immigrants": "496"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1990", "Total number of international immigrants": "1025009"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1995", "Total number of international immigrants": "1019996"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2000", "Total number of international immigrants": "1013738"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2005", "Total number of international immigrants": "1076474"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2010", "Total number of international immigrants": "1347347"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2015", "Total number of international immigrants": "1404448"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2020", "Total number of international immigrants": "1324193"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2024", "Total number of international immigrants": "1263304"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "1990", "Total number of international immigrants": "88560"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "1995", "Total number of international immigrants": "88376"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2000", "Total number of international immigrants": "86213"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2005", "Total number of international immigrants": "81997"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2010", "Total number of international immigrants": "84408"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2015", "Total number of international immigrants": "101386"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2020", "Total number of international immigrants": "200639"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2024", "Total number of international immigrants": "326418"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "1990", "Total number of international immigrants": "1402"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "1995", "Total number of international immigrants": "1704"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2000", "Total number of international immigrants": "2015"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2005", "Total number of international immigrants": "2191"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2010", "Total number of international immigrants": "2111"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2015", "Total number of international immigrants": "2050"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2020", "Total number of international immigrants": "2040"}, {"Entity": "Wallis and Futuna", "Code": "WLF", "Year": "2024", "Total number of international immigrants": "2032"}, {"Entity": "Western Africa (UN)", "Code": "", "Year": "1990", "Total number of international immigrants": "4476686"}, {"Entity": "Western Africa (UN)", "Code": "", "Year": "1995", "Total number of international immigrants": "5350182"}, {"Entity": "Western Africa (UN)", "Code": "", "Year": "2000", "Total number of international immigrants": "5254826"}, {"Entity": "Western Africa (UN)", "Code": "", "Year": "2005", "Total number of international immigrants": "6000026"}, {"Entity": "Western Africa (UN)", "Code": "", "Year": "2010", "Total number of international immigrants": "6413896"}, {"Entity": "Western Africa (UN)", "Code": "", "Year": "2015", "Total number of international immigrants": "7206050"}, {"Entity": "Western Africa (UN)", "Code": "", "Year": "2020", "Total number of international immigrants": "7747495"}, {"Entity": "Western Africa (UN)", "Code": "", "Year": "2024", "Total number of international immigrants": "8236829"}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "1990", "Total number of international immigrants": "14489476"}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "1995", "Total number of international immigrants": "16497765"}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2000", "Total number of international immigrants": "18138496"}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2005", "Total number of international immigrants": "21262754"}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2010", "Total number of international immigrants": "30063216"}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2015", "Total number of international immigrants": "41799209"}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2020", "Total number of international immigrants": "45765868"}, {"Entity": "Western Asia (UN)", "Code": "", "Year": "2024", "Total number of international immigrants": "49211670"}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "1990", "Total number of international immigrants": "17019914"}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "1995", "Total number of international immigrants": "20263560"}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2000", "Total number of international immigrants": "21781702"}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2005", "Total number of international immigrants": "24134692"}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2010", "Total number of international immigrants": "25952723"}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2015", "Total number of international immigrants": "28688766"}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2020", "Total number of international immigrants": "32846290"}, {"Entity": "Western Europe (UN)", "Code": "", "Year": "2024", "Total number of international immigrants": "36742379"}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "1990", "Total number of international immigrants": "2688"}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "1995", "Total number of international immigrants": "2989"}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "2000", "Total number of international immigrants": "3289"}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "2005", "Total number of international immigrants": "3891"}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "2010", "Total number of international immigrants": "4493"}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "2015", "Total number of international immigrants": "5179"}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "2020", "Total number of international immigrants": "5424"}, {"Entity": "Western Sahara", "Code": "ESH", "Year": "2024", "Total number of international immigrants": "5628"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1990", "Total number of international immigrants": "153916063"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1995", "Total number of international immigrants": "163176002"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Total number of international immigrants": "174566152"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Total number of international immigrants": "192788721"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Total number of international immigrants": "221020392"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Total number of international immigrants": "250042020"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Total number of international immigrants": "275284032"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Total number of international immigrants": "304021813"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1990", "Total number of international immigrants": "118863"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1995", "Total number of international immigrants": "136515"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Total number of international immigrants": "144940"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Total number of international immigrants": "171871"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Total number of international immigrants": "288394"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Total number of international immigrants": "379882"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2020", "Total number of international immigrants": "387113"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2024", "Total number of international immigrants": "392997"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Total number of international immigrants": "279463"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Total number of international immigrants": "244338"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Total number of international immigrants": "343703"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Total number of international immigrants": "252895"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Total number of international immigrants": "149962"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Total number of international immigrants": "132107"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Total number of international immigrants": "187955"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Total number of international immigrants": "249205"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Total number of international immigrants": "634621"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Total number of international immigrants": "431226"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Total number of international immigrants": "410109"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Total number of international immigrants": "402226"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Total number of international immigrants": "398307"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Total number of international immigrants": "400482"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Total number of international immigrants": "416141"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2024", "Total number of international immigrants": "429108"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "migrant-stock-total", "metadata_url": "https://ourworldindata.org/grapher/migrant-stock-total.metadata.json", "chart_title": "Total number of international immigrants", "chart_subtitle": "An immigrant is someone who lives in a country that they were not born in.", "chart_note": null, "chart_citation": "United Nations 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Reuse should follow the source and license information in this chart metadata."}, {"title": "Population census recently completed", "source_url": "https://ourworldindata.org/grapher/population-census-world-bank.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Population census recently completed"], "row_count_total": 7745, "rows_head": [{"Entity": "Albania", "Code": "ALB", "Year": "1989", "Population census recently completed": "1"}, {"Entity": "Albania", "Code": "ALB", "Year": "1990", "Population census recently completed": "1"}, {"Entity": "Albania", "Code": "ALB", "Year": "1991", "Population census recently completed": "1"}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "Population census recently completed": "1"}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "Population census recently completed": "1"}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "Population census recently completed": "1"}, {"Entity": "Albania", "Code": "ALB", "Year": 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{"raw_link": "https://ourworldindata.org/hpv-vaccination-world-can-eliminate-cervical-cancer", "title": "HPV vaccination: How the world can eliminate cervical cancer", "context": "Home\nCancer\nHPV vaccination: How the world can eliminate cervical cancer\nHPV vaccines offer a rare opportunity to effectively eliminate one type of cancer. By taking this opportunity, it’s possible to save hundreds of thousands of women each year.\nBy\nSaloni Dattani\nand\nVeronika Samborska\nNovember 4, 2024\nBrowse past versions\nCite this article\nReuse our work freely\nEvery year, over half a million women develop cervical cancer, and more than 300,000 die from the disease. These deaths are particularly tragic because we can prevent them through vaccination and early screening.\nIt’s possible to virtually eliminate this type of cancer with existing vaccines. Some countries are already on track to achieve this goal within a decade, but many others lag behind.\nThis is a huge opportunity to save hundreds of thousands of women every year.\nIn this article, we describe the cause of cervical cancer and how we can use incredibly effective vaccines to eliminate it.\nCervical cancer is caused by a virus, which makes it preventable\nCervical cancer is one of several cancers caused by\npathogens\n— in this case, the “\nhuman papillomavirus\n” (HPV).\nThere are hundreds of types of the virus, but\nonly a handful\nare responsible for most cases of cervical cancer.\nThe virus is also responsible for a large share of other cancers, including anal cancer, penile cancer, vulval cancer, vaginal cancer, and some head and neck cancers.\n1\nWhat share of invasive cervical cancers are caused by each type of the human papillomavirus?\nThe estimated relative share of all invasive cervical cancers globally that are caused by each type of human papillomavirus. Invasive cervical cancer are those that have spread beyond the cervix's surface layer into deeper tissues or other areas.\nWhat share of different cancers are caused by the human papillomavirus?\nCertain types of human papillomavirus (HPV) can cause a range of different cancers. This shows the estimated share of HPV-related cancers globally that are directly caused by the nine types (6, 11, 16, 18, 31, 33, 45, 52 and 58) targeted by some HPV vaccines.\nHow does the virus cause these cancers?\nHPV is a very common virus that spreads through physical contact — especially through sex, kissing, or touching — and can infect cells in the cervix, vagina, penis, mouth, and some other parts of the body.\nIn some people infected by HPV, the virus integrates itself into cells’ DNA and damages key proteins that protect us from uncontrolled cell growth — eventually leading to cancer.\nThis link between HPV and cervical cancer was uncovered in the 1980s by the scientist\nHarald zur Hausen\n, who later won a Nobel prize for the discovery.\n2\nBefore this, researchers debated whether hormones, environmental toxins, or other factors were responsible. Zur Hausen’s team focused on human papillomaviruses, which were then known to cause genital warts.\nThe team identified viral DNA in cervical cancer cells and found that the virus was present in the cancer cells and integrated their DNA. HPV was able to disrupt key functions of cells by damaging proteins like “p53”, which is critical in stopping cells from multiplying if they develop potentially harmful mutations.\n3\nFurther research confirmed their findings and helped uncover the role of HPV in a range of other cancers.\n1\nHPV vaccines are highly effective in preventing infections and cervical cancer\nBecause cervical cancer is caused by a virus, it can be prevented through vaccination.\nThe first HPV vaccine was developed in the 1990s by researchers in Australia.\n4\nIt was first introduced in 2006 and is effective against four major types of HPV.\n5\nAnother version, introduced in 2014, expanded its coverage to protect against additional cancer-causing HPV types.\n6\nThese vaccines work by stimulating the immune system to produce antibodies that can eliminate HPV and prevent infections.\n7\nHPV vaccines are highly effective, which has been demonstrated in large-scale\nrandomized controlled trials\n. Research shows that, if given early, vaccination reduces the risk of serious cervical cell changes by 99% for the HPV types most likely to cause cervical cancer.\n8\nRecent research has found that even a single dose provides high efficacy against infections.\n9\nThe vaccine is most effective when given early — before people are exposed to the virus. Vaccination programs in schools are, therefore, very impactful in making the most of HPV vaccines.\nThere is also another important reason for giving out these vaccines in schools: the vaccine’s efficacy is long-lasting\n10\n, and vaccination programs are much easier to implement at scale through schools than vaccinating individuals at clinics.\n11\nCervical cancer rates have declined greatly among younger generations with vaccination\nIn England, younger cohorts vaccinated at school show dramatically lower rates of cervical cancer than older cohorts when they reached the same age. This is shown in the chart below.\n12\nIn the youngest cohort, which had a vaccination rate of 89%, cervical cancer rates were around 87% lower than in the oldest cohorts.\nLarge reductions in cervical cancer rates have also been seen in other countries with high vaccination rates.\n13\nDownload\nMilena Falcaro et al. (2021).\n12\nBecause of highly effective vaccination and early screening programs, several countries have substantially reduced cervical cancer rates.\nRates in Australia and the United Kingdom, for example, have\nrecently fallen below\n8 per 100,000 and are projected to fall below 4 per 100,000 in the coming decade.\n14\nBut, at current vaccination rates, it will take decades for other countries to reach these levels — this is why raising vaccination rates is crucial.\n15\nHundreds of thousands of women still get cervical cancer each year\nCervical cancer still affects hundreds of thousands of women globally, despite the fact that vaccines and screening are highly effective. Screening and diagnosis can inform surgeries that could cure cervical cancer if identified in its early stages.\nIt’s estimated that there were more than 660,000 new cases of cervical cancer and around 350,000 deaths globally in 2022.\n16\nThe map shows that cervical cancer is much more common in Africa and South America than in other regions.\nHow common is cervical cancer screening within countries?\nExplore cervical cancer screening rates in European countries\nThere are two main reasons that cervical cancer deaths remain high.\nOne reason is that women in many poor countries lack access to early screening, as shown in the map above. Another is that women living with\nHIV\n, which is much more common in sub-Saharan Africa, are much more at risk of cervical cancer because their immune system is weakened.\n17\nThe final reason is that HPV vaccination rates are low or vaccines are unavailable in many countries.\nHPV vaccination is limited in many countries\nIn many countries in Africa and Southeast Asia, vaccination rates are low. This is shown in the chart below. In many countries, vaccination rates are very low.\nHowever, this is not true everywhere in these regions. Ethiopia and Rwanda, for example, have had relatively high vaccination rates in recent years due to targeted campaigns.\nThis is visible in the second map, which shows the national policy for offering HPV vaccines. In the countries shown in blue — much of the Americas, Europe, and Australia — HPV vaccines are offered routinely at no cost at a national level.\nBut in many other countries across Africa and Asia, shown in red, they are not.\nIn China, for example, the HPV vaccine has only recently been offered in certain cities and regions, but not across the country, and the national vaccination rate is very low.\n18\nAnother example is Japan, where the HPV vaccine had been withdrawn for several years (between 2013 and 2021) because of inaccurate media reports that the vaccine was linked to pregnancy complications.\n19\nLarge-scale studies have shown no difference in side effects in those taking the HPV vaccine\n20\n, and the vaccine has been recently reintroduced.\nFinally, many countries have low vaccination rates due to limited supplies and the upfront costs of bulk buying the vaccine, which have led many to delay its introduction. This is especially true in middle-income countries that are not eligible for funding through Gavi, which helps improve access to vaccines in low-income countries.\n21\nRecently, new HPV vaccines have been developed, which are expected to be cheaper to manufacture, but they are not yet available internationally.\n22\nThese could increase the global supply, help improve vaccination rates in lower- and middle-income countries, and help push the elimination of cervical cancer globally.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nThe world is missing out on the opportunity to prevent millions of cervical cancer cases\nCountries like the UK and Australia are showing that the elimination of cervical cancer is possible, but much of the world is missing out.\nHigher vaccination rates could prevent hundreds of thousands of cervical cancer cases every year.\nThis is shown in the chart below, which presents projections by Kate T. Simms and colleagues based on estimates of current cases, vaccination, and screening rates worldwide and modeling of the transmission of HPV and the vaccine’s effectiveness.\n23\nThey estimate that 80% to 100% vaccination rates for both boys and girls, with catch-up vaccinations for adults, could prevent nearly 50 million cervical cancer cases by 2100.\nDownload\nKate T. Simms et al. (2019)\n23\nCountries with widespread HPV vaccination and regular cervical cancer screenings are already seeing dramatic reductions in cancer rates. They demonstrate that it’s possible to effectively eliminate the disease.\nYet many countries still lag behind. Some face barriers in accessing limited supplies of vaccines, while others have had low vaccination rates due to popular misconceptions.\nHowever, HPV vaccines offer a rare opportunity to effectively eliminate a common type of cancer.\nBy investing in vaccination and screening, the world could save hundreds of thousands of lives each year, prevent avoidable suffering, and make cervical cancer a disease of the past.\nEndnotes\nDe Sanjosé, S., Serrano, B., Tous, S., Alejo, M., Lloveras, B., Quirós, B., Clavero, O., Vidal, A., Ferrándiz-Pulido, C., Pavón, M. Á., Holzinger, D., Halec, G., Tommasino, M., Quint, W., Pawlita, M., Muñoz, N., Bosch, F. X., Alemany, L., RIS HPV TT, VVAP and Head and Neck study groups, & Kulkarni, A. (2018). Burden of Human Papillomavirus (HPV)-Related Cancers Attributable to HPVs 6/11/16/18/31/33/45/52 and 58.\nJNCI Cancer Spectrum\n,\n2\n(4), pky045.\nhttps://doi.org/10.1093/jncics/pky045\nSchiffman, M., Doorbar, J., Wentzensen, N., De Sanjosé, S., Fakhry, C., Monk, B. J., Stanley, M. A., & Franceschi, S. (2016). Carcinogenic human papillomavirus infection. Nature Reviews Disease Primers, 2(1), 16086.\nhttps://doi.org/10.1038/nrdp.2016.86\nzur Hausen, H. (2009). The search for infectious causes of human cancers: where and why (Nobel lecture). Angewandte Chemie International Edition, 48(32), 5798-5808.\nhttps://doi.org/10.1002/anie.200901917\nZur Hausen, H. (2009). Papillomaviruses in the causation of human cancers—A brief historical account.\nVirology\n,\n384\n(2), 260–265.\nhttps://doi.org/10.1016/j.virol.2008.11.046\nFrazer, I. H. (2019). The HPV Vaccine Story.\nACS Pharmacology & Translational Science\n,\n2\n(3), 210–212.\nhttps://doi.org/10.1021/acsptsci.9b00032\nSpecifically, it is effective against types 6, 11, 16, and 18.\nMarkowitz, L. E., & Schiller, J. T. (2021). Human Papillomavirus Vaccines.\nThe Journal of Infectious Diseases\n,\n224\n(Supplement_4), S367–S378.\nhttps://doi.org/10.1093/infdis/jiaa621\nMarkowitz, L. E., & Schiller, J. T. (2021). Human Papillomavirus Vaccines.\nThe Journal of Infectious Diseases\n,\n224\n(Supplement_4), S367–S378.\nhttps://doi.org/10.1093/infdis/jiaa621\nThese efficacy estimates are shown in the results of Analysis 1.1 -\nArbyn, M., Xu, L., Simoens, C., & Martin-Hirsch, P. P. (2018). Prophylactic vaccination against human papillomaviruses to prevent cervical cancer and its precursors.\nCochrane Database of Systematic Reviews\n,\n2020\n(3).\nhttps://doi.org/10.1002/14651858.CD009069.pub3\nInternational Agency for Research on Cancer. (2023).\nProtection from a Single Dose of HPV Vaccine\n.\nAvailable online.\nBasu, P., Malvi, S. G., Joshi, S., Bhatla, N., Muwonge, R., Lucas, E., Verma, Y., Esmy, P. O., Poli, U. R. R., Shah, A., Zomawia, E., Pimple, S., Jayant, K., Hingmire, S., Chiwate, A., Divate, U., Vashist, S., Mishra, G., Jadhav, R., … Sankaranarayanan, R. (2021). Vaccine efficacy against persistent human papillomavirus (HPV) 16/18 infection at 10 years after one, two, and three doses of quadrivalent HPV vaccine in girls in India: A multicentre, prospective, cohort study. The Lancet Oncology, 22(11), 1518–1529.\nhttps://doi.org/10.1016/S1470-2045(21)00453-8\nBasu, P., Malvi, S. G., Joshi, S., Bhatla, N., Muwonge, R., Lucas, E., Verma, Y., Esmy, P. O., Poli, U. R. R., Shah, A., Zomawia, E., Pimple, S., Jayant, K., Hingmire, S., Chiwate, A., Divate, U., Vashist, S., Mishra, G., Jadhav, R., … Sankaranarayanan, R. (2021). Vaccine efficacy against persistent human papillomavirus (HPV) 16/18 infection at 10 years after one, two, and three doses of quadrivalent HPV vaccine in girls in India: A multicentre, prospective, cohort study. The Lancet Oncology, 22(11), 1518–1529.\nhttps://doi.org/10.1016/S1470-2045(21)00453-8\nLadner, J., Besson, M. H., Rodrigues, M., Audureau, E., & Saba, J. (2014). Performance of 21 HPV vaccination programs implemented in low and middle-income countries, 2009–2013. BMC public health, 14, 1-11.\nAvailable online\n.\nKempe, A., Allison, M. A., & Daley, M. F. (2018). Can School-Located Vaccination Have a Major Impact on Human Papillomavirus Vaccination Rates in the United States? Academic Pediatrics, 18(2), S101–S105.\nhttps://doi.org/10.1016/j.acap.2017.08.010\nFalcaro, M., Castañon, A., Ndlela, B., Checchi, M., Soldan, K., Lopez-Bernal, J., Elliss-Brookes, L., & Sasieni, P. (2021). The effects of the national HPV vaccination programme in England, UK, on cervical cancer and grade 3 cervical intraepithelial neoplasia incidence: A register-based observational study.\nThe Lancet\n,\n398\n(10316), 2084–2092.\nhttps://doi.org/10.1016/S0140-6736(21)02178-4\nJansen, E. E. L., Zielonke, N., Gini, A., Anttila, A., Segnan, N., Vokó, Z., Ivanuš, U., McKee, M., De Koning, H. J., De Kok, I. M. C. M., Veerus, P., Anttila, A., Heinävaara, S., Sarkeala, T., Csanádi, M., Pitter, J., Széles, G., Vokó, Z., Minozzi, S., … Priaulx, J. (2020). Effect of organised cervical cancer screening on cervical cancer mortality in Europe: A systematic review.\nEuropean Journal of Cancer\n,\n127\n, 207–223.\nhttps://doi.org/10.1016/j.ejca.2019.12.013\nBrisson, M., Kim, J. J., Canfell, K., Drolet, M., Gingras, G., Burger, E. A., Martin, D., Simms, K. T., Bénard, É., Boily, M.-C., Sy, S., Regan, C., Keane, A., Caruana, M., Nguyen, D. T. N., Smith, M. A., Laprise, J.-F., Jit, M., Alary, M., … Hutubessy, R. (2020). Impact of HPV vaccination and cervical screening on cervical cancer elimination: A comparative modelling analysis in 78 low-income and lower-middle-income countries. The Lancet, 395(10224), 575–590.\nHall, M. T., Simms, K. T., Lew, J.-B., Smith, M. A., Brotherton, J. M., Saville, M., Frazer, I. H., & Canfell, K. (2019). The projected timeframe until cervical cancer elimination in Australia: A modelling study.\nThe Lancet Public Health\n,\n4\n(1), e19–e27.\nhttps://doi.org/10.1016/S2468-2667(18)30183-X\nDavies-Oliveira, J. C., Smith, M. A., Grover, S., Canfell, K., & Crosbie, E. J. (2021). Eliminating Cervical Cancer: Progress and Challenges for High-income Countries. Clinical Oncology, 33(9), 550–559.\nhttps://doi.org/10.1016/j.clon.2021.06.013\nSimms, K. T., Steinberg, J., Caruana, M., Smith, M. A., Lew, J.-B., Soerjomataram, I., Castle, P. E., Bray, F., & Canfell, K. (2019). Impact of scaled up human papillomavirus vaccination and cervical screening and the potential for global elimination of cervical cancer in 181 countries, 2020–99: A modelling study. The Lancet Oncology, 20(3), 394–407.\nhttps://doi.org/10.1016/S1470-2045(18)30836-2\nCanfell, K., Kim, J. J., Brisson, M., Keane, A., Simms, K. T., Caruana, M., Burger, E. A., Martin, D., Nguyen, D. T. N., Bénard, É., Sy, S., Regan, C., Drolet, M., Gingras, G., Laprise, J.-F., Torode, J., Smith, M. A., Fidarova, E., Trapani, D., … Hutubessy, R. (2020). Mortality impact of achieving WHO cervical cancer elimination targets: A comparative modelling analysis in 78 low-income and lower-middle-income countries. The Lancet, 395(10224), 591–603.\nhttps://doi.org/10.1016/S0140-6736(20)30157-4\nThis was\nestimated\nby the WHO’s Global Cancer Observatory for 2022.\nLiu, G., Sharma, M., Tan, N., & Barnabas, R. V. (2018). HIV-positive women have higher risk of human papilloma virus infection, precancerous lesions, and cervical cancer. AIDS, 32(6), 795–808.\nhttps://doi.org/10.1097/QAD.0000000000001765\nRohner, E., Bütikofer, L., Schmidlin, K., Sengayi, M., Maskew, M., Giddy, J., Taghavi, K., Moore, R. D., Goedert, J. J., Gill, M. J., Silverberg, M. J., D’Souza, G., Patel, P., Castilho, J. L., Ross, J., Sohn, A., Bani‐Sadr, F., Taylor, N., Paparizos, V., … Bohlius, J. (2020). Cervical cancer risk in women living with HIV across four continents: A multicohort study. International Journal of Cancer, 146(3), 601–609.\nhttps://doi.org/10.1002/ijc.32260\nStelzle, D., Tanaka, L. F., Lee, K. K., Ibrahim Khalil, A., Baussano, I., Shah, A. S. V., McAllister, D. A., Gottlieb, S. L., Klug, S. J., Winkler, A. S., Bray, F., Baggaley, R., Clifford, G. M., Broutet, N., & Dalal, S. (2021). Estimates of the global burden of cervical cancer associated with HIV. The Lancet Global Health, 9(2), e161–e169.\nhttps://doi.org/10.1016/S2214-109X(20)30459-9\nWang, H., Jiang, Y., Wang, Q., Lai, Y., & Holloway, A. (2023). The status and challenges of HPV vaccine programme in China: An exploration of the related policy obstacles.\nBMJ Global Health\n,\n8\n(8), e012554.\nhttps://doi.org/10.1136/bmjgh-2023-012554\nHaruyama, R., Obara, H., & Fujita, N. (2022). Japan resumes active recommendations of HPV vaccine after 8·5 years of suspension.\nThe Lancet Oncology\n,\n23\n(2), 197–198.\nhttps://doi.org/10.1016/S1470-2045(22)00002-X\nIkeda, S., Ueda, Y., Yagi, A., Matsuzaki, S., Kobayashi, E., Kimura, T., ... & Kudoh, K. (2019). HPV vaccination in Japan: what is happening in Japan?. Expert review of vaccines, 18(4), 323-325.\nhttps://www.tandfonline.com/doi/abs/10.1080/14760584.2019.1584040\nHanley, S. J., Yoshioka, E., Ito, Y., & Kishi, R. (2015). HPV vaccination crisis in Japan. The Lancet, 385(9987), 2571.\nhttps://doi.org/10.1016/S0140-6736(15)61152-7\nArbyn, M., & Xu, L. (2018). Efficacy and safety of prophylactic HPV vaccines. A Cochrane review of randomized trials.\nExpert Review of Vaccines\n,\n17\n(12), 1085–1091.\nhttps://doi.org/10.1080/14760584.2018.1548282\nFaksová, K., Laksafoss, A. D., & Hviid, A. (2024). Human papillomavirus nonavalent (HPV9) vaccination and risk of immune mediated diseases, myocarditis, pericarditis, and thromboembolic outcomes in Denmark: Self-controlled case series study. BMJ Medicine, 3(1), e000854.\nhttps://doi.org/10.1136/bmjmed-2024-000854\nWorld Health Organization. (2018). Global Market Study: HPV.\nAvailable online.\nWorld Health Organization. (2020). Global strategy to accelerate the elimination of cervical cancer as a public health problem.\nAvailable online\n.\nWorld Health Organization. (2024).\nConsiderations for Human Papillomavirus (HPV) Vaccine Product Choice\n.\nAvailable online.\nInternational Agency for Research on Cancer. (2023).\nProtection from a Single Dose of HPV Vaccine\n.\nAvailable online.\nSimms, K. T., Steinberg, J., Caruana, M., Smith, M. A., Lew, J.-B., Soerjomataram, I., Castle, P. E., Bray, F., & Canfell, K. (2019). Impact of scaled up human papillomavirus vaccination and cervical screening and the potential for global elimination of cervical cancer in 181 countries, 2020–99: A modelling study. The Lancet Oncology, 20(3), 394–407.\nhttps://doi.org/10.1016/S1470-2045(18)30836-2\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nSaloni Dattani and Veronika Samborska (2024) - “HPV vaccination: How the world can eliminate cervical cancer” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260604-074426/hpv-vaccination-world-can-eliminate-cervical-cancer.html' [Online Resource] (archived on June 4, 2026).\nBibTeX citation\n@article{owid-hpv-vaccination-world-can-eliminate-cervical-cancer,\nauthor = {Saloni Dattani and Veronika Samborska},\ntitle = {HPV vaccination: How the world can eliminate cervical cancer},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://archive.ourworldindata.org/20260604-074426/hpv-vaccination-world-can-eliminate-cervical-cancer.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "hpv-vaccination-world-can-eliminate-cervical-cancer", "source_url": "https://ourworldindata.org/hpv-vaccination-world-can-eliminate-cervical-cancer", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "HPV vaccines offer a rare opportunity to effectively eliminate one type of cancer. By taking this opportunity, it’s possible to save hundreds of thousands of women each year.", "numeric_mentions": ["4,", "2024", "300,000", "1", "6,", "11,", "16,", "18,", "31,", "33,", "45,", "52", "58", "1980", "2", "3", "1990", "4", "2006", "5", "2014,", "6", "7", "99%", "8", "9", "10", "11", "12", "89%", "87%", "13", "2021", "100,000", "14", "15", "660,000", "350,000", "2022", "16", "17", "18", "2013", "19", "20", "21", "22", "23", "80%", "100%", "50 million", "2100", "2019", "2018", "31", "33", "45", "10.1093", "2016", "16086", "10.1038", "2016.86", "2009", "48", "32", "5798", "5808", "10.1002", "200901917", "384", "260", "265", "10.1016", "2008.11", "046", "210", "212", "10.1021", "224", "1.1"], "numeric_evidence": [{"title": "Share of invasive cervical cancers caused by each HPV type", "source_url": "https://ourworldindata.org/grapher/share-of-invasive-cervical-cancers-caused-by-each-hpv-type.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "HPV type 18", "HPV type 16", "HPV type 26", "HPV type 33", "HPV type 51", "HPV type 82", "HPV type 69", "HPV type 59", "HPV type 56", "HPV type 39", "HPV type 31", "HPV type 35", "HPV type 45", "HPV type 52", "HPV type 58", "HPV type 68", "HPV type 73"], "row_count_total": 1, "rows_head": [{"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "HPV type 18": "15.3", "HPV type 16": "61.7", "HPV type 26": "0.2", "HPV type 33": "3.8", "HPV type 51": "0.5", "HPV type 82": "0.1", "HPV type 69": "0.2", "HPV type 59": "0.9", "HPV type 56": "0.6", "HPV type 39": "0.7", "HPV type 31": "2.8", "HPV type 35": "1.4", "HPV type 45": "4.8", "HPV type 52": "2.8", "HPV type 58": "3.5", "HPV type 68": "0.4", "HPV type 73": "0.3"}], "rows_tail": [], "sampling_note": "Stored first 1 rows and last 1 rows when the table is larger.", "grapher_slug": "share-of-invasive-cervical-cancers-caused-by-each-hpv-type", "metadata_url": "https://ourworldindata.org/grapher/share-of-invasive-cervical-cancers-caused-by-each-hpv-type.metadata.json", "chart_title": "Share of invasive cervical cancers caused by each HPV type", "chart_subtitle": "The estimated relative share of all invasive cervical cancers globally that are caused by each type of human papillomavirus. Invasive cervical cancer are those that have spread beyond the cervix's surface layer into deeper tissues or other areas.", "chart_note": "", "chart_citation": "Wei et al. (2024)", "original_chart_url": "https://ourworldindata.org/grapher/share-of-invasive-cervical-cancers-caused-by-each-hpv-type", "owid_column_metadata": {"HPV_type_18": {"titleShort": "HPV type 18", "titleLong": "HPV type 18", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989862, "shortName": "hpv_type_18", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 18” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989862.metadata.json"}, "HPV_type_16": {"titleShort": "HPV type 16", "titleLong": "HPV type 16", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989863, "shortName": "hpv_type_16", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 16” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989863.metadata.json"}, "HPV_type_26": {"titleShort": "HPV type 26", "titleLong": "HPV type 26", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989864, "shortName": "hpv_type_26", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 26” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989864.metadata.json"}, "HPV_type_33": {"titleShort": "HPV type 33", "titleLong": "HPV type 33", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989869, "shortName": "hpv_type_33", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 33” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989869.metadata.json"}, "HPV_type_51": {"titleShort": "HPV type 51", "titleLong": "HPV type 51", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989873, "shortName": "hpv_type_51", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 51” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989873.metadata.json"}, "HPV_type_82": {"titleShort": "HPV type 82", "titleLong": "HPV type 82", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989875, "shortName": "hpv_type_82", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 82” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989875.metadata.json"}, "HPV_type_69": {"titleShort": "HPV type 69", "titleLong": "HPV type 69", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989876, "shortName": "hpv_type_69", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 69” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989876.metadata.json"}, "HPV_type_59": {"titleShort": "HPV type 59", "titleLong": "HPV type 59", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989870, "shortName": "hpv_type_59", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 59” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989870.metadata.json"}, "HPV_type_56": {"titleShort": "HPV type 56", "titleLong": "HPV type 56", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989868, "shortName": "hpv_type_56", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 56” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989868.metadata.json"}, "HPV_type_39": {"titleShort": "HPV type 39", "titleLong": "HPV type 39", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989866, "shortName": "hpv_type_39", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 39” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989866.metadata.json"}, "HPV_type_31": {"titleShort": "HPV type 31", "titleLong": "HPV type 31", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989865, "shortName": "hpv_type_31", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 31” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989865.metadata.json"}, "HPV_type_35": {"titleShort": "HPV type 35", "titleLong": "HPV type 35", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989867, "shortName": "hpv_type_35", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 35” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989867.metadata.json"}, "HPV_type_45": {"titleShort": "HPV type 45", "titleLong": "HPV type 45", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989872, "shortName": "hpv_type_45", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 45” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989872.metadata.json"}, "HPV_type_52": {"titleShort": "HPV type 52", "titleLong": "HPV type 52", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989877, "shortName": "hpv_type_52", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 52” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989877.metadata.json"}, "HPV_type_58": {"titleShort": "HPV type 58", "titleLong": "HPV type 58", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989878, "shortName": "hpv_type_58", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 58” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989878.metadata.json"}, "HPV_type_68": {"titleShort": "HPV type 68", "titleLong": "HPV type 68", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989871, "shortName": "hpv_type_68", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 68” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989871.metadata.json"}, "HPV_type_73": {"titleShort": "HPV type 73", "titleLong": "HPV type 73", "unit": "%", "timespan": "2024-2024", "type": "Numeric", "owidVariableId": 989874, "shortName": "hpv_type_73", "lastUpdated": "2024-10-07", "citationShort": "Wei et al. (2024) – processed by Our World in Data", "citationLong": "Wei et al. (2024) – processed by Our World in Data. “HPV type 73” [dataset]. Wei et al., “Causal attribution of HPV genotypes to invasive cervical cancer worldwide” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989874.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Share of cancers attributed to nine HPV types", "source_url": "https://ourworldindata.org/grapher/share-of-cancers-attributed-to-9-hpv-types.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Anus", "Larynx", "Oropharynx", "Vagina", "Cervix", "Oral cavity", "Penis", "Vulva"], "row_count_total": 1, "rows_head": [{"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Anus": "95.7", "Larynx": "77.8", "Oropharynx": "95.1", "Vagina": "85.6", "Cervix": "89.3", "Oral cavity": "92.7", "Penis": "88.1", "Vulva": "92.8"}], "rows_tail": [], "sampling_note": "Stored first 1 rows and last 1 rows when the table is larger.", "grapher_slug": "share-of-cancers-attributed-to-9-hpv-types", "metadata_url": "https://ourworldindata.org/grapher/share-of-cancers-attributed-to-9-hpv-types.metadata.json", "chart_title": "Share of cancers attributed to nine HPV types", "chart_subtitle": "Certain types of human papillomavirus (HPV) can cause a range of different cancers. This shows the estimated share of HPV-related cancers globally that are directly caused by the nine types (6, 11, 16, 18, 31, 33, 45, 52 and 58) targeted by some HPV vaccines.", "chart_note": null, "chart_citation": "De Sanjosé et al. (2019)", "original_chart_url": "https://ourworldindata.org/grapher/share-of-cancers-attributed-to-9-hpv-types", "owid_column_metadata": {"Anus_attributable_fraction_HPV_nine_types": {"titleShort": "Anus", "titleLong": "Anus", "unit": "%", "timespan": "2019-2019", "type": "Numeric", "owidVariableId": 989879, "shortName": "anus_attributable_fraction_hpv_nine_types", "lastUpdated": "2024-10-07", "citationShort": "De Sanjosé et al. (2019) – processed by Our World in Data", "citationLong": "De Sanjosé et al. (2019) – processed by Our World in Data. “Anus” [dataset]. De Sanjosé et al., “Burden of Human Papillomavirus (HPV)-Related Cancers Attributable to HPVs 6/11/16/18/31/33/45/52 and 58” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989879.metadata.json"}, "Larynx_attributable_fraction_HPV_nine_types": {"titleShort": "Larynx", "titleLong": "Larynx", "unit": "%", "timespan": "2019-2019", "type": "Numeric", "owidVariableId": 989882, "shortName": "larynx_attributable_fraction_hpv_nine_types", "lastUpdated": "2024-10-07", "citationShort": "De Sanjosé et al. (2019) – processed by Our World in Data", "citationLong": "De Sanjosé et al. (2019) – processed by Our World in Data. “Larynx” [dataset]. De Sanjosé et al., “Burden of Human Papillomavirus (HPV)-Related Cancers Attributable to HPVs 6/11/16/18/31/33/45/52 and 58” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989882.metadata.json"}, "Oropharynx_attributable_fraction_HPV_nine_types": {"titleShort": "Oropharynx", "titleLong": "Oropharynx", "unit": "%", "timespan": "2019-2019", "type": "Numeric", "owidVariableId": 989883, "shortName": "oropharynx_attributable_fraction_hpv_nine_types", "lastUpdated": "2024-10-07", "citationShort": "De Sanjosé et al. (2019) – processed by Our World in Data", "citationLong": "De Sanjosé et al. (2019) – processed by Our World in Data. “Oropharynx” [dataset]. De Sanjosé et al., “Burden of Human Papillomavirus (HPV)-Related Cancers Attributable to HPVs 6/11/16/18/31/33/45/52 and 58” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989883.metadata.json"}, "Vagina_attributable_fraction_HPV_nine_types": {"titleShort": "Vagina", "titleLong": "Vagina", "unit": "%", "timespan": "2019-2019", "type": "Numeric", "owidVariableId": 989884, "shortName": "vagina_attributable_fraction_hpv_nine_types", "lastUpdated": "2024-10-07", "citationShort": "De Sanjosé et al. (2019) – processed by Our World in Data", "citationLong": "De Sanjosé et al. (2019) – processed by Our World in Data. “Vagina” [dataset]. De Sanjosé et al., “Burden of Human Papillomavirus (HPV)-Related Cancers Attributable to HPVs 6/11/16/18/31/33/45/52 and 58” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989884.metadata.json"}, "Cervix_attributable_fraction_HPV_nine_types": {"titleShort": "Cervix", "titleLong": "Cervix", "unit": "%", "timespan": "2019-2019", "type": "Numeric", "owidVariableId": 989880, "shortName": "cervix_attributable_fraction_hpv_nine_types", "lastUpdated": "2024-10-07", "citationShort": "De Sanjosé et al. (2019) – processed by Our World in Data", "citationLong": "De Sanjosé et al. (2019) – processed by Our World in Data. “Cervix” [dataset]. De Sanjosé et al., “Burden of Human Papillomavirus (HPV)-Related Cancers Attributable to HPVs 6/11/16/18/31/33/45/52 and 58” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989880.metadata.json"}, "Oral_cavity_attributable_fraction_HPV_nine_types": {"titleShort": "Oral cavity", "titleLong": "Oral cavity", "unit": "%", "timespan": "2019-2019", "type": "Numeric", "owidVariableId": 989881, "shortName": "oral_cavity_attributable_fraction_hpv_nine_types", "lastUpdated": "2024-10-07", "citationShort": "De Sanjosé et al. (2019) – processed by Our World in Data", "citationLong": "De Sanjosé et al. (2019) – processed by Our World in Data. “Oral cavity” [dataset]. De Sanjosé et al., “Burden of Human Papillomavirus (HPV)-Related Cancers Attributable to HPVs 6/11/16/18/31/33/45/52 and 58” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989881.metadata.json"}, "Penis_attributable_fraction_HPV_nine_types": {"titleShort": "Penis", "titleLong": "Penis", "unit": "%", "timespan": "2019-2019", "type": "Numeric", "owidVariableId": 989886, "shortName": "penis_attributable_fraction_hpv_nine_types", "lastUpdated": "2024-10-07", "citationShort": "De Sanjosé et al. (2019) – processed by Our World in Data", "citationLong": "De Sanjosé et al. (2019) – processed by Our World in Data. “Penis” [dataset]. De Sanjosé et al., “Burden of Human Papillomavirus (HPV)-Related Cancers Attributable to HPVs 6/11/16/18/31/33/45/52 and 58” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/989886.metadata.json"}, "Vulva_attributable_fraction_HPV_nine_types": {"titleShort": "Vulva", "titleLong": "Vulva", "unit": "%", "timespan": "2019-2019", "type": "Numeric", "owidVariableId": 989885, "shortName": "vulva_attributable_fraction_hpv_nine_types", "lastUpdated": "2024-10-07", "citationShort": "De Sanjosé et al. (2019) – processed by Our World in Data", "citationLong": "De Sanjosé et al. (2019) – processed by Our World in Data. “Vulva” [dataset]. 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Reuse should follow the source and license information in this chart metadata."}, {"grapher_slug": "rate-of-new-cervical-cancer-cases", "source_url": "https://ourworldindata.org/grapher/rate-of-new-cervical-cancer-cases", "parse_error": "Failed to fetch https://ourworldindata.org/grapher/rate-of-new-cervical-cancer-cases.csv"}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "93bd519a69781213ca3a"},
{"raw_link": "https://ourworldindata.org/climate-change-will-affect-food-production-things-can-adapt", "title": "Climate change will affect food production, but here are the things we can do to adapt", "context": "Home\nClimate Change\nClimate change will affect food production, but here are the things we can do to adapt\nAdapting planting dates, selecting better crop varieties, and increasing access to irrigation and fertilizers could offset potential declines in crop yields.\nBy\nHannah Ritchie\nOctober 28, 2024\nBrowse past versions\nCite this article\nReuse our work freely\nClimate change could have large impacts on food production across the world.\nI explored this in my previous two articles, looking at the impact of climate change on food production\nso far\n, and what we might expect\nin the\nfuture\n.\nIn short, it might boost crop yields at high latitudes but negatively impact yields in the tropics and subtropics. Wheat and rice — which benefit from more carbon dioxide (CO\n2\n) in the atmosphere — could see yields increase, while maize, sorghum, and millet could see a decline with warmer temperatures. If you want to know more, you can read the previous two articles to get some grounding in the scale and distribution of climate impacts.\nThis article is the third and final one in this series. It examines whether the world can adapt its food systems to climate change. What are those changes, and can the negative impacts on yields be offset?\nThese answers are crucial to ensure that countries can further improve food security in a warmer world.\nWhat, where, when, and how: the four ways to adapt crop production\nFarmers can adjust their practices in four key ways. They can change:\nFarmers can change\nwhat\nthey plant.\nThis could be an entirely different\ntype\nof crop: maize instead of wheat, for example.\n1\nOr a different\nvariety\nof a specific crop. There isn’t just one variety of maize or rice; scientists breed various \"cultivars\" with different characteristics and grow best in different conditions. Some are more drought-tolerant than others, for example. Or they take shorter or longer periods to mature and be ready to harvest.\nThis means farmers can pick crop varieties best suited to different climate conditions.\nFarmers can change\nwhere\ncrops are planted. If temperatures rise or fall, crop production can shift north or southwards towards more optimal temperatures. In mountainous areas, it can move up and down the slopes. Agriculture can also shift to drier or wetter regions of a country, depending on the local conditions.\nFarmers can change\nwhen\nthey plant and harvest\n.\nFarmers can plant earlier or later in the year, depending on when spring arrives. The same applies to winter crops: they might choose to plant earlier or later in autumn.\nFarmers can change\nh\now\ncrops are managed. Crops need the right amount of water, nutrients, and protection from pests and disease. Making sure that they have enough through the use of irrigation, fertilizers, and pesticides can help offset some impacts of climate change.\nIn the following sections, I’ll look at whether these changes can make food systems more resilient to rising temperatures in the future. However, it’s important to note that farmers\nhave already implemented\nmany of these strategies. For example, an extensive study by Lindsey Sloat and colleagues showed that the movement of farming and more irrigation has already offset most of the negative impacts that we’d expect from the 1.3°C of warming that the world has already seen.\n2\nAdaptation can offset many, if not all, of the negative impacts of climate change on crop yields\nBefore I jump into the specific changes and investments that will allow us to adapt, it’s worth looking at the opportunity we have to make our food system more resilient at a global level.\nHere, I’ll focus on a recent study by Sara Minoli, Jonas Jägermeyr, and colleagues.\n3\nThe study looks at the impact of climate change on yields of key staple crops — maize, rice, sorghum, soybean, and rice — towards the end of the century under a pretty high (i.e., bad) climate scenario called “RCP6.0”. In this scenario, the world would warm by around 3°C by 2100.\n4\nThey look at\nglobal\nyields for each crop (I’ll come on to some regional impacts later) under scenarios with and without adaptation. Adaptation in their study involves farmers changing the when and what — the timing of planting and harvesting — and picking more climate-suited breeds of crops.\nThey find that adaptation\ncould\noffset climate impacts, at least at a global level. Crops such as maize would see yield losses without adaptation but could see an increase in yields with a change in practices and investments in the right places.\nLet’s look at what these adaptation measures would mean in practice.\nPlanting and harvesting crops at different times and using more suitable crop types can improve yields\nSara Minoli and colleagues modeled two adaptation methods. The first is changing the dates that farmers planted their crops — in the chart, this is shown in brown.\nThere are two key stages in the year when farmers plant their crops: either at the start of warm weather for spring crops or the beginning of colder weather for winter crops, such as winter wheat. Climate change will affect the optimal time to plant. Warmer temperatures mean that in many countries, spring crops can be planted earlier.\n5\nFor the scenario of 3°C warming, The researchers estimate that toward the end of the century, spring crops in many places should be planted 10 to 30 days earlier than they are today. On the other hand, winter wheat should be planted 10 to 30 days later so the crop doesn’t develop too early or too quickly when it’s vulnerable to damaging conditions like frost.\nIn the chart below, you can see the estimated impact of changing the sowing date and adopting different crop varieties on crop yields at the end of the century under the RCP6.0 scenario that we looked at before.\nDownload\nAt high latitudes — especially across Northern Europe and Canada — farmers who grow spring wheat might benefit from switching to winter wheat.\nThe second adaptation measure is choosing better-suited crop strains — in the chart this is shown in purple. Through plant breeding, scientists have adapted crop varieties to suit the local climatic conditions. Some varieties will need longer or shorter periods to reach maturity, will be more adapted to warmer, drier, or wetter conditions, have different optimal day lengths, and need different exposures to cold conditions to flower properly. Farmers can, therefore, select better-suited crop varieties over time as the climate changes. They already do this.\n6\nBoth measures help farmers to adapt, although changing crop varieties is expected to have a bigger impact on improving yields for all crops. The adaptation benefits for maize, rice, and sorghum are much larger than for wheat and soybean. This is good news because maize, millet, and sorghum could suffer the most from higher temperatures and don’t benefit much from higher levels of CO\n2\n. Wheat yields, on the other hand,\nare projected to increase\nunder climate change regardless of adaptation.\nOther studies that focus specifically on the increased risk of waterlogging find that adaptation using more tolerant crop strains, and changes in planting dates can offset many of the declines expected with warming.\n7\nData is lacking on impacts and adaptation in the regions that are likely to be most affected\nOf course, we don’t only care about crop yields at the\nglobal\nlevel. If farmers in particular regions — especially those that are most food insecure — cannot adapt to climate change, this is still a major problem.\nOne extra challenge in assessing the impacts of adaptation in these regions is a lack of data and representative research. Only a fraction of the studies that look at climate change impacts and adaptation strategies are in Sub-Saharan Africa and South Asia. Investment into agricultural research in these regions is essential, given that this is where the most severe climate impacts will be.\nWhat can we say from the small number of high-quality studies that have been done?\nChanges in planting dates and the selection of the best crop breeds can already go some way to offsetting climate pressures in countries closer to the equator.\nA study looking at agriculture in West Africa found that crop yields could decline by an average of 6% due to climate change.\n8\nBut, adaptation through changing planting dates and selecting better crop varieties could offset these declines, resulting in a 13% increase in yields.\nAnother study focused on the large breadbasket of Punjab in India found that a decline in wheat yields could also be turned into a yield gain by adapting planting dates and using improved crop varieties.\n9\nBut in some countries — and for specific crops like maize or millet — these strategies will probably not be enough. They can offset some of the declines in yields but not all of them.\nA recent study looked at the potential for adaptation in rainfed cereal crops in West and East Africa.\n10\nIt estimated the share of current cereal production that would see increases or decreases in yield — and the stability of those yields from year to year — in 2050 and 2090.\n11\nIt then modeled how this would change through adaptation. You can see the results in the chart below.\nWithout adaptation you can see that a large share of cereal production could see a decline in yield. This is shown in red. But adaptation makes a big difference, especially in East and South Africa. In 2050, more than half of cereal production could see a decline in yield or stability. With adaptation, this shrinks to less than 20%.\nThere is still some red in the scenarios with adaptation, though. These regions will need to look towards other changes to farming practices and inputs to fully compensate for the impacts of climate change. Increasing access to agricultural inputs will be essential, not just to offset damages from climate change but to feed a growing population over the next 50 years.\nDownload\nIncreased access to irrigation, fertilizers, and other inputs will be crucial\nGlobal crop yields have\nincreased dramatically\nover the last half-century. Improved crop varieties have been one of the big drivers, but increased access to irrigation, fertilizers, and other inputs has also been crucial.\nThis will still be the case in a changing climate. Perhaps even more so.\nA large review of climate impacts on yields by Rezaei et al. (2023) highlights that irrigation and nutrient management (i.e., fertilizer use and efficiency) could be the most effective adaptation options.\n12\nIn areas where water stress increases with climate change, the need for more irrigation is obvious. However, a key point is that crop yields in many countries are\nalready\nlimited by poor access to irrigation and\nfertilizers\n.\nIn the two maps below, you can see the large differences in the use of these inputs across the world.\nFarmers in richer countries get much\nhigher crop yields\n. One reason for this is fertilizer use. Some of the world’s poorest countries — particularly across Sub-Saharan Africa — use more than 100 times less fertilizer than farmers in richer countries. Many don’t have access to additional nutrient inputs at all.\nData coverage on the use of irrigation is less complete. But as you can see on the map, there are large differences in rates of irrigation even along the tropics and sub-tropics. More than 75% of farmland in Bangladesh and 40% in India is irrigated, compared to less than 1% in Ethiopia, Nigeria and Niger. It would also be reasonable to assume that countries without available data — particularly across Sub-Saharan Africa — also have very poor access to irrigation.\nOf course, the\ndemand\nfor irrigation across the world is not equal. Countries at higher latitudes, such as the United Kingdom, get more rain and don’t have to rely on irrigation as much.\nAs I mentioned in my\nprevious article\n, the “yield gap” — the difference between yields that countries currently achieve and\ncould\nachieve if they had access to best practices and inputs that are already available — in many countries is huge. Crucially, the gap is often far larger than the potential yield reductions due to climate change, even in the worst-case scenarios.\nIf farmers were given the means to boost yields by improving access to irrigation, fertilizers, and other necessary resources, then countries would be much more resilient and safeguarded against climate impacts. A 0.5 tonnes reduction in crop yields would be much more damaging to food security for a farmer who harvests 1.5 tonnes per hectare than for one who achieves 5 tonnes.\nThe earlier study looking at climate impacts in West Africa found that increasing fertilizer use alone would not only make crops more resilient to climate impacts but also boost the “baseline” yields.\n13\nCombining climate adaptation measures with better access to fertilizers and irrigation would not only offset yield declines due to climate change but would actually result in much higher yields than farmers achieve today.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nThe world will need to take investment and innovation seriously to adapt to climate change\nAll of the studies above have focused on the\nbiophysical\npotential that we have to build more resilient food systems.\nThe roadblocks to maintaining food security for 8, 9, or 10 billion people are not technical. With the right crop breeds, access to water and nutrients, and smart decisions around planting, we can offset many of the negative impacts of climate change on agriculture.\nThat doesn’t mean we should be complacent.\nIt means we have the chance to build a more productive and resilient food system, but it’s not guaranteed that we will. It depends on whether the seeds, irrigation, and adaptation practices will be available. That will require real and sustained investment from governments, donors, and private companies. Without it, many of these gains will not happen. Climate change will put increasing — and growing — pressure on yields in countries where\nfood insecurity\nis already high.\nThe scientific research suggests this is a tractable problem we can tackle. Whether the world commits to making it a reality is up to us.\nThis article is the last in our series on climate change and agriculture:\nCrop yields have increased dramatically in recent decades, but crops like maize would have improved more without climate change\nClimate change has slowed the productivity of key crops such as maize and soybeans, but might have had small positive impacts on wheat.\nHow will climate change affect crop yields in the future?\nMaize yields could see significant declines, but wheat could increase. Impacts across the world will be very different.\nClimate change will affect food production, but here are the things we can do to adapt\nAdapting planting dates, selecting better crop varieties, and increasing access to irrigation and fertilizers could offset potential declines in crop yields.\nEndnotes\nThis study finds significant benefits to crop switching in China:\nXie, W., Zhu, A., Ali, T., Zhang, Z., Chen, X., Wu, F., ... & Davis, K. F. (2023). Crop switching can enhance environmental sustainability and farmer incomes in China. Nature.\nSloat, L. L., Davis, S. J., Gerber, J. S., Moore, F. C., Ray, D. K., West, P. C., & Mueller, N. D. (2020). Climate adaptation by crop migration.\nNature Communications\n.\nMinoli, S., Jägermeyr, J., Asseng, S., Urfels, A., & Müller, C. (2022). Global crop yields can be lifted by timely adaptation of growing periods to climate change. Nature Communications.\nThat is, 3°C compared to pre-industrial temperatures, not 3°C more warming from today’s levels.\nIn some places, farmers plant crops at the end of the rainy season. Climate change could shift these dates — although the change would be much less than temperature — so they might also shift their planting date slightly.\nTao, F., Zhang, S., Zhang, Z., & Rötter, R. P. (2014). Maize growing duration was prolonged across China in the past three decades under the combined effects of temperature, agronomic management, and cultivar shift. Global Change Biology.\nLiu, K., Harrison, M. T., Yan, H., Liu, D. L., Meinke, H., Hoogenboom, G., ... & Zhou, M. (2023). Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates. Nature Communications.\nThis decline reached as much as 18% in the most extreme, but unlikely climate scenario.\nCarr, T. W., Mkuhlani, S., Segnon, A. C., Ali, Z., Zougmoré, R., Dangour, A. D., ... & Scheelbeek, P. (2022). Climate change impacts and adaptation strategies for crops in West Africa: a systematic review. Environmental research letters.\nSandhu, S. S., Kothiyal, S., & Kaur, J. (2023). An assessment of adaptation measures to enhance wheat productivity under climate change during early, mid and end of 21st century in Indian Punjab. The Journal of Agricultural Science.\nAlimagham, S., van Loon, M. P., Ramirez-Villegas, J., Adjei-Nsiah, S., Baijukya, F., Bala, A., ... & van Ittersum, M. K. (2024). Climate change impact and adaptation of rainfed cereal crops in sub-Saharan Africa. European Journal of Agronomy.\nThis was based on the average of five general circulation models and two climate scenarios: SSP3-7.0 and SSP5-8.5. Both of these scenarios are pretty pessimistic about our climate trajectory. The SSP5-8.5 scenario is now very unlikely, and the SSP3-7.0 also seems very pessimistic. Warming is likely to be lower than these scenarios would suggest.\nRezaei, E. E., Webber, H., Asseng, S., Boote, K., Durand, J. L., Ewert, F., ... & MacCarthy, D. S. (2023). Climate change impacts on crop yields. Nature Reviews Earth & Environment/\nCarr, T. W., Mkuhlani, S., Segnon, A. C., Ali, Z., Zougmoré, R., Dangour, A. D., ... & Scheelbeek, P. (2022). Climate change impacts and adaptation strategies for crops in West Africa: a systematic review. Environmental research letters.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2024) - “Climate change will affect food production, but here are the things we can do to adapt” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260619-135635/climate-change-will-affect-food-production-things-can-adapt.html' [Online Resource] (archived on June 19, 2026).\nBibTeX citation\n@article{owid-climate-change-will-affect-food-production-things-can-adapt,\nauthor = {Hannah Ritchie},\ntitle = {Climate change will affect food production, but here are the things we can do to adapt},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://archive.ourworldindata.org/20260619-135635/climate-change-will-affect-food-production-things-can-adapt.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "climate-change-will-affect-food-production-things-can-adapt", "source_url": "https://ourworldindata.org/climate-change-will-affect-food-production-things-can-adapt", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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Yield (tonnes per hectare)": "1.0069001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Cereals - Yield (tonnes per hectare)": "1.2796"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Cereals - Yield (tonnes per hectare)": "1.3019"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Cereals - Yield (tonnes per hectare)": "1.3164"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Cereals - Yield (tonnes per hectare)": "1.3624"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Cereals - Yield (tonnes per hectare)": "1.2240001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Cereals - Yield (tonnes per hectare)": "1.2919"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Cereals - Yield (tonnes per hectare)": "1.3237001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Cereals - Yield (tonnes per hectare)": "1.3490001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Cereals - Yield (tonnes per hectare)": "1.3383001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Cereals - Yield (tonnes per hectare)": "1.3302"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Cereals - Yield (tonnes per hectare)": "1.3556"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Cereals - Yield (tonnes per hectare)": "1.3341"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Cereals - Yield (tonnes per hectare)": "1.3317"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Cereals - Yield (tonnes per hectare)": "1.314"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Cereals - Yield (tonnes per hectare)": "1.3598001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Cereals - Yield (tonnes per hectare)": "1.2857"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Cereals - Yield (tonnes per hectare)": "1.2376001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Cereals - Yield (tonnes per hectare)": "1.2006"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Cereals - Yield (tonnes per hectare)": "1.1604"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Cereals - Yield (tonnes per hectare)": "1.0978001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Cereals - Yield (tonnes per hectare)": "1.1329001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Cereals - Yield (tonnes per hectare)": "1.1404"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Cereals - Yield (tonnes per hectare)": "1.2145001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Cereals - Yield (tonnes per hectare)": "1.2044001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Cereals - Yield (tonnes per hectare)": "1.3488001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Cereals - Yield (tonnes per hectare)": "1.388"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Cereals - Yield (tonnes per hectare)": "1.2857"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Cereals - Yield (tonnes per hectare)": "0.80630004"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Cereals - Yield (tonnes per hectare)": "1.0067"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Cereals - Yield (tonnes per hectare)": "1.6698002"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Cereals - Yield (tonnes per hectare)": "1.4580001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Cereals - Yield (tonnes per hectare)": "1.3348001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Cereals - Yield (tonnes per hectare)": "1.7904001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Cereals - Yield (tonnes per hectare)": "1.5517"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Cereals - Yield (tonnes per hectare)": "1.9153001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Cereals - Yield (tonnes per hectare)": "1.4554001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Cereals - Yield (tonnes per hectare)": "2.0407"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Cereals - Yield (tonnes per hectare)": "2.0111"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Cereals - Yield (tonnes per hectare)": "1.6599001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Cereals - Yield (tonnes per hectare)": "2.0942001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Cereals - Yield (tonnes per hectare)": "2.1277"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Cereals - Yield (tonnes per hectare)": "2.0966003"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Cereals - Yield (tonnes per hectare)": "2.2064"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Cereals - Yield (tonnes per hectare)": "2.0433002"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Cereals - Yield (tonnes per hectare)": "2.0914"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Cereals - Yield (tonnes per hectare)": "2.2532"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Cereals - Yield (tonnes per hectare)": "2.1847"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Cereals - Yield (tonnes per hectare)": "2.0508"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Cereals - Yield (tonnes per hectare)": "2.0991"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Cereals - 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"Phosphate": "4.76", "Nitrogen": "9.12"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1987", "Potassium": "2.96", "Phosphate": "4.21", "Nitrogen": "7.91"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1988", "Potassium": "3.14", "Phosphate": "4.29", "Nitrogen": "9.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1989", "Potassium": "3.21", "Phosphate": "4.23", "Nitrogen": "8.34"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1990", "Potassium": "3.07", "Phosphate": "4.1", "Nitrogen": "9.37"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Potassium": "3.01", "Phosphate": "4.15", "Nitrogen": "8.63"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Potassium": "2", "Phosphate": "3.23", "Nitrogen": "5.38"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Potassium": "2.87", "Phosphate": "4.24", "Nitrogen": "7.56"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Potassium": "3.26", "Phosphate": "3.89", "Nitrogen": "8.54"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Potassium": "2.82", "Phosphate": "3.46", "Nitrogen": "6.93"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Potassium": "3.32", "Phosphate": "3.32", "Nitrogen": "8.42"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Potassium": "3.25", "Phosphate": "3.87", "Nitrogen": "8.27"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Potassium": "3.28", "Phosphate": "3.62", "Nitrogen": "8.19"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Potassium": "3.56", "Phosphate": "3.65", "Nitrogen": "8.49"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Potassium": "2.9", "Phosphate": "3.65", "Nitrogen": "7.35"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Potassium": "2.94", "Phosphate": "2.92", "Nitrogen": "6.85"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Potassium": "0.95", "Phosphate": "3.98", "Nitrogen": "5.85"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Potassium": "1.97", "Phosphate": "3.63", "Nitrogen": "6.34"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Potassium": "1.16", "Phosphate": "1.65", "Nitrogen": "4.17"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Potassium": "1.27", "Phosphate": "1.66", "Nitrogen": "3.88"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Potassium": "2.12", "Phosphate": "3.58", "Nitrogen": "4.79"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Potassium": "1.6", "Phosphate": "2.37", "Nitrogen": "4.48"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Potassium": "1.1", "Phosphate": "2.36", "Nitrogen": "3.75"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Potassium": "0.83", "Phosphate": "3.12", "Nitrogen": "5.03"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Potassium": "1.35", "Phosphate": "3.58", "Nitrogen": "5.28"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Potassium": "0.95", "Phosphate": "2.87", "Nitrogen": "4.38"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Potassium": "0.87", "Phosphate": "1.45", "Nitrogen": "2.97"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Potassium": "1.08", "Phosphate": "2.53", "Nitrogen": "3.7"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Potassium": "1.52", "Phosphate": "2.22", "Nitrogen": "3.52"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Potassium": "0.9", "Phosphate": "1.6", "Nitrogen": "2.96"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "Potassium": "2.27", "Phosphate": "2.67", "Nitrogen": "3.98"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "Potassium": "2.7", "Phosphate": "2.79", "Nitrogen": "4.39"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "Potassium": "2.45", "Phosphate": "3.05", "Nitrogen": "3.33"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "Potassium": "2.42", "Phosphate": "3", "Nitrogen": "3.28"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "Potassium": "0.04", "Phosphate": "0.06", "Nitrogen": "5.14"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "Potassium": "0.18", "Phosphate": "0.41", "Nitrogen": "8.81"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "Potassium": "0.51", "Phosphate": "0.53", "Nitrogen": "6.67"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "Potassium": "0.28", "Phosphate": "0.26", "Nitrogen": "5.91"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "fertilizer-per-capita", "metadata_url": "https://ourworldindata.org/grapher/fertilizer-per-capita.metadata.json", "chart_title": "Fertilizer use per capita", "chart_subtitle": "Fertilizer use is the sum of synthetic inputs of nitrogen, potassium and phosphorous, plus organic nitrogen inputs. This is given in kilograms per person per year.", "chart_note": null, "chart_citation": "Food and Agriculture Organization of the United Nations", "original_chart_url": "https://ourworldindata.org/grapher/fertilizer-per-capita", "owid_column_metadata": {"Nutrient potash K2O (total) | 00003104 || Use per capita | 005172 || Kilograms per capita": {"titleShort": "Nutrient potash k2o (total) - Use per capita (Kilograms per capita)", "titleLong": "Nutrient potash k2o (total) - Use per capita (Kilograms per capita) - UN FAO", "shortUnit": "kg/cap", "unit": "Kilograms per capita", "timespan": "1961-2023", "type": "Numeric", "owidVariableId": 1196065, "shortName": "nutrient_potash_k2o__total__00003104__use_per_capita__005172__kilograms_per_capita", "lastUpdated": "2026-02-25", "nextUpdate": "2027-02-25", "citationShort": "Food and Agriculture Organization of the United Nations (2025) – with major processing by Our World in Data", "citationLong": "Food and Agriculture Organization of the United Nations (2025) – with major processing by Our World in Data. “Nutrient potash k2o (total) - Use per capita (Kilograms per capita) – UN FAO” [dataset]. Food and Agriculture Organization of the United Nations, “Land, Inputs and Sustainability: Fertilizers by Nutrient” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1196065.metadata.json"}, "Nutrient phosphate P2O5 (total) | 00003103 || Use per capita | 005172 || Kilograms per capita": {"titleShort": "Nutrient phosphate p2o5 (total) - Use per capita (Kilograms per capita)", "titleLong": "Nutrient phosphate p2o5 (total) - Use per capita (Kilograms per capita) - UN FAO", "shortUnit": "kg/cap", "unit": "Kilograms per capita", "timespan": "1961-2023", "type": "Numeric", "owidVariableId": 1196055, "shortName": "nutrient_phosphate_p2o5__total__00003103__use_per_capita__005172__kilograms_per_capita", "lastUpdated": "2026-02-25", "nextUpdate": "2027-02-25", "citationShort": "Food and Agriculture Organization of the United Nations (2025) – with major processing by Our World in Data", "citationLong": "Food and Agriculture Organization of the United Nations (2025) – with major processing by Our World in Data. “Nutrient phosphate p2o5 (total) - Use per capita (Kilograms per capita) – UN FAO” [dataset]. Food and Agriculture Organization of the United Nations, “Land, Inputs and Sustainability: Fertilizers by Nutrient” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1196055.metadata.json"}, "Nutrient nitrogen N (total) | 00003102 || Use per capita | 005172 || Kilograms per capita": {"titleShort": "Nutrient nitrogen n (total) - Use per capita (Kilograms per capita)", "titleLong": "Nutrient nitrogen n (total) - Use per capita (Kilograms per capita) - UN FAO", "shortUnit": "kg/cap", "unit": "Kilograms per capita", "timespan": "1961-2023", "type": "Numeric", "owidVariableId": 1196029, "shortName": "nutrient_nitrogen_n__total__00003102__use_per_capita__005172__kilograms_per_capita", "lastUpdated": "2026-02-25", "nextUpdate": "2027-02-25", "citationShort": "Food and Agriculture Organization of the United Nations (2025) – with major processing by Our World in Data", "citationLong": "Food and Agriculture Organization of the United Nations (2025) – with major processing by Our World in Data. “Nutrient nitrogen n (total) - Use per capita (Kilograms per capita) – UN FAO” [dataset]. Food and Agriculture Organization of the United Nations, “Land, Inputs and Sustainability: Fertilizers by Nutrient” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1196029.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Share of agricultural land which is irrigated", "source_url": "https://ourworldindata.org/grapher/agricultural-land-irrigation.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Agricultural irrigated land (% of total agricultural land)"], "row_count_total": 1228, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Agricultural irrigated land (% of total agricultural land)": "5.6621246"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Agricultural irrigated land (% of total agricultural land)": "4.617624"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Agricultural irrigated land (% of total agricultural land)": "7.2642803"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Agricultural irrigated land (% of total agricultural land)": "5.4998946"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Agricultural irrigated land (% of total agricultural land)": "5.8390694"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Agricultural irrigated land (% of total agricultural land)": "6.0854654"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Agricultural irrigated land (% of total agricultural land)": "5.9397583"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Agricultural irrigated land (% of total agricultural land)": "5.778563"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Agricultural irrigated land (% of total agricultural land)": "4.842283"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Agricultural irrigated land (% of total agricultural land)": "5.000396"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Agricultural irrigated land (% of total agricultural land)": "5.391006"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Agricultural irrigated land (% of total agricultural land)": "5.4649997"}, {"Entity": 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"ARE", "Year": "2012", "Agricultural irrigated land (% of total agricultural land)": "12.768782"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2013", "Agricultural irrigated land (% of total agricultural land)": "20.92778"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2014", "Agricultural irrigated land (% of total agricultural land)": "21.568628"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2015", "Agricultural irrigated land (% of total agricultural land)": "21.630095"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2016", "Agricultural irrigated land (% of total agricultural land)": "21.83429"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2017", "Agricultural irrigated land (% of total agricultural land)": "21.83429"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2018", "Agricultural irrigated land (% of total agricultural land)": "21.914865"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2019", "Agricultural irrigated land (% of total agricultural land)": "23.657787"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2020", "Agricultural irrigated land (% of total agricultural land)": "23.55567"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2021", "Agricultural irrigated land (% of total agricultural land)": "23.996231"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2022", "Agricultural irrigated land (% of total agricultural land)": "23.996231"}, {"Entity": "United Arab Emirates", "Code": "ARE", "Year": "2023", "Agricultural irrigated land (% of total agricultural land)": "23.987518"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2003", "Agricultural irrigated land (% of total agricultural land)": "1.3387592"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2005", "Agricultural irrigated land (% of total agricultural land)": "1.2290635"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2007", "Agricultural irrigated land (% of total agricultural land)": "0.78313595"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2010", "Agricultural irrigated land (% of total agricultural land)": "0.3855086"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2013", "Agricultural irrigated land (% of total agricultural land)": "0.28364888"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2018", "Agricultural irrigated land (% of total agricultural land)": "0.41390508"}, {"Entity": "United States", "Code": "USA", "Year": "2002", "Agricultural irrigated land (% of total agricultural land)": "5.419015"}, {"Entity": "United States", "Code": "USA", "Year": "2007", "Agricultural irrigated land (% of total agricultural land)": "5.54816"}, {"Entity": "United States", "Code": "USA", "Year": "2008", "Agricultural irrigated land (% of total agricultural land)": "5.357924"}, {"Entity": "United States", "Code": "USA", "Year": "2012", "Agricultural irrigated land (% of total agricultural land)": "5.3407574"}, {"Entity": "United States", "Code": "USA", "Year": "2017", "Agricultural irrigated land (% of total agricultural land)": "5.4952865"}, {"Entity": "United States", "Code": "USA", "Year": "2023", "Agricultural irrigated land (% of total agricultural land)": "5.1010633"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2006", "Agricultural irrigated land (% of total agricultural land)": "1.150051"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2007", "Agricultural irrigated land (% of total agricultural land)": "1.0790378"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2008", "Agricultural irrigated land (% of total agricultural land)": "1.2266594"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2009", "Agricultural irrigated land (% of total agricultural land)": "1.1678143"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2010", "Agricultural irrigated land (% of total agricultural land)": "1.2332848"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2011", "Agricultural irrigated land (% of total agricultural land)": "1.3768415"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2012", "Agricultural irrigated land (% of total agricultural land)": "1.4047903"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2016", "Agricultural irrigated land (% of total agricultural land)": "1.5632339"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2017", "Agricultural irrigated land (% of total agricultural land)": "1.567894"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2018", "Agricultural irrigated land (% of total agricultural land)": "1.5714407"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2015", "Agricultural irrigated land (% of total agricultural land)": "14.3529825"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2016", "Agricultural irrigated land (% of total agricultural land)": "14.341748"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2017", "Agricultural irrigated land (% of total agricultural land)": "14.632713"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2018", "Agricultural irrigated land (% of total agricultural land)": "14.632273"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2019", "Agricultural irrigated land (% of total agricultural land)": "14.592668"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2020", "Agricultural irrigated land (% of total agricultural land)": "14.531296"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2021", "Agricultural irrigated land (% of total agricultural land)": "14.524768"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2022", "Agricultural irrigated land (% of total agricultural land)": "14.476697"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2023", "Agricultural irrigated land (% of total agricultural land)": "14.582519"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2007", "Agricultural irrigated land (% of total agricultural land)": "3.1680505"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", 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{"raw_link": "https://ourworldindata.org/foreign-aid", "title": "Foreign Aid", "context": "Foreign Aid\nBy\nBastian Herre\n,\nPablo Arriagada\n,\nSimon van Teutem\n,\nand\nHannah Ritchie\nCite this work\nReuse this work\nForeign aid is the provision of money, goods, or services from one country to another, usually to support the people in a lower-income country. It can be used to build public infrastructure, improve health or education, increase economic growth, reduce conflict, support institutions, or recover from disasters or crises.\nSupporters of aid highlight successful organizations and programs, such as PEPFAR, which has\nsaved millions of lives\nfrom HIV and AIDS; GAVI, which has vaccinated\nhundreds of millions of children\nagainst diseases; USAID and private foundations, which supported the Green Revolution to\nincrease crop yields\n; and the Carter Center, which has led\nthe near-eradication\nof guinea worm disease.\n1\nBut foreign aid projects can fail and, in the worst cases, cause harm. Foreign aid can disrupt local economies and political systems and make governments more responsive to foreign powers than their own citizens.\n2\nCritics of foreign aid point to failures like the WHO’s\nGlobal Malaria Eradication Program\nin the 1950s and ’60s, or India’s aid-supported\nfamily planning\nand sterilization program in the 1970s.\nForeign aid comes in many forms.\nGovernments\nprovide most assistance, but civil society organizations and international organizations, such as the World Bank, also play an influential role. Aid is often directed towards\nthe poorest countries\n, but other countries that are not among the poorest also receive significant amounts. Some aid supports government operations generally, but most aid is focused on\nspecific sectors\n.\nOn this topic page, you can explore data on who gives and receives foreign aid, the different types of assistance, and a few examples of when it has been successful.\nRelated topics\nGovernment Spending\nEconomic Growth\nEradication of Diseases\nCrop Yields\nResearch & Writing\nMarch 10, 2025\nFor many of us, it doesn’t cost much to improve someone’s life, and we can do much more of it\nMost countries spend less than 1% of their national income on foreign aid; even small increases could make a big difference.\nHannah Ritchie\nApril 14, 2025\nWhat is foreign aid? How “Official Development Assistance” is measured\nForeign aid measurement is complicated — what exactly counts as Official Development Assistance, what doesn’t, and how much is actually spent abroad?\nSimon van Teutem and Pablo Arriagada\nSeptember 29, 2025\nForeign aid from the United States saved millions of lives each year\nFor decades, these aid programs received bipartisan support and made a difference. Cutting them will cost lives.\nSimon van Teutem and Hannah Ritchie\nAugust 25, 2025\nGlobal inequality is huge — but so is the opportunity for people in high-income countries to support poor people\nPeople in high-income countries could dramatically improve lives worldwide with minimal financial commitment, yet few do.\nSimon van Teutem and Joe Hasell\nMarch 24, 2025\nHow much foreign aid is spent domestically rather than overseas?\nIn many countries, a significant share of aid is spent domestically on hosting refugees, offering student scholarships, and administrative costs.\nSimon van Teutem, Hannah Ritchie, and Pablo Arriagada\nJuly 21, 2025\nThe Demographic and Health Surveys brought crucial data for more than 90 countries — without them, we risk darkness\nCuts to US aid could end the Demographic and Health Surveys. This would leave a massive gap in our understanding of global health, mortality, and development.\nSaloni Dattani\nKey Charts on Foreign Aid\nSee all charts on this topic\nForeign aid received\nForeign aid given\nNet flows\nForeign aid given as a share of national income\nForeign aid received as a share of national income\nForeign aid received by income group\nForeign aid given\nGrant equivalents\nForeign aid given as grants and concessional loans\nForeign aid given bilaterally and multilaterally\nForeign aid given by governments and civil society organizations\nForeign aid given by sector\nForeign aid given by sector, per capita\nForeign aid given for emergencies and longer-term development\nForeign aid given for emergencies and longer-term development, per capita\nForeign aid given per capita\nNet flows\nForeign aid given per capita vs. GDP per capita\nForeign aid received as grants and concessional loans\nForeign aid received from governments and private foundations\nForeign aid received per capita\nForeign aid received per capita vs. GDP per capita\nForeign aid spent domestically within donor countries\nShare of foreign aid spent domestically within donor countries\nForeign aid and investments given\nForeign aid and investments received\nForeign aid given for social infrastructure and services\nForeign aid given for social infrastructure and services, per capita\nForeign aid given to least-developed countries as a share of donor's national income\nGlobal infant mortality rate with and without vaccines\nHIV/AIDS deaths averted by antiretroviral therapy\nNet and grant-equivalent foreign aid given\nLine chart\nNumber of one-year-olds who have received different vaccinations\nPeople receiving antiretroviral therapy through PEPFAR\nReported cases of guinea worm disease\nChart 1 of 32\nFeatured Data on\nForeign Aid\nAcknowledgments\nWe thank Elena Bernaldo de Quirós, Saloni Dattani, Harsh Desai, Edouard Mathieu, Hannah Ritchie, Jorge Rivera, Pablo Rosado, and Max Roser for their helpful suggestions and ideas for this topic page.\nEndnotes\nFor more research and data on these topics, see our pages on\nHIV / AIDS\n,\nvaccination\n,\neradication of diseases\n,\ncrop yields\n, and\nneglected tropical diseases\n.\nFor a more detailed overview of aid’s positive and negative effects, read Qian, Nancy. 2015.\nMaking Progress on Foreign Aid\n. Annual Review of Economics 7: 277-308.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this topic page, please also cite the underlying data sources. This topic page can be cited as:\nBastian Herre, Pablo Arriagada, Simon van Teutem, and Hannah Ritchie (2024) - “Foreign Aid” Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/foreign-aid' [Online Resource]\nBibTeX citation\n@article{owid-foreign-aid,\nauthor = {Bastian Herre and Pablo Arriagada and Simon van Teutem and Hannah Ritchie},\ntitle = {Foreign Aid},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://ourworldindata.org/foreign-aid}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "foreign-aid", "source_url": "https://ourworldindata.org/foreign-aid", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Who gives and receives foreign aid? Which forms does it take? What are examples of when it was (un-)successful?", "numeric_mentions": ["1", "2", "1950", "60", "1970", "10,", "2025", "1%", "14,", "29,", "25,", "24,", "21,", "90", "32", "2015", "7", "277", "308", "2024"], "numeric_evidence": [{"title": "Foreign aid given by governments and civil society organizations", "source_url": "https://ourworldindata.org/grapher/government-vs-private-aid-by-donor.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Aid by governments", "Aid by civil society organizations", "Aid by governments (Annotations)", "Aid by civil society organizations (Annotations)"], "row_count_total": 2449, "rows_head": [{"Entity": "Australia", "Code": "AUS", "Year": "1960", "Aid by governments": "669527600", "Aid by civil society organizations": "", "Aid by governments (Annotations)": "", "Aid by civil society organizations (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1961", "Aid by governments": "797958200", "Aid by civil society organizations": "", "Aid by governments (Annotations)": "", "Aid by civil society organizations (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1962", "Aid by governments": "824214800", "Aid by civil society organizations": "", "Aid by governments (Annotations)": "", "Aid by civil society organizations (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1963", "Aid by governments": "1039639740", "Aid by civil society organizations": "", "Aid by governments (Annotations)": "", "Aid by civil society organizations (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1964", "Aid by governments": "1054036740", "Aid by civil society organizations": "", "Aid by governments (Annotations)": "", "Aid by civil society organizations (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1965", "Aid by governments": "1217696100", "Aid by civil society organizations": "", "Aid by governments (Annotations)": "", "Aid by civil society organizations (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1966", "Aid by governments": "1312837500", "Aid by civil society organizations": "", "Aid by governments (Annotations)": "", "Aid by civil society organizations (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1967", "Aid by governments": "1545242200", "Aid by civil society organizations": "", "Aid by governments (Annotations)": "", "Aid by civil society organizations (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1968", "Aid by governments": "1484232000", "Aid by civil society organizations": "", "Aid by governments (Annotations)": "", "Aid by civil society organizations (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1969", "Aid by governments": "1650966000", "Aid by civil society organizations": "", "Aid by governments (Annotations)": "", "Aid by civil society organizations (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1970", "Aid by governments": "1787921500", "Aid by civil society organizations": "132582504", "Aid by governments (Annotations)": "", "Aid by civil society organizations (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1971", "Aid by governments": "1679199100", "Aid by civil society organizations": "145432020", "Aid by governments (Annotations)": "", "Aid by civil society organizations (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1972", "Aid by governments": "1932812800", "Aid by civil society organizations": "120122216", "Aid by governments (Annotations)": "", "Aid by civil society organizations (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1973", "Aid by governments": "1542871900", "Aid by civil society organizations": "112697176", "Aid by governments (Annotations)": "", "Aid by civil society organizations (Annotations)": ""}, {"Entity": "Australia", "Code": "AUS", "Year": "1974", "Aid by governments": 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This data is expressed in US dollars and adjusted for inflation.", "chart_note": "This data is expressed in constant 2023 US$.", "chart_citation": "OECD (2025)", "original_chart_url": "https://ourworldindata.org/grapher/government-vs-private-aid-by-donor", "owid_column_metadata": {"Official development assistance (ODA) by donor - Net disbursements": {"titleShort": "Aid by governments", "titleLong": "Aid by governments", "descriptionShort": "Official development assistance (ODA) is defined as government aid designed to promote the economic development and welfare of developing countries. Monetary aid is estimated as net disbursements. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "The data is reported as net disbursements. This refers to aid ultimately given and is different from commitments, which is only aid that has been pledged. These are net amounts because any money coming in (like loan repayments or interest) has been subtracted from money going out (like new grants or loans).", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We have combined net disbursements aid data from the [DAC1: Flows by donor (ODA+OOF+Private) dataset](https://data-explorer.oecd.org/vis?fs[0]=Topic%2C1%7CDevelopment%23DEV%23%7COfficial%20Development%20Assistance%20%28ODA%29%23DEV_ODA%23&pg=0&fc=Topic&bp=true&snb=20&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_DAC1%40DF_DAC1&df[ag]=OECD.DCD.FSD&df[vs]=1.2&dq=DAC...1140%2B1160..Q.&lom=LASTNPERIODS&lo=10&to[TIME_PERIOD]=false) with the [DAC2A: Aid (ODA) disbursements to countries and regions dataset](https://data-explorer.oecd.org/vis?fs[0]=Topic%2C1%7CDevelopment%23DEV%23%7COfficial%20Development%20Assistance%20%28ODA%29%23DEV_ODA%23&pg=0&fc=Topic&bp=true&snb=20&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_DAC2%40DF_DAC2A&df[ag]=OECD.DCD.FSD&df[vs]=1.1&dq=.DPGC.206.USD.Q&lom=LASTNPERIODS&lo=5&to[TIME_PERIOD]=false) to add aid given by multilateral organizations and grants given by civil society organizations.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1960-2024", "type": "Numeric", "owidVariableId": 1132264, "shortName": "i_oda_net_disbursements", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Aid by governments – Net disbursements” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC1: Flows by provider (ODA+OOF+Private)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132264.metadata.json"}, "Net private grants by donor - Net disbursements": {"titleShort": "Aid by civil society organizations", "titleLong": "Aid by civil society organizations", "descriptionShort": "Grants by private voluntary agencies and non-government organizations (NGOs) are defined as transfers for development made by private voluntary agencies and NGOs in cash, goods or services for which no payment is required. Monetary aid is estimated as net disbursements. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["The private sector comprises private corporations, households and non-profit institutions serving households. Development funding from the private sector is becoming more significant. This includes civil society organizations, which play an increasing role in funding development and in finding innovative ways to promote it; non-government organizations; and the for-profit private sector.", "The data is reported as net disbursements. This refers to aid ultimately given and is different from commitments, which is only aid that has been pledged. These are net amounts because any money coming in (like loan repayments or interest) has been subtracted from money going out (like new grants or loans).", "Not all countries report aid by civil society organizations, so the total does not reflect all giving by them.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1970-2024", "type": "Numeric", "owidVariableId": 1132295, "shortName": "v_net_private_grants_net_disbursements", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with minor processing by Our World in Data", "citationLong": "OECD (2025) – with minor processing by Our World in Data. “Aid by civil society organizations – Net disbursements” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC1: Flows by provider (ODA+OOF+Private)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132295.metadata.json"}, "1132264-annotations": {"titleShort": "1132264-annotations", "titleLong": "1132264-annotations", "type": "SeriesAnnotation", "citationShort": " – processed by Our World in Data", "citationLong": " – processed by Our World in Data. “1132264-annotations” [dataset].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/undefined.metadata.json"}, "1132295-annotations": {"titleShort": "1132295-annotations", "titleLong": "1132295-annotations", "type": "SeriesAnnotation", "citationShort": " – processed by Our World in Data", "citationLong": " – processed by Our World in Data. “1132295-annotations” [dataset].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/undefined.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Foreign aid received by income group", "source_url": "https://ourworldindata.org/grapher/foreign-aid-received-by-income-group.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Foreign aid received"], "row_count_total": 13799, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1960", "Foreign aid received": "136674850"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Foreign aid received": "268410060"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Foreign aid received": "129123580"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Foreign aid received": "276782660"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Foreign aid received": "339513950"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Foreign aid received": "378861800"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Foreign aid received": "342813150"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Foreign aid received": "276872930"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Foreign aid received": "190177950"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1969", "Foreign aid received": "172307900"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1970", "Foreign aid received": "185960620"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1971", "Foreign aid received": "276397250"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1972", "Foreign aid received": "315569860"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Foreign aid received": "288308700"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Foreign aid received": "225383840"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Foreign aid received": "274014700"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Foreign aid received": "279548000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Foreign aid received": "297755700"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Foreign aid received": "235573860"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Foreign aid received": "271708830"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Foreign aid received": "73027450"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Foreign aid received": "57485176"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Foreign aid received": "26716752"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Foreign aid received": "39137644"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Foreign aid received": "18835416"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Foreign aid received": "45849910"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Foreign aid received": "4740680"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Foreign aid received": "99161790"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Foreign aid received": "143264830"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Foreign aid received": "279902400"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Foreign aid received": "219764270"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Foreign aid received": "816740500"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Foreign aid received": "335388800"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Foreign aid received": "368071970"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Foreign aid received": "365740640"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Foreign aid received": "293867700"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Foreign aid received": "238301820"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Foreign aid received": "362572830"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Foreign aid received": "259684780"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Foreign aid received": "232112480"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Foreign aid received": "234932640"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Foreign aid received": "748375800"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Foreign aid received": "1999898400"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Foreign aid received": "2181915400"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Foreign aid received": "2987615000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Foreign aid received": "3758884400"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Foreign aid received": "3739759600"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Foreign aid received": "5876944000"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Foreign aid 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{"Entity": "Africa (OECD)", "Code": "", "Year": "2006", "Foreign aid received": "52646715000"}, {"Entity": "Africa (OECD)", "Code": "", "Year": "2007", "Foreign aid received": "44210180000"}, {"Entity": "Africa (OECD)", "Code": "", "Year": "2008", "Foreign aid received": "47992860000"}, {"Entity": "Africa (OECD)", "Code": "", "Year": "2009", "Foreign aid received": "52996215000"}, {"Entity": "Africa (OECD)", "Code": "", "Year": "2010", "Foreign aid received": "46270480000"}, {"Entity": "Africa (OECD)", "Code": "", "Year": "2011", "Foreign aid received": "53860917000"}, {"Entity": "Africa (OECD)", "Code": "", "Year": "2012", "Foreign aid received": "55352627000"}, {"Entity": "Africa (OECD)", "Code": "", "Year": "2013", "Foreign aid received": "59633357000"}, {"Entity": "Africa (OECD)", "Code": "", "Year": "2014", "Foreign aid received": "56707890000"}], "rows_tail": [{"Entity": "Zambia", "Code": "ZMB", "Year": "1968", "Foreign aid received": "285176320"}, {"Entity": "Zambia", "Code": 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"Year": "1980", "Foreign aid received": "792015600"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1981", "Foreign aid received": "601543300"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1982", "Foreign aid received": "844928260"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1983", "Foreign aid received": "583058600"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1984", "Foreign aid received": "684931400"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1985", "Foreign aid received": "883363800"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1986", "Foreign aid received": "1007418300"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1987", "Foreign aid received": "827795260"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1988", "Foreign aid received": "816154700"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1989", "Foreign aid received": "638310000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1990", "Foreign aid received": "754414660"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Foreign aid received": "1307458400"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Foreign aid received": "1485278800"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Foreign aid received": "1318296600"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Foreign aid received": "1020128000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Foreign aid received": "2615052300"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Foreign aid received": "830138000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Foreign aid received": "901771600"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Foreign aid received": "525356860"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Foreign aid received": "914878300"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Foreign aid received": "1275250200"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Foreign aid received": "916213100"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Foreign aid received": "1256797000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Foreign aid received": "1068768600"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Foreign aid received": "1387769600"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Foreign aid received": "1386224100"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Foreign aid received": "1805025800"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Foreign aid received": "1120681600"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Foreign aid received": "1197033100"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Foreign aid received": "1413573000"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Foreign aid received": "993232640"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Foreign aid received": "1101177900"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Foreign aid received": "1047845950"}, {"Entity": "Zambia", "Code": "ZMB", "Year": 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This data is expressed in constant 2023 US$.", "chart_citation": "OECD (2025)", "original_chart_url": "https://ourworldindata.org/grapher/foreign-aid-received-by-income-group", "owid_column_metadata": {"Official development assistance (ODA) by recipient (Official donors)": {"titleShort": "Foreign aid received", "titleLong": "Foreign aid received", "descriptionShort": "Official development assistance (ODA) is defined as government aid designed to promote the economic development and welfare of developing countries. Monetary aid is estimated as net disbursements. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "The data is reported as net disbursements. This refers to aid ultimately given and is different from commitments, which is only aid that has been pledged. These are net amounts because any money coming in (like loan repayments or interest) has been subtracted from money going out (like new grants or loans).", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1960-2024", "type": "Numeric", "owidVariableId": 1132358, "shortName": "oda_recipient__donor_official_donors", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Foreign aid received – Official donors” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC2A: Aid (ODA) disbursements to countries and regions” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132358.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. 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This data is expressed in US dollars and adjusted for inflation.", "chart_note": "This data is expressed in constant 2023 US$.", "chart_citation": "OECD (2025)", "original_chart_url": "https://ourworldindata.org/grapher/foreign-aid-given-by-sector", "owid_column_metadata": {"ODA by donor and sector (Unallocated / Unspecified)": {"titleShort": "Unspecified aid", "titleLong": "Unspecified aid", "descriptionShort": "Official development assistance given that is unallocated / unspecified. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1967-2024", "type": "Numeric", "owidVariableId": 1132412, "shortName": "oda_by_sector__sector_unallocated__unspecified", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Unspecified aid” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC5: Aid (ODA) by sector and provider” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132412.metadata.json"}, "ODA by donor and sector (Humanitarian aid)": {"titleShort": "Humanitarian aid", "titleLong": "Humanitarian aid", "descriptionShort": "Official development assistance given that is humanitarian aid. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Humanitarian aid is assistance designed to save lives, alleviate suffering and maintain and protect human dignity during and in the aftermath of emergencies. To be classified as humanitarian, aid should be consistent with the humanitarian principles of humanity, impartiality, neutrality and independence. It broadly includes aid given for emergencies, such as natural disasters and wars, reconstruction in their aftermath, but also prevention and preparation for future emergencies.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1971-2024", "type": "Numeric", "owidVariableId": 1132389, "shortName": "oda_by_sector__sector_humanitarian_aid", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Humanitarian aid” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC5: Aid (ODA) by sector and provider” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132389.metadata.json"}, "ODA by donor and sector (Action relating to debt)": {"titleShort": "Aid related to debt", "titleLong": "Aid related to debt", "descriptionShort": "Official development assistance given that is action relating to debt. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1967-2024", "type": "Numeric", "owidVariableId": 1132405, "shortName": "oda_by_sector__sector_action_relating_to_debt", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Aid related to debt” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC5: Aid (ODA) by sector and provider” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132405.metadata.json"}, "ODA by donor and sector (Commodity aid / General programme assistance)": {"titleShort": "Commodity aid", "titleLong": "Commodity aid", "descriptionShort": "Official development assistance given that is commodity aid / general programme assistance. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Commodity aid / General programme assistance aid includes general budget support, development food assistance and other commodity assistance.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1967-2024", "type": "Numeric", "owidVariableId": 1132385, "shortName": "oda_by_sector__sector_commodity_aid__general_programme_assistance", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Commodity aid” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC5: Aid (ODA) by sector and provider” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132385.metadata.json"}, "ODA by donor and sector (Multi-sector / Cross-cutting)": {"titleShort": "Multi-sector aid", "titleLong": "Multi-sector aid", "descriptionShort": "Official development assistance given that is multi-sector / cross-cutting. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Multi-sector / Cross-cutting aid includes aid that is not sector-specific, such as aid for general environmental protection not allocable by sector.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1968-2024", "type": "Numeric", "owidVariableId": 1132378, "shortName": "oda_by_sector__sector_multi_sector__cross_cutting", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Multi-sector aid” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC5: Aid (ODA) by sector and provider” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132378.metadata.json"}, "ODA by donor and sector (Production sectors)": {"titleShort": "Aid for production sectors", "titleLong": "Aid for production sectors", "descriptionShort": "Official development assistance given that is production sectors. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Production sectors aid groups contributions to all directly productive sectors. Includes agriculture, forestry, fishing, industry, mineral resources and mining, construction, trade and tourism.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1967-2024", "type": "Numeric", "owidVariableId": 1132391, "shortName": "oda_by_sector__sector_production_sectors", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Aid for production sectors” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC5: Aid (ODA) by sector and provider” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132391.metadata.json"}, "ODA by donor and sector (Economic infrastructure and services)": {"titleShort": "Aid for economic infrastructure and services", "titleLong": "Aid for economic infrastructure and services", "descriptionShort": "Official development assistance given that is economic infrastructure and services. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Economic infrastructure and services aid groups assistance for networks, utilities and services that facilitate economic activity. Includes transport and storage, communications, energy, banking and financial services, and business and other services.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1967-2024", "type": "Numeric", "owidVariableId": 1132393, "shortName": "oda_by_sector__sector_economic_infrastructure_and_services", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Aid for economic infrastructure and services” [dataset]. OECD, “OECD Official Development Assistance (ODA) - DAC5: Aid (ODA) by sector and provider” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1132393.metadata.json"}, "ODA by donor and sector (Social infrastructure and services)": {"titleShort": "Aid for social infrastructure and services", "titleLong": "Aid for social infrastructure and services", "descriptionShort": "Official development assistance given that is social infrastructure and services. Monetary aid is estimated using commitment or gross disbursement data. This data is expressed in US dollars. It is adjusted for inflation.", "descriptionKey": ["Official development assistance (ODA) is aid given to countries and territories on the OECD Development Assistance Committee (DAC) list of recipients and multilateral development institutions. To qualify as ODA, the aid has to serve the economic development and welfare of recipient countries and be either a grant or a loan with favorable terms.", "DAC country recipients are all low- and middle-income countries as defined by the World Bank, or least-developed countries as defined by the United Nations. All recipients are listed on [the OECD website](https://www.oecd.org/en/topics/sub-issues/oda-eligibility-and-conditions/dac-list-of-oda-recipients.html).", "Most ODA is provided by [DAC members](https://www.oecd.org/en/about/committees/development-assistance-committee.html), which are major aid donors in the OECD plus the European Union. However, some non-DAC countries, such as Turkey or Saudi Arabia, also give aid that follows ODA guidelines.", "ODA does not include military aid, except for the cost of using armed forces to deliver humanitarian aid. It also excludes spending on peacekeeping unless it is closely related to development.", "Social infrastructure and services aid relates essentially to efforts to develop the human resource potential of developing countries. Includes education, health, population and reproductive health policies, water supply and sanitation, government and sanitation and other social services.", "Aid by sector is presented as commitment or gross disbursement data. Commitments refer to money pledged, and may be different from how much and how it is ultimately given/received. Gross disbursements measure total new flows given/received before repayments are deducted. Net disbursements are not used because there is no evidence that the source of financing to reimburse loans in a sector is actually the sector itself.", "The OECD adjusts this data for inflation and expresses it in constant 2023 US$. It makes this adjustment using annual average market exchange rates and GDP deflators for each donor country. For more information on the methodology, visit [the OECD's documentation](https://www.oecd.org/content/oecd/en/data/insights/data-explainers/2024/10/resources-for-reporting-development-finance-statistics.html) on DAC deflators."], "descriptionProcessing": "We calculated aggregates for the [World Bank income groups](https://ourworldindata.org/world-bank-income-groups-explained) by summing the indicator across all countries in each income group.", "shortUnit": "$", "unit": "constant 2023 US$", "timespan": "1967-2024", "type": "Numeric", "owidVariableId": 1132381, "shortName": "oda_by_sector__sector_social_infrastructure_and_services", "lastUpdated": "2025-12-23", "nextUpdate": "2026-12-23", "citationShort": "OECD (2025) – with major processing by Our World in Data", "citationLong": "OECD (2025) – with major processing by Our World in Data. “Aid for social infrastructure and services” [dataset]. 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{"raw_link": "https://ourworldindata.org/will-climate-change-affect-crop-yields-future", "title": "How will climate change affect crop yields in the future?", "context": "Home\nClimate Change\nHow will climate change affect crop yields in the future?\nMaize yields could see significant declines, but wheat could increase. Impacts across the world will be very different.\nBy\nHannah Ritchie\nOctober 14, 2024\nBrowse past versions\nCite this article\nReuse our work freely\nHow much will climate change affect food production? Will it hurt or benefit crop yields? Can we feed 8, 9, or 10 billion people in a warmer world?\nThese are crucial questions that I’m trying to tackle in a three-part series on climate change and agriculture.\nIn my first article, I discussed the different ways climate change impacts crop yields and the effect they have\nalready had\non global food production. In this installment, we’ll look at how climate change could affect crop yields in the future.\nAs a quick reminder, there are three ways that CO\n2\nemissions and climate change can affect agriculture.\nFirst, plants can benefit from\nhigher CO\n2\nlevels\nin the atmosphere; this is called “carbon fertilization”. Wheat and rice — so-called “C3” crops — can significantly benefit from more CO\n2\n. Maize, millet, and sorghum — “C4” crops — benefit very little, except under drought conditions.\nSecond, crops are affected by\nhigher temperatures\n. This can increase or decrease yields depending on the type of crop and where in the world it is grown. For farmers in temperate climates, where temperatures are\nlower\nthan the “optimal” for that crop, moderate climate change can potentially increase average yields. For farmers in the tropics or subtropics where temperatures are already at or past the “optimal”, higher temperatures will directly reduce yields.\nFinally, crops are affected by\nwater availability\n. Yields decline significantly under water stress and the opposite — flood and waterlogging — so crop productivity will decrease if climate change increases the frequency or intensity of these events.\nThe ultimate impact on crop yields combines all of the above. They can either offset or boost one another. Considering just one could lead to the wrong conclusions. That’s why we get oversimplified and opposing headlines, such as “More CO\n2\nand climate change is good for agriculture” or “Higher temperatures will cause crop yields to collapse worldwide.”\nThe reality is more complex. Some crops in some places could benefit. Elsewhere, crop yields are at risk of a severe decline. Extreme events pose additional risks that could destabilize food systems in the future.\nHow will\nglobal\nyields be affected by climate change?\nThe impact of climate change on yields will depend on several factors: the type of crop, how much warmer the world gets (which will depend on how quickly we reduce our\ncarbon emissions\n), where in the world you are, and what we do to adapt.\nBefore considering what adaptation is necessary, we must understand what to expect in a world in which we don't adapt. For this reason, I'm focusing on that in this article, and in the third and final article of the series, I’ll then consider how to adapt.\nLet’s start by focusing on the first two factors: the sensitivity of different crops at the global level under a range of warming scenarios.\nFor\nmaize\n, the expected change is shown in the first panel of the chart: more warming means lower yields. Jonas Jägermeyr and colleagues used the latest modeling techniques to look at the impact of climate change on yields under a range of climate scenarios.\n1\nIn the lowest warming scenario — so-called “RCP2.6”, where we keep global warming levels below 2°C compared to preindustrial levels — global yields decline by about 6%. In the most extreme case — “RCP8.5”, a pessimistic high-end scenario that leads to 3°C to 5°C of warming — they decline by about 24%. This worst-case scenario provides an upper limit on the potential magnitude of isolated climate impacts without any efforts to adapt to these new conditions.\n2\nOther studies on maize find similar results.\n3\nSince maize benefits little from carbon fertilization and maize is usually grown in warmer regions, global warming directly reduces global maize productivity. Even Europe, where temperatures are cooler, could see a decrease of up to 20%.\n4\nDownload\nThe opposite is true for\nwheat\n. Global yields are expected to increase.\n5\nThe impact of carbon fertilization makes a crucial difference.\nAt 2°C of warming, one study estimated that wheat yields would decline by 6.6% without carbon fertilization. Once that was included, they projected a 1.7%\nincrease\n.\n6\nWinter wheat yields in Europe could decline by 9% by 2050 without CO\n2\neffects, but with them, this changes to a 4% increase.\n4\nIn the most extreme warming scenario — RCP8.5 — wheat yields are projected to increase by 18%.\n1\nThe climate impacts on\nrice\nand\nsoybeans\nare smaller. Higher temperatures will tend to have a negative impact on yields. But this is largely offset by gains from carbon fertilization. Uncertainties are large at the global level, especially for soybeans and rice, without clear negative or positive climate impacts. Regionally, the models show higher agreement and more robust results.\nThis result is mirrored in other studies.\n7\nOne large meta-analysis concludes that rice yields in China, India, Bangladesh, and Indonesia would see small yield increases, ranging from 0% to 10% in the most optimistic and extreme scenario.\n5\nChanges in crop yields will vary a lot across the world\nThe previous section discussed what we can expect on the global level. What does it look like on the local level?\nAs a general rule, high-latitude or temperate countries will likely see less severe adverse climate impacts — and potentially even increases in yields, despite additional extreme weather events — while farmers in the tropics and subtropics face the largest yield declines, while also having a lower capacity to adapt.\nThere are several reasons for this.\nFirst, in the warmer tropics, many crops are already close to their “optimal” growing temperature. Further warming will push them well past it.\nSecond, crops that benefit very little from carbon fertilization — maize, millet, and sorghum — are much more common in warmer climates. So, they’ll see a decline in yields due to increased temperatures without the benefits of carbon fertilization to offset it.\nIn the figure below, I have visualized the projected changes in crop yields for several crops across different latitudes. These results come from the work of Jonas Jägermeyr and colleagues, published in\nNature Food\n.\n1\nThe vertical line for each crop extends from 90° North at the top down to 60°S South of the equator. The horizontal axis shows us the projected change in yield due to climate change. On the right — and in blue — yields increase. On the left — shown in red — they decline.\nDownload\nOf course, where crops are grown matters. An increase or decrease in rice yields in Northern Europe makes no difference since almost no rice is grown there. So, for each crop, I’ve also shown a map of where these crops are grown\ntoday\n.\n8\nThis is lined up with the yield chart, so you can see where in the world yields might benefit or be vulnerable.\nWhat we see is that most crops see yield benefits at higher latitudes.\nWe also see that crop yields are expected to decline around the equator and the tropics. But this zone is much larger for maize, stretching from around 60°N to 30°S of the equator. That’s further north than the UK, which means that the only regions where maize yields wouldn’t decline are Scandinavia, Russia, and Canada, regions in which very little maize is currently grown.\nMost of the world’s biggest maize-growing regions, the United States, China, South America, and Sub-Saharan Africa, could see significant declines, reaching 20% to 25% in some of the most extreme scenarios.\nProjections for other crops are less bleak. Wheat production in Europe could see a yield increase. Production in northern India could, too, but there would be declines in southern states.\nImpacts on soybean and rice yields will probably be smaller. Most of these crops are grown in regions where there are not huge increases or declines. Soybean production in the United States might benefit, but less so in South America.\nA range of other studies find similar results. A major review by Ehsan Eyshi Rezaei and colleagues looked at projected crop yields under low and high warming scenarios.\n5\nEven in low-warming scenarios, the impacts on maize yields were negative everywhere, from France to the United States, and China to Brazil.\nWheat yields were expected to benefit almost everywhere. Rice yields were expected to change very little. Millet and sorghum yields in India and West Africa, where these crops are widely grown, were negatively impacted.\nIn the footnote, I list other studies — they all come to similar conclusions.\n9\nUnequal impacts could lead to more price volatility and food insecurity\nMost of the studies so far have focused on the average impacts on crop yields based on changes in temperature, and carbon fertilization. One aspect that could introduce more volatility in production (and food prices) is more extreme changes in water patterns. As we saw earlier, flooding or high drought stress can have a large impact on yields.\nA large study that looks at the potential increase in waterlogging in future climate scenarios finds that yield penalties under high climate scenarios could increase from 3% to 11% in the past, to 10–20% by 2080.\n10\nThe authors highlight that these impacts can be offset by changing crop practices (which I’ll cover in my next article) but without adaptation, more intense rainfall could make food markets more volatile.\nAnother key point is that even if global yields of wheat increase, and rice and soybean are positive, the negative expectations for maize, millet, and sorghum are very concerning.\nThese are staple crops for many of the poorest and most food-insecure countries in the world. The injustice of climate change is that it will be those who are already the worst off who will be hit hardest.\nThe charts below show per capita supplies of maize and millet per person each year. This is the amount of these crops that are available for consumption at the end of the supply chain. Maize makes up a huge amount of diets in South America and Southern Africa. Millet is heavily concentrated in Sub-Saharan Africa. You can see that this\nis the same\nfor sorghum.\nThis is also where\nhunger rates are highest\n, crop yields\nare already lowest\n, most of the population\nworks in agriculture\n, and\nearns very little\n. Yield declines not only threaten food security but could also push farmers deeper into poverty as they get worse and more volatile harvests.\nThis is the brutal reality of climate change — which I’ve\nwritten about before\nin the context of temperature-related deaths. Richer countries that have contributed most to carbon emissions could see yields\nincrease\nbecause wheat happens to benefit from climate change. Those who are suffering from hunger today are expected to see their yield decline.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nWhether crop yields increase or decrease in the future will not\nonly\ndepend on climate change\nNow that we see what we can expect from the world’s food production, we can ask what we can do to counter this injustice. Two points are important: climate change is not the\nonly\nfactor that matters for the level of agricultural production in the future and even based on current knowledge we know that the scope for improvements thanks to all those other factors is large.\nIn my previous article on the\nhistorical\nimpacts of climate change on yields, I pointed out that reporting a “5% decline” is often not what people imagine it to be. They might assume that this means yields in 10, 20, or 50 years will be 5% lower than they are today.\nBut that’s not the case. I used the example of changes in yields from 1961 through to today. Studies suggest that yields of key crops have “declined” by around 5% due to climate change. Yet global yields have increased by more than 200% over this period.\nWhat this “5% decline” actually means is that yields are 5% lower than they would have been in a world without climate change. Yields\nhave\nincreased but would have increased even more without warming.\nThe same is true when we think about changes in the future. Climate change will make crop production in some regions much more vulnerable. Holding everything else constant, yields\nwould\ndecline. But there are other things we can do to mitigate this risk and counteract some of these pressures.\nThere are still huge yield gaps across the world today. “Yield gaps” are the difference between the yields that farmers currently get and\ncould\nget if they had access to the best seeds, fertilizers, pesticides, irrigation, and practices that already exist today.\nLet’s take the example of Kenya and maize. Farmers currently grow around 1.4 tonnes per hectare. However, researchers\nestimate that\nfarmers\ncould\nget 4.2 tonnes if they had access to the best technologies and practices available today.\n11\nThat means the yield gap is 2.8 tonnes. You can see this in the chart below.\nIn some of the worst climate scenarios, Kenya could see a 20% to 25% decline in maize yields. If nothing else changed, that would cut its current yield of 1.4 tonnes to around 1.1 tonnes: a drop of 0.3 tonnes.\nHowever, the current yield gap of 2.8 tonnes is much larger than the 0.3 tonnes drop that might be expected with climate change.\nDownload\nYield gaps for maize in Kenya are much higher than the potential reduction in yield due to climate change, even in extreme scenarios\n12\nOf course, adopting the best agricultural practices and technologies\nand\nstopping climate change would increase yields even more. But it’s still possible for yields to increase over time, even if they rise at a slower rate than they would in a more stable climate. This won’t happen on its own. It will require serious investments to shrink existing yield gaps, and new ones into developing more temperature and drought-resistant crops that are more resilient.\nThat brings us to the third article in this series: how we can adapt our food systems to protect farmers from the negative impacts of climate change.\nAppendix: What about non-staple crops such as fruits, vegetables, and legumes?\nIn this article, I’ve focused on the main staple crops: cereals and soybeans. These crops make up the largest share of the world’s calories (for direct human consumption, and as animal feed for livestock).\nBut non-staple crops are also crucial for human nutrition. They provide vital micronutrients, fats, and proteins that are essential for a healthy diet.\nUnfortunately, there are far fewer studies on the impacts of climate change on the yields of fruits, vegetables, legumes, and seeds. Here is a quick summary of some of the research that is available.\nSeveral systematic reviews have attempted to summarize the existing literature on how yields of these crops could respond to increases in CO\n2\nconcentrations and temperatures.\nA review by Scheelbeek et al. (2017) found that an increase in CO\n2\nconcentrations of 250 parts per million (ppm) — a bit less than a doubling from pre-industrial levels — increased yields by around 22%, on average.\n13\nThis ranged from a 12% to 33% increase, with larger gains for legumes than leafy vegetables.\nMost studies on warming assumed a very high increase of 4°C, which is far more than we’d expect on our current trajectory (which is 2.5°C to 3°C). This temperature increase resulted in a\nmean\nyield decline of 5%. However, there was huge heterogeneity across crop types and locations — extending from a yield decline of 48% to a yield increase of 38%. Regions with current average temperatures below 20°C saw yields increase by around 35%. Those with baseline temperatures above 20°C saw a decline of 32%.\nThese warming effects would be somewhat moderated by increased yields from carbon fertilization. So vegetable crops in warmer regions might see a small-to-moderate decline in yields in higher warming scenarios. Those in temperate regions might see an increase since warming and CO\n2\nboth increase yields in these regions.\nA later review by Alae-Carew et al. (2020) focused on fruit, nuts, and seeds.\n14\nIt found similar results: that CO\n2\nfertilization benefited yields, but was offset by yield declines due to increased temperatures, particularly in warmer regions.\nBoth reviews highlight again, the very different responses of crops across different parts of the world. Even if changes in yield at a\nglobal\nlevel are relatively small, the distribution of food production could change significantly with clear winners and losers. This could have significant impacts on farmers, economies that rely on agriculture for exports, and food prices.\nShow more\nAcknowledgments\nMany thanks to Max Roser and Edouard Mathieu for their comments on this article, and to Jonas Jägermeyr for invaluable suggestions and feedback on this series of work.\nThis article is the second in our series on climate change and agriculture:\nCrop yields have increased dramatically in recent decades, but crops like maize would have improved more without climate change\nClimate change has slowed the productivity of key crops such as maize and soybeans, but might have had small positive impacts on wheat.\nHow will climate change affect crop yields in the future?\nMaize yields could see significant declines, but wheat could increase. Impacts across the world will be very different.\nClimate change will affect food production, but here are the things we can do to adapt\nAdapting planting dates, selecting better crop varieties, and increasing access to irrigation and fertilizers could offset potential declines in crop yields.\nEndnotes\nJägermeyr, J., Müller, C., Ruane, A. C., Elliott, J., Balkovic, J., Castillo, O., ... & Rosenzweig, C. (2021). Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nature Food.\nIdeally, I would rather focus on more realistic climate scenarios — such as RCP4.5 — which will have more severe impacts than RCP2.6 but much less so than RCP8.5. Unfortunately, many studies do not model these middle-of-the-road scenarios.\nMinoli, S., Jägermeyr, J., Asseng, S., Urfels, A., & Müller, C. (2022). Global crop yields can be lifted by timely adaptation of growing periods to climate change. Nature Communications.\nWebber, H., Ewert, F., Olesen, J. E., Müller, C., Fronzek, S., Ruane, A. C., ... & Wallach, D. (2018). Diverging importance of drought stress for maize and winter wheat in Europe. Nature communications.\nRezaei, E. E., Webber, H., Asseng, S., Boote, K., Durand, J. L., Ewert, F., ... & MacCarthy, D. S. (2023). Climate change impacts on crop yields. Nature Reviews Earth & Environment.\nZhang, T., van der Wiel, K., Wei, T., Screen, J., Yue, X., Zheng, B., ... & Yang, X. (2022). Increased wheat price spikes and larger economic inequality with 2 C global warming. One Earth.\nHosokawa, N., Doi, Y., Kim, W., & Iizumi, T. (2023). Contrasting area and yield responses to extreme climate contributes to climate-resilient rice production in Asia. Scientific Reports.\nTo understand the importance of specific crops to each country, I’ve shown production in\nper capita\nterms. If you want to explore the totals (not adjusted for population), you can do so in our\nGlobal Food Explorer\nfor all crops.\nMinoli, S., Jägermeyr, J., Asseng, S., Urfels, A., & Müller, C. (2022). Global crop yields can be lifted by timely adaptation of growing periods to climate change. Nature Communications.\nAgnolucci, P., Rapti, C., Alexander, P., De Lipsis, V., Holland, R. A., Eigenbrod, F., & Ekins, P. (2020). Impacts of rising temperatures and farm management practices on global yields of 18 crops. Nature Food.\nWebber, H., Ewert, F., Olesen, J. E., Müller, C., Fronzek, S., Ruane, A. C., ... & Wallach, D. (2018). Diverging importance of drought stress for maize and winter wheat in Europe. Nature Communications.\nPutelat, T., Whitmore, A. P., Senapati, N., & Semenov, M. A. (2021). Local impacts of climate change on winter wheat in Great Britain. Royal Society Open Science.\nYu, Y., Clark, J. S., Tian, Q., & Yan, F. (2022). Rice yield response to climate and price policy in high-latitude regions of China. Food Security.\nElsadek, E. A., Zhang, K., Hamoud, Y. A., Mousa, A., Awad, A., Abdallah, M., ... & Elbeltagi, A. (2024). Impacts of climate change on rice yields in the Nile River Delta of Egypt: a large-scale projection analysis based on CMIP6. Agricultural Water Management.\nhttps://svs.gsfc.nasa.gov/4914/\n.\nLiu, K., Harrison, M.T., Yan, H. et al. (2023). Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates. Nature Communications.\nThis is based on research on “attainable yields” published by Nathaniel Meuller and colleagues. Mueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., & Foley, J. A. (2012). Closing yield gaps through nutrient and water management. Nature.\nYield data for 2022 comes from the Food and Agriculture Organization of the United Nations. Attainable yields come from Mueller et al. (2012). Potential yield reductions due to climate change is based on a 25% reduction compared to yields today, which is possible in some of the most extreme climate scenarios (RCP8.5). This extreme scenario is unlikely but gives a sense of the upper limit of these reductions.\nMueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., & Foley, J. A. (2012). Closing yield gaps through nutrient and water management. Nature.\nScheelbeek, P. F., Bird, F. A., Tuomisto, H. L., Green, R., Harris, F. B., Joy, E. J., ... & Dangour, A. D. (2018). Effect of environmental changes on vegetable and legume yields and nutritional quality. Proceedings of the National Academy of Sciences.\nAlae-Carew, C., Nicoleau, S., Bird, F. A., Hawkins, P., Tuomisto, H. L., Haines, A., ... & Scheelbeek, P. F. (2020). The impact of environmental changes on the yield and nutritional quality of fruits, nuts and seeds: a systematic review. Environmental Research Letters.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2024) - “How will climate change affect crop yields in the future?” Published online at OurWorldinData.org. 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We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "will-climate-change-affect-crop-yields-future", "source_url": "https://ourworldindata.org/will-climate-change-affect-crop-yields-future", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Maize yields could see significant declines, but wheat could increase. 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Value (percent)": "15.5"}, {"Entity": "Africa (FAO)", "Code": "", "Year": "2015", "2.1.1 prevalence of undernourishment - Value (percent)": "15.9"}, {"Entity": "Africa (FAO)", "Code": "", "Year": "2016", "2.1.1 prevalence of undernourishment - Value (percent)": "16.8"}, {"Entity": "Africa (FAO)", "Code": "", "Year": "2017", "2.1.1 prevalence of undernourishment - Value (percent)": "16.4"}, {"Entity": "Africa (FAO)", "Code": "", "Year": "2018", "2.1.1 prevalence of undernourishment - Value (percent)": "16.6"}, {"Entity": "Africa (FAO)", "Code": "", "Year": "2019", "2.1.1 prevalence of undernourishment - Value (percent)": "17.4"}, {"Entity": "Africa (FAO)", "Code": "", "Year": "2020", "2.1.1 prevalence of undernourishment - Value (percent)": "18.5"}, {"Entity": "Africa (FAO)", "Code": "", "Year": "2021", "2.1.1 prevalence of undernourishment - Value (percent)": "18.9"}, {"Entity": "Africa (FAO)", "Code": "", "Year": "2022", "2.1.1 prevalence of undernourishment - Value (percent)": "18.9"}, {"Entity": "Africa (FAO)", "Code": "", "Year": "2023", "2.1.1 prevalence of undernourishment - Value (percent)": "20"}, {"Entity": "Africa (FAO)", "Code": "", "Year": "2024", "2.1.1 prevalence of undernourishment - Value (percent)": "20.2"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "2.1.1 prevalence of undernourishment - Value (percent)": "4.8"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "2.1.1 prevalence of undernourishment - Value (percent)": "5.9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "2.1.1 prevalence of undernourishment - Value (percent)": "7.3"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "2.1.1 prevalence of undernourishment - Value (percent)": "8.5"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "2.1.1 prevalence of undernourishment - Value (percent)": "8.8"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "2.1.1 prevalence of undernourishment - Value (percent)": "8.7"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "2.1.1 prevalence of undernourishment - Value (percent)": "8.2"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "2.1.1 prevalence of undernourishment - Value (percent)": "7.3"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "2.1.1 prevalence of undernourishment - Value (percent)": "6.1"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "2.1.1 prevalence of undernourishment - Value (percent)": "5.4"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "2.1.1 prevalence of undernourishment - Value (percent)": "4.9"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "2.1.1 prevalence of undernourishment - Value (percent)": "4.6"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "2.1.1 prevalence of undernourishment - Value (percent)": "4.6"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "2.1.1 prevalence of undernourishment - Value (percent)": "4.6"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "2.1.1 prevalence of undernourishment - Value (percent)": "4.5"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "2.1.1 prevalence of undernourishment - Value (percent)": "4.4"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "2.1.1 prevalence of undernourishment - Value (percent)": "4.3"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "2.1.1 prevalence of undernourishment - Value (percent)": "4.3"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "2.1.1 prevalence of undernourishment - Value (percent)": "4.3"}, {"Entity": "Albania", "Code": "ALB", "Year": "2020", "2.1.1 prevalence of undernourishment - Value (percent)": "5.4"}, {"Entity": "Albania", "Code": "ALB", "Year": "2021", "2.1.1 prevalence of undernourishment - Value (percent)": "5.8"}, {"Entity": "Albania", "Code": "ALB", "Year": "2022", "2.1.1 prevalence of undernourishment - Value (percent)": "6.2"}, {"Entity": "Albania", "Code": "ALB", "Year": "2023", "2.1.1 prevalence of undernourishment - Value (percent)": "5.4"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2001", "2.1.1 prevalence of undernourishment - Value (percent)": "7.3"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2002", "2.1.1 prevalence of undernourishment - Value (percent)": "6.7"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2003", "2.1.1 prevalence of undernourishment - Value (percent)": "6.4"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2004", "2.1.1 prevalence of undernourishment - Value (percent)": "6.4"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2005", "2.1.1 prevalence of undernourishment - Value (percent)": "6.1"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2006", "2.1.1 prevalence of undernourishment - Value (percent)": "5.8"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2007", "2.1.1 prevalence of undernourishment - Value (percent)": "5.3"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2008", "2.1.1 prevalence of undernourishment - Value (percent)": "5"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2009", "2.1.1 prevalence of undernourishment - Value (percent)": "4.7"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2010", "2.1.1 prevalence of undernourishment - Value (percent)": "4.1"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2011", "2.1.1 prevalence of undernourishment - Value (percent)": "3.7"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2012", "2.1.1 prevalence of undernourishment - Value (percent)": "3.1"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2013", "2.1.1 prevalence of undernourishment - Value (percent)": "2.9"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2014", "2.1.1 prevalence of undernourishment - Value (percent)": "2.7"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2015", "2.1.1 prevalence of undernourishment - Value (percent)": "2.6"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2016", "2.1.1 prevalence of undernourishment - Value (percent)": "2.6"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2017", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2018", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2019", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2020", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2021", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2022", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Algeria", "Code": "DZA", "Year": "2023", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2000", "2.1.1 prevalence of undernourishment - Value (percent)": "6.5"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2001", "2.1.1 prevalence of undernourishment - Value (percent)": "6.3"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2002", "2.1.1 prevalence of undernourishment - Value (percent)": "6.5"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2003", "2.1.1 prevalence of undernourishment - Value (percent)": "6"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2004", "2.1.1 prevalence of undernourishment - Value (percent)": "5.8"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2005", "2.1.1 prevalence of undernourishment - Value (percent)": "5.3"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2006", "2.1.1 prevalence of undernourishment - Value (percent)": "4.9"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2007", "2.1.1 prevalence of undernourishment - Value (percent)": "4.5"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2008", "2.1.1 prevalence of undernourishment - Value (percent)": "4.2"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2009", "2.1.1 prevalence of undernourishment - Value (percent)": "4"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2010", "2.1.1 prevalence of undernourishment - Value (percent)": "3.7"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2011", "2.1.1 prevalence of undernourishment - Value (percent)": "3.6"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2012", "2.1.1 prevalence of undernourishment - Value (percent)": "3.4"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2013", "2.1.1 prevalence of undernourishment - Value (percent)": "3.3"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2014", "2.1.1 prevalence of undernourishment - Value (percent)": "3.1"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2015", "2.1.1 prevalence of undernourishment - Value (percent)": "3.2"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2016", "2.1.1 prevalence of undernourishment - Value (percent)": "3.7"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2017", "2.1.1 prevalence of undernourishment - Value (percent)": "3.5"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2018", "2.1.1 prevalence of undernourishment - Value (percent)": "3.6"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2019", "2.1.1 prevalence of undernourishment - Value (percent)": "3.4"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2020", "2.1.1 prevalence of undernourishment - Value (percent)": "3.9"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2021", "2.1.1 prevalence of undernourishment - Value (percent)": "3.7"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2022", "2.1.1 prevalence of undernourishment - Value (percent)": "3.6"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2023", "2.1.1 prevalence of undernourishment - Value (percent)": "3.4"}, {"Entity": "Americas (FAO)", "Code": "", "Year": "2024", "2.1.1 prevalence of undernourishment - Value (percent)": "3.2"}, {"Entity": "Angola", "Code": "AGO", "Year": "2001", "2.1.1 prevalence of undernourishment - Value (percent)": "67.8"}], "rows_tail": [{"Entity": "Western Asia (FAO)", "Code": "", "Year": "2001", "2.1.1 prevalence of undernourishment - Value (percent)": "13.1"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2002", "2.1.1 prevalence of undernourishment - Value (percent)": "13.1"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2003", "2.1.1 prevalence of undernourishment - Value (percent)": "13.4"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2004", "2.1.1 prevalence of undernourishment - Value (percent)": "11.7"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2005", "2.1.1 prevalence of undernourishment - Value (percent)": "10.3"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2006", "2.1.1 prevalence of undernourishment - Value (percent)": "8.7"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2007", "2.1.1 prevalence of undernourishment - Value (percent)": "7.7"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2008", "2.1.1 prevalence of undernourishment - Value (percent)": "7.3"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2009", "2.1.1 prevalence of undernourishment - Value (percent)": "6.6"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2010", "2.1.1 prevalence of undernourishment - Value (percent)": "6.1"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2011", "2.1.1 prevalence of undernourishment - Value (percent)": "7.7"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2012", "2.1.1 prevalence of undernourishment - Value (percent)": "7.6"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2013", "2.1.1 prevalence of undernourishment - Value (percent)": "7.8"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2014", "2.1.1 prevalence of undernourishment - Value (percent)": "8.3"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2015", "2.1.1 prevalence of undernourishment - Value (percent)": "9.3"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2016", "2.1.1 prevalence of undernourishment - Value (percent)": "10.5"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2017", "2.1.1 prevalence of undernourishment - Value (percent)": "10.2"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2018", "2.1.1 prevalence of undernourishment - Value (percent)": "10.6"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2019", "2.1.1 prevalence of undernourishment - Value (percent)": "10.6"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2020", "2.1.1 prevalence of undernourishment - Value (percent)": "10.9"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2021", "2.1.1 prevalence of undernourishment - Value (percent)": "11.4"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2022", "2.1.1 prevalence of undernourishment - Value (percent)": "11.9"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2023", "2.1.1 prevalence of undernourishment - Value (percent)": "12.5"}, {"Entity": "Western Asia (FAO)", "Code": "", "Year": "2024", "2.1.1 prevalence of undernourishment - Value (percent)": "12.7"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2000", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2001", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2002", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2003", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2004", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2005", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2006", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2007", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2008", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2009", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2010", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2011", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2012", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2013", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2014", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2015", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2016", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2017", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2018", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2019", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2020", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2021", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2022", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2023", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "Western Europe (FAO)", "Code": "", "Year": "2024", "2.1.1 prevalence of undernourishment - Value (percent)": "2.5"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "2.1.1 prevalence of undernourishment - Value (percent)": "12.7"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "2.1.1 prevalence of undernourishment - Value (percent)": "12.8"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "2.1.1 prevalence of undernourishment - Value (percent)": "13"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "2.1.1 prevalence of undernourishment - Value (percent)": "12.7"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "2.1.1 prevalence of undernourishment - Value (percent)": "12.6"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "2.1.1 prevalence of undernourishment - Value (percent)": "12"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "2.1.1 prevalence of undernourishment - Value (percent)": "11"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "2.1.1 prevalence of undernourishment - Value (percent)": "10"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "2.1.1 prevalence of undernourishment - Value (percent)": "9.3"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "2.1.1 prevalence of undernourishment - Value (percent)": "9.1"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "2.1.1 prevalence of undernourishment - Value (percent)": "8.7"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "2.1.1 prevalence of undernourishment - Value (percent)": "8.2"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "2.1.1 prevalence of undernourishment - Value (percent)": "8.1"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "2.1.1 prevalence of undernourishment - Value (percent)": "7.9"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "2.1.1 prevalence of undernourishment - Value (percent)": "7.5"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "2.1.1 prevalence of undernourishment - Value (percent)": "7.7"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "2.1.1 prevalence of undernourishment - Value (percent)": "7.6"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "2.1.1 prevalence of undernourishment - Value (percent)": "7.1"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "2.1.1 prevalence of undernourishment - Value (percent)": "7.3"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "2.1.1 prevalence of undernourishment - Value (percent)": "7.5"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "2.1.1 prevalence of undernourishment - Value (percent)": "8.5"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "2.1.1 prevalence of undernourishment - Value (percent)": "8.8"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "2.1.1 prevalence of undernourishment - Value (percent)": "8.7"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "2.1.1 prevalence of undernourishment - Value (percent)": "8.5"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "2.1.1 prevalence of undernourishment - Value (percent)": "8.2"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "2.1.1 prevalence of undernourishment - Value (percent)": "50.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "2.1.1 prevalence of undernourishment - Value (percent)": "50.1"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "2.1.1 prevalence of undernourishment - Value (percent)": "50.1"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "2.1.1 prevalence of undernourishment - Value (percent)": "49.5"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "2.1.1 prevalence of undernourishment - Value (percent)": "49.4"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "2.1.1 prevalence of undernourishment - Value (percent)": "48.1"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "2.1.1 prevalence of undernourishment - Value (percent)": "46.8"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "2.1.1 prevalence of undernourishment - Value (percent)": "45.6"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "2.1.1 prevalence of undernourishment - Value (percent)": "44.9"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "2.1.1 prevalence of undernourishment - Value (percent)": "43.9"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "2.1.1 prevalence of undernourishment - Value (percent)": "42.8"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "2.1.1 prevalence of undernourishment - Value (percent)": "41.1"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "2.1.1 prevalence of undernourishment - Value (percent)": "39.1"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "2.1.1 prevalence of undernourishment - Value (percent)": "37.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "2.1.1 prevalence of undernourishment - Value (percent)": "35.1"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "2.1.1 prevalence of undernourishment - Value (percent)": "33.4"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "2.1.1 prevalence of undernourishment - Value (percent)": "31.8"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "2.1.1 prevalence of undernourishment - Value (percent)": "31.5"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "2.1.1 prevalence of undernourishment - Value (percent)": "32.1"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "2.1.1 prevalence of undernourishment - Value (percent)": "33.9"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "2.1.1 prevalence of undernourishment - Value (percent)": "35.7"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "2.1.1 prevalence of undernourishment - Value (percent)": "36.2"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "2.1.1 prevalence of undernourishment - Value (percent)": "37.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "2.1.1 prevalence of undernourishment - Value (percent)": "32.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "2.1.1 prevalence of undernourishment - Value (percent)": "31.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "2.1.1 prevalence of undernourishment - Value (percent)": "31.4"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "2.1.1 prevalence of undernourishment - Value (percent)": "30.6"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "2.1.1 prevalence of undernourishment - Value (percent)": "29.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "2.1.1 prevalence of undernourishment - Value (percent)": "28.7"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "2.1.1 prevalence of undernourishment - Value (percent)": "27.9"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "2.1.1 prevalence of undernourishment - Value (percent)": "27.1"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "2.1.1 prevalence of undernourishment - Value (percent)": "28"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "2.1.1 prevalence of undernourishment - Value (percent)": "29.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "2.1.1 prevalence of undernourishment - Value (percent)": "29.9"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "2.1.1 prevalence of undernourishment - Value (percent)": "29.6"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "2.1.1 prevalence of undernourishment - Value (percent)": "31.1"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "2.1.1 prevalence of undernourishment - Value (percent)": "33.4"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "2.1.1 prevalence of undernourishment - Value (percent)": "34.3"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2016", "2.1.1 prevalence of undernourishment - Value (percent)": "32.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2017", "2.1.1 prevalence of undernourishment - Value (percent)": "30.9"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2018", "2.1.1 prevalence of undernourishment - Value (percent)": "31"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2019", "2.1.1 prevalence of undernourishment - Value (percent)": "30"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2020", "2.1.1 prevalence of undernourishment - Value (percent)": "28.2"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2021", "2.1.1 prevalence of undernourishment - Value (percent)": "24.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2022", "2.1.1 prevalence of undernourishment - Value (percent)": "21.4"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2023", "2.1.1 prevalence of undernourishment - Value (percent)": "19.7"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "prevalence-of-undernourishment", "metadata_url": "https://ourworldindata.org/grapher/prevalence-of-undernourishment.metadata.json", "chart_title": "Share of people who are undernourished", "chart_subtitle": null, "chart_note": "The FAO reports all values below 2.5% as \"<2.5%\" due to high uncertainty at very low levels of undernourishment.\n", "chart_citation": "Food and Agriculture Organization of the United Nations (2025)", "original_chart_url": "https://ourworldindata.org/grapher/prevalence-of-undernourishment", "owid_column_metadata": {"2.1.1 Prevalence of undernourishment | 000000000024000 || Value | 006121 || percent": {"titleShort": "Share of people who are undernourished", "titleLong": "Share of people who are undernourished - UN FAO", "descriptionShort": "Share of the population whose daily food intake does not provide enough energy to maintain a normal, active, and healthy life.\n", "descriptionKey": ["[Hunger](https://ourworldindata.org/hunger-and-undernourishment) has been a severe problem throughout history. For most people, growing enough food to feed their family was a daily struggle. Food shortages, malnutrition, and [famines](https://ourworldindata.org/famines) were common around the world.", "This data estimates the share of people who are undernourished — those whose daily energy (calorie) intake is too low to support a normal, active, and healthy life.", "Undernourishment is determined solely by whether a person gets enough calories. It does not account for the quality or diversity of their diet. Therefore, it is only one aspect of malnutrition, a broader term that captures other deficiencies, such as micronutrients.", "Minimum calorie needs vary by sex, age, body size, and activity level. Researchers use demographic data to account for these differences in each country's estimates.", "The data is published by the Food and Agriculture Organization of the United Nations (FAO). It is based on a statistical model that combines national food supply data, demographic projections, and, where available, household food consumption surveys. To reduce short-term variability on country-level data, the FAO sets the values for a given year to the average of the last three years.", "Many countries, especially high-income ones, are shown at 2.5% because the FAO reports values between 0% and 2.5% as \"<2.5%\", due to uncertainty at very low levels of undernourishment.", "The world has made significant progress in reducing undernourishment. However, this data shows we are still far from ending hunger: nearly 1 in 10 people globally don't get enough to eat. Hunger worsened during the COVID-19 pandemic and remains a major challenge."], "shortUnit": "%", "unit": "percent", "timespan": "2000-2024", "type": "Numeric", "owidVariableId": 1196353, "shortName": "_2_1_1_prevalence_of_undernourishment__000000000024000__value__006121__percent", "lastUpdated": "2026-02-25", "nextUpdate": "2027-02-25", "citationShort": "Food and Agriculture Organization of the United Nations (2025) – with major processing by Our World in Data", "citationLong": "Food and Agriculture Organization of the United Nations (2025) – with major processing by Our World in Data. “Share of people who are undernourished – UN FAO” [dataset]. Food and Agriculture Organization of the United Nations, “SDG Indicators” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1196353.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Cereal yields", "source_url": "https://ourworldindata.org/grapher/cereal-yield.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Cereals - Yield (tonnes per hectare)"], "row_count_total": 13424, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1961", "Cereals - Yield (tonnes per hectare)": "1.1151"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1962", "Cereals - Yield (tonnes per hectare)": "1.079"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1963", "Cereals - Yield (tonnes per hectare)": "0.9858"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1964", "Cereals - Yield (tonnes per hectare)": "1.0828001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1965", "Cereals - Yield (tonnes per hectare)": "1.0989001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1966", "Cereals - Yield (tonnes per hectare)": "1.0123"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1967", "Cereals - Yield (tonnes per hectare)": "1.2245001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1968", "Cereals - Yield (tonnes per hectare)": "1.2875"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1969", "Cereals - Yield (tonnes per hectare)": "1.3104001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1970", "Cereals - Yield (tonnes per hectare)": "1.1051"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1971", "Cereals - Yield (tonnes per hectare)": "0.97620004"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1972", "Cereals - Yield (tonnes per hectare)": "1.0069001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1973", "Cereals - Yield (tonnes per hectare)": "1.2796"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1974", "Cereals - Yield (tonnes per hectare)": "1.3019"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1975", "Cereals - Yield (tonnes per hectare)": "1.3164"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1976", "Cereals - Yield (tonnes per hectare)": "1.3624"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1977", "Cereals - Yield (tonnes per hectare)": "1.2240001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1978", "Cereals - Yield (tonnes per hectare)": "1.2919"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1979", "Cereals - Yield (tonnes per hectare)": "1.3237001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1980", "Cereals - Yield (tonnes per hectare)": "1.3490001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1981", "Cereals - Yield (tonnes per hectare)": "1.3383001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1982", "Cereals - Yield (tonnes per hectare)": "1.3302"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1983", "Cereals - Yield (tonnes per hectare)": "1.3556"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1984", "Cereals - Yield (tonnes per hectare)": "1.3341"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1985", "Cereals - Yield (tonnes per hectare)": "1.3317"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1986", "Cereals - Yield (tonnes per hectare)": "1.314"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1987", "Cereals - Yield (tonnes per hectare)": "1.3598001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1988", "Cereals - Yield (tonnes per hectare)": "1.2857"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1989", "Cereals - Yield (tonnes per hectare)": "1.2376001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1990", "Cereals - Yield (tonnes per hectare)": "1.2006"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Cereals - Yield (tonnes per hectare)": "1.1604"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Cereals - Yield (tonnes per hectare)": "1.0978001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Cereals - Yield (tonnes per hectare)": "1.1329001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Cereals - Yield (tonnes per hectare)": "1.1404"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Cereals - Yield (tonnes per hectare)": "1.2145001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1996", "Cereals - Yield (tonnes per hectare)": "1.2044001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1997", "Cereals - Yield (tonnes per hectare)": "1.3488001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1998", "Cereals - Yield (tonnes per hectare)": "1.388"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1999", "Cereals - Yield (tonnes per hectare)": "1.2857"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2000", "Cereals - Yield (tonnes per hectare)": "0.80630004"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2001", "Cereals - Yield (tonnes per hectare)": "1.0067"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2002", "Cereals - Yield (tonnes per hectare)": "1.6698002"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2003", "Cereals - Yield (tonnes per hectare)": "1.4580001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2004", "Cereals - Yield (tonnes per hectare)": "1.3348001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2005", "Cereals - Yield (tonnes per hectare)": "1.7904001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2006", "Cereals - Yield (tonnes per hectare)": "1.5517"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2007", "Cereals - Yield (tonnes per hectare)": "1.9153001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2008", "Cereals - Yield (tonnes per hectare)": "1.4554001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2009", "Cereals - Yield (tonnes per hectare)": "2.0407"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2010", "Cereals - Yield (tonnes per hectare)": "2.0111"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2011", "Cereals - Yield (tonnes per hectare)": "1.6599001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2012", "Cereals - Yield (tonnes per hectare)": "2.0942001"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2013", "Cereals - Yield (tonnes per hectare)": "2.1277"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2014", "Cereals - Yield (tonnes per hectare)": "2.0966003"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2015", "Cereals - Yield (tonnes per hectare)": "2.2064"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2016", "Cereals - Yield (tonnes per hectare)": "2.0433002"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2017", "Cereals - Yield (tonnes per hectare)": "2.0914"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2018", "Cereals - Yield (tonnes per hectare)": "2.2532"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2019", "Cereals - Yield (tonnes per hectare)": "2.1847"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2020", "Cereals - Yield (tonnes per hectare)": "2.0508"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2021", "Cereals - Yield (tonnes per hectare)": "2.0991"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2022", "Cereals - Yield (tonnes per hectare)": "2.1934"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2023", "Cereals - Yield (tonnes per hectare)": "2.3030002"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "2024", "Cereals - Yield (tonnes per hectare)": "2.3586001"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1961", "Cereals - Yield (tonnes per hectare)": "0.8102074"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1962", "Cereals - Yield (tonnes per hectare)": "0.9107355"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1963", "Cereals - 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Yield (tonnes per hectare)": "1.1616954"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1983", "Cereals - Yield (tonnes per hectare)": "1.0265635"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1984", "Cereals - Yield (tonnes per hectare)": "0.99604636"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Cereals - Yield (tonnes per hectare)": "1.1413453"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Cereals - Yield (tonnes per hectare)": "1.1560067"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Cereals - Yield (tonnes per hectare)": "1.1120484"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Cereals - Yield (tonnes per hectare)": "1.2057401"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Cereals - Yield (tonnes per hectare)": "1.2330301"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Cereals - Yield (tonnes per hectare)": "1.1807389"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Cereals - Yield (tonnes per hectare)": "1.2371025"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Cereals - Yield (tonnes per hectare)": "1.0424464"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Cereals - Yield (tonnes per hectare)": "1.1893716"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Cereals - Yield (tonnes per hectare)": "1.1947286"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Cereals - Yield (tonnes per hectare)": "1.1149794"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1996", "Cereals - Yield (tonnes per hectare)": "1.3236339"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1997", "Cereals - Yield (tonnes per hectare)": "1.2007781"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1998", "Cereals - Yield (tonnes per hectare)": "1.2277457"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1999", "Cereals - Yield (tonnes per hectare)": "1.2712792"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Cereals - Yield (tonnes per hectare)": "1.2659948"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Cereals - 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Reuse should follow the source and license information in this chart metadata."}, {"title": "Share of the labor force employed in agriculture", "source_url": "https://ourworldindata.org/grapher/share-of-the-labor-force-employed-in-agriculture.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Share of the labor force employed in agriculture"], "row_count_total": 6849, "rows_head": [{"Entity": "Afghanistan", "Code": "AFG", "Year": "1991", "Share of the labor force employed in agriculture": "63.43"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1992", "Share of the labor force employed in agriculture": "63.65"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1993", "Share of the labor force employed in agriculture": "64.41"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1994", "Share of the labor force employed in agriculture": "64.44"}, {"Entity": "Afghanistan", "Code": "AFG", "Year": "1995", "Share of the labor force employed in agriculture": "64.3"}, 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agriculture": "65.27195"}, {"Entity": "Africa Eastern and Southern", "Code": "", "Year": "2007", "Share of the labor force employed in agriculture": "64.7639"}, {"Entity": "Africa Eastern and Southern", "Code": "", "Year": "2008", "Share of the labor force employed in agriculture": "64.14392"}, {"Entity": "Africa Eastern and Southern", "Code": "", "Year": "2009", "Share of the labor force employed in agriculture": "63.86325"}, {"Entity": "Africa Eastern and Southern", "Code": "", "Year": "2010", "Share of the labor force employed in agriculture": "63.546383"}, {"Entity": "Africa Eastern and Southern", "Code": "", "Year": "2011", "Share of the labor force employed in agriculture": "62.92116"}, {"Entity": "Africa Eastern and Southern", "Code": "", "Year": "2012", "Share of the labor force employed in agriculture": "62.19353"}, {"Entity": "Africa Eastern and Southern", "Code": "", "Year": "2013", "Share of the labor force employed in agriculture": "61.855755"}, {"Entity": "Africa Eastern and Southern", "Code": "", "Year": "2014", "Share of the labor force employed in agriculture": "61.248016"}, {"Entity": "Africa Eastern and Southern", "Code": "", "Year": "2015", "Share of the labor force employed in agriculture": "60.739735"}, {"Entity": "Africa Eastern and Southern", "Code": "", "Year": "2016", "Share of the labor force employed in agriculture": "60.377964"}, {"Entity": "Africa Eastern and Southern", "Code": "", "Year": "2017", "Share of the labor force employed in agriculture": "59.9512"}, {"Entity": "Africa Eastern and Southern", "Code": "", "Year": "2018", "Share of the labor force employed in agriculture": "59.527874"}, {"Entity": "Africa Eastern and Southern", "Code": "", "Year": "2019", "Share of the labor force employed in agriculture": "59.19997"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "1991", "Share of the labor force employed in agriculture": "59.12681"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "1992", "Share of the labor force employed in agriculture": "59.030865"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "1993", "Share of the labor force employed in agriculture": "58.97454"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "1994", "Share of the labor force employed in agriculture": "58.84168"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "1995", "Share of the labor force employed in agriculture": "58.64951"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "1996", "Share of the labor force employed in agriculture": "58.358322"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "1997", "Share of the labor force employed in agriculture": "58.029167"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "1998", "Share of the labor force employed in agriculture": "57.67624"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "1999", "Share of the labor force employed in agriculture": "57.355335"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2000", "Share of the labor force employed in agriculture": "56.97303"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2001", "Share of the labor force employed in agriculture": "56.43532"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2002", "Share of the labor force employed in agriculture": "55.70441"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2003", "Share of the labor force employed in agriculture": "55.079296"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2004", "Share of the labor force employed in agriculture": "54.38082"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2005", "Share of the labor force employed in agriculture": "53.736336"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2006", "Share of the labor force employed in agriculture": "53.1014"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2007", "Share of the labor force employed in agriculture": "52.327724"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2008", "Share of the labor force employed in agriculture": "51.4563"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2009", "Share of the labor force employed in agriculture": "50.577896"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2010", "Share of the labor force employed in agriculture": "49.60382"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2011", "Share of the labor force employed in agriculture": "48.66133"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2012", "Share of the labor force employed in agriculture": "47.557747"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2013", "Share of the labor force employed in agriculture": "46.49208"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2014", "Share of the labor force employed in agriculture": "45.196457"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2015", "Share of the labor force employed in agriculture": "44.05116"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2016", "Share of the labor force employed in agriculture": "43.72226"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2017", "Share of the labor force employed in agriculture": "42.992603"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2018", "Share of the labor force employed in agriculture": "42.321785"}, {"Entity": "Africa Western and Central", "Code": "", "Year": "2019", "Share of the labor force employed in agriculture": "41.658943"}, {"Entity": "Albania", "Code": "ALB", "Year": "1991", "Share of the labor force employed in agriculture": "57.91"}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "Share of the labor force employed in agriculture": "58.11"}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "Share of the labor force employed in agriculture": "57.62"}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "Share of the labor force employed in agriculture": "57.08"}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "Share of the labor force employed in agriculture": "56.36"}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "Share of the labor force employed in agriculture": "55.94"}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "Share of the labor force employed in agriculture": "56.06"}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "Share of the labor force employed in agriculture": "55.25"}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "Share of the labor force employed in agriculture": "54.03"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Share of the labor force employed in agriculture": "52.82"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Share of the labor force employed in agriculture": "51.31"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Share of the labor force employed in agriculture": "50.16"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Share of the labor force employed in agriculture": "49.27"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Share of the labor force employed in agriculture": "48.34"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Share of the labor force employed in agriculture": "47.38"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Share of the labor force employed in agriculture": "46.3"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Share of the labor force employed in agriculture": "45.55"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Share of the labor force employed in agriculture": "44.66"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Share of the labor force employed in agriculture": "44.05"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Share of the labor force employed in agriculture": "42.08"}, {"Entity": "Albania", "Code": "ALB", "Year": "2011", "Share of the labor force employed in agriculture": "45.39"}, {"Entity": "Albania", "Code": "ALB", "Year": "2012", "Share of the labor force employed in agriculture": "46"}, {"Entity": "Albania", "Code": "ALB", "Year": "2013", "Share of the labor force employed in agriculture": "44.2"}, {"Entity": "Albania", "Code": "ALB", "Year": "2014", "Share of the labor force employed in agriculture": "42.26"}, {"Entity": "Albania", "Code": "ALB", "Year": "2015", "Share of the labor force employed in agriculture": "41.28"}, {"Entity": "Albania", "Code": "ALB", "Year": "2016", "Share of the labor force employed in agriculture": "40.04"}, {"Entity": "Albania", "Code": "ALB", "Year": "2017", "Share of the labor force employed in agriculture": "38.08"}, {"Entity": "Albania", "Code": "ALB", "Year": "2018", "Share of the labor force employed in agriculture": "37.29"}, {"Entity": "Albania", "Code": "ALB", "Year": "2019", "Share of the labor force employed in agriculture": "36.42"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1991", "Share of the labor force employed in agriculture": "24.88"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1992", "Share of the labor force employed in agriculture": "24.73"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1993", "Share of the labor force employed in agriculture": "24.56"}, {"Entity": "Algeria", "Code": "DZA", "Year": "1994", "Share of the labor force employed in agriculture": "24.36"}], "rows_tail": [{"Entity": "Vietnam", "Code": "VNM", "Year": "2016", "Share of the labor force employed in agriculture": "41.87"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2017", "Share of the labor force employed in agriculture": "40.16"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2018", "Share of the labor force employed in agriculture": "38.7"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2019", "Share of the labor force employed in agriculture": "37.22"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1991", "Share of the labor force employed in agriculture": "43.69909"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1992", "Share of the labor force employed in agriculture": "43.474453"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1993", "Share of the labor force employed in agriculture": "42.761868"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1994", "Share of the labor force employed in agriculture": "42.020775"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1995", "Share of the labor force employed in agriculture": "41.115856"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1996", "Share of the labor force employed in agriculture": "40.45169"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1997", "Share of the labor force employed in agriculture": "40.078907"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1998", "Share of the labor force employed in agriculture": "40.05212"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1999", "Share of the labor force employed in agriculture": "40.06743"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Share of the labor force employed in agriculture": "39.910355"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Share of the labor force employed in agriculture": "39.57007"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Share of the labor force employed in agriculture": "39.34763"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Share of the labor force employed in agriculture": "38.87109"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Share of the labor force employed in agriculture": "37.823814"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Share of the labor force employed in agriculture": "36.930977"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Share of the labor force employed in agriculture": "35.79818"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Share of the labor force employed in agriculture": "34.852097"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Share of the labor force employed in agriculture": "34.261242"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Share of the labor force employed in agriculture": "33.697765"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Share of the labor force employed in agriculture": "33.03143"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Share of the labor force employed in agriculture": "32.048603"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Share of the labor force employed in agriculture": "31.14652"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Share of the labor force employed in agriculture": "30.339811"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Share of the labor force employed in agriculture": "29.430202"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Share of the labor force employed in agriculture": "28.834824"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Share of the labor force employed in agriculture": "28.32059"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Share of the labor force employed in agriculture": "27.801733"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Share of the labor force employed in agriculture": "27.22024"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Share of the labor force employed in agriculture": "26.756779"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1991", "Share of the labor force employed in agriculture": "54.82"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1992", "Share of the labor force employed in agriculture": "54.69"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1993", "Share of the labor force employed in agriculture": "54.74"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1994", "Share of the labor force employed in agriculture": "54.65"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1995", "Share of the labor force employed in agriculture": "52.49"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1996", "Share of the labor force employed in agriculture": "50.31"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1997", "Share of the labor force employed in agriculture": "47.99"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1998", "Share of the labor force employed in agriculture": "45.57"}, {"Entity": "Yemen", "Code": "YEM", "Year": "1999", "Share of the labor force employed in agriculture": "43.26"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2000", "Share of the labor force employed in agriculture": "40.84"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2001", "Share of the labor force employed in agriculture": "38.42"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2002", "Share of the labor force employed in agriculture": "35.96"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2003", "Share of the labor force employed in agriculture": "33.48"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2004", "Share of the labor force employed in agriculture": "31.03"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2005", "Share of the labor force employed in agriculture": "29.9"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2006", "Share of the labor force employed in agriculture": "28.82"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2007", "Share of the labor force employed in agriculture": "27.72"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2008", "Share of the labor force employed in agriculture": "26.53"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2009", "Share of the labor force employed in agriculture": "25.3"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2010", "Share of the labor force employed in agriculture": "24.11"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2011", "Share of the labor force employed in agriculture": "25.87"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2012", "Share of the labor force employed in agriculture": "27.01"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2013", "Share of the labor force employed in agriculture": "28.02"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2014", "Share of the labor force employed in agriculture": "29.25"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2015", "Share of the labor force employed in agriculture": "29.47"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2016", "Share of the labor force employed in agriculture": "29.26"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2017", "Share of the labor force employed in agriculture": "28.9"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2018", "Share of the labor force employed in agriculture": "28.31"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2019", "Share of the labor force employed in agriculture": "27.55"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1991", "Share of the labor force employed in agriculture": "70.21"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1992", "Share of the labor force employed in agriculture": "70.21"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1993", "Share of the labor force employed in agriculture": "70.07"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1994", "Share of the labor force employed in agriculture": "70.42"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1995", "Share of the labor force employed in agriculture": "70.3"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1996", "Share of the labor force employed in agriculture": "70.05"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1997", "Share of the labor force employed in agriculture": "69.89"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1998", "Share of the labor force employed in agriculture": "69.97"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "1999", "Share of the labor force employed in agriculture": "70.42"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2000", "Share of the labor force employed in agriculture": "70.88"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2001", "Share of the labor force employed in agriculture": "71.23"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2002", "Share of the labor force employed in agriculture": "71.57"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2003", "Share of the labor force employed in agriculture": "71.78"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Share of the labor force employed in agriculture": "71.98"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Share of the labor force employed in agriculture": "72.26"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Share of the labor force employed in agriculture": "72.02"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Share of the labor force employed in agriculture": "71.73"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Share of the labor force employed in agriculture": "71.43"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Share of the labor force employed in agriculture": "68.09"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Share of the labor force employed in agriculture": "64.33"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Share of the labor force employed in agriculture": "60.43"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Share of the labor force employed in agriculture": "56.03"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Share of the labor force employed in agriculture": "54.47"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Share of the labor force employed in agriculture": "53.28"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Share of the labor force employed in agriculture": "52.27"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Share of the labor force employed in agriculture": "51.37"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Share of the labor force employed in agriculture": "50.71"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Share of the labor force employed in agriculture": "50.11"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Share of the labor force employed in agriculture": "49.64"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1991", "Share of the labor force employed in agriculture": "61.43"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1992", "Share of the labor force employed in agriculture": "61.47"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1993", "Share of the labor force employed in agriculture": "61.4"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1994", "Share of the labor force employed in agriculture": "61.08"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Share of the labor force employed in agriculture": "60.92"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Share of the labor force employed in agriculture": "60.52"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Share of the labor force employed in agriculture": "60.31"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Share of the labor force employed in agriculture": "60.08"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1999", "Share of the labor force employed in agriculture": "60.03"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2000", "Share of the labor force employed in agriculture": "60.62"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2001", "Share of the labor force employed in agriculture": "61"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2002", "Share of the labor force employed in agriculture": "61.9"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2003", "Share of the labor force employed in agriculture": "62.95"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2004", "Share of the labor force employed in agriculture": "63.67"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "Share of the labor force employed in agriculture": "64.51"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2006", "Share of the labor force employed in agriculture": "64.72"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Share of the labor force employed in agriculture": "65.55"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2008", "Share of the labor force employed in agriculture": "66.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2009", "Share of the labor force employed in agriculture": "66.07"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2010", "Share of the labor force employed in agriculture": "65.54"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2011", "Share of the labor force employed in agriculture": "65.86"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2012", "Share of the labor force employed in agriculture": "65.99"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2013", "Share of the labor force employed in agriculture": "66.77"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2014", "Share of the labor force employed in agriculture": "67.24"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Share of the labor force employed in agriculture": 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{"raw_link": "https://ourworldindata.org/2024-living-planet-index", "title": "The 2024 Living Planet Index reports a 73% average decline in wildlife populations — what’s changed since the last report?", "context": "Home\nBiodiversity\nThe 2024 Living Planet Index reports a 73% average decline in wildlife populations — what’s changed since the last report?\nA guide to understanding the Living Planet Index and what it does and doesn’t mean.\nBy\nHannah Ritchie\nand\nFiona Spooner\n10th October 2024\nBrowse past versions\nCite this article\nReuse our work freely\nThe 2024\nLiving Planet Index report\nis published today and makes for some grim reading.\n1\nThe headline is a 73% average decline in wildlife populations since 1970.\nWhile these trends are extremely worrying, the numbers presented in the Living Planet Index (LPI) report are often misunderstood or misreported. In this article, we give a short overview of what these numbers mean — and don’t mean — and what some of the data underneath the headline figure shows.\nWe’ve written about the LPI several times before, including more technical texts that explain how it should be interpreted, so we’ll link to those when we can if you want to dig deeper.\nThe headline figure from the 2024 update of the LPI is that studied wildlife populations have seen an average decline of 73% from 1970 through 2020. You can see this decline in the chart below.\nFirst, let’s clarify what this\ndoesn’t\nmean. It doesn’t tell us anything about:\nThe number of species lost\nThe number of populations or individuals that have been lost\nThe number or percentage of species or populations that are declining\nThe number of extinctions\nAny headlines claiming that we’ve “lost 73% of wildlife”, “73% of species have gone extinct” or “73% of species are declining” are incorrect.\nWhat this metric tells us is that across the 34,836 studied wildlife populations, the\naverage\ndecline was 73%. As we’ll see later, this doesn’t mean 73% of populations are in decline; in fact, around half of the studied populations were in decline, while half were either increasing or stable.\nHow the Living Planet Index (LPI) is calculated\nWe go into how the LPI is calculated in a\ndedicated article\n.\nBut as a summary: researchers collect data on the change in wildlife numbers across tens of thousands of populations. While the data is measured relative to 1970, very few populations have data stretching back this far. Many populations only have data for much shorter and sparser timelines.\nFor every population, the change in numbers over time is calculated, giving a rate of change. It can be positive, negative, or zero.\nTo get the final LPI number, researchers take the\ngeometric mean\nof the rate of change across all of these populations.\nWhat’s changed since the last Living Planet Index Report?\nThe 2022 Living Planet Index reported an average decline of 69% since 1970. This 2024 update reports a 73% decline.\nFrom this, you might assume that wildlife populations have dropped by another 4 percentage points in the additional two years of data.\nHowever, almost none of this change has happened in the last few years, and the new LPI should not be seen as a simple extension of the previous one.\nInstead, the whole trend line for the LPI has shifted as the project has access to data for more populations and species, and the type of populations that are included has changed. The number of populations in the LPI increased by around 3,000 (just short of a 10% rise), and the number of species increased by around 250 (a 5% rise).\nThe other major change is that only\nnative species\nare included in this update, whereas native\nand\nnon-native species\nwere included in previous reports. This explains a lot of the difference in this year’s report. In the\ntechnical supplement\n, the authors compare what this year’s LPI looks like with and without non-native species. When non-native species\nare\nincluded, the LPI trend is closer to previous reports.\nThe chart below compares the LPI in 2022 and 2024 globally and for different regions. You can see that the whole lines have shifted, rather than the decline accelerating in the two most recent years.\nThe biggest shifts in this report, compared to the last, are for Europe and Central Asia; and Africa, where the reductions are more substantial in the most recent update.\nThe reason for the change in Africa is a 45% increase in the number of populations included, many of which are declining. This has caused the LPI for this region to shift down.\nThe exclusion of non-native species from the LPI has meant there are fewer populations counted in Europe and Central Asia, and this has been the main reason for the change in the trend in this region.\nAround half of the studied populations are decreasing; the rest are increasing or stable\nWhen you hear an average decline of 73%, you’d probably imagine that most of the world’s studied wildlife populations are in decline.\nBut when we look at how many populations that are increasing, stable, or decreasing, this isn’t the case. Almost as many populations are increasing as decreasing.\nAcross the whole dataset, exactly 50% of populations were in decline; 43% were increasing; and the remaining 7% were stable.\n2\nIn the chart, we can see the share of populations in LPI that have trended in a given direction for different taxonomic groups. What you’ll notice is that “only” around half of wildlife populations declined in numbers. The rest were either increasing or stable.\nYou can see the share of populations that are strongly or moderately increasing and decreasing\nin this chart\n.\nDownload\nSo, not all populations are struggling. This is not to dismiss the steep decline many populations are seeing. Instead, it’s useful to know so that we can direct resources more efficiently to those areas and populations that need it most. It’s more effective to target restorative and protection efforts to the populations that are in serious trouble than to assume that\nall\npopulations are struggling.\nIf some populations are doing well and others very poorly, we want to know so that we can understand why and work out how to stop it.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nDirect exploitation and habitat loss are still the biggest threats to biodiversity\nEstimates of the magnitude of population decline are updated with every Living Planet Index report, but the main drivers of biodiversity loss remain the same.\nDirect pressures — including\ndeforestation\n, habitat loss, hunting,\noverfishing\n, and other\nenvironmental impacts of food production\n— are the biggest causes.\n3\nClimate change is also impacting biodiversity. For example, species are shifting towards the poles and to higher elevations to track suitable climates. While the impacts of climate change on biodiversity are currently less substantial than the direct pressures listed above, they are expected to intensify as global temperatures rise.\nWhat we need to do to slow biodiversity loss is clear. Reduce the amount of land we use for agriculture. Bring deforestation to an end. Use fertilizers and agricultural inputs more efficiently (to have high productivity but low levels of excess nutrients and pollution). Reduce levels of overfishing. Protect areas of high species richness and areas with unique biodiversity.\nWhen combined with restoration and reintroduction programs, it’s not only possible to slow down biodiversity loss: we can also\nbring species back\nfrom the brink. We’ve seen this for species such as the European bison, Eurasian beaver, and wolves across Europe.\nMany of the world’s wildlife populations are struggling, but we have solutions to turn things around.\nExplore more of our articles on the Living Planet Index:\nLiving Planet Index: what does it really mean?\nThe Living Planet Index is the biodiversity metric that always claims the headlines. It’s often misinterpreted. How should we understand it?\nFAQs on the Living Planet Index\nThe Living Planet Index is one of the most common measures used in biodiversity monitoring. But what is it, and where does this data come from?\nHow does the Living Planet Index vary by region?\nThe Living Planet Index shows an average decline of 73% across studied animal populations globally. But how does this vary by region?\nHow the Living Planet project helps us understand changes in the world’s wildlife\nBeneath the popular index, the Living Planet database helps us understand where and what animals are deeply threatened, and what animals are thriving.\nEndnotes\nWWF (2024) Living Planet Report 2024 – A System in Peril. WWF, Gland, Switzerland.\nDeinet S, Marconi V, Freeman R, Puleston H, McRae L. Living Planet Report 2024 Technical Supplement: Living Planet Index. ZSL, 2024.\nMaxwell, S. L., Fuller, R. A., Brooks, T. M., & Watson, J. E. (2016). Biodiversity: The ravages of guns, nets and bulldozers. Nature.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie and Fiona Spooner (2024) - “The 2024 Living Planet Index reports a 73% average decline in wildlife populations — what’s changed since the last report?” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-110921/2024-living-planet-index.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-2024-living-planet-index,\nauthor = {Hannah Ritchie and Fiona Spooner},\ntitle = {The 2024 Living Planet Index reports a 73% average decline in wildlife populations — what’s changed since the last report?},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://archive.ourworldindata.org/20260518-110921/2024-living-planet-index.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "2024-living-planet-index", "source_url": "https://ourworldindata.org/2024-living-planet-index", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "A guide to understanding the Living Planet Index and what it does and doesn’t mean.", "numeric_mentions": ["2024", "73%", "10", "1", "1970", "2020", "34,836", "1970,", "2022", "69%", "4 percentage points", "3,000", "10%", "250", "5%", "45%", "50%", "43%", "7%", "2", "3", "2016", "20260518", "110921", "18,", "2026"], "numeric_evidence": [{"title": "Living Planet Index", "source_url": "https://ourworldindata.org/grapher/global-living-planet-index.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Central estimate", "Upper estimate", "Lower estimate"], "row_count_total": 357, "rows_head": [{"Entity": "Africa", "Code": "OWID_AFR", "Year": "1970", "Central estimate": "100", "Upper estimate": "100", "Lower estimate": "100"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1971", "Central estimate": "94.004613", "Upper estimate": "103.67786000000001", "Lower estimate": "86.47847"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1972", "Central estimate": "89.996994", "Upper estimate": "105.99841", "Lower estimate": "77.636576"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1973", "Central estimate": "89.674807", "Upper estimate": "109.67456", "Lower estimate": "74.525344"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1974", "Central estimate": "85.61314", "Upper estimate": "109.10656", "Lower estimate": "67.872155"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1975", "Central estimate": "79.87522", "Upper estimate": "107.55627000000001", "Lower estimate": "59.519064"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1976", "Central estimate": "73.681283", "Upper estimate": "105.57764999999999", "Lower estimate": "51.21531"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1977", "Central estimate": "68.55669599999999", "Upper estimate": "102.30387", "Lower estimate": "45.856735"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1978", "Central estimate": "63.46199", "Upper estimate": "97.801065", "Lower estimate": "41.300281999999996"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1979", "Central estimate": "61.046195000000004", "Upper estimate": "98.52561", "Lower estimate": "37.959984000000006"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1980", "Central estimate": "61.535764", "Upper estimate": "105.41126999999999", "Lower estimate": "36.1236"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1981", "Central estimate": "64.33878", "Upper estimate": "114.8395", "Lower estimate": "36.375924999999995"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1982", "Central estimate": "62.54752", "Upper estimate": "114.23649", "Lower estimate": "34.717086"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1983", "Central estimate": "60.948840000000004", "Upper estimate": "111.78066", "Lower estimate": "33.728993"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1984", "Central estimate": "57.786286", "Upper estimate": "106.25089999999999", "Lower estimate": "31.87623"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1985", "Central estimate": "54.21002", "Upper estimate": "100.20067999999999", "Lower estimate": "29.702437"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1986", "Central estimate": "50.9055", "Upper estimate": "94.659775", "Lower estimate": "27.644170000000003"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1987", "Central estimate": "48.512694", "Upper estimate": "90.78437000000001", "Lower estimate": "26.075930000000003"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1988", "Central estimate": "46.974334", "Upper estimate": "88.48914", "Lower estimate": "25.146198"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1989", "Central estimate": "45.379722", "Upper estimate": "86.04856", "Lower estimate": "24.232860000000002"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1990", "Central estimate": "43.616715", "Upper estimate": "83.13535", "Lower estimate": "23.154394"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1991", "Central estimate": "42.95254", "Upper estimate": "82.305026", "Lower estimate": "22.641375999999998"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1992", "Central estimate": "43.475807", "Upper estimate": "83.721936", "Lower estimate": "22.714485"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1993", "Central estimate": "43.948746", "Upper estimate": "85.25987", "Lower estimate": "22.794339"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1994", "Central estimate": "44.49087", "Upper estimate": "87.306833", "Lower estimate": "22.81294"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Central estimate": "44.059843", "Upper estimate": "87.331957", "Lower estimate": "22.427095"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1996", "Central estimate": "43.973678", "Upper estimate": "87.73531", "Lower estimate": "22.282863"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1997", "Central estimate": "43.074483", "Upper estimate": "86.01516000000001", "Lower estimate": "21.814700000000002"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1998", "Central estimate": "42.116538", "Upper estimate": "84.01783", "Lower estimate": "21.278445"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1999", "Central estimate": "41.479552", "Upper estimate": "82.84506", "Lower estimate": "20.922288"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2000", "Central estimate": "40.746465", "Upper estimate": "81.522363", "Lower estimate": "20.531979"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2001", "Central estimate": "39.108196", "Upper estimate": "78.84755", "Lower estimate": "19.589019"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Central estimate": "37.214458", "Upper estimate": "75.62147999999999", "Lower estimate": "18.52329"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2003", "Central estimate": "35.666745999999996", "Upper estimate": "73.148537", "Lower estimate": "17.585754"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2004", "Central estimate": "34.566407999999996", "Upper estimate": "71.24281", "Lower estimate": "16.924919"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2005", "Central estimate": "33.269107", "Upper estimate": "69.08185999999999", "Lower estimate": "16.122182"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2006", "Central estimate": "32.03177", "Upper estimate": "66.88021400000001", "Lower estimate": "15.417819"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Central estimate": "32.009085999999996", "Upper estimate": "67.16938", "Lower estimate": "15.344945"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2008", "Central estimate": "32.298052", "Upper estimate": "67.71928", "Lower estimate": "15.464692"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2009", "Central estimate": "32.467926000000006", "Upper estimate": "68.22785", "Lower estimate": "15.528197999999998"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2010", "Central estimate": "31.544983", "Upper estimate": "66.406804", "Lower estimate": "15.069509"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2011", "Central estimate": "30.375797", "Upper estimate": "64.143234", "Lower estimate": "14.504717"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2012", "Central estimate": "29.082092999999997", "Upper estimate": "61.417719999999996", "Lower estimate": "13.882273000000001"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Central estimate": "28.170413", "Upper estimate": "59.51395", "Lower estimate": "13.459119"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2014", "Central estimate": "26.960372999999997", "Upper estimate": "57.083850000000005", "Lower estimate": "12.890699999999999"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2015", "Central estimate": "26.047169999999998", "Upper estimate": "55.269647", "Lower estimate": "12.404774"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Central estimate": "25.596067", "Upper estimate": "54.444873", "Lower estimate": "12.148655999999999"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2017", "Central estimate": "25.551108", "Upper estimate": "54.49539", "Lower estimate": "12.073449"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2018", "Central estimate": "25.256573999999997", "Upper estimate": "53.997785", "Lower estimate": "11.910943999999999"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2019", "Central estimate": "24.632017", "Upper estimate": "52.640414", "Lower estimate": "11.588701"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2020", "Central estimate": "23.96703", "Upper estimate": "51.069075", "Lower estimate": "11.251467499999999"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1970", "Central estimate": "100", "Upper estimate": "100", "Lower estimate": "100"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1971", "Central estimate": "103.67014", "Upper estimate": "109.47583", "Lower estimate": "99.857605"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1972", "Central estimate": "105.45609", "Upper estimate": "114.56957999999999", "Lower estimate": "99.22840000000001"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1973", "Central estimate": "106.37546", "Upper estimate": "116.35312", "Lower estimate": "99.042416"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1974", "Central estimate": "107.30608", "Upper estimate": "117.88788", "Lower estimate": "99.29126000000001"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1975", "Central estimate": "108.42007", "Upper estimate": "119.52136", "Lower estimate": "99.85553"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1976", "Central estimate": "107.95983", "Upper estimate": "119.40915999999999", "Lower estimate": "99.06393999999999"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1977", "Central estimate": "106.00847", "Upper estimate": "117.61702999999999", "Lower estimate": "96.93238"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1978", "Central estimate": "103.8813", "Upper estimate": "115.62893000000001", "Lower estimate": "94.64202"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1979", "Central estimate": "102.26315", "Upper estimate": "114.1453", "Lower estimate": "92.888796"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1980", "Central estimate": "101.17103999999999", "Upper estimate": "113.24356", "Lower estimate": "91.60987"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1981", "Central estimate": "99.90027500000001", "Upper estimate": "112.09414", "Lower estimate": "90.06607"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1982", "Central estimate": "98.06892", "Upper estimate": "110.2777", "Lower estimate": "88.05967"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1983", "Central estimate": "95.40395000000001", "Upper estimate": "107.56749", "Lower estimate": "85.37002"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1984", "Central estimate": "93.619156", "Upper estimate": "106.72512", "Lower estimate": "82.811826"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1985", "Central estimate": "93.40339", "Upper estimate": "108.32638000000001", "Lower estimate": "81.15850999999999"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1986", "Central estimate": "94.81911", "Upper estimate": "113.22140999999999", "Lower estimate": "80.31374"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1987", "Central estimate": "95.18263", "Upper estimate": "116.10952999999999", "Lower estimate": "79.192847"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1988", "Central estimate": "94.774324", "Upper estimate": "117.61899", "Lower estimate": "77.78521"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1989", "Central estimate": "93.57719399999999", "Upper estimate": "116.84057999999999", "Lower estimate": "76.32293999999999"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1990", "Central estimate": "90.70645", "Upper estimate": "114.5702", "Lower estimate": "73.153824"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1991", "Central estimate": "87.318444", "Upper estimate": "111.8993", "Lower estimate": "69.55493"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1992", "Central estimate": "83.626175", "Upper estimate": "108.72444000000002", "Lower estimate": "65.65954699999999"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1993", "Central estimate": "79.48206", "Upper estimate": "104.41960999999999", "Lower estimate": "61.76587"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1994", "Central estimate": "76.156116", "Upper estimate": "101.03317000000001", "Lower estimate": "58.486366000000004"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1995", "Central estimate": "72.777075", "Upper estimate": "97.607833", "Lower estimate": "55.14763000000001"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1996", "Central estimate": "71.23953", "Upper estimate": "96.35527", "Lower estimate": "53.36959"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1997", "Central estimate": "68.88039", "Upper estimate": "93.92285", "Lower estimate": "51.06280399999999"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1998", "Central estimate": "66.21564", "Upper estimate": "90.9207", "Lower estimate": "48.693627"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "1999", "Central estimate": "63.25674", "Upper estimate": "87.43254999999999", "Lower estimate": "46.180665"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2000", "Central estimate": "60.836875", "Upper estimate": "84.53049700000001", "Lower estimate": "44.153407"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2001", "Central estimate": "59.460074", "Upper estimate": "83.03233399999999", "Lower estimate": "43.010604"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2002", "Central estimate": "59.40582", "Upper estimate": "83.218604", "Lower estimate": "42.947298"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2003", "Central estimate": "59.789216999999994", "Upper estimate": "83.91807999999999", "Lower estimate": "43.122348"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2004", "Central estimate": "59.64404", "Upper estimate": "84.05449", "Lower estimate": "42.785895000000004"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2005", "Central estimate": "57.462436", "Upper estimate": "81.37725999999999", "Lower estimate": "40.99198"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2006", "Central estimate": "54.030376999999994", "Upper estimate": "77.179766", "Lower estimate": "38.320583"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2007", "Central estimate": "49.774006", "Upper estimate": "71.47019999999999", "Lower estimate": "35.142812"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2008", "Central estimate": "46.577754999999996", "Upper estimate": "67.28805", "Lower estimate": "32.710785"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2009", "Central estimate": "43.547058", "Upper estimate": "63.20089", "Lower estimate": "30.370303999999997"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2010", "Central estimate": "42.038888", "Upper estimate": "61.53361", "Lower estimate": "29.038098"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2011", "Central estimate": "40.70394", "Upper estimate": "60.29845", "Lower estimate": "27.806925999999997"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2012", "Central estimate": "40.625206000000006", "Upper estimate": "60.90478", "Lower estimate": "27.48775"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2013", "Central estimate": "42.730197", "Upper estimate": "65.05145", "Lower estimate": "28.516975"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2014", "Central estimate": "45.025682", "Upper estimate": "69.36964", "Lower estimate": "29.657936000000003"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2015", "Central estimate": "47.193745", "Upper estimate": "73.76045", "Lower estimate": "30.619397999999997"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2016", "Central estimate": "45.737422", "Upper estimate": "72.21392", "Lower estimate": "29.359853"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2017", "Central estimate": "45.325962", "Upper estimate": "72.19799", "Lower estimate": "28.810632000000002"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2018", "Central estimate": "43.19193", "Upper estimate": "69.18523", "Lower estimate": "27.244220000000002"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2019", "Central estimate": "41.926342", "Upper estimate": "67.602247", "Lower estimate": "26.193655"}, {"Entity": "Asia and Pacific", "Code": "", "Year": "2020", "Central estimate": "39.603937", "Upper estimate": "64.33672", "Lower estimate": "24.470406999999998"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1970", "Central estimate": "100", "Upper estimate": "100", "Lower estimate": "100"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1971", "Central estimate": "101.56175999999999", "Upper estimate": "104.97311", "Lower estimate": "98.64034000000001"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1972", "Central estimate": "103.28828000000001", "Upper estimate": "108.97547999999999", "Lower estimate": "98.400074"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1973", "Central estimate": "105.34041", "Upper estimate": "112.16738", "Lower estimate": "99.39167"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1974", "Central estimate": "107.97343", "Upper estimate": "116.07008", "Lower estimate": "100.90113000000001"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1975", "Central estimate": "110.22451", "Upper estimate": "119.48347", "Lower estimate": "102.0718"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1976", "Central estimate": "111.41124", "Upper estimate": "121.77454", "Lower estimate": "102.13884"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1977", "Central estimate": "111.57060999999999", "Upper estimate": "122.80009000000001", "Lower estimate": "101.41228"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1978", "Central estimate": "111.54101999999999", "Upper estimate": "123.44744999999999", "Lower estimate": "100.65097"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1979", "Central estimate": "111.60547999999999", "Upper estimate": "123.94941", "Lower estimate": "100.37164999999999"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1980", "Central estimate": "111.79069", "Upper estimate": "124.45517", "Lower estimate": "100.22746000000001"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1981", "Central estimate": "112.12618", "Upper estimate": "125.13522999999999", "Lower estimate": "100.27862"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1982", "Central estimate": "113.51088", "Upper estimate": "127.14566", "Lower estimate": "101.2205"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1983", "Central estimate": "113.3673", "Upper estimate": "127.69225", "Lower estimate": "100.55945"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1984", "Central estimate": "114.12020000000001", "Upper estimate": "129.32388", "Lower estimate": "100.62209"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1985", "Central estimate": "113.89959", "Upper estimate": "129.69565", "Lower estimate": "99.86242"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1986", "Central estimate": "115.30493", "Upper estimate": "131.66134", "Lower estimate": "100.76907"}, {"Entity": "Europe and Central Asia", "Code": "", "Year": "1987", "Central estimate": "117.54643", "Upper estimate": "134.59896", "Lower estimate": "102.41672"}], "rows_tail": [{"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2003", "Central estimate": "16.717969", "Upper estimate": "23.513713", "Lower estimate": "11.887056"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2004", "Central estimate": "15.834408", "Upper estimate": "22.500454", "Lower estimate": "11.162569999999999"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2005", "Central estimate": "15.409875000000001", "Upper estimate": "22.153672999999998", "Lower estimate": "10.751541000000001"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2006", "Central estimate": "15.119104", "Upper estimate": "21.938896", "Lower estimate": "10.431735999999999"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2007", "Central estimate": "14.422368999999998", "Upper estimate": "21.204594", "Lower estimate": "9.833661000000001"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2008", "Central estimate": "13.092011000000001", "Upper estimate": "19.457492000000002", "Lower estimate": "8.802731"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2009", "Central estimate": "11.188099999999999", "Upper estimate": "16.914700999999997", "Lower estimate": "7.402469"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2010", "Central estimate": "9.4993964", "Upper estimate": "14.578323000000001", "Lower estimate": "6.180943"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2011", "Central estimate": "8.084096", "Upper estimate": "12.643512000000001", "Lower estimate": "5.1746633"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2012", "Central estimate": "7.323765", "Upper estimate": "11.6158985", "Lower estimate": "4.634344"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2013", "Central estimate": "6.918912000000001", "Upper estimate": "11.055605", "Lower estimate": "4.3535337"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2014", "Central estimate": "6.663473000000001", "Upper estimate": "10.695252", "Lower estimate": "4.179081"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2015", "Central estimate": "6.525281", "Upper estimate": "10.489141", "Lower estimate": "4.076643"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2016", "Central estimate": "5.97616", "Upper estimate": "9.739527", "Lower estimate": "3.6763209999999997"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2017", "Central estimate": "5.5900894", "Upper estimate": "9.517394999999999", "Lower estimate": "3.300528"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2018", "Central estimate": "5.621798699999999", "Upper estimate": "10.154876999999999", "Lower estimate": "3.1272035"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2019", "Central estimate": "5.7098597", "Upper estimate": "10.694054", "Lower estimate": "3.0585924"}, {"Entity": "Latin America and the Caribbean", "Code": "", "Year": "2020", "Central estimate": "5.377315", "Upper estimate": "10.206671", "Lower estimate": "2.8329503"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1970", "Central estimate": "100", "Upper estimate": "100", "Lower estimate": "100"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1971", "Central estimate": "99.518985", "Upper estimate": "105.29366", "Lower estimate": "95.09994400000001"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1972", "Central estimate": "97.91978", "Upper estimate": "108.01061000000001", "Lower estimate": "90.42873"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1973", "Central estimate": "95.73132", "Upper estimate": "108.67142999999999", "Lower estimate": "86.5427"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1974", "Central estimate": "91.89177", "Upper estimate": "104.81436", "Lower estimate": "82.405466"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1975", "Central estimate": "89.91088", "Upper estimate": "103.06742", "Lower estimate": "80.09464"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1976", "Central estimate": "89.314854", "Upper estimate": "102.92424", "Lower estimate": "78.98818"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1977", "Central estimate": "89.9167", "Upper estimate": "104.46526", "Lower estimate": "78.71918"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1978", "Central estimate": "90.695524", "Upper estimate": "106.36749999999999", "Lower estimate": "78.30669999999999"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1979", "Central estimate": "91.205144", "Upper estimate": "107.90823", "Lower estimate": "77.85505"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1980", "Central estimate": "91.358733", "Upper estimate": "108.77844", "Lower estimate": "77.354676"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1981", "Central estimate": "91.43845", "Upper estimate": "109.23765", "Lower estimate": "77.10903"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1982", "Central estimate": "91.13525", "Upper estimate": "109.28298000000001", "Lower estimate": "76.58759"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1983", "Central estimate": "90.957445", "Upper estimate": "109.65796999999999", "Lower estimate": "76.08235"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1984", "Central estimate": "90.03016000000001", "Upper estimate": "109.25882", "Lower estimate": "74.96255"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1985", "Central estimate": "89.89684", "Upper estimate": "109.70219999999999", "Lower estimate": "74.44145"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1986", "Central estimate": "89.44456", "Upper estimate": "109.62118000000001", "Lower estimate": "73.72708999999999"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1987", "Central estimate": "88.58817", "Upper estimate": "108.9242", "Lower estimate": "72.73995"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1988", "Central estimate": "87.6093", "Upper estimate": "108.02797", "Lower estimate": "71.68628"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1989", "Central estimate": "85.875744", "Upper estimate": "106.24365", "Lower estimate": "69.95405"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1990", "Central estimate": "85.06336", "Upper estimate": "105.58866", "Lower estimate": "69.00129"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1991", "Central estimate": "83.289903", "Upper estimate": "103.738", "Lower estimate": "67.312616"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1992", "Central estimate": "83.01433", "Upper estimate": "103.74209", "Lower estimate": "66.89699"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1993", "Central estimate": "83.00382499999999", "Upper estimate": "104.02765000000001", "Lower estimate": "66.65404000000001"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1994", "Central estimate": "83.59228", "Upper estimate": "105.17985", "Lower estimate": "66.879445"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1995", "Central estimate": "81.87896", "Upper estimate": "103.4384", "Lower estimate": "65.228313"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1996", "Central estimate": "79.80807399999999", "Upper estimate": "101.37922999999999", "Lower estimate": "63.276889999999995"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1997", "Central estimate": "77.850854", "Upper estimate": "99.39235400000001", "Lower estimate": "61.36907299999999"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1998", "Central estimate": "75.9126", "Upper estimate": "97.48167", "Lower estimate": "59.53518"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "1999", "Central estimate": "74.187624", "Upper estimate": "95.75059", "Lower estimate": "57.96633"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2000", "Central estimate": "72.710913", "Upper estimate": "94.35578", "Lower estimate": "56.55965"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2001", "Central estimate": "71.56855", "Upper estimate": "93.304944", "Lower estimate": "55.379003000000004"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2002", "Central estimate": "71.810484", "Upper estimate": "94.27451", "Lower estimate": "55.1211"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2003", "Central estimate": "72.29514999999999", "Upper estimate": "95.55068", "Lower estimate": "55.06054"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2004", "Central estimate": "73.93665", "Upper estimate": "98.28551", "Lower estimate": "55.924110000000006"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2005", "Central estimate": "73.84296", "Upper estimate": "98.758054", "Lower estimate": "55.52636"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2006", "Central estimate": "73.64116299999999", "Upper estimate": "99.03309999999999", "Lower estimate": "55.12054"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2007", "Central estimate": "73.413026", "Upper estimate": "99.22538", "Lower estimate": "54.688393999999995"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2008", "Central estimate": "73.19164", "Upper estimate": "99.24884", "Lower estimate": "54.342383000000005"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2009", "Central estimate": "72.78037", "Upper estimate": "99.07809", "Lower estimate": "53.75991"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2010", "Central estimate": "72.07213", "Upper estimate": "98.60414", "Lower estimate": "53.030676"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2011", "Central estimate": "71.83682300000001", "Upper estimate": "98.56849", "Lower estimate": "52.636987000000005"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2012", "Central estimate": "70.69316", "Upper estimate": "97.48509", "Lower estimate": "51.549034999999996"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2013", "Central estimate": "69.847405", "Upper estimate": "96.694535", "Lower estimate": "50.62183"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2014", "Central estimate": "69.22380299999999", "Upper estimate": "96.270025", "Lower estimate": "49.837613"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2015", "Central estimate": "69.80753", "Upper estimate": "97.210073", "Lower estimate": "50.08576"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2016", "Central estimate": "69.36165", "Upper estimate": "96.84109", "Lower estimate": "49.605286"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2017", "Central estimate": "68.66212", "Upper estimate": "96.21347", "Lower estimate": "48.981172"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2018", "Central estimate": "66.51272", "Upper estimate": "93.616486", "Lower estimate": "47.361547"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2019", "Central estimate": "64.22505", "Upper estimate": "90.66618", "Lower estimate": "45.685416000000004"}, {"Entity": "North America", "Code": "OWID_NAM", "Year": "2020", "Central estimate": "60.95505", "Upper estimate": "86.24542", "Lower estimate": "43.291467"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1970", "Central estimate": "100", "Upper estimate": "100", "Lower estimate": "100"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1971", "Central estimate": "99.406844", "Upper estimate": "101.59934", "Lower estimate": "97.358286"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1972", "Central estimate": "98.14285000000001", "Upper estimate": "101.83256999999999", "Lower estimate": "94.61055"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1973", "Central estimate": "96.61677", "Upper estimate": "101.14101", "Lower estimate": "92.20869499999999"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1974", "Central estimate": "94.809854", "Upper estimate": "100.25398999999999", "Lower estimate": "89.58937999999999"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1975", "Central estimate": "92.65501499999999", "Upper estimate": "98.937094", "Lower estimate": "86.78831500000001"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1976", "Central estimate": "89.991647", "Upper estimate": "96.91421", "Lower estimate": "83.68637"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1977", "Central estimate": "86.367744", "Upper estimate": "93.39025600000001", "Lower estimate": "79.97391"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1978", "Central estimate": "82.86782000000001", "Upper estimate": "89.899683", "Lower estimate": "76.46518"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1979", "Central estimate": "80.259967", "Upper estimate": "87.47364", "Lower estimate": "73.666024"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1980", "Central estimate": "78.43339", "Upper estimate": "85.9839", "Lower estimate": "71.546197"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1981", "Central estimate": "77.014565", "Upper estimate": "84.86694700000001", "Lower estimate": "69.877255"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1982", "Central estimate": "74.73171400000001", "Upper estimate": "82.62671999999999", "Lower estimate": "67.60401999999999"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1983", "Central estimate": "72.17754", "Upper estimate": "80.02931", "Lower estimate": "65.15689"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1984", "Central estimate": "69.707686", "Upper estimate": "77.48042000000001", "Lower estimate": "62.779019999999996"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1985", "Central estimate": "67.67479", "Upper estimate": "75.47232000000001", "Lower estimate": "60.769224"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1986", "Central estimate": "66.16929999999999", "Upper estimate": "73.98204", "Lower estimate": "59.270376"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1987", "Central estimate": "64.42259", "Upper estimate": "72.228897", "Lower estimate": "57.558960000000006"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1988", "Central estimate": "62.675349999999995", "Upper estimate": "70.365506", "Lower estimate": "55.854079999999996"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1989", "Central estimate": "61.357963000000005", "Upper estimate": "68.98210999999999", "Lower estimate": "54.568404"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1990", "Central estimate": "60.09213", "Upper estimate": "67.74154300000001", "Lower estimate": "53.296600000000005"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1991", "Central estimate": "58.816165", "Upper estimate": "66.497713", "Lower estimate": "52.005849999999995"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1992", "Central estimate": "57.28322", "Upper estimate": "64.97869", "Lower estimate": "50.5043"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1993", "Central estimate": "55.24195400000001", "Upper estimate": "62.799525", "Lower estimate": "48.570102"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1994", "Central estimate": "53.247344", "Upper estimate": "60.675395", "Lower estimate": "46.72641"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1995", "Central estimate": "51.149136", "Upper estimate": "58.432174", "Lower estimate": "44.77669"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1996", "Central estimate": "50.24958", "Upper estimate": "57.51728000000001", "Lower estimate": "43.90801"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1997", "Central estimate": "48.922349999999994", "Upper estimate": "56.10058", "Lower estimate": "42.653722"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1998", "Central estimate": "47.500777", "Upper estimate": "54.567589999999996", "Lower estimate": "41.331362999999996"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1999", "Central estimate": "45.67586", "Upper estimate": "52.53559", "Lower estimate": "39.681675999999996"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Central estimate": "44.373488", "Upper estimate": "51.088710000000006", "Lower estimate": "38.475"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Central estimate": "43.125597", "Upper estimate": "49.715665", "Lower estimate": "37.24875"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Central estimate": "42.013386000000004", "Upper estimate": "48.545527", "Lower estimate": "36.136833"}, {"Entity": 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"2024 Report": "27.369870000000002"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "2022 Report": "30.897567", "2024 Report": "27.097133"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "2022 Report": "", "2024 Report": "27.327447999999997"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "2022 Report": "", "2024 Report": "27.134067"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "living-planet-index-comparison", "metadata_url": "https://ourworldindata.org/grapher/living-planet-index-comparison.metadata.json", "chart_title": "Comparison of the Living Planet Index across reports", "chart_subtitle": "The Living Planet Index (LPI) measures the average decline in monitored wildlife populations, where values in 1970 are equal to 100. The LPI published in the 2022 and 2024 report are shown for comparison.", "chart_note": null, "chart_citation": "World Wildlife Fund and Zoological Society of London (2022; 2024)", "original_chart_url": "https://ourworldindata.org/grapher/living-planet-index-comparison", "owid_column_metadata": {"Living Planet Index": {"titleShort": "2024 Report", "titleLong": "2024 Report", "descriptionShort": "The Living Planet Index (LPI) is a measure of the state of global biological diversity based on population trends of vertebrate species from around the world. The index value is measured relative to species' populations in 1970 (i.e. 1970 = 1).", "unit": "(1970 = 1)", "timespan": "1970-2020", "type": "Numeric", "conversionFactor": 100, "owidVariableId": 990513, "shortName": "lpi_final", "lastUpdated": "2024-09-30", "nextUpdate": "2026-09-30", "citationShort": "World Wildlife Fund and Zoological Society of London (2024) – processed by Our World in Data", "citationLong": "World Wildlife Fund and Zoological Society of London (2024) – processed by Our World in Data. “2024 Report” [dataset]. World Wildlife Fund and Zoological Society of London, “Living Planet Index” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/990513.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Share of populations increasing, stable or declining in the Living Planet Index", "source_url": "https://ourworldindata.org/grapher/share-of-populations-increasing-stable-or-decreasing-in-the-living-planet-index.csv", "file_type": "csv", "columns": ["Entity", "Year", "Strong increase", "Moderate increase", "No change", "Moderate decline", "Strong decline"], "row_count_total": 4, "rows_head": [{"Entity": "Birds", "Year": "2022", "Strong increase": "33.5", "Moderate increase": "4.5", "No change": "14.3", "Moderate decline": "5.7", "Strong decline": "42"}, {"Entity": "Fishes", "Year": "2022", "Strong increase": "38.3", "Moderate increase": "4.4", "No change": "8.5", "Moderate decline": "4.7", "Strong decline": "44.2"}, {"Entity": "Mammals", "Year": "2022", "Strong increase": "34.8", "Moderate increase": "7.1", "No change": "15.4", "Moderate decline": "6", "Strong decline": "36.7"}, {"Entity": "Reptiles and amphibians", "Year": "2022", "Strong increase": "36.6", "Moderate increase": "4.6", "No change": "9.6", "Moderate decline": "5.3", "Strong decline": "44"}], "rows_tail": [], "sampling_note": "Stored first 4 rows and last 4 rows when the table is larger.", "grapher_slug": "share-of-populations-increasing-stable-or-decreasing-in-the-living-planet-index", "metadata_url": "https://ourworldindata.org/grapher/share-of-populations-increasing-stable-or-decreasing-in-the-living-planet-index.metadata.json", "chart_title": "Share of populations increasing, stable or declining in the Living Planet Index", "chart_subtitle": "The Living Planet Index (LPI) measures the change in 34,836 wildlife populations. This chart shows the share of populations in taxonomic groups that have been increasing, stable, or declining.", "chart_note": "Categories are based on annual changes: +2.81% is a strong increase; +1.16% to +2.81% is a moderate increase; -2.73% to -1.14% is a moderate decrease; and greater than -2.73% is a strong decrease.", "chart_citation": "Deinet et al. (2024)", "original_chart_url": "https://ourworldindata.org/grapher/share-of-populations-increasing-stable-or-decreasing-in-the-living-planet-index", "owid_column_metadata": {"Share of populations strongly increasing": {"titleShort": "Strong increase", "titleLong": "Strong increase", "descriptionShort": "The share of native, vertebrate populations that are increasing on average by 2.81% or more per year.", "descriptionProcessing": "Categories are based on [Burns et al. (2023)](https://stateofnature.org.uk/wp-content/uploads/2023/09/TP25999-State-of-Nature-main-report_2023_FULL-DOC-v12.pdf).", "shortUnit": "%", "unit": "%", "timespan": "2022-2022", "type": "Numeric", "owidVariableId": 990510, "shortName": "share_strong_increase", "lastUpdated": "2024-09-30", "nextUpdate": "2026-09-30", "citationShort": "Deinet et al. (2024) – processed by Our World in Data", "citationLong": "Deinet et al. (2024) – processed by Our World in Data. “Strong increase” [dataset]. Deinet et al., “Living Planet Index - Share of populations with increasing and decreasing trends” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/990510.metadata.json"}, "Share of populations moderately increasing": {"titleShort": "Moderate increase", "titleLong": "Moderate increase", "descriptionShort": "The share of native, vertebrate populations that are increasing on average by 1.16% to 2.81% per year.", "descriptionProcessing": "Categories are based on [Burns et al. (2023)](https://stateofnature.org.uk/wp-content/uploads/2023/09/TP25999-State-of-Nature-main-report_2023_FULL-DOC-v12.pdf).", "shortUnit": "%", "unit": "%", "timespan": "2022-2022", "type": "Numeric", "owidVariableId": 990512, "shortName": "share_moderate_increase", "lastUpdated": "2024-09-30", "nextUpdate": "2026-09-30", "citationShort": "Deinet et al. (2024) – processed by Our World in Data", "citationLong": "Deinet et al. (2024) – processed by Our World in Data. “Moderate increase” [dataset]. Deinet et al., “Living Planet Index - Share of populations with increasing and decreasing trends” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/990512.metadata.json"}, "Share of populations with little change": {"titleShort": "No change", "titleLong": "No change", "descriptionShort": "The share of native, vertebrate populations that are decreasing by less than 1.14% per year and increasing by less than 1.16% per year.", "descriptionProcessing": "Categories are based on [Burns et al. (2023)](https://stateofnature.org.uk/wp-content/uploads/2023/09/TP25999-State-of-Nature-main-report_2023_FULL-DOC-v12.pdf).", "shortUnit": "%", "unit": "%", "timespan": "2022-2022", "type": "Numeric", "owidVariableId": 990516, "shortName": "share_little_change", "lastUpdated": "2024-09-30", "nextUpdate": "2026-09-30", "citationShort": "Deinet et al. (2024) – processed by Our World in Data", "citationLong": "Deinet et al. (2024) – processed by Our World in Data. “No change” [dataset]. Deinet et al., “Living Planet Index - Share of populations with increasing and decreasing trends” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/990516.metadata.json"}, "Share of populations moderately decreasing": {"titleShort": "Moderate decline", "titleLong": "Moderate decline", "descriptionShort": "The share of native, vertebrate populations that are decreasing on average by 1.14% to 2.73% per year.", "descriptionProcessing": "Categories are based on [Burns et al. (2023)](https://stateofnature.org.uk/wp-content/uploads/2023/09/TP25999-State-of-Nature-main-report_2023_FULL-DOC-v12.pdf).", "shortUnit": "%", "unit": "%", "timespan": "2022-2022", "type": "Numeric", "owidVariableId": 990517, "shortName": "share_moderate_decrease", "lastUpdated": "2024-09-30", "nextUpdate": "2026-09-30", "citationShort": "Deinet et al. 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{"raw_link": "https://ourworldindata.org/cancer", "title": "Cancer", "context": "Cancer\nCancers are one of the leading causes of death globally. Are we making progress against them?\nBy\nSaloni Dattani\n,\nVeronika Samborska\n,\nHannah Ritchie\n,\nand\nMax Roser\nContents\nCancer\nis one of the biggest health challenges worldwide. As of 2021,\naround 15%\nof all deaths were cancer deaths, making it one of the most common\ncauses of death\nglobally.\nCancers are a group of diseases in which abnormal cells multiply rapidly and can grow into tumors. They can develop in different parts of the body and, in some cases, spread to other organs through the blood and\nlymph systems\n.\nAs the global population grows larger and older, the number of cancer cases has also increased. However, the\nage-standardized\ndeath rate from cancer has declined over time in many countries — due to improvements in diagnosis, research, medical advances, and public health efforts, as well as reductions in\nrisk factors\nsuch as\nsmoking\nand some\ncancer-causing pathogens\n.\nOn this page, we explore global data and research on different types of cancer. This can help us better understand the risk factors for cancer, how cancer risks vary across the lifespan, how they differ worldwide, and how they have changed over time.\nResearch & Writing\nSmoking: How large of a global problem is it? And how can we make progress against it?\nEvery year, around eight million people die prematurely as a result of smoking. But there are things we can do to prevent this.\nChildhood leukemia: how a deadly cancer became treatable\nBefore the 1970s, most children affected by leukemia would quickly die from it. Now, most children in rich countries are cured.\nHPV vaccination: How the world can eliminate cervical cancer\nHPV vaccines offer a rare opportunity to effectively eliminate one type of cancer. By taking this opportunity, it’s possible to save hundreds of thousands of women each year.\nRelated topics\nCauses of Death\nTo find ways to save lives, it’s essential to know what people are dying from. Explore global data and research on causes of death.\nLife Expectancy\nPeople are living longer across the world, but large differences remain. Explore global data on life expectancy and how it has changed over time.\nSmoking\nTobacco smoking is one of the world’s largest health problems today.\nSee all interactive charts on cancer ↓\nLung, colorectal, stomach, and breast cancers are the leading cancer causes of death worldwide\nThe chart below shows the cancers with the highest\ndeath rates\nglobally.\nThese estimates come from the Institute for Health Metrics and Evaluation (IHME), which uses data from\nvital registries\n, cancer registries, hospital records, and\nverbal autopsies\nand applies statistical modeling to make global estimates, including for countries where data is lacking.\nAs you can see, lung cancer has the highest death rate, followed by colorectal, stomach, and breast cancers.\nThe high death rate from lung cancer is\nprimarily\ndue to the impact of smoking.\nThe two maps below show the leading cause of cancer deaths in men and women.\nThe data comes from the WHO Mortality Database, which presents reported data from the “\nunderlying cause of death\n” listed on death certificates. These are filled in by health and legal professionals and collected by national vital registries. The dataset only includes countries that have sufficient death registration.\nIn men, you can see that lung cancer kills the most in many countries. Other cancers — such as prostate, liver, and stomach cancer — are the most common in others.\nIn women, the picture is different. Breast cancer is the most common cause of cancer death in women in many countries, but in others, lung, liver, and stomach cancer dominate.\nCancer risks rise steeply with age\nCancer risks rise with age, so most\ncancer cases\nand\ndeaths\noccur in older people.\nThe chart below presents death rates from cancers across ages in the United States. It is based on national data from the Centers for Disease Control and Prevention (CDC), using the “underlying cause of death” on death certificates between 2018 and 2022.\nAs you can see, cancer death rates are much higher at older ages.\nSome cancers — such as brain and central nervous system cancers,\nleukemias\n,\nlymphoid\nand blood cancers — are also seen in children. Some cancers begin to rise much earlier than others.\nDownload\nScripts to recreate this chart can be found\non GitHub\n.\nWhy does cancer risk increase with age?\nSeveral factors make cancer more likely at older ages.\nOne reason is that as we get older, our cells accumulate more DNA damage, which increases the chances of mutations that can lead to cancer. At the same time, our cells’ ability to repair this damage becomes less effective, which allows harmful mutations to build up.\n1\nOur immune system also weakens with age, which makes it harder to identify and eliminate abnormal cells before they can multiply and spread.\nIn addition, we accumulate more exposure to\nrisk factors\n, such as smoking and radiation, which can damage our cells.\nIn some organs, it is rare for cancers to develop without the effect of risk factors, and the risk factors may only affect people in some age groups, such as the\nhuman papillomavirus\n, which is spread through sexual contact.\nThese factors mean that countries with older populations tend to have a higher prevalence of cancer. This is shown in the chart below.\nIn many countries, cancer death rates have fallen for people of a given age\nThe world has seen a rise in cancer deaths over time, as the chart below shows. This comes largely from more deaths in the oldest age groups as populations have been\ngrowing\nand\naging\n.\nAs we saw earlier, death rates from cancer rise steeply with age. This means that, without any other changes, we would expect more people to die from cancer as populations become larger and older.\nIf we account for the population\nsize\nalone — by looking at the “\ncrude death rate\n” — we may see an increase or a slight decrease over time.\nBut if we also account for the population\ngrowing older\n— by looking at the “\nage-standardized\ndeath rate” — we see a more significant decline.\nThe chart below compares the crude to the age-standardized rate in the United States. You can click to change the country.\nThe chart shows that the\nage-standardized\ncancer death rate has fallen by one-third from 1990 to 2021. This means that, among people of the same ages, those in 2021 had a cancer mortality rate one-third lower than those in 1990.\nThe decline in cancer mortality is due to many factors, including:\nBetter screening and earlier diagnosis of cancers;\nResearch into the biological mechanisms of cancer, and medical advances such as chemotherapy, radiation therapy, immunotherapy, and surgery;\nPublic health efforts in reducing risk factors — including behavioral risk factors like declining smoking, environmental toxins and\ncarcinogens\n, and infectious causes of cancers.\nOne notable example is stomach cancer, which used to be one of the top causes of cancer death in countries like the United States. But age-standardized death rates from stomach cancer have declined a lot over time, as the chart below shows.\nFor example, in the United States, the death rate of stomach cancer was around 9 times lower in 2021 than it was in 1950.\nMost stomach cancer cases are caused by the bacteria\nHelicobacter pylori\n, which can cause chronic inflammation and gradually result in stomach cancer in a fraction of people. It spreads between people through close contact, contaminated food and water, and poor hygiene.\n2\nIt’s likely that stomach cancer declined without specific interventions but rather over time due to general improvements in clean water and sanitation, food safety practices, antibiotics, and hygiene.\n3\nSince the 1990s, when the bacterium was identified as its cause, doctors have used screening, testing, and antibiotic treatment to prevent their progression into stomach cancer in individuals.\n4\nSome countries have also piloted screening and antibiotic programs to eliminate the bacteria nationwide after\nrandomized controlled trials\n.\n5\nThese have shown large declines in the prevalence of bacteria, stomach infections, and cancers; research is ongoing.\n6\nCancers in childhood are often different from cancers at older ages\nAlthough cancer rates rise with age, some cancers can develop much earlier.\nThe chart below shows the relative share of deaths from each cancer by age. This is based on national data from the United States Centers for Disease Control and Prevention (CDC) between 2018 and 2022, using the “\nunderlying cause of death\n” on death certificates.\nDownload\nScripts to recreate this chart can be found\non GitHub\n. We published a new version of this chart in May 2025, fixing an error in the data on lymphatic and blood cancers.\nIn the United States, the leading causes of cancer death in children are brain and central nervous system cancers and\nleukemias\n. These organs rapidly grow and develop during childhood, which makes them more vulnerable to cancerous changes.\nThese tend to be linked to risk factors early in development or genetic mutations. Some cancer-causing genetic mutations are inherited, but others are “de novo mutations” — meaning they arise by chance around the time of conception or early in development.\n7\nIn contrast, cancers often develop in adults due to long-term exposure to risk factors, typically in organs such as the lungs, colon, pancreas, breasts, or prostate.\nThe relative share of deaths from each cancer type in this chart is affected by the rise of other cancer types: some cancers may grow rapidly and outrank other cancers on the chart. This is why some cancer types, like colorectal and breast cancer, appear to shrink at the oldest ages, even though their risks actually continue to rise with age.\nThe share of cancer deaths from each type varies between countries. You can explore an interactive chart for different countries showing the relative share of all cancer deaths from each cancer type here:\nShare of cancer deaths by cancer type\nThe estimated percentage of deaths from different types of cancers as a proportion of all cancer-related deaths.\nThe world has made much progress in treating childhood cancers\nThanks to scientific research, medical advances, and public health efforts, the world has made significant progress in treating childhood cancers.\nThe chart below shows this. There have been declines in cancer death rates across age groups, but especially among children.\nOne reason for this progress is that scientists have learned more about the genetic causes of childhood cancers. This has helped identify children at risk earlier and develop targeted treatments with fewer side effects.\n8\nThere have also been advances in immunotherapy, stem cell transplants, radiation, and surgeries used to treat different types of childhood cancers.\n9\nA prominent example is acute lymphoblastic leukemia (ALL), a common form of\nleukemia\nin children. Survival rates for ALL in children have increased greatly, thanks to improved treatments and bone marrow transplants. Genetic research helped identify specific mutations responsible for ALL, which led to the development of highly effective targeted chemotherapy drugs.\n10\nThe chart below shows the decline in death rates for some common childhood cancers. You can see that childhood cancer death rates have declined greatly in the United States, especially for\nleukemias\nand\nlymphomas\n.\nThere has also been progress in protecting children with cancer from other complications while they are undergoing chemotherapy.\nFor example, as chemotherapy weakens the immune system, it’s harder for children with cancer to fight off infections. This means vaccination across the population — against diseases like the flu, measles, whooping cough, and pneumonia — can help protect these children from catching infections that can be much more serious for them.\n11\nSmoking was a major driver of cancer in the 20th century\nTobacco smoking became much more common over the 20th century in many countries. This led to a rise in lung cancer death rates, as the chart below shows.\nSmoking causes lung cancer because some of the chemicals in cigarette smoke damage the cells in our lungs and their DNA.\nOver time, constant exposure overwhelms the body’s ability to repair the damage, which allows mutated cells to multiply and form tumors. Smoking also causes chronic inflammation and weakens the immune system, making it harder to stop cancer from developing.\n12\nDownload\nRead more in our article:\nSmoking: How large of a global problem is it? And how can we make progress against it?\nEvery year, around eight million people die prematurely as a result of smoking. But there are things we can do to prevent this.\nLung cancer isn’t the only risk from smoking.\nSmoking raises the risks of various other cancers as well, because the chemicals in cigarette smoke can affect multiple parts of the respiratory system, and can travel via the bloodstream to other organs.\nAs the chart below shows, smoking increases the risk of death from cancers in various organs like the bladder, kidneys, pancreas, stomach, cervix, lungs, mouth, and throat.\n13\nSmoking also increases the risks of death from\ncardiovascular diseases\n,\ntuberculosis\n, and\nchronic obstructive pulmonary disease\n.\nDownload\nSmoking has an impact on many cancers, and its rise and fall over time was a major driver of cancer in the 20th century.\nThe chart below shows a historical view of reported cancer death rates since 1950 in the United States. You can see several major changes: colorectal and stomach cancer were previously the two most common cancer causes of death, but both have declined greatly over time.\nAlthough both colorectal and stomach cancer are also affected by smoking, other factors reduced their rates during this time — such as colorectal cancer screening, improved hygiene, and the use of antibiotics against the bacteria\nH. pylori\n, which can cause stomach cancer.\nIn contrast, lung cancer rose greatly over the 20th century and became the leading cause of cancer death by far, and pushed up the\noverall death rate from cancer\n.\nSome cancers are caused by infections, which can be effectively prevented or treated\nSome infections can increase the risk of cancer, by mechanisms such as causing inflammation, harming key proteins, or directly damaging our cells’ DNA.\nThis includes the\nhuman papillomavirus (HPV)\n, which can cause various cancers, including cervical cancer and penile cancer; the\nhepatitis B and C viruses\n, which can cause liver cancer; and the bacteria\nHelicobacter pylori\n, which can cause stomach cancer.\n14\nThe different pathogens spread through various routes. HPV is typically spread through sexual contact, while hepatitis B and C viruses spread via blood, including needle sharing or unprotected sex. In contrast,\nH. pylori\ntypically spreads through contaminated food and water or close human contact, often in places with poor sanitation.\nThe chart below shows the estimated share of cancers caused by infectious diseases. This comes from the International Agency for Research on Cancer.\n15\nYou can see that, for some cancers — such as\nKaposi’s sarcoma\n, cervical cancer, T-cell\nleukemia\nand\nlymphoma\n, and non-cardia stomach cancer — it’s estimated that all, or almost all, cases are caused by pathogens.\nWithout these pathogens, these organs tend to remain stable and rarely form cancers.\nFor many others, such as lung cancer, pathogens are not established as a causal\nrisk factor\nand are not shown in the chart.\nIn total, it’s estimated that around 13% of all cancers worldwide were caused by infections in 2020.\nThese infection-caused cancers can be prevented or treated in different ways.\nThis includes vaccination (for\nHPV\nand\nhepatitis B\n), antibiotic treatment and improved hygiene (for\nH. pylori\n), antiviral treatments (for\nhepatitis C\n), and various types of chemotherapies, radiation therapies, immunotherapies, and surgeries.\nBy controlling these infections, their cancers can be reduced or prevented effectively.\nRead more in our article about human papillomavirus:\nHPV vaccination: How the world can eliminate cervical cancer\nHPV vaccines offer a rare opportunity to effectively eliminate one type of cancer. By taking this opportunity, it’s possible to save hundreds of thousands of women each year.\nThe map below shows that the share of cancers caused by infections varies widely worldwide — with around a quarter of cancer cases caused by infections in many African countries versus less than 10% in many European countries and North America.\nThese disparities are due to many factors, including the risks of infection and public health efforts, such as vaccination, sanitation, and antibiotics.\nMuch more progress can be made against cancers\nAlthough the world has made progress against several cancers, it remains one of the largest health problems, and it is the\nmost common cause of death\nin many countries.\nIt’s estimated that around 10 million people died from cancer in 2021. This number is expected to rise over time, with a growing and aging population.\nMuch more progress is possible. As the chart below shows, death rates from cancers remain high.\nOur ability to treat them can improve with a better understanding of the risk factors and causes and research into potential new treatments and public health policies.\nWe can also make more use of existing tools — including vaccines, antibiotics, and antivirals for cancers caused by infections.\nWe can also save lives by expanding access to screening and early treatment, especially in places where healthcare is limited.\nOn a broader scale, we can use public health efforts that reduce smoking, improve sanitation, and lower exposure to\ncarcinogens\nto drive further progress.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nDefinitions and measurement\nDifferences in screening and diagnosis can affect the number of cancer cases detected\nIn the past, many cancers went undetected, especially at the early stages of cancer. Improvements in screening and diagnosis methods have helped detect cancers earlier before they might progress and lead to noticeable symptoms or disease.\nThese can include\nmammography\nfor breast cancer,\nPap smears\nor\nHPV\ntesting for cervical cancer, and\ncolonoscopies\nfor colorectal cancer.\nSome countries have screening programs to test populations at risk of specific cancers. For example, all women of certain ages may be invited for Pap smear tests to screen for cervical cancer, or long-term smokers may be invited for lung cancer screening.\nOther cases may be detected during routine check-ups, other healthcare visits, or emergency admissions.\nThe chart below shows these pathways to diagnosis in England. As you can see, a significant share of stage 1 cases of cervical, breast, and colorectal cancer, are detected during screening in England. For others, most stage 1 cases are diagnosed during general practitioner (GP) referrals or other routes.\nScreening rates and policies can vary greatly between countries. They have also changed over time.\n16\nThe chart below shows data from Europe on screening rates for different cancers. Screening rates vary between countries and\ndropped\nin several countries during the COVID-19 pandemic.\nThese differences can result in differences in the reported number of cancer cases between countries and over time, even without underlying changes in cancer rates.\nIn many countries, a national cancer registry collects data on the number of cancer cases. This only includes cancers that are detected and reported, which means that new and expanded screening programs can increase the number of cancer cases reported.\n16\nInternational statistical organizations aim to adjust for underdiagnosis of cancer in countries without cancer registries, by using other hospital or death records. However, this can be challenging without sufficient data.\nEven in countries with high-quality data from cancer registries, it can be difficult to account for new screening policies and improvements in survival rates over time.\nNon-melanoma skin cancers are typically excluded from cancer statistics\nNon-melanoma skin cancer (NMSC)\nrefers to skin cancers aside from\nmelanoma\n. This group of cancers is often not included in international cancer statistics.\nThis is because NMSC cases are usually\nbenign\nand easily treatable, and awareness and diagnosis of them have increased greatly over time.\n17\nIn addition, different countriesʼ reporting standards vary. Some countries don’t include NMSC in their cancer registries, while others do.\n18\nMany international statistical organizations exclude them from overall cancer statistics, as we do on this page, to make comparisons more valid.\nKey Charts on Cancer\nSee all charts on this topic\nCancer death rate by type\nIHME, estimated\nCancer death rate: crude versus age-standardized\nWHO Mortality Database, age-standardized\nCancer death rates by type\nWHO Mortality Database, age-standardized, reported\nCancer deaths by type\nStacked area chart\nCancer incidence rate by age group\nDeath rate from cancer\nIHME, age-standardized, estimated\nDeath rate from cancer\nWHO, crude, estimated\nDeath rate from cancer\nIHME, crude, estimated\nDeath rate from cancer\nWHO Mortality Database, age-standardized, reported\nDeath rate from cancer by sex\nWHO Mortality Database, age-standardized, reported\nDeaths from cancer\nIHME\nDeaths from cancer\nWHO\nDeaths from cancer by age\nLeading cancer types causing death in men\nLeading cancer types causing death in women\nPeople who currently have cancer, by age group\nRate of new cases of cancer by type\nScreening rates for cancers in Europe\nShare of cancer deaths by cancer type\nShare of deaths from underlying causes\nShare of new cancers caused by infections\nThree measures of cancer mortality\nBreast cancer death rate in women\nCancer death rate by age group\nIHME\nCancer death rate in children under 5 years old by type\nIHME, estimated\nCancer death rate in children under five years old by type\nIHME, estimated\nCancer death rates in children under 10 years old\nWHO MDB, reported\nCancer incidence rate\nCervical cancer death rate in women\nColon and rectum cancer death rate\nDeath rate from cancers vs. GDP per capita\nDeath rates from different causes\nWHO Mortality Database, age-standardized\nFive-year net cancer survival rates by sex\nFive-year net survival rates by cancer type\nLung cancer death rates\nNumber of people with cancer\nNumber of people with cancer by type\nPancreatic cancer death rate\nProstate cancer death rate in men\nShare of cancer deaths attributed to different risk factors\nby risk factor\nShare of cancer deaths attributed to risk factors\nby cancer\nShare of population with cancer vs. median age\nStomach cancer death rate\nBreast cancer screening coverage in Europe\nCervical cancer screening coverage in Europe\nColon and rectum cancer screening coverage in Europe\nLiver cancer incidence rate by risk factor\nShare of Stage 4 cancers diagnosed by different pathways in England\nShare of all cancer deaths attributable to alcohol use\nShare of cancer deaths attributed to alcohol use by type\nShare of cancer deaths linked to dietary risk factors by cancer type\nShare of cancer deaths linked to high body mass index (BMI) by type\nShare of cancer deaths linked to tobacco use by cancer type\nShare of lung cancer deaths attributed to air pollution\nShare of new cancers caused by Helicobacter pylori bacteria\nShare of new cancers caused by all known infectious agents\nShare of population with cancer\nShare of population with cancer\nCrude, by country\nShare of population with cancer by age\nShare of population with cancer by type\nBy cancer type\nShare of stage 1 cancers diagnosed by each pathway in England\nChart 1 of 61\nFeatured Data on\nCancer\nEndnotes\nSas, A. A., Snieder, H., & Korf, J. (2012). Gompertz’ survivorship law as an intrinsic principle of aging.\nMedical Hypotheses\n,\n78\n(5), 659–663.\nhttps://doi.org/10.1016/j.mehy.2012.02.004\nMoss, Steven F. “The Clinical Evidence Linking Helicobacter Pylori to Gastric Cancer.”\nCellular and Molecular Gastroenterology and Hepatology\n3, no. 2 (March 2017): 183–91.\nhttps://doi.org/10.1016/j.jcmgh.2016.12.001\nBalakrishnan, Maya, Rollin George, Ashish Sharma, and David Y. Graham. “Changing Trends in Stomach Cancer Throughout the World.”\nCurrent Gastroenterology Reports\n19, no. 8 (August 2017): 36.\nhttps://doi.org/10.1007/s11894-017-0575-8\nLee, Y.-C., Dore, M. P., & Graham, D. Y. (2022). Diagnosis and Treatment of Helicobacter pylori Infection. Annual Review of Medicine, 73(1), 183–195.\nhttps://doi.org/10.1146/annurev-med-042220-020814\nChiang, T.-H., Cheng, H.-C., Chuang, S.-L., Chen, Y.-R., Hsu, Y.-H., Hsu, T.-H., Lin, L.-J., Lin, Y.-W., Chu, C.-H., Wu, M.-S., & Lee, Y.-C. (2022). Mass screening and eradication of Helicobacter pylori as the policy recommendations for gastric cancer prevention. Journal of the Formosan Medical Association, 121(12), 2378–2392.\nhttps://doi.org/10.1016/j.jfma.2022.08.012\nKowada, A., & Asaka, M. (2021). Economic and health impacts of introducing Helicobacter pylori eradication strategy into national gastric cancer policy in Japan: A cost‐effectiveness analysis. Helicobacter, 26(5), e12837.\nhttps://doi.org/10.1111/hel.12837\nChen, M., Bair, M., Chen, P., Lee, J., Yang, T., Fang, Y., Chen, C., Chang, A., Hsiao, W., Yu, J., Kuo, C., Chiu, M., Lin, K., Tsai, M., Hsu, Y., Chou, C., Chen, C., Lin, J., Lee, Y., … for the Taiwan Gastrointestinal Disease and Helicobacter Consortium. (2022). Declining trends of prevalence of Helicobacter pylori infection and incidence of gastric cancer in Taiwan: An updated cross‐sectional survey and meta‐analysis. Helicobacter, 27(5), e12914.\nhttps://doi.org/10.1111/hel.12914\nChiang, T.-H., Chang, W.-J., Chen, S. L.-S., Yen, A. M.-F., Fann, J. C.-Y., Chiu, S. Y.-H., Chen, Y.-R., Chuang, S.-L., Shieh, C.-F., Liu, C.-Y., Chiu, H.-M., Chiang, H., Shun, C.-T., Lin, M.-W., Wu, M.-S., Lin, J.-T., Chan, C.-C., Graham, D. Y., Chen, H.-H., & Lee, Y.-C. (2020). Mass eradication of Helicobacter pylori to reduce gastric cancer incidence and mortality: A long-term cohort study on Matsu Islands. Gut, gutjnl-2020-322200.\nhttps://doi.org/10.1136/gutjnl-2020-322200\nKuhlen, M., Taeubner, J., Brozou, T., Wieczorek, D., Siebert, R., & Borkhardt, A. (2019). Family-based germline sequencing in children with cancer. Oncogene, 38(9), 1367–1380.\nhttps://doi.org/10.1038/s41388-018-0520-9\nPlon, S. E., & Lupo, P. J. (2019). Genetic Predisposition to Childhood Cancer in the Genomic Era. Annual Review of Genomics and Human Genetics, 20, 241–263.\nhttps://doi.org/10.1146/annurev-genom-083118-015415\nKuhlen, M., Taeubner, J., Brozou, T., Wieczorek, D., Siebert, R., & Borkhardt, A. (2019). Family-based germline sequencing in children with cancer. Oncogene, 38(9), 1367–1380.\nhttps://doi.org/10.1038/s41388-018-0520-9\nPlon, S. E., & Lupo, P. J. (2019). Genetic Predisposition to Childhood Cancer in the Genomic Era. Annual Review of Genomics and Human Genetics, 20, 241–263.\nhttps://doi.org/10.1146/annurev-genom-083118-015415\nRashed, W. M., Marcotte, E. L., & Spector, L. G. (2022). Germline De Novo Mutations as a Cause of Childhood Cancer. JCO Precision Oncology, 6, e2100505.\nhttps://doi.org/10.1200/PO.21.00505\nSkalet, A. H., Gombos, D. S., Gallie, B. L., Kim, J. W., Shields, C. L., Marr, B. P., Plon, S. E., & Chévez-Barrios, P. (2018). Screening Children at Risk for Retinoblastoma. Ophthalmology, 125(3), 453–458.\nhttps://doi.org/10.1016/j.ophtha.2017.09.001\nSmith, M. A., Altekruse, S. F., Adamson, P. C., Reaman, G. H., & Seibel, N. L. (2014). Declining childhood and adolescent cancer mortality.\nCancer\n,\n120\n(16), 2497–2506.\nhttps://doi.org/10.1002/cncr.28748\nErdmann, F., Frederiksen, L. E., Bonaventure, A., Mader, L., Hasle, H., Robison, L. L., & Winther, J. F. (2021). Childhood cancer: Survival, treatment modalities, late effects and improvements over time. Cancer Epidemiology, 71, 101733.\nhttps://doi.org/10.1016/j.canep.2020.101733\nTran, T. H., & Hunger, S. P. (2022). The genomic landscape of pediatric acute lymphoblastic leukemia and precision medicine opportunities.\nSeminars in Cancer Biology\n,\n84\n, 144–152.\nhttps://doi.org/10.1016/j.semcancer.2020.10.013\nStarý, J., & Hrušák, O. (2016). Recent advances in the management of pediatric acute lymphoblastic leukemia. F1000Research, 5, 2635.\nhttps://doi.org/10.12688/f1000research.9548.1\nPlon, S. E., & Lupo, P. J. (2019). Genetic Predisposition to Childhood Cancer in the Genomic Era. Annual Review of Genomics and Human Genetics, 20, 241–263.\nhttps://doi.org/10.1146/annurev-genom-083118-015415\nWard, Elizabeth M., Christopher R. Flowers, Ted Gansler, Saad B. Omer, and Robert A. Bednarczyk. “The Importance of Immunization in Cancer Prevention, Treatment, and Survivorship.”\nCA: A Cancer Journal for Clinicians\n67, no. 5 (September 2017): 398–410.\nhttps://doi.org/10.3322/caac.21407\nLee, J., V. Taneja, and R. Vassallo. “Cigarette Smoking and Inflammation: Cellular and Molecular Mechanisms.”\nJournal of Dental Research\n91, no. 2 (February 2012): 142–49.\nhttps://doi.org/10.1177/0022034511421200\nOza, 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.\nhttps://doi.org/10.1016/j.ypmed.2011.04.007\nSchiffman, M., Doorbar, J., Wentzensen, N., De Sanjosé, S., Fakhry, C., Monk, B. J., Stanley, M. A., & Franceschi, S. (2016). Carcinogenic human papillomavirus infection.\nNature Reviews Disease Primers\n,\n2\n(1), 16086.\nhttps://doi.org/10.1038/nrdp.2016.86\nMoss, S. F. (2017). The Clinical Evidence Linking Helicobacter pylori to Gastric Cancer. Cellular and Molecular Gastroenterology and Hepatology, 3(2), 183–191.\nhttps://doi.org/10.1016/j.jcmgh.2016.12.001\nLlovet, J. M., Kelley, R. K., Villanueva, A., Singal, A. G., Pikarsky, E., Roayaie, S., Lencioni, R., Koike, K., Zucman-Rossi, J., & Finn, R. S. (2021). Hepatocellular carcinoma. Nature Reviews Disease Primers, 7(1), 6.\nhttps://doi.org/10.1038/s41572-020-00240-3\nDe Martel, Catherine, Damien Georges, Freddie Bray, Jacques Ferlay, and Gary M Clifford. “Global Burden of Cancer Attributable to Infections in 2018: A Worldwide Incidence Analysis.”\nThe Lancet Global Health\n8, no. 2 (February 2020): e180–90.\nhttps://doi.org/10.1016/S2214-109X(19)30488-7\nCardoso, R., Guo, F., Heisser, T., Hackl, M., Ihle, P., De Schutter, H., Van Damme, N., Valerianova, Z., Atanasov, T., Májek, O., Mužík, J., Nilbert, M. C., Tybjerg, A. J., Innos, K., Mägi, M., Malila, N., Bouvier, A.-M., Bouvier, V., Launoy, G., … Brenner, H. (2021). Colorectal cancer incidence, mortality, and stage distribution in European countries in the colorectal cancer screening era: An international population-based study.\nThe Lancet Oncology\n,\n22\n(7), 1002–1013.\nhttps://doi.org/10.1016/S1470-2045(21)00199-6\nSome forms of NMSC are not mild; for example, squamous cell carcinomas can sometimes spread to other parts of the body if not treated. Non-melanoma skin cancers can also cause damage to the skin and surrounding tissues if they are left untreated for too long.\nCiążyńska, M., Kamińska-Winciorek, G., Lange, D., Lewandowski, B., Reich, A., Sławińska, M., Pabianek, M., Szczepaniak, K., Hankiewicz, A., Ułańska, M., Morawiec, J., Błasińska-Morawiec, M., Morawiec, Z., Piekarski, J., Nejc, D., Brodowski, R., Zaryczańska, A., Sobjanek, M., Nowicki, R. J., … Lesiak, A. (2021). The incidence and clinical analysis of non-melanoma skin cancer. Scientific Reports, 11(1), 4337.\nhttps://doi.org/10.1038/s41598-021-83502-8\nEisemann, N., Waldmann, A., Geller, A. C., Weinstock, M. A., Volkmer, B., Greinert, R., Breitbart, E. W., & Katalinic, A. (2014). Non-Melanoma Skin Cancer Incidence and Impact of Skin Cancer Screening on Incidence. Journal of Investigative Dermatology, 134(1), 43–50.\nhttps://doi.org/10.1038/jid.2013.304\nThe National Cancer Registry of Ireland provides an example. They write:\n”Registrations for non-melanoma skin cancer (ICD-10 C44) are likely to be less complete and less accurate than for other cancer sites. Such cancers are relatively common and usually non-fatal. There is a propensity for multiple tumours to occur in one individual and cancer registries adopt different practices in recording these. The tumours are most common in the elderly population and the completeness of registration in the very elderly is likely to be less than for younger patients. Furthermore, increasing numbers of these cancers are diagnosed and treated within GP surgeries and the registration scheme is not confident that all such cases are notified. Because cancer registries across the world have different practices for recording non-melanoma skin cancer (some do not record them at all), the category \"all cancers combined\" often omits these tumours in the interests of making international comparisons of cancer incidence more valid.”\nNational Cancer Registry of Ireland (2024). Why are data for non-melanoma skin cancer sometimes excluded? Available\nonline\n.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this topic page, please also cite the underlying data sources. This topic page can be cited as:\nSaloni Dattani, Veronika Samborska, Hannah Ritchie, and Max Roser (2024) - “Cancer” Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/cancer' [Online Resource]\nBibTeX citation\n@article{owid-cancer,\nauthor = {Saloni Dattani and Veronika Samborska and Hannah Ritchie and Max Roser},\ntitle = {Cancer},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://ourworldindata.org/cancer}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "cancer", "source_url": "https://ourworldindata.org/cancer", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Cancers are one of the leading causes of death globally. Are we making progress against them?", "numeric_mentions": ["2021,", "15%", "1970", "2018", "2022", "1", "1990", "2021", "9", "1950", "2", "3", "4", "5", "6", "2022,", "2025,", "7", "8", "10", "11", "20", "12", "13", "14", "15", "13%", "2020", "10%", "10 million", "16", "19", "17", "18", "5 years", "10 years", "61", "2012", "78", "659", "663", "10.1016", "2012.02", "004", "3,", "2017", "183", "91", "2016.12", "001", "19,", "36", "10.1007", "017", "0575", "73", "195", "10.1146", "042220", "020814", "121", "2378", "2392", "2022.08", "012", "26", "10.1111", "12837", "27", "12914", "322200", "10.1136", "2019", "38", "1367", "1380", "10.1038", "018", "0520", "20,"], "numeric_evidence": [{"grapher_slug": "share-of-deaths-by-cause", "source_url": "https://ourworldindata.org/grapher/share-of-deaths-by-cause", "parse_error": "Failed to fetch https://ourworldindata.org/grapher/share-of-deaths-by-cause.csv"}, {"grapher_slug": "cancer-crude-death-rate-by-type", "source_url": "https://ourworldindata.org/grapher/cancer-crude-death-rate-by-type", "parse_error": "Failed to fetch https://ourworldindata.org/grapher/cancer-crude-death-rate-by-type.csv"}, {"title": "Leading cancer types causing death in men", "source_url": "https://ourworldindata.org/grapher/leading-cancer-types-causing-death-in-men.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Leading cancer types causing death in males"], "row_count_total": 4904, "rows_head": [{"Entity": "Albania", "Code": "ALB", "Year": "1987", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "1988", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "1989", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "1992", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "1993", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "1994", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "1995", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "1996", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "1997", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "1998", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "1999", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "2000", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "2001", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "2002", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "2003", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "2004", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "2005", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "2006", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "2007", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "2008", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "2009", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Albania", "Code": "ALB", "Year": "2010", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1961", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1962", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1963", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1964", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1966", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1969", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1970", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1971", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1972", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1973", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1974", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1975", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1976", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1977", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1978", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1983", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1985", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1986", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1987", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1988", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1989", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1990", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1991", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1992", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1993", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1994", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1995", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1998", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "1999", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2000", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2001", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2002", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2003", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2004", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2005", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2006", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2007", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2008", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2009", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2010", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2011", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2012", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2013", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2014", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2015", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2016", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2017", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2018", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2019", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2020", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2021", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1966", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1967", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1968", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1969", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1970", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1977", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1978", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1979", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1980", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1981", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1982", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1983", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1984", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1985", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1986", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1987", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1988", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1989", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1990", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1991", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1992", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1993", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1994", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1995", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1996", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1997", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1998", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1999", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2000", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2001", "Leading cancer types causing death in males": "Trachea and bronchus 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bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2004", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2005", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2006", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2007", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2008", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2009", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2010", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2012", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2013", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2014", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2015", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2016", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2017", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2018", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2019", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2020", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2021", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2022", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1981", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1982", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1985", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1986", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1987", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1988", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1989", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1990", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1991", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1992", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1993", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1994", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1995", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1996", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1997", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1998", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "1999", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2000", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2001", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2002", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2003", "Leading cancer types causing death in males": "Other malignant neoplasms"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2004", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2005", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2009", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2010", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2011", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2012", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2013", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2014", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2015", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2016", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2017", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2018", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2019", "Leading cancer types causing death in males": "Trachea and bronchus and lung"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2020", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2021", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2022", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Uzbekistan", "Code": "UZB", "Year": "2023", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1955", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1956", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1957", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1958", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1959", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1960", "Leading cancer types causing death in males": "Stomach"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "1961", "Leading cancer types causing death in 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"1999", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2000", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2001", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2002", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2003", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2004", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2005", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2006", "Leading cancer types causing death in males": "Prostate"}, {"Entity": "Venezuela", "Code": "VEN", "Year": "2007", "Leading cancer types causing death in males": "Prostate"}, {"Entity": 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{"raw_link": "https://ourworldindata.org/crop-yields-climate-impact", "title": "Crop yields have increased dramatically in recent decades, but crops like maize would have improved more without climate change", "context": "Home\nClimate Change\nCrop yields have increased dramatically in recent decades, but crops like maize would have improved more without climate change\nClimate change has slowed the productivity of key crops such as maize and soybeans, but might have had small positive impacts on wheat.\nBy\nHannah Ritchie\nSeptember 30, 2024\nBrowse past versions\nCite this article\nReuse our work freely\nAgriculture is arguably the industry most sensitive to changes in the climate. Crops need CO\n2\n, water — not too little or too much — and the right temperatures to grow. That’s why it’s the impact of climate change that I’m most worried about.\nIn this 3-part series on climate change and food, I’m going to look at three important questions: how has climate change\nalready\naffected food production; what could these impacts look like in the future; and how can we feed a growing population in a warmer world?\nIn this first article, I’ll focus on the first question, looking at how different factors affect plant growth and how large these effects have\nalready\nbeen.\nHow do CO\n2\n, temperature, and water impact crop yields?\nYou might have heard people argue over whether climate change would help or hurt crop yields.\nSome argue that having more CO\n2\nin the atmosphere is good because it increases plant growth, while others argue that higher temperatures reduce growth. Both are true at the same time. The combined impacts on yields vary a lot depending on the type of crop and location.\nWhen considering the net impacts of climate on food production, we need to consider three key factors: higher concentrations of CO\n2\n, warmer temperatures, and changes in rainfall (which can cause too much, or not enough, water).\n1\nLet’s look at each of these effects in isolation.\nThe first climate impact: carbon dioxide fertilisation\nCarbon dioxide helps plants grow in two ways.\n2\nFirst, it increases the rate of photosynthesis. Plants use sunlight to create sugars out of CO\n2\nand water. When there’s more CO\n2\nin the atmosphere, this process can go faster.\nSecond, it means plants can use water more efficiently. When there’s more CO\n2\nin the atmosphere, the pores on the surface of the leaves that release water can close slightly, allowing them to take in the same amount of CO\n2\nwhile losing less water.\nBoth have a positive impact, CO\n2\ndoes help plant growth.\nBut, some crops benefit much more than others. Wheat and rice benefit quite a lot. Maize, millet, and sorghum benefit much less, and only when under water stress conditions. The chart below shows the results of a large review of studies assessing how higher concentrations of CO\n2\nimpact crop yields.\n3\nHere, CO\n2\nconcentrations were increased from 350 to 570 parts per million (ppm). For context, we’re\ncurrently at\naround 420 ppm, and reaching 570 ppm would mean global temperatures are 2°C to 2.5°C higher than pre-industrial times.\nWheat and rice yields increased by around 15% to 20%. Yields of maize and sorghum responded very little to CO\n2\nunder “normal” conditions but increased by 30% under water stress.\nWhile non-cereal crops weren’t included in this review, other studies suggest that crops such as soybeans, potatoes, tomatoes, and other vegetables get large yield benefits from higher CO\n2\nlevels.\n4\nDoubling levels of CO\n2\nincreased soybean yields by 22% to 45%; potato by 51%; lettuce by 35% to 44%; and tomato by around one-quarter.\nDownload\nThe second climate impact: warmer temperatures\nWarmer temperatures can both benefit or hurt crop yields.\n5\nThere are a few reasons for this.\nWarmer temperatures can\nincrease the length\nof the potential growing season (sometimes called the “climatological growing season”).\n6\nCrops have a minimum temperature below which they can’t be grown. The “growing season” is, therefore, often defined as the number of days across the year where mean temperatures are higher than this. For example, temperatures of 5°C or more are needed to grow winter or spring wheat.\n7\nAt high latitudes, warming will increase the number of days across the year when wheat can be planted, making the potential growing season longer.\nAt the same time, higher temperatures mean that crops mature faster, so their individual “growing period” is shorter.\nWhether these changes positively or negatively impact productivity across the year depends on the type of crop, where it’s grown, and how farmers respond. Farmers could, for example, switch to “early-maturing” crop varieties so they can replant and produce a second harvest within a single year (also known as “double-cropping”).\nCurrently, temperature-limited regions — such as Northern Europe, Canada, and Russia — can potentially benefit from some warming, whereas tropical regions largely face losses.\nCrops can grow at various temperatures, but each crop variety has an “optimum” temperature where they grow best. If temperatures are below or above the “optimum” temperature, crops grow more slowly and produce smaller grain seeds. Even brief exposure to extreme warm or cold temperatures (such as a heatwave or cold snap) can damage the plant tissue and kill the crop completely.\nI’ve shown this in the schematic below, which shows how crop growth varies by temperature.\nEach crop can grow across a range of temperatures and will have a slightly different curve. Crops like maize, millet, and sorghum tend to grow best at higher temperatures — reaching their optimum growth at 25°C to 30°C for maize and more than 30°C for millet and sorghum.\nWheat grows much better at lower temperatures of around 15°C to 20°C.\nDownload\nThis explains where crops are grown across the world today. Europe\nproduces lots of wheat\n, but\nvery little maize\n. Millet is\nmostly grown\nacross Sub-Saharan Africa and South Asia, where temperatures are higher. Sorghum,\ntoo\n.\nHow warmer temperatures will affect crop yields, therefore, depends on the choice of crop variety and how far a region’s temperature is away from the optimum. If the local temperatures are currently colder than the optimum, warming could increase yields. If temperatures are higher than is optimal, more warming will reduce them.\nThe third climate impact: water availability\nPlants need water to grow and, therefore, struggle under water stress. But having\ntoo much\nwater is also a problem. Too little water hampers photosynthesis, and the plant can even die; waterlogged soils, on the other hand, reduce the amount of oxygen available to the crop roots, which reduces plant growth.\n8\nChanging rainfall patterns means crops could experience more frequent or intense drought or excess water.\nHow much does water availability impact crop yields?\nThe data in the chart below comes from a meta-analysis of tens to hundreds of experiments on different crops.\n3\nIt gives us some indication of the size of these impacts, but this can vary a lot depending on the crop type and local context.\nThe first panel in the chart below shows the reduction in yields that different crops experience under drought conditions. The average reduction in water availability was around 40% to 50%, but as I mentioned, this can vary a lot depending on the context.\nWheat tends to be much more tolerant of water stress than crops like maize, so it experiences lower yield declines under drought.\nThe second panel shows how yields are affected by waterlogging. Wheat and maize yields drop by 25% to 35%. Sorghum by more than 40%, on average.\nNotice that rice isn’t shown here because it’s usually grown in water-logged paddies anyway.\nDownload\nTaking all three effects together\nIn the real world, all three impacts come together. Atmospheric CO\n2\nlevels are rising\nand\nthe world is getting warmer\nand\nrainfall patterns are changing.\nAgain, how these effects interact will depend on the crop type, how it is managed, and where it’s being grown. If a crop is under irrigation, for example, water stress is less of a concern.\nFor example, wheat harvests in Northern Europe could increase with climate change. Wheat benefits from higher levels of CO\n2\n, and since temperatures are often\nbelow\nthe “optimum” for wheat, warming could push them closer to it.\nThat isn’t necessarily the case in Southern Europe. It’ll still benefit from carbon fertilization, but warming might push temperatures further away from the “optimum”. The losses from higher temperatures might offset the gains from having more CO\n2\n. The net effect will depend on how much the world warms, and how high CO\n2\nconcentrations are.\nMaize production in the tropics and subtropics is different again. More CO\n2\ndoesn’t benefit it much, and temperatures are already warmer than optimal, so more warming will hurt yields even more. The exception is under drought conditions when more CO\n2\ncould unlock semi-arid areas for agriculture because plants can use water more efficiently.\nOne important point is that climate and crop models mostly capture the\naverage\neffects that CO\n2\n, temperature, and water availability have on crop yields. Even if the average changes are small, there could be a higher frequency of extreme events like heatwaves or droughts that lead to more variability from year to year.\nIn my next article, I’ll look at some concrete projections — by crop and by country — for how crop yields could change in the future. For now, the important takeaway is that the impacts differ between regions.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nHow has climate change already impacted crop yields?\nThe world has\nalready warmed\nby around 1.3°C compared to pre-industrial temperatures. Atmospheric concentrations of CO\n2\nhave increased\nfrom around 280 to 420 ppm. What was the impact on crop yields and food production?\nA large number of studies investigate the impact of increased temperatures, changes in water availability, or CO\n2\nconcentrations individually, but a much smaller number of studies combine all three.\n9\nThose that do — including those cited in the\nlatest AR6 report\nfrom the Intergovernmental Panel on Climate Change (IPCC) — report mixed results.\n10\nA study from Frances Moore found that the impacts of increased temperatures alone harmed the yields of three staple crops, but the drop “has likely been fully compensated, at a global level, by gains from CO\n2\nfertilization”.\n11\nIn other words, the combined effect of these variables was probably close to zero.\n12\nThe IPCC cites another study — by Toshichika Iizumi and colleagues — that finds that CO\n2\nfertilization only offsets the losses from increased temperatures for wheat and rice. Maize and soybeans still saw a net decline.\n13\nThis matches what we saw earlier: wheat and rice benefit far more from higher levels of CO\n2\nthan maize.\nThe study found that rice and wheat did not show a significant decline (and in some cases saw a small positive impact), but maize yields were 2.4% to 4.1%, and soybeans were 4.5% lower.\n14\nCombined, the world\nproduces around\n1.5 billion tonnes of maize and soybeans each year. This is about the same as the sum of wheat and rice. If the yields of the first two have seen a net decline, and the latter two have seen little change, then the\nnet\nimpact across these four crops is probably a more moderate decline of a few percent.\nIt’s important to give context to what these reported yield losses mean. It would be a mistake to read this as suggesting that crop yields today are\nlower\nthan they were decades ago. That reports about a “5% decline” means that yields have dropped below what they were in the past. That’s far from the truth.\nThe chart below shows the actual change in crop yields globally over this period. Wheat yields have grown by 225%, maize by 196%, soybean by 153%, and rice by 146%. This growth has been driven by improvements in seed varieties, fertilizers, pesticides, irrigation, and better farming practices.\nWhat these numbers actually tell us is how different yields would be in a world\nwithout\nclimate change compared to our current one; a “decline” in this case means that in such a world yield growth would have been even higher.\nIn a world without climate change, maize yields might have been 4% to 5% higher than they are today. Instead of the actual 196% increase, we would have seen a 208% increase. Climate change has been a drag on yield increases, but this has not been enough to stop them from growing. Advances in crop breeding, the development of better seeds, and access to agricultural inputs have vastly outpaced the impacts of climate.\nIn the chart below I show how crop yield growth might have been different for four staple crops in a world without warming. This is based on the study by Toshichika Iizumi and colleagues, which\ndid\nfactor in changes in climate\nand\nCO\n2\nfertilization on yield growth between 1981 and 2010.\nIn 2010, average maize yields were 5.2 tonnes per hectare — 47% higher than the 3.5 tonnes you’d get in 1981. If there had been no climate change, we might have achieved 5.4 tonnes instead. A gain of 0.2 tonnes per hectare.\nSoybean increased from 1.75 to 2.6 tonnes per hectare. Without climate change, that might have been 2.7 tonnes.\nThe net impacts on rice and wheat have been marginal because reductions due to warming have been offset by CO\n2\nfertilization.\nDownload\nOverall, climate change has probably already suppressed crop yields of key crops like maize at a global level (with varying patterns across regions). But we’ve also seen massive improvements in yields thanks to non-climate developments in agricultural technologies.\nThis is crucial to keep in mind when we look at the future impacts of climate change. It will put increasing pressure on many crops, but we are not helpless in adapting and enhancing our food systems to deal with it.\nAcknowledgments\nMany thanks to Max Roser and Edouard Mathieu for their comments on this article and to Jonas Jägermeyr for invaluable suggestions and feedback on this series of work.\nThis article is the first in our series on climate change and agriculture:\nCrop yields have increased dramatically in recent decades, but crops like maize would have improved more without climate change\nClimate change has slowed the productivity of key crops such as maize and soybeans, but might have had small positive impacts on wheat.\nHow will climate change affect crop yields in the future?\nMaize yields could see significant declines, but wheat could increase. Impacts across the world will be very different.\nClimate change will affect food production, but here are the things we can do to adapt\nAdapting planting dates, selecting better crop varieties, and increasing access to irrigation and fertilizers could offset potential declines in crop yields.\nEndnotes\nThere are many more factors that determine crop growth, including the amount of radiation, air pollution, aerosol concentrations, but when it comes to climate impacts, temperature, CO\n2\nand water are the biggest environmental factors. How we breed, select, and manage these crops plays a massive role, which we’ll come to later.\nHaverd, V., Smith, B., Canadell, J. G., Cuntz, M., Mikaloff‐Fletcher, S., Farquhar, G., ... & Trudinger, C. M. (2020). Higher than expected CO2 fertilization inferred from leaf to global observations. Global Change Biology.\nRezaei, E. E., Webber, H., Asseng, S., Boote, K., Durand, J. L., Ewert, F., ... & MacCarthy, D. S. (2023). Climate change impacts on crop yields. Nature Reviews Earth & Environment.\nKorres, N. E., Norsworthy, J. K., Tehranchian, P., Gitsopoulos, T. K., Loka, D. A., Oosterhuis, D. M., ... & Palhano, M. (2016). Cultivars to face climate change effects on crops and weeds: a review. Agronomy for Sustainable Development.\nKorres, N. E., Norsworthy, J. K., Tehranchian, P., Gitsopoulos, T. K., Loka, D. A., Oosterhuis, D. M., ... & Palhano, M. (2016). Cultivars to face climate change effects on crops and weeds: a review. Agronomy for Sustainable Development.\nKukal, M. S., & Irmak, S. (2018). US agro-climate in 20th century: Growing degree days, first and last frost, growing season length, and impacts on crop yields. Scientific reports.\nMueller, B., Hauser, M., Iles, C., Rimi, R. H., Zwiers, F. W., & Wan, H. (2015). Lengthening of the growing season in wheat and maize producing regions. Weather and Climate Extremes.\nManghwar, H., Hussain, A., Alam, I., Khoso, M. A., Ali, Q., & Liu, F. (2024). Waterlogging stress in plants: Unraveling the mechanisms and impacts on growth, development, and productivity.\nEnvironmental and Experimental Botany\n.\nFor example, a study by Deepak Ray and colleagues looked at the impact of past climate change on the yield of 10 staple crops. Combined, it estimated that climate change had led to a small reduction — around 1% — in consumable calories compared to a world without climate change. One key caveat is that the impacts of increased CO2 concentrations were not included, which could have offset the negative impacts of increased temperatures.\nRay, D. K., West, P. C., Clark, M., Gerber, J. S., Prishchepov, A. V., & Chatterjee, S. (2019). Climate change has likely already affected global food production. PloS one.\nIPCC, 2022: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, 3056 pp., doi:10.1017/9781009325844.\nMoore, F., 2020: The fingerprint of anthropogenic warming on global agriculture. EarthArXiv, doi:10.31223/x5q30z. The study estimated that “CO2 fertilization over the 1961-2017 period has raised yields of C3 crops (rice and wheat) by 7.1% and of C4 crops (maize) by 6.0%. These are more than enough to offset, at a global level, the negative effects of anthropogenic warming documented here.”\nIn some countries, this net effect was actually positive. For example, in the United States: Butler, E. E., Mueller, N. D., & Huybers, P. (2018). Peculiarly pleasant weather for US maize. Proceedings of the National Academy of Sciences.\nIizumi, T., Shiogama, H., Imada, Y., Hanasaki, N., Takikawa, H., & Nishimori, M. (2018). Crop production losses associated with anthropogenic climate change for 1981–2010 compared with preindustrial levels. International Journal of Climatology.\nThe final results depended on assumptions about how sensitive crops were to CO\n2\nfertilization. Results for wheat ranged from slightly negative (-1.8%) to no impact. Results for rice showed little net impact or slightly positive. Maize and soybean yields were consistently negative, but the size of the impact was dependent on CO\n2\nsensitivity.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2024) - “Crop yields have increased dramatically in recent decades, but crops like maize would have improved more without climate change” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-090244/crop-yields-climate-impact.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-crop-yields-climate-impact,\nauthor = {Hannah Ritchie},\ntitle = {Crop yields have increased dramatically in recent decades, but crops like maize would have improved more without climate change},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://archive.ourworldindata.org/20260518-090244/crop-yields-climate-impact.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "crop-yields-climate-impact", "source_url": "https://ourworldindata.org/crop-yields-climate-impact", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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"Temperature anomaly, measured as the difference between the average land-sea surface temperature in a given year and the 1861-1890 mean, in degrees Celsius.", "chart_note": "The period 1861–1890 is used as the baseline to measure temperature changes relative to pre-industrial times, [as recommended by the source](https://www.metoffice.gov.uk/hadobs/indicators/index.html#:~:text=For%20global%20average%20temperatures%2C%20an%201861%2D1890%20period%20is%20sometimes%20used%20to%20show%20the%20warming%20since%20the%20%22pre%2Dindustrial%22%20period.).", "chart_citation": "Met Office Hadley Centre - HadCRUT5 (2026)", "original_chart_url": "https://ourworldindata.org/grapher/temperature-anomaly", "owid_column_metadata": {"Global average temperature anomaly relative to 1861-1890": {"titleShort": "Global average temperature anomaly relative to 1861-1890", "titleLong": "Global average temperature anomaly relative to 1861-1890", "descriptionShort": "The difference in average land-sea surface 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This baseline period is commonly used to highlight the changes in temperature since pre-industrial times, prior to major human impacts.", "Temperature averages and anomalies are calculated over all land and ocean surfaces.", "The data includes separate measurements for the Northern and Southern Hemispheres, which helps researchers analyze regional differences.", "The global temperature anomaly is the average of both hemisphere measurements.", "This data is based on the HadCRUT5 method. This method averages temperature measurements onto a fixed grid. 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Met Office Hadley Centre, “HadCRUT5 HadCRUT.5.1.0.0” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1271207.metadata.json"}, "Lower bound of the annual temperature anomaly (95% confidence interval)": {"titleShort": "Lower bound of the annual temperature anomaly (95% confidence interval)", "titleLong": "Lower bound of the annual temperature anomaly (95% confidence interval)", "descriptionKey": ["Temperature anomalies show how many degrees Celsius temperatures have changed compared to the 1861-1890 period. This baseline period is commonly used to highlight the changes in temperature since pre-industrial times, prior to major human impacts.", "Temperature averages and anomalies are calculated over all land and ocean surfaces.", "The data includes separate measurements for the Northern and Southern Hemispheres, which helps researchers analyze regional differences.", "The global temperature anomaly is the average of both hemisphere measurements.", "This data is based on the HadCRUT5 method. This method averages temperature measurements onto a fixed grid. If no data is available for a grid cell, it remains empty and adds extra uncertainty when calculating averages like the global mean.", "Despite different approaches, HadCRUT5 and other methods show similar global temperature trends."], "descriptionProcessing": "- We switch from using 1961-1990 to using 1861-1890 as our baseline to better show how temperatures have changed since pre-industrial times.\n- For each region, we calculate the mean temperature anomalies for 1961-1990 and for 1861-1890. The difference between these two means serves as the adjustment factor.\n- This factor is applied uniformly to both the temperature anomalies and the confidence intervals to ensure that both the central values and the associated uncertainty bounds are correctly shifted relative to the new 1861-1890 baseline.", "shortUnit": "°C", "unit": "degrees Celsius", "timespan": "1850-2026", "type": "Numeric", "owidVariableId": 1271208, "shortName": "near_surface_temperature_anomaly_lower", "lastUpdated": "2026-06-19", "nextUpdate": "2026-08-18", "citationShort": "Met Office Hadley Centre - HadCRUT5 (2026) – with major processing by Our World in Data", "citationLong": "Met Office Hadley Centre - HadCRUT5 (2026) – with major processing by Our World in Data. “Lower bound of the annual temperature anomaly (95% confidence interval)” [dataset]. Met Office Hadley Centre, “HadCRUT5 HadCRUT.5.1.0.0” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1271208.metadata.json"}, "Upper bound of the annual temperature anomaly (95% confidence interval)": {"titleShort": "Upper bound of the annual temperature anomaly (95% confidence interval)", "titleLong": "Upper bound of the annual temperature anomaly (95% confidence interval)", "descriptionKey": ["Temperature anomalies show how many degrees Celsius temperatures have changed compared to the 1861-1890 period. This baseline period is commonly used to highlight the changes in temperature since pre-industrial times, prior to major human impacts.", "Temperature averages and anomalies are calculated over all land and ocean surfaces.", "The data includes separate measurements for the Northern and Southern Hemispheres, which helps researchers analyze regional differences.", "The global temperature anomaly is the average of both hemisphere measurements.", "This data is based on the HadCRUT5 method. This method averages temperature measurements onto a fixed grid. If no data is available for a grid cell, it remains empty and adds extra uncertainty when calculating averages like the global mean.", "Despite different approaches, HadCRUT5 and other methods show similar global temperature trends."], "descriptionProcessing": "- We switch from using 1961-1990 to using 1861-1890 as our baseline to better show how temperatures have changed since pre-industrial times.\n- For each region, we calculate the mean temperature anomalies for 1961-1990 and for 1861-1890. The difference between these two means serves as the adjustment factor.\n- This factor is applied uniformly to both the temperature anomalies and the confidence intervals to ensure that both the central values and the associated uncertainty bounds are correctly shifted relative to the new 1861-1890 baseline.", "shortUnit": "°C", "unit": "degrees Celsius", "timespan": "1850-2026", "type": "Numeric", "owidVariableId": 1271209, "shortName": "near_surface_temperature_anomaly_upper", "lastUpdated": "2026-06-19", "nextUpdate": "2026-08-18", "citationShort": "Met Office Hadley Centre - HadCRUT5 (2026) – with major processing by Our World in Data", "citationLong": "Met Office Hadley Centre - HadCRUT5 (2026) – with major processing by Our World in Data. “Upper bound of the annual temperature anomaly (95% confidence interval)” [dataset]. Met Office Hadley Centre, “HadCRUT5 HadCRUT.5.1.0.0” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1271209.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. 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{"raw_link": "https://ourworldindata.org/low-carbon-technologies-need-far-less-mining-fossil-fuels", "title": "Low-carbon technologies need far less mining than fossil fuels", "context": "Home\nMetals & Minerals\nLow-carbon technologies need far less mining than fossil fuels\nMining for coal is much more resource-intensive than renewables or nuclear power.\nBy\nHannah Ritchie\nSeptember 23, 2024\nBrowse past versions\nCite this article\nReuse our work freely\nIf we want to build a low-carbon economy, we'll need to mine a lot of different minerals. To build solar panels, we’ll need silicon, nickel, silver, and manganese. We’ll need iron and steel for wind turbines, uranium for nuclear power, and lithium and graphite for batteries.\n1\nThis raises the concern that a move to clean energy might drive a huge increase in global mining.\nIt looks this way if you only look at the mining requirements of a low-carbon energy system in isolation. We’ll indeed\nneed to dig out\ntens to hundreds of millions of tonnes of minerals every year for decades.\nBut zero mining is not the right baseline to compare it to. The relevant comparison is what we already mine for our current fossil fuel system. The alternative to low-carbon energy is not a zero-energy economy: it’s maintaining the status quo of a system powered mostly by fossil fuels.\nWhen we run the numbers, we find that moving to renewables or nuclear power actually\nreduces\nthe material requirements for electricity.\nLet’s take a look at the data.\nNuclear power has the lowest material footprint\nHow much concrete, steel, silicon, and other materials do different sources of clean energy need?\nSeaver Wang and his colleagues at The Breakthrough Institute\nrecently published\nan updated study looking at the material requirements of different electricity sources.\n2\nI’ve used this study for a few reasons. First, it’s a very recent, up-to-date assessment, which is essential because many of these technologies have reduced their material footprints dramatically in recent years. Solar panels, batteries, and wind turbines need fewer materials than they used to, thanks to design and efficiency improvements. Second, unlike other (and often outdated) studies, it not only considers the amount of each material needed to build electricity sources, it also calculates the total mining requirements, including waste rock. As we’ll see later, this can make a big difference. Finally, it not only looks at metal and mineral requirements for low-carbon technologies but also puts this into the context of the mining footprint for the\nfuel\n. Some studies look at the materials to build a coal or gas plant but leave out the mining of the fuel itself.\nWhile I focus on the numbers from Wang et al. (2024) here, other high-quality studies have found the same result: that shifting to clean energy will reduce mining for energy rather than increase it. I’ve included some of these in the footnote.\n3\nThe chart below shows how much material — including metals, minerals, and concrete — is needed to produce\none gigawatt-hour of electricity\n. For context, that’s the annual electricity consumption of around 230 British people.\n4\nConcrete (in gray) and steel (in light blue) tend to dominate the material footprint of all of these technologies, consuming hundreds to thousands of kilograms, compared to just tens of kilograms of nickel or manganese, and a few kilograms or less of rarer elements such as silver, graphite or cobalt.\nAs you can see, onshore wind power uses far more materials than solar or nuclear, primarily because of the need for concrete.\nNuclear power — shown with two designs, a European Pressurized water Reactor (EPR) and the smaller AP1000 — has the lowest material intensity.\nMining for metals also produces a lot of waste rock\nThe figures above measure the amount of material that’s used in the final product — the amount of silver or silicon in a solar panel, or the amount of steel used in the turbine. But these figures don’t tell us the total amount of material that has to be dug out of the ground to provide these usable minerals.\nMetals are often found in ores or rocks in low concentrations. Unfortunately, we can’t just extract the bits we need — the valuable metals or minerals — and leave the rest of the rock intact. We usually need to mine much more, leaving a lot of waste rock behind.\nTo get a complete picture of the mining requirements of different energy sources, we need to include this excavated rock, too. We can do this using estimates of the rock-to-metal ratio (RMR).\nThe RMR tells us how much rock needs to be mined to produce one unit of metal. For example, nickel has a ratio of 250. This means we need to extract 250 kilograms of rock to get one kilogram of nickel.\nThe chart below shows the RMR for metals used in low-carbon energy technologies. This data\ncomes from the work\nof Nedal Nassar and colleagues and is one of the most widely cited sources used for mining analyses.\n5\nI’ve combined it with estimates from Seaver Wang and colleagues for a few missing materials, such as uranium and lead.\nCoal requires much more mining than solar, wind, or nuclear power\nSeaver Wang and his colleagues used these rock-to-metal ratios to calculate the total mining requirements of different electricity technologies. They also compared this to the mining requirements for coal (we’ll look at natural gas in the next section).\nNow, just building a coal plant uses less metals and minerals than solar, wind, or nuclear power. But coal relies on vast amounts of mining for the\nfuel\nitself. We need to add that in as well.\nIn the chart below, we see the comparison with waste rock also included for renewables and nuclear. Again, this is measured per gigawatt-hour of electricity generation.\nCoal has a much higher mining footprint than any other source. It’s 26 times higher than solar power and more than 50 times higher than nuclear.\nRenewables have a\nmuch\nlower mining footprint, even when battery storage is included. Moving from coal power to any low-carbon source — solar, wind, or nuclear — would reduce the mining footprint of our electricity systems.\nHow does the material footprint of gas compare?\nThe mining impacts of natural gas are more complicated to estimate and compare.\nWhen we examine the minerals and materials for coal, renewables, and nuclear, we’re mostly comparing the amount of\nrock\nthat has to be mined. Natural gas is, of course, a gas rather than a solid, which makes direct comparison a bit less relevant.\nThere is some disruption and use of rocks for gas extraction: the rock extracted during drilling, the materials used for pipelines, and well casing. Unfortunately, I haven’t seen reliable and comparable estimates on the total amount of rock moved or used for this process.\nThe chart below is the same as the previous one, but with natural gas included. This shows the amount of\ngas\nthat has to be extracted per unit of electricity. Again, these sources are not directly comparable: we’re comparing rock extraction to the quantity of gas that’s extracted, but it gives some context to the quantities involved.\nWhen compared purely on the basis of mass, gas has a much lower footprint than coal, but higher than either renewables or nuclear.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nWe still need to find ways to mine more responsibly\nMoving to renewables or nuclear power reduces the amount of mining needed, compared to the status quo of fossil fuels.\nHowever, this fact doesn’t mean we should dismiss concerns about the environmental damage and working conditions associated with mining — for low-carbon energy or any other industry.\nThe move to low-carbon energy will shift\nwhat\nmaterials we extract and where this mining will take place. There are still important discussions to be had about how to manage this responsibly.\nFurther improvements in the material intensity of low-carbon energy sources are still needed, and recycling and strong governance will play a crucial role in reducing its impacts.\nWhat these results do show is that maintaining our current energy systems —\nmostly running\non fossil fuels — is not only worse for the climate and\nair pollution\n: it’s worse for mining amounts, too.\nAcknowledgements\nMany thanks to Max Roser, Edouard Mathieu, Seaver Wang, and Peter Cook for their comments and feedback on this article.\nEndnotes\nTypical lithium-ion batteries obviously use lithium, although there are alternatives such as sodium-ion batteries, which could reduce this demand in the future.\nWang, Cook, Stein, Lloyd, Smith (2024). Updated Mining Footprints and Raw Material Needs for Clean Energy.\nNijnens, J., Behrens, P., Kraan, O., Sprecher, B., & Kleijn, R. (2023). Energy transition will require substantially less mining than the current fossil system. Joule.\nWatari, T., McLellan, B. C., Giurco, D., Dominish, E., Yamasue, E., & Nansai, K. (2019). Total material requirement for the global energy transition to 2050: A focus on transport and electricity. Resources, Conservation and Recycling.\nWatari, T., Nansai, K., Nakajima, K., & Giurco, D. (2021). Sustainable energy transitions require enhanced resource governance. Journal of Cleaner Production.\nPer capita electricity generation in the United Kingdom\nis around\n4.3 megawatt-hours (or 0.0043 GWh) per year. 232 people * 0.0043 is equal to one gigawatt-hour.\nNassar, N. T., Lederer, G. W., Brainard, J. L., Padilla, A. J., & Lessard, J. D. (2022). Rock-to-metal ratio: a foundational metric for understanding mine wastes. Environmental Science & Technology.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2024) - “Low-carbon technologies need far less mining than fossil fuels” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-093348/low-carbon-technologies-need-far-less-mining-fossil-fuels.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-low-carbon-technologies-need-far-less-mining-fossil-fuels,\nauthor = {Hannah Ritchie},\ntitle = {Low-carbon technologies need far less mining than fossil fuels},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://archive.ourworldindata.org/20260518-093348/low-carbon-technologies-need-far-less-mining-fossil-fuels.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "low-carbon-technologies-need-far-less-mining-fossil-fuels", "source_url": "https://ourworldindata.org/low-carbon-technologies-need-far-less-mining-fossil-fuels", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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{"raw_link": "https://ourworldindata.org/metals-minerals", "title": "Metals and Minerals", "context": "Metals and Minerals\nWhich countries produce the world’s critical minerals? How is production changing over time?\nBy\nHannah Ritchie\nand\nPablo Rosado\nCite this work\nReuse this work\nMetals and minerals have played a crucial role in building the modern world. These materials are essential in construction and manufacturing, from buildings and bridges to cars and electronics.\nCritical minerals like lithium, copper, and cobalt will play an increasingly important role in the energy transition as countries move away from fossil fuels towards clean energy.\nThis raises important questions about whether the world has enough of these minerals to power the energy transition, the environmental impacts of mining, and socioeconomic issues such as working conditions in supply chains.\nOn this page, you find our data, charts, and writing related to metals and minerals. It gives an overview of global statistics on crucial minerals: which countries have these resources, where they are mined and refined, and how they’re traded across the world.\nSee all of our interactive charts on metals and minerals ↓\nRelated topics\nEnergy\nRenewable Energy\nTransport\nExplore Data on Metals and Minerals\nResearch & Writing\nSeptember 23, 2024\nLow-carbon technologies need far less mining than fossil fuels\nMining for coal is much more resource-intensive than renewables or nuclear power.\nHannah Ritchie\nSeptember 16, 2024\nWhich countries have the critical minerals needed for the energy transition?\nAn overview of the distribution of critical minerals for clean energy.\nHannah Ritchie and Pablo Rosado\nJuly 29, 2024\nWhat’s the difference between mineral reserves and resources?\nEvery reserve is a resource, but not every resource is a reserve.\nHannah Ritchie and Max Roser\nRelated work on minerals and energy\nFebruary 26, 2024\nTracking global data on electric vehicles\nHannah Ritchie\nJune 16, 2022\nHow does the land use of different electricity sources compare?\nHannah Ritchie\nKey Charts on Metals & Minerals\nSee all charts on this topic\nMaterials used for low-carbon electricity sources\nMining requirements of different electricity sources\nRock-to-metal ratios of mined materials\nCobalt production\nLithium production\nRaw material consumption in the EU-27 by the final product\nTotal natural resource rents\nReal commodity price index, metals\nChart 1 of 8\nFeatured Data on\nMetals & Minerals\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this topic page, please also cite the underlying data sources. This topic page can be cited as:\nHannah Ritchie and Pablo Rosado (2024) - “Metals and Minerals” Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/metals-minerals' [Online Resource]\nBibTeX citation\n@article{owid-metals-minerals,\nauthor = {Hannah Ritchie and Pablo Rosado},\ntitle = {Metals and Minerals},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://ourworldindata.org/metals-minerals}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "metals-minerals", "source_url": "https://ourworldindata.org/metals-minerals", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "Which countries produce the world’s critical minerals? How is production changing over time?", "numeric_mentions": ["23,", "2024", "16,", "29,", "26,", "2022", "27", "1", "8"], "numeric_evidence": [{"title": "Cobalt production", "source_url": "https://ourworldindata.org/grapher/cobalt-production.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Cobalt production"], "row_count_total": 690, "rows_head": [{"Entity": "Asia", "Code": "OWID_ASI", "Year": "1995", "Cobalt production": "980"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "1996", "Cobalt production": "190"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "1997", "Cobalt production": "200"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "1998", "Cobalt production": "40"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "1999", "Cobalt production": "250"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2000", "Cobalt production": "90"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2001", "Cobalt production": "150"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2002", "Cobalt production": "1000"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2003", "Cobalt production": "800"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2004", "Cobalt production": "1360"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2005", "Cobalt production": "2399.9999000000003"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2006", "Cobalt production": "2740"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2007", "Cobalt production": "2840.0002"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2008", "Cobalt production": "3040"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2009", "Cobalt production": "3240"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2010", "Cobalt production": "3600"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2011", "Cobalt production": "3500"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2012", "Cobalt production": "4900"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2013", "Cobalt production": "5399.9996"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2014", "Cobalt production": "7399.9996"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2015", "Cobalt production": "6900"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2016", "Cobalt production": "6300"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2017", "Cobalt production": "7100"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2018", "Cobalt production": "6750"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2019", "Cobalt production": "7600"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2020", "Cobalt production": "7122.504"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2021", "Cobalt production": "6529.723"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2022", "Cobalt production": "6400.777"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2023", "Cobalt production": "6223.510700000001"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2024", "Cobalt production": "5873.0599999999995"}, {"Entity": "Australia", "Code": "AUS", "Year": "1995", "Cobalt production": "824"}, {"Entity": "Australia", "Code": "AUS", "Year": "1996", "Cobalt production": "942"}, {"Entity": "Australia", "Code": "AUS", "Year": "1997", "Cobalt production": "1219"}, {"Entity": "Australia", "Code": "AUS", "Year": "1998", "Cobalt production": "1269"}, {"Entity": "Australia", "Code": "AUS", "Year": "1999", "Cobalt production": "1010.3448999999999"}, {"Entity": "Australia", "Code": "AUS", "Year": "2000", "Cobalt production": "3598.48"}, {"Entity": "Australia", "Code": "AUS", "Year": "2001", "Cobalt production": "4311.9397"}, {"Entity": "Australia", "Code": "AUS", "Year": "2002", "Cobalt production": "4854"}, {"Entity": "Australia", "Code": "AUS", "Year": "2003", "Cobalt production": "5120"}, {"Entity": "Australia", "Code": "AUS", "Year": "2004", "Cobalt production": "4550"}, {"Entity": "Australia", "Code": "AUS", "Year": "2005", "Cobalt production": "4110"}, {"Entity": "Australia", "Code": "AUS", "Year": "2006", "Cobalt production": "5050"}, {"Entity": "Australia", "Code": "AUS", "Year": "2007", "Cobalt production": "4740"}, {"Entity": "Australia", "Code": "AUS", "Year": "2008", "Cobalt production": "4790"}, {"Entity": "Australia", "Code": "AUS", "Year": "2009", "Cobalt production": "4630"}, {"Entity": "Australia", "Code": "AUS", "Year": "2010", "Cobalt production": "3850"}, {"Entity": "Australia", "Code": "AUS", "Year": "2011", "Cobalt production": "3850"}, {"Entity": "Australia", "Code": "AUS", "Year": "2012", "Cobalt production": "5880"}, {"Entity": "Australia", "Code": "AUS", "Year": "2013", "Cobalt production": "6400"}, {"Entity": "Australia", "Code": "AUS", "Year": "2014", "Cobalt production": "6201.256"}, {"Entity": "Australia", "Code": "AUS", "Year": "2015", "Cobalt production": "6030"}, {"Entity": "Australia", "Code": "AUS", "Year": "2016", "Cobalt production": "5470"}, {"Entity": "Australia", "Code": "AUS", "Year": "2017", "Cobalt production": "5800"}, {"Entity": "Australia", "Code": 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{"Entity": "China", "Code": "CHN", "Year": "2007", "Cobalt production": "1840"}, {"Entity": "China", "Code": "CHN", "Year": "2008", "Cobalt production": "1840"}, {"Entity": "China", "Code": "CHN", "Year": "2009", "Cobalt production": "1840"}, {"Entity": "China", "Code": "CHN", "Year": "2010", "Cobalt production": "1500"}, {"Entity": "China", "Code": "CHN", "Year": "2011", "Cobalt production": "1500"}, {"Entity": "China", "Code": "CHN", "Year": "2012", "Cobalt production": "2200"}, {"Entity": "China", "Code": "CHN", "Year": "2013", "Cobalt production": "2600"}, {"Entity": "China", "Code": "CHN", "Year": "2014", "Cobalt production": "2800"}, {"Entity": "China", "Code": "CHN", "Year": "2015", "Cobalt production": "2600"}, {"Entity": "China", "Code": "CHN", "Year": "2016", "Cobalt production": "2300"}, {"Entity": "China", "Code": "CHN", "Year": "2017", "Cobalt production": "2500"}, {"Entity": "China", "Code": "CHN", "Year": "2018", "Cobalt production": "2000"}, {"Entity": "China", "Code": "CHN", "Year": "2019", "Cobalt production": "2500"}, {"Entity": "China", "Code": "CHN", "Year": "2020", "Cobalt production": "2200"}, {"Entity": "China", "Code": "CHN", "Year": "2021", "Cobalt production": "2200"}, {"Entity": "China", "Code": "CHN", "Year": "2022", "Cobalt production": "1750"}, {"Entity": "China", "Code": "CHN", "Year": "2023", "Cobalt production": "1750"}, {"Entity": "China", "Code": "CHN", "Year": "2024", "Cobalt production": "1750"}], "rows_tail": [{"Entity": "South Africa", "Code": "ZAF", "Year": "1995", "Cobalt production": "288"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "1996", "Cobalt production": "350"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "1997", "Cobalt production": "465"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "1998", "Cobalt production": "435"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "1999", "Cobalt production": "450"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2000", "Cobalt production": "580"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2001", "Cobalt production": "560"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2002", "Cobalt production": "520"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2003", "Cobalt production": "480"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2004", "Cobalt production": "610"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2005", "Cobalt production": "620"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2006", "Cobalt production": "600"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2007", "Cobalt production": "600"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2008", "Cobalt production": "590"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2009", "Cobalt production": "610"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2010", "Cobalt production": "1800"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2011", "Cobalt production": "1600"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2012", "Cobalt production": "2500"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2013", "Cobalt production": "3000"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2014", "Cobalt production": "3000"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2015", "Cobalt production": "2900"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2016", "Cobalt production": "2300"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2017", "Cobalt production": "2300"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2018", "Cobalt production": "2300"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2019", "Cobalt production": "2100"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2020", "Cobalt production": "1800"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2021", "Cobalt production": "1197"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2022", "Cobalt production": "1000"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2023", "Cobalt 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"Cobalt production": "148242"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Cobalt production": "135436"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Cobalt production": "131582"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Cobalt production": "130733.21999999999"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Cobalt production": "137217.47999999998"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Cobalt production": "118418.44"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Cobalt production": "137319.23"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Cobalt production": "150927.3"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Cobalt production": "120029.70000000001"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Cobalt production": "131468.75"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Cobalt production": "137523.56"}, {"Entity": "World", "Code": 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"3211"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2004", "Cobalt production": "6082"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2005", "Cobalt production": "5529"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2006", "Cobalt production": "4658"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Cobalt production": "4690"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2008", "Cobalt production": "4613"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2009", "Cobalt production": "5879"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2010", "Cobalt production": "8648"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2011", "Cobalt production": "7702"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2012", "Cobalt production": "5435"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2013", "Cobalt production": "5919"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2014", "Cobalt production": "4562"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Cobalt production": "2979"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2016", "Cobalt production": "4982"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2017", "Cobalt production": "2648.8999999999996"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2018", "Cobalt production": "1585"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2019", "Cobalt production": "367"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2020", "Cobalt production": "287"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2021", "Cobalt production": "247"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "Cobalt production": "252"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2023", "Cobalt production": "252"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2024", "Cobalt production": "252"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "cobalt-production", "metadata_url": "https://ourworldindata.org/grapher/cobalt-production.metadata.json", "chart_title": "Cobalt production", "chart_subtitle": "Cobalt production is measured in tonnes.", "chart_note": null, "chart_citation": "Energy Institute - Statistical Review of World Energy (2025)", "original_chart_url": "https://ourworldindata.org/grapher/cobalt-production", "owid_column_metadata": {"Cobalt production-reserves - kt": {"titleShort": "Cobalt production-reserves", "titleLong": "Cobalt production-reserves", "shortUnit": "kt", "unit": "thousand tonnes", "timespan": "1995-2024", "type": "Numeric", "conversionFactor": 1000, "owidVariableId": 1077559, "shortName": "cobalt_production_kt", "lastUpdated": "2025-06-27", "nextUpdate": "2026-06-27", "citationShort": "Energy Institute - Statistical Review of World Energy (2025) – with major processing by Our World in Data", "citationLong": "Energy Institute - Statistical Review of World Energy (2025) – with major processing by Our World in Data. “Cobalt production-reserves” [dataset]. Energy Institute, “Statistical Review of World Energy” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1077559.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Global mine production of minerals", "source_url": "https://ourworldindata.org/grapher/global-mine-production-minerals.csv", "file_type": "csv", "columns": ["Entity", "Year", "Global mine production of different minerals"], "row_count_total": 4659, "rows_head": [{"Entity": "Antimony", "Year": "1900", "Global mine production of different minerals": "7710"}, {"Entity": "Antimony", "Year": "1901", "Global mine production of different minerals": "7890"}, {"Entity": "Antimony", "Year": "1902", "Global mine production of different minerals": "8550"}, {"Entity": "Antimony", "Year": "1903", "Global mine production of different minerals": "8140"}, {"Entity": "Antimony", "Year": "1904", "Global mine production of different minerals": "8000"}, {"Entity": "Antimony", "Year": "1905", "Global mine production of different minerals": "8000"}, {"Entity": "Antimony", "Year": "1906", "Global mine production of 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{"Entity": "Antimony", "Year": "1917", "Global mine production of different minerals": "57200"}, {"Entity": "Antimony", "Year": "1918", "Global mine production of different minerals": "30800"}, {"Entity": "Antimony", "Year": "1919", "Global mine production of different minerals": "11800"}, {"Entity": "Antimony", "Year": "1920", "Global mine production of different minerals": "29000"}, {"Entity": "Antimony", "Year": "1921", "Global mine production of different minerals": "18300"}, {"Entity": "Antimony", "Year": "1922", "Global mine production of different minerals": "18900"}, {"Entity": "Antimony", "Year": "1923", "Global mine production of different minerals": "17600"}, {"Entity": "Antimony", "Year": "1924", "Global mine production of different minerals": "17500"}, {"Entity": "Antimony", "Year": "1925", "Global mine production of different minerals": "25500"}, {"Entity": "Antimony", "Year": "1926", "Global mine production of different minerals": "29000"}, {"Entity": "Antimony", "Year": 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{"Entity": "Antimony", "Year": "1958", "Global mine production of different minerals": "46300"}, {"Entity": "Antimony", "Year": "1959", "Global mine production of different minerals": "53300"}, {"Entity": "Antimony", "Year": "1960", "Global mine production of different minerals": "53300"}, {"Entity": "Antimony", "Year": "1961", "Global mine production of different minerals": "51900"}, {"Entity": "Antimony", "Year": "1962", "Global mine production of different minerals": "53700"}, {"Entity": "Antimony", "Year": "1963", "Global mine production of different minerals": "58000"}, {"Entity": "Antimony", "Year": "1964", "Global mine production of different minerals": "63000"}, {"Entity": "Antimony", "Year": "1965", "Global mine production of different minerals": "63000"}, {"Entity": "Antimony", "Year": "1966", "Global mine production of different minerals": "61400"}, {"Entity": "Antimony", "Year": "1967", "Global mine production of different minerals": "58400"}, {"Entity": "Antimony", "Year": "1968", "Global mine production of different minerals": "61500"}, {"Entity": "Antimony", "Year": "1969", "Global mine production of different minerals": "66200"}, {"Entity": "Antimony", "Year": "1970", "Global mine production of different minerals": "70000"}, {"Entity": "Antimony", "Year": "1971", "Global mine production of different minerals": "64100"}, {"Entity": "Antimony", "Year": "1972", "Global mine production of different minerals": "68100"}, {"Entity": "Antimony", "Year": "1973", "Global mine production of different minerals": "69300"}, {"Entity": "Antimony", "Year": "1974", "Global mine production of different minerals": "70500"}, {"Entity": "Antimony", "Year": "1975", "Global mine production of different minerals": "67900"}, {"Entity": "Antimony", "Year": "1976", "Global mine production of different minerals": "69200"}, {"Entity": "Antimony", "Year": "1977", "Global mine production of different minerals": "72200"}, {"Entity": "Antimony", "Year": "1978", "Global mine production of different minerals": "68800"}, {"Entity": "Antimony", "Year": "1979", "Global mine production of different minerals": "71900"}, {"Entity": "Antimony", "Year": "1980", "Global mine production of different minerals": "67200"}, {"Entity": "Antimony", "Year": "1981", "Global mine production of different minerals": "59200"}, {"Entity": "Antimony", "Year": "1982", "Global mine production of different minerals": "53800"}, {"Entity": "Antimony", "Year": "1983", "Global mine production of different minerals": "48400"}, {"Entity": "Antimony", "Year": "1984", "Global mine production of different minerals": "53400"}, {"Entity": "Antimony", "Year": "1985", "Global mine production of different minerals": "55000"}, {"Entity": "Antimony", "Year": "1986", "Global mine production of different minerals": "59900"}, {"Entity": "Antimony", "Year": "1987", "Global mine production of different minerals": "56100"}, {"Entity": "Antimony", "Year": "1988", "Global mine production of different minerals": "64400"}, {"Entity": "Antimony", "Year": "1989", "Global mine production of different minerals": "68400"}, {"Entity": "Antimony", "Year": "1990", "Global mine production of different minerals": "60400"}, {"Entity": "Antimony", "Year": "1991", "Global mine production of different minerals": "64700"}, {"Entity": "Antimony", "Year": "1992", "Global mine production of different minerals": "76000"}, {"Entity": "Antimony", "Year": "1993", "Global mine production of different minerals": "73000"}, {"Entity": "Antimony", "Year": "1994", "Global mine production of different minerals": "106000"}, {"Entity": "Antimony", "Year": "1995", "Global mine production of different minerals": "103000"}, {"Entity": "Antimony", "Year": "1996", "Global mine production of different minerals": "156000"}, {"Entity": "Antimony", "Year": "1997", "Global mine production of different minerals": "155000"}, {"Entity": "Antimony", "Year": "1998", "Global mine production of different minerals": "117000"}, {"Entity": "Antimony", "Year": "1999", "Global mine production of different minerals": "108000"}, {"Entity": "Antimony", "Year": "2000", "Global mine production of different minerals": "118000"}, {"Entity": "Antimony", "Year": "2001", "Global mine production of different minerals": "157000"}, {"Entity": "Antimony", "Year": "2002", "Global mine production of different minerals": "118000"}, {"Entity": "Antimony", "Year": "2003", "Global mine production of different minerals": "116000"}, {"Entity": "Antimony", "Year": "2004", "Global mine production of different minerals": "142000"}, {"Entity": "Antimony", "Year": "2005", "Global mine production of different minerals": "172000"}, {"Entity": "Antimony", "Year": "2006", "Global mine production of different minerals": "173000"}, {"Entity": "Antimony", "Year": "2007", "Global mine production of different minerals": "180000"}, {"Entity": "Antimony", "Year": "2008", "Global mine production of different minerals": "185000"}, {"Entity": "Antimony", "Year": "2009", "Global mine production of different minerals": "158000"}, {"Entity": "Antimony", "Year": "2010", "Global mine production of different minerals": "182000"}, {"Entity": "Antimony", "Year": "2011", "Global mine production of different minerals": "187000"}, {"Entity": "Antimony", "Year": "2012", "Global mine production of different minerals": "181000"}, {"Entity": "Antimony", "Year": "2013", "Global mine production of different minerals": "193000"}, {"Entity": "Antimony", "Year": "2014", "Global mine production of different minerals": "175000"}, {"Entity": "Antimony", "Year": "2015", "Global mine production of different minerals": "150000"}, {"Entity": "Antimony", "Year": "2016", "Global mine production of different minerals": "148000"}, {"Entity": "Antimony", "Year": "2017", "Global mine production of different minerals": "144000"}, {"Entity": "Antimony", "Year": "2018", "Global mine production of different minerals": "147000"}, {"Entity": "Antimony", "Year": "2019", "Global mine production of different minerals": "162000"}], "rows_tail": [{"Entity": "Zinc", "Year": "1910", "Global mine production of different minerals": "810000"}, {"Entity": "Zinc", "Year": "1911", "Global mine production of different minerals": "895000"}, {"Entity": "Zinc", "Year": "1912", "Global mine production of different minerals": "971000"}, {"Entity": "Zinc", "Year": "1913", "Global mine production of different minerals": "939000"}, {"Entity": "Zinc", "Year": "1914", "Global mine production of different minerals": "795000"}, {"Entity": "Zinc", "Year": "1915", "Global mine production of different minerals": "760000"}, {"Entity": "Zinc", "Year": "1916", "Global mine production of different minerals": "882000"}, {"Entity": "Zinc", "Year": "1917", "Global mine production of different minerals": "901000"}, {"Entity": "Zinc", "Year": "1918", "Global mine production of different minerals": "849000"}, {"Entity": "Zinc", "Year": "1919", "Global mine production of different minerals": "719000"}, {"Entity": "Zinc", "Year": "1920", "Global mine production of different minerals": "682000"}, {"Entity": "Zinc", "Year": "1921", "Global mine production of different minerals": "464000"}, {"Entity": "Zinc", "Year": "1922", "Global mine production of different minerals": "730000"}, {"Entity": "Zinc", "Year": "1923", "Global mine production of different minerals": "889000"}, {"Entity": "Zinc", "Year": "1924", "Global mine production of different minerals": "986000"}, {"Entity": "Zinc", "Year": "1925", "Global mine production of different minerals": "1190000"}, {"Entity": "Zinc", "Year": "1926", "Global mine production of different minerals": "1410000"}, {"Entity": "Zinc", "Year": "1927", "Global mine production of different minerals": "1420000"}, {"Entity": "Zinc", "Year": "1928", "Global mine production of different minerals": "1360000"}, {"Entity": "Zinc", "Year": "1929", "Global mine production of different minerals": "1320000"}, {"Entity": "Zinc", "Year": "1930", "Global mine production of different minerals": "1260000"}, {"Entity": "Zinc", "Year": "1931", "Global mine production of different minerals": "904000"}, {"Entity": "Zinc", "Year": "1932", "Global mine production of different minerals": "709000"}, {"Entity": "Zinc", "Year": "1933", "Global mine production of different minerals": "892000"}, {"Entity": "Zinc", "Year": "1934", "Global mine production of different minerals": "1060000"}, {"Entity": "Zinc", "Year": "1935", "Global mine production of different minerals": "1210000"}, {"Entity": "Zinc", "Year": "1936", "Global mine production of different minerals": "1330000"}, {"Entity": "Zinc", "Year": "1937", "Global mine production of different minerals": "1470000"}, {"Entity": "Zinc", "Year": "1938", "Global mine production of different minerals": "1420000"}, {"Entity": "Zinc", "Year": "1939", "Global mine production of different minerals": "1500000"}, {"Entity": "Zinc", "Year": "1940", "Global mine production of different minerals": "1470000"}, {"Entity": "Zinc", "Year": "1941", "Global mine production of different minerals": "1590000"}, {"Entity": "Zinc", "Year": "1942", "Global mine production of different minerals": "1630000"}, {"Entity": "Zinc", "Year": "1943", "Global mine production of different minerals": "1830000"}, {"Entity": "Zinc", "Year": "1944", "Global mine production of different minerals": "1870000"}, {"Entity": "Zinc", "Year": "1945", "Global mine production of different minerals": "1470000"}, {"Entity": "Zinc", "Year": "1946", "Global mine production of different minerals": "1440000"}, {"Entity": "Zinc", "Year": "1947", "Global mine production of different minerals": "1600000"}, {"Entity": "Zinc", "Year": "1948", "Global mine production of different minerals": "1690000"}, {"Entity": "Zinc", "Year": "1949", "Global mine production of different minerals": "1730000"}, {"Entity": "Zinc", "Year": "1950", "Global mine production of different minerals": "2150000"}, {"Entity": "Zinc", "Year": "1951", "Global mine production of different minerals": "2360000"}, {"Entity": "Zinc", "Year": "1952", "Global mine production of different minerals": "2590000"}, {"Entity": "Zinc", "Year": "1953", "Global mine production of different minerals": "2670000"}, {"Entity": "Zinc", "Year": "1954", "Global mine production of different minerals": "2660000"}, {"Entity": "Zinc", "Year": "1955", "Global mine production of different minerals": "2900000"}, {"Entity": "Zinc", "Year": "1956", "Global mine production of different minerals": "3110000"}, {"Entity": "Zinc", "Year": "1957", "Global mine production of different minerals": "3150000"}, {"Entity": "Zinc", "Year": "1958", "Global mine production of different minerals": "2950000"}, {"Entity": "Zinc", "Year": "1959", "Global mine production of different minerals": "3020000"}, {"Entity": "Zinc", "Year": "1960", "Global mine production of different minerals": "3090000"}, {"Entity": "Zinc", "Year": "1961", "Global mine production of different minerals": "3490000"}, {"Entity": "Zinc", "Year": "1962", "Global mine production of different minerals": "3570000"}, {"Entity": "Zinc", "Year": "1963", "Global mine production of different minerals": "3660000"}, {"Entity": "Zinc", "Year": "1964", "Global mine production of different minerals": "4030000"}, {"Entity": "Zinc", "Year": "1965", "Global mine production of different minerals": "4310000"}, {"Entity": "Zinc", "Year": "1966", "Global mine production of different minerals": "4500000"}, {"Entity": "Zinc", "Year": "1967", "Global mine production of different minerals": "4840000"}, {"Entity": "Zinc", "Year": "1968", "Global mine production of different minerals": "4970000"}, {"Entity": "Zinc", "Year": "1969", "Global mine production of different minerals": "5340000"}, {"Entity": "Zinc", "Year": "1970", "Global mine production of different minerals": "5460000"}, {"Entity": "Zinc", "Year": "1971", "Global mine production of different minerals": "5520000"}, {"Entity": "Zinc", "Year": "1972", "Global mine production of different minerals": "5440000"}, {"Entity": "Zinc", "Year": "1973", "Global mine production of different minerals": "5710000"}, {"Entity": "Zinc", "Year": "1974", "Global mine production of different minerals": "5780000"}, {"Entity": "Zinc", "Year": "1975", "Global mine production of different minerals": "5850000"}, {"Entity": "Zinc", "Year": "1976", "Global mine production of different minerals": "5690000"}, {"Entity": "Zinc", "Year": "1977", "Global mine production of different minerals": "5920000"}, {"Entity": "Zinc", "Year": "1978", "Global mine production of different minerals": "5850000"}, {"Entity": "Zinc", "Year": "1979", "Global mine production of different minerals": "5990000"}, {"Entity": "Zinc", "Year": "1980", "Global mine production of different minerals": "5950000"}, {"Entity": "Zinc", "Year": "1981", "Global mine production of different minerals": "5950000"}, {"Entity": "Zinc", "Year": "1982", "Global mine production of different minerals": "6130000"}, {"Entity": "Zinc", "Year": "1983", "Global mine production of different minerals": "6280000"}, {"Entity": "Zinc", "Year": "1984", "Global mine production of different minerals": "6520000"}, {"Entity": "Zinc", "Year": "1985", "Global mine production of different minerals": "6760000"}, {"Entity": "Zinc", "Year": "1986", "Global mine production of different minerals": "6840000"}, {"Entity": "Zinc", "Year": "1987", "Global mine production of different minerals": "7190000"}, {"Entity": "Zinc", "Year": "1988", "Global mine production of different minerals": "6770000"}, {"Entity": "Zinc", "Year": "1989", "Global mine production of different minerals": "6820000"}, {"Entity": "Zinc", "Year": "1990", "Global mine production of different minerals": "7150000"}, {"Entity": "Zinc", "Year": "1991", "Global mine production of different minerals": "7270000"}, {"Entity": "Zinc", "Year": "1992", "Global mine production of different minerals": "7250000"}, {"Entity": "Zinc", "Year": "1993", "Global mine production of different minerals": "6910000"}, {"Entity": "Zinc", "Year": "1994", "Global mine production of different minerals": "7050000"}, {"Entity": "Zinc", "Year": "1995", "Global mine production of different minerals": "7280000"}, {"Entity": "Zinc", "Year": "1996", "Global mine production of different minerals": "7480000"}, {"Entity": "Zinc", "Year": "1997", "Global mine production of different minerals": "7540000"}, {"Entity": "Zinc", "Year": "1998", "Global mine production of different minerals": "7570000"}, {"Entity": "Zinc", "Year": "1999", "Global mine production of different minerals": "7960000"}, {"Entity": "Zinc", "Year": "2000", "Global mine production of different minerals": "8770000"}, {"Entity": "Zinc", "Year": "2001", "Global mine production of different minerals": "8910000"}, {"Entity": "Zinc", "Year": "2002", "Global mine production of different minerals": "8880000"}, {"Entity": "Zinc", "Year": "2003", "Global mine production of different minerals": "9520000"}, {"Entity": "Zinc", "Year": "2004", "Global mine production of different minerals": "9600000"}, {"Entity": "Zinc", "Year": "2005", "Global mine production of different minerals": "10000000"}, {"Entity": "Zinc", "Year": "2006", "Global mine production of different minerals": "10300000"}, {"Entity": "Zinc", "Year": "2007", "Global mine production of different minerals": "11100000"}, {"Entity": "Zinc", "Year": "2008", "Global mine production of different minerals": "11900000"}, {"Entity": "Zinc", "Year": "2009", "Global mine production of different minerals": "11600000"}, {"Entity": "Zinc", "Year": "2010", "Global mine production of different minerals": "12300000"}, {"Entity": "Zinc", "Year": "2011", "Global mine production of different minerals": "12500000"}, {"Entity": "Zinc", "Year": "2012", "Global mine production of different minerals": "13300000"}, {"Entity": "Zinc", "Year": "2013", "Global mine production of different minerals": "13200000"}, {"Entity": "Zinc", "Year": "2014", "Global mine production of different minerals": "13500000"}, {"Entity": "Zinc", "Year": "2015", "Global mine production of different minerals": "13300000"}, {"Entity": "Zinc", "Year": "2016", "Global mine production of different minerals": "13800000"}, {"Entity": "Zinc", "Year": "2017", "Global mine production of different minerals": "13700000"}, {"Entity": "Zinc", "Year": "2018", "Global mine production of different minerals": "13100000"}, {"Entity": "Zinc", "Year": "2019", "Global mine production of different minerals": "13600000"}, {"Entity": "Zinc", "Year": "2020", "Global mine production of different minerals": "12000000"}, {"Entity": "Zinc", "Year": "2021", "Global mine production of different minerals": "12700000"}, {"Entity": "Zinc", "Year": "2022", "Global mine production of different minerals": "12500000"}, {"Entity": "Zinc", "Year": "2023", "Global mine production of different minerals": "12100000"}, {"Entity": "Zinc", "Year": "2024", "Global mine production of different minerals": "12000000"}, {"Entity": "Zirconium and hafnium", "Year": "2020", "Global mine production of different minerals": "1200000"}, {"Entity": "Zirconium and hafnium", "Year": "2021", "Global mine production of different minerals": "1300000"}, {"Entity": "Zirconium and hafnium", "Year": "2022", "Global mine production of different minerals": "1440000"}, {"Entity": "Zirconium and hafnium", "Year": "2023", "Global mine production of different minerals": "1440000"}, {"Entity": "Zirconium and hafnium", "Year": "2024", "Global mine production of different minerals": "1500000"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "global-mine-production-minerals", "metadata_url": "https://ourworldindata.org/grapher/global-mine-production-minerals.metadata.json", "chart_title": "Global mine production of minerals", "chart_subtitle": null, "chart_note": null, "chart_citation": "USGS - Mineral Commodity Summaries (2025); USGS - Historical Statistics for Mineral and Material Commodities (2024); BGS - World Mineral Statistics (2025)", "original_chart_url": "https://ourworldindata.org/grapher/global-mine-production-minerals", "owid_column_metadata": {"Global mine production of different minerals": {"titleShort": "Global mine production of different minerals", "titleLong": "Global mine production of different minerals", "descriptionShort": "Measured in tonnes of mined, rather than refined production.", "descriptionKey": ["Antimony - Values are reported as tonnes of metal content.", "Asbestos - Values are reported as gross weight.", "Bismuth - Values are reported as tonnes of metal content.", "Chromium - Values are reported as tonnes of contained chromium.", "Gemstones - Values are reported as tonnes of gemstone-quality diamonds.", "Graphite - Values refer to natural graphite.", "Lithium - Values are reported as tonnes of lithium content.", "Platinum group metals (iridium) - Values are reported as tonnes of metal content.", "Platinum group metals (other) - Values are reported as tonnes of metal content.", "Platinum group metals (rhodium) - Values are reported as tonnes of metal content.", "Potash (chloride) - Values are reported as tonnes of potassium oxide content.", "Potash (polyhalite) - Values are reported as tonnes of potassium oxide content.", "Potash (potassic salts) - Values are reported as tonnes of potassium oxide content.", "Potash - Values are reported in tonnes of potassium oxide equivalent.", "Rare earths - Values are reported in tonnes of rare-earth-oxide equivalent.", "Titanium (ilmenite) - Values are reported as tonnes of titanium dioxide content.", "Uranium - Values are reported as tonnes of metal content."], "descriptionProcessing": "- The majority of the data is sourced from USGS, supplemented by BGS data where available. Where both overlap, USGS data is prioritized.\n- As BGS does not provide global data, we calculated the world total by summing the data from individual countries, using this as a cross-check against USGS global figures.\n- Due to the inherent uncertainties in the data for certain minerals and countries, we allowed a maximum deviation of 10% between the global totals reported by USGS and the calculated ones for BGS. If the deviation exceeded this threshold, we excluded the BGS data.\n- The calculated global total from BGS data was used only on exceptional occasions, after ensuring that the resulting aggregate was sufficiently complete.\n- Both BGS and USGS datasets include numerous notes and footnotes. We have retained most of these, making only minor edits or deletions where necessary to maintain clarity.", "shortUnit": "t", "unit": "tonnes", "timespan": "1900-2024", "type": "Numeric", "owidVariableId": 1131074, "shortName": "mine_production", "lastUpdated": "2025-12-15", "nextUpdate": "2026-12-15", "citationShort": "USGS - Mineral Commodity Summaries (2025); USGS - Historical Statistics for Mineral and Material Commodities (2024); BGS - World Mineral Statistics (2025) – with major processing by Our World in Data", "citationLong": "USGS - Mineral Commodity Summaries (2025); USGS - Historical Statistics for Mineral and Material Commodities (2024); BGS - World Mineral Statistics (2025) – with major processing by Our World in Data. “Global mine production of different minerals” [dataset]. United States Geological Survey, “Mineral Commodity Summaries”; United States Geological Survey, “Historical Statistics for Mineral and Material Commodities”; British Geological Survey, “World Mineral Statistics” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/1131074.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Lithium production", "source_url": "https://ourworldindata.org/grapher/lithium-production.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Lithium Production"], "row_count_total": 540, "rows_head": [{"Entity": "Argentina", "Code": "ARG", "Year": "1995", "Lithium Production": "8"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1996", "Lithium Production": "8"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1997", "Lithium Production": "8"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1998", "Lithium Production": "1130"}, {"Entity": "Argentina", "Code": "ARG", "Year": "1999", "Lithium Production": "200"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2000", "Lithium Production": "200"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2001", "Lithium Production": "200"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2002", "Lithium Production": "946"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2003", "Lithium Production": "960"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2004", "Lithium Production": "1970"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2005", "Lithium Production": "1980"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2006", "Lithium Production": "2900"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2007", "Lithium Production": "3000"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2008", "Lithium Production": "3170"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2009", "Lithium Production": "2220"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2010", "Lithium Production": "2950"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2011", "Lithium Production": "2950"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2012", "Lithium Production": "2700"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2013", "Lithium Production": "2500"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2014", "Lithium Production": "3200"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2015", "Lithium Production": "3600"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2016", "Lithium Production": "5800"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2017", "Lithium Production": "5700"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2018", "Lithium Production": "6400"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2019", "Lithium Production": "6300"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2020", "Lithium Production": "5900"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2021", "Lithium Production": "5966.9123"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2022", "Lithium Production": "6377.9364000000005"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2023", "Lithium Production": "8630"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2024", "Lithium Production": "18000"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "1995", "Lithium Production": "320"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "1996", "Lithium Production": "2800"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "1997", "Lithium Production": "2900"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "1998", "Lithium Production": "3000"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "1999", "Lithium Production": "2300"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2000", "Lithium Production": "2400"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2001", "Lithium Production": "2400"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2002", "Lithium Production": "2400"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2003", "Lithium Production": "2500"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2004", "Lithium Production": "2630"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2005", "Lithium Production": "2820"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2006", "Lithium Production": "2820"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2007", "Lithium Production": "3010"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2008", "Lithium Production": "3290"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2009", "Lithium Production": "3760"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2010", "Lithium Production": "3950"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2011", "Lithium Production": "4140"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2012", "Lithium Production": "4500"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2013", "Lithium Production": "4700"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2014", "Lithium Production": "2300"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2015", "Lithium Production": "2000"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2016", "Lithium Production": "2300"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2017", "Lithium Production": "6800"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2018", "Lithium Production": "7100"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2019", "Lithium Production": "10800"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2020", "Lithium Production": "13300"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2021", "Lithium Production": "14000"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2022", "Lithium Production": "22600"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2023", "Lithium Production": "35700"}, {"Entity": "Asia", "Code": "OWID_ASI", "Year": "2024", "Lithium Production": "41000"}, {"Entity": "Australia", "Code": "AUS", "Year": "1995", "Lithium Production": "2233.4814"}, {"Entity": "Australia", "Code": "AUS", "Year": "1996", "Lithium Production": "3882.1355999999996"}, {"Entity": "Australia", "Code": "AUS", "Year": "1997", "Lithium Production": "1576.6063"}, {"Entity": "Australia", "Code": "AUS", "Year": "1998", "Lithium Production": "1179.9950999999999"}, {"Entity": "Australia", "Code": "AUS", "Year": "1999", "Lithium Production": "1505.7013"}, {"Entity": "Australia", "Code": "AUS", "Year": "2000", "Lithium Production": "1825.6938"}, {"Entity": "Australia", "Code": "AUS", "Year": "2001", "Lithium Production": "2225.7889999999998"}, {"Entity": "Australia", "Code": "AUS", "Year": "2002", "Lithium Production": "2204.2165"}, {"Entity": "Australia", "Code": "AUS", "Year": "2003", "Lithium Production": "3500"}, {"Entity": "Australia", "Code": "AUS", "Year": "2004", "Lithium Production": "3301.4052"}, {"Entity": "Australia", "Code": "AUS", "Year": "2005", "Lithium Production": "4839.464999999999"}, {"Entity": "Australia", "Code": "AUS", "Year": "2006", "Lithium Production": "6190.2847"}, {"Entity": "Australia", "Code": "AUS", "Year": "2007", "Lithium Production": "6836.29"}, {"Entity": "Australia", "Code": "AUS", "Year": "2008", "Lithium Production": "6676.0015"}, {"Entity": "Australia", "Code": "AUS", "Year": "2009", "Lithium Production": "5504.1165"}, {"Entity": "Australia", "Code": "AUS", "Year": "2010", "Lithium Production": "8465.379"}, {"Entity": 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"Lithium Production": "224.93218000000002"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2017", "Lithium Production": "269.46384"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2018", "Lithium Production": "1047.5034"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2019", "Lithium Production": "2171.6533"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2020", "Lithium Production": "1420"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2021", "Lithium Production": "1700"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2022", "Lithium Production": "2630"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2023", "Lithium Production": "5260"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2024", "Lithium Production": "10000"}], "rows_tail": [{"Entity": "United States", "Code": "USA", "Year": "1995", "Lithium Production": "3500"}, {"Entity": "United States", "Code": "USA", "Year": "1996", "Lithium Production": "4000"}, {"Entity": "United States", "Code": "USA", "Year": "1997", "Lithium Production": "4000"}, {"Entity": "United States", "Code": "USA", "Year": "1998", "Lithium Production": "1500"}, {"Entity": "United States", "Code": "USA", "Year": "1999", "Lithium Production": "1500"}, {"Entity": "United States", "Code": "USA", "Year": "2000", "Lithium Production": "1500"}, {"Entity": "United States", "Code": "USA", "Year": "2001", "Lithium Production": "1500"}, {"Entity": "United States", "Code": "USA", "Year": "2002", "Lithium Production": "1500"}, {"Entity": "United States", "Code": "USA", "Year": "2003", "Lithium Production": "1500"}, {"Entity": "United States", "Code": "USA", "Year": "2004", "Lithium Production": "1500"}, {"Entity": "United States", "Code": "USA", "Year": "2005", "Lithium Production": "1500"}, {"Entity": "United States", "Code": "USA", "Year": "2006", "Lithium Production": "1500"}, {"Entity": "United States", "Code": "USA", "Year": "2007", "Lithium Production": "1500"}, {"Entity": "United States", "Code": "USA", "Year": "2008", "Lithium Production": "1500"}, {"Entity": "United States", "Code": "USA", "Year": "2009", "Lithium Production": "1500"}, {"Entity": "United States", "Code": "USA", "Year": "2010", "Lithium Production": "1000"}, {"Entity": "United States", "Code": "USA", "Year": "2011", "Lithium Production": "1000"}, {"Entity": "United States", "Code": "USA", "Year": "2012", "Lithium Production": "1000"}, {"Entity": "United States", "Code": "USA", "Year": "2013", "Lithium Production": "870"}, {"Entity": "United States", "Code": "USA", "Year": "2014", "Lithium Production": "900"}, {"Entity": "United States", "Code": "USA", "Year": "2015", "Lithium Production": "900"}, {"Entity": "United States", "Code": "USA", "Year": "2016", "Lithium Production": "900"}, {"Entity": "United States", "Code": "USA", "Year": "2017", "Lithium Production": "900"}, {"Entity": "United States", "Code": "USA", "Year": "2018", "Lithium Production": "900"}, {"Entity": "United States", "Code": "USA", "Year": "2019", "Lithium Production": "900"}, {"Entity": "United States", "Code": "USA", "Year": "2020", "Lithium Production": "900"}, {"Entity": "United States", "Code": "USA", "Year": "2021", "Lithium Production": "939.234"}, {"Entity": "United States", "Code": "USA", "Year": "2022", "Lithium Production": "614.9077"}, {"Entity": "United States", "Code": "USA", "Year": "2023", "Lithium Production": "614.9077"}, {"Entity": "United States", "Code": "USA", "Year": "2024", "Lithium Production": "920.563"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1995", "Lithium Production": "359.99998"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1996", "Lithium Production": "2840"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1997", "Lithium Production": "2940"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1998", "Lithium Production": "4162"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "1999", "Lithium Production": "2532"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2000", "Lithium Production": "2630"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2001", "Lithium Production": "2820.0002"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2002", "Lithium Production": "3570.0002"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2003", "Lithium Production": "3700"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2004", "Lithium Production": "4842"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2005", "Lithium Production": "5042"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2006", "Lithium Production": "5962"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2007", "Lithium Production": "6190"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2008", "Lithium Production": "6620"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2009", "Lithium Production": "6140"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2010", "Lithium Production": "7060"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2011", "Lithium Production": "7410"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2012", "Lithium Production": "7350"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2013", "Lithium Production": "7600"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2014", "Lithium Production": "5660"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2015", "Lithium Production": "5747.697999999999"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2016", "Lithium Production": "8324.932"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2017", "Lithium Production": "12769.464"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2018", "Lithium Production": "14547.503"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2019", "Lithium Production": "19271.654000000002"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2020", "Lithium Production": "20620"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2021", "Lithium Production": "21666.912"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2022", "Lithium Production": "31607.936999999998"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2023", "Lithium Production": "49590"}, {"Entity": "Upper-middle-income countries", "Code": "OWID_UMC", "Year": "2024", "Lithium Production": "69000"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1995", "Lithium Production": "9485.481"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1996", "Lithium Production": "14783.804"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1997", "Lithium Production": "15591.14"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1998", "Lithium Production": "14192.644"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "1999", "Lithium Production": "12875.787"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2000", "Lithium Production": "14283.851"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2001", "Lithium Production": "14002.716"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2002", "Lithium Production": "15486.188"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2003", "Lithium Production": "18086.103000000003"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2004", "Lithium Production": "19361.534"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2005", "Lithium Production": "20947.239"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2006", "Lithium Production": "24263.863"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2007", "Lithium Production": "26374.807"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2008", "Lithium Production": "26328.201"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2009", "Lithium Production": "19532.03"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2010", "Lithium Production": "26476.818000000003"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2011", "Lithium Production": "33033.84"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2012", "Lithium Production": "34745.956"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2013", "Lithium Production": "30391.504"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2014", "Lithium Production": "30951.284"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2015", "Lithium Production": "29543.085000000003"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2016", "Lithium Production": "38217.278"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2017", "Lithium Production": "50850.212"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2018", "Lithium Production": "95134.28"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2019", "Lithium Production": "86921.616"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2020", "Lithium Production": "83705.12999999999"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2021", "Lithium Production": "107859.66500000001"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2022", "Lithium Production": "157809.71"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2023", "Lithium Production": "211411.68"}, {"Entity": "World", "Code": "OWID_WRL", "Year": "2024", "Lithium Production": "244636.8"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1995", "Lithium Production": "520"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1996", "Lithium Production": "500"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1997", "Lithium Production": "700"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "1998", "Lithium Production": 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{"raw_link": "https://ourworldindata.org/elephant-populations", "title": "The state of the world's elephant populations", "context": "Home\nBiodiversity\nThe state of the world's elephant populations\nHow have elephant populations changed over time? What species are at risk of extinction today?\nBy\nHannah Ritchie\nSeptember 10, 2024\nBrowse past versions\nCite this article\nReuse our work freely\nElephants are the world’s largest living land animals, weighing in at\nup to 7.5 tonnes\n.\n1\nTheir size has made them a prime target for poaching. History has shown us that it is usually the largest mammals that are most at risk from human hunting. Elephants are no different. We hunt them for their meat, their trunks, and their lucrative tusks.\nThere are around 450,000 elephants in the world. But many populations are much smaller than they used to be.\nIn this article we look at the state of elephant populations, and how these populations have changed over time.\nState of elephant populations today\nThere are two species of elephant: the African (with the official name\nLoxodonta africana\n) and the Asian (\nElephas maximus\n) elephant.\nIf you want to tell the difference between them, look at their ears: the African elephant has much bigger ears, very similar in shape to the African continent; the Asian elephant has much smaller, rounded ears.\n2\nTheir tusks are also a useful indicator: both male and female African elephants can grow tusks, but only male Asian ones can.\nIn the table, I have summarized the status of their populations.\nElephant species\nPopulation\n(latest estimate)\nExtinction risk\nPopulation trend\nAfrican elephant\n(\nLoxodonta africana\n)\n415,000\nBy subspecies below\nBy subspecies below\nAfrican forest elephant (Loxodonta cyclotis)\nCritically endangered\nDecreasing\nAfrican savanna elephant (Loxodonta africana)\nEndangered\nDecreasing\nAsian elephant\n(\nElephas maximus\n)\n40,000 – 50,000\nEndangered\nDecreasing\nThere are around ten times as many African than Asian elephants in the world. In 2015, there were\naround 415,000\nAfrican elephants left. For the Asian species, this is in the range of\n40,000 to 50,000\n.\nThe Asian elephant is\nclassified as\n‘endangered’, one level down from ‘critically endangered’ before extinction, on the IUCN Red List.\n3\nThe African elephant was previously treated as a single species, but has\nrecently been separated\ninto the African forest elephant (Loxodonta cyclotis) and African savanna elephant (Loxodonta africana) for evaluation. The forest elephant is listed as ‘critically endangered’ and the savanna elephant as ‘endangered’. The populations of both species are declining.\nAre elephant populations increasing or decreasing?\nTo understand the vulnerability of elephant populations, knowing the the number of animals alive today is not enough. We also need to know the direction and rate of change. If population numbers are falling quickly, we should be concerned even if there are hundreds of thousands left.\nLet’s take a look at the African and Asian elephant species one by one.\nAfrican elephant (\nLoxodonta africana\n)\nThere are ten times as many African elephants as Asian elephants in the world. That makes them seem abundant. But their numbers are, by several estimates, much smaller than they were in the past.\nIn the chart below I’ve shown estimates from 1995 onwards\nfrom the\nIUCN’s SSC African Elephant Specialist Group (AfESG). Two lines are shown: in red we have “definitive” estimates that are based on sightings or improved survey counts; and in brown we have these figures plus “probable” counts, which are less certain. As you can see, this affects the total number of estimated elephants but the overall trend in the last few decades is similar.\nI’ve also included a single point estimate — of 1.3 million — for 1979. This comes from an earlier paper from Douglas-Hamilton.\n4\nEstimates for 1979 were also modeled by Milner-Gulland and Beddington, as part of a study looking elephant populations over the last few centuries; their models suggested 1979 populations in the range of around 1.1 to 1.8 million.\n5\nThis data point not connected to the overall time-series because it’s much less certain, and could be an overestimate.\nIn a previous version of this chart, I showed a time-series of elephant populations dating back to 1500. This estimated that centuries ago, there were 26 million elephants on the continent. Since then, there\nhas been debate\naround the quality and credibility of these figures. While in the original chart we made clear that long-run estimates — especially those for from 1900 or earlier — are very crude and come with large uncertainty, the quality of these estimates may be too low to include in our longer time-series.\nIn the footnote I describe the original sources of some of these estimates.\n6\nDownload\nWhile earlier estimates are more uncertain, there have been relatively consistent measurement methods from 2007 onwards. As you can see in the chart above, there was a gradual decline in elephant populations through to 2016. This decline is consistent with trends from the Great Elephant Census, which estimated a reduction of 144,000 from 2007 to 2014.\n7\nAnother piece of evidence we have that populations have been in decline comes from a metric called the ‘carcass ratio’.\nDuring population surveys, researchers don’t only count the number of alive elephants, they also count the number of dead elephants (carcasses). The carcass ratio is the number of dead elephants observed during surveys, given as a percentage of the total population.\nThe carcass ratio\nacross Africa as a whole was 11.9%.\n8\nThis means that for every 100 live elephants, there were around 12 dead elephants. A carcass ratio greater than 8% usually means the population is shrinking, because this will be greater than the replacement rate.\n9\nThe overall population of African elephants has been falling in recent years. But this varies significantly across countries. In some, the\ncarcass ratio\nwas very high: in Cameroon, it was 83%, it was 32% in Mozambique, and 30% in Angola.\n10\nIn the map below you can see the distribution of African elephants across the continent in 2016.\nAsian elephant (\nElephas maximus\n)\nThere are fewer estimates of Asian Elephant populations. This is more worrying because the Asian elephant is at a higher risk of extinction. We should be tracking these numbers more, not less, closely.\nWe do have some data for select countries, and some longer-term estimates.\nThe IUCN estimates that the total population of Asian elephants has more than halved over the past century. It\nestimates\nthat there were 100,000 animals in the early 1900s; today that figure is in the range of 40,000 to 50,000.\nPopulation data over time is available for some countries in Asia: the Indian government, for example, has\npublished estimates\nperiodically since 1970.\nIn the map, you can explore the latest population estimates from the Asian IUCN SSC Asian Elephant Specialist Group (\nAsESG\n) for each country.\nIn India, populations have been steadily increasing since 1980, rising from around 16,000 to over 27,000 in 2017. This shows that it’s possible to protect these species and help their populations rebuild.\nHowever, the lack of data over time for many countries makes it difficult to properly assess the health of Asian elephant populations.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nHow to save our elephant populations\nBy far the biggest threat to both African and Asian elephants is poaching. Elephants are killed for their trunks and their tusks. Ivory is a lucrative business.\nIt’s not just elephants that are under pressure.\nPoaching is the leading threat to all large mammals. But as we’ve seen from some country-level examples: protecting these species is possible: India has managed to protect and restore elephant populations. Namibia, Zimbabwe, and Angola have also managed to turn the trend.\nUpdated\nThis article and its charts were first published in 2019. It was updated in 2024, when longer-run estimates of African elephant populations were excluded due to concerns about data quality. You can find more context on this within the article. Thanks to Tin Fischer for flagging this issue.\nEndnotes\nAfrican elephants are typically bigger than the Asian species. African elephants can weigh up to 7.5 tonnes; Asian elephants\nup to 5 tonnes\n.\nElephants dissipate heat via their ears, and so use them for temperature regulation. The reason that African elephants have larger ears is that they live in warmer climates and therefore need to dissipate more heat.\nChoudhury, A., Lahiri Choudhury, D.K., Desai, A., Duckworth, J.W., Easa, P.S., Johnsingh, A.J.T., Fernando, P., Hedges, S., Gunawardena, M., Kurt, F., Karanth, U., Lister, A., Menon, V., Riddle, H., Rübel, A. & Wikramanayake, E. (IUCN SSC Asian Elephant Specialist Group) 2008.\nElephas maximus\n.\nThe IUCN Red List of Threatened Species\n2008.\nDouglas-Hamilton (1979).\nElephant Survey and Conservation Programme: final and annual report 1979\n. IUCN/WWF.\nMilner-Gulland, E. J., & Beddington, J. R. (1993). The exploitation of elephants for the ivory trade: an historical perspective. Proceedings of the Royal Society of London. Series B: Biological Sciences.\nThe original estimates for 1500 and 1913 came from the Great Elephant Census project. These estimates were published on its main project page here (which has subsequently been taken down, but can be accessed\nvia web archive\n).\nThe Great Elephant Census (GEC) was one of the largest wildlife surveys to date, and attempted to provide continent-wide estimates of elephant populations and trends using aerial surveys. Its results\nwere published\nin a peer-reviewed journal in 2016.\nThere, the authors note that “Africa may have held over 20 million elephants before European colonization and 1 million as recently as the 1970s.” The papers they reference there\ndo\nprovide estimates of this order of magnitude for the 1800s and 1970s, but do not provide estimates for\n1500\n. I can only assume that there was an error in translating this on to the project’s\nmain page\n, hence why it assumed a 1500 estimate of 26 million. This is likely to have been an estimate for 1813.\nThe underlying paper by Milner-Gulland and Beddington (1993) does estimate that elephant populations were around 13.5 to 27 million in 1813. It estimates that the “pristine carrying capacity” was around 27 million, and that the population was 50% to 100% of this carrying capacity.\nAs I mentioned, these estimates — as the authors acknowledge — are crude and come with large uncertainty. Other researchers in this area\nfind them\nto be unlikely. While they could still be solid estimates, I think the lack of confirmatory studies since then makes their quality too low to include in our longer time-series.\nChase, M. J., Schlossberg, S., Griffin, C. R., Bouché, P. J., Djene, S. W., Elkan, P. W., ... & Sutcliffe, R. (2016). Continent-wide survey reveals massive decline in African savannah elephants. PeerJ.\nMilner-Gulland, E. J., & Beddington, J. R. (1993). The exploitation of elephants for the ivory trade: an historical perspective. Proceedings of the Royal Society of London. Series B: Biological Sciences.\nDouglas-Hamilton, I. 1988 African elephant population study - phase 2. Nairobi: WWF/UNEP.\nChase, M. J., Schlossberg, S., Griffin, C. R., Bouché, P. J., Djene, S. W., Elkan, P. W., ... & Sutcliffe, R. (2016). Continent-wide survey reveals massive decline in African savannah elephants. PeerJ.\nChase, M. J., Schlossberg, S., Griffin, C. R., Bouché, P. J., Djene, S. W., Elkan, P. W., ... & Omondi, P. (2016).\nContinent-wide survey reveals massive decline in African savannah elephants\n.\nPeerJ\n, 4, e2354.\nDouglas-Hamilton, I., & Burrill, A. (1991).\nUsing elephant carcass ratios to determine population trends\n.\nAfrican wildlife: research and management\n, 98-105.\nData is only available for the years 2007 and 2015. So even countries which show an increase in over this decade – Cameroon, for example – might have seen a decline in very recent years, which is reflected in carcass ratio data.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2024) - “The state of the world's elephant populations” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-090244/elephant-populations.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-elephant-populations,\nauthor = {Hannah Ritchie},\ntitle = {The state of the world's elephant populations},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://archive.ourworldindata.org/20260518-090244/elephant-populations.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "elephant-populations", "source_url": "https://ourworldindata.org/elephant-populations", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "How have elephant populations changed over time? What species are at risk of extinction today?", "numeric_mentions": ["10,", "2024", "7.5", "1", "450,000", "2", "415,000", "40,000", "50,000", "2015,", "3", "1995", "1.3 million", "1979", "4", "1.1", "1.8 million", "5", "1500", "26 million", "1900", "6", "2007", "2016", "144,000", "2014", "7", "11.9%", "8", "100", "12", "8%", "9", "83%", "32%", "30%", "10", "100,000", "1970", "1980,", "16,000", "27,000", "2017", "2019", "2024,", "2008", "1993", "1913", "20 million", "1 million", "1800", "1813", "13.5", "27 million", "50%", "100%", "1988", "4,", "1991", "98", "105", "2015", "20260518", "090244", "18,", "2026"], "numeric_evidence": [{"title": "African elephant carcass ratio", "source_url": "https://ourworldindata.org/grapher/african-elephant-carcass-ratio.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Carcass ratio"], "row_count_total": 12, "rows_head": [{"Entity": "Angola", "Code": "AGO", "Year": "2015", "Carcass ratio": "30"}, {"Entity": "Botswana", "Code": "BWA", "Year": "2015", "Carcass ratio": "7"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2015", "Carcass ratio": "83"}, {"Entity": "Chad", "Code": "TCD", "Year": "2015", "Carcass ratio": "17"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2015", "Carcass ratio": "13"}, {"Entity": "Malawi", "Code": "MWI", "Year": "2015", "Carcass ratio": "2"}, {"Entity": "Mali", "Code": "MLI", "Year": "2015", "Carcass ratio": "10"}, {"Entity": "Mozambique", "Code": "MOZ", "Year": "2015", "Carcass ratio": "32"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2015", "Carcass ratio": "26"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2015", "Carcass ratio": "0.5"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Carcass ratio": "4.5"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Carcass ratio": "8"}], "rows_tail": [], "sampling_note": "Stored first 12 rows and last 12 rows when the table is larger.", "grapher_slug": "african-elephant-carcass-ratio", "metadata_url": "https://ourworldindata.org/grapher/african-elephant-carcass-ratio.metadata.json", "chart_title": "African elephant carcass ratio", "chart_subtitle": "Carcass ratio is the number of dead elephants observed during survey counts as a percentage of the total population. Carcass ratios greater than 8% are considered to be a strong indication of a declining population.", "chart_note": null, "chart_citation": "Great Elephant Census (GEC)", "original_chart_url": "https://ourworldindata.org/grapher/african-elephant-carcass-ratio", "owid_column_metadata": {"Carcass ratio": {"titleShort": "Carcass ratio", "titleLong": "Carcass ratio", "unit": "", "timespan": "2015-2015", "type": "Numeric", "owidVariableId": 987565, "shortName": "african_elephant_carcass_ratio", "lastUpdated": "2025-05-30", "citationShort": "African Elephant Specialist Group (AfESG); Great Elephant Census; Asian Elephant Specialist Group (AsESG) – processed by Our World in Data", "citationLong": "African Elephant Specialist Group (AfESG); Great Elephant Census; Asian Elephant Specialist Group (AsESG) – processed by Our World in Data. “Carcass ratio” [dataset]. 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Reuse should follow the source and license information in this chart metadata."}, {"title": "Number of African elephants", "source_url": "https://ourworldindata.org/grapher/african-elephants.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Number of African elephants"], "row_count_total": 90, "rows_head": [{"Entity": "Africa", "Code": "OWID_AFR", "Year": "1979", "Number of African elephants": "1300000"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1995", "Number of African elephants": "286233"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "1998", "Number of African elephants": "301733"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2002", "Number of African elephants": "402067"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2007", "Number of African elephants": "472269"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2013", "Number of African elephants": "404247"}, {"Entity": "Africa", "Code": "OWID_AFR", "Year": "2016", "Number of African 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"", "Year": "1995", "Number of African elephants": "7320"}, {"Entity": "Central Africa", "Code": "", "Year": "1998", "Number of African elephants": "7322"}, {"Entity": "Central Africa", "Code": "", "Year": "2002", "Number of African elephants": "16450"}, {"Entity": "Central Africa", "Code": "", "Year": "2007", "Number of African elephants": "10383"}, {"Entity": "Central Africa", "Code": "", "Year": "2013", "Number of African elephants": "18980"}, {"Entity": "Central Africa", "Code": "", "Year": "2015", "Number of African elephants": "24119"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2007", "Number of African elephants": "109"}, {"Entity": "Central African Republic", "Code": "CAF", "Year": "2015", "Number of African elephants": "702"}, {"Entity": "Chad", "Code": "TCD", "Year": "2007", "Number of African elephants": "3885"}, {"Entity": "Chad", "Code": "TCD", "Year": "2015", "Number of African elephants": "794"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2007", "Number of African elephants": "188"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2015", "Number of African elephants": "189"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2007", "Number of African elephants": "2447"}, {"Entity": "Democratic Republic of Congo", "Code": "COD", "Year": "2015", "Number of African elephants": "1794"}, {"Entity": "Eastern Africa", "Code": "", "Year": "1995", "Number of African elephants": "90482"}, {"Entity": "Eastern Africa", "Code": "", "Year": "1998", "Number of African elephants": "83770"}, {"Entity": "Eastern Africa", "Code": "", "Year": "2002", "Number of African elephants": "117716"}, {"Entity": "Eastern Africa", "Code": "", "Year": "2007", "Number of African elephants": "137485"}, {"Entity": "Eastern Africa", "Code": "", "Year": "2013", "Number of African elephants": "99751"}, {"Entity": "Eastern Africa", "Code": "", "Year": "2015", "Number of African elephants": "86373"}, {"Entity": "Equatorial Guinea", "Code": "GNQ", "Year": "2015", "Number of African elephants": "884"}, {"Entity": "Eritrea", "Code": "ERI", "Year": "2007", "Number of African elephants": "96"}, {"Entity": "Eswatini", "Code": "SWZ", "Year": "2007", "Number of African elephants": "31"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2007", "Number of African elephants": "634"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2015", "Number of African elephants": "1017"}, {"Entity": "Gabon", "Code": "GAB", "Year": "2007", "Number of African elephants": "1523"}, {"Entity": "Gabon", "Code": "GAB", "Year": "2015", "Number of African elephants": "7058"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2007", "Number of African elephants": "789"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2015", "Number of African elephants": "994"}, {"Entity": "Guinea", "Code": "GIN", "Year": "2007", "Number of African elephants": "135"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2007", "Number of African elephants": "23353"}, {"Entity": "Kenya", "Code": "KEN", "Year": "2015", "Number of African elephants": "22809"}, {"Entity": "Liberia", "Code": "LBR", "Year": "2015", "Number of African elephants": "124"}, {"Entity": "Malawi", "Code": "MWI", "Year": "2007", "Number of African elephants": "185"}, {"Entity": "Malawi", "Code": "MWI", "Year": "2015", "Number of African elephants": "1307"}, {"Entity": "Mali", "Code": "MLI", "Year": "2007", "Number of African elephants": "357"}, {"Entity": "Mali", "Code": "MLI", "Year": "2015", "Number of African elephants": "253"}, {"Entity": "Mozambique", "Code": "MOZ", "Year": "2007", "Number of African elephants": "14079"}, {"Entity": "Mozambique", "Code": "MOZ", "Year": "2015", "Number of African elephants": "10884"}, {"Entity": "Namibia", "Code": "NAM", "Year": "2007", "Number of African elephants": "12531"}, {"Entity": "Namibia", "Code": "NAM", "Year": "2015", "Number of African elephants": "22754"}, {"Entity": "Niger", "Code": "NER", "Year": "2007", "Number of African elephants": "85"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2007", "Number of African elephants": "348"}, {"Entity": "Nigeria", "Code": "NGA", "Year": "2015", "Number of African elephants": "94"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2007", "Number of African elephants": "34"}, {"Entity": "Rwanda", "Code": "RWA", "Year": "2015", "Number of African elephants": "88"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2007", "Number of African elephants": "1"}, {"Entity": "Senegal", "Code": "SEN", "Year": "2015", "Number of African elephants": "1"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2007", "Number of African elephants": "17847"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2015", "Number of African elephants": "18841"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2015", "Number of African elephants": "7130"}, {"Entity": "Southern Africa", "Code": "", "Year": "1995", "Number of African elephants": "170837"}, {"Entity": "Southern Africa", "Code": "", "Year": "1998", "Number of African elephants": "196845"}, {"Entity": "Southern Africa", "Code": "", "Year": "2002", "Number of African elephants": "246592"}, {"Entity": "Southern Africa", "Code": "", "Year": "2007", "Number of African elephants": "297718"}, {"Entity": "Southern Africa", "Code": "", "Year": "2013", "Number of African elephants": "297603"}, {"Entity": "Southern Africa", "Code": "", "Year": "2015", "Number of African elephants": "293447"}, {"Entity": "Sudan", "Code": "SDN", "Year": "2007", "Number of African elephants": "20"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2007", "Number of African elephants": "108816"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2015", "Number of African elephants": "50433"}, {"Entity": "Togo", "Code": "TGO", "Year": "2007", "Number of African elephants": "4"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2007", "Number of African elephants": "2337"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2015", "Number of African elephants": "4923"}, {"Entity": "Western Africa", "Code": "", "Year": "1995", "Number of African elephants": "2760"}, {"Entity": "Western Africa", "Code": "", "Year": "1998", "Number of African elephants": "2489"}, {"Entity": "Western Africa", "Code": "", "Year": "2002", "Number of African elephants": "5458"}, {"Entity": "Western Africa", "Code": "", "Year": "2007", "Number of African elephants": "7487"}, {"Entity": "Western Africa", "Code": "", "Year": "2013", "Number of African elephants": "9959"}, {"Entity": "Western Africa", "Code": "", "Year": "2015", "Number of African elephants": "11489"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2007", "Number of African elephants": "16562"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2015", "Number of African elephants": "21967"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2007", "Number of African elephants": "84416"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2015", "Number of African elephants": "82630"}], "rows_tail": [], "sampling_note": 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Reuse should follow the source and license information in this chart metadata."}, {"title": "Number of Asian elephants", "source_url": "https://ourworldindata.org/grapher/number-of-asian-elephants.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Number of Asian elephants"], "row_count_total": 41, "rows_head": [{"Entity": "Bangladesh", "Code": "BGD", "Year": "2017", "Number of Asian elephants": "268"}, {"Entity": "Bhutan", "Code": "BTN", "Year": "2010", "Number of Asian elephants": "513"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2016", "Number of Asian elephants": "500"}, {"Entity": "China", "Code": "CHN", "Year": "1976", "Number of Asian elephants": "146"}, {"Entity": "China", "Code": "CHN", "Year": "1983", "Number of Asian elephants": "225"}, {"Entity": "China", "Code": "CHN", "Year": "1997", "Number of Asian elephants": "216"}, {"Entity": "China", "Code": "CHN", "Year": "2003", "Number of Asian elephants": "230"}, {"Entity": "China", "Code": "CHN", "Year": "2006", "Number of Asian elephants": "189"}, {"Entity": "China", "Code": "CHN", "Year": "2009", "Number of Asian elephants": "197"}, {"Entity": "China", "Code": "CHN", "Year": "2014", "Number of Asian elephants": "232"}, {"Entity": "China", "Code": "CHN", "Year": "2017", "Number of Asian elephants": "300"}, {"Entity": "India", "Code": "IND", "Year": "1980", "Number of Asian elephants": "15627"}, {"Entity": "India", "Code": "IND", "Year": "1985", "Number of Asian elephants": "18975"}, {"Entity": "India", "Code": "IND", "Year": "1989", "Number of Asian elephants": "20862"}, {"Entity": "India", "Code": "IND", "Year": "1993", "Number of Asian elephants": "15604"}, {"Entity": "India", "Code": "IND", "Year": "1997", "Number of Asian elephants": "25877"}, {"Entity": "India", "Code": "IND", "Year": "2002", "Number of Asian elephants": "26413"}, {"Entity": "India", "Code": "IND", "Year": "2007", "Number of Asian elephants": "27694"}, {"Entity": "India", "Code": "IND", "Year": "2012", "Number of Asian elephants": "29391"}, {"Entity": "India", "Code": "IND", "Year": "2017", "Number of Asian elephants": "27312"}, {"Entity": "Indonesia", "Code": "IDN", "Year": "2007", "Number of Asian elephants": "2600"}, {"Entity": "Indonesia", "Code": "IDN", "Year": "2012", "Number of Asian elephants": "1724"}, {"Entity": "Laos", "Code": "LAO", "Year": "2012", "Number of Asian elephants": "700"}, {"Entity": "Malaysia", "Code": "MYS", "Year": "2010", "Number of Asian elephants": "1223"}, {"Entity": "Myanmar", "Code": "MMR", "Year": "2017", "Number of Asian elephants": "2000"}, {"Entity": "Nepal", "Code": "NPL", "Year": "1903", "Number of Asian elephants": "328"}, {"Entity": "Nepal", "Code": "NPL", "Year": "1913", "Number of Asian elephants": "234"}, {"Entity": "Nepal", "Code": "NPL", "Year": "1923", "Number of Asian elephants": "198"}, {"Entity": "Nepal", "Code": "NPL", "Year": "1933", "Number of Asian elephants": "199"}, {"Entity": "Nepal", "Code": "NPL", "Year": "1943", "Number of Asian elephants": "180"}, {"Entity": "Nepal", "Code": "NPL", "Year": "1953", "Number of Asian elephants": "136"}, {"Entity": "Nepal", "Code": "NPL", "Year": "1963", "Number of Asian elephants": "80"}, {"Entity": "Nepal", "Code": "NPL", "Year": "1973", "Number of Asian elephants": "47"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2010", "Number of Asian elephants": "5879"}, {"Entity": "Thailand", "Code": "THA", "Year": "2017", "Number of Asian elephants": "3100"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "1980", "Number of Asian elephants": "1750"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "1992", "Number of Asian elephants": "500"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "1995", "Number of Asian elephants": "280"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "1997", "Number of Asian elephants": "165"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2000", "Number of Asian elephants": "100"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2016", "Number of Asian elephants": "101"}], "rows_tail": [], "sampling_note": "Stored first 41 rows and last 41 rows when the table is larger.", "grapher_slug": "number-of-asian-elephants", "metadata_url": "https://ourworldindata.org/grapher/number-of-asian-elephants.metadata.json", "chart_title": "Number of Asian elephants", "chart_subtitle": "Estimates on wild mammal populations tend to come with significant uncertainty.", "chart_note": null, "chart_citation": "African Elephant Specialist Group (AfESG); 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{"raw_link": "https://ourworldindata.org/half-child-deaths-linked-malnutrition", "title": "Half of all child deaths are linked to malnutrition", "context": "Home\nHunger & Undernourishment\nHalf of all child deaths are linked to malnutrition\nImproving the nutrition of mothers and children could save many lives at a relatively low cost.\nBy\nHannah Ritchie\nSeptember 9, 2024\nBrowse past versions\nCite this article\nReuse our work freely\nIn 2021,\n4.7 million\nchildren under the age of five died; 2.4 million of those were attributed to child and maternal malnutrition.\n1\nThat means around half of child deaths were linked to nutritional deficiencies.\nWhen you think about these deaths, you might imagine a very acute form of hunger: a starving child. While this can happen during\nfamines\nor in areas with very low levels of food availability, it’s only a small fraction of the total deaths linked to malnutrition.\n2\nIn most cases, children don’t die\nof\nmalnutrition. They die from conditions that are exacerbated or are triggered by it. In most cases, it’s a\nrisk factor\nfor premature death. Take the example of the risk factor of smoking. People die\nfrom\nlung cancer, but their risk of developing it\nis significantly increased\nif they’ve been a smoker.\nYou can read more about how risk factors are calculated and how they should be interpreted, in\nan article\nby my colleague, Saloni Dattani.\nIn the chart below, we can see how many child deaths are attributed to different nutritional risk factors.\nBy far, the biggest is low birth weight, which often happens because the mother is malnourished or has experienced infectious diseases during pregnancy. Infants that are born with a low birth weight — which the World Health Organization\ndefines as\nweighing less than 2,500 grams or 5.5 pounds — have a much higher risk of infant mortality and health complications.\n3\nAfter the first few weeks or months of life, children are also more vulnerable to infection and disease when they’re underweight or are malnourished and don’t develop at a healthy rate. Hundreds of thousands die as a result of “\nwasting\n”, which means their weight is too low for their height. Or “\nstunting\n”, meaning they are too short for their age.\nNote that individual risk factors can overlap, such that deaths attributed to all forms of malnutrition can be lower than the sum of all individual nutrition-related risk factors.\nThis is not only about getting enough calories. Children are also malnourished when they don’t eat diverse foods, so they don’t get enough protein, vitamins, and\nmicronutrients\n.\nDeath rates from malnutrition are much higher in low-income countries, where children often don’t get the diversity of nutrients they need and where infectious diseases are much more common.\nYou can see this in the scatterplot below, where malnutrition deaths are plotted on the vertical axis and gross domestic product (GDP) per person on the horizontal axis. In rich countries — on the right of the chart — rates are 20 to 50 times lower than in the poorest countries, on the left.\nBecause of this, most\nmalnutrition deaths\noccur in Sub-Saharan Africa and South Asia.\nThe world is making progress due to improvements in malnutrition and tackling infectious diseases\nThankfully, we know that we can make progress on this problem. That’s because the world has\nalready\nmade progress. Fewer children are dying from malnutrition than a few decades ago.\nThe chart below shows the IHME’s estimates of the number of child deaths related to malnutrition since 1990.\nAround 6.6 million deaths were linked to these risks in 1990. By 2021, this had fallen to around 2.4 million.\nImprovements in nutrition have driven some of this decline.\nChildhood stunting\nhas fallen from 33% to 23% since 1990, and\nwasting\nhas dropped from 9% to 7% since 2000. The\nshare of children who are underweight\nhas also gone down from 21% to 12%.\nProgress in tackling infectious diseases has also been crucial. Disease and malnutrition have a bidirectional relationship, where one makes people more vulnerable to the other. As I wrote earlier, most people don’t die\nof\nmalnutrition; poor nutrition can make them more vulnerable to infections, and vice versa.\nTake the example of diarrheal diseases. Malnourished kids have weaker immune systems and are more susceptible to these diseases. Once they are sick, it’s much harder to retain nutrients from food, which is already in short supply. This makes them even weaker and more malnourished, locking them into a cycle that is hard to break.\nThis means that if diseases are less common, the health risks from being malnourished are also lower.\nThis has\nhappened in the last few decades.\nDeaths from diarrheal diseases\nhave plummeted thanks to clean water, improvements in sanitation, handwashing, and better and more widespread\ntreatments\nfor diarrheal diseases. Antimalarials and bednets have reduced\nmalaria death rates\n. Most children are\nvaccinated against tuberculosis\n, and a growing number\nagainst rotavirus\n.\nSupport for mothers and babies during pregnancy and after birth has also improved. More births are\nattended by skilled health workers\n, which means that when babies are born with very low birth weights, professional medical workers are there to help and advise.\nTackling the diseases and health conditions that affect malnourished children is another way to reduce the poor health\noutcomes\nof malnutrition. But of course, improving the nutrition of children and mothers is crucial.\nSubscribe to our newsletters\nWe send two regular newsletters so you can stay up to date on our work and receive curated highlights from across Our World in Data.\nSubscribe\nIt’s critical to invest in good nutrition for kids and mothers\nIf we’re trying to reduce childhood malnutrition, it’s tempting to focus on what kids eat. But this challenge starts with the nutrition of mothers, particularly during pregnancy.\nSeveral factors, including genetics, contribute to low birth weight in infants — but the risk tends to be higher when the mother has poor nutrition and dietary deficiencies during pregnancy.\n4\nWithout sufficient supplies of nutrients, fetal development is restricted, and babies cannot grow fully. In addition, the IHME estimates that around\n34,000 women died from pregnancy-related causes\nin 2021 as a result of being malnourished.\nAgain, this is not just about calories. Women who don’t get enough vital nutrients — such as iron, zinc, iodine, calcium, and vitamin B\n12\n— are not only at much higher risk of complications during pregnancy and childbirth themselves but are also much more likely to have infants with low birth weight and delays in development.\nWe discuss this on our page on\nmicronutrient deficiency\n.\nThe obvious solution is to ensure women have a diverse diet with lots of fruits, vegetables, pulses, and other nutrient-dense foods. The problem is that this is not affordable for many of the poorest women in the world.\nAs I previously wrote,\nbillions of people can’t afford a “healthy”, sufficient diet\neven if they spend most of their income on food. Hopefully, this will improve over time as incomes rise and healthy food becomes cheaper and more easily accessible. However, these changes will take time and will not solve the problem soon.\nBecause of this, we need to fast-track alternative solutions that deliver essential nutrients more efficiently to women\nand\nchildren. Doing so can save hundreds of thousands, perhaps millions, of lives every year.\nProviding dietary supplements is one option. There is good evidence that oral supplementation of various nutrients during pregnancy reduces the risk of low birth weight or premature births.\n5\nStudies also show that giving supplements to children who are malnourished reduces the risk of dying from various causes, including diarrheal diseases and measles.\n6\nThe charity evaluator GiveWell\nlists vitamin-A supplementation\nas one of its most cost-effective interventions to reduce child mortality and improve lives. It estimates that delivering vitamin A costs just $1 per capsule and could lead to a significant reduction in child deaths.\n7\nAnother is\nfortifying staple foods\nlike cereals with crucial micronutrients, directly adding small quantities of iron, zinc, iodine, or vitamins to products before they reach the market. This is very common across the world. In the UK, for example, I can buy breakfast cereals, bread, or milk with added nutrients. Food fortification is also incredibly cheap,\nranging from\njust $0.05 to $0.25 per person per year. One problem, however, is that food products need to go through processing, which means that fortification is often not a solution for rural communities.\nBiofortification\ncould be an alternative. This is when crops are bred to have more nutrients. Rice or corn grown through biofortification has higher levels of zinc, iron, or vitamin A. “Golden rice” — where rice is grown with higher levels of vitamin A — is the most well-known example of biofortification.\n8\nUltimately, we want people to be able to afford healthy and diverse diets so that they don’t need to rely on supplementation. But this will take time. There\nare\ncheaper and faster ways to deliver better nutrition for mothers and children that could save lives today.\nEndnotes\nThese estimates come from the latest Global Burden of Disease study from the Institute for Health Metrics and Evaluation (IHME). UNICEF and WHO\nalso attribute\naround half of child deaths to malnutrition.\nIf we look at\nwhat children die from globally\n, direct malnutrition deaths (from hunger) are a relatively small share: 97,000 deaths out of a total of 5 million.\nAshorn, P., Ashorn, U., Muthiani, Y., Aboubaker, S., Askari, S., Bahl, R., ... & Hayashi, C. (2023). Small vulnerable newborns—big potential for impact. The Lancet.\nJana, A., Saha, U. R., Reshmi, R. S., & Muhammad, T. (2023). Relationship between low birth weight and infant mortality: evidence from National Family Health Survey 2019-21, India. Archives of Public Health.\nVilanova, C. S., Hirakata, V. N., de Souza Buriol, V. C., Nunes, M., Goldani, M. Z., & da Silva, C. H. (2019). The relationship between the different low birth weight strata of newborns with infant mortality and the influence of the main health determinants in the extreme south of Brazil. Population health metrics.\nKheirouri, S., & Alizadeh, M. (2021). Maternal dietary diversity during pregnancy and risk of low birth weight in newborns: a systematic review. Public Health Nutrition.\nPerry, I. J., & Lumey, L. H. (1997). Fetal growth and development: the role of nutrition and other factors. A life course approach to chronic disease epidemiology.\nCetin, I., Mando, C., & Calabrese, S. (2013). Maternal predictors of intrauterine growth restriction. Current Opinion in Clinical Nutrition & Metabolic Care.\nNyamasege, C. K., Kimani-Murage, E. W., Wanjohi, M., Kaindi, D. W. M., Ma, E., Fukushige, M., & Wagatsuma, Y. (2019). Determinants of low birth weight in the context of maternal nutrition education in urban informal settlements, Kenya. Journal of developmental origins of health and disease.\nda Silva Lopes, K., Ota, E., Shakya, P., Dagvadorj, A., Balogun, O. O., Peña-Rosas, J. P., ... & Mori, R. (2017). Effects of nutrition interventions during pregnancy on low birth weight: an overview of systematic reviews. BMJ Global Health.\nPersson, L. Å., Arifeen, S., Ekström, E. C., Rasmussen, K. M., Frongillo, E. A., & MINIMat Study Team. (2012). Effects of prenatal micronutrient and early food supplementation on maternal hemoglobin, birth weight, and infant mortality among children in Bangladesh: the MINIMat randomized trial. Jama.\nLeung, D. T., Chisti, M. J., & Pavia, A. T. (2016). Prevention and control of childhood pneumonia and diarrhea. Pediatric Clinics.\nMayo-Wilson, E., Imdad, A., Herzer, K., Yakoob, M. Y., & Bhutta, Z. A. (2011). Vitamin-A supplements for preventing mortality, illness, and blindness in children aged under 5: systematic review and meta-analysis. Bmj.\nYou can read more about GiveWell’s evaluation — including a summary of the literature on the effectiveness of vitamin A supplementation in its report here:\nhttps://www.givewell.org/international/technical/programs/vitamin-A\n. It notes that it is more uncertain on the size of the impact on child mortality than other causes it recommends — such as malaria bednets — but thinks it is still an excellent investment to improve lives.\nMallikarjuna Swamy, B. P., Marundan Jr, S., Samia, M., Ordonio, R. L., Rebong, D. B., Miranda, R., ... & MacKenzie, D. J. (2021). Development and characterization of GR2E Golden rice introgression lines. Scientific Reports.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this article, please also cite the underlying data sources. This article can be cited as:\nHannah Ritchie (2024) - “Half of all child deaths are linked to malnutrition” Published online at OurWorldinData.org. Retrieved from: 'https://archive.ourworldindata.org/20260518-093348/half-child-deaths-linked-malnutrition.html' [Online Resource] (archived on May 18, 2026).\nBibTeX citation\n@article{owid-half-child-deaths-linked-malnutrition,\nauthor = {Hannah Ritchie},\ntitle = {Half of all child deaths are linked to malnutrition},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://archive.ourworldindata.org/20260518-093348/half-child-deaths-linked-malnutrition.html}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "half-child-deaths-linked-malnutrition", "source_url": "https://ourworldindata.org/half-child-deaths-linked-malnutrition", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. 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{"raw_link": "https://ourworldindata.org/homelessness", "title": "Homelessness", "context": "Homelessness\nHow many people are affected by homelessness? How is their number changing over time? How does it look by gender?\nBy\nBastian Herre\nand\nPablo Arriagada\nContents\nHomelessness can take various forms: some people sleep in the streets, some in public spaces, and others are temporarily housed in emergency shelters.\nThis makes measuring the extent of homelessness difficult. First, countries define and measure homelessness differently, making numbers challenging to compare. Methods sometimes even differ for the same country over time or across regions.\nSecond, these statistics can miss people who are only briefly homeless, stay with friends, live in their car, or do not seek formal support.\nHowever, while it is challenging to measure homelessness, it is essential to try. Housing is a basic human need and homelessness matters for many other problems that we focus on at Our World in Data. People who are homeless often face\npoverty\n, poorer\nphysical\nand\nmental health\n, and shorter\nlifespans\n.\nOn this page, you can find data and visualizations on the number of people affected by homelessness across different countries and how these numbers have changed over time. When presenting this data, we’ve been careful to clarify where comparisons can and can’t be made directly due to differences in definitions and methods.\nSee all interactive charts on homelessness ↓\nHow common is homelessness across the world?\nCounting how many people are affected by homelessness across different countries is challenging because countries differ in how they\ndefine\nand measure homelessness.\nThe Organisation for Economic Co-operation and Development (OECD) works to make the definitions and methods as similar as possible. The charts below show this harmonized data.\nThe first chart presents countries that count the people affected by homelessness by counting on a single night each year those living on the street or staying in shelters. To adjust for the fact that countries have different populations, this is expressed as a\nrate\n: the number affected by homelessness per 100,000 people.\nWe see that countries have very different rates of homelessness. For example, 300 out of every 100,000 are reported homeless in France, while it’s fewer than 20 in Finland.\nIn about half of the countries, more than 100 in every 100,000 people are homeless. That means more than one per thousand people.\nThere are also differences in\ntypes\nof homelessness. The United States, for example, has relatively high numbers of people living in the streets or public spaces but fewer in temporary accommodations or shelters.\nNot all countries on the chart can be directly compared. For example, Japan and Greece only report data on one type of homelessness, so they don’t give a complete picture of the total population affected by homelessness.\nOther countries measure homelessness over the entire year instead of just on one night. They include anyone who lived on the street or stayed in a shelter at\nany time\nduring the year. This data is shown below.\nAgain, we see significant differences. In Austria, more than 200 out of every 100,000 people were affected by homelessness, while in Croatia, it was less than a tenth of that.\nWhen we look only at those in temporary accommodation and shelters, the numbers vary too. In Latvia, several hundred out of every 100,000 people are in shelters, whereas in Turkey, it’s fewer than 10.\nImportantly, we can’t directly compare the data from the two charts above. Counting over a year will naturally include more people than counting on a single night.\nThe data in both charts mainly covers OECD member countries. Comparing data from non-OECD countries is even more complex because\ntheir definitions are often unclear\n, and\nthe data is often sparse\n.\nHow is the share of people affected by homelessness changing?\nThe OECD provides data on the reported share of people experiencing homelessness over time for selected countries.\nThis data can’t be directly compared between countries because their definitions and methods differ significantly. Therefore, you can only choose one country at a time in the chart.\nFor example, the chart shows that the share of people affected by homelessness in the United States first slightly declined and then increased again over the last decade.\nOther countries have seen different trends: during the same period, homelessness has\ndoubled in England\nbut\nhalved in Finland\n.\nMen are more likely to be homeless in most countries, but there are exceptions\nThis chart shows the gender breakdown of people affected by homelessness.\nIn most countries, men tend to be more likely to experience homelessness than women. In many, women make up 20% to 40% of the homeless population.\nBut this ratio varies a lot by country. In Colombia and Costa Rica, men are\nmuch\nmore likely to be affected by homelessness, with only around one in ten being women.\nIn the United Kingdom and New Zealand, it’s even the opposite: more women experience homelessness than men.\nDifferent definitions of homelessness make international comparisons difficult\nHomelessness is defined differently around the world, making it difficult to compare the issue across countries.\nThe map shows the forms of homelessness included in country statistics, as recorded by the\nInstitute of Global Homelessness\n.\nSources distinguish three broad forms of homelessness: people with no accommodation who sleep in the streets or public spaces; people in temporary accommodation, such as emergency shelters; and people in severely inadequate housing, such as tents or slums.\nThe available statistics vary in which forms they include, with some focusing on just one type, while others cover multiple combinations. Many sources do not provide enough details to know which forms of homelessness they refer to.\nCountries also differ in their data collection methods. For example, some countries count\nthe number of people experiencing homelessness on one night of the year\n, while others do so\nover the entire year\n.\nDespite these challenges, progress has recently been made. The Institute of Global Homelessness has collected data on the\ncompleteness of country statistics\n, and the OECD has worked on making the statistics of their members\ncomparable\nby using the same definitions of homelessness across them.\nKey Charts on Homelessness\nSee all charts on this topic\nHomelessness rate\nOne-night count\nHomelessness rate\nAnnual count\nForms of homelessness included in available statistics\nReported share of people experiencing homelessness\nCompleteness of homelessness data\nHomelessness by gender\nReported share of people experiencing homelessness who are women\nOne-night count\nReported share of people experiencing homelessness who are women\nAnnual count\nChart 1 of 8\nFeatured Data on\nHomelessness\nRelated topics on Our World in Data\nPoverty\nIn order to make progress against poverty in the future, we need to understand poverty around the world today and how it has changed.\nEconomic Inequality\nSee all our data, visualizations, and writing on economic inequality.\nMental Health\nMental health has a significant impact on people’s lives and wellbeing. To support people with mental illnesses, we need better data to understand them.\nAcknowledgements\nWe thank Ali Bargu, Esteban Ortiz-Ospina, Edouard Mathieu, Hannah Ritchie, Max Roser, and Julia Wagner for their very helpful suggestions and ideas.\nCite this work\nOur articles and data visualizations rely on work from many different people and organizations. When citing this topic page, please also cite the underlying data sources. This topic page can be cited as:\nBastian Herre and Pablo Arriagada (2024) - “Homelessness” Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/homelessness' [Online Resource]\nBibTeX citation\n@article{owid-homelessness,\nauthor = {Bastian Herre and Pablo Arriagada},\ntitle = {Homelessness},\njournal = {Our World in Data},\nyear = {2024},\nnote = {https://ourworldindata.org/homelessness}\n}\nReuse this work freely\nAll visualizations, data, and articles produced by Our World in Data are completely open access under the\nCreative Commons BY license\n. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.\nThe data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution.\nAll of\nour charts can be embedded\nin any site.", "source": "Our World in Data articles with Grapher numeric data", "source_record_id": "homelessness", "source_url": "https://ourworldindata.org/homelessness", "record_type": "analysis_result_summary_with_numeric_evidence", "source_license": "CC BY unless otherwise stated", "license_note": "OWID says its charts, articles, and data are licensed under CC BY unless otherwise stated. Some underlying data and third-party materials have separate licenses, so chart metadata is preserved.", "published_date": "", "description": "How many people are affected by homelessness? How is their number changing over time? Explore global data and research on homelessness.", "numeric_mentions": ["100,000", "300", "20", "100", "200", "10", "20%", "40%", "1", "8", "2024"], "numeric_evidence": [{"title": "Forms of homelessness included in available statistics", "source_url": "https://ourworldindata.org/grapher/forms-of-homelessness-included-in-available-statistics.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "IGH Framework Category"], "row_count_total": 129, "rows_head": [{"Entity": "Albania", "Code": "ALB", "Year": "2019", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "Angola", "Code": "AGO", "Year": "2007", "IGH Framework Category": "Not enough information"}, {"Entity": "Antigua and Barbuda", "Code": "ATG", "Year": "2001", "IGH Framework Category": "Not enough information"}, {"Entity": "Argentina", "Code": "ARG", "Year": "2022", "IGH Framework Category": "None or temporary"}, {"Entity": "Armenia", "Code": "ARM", "Year": "2016", "IGH Framework Category": "Not enough information"}, {"Entity": "Australia", "Code": "AUS", "Year": "2021", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "Austria", "Code": "AUT", "Year": "2021", "IGH Framework Category": "None or temporary"}, {"Entity": "Azerbaijan", "Code": "AZE", "Year": "2017", "IGH Framework Category": "Temporary and crisis accommodation"}, {"Entity": "Bahrain", "Code": "BHR", "Year": "2015", "IGH Framework Category": "No accommodation"}, {"Entity": "Bangladesh", "Code": "BGD", "Year": "2024", "IGH Framework Category": "No accommodation"}, {"Entity": "Belarus", "Code": "BLR", "Year": "2013", "IGH Framework Category": "Not enough information"}, {"Entity": "Belgium", "Code": "BEL", "Year": "2022", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "Belize", "Code": "BLZ", "Year": "2010", "IGH Framework Category": "No accommodation"}, {"Entity": "Bolivia", "Code": "BOL", "Year": "2012", "IGH Framework Category": "No accommodation"}, {"Entity": "Bosnia and Herzegovina", "Code": "BIH", "Year": "2013", "IGH Framework Category": "None or temporary"}, {"Entity": "Brazil", "Code": "BRA", "Year": "2022", "IGH Framework Category": "None or temporary"}, {"Entity": "Burundi", "Code": "BDI", "Year": "2021", "IGH Framework Category": "Not enough information"}, {"Entity": "Cambodia", "Code": "KHM", "Year": "2017", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "Cameroon", "Code": "CMR", "Year": "2023", "IGH Framework Category": "Not enough information"}, {"Entity": "Canada", "Code": "CAN", "Year": "2022", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "Cape Verde", "Code": "CPV", "Year": "2021", "IGH Framework Category": "Not enough information"}, {"Entity": "Chile", "Code": "CHL", "Year": "2023", "IGH Framework Category": "None or temporary"}, {"Entity": "China", "Code": "CHN", "Year": "2020", "IGH Framework Category": "None or inadequate"}, {"Entity": "Colombia", "Code": "COL", "Year": "2021", "IGH Framework Category": "No accommodation"}, {"Entity": "Congo", "Code": "COG", "Year": "2018", "IGH Framework Category": "Not enough information"}, {"Entity": "Costa Rica", "Code": "CRI", "Year": "2021", "IGH Framework Category": "No accommodation"}, {"Entity": "Cote d'Ivoire", "Code": "CIV", "Year": "2021", "IGH Framework Category": "No accommodation"}, {"Entity": "Croatia", "Code": "HRV", "Year": "2021", "IGH Framework Category": "Temporary and crisis accommodation"}, {"Entity": "Cuba", "Code": "CUB", "Year": "2012", "IGH Framework Category": "No accommodation"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2017", "IGH Framework Category": "Not enough information"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2019", "IGH Framework Category": "None or temporary"}, {"Entity": "Denmark", "Code": "DNK", "Year": "2022", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "East Timor", "Code": "TLS", "Year": "2010", "IGH Framework Category": "No accommodation"}, {"Entity": "Ecuador", "Code": "ECU", "Year": "2021", "IGH Framework Category": "Temporary and crisis accommodation"}, {"Entity": "Egypt", "Code": "EGY", "Year": "2024", "IGH Framework Category": "Not enough information"}, {"Entity": "Estonia", "Code": "EST", "Year": "2021", "IGH Framework Category": "None or temporary"}, {"Entity": "Eswatini", "Code": "SWZ", "Year": "2018", "IGH Framework Category": "Not enough information"}, {"Entity": "Ethiopia", "Code": "ETH", "Year": "2007", "IGH Framework Category": "Not enough information"}, {"Entity": "Fiji", "Code": "FJI", "Year": "2022", "IGH Framework Category": "Not enough information"}, {"Entity": "Finland", "Code": "FIN", "Year": "2023", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "France", "Code": "FRA", "Year": "2012", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "Georgia", "Code": "GEO", "Year": "2019", "IGH Framework Category": "Not enough information"}, {"Entity": "Germany", "Code": "DEU", "Year": "2022", "IGH Framework Category": "None or temporary"}, {"Entity": "Ghana", "Code": "GHA", "Year": "2023", "IGH Framework Category": "Not enough information"}, {"Entity": "Greece", "Code": "GRC", "Year": "2023", "IGH Framework Category": "Temporary and crisis accommodation"}, {"Entity": "Grenada", "Code": "GRD", "Year": "2023", "IGH Framework Category": "No accommodation"}, {"Entity": "Guatemala", "Code": "GTM", "Year": "2018", "IGH Framework Category": "None or temporary"}, {"Entity": "Honduras", "Code": "HND", "Year": "2024", "IGH Framework Category": "Not enough information"}, {"Entity": "Hungary", "Code": "HUN", "Year": "2011", "IGH Framework Category": "Not enough information"}, {"Entity": "Iceland", "Code": "ISL", "Year": "2021", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "India", "Code": "IND", "Year": "2011", "IGH Framework Category": "No accommodation"}, {"Entity": "Indonesia", "Code": "IDN", "Year": "2024", "IGH Framework Category": 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"Code": "KGZ", "Year": "2022", "IGH Framework Category": "Not enough information"}, {"Entity": "Latvia", "Code": "LVA", "Year": "2022", "IGH Framework Category": "Temporary and crisis accommodation"}, {"Entity": "Lesotho", "Code": "LSO", "Year": "2015", "IGH Framework Category": "Not enough information"}, {"Entity": "Lithuania", "Code": "LTU", "Year": "2022", "IGH Framework Category": "None or temporary"}, {"Entity": "Luxembourg", "Code": "LUX", "Year": "2022", "IGH Framework Category": "None or temporary"}, {"Entity": "Madagascar", "Code": "MDG", "Year": "2022", "IGH Framework Category": "No accommodation"}, {"Entity": "Malawi", "Code": "MWI", "Year": "2022", "IGH Framework Category": "Not enough information"}, {"Entity": "Malaysia", "Code": "MYS", "Year": "2015", "IGH Framework Category": "No accommodation"}, {"Entity": "Maldives", "Code": "MDV", "Year": "2020", "IGH Framework Category": "Temporary and crisis accommodation"}, {"Entity": "Mali", "Code": "MLI", "Year": "2002", "IGH 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{"Entity": "Spain", "Code": "ESP", "Year": "2022", "IGH Framework Category": "Temporary and crisis accommodation"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2021", "IGH Framework Category": "Not enough information"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2017", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2021", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2016", "IGH Framework Category": "Not enough information"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2010", "IGH Framework Category": "Not enough information"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2022", "IGH Framework Category": "No accommodation"}, {"Entity": "Thailand", "Code": "THA", "Year": "2023", "IGH Framework Category": "None or temporary"}, {"Entity": "Togo", "Code": "TGO", "Year": "2022", "IGH Framework Category": "Not enough information"}, {"Entity": "Trinidad and 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information"}, {"Entity": "San Marino", "Code": "SMR", "Year": "2023", "IGH Framework Category": "Not enough information"}, {"Entity": "Saudi Arabia", "Code": "SAU", "Year": "2015", "IGH Framework Category": "Not enough information"}, {"Entity": "Serbia", "Code": "SRB", "Year": "2011", "IGH Framework Category": "None or inadequate"}, {"Entity": "Singapore", "Code": "SGP", "Year": "2022", "IGH Framework Category": "Not enough information"}, {"Entity": "Slovakia", "Code": "SVK", "Year": "2021", "IGH Framework Category": "Temporary or inadequate"}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2022", "IGH Framework Category": "Temporary and crisis accommodation"}, {"Entity": "South Africa", "Code": "ZAF", "Year": "2022", "IGH Framework Category": "None or temporary"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2021", "IGH Framework Category": "None or temporary"}, {"Entity": "South Sudan", "Code": "SSD", "Year": "2017", "IGH Framework Category": "Not enough information"}, {"Entity": "Spain", "Code": "ESP", "Year": "2022", "IGH Framework Category": "Temporary and crisis accommodation"}, {"Entity": "Sri Lanka", "Code": "LKA", "Year": "2021", "IGH Framework Category": "Not enough information"}, {"Entity": "Sweden", "Code": "SWE", "Year": "2017", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "Switzerland", "Code": "CHE", "Year": "2021", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "Taiwan", "Code": "TWN", "Year": "2016", "IGH Framework Category": "Not enough information"}, {"Entity": "Tajikistan", "Code": "TJK", "Year": "2010", "IGH Framework Category": "Not enough information"}, {"Entity": "Tanzania", "Code": "TZA", "Year": "2022", "IGH Framework Category": "No accommodation"}, {"Entity": "Thailand", "Code": "THA", "Year": "2023", "IGH Framework Category": "None or temporary"}, {"Entity": "Togo", "Code": "TGO", "Year": "2022", "IGH Framework Category": "Not enough information"}, {"Entity": "Trinidad and Tobago", "Code": "TTO", "Year": "2011", "IGH Framework Category": "No accommodation"}, {"Entity": "Tunisia", "Code": "TUN", "Year": "2019", "IGH Framework Category": "Not enough information"}, {"Entity": "Turkey", "Code": "TUR", "Year": "2022", "IGH Framework Category": "Temporary and crisis accommodation"}, {"Entity": "Turkmenistan", "Code": "TKM", "Year": "2015", "IGH Framework Category": "Not enough information"}, {"Entity": "Uganda", "Code": "UGA", "Year": "2014", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "Ukraine", "Code": "UKR", "Year": "2016", "IGH Framework Category": "Not enough information"}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2020", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "United States", "Code": "USA", "Year": "2023", "IGH Framework Category": "None, temporary or inadequate"}, {"Entity": "Uruguay", "Code": "URY", "Year": "2020", "IGH Framework Category": "None or temporary"}, {"Entity": "Vietnam", "Code": "VNM", "Year": "2019", "IGH Framework Category": "No accommodation"}, {"Entity": "Yemen", "Code": "YEM", "Year": "2023", "IGH Framework Category": "Temporary and crisis accommodation"}, {"Entity": "Zambia", "Code": "ZMB", "Year": "2022", "IGH Framework Category": "Not enough information"}, {"Entity": "Zimbabwe", "Code": "ZWE", "Year": "2005", "IGH Framework Category": "Not enough information"}], "sampling_note": "Stored first 120 rows and last 120 rows when the table is larger.", "grapher_slug": "forms-of-homelessness-included-in-available-statistics", "metadata_url": "https://ourworldindata.org/grapher/forms-of-homelessness-included-in-available-statistics.metadata.json", "chart_title": "Forms of homelessness included in available statistics", "chart_subtitle": "Categories of homelessness covered in the available data source, such as official government data, news reports, or information from non-governmental organizations.", "chart_note": null, "chart_citation": "Institute of Global Homelessness (2024)", "original_chart_url": "https://ourworldindata.org/grapher/forms-of-homelessness-included-in-available-statistics", "owid_column_metadata": {"IGH Framework Category (simplified)": {"titleShort": "Forms of homelessness included in available statistics", "titleLong": "Forms of homelessness included in available statistics", "descriptionShort": "The category of the IGH Framework that the homelessness data falls under.", "descriptionKey": ["The [IGH Global Framework](https://ighomelessness.org/wp-content/uploads/2019/10/globalframeworkforundertanding.pdf) captures three broad categories of people who may be considered homeless, defined as \"lacking access to minimally adequate housing\". These categories are (1) People without accommodation, (2) People living in temporary or crisis accommodation, and (3) People living in severely inadequate or insecure accommodation.", "Among the first category, people without accommodation, the IGH Framework distinguishes (1A) People sleeping in the streets or in other open spaces, (1B) People sleeping in public roofed spaces or buildings not intended for human habitation, (1C) People sleeping in their cars, rickshaws, open fishing boats and other forms of transport, and (1D) \"Pavement dwellers\" - individuals or households who live on the street in a regular spot, usually with some form of makeshift cover.", "Among the second category, people living in temporary or crisis accommodation, the IGH Framework distinguishes (2A) People staying in night shelters, (2B) People living in homeless hostels and other types of temporary accommodation, (2C) Women and children living in refuges for those fleeing domestic violence, (2D) People living in camps provided for \"internally displaced people\", and (2E) People living in camps or reception centres/temporary accommodation for asylum seekers, refugees and other immigrants.", "Among the third category, people living in severely inadequate or insecure accommodation, the IGH Framework distinguishes (3A) People sharing with friends and relatives on a temporary basis, (3B) People living under threat of violence, (3C) People living in cheap hotels, bed and breakfasts and similar, (3D) People squatting in conventional housing, (3E) People living in conventional housing that is unfit for human habitation, (3F) People living in trailers, caravans and tents, (3G) People living in extremely overcrowded conditions, and (3H) People living in non-conventional buildings and temporary structures, including those living in slums/informal settlements.", "Within the framework, IGH targets programs and research primarily toward those in Category 1 and in a subset of Category 2 (2A-2C).", "We only consider the data from the source that is at most five years old."], "descriptionProcessing": "We have simplified the original version of the IGH Framework Category in order to make the metric more clear in a chart. Regardless of the subcategories, we classify the homelessness data into these categories:\n\n- \"No accommodation\" refers to mentions to the category 1 of the IGH Framework.\n- \"Temporary and crisis accommodation\" refers to mentions to the category 2 of the IGH Framework.\n- \"Severely inadequate accommodation\" refers to mentions to the category 3 of the IGH Framework.\n- \"None or temporary\" refers to mentions to the categories 1 and 2 of the IGH Framework.\n- \"None or inadequate\" refers to mentions to the categories 1 and 3 of the IGH Framework.\n- \"Temporary or inadequate\" refers to mentions to the categories 2 and 3 of the IGH Framework.\n- \"None, temporary or inadequate\" refers to mentions to the categories 1, 2 and 3 of the IGH Framework.\n- \"Not enough information\" refers to the cases where the definition does not align or provide enough detail for IGH Framework classification.", "shortUnit": "", "unit": "", "timespan": "2001-2024", "type": "Ordinal", "owidVariableId": 946990, "shortName": "igh_framework_category_simplified", "lastUpdated": "2024-07-05", "nextUpdate": "2026-07-22", "citationShort": "Institute of Global Homelessness (2024) – with major processing by Our World in Data", "citationLong": "Institute of Global Homelessness (2024) – with major processing by Our World in Data. “Forms of homelessness included in available statistics” [dataset]. Institute of Global Homelessness, “Homelessness - Better Data Project” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/946990.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Homelessness rate", "source_url": "https://ourworldindata.org/grapher/homelessness-rate-point-in-time-count.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Living in the streets or public spaces", "Staying in temporary accommodation or shelters", "Either"], "row_count_total": 22, "rows_head": [{"Entity": "Australia", "Code": "AUS", "Year": "2021", "Living in the streets or public spaces": "29.73753", "Staying in temporary accommodation or shelters": "94.5497", "Either": ""}, {"Entity": "Canada", "Code": "CAN", "Year": "2022", "Living in the streets or public spaces": "23.89166", "Staying in temporary accommodation or shelters": "80.68862", "Either": ""}, {"Entity": "Costa Rica", "Code": "CRI", "Year": "2023", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "", "Either": "86.08506"}, {"Entity": "Czechia", "Code": "CZE", "Year": "2022", "Living in the streets or public spaces": "85.877396", "Staying in temporary accommodation or shelters": "198.42883", "Either": ""}, {"Entity": "Denmark", "Code": "DNK", "Year": "2022", "Living in the streets or public spaces": "9.05157", "Staying in temporary accommodation or shelters": "54.190987", "Either": ""}, {"Entity": "Finland", "Code": "FIN", "Year": "2023", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "", "Either": "15.621677"}, {"Entity": "France", "Code": "FRA", "Year": "2022", "Living in the streets or public spaces": "8.09507", "Staying in temporary accommodation or shelters": "298.78168", "Either": ""}, {"Entity": "Germany", "Code": "DEU", "Year": "2022", "Living in the streets or public spaces": "45.943825", "Staying in temporary accommodation or shelters": "212.53494", "Either": ""}, {"Entity": "Greece", "Code": "GRC", "Year": "2023", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "13.344173", "Either": ""}, {"Entity": "Iceland", "Code": "ISL", "Year": "2021", "Living in the streets or public spaces": "52.077324", "Staying in temporary accommodation or shelters": "", "Either": ""}, {"Entity": "Ireland", "Code": "IRL", "Year": "2023", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "253.43826", "Either": ""}, {"Entity": "Japan", "Code": "JPN", "Year": "2023", "Living in the streets or public spaces": "2.4767559", "Staying in temporary accommodation or shelters": "", "Either": ""}, {"Entity": "Mexico", "Code": "MEX", "Year": "2020", "Living in the streets or public spaces": "4.5213995", "Staying in temporary accommodation or shelters": "31.673275", "Either": ""}, {"Entity": "New Zealand", "Code": "NZL", "Year": "2018", "Living in the streets or public spaces": "4.2239723", "Staying in temporary accommodation or shelters": "130.75949", "Either": ""}, {"Entity": "Norway", "Code": "NOR", "Year": "2020", "Living in the streets or public spaces": "1.2268863", "Staying in temporary accommodation or shelters": "24.723616", "Either": ""}, {"Entity": "Poland", "Code": "POL", "Year": "2019", "Living in the streets or public spaces": "6.64557", "Staying in temporary accommodation or shelters": "48.76978", "Either": ""}, {"Entity": "Portugal", "Code": "PRT", "Year": "2022", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "", "Either": "103.14774"}, {"Entity": "South Korea", "Code": "KOR", "Year": "2022", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "", "Either": "16.403852"}, {"Entity": "Spain", "Code": "ESP", "Year": "2022", "Living in the streets or public spaces": "15.282989", "Staying in temporary accommodation or shelters": "38.993984", "Either": ""}, {"Entity": "Sweden", "Code": "SWE", "Year": "2017", "Living in the streets or public spaces": "6.4328856", "Staying in temporary accommodation or shelters": "48.92771", "Either": ""}, {"Entity": "United Kingdom", "Code": "GBR", "Year": "2023", "Living in the streets or public spaces": "16.498774", "Staying in temporary accommodation or shelters": "409.80273", "Either": ""}, {"Entity": "United States", "Code": "USA", "Year": "2023", "Living in the streets or public spaces": "75.547935", "Staying in temporary accommodation or shelters": "116.73085", "Either": ""}], "rows_tail": [], "sampling_note": "Stored first 22 rows and last 22 rows when the table is larger.", "grapher_slug": "homelessness-rate-point-in-time-count", "metadata_url": "https://ourworldindata.org/grapher/homelessness-rate-point-in-time-count.metadata.json", "chart_title": "Homelessness rate", "chart_subtitle": "Population reported as experiencing homelessness at a single point in time per 100,000 people. The data is collected by counting the people living on the street or staying in shelters on one night of the year. Countries use different definitions and data collection methods and are harmonized to the extent possible.", "chart_note": "Data for the United Kingdom only considers England and is expressed in households.", "chart_citation": "OECD (2024)", "original_chart_url": "https://ourworldindata.org/grapher/homelessness-rate-point-in-time-count", "owid_column_metadata": {"Rate of people experiencing homelessness (point-in-time, ETHOS 1)": {"titleShort": "Rate of people experiencing homelessness (ETHOS 1)", "titleLong": "Rate of people experiencing homelessness (ETHOS 1)", "descriptionShort": "Includes people living in the streets or public spaces without a shelter that can be defined as living quarters. This data is collected at a single point in time, generally through a coordinated street count.", "descriptionKey": ["This data has been categorized within the European Typology of Homelessness and Housing Exclusion (ETHOS) typology as _ETHOS Light 1_. ETHOS Light 1 is defined as people living rough, living in the streets or public spaces without a shelter that can be defined as living quarters.", "This data is collected with a _point-in-time count_, in which data are collected at a single point in time, generally through a coordinated street count or an enumeration of people staying in shelters for people experiencing homelessness on a given night. Point-in-time counts thus present a “snapshot” of homelessness at a single time and place.", "Countries use different definitions and data collection methods and are harmonized to the extent possible.", "Data for Australia, Canada, France, Germany, South Korea, Norway, and the United States includes people living in unconventional dwellings (e.g. tents).", "Data for the United Kingdom only considers England and refers to the number of households experiencing homelessness per 100,000 households.", "Data for France exclude asylum seekers to facilitate cross-country comparison.", "More details about definitions, methodology and comparability issues can be found in the [OECD Population Experiencing Homelessness documentation](https://www.oecd.org/els/family/HC3-1-Population-experiencing-homelessness.pdf).", "For more information on the statistical definitions for each country, please check the [OECD's Country Notes on Homelessness data](https://www.oecd.org/social/homelessness-country-notes.htm)."], "descriptionProcessing": "We have transformed the data from homelessness per 10,000 population to homeless per 100,000 population.\n\nWe have included the years the data has been captured for each country, according to the source's notes. Whenever the data was captured over different years, we have included the latest year.", "shortUnit": "", "unit": "people in homelessness per 100,000 population", "timespan": "2017-2023", "type": "Numeric", "owidVariableId": 901091, "shortName": "point_in_time_1", "lastUpdated": "2024-04-30", "nextUpdate": "2026-07-22", "citationShort": "OECD (2024) – with major processing by Our World in Data", "citationLong": "OECD (2024) – with major processing by Our World in Data. “Rate of people experiencing homelessness (ETHOS 1)” [dataset]. OECD, “OECD Affordable Housing Database (AHD)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/901091.metadata.json"}, "Rate of people experiencing homelessness (point-in-time, ETHOS 2 and 3)": {"titleShort": "Rate of people experiencing homelessness (ETHOS 2 and 3)", "titleLong": "Rate of people experiencing homelessness (ETHOS 2 and 3)", "descriptionShort": "Includes people living in emergency accommodation, people with no place of usual residence who move frequently between various types of accommodation, and people living in accommodation for the homeless. This data is collected at a single point in time, generally through a coordinated street count.", "descriptionKey": ["This data has been categorized within the European Typology of Homelessness and Housing Exclusion (ETHOS) typology as _ETHOS Light 2 and 3_. ETHOS Light 2 is defined as people living in emergency accommodation, people with no place of usual residence who move frequently between various types of accommodation. ETHOS Light 3 is defined as people living in accommodation for the homeless, including hostels, temporary and transitional accommodation, and women's shelters or refuge accommodation.", "This data is collected with a _point-in-time count_, in which data are collected at a single point in time, generally through a coordinated street count or an enumeration of people staying in shelters for people experiencing homelessness on a given night. Point-in-time counts thus present a “snapshot” of homelessness at a single time and place.", "Countries use different definitions and data collection methods and are harmonized to the extent possible.", "Data for Australia, Canada, France, Germany, South Korea, Norway, and the United States includes people living in unconventional dwellings (e.g. tents).", "Data for the United Kingdom only considers England and refers to the number of households experiencing homelessness per 100,000 households.", "Data for France exclude asylum seekers to facilitate cross-country comparison.", "More details about definitions, methodology and comparability issues can be found in the [OECD Population Experiencing Homelessness documentation](https://www.oecd.org/els/family/HC3-1-Population-experiencing-homelessness.pdf).", "For more information on the statistical definitions for each country, please check the [OECD's Country Notes on Homelessness data](https://www.oecd.org/social/homelessness-country-notes.htm)."], "descriptionProcessing": "We have transformed the data from homelessness per 10,000 population to homeless per 100,000 population.\n\nWe have included the years the data has been captured for each country, according to the source's notes. Whenever the data was captured over different years, we have included the latest year.", "shortUnit": "", "unit": "people in homelessness per 100,000 population", "timespan": "2017-2023", "type": "Numeric", "owidVariableId": 901092, "shortName": "point_in_time_2_3", "lastUpdated": "2024-04-30", "nextUpdate": "2026-07-22", "citationShort": "OECD (2024) – with major processing by Our World in Data", "citationLong": "OECD (2024) – with major processing by Our World in Data. “Rate of people experiencing homelessness (ETHOS 2 and 3)” [dataset]. OECD, “OECD Affordable Housing Database (AHD)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/901092.metadata.json"}, "Rate of people experiencing homelessness (point-in-time, ETHOS 1, 2 and 3)": {"titleShort": "Rate of people experiencing homelessness (ETHOS 1, 2 and 3)", "titleLong": "Rate of people experiencing homelessness (ETHOS 1, 2 and 3)", "descriptionShort": "Includes people living in the streets or public spaces, in emergency accommodation, and in accommodation for the homeless. This data is collected at a single point in time, generally through a coordinated street count.", "descriptionKey": ["This data has been categorized within the European Typology of Homelessness and Housing Exclusion (ETHOS) typology as _ETHOS Light 1, 2 and 3_. It includes people living in the streets or public spaces, in emergency accommodation and in accommodation for the homeless, but it has not been dissagregated.", "This data is collected with a _point-in-time count_, in which data are collected at a single point in time, generally through a coordinated street count or an enumeration of people staying in shelters for people experiencing homelessness on a given night. Point-in-time counts thus present a “snapshot” of homelessness at a single time and place.", "Countries use different definitions and data collection methods and are harmonized to the extent possible.", "Data for Australia, Canada, France, Germany, South Korea, Norway, and the United States includes people living in unconventional dwellings (e.g. tents).", "Data for the United Kingdom only considers England and refers to the number of households experiencing homelessness per 100,000 households.", "Data for France exclude asylum seekers to facilitate cross-country comparison.", "More details about definitions, methodology and comparability issues can be found in the [OECD Population Experiencing Homelessness documentation](https://www.oecd.org/els/family/HC3-1-Population-experiencing-homelessness.pdf).", "For more information on the statistical definitions for each country, please check the [OECD's Country Notes on Homelessness data](https://www.oecd.org/social/homelessness-country-notes.htm)."], "descriptionProcessing": "We have transformed the data from homelessness per 10,000 population to homeless per 100,000 population.\n\nWe have included the years the data has been captured for each country, according to the source's notes. Whenever the data was captured over different years, we have included the latest year.", "shortUnit": "", "unit": "people in homelessness per 100,000 population", "timespan": "2022-2023", "type": "Numeric", "owidVariableId": 901093, "shortName": "point_in_time_1_2_3", "lastUpdated": "2024-04-30", "nextUpdate": "2026-07-22", "citationShort": "OECD (2024) – with major processing by Our World in Data", "citationLong": "OECD (2024) – with major processing by Our World in Data. “Rate of people experiencing homelessness (ETHOS 1, 2 and 3)” [dataset]. OECD, “OECD Affordable Housing Database (AHD)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/901093.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}, {"title": "Homelessness rate", "source_url": "https://ourworldindata.org/grapher/homelessness-rate-flow-count.csv", "file_type": "csv", "columns": ["Entity", "Code", "Year", "Living in the streets or public spaces", "Staying in temporary accommodation or shelters", "Either"], "row_count_total": 11, "rows_head": [{"Entity": "Austria", "Code": "AUT", "Year": "2022", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "", "Either": "217.24637"}, {"Entity": "Chile", "Code": "CHL", "Year": "2023", "Living in the streets or public spaces": "51.160046", "Staying in temporary accommodation or shelters": "28.916548", "Either": ""}, {"Entity": "Croatia", "Code": "HRV", "Year": "2021", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "", "Either": "14.462562"}, {"Entity": "Cyprus", "Code": "CYP", "Year": "2017", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "15.008377", "Either": ""}, {"Entity": "Israel", "Code": "ISR", "Year": "2023", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "", "Either": "34.477238"}, {"Entity": "Italy", "Code": "ITA", "Year": "2021", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "162.67856", "Either": ""}, {"Entity": "Latvia", "Code": "LVA", "Year": "2022", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "319.0956", "Either": ""}, {"Entity": "Lithuania", "Code": "LTU", "Year": "2022", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "152.38263", "Either": ""}, {"Entity": "Luxembourg", "Code": "LUX", "Year": "2022", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "106.56737", "Either": ""}, {"Entity": "Slovenia", "Code": "SVN", "Year": "2022", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "168.1105", "Either": ""}, {"Entity": "Turkey", "Code": "TUR", "Year": "2022", "Living in the streets or public spaces": "", "Staying in temporary accommodation or shelters": "6.219116", "Either": ""}], "rows_tail": [], "sampling_note": "Stored first 11 rows and last 11 rows when the table is larger.", "grapher_slug": "homelessness-rate-flow-count", "metadata_url": "https://ourworldindata.org/grapher/homelessness-rate-flow-count.metadata.json", "chart_title": "Homelessness rate", "chart_subtitle": "Population reported as experiencing homelessness over the course of the year per 100,000 people. The data is collected by counting the people who at any point lived on the street or stayed in a shelter. Countries use different definitions and data collection methods and are harmonized to the extent possible.", "chart_note": null, "chart_citation": "OECD (2024)", "original_chart_url": "https://ourworldindata.org/grapher/homelessness-rate-flow-count", "owid_column_metadata": {"Rate of people experiencing homelessness (flow, ETHOS 1)": {"titleShort": "Rate of people experiencing homelessness (ETHOS 1)", "titleLong": "Rate of people experiencing homelessness (ETHOS 1)", "descriptionShort": "Includes people living in the streets or public spaces without a shelter that can be defined as living quarters. This data is collected over a given period of time, such as the enumeration of all people who have stayed in a shelter over the course of the year.", "descriptionKey": ["This data has been categorized within the European Typology of Homelessness and Housing Exclusion (ETHOS) typology as _ETHOS Light 1_. ETHOS Light 1 is defined as people living rough, living in the streets or public spaces without a shelter that can be defined as living quarters.", "This data is collected with a _flow count_, in which data are collected over a given period of time, such as the enumeration of all people who have stayed in a shelter over the course of the year.", "Countries use different definitions and data collection methods and are harmonized to the extent possible.", "More details about definitions, methodology and comparability issues can be found in the [OECD Population Experiencing Homelessness documentation](https://www.oecd.org/els/family/HC3-1-Population-experiencing-homelessness.pdf).", "For more information on the statistical definitions for each country, please check the [OECD's Country Notes on Homelessness data](https://www.oecd.org/social/homelessness-country-notes.htm)."], "descriptionProcessing": "We have transformed the data from homelessness per 10,000 population to homeless per 100,000 population.\n\nWe have included the years the data has been captured for each country, according to the source's notes. Whenever the data was captured over different years, we have included the latest year.", "shortUnit": "", "unit": "people in homelessness per 100,000 population", "timespan": "2023-2023", "type": "Numeric", "owidVariableId": 901094, "shortName": "flow_1", "lastUpdated": "2024-04-30", "nextUpdate": "2026-07-22", "citationShort": "OECD (2024) – with major processing by Our World in Data", "citationLong": "OECD (2024) – with major processing by Our World in Data. “Rate of people experiencing homelessness (ETHOS 1)” [dataset]. OECD, “OECD Affordable Housing Database (AHD)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/901094.metadata.json"}, "Rate of people experiencing homelessness (flow, ETHOS 2 and 3)": {"titleShort": "Rate of people experiencing homelessness (ETHOS 2 and 3)", "titleLong": "Rate of people experiencing homelessness (ETHOS 2 and 3)", "descriptionShort": "Includes people living in emergency accommodation, people with no place of usual residence who move frequently between various types of accommodation, and people living in accommodation for the homeless. This data is collected over a given period of time, such as the enumeration of all people who have stayed in a shelter over the course of the year.", "descriptionKey": ["This data has been categorized within the European Typology of Homelessness and Housing Exclusion (ETHOS) typology as _ETHOS Light 2 and 3_. ETHOS Light 2 is defined as people living in emergency accommodation, people with no place of usual residence who move frequently between various types of accommodation. ETHOS Light 3 is defined as people living in accommodation for the homeless, including hostels, temporary and transitional accommodation, and women's shelters or refuge accommodation.", "This data is collected with a _flow count_, in which data are collected over a given period of time, such as the enumeration of all people who have stayed in a shelter over the course of the year.", "Countries use different definitions and data collection methods and are harmonized to the extent possible.", "More details about definitions, methodology and comparability issues can be found in the [OECD Population Experiencing Homelessness documentation](https://www.oecd.org/els/family/HC3-1-Population-experiencing-homelessness.pdf).", "For more information on the statistical definitions for each country, please check the [OECD's Country Notes on Homelessness data](https://www.oecd.org/social/homelessness-country-notes.htm)."], "descriptionProcessing": "We have transformed the data from homelessness per 10,000 population to homeless per 100,000 population.\n\nWe have included the years the data has been captured for each country, according to the source's notes. Whenever the data was captured over different years, we have included the latest year.", "shortUnit": "", "unit": "people in homelessness per 100,000 population", "timespan": "2017-2023", "type": "Numeric", "owidVariableId": 901095, "shortName": "flow_2_3", "lastUpdated": "2024-04-30", "nextUpdate": "2026-07-22", "citationShort": "OECD (2024) – with major processing by Our World in Data", "citationLong": "OECD (2024) – with major processing by Our World in Data. “Rate of people experiencing homelessness (ETHOS 2 and 3)” [dataset]. OECD, “OECD Affordable Housing Database (AHD)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/901095.metadata.json"}, "Rate of people experiencing homelessness (flow, ETHOS 1, 2 and 3)": {"titleShort": "Rate of people experiencing homelessness (ETHOS 1, 2 and 3)", "titleLong": "Rate of people experiencing homelessness (ETHOS 1, 2 and 3)", "descriptionShort": "Includes people living in the streets or public spaces, in emergency accommodation, and in accommodation for the homeless. This data is collected over a given period of time, such as the enumeration of all people who have stayed in a shelter over the course of the year.", "descriptionKey": ["This data has been categorized within the European Typology of Homelessness and Housing Exclusion (ETHOS) typology as _ETHOS Light 1, 2 and 3_. It includes people living in the streets or public spaces, in emergency accommodation and in accommodation for the homeless, but it has not been dissagregated.", "This data is collected with a _flow count_, in which data are collected over a given period of time, such as the enumeration of all people who have stayed in a shelter over the course of the year.", "Countries use different definitions and data collection methods and are harmonized to the extent possible.", "More details about definitions, methodology and comparability issues can be found in the [OECD Population Experiencing Homelessness documentation](https://www.oecd.org/els/family/HC3-1-Population-experiencing-homelessness.pdf).", "For more information on the statistical definitions for each country, please check the [OECD's Country Notes on Homelessness data](https://www.oecd.org/social/homelessness-country-notes.htm)."], "descriptionProcessing": "We have transformed the data from homelessness per 10,000 population to homeless per 100,000 population.\n\nWe have included the years the data has been captured for each country, according to the source's notes. Whenever the data was captured over different years, we have included the latest year.", "shortUnit": "", "unit": "people in homelessness per 100,000 population", "timespan": "2021-2023", "type": "Numeric", "owidVariableId": 901097, "shortName": "flow_1_2_3", "lastUpdated": "2024-04-30", "nextUpdate": "2026-07-22", "citationShort": "OECD (2024) – with major processing by Our World in Data", "citationLong": "OECD (2024) – with major processing by Our World in Data. “Rate of people experiencing homelessness (ETHOS 1, 2 and 3)” [dataset]. OECD, “OECD Affordable Housing Database (AHD)” [original data].", "fullMetadata": "https://api.ourworldindata.org/v1/indicators/901097.metadata.json"}}, "source_license": "CC BY for OWID-produced material unless otherwise stated", "underlying_data_license_note": "OWID chart data can include third-party data. Reuse should follow the source and license information in this chart metadata."}], "task_hint": "Summarize the analysis result using the main findings, key numeric evidence, trends, and caveats.", "attachments": [], "domain": "데이터/분석 실무", "subdomain": "분석결과 요약", "matched_subdomain_folder": "analysis_result_summary_owid_articles_numeric_data", "record_id": "db655688f38069cbdeb5"}
]