# Empirical View
The first chart shows the total fertility rate for the entire world population at 5 different points in time. The total fertility rate is the number of children born per woman. More specifically, it tells you the average number of children that would be born to a woman (who would live until the end of her childbearing years) if she were to experience the exact current age-specific fertility rates through her lifetime.
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 descending by the country’s fertility rate. This makes it possible to see the fertility rate for each country, and it is also possible to see which share of the world population had a fertility rate higher than a given level. For the blue line – referring to the latest data (2005-10) – we see that 20% of the world population have a fertility rate of higher than 3.
For 1950-55 (red line), you see that the countries on the very left – Rwanda, Kenya, the Philippines and others that are not labelled – had a fertility rate of more than 7. China had a fertility rate of just over 6 and India a fertility rate of just under 6! On the very right of the red line you see that in 1950-55 there was only one country in the world with a fertility rate below 2: tiny Luxembourg.
Looking at the orange line, you see that until 1975-80 some countries substantially reduced their fertility: India’s fertility rate fell to a still very high 4.9, and China’s fell to 3. Other countries maintained very high fertility levels. In Yemen, the fertility rate was 8.6 children per woman.
The latest data from the UN refers to 2005-2010. 80% of the world population lives in countries where women have on average fewer than 3 children. The global average fertility rate is 2.5. This means that global fertility is barely higher than the global replacement fertility. The replacement fertility is the total fertility rate at which the population size stays constant. If there were no mortality in the female population until the end of the childbearing years, the replacement fertility would be exactly 2. With the current level of mortality the global replace fertility is 2.3 – the narrow gap between the current global fertility and the global replacement rate means that the increase of the world population is due to the increasing length of life and population momentum.1
We also see convergence in fertility rates: the countries that already had low fertility in the 1950s only slightly decreased fertility, while many of the countries that had the highest fertility back then saw a rapid reduction of the number of children per woman.
Comparing the red, orange and blue lines also makes it possible to see the change in single countries: In Iran, the fertility rate in 1980-85 was 6.3; in the latest data we see that it is down to the level of Sweden: 1.9 children per woman. In Thailand, the fertility rate in 1950-55 was 6.14, in 1980-85 it was 3.92, and today it is 1.49.
In the chart I have also included the projections for the 21st century. The UN – which has a very good track record for its past demographic projections – expects global fertility to fall further in most countries so that the rate will be below 2 by the end of the century.
# World population by level of fertility over time, 1950-2050
# Total Fertility Rate from 1950 to 2015
The following map shows the total fertility rate around the world. Press play to see how fertility changed since the 1950s.
# Total Fertility Rate around the world over the last two centuries
The following chart shows the Total Fertility Rate dataset published by Gapminder.org. It includes estimates for all countries over the last 200 years: Add any country to the chart by clicking ‘+ Add Country’.
# Birth Rates Around the World
In addition to the fertility rate a second commonly used measure is the birth rate. The birth rate is expressed as the annual number of births per 1,000 people in the population.
This measure is visualized in the interactive chart below.
# The decline of global fertility over the last 50 years
World maps of the total fertility rate (children per woman), 1960-65 and 2005-102
# Fertility was high in the time before population growth
The table shows that in European countries one woman gave on average birth to 4.5 to 6.2 children in the 18th century. The population of a society does not increase when every woman is replaced on average by two children. As the tables presents fertility rates when the population did not yet grow rapidly we can infer that on average 2.5 to 4.2 children died per woman.
Age of Marriage of Women and Marital Fertility in Europe before 17903
|Country or Region||Mean age at first marriage||Births per married women||Percentage never married||Total fertility rate|
# Fertility can decline extremely fast
The decline of fertility is one of the most fundamental social changes that happened in human history.
It is therefore especially surprising how very rapidly this transition can indeed happen. As we see from the chart below it took Iran only 10 years for fertility to fall from more than 6 children per woman to fewer than 3 children per woman. (Iran made this transition under a conservative Muslim government.)
We also see from the chart that the speed with which countries can achieve low fertility has increased over time. A century ago it took the United Kingdom 95 years and the US 82 years to reduce fertility from more than 6 to less than 3. This is a pattern that we see often in development: those countries that first experience social change take much longer for transitions than those who catch up later: Countries that were catching up increased life expectancy much faster, they reduced child mortality more quickly and were able to grow their incomes much more rapidly.
# How long did it take for fertility to fall from 6 children per woman to fewer than 3 children per woman?
# Correlates, Determinants & Consequences
# When more children survive fertility decreases
On the y-axis we measure the number of annual live births per 1,000 people. On the x-axis we measure how many infants, who were born alive, survive their 1st year of life – this is the infant survival rate.
The chart shows how these two aspects changed over the course of the 20th century: At the beginning of the century all 4 countries can be found in the upper left corner – they are characterized by high fertility and an infant survival rate below 85%. If we follow the 4 lines we are taken to the bottom right corner and see that women have fewer children when the mortality rate of babies goes down.
The causal link between infant survival and fertility is established in both directions: Firstly, increasing infant survival reduces the parents’ demand for children. And secondly, a decreasing fertility allows the parents to devote more attention and resources to their children.
Infant survival and fertility through time4
The following chart takes a global perspective on the same phenomenon: The chart shows data for all countries in the world over more than 5 decade. For each country in the world I plotted the level of child survival and fertility for all years. We see that the regularity that the previous chart showed is also true for the world as a whole, as more children survive the number of children per woman is declining.
Emphasized is the world average in blue: For the world as a whole we see this connection between child health and fertility. Every 5th child born in 1960 died during childhood; corresponding to this high level of child mortality the fertility rate was very high: Women in the world had 5 children on average! Over the following 5 decades child health improved globally and 96% of the children born in 2014 survived childhood; correspondingly global fertility declined and in fact is now less than half the level of 1960 (2.45 per woman).
This link between fertility and child mortality is an immensely important insight and tells us what drives the acceleration and slowdown of population growth: In the initial stage of the transition, when fertility rates are still high but health is already improving, the population starts to grow. But then, a bit later, we see that this transition works to decrease population growth since improving health of the children leads to lower fertility. It is an important part of the mechanism behind the demographic transition.
A very cynical view is that a decrease in child mortality is bad for the world since it would contribute to the overpopulation of the planet. The chart below shows that this opinion is not just contemptuous of human life but plainly wrong: When more infants survive fertility goes down and the temporary population growth comes to an end. If we want to ensure that the world’s population increase comes to an end soon we must work to increase child survival.
# The richer the people, the lower the fertility
The following plot shows the close relation between the income level (measured by GDP per capita) and the total fertility rate. It is not just showing country averages but also taking the within-country inequality into account: For most poor countries it shows the fertility by wealth quintile against the income quintile in the same country.
# Fertility is first falling with development – and then rising with development
We have already seen that as a country develops – child mortality declines and incomes grow – the fertility declines rapidly.
The demographers Mikko Myrskylä, Hans-Peter Kohler & Francesco Billari studied what happens at very high levels of development. To measure development they relied on the Human Development Index – a measure published by the UN that combines with equal weight indicators of a country’s health, material living standards and level of education.
In their study – published in Nature in 20095 – they found “a fundamental change in the well-established negative relationship between fertility and development as the global population entered the twenty-first century.”
The visualization below shows their finding. Again they found the strong negative association between a country’s level of development and the fertility level. But the most recent data also indicate that at very high levels of development – HDI over 0.85 or even 0.9 – this association is reversed. Higher development then leads to increasing fertility.
In the paper the authors do not only show this relationship cross-sectionally but also over time: after reaching the lowest Total Fertility Rate at HDI values between 0.85 and 0.9 fertility then increased again as countries advanced to the highest development levels.
It is a finding with important consequences. The authors note that this reversal “has the potential to slow the rates of population ageing, thereby ameliorating the social and economic problems that have been associated with the emergence and persistence of very low fertility”.
# Data Quality & Definition
Fertility is measured in different period and cohort measures:
# Period Measures
The definition of the crude birth rate (CBR) – or simply birth rate – is “the number of live births occurring among the population of a given geographical area during a given year, per 1,000 mid-year total population of the given geographical area during the same year”.6
The total fertility rate (TFR)7 – or simply Fertility Rate – is defined as the average number of children that would be born to a woman over her lifetime if:
- The woman were to experience the exact current age-specific fertility rates (ASFRs) through her lifetime, and
- The woman were to survive from birth through the end of her reproductive life.
It is expressed as children per woman.
The age-specific fertility rate (ASFR) “measures the annual number of births to women of a specified age or age group per 1,000 women in that age group.”8
# Cohort Measures
The net reproduction rate (NRR) is defined as “the average number of daughters a hypothetical cohort of women would have at the end of their reproductive period if they were subject during their whole lives to the fertility rates and the mortality rates of a given period. It is expressed as number of daughters per woman”.9
# Incomplete civil registration
Civil registration systems register vital events – births, deaths, fetal deaths, marriages and divorces – for governments. As such they are a key source for vital statistics and fertility measures. As the following map shows the civil registration coverage of births is incomplete in many countries in Africa and Asia.
Civil registration coverage of births (in percent) – latest available data (in 2014)10
# Within-country differences
A second aspect about country level data of fertility to keep in mind is that there can be considerable heterogeneity within countries, which are hidden in the mean fertility which were discussed in this entry. The mean Total Fertility Rate for India in 2010 was 2.8 (UN Data): But this average hides the fact that the fertility in many Southern Indian regions was below 1.5 (which is similar to the mean fertility in many European countries), while the fertility in Northern India was still higher than 5 children per woman (which is as high as the mean of the African countries with the highest fertility).
Total fertility rate map: average births per woman by districts, 201111
# Data Sources
# United Nations World Population Prospects
- Data: Number of annual births, the Crude Birth Rate (CBR), Total fertility (TFR), Net Reproduction Rate (NRR) among other measures.
- Geographical coverage: The entire world – countries and world regions.
- Time span: From 1950 onwards.
- Available at: Online here
- The League of Nations records are available via Northwestern University and these records include population figures, birth rates, and death rates. They are available by world region and country (a global total is missing however).
# Human Fertility Database (HFD) and the associated Human Fertility Data website
- Data: The data is detailed and includes Data fertility rates (by age (ASFR), cohort and period) and mean ages at childbearing.
- Geographical coverage: More than 70 countries
- Time span: Mostly the 2nd half of the 20th century although data for the 1st half of the century is available for some countries
- Available at: Human Fertility Database and Human Fertility data website
# The International Historical Statistics (IHS)
- Data: Birth rates
- Geographical coverage: Various countries around the world
- Time span: From 1750 onwards
- Available at: Published in three volumes covering more than 5000 pages.12 At some universities you can access the online version of the books where data tables can be downloaded as ePDFs and Excel files. The online access is here.
- These statistics – originally published under the editorial leadership of Brian Mitchell (since 1983) – are a collection of data sets taken from many primary sources, including both official national and international abstracts dating back to 1750.
- Data: The total fertility rate
- Geographical coverage: Data is available for a great number of countries.
- Time span: Data goes back to 1800
- Available at: The data documentation and the spreadsheets available for download can be found here.
- The documentation includes a discussion of the quality of the available Data fertility.
# World Development Indicators (WDI) published by the World Bank.
- Data: Total fertility rate and on the crude birth rate
- Geographical coverage: Entire world, by country and world region
- Time span: Data is available only for the last decades.
- Available at: Data for total fertility rate is here, and data for crude birth rate is here.
# The Demographic and Health Surveys (DHS)
- Data: Fertility and related data.
- Geographical coverage: More than 90 countries – here is the list of countries.
- Time span: Data is available from the early 1990s onwards
- Available at: Online here
- For the time preceding the DHS data timeframe the World Fertility Survey is available at Princeton University here.
# Important regular publications
The UN’s World Fertility Report 2012 is online here. The Eurostat website ‘Statistics Explained’ publishes up-to-date statistical information on fertility here. For a comparison of the latest available data of different data sets, see Wikipedia here.