At Our World in Data our mission is to present research and data to make progress against the world’s largest problems. These problems are broad and varied, ranging from poverty, to health, education, environment, conflict and human rights.
We therefore present lots of charts – now more than 3000 – across many topics. All of these represent a piece in the jigsaw that gives us a perspective on the state of the world and how to make progress against the problems we face. But some metrics stand out: they are core to our work and understanding of the world. You will find them appearing again and again throughout Our World in Data.
We have tried to distill this list down to 12 core metrics. Making these choices isn’t easy– all the indicators we cover matter to understanding global development. As everything else on Our World in Data, we continue to update our content as new data becomes available so you can keep coming back and see how the world is changing. It is also worth mentioning that we make sure that links to us don’t break – if you embed one of these visualizations, you can be sure that it will remain available for many years to come.
To make them more easily findable, we have gathered them together, here, in a single place:
(1) Extreme Poverty
(2) GDP per capita
(4) Child Mortality
(5) Fertility Rate
(6) Life Expectancy
(7) Hunger and Undernourishment
(8) Education – years of schooling and literacy
(9) Access to improved water sources and sanitation
(10) Energy Access
(11) Energy Use
(12) CO2 emissions
Ending global poverty is surely one of the world’s most pressing problems. Leaving the worst poverty behind is critical to so many of the other aspects of development we cover – ranging from hunger and malnutrition, to health, education and access to basic resources.
Extreme poverty – indeed a very extreme poverty line – is defined by the ‘international poverty line’, which is living on less than $1.90 per day. This line is set by the United Nations and estimates are available for most countries in the world. In these measurement statisticians adjust for inflation and for price differences between countries (PPP-adjustment).
The visualization below shows the share living in extreme poverty in recent decades. This provides the most up-to-date estimates of extreme poverty from the World Bank. You can find estimates of global extreme poverty over the past few centuries here.
As we emphasize, the international poverty line is very low – well below what would be required to live a healthy and comfortable life. In the following chart we also show data on the share (or you can switch to absolute numbers using the ‘Relative’ checkbox) of the population living above or below higher poverty lines.
You find more research in our entry on Global Extreme Poverty.
Lifting people out of poverty – not just relative to the most extreme poverty line, but relative to higher thresholds – relies on economic growth across the world.
For most of human history, our ancestors were stuck in a world of poor health, hunger and little access to formal education. Economic growth – particularly over the past few centuries – has allowed some part of the world population to break out of these conditions.
Economic prosperity is measured as gross domestic product (GDP) per capita. Just as the poverty measure above, this metric is adjusted for price changes over time, and price differences between countries – it is measured in international-$ in 2011 prices.
The visualizations below shows both our long-run (dating back to year 1, extending to 2016) dataset on GDP per capita from the Maddison Project Database; and the World Bank that we rely on when we are focussing on the development over the last three decades
You find more research in our entry on Economic Growth.
The size of the population is our most commonly used metric, either directly or indirectly. You will find that throughout Our World in Data we present many metrics – ranging from poverty and income in the above examples, to child mortality (below), electricity access and CO2 emissions – in per capita terms as well as in absolute numbers.
As we describe here in our post on the global population cartogram, if we want to understand how living conditions across the world are changing, knowing how people are distributed across the world is key.
The visualization below shows our long-run dataset on population. This provides data on global and regional populations dating back to 10,000 BC, and country population back to the year 1800.
You find more research in our entry on World Population Growth.
One of the best indicators of how a country is doing is a newborn’s chances of surviving childhood. How able societies are to protect their children from dying is a crucial benchmark, one that reflects many aspects of development: healthcare, nutrition, maternal health, disease prevention and treatment. We can think of child mortality in some sense as an aggregate indicator of a country’s living conditions.
Child mortality is measured as the share of newborns that die before reaching the age of five.
In the visualizations below we present our long-run and short-run datasets on child mortality across the world. The long-run estimates come from a combination of Gapminder and data from the UN Inter-agency Group for Child Mortality Estimation (UN IGME).
In this dataset we show only data backed up with published estimates within the academic literature or the UN Inter-agency Group for Child Mortality Estimation. Gapminder also publishes long-run estimates for all countries – but stresses that these estimates come with high uncertainty. The full dataset can be found here.
Our short-run and most up-to-date dataset on child mortality comes from the UN Inter-agency Group for Child Mortality Estimation (UN IGME).
You find more research in our entry on Child & Infant Mortality.
Fertility rate – the average number of children per woman – is an important development indicator, not only in its impact on population and demographics but also as a proxy measure of other aspects of progress such as education, access to family planning and contraception, and child mortality.
The following visualization shows the long-run dataset on fertility rates based on estimates published by the UN Population Division and historical estimates that Gapminder has assembled. These historical estimates go back to the year 1541 for some countries.
In this dataset we show only data backed up with published estimates within the academic literature or United Nations Population Division. Gapminder also publishes long-run estimates for all countries – but stresses that these estimates come with high uncertainty. The full dataset can be found here.
You find more research in our entry on Fertility Rate.
Life expectancy is one of the most indicative metrics to evaluate our progress on improving health across the world. It is not only reflective of increasing longevity and maximum lifespans, but is also a strong reflection of child health and mortality.
We describe how life expectancy is measured in detail here.
Long-run estimates of life expectancy across the world are shown in the visualization below. For countries where historical records are much more readily available – such as the UK – estimates can extend as far back as 1543 – click on the UK to see this long-run perspective. Global and regional estimates extend back to the year 1770.
This dataset is based on a combination of data from the Clio Infra project, the UN Population Division, and global and estimates for world regions from James Riley (2005).1
You find more research in our entry on Life Expectancy.
Hunger has been a severe problem of humanity throughout most of our history. Growing enough food to feed one’s family was a constant struggle of daily life. Malnutrition and Famines were common around the world.
The visualization below shows our dataset on hunger across the world. This data is sourced from the UN Food and Agriculture Organization (FAO).
Hunger – also often referred to as undernourishment – is defined as having a calorie (i.e. energy) intake which is below an individual’s minimum requirements to lead a healthy life. Energy requirements of course vary depending on a person’s sex, weight, height, and activity levels, and this is taken into account in national and global estimates.
Having enough food to eat in energy terms is not the only requirement for good nutrition and health. The quality and diversity of diets in terms of protein and micronutrient intake is also important. We cover micronutrient deficiencies across the world in our entry on that topic here.
You find more research in our entry on Hunger and Undernourishment.
Education has been one of the most integral drivers and outcomes of global development.
The provision of education is now viewed in most parts of the world as a basic right – with pressure on governments to ensure high-quality education for all.
There are many metrics we can use to assess education access, quality, and attainment – we cover many of them throughout our work on education. In this list on the most important metrics we include two encompassing indicators: one as an educational input (the time adults over the age of 25 have been in formal education), and an output variable (literacy rates).
The visualizations below present these metrics over the long-term:
- Mean years of schooling estimates the average number of years of total schooling adults aged 25 years and older have received. This data extends back to the year 1870 and is based on the combination of data from Lee and Lee (2016); Barro-Lee (2018); and the UN Development Programme.
- The literacy rate measures the share of the population older than 14 years who can read and write. It is based on the combination of sources from the World Bank, CIA Factbook and additional academic sources. You can find more information on literacy here.
Unsafe water and poor sanitation are leading risk factors for death, particularly in low-income countries. This is especially true for children: around one million annual child deaths are attributed to unsafe water, sanitation and poor hygiene.
In the visualizations below we present our datasets on the share of populations across the world that have access to ‘improved’ drinking water and sanitation facilities.
An ‘improved’ drinking water source is defined as piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), or other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection). ‘Improved’ sanitation facilities include flush/pour flush (to piped sewer system, septic tank, pit latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and composting toilet.
One caveat to these definitions is that they make it much more likely that the source is clean and safe, but do not guarantee it. Additional metrics are being developed which aim to assess more directly the availability of clean water and safe sanitation. These metrics are the basis of two Sustainable Development Goal targets, and we present this data in our SDG-Tracker (for water here, and sanitation here).
Unfortunately, there are still large gaps in data availability for these direct metrics for clean water and safe sanitation across the world – many countries have no estimates currently available. For this reason, we continue to present data on ‘improved’ water sources and sanitation to provide a complete global picture. Once much wider data on safe drinking water and sanitation is available, we will adopt these as our core metrics.
You find more research in our entry on Water Use and Sanitation.
Access to energy is a critical aspect of global development. It is enormously important to improve living conditions: through the freeing of time from household chores (for example, washing clothes or cooking); increased productivity; improved healthcare; and digital connections to local and global networks. By switching to the Chart view you see that the share of the global population that has access to electricity increased from x to y percent over the last generation.
The lack of access to clean fuels for cooking is one of the most pressing environmental health problems: an estimated 1.6 million die from household air pollution every year because they do not have access to clean fuels for cooking and heating. Particularly in Africa and parts of Asia…
Basic energy access is measured through two indicators: access to electricity, and clean fuels for cooking. The visualizations below show the data on these two metrics across the world.
You find more research in our entry on Energy Access.
Having access to electricity and clean cooking and heating fuels (as we look at above) is a basic requirement to having sufficient energy to live a productive and healthy life. But this doesn’t tell us a lot about the quantity of energy people use. Some households may have access to electricity, but can afford to use only very little. This could be insufficient to meet their needs.
Energy use – average per capita energy consumption – is therefore another important metric to monitor to understand global development. The visualization below provides coverage of energy use per capita – from all sources, including biomass – across the world.
Total energy consumption (and the sources that make up this energy mix) are also important to resource and environmental concerns. We cover these metrics in detail here.
You find more research in our entry on Energy Access.
Economic growth and improving living standards have come at the cost of environmental degradation. One of the key trade-offs between human development and the environment is rising carbon dioxide (CO2) emissions. We know that as people get richer, their CO2 emissions tend to increase.
CO2 emissions have increased rapidly in recent decades; globally we now emit more than 36 billion tonnes each year.
There are many indicators we can use to look at CO2 emissions across the world: cumulative, annual, per capita, production vs. consumption-based, carbon intensity, sectorial or emissions embedded in trade. All of these metrics you can find in detail here.
The two core datasets we present below are production-based per capita CO2 emissions. This is shown in the visualization below. This data is sourced from the Global Carbon Project and the Carbon Dioxide Information Analysis Centre (CDIAC), with population from Gapminder and the UN Population Division used to calculate per capita figures.
You find more research in our entry on CO2 and Greenhouse Gas Emissions.