Life expectancy has increased rapidly since the Enlightenment. Estimates suggest that in a pre-modern, poor world, life expectancy was around 30 years in all regions of the world. In the early 19th century, life expectancy started to increase in the early industrialized countries while it stayed low in the rest of the world. This led to a very high inequality in how health was distributed across the world. Good health in the rich countries and persistently bad health in those countries that remained poor. Over the last decades this global inequality decreased. Countries that not long ago were suffering from bad health are catching up rapidly. Since 1900 the global average life expectancy has more than doubled and is now approaching 70 years. No country in the world has a lower life expectancy than the the countries with the highest life expectancy in 1800.
# Empirical View
# Rising life expectancy around the world
The visualization below shows the dramatic increase in life expectancy over the last few centuries. For the UK – the country for which we have the longest time-series – we see that before the 19th century there was no trend for life expectancy: life expectancy fluctuated between 30 and 40 years.
Over the last 200 years people in all countries in the world achieved impressive progress in health that lead to increases in life expectancy. In the UK, life expectancy doubled and is now higher than 80 years. In Japan health started to improve later, but the country caught up quickly with the UK and surpassed it in the late 1960s. In South Korea health started to improve later still and the country achieved even faster progress than the UK and Japan; by now life expectancy in South Korea has surpassed life expectancy in the UK.
The chart also shows how low life expectancy was in some countries in the past: A century ago life expectancy in India and South Korea was as low as 23 years. A century later, life expectancy in India has almost tripled and in South Korea it has almost quadrupled.
You can switch to the map view to compare life expectancy across countries. This view shows that there are still huge differences between countries: people in Sub-Saharan countries have a life expectancy of less than 50 years, while in Japan it exceeds 80.
# It is not only about child mortality – life expectancy by age
Yes, the decline of child mortality matters a lot for the increase of life expectancy. But as this chart below shows, there is much more to it.
Child mortality is defined as the number of children dying before their 5th birthday. To see how life expectancy has improved without taking child mortality into account we therefore have to look at the prospects of a child who just survived their 5th birthday: in 1841 a 5-year old could expect to live 55 years. Today a 5-year old can expect to live 82 years. An increase of 27 years.
At higher ages mortality patterns have also changed. A 50-year old could once expect to live an additional twenty years. Today the life expectancy of a 50-year old has increased to an additional 33 years.
And another important change can be studied in this chart: health inequality decreased very substantially. Look by how much life expectancy differed by age in 1845 – from 40 years for newborns to 79 for 70-year olds. Today this span is much smaller – from 81 to 86. This is because the chance of dying at a younger age has been steadily decreasing, which means that the equality of life spans across all people has increased.
The exceptionally high mortality of the 1918 flu epidemic stands out in this visualisation. Life expectancy (the period measure here) drops sharply.
Interestingly this epidemic affected few old people, life expectancy at an old age hardly changed. This was the case presumably since older people had some immunity from the previous Russian flu pandemic of 1889–90.1
Total life expectancy by age in England and Wales, 1800-20132
For the entire world the following visualisation presents the estimates and UN-projections of the remaining expected life years for 10-year-olds. The rise – best visible on the Map-view – shows that the increasing life expectancy is not only due to declining child mortality, but that mortality at higher ages also declined globally.
# Life expectancy has improved globally
Life expectancy in each region of the world stayed fairly stable for most of history until the onset of the “health transition,” the period in which life expectancy began to increase. The chart below shows that the health transition began at different times in different regions; Oceania began to see increases in life expectancy around 1870, while Africa didn’t begin to see increases until around 1920.
The source of the historical estimates is Riley (2005):
# Estimates of life expectancy before and after health transition by region, 1800-2001 – Riley (2005)3
|Africa||Americas||Asia||Europe||Former Soviet Union||Oceania||Global average (a)|
|Period when earliest health transition in region began||1920s||1820s or 1830s||1870s-1890s||1770s||1890s or 1900s||1860s or 1870s|
|Life expectancy before health transition||26.4||34.8||27.5||34.3||29||22.5 (b)|
# Life expectancy increased in all countries of the world
There is a lot of information in the following – rather unusual – chart. On the x-axis you find the cumulative share of the world population. And all the countries of the world are ordered along the x-axis ascending by the life expectancy of the population. On the y-axis you see the life expectancy of each country.
For 1800 (red line) you see that the countries on the left – India and also South Korea – have a life expectancy around 25. On the very right you see that in 1800 no country had a life expectancy above 40 (Belgium had the highest life expectancy with just 40 years).
In 1950 the life expectancy of all countries was higher than in 1800 and the richer countries in Europe and North America had life expectancies over 60 years – over the course of modernization and industrialization the health of the population improved dramatically. But half of the world’s population – look at India and China – made only little progress. Therefore the world in 1950 was highly unequal in living standards – clearly devided between developed countries and developing countries.
This division is ending: Look at the change between 1950 and 2012! Now it is the former developing countries – the countries that were worst off in 1950 – that achieved the fastest progress. While some countries (mostly in Africa) are lacking behind. But many of the former developing countries have caught up and we achieved a dramatic reduction of global health inequality.
The world developed from equally poor health in 1800 to great inequality in 1950 and back to more equality today – but equality on a much higher level.
How to read the following graph: On the x-axis you find the cumulative share of the world population. The countries are ordered along the x-axis ascending by the life expectancy of the population.
# The interactive world map of life expectancy
The world map below shows the historical data that we have for life expectancy. Use the slider below the map to see the change over time.
Download a map that shows life expectancy in 1800, 1950, and 2011
# The rise of maximum life expectancy
- The colored symbols represent the highest life expectancy of women from 1840 to today – indicating that country with the highest life expectancy at each point in time. For instance, we can see that in the mid-1800s, Norway had the highest life expectancy, but then by 1880 people in non-Maori New Zealand were expected to live the longest lives. The data shows that in the life expectancy in the leading country of the world has increased by three months every single year.
- The solid horizontal line represents the results of the linear regression on all these points; remarkably, the maximum life expectancy seems to follow this linear trend very closely. The gray dashed line is the extension of this trend into the future, and the red dashed lines represent ‘projections of female life expectancy in Japan published by the UN in 1986, 1999, and 2001.’
- The author names listed on the right refer to multiple predictions of the maximum possible life expectancy for humans. The horizontal black lines extending from the publication denote the prediction in each publication of the asserted ceiling on life expectancy attainable by humans and the year in which the study was published. Dublin published a study in 1928 that asserted that the maximum life expectancy possible was less than 65 while at the same time life expectancy in New Zealand was already over 65. The predictions of maximum life expectancy were proven wrong again and again over the course of the last century. On average the predictions have been broken within 5 years after publication.
# Record female life expectancy including time trend and asserted ceilings on life expectancy, 1840 to the present – Oeppen and Vaupel (2002)6
# Median age by country
The median age of a country’s population is an indicator of demographic makeup of the country and of its the population growth. These maps show how the world population is aging;the median age is increasing around the world. However, there are considerable differences between world regions – many parts of sub-Saharan Africa are much younger since both birth rates and mortality are higher.
# Number of deaths
The visualization below shows the number of global deaths per year since 1950.
# Correlates, Determinants & Consequences
# Better science and better health
David Cutler, Angus Deaton, and Adriana Lleras-Muney7 write: “Knowledge, science and technology are the keys to any coherent explanation. Mortality in England began to decline in the wake of the Enlightenment, directly through the application to health of new ideas about personal health and public administration, and indirectly through increased productivity that permitted (albeit with some terrible reversals) better levels of living, better nutrition, better housing and better sanitation. Ideas about the germ theory of disease were critical to changing both public health infrastructure and personal behavior. Similarly, knowledge about the health effects of smoking in the middle of the twentieth century has had profound effects on behavior and on health. Most recently, the major life-saving scientific innovations in medical procedures and new pharmaceuticals have had a major effect, particularly on reduced mortality from cardiovascular disease. There have also been important health innovations whose effect has been mainly in poor countries: for example, the development of freeze-dried serums that can be transported without refrigeration, and of oral rehydration therapy for preventing the death of children from diarrhea.”
# Life expectancy and GDP
This graph displays the correlation between life expectancy and gross domestic product (GDP) per capita. In general, countries with higher GDP have a higher life expectancy. The relationship seems to follow a logarithmic trend: the unit increase in life expectancy per unit increase in GDP decreases as GDP per capita increases.
The cross-sectional relationship between life expectancy and per capita income is known as the Preston Curve, named after Samuel H. Preston who first described it in 1975. In the chart I plot the cross-sectional relationship for the years 1800, 1950, 1980, and 2012. Interestingly we then find that the life expectancy associated with a given level of real income is rising over time. If economic development were the only determinant of health countries that get richer would just move along the same curve. Since this is not the case we can conclude that economic development cannot be the sole determinant of health. A possible explanation for this changing relationship is that scientific understanding and technological progress makes some very efficient public health interventions – such as vaccinations, hygiene measures, oral rehydration therapy, and public health measures – cheaper and brings these more and more into the reach of populations with lower incomes.
# Life expectancy and intelligence
A recent study by Rosalind Arden et al (2015)9 analyzes the causes for the link between intelligence and longer lifespan. They note that many previous studies have found this correlation but that distinguishing the direction of the causality in this relationship is difficult. Common causes posited include socioeconomic status affecting both intelligence and life expectancy, higher intelligence causing more healthy behavior choices, and shared genetic factors influencing both intelligence and health. By analyzing three data sets of twins from the US, Sweden and Denmark, they determined that genetic factors contributed the most to the correlation between lifespan and intelligence.
# Data Quality & Definition
Life expectancy is the average number of years a child born now would live if current mortality patterns were to stay the same.
# Data Sources
- Long-run data on life expectancy at birth for the time period since 1800 is available from the Clio Infra project.
- Long-term data is available at Lifetable.de. Lifetable.de – a project by researchers at the Max Planck Institute in Rostock, the University of Berkeley and the Institut national d’études démographiques in Paris – presents life expectancy estimates drawn from some 700 sources. The estimates along with the sources are presented at lifetable.de.
- Gapminder presents estimates for life expectancy since 1800. Here is the corresponding documentation.
# Post 1960
- Annual data on ‘Life expectancy at birth’ [by country] – since 1961 – is available in the World Development Indicators (WDI) published by the World Bank. For the male population, the female population and the total population.
- The World Health Organization (WHO) publishes data on life expectancy. Data are only available for the time after 1990.
- Other more specialized data are available in the The Human Mortality Database (free – but registration is necessary).
- The Eurostat website ‘Statistics Explained’ publishes up-to-date statistical information on mortality and life expectancy.
- Wikipedia includes a list of countries by life expectancy which includes up-to-date data from different sources.