Our world in data

03.09.202203.09.2022 admin 0 Comments

Our world in data

Coronavirus Pandemic (COVID-19)

The data on the coronavirus pandemic is updated daily.

Explore all metrics – including cases, deaths, testing, and vaccinations – in one place.

Get an overview of the pandemic for any country on a single page.

Download our complete dataset of COVID-19 metrics on GitHub. It’s open access and free for anyone to use.

Explore our global dataset on COVID-19 vaccinations.

See state-by-state data on vaccinations in the United States.

Explore the data on confirmed COVID-19 cases for all countries.

Explore the data on confirmed COVID-19 deaths for all countries.

Explore our data on COVID-19 testing to see how confirmed cases compare to actual infections.

See data on how many people are being hospitalized for COVID-19.

See how government policy responses – on travel, testing, vaccinations, face coverings, and more – vary across the world.

Learn what we know about the mortality risk of COVID-19 and explore the data used to calculate it.

Compare the number of deaths from all causes during COVID-19 to the years before to gauge the total impact of the pandemic on deaths.

Explore the global situation

Coronavirus Country Profiles

We built 207 country profiles which allow you to explore the statistics on the coronavirus pandemic for every country in the world.

In a fast-evolving pandemic it is not a simple matter to identify the countries that are most successful in making progress against it. For a comprehensive assessment, we track the impact of the pandemic across our publication and we built country profiles for 207 countries to study in depth the statistics on the coronavirus pandemic for every country in the world.

Each profile includes interactive visualizations, explanations of the presented metrics, and the details on the sources of the data.

Every country profile is updated daily.

Our 12 most visited country profiles

Every profile includes five sections:

Acknowledgements

We would like to acknowledge and thank a number of people in the development of this work: Carl Bergstrom, Bernadeta Dadonaite, Natalie Dean, Joel Hellewell, Jason Hendry, Adam Kucharski, Moritz Kraemer and Eric Topol for their very helpful and detailed comments and suggestions on earlier versions of this work. We thank Tom Chivers for his editorial review and feedback.

And we would like to thank the many hundreds of readers who give us feedback on this work. Your feedback is what allows us to continuously clarify and improve it. We very much appreciate you taking the time to write. We cannot respond to every message we receive, but we do read all feedback and aim to take the many helpful ideas into account.

Reuse our work freely

All visualizations, data, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

The 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.

Cite our work

Our articles and data visualizations rely on work from many different people and organizations. When citing this entry, please also cite the underlying data sources. This entry can be cited as:

Intelligence

Notice: This is only a preliminary collection of relevant material

The data and research currently presented here is a preliminary collection or relevant material. We will further develop our work on this topic in the future (to cover it in the same detail as for example our entry on World Population Growth).

If you have expertise in this area and would like to contribute, apply here to join us as a researcher.

In this entry we focus on how IQ has changed over time. The most common way of assessing intelligence is IQ testing.

All our interactive charts on Intelligence

The Flynn Effect: IQ gains over time

The ‘Flynn Effect’ describes the phenomenon that over time average IQ scores have been increasing. The change in IQ scores has been approximately three IQ points per decade. One major implications of this trend is that an average individual alive today would have an IQ of 130 by the standards of 1910, placing them higher than 98% of the population at that time. Equivalently, an individual alive in 1910 would have an IQ of 70 by today’s standards.

By world region

This visualization shows the gains in IQ that different world regions have made since the first year for which data is available for a particular region.

For each region this visualization shows the change since the first year for which there is data for that particular region. This means it is not possible to make comparisons between different regions and only possible to compare IQ scores over time.

By country

In a comprehensive study of the Flynn Effect, Jakob Pietschnig and Martin Voracek looked at 271 independent samples comprising 3,987,892 participants covering a time span of 105 years (1909–2013). 1

They find strong evidence to support the claim that IQ has been increasing substantially over time. 2 The paper discusses several explanations for the observed increases, namely education, exposure to technology and nutrition. For the definitions of the different IQ measures presented in the chart click here.

The charts show the estimated gain in average IQ for a selection of countries and world regions.

It is important to note that this is not a reflection of how intelligent a country/region is but instead how quickly advances were being made. As the visualization above it is only helpful for understanding changes over time. For more information on the drivers and composition of IQ gains, see our section here

The different measures of IQ displayed in the figure correspond to different types of intelligence.

Domain-specific IQ gain trajectories, 1909-2013 – Pietschnig and Voracek 3

Click to open interactive version

Why is IQ changing?

Composition of IQ Gains

What has been driving the gains in intelligence?

There are many competing and complementary explanations: from nutrition and health improvements, greater levels of education, to the increasingly abstract nature of human existence.

In James Flynn’s book What Is Intelligence?: Beyond the Flynn Effect, he decomposes the gains in IQ found for American children in the Wechsler Intelligence Scale for Children (WISC) and finds that much of the gains have come from the subtests that focus on abstract thinking (similarities test and Raven’s progressive matrices). Only a small portion of the gains is due to improvements in knowledge of basic information, arithmetic and vocabulary. This observation would support the idea that increases in IQ have been driven by the changing way in which we live.

WISC IQ gains for America, 1947-2002 – Flynn (2007) 4

Domain-specific IQ gains

Researchers distinguish between intelligence across a range of domains:

The chart shows domain-specific IQ gain trajectories over the last century.

Domain-specific IQ gain trajectories, 1909-2013 – Pietschnig and Voracek 5

Alexander Luria’s Studies of Reasoning

Alexander Luria, a Russian neuropsychologist, conducted a series of interviews with headmen of villages in rural 1920s Russia as part of a study of reasoning. His research was published in a book titled Cognitive Development: Its Cultural and Social Foundations in 1976. The following extract from James Flynn’s What is intelligence? is just one example of the types of responses Luria received from the villagers.

Today we have no difficulty freeing logic from concrete referents and reasoning about purely hypothetical situations. People were not always thus. Christopher Hallpike (1979) and Nick Mackintosh (2006) have drawn my attention to the seminal book on the social foundations of cognitive development by Luria (1976). His interviews with peasants in remote areas of the Soviet Union offer some wonderful examples. The dialogues paraphrased run as follows:

White bears and Novaya Zemlya (pp. 108-109):

Q: All bears are white where there is always snow; in Zovaya Zemlya there is always snow; what color are the bears there?
A: I have seen only black bears and I do not talk of what I have not seen.
Q: But what do my words imply?
A: If a person has not been there he can not say anything on the basis of words. If a man was 60 or 80 and had seen a white bear there and told me about it, he could be believed.

Population aging and The Flynn Effect

The effect of an aging population, especially in advanced economies, has an attenuating effect on average cognitive abilities over time.

Skirbekk et al. writing in Intelligence create projections of future cognitive abilities and find that if the Flynn effect reaches a saturation point, then average cognitive ability is expected to decline in the future. However, if the current Flynn effect persists, average intelligence will continue to rise in spite of an aging population. Consider the following simulations.

Cognitive score in scenario with no cohort effect and constant age variation, 2002-2042 – Skirbekk et al. (2013) 6

Projections of cognitive ability, age profile of cognition by cohort in scenario with continued improvement along cohort lines and constant lifespan trajectories – Skirbekk et al. (2013) 7

Disease Burden and IQ

Disease during pregnancy or early childhood can impair the cognitive development of children permanently. The driving force behind this theory is that if a child becomes seriously ill, the body transfers resources (energy) into fighting off the infection, reducing the amount left for brain development.

Nutrition and prosperity

An examination of the differences in IQ between two cohorts, one group born in 1921 and the other 15 years apart in 1936, finds substantial differences in IQ over their lifetimes. The study conducted by Staff et al. uses panel (longitudinal) data on the same groups of individuals. 8

All students born in either 1921 or 1936 and attending school in Scotland on June 1, 1932 or June 4, 1947 were made to sit intelligence examinations. The authors report that scores on the Raven’s Progressive Matrices (RPM) test increased annually by over one-half point. At age 77 (where there is an overlap in data) there is an estimated difference of 16.5 IQ points between the two cohorts, which is roughly three times larger than expected.

Dr Roger Staff explains that “those born in 1936 were children during the war and experienced food rationing. Although rationing meant that the food was not particularly appetising it was nutritious and probably superior to the older group. In addition, post-war political changes such as the introduction of the welfare state and a greater emphasis on education probably ensured better health and greater opportunities. Finally, in their thirties and forties the 1936 group experienced the oil boom which brought them and the city prosperity. Taken together, good nutrition, education and occupational opportunities have resulted in this life long improvement in their intelligence. Aberdeen has been good for their IQ!” More information on this research can be found here.

Endnotes

Pietschnig, Jakob, and Martin Voracek. “One Century of Global IQ Gains: A Formal Meta-Analysis of the Flynn Effect (1909-2010).” Perspectives on Psychological Science, 2015, 282-306. doi:10.1177/1745691615577701.

In particular, the estimated increase is 0.41, 0.30, 0.28, and 0.21 IQ points annually for fluid, spatial, full-scale, and crystallized IQ test performance, respectively.

Flynn, James R. What is intelligence?: Beyond the Flynn effect. Cambridge University Press, 2007.

S Vegard Skirbekk, Marcin Stonawski, Eric Bonsang, Ursula M. Staudinger, The Flynn effect and population aging, Intelligence, Volume 41, Issue 3, May–June 2013, Pages 169-177, ISSN 0160-2896. Available online here.

Coronavirus (COVID-19) Cases

We are grateful to everyone whose editorial review and expert feedback on this work helps us to continuously improve our work on the pandemic. Thank you. Here you find the acknowledgements.

The data on the coronavirus pandemic is updated daily.

Our work belongs to everyone

Explore the global data on confirmed COVID-19 cases

Select countries to show in all charts

This page has a large number of charts on the pandemic. In the box below you can select any country you are interested in – or several, if you want to compare countries.

All charts on this page will then show data for the countries that you selected.

Confirmed cases

What is the daily number of confirmed cases?

Related charts:

Which world regions have the most daily confirmed cases?

This chart shows the number of confirmed COVID-19 cases per day.

What is important to note about these case figures?

→ We provide more detail on these points in the section ‘Cases of COVID-19: background‘.

Five quick reminders on how to interact with this chart

Daily confirmed cases per million people

Differences in the population size between different countries are often large – it is insightful to compare the number of confirmed cases per million people.

Keep in mind that in countries that do very little testing the actual number of cases can be much higher than the number of confirmed cases shown here.

Three tips on how to interact with this map

What is the cumulative number of confirmed cases?

Related charts:

Which world regions have the most cumulative confirmed cases?

How do the number of tests compare to the number of confirmed COVID-19 cases? See them plotted against each other.

The previous charts looked at the number of confirmed cases per day – this chart shows the cumulative number of confirmed cases since the beginning of the COVID-19 pandemic.

Cumulative confirmed cases per million people

This chart shows the cumulative number of confirmed cases per million people.

Weekly and biweekly cases: where are confirmed cases increasing or falling?

Why is it useful to look at weekly or biweekly changes in confirmed cases?

For all global data sources on the pandemic, daily data does not necessarily refer to the number of new confirmed cases on that day – but to the cases reported on that day.

Since reporting can vary significantly from day to day – irrespectively of any actual variation of cases – it is helpful to look at changes from week to week. This provides a slightly clearer picture of where the pandemic is accelerating, slowing, or in fact reducing.

The maps shown here provide figures on weekly and biweekly confirmed cases: one set shows the number of confirmed cases per million people in the previous seven (or fourteen) days (the weekly or biweekly cumulative total); the other set shows the percentage change (growth rate) over these periods.

Click to open interactive version

Global comparison: where are confirmed cases increasing most rapidly?

Simply looking at the cumulative total or daily number of confirmed cases does not allow us to understand or compare the speed at which these figures are rising.

The table here shows how long it has taken for the number of confirmed cases to double in each country for which we have data. The table also shows both the cumulative total and daily new number of confirmed cases, and how those numbers have changed over the last 14 days.

How you can interact with this table

You can sort the table by any of the columns by clicking on the column header.

Coronavirus sequences by variant

About this data

Our data on SARS-CoV-2 sequencing and variants is sourced from GISAID, a global science initiative that provides open-access to genomic data of SARS-CoV-2. We recognize the work of the authors and laboratories responsible for producing this data and sharing it via the GISAID initiative.

Khare, S., et al (2021) GISAID’s Role in Pandemic Response. China CDC Weekly, 3(49): 1049-1051. doi: 10.46234/ccdcw2021.255 PMCID: 8668406

Elbe, S. and Buckland-Merrett, G. (2017) Data, disease and diplomacy: GISAID’s innovative contribution to global health. Global Challenges, 1:33-46. doi:10.1002/gch2.1018 PMCID: 31565258

Shu, Y. and McCauley, J. (2017) GISAID: from vision to reality. EuroSurveillance, 22(13) doi:10.2807/1560-7917.ES.2017.22.13.30494 PMCID: PMC5388101

We download aggregate-level data via CoVariants.org.

All countries report data on the results from sequenced samples every 14 days, although some of them may share partial data in advance. We obtain the share of each variant by dividing the number of sequences labelled for that variant by the total number of sequences. Since only a fraction of all cases are sequenced, this share may not reflect the complete breakdown of cases. In addition, recently-discovered or actively-monitored variants may be overrepresented, as suspected cases of these variants are likely to be sequenced preferentially or faster than other cases.

Click to open interactive version

Confirmed deaths and cases: our data source

Our World in Data relies on data from Johns Hopkins University

The Johns Hopkins University dashboard and dataset is maintained by a team at its Center for Systems Science and Engineering (CSSE). It has been publishing updates on confirmed cases and deaths for all countries since January 22, 2020. A feature on the JHU dashboard and dataset was published in The Lancet in early May 2020. 1 This has allowed millions of people across the world to track the course and evolution of the pandemic.

JHU updates its data multiple times each day. This data is sourced from governments, national and subnational agencies across the world — a full list of data sources for each country is published on Johns Hopkins’s GitHub site. It also makes its data publicly available there.

Cases of COVID-19: background

How is a COVID-19 case defined?

In epidemiology, individuals who meet the case definition of a disease are often categorized on three different levels.

These definitions are often specific to the particular disease, but generally have some clear and overlapping criteria.

Cases of COVID-19 – as with other diseases – are broadly defined under a three-level system: suspected, probable and confirmed cases.

Typically, for a case to be confirmed, a person must have a positive result from laboratory tests. This is true regardless of whether they have shown symptoms of COVID-19 or not.

This means that the number of confirmed cases is lower than the number of probable cases, which is in turn lower than the number of suspected cases. The gap between these figures is partially explained by limited testing for the disease.

How are cases reported?

We have three levels of case definition: suspected, probable and confirmed cases. What is measured and reported by governments and international organizations?

International organizations – namely the WHO and European CDC – report case figures submitted by national governments. Wherever possible they aim to report confirmed cases, for two key reasons:

1. They have a higher degree of certainty because they have laboratory confirmation;

2. They help to provide standardised comparisons between countries.

However, international bodies can only provide figures as submitted by national governments and reporting institutions. Countries can define slightly different criteria for how cases are defined and reported. 4 Some countries have, over the course of the outbreak, changed their reporting methodologies to also include probable cases.

One example of this is the United States. Until 14 th April 2020 the US CDC provided daily reports on the number of confirmed cases. However, as of 14 th April, it now provides a single figure of cases: the sum of confirmed and probable cases.

Suspected case figures are usually not reported. The European CDC notes that suspected cases should not be reported at the European level (although countries may record this information for national records) but are used to understand who should be tested for the disease.

Reported new cases on a particular day do not necessarily represent new cases on that day

The number of confirmed cases reported by any institution – including the WHO, the ECDC, Johns Hopkins and others – on a given day does not represent the actual number of new cases on that date. This is because of the long reporting chain that exists between a new case and its inclusion in national or international statistics.

The steps in this chain are different across countries, but for many countries the reporting chain includes most of the following steps:

This reporting chain can take several days. This is why the figures reported on any given date do not necessarily reflect the number of new cases on that specific date.

The number of actual cases is higher than the number of confirmed cases

To understand the scale of the COVID-19 outbreak, and respond appropriately, we would want to know how many people are infected by COVID-19. We would want to know the actual number of cases.

However, the actual number of COVID-19 cases is not known. When media outlets claim to report the ‘number of cases’ they are not being precise and omit to say that it is the number of confirmed cases they speak about.

The actual number of cases is not known, not by us at Our World in Data, nor by any other research, governmental or reporting institution.

The number of confirmed cases is lower than the number of actual cases because not everyone is tested. Not all cases have a “laboratory confirmation”; testing is what makes the difference between the number of confirmed and actual cases.

All countries have been struggling to test a large number of cases, which means that not every person that should have been tested has been tested.

Since an understanding of testing for COVID-19 is crucial for an interpretation of the reported numbers of confirmed cases we have looked into the testing for COVID-19 in more detail.

You find our work on testing here. In a separate post we discuss how models of COVID-19 help us estimate the actual number of cases.

Acknowledgements

Endnotes

The European CDC discusses the criteria for what constitutes a probable case, and a ‘close contact’ here.

See any Situation Report by the WHO – for example Situation Report 50.

The WHO also speaks of ‘suspected cases’ and ‘probable cases’, but the WHO Situation Reports do not provide figures on ‘probable cases’, and only report ‘suspected cases’ for Chinese provinces (‘suspected cases’ by country is not available).

In Situation Report 50 they define these as follows:
Suspect case
A. A patient with acute respiratory illness (fever and at least one sign/symptom of respiratory disease (e.g., cough, shortness of breath), AND with no other etiology that fully explains the clinical presentation AND a history of travel to or residence in a country/area or territory reporting local transmission (See situation report) of COVID-19 disease during the 14 days prior to symptom onset.
OR
B. A patient with any acute respiratory illness AND having been in contact with a confirmed or probable COVID19 case (see definition of contact) in the last 14 days prior to onset of symptoms;
OR
C. A patient with severe acute respiratory infection (fever and at least one sign/symptom of respiratory disease (e.g., cough, shortness breath) AND requiring hospitalization AND with no other etiology that fully explains the clinical presentation.

Probable case
A suspect case for whom testing for COVID-19 is inconclusive. Inconclusive being the result of the test reported by the laboratory.

The US, for example, uses the following definitions: “A confirmed case or death is defined by meeting confirmatory laboratory evidence for COVID-19. A probable case or death is defined by i) meeting clinical criteria AND epidemiologic evidence with no confirmatory laboratory testing performed for COVID-19; or ii) meeting presumptive laboratory evidence AND either clinical criteria OR epidemiologic evidence; or iii) meeting vital records criteria with no confirmatory laboratory testing performed for COVID19.”

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Cite our work

Our articles and data visualizations rely on work from many different people and organizations. When citing this entry, please also cite the underlying data sources. This entry can be cited as:

About

Poverty, disease, hunger, climate change, war, existential risks, and inequality: The world faces many great and terrifying problems. It is these large problems that our work at Our World in Data focuses on.

Thanks to the work of thousands of researchers around the world who dedicate their lives to it, we often have a good understanding of how it is possible to make progress against the large problems we are facing. The world has the resources to do much better and reduce the suffering in the world.

We believe that a key reason why we fail to achieve the progress we are capable of is that we do not make enough use of this existing research and data: the important knowledge is often stored in inaccessible databases, locked away behind paywalls and buried under jargon in academic papers.

The goal of our work is to make the knowledge on the big problems accessible and understandable. As we say on our homepage, Our World in Data’s mission is to publish the “research and data to make progress against the world’s largest problems”.

Why have we made this our mission?

This is the question our founder Max Roser answers in this text:

A publication to see the large global problems and the powerful changes that reshape our world

If you want to contribute to a better future you need to know the problems the world faces. To understand these problems the daily news is not enough. The news media focuses on events and therefore largely fails to report the two aspects that Our World in Data focuses on: the large problems that continue to confront us for centuries or much longer and the long-lasting, forceful changes that gradually reshape our world.

The criterion by which the news select what they focus our attention on is whether it is new. The criterion by which we at Our World in Data decide what to focus our attention on is whether it is important.

The front page of Our World in Data lists the same big global problems every day, because they matter every day. One of the biggest mistakes that the news media makes is to suggest that different things matter on different days.

To understand issues that are affecting billions, we need data. We need to carefully measure what we care about and make the results accessible in an understandable and public platform. This allows everyone to see the state of the world today and track where we are making progress, and where we are falling behind. The publication we are building has this goal. Through interactive data visualizations we can see how the world has changed; by summarizing the scientific literature we can understand why.

It is possible to change the world

To work towards a better future, we also need to understand how and why the world is changing.

The historical data and research shows that it is possible to change the world. Historical research shows that until a few generations ago around half of all newborns died as children. Since then the health of children has rapidly improved around the world and life expectancy has doubled in all regions. Progress is possible.

In other important ways global living conditions have improved as well. While we believe this is one of the most important facts to know about the world we live in, it is known by surprisingly few.

Instead, many believe that global living conditions are stagnating or getting worse and much of the news media’s reporting is doing little to challenge this perception. It is wrong to believe that one can understand the world by following the news alone and the media’s focus on single events and things that go wrong can mean that well-intentioned people who want to contribute to positive change become overwhelmed, hopeless, cynical and in the worst cases give up on their ideals. Much of our effort throughout these years has been dedicated to countering this threat.

Researching how it was possible to make progress against large problems in the past allows us to learn. Progress is possible, but it is not a given. If we want to know how to reduce suffering and tackle the world’s problems we should learn from what was successful in the past.

Comprehensive perspective on global living conditions and the earth’s environment

We take a broad perspective, covering an extensive range of aspects that matter for our lives. Measuring economic growth is not enough. The research publications on Our World in Data are dedicated to a large range of global problems in health, education, violence, political power, human rights, war, poverty, inequality, energy, hunger, and humanity’s impact on the environment. On the homepage we list all the global problems and important long-term changes that we have researched. The complete list of aspects that we eventually want to cover is longer still and can be found here.

As becomes obvious from our publication we always aim to provide a global perspective, but our focus are the living conditions of the worst-off.

Covering all of these aspects in one resource makes it possible to understand how long-run global trends are interlinked.

Measuring what matters

On the closely integrated website SDG-Tracker.org we present the data and research on the UN’s Sustainable Development Goals (SDGs). In 2015, all countries in the world signed up to reach the SDGs by 2030 and we built this site to track progress towards them. Our SDG-Tracker is a widely accessed publication that presents all the latest available data on the 232 SDG-Indicators with which the 17 Goals are assessed.

This is the core of our mission and extends beyond the SDGs. We all, the citizens of this world, are investing vast resources towards the ambitious goal of making the world a better place: we dedicate our lives to medical care and education, we are developing new technologies, we are spending large sums of money on infrastructure and the education of the next generation. What we do not do enough is to investigate whether these efforts are actually getting us closer to achieving our goals.

If the world wants to be serious about achieving progress we need to be much more serious about measuring what matters.

Our World in Data is based on the work of others – and should in turn be a base for others

The research we publish here is not only the work of our small team. Instead we rely on the work of a global community of scholars and wherever possible we see our role as presenting the best available research and data in an understandable and accessible way. Only when we find that important questions have not yet been answered do we do the necessary research ourselves and fill in the gaps.

Newton said, “If I have seen further than others, it is because I’ve stood on the shoulders of giants.” This is how science should work. Those who want to understand the world should be able to stand on the shoulders of those who came before them. A key part of our mission is therefore to build an infrastructure that makes research and data openly available and useful for all.

A publication to cover the long-run, built for the long-run

Making progress against the large problems that our world is facing will require dedicated work for a long time. We are therefore building a publication that aims to remain helpful for several decades: we regularly update our existing work as new research improves our understanding of the world; and we are building and expanding a central database, which allows us to continuously update the entire publication with the best available data.

Building the infrastructure to make data and research accessible and understandable

The web allows us to publish in a way that was unimaginable just a few years ago: distribution is free and research and data can be explored through interactive documents. Yet much of today’s research is published in a format that is essentially the same as that made available by Gutenberg’s printing press, 500 years ago.

To make research and data as accessible as possible we are a team in which researchers are collaborating with web developers. Together we are building the infrastructure that allows everyone in the world to understand how we make progress against our most pressing problems.

If you want to join us as a developer or researcher, see our Jobs page.

Our World in Data is a public good

We have big plans for the coming decades, but we are already having an impact. More than a million readers come to our site every month. Our work is very regularly covered by the media, our publication is informing many writers in their often widely read work, our writing is widely shared through social media, and we are regularly cited in top journals including Science and Nature.

For many relevant search queries – ‘CO2 emissions’, ‘world poverty’, ‘child mortality’, ‘population growth’ – we are one of the top search results in many parts of the world. And our work is commonly used as teaching material in schools and universities.

We design our work with the aim of generating an impact beyond what our team can achieve directly. By producing charts and data that can be freely downloaded and embedded in others’ work, we support and empower colleagues in policy, media and civil society also working on the problems we focus on.

This is why all the work we ever do is made available in its entirety as a public good:

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Reader donations are essential to our work, providing us with the stability and independence we need, so we can focus on showing the data and evidence we think everyone needs to know.

You can learn more about our funding in our How We’re Funded page, and you can help us do more by donating here – it will make a real difference.

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For selected topics we have created interactive teaching notes, presentation slides, and chart sets, which we designed specifically for students and teachers. You are welcome to use, edit and share these materials for free.

These materials were created a few years ago, so some slides and graphs do not reflect our most recent data and research. We are working on updating and expanding the materials. As the interactive charts have usually not changed substantially, you might still find them useful. That is why we list them here, so you can find the most recent data available.

Extreme Poverty

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Transport

Road travel

Passenger vehicle registrations by type

These interactive charts show the breakdown of new passenger vehicle registrations by type.

This is broken down by: petroleum; diesel; full hybrid (excluding plug-in hybrids); plug-in electric hybrids; and fully electric battery vehicles.

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Electric vehicle registrations

This interactive chart shows the share of new passenger vehicle registrations that are battery electric vehicles. This does not include plug-in hybrid vehicles.

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This interactive chart shows the share of new passenger vehicle registrations that are battery electric plus plug-in hybrid vehicles.

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Carbon intensity of new passenger vehicles

This interactive chart shows the average carbon intensity of new passenger vehicles in each country.

This is measured as the average emissions of CO₂ (in grams) per kilometer travelled across all types of passenger vehicles.

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Fuel economy of new passenger vehicles

This interacrive chart shows the average fuel economy of new passenger vehicles in each country.

This is measured as the average liters consumed per 1000 kilometers travelled, across all types of passenger vehicles.

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Aviation

What share of global CO₂ emissions come from aviation?

Flying is a highly controversial topic in climate debates. There are a few reasons for this.

The first is the disconnect between its role in our personal and collective carbon emissions. Air travel dominates a frequent traveller’s individual contribution to climate change. Yet aviation overall accounts for only 2.5% of global carbon dioxide (CO₂) emissions. This is because there are large inequalities in how much people fly – many do not, or cannot afford to, fly at all [best estimates put this figure at around 80% of the world population – we will look at this in more detail in an upcoming article].

The second is how aviation emissions are attributed to countries. CO₂ emissions from domestic flights are counted in a country’s emission accounts. International flights are not – instead they are counted as their own category: ‘bunker fuels’. The fact that they don’t count towards the emissions of any country means there are few incentives for countries to reduce them.

It’s also important to note that unlike the most common greenhouse gases – carbon dioxide, methane or nitrous oxide – non-CO₂ forcings from aviation are not included in the Paris Agreement. This means they could be easily overlooked – especially since international aviation is not counted within any country’s emissions inventories or targets.

How much of a role does aviation play in global emissions and climate change? In this article we take a look at the key numbers that are useful to know.

Global aviation (including domestic and international; passenger and freight) accounts for:

The latter two numbers refer to 2018, and the first to 2016, the latest year for which such data are available.

Aviation accounts for 2.5% of global CO₂ emissions

As we will see later in this article, there are a number of processes by which aviation contributes to climate change. But the one that gets the most attention is its contribution via CO₂ emissions. Most flights are powered by jet gasoline – although some partially run on biofuels – which is converted to CO₂ when burned.

In a recent paper, researchers – David Lee and colleagues – reconstructed annual CO₂ emissions from global aviation dating back to 1940. 1 This was calculated based on fuel consumption data from the International Energy Agency (IEA), and earlier estimates from Robert Sausen and Ulrich Schumann (2000). 2

The time series of global emissions from aviation since 1940 is shown in the accompanying chart. In 2018, it’s estimated that global aviation – which includes both passenger and freight – emitted 1.04 billion tonnes of CO₂.

Aviation emissions have doubled since the mid-1980s. But, they’ve been growing at a similar rate as total CO₂ emissions – this means its share of global emissions has been relatively stable: in the range of 2% to 2.5%. 5

Non-CO₂ climate impacts mean aviation accounts for 3.5% of global warming

Aviation accounts for around 2.5% of global CO₂ emissions, but it’s overall contribution to climate change is higher. This is because air travel does not only emit CO₂: it affects the climate in a number of more complex ways.

As well as emitting CO₂ from burning fuel, planes affect the concentration of other gases and pollutants in the atmosphere. They result in a short-term increase, but long-term decrease in ozone (O₃); a decrease in methane (CH₄); emissions of water vapour; soot; sulfur aerosols; and water contrails. While some of these impacts result in warming, others induce a cooling effect. Overall, the warming effect is stronger.

David Lee et al. (2020) quantified the overall effect of aviation on global warming when all of these impacts were included. 6 To do this they calculated the so-called ‘Radiative Forcing’. Radiative forcing measures the difference between incoming energy and the energy radiated back to space. If more energy is absorbed than radiated, the atmosphere becomes warmer.

In this chart we see their estimates for the radiative forcing of the different elements. When we combine them, aviation accounts for approximately 3.5% of effective radiative forcing: that is, 3.5% of warming.

Although CO₂ gets most of the attention, it accounts for less than half of this warming. Two-thirds (66%) comes from non-CO₂ forcings. Contrails – water vapor trails from aircraft exhausts – account for the largest share.

We don’t yet have the technologies to decarbonize air travel

Aviation’s contribution to climate change – 3.5% of warming, or 2.5% of CO₂ emissions – is often less than people think. It’s currently a relatively small chunk of emissions compared to other sectors.

The key challenge is that it is particularly hard to decarbonize. We have solutions to reduce emissions for many of the largest emitters – such as power or road transport – and it’s now a matter of scaling them. We can deploy renewable and nuclear energy technologies, and transition to electric cars. But we don’t have proven solutions to tackle aviation yet.

There are some design concepts emerging – Airbus, for example, have announced plans to have the first zero-emission aircraft by 2035, using hydrogen fuel cells. Electric planes may be a viable concept, but are likely to be limited to very small aircraft due to the limitations of battery technologies and capacity.

Innovative solutions may be on the horizon, but they’re likely to be far in the distance.

Appendix: Efficiency improvements means air traffic has increased more rapidly than emissions

Global emissions from aviation have increased a lot over the past half-century. However, air travel volumes increased even more rapidly.

Since 1950, aviation emissions increased almost seven-fold; since 1960 they’ve tripled. Air traffic volume – here defined as revenue passenger kilometers (RPK) traveled – increased by orders of magnitude more: almost 300-fold since 1950; and 75-fold since 1960 [you find this data in our interactive chart here]. 7

The much slower growth in emissions means aviation efficiency has seen massive improvements. In the chart we show both the increase in global airline traffic since 1950, and aviation efficiency, measured as the quantity of CO₂ emitted per revenue passenger kilometer traveled. In 2018, approximately 125 grams of CO₂ were emitted per RPK. In 1960, this was eleven-fold higher; in 1950 it was twenty-fold higher. Aviation has seen massive efficiency improvements over the past 50 years.

These improvements have come from several sources: improvements in the design and technology of aircraft; larger aircraft sizes (allowing for more passengers per flight); and an increase in how ‘full’ passenger flights are. This last metric is termed the ‘passenger load factor’. The passenger load factor measures the actual number of kilometers traveled by paying customers (RPK) as a percentage of the available seat kilometers (ASK) – the kilometers traveled if every plane was full. If every plane was full the passenger load factor would be 100%. If only three-quarters of the seats were filled, it would be 75%.

The global passenger load factor increased from 61% in 1950 to 82% in 2018 [you find this data in our interactive chart here].

Passenger vs. freight; domestic vs. international: where do aviation emissions come from?

Global aviation – both passenger flights and freight – emits around one billion tonnes of carbon dioxide (CO₂) each year. This was equivalent to around 2.4% of CO₂ emissions in 2018.

How do global aviation emissions break down?

The chart gives the answer. This data is sourced from the 2019 International Council on Clean Transportation (ICCT) report on global aviation. 8

Most emissions come from passenger flights – in 2018, they accounted for 81% of aviation’s emissions; the remaining 19% came from freight, the transport of goods.

Sixty percent of emissions from passenger flights come from international travel; the other 40% come from domestic (in-country) flights.

When we break passenger flight emissions down by travel distance, we get a (surprisingly) equal three-way split in emissions between short-haul (less than 1,500 kilometers); medium-haul (1,500 to 4,000 km); and long-haul (greater than 4,000 km) journeys.

The richest half are responsible for 90% of air travel CO₂ emissions

The global inequalities in how much people fly become clear when we compare aviation emissions across countries of different income levels. The ICCT split these emissions based on World Bank’s four income groups.

A further study by Susanne Becek and Paresh Pant (2019) compared the contribution of each income group to global air travel emissions versus its share of world population. 9 This comparison is shown in the visualization.

The ‘richest’ half of the world (high and upper-middle income countries) were responsible for 90% of air travel emissions. 10

Looking at specific income groups:

In an upcoming article we will look in more detail at the contribution of each country to global aviation emissions.

Where in the world do people have the highest carbon footprint from flying?

Aviation accounts for around 2.5% of global carbon dioxide (CO₂) emissions. But if you are someone who does fly, air travel will make up a much larger share of your personal carbon footprint.

The fact that aviation is relatively small for global emissions as a whole, but of large importance for individuals that fly is due to large inequalities in the world. Most people in the world do not take flights. There is no global reliable figure, but often cited estimates suggest that more than 80% of the global population have never flown. 11

How do emissions from aviation vary across the world? Where do people have the highest footprint from flying?

Per capita emissions from domestic flights

The first and most straightforward comparison is to look at emissions from domestic aviation – that is, flights that depart and arrive in the same country.

This is easiest to compare because domestic aviation is counted in each country’s inventory of greenhouse gas emissions. International flights, on the other hand, are not attributed to specific countries – partly because of contention as to who should take responsibility (should it be the country of departure or arrival? What about layover flights?).

We see large differences in emissions from domestic flights across the world. In the United States the average person emits around 386 kilograms of CO₂ each year from internal flights. This is followed by Australia (267 kg); Norway (209 kg); New Zealand (174 kg); and Canada (168 kg). Compare this with countries at the bottom of the table – many across Africa, Asia, and Eastern Europe in particular emit less than one kilogram per person – just 0.8 kilograms; or 0.14 kilograms in Rwanda. For very small countries where there are no internal commercial flights, domestic emissions are of course, zero.

There are some obvious factors that explain some of these cross-country differences. Firstly, countries that are richer are more likely to have higher emissions because people can afford to fly. Second, countries that have a larger land mass may have more internal flights – and indeed we see a correlation between land area and domestic flight emissions; in small countries people are more likely to travel by other means such as car or train. And third, countries that are more geographically-isolated – such as Australia and New Zealand – may have more internal travel.

Click to open interactive version

Related charts:

Per capita emissions from international flights

Allocating emissions from international flights is more complex. International databases report these emissions separately as a category termed ‘bunker fuels’. The term ‘bunker fuel’ is used to describe emissions which come from international transport – either aviation or shipping.

Because they are not counted towards any particular country these emissions are also not taken into account in the goals that are set by countries in international treaties like the Kyoto protocol or the Paris Agreement. 14

But if we wanted to allocate them to a particular country, how would we do it? Who do emissions from international flights belong to: the country that owns the airline; the country of departure; the country of arrival?

Let’s first take a look at how emissions would compare if we allocated them to the country of departure. This means, for example, that emissions from any flight that departs from Spain are counted towards Spain’s total. In the chart here we see international aviation emissions in per capita terms.

Some of the largest emitters per person in 2018 were Iceland (3.5 tonnes of CO₂ per person); Qatar (2.5 tonnes); United Arab Emirates (2.2 tonnes); Singapore (1.7 tonnes); and Malta (992 kilograms).

Again, we see large inequalities in emissions across the world – in many lower-income countries per capita emissions are only a few kilograms: 6 kilograms in India, 4 kilograms in Nigeria; and only 1.4 kilograms in the Democratic Republic of Congo.

Click to open interactive version

Related charts:

Per capita emissions from international flights – adjusted for tourism

The above allocation of international aviation emissions to the country of departure raises some issues. It is not an accurate reflection of the local population of countries that rely a lot on tourism, for example. Most of the departing flights from these countries are carrying visiting tourists rather than locals.

One way to correct for this is to adjust these figures for the ratio of inbound to outbound travellers. This approach was applied in an analysis by Sola Zheng for the International Council on Clean Transportation. This attempts to distinguish between locals traveling abroad and foreign visitors traveling to that country on the same flight. 15 For example, if we calculated that Spain had 50% more incoming than outgoing travellers, we would reduce its per capita footprint from flying by 50%. If the UK had 75% more outgoing travellers than incoming, we’d increase its footprint by 75%.

We have replicated this approach and applied this adjustment to these figures by calculating the inbound:outbound tourist ratio based on flight departures and arrival data from the World Bank.

How does this affect per capita emissions from international flights? The adjusted figures are shown in the chart here.

As we would expect, countries which are tourist hotspots see the largest change. Portugal’s emissions, for example, fall from 388 to just 60 kilograms per person. Portugese locals are responsible for much fewer travel emissions than outgoing tourists. Spanish emissions fall from 335 to 77 kilograms per person.

On the other hand, countries where the locals travel elsewhere see a large increase. In the UK, they almost double from 422 to 818 kilograms.

Click to open interactive version

Per capita emissions from domestic and international flights

Let’s combine per capita emissions from domestic and international travel to compare the total footprint from flying.

This is shown in the interactive map [we’ve taken the adjusted international figures – you can find the combined figures without tourism-adjustment here].

The global average emissions from aviation were 103 kilograms. The inequality in emissions across the world becomes clear when this is broken down by country.

At the top of the table lies the United Arab Emirates – each person emits close to two tonnes – 1950 kg – of CO₂ from flying each year. That’s 200 times the global average. This was followed by Singapore (1173 kilograms); Iceland (1070 kg); Finland (1000 kg); and Australia (878 kilograms).

To put this into perspective: a return flight (in economy class) from London to Dubai/United Arab Emirates would emit around one tonne of CO₂. 16 So the two-tonne average for the UAE is equivalent to around two return trips to London.

In many countries, most people do not fly at all. The average Indian emits just 18 kilograms from aviation – this is much, much less than even a short-haul flight which confirms that most did not take a flight.

In fact, we can compare just the aviation emissions for the top countries to the total carbon footprint of citizens elsewhere. The average UAE citizen emits 1950 kilograms of CO₂ from flying. This is the same as the total CO₂ footprint of the average Indian (including everything from electricity to road transport, heating and industry). Or, to take a more extreme example, 200 times the total footprint of the average Nigerien, Ugandan or Ethiopian, which have per capita emissions of around 100 kilograms.

This again emphasises the large difference between the global average and the individual emissions of people who fly. Aviation contributes a few percent of total CO₂ emissions each year – this is not insignificant, but far from being the largest sector to tackle. Yet from the perspective of the individual, flying is often one of the largest chunks of our carbon footprint. The average rich person emits tonnes of CO₂ from flying each year – this is equivalent to the total carbon footprint of tens or hundreds of people in many countries of the world.

Click to open interactive version

Related charts:

Where in the world do people fly the most?

Domestic air travel

This interactive chart shows the average distance travelled per person through domestic air travel each year. This data is for passenger flights only and does not include freight.

Click to open interactive version

Related charts:

What share of global domestic air travel does each country account for?

International air travel

This interactive chart shows the average distance travelled per person through international air travel each year. This data is for passenger flights only and does not include freight.

Click to open interactive version

Related charts:

What share of global international air travel does each country account for?

Total air travel

This interactive chart shows the average distance travelled per person through domestic and international air travel each year. This data is for passenger flights only and does not include freight.

Click to open interactive version

Related charts:

What share of global air travel does each country account for?

This interactive chart shows the total rail travel in each country, measured in passenger-kilometers per year.

This includes passenger travel only and does not include freight.

Click to open interactive version

Energy intensity of transport

This chart shows the average energy intensity of transport across different modes of travel. It is measured as the average kilowatt-hours required per passenger-kilometer.

This data comes from the United States Department of Transportation’s Bureau of Transportation Statistics (BTS). The energy intensity of public transport depends on the assumptions made about the capacity of transport modes i.e. how many passengers travel on a given train or bus journey. This data thererfore reflects average capacities in the United States, but will vary from country-to-country.

Click to open interactive version

CO₂ emissions from transport

Per capita transport emissions from transport

This interactive shows the average per capita emissions of carbon dioxide from transport each year. This includes road, train, bus and domestic air travel but does not include international aviation and shipping.

Click to open interactive version

Total transport emissions

This interactive shows the emissions of carbon dioxide from transport each year. This includes road, train, bus and domestic air travel but does not include international aviation and shipping.

Click to open interactive version

CO₂ emissions by mode of transport

Transport accounts for around one-fifth of global carbon dioxide (CO₂) emissions [24% if we only consider CO₂ emissions from energy]. 17

How do these emissions break down? Is it cars, trucks, planes or trains that dominate?

In the chart here we see global transport emissions in 2018. This data is sourced from the International Energy Agency (IEA).

Road travel accounts for three-quarters of transport emissions. Most of this comes from passenger vehicles – cars and buses – which contribute 45.1%. The other 29.4% comes from trucks carrying freight.

Since the entire transport sector accounts for 21% of total emissions, and road transport accounts for three-quarters of transport emissions, road transport accounts for 15% of total CO₂ emissions.

Aviation – while it often gets the most attention in discussions on action against climate change – accounts for only 11.6% of transport emissions. It emits just under one billion tonnes of CO₂ each year – around 2.5% of total global emissions [we look at the role that air travel plays in climate change in more detail in an upcoming article]. International shipping contributes a similar amount, at 10.6%.

Rail travel and freight emits very little – only 1% of transport emissions. Other transport – which is mainly the movement of materials such as water, oil, and gas via pipelines – is responsible for 2.2%.

Towards zero-carbon transport: how can we expect the sector’s CO₂ emissions to change in the future?

Transport demand is expected to grow across the world in the coming decades as the global population increases, incomes rise, and more people can afford cars, trains and flights. In its Energy Technology Perspectives report, the International Energy Agency (IEA) expects global transport (measured in passenger-kilometers) to double, car ownership rates to increase by 60%, and demand for passenger and freight aviation to triple by 2070. 18 Combined, these factors would result in a large increase in transport emissions.

But major technological innovations can help offset this rise in demand. As the world shifts towards lower-carbon electricity sources, the rise of electric vehicles offers a viable option to reduce emissions from passenger vehicles.

This is reflected in the IEA’s Energy Technology Perspective report. There it outlines its “Sustainable Development Scenario” for reaching net-zero CO2 emissions from global energy by 2070. The pathways for the different elements of the transport sector in this optimistic scenario are shown in the visualization.

We see that with electrification- and hydrogen- technologies some of these sub-sectors could decarbonize within decades. The IEA scenario assumes the phase-out of emissions from motorcycles by 2040; rail by 2050; small trucks by 2060; and although emissions from cars and buses are not completely eliminated until 2070, it expects many regions, including the European Union; United States; China and Japan to have phased-out conventional vehicles as early as 2040.

Other transport sectors will be much more difficult to decarbonize.

So, despite falling by three-quarters in the visualized scenario, emissions from these sub-sectors would still make transport the largest contributor to energy-related emissions in 2070. To reach net-zero for the energy sector as a whole, these emissions would have to be offset by ‘negative emissions’ (e.g. the capture and storage of carbon from bioenergy or direct air capture) from other parts of the energy system.

In the IEA’s net-zero scenario, nearly two-thirds of the emissions reductions come from technologies that are not yet commercially available. As the IEA states, “Reducing CO₂ emissions in the transport sector over the next half-century will be a formidable task.” 22

Global CO2 emissions from transport in the IEA’s Sustainable Development Scenario to 2070 23

Endnotes

Lee, D. S., Fahey, D. W., Skowron, A., Allen, M. R., Burkhardt, U., Chen, Q., … & Gettelman, A. (2020). The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018. Atmospheric Environment, 117834.

The Global Carbon Budget estimated total CO₂ emissions from all fossil fuels, cement production and land-use change to be 42.1 billion tonnes in 2018. This means aviation accounted for [1 / 42.1 * 100] = 2.5% of total emissions.

Global Carbon Project. (2019). Supplemental data of Global Carbon Budget 2019 (Version 1.0) [Data set]. Global Carbon Project. https://doi.org/10.18160/gcp-2019.

If we were to exclude land use change emissions, aviation accounted for 2.8% of fossil fuel emissions. The Global Carbon Budget estimated total CO₂ emissions from fossil fuels and cement production to be 36.6 billion tonnes in 2018. This means aviation accounted for [1 / 36.6 * 100] = 2.8% of total emissions.

2.3% to 2.8% of emissions if land use is excluded.

Airline traffic data comes from the International Civil Aviation Organization (ICAO) via Airlines for America. Revenue passenger kilometers (RPK) measures the number of paying passengers multiplied by their distance traveled.

Graver, B., Zhang, K., & Rutherford, D. (2019). CO2 emissions from commercial aviation, 2018. The International Council of Clean Transportation.

Note that this is based on categorisations from the average income level of countries, and does not take account of variation in income within countries. If we were to look at this distribution based on the income level of individuals rather than countries, the inequality in aviation emissions would be even larger.

There is no global database available on who in the world flies each year. Passenger information is maintained by private airlines. Therefore, deriving estimates of this exact percentage is challenging. The most-cited estimate I’ve seen on this is that around 80% of the world population have never flown. This figure seems to circle back to a quoted estimate from the Boeing CEO.

Even in some of the world’s richest countries, a large share of the population do not fly frequently. Gallup survey data from the United States suggests that in 2015, half of the population did not take a flight. Survey data from the UK provides similar estimates: 46% had not flown in the previous year.

Graver, B., Zhang, K., & Rutherford, D. (2019). CO2 emissions from commercial aviation, 2018. The International Council of Clean Transportation.

Note that this gives us mean per capita emissions, which does not account for in-country inequalities in the amount of flights people take.

A country with a ratio greater than one will have more incoming travellers than outgoing locals i.e. they are more of a hotspot for tourism.

We can calculate this by taking the standard CO2 conversion factors for travel, used in the UK greenhouse gas accounting framework. For a long-haul flight in economy class, around 0.079 kilograms of CO₂ are emitted per passenger-kilometer. This means that you would travel around 12,600 kilometers to emit one tonne [1,000,000 / 0.079 kg = 12,626 kilometers]. Since we’re taking a return flight, the travel distance would be half of that figure: around 6300 kilometers. The direct distance from London to Dubai is around 5,500 kilometers. Depending on the flight path, it’s likely to be slightly longer than this, and in the range of 5500 to 6500 kilometers.

Note that in this case we’re looking at CO₂ emissions without the extra warming effects of these emissions at high altitudes. This is to allow us to compare with the ICCT figures by country presented in this article. You find additional data on how the footprint of flying is impacted by non-CO₂ warming effects here.

The World Resource Institute’s Climate Data Explorer provides data from CAIT on the breakdown of emissions by sector. In 2016, global CO₂ emissions (including land use) were 36.7 billion tonnes CO₂; emissions from transport were 7.9 billion tonnes CO₂. Transport therefore accounted for 7.9 / 36.7 = 21% of global emissions.

The IEA looks at CO₂ emissions from energy production alone – in 2018 it reported 33.5 billion tonnes of energy-related CO₂ [hence, transport accounted for 8 billion / 33.5 billion = 24% of energy-related emissions.

Davis, S. J., Lewis, N. S., Shaner, M., Aggarwal, S., Arent, D., Azevedo, I. L., … & Clack, C. T. (2018). Net-zero emissions energy systems. Science, 360(6396).

Cecere, D., Giacomazzi, E., & Ingenito, A. (2014). A review on hydrogen industrial aerospace applications. International Journal of Hydrogen Energy, 39(20), 10731-10747.

Fulton, L. M., Lynd, L. R., Körner, A., Greene, N., & Tonachel, L. R. (2015). The need for biofuels as part of a low carbon energy future. Biofuels, Bioproducts and Biorefining, 9(5), 476-483.

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Drug Use

620,000 people die prematurely from illicit drug use every year.

45% are younger than 50 years old.

How common is the use and addiction to opioids, cocaine, amphetamines and cannabis? What is the impact?

Opioids, cocaine, cannabis and illicit drugs

Infectious diseases

COVID-19 developed into a global pandemic.

Country-by-country data and research on the pandemic. Updated daily.

Coronavirus Pandemic (COVID-19)

860,000 people die from HIV/AIDS each year.

77% are younger than 50 years old.

A global epidemic and the leading cause of death in some countries.

HIV / AIDS

630,000 people die from malaria every year.

55% are children younger than 5 years old.

The 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?

Malaria

Humanity has already eradicated one severe disease.

Which ones could we eradicate in our lifetimes and how?

Eradication of Diseases

1.5 million people die from diarrheal diseases every year.

One-third are children under five years old.

Diarrheal diseases are one of the leading cause of child deaths while they are largely preventable. How can we continue to make progress against these diseases?

Diarrheal diseases

In the past smallpox killed millions every year.

Humanity eradicated this infectious disease globally. How was this possible?

Smallpox

One generation ago polio paralyzed hundreds of thousands of children every year.

Now the world can possibly eradicate it: polio remains endemic in only 2 countries.

Polio

2.5 million people die from pneumonia every year.

One-fourth are children younger than five years old.

Pneumonia is the leading cause of death for children younger than 5 years.

Pneumonia

35,000 people die from Tetanus every year.

Half are children under the age of five.

Tetanus is a bacterial infection that leads to painful muscle contractions and possibly death.

Tetanus

Health institutions and interventions

Healthcare funding is essential for good health.

Healthcare is key to make progress against poor health. How is it financed?

Financing Healthcare

Vaccines are key in making progress against infectious diseases and save millions of lives every year.

Vaccination

Food and Agriculture

Most of the world’s farmers are smallholders, with less than two hectares of land

How does farm size vary across the world? How much of farmland is held by smallholders?

Farm Size

Nutrition

9% of the world population – 660 million people – are undernourished.

22% of children younger than five are ‘stunted’.

What are the consequences of undernourishment and how can we make progress against hunger and undernourishment?

Hunger and Undernourishment

Around 130 million people died in famines during the last 150 years.

This estimate is based on our historical reconstructions.

In many parts of the world famines have been common in the past. What causes famines? How can famines be averted?

Famines

2,861 kcal per day is the global average food supply per person.

There are large inequalities in food supply across countries.

How had the availability of food changed over time? How does food supply vary across the world today?

Food Supply

The average young adult is about 5% taller today than 100 years ago.

Human height gives us an indicator of changes in health and nutrition in the past.

The average height of a population can inform us about the nutrition and living conditions of populations in the past for which we have little other data.

Human Height

An estimated 2 billion people are deficient in essential micronutrients.

Food is not only a source of energy and protein, but also micronutrients – vitamins and minerals – which are essential to good health. Who is most affected by the ‘hidden hunger’ of micronutrient deficiency?

Micronutrient Deficiency

A diverse diet is essential for good nutrition.

There are large inequalities in dietary diversity between countries.

What do people across the world eat?

Diet Compositions

Three billion people cannot afford a healthy diet.

Food prices are important for both consumers and farmers.

Food needs to be affordable for people, and at the same it is a key source of income for one-quarter of the world’s labor force.

Food Prices

13% of adults are obese, globally.

Obesity is responsible for 5 million premature deaths every year.

When did obesity increase? How do rates vary across the world? What is the health impact?

Obesity

2.4 million people die prematurely from alcohol consumption every year.

71% are younger than 70 years old.

Who consumes the most alcohol? How has consumption changed over time? And what are the health impacts?

Alcohol Consumption

Food Production

The world produces more than enough food for everyone.

But it’s unequally distributed across the world.

How much food do countries produce across the world?

Agricultural Production

26% of global greenhouse gas emissions come from food production.

50% of the world’s habitable land is used for agriculture.

What are the environmental impacts of food production? How do we reduce the impacts of agriculture on the environment?

Environmental Impacts of Food Production

Global average cereal yield is 4 tonnes per hectare.

But in many regions yields are much lower.

Increasing the production of crops per area of land is of key importance for food security, living standards, and decreasing humanity’s impact on the environment.

Crop Yields

72 billion animals are slaughtered every year for meat production.

Meat is an important source of nutrition for people around the world. How quickly is demand growing? And what are the implications for animal welfare and the earth’s environment?

Meat and Dairy Production

Agricultural inputs

27% of the world’s labor force is employed in agriculture.

Once the majority of human labor was dedicated to food production. When and how did this change? What is the global distribution of agricultural labor today?

Employment in Agriculture

How is humanity using the Earth’s land? And how can we decrease our land use so that more land is left for wildlife?

Land Use

The world produces more than 200 million tonnes of fertilizer each year.

Fertilizers supply plants with nutrients that are essential for growth. How important are fertilizers? How does their use vary across the world?

Fertilizers

Pesticides are often used to protect crop yields.

There are large differences in its use across the world.

Pesticides are used to protect plants from weeds, fungi, or insects. Where are they used? What is their impact?

Pesticides

Energy and Environment

Around one-in-three children globally suffer from lead poisoning.

Lead pollution is a widespread problem that receives little attention. What is the scale of the problem and how can we tackle it?

Lead Pollution

Global trends in biodiversity are mixed, but overall it’s in steep decline

Explore the diversity of wildlife across the planet. What are species threatened with? What can we do to prevent biodiversity loss?

Biodiversity

Transport is an important measure of technological progress

Explore trends in transport technologies and emissions across the world.

Transport

Average global temperature has increased by 1.2°C since pre-industrial times

How are increasing greenhouse gas emissions affecting the climate? What are the implications for sea level rise; sea ice area; and other impacts?

Climate Change

Energy

10% of the world do not have access to electricity.

40% do not have access to clean fuels for cooking.

Access to electricity and clean cooking fuels are vital for a good standard of living and good health.

Access to Energy

Global energy production has grown 2.5-fold in the past 50 years.

What energy sources does the world rely on? What is this energy used for? And how does it change over time?

Energy

10% of global primary energy production comes from modern renewable sources.

Solar, wind, hydropower, and other renewable energy sources currently account for a small share of global energy. But they’re growing quickly and can play a key role in tackling climate change.

Renewable Energy

84% of global primary energy comes from fossil fuels.

Deaths per TWh range from 2.8 for gas to 24.6 for coal.

Coal, gas and oil were key to industrialization and rising prosperity, but their large impact on health and the climate mean that we should transition away from these sources of energy.

Fossil Fuels

Waste

The use of plastics has many benefits – it is affordable, versatile, resistant, and can help reduce other forms of waste – especially food waste. However, when poorly managed it can pollute the environment and our oceans. Where does the plastic in our oceans come from and what can we do to reduce plastic pollution?

Plastic Pollution

Oil spills can have a large negative impact on the environment.

How often do oil spills happen? How did it change over time?

Oil Spills

Air and Climate

35 billion tonnes of CO₂ are emitted every year.

Who is emitting greenhouse gases? Which countries and which sectors? And what needs to happen to reduce emissions?

CO₂ and Greenhouse Gas Emissions

6.7 million people die prematurely from air pollution each year.

Our overview on both indoor and outdoor air pollution.

Air Pollution

4.5 million people die prematurely from outdoor air pollution every year.

44% are younger than 70 years old.

Outdoor air pollution is one of the world’s largest health and environmental problems.

Outdoor Air Pollution

2.3 million people die prematurely from indoor air pollution every year.

58% are younger than 70 years old.

Indoor air pollution – caused by the burning of firewood, crop waste, and dung for cooking and heating – is a major health risk of the world’s poorest.

Indoor Air Pollution

Emissions of ozone-depleting gases have fallen by 98%.

But it will take decades for the ozone layer to recover.

The emission of ozone-depleting gases are threatening the earth’s ozone layer. Global collaboration and regulation aims to reduce the emissions. Are these efforts successful?

Ozone Layer

Water

One-quarter of the world do not have access to safe drinking water

Explore global access to clean water and sanitation.

Clean Water and Sanitation

2.1 billion people do not have access to safe drinking water.

That’s 26% of the world population.

Clean and safe water is essential for good health. How did access change over time? Where do people lack access?

Clean Water

3.7 billion people do not have access to safely managed sanitation.

That’s 46% of the world population.

Access to safe sanitation is essential for reducing deaths from infectious disease, preventing malnutrition and providing dignity. What is the global situation today and how can we make progress?

Sanitation

Freshwater resources across the world are the focus of this entry. How much water do we use? How did it change over time?

Water Use and Stress

Land and Ecosystems

26% of global greenhouse gas emissions come from food production.

50% of the world’s habitable land is used for agriculture.

What are the environmental impacts of food production? How do we reduce the impacts of agriculture on the environment?

Environmental Impacts of Food Production

31% of the world’s land area is covered by forest.

How are forests distributed across the world? How much do we lose to deforestation every year?

Forests and Deforestation

How is humanity using the Earth’s land? And how can we decrease our land use so that more land is left for wildlife?

Land Use

Where and from which disasters do people die? What can we do to prevent deaths from natural disasters?

Natural Disasters

Innovation and Technological Change

Explore data and developments in space travel and satellite technologies.

Space Exploration and Satellites

Transport is an important measure of technological progress

Explore trends in transport technologies and emissions across the world.

Transport

Technological progress has been key a key driver of improved living standards.

Technology is a key driver of change that matters for all the big problems that we consider in this publication.

Technological Change

Technology adoption has been a key driver of improved living conditions.

Technology has been a leading driver of global change – disrupting the way we work, travel, and live. How quickly have different technologies been adopted across the world? Explore global and country-level data and research on technology adoption.

Technology Adoption

Poverty and Economic Development

Public Sector

Government spending has increased significantly, but with large differences across the world.

What do governments spend their financial resources on?

Government Spending

Tax revenues account for more than 80% of total government revenue in about half of the countries in the world.

And for more than 50% in almost every country.

Taxes are the most important source of government revenue. Who is paying how much and how do tax systems differ?

Taxation

How much do different countries spend on their military? How did it change over time?

Military Spending

Healthcare funding is essential for good health.

Healthcare is key to make progress against poor health. How is it financed?

Financing Healthcare

Funding for education is growing across the world, but large gaps still exist.

How is education financed? How much do we spend on it? What are the returns?

Financing Education

Poverty and Prosperity

The world has become much more prosperous, but in some countries incomes remain very low.

All of today’s rich countries were poor in the past – how do poor countries become rich?

Economic Growth

Global Extreme Poverty

Economic Inequality

Many countries have high levels of income inequality.

How are incomes distributed and how and why did the distribution change over time?

Income Inequality

In most countries the gender pay gap has reduced, but inequalities are still large and common.

What is determining the inequality in incomes, jobs, and wealth between men and women?

Economic inequality by gender

Global inequality has fallen but living conditions are still vastly unequal across the world.

Living conditions around the world are vastly unequal and economic differences are a major reason for this. How is this distribution changing?

Global Economic Inequality

Labor

An estimated 17% of children globally work.

Why and where do children work? How did child labor change over time?

Child Labor

Many people have to work long hours with for very low incomes.

How much time do people across the world spend working? How have working hours changed over time, and what do these changes matter for people’s lives? Explore data and research on working hours.

Working Hours

Women’s labor force participation is 47% globally with large differences between countries.

What is determining whether women participate in the labor market? How is it changing?

Women’s employment

Corruption

Corruption is a common problem in many countries and sectors.

How common is corruption? What impact does it have? And what can be done to reduce it?

Corruption

Living conditions, Community and Wellbeing

Time is the ultimate limited resource

How do people across the world spend their time? How do daily activities differ across countries, and how do these differences matter for people’s lives? Explore data and research on time use.

Time Use

The institution of marriage is changing quickly

How is the institution of marriage changing? What percentage of marriages end in divorce? Explore global data on marriages and divorces.

Marriages and Divorces

Loneliness is common across the world.

Family and friends are important for our well-being. In this article we explore data on loneliness and social connections, and review available evidence on the link between social connections and well-being.

Loneliness and Social Connections

Life satisfaction and happiness vary widely both within and among countries.

Self-reported life satisfaction differs widely between people and between countries. What explains these differences?

Happiness and Life Satisfaction

Health, education and living standards have increased in recent decades, but more progress is needed.

The HDI is a measure of human development that captures health, education, and income. How does the index vary around the world, and how did it change over time?

Human Development Index (HDI)

An estimated 17% of children globally work.

Why and where do children work? How did child labor change over time?

Child Labor

Many people have to work long hours with for very low incomes.

How much time do people across the world spend working? How have working hours changed over time, and what do these changes matter for people’s lives? Explore data and research on working hours.

Working Hours

56% of the world population live in urban areas.

In 1960 it was 34%.

The world population is moving to cities. Why is urbanization happening and what are the consequences?

Urbanization

Tourism is an important source of income and employment for many countries.

How many travel for tourism? Where do they go?

Tourism

Culture

Misconceptions about past development means many are pessimistic about future progress.

What is people’s outlook on the future – personally and for the world as a whole?

Optimism and Pessimism

Trust levels can vary a lot between countries and groups of society.

Trust is essential for community, wellbeing, and effective cooperation. How does trust vary between different societies and locations and what matters for levels of trust?

Trust

Housing

One-quarter of the world do not have access to safe drinking water

Explore global access to clean water and sanitation.

Clean Water and Sanitation

10% of the world do not have access to electricity.

40% do not have access to clean fuels for cooking.

Access to electricity and clean cooking fuels are vital for a good standard of living and good health.

Access to Energy

2.1 billion people do not have access to safe drinking water.

That’s 26% of the world population.

Clean and safe water is essential for good health. How did access change over time? Where do people lack access?

Clean Water

Homelessness is a problem in countries around the world.

How many are homeless? How did homelessness change over time?

Homelessness

2.3 million people die prematurely from indoor air pollution every year.

58% are younger than 70 years old.

Indoor air pollution – caused by the burning of firewood, crop waste, and dung for cooking and heating – is a major health risk of the world’s poorest.

Indoor Air Pollution

Many do not have light at night

Light at night was once expensive everywhere. In some places people are still lacking light at night, while in other places light became extremely cheap.

Light at Night

3.7 billion people do not have access to safely managed sanitation.

That’s 46% of the world population.

Access to safe sanitation is essential for reducing deaths from infectious disease, preventing malnutrition and providing dignity. What is the global situation today and how can we make progress?

Sanitation

Human rights and Democracy

How democratic is the world? Why do countries become democratic? What is the impact of democratisation on people’s lives?

Democracy

Violence against children in various forms has fallen, but still occurs today.

How common is physical and emotional violence against children? How did it change over time?

Violence against children and children’s rights

In most countries the gender pay gap has reduced, but inequalities are still large and common.

What is determining the inequality in incomes, jobs, and wealth between men and women?

Economic inequality by gender

Corruption is a common problem in many countries and sectors.

How common is corruption? What impact does it have? And what can be done to reduce it?

Corruption

Human Rights violations are still common in many countries.

From freedom of the press to racism, this entry presents an overview of quantitative measures of human rights.

Human Rights

Violence and War

War and Peace

Humans are capable of atrocious cruelty – the history of war makes this all too clear. How many died in war? And what are the prospects for making the world more peaceful?

War and Peace

How much do different countries spend on their military? How did it change over time?

Military Spending

The attacks of terrorists receive a lot of attention from the media and often dominate the public discourse. How many people die from these attacks and how did it change over time?

Terrorism

The world’s nuclear powers have more than 9,000 nuclear warheads.

The world’s nuclear powers possess around 9,500 nuclear warheads in total. These weapons have the capacity to kill hundreds of millions of people directly, and billions due to subsequent effects on agriculture.

Nuclear Weapons

Peacekeeping operations are used in conflict prevention, but are not always successful.

Peacekeeping aims to help countries transition from conflict towards peace. How have peacekeeping operations and forces changed over time? See global data on peacekeeping activities.

Peacekeeping

Violence

Violence against children in various forms has fallen, but still occurs today.

How common is physical and emotional violence against children? How did it change over time?

Violence against children and children’s rights

Globally around 415,000 people die from homicide each year.

Where are people dying from homicides? How did the homicide rate change over time?

Homicides

Education and Knowledge

Global education has improved over recent decades, but much more progress is possible.

The overview of our research on global education.

Global Education

Educational outcomes

Being able to read and write opens up the world of education and knowledge. When and why did more people become literate? How can progress continue?

Literacy

Schools often do not live up to their promise: in many schools children learn very little.

How do learning outcomes differ between countries? How has the quality of education changed over time?

Quality of Education

Access to Education

Many children have very few opportunities in learning before primary education.

Access to education early in life can improve outcomes for the rest of life. How does pre-primary education differ between countries and how did it change over time?

Pre-Primary Education

58 million children of primary school age are not in school.

202 million children of secondary school age are not in school.

How does access to school differ around the world? How does it between boys and girls? And how did it change over time?

Primary and Secondary Education

Globally 36% of those within 5 years of secondary education are enrolled in tertiary education.

When did access to universities and tertiary education increase? How does it differ between countries?

Tertiary Education

Inputs to education

Funding for education is growing across the world, but large gaps still exist.

How is education financed? How much do we spend on it? What are the returns?

Financing Education

Many teachers across the world do not receive sufficient training.

A global overview of teaching professionals. How many teachers are there? At what level do they teach? What are their qualifications?

Teachers and Professors

Media

Book publication has been a key driver of knowledge-sharing and education.

Books have been at the center of science and the arts for centuries. Their history and relevance is the focus of this entry.

Books

For many, the internet is now essential for work, finding information, and connecting with others. How did half the world get online in just one generation? And what are the challenges ahead?

Internet

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Licenses: All visualizations, data, and articles produced by Our World in Data are open access under the Creative Commons BY license. You have permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited. All the software and code that we write is open source and made available via GitHub under the permissive MIT license. All other material, including 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.

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Tourism

Notice: This is only a preliminary collection of relevant material

If you have expertise in this area and would like to contribute, apply here to join us as a researcher.

All our interactive charts on Tourism

International arrivals by world region

Arrivals by world region

This visualization shows how tourist arrivals have increased since shortly after the Second World War in 1950.

The United Nations World Tourism Organization (UNWTO) estimates that internationally there were just 25 million tourist arrivals in 1950. 68 years later this number has increased to 1.4 billion international arrivals per year. This is a 56-fold increase.

Click to open interactive version

Changing relative distribution of tourist arrivals

This chart shows the relative distribution of tourist arrivals by region. In 1950 two-thirds of tourists arrived in Europe. Over the following 68 years, the relative importance declined to around 50%, but it is still the most important touristic region.

Asia and the Pacific had only very small importance as tourist destinations in 1950. In 2018 however, every fourth tourist arrived in the region.

Click to open interactive version

International arrivals by country

The map shows the number of tourists by country. France is today the country that receives most tourists.

Click to open interactive version

Safety of aviation

Fatal accidents

Number of fatal accidents

The chart shows the number of fatal accidents globally from airliners. These figures are given based on commercial flights with a minimum of 14 passengers’ capacity. The number of fatal incidents from airline hijacking or sabotage is also shown (and included in total figures).

Click to open interactive version

Number of fatal accidents per million flights

The chart shows the number of fatal aviation accidents per million commercial flights.

Click to open interactive version

Aviation deaths

Number of fatalities

The chart shows the number of fatalities globally from airliners; these figures are given based on commercial flights with a minimum of 14 passengers’ capacity. The number of fatalities from airline hijacking or sabotage is also shown (and included in total figures).

Click to open interactive version

Number of fatalities per million passengers

The visualization shows the number of aviation fatalities per million passengers on commercial airlines (with a minimum capacity of 14 passengers).

Click to open interactive version

Number of passengers per fatality

The visualization shows the inverse of the trend above: the number of air passengers per fatality on commercial airlines. In 2017, 90 million passengers flew per death from commercial airlines.

Click to open interactive version

Data Sources

United Nations World Tourism Organization (UNWTO)

World Bank – World Development Indicators

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Cite our work

Our articles and data visualizations rely on work from many different people and organizations. When citing this entry, please also cite the underlying data sources. This entry can be cited as:

Internet

All our interactive charts on Internet

The Internet’s history has just begun

But it was the creation of the World Wide Web in 1989 that revolutionized our history of communication. The inventor of the World Wide Web was the British scientist Tim Berners-Lee who created a system to share information through a network of computers. At the time he was working for the European physics laboratory CERN in the Swiss Alps.

Here I want to look at the global expansion of the internet since then.

This chart shows the share and number of people that are using the internet, which in these statistics refers to all those who have used the internet in the last 3 months. 1

You can also explore interactive versions of the chart with the most recent available global data.

The chart starts in 1990, still one year before Berners-Lee released the first web browser and before the very first website was online (the site of CERN, which is still online ). At that time very few computers around the world were connected to a network; estimates for 1990 suggest that only half of a percent of the world population were online.

As the chart shows, this started to change in the 1990s, at least in some parts of the world: By the year 2000 almost half of the population in the US was accessing information through the internet. But across most of the world, the internet had not yet had much influence – 93% in the East Asia and Pacific region and 99% in South Asia and in Sub-Saharan Africa were still offline in 2000. At the time of the Dot-com-crash less than 7% of the world was online.

Fifteen years later, in 2016, three-quarters (76%) of people in the US were online and during these years countries from many parts of the world caught up: in Malaysia 79% used the internet; in Spain and Singapore 81%; in France 86%; in South Korea and Japan 93%; in Denmark and Norway 97%; and Iceland tops the ranking with 98% of the population online. 2

At the other end of the spectrum, there are still countries where almost nothing has changed since 1990. In the very poorest countries – including Eritrea, Somalia, Guinea-Bissau, the Central African Republic, Niger, and Madagascar – fewer than 5% are online. And at the very bottom is North Korea, where the country’s oppressive regime restricts the access to the walled-off North Korean intranet Kwangmyong and access to the global internet is only granted to a very small elite.

But the overarching trend globally – and, as the chart shows, in all world regions – is clear: more and more people are online every year. The speed with which the world is changing is incredibly fast. On any day in the last 5 years there were on average 640,000 people online for the first time. 3

This was 27,000 every hour.

For those who are online most days it is easy to forget how young the internet still is. The timeline below the chart reminds you how recent websites and technologies became available that are integrated to the everyday lives of millions: In the 1990s there was no Wikipedia, Twitter launched in 2006, and Our World in Data is only 4 years old (and look how many people have joined since then 4 ).

And while many of us cannot imagine their lives without the services that the internet provides, the key message for me from this overview of the global history of the internet is that we are still in the very early stages of the internet. It was only in 2017 that half of the world population was online; and in 2018 it is therefore still the case that close to half of the world population is not using the internet. 5

The internet has already changed the world, but the big changes that the internet will bring still lie ahead. Its history has just begun.

Books

Notice: This is only a preliminary collection of relevant material

If you have expertise in this area and would like to contribute, apply here to join us as a researcher.

In this entry we study the history of books over the last centuries.

Several recent research papers and books have made it possible to follow the rise of book production. The crucial event was the invention of the printing press by Gutenberg around 1440. But we will also study the history of manuscripts that preceded printed books. Particularly interesting is the transition from manuscripts to books – book production became more efficient, prices decreased and the consumption of books increased.

A major driver for the increased production of books is the revolution of literacy, which we study in detail in our literacy entry.

All our interactive charts on Books

Production of manuscripts and books from 500 to 1800

The increased production of manuscripts and books was estimated by Buringh and Van Zanden (2009). 1

The unit of analysis for the estimates of manuscripts is the number of individual manuscripts. The unit of analysis for the estimates of printed books is (new) ‘title’ or ‘edition’. The authors corrected the numbers to account for the underrepresentation of estimates for different geographical regions and different times. The authors consider their estimates to be conservative and note that the “figures should be interpreted as low estimates”.

Titles are either books (which have by definition more than 49 pages) or pamphlets (less than 50 pages). The authors define a title as ‘a printed publication which forms a separate whole, whether issued in one or several volumes. Different language versions of the same title published in a particular country should be considered as individual titles’; this includes first editions and reeditions. The authors give the following example: ‘The first printing of Gutenberg’s Bible is one title, and new editions of the Bible will again be counted, but a reprint of exactly the same manuscript would not be included.’

Buringh and Van Zanden note that the aggregation of the data to country levels obscures inequalities within countries – ‘if we could isolate data on, for example, northern Italy or the north of France (including Paris), these regions rank much higher in output per capita’.

The growth of the book sector in Western Europe over the 1300 years studied by the authors is enormous. The most decisive development for the increased book production was the invention of the printing press. Buringh and Van Zanden note: “in the year 1550 alone, for example, some 3 million books were produced in Western Europe, more than the total number of manuscripts produced during the fourteenth century as a whole”.

Our world in data

All the visualizations, data, and articles produced by Our World in Data are free for you to take and use — no permission required. You just need to provide credit to Our World in Data (more details below). This part of our work is licensed under a very permissive ‘Creative Commons’ (CC) license: the CC-BY license (the BY stands for ‘by attribution’).

How can you tell what is produced by Our World in Data? For the visualization here on COVID-19 vaccinations, we produced both the chart and the underlying data. You can tell this because visualizations produced by Our World in Data will have our logo on them, and data we produced will say in the sources section “Our World in Data based on…” or “Official data collated by Our World in Data.”

All other material, such as charts and 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.

Note: In early 2019 we changed our Creative Commons license from “By Attribution-Share Alike” (CC-BY-SA) to “By Attribution” (CC-BY). Some of our static charts still have the CC-BY-SA mark in the bottom right corner. You can disregard this, and consider all our work as licensed under CC-BY.

Our visualization software and code is free and open-source under the MIT License

We develop our own data visualization and database tool: The Our World in Data-Grapher. This tool is completely open-source – here on GitHub – and is free to use on any other web publication. The code is licensed under the MIT License.

Third-party charts and data are subject to third-party licenses

All charts and data produced by third parties and made available by Our World in Data are subject to the license terms from the original third-party authors.

For example, the chart here on time spent on unpaid care work, from our entry on women’s employment, was produced by the OECD and is subject to the OECD’s license.

The chart on the number of deaths by cause was produced by Our World in Data and is subject to our CC-BY license — however, the chart’s underlying data was produced by the Institute for Health Metrics and Evaluation (IHME) and is subject to IHME’s license. If you wanted to download the underlying data and use it, e.g., to make a new chart or put it in a table, your use would be subject to the terms of IHME’s license.

Average daily time spent on unpaid care work by world regions, OECD (2014) 1

Click to open interactive version

Can I use or reproduce your data?

All data produced by Our World in Data is completely open access under the Creative Commons BY license. You have the permission to use, reproduce, and distribute it in any medium, provided the source and authors are credited.

How should I cite your work?

Citing work produced by Our World in Data

If you are using one of our original charts (those with the Our World in Data logo and CC-BY copyright stamp) — cite the corresponding entry from Our World in Data where the chart is located.

For example, if using this chart on literacy rate, you should cite: Max Roser and Esteban Ortiz-Ospina (2019) – “Global Rise of Education”. Published online at OurWorldInData.org. Retrieved from: ‘https://ourworldindata.org/global-rise-of-education’ [Online Resource]

We provide citation details at the bottom of each entry. For example, here you can find the citation for our entry on CO2 and Greenhouse Gas Emissions.

Citing work produced by third parties and made available by Our World in Data

If you are using the underlying data from an Our World in Data chart or entry — cite both Our World in Data and the underlying data source(s).

For example, if you downloaded the data from this chart on real GDP per capita and are using it for analysis or to redraw the chart, you should write:

Additional points

If you can, send us a quick note telling us where you used our work. It is encouraging to hear that our work is helpful and we can learn from seeing how you used it.

In online publications, embed interactive charts when possible. We encourage online publications to embed our interactive charts. This is easy to do and you can trust that the embedded chart won’t break — all our links are stable.

Can I use your software to make my own visualizations?

Our interactive visualizations are made through the Our World in Data Grapher, developed by us. All our software is open-source and free for everyone to use, but the code will require a relatively experienced developer to implement. If you are looking only to publish one or a few interactive visualizations on the web we recommend https://www.datawrapper.de.

The Grapher is very helpful for publications looking to bring together many different datasets and publish hundreds of visualizations based on this data. You can read more about the Grapher here: https://ourworldindata.org/about/owid-grapher.

Can I use your data and visualizations in my article, blog, book, presentations?

All the visualizations, data, and articles produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Please bear in mind that all other material, including 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 for every chart in our documentation, so you should always check the license of any such third-party data before use and redistribution.

Can I republish your articles and posts?

Yes, you can republish our articles online or in print for free, provided you follow these guidelines:

Signed consent / copyright release forms are not required, providing you are following these guidelines.

Can I use Our World in Data material for teaching?

Yes, we love to see people using our work for teaching! We know – based on surveys from users – that many teachers do use our work. Surprisingly, this extends from primary school children through to postgraduate university students.

We also have a Teaching Hub where we provide resources for teaching and learning about global development. For specific topics you will find interactive teaching notes, presentation slides, charts and many other resources.

Our interactive charts are featured in the Core Econ textbooks, and in their teaching resources you find many great ideas on how to include interactive charts on courses taught online.

If you use our teaching already we’d love to hear from you and would be happy if you send us your slides or teaching material. And if something is missing for you or you have any ideas or suggestions for how to make or work more useful for teaching, please do get in touch at info@ourworldindata.org or through our Feedback page.

How can I get a static image from your interactive charts?

It is straightforward to make static versions of Our World in Data interactive visualizations. Just click the downward arrow below the visualization and then chose ‘PNG’.

In case you need to edit the chart further, you can click the downward arrow and select the option “Save as SVG”, and the chart will open as a Scalable Vector Graphic (.svg) file in a new tab in your browser. You can then save it in your browser – through “save page as” – and you will have a vector graphic of the chart that you can then edit in Inkscape (free), Adobe Illustrator or similar software.

For a step-by-step explanation with examples, see here.

Where do you get your data from?

One of our key tasks in producing this publication is to bring together the most reliable and informative data sets on a particular topic.

There are four main sources for the data that we bring together:

In every visualization we indicate clearly the source of the presented data. Where we have combined data sources or made changes to original datasets (such as regional aggregations, per capita transformations, etc.), this is also indicated.

How do you decide what data sources to use?

We have six guidelines to decide which sources to accept and which data to present.

1) As far back into the past as possible – but up to today

The goal is to give a perspective on the long-term development and therefore we always aim to find time series data that reach back as far as possible. Unfortunately the availability of data is often itself an achievement of modern development and data is not available for the more distant past. A solution for this problem is data that has later been reconstructed and we aim to give a more complete picture by taking this data into account.

At the same time the idea is also to present a ‘history of today’ and we therefore also want to ensure that the data presented reaches until today. The limitation here is often that it takes up to several years for researchers and international institutions to publish important data for the most recent period.

2) As global as possible

A second objective is to give an account of each topic that includes as many societies, countries, and world regions as possible.

3) Present data in its entirety

Shorter sample periods may mask important trends and a recent reversal of a long-term trend could be falsely interpreted as the direction of the long-term trend. The merit of taking a historical perspective that studies long-term trends is that it shows the direction in which some aspect of our world is developing. Therefore we also always ensure to present the whole dataset and we do not want to cut off the original data.

4) Comparable through time and across societies

A third objective is to ensure that the data we present is comparable across time and across societies.

When data is not comparable across countries and through time we highlight this in the text accompanying the visualisation.

5) There is no other data – or we would include this data

An important promise is that we are not withholding any data that would give a different impression of the long-term development of some aspect. If two credible sources would publish statistics that contradict each other – indicating an open debate between researchers – then we would say so.

6) Reference the original source

To make the database useful for readers and credit the important work of those who construct the data presented here we aim to always reference the original source of the data.

We take great care to follow these guidelines. Unintentional mistakes or omissions, whilst hopefully rare, are of course possible. If you find any instance where we have not followed these guidelines, or you have any other complaints, please do get in touch at info@ourworldindata.org or through our Feedback page.

How do you decide which topics to cover?

We have a list of all the topics we want to cover and have been working through this list for several years now. Our goal here is to cover all quantifiable aspects that matter for our living conditions and the earth’s environment.

In deciding which topics to cover next we take several aspects into account:

Do you only cover positive trends and stories of progress?

We are interested in the state of the world and how it changed. Many of the trends that we discuss in our articles are positive; and since fewer people are aware of these positive developments, these trends often get considerable attention from our audience. But it’s not that we have an editorial agenda to only study positive trends. Indeed, in our publication you will also find very worrying trends: inequality is rising in many countries, obesity is rising in all world regions, CO2 emissions have increased for many decades while they need to fall urgently).

Additionally, we are convinced that covering positive trends is not in conflict with acknowledging just how awful the world continues to be in many ways, for many people. In fact the opposite is true: charting the progress of the past helps us see just how much better the world could be in the future, if we make this our goal.

Consider a concrete example. Every tenth person in the world today lives in extreme poverty. That statistic summarises a degree of suffering that is barely comprehensible. But the fact that, since 2000, there are a billion fewer people living beneath this very low poverty line shows us that ending extreme poverty is possible, if we choose to make it happen. This is the very reason we write about extreme poverty.

How can I embed one of your interactive charts in my website?

You can embed any of the interactive visualizations from Our World In Data in your articles.

Here are three examples of articles that embed OWID visualizations:

Our Audience & Coverage

Audience

In 2021 we had 89 million unique visitors, and we are very happy about the fact that readers come from every country in the world.

Our visitors range from students and school children doing their classwork; to individuals trying to better understand the world and how it’s changing; to journalists, researchers, and policymakers looking for the data and research to inform their work.

In the section on Teaching we discuss how many teachers, lecturers, and professors use Our World in Data material in their classrooms.

Coverage

Our work is cited and referenced in many hundreds of articles, reports, books, lectures, videos, radio shows, podcasts, and talks every year.

In the section below is a partial list of such citations — the ones that have come to our attention. Since all our work is open, it is difficult to keep track of our citations.

If you would like to suggest an addition to our list of citations, send us an email at info@ourworldindata.org.

Academic coverage

List of coverage by year

Coverage in 2022

Aug 10 – Los Angeles Times – Fears of losing battle to control monkeypox in California, U.S. as cases surge – Rong-gong Lin Ii, Luke Money, Melody Gutierrez

Aug 8 – Nature Human Behaviour – Resilient government requires data science reform – Ben D. MacArthur et al.

Aug 5 – The New York Times – The Case for Longtermism – Will MacAskill

Aug 3 – The Financial Times – Global inflation tracker: see how your country compares on rising prices – Valentina Romei and Alan Smith

Jun 30 – The Financial Times – Would carbon food labels change the way you shop? – Niko Kommenda and colleagues

Jun 5 – The New York Times – Your Kids Are Not Doomed – Ezra Klein

Jun 1 – The Atlantic – ‘Everything Is Terrible, but I’m Fine’ – Derek Thompson

May 25 – The New York Times – The Morning: 19 Murdered Children – David Leonhardt

May 24 – The Atlantic – The People Who Hate People – Jerusalem Demsas

May 2 (accessed) – UN Food and Agriculture Organization (FAO) – The State of the World’s Forests 2022 Report

May 2 – cited in the book How to Prevent the Next Pandemic by Bill Gates

Apr 27 – Financial Times – How Russia’s war in Ukraine upended the breadbasket of Europe – John Reed and colleagues

Apr 25 – cited in the book Gambling on Development by Stefan Dercon

Apr 21 – Works in Progress – Scientific slowdown is not inevitable – Ben Southwood

Apr 14 – Council on Foreign Relations – Why Hasn’t the World Eradicated Polio? – Claire Felter and Danielle Renwick

Apr 13 – Carbon Brief – The Carbon Brief Country Profiles – Josh Gabbatiss

Apr 6 – The New York Times – The Morning: A New COVID Mystery – David Leonhardt

Apr 4 – Intergovernmental Panel on Climate Change (IPCC) – Sixth Assessment Report: Mitigation of Climate Change

Mar – cited in the book How the World Became Rich: The Historical Origins of Economic Growth by Mark Koyama and Jared Rubin

Mar 28 (accessed) – Wikipedia – Longtermism

Mar 27 – The New York Times – The Morning Newsletter: Ukraine’s Warning – German Lopez

Mar 23 – Nature News – Lessons from the COVID data wizards – Lynne Peeples

Mar 8 – The New York Times – Paul Krugman Newsletter

Feb 13 – Metaculus – Renewables Forecasting – Ryan Beck

Feb 13 – The Telegraph – Was Sweden right about Covid all along? – Fraser Nelson

Feb 9 – Center for Global Development – COVID-19 Vaccine Development and Rollout in Historical Perspective – Amanda Glassman, Charles Kenny, & George Yang

Feb 7 – The New York Times – The Morning: The Booster Problem – David Leonhardt

Feb 7 – Inside Climate News – Activists Urge the International Energy Agency to Remove Paywalls Around its Data – Andrew Marquardt and Jeannie Kopstein

Feb 1 – The New York Times – U.S. Has Far Higher Covid Death Rate Than Other Wealthy Countries – Benjamin Mueller and Eleanor Lutz

Jan 28 – Zeit Online – Ten billion vaccine doses, unfairly distributed – Annick Ehmann and colleagues

Jan 21 – Works in Progress – Parenting as a public good – Ellen Pasternack

Jan 19 – The New York Times – The Morning: Omicron is in retreat – David Leonhardt

Coverage in 2021

Dec 26 – BBC: More or Less – Numbers of 2021

Dec 21 – Visual Capitalist – Mapped: Food Production Around the World – Govind Bhutada

Dec 6 (accessed) – ONE Campaign – Africa COVID-19 Tracker

Dec 1 – Financial Times – Omicron variant highlights uneven genomic sequencing worldwide – Donato Paolo Mancini and Oliver Barnes

Nov 24 (accessed) – World Trade Organization & International Monetary Fund – COVID-19 Vaccine Trade Tracker

Nov 23 – Forskning & Framsteg – 60 miljoner barn går inte i skolan – Oskar Alex

Nov 17 (accessed) – Land Calorie Protein – Freddie Yauner

Oct 25 – The New York Times – Yes, There Has Been Progress on Climate. No, It’s Not Nearly Enough. – Brad Plumer and Nadja Popovich

Oct 11 – Financial Times – Covid response hampered by population data glitches – Oliver Barnes and John Burn-Murdoch

Oct 8 – The Bureau of Investigative Journalism – How COVAX failed on its promise to vaccinate the world – Rosa Furneaux and Olivia Goldhill

Oct 7 – cited in the book Covid by Numbers: Making Sense of the Pandemic with Data by David Spiegelhalter and Anthony Masters

Oct 6 – Neue Zürcher Zeitung – Wie schützen wir das Klima, wenn die Bevölkerung rasant wächst? – Pauline Voss, Charlotte Eckstein, and Nikolai Thelitz

Sep 28 – cited in the book Rationality by Steven Pinker

Sep 14 (accessed) – Bill & Melinda Gates Foundation – Goalkeepers Report 2021: Innovation and Inequity

Aug 31 – Nature Medicine – COVID-19 boosters in rich nations will delay vaccines for all – Zain Chagla and Madhukar Pai

Aug 23 (accessed) – US Centers for Disease Control and Prevention (CDC) – COVID Data Tracker: Global COVID-19 Vaccinations

Aug 17 – The New York Times – Who created the renewable-energy miracle? – Paul Krugman

Aug 12 (accessed) – International Monetary Fund, World Bank, World Health Organization, & World Trade Organization – Multilateral Leaders Task Force on COVID-19

Jul 26 (accessed) – World Health Organization – Europe COVID-19 Vaccine Programme Monitor

Jul 22 (accessed) – United Nations Development Programme – Global Dashboard for Vaccine Equity

Jul 20 (accessed) – Google – Coronavirus (COVID-19) dashboard

Jul 15 – England’s National Food Strategy – Henry Dimbleby et al

Jul 9 – The New York Times – The Interpreter Newsletter 9 July 2021 Edition – Max Fisher

Jun 28 – International Monetary Fund (IMF) Blog – Sub-Saharan Africa: We Need to Act Now – Kristalina Georgieva and Abebe Aemro Selassie

Jun 11 – BBC News – Fact-checking Matt Hancock’s Covid claims – Reality Check team

Jun 9 (accessed) – University of Oxford – Annual Review 2020

Jun 8 – Falter – Der Meister der Zahlen – Eva Konzett

May 13 – Nature Sustainability – Data needed to decarbonize paratransit in Sub-Saharan Africa – Katherine A. Collett and Stephanie A. Hirmer

Apr 30 – Bistandsaktuelt – Vaksinegapet i verden vil fortsette å øke – Sofi Lundin and Asle Olav Rønning

Apr 24 – The New York Times – Let’s Launch a Moonshot for Meatless Meat – Ezra Klein

Apr 24 – BBC: More or Less – Will 2021 have more Covid deaths than 2020? – Tim Harford

Apr 22 – The Washington Post – Here’s just how unequal the global coronavirus vaccine rollout has been – Atthar Mirza and Emily Rauhala

Apr 14 – Süddeutsche Zeitung – Zahlen lügen nicht – oder doch? – Christian Endt

Mar 18 (accessed) – CNN – Tracking Covid-19 vaccinations worldwide – Henrik Pettersson, Byron Manley, Sergio Hernandez and Deidre McPhillips

Mar 18 – cited in the book How to Read Numbers by Tom Chivers & David Chivers

Mar 2 – cited in the book The Physics of Climate Change by Lawrence M. Krauss

Mar 2 – FactCheck.org – Pace of U.S. Vaccinations vs. the World – Robert Farley

Feb 19 – The New Yorker – The Activists Who Embrace Nuclear Power – Rebecca Tuhus–Dubrow

Feb 18 – German Federal Ministry of Health – Current vaccination status dashboard

Feb 16 – cited in the book How to Avoid a Climate Disaster by Bill Gates

Feb 13 – Süddeutsche Zeitung – Chile drückt aufs Tempo – Sebastian Schoepp

Feb 9 (accessed) – Wikipedia – Smoking

Feb 5 – Financial Times – Why investing in data is never money wasted – Tim Harford

Feb 2 – Peterson Institute for International Economics (PIIE) – It’s taking too long to get vaccine doses from refrigerators into arms – David Wilcox

Jan 27 – GatesNotes Annual Letter – The year global health went local – Bill and Melinda Gates

Jan 22 – Financial Times – Covid-19 vaccine tracker: the global race to vaccinate – FT Visual and Data Journalism team

Jan 16 – New Scientist – Vaccine roll-out around the world

Jan 12 – Financial Times – IEA chief: Net zero by 2050 plan for energy sector is coming – Faith Birol (head of the International Energy Agency)

Jan 6 – Katapult Magazine (Instagram account) – Anteil von Corona-Tests, die positiv sind

Jan 5 – The Guardian (Instagram account) – Who is ahead in the race to vaccinate?

Jan 4 – The Washington Post – Israel is vaccinating so fast it’s running out of vaccine – Steve Hendrix and Shira Rubin

Coverage in 2020

Dec 29 – Zeit Online – Corona vaccinations in Germany – Paul Blickle et al

Dec 27 – Marginal Revolution – Vaccinate, 24/7 – Alex Tabarrok

Dec 15 – UN Development Programme – Human Development Report 2020

Dec 10 – Quillette – The End of the World as We Know It? – Glenn T. Stanton

Nov 23 – Agenda (World Economic Forum) – This is why food security matters now more than ever – Keith Breene

Nov 18 – Agenda (World Economic Forum) – Here’s how technology has changed the world since 2000 – Madeleine Hillyer

Nov 16 – Agenda (World Economic Forum) – How cities can overcome their growing transport pains – Alexander Wachtmeister

Nov 13 – BBC – Fact-checking the US and China on climate and environment – Wanyuan Song, Pratik Jakhar, Upasana Bhat, Shruti Menon

Nov 7 – More or Less: Behind the Stats – How deadly is Covid 19? – Tim Harford

Nov 2 – Agenda (World Economic Forum) – We’re living longer – but how can we ensure we stay healthy, too? – Kazumi Nishikawa

Nov 1 – PoliticsHome – Anneliese Dodds: “The test and trace failure isn’t just costing lives – it’s costing our economy too” – Frances O’Grady, Seema Malhotra MP, Stella Creasy MP, Rob Halfon MP

Oct 23 – Nature – Modeling COVID-19 scenarios for the United States – Ryan M. Barber, James K. Collins, Peng Zheng, Christopher Adolph

Oct 22 – El Pais – El exceso de muertes desde julio supera los 13.000 fallecidos – Borja Andrino, Daniele Grasso, Kiko Llaneras

Oct 18 – FT – Covid-19: The global crisis — in data – FT Visual & Data Journalism team

Oct 14 – Unherd – The German coronavirus mystery – Tom Chivers

Oct 1 – Nature – Challenges of access to kidney care for children in low-resource settings – Mignon McCulloch, Valerie A. Luyckx, Simon J. Davies, Brett Cullis

Sep 17 – cited in the book How to Make the World Add Up (titled The Data Detective in North America) by Tim Harford

Sep 15 – Towards Data Science – 5 Steps to Develop Unique Data Science Project Ideas – Julia Nikulski

Sep 9 – Medium – 2020, hindsight – Dr. Nechama Brodie

Sep 3 – The Lancet – Make it new: reformism and British public health – Claas Kirchhelle, Gordon Dougan

Sep 2 – Indian Express – Not prepared for the worst – Deepankar Basu

Aug 31 – cited in the book Ten Global Trends Every Smart Person Should Know by Ronald A. Bailey and Marian L. Tupy

Aug 30 – Geneva Solutions – Why enabling all children to flourish helps tackle the climate crisis – Bruno Jochum, Malka Gouzer, Florent Hiard

Jul 18 – Business Recorder – The curious case of reduced testing in Pakistan – Safia Mahmood

Jul 6 – BBC – Coronavirus: How fast is it spreading in Africa? – Peter Mwai, Christopher Giles

Jun 28 – Daily Mail – Global coronavirus death toll passes half a million – Marlene Lenthang, Sam Blanchard

Jun 24 – Vegan Food & Living – The (un)meaty business of the vegan meat industry – Sara Colohan

Jun 19 – Nature – COVID-19 pandemic reveals the peril of ignoring metadata standards – Neil Davies, Emiley A. Eloe-Fadrosh, Robert D. Finn, Philip Hugenholtz

Jun 15 – Nature – How behavioural sciences can promote truth, autonomy and democratic discourse online – Philipp Lorenz-Spreen, Stephan Lewandowsky, Cass R. Sunstein, Ralph Hertwig

Jun 3 – The Spectator – Newsnight’s dodgy coronavirus data – Steerpike

May 6 – The Lancet – COVID-19 on the African continent – Chad R Wells, Jason K Stearns, Pascal Lutumba, Alison P Galvani

May 3 – Medium – El Martillazo y el Huayno – Rhea Moutafis, Gillian Sisley

April 29 – Business Insider – Why Mike Pence didn’t wear a mask at Mayo Clinic coronavirus visit – Henry Blodget, David Plotz

April 29 – Agenda (Word Economic Forum) – The pandemic is just another sign of our broken food system – Chandran Nair

April 27 – The Lancet – Estimation of COVID-19 burden in Egypt – Khaled Elmeleegy

April 26 – Agenda (Word Economic Forum) – COVID-19: What you need to know about coronavirus on 26 April – Briony Harris

April 22 – Reuters – Vietnam to ease nationwide coronavirus lockdown – James Pearson, Phuong Nguyen

April 17 – The Lancet – Hydroxychloroquine prophylaxis for COVID-19 contacts in India – Sahaj Rathi, Pranav Ish, Ashwini Kalantri, Shriprakash Kalantri

April 12 – The Observer – Coronavirus statistics: what can we trust and what should we ignore? – Sylvia Richardson, David Spiegelhalter

April 10 – The Times of India – When will countries see Covid-19 deaths peak? – Kenneth Mohanty, Anjishnu Das

April 9 – Bloomberg News – Global Food Exports Get Paralyzed by Growing Problems for Ports – Jen Skerritt, Leslie Patton, Emele Onu

April 8 – Nature – Humanity tested – Editorial

April 6 – Bloomberg News – Key Food Prices Are Surging After Virus Upends Supply Chains – Agnieszka de Sousa, Ruth Olurounbi, Pratik Parija

April 6 – Frankfurter Allgemeine Zeitung – Hanks Welt: Beten hilft nicht mehr – Rainer Hank

April 4 – Australian Broadcasting Corporation (ABC) News – Mapping the lockdown effect: How coronavirus turned cities into ghost towns – Inga Ting, Alex Palmer, Stephen Hutcheon

April 4 – voxeu.org – Data needs for shutdown policy – James Stock

April 3 – 80000hours.org – Good news about COVID-19 – Robert Wiblin

April 1 – Agenda (Word Economic Forum) – To test or not to test? Two experts explain COVID-19 testing – Gayle Markovitz

March 28 – The Times of India – Why Covid-19 testing has been slow to take off – Anjishnu Das

March 27 – Forbes – School Closures – Let’s Talk Stem For Parents – Shaheena Janjuha-Jivraj

March 27 – Bloomberg Opinion – Want to Escape a Lockdown? Try Sweden – Lionel Laurent

March 25 – Washington Times – Scores of data released on coronavirus – Rowan Scarborough

March 25 – The Conversation – How to model a pandemic – Christian Yates

March 24 – Agenda (Word Economic Forum) – A virology expert answers key questions on COVID-19 – Maria Epifanova

March 20 – WIRED Magazine – How Fast Does a Virus Spread? Let’s Do the Math – Rhett Allain

March 18 – Times of Israel – Are governments overreacting? – Rachel M. Roth

March 17 – The Conversation – Coronavirus in Japan: why is the infection rate relatively low? – Hiroaki Richard Watanabe

March 15 – Psychology Today – DeepMind Uses AI to Help Fight COVID-19 – Cami Rosso

March 13 – The New York Times – The Exponential Power of Now – Siobhan Roberts

March 5 – Nature – Social media, nature, and life satisfaction: global evidence of the biophilia hypothesis – Xiao Ping Song, Daniel R. Richards, L. Roman Carrasco, Chia-chen Chang

March 2 – The Telegraph (India) – terrific panic, unthinking nonchalance – Samantak Das

February 28 – Agenda (Word Economic Forum) – How to bring electricity to 600 million Africans – Andrew Herscowitz

February 21 – Nature – SelectEarly-onset colorectal cancer: initial clues and current views – James R. Hebert, Hexin Chen, Lorne J. Hofseth, Anindya Chanda

February 19 – The San Francisco Examiner – Is plant-based food always the best choice? – Robyn Purchia

February 18 – Bloomberg Opinion – Put a Stop to Economic Growth? Huge Mistake – Noah Smith

February 13 – The Washington Post – Opinion | Bernie Sanders’s magical thinking on climate change – David Byler, Fareed Zakaria

February 10 – Psychology Today – The Monster Within: How We Feed the Appetite of Anxiety – Jamie Cannon MS

February 4 – business.financialpost.com – Banish ‘Eat Local’ From Your Environmental Playbook – Akshat Rathi

February 4 – Agenda (Word Economic Forum) – How city living could be making you anxious – and how to deal with it – Andrea Mechelli

January 20 – Agenda (Word Economic Forum) – China has announced plans to cut single-use plastics – Joe Myers

January 19 – Agenda (Word Economic Forum) – 7 ways to make the workplace better for our mental health – Nancy Brown

January 17 – Agenda (Word Economic Forum) – Davos 2020 is offering sustainable meal choices for guests – Lisa Sweet

January 14 – Agenda (Word Economic Forum) – How can we better align climate and trade? – John Denton

January 7 – Agenda (Word Economic Forum) – The biggest emitters could be held to account by accountants – Zubair Abid Arain

Coverage in 2019

December 28 – The New York Times – Opinion | This Has Been the Best Year Ever – Nicholas Kristof

December 26 – Forbes – Trade In The Age Of Terrorism – Pushkar Mukewar

December 16 – The Wall Street Journal – Opinion | The 2010s Have Been Amazing – Johan Norberg

December 3 – The Conversation – How can we actually create happy societies? – Sam Wren-Lewis

November 7 – The Conversation – Even the most beautiful maps can be misleading – Samuel Langton

November 6 – The Washington Post – Opinion | The Berlin Wall fell 30 years ago. Its shadow looms large. – Stefan Kornelius, Christian Caryl, Emily Tamkin, Brian Klaas

October 30 – Future Farming – Fertiliser feeds half of the world’s population – David Nabhan

October 16 – Nature – Mapping 123 million neonatal, infant and child deaths between 2000 and 2017 – Roy Burstein, Nathaniel J. Henry, Michael L. Collison, Laurie B. Marczak

October 15 – cited in the book How Charts Lie: Getting Smarter about Visual Information by Alberto Cairo

October 14 – Econtalk.org – Andrew McAfee on More from Less – Russ Roberts

October 10 – BBC – Climate change and meat – what’s the beef? – The Briefing Room

October 8 – Project Syndicate – Why We Need More Economists – Roger E.A. Farmer

September 30 – Medical News Today – Brain scans could help predict whether antidepressants will work – Catharine Paddock Ph.D

September 3 – Prospect Magazine – Prospect world’s top thinkers, 2019: the top ten – Sameer Rahim

June 28 – Axios – Axios World – July 1, 2019 – Axios – Shane Savitsky

June 5 – weforum.org – 5 ways to #BeatAirPollution – Emma Charlton

May 15 – The Conversation – Inégalités face au changement climatique : la balle est dans le camp des plus riches – Anda David, Étienne Espagne, Nicolas Longuet Marx

May 7 – Psychology Today – How to Be More Positive – Gustavo Razzetti

Apr 29 – The Wall Street Journal – Why Women Live Longer Than Men – Heidi Mitchell

Feb 18 – Scientific American – Revolt against the Rich – John Horgan

Feb 4 – BBC (Good Morning Wales) – Which Countries Eat the Most Meat? (1:54:00)

Feb 4 – The New York Times – Giant Strides in World Health, but It Could Be So Much Better – Austin Frakt and Aaron E. Carroll

Jan 22 – Forbes Slovakia – Nebuďte takí pesimisti. Naša planéta sa nerúti do záhuby, tu je 16 dôkazov (Don’t be so pessimistic. Our planet is not in ruin, there are 16 proofs) – Lucia Vanková

Jan 16 – CNN en Español – El proyecto digital “Nuestro Mundo en Datos” trae una nueva perspectiva sobre los grandes cambios globales para la humanidad (“The digital project Our World in Data brings a new perspective on the great global changes for humanity”)

Jan 14 – Breakthrough Dialogues – Zooming Out: Big Picture Data with Hannah Ritchie – by Alex Trembath

Jan 11 – El Universal – ¿Combatir el cambio climático?: Sí. ¿Generar más empleos?: También (Combat climate change? Yes. Generate more jobs? Also – Sandra Herrera López

Jan 8 – Financial Times – Household food spending divides the world – Michael Kavanagh

Jan 7 – FOCUS Online – US-Journalist: Darum war 2018 das beste Jahr der Menschheitsgeschichte (US Journalist: That’s why 2018 was the best year in human history)

Jan 5 – The New York Times – Why 2018 Was the Best Year in Human History – Nicholas Kristof

Coverage in 2018

Dec 31 – Le Monde – Steven Pinker : “Notre pessimisme nous conduit à croire que tout effort pour améliorer le monde est une perte de temps” (“Our pessimism leads us to believe that any effort to improve the world is a waste of time”) – Marc-Olivier Bherer

Dec 24 – n-tv – Und die Welt wird doch besser (And the world is getting better) – Von Gudula Hörr

Dec 24 – Volksstimme – So schlimm ist es gar nicht (It’s not that bad) – Alois Kösters

Dec 18 – The Conversation – The glass is more than half full – Robert Stavins

Dec 8 (undated, hence the date of access) – Tutor 2 U – Absolute and Relative Poverty – Geoff Riley

Dec 4 – Ritholtz – Urbanization Over the Past 500 Years – Barry Ritholtz

Dec 3 – cited in Journal of Geriatric Oncology – Global geriatric oncology: One size does not fit all – Enrique Soto-Perez-de-Celis

Nov 30 – Barrons – A Toast to J.D. Wetherspoon – Simon Constable

Nov 28 – Videnskab – Se, hvilken massiv gavn vacciner har gjort indtil videre (See what massive benefit vaccines have produced so far) – Thomas Hoffmann

Nov 23 – World Economic Forum – Is income inequality rising around the world? – Joe Hasell

Nov 23 – Psychology today – How the World is Becoming Better – Iddo Landau

Nov 13 – Index – Puljak objasnio zašto bismo se više trebali bojati čokolade nego terorista (Puljak explained why we should be more afraid of chocolate than terrorists) – Nenad Jarić Dauenhauer

Nov 7 – Corriere – Il declino italiano si legge nei libri (The Italian decline can be read in books) – Danilo Taino

Oct 29 – Initiative for Free Trade – The Secret of Eternal Growth – Michael Liebreich

Oct 29 – cited in Peterson Institute for International Economics – What is Globalization?

Oct 4 – Indian Express – Suicide kills more people than war every year: Survey – by their Lifestyle Desk

Oct 1 – cited in 80,000 hours career guide on Academic Research by Jess Whittlestone

Sep 30 – Gottesman Libraries, Columbia University – Find Data Visualizations and Sources Through Our World in Data – Ryan Allen

Sep 27 – cited in the book Social Progress in Britain by Anthony F. Heath

Sep 25 – Theory and practice – Находка T&P: карта, где территория стран соответствует числу жителей (map, where the territory of the country corresponds to the number of inhabitants)

Sep 17 – Vanguardia – Pablo y los pobres (Pablo and the poor) – by José De Nigris Felán

Aug 15 – L’Obs – Nous vivons la meilleure période de toute l’histoire de l’humanité (We are living in the best period of human history) – Clément Lacombe & Rémi Noyon

Aug 9 – Irish Times – How to combat climate change – Rob Sadlier

Aug 2 – Handelszeitung – Wo die Steuerlast besonders hoch ist – and wo tief (Where the tax burden is especially high – and where low) – Editorial team

Jul 29 – Kurier – Warum wir uns trotz guter Nachrichten fürchten (Why we are scared despite good news) – Ute Brühl

Jul 23 – The New Yorker – Are Things Getting Better or Worse? – by Joshua Rothman

Jul 21 – HN Online – Nevhodné pre pesimistov: Päť dôvodov, ktoré vás presvedčia, že svet nemieri do záhuby (Unsuitable for pessimists: Five reasons to convince you that the world is not destroying itself) – by Stanislava Luppová

Jul 10 – Vlogbrothers (Youtube) – The General Crisis – by John Green

Jun 30 – Huffington Post – “Verstand verloren”: Ökonom zerlegt mit einem Tweet die “Spiegel”-Titelgeschichte (Out of their mind: Economist destroys the Spiegel’s title story with a tweet) – by editorial team

Jun 28 – People’s Pundit Daily – The Western World’s Most Depressing Chart – by Daniel Mitchell

Jun 24 – Slate Star Codex – Book Review: Capital in the Twenty-First Century – by Scott Alexander

Jun 17 – El País – Entrevista Steven Pinker: “Los populistas están en el lado oscuro de la historia (The populists are on the dark side of history) – by Jan Martínez Ahrens

Jun 13 – cited in the British Medical Journal (BMJ) Hunger and malnutrition in the 21st century – by Patrick Webb, Gunhild Anker Stordalen, Sudhvir Sing, Ramani Wijesinha-Bettoni, Prakash Shetty, and Anna Lartey

Jun 11 – cited in the RAPID project Why industrialisation – by Stefan Dercon and Nicolas Lippolis

Jun – cited in Nicholas Stern’s Fulbright Legacy Lecture ‘The best of centuries or the worst of centuries: Leadership, governance and cohesion in an interdependent world.

May 28 – Index – Jesu li jugonostalgičari u pravu? Je li prije zaista bilo bolje? (Are the Southeast Asians right? Was it really better before?) – by Nenad Jarić Dauenhauer

May 23 – El País – Los números explican el mundo – by Kiko Llaneras

May 15 – Vlogbrothers (Youtube) – Seven Maps to Better Understand The World – by John Green

Apr 30 – Royal Statistics Society – Risk, statistics and the media: David Spiegelhalter’s IPSO lecture – by David Spiegelhalter

Apr 24 – Forskning – Nordmenn best i verden på å slappe av (Norwegians are best in the world to relax) – by Bård Amundsen

Apr 21 (discovery date as article undated) – WeekendSwitzer – 5 ways women are remaking the global economy – by Fi Bendall

Apr 16 – El Gato y La Caja – Pescado podrido (Rotten fish) – by Guadalupe Nogués

Apr 15 – Der Spiegel – Die Welt wird besser – es will nur kaum jemand glauben (The world is getting better – but hardly anyone wants to believe it) – by Christian Stöcker

Apr 13 – Financial Times – FT Health: Why ‘sin taxes’ are good economics – by Darren Dodd and Andrew Jack

Apr 13 – Pathways for Prosperity – Can machine learning predict poverty? – by Sophie Ochmann

Apr 11 – NASA Earth Observatory – Finding New Ways to Feed the World – by Adam Voiland

Apr 9 – Financial Times – Japan’s economic miracle – by Dan McCrum

Apr 9 – Frankfurter Allgemeine Zeitung – Die Welt wird immer besser (The world is getting better and better) – by Hans Rolling

Apr 5 – Morbidity, Peak Child, And Collective Pessimism – NPR podcast The Indicator interviews Hannah Ritchie and Max Roser of Our World in Data

Apr 5 – NPR Planet Money – Morbidity, Peak Child and Collective Pessimism – by Stacey Vanek Smith and Cardiff Garcia

Apr 3 – cited in the book Factfulness by Hans Rosling, Ola Rosling, and Anna Rosling Rönnlund

Mar 28 – Il Post (Italy) – Una vecchia alla finestra (An old woman at the window) – by Giacomo Papi

Mar 27 – Breakthrough Institute – Organic or conventional farming? Wrong question – by Hannah Ritchie of Our World in Data

Mar 20 – Info Data (Italy) – Scopri la tecnologia che è entrata più velocemente nelle nostre case (Discover the technology that has entered fastest in our homes) – by Luca Tremolada

Mar 17 – Spiegel Online (Germany) – Angst: Das It-Girl unter den Gefühlen (Fear: The It-girl among all feelings) – by Sibylle Berg

Mar 17 – Origo (Hungary) – Elképesztő ábrák, amelyek bemutatják a világ fejlődését (Impressive figures that show the evolution of the world) – by Origo

Mar 16 – Bloomberg – The Population Bomb Has Been Defused – by Noah Smith

Mar 15 – Project Syndicate – A Trade War On the World’s Poorest – by Bjørn Lomborg

Mar 9 – New York Magazine – The World Is Better Than Ever. Why Are We Miserable? – by Andrew Sullivan

Mar 8 – coreecon – Learning by “Doing Economics” – by Tim Phillips

Mar 2 – Financial Times – Orphan diseases move into the spotlight – by Andrew Jack and Darren Dodd

Feb – Mega Online – Power to the people – by MEGA

Feb 28 – El confidencial (Spain) – Los países nórdicos se desarrollaron antes del Estado de bienestar (The Nordic countries developed before the welfare state) – by Juan Ramón Rallo

Feb 22 – Gazeta do Povo (Brazil) – O segredo dos países mais honestos do mundo (The secret of the most honest country in the world) – by Tiago Cordeiro

Feb 22 – Folha de S.Paulo (Brazil) – Do que morre o mundo (What the world dies of) – by Roberto Lameirinhas

Feb 21 – P Magazine (Belgium) – Hieraan sterven belgen het vaakst (This is what Belgians die of most frequently) – by P-Magazine

Feb 16 – cited in the Nature book review of Steven Pinker’s Enlightenment Now: The limitations of Steven Pinker’s optimism – Ian Goldin

Feb 14 – Visual Capitalist – The Rising Speed of Technological Adoption – by Jeff Desjardins

Feb 13 – Gates notes – 10 tough questions we get asked – The annual Gates Letter by Bill and Melinda Gates

Feb 13 – Republik (Switzerland) – Warum so pessimistisch? – by Olivia Kühni

Feb 12 – cited in the book The Routledge Companion to Business Ethics by Eugene Heath, Byron Kaldis, Alexei Marcoux

Feb 09 – Wall Street Journal – The Enlightenment is working – Steven Pinker

Feb 08 – Bank of Canada – At the Crossroads: Innovation and Inclusive Growth – by Carolyn A. Wilkins

Feb 01 – Datawrapper – Our world in the long term – by Lisa Charlotte Rost

Feb 1 – The Quarterly Journal of Economics – The Global Distribution of Economic Activity: Nature, History, and the Role of Trade by J Vernon Henderson, Tim Squires, Adam Storeygard, and David Weil

Jan 28 – El Comercio – Más ricos significa más desigualdad? – by César Augusto Sosa

Jan 23 – cited in the book Journal of Moral Theology by Mary Doyle Roche

Jan 18 – Uses This – Uses This: Jaiden Mispy – Interview with the developer in our team

Jan 12 – Chicago Tribune – Was 2017 ‘The Best Year in Human History’? – by Editorial Board

Jan 06 – The New York Times – Why 2017 Was the Best Year in Human History – by Nicholas Kristof

Jan 04 – Le Monde – 2018, année optimiste? Enquête sur les raisons de se réjouir (2018, an optimistic year? Investigation reasons to rejoice) – Fréderic Joignot

Coverage in 2017

Dec 31 – El Mundo – Todo va bien menos la política – by Arcadi Espada

Dec 29 – NPR Planet Money – The 50-Year Newspaper – by Stacey Vanek Smith and Cardiff Garcia

Dec 19 – Die ZEIT – Spenden: Daten gegen Armut – by Johannes Haushofer, Dina Pomeranz, Max Roser und Frank Schilbach

Dec 19 – Marginal Revolution – Political incorrect paper of the day: Food deserts – by Alex Tabarrok

Dec 18 – World Bank – Year in Review: 2017 in 12 Charts – by Donna Barne and Tariq Khokhar

Dec 15 – Frankfurter Allgemeine Zeitung (FAZ) – Arm und Reich nähern sich an

Dec 12 – Vlogbrothers (Youtube) – 2017 is the Best? – by John Green

Nov 28 – The conversation – Peut-on encore croire au progrès social? – by Göran Therborn and Marc Fleurbaey

Nov 28 – Tagesanzeiger – Die Reichen waren auch schon reicher – by Ralph Pöhner

Nov 23 – Reuters Breaking views – Be thankful for good economic news – by Edward Hadas

Nov 23 – Global Carbon Project Startpage – Visualizations

Nov 23 – Neue Zürcher Zeitung – Wie sich Essgewohnheiten weltweit verändern – by Alexandra Kohler

Nov 21 – Inverse – How Much Do Humans Eat? – by Sarah Sloat

Nov 14 – cited in the book An Introduction to Global Health Delivery by Joia S. Mukherjee

Nov 09 – Latin American Post – New Technology: Neither Feared Nor Trusted – by Noah Smith

Nov 09 – Lindau Nobel Laureate Meetings – Only as Strong as the Weakest Link: Global Food Supply Chains – by Howard-Yana Shapiro

Oct 30 – Deutscher Arbeitgeber Verband – Homo sapiens: Sündenfall oder Hoffnung? – by Martin Schlumpf

Oct 23 – El Mundo – Steven Pinker: “Los progresistas detestan el progreso” – by Cayetana Alvarez De Toledo

Oct 10 – cited in the book “What’s The Future and Why It’s Up To Us” by Tim O’Reilly

Sept 21 – Cause and Effect – Invisible effects, invisible causes – by Felix Salmon

Sept 15 – World Economic Forum – After 35 years with the UN, here’s what I learned – by Yoriko Yasukawa

Sept 12 – World Economic Forum – In 10 years, the world may not be able to feed itself – by Abdi Latif Dahir

Sept 09 – Gazeta Do Povo (Brazil) – O QI brasileiro pode estar diminuindo, e a culpa é da escola – by Gabriel de Arruda Castro

Sept 08 – Financial Times (UK) – FT Health: Stem cells — pushing the boundaries – by Darren Dodd

Aug 31 – The New York Times – Hurricanes, Climate and the Capitalist Offset – by Bret Stephens

Aug 31 – Marginal Revolution – Is storm damage getting worse? – by Tyler Cowen

Aug 28 – Tygodnik Powszechny (Poland) – ŻYJEMY W NAJLEPSZYCH CZASACH – by Marcin Napiorkowski

Aug – Graziadio Business Review – The Great Escape from Global Poverty – by Walker Wright

Aug – cited in the book “Grundforløbet i engelsk” by Mette Grønvold, Hanne Ohland-Andersen

July 25 – The Big Picture (rithotz.com) – How Many Deaths Are Newsworthy…? – by Barry Ritholtz

July 24 – Marginal Revolution – Newsworthy Deaths – by Alex Tabarrok

July 23 – Quilette – Some Countries Are Much Richer Than Others. Is That Unjust? – by Jonny Anomaly, Hrishikesh Joshi

July 13 – The Conversation – What’s behind the sudden rise in measles deaths in Europe? – by Anita Milicic, Samantha Vanderslott, Sarah Loving

July – Alumniportal (Germany) – Economist Max Roser: “We overstate the negative” – by Friederike Bauer

June 30 – Konrad Adenauer Stiftung (Germany) – Zuversichtlich in die Zukunft – aber nicht zurücklehnen! – by Stefan Stahlberg

June 15 – El Economista – ¿Ahora la gente tiene más en común con los CEO que antes? – by Ruy Alonso Rebolledo

June 13 – Visão (Portugal) – O Mundo está hoje muito melhor – by Adolfo Mesquita Nunes

June – SPAG – Is the World Going Down the Gurgler? (pg 32) – by Vicki Nunn

June – Proceedings of the National Academy of Sciences of the United States of America – Future of fundamental discovery in US biomedical research – by Michael Levitt & Jonathan M. Levitt

June – Journal of the American College of Cardiology – Changing Demographics: A New Approach to Global Health Care Due to the Aging Population – by Valentin Fuster

May 24 – CTXT (Spain) – Un futuro para las mayorías – by Ivan Krastev

May 11 – Frankfurter Allgemeine – Wie gut es uns geht! – by Marc Felix Serrao

May 01 – The Independent (UK) – Despite Scepticism, Europe Has High Vaccination Rates – But It Shouldn’t be Complacent – by Samantha Vanderslott

May – Anuario Internacional CIDOB – Buenas Noticias: En el Mundo Hay Menos Pobreza Que Munca (III) – by Esteban Ortiz-Ospina

Apr 21 – Frankfurter Allgemeine (Germany) – Besser als gedacht – by Patrick Bernau

Apr 21 – Science – Ecosystem management as a wicked problem – Ruth DeFries & Harini Nagendra

Apr 12 – Freakonomics Radio – Earth 2.0: What Would Our Economy Look Like? – Stephen J. Dubner

Mar 15 – Conversable Economist – US Health Care: The Case For Going Upstream – by Timothy Taylor

Mar 15 – La Nación – La globalización no tiene la culpa – by Juan J. Llach

Feb – The Foreign Service Journal by the American Foreign Service Association – Site of the month: Our WorldInData.org

Feb 23 – Future World Foundation – Global Extreme Poverty

Feb 22 – Cash (Switzerland) – Zehn Gründe, die Welt positiver zu sehen – by Pascal Züger

Feb 22 – the Sun Daily (Malaysia) – Trump’s trade challenge and opportunity – by Tan Siok Choo

Feb 8 – Adam Smith Institute (UK) – Sadly, Hans Rosling Has Died – by Tim Worstall

Feb 6 — TEDx Talks — With data, the future is different — by Wali Zahid

Feb 3 – Finanz und Wirtschaft (Switzerland) – Doch, doch, die Welt wird besser – by Mark Dittli

Feb – Schweizer Monat (Germany) – Der Kartograph von Oxford – by Olivia Kühni, Max Roser

Jan 28 – Tichys Einblick (Germany) – Von Malthus zum Ökologismus: Pessimismus – by Von Peter Heller

Jan 27 – Libre Mercado (Spain) – Cinco gráficos para el Papa – by Manuel Llamas

Jan 21 – Economia Online (Portugal) – O mundo (e Portugal) está assim tão mal? – by Marta Santos Silva

Jan 18 – Bl.ocks – War Deaths in the World Each Year – by David Curran

Jan 17 – Bl.ocks – World Births per Woman Test – by David Curran

Jan 17 – Financial Times (UK) – The problem with US healthcare in one chart by Federica Cocco

Jan 17 – Financial Times (UK) – The problem with US healthcare in one chart – by Federica Cocco

Jan 17 – akzente (Germany) – ‘We Overstate the Negative’ (interview with Max Roser)

Jan 16 – Semana Económica (Peru) – Perspectivas y optimismo – by Oswaldo Molina

Jan 6 – StarTribune (US) – Something’s right in the world today – by D.J. Tice

RAND Corporation – Strategic Choices For a Turbulent World – Hoehn, Solomon, Efron et al

ZukunftsInstitut (Germany) – Zukunftsreport 2017 – by Matthias Horx

Coverage in 2016

Dec 31 – Bloomberg View – Here Comes the Cleanup Crew for 2016 – by Barry Ritholtz

Dec 30 – The Washington Post (US) – Why 2016 was actually one of the best years on record – by Annie Duflo & Jeffrey Mosenkis

Dec 30 – The Washington Post (US) – The best work on political economy in 2016 – by Daniel Drezner

Dec 30 – der Freitag (Germany) – „99 Prozent können irren“ (interview with Max Roser) – by Christine Käppeler

Dec 30 – El País (Spain) – Las paradojas del progreso: datos para el optimismo – by Kiko Llaneras & Nacho Carretero

Dec 30 – Radio SRF (Switzerland) – Vieles wird besser (interview with Max Roser) – by Thomas Häusler

Dec 29 – NRC.nl (The Netherlands) – De wereld wordt steeds beter. Kijk maar naar de data – by Wouter van DijkeLen Maessen

Dec 28 – The Huffington Post (Germany) – Ihr glaubt, 2016 war ein beschissenes Jahr… dann lest das hier – by Julius Zimmer

Dec 27 – Business Insider (Nordic) – 14 charts that should restore your faith in humanity – by Chris Weller

Dec 27 – Valencia Plaza (Spain) – Merry Christmas… and Happy New Year? – Regina Laguna

Dec 26 – KiwiBlog (New Zealand) – World poverty has fallen by 130,000 since yesterday! – by David Farrar

Dec 25 – NZZ.at (Austria) – Die Welt ist besser als ihr Ruf – by Lukas Sustala

Dec 24 – bento (Germany) – Nein, die Welt wird nicht immer schlimmer – by Marc Röhlig

Dec 24 – Toska.com.mk (Macedonia) – График на денот: Како се менувало светското богатство во последните 2 века?

Dec 16 – Bloomberg View – Stereotypes Are Poisoning American Politics – by William R. Easterly

Dec 9 – El País (Spain) – Así se multiplicó la educación en España – by Kiko Llaneras

Nov 11 – Visual Capitalist (Canada) – Chart: The End of World Poverty is in Sight – by Jeff Desjardins

Nov 9 – Scientific American (USA) – Yes, Trump Is Scary, but Don’t Lose Faith in Progress – by John Horgan

Nov 7 – Global Nomadic – The World is More Full of Goodness Than We Think – by Elaina Giolando

Nov 5 – Renaissance for Leaders – Soft Power vs Hard Power – by Andrew John Harrison

Nov – Scientific American (USA) – Why Political Pessimism Trumps Optimism – by Michael Shermer

Nov 1 – Singularity University via Medium (US) – Why the World Is Better Than You Think in 10 Powerful Charts – by Peter Diamandis

Oct – Anthropocene Magazine – An Anthropocene Journey – By Andrew C. Revkin

Oct 31 – American Enterprise Institute (US) – What does America owe Americans vs. non-Americans? – by James Pethokoukis

Oct 30 – VOzes Mórmons (Brazil) – Vivemos nos Últimos Dias?

Oct 26 – Foundation for Economic Education (US) – We See the Glass as Half Empty, Yet Our Cup Is Overflowing – by Marian L. Tupy

Oct 18 – Ökonomenstimme (Germany) – Antworten an den Club of Rome – by Hans-Jörg Naumer

Oct 16 – FAZ – Viel BIP, langes Leben Grafik

Oct – “ourworldindata: an R data package” – by Simon Jackson (description of a software package)

September 23 – Weser Kurier (Germany) – ‘Frohe Botschaft’ – Article about Our World in Data by Martin Wein

September – Onyx Magazin (Germany) – Lichtblicke im Kampf gegen die Armut

22 September 2016 – The New York Times – The Best News You Don’t Know – by Nicholas Kristof

September 2016 – Resolution Foundation – Examining an Elephant – by Adam Corlett

August 15 – Het gaat ondanks alles ook gewoon heel goed met de wereld (translation of the article by Amy Robinson Sterling)

August 16 – Marginal Revolution – Regulation and Distrust–The Ominous Update – by Alex Tabarrok

August 7 – NZZ – Unsere Welt kollabiert nicht, ganz im Gegenteil – by Professor Monika Bütler

Aug 02 – cited in the book “What’s Really Happening to Our Planet” by Tony Juniper

July 23 – Kurier (Austria) – Das Ende extremer Armut wird greifbar (with nicely embedded visualisations from Our World in Data)

July 22 – Berner Zeitung and other swiss newspapers (Switzerland) – Es hat wieder gepiepst

July 22 – La Nación (Argentina) – El que no arriesga no gana

July 18 – Huffington Post – Volver al 77? (Going back to 77?)

July 12, 2016 – Die Welt wird immer besser – longer portrait of Our World in Data and Max Roser in the German daily Süddeutsche Zeitung by Björn Finke

July 7 – Hürriyet Daily News (Turkey) – Europe’s troubled future

July 3 – American Council on Science and Health – Only 6% of Americans Think the World is Getting Better

June 21 – Bloomberg View – There’s Great News on Inequality and Poverty – by Noah Smith

27 May – World Economic Forum – US and European success was built on free trade. It must be defended – by Carl Bildt (former Prime Minister of Sweden)

Series with New York University’s Development Research Institute (for example here)

20 May – Vox.com – 100 years of global aging – by Zack Beauchamp

19 April – Washington Post (USA) and the Independent (UK) – What men and women wanted in a spouse in 1939 — and how different it is today

9 April – O futuro das coisas (Brazil) – Projeções para a educação até 2050

2016 – Book – Alberto Cairo – The Truthful Art

15 March – Huffington Post (Germany) – ’Teil einer besseren Zukunft: Darum brauchen wir einen kämpferischen Optimismus’ (Part of a better future: This is why we need optimism)

7 March – FAZ – Fazit – das Wirtschaftsboom – Sind die Reichen wirklich so reich? – Austausch zwischen Patrick Bernau und Oxfam

15 December 2015 – World Economic Forum – Why we need to reconnect with the sun

31 January – FAZ – Weltbevölkerung: Die Grenzen des Wachstums (world population: the limits to growth)

14 Jan – Publik-Forum (Germany) – Ohne Hoffnung keine Zukunft – by Bettina Röder, Barbara Tambour

3 Jan – Wortgebrauch (Germany) – Optimismus benötigt Verstand und Mut (‘Optimism requires reason and courage’)

2 Jan – Il Foglio (Italy) – Toh! Non siamo mai stati così “ricchi” (“We have never been so rich”) –

1 Jan – Spiegel (Germany) – Frohe Botschaft – long portrait of Max Roser and Our World in Data

1 Jan – Hannoversche Allgemeine Zeitung (Germany) – Die Zukunft ist rosig – Alles wird gut! – Interview with Max Roser and article on Our World in Data

cited in the book “The Truthful Art book” by Alberto Cairo

cited in the book Society and the Environment by Michael Carolan

Coverage in 2015

29 Dec – Grist (USA) – Here’s the good news about Earth from this year – An article on the action against climate change by Katie Herzog

30 Dec – Frankfurter Allgemeine Zeitung (German) – Book Review of “Joseph Stiglitz: „Reich und Arm“” – Patrick Bernau

21 Dec Quartz (USA) – Quartz’s Chart of the Year™ for 2015 – by Matt Philipp (the chart on the decline of extreme poverty over the last 200 years was chosen as the Chart of the Year)

14 December – Hamburger Abendblatt (Germany) – Die Welt ist besser, als wir denken – by Thomas Frankenfeld

December – Neue Zürcher Zeitung (Switzerland) – Es wird besser – Portrait of Our World in Data & Interview of Max Roser by Peter Glaser

December 1 – The New York Times – Imagining a World Without Growth – by Eduardo Porter

26 November – Discover magazine – What I’m Thankful For: The Science and Technology Edition – by Corey S. Powell

23 November – New Yorker (USA) – Terrorism in the Age of Twitter – by John Cassidy

16 November – Zurich (Insurance company) – The way we work

15 November – The Guardian (UK) – The scientists with reasons to be cheerful – long portrait of Ola & Hans Rosling, Ruth deFries and Our World in Data by Ed Cumming

3 November – Inequality in Education: ‘Inequality over the last century…’

October 28, 2015 – The Atlantic – City Lab (USA) – Stop Complaining About Your ‘Long’ Work Week, in 2 Charts – by Eric Jaffe

October 27 – Bloomberg (USA) – Lowering World Poverty Depends on India – by Noah Smith

October 21 – (Greece) – Our World in Data: Η φτώχεια τα τελευταία 1000 χρόνια – Κάτι αλλάζει προς το καλύτερο διεθνώς (‘Our World in Data: Poverty in the last 1000 years – Something is changing for the better internationally’)

October 20 – American Enterprise Institute – Today is World Statistics Day 2015

October 6 – Libra Mercado (Spain) – La pobreza extrema sigue cayendo y podría ser historia en 2030 (‘Extreme poverty continues to fall and could be history in 2030’)

September – SAS Inflight magazine – Getting Better

September 28 – Foreign Policy – The SDGs Should Stand for Senseless, Dreamy, Garbled – By Bill Easterly

September 24 – Washington Post (USA) – 30 charts and maps that explain China today – by Ana Swanson

September 24 – Council for European Studies – Book review “Inequality: What Can Be Done?” by Stefan Thewissen

September 23 – BBC (UK) – Material from Our World in Data was used for “Don’t Panic, How to End Poverty” – a one-hour documentary film with Hans Rosling and produced by Wingspan Productions for This World on BBC2. All sources and material used in the documentary are listed here. The film is available here.

September 18 – Bloomberg (USA) – Africa Starts to Emerge – by Noah Smith

September 9 – CapX – Why child mortality is falling – by Zac Tate

September 7, 2015 – Xataka (Spain) – ’17 gráficos para enseñar a quien todavía no crea que el mundo va cada vez mejor’ (17 graphics to teach those who do not yet believe that the world is getting better)

September 7 – Austrian Economics Center (Austria) – Inequality, Poverty, “The Free Market” And Capitalism: The Story Of A Wonderful Success – By Giovanni Caccavello for the Austrian Economics Center

September 1 – Vice (Italy) – Stiamo vivendo il periodo più pacifico della storia degli esseri umani – Interview with Max Roser and coverage of the long-term picture of violence on Our World in Data by Federico Nejrotti

August 19 – Süddeutsche Zeitung (Germany) – ‘So schön wie früher wird’s nicht mehr – findet euch damit ab’ – by Armin Wolf

August 17 – Exame (Brazil) – Um gráfico para ficar otimista com a queda da pobreza global (A graph that makes you optimistic about the decline of global poverty’)

August 10 – Mediapool (Bulgaria) – Светът по-много критерии е в доста по-добро състояние, отколкото е бил преди (‘In many aspects the world is much better than before’)August 16 – Inside HigherEd – Pushing Back on the Collapse Meme

August 10 – Vesti (Bulgaria) – Смятате, че светът е по-ужасен от всякога? Помислете пак (‘You think the world is more terrible than ever? Think again’)

August 7 – Deutschland Radio (Germany) – Heute ist alles besser als früher

Aug 4 – Süddeutsche Zeitung (German) – “Die Menschheit war früher viel gewalttätiger.” (Humanity was more violent in the past)

July 8 – Data Stories (Podcast on data visualisation) – #57: Visualizing Human Development w/ Max Roser

July 3 – Huffington Post (France) – Grèce: ces économistes dénoncent les politiques d’austérité qui ont viré cauchemar – Greece: these economists denounce the austerity policies that have turned into a nightmare”

July 1 2015 – Süddeutsche Zeitung (Germany) – Kurven des Kapitalismus

June 30 – Vox (USA) – In defense of the Eurozone – by Zack Beauchamp

June 25 – Magnet (Spain) – Este gráfico cuenta cuánta gente ha muerto en guerras desde 1400 (‘This graphic shows how many people have died in wars since 1400’)

June 8 – Strategy and Business (USA) – The Data-Driven Optimist – Longer portrait of Our World in Data and Max Roser

June 8 – Sputniks (Portugal) – Este vídeo irá mostrar por que tudo que você pensa sobre o capitalismo está errado (‘This video will show why everything you think about capitalism is wrong’)

May 31 – Bild am Sonntag (‘Germany’) – Von wegen früher war alles besser… (Interview passages with Max Roser on the content of Our World in Data. In print edition of the newspaper.)

May 21 – Chicago Tribune (USA) – GOP candidates want you to be terrified – By Rex W. Huppke (behind pay wall)

May 19 – Rivista Studio (Italy) – Com’è cambiata l’Africa (in meglio) in tre grafici (‘How Africa has changed (for the better) in three graphs’)

May 9 – Bill Gates shares data from the Malaria data entry on his official Facebook page. Previously he used material from the life expectancy chart.

May 8 – RTS (Switzerland) – La pauvreté mondiale atteindrait son niveau le plus bas de l’Histoire (‘Global inequality reaches the lowest level in history’)

May 6 – News Monkey (Belgium) – Yep, we leven allemaal steeds langer: deze kaartjes tonen hoe snel dat gaat (‘Yes, we all live increasingly longer: these cards show how fast it goes’)

May – Folha de Sao Paulo (Brasil) – Reforma política do século 21 (‘’Political reform in the 21st century”)

April 16 – Slate (France) – La pauvreté n’a jamais été à un niveau aussi bas dans l’histoire (‘Poverty was never lower in history’) – by Eric Leser

April 15 – Express (Belgium) Indrukwekkende grafiek toont hoe het kapitalisme de armoede in de wereld op een historisch laag niveau bracht (‘Impressive graph shows how capitalism brought the poverty in the world at a historically low level’)

April 14 – Linke Zeitung and also in Griechenland Blog (Germany) – So wurden in Griechenland die Armen ärmer (‘This is how the poor in Greece got poorer’)

April 13 – Oxford University (UK) – Interview with Max Roser

April 10 – The Washington Post (USA) – Greece’s poor are back to where they were in 1980 – By Matt O’Brien

April 9 – The New York Times – Turning to Big, Big Data to See What Ails the World – By Tina Rosenberg

April 9 – Institute for New Economic Thinking (USA/UK) – Inequality or Living Standards: Which Matters More? By Max Roser, Brian Nolan, and Stefan Thewissen

April 5 – Svenska Dagbladet (Sweden) – Tro inte allt ni hör – fattigdomen minskar (‘Do not believe everything you hear – there is less poverty’)

March 29 – Capital of Statistics (China) – Let us talk about statistics

Jan 20 – Berlingske (Denmark) – Naturkatastrofer dræber stadig færre (‘Natural disasters kill fewer and fewer’)– Article by Bjorn Lomborg

Jan 20 – Journal Montreal (Canada) – Au niveau planétaire, les inégalités baissent! (‘Globally, the inequalities fall!’) – by Vincent Geloso

Jan 19 – Europa Press (Spain) – ‘Así ha mejorado el acceso a la educación en los últimos 200 años en todo el mundo’ (‘This is how much access to education has globally improved over the last 200 years’)

Jan 19 – Financial Times (UK) – Give the middle classes their fair share of the pie – by Izabella Kaminska

Jan 12 – Macro Business (Australia) – Chart of the Day – by Chris Becker

Jan – Edge Question of the Year 2016 – What do you consider the most interesting recent [scientific] news? what makes it important? by S Pinker – Steven Pinker answers Quantified Human Progress and talks about Our World in Data

Coverage in 2014

Nov 25 – TIME Magazine (USA) – ‘The Reason Every One of Us Should Be Thankful’ – By Michael Shermer

Nov 24 – Vox.com (USA) – ‘26 charts and maps to be thankful for‘ – by Dylan Matthews

Nov 12 – L’actualité (Canada) – ‘Nous sommes moins violents, plus tolérants, parfois désespérants‘ (”) – Longer coverage by Vincent Destouches

Nov 10 – Contrepoints (France) – ‘La moitié de l’humanité vit en démocratie’ (‘Half of humanity lives in Democacies’) – Short coverage

Nov 8 – Blic (Serbia) – ‘Ovi grafikoni ruše sve što mislite da znate o surovom svetu u kom živimo’ (These graphs crush everything you think you know about the cruel world we live in) – Longer coverage by Č. Vučinić

Nov 8 – Geenstijl (Netherlands) – ‘Dit is het beste tijdperk ooit’ (‘This is the best time ever’) – Short coverage

Nov 7 – Index.hr (Croatia) – ‘Mislite da nam nikad nije bilo gore? Varate se’ (You think it was never worse? You are wrong’) – Longer coverage

Nov 6 – Business Insider (USA) – ’25 Ways The World Is Becoming Much Better‘ – Longer coverage by Natasha Bertrand

Nov 6 – Mundo Tecno (Argentina) – ‘Prótesis biónicas – Esperanza de futuro‘ (‘Bionic prostheses – Hope for the future’) – Use (& spanish translation) of an Our World in Data graph by Alicia Bañuelos

Nov 5 – Mic.com (USA) – ‘Good News: The World Is More Democratic Than Ever‘. – Longer coverage by Coleen Jose.

Nov 5 – Chabad.org (Israel) – ‘What Makes You Think the World Is Getting Better?‘ – Long text referring to Our World in Data and embedding some data visualisations by Tzvi Freeman

Nov 1 – NextBillion.net (USA) – covered in the ‘Weekly Roundup’. – Short coverage

Oct 30 – Scientias.nl (Netherlands) – ‘De wereld vergaat niet en deze statistieken bewijzen het‘ (The world is not coming to an end and these statistics prove it). – – Detailed coverage (title page) by Tim Kraaijvanger

Oct 30 – Jutarnji (Croatia) – ‘Nova Studija S Oxforda ‘Zbog ebole i terorizma čini nam se da je svijet užasan, ali istina je suprotna‘ (‘A new publication from Oxford on the development of the world – we never had it so good’). – Detailed coverage (title page) by Tanja Rudež

Oct 30 – Rusplt (Russia) – ‘Жить стало лучше‘ (‘Life is better’). – Detailed coverage by Михаил Карпов

Oct 30 – PlanetSiol (Slovenia) – Kljub terorizmu, eboli in drugim tegobam je svet boljši, kot je bil nekoč (‘Despite terrorism, Ebola and other problems the world is better than it once was’) – Detailed coverage

Oct 30 – Kurir-info (Serbia) – ‘Mi samo mislimo da je svet užasan, u stvari nam je sve bolje!‘ (‘We think that the world is horrible, in fact we are all better!’) – Detailed coverage

Oct 30 – Bayerischer Rundfunk (Germany) – ‘Weniger Krieg und Armut – Von wegen alles wird immer schlechter‘ (‘Fewer wars and less poverty – so much for everything getting worse’) – Short coverage by Nina Heinisch

Oct 29 – SPLOID Gizmodo (USA) – ‘The world is now safer and better than ever and here’s the evidence‘ – Short coverage by Jesus Diaz

Oct 28 – Business Insider (USA) – ‘OK, Haters, It’s Time To Admit It: The World Is Becoming A Better Place‘ – Longer coverage, title coverage by Henry Blodget

Oct 27 – Gizmodo Espanol (Spain) – ‘Aunque no lo parezca, el mundo es ahora más seguro que nunca‘ (‘Believe it or not, the world is now safer than ever’) – Short coverage by Saavedra Yare

Oct 27 – INET Oxford (UK) – ‘OurWorldInData.org – a new web publication shows how the world is changing‘ – Longer coverage by Max Roser on his publication

Oct 27 – Marisol Collazos Soto (Spain) – ‘El mundo es ahora más seguro que nunca’ (‘The world is now safer than ever’)

Oct 24 – Andrew Hidas Blog (USA) – ‘These are the best of times’ – Short coverage

October 22 – Radio interview with Max Roser covering Our World in Data with morning show ‘The Takeaway’ on WNYC (public radio in New York). The ‘program reaches nearly 2 million listeners across 230 stations nationwide’.

October 21 – CapX by The Centre for Policy Studies (UK) – ‘The world is becoming a better place’– Short coverage

Oct 20 – kocka.sda.sk (Slovakia) – ‘Ako rastie vzdelanosť vo svete’ (‘How to extend education around the world’) – Short coverage

Oct 20 – Oxford Martin School blog (UK) – ‘It’s a cold, hard fact: our world is becoming a better place’ – Longer coverage by Max Roser

Oct 6 – Weather and Climate @ Reading University (UK) – ‘Healthy eating, healthy planet?‘ – Short coverage by Joy Singarayer

September – Econogist.org (USA) – ‘Deciphering Data: Earthquakes, Music, Love, and Violence‘ – Short coverage by Isabel Munson

September 12 – Humanosphere.org (USA) – ‘Fewer children die each year’ – Short coverage by Tom Murphy

August 28 – SupplyChainBeyond – ‘6 Maps That Explain Global Supply Chains’ – Short coverage by Bruce Jacquemard

August 26 – Vox.com (USA) – ‘38 maps that explain the global economy‘ – Short coverage by Matthew Yglesias

August 16 – Health, Education, Social Protection News & Notes – ‘Our World in Data‘ – Short coverage

August 16 – Geographical Imaginations (USA) – ‘Peace in our time‘ – Short text

August 15 – Vox.com (USA) – ‘The world economy since 1 AD, in one chart’ – Detailed coverage by Dylan Matthews

August 15 – DineroEnImagen (Mexico) – ‘Dos gráficos muestran la riqueza mundial de los últimos 2000 años’ (‘Two graphs show the global prosperity of the last 2000 years’) – Short coverage

August 2 – The Washington Post (USA) uses Our World in Data as a source for ‘The State of Africa‘

July 31 – The Aspen Institute (USA) – The Our World in Data presentation at AfricaInData.org is one of the ‘Five Best Ideas of the Day‘.

July 30 – Marginal Revolution (USA) – Link to Africa in Data

July 23 – Kushima (Japan) – Our World in Data – Short coverage

July 8 – Treehugger (USA) – ‘Deaths from malaria since 2010 in one graph (and a 1870 map showing U.S. deaths)‘ – Longer coverage by Michael Graham Richard

July 8 – Marginal Revolution (USA) – Link to OurWorldInData.org

July 6 – Xataka (Spain) – ‘Nuestro mundo en datos: visualizando cómo hemos cambiado los últimos siglos’ (‘Our world in data: visualizing how we have changed over the last centuries”) – Longer coverage

Teaching

Many teachers, lecturers, and professors use Our World in Data material in their teaching. The list of institutions where our work is being used for teaching is long and varied. The spectrum spans primary and secondary schools through to higher education institutions across the world, including the University of Oxford, Stanford University, the University of Chicago, The University of Cambridge, the University of California Berkeley, and many more institutions in Europe, Oceania, Asia, Africa, and the Americas.

One of the most inspiring and innovative teachers we heard from was Matthew Cone, a high-school teacher at Carrboro High School in North Carolina. He wrote a post on our site about how he uses our work in his teaching.

As a response to this use of our work we built the Our World in Data-Teaching Hub.

In 2018 we conducted a public survey to understand how people use our work in teaching. Below is a chart summarizing what we learned from this exercise covering nearly 100 educators from around the world. 1

Three findings from our survey stood out to us:

If you know of people using Our World in Data not listed here, please let us know and we will add it to this page!

Endnotes

Here is the full list of institutions, as provided by survey respondents: University of Oxford (UK); Ben-Gurion University of the Negev (Israel); Tribhuvan University (Nepal); University of Zurich (Switzerland); University of Massachusetts Boston (USA); Grand Valley State University (USA); Queen Mary University of London (UK); University of California, Berkeley (USA); Touro University California (USA); Adlai E. Stevenson High School (USA); Universidad Iberoamericana, Ciudad de Mèxico (Mexico); Rugby School (UK); IES Corona de Aragón (Spain); Northern Illinois University (USA); Monroe One Board of Cooperative Education Services (USA); Mount Saint Mary Academy (USA); Washington State University (USA); Universidad Católica Santiago de Guayaquil (Ecuador); Stanford University (USA); Harvard University (USA); Texas A&M University (USA); EM Lyon Business School (France); Danville Park Girls’ High School (South Africa); American University (USA); Virginia Polytechnic Institute and State University (USA); Rotterdam Business School (Netherlands); University of Salamanca (Spain); Ohlone College (USA); St. George’s School (UK); Ernst-Reuter-Schule (Germany); Overnewton College (Australia); Seattle University (USA); Olin College (USA); American Community School of Abu Dhabi (UAE); Glenforest Secondary School (Canada); Pennsylvania State University (USA); Associazione Sulleregole (Italy); Wingate University (USA); University of Milano-Bicocca (Italy); University of Chicago (USA); Iowa State University (USA); University of Queensland (Australia).

War and Peace

We are currently working on a historical dataset on conflict deaths and casualties. If you want to contribute to this research please get in touch.

This entry presents an empirical perspective on the history of war and peace. We also published a visual history of human violence which shows that we may now live in the most peaceful time in our species’ existence.

All our interactive charts on War and Peace

War and Peace after 1945

The absolute number of war deaths has declined since 1945

The absolute number of war deaths has been declining since 1946. In some years in the early post-war era, around half a million people died through direct violence in wars. In recent years, the annual death toll tends to be less than 100,000.

The decline of the absolute number of battle deaths can be seen in the visualization here that shows global battle deaths per year by world region. There are three marked peaks in war deaths since then: the Korean War (early 1950s), the Vietnam War (around 1970), and the Iran-Iraq and Afghanistan wars (1980s). There has been a recent increase in battle deaths driven by conflict in the Middle East, particularly in Syria, Iraq and Afghanistan.

Click to open interactive version

Other forms of large-scale violence

The chart above refers only to battle deaths occurring in conflicts that involved at least one state on one of the opposing sides. For more recent years, we show these ‘state-based’ conflict deaths alongside battle deaths in ‘non-state’ conflicts (where two or more organisations are fighting but no state is involved), and also violent deaths in ‘one-sided violence’ (where there is only one organised aggressor, such as in genocidal violence).

We see that, in recent years, state-based conflicts form the majority of such deaths, though the genocide in Rwanda in 1994 stands out for its very high death-toll.

Click to open interactive version

The share of battle deaths is declining even faster

The previous two graphs showed absolute numbers, but as the world has seen rapid population growth (see our entry on global population growth), it is more appropriate to look at relative numbers. Here we show the battle death in state-based conflicts per 100,000 people per year. The figures are shown by type of conflict.

Click to open interactive version

A greater number of increasingly less-deadly conflicts

The stacked area chart here shows the number of ongoing conflicts each year has risen. This increase however only relates to civil conflicts within states. Conflicts related to the expansion or defence of colonial empires ended with decolonisation. Conflicts between states have almost ceased to exist.

But the number of war victims varies hugely between different wars: whereas 1,200,000 died during the the Korean War (1950–1953), other wars had ‘just’ 1,000 victims. For this reason, statistics on the number of wars need to be interpreted alongside data on the size of these conflicts.

The increase in the number of wars is predominantly an increase of smaller conflicts. This follows from the previously shown declining number of war victims while the number of conflicts increased. The decreasing deadliness of conflicts can be seen in the bar chart.

Click to open interactive version

Deadliness of wars – average battle deaths per conflict by decade, 1950-2016

The past was not peaceful

It would be wrong to believe that the past was peaceful. One reason why some people might have this impression is that many of the past conflicts feature less prominently in our memories; they are simply forgotten.

The decline of wars between ‘Great Powers’

Data Quality & Definition

Counting world conflict deaths: why do sources differ?

To answer the question of how many people die in conflicts today, and how this has changed over time, we can turn to a number of different datasets.

Here we show the world conflict death rate since 1989 according to five sources.

The ‘UCDP all’ series is an aggregation of the deaths recorded in each of the three categories of conflict used by Uppsala Conflict Data Program: state-based conflict, non-state conflict and one-sided violence. (We show the data for these categories separately here).

We’ve summarised more information about the data sources and how we handled them to produce the chart above in a document, World conflict deaths since 1989: Notes on five sources.

You see in the chart that there are certainly similarities across the different sources. Overall they show a decline in conflict deaths into the 2000s, followed by an increase in the 2010s. 1

But there are also large differences. Most noticeably, there is a large jump in 1994 – marking the Rwandan genocide – which is present in some series, but absent from others.

If you look closely, you’ll see that there are large relative differences between the series over the entire period as well, though they are understated by the 1994 peak. If you hover over the datapoints, you can see the exact figures: the highest figure for a given year is typically well more than double the lowest.

Click to open interactive version

What explains the differences?

Discrepancies between different sources of conflict deaths data are partly to do with the differences in how the underlying source information – for instance newswires, death registers, government or NGO reports, or indeed other conflict databases – are selected and interpreted.

But they also reflect conceptual differences in terms of which deaths are and aren’t included in the source’s definition.

Below we relate some of the differences visible in the chart above in terms of some key conceptual differences lying along three dimensions: Who, How and What.

The Who, How, and What of conflict deaths data

Who: civillian vs military deaths

The Correlates of War series aims to include only deaths of military personnel, whereas the other sources capture – at least to some extent – civilian deaths too. As we would expect then, the Correlates of War figures are generally lower than the others.

How: direct vs indirect deaths

In addition to those deaths caused directly by violence – for instance those from gunshot or explosions – a significant proportion of lives lost in conflict are indirect: due to disease, starvation or exposure. This is particularly true where conflicts lead to famine or outbreaks of disease among the civilian population. But historically, such indirect deaths were also a major cause of military fatalities. 2

The UCDP and IHME data include only direct deaths. The Correlates of War series includes military personnel that died from diseases ‘contracted in the war theatre’. The Conflict Catalog series (running to 2000 only) tries to include indirect deaths of both the military and civilian populations. Peter Brecke, the author of the dataset, however acknowledges that the degree to which this is in fact achieved varies considerably across conflicts. 3

While indirect deaths represent a substantial proportion of the social costs of conflict, t here is a conceptual difficulty in drawing a consistent boundary between indirect deaths attributable to the conflict and those due to other factors. For instance, whilst famines are often triggered by conflicts, many factors contribute to their onset and severity, such as the level of sanitation or the transportation infrastructure present.

Brecke does not attempt to provide a clear-cut definition, and this conceptual boundary has been largely dictated by the available primary sources he used in each estimate. Nevertheless, as we would expect, the death rates reported in the Conflict Catalog do come out the highest.

What: state involvement and one-sided violence

Across the various sources there three broad kinds of violent event distinguished: state-based conflict, non-state conflict and one-sided violence. The kind of event depends on the type of actors involved. State-based conflict is that involving at least one state-actor, and includes conflicts between states (“inter-state” conflicts) and those between a state and non-state actor (“extra-state” conflicts), such as civil wars or colonial wars.

Non-state actors are those that demonstrate a degree of coordinated military organisation but whose identity falls short of statehood. Non-state conflicts are those between two or more non-state actors, with no state involvement.

‘One-sided violence’ on the other hand is where one organized actor (either a state or non-state group) attacks people that do not have any organized military capability to defend themselves, as in the case of genocide or ‘politicide‘.

The UCDP state-based and Correlates of War series do not include such events. It is for this reason that they do not show the jump in 1994 that marks the Rwandan genocide.

Here we picture the definitions used in the various sources across these three dimensions.

Our world in data

Research and data are crucial to making progress against the large problems the world is facing and to build a better future. At Our World in Data, we make research and data on the world’s largest problems accessible and understandable.

The problems the world faces are very diverse – global poverty, CO₂ emissions, child mortality, mental health, and many more. Our World in Data readers who are concerned about these problems should be able to rely on our compilation of research, our database, and our visualizations to understand them clearly, and learn how it is possible to make progress against them.

All data on Our World in Data is available for download, all visualisations are Creative Commons licensed, and all tools are open source.

Join us if you are committed to helping us achieve this mission!

Impact

We reach a large audience (over 5 million visitors every month) and we rank within the top few results on Google Search for queries including ‘CO₂ emissions’, ‘population growth’, ‘global poverty’, ‘COVID-19 vaccinations’, and many others. Our readers include researchers, journalists, teachers, policymakers, students, and engineers looking for the data and research that enables them to do their work.

Our work is cited and referenced in hundreds of articles, reports, books, lectures, videos and talks every year, including high-profile publications from academics and multilateral organizations such as the UN. In 2020 our rate of academic citations was above 15 per day and citations in the popular media are even more common.

During the pandemic hundreds of media outlets cite our work daily and all major international organizations rely on the data that we bring together. Dr. Anthony Fauci has used our charts to answer questions about the COVID-19 pandemic in the US Congress, and our work has been used by the White House as well.

It is very motivating for us to work on this publication because we receive great feedback from readers. Many teachers and professors around the world get in touch because they use our work as assigned reading or rely on our visualizations for their in-class presentations – high-school teacher Matt Cone wrote about it here. Professionals in international organizations, governments, and NGOs rely on our work regularly to understand the challenges ahead.

We have even heard from medics and psychologists who use our work to help their patients and give them a more fact-based, positive attitude towards the world – Dr Jill Gordon wrote about how she uses Our World in Data in her work here.

But we are far from achieving the impact we can have. Our main constraint is that we are a small team; this is why we are looking for new talented colleagues to join us. There is a lot more we can do, in all areas of our work – research, writing, web development and high-impact collaborations. This is why we want to work with you.

What is it like to work for Our World in Data?

Mission and impact: The mission of this work is our motivation to do it: we are committed to making progress against the world’s largest problems; to do so we need research and data to be available and accessible; and we believe that our work on this has a large positive impact in bringing us closer to a better future.

Team: We are a lovely bunch of people with an overwhelming passion for what we do. Well, of course we are a bit biased here. But it’s not an exaggeration to say that we would still want to work on this publication even if we weren’t paid for it (we know this is true because in the past we weren’t paid for this work). We are a small team trying to have a large impact: this means each of us has to take on large projects and lead them. We enjoy working together and have a lot of respect for each other’s work, knowledge, skills, and personalities. Outside of work we are good friends.

Flexibility: The type of work we do can be done from anywhere, anytime. We are currently a global team – this lets us find the best people in the world without a geographical constraint. We are in constant contact electronically.

Audience: We receive tremendous support and engagement from our readers. The feedback and discussion that our content generates creates a mutual learning experience between us and our readers. Unfortunately, mutual respect is not always a given online and sometimes you need to have a thick skin. Nonetheless, it is true that close to 100% of all feedback is constructive and respectful, and we find this very motivating. It makes our work much better in the process.

Contacts and collaboration: We are a unique project with a wide reach, and we bridge the gap between academia and high-level policy and international journalism; this means we often work with fantastic people and impactful institutions.

Learning: We learn every day. By studying the data, working through the research, and having discussions with the other members of the team, readers and collaborators, we continually re-evaluate and challenge our understanding. It is a personal goal for all of us to understand our world a bit better every day. This makes working on Our World in Data an exciting journey for everyone in the team.

Biodiversity

Explore the diversity of wildlife across the planet – how many species are in each group, and where they live.

See how wild mammal populations have changed over time; where they live today; and where they are threatened with extinction.

Explore the diversity of birds across the world; how many species have gone extinct; and how populations are changing.

See data on wild fish stocks; threatened species and how seafood production is changing.

Explore the distribution of coral reefs across the world and how they are changing from human pressures.

The Living Planet Index is one of the most-common biodiversity metrics. What does it tell us about the world’s wildlife?

Explore the long-term history of Earth’s extinctions, and where we are today.

Explore the latest data and research on the biggest threats to global wildlife.

Hunting is one of the largest threats to wildlife. See how poaching rates and trade has changed over time, and across species.

See how human expansion and habitat loss has changed landscapes over millennia, and how this has impacted global biodiversity.

See how protected areas and conservation efforts are changing over time, and their impacts on protecting the world’s wildlife.

Upcoming biodiversity topics to cover

This is a growing collection of resources and research on biodiversity. Biodiversity aspects we have not yet covered but aim to in the near future include:

Key articles on Biodiversity

Reuse our work freely

Cite our work

Our articles and data visualizations rely on work from many different people and organizations. When citing this entry, please also cite the underlying data sources. This entry can be cited as:

How We’re Funded

Our World in Data and the SDG-Tracker are collaborative efforts between researchers at the University of Oxford, who are the scientific editors of the website content; and the non-profit organization Global Change Data Lab, who publishes and maintains the website and the data tools that make our work possible.

You find our accounts and a full description of how we are funded in our Annual Report. In this document you can also find details about our organizational structure and the latest report on how we are spending our funds.

The following is an overview of our funding sources.

Grants

Our World in Data is supported by grants from the Quadrature Climate Foundation, the Bill and Melinda Gates Foundation, and a grant from the German entrepreneur, businesswoman and philanthropist Susanne Klatten.

In the past we have also received grants from the World Health Organization, the Department of Health and Social Care in the United Kingdom, the Centre for Effective Altruism – Effective Altruism Meta Fund, and the Nuffield Foundation.

Individual donations and contributions

We’d also like to thank the people who donate directly on our website to support our work – it is only through the sum of all these kind collective efforts that our work is possible.

More than 4,000 people have donated on our website over the past couple of years, and many of them have agreed to be listed publicly – here are the names and dedications provided by these readers on our donations page. Huge thanks to everyone!

History of Our World in Data

[This history was written in autumn 2019]

Growing up I learned much of what is wrong about the world: war, poor health, environmental degradation, hunger, and poverty. Just as it was for everyone else, it was clear to me that the world faces many serious problems.

And like most of us I had no idea how these issues changed over time. My perception was that all of these problems were getting worse and my fear was that during my lifetime they will get worse still.

I was angry when I found out that in many important points I was wrong. Not in all, but in many ways the change over time was actually positive. I didn’t know it growing up, but it is actually possible to make progress against the big problems we face. The world had actually achieved positive changes.

How was it possible that I wasn’t aware of this progress? And especially, why did I believe the opposite of what was true, even after many years of education, and despite following the news and public discussions?

I was angry because my misconception of how the world changed had made me feel powerless for years. The straightforward evidence should have encouraged me to choose an important problem to work on and to do what I can to make progress against it. But since I lacked the knowledge that progress is possible I also didn’t know that it is possible to have a positive impact on the world. In retrospect I still feel bitter about it: the absence of a factual discussion of global problems and achievements destroyed my confidence in our world and robbed me of my hope for the future.

There was no single moment in which I realized how wrong I was. I slowly realized it as I studied social and economic history and read more research on global development. The lectures by Jesus Crespo-Cuaresma at the University of Innsbruck were important and the book that had the biggest influence on me at that time was Amartya Sen’s ‘Development as Freedom’.

This slow realization, more than a decade ago, set me on the path to build Our World in Data. Since then it has been my aim to make the research and data accessible that would offer this perspective to everyone. To provide the perspective that I needed myself, when I was younger, has been a big motivation for spending the last 8 years on this.

Many of us don’t have a good understanding of global problems and change. This is not because the evidence isn’t available. Many of the relevant questions are very well-studied by thousands of researchers in the environmental and social sciences. But unfortunately it is very poorly communicated, with the research hidden behind paywalls and the data stored in dull, inaccessible databases. And science that is not communicated is of not much help, it is just a stack of papers in a drawer.

From the start, Our World in Data was intended as a platform that brings this research together and makes it accessible and understandable. Our mission has always been to present the research and data we need to make progress against the world’s largest problems. To achieve this mission the focus since 2011 has been to build a team of researchers that understands and communicates the research on a wide range of important global problems. The web makes it possible to provide this completely free for everyone as a public good. For this reason we have strong web developers in our team that build the tools – the site, visualization tools, and a growing database – that make this research accessible and understandable. This is the history of how I got started and where we are today (October 2019) as a team.

It was during 2011, as I was working in Brazil, when I started working on my original idea for how to publish the data and research on global change. The original idea was to write a book on the big global problems.

My plan was to address many of the big problems with a broad overview. To be able to write the book I started to collect all the data and research that would give me an understanding on where the world stands today and what we know about how to change it. In the first few years of working on this, my collection grew to many thousand datasets, visualizations, and publications. This collection later became the starting point for Our World in Data.

In early 2012, I moved to Oxford to work with Sir Tony Atkinson on income inequality. Atkinson had spent several decades researching inequality and poverty. His work has an immense impact on all scholars who work in the field today and this is also true for my work and Our World in Data. None of it would have been possible without him.

In the summer of 2012 I shared my very early plan and outline for the book. The plan was, at the time, a private side-project of mine, but from the start Tony was extremely supportive and we continued to discuss the book concept and studied the data together.

In these conversations we realized that it would be valuable to make the research available online. One discussion I remember well was about a typo Tony had found in his book on earnings inequality in OECD countries. 1 It was a small typo in one of the tables, but he was very unhappy about it and explained how much better it would be if he had published the book online instead. We decided to do that for this project on the world’s problems. In an online publication typos can be fixed, research can stay up-to-date, and many more people can benefit from it. Later that year I shifted the effort from writing a book to building this web publication.

While I was spending my days working on inequality, I spent nights and weekends building first versions of what is now Our World in Data. At the beginning I had of course no financial or institutional support to work on it. To finance it, I worked as a bicycle tour guide in Poland, Portugal, France and other places around Europe. For many years it was only me working on this project.

Work was moving very slowly at first because I had to learn the basic web technologies to build the first versions of the website. Coming up with a name for the online publication also took me an embarrassingly long time. The list that I discussed with friends included maybe 200 or 300 names. I finally decided on ‘Our World in Data’ and my friends were happy that the endless discussions about it were finally over.

In early 2013 the Department of Economics at Oxford hired me as a post-doctoral researcher and I continued working with Tony Atkinson. Our World in Data was still my side-project, but the coverage of data and research grew. I managed to build a first version of the website and some early interactive visualizations (based on d3 and nvd3), and so I launched a first version of Our World in Data in the summer of that year. For many months the site was password-protected and I only shared it with a few friends. Looking back at old records, I see that I had just 202 visitors to the site in the first year.

It was Tony who suggested the research that is published on Our World in Data could possibly be based at the University. I had previously thought of it as an entirely private project, but Tony’s idea made sense: while most researchers at universities publish in specialized scientific journals, this new research team would publish their work in an online publication that is freely accessible for everyone. In November 2013, we applied for a research grant from the London-based Nuffield Foundation. Principal Investigator was Sir David Hendry, who was at the time my boss at Oxford’s Institute for New Economic Thinking (INET). Half a year later we received a rejection from the Nuffield Foundation. I rewrote the application and reapplied. One year later, just before Christmas 2014, we received a letter from the Nuffield Foundation with the fantastic news that the work would now receive some funding.

In May 2014 I launched Our World in Data publicly. Over the following six months I had on average 20,000 visitors every month. This is of course a low number when compared to the reach of mass media, but it was much higher than I had expected. Academic publications usually do not reach many readers and I remember well how exciting it was to watch Google Analytics and see whenever a new reader found their way to the site.

During this time I was working at the Institute for New Economic Thinking (INET) Oxford in Eagle House. It was there where I started working on inequality with Tony Atkinson in 2012 and INET stayed Our World in Data’s home during these years. It was a great place to work that provided a lot of freedom, a great community of researchers, and I remain very grateful for their support during these years.

At the end of the year we received the grant from the Nuffield Foundation in London. The Nuffield Foundation grant of £75,883 financed the project from December 2014 to November 2015; it was the first time I had a budget that would pay for at least some part-time team to work on Our World in Data.

By 2015, two important steps had been taken: A few people knew the site and found it useful and I had some research budget to pay colleagues for some time.

The next important step was to find a web developer and two researchers that shared the site’s mission and were willing to contribute to the publication.

In May 2015 I started working with Zdenek Hynek. Zdenek is a web developer, who was at the time based in London, and he built the first version of the Our World in Data-Grapher and redid the website in many ways.

From July 2015 onwards Lindsay Lee and Mohamed Nagdy worked as research assistants for the project. Lindsay concentrated on health and Mohamed on economic topics.

Four years of working on this by myself were over.

We grew the audience by spreading the word. I emailed academics, writers, and anyone who I thought might be interested in having a look. Journalists found the site helpful and wrote about it – here is an article from The Guardian from late 2015. I traveled to spread the word at conferences. Both at universities (a highlight that year was a talk in a busy, big lecture theatre at Harvard) to non-academic conferences (like Wired in London or Transition at the New York Times offices).

Financially this was a difficult time for the research project. There were times when I paid for the work of my colleagues from my own money.

In early 2016 I started working with my very good friend Esteban Ortiz-Ospina. Esteban and I had known each other for several years and since the early days of Our World in Data we often discussed this work. Esteban is a fantastic economist and a very good manager, and it quickly became clear that we worked very well together. I think what is special about our collaboration is that we have a very similar view of the world, but very different skills that complement each other well.

A month later, in February 2016, Jaiden Mispy joined our small team as a web developer. Jaiden was not just interested in the web technology of the publication, but also very much in the content of Our World in Data. In fact, he had started to work on a similar web publication before he joined us. Jaiden worked with us until spring 2019 and he was an exceptionally smart colleague who has skills in all aspects of web development. He also has a strong personal drive to build technology that helps people around the world.

Financially we were able to keep going because of the donations we received from readers. This direct support kept the project alive during 2015 and 2016.

To give us some stability I had been working on a grant application for the Bill and Melinda Gates Foundation. In August 2016 we were given a one-year grant to expand our open-access online publication. It was the first time that we could plan for longer than just the next few months.In 2016 we also started a collaboration with the YouTube channel Kurz Gesagt and we published our first video in December. Both the partnership with the Gates Foundation and with Kurz Gesagt continue to this day.

On April 4 2017 Hannah Ritchie sent me an email and explained why she would want to contribute to our research. She had an unusual profile and was in an unusual position: Hannah has very broad interests – environmental research, energy, malnutrition, agriculture, health, and really the entire spectrum of topics that we work on – and she had published several very good academic papers. The very unusual position she was in was that she finished her PhD several years earlier than planned. Her application was particularly strong because she linked to several very good articles she had published on her own website.

The fit could not have been more perfect. Hannah joined soon after she sent this first email and was quickly leading big research projects within our team. She studies, in particular, humanity’s impact on the environment and asks how we can make progress against the severe problems that we face as a result. Since 2019 Hannah has been Head of Research for Our World in Data. Without her work, Our World in Data’s work today would be unimaginable.

Our publication grew: we published longer and more-in-depth articles on population growth, global poverty, greenhouse gas emissions, trade, fertility rates, and many other topics. Our research got cited in the best academic journals including Science, the Proceedings of the National Academy of Sciences, the QJE, and The Lancet. And more readers came to our site to understand global problems.

After the end of the academic year Diana Beltekian, Sophie Ochman, and Ruby Mittal joined us as research assistants after they had finished their Master’s in Oxford. Ruby focussed on demographic changes. Sophie researched topics in global health, including the history of polio and smallpox. Diana has supported our research on many topics – the long-term history of trade, education, technological change, and much more – and since she knows about so many aspects of our publication, she is also the one who answers most of the many questions and ideas we receive from colleagues and readers.

And Aibek Aldabergenov joined us as the first database manager. The big central database, which by now, in 2019, includes more than 70,000 variables is at the heart of our work. For the first years, each author had to upload each variable by hand (at the beginning I had to hardcode each visualization and connect it to a specific file). Aibek changed all of this in the year he worked with us. We now mass import the most important datasets in one go and keep the publication up-to-date as new and better data becomes available. One big project that we will be working on in 2020 and ‘21 is to build the OWID-‘Data Explorer’ that will allow you as the reader to explore this entire big database (if you are interested in helping to build this tool or improve the database, join us – we are looking for two developers to join our team).

In 2015, all countries in the world signed up to reach the Sustainable Development Goals by 2030. Yet by 2018 – three years into the SDG era – there was no publication that would allow the world’s citizens to see and understand whether a country, and the world as a whole, was on its way to reaching the goals. This is why we decided to do it ourselves and we developed the site: SDG-Tracker.org.

Joe Hasell joined us in 2017 when he was working remotely from Italy, since 2018 he works with us in Oxford. Since then he contributed research on two very different global problems. The first big focus of his work is the study of economic inequality. Joe worked with Tony Atkinson, Salvatore Morelli, and me on our open-access Chartbook of Economic Inequality; his PhD research focuses on inequality and housing.

The second focus of his work is the history of events that caused large-scale suffering – famines, genocides and war. His very first OWID project investigated the causes of famines and lead to a new dataset on the global history of famines over the last 150 years. Since 2018 he has been leading a large research project into the history of war and is currently building a global database of war over the last centuries. Joe also has a strong interest in ethics and philosophy more broadly, and has had a large influence on defining the mission of our work. We value him as a careful researcher, a philosopher, but also because he is the best musician of the team.

Anstey Brock joined us as a part-time researcher in 2018 for the research project on the history of war. She has a background in conflict studies and is working her way through stacks of historical accounts of wars and genocides to produce this much-needed perspective on the history of violence. We will publish this database in a way that will allow historians and war researchers to use the data for their own research, but also to contribute so the database can become more complete over time. This project is among the biggest we have taken on and it is possible thanks to this very good collaboration.

By 2018, the reach of Our World in Data had increased significantly. Many more readers were coming to the site and for many relevant search queries – ‘CO2 emissions’, ‘world poverty’, ‘child mortality’, ‘population growth’ – we became the top search results in many parts of the world. Many teachers and professors relied on our work in their classrooms, but we also had very unexpected uses: parents told us they had taken our research into account when they decided whether they should have children; and doctors told us that they use our work to help patients who suffer from depression – Dr Jill Gordon’s account of how she uses Our World in Data in her work can be found here.

Thanks to the help of many of you, the financial situation improved too. Over the last years hundreds of you have donated via OurWorldInData.org/donate to make our work possible. We have also applied and received more research grants and all of this meant that we were able to spend a bit less time fundraising and more time researching. We list our supporters here.

The web developer Daniel Gavrilov joined us in October. Out of a large number of applicants Daniel was obviously the ideal candidate – he had a background in data visualization in particular, and had shown that he is careful when thinking about the site’s design and user-interface from the reader’s perspective. We were very happy when he joined our team. He didn’t know what he was getting into: he hadn’t yet moved to Oxford when he learned that we would all be moving to Silicon Valley instead (more on this in a moment) and just a few months later he became the lead developer and had the challenging job of working on all aspects of the site, the database, and the visualization tool, whilst building a development team around him.

For our research we could have never asked for a better home than the University of Oxford and there the Institute for New Economic Thinking in the early years and the Oxford Martin School today. But the research is only one half of our work; the other half is dedicated to building the scientific publication and all the tools that are necessary for it. Sir Charles Godfray, the director of the Martin School, suggested that this work of the developers should have its own base: a nonprofit that is dedicated to the production and maintenance of the open-access online publication and tools. In 2018 we started building a non-profit that would serve this purpose. The Global Change Data Lab was founded and is today under the leadership of three outstanding trustees: Professor Wendy Carlin, Sir David Hendry, and Professor Stefano Caria.

Y Combinator is certainly the world’s most prestigious and arguably the best startup incubator. While they focus on for-profit startups they had also accepted a few non-profits over the past years. We decided to apply and were actually accepted! This meant that 2018 ended with some hectic weeks in which we prepared for our work in Silicon Valley.

Y Combinator started right after New Year’s Day. We all moved to Silicon Valley and started working. We wrote about our time there here. The one thing I’d now add in retrospect is that it became even clearer just how helpful YC has been the further away from it we are.

During the summer the web developer Matthieu Bergel joined our team. Matthieu is rebuilding the ‘content management system’ and overall has the important task of finding an architecture that allows you as the reader to navigate the site and find the relevant data and research. What was impressive with Matthieu was how very well he considers a wide range of options, weighs the pros and cons of each of them, and finally comes to a well-argued conclusion of what the right approach is.

The newest member of the team is Bernadeta Dadoinaite. Her research and experience have prepared her for exactly the kind of work we are doing: after completing a degree in microbiology she graduated with a PhD in immunology and translational medicine from Oxford; she has a Master’s degree in ‘science media production’ from Imperial College in London; and beyond that researches and visualizes data to understand global health. An understanding of health issues from the micro- to the macroscopic level makes her the perfect addition to our small Our World in Data team.

We are very much looking forward to working with Bernadeta and Matthieu much more!

It’s been a long journey over the last 8 years. But I am very happy that we embarked on it. Everyone in the team believes that research on big problems and studying how we can make progress against them is extremely important (we actually believe it is a duty). The key is to make the data and research we need for this accessible. But this is, unfortunately, in very short supply: it is not the kind of work that gets you a permanent position in universities; you have to raise funding for it constantly; and there are few incentives to do it. But as the work over the last years shows, it is possible.

The very best thing that happened on this journey is that many outstanding researchers and developers joined on the way. The team is what makes Our World in Data possible and I’m very grateful to be working with all of them.

The mission of this work has never changed: from the first days in 2011 Our World in Data focussed on the big global problems and asked how it is possible to make progress against them. The enemies of this effort were also always the same: apathy and cynicism. It is very easy to become cynical and give up in the face of the world’s big problems. For much too long I believed the cynics that tell us that it isn’t possible to change the world. That this belief is wrong is what unites our team. This is why we study how to make progress against the world’s biggest problems and we are looking forward to our joint work over the coming decades.

If you want to contribute to our mission, we are looking for researchers and developers to join us.

Millions of children learn only very little. How can the world provide a better education to the next generation?

Introduction: What is the problem?

For many children schools do not live up to their promise: in many schools children learn very little.

This is a problem in rich countries. By the end of primary school about 9% of children in high-income countries cannot read with comprehension. 1

But it tends to be a much larger problem in poorer countries. This is what the chart below shows. The education researcher Joāo Pedro Azevedo and his colleagues estimate that in the very poorest countries of the world 90% of children are not able to read with comprehension when they reach the end of primary school.

Many of these children do eventually learn how to read, but the problem of poor learning persists: these children are already behind by the end of primary school and the issue compounds over the years so that many of them leave school with a poor education.

The same data also shows that it doesn’t have to be this way: in the best-off countries the share of children that fail to learn how to read with comprehension at that age is less than 2%. 2

Children need to learn to read so that they can read to learn. When we fail to provide this to the next generation, they have fewer opportunities to lead rich and interesting lives that a good education offers. It crucially also leaves them in a poorer position to solve the problems of tomorrow.

What explains this large problem and how we can do better?

Schooling doesn’t necessarily mean learning: To make progress we need data that lets us see the difference

One obvious reason why many children don’t learn is that they are not in school or that they drop out; this is the case for 8% of the world’s children and I discussed this problem before here.

But the problem is bigger than that. Many children who don’t learn are in school.

What the research shows is that getting children into the classroom is only half the battle. Many education systems are failing to ensure that the children who arrive at school every morning actually learn.

For this we need data. But the international statistics on education have not yet caught up with this reality. They still very much focus on school attendance. 3 Even the most prominent index measure of development – the UN’s Human Development Index – only captures attendance. 4 It doesn’t capture whether or not children learn.

To be clear, we should also keep tracking access to schools. Schools are not just about learning – it is where children socialize, they provide safety and often food, and they make it possible for parents to work.

We need the statistics to capture both aspects: the quantity of education – how many years a child spends at school –, but also the quality of education.

One way of assessing which schools live up to their promise is to study test scores. I think that an excessive emphasis on tests in school education is misplaced. But I also believe that the vast differences in test scores that this data reveals tell us something important about the world. It offers us the opportunity to understand why some schools are failing and how we can do better.

The inequality in learning largely mirrors the economic inequality – but it does not have to be that way

In recent years several research teams have done the hard work of piecing together test results to produce global data on learning outcomes. 5

The one that I rely on was produced by researchers Dev Patel and Justin Sandefur. 6

The bar chart at the beginning showed the large differences in learning outcomes between rich and poor countries. The data by Patel and Sandefur also reveals the differences within countries. Their data also complements the literacy scores above with the other basic educational skill: numeracy.

In the large visualisation below I show all of their data on test scores in mathematics. But to see clearly what this data tells us let’s go through it step by step – first for one country, then for several, until we arrive at the global picture.

The sloping line in the small chart below shows the distribution of test scores in Brazil. It plots the students’ mathematics scores on the vertical axis against their family’s incomes on the horizontal axis.

It shows the large inequality in incomes within Brazil and it shows that the learning outcomes of Brazilian children map onto this economic inequality. The average students from rich households achieve much better scores than the average poor students.

The fact that educational outcomes correlate with the household’s income doesn’t mean that income is the only variable that matters. This is because income itself is correlated with other aspects that matter, for example the parents’ education. 7

It also doesn’t mean that children from poor families cannot possibly achieve a very good education. The data shows the averages along the income distribution and makes clear that poor children face much steeper odds.

Let’s add more countries to the chart.

At the center of this next plot we see again the data for Brazil, but now we can compare it with the results in six other countries.

This data shows that the differences between countries are often much larger than the differences within countries:

Another insight from this chart is that some of the most successful countries – including Finland – avoid educational inequalities along the income distribution almost entirely. The steepness of the line indicates how unequal the learning outcomes in a particular country are: a steep line shows a high inequality between the poorest and richest kids in terms of learning outcomes, while a less steep line – like the line for Finland – indicates that kids from all family backgrounds do similarly well.

Finally let’s also add the data for the other 58 countries for which data is available.

For most countries the lines slope upwards: students from richer families do better in maths. Patel and Sandefur document that these within-country differences in learning outcomes are particularly large in those countries with the largest economic inequalities. Brazil is one of them.

Because test scores are such an abstract metric it is hard to grasp how very large the disparities between countries are – it’s hard for anyone to relate to a test score of 380 (the score of the richest children in Cote d’Ivoire) or a score of 545 (the score of the poorest children in the UK).

One way to make such a 165-point difference understandable is to compare it with the inequality within countries. The difference in test scores between the richest and poorest students in the US is 53 points. This tells us that the differences between countries are several times larger than the differences within countries, even a highly unequal country like the US. 8

This is one of the main insights from this data, the differences between countries are enormous.

Students with the same household income tend to reach better educational outcomes if they live in a richer country

There is a second key insight from this research that is worth highlighting: the average income level of the country is more important for a student’s learning than the income of the particular family within that country. 9

Look, for example, at the test results of the poorest students in Korea or Finland to see this striking result. The poorest Korean or Finnish students are poorer than the rich students in Brazil, but their math scores are much higher.

Let’s think about the implication of this.

In some of the world’s richest countries, like Finland, the education system is a great equalizer – it gives every child a chance, no matter what their family background is.

But in most places – and even more so in a global perspective – these educational differences are actually perpetuating the high levels of inequality. Children from richer backgrounds tend to learn much more and grow up to become more skilled and productive and make themselves and their countries richer in turn. 10 If we want to stop inequality perpetuating itself through education, we have to raise the quality of education for hundreds of millions of children. The most successful countries show that it is possible.

Can we make progress and provide much better education?

Now that we have an idea of the problem let’s see what can be done to provide better education to the world’s children,

The fact that every morning millions of children go to schools in which they learn very little is a massive challenge. I can’t blame you if you feel disheartened when you consider how we can overcome this.

But I do think it is very much possible to make progress. Let me explain why.

As always in this series on ‘The world’s largest problems in brief’, I won’t pretend that I can lay out an exact plan for how we should solve it. Particularly for education, this very much depends on the local situation. But I do want to explain why I am optimistic that change is possible.

We know that change is possible, because we’ve done it already

Today a large share of the world’s children gets a poor education. But until recently almost every child had a terrible education.

We know that change is possible because it has already happened. If we look at the places where children now get a good education, nearly everyone was illiterate until recently.

Even basic skills – such as reading and writing – were only attainable for a small elite. This chart brings together estimates of basic literacy from around the world to show how this has changed. 11

And the world isn’t just making progress in learning basic skills. The fact that many children learn very little is often referred to as the ‘learning crisis’. But I think this is a misnomer. The word ‘crisis’ suggests that we are in an extraordinary period, worse than before. But this isn’t the case. Learning was worse in the past. In the majority of countries children are learning more now than some years ago, the world is making progress. 12

The change that we are seeing makes clear that there are ways forward.

→ You can explore this data in detail in the interactive version of this chart.

Living standards matter: poor education is about more than just poor education

It’s not only schools that matter for how much children learn. Many children struggle to learn because they suffer from poor nutrition, poverty and poor health. 13

What we’ve seen above, that those children in richer countries and those from richer families do much better in school, is also due to the differences in living conditions more broadly.

Better health, less poverty, and a more nutritious diet can often do more for a child’s education than the best teacher. This is why progress against poverty, against poor child health, and against malnutrition are key to improving the education of the next generation. The fact that the world is making progress against these problems is a big reason why I am optimistic about the future of education.

Even in the very poorest corners of the world children can learn very well, but without large economic growth it remains unaffordable

Looking at the evidence so far might have convinced you that improvements have been possible, but you may raise the skeptical question of whether this implies that further improvements can be achieved. What needs to happen to achieve a good education in those places where children learn so very little today?

There are studies that set out to answer this question.

One of the countries with the poorest education today is Guinea-Bissau. 14 A study in the rural parts of the small Western-African country found that most children do not learn how to read and write. From their parents they can’t learn it, less than 3% of mothers were able to pass a simple literacy test. This study concluded that the quality of teaching was poor because “teachers are isolated, underequipped, receive salaries after long delays, and have little training.”

A recent study by Ila Fazzio and her colleagues set itself the goal to see what can be done when these constraints are lifted. 15

The researchers went to the most difficult places within the country – those regions with the lowest learning levels – and worked with the people there to set up simple primary schools. 16

The study’s schools trained teachers, provided them with scripted lessons, monitored children and teachers regularly, involved the village communities, and provided adequate resources to support all operations. To see whether these well-resourced schools made a difference they set up a randomized controlled trial: they compared how much the children learned in the study’s schools with children in the control group who went to schools that carried on with their teaching as they did before.

After 4 years they compared whether children learned more in the study’s schools.

In the control group the results were very poor: after 4 years only 0.09% of children were able to read. Among those children that attended the study’s school learning was much better: 64% of them had learned how to read.

The chart below shows the overall test scores, which also take into account the kids’ numerical skills. Overall test scores increased hugely – by 59 percentage points.

Other recent studies also show that it is possible to achieve very large improvements in those places where young children are otherwise illiterate and innumerate. 17

Even in the most challenging places – extreme poverty, very low education of parents, almost no infrastructure (no internet, no electricity, no roads) – it is possible to teach primary school children to read fluently and do basic math very well.

If it is possible to run schools in which children learn very successfully, what is the catch?

This highlights one reason why a country’s prosperity is so important for its education. What a rich country spends annually per primary school student is about 10 times as much as the average income in a poor country.

Countries need to become much richer to build schools that are as well-resourced as those in this study. Big change is possible, but it requires large increases in prosperity.

For countries that are poor we need to find out which opportunities are the most cost-effective

Education in those places where children learn very well is expensive. High-income countries spend more than 150-times as much on the education of each child than poor countries. 19

In the long run, countries will hopefully have achieved the growth they need to afford better schools, but is there anything they can do right now?

To answer this question, researchers have made a big effort in recent years to identify the most cost-effective ways to improve schools.

Instead of trying to change the entire school system, as in the study above, this research tries to find out what exactly it is that means that children learn little in a particular place, and to change those things that have the biggest possible impact for the smallest cost. 20

Since the problems which hold children back differ from place to place there are no universal solutions. What works in one context, might not work in another. 21

What are the changes that can achieve so much with so little? The recent review by Noam Angrist and colleagues highlights three in particular. 23

1] Avoiding overly ambitious curricula and ‘teaching at the right level’

Perhaps somewhat paradoxically one reason why children in some countries learn very little is that the school curricula are too ambitious. Instead of being aligned to the students’ learning levels, most of the content goes over the students’ heads. 24

The suggested solution is simple: match the teaching to the learning level of the students. The kids do a test and the teaching they receive then depends on how much they already know.

2] Improved pedagogy and lesson plans

Another problem in many places is that teachers are left to fend for themselves. They are isolated, have little training, and on top of the teaching they have to write their own daily lesson plans.

In such situations it has been shown to be very cost-effective to introduce structured pedagogy programmes in which teachers receive support and are provided with structured lesson plans. 25

There are also encouraging studies that show that the work of teachers can be complemented by technology-aided instruction programs. 26

3] Providing information on the returns to education

A third cost-effective approach is to simply inform people about how very high the returns from a better education are.

Some parents and students are not aware of the enormous pay-offs of having a good education. Learning this can increase the demand for education and improve children’s learning for very little cost. 27

In the previous section we have seen that it is costly to bring the entire education system to fruition. In this section the takeaway is that there are some possibilities to achieve a lot with very little – there are some very low-hanging fruits.

A big opportunity

The first insight from this research is that schooling is not the same as learning. The new data on global learning outcomes makes clear just how big of a problem this is.

The second insight is that it doesn’t have to be like this – we can change this. All children can learn.

We have a huge opportunity. The world has made big strides in getting children into schools. These children are no longer isolated; teachers are in contact with them. At the same time, researchers have identified low-cost ways to improve their learning outcomes. Taken together this gives us the possibility to turn schooling into learning.

The evidence also made clear that poor schooling is not only a problem in poor countries. Some of the most striking data discussed above showed how very unequal learning outcomes in most countries are – while some other countries show that it doesn’t have to be that way.

Much is at stake here: Humanity solves problems by understanding the world and implementing ideas for how to do better. Whether tomorrow’s generation continues to make progress against disease, poverty, poor nutrition, and environmental problems will depend on their understanding. 28 Those of us who dedicate our lives to teaching therefore have the responsibility – and opportunity – to enable the next generation to develop these new ideas and grow up to lead a fulfilling life.

Acknowledgements: Many thanks to Hannah Ritchie, Noam Angrist, Bastian Herre, Dev Patel, and Pablo Rosado who provided feedback, help, and data.

Recommendations: In addition to the referenced research in this article I recommend listening to the 80,000 hours podcast episode with Rachel Glennerster. It is called “A year’s worth of education for under a dollar and other ‘best buys’ in development, from the UK aid agency’s Chief Economist”.

The Rise Programme at the Blavatnik School of Government at the University of Oxford is dedicated to finding solutions to poor learning. Plenty of research articles, background information, blogs and more can be found on their site.

And overall the literature on how to improve teaching is fascinating – at the footnote you find many additional references. 29

Our World in Data presents the data and research to make progress against the world’s largest problems.
This article draws on data and research discussed in our entry on Quality of Education.

Endnotes

This figure and the figures in the following bar chart are from João Pedro Azevedo, Diana Goldemberg, Silvia Montoya, Reema Nayar, Halsey Rogers, Jaime Saavedra, Brian William Stacy (2021) – “Will Every Child Be Able to Read by 2030? Why Eliminating Learning Poverty Will Be Harder Than You Think, and What to Do About It.” World Bank Policy Research Working Paper 9588, March 2021.

You find these estimates for particular countries in the previously cited study and updates for some countries can be found in João Pedro Azevedo, Silvia Montoya, Maryam Akmal, Yi Ning Wong, Laura Gregory, Koen Martijn Geven, Marie-Helene Cloutier, Syedah Aroob Iqbal, Adolfo Gustavo Imhof, Natasha de Andrade Falcão, Cristelle Kouame, Mahesh Dahal, Tihtina Zenebe Gebre, and Maria Jose Vargas Mancera (2021) – Learning Poverty Updates and Revisions What’s New?. July 2021

One country that does very well is the Netherlands. 98.4% of all children read with comprehension by the end of primary school. Other countries have also very low (2-3%) of children who don’t learn how to read with comprehension by that age: Austria, Finland, Hong Kong, Italy, Kazakhstan, Lithuania, Russia, Sweden, Singapore, and the UK are among them.

Lant Pritchett (2013) – The Rebirth of Education: Schooling ain’t Learning (CGD Books, 2013).

Existing large testing efforts are restricted to particular world regions [To give two examples: SACMEQ – the Southern and Eastern Africa Consortium for Monitoring Education Quality – focuses on that region of the world while the OECD’s PISA test focuses largely on high-income countries.]. The key difficulty that these researchers have to find solutions for is to bring these regional results together to obtain a global perspective through harmonized test scores.

Three recent key efforts in this area are:

Dev Patel and Justin Sandefur (2020) – A Rosetta Stone for Human Capital. Working Paper.

Data and Code for this research paper are made available by Dev Patel on his website. The authors also summarized their findings in a blog post. Many thanks to Dev Patel who helped me to access and understand the data.

On this aspect see for example: Alex Bell, Raj Chetty, Xavier Jaravel, Neviana Petkova, and John Van Reenen (2019) – Who Becomes an Inventor in America? The Importance of Exposure to Innovation. In The Quarterly Journal of Economics, Volume 134, Issue 2, May 2019, Pages 647–713, https://doi.org/10.1093/qje/qjy028 Alex Bell makes the research available on his website.

See my summary of this research article in Talent is everywhere, Opportunity is not.

Another way to make these test score differences relatable is to relate them to changes over time. Patel and Sandefur have converted the international data on test scores into the TIMSS scale. Most countries have made progress in the TIMSS study. The US average score for students in grade 4 increased by 23 points over the course of the last generation (from 492 points in 1995 to 515 points in 2019). This means that a 165-point difference is more than 7-times larger than the progress the US made in the last generation.

The strength of those country effects is very large. Patel and Sandefur write: “Controlling for a household income as flexibly as possible, we still find that country fixed effects explain over half of the pupil-level variation in reading scores, and about two-thirds of the variation in math scores.”

The evidence shows that it is education in the form of skills and learning – rather than mere school attendance – that matters for individual earnings and economic growth.

On the impact of education on economic growth see the research by Hanushek and Woessman:

Eric A Hanushek and Ludger Woessmann (2008) – The Role of Cognitive Skills in Economic Development. In Journal of Economic Literature 46 (3): 607–68.

Eric A. Hanushek, Ludger Woessmann (2010) – Education and Economic Growth. In Economics of Education (Amsterdam: Elsevier, 2010), Pages: pp. 60-67

Eric A Hanushek and Ludger Woessmann (2012) – Do Better Schools Lead to More Growth? Cognitive Skills, Economic Outcomes, and Causation. In Journal of Economic Growth 17 (4): 267–321.

And for a more detailed account: Eric A Hanushek and Ludger Woessmann (2015) – The Knowledge Capital of Nations: Education and the Economics of Growth. MIT Press.

Alan B. Krueger and Lindahl, M. (2001) – Education for growth: why and for whom? In J. Econ. Lit. 39, 1101–1136 (2001).

Literacy is a skill that is distributed along a continuum, to turn it into a binary variable a cutoff has to be chosen and there are different reasonable ways to choose that cutoff. In this statistic here the cutoff for what it means to be literate is lower than in the study that I cited first in this text (that’s why I emphasized the comprehension aspect in that study there). We explain this in more detail in How is literacy measured?

And for more recent data read this paper in Nature: Angrist, N., Djankov, S., Goldberg, P.K. et al. (2021) – Measuring human capital using global learning data. In Nature 592, 403–408 (2021). doi.org/10.1038/s41586-021-03323-7

McCoy, Dana Charles, Evan D. Peet, Majid Ezzati, Goodarz Danaei, Maureen M. Black, Christopher R. Sudfeld, Wafaie Fawzi, et al. (2016) – Early Childhood Developmental Status in Low- and Middle-Income Countries: National, Regional, and Global Prevalence Estimates Using Predictive Modeling. PLOS Medicine 13 (6): e1002034.

Walker, Susan P., Theodore D. Wachs, Julie Meeks Gardner, Betsy Lozoff, Gail A. Wasserman, Ernesto Pollitt, Julie A. Carter, et al. (2007) – Child Development: Risk Factors for Adverse Outcomes in Developing Countries. In Lancet 369 (9556): 145–57.

For an overview see “SPOTLIGHT 2 – Poverty hinders biological development and undermines learning” in World Bank (2018) – World Development Report 2018: Learning to Realize Education’s Promise. Washington, DC: World Bank. doi:10.1596/978-1-4648-1096-1.

Peter Boone, Ila Fazzio, Kameshwari Jandhyala, Chitra Jayanty, Gangadhar Jayanty, Simon Johnson, Vimala Ramachandrin, Filipa Silva & Zhaoguo Zhan (2013) – The Surprisingly Dire Situation of Children’s Education in Rural West Africa: Results from the CREO Study in Guinea-Bissau (Comprehensive Review of Education Outcomes). NBER Working Paper 18971. They have also summarized their findings in an article for VoxEU.

Fazzio, I., Eble, A., Lumsdaine, R. L., Boone, P., Bouy, B., Hsieh, P.-T. J., Jayanty, C., Johnson, S., & Silva, A. F. (2021) – Large learning gains in pockets of extreme poverty: Experimental evidence from Guinea Bissau. In Journal of Public Economics, 199, 104385.

In these places teaching has to come from the school, there is little chance for parents to reinforce learning, the literacy rates among parents is very low. What makes the situation additionally hard is that in this region multiple languages are spoken, none of which have their own script. The students in this study therefore first learned Portuguese (the country’s official language) in the first year of the program, before they attended three years of primary school within the study’s schools.

Banerjee, Abhijit, Rukmini Banerji, James Berry, Esther Duflo, Harini Kannan, Shobhini Mukerji, Marc Shotland, and Michael Walton (2017) – From Proof of Concept to Scalable Policies: Challenges and Solutions, with an Application. In Journal of Economic Perspectives, 31 (4): 73-102.

Gertler, Paul J., James J. Heckman, Rodrigo Pinto, Arianna Zanolini, Christel Vermeersch, Susan Walker, Susan M. Chang, et al. (2014) – Labor Market Returns to an Early Childhood Stimulation Intervention in Jamaica. In Science 344 (6187): 998–1001.

The authors find the intervention to be cost-effective, which could mean that some well-resourced organizations and the governments in some countries that are richer than Guinea-Bissau can adopt it. The authors also suggest that it would be valuable to find out exactly which aspect of these schools was so very important. That might offer the opportunity to leave out some expensive yet less-important aspects of the school program and achieve similar results for a smaller cost. This connects to the next section in my text that focuses on cost-effective small interventions rather than the bundled intervention that this study conducted.

The differences in spending on education are vast. According to the latest data Guinea-Bissau spends about int.-$ 66 per primary school student per year. High-income countries spend more than 150-times more on each child.

The latest data for Guinea-Bissau is for 2010, a long time ago, but unfortunately the country has only had very little economic growth since then. Back then the government spending per primary school student was international-$ 66.41. In a high-income country like Austria the spending at the same time was international-$ 10,469 per student per year. The ratio is 10,469/66.41=157.6. Other high-income countries spend even more than Austria.

Large implementations like the one in Guinea-Bissau can be a first step in that direction. Research in Kenya tried to identify exactly what of the Tusome program was crucial. Piper, B., Destefano, J., Kinyanjui, E.M. et al. (2018) – Scaling up successfully: Lessons from Kenya’s Tusome national literacy program. J Educ Change 19, 293–321 (2018). https://doi.org/10.1007/s10833-018-9325-4

On the point that many social science findings don’t generalize well see the research by Eva Vivalt.

See: Noam Angrist; Evans, David K.; Filmer, Deon; Glennerster, Rachel; Rogers, F. Halsey; Sabarwal, Shwetlena (2020) – How to Improve Education Outcomes Most Efficiently? A Comparison of 150 Interventions Using the New Learning-Adjusted Years of Schooling Metric. Policy Research Working Paper; No. 9450. World Bank.

There is a very wide literature on this fact. For a recent major paper see the following (and the references therein): Banerjee, Abhijit, Rukmini Banerji, James Berry, Esther Duflo, Harini Kannan, Shobhini Mukerji, Marc Shotland, and Michael Walton (2017) – From Proof of Concept to Scalable Policies: Challenges and Solutions, with an Application. In Journal of Economic Perspectives, 31 (4): 73-102.

And see the references in the previously cited Angrist et al. (2020) paper.

On its cost-effectiveness see: Angrist, Noam; Evans, David K.; Filmer, Deon; Glennerster, Rachel; Rogers, F. Halsey; Sabarwal, Shwetlena (2020) – How to Improve Education Outcomes Most Efficiently? A Comparison of 150 Interventions Using the New Learning-Adjusted Years of Schooling Metric. Policy Research Working Paper; No. 9450. World Bank.

Muralidharan, Karthik, Abhijeet Singh, and Alejandro J. Ganimian – (2019) – Disrupting Education? Experimental Evidence on Technology-Aided Instruction in India. In American Economic Review, 109 (4): 1426-60.

Angrist et al (2020) cite also other studies on whether these types of interventions work (for these they unfortunately lack information on costs so that effectiveness can be established, but the cost-effectiveness is unknown). See: Tahir Andrabi, Jishnu Das, and Asim Ijaz Khwaja (2017) – Report Cards: The Impact of Providing School and Child Test Scores on Educational Markets. In American Economic Review vol. 107, no. 6, June 2017 (pp. 1535-63).

On this point I recommend the excellent book by David Deutsch. Deutsch (2011) – The Beginning of Infinity: Explanations that Transform the World

Two texts that give a background on the overall problem are:

Michael Kremer, Conner Brannen, Rachel Glennerster (2013) – The challenge of education and learning in the developing world. In Science, 340 (6130) (2013), pp. 297-300

More recent literature on specific interventions or overviews that are relevant:

Paul Glewwe, Karthik Muralidharan (2016) – Improving education outcomes in developing countries: evidence, knowledge gaps, and policy implications. In Handbook of the Economics of Education, 5, Elsevier, Amsterdam, Holland (2016), pp. 653-743

Bold, Tessa, Kimenyi, Mwangi, Mwabu, Germano, Ng’ang’a, Alice, Sandefur, Justin (2018) – Experimental evidence on scaling up education reforms in Kenya. J. Public Econ. 168 (December): 1–20.

Abhijit Banerjee, Rukmini Banerji, James Berry, Esther Duflo, Harini Kannan, Shobhini Mukerji, Marc Shotland, Michael Walton (2017) – From proof of concept to scalable policies: challenges and solutions, with an application. J. Econ. Perspect., 31 (4) (2017), pp. 73-102

Dana Burde, Linden, L. Leigh (2013) – Bringing education to Afghan girls: a randomized controlled trial of village-based schools. In Am. Econ. J.: Appl. Econ., 5 (3) (2013), pp. 27-40

GiveWell has studied the cost-effectiveness of programs that focus on ‘Education in developing countries’. It was written in 2018 and therefore doesn’t take the recent literature into account.

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Coronavirus (COVID-19) Deaths

We are grateful to everyone whose editorial review and expert feedback on this work helps us to continuously improve our work on the pandemic. Thank you. Here you find the acknowledgements.

The data on the coronavirus pandemic is updated daily.

Our work belongs to everyone

This page provides data on the number of confirmed deaths from COVID-19.

We know – based on reports and estimates of excess deaths – that these figures underestimate the total impact of the pandemic on mortality globally. We provide data on excess deaths across the world here:

Explore the global data on confirmed COVID-19 deaths

Select countries to show in all charts

This page has a large number of charts on the pandemic. In the box below you can select any country you are interested in – or several, if you want to compare countries.

All charts on this page will then show data for the countries that you selected.

Confirmed deaths

What is the daily number of confirmed deaths?

Related charts:

Which world regions have the most daily confirmed deaths?

This chart shows the number of confirmed COVID-19 deaths per day.

Three points on confirmed death figures to keep in mind

All three points are true for all currently available international data sources on COVID-19 deaths:

→ We provide more detail on these three points in the section ‘Deaths from COVID-19: background‘.

Three tips on how you can interact with this chart

Daily confirmed deaths per million people

Why adjust for the size of the population?

Differences in the population size between countries are often large, and the COVID-19 death count in more populous countries tends to be higher. Because of this it can be insightful to know how the number of confirmed deaths in a country compares to the number of people who live there, especially when comparing across countries.

For instance, if 1,000 people died in Iceland, out of a population of about 340,000, that would have a far bigger impact than the same number dying in the United States, with its population of 331 million. 1 This difference in impact is clear when comparing deaths per million people of each country’s population – in this example it would be roughly 3 deaths/million people in the US compared to a staggering 2,941 deaths/million people in Iceland.

Three tips on how to interact with this map

What is the cumulative number of confirmed deaths?

Related charts:

Which world regions have the most cumulative confirmed deaths?

The previous charts looked at the number of confirmed deaths per day – this chart shows the cumulative number of confirmed deaths since the beginning of the COVID-19 pandemic.

Another tip on how you can interact with this chart

By pulling the ends of the blue time slider you can focus the chart on a particular period. If you bring them together to one point in time then the line chart becomes a bar chart – this of course only makes sense if you compare countries (that is what the

Add country button is for).

Cumulative confirmed deaths per million people

This chart shows the cumulative number of confirmed deaths per million people.

Weekly and biweekly deaths: where are confirmed deaths increasing or falling?

Why is it useful to look at weekly or biweekly changes in deaths?

For all global data sources on the pandemic, daily data does not necessarily refer to deaths on that day – but to the deaths reported on that day.

Since reporting can vary significantly from day to day – irrespectively of any actual variation of deaths – it is helpful to look at changes from week to week. This provides a slightly clearer picture of where the pandemic is accelerating, slowing, or in fact reducing.

The maps shown here provide figures on weekly and biweekly deaths: one set shows the number of deaths per million people in the previous seven or fourteen days (the weekly or biweekly cumulative total); the other set shows the percentage change (growth rate) over these periods.

Click to open interactive version

Global comparison: where are confirmed deaths increasing most rapidly?

Simply looking at the cumulative total or daily number of confirmed deaths does not allow us to understand or compare the speed at which these figures are rising.

The table here shows how long it has taken for the number of confirmed deaths to double in each country for which we have data. The table also shows both the cumulative total and daily new number of confirmed deaths, and how those numbers have changed over the last 14 days.

A tip on how to interact with this table

You can sort the table by any of the columns by clicking on the column header.

Confirmed deaths and cases: our data source

Our World in Data relies on data from Johns Hopkins University

The Johns Hopkins University dashboard and dataset is maintained by a team at its Center for Systems Science and Engineering (CSSE). It has been publishing updates on confirmed cases and deaths for all countries since January 22, 2020. A feature on the JHU dashboard and dataset was published in The Lancet in early May 2020. 2 This has allowed millions of people across the world to track the course and evolution of the pandemic.

Deaths from COVID-19: background

What is counted as a death from COVID-19?

The attribution of deaths to specific causes can be challenging under any circumstances. Health problems are often connected, and multiplicative, meaning an underlying condition can often lead to complications which ultimately result in death.

This is also true in the case of COVID-19: the disease can lead to other health problems such as pneumonia and acute respiratory distress syndrome (ARDS).

So, how are deaths from COVID-19 recorded? What is and isn’t included in these totals?

As is standard in death reporting, countries are asked to follow the ‘cause of death’ classifications from the WHO’s International Classification of Diseases guidelines. 3 However, countries also typically provide their own guidance to practitioners on how and when COVID-19 deaths should be recorded.

Let’s take a look at two concrete examples of national guidance: the United States and the UK. Both provide very similar guidelines for medical practitioners on the completion of death certificates. Here is the US CDC’s Vital Statistics Reporting Guidance; here is the UK Government guidance. 4

Both guidelines state that if the practitioner suspects that COVID-19 played a role in an individual’s death it should be specified on the death certificate. In some cases, COVID-19 may be the underlying cause of death, having led to complications such as pneumonia or ARDS. Even when it’s the underlying and not the direct cause, COVID-19 should be listed. 5

Although confirmed cases are reliant on a positive laboratory confirmation of the COVID-19 test, a laboratory diagnosis may not be required for it to be listed as the cause of death. In the UK guidelines, for example, it makes clear that practitioners should complete death certificates to the best of their knowledge, stating that “if before death the patient had symptoms typical of COVID-19 infection, but the test result has not been received, it would be satisfactory to give ‘COVID-19’ as the cause of death, and then share the test result when it becomes available. In the circumstances of there being no swab, it is satisfactory to apply clinical judgement.”

This means a positive COVID-19 test result is not required for a death to be registered as COVID-19. In some circumstances, depending on national guidelines, medical practitioners can record COVID-19 deaths if they think the signs and symptoms point towards this as the underlying cause.

The US CDC guidelines also make this clear with an example: the death of an 86-year-old female with an unconfirmed case of COVID–19. It was reported that the woman had typical COVID-19 symptoms five days prior to suffering an ischemic stroke at home. Despite not being tested for COVID-19, the coroner determined that the likely underlying cause of death was COVID–19 given her symptoms and exposure to an infected individual.

Why are there delays in death reports?

Just as with confirmed cases, the number of deaths reported on a given day does not necessarily reflect the actual number of COVID-19 deaths on that day, or in the previous 24 hours. This is due to lags and delays in reporting.

Delays can occur for several reasons:

The delay in reporting can be on the order of days and sometimes as long as a week or more. This means the number of deaths reported on a given day is not reflective of the actual number of deaths that occurred on that day.

Actual death figures are likely to be higher than confirmed deaths

What we know is the number of confirmed deaths due to COVID-19 to date. Limited testing and challenges in the attribution of the cause of death means that the number of confirmed deaths may not be an accurate count of the actual number of deaths from COVID-19.

In an ongoing outbreak the final outcomes – death or recovery – for all cases is not yet known. The time from symptom onset to death ranges from 2 to 8 weeks for COVID-19. 6 This means that some people who are currently infected with COVID-19 will die at a later date. This needs to be kept in mind when comparing the current number of deaths with the current number of cases.

What does the data on deaths and cases tell us about the mortality risk of COVID-19?

To understand the risks and respond appropriately we would also want to know the mortality risk of COVID-19 – the likelihood that someone who is infected with the disease will die from it.

We look into this question in more detail on our page about the mortality risk of COVID-19.

Acknowledgements

Endnotes

Here is our visualization for the population of Iceland and the US. Any other country can be added to this chart.

National Center for Health Statistics. Guidance for certifying deaths due to COVID–19. Hyattsville, MD. 2020.

The WHO, in its ICD documentation, defines the underlying cause of death as “a) the disease or injury which initiated the train of morbid events leading directly to death, or b) the circumstances of the accident or violence which produced the fatal injury.”

World Health Organization (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Available online at: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf

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