# 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
# The decline of global fertility over the last 50 years
The map shows the total fertility rate from the 1950s to 2015. Press play to see how fertility changed since the 1950s.
# Total Fertility Rate around the world over the last two centuries
The following map shows the Total Fertility Rate dataset published by Gapminder.org. It includes estimates for all countries over the last 200 years and includes projections from the UN until the end of the century.
# 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.
# 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 17902
|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?
# The wanted fertility rate vs the actual fertility rate
The visualisation below shows the discrepancy between the wanted fertility rate and the actual fertility rate.
This visualisation shows the two measures over time.
# Correlates, Determinants & Consequences
# Child mortality and fertility
The model of the demographic transition – presented in the entry on population growth – links mortality and fertility. In the third stage of the model, the fertility rate starts to follow the downward trend in mortality, thus slowing population growth. Researchers have been particularly interested in examining this relationship: does a falling mortality rate lead to a falling fertility rate?
The academic literature outlines several mechanisms that have been used to link mortality and fertility rates. There are three mechanisms commonly used to link mortality and fertility rates:
1). The physiological effect: The likelihood of pregnancy is increased following the death of a child due to the sudden termination of breastfeeding, which triggers a resumption of ovulation and thus increases the period of exposure to a new conception3
2). The replacement effect: A couple deliberately has an additional birth in order to “compensate” for the death of an offspring; possibly because of the existence of a target family size4
3). The hoarding effect: When a family decides to have more births than their optimal number of children in order to protect themselves against the possibility of future high mortality in the family5
Recent studies provided evidence consistent with these channels. Reher, David Sven, et al. (2017)6 find evidence that couples are continuously regulating their fertility toward reproductive goals, and that families experiencing child fatalities “show significant increases in the hazard of additional births.”
In the below visualization, we can see the observed correlation between fertility and child mortality across the world. Countries with high child mortality rates tend to have much higher fertility rates, while countries with low child mortality rates experience lower fertility rates.
# When more children survive fertility decreases
The following visualisation shows the relationship between infant survival and fertility over time.
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 time7
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.
# Economic development and fertility
# 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 and each population is split into 5 quintiles, from the poorest 20% to the richest 20%. Unfortunately there is no data available to do this perfectly, we have to combine income and wealth data to approximate the relationship: Fertility is shown by the wealth quintile and economic prosperity is shown by the income quintile. The match is imperfect because we assumed that each household is in the same income and wealth quintile.
The correlation between high fertility and low economic prosperity is very strong. In the following we aim to understand in which way this relationship is causal.
# ‘Quantity’ vs. ‘quality’ of children
An influential strand of economic theory suggests that the process of modernization alters the structure of an economy, and with it, the dynamics that drive parent choices about how many children to have. As an economy shifts away from the production of physical capital and towards more human capital-intensive industries, the return on human capital becomes larger. Thus, households that may have previously benefitted from having more children as a source of labor are instead incentivized to invest in their children—for example through education—and to ultimately have fewer children. In slightly cruder words, parents choose “quality” over “quantity” of children8. The economist Oded Galor argues that in the context of the Industrial Revolution, this has had a two-fold effect: “On the one hand, the rise in income eased households’ budget constraints and provided more resources for quality as well as quantity of children. On the other hand, it induced a reallocation of these increased resources toward child quality. In the course of transition from the Malthusian Epoch, the effect of technological progress on parental income dominated, and population growth as well as the average population quality increased. Ultimately, further increases in the rate of technological progress induced a reduction in fertility, generating a decline in population growth and an increase in the average level of education.”9 This effect can be seen in the chart below, which plots the relationship between income and fertility during 1870 and 1930. While in 1870 fertility increased with income, in 1930 the relationship is reversed.
# More than just income at play
Above we point out that there is a strong link between income and fertility. Yet this relationship alone is not enough to tell the whole story. In the scatterplot below, for example, we can see that a country such as Vietnam—which had 10% of the income per capita of the US in 2014—had a slightly lower fertility rate than the US.
Drilling down beyond income per capita figures further complicates things, however. Looking at averages can obscure sometimes-large differences at a country level. In the chart below from the 2012 World Bank Development Report, we see that there are often large gaps between the poorest and richest quintile in a country, and that the poorest women bear the most children. Additionally, the poorer the country, the larger the gap between rich and poor.
# Women, education, and fertility
The economist James Duesenberry once said, “economics is all about how people make choices; sociology is all about how they don’t have choices to make.” In the previous section we took a look at the economics of fertility. In this section, we take a look the increasing availability of choices regarding fertility. Through developments in education, gender roles, and contraception, women (and to some extent men) are gaining reproductive agency, and this has contributed to pushing fertility rates down.
# Religion and fertility
The visualization below shows the children per woman plotted against the share of children that survive the first 5 years of life. Each country is colored according to the largest religious group in that country. The obvious relationship here is that in countries where more children survive, fertility is lower. This relationship holds independently of the major religion of the country.
Religious background can especially hardly explain the rapid change in the level of fertility that we see. Explanations that refer to the cultural background of a population regularly run into this problem that they not explain the very fast socio-economic changes over time, as in this case for religion.
Despite the ascribed values of a particular religion we have seen fertility falling in catholic Italy the fertility declined from 2.5 in 1966 to 1.2 at its lowest rate in 1997, and in Muslim Iran the fertility declined from 6.5 children per woman in 1982 to 1.8 in 2005!
# Child survival vs children per woman, by religion
# Coercive policy interventions
# Did China’s one-child policy reduce fertility?
A common claim—and one originated by the Chinese Government—is that China’s one-child policy has prevented approximately 400 million Chinese births. The view of many has been that this policy shaped a population age structure that contributed to economic growth (through the effect of the “demographic dividend”) and even contributed to global efforts to address climate change. But was the policy necessary to drive down fertility?
But is it really? The chart shows fertility in China since 1945. The striking decline and rebound of fertility around 1960 is due to the Great Leap Forward famine. But otherwise fertility in China was over 5 and even as high as 7 children per woman in the 1950s and 60s. Then, fertility started to decline – and as we see from the chart this decline started in 1970, long before the introduction of the one-child-policy. China promoted family planning policy in the 1960s and 70s, but the one-child policy was phased in between 1978 and 1980. By the time of the introduction of the one-child-policy, fertility in China had already more than halved. The huge reduction in fertility happened irrespective of the one-child-policy. Until 1980 child mortality – which we saw is an important determinant of fertility – had already halved from 12% (in 1969) to 6%.
In 2013 the researchers Wang Feng, Yong Cai, and Baochang Gu examined what China’s fertility rate would have been in the absence of the one-child policy.12 Using data from countries that had a similar birth rate to China’s in 1970, they compared the trajectories of change in those countries with that of China. The study found that “in other countries without a one-child policy the birth rate also declined, and it declined below the level predicted for China.” Additionally, the researchers estimated what China’s fertility would have been without the one-child policy by using the UN’s 2011 population projection model. The results showed that China’s fertility rate, which was already on a rapid decline in 1970, would have continued to decline after 1980 and by 2010 “fertility would have fallen to its currently observed level.” The continuation of the decline is due to the continuations of improving living conditions in China over this period.
Also shown in the chart is the evolution of fertility in Taiwan. Taiwan – which is claimed by China as part of China – never introduced a one-child-policy. Yet, Taiwan experienced the same decline that China did. From around 7 children per woman to fewer than two. Today the fertility in Taiwan is even lower than in China. In fact the fertility is close to 1 child per woman – just the aim that China had and never reached despite this being the planned outcome of the Chinese government. The point here is that economic and social development is truly important and ultimately what influences women’s decision about how many children they want to have.
# Did the one-child-policy work? Fertility in China and Taiwan (1945-2015)
# 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 200913 – 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, we can see the strong negative association between a country’s level of development and the fertility level. But at very high levels of development—HDI over 0.85 or even 0.9—this association is reversed. While causality cannot be established in this relationship, it is evident that after a given point, higher development is associated with increasing fertility. Not only do the authors 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 increases 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”.
For an interactive version of the above visualization, see here.
# Data Quality & Definition
# Definitions of fertility measures
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”.15
The total fertility rate (TFR)16 – 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.”17
# 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”.18
# 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.
# Data quality
# 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, 201119
# 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.20 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.