Niger had the highest birth rate in the world in 2024, with a birth rate of 46.6 births per 1,000 inhabitants. Angola, Benin, Mali, and Uganda followed. Except for Afghanistan, all 20 countries with the highest birth rates in the world were located in Sub-Saharan Africa. High infant mortality The reasons behind the high birth rates in many Sub-Saharan African countries are manyfold, but a major reason is that infant mortality remains high on the continent, despite decreasing steadily over the past decades, resulting in high birth rates to counter death rates. Moreover, many nations in Sub-Saharan Africa are highly reliant on small-scale farming, meaning that more hands are of importance. Additionally, polygamy is not uncommon in the region, and having many children is often seen as a symbol of status. Fastest-growing populations As the high fertility rates coincide with decreasing death rates, countries in Sub-Saharan Africa have the highest population growth rates in the world. As a result, Africa's population is forecast to increase from 1.4 billion in 2022 to over 3.9 billion by 2100.

The average for 2022 based on 195 countries was 18.38 births per 1000 people. The highest value was in Niger: 45.03 births per 1000 people and the lowest value was in Hong Kong: 4.4 births per 1000 people. The indicator is available from 1960 to 2022. Below is a chart for all countries where data are available.

In 2024, Monaco was the European country estimated to have the highest fertility rate. The country had a fertility rate of 2.1 children per woman. Other small countries such as Gibraltar or Montenegro also came towards the top of the list for 2024, while the large country with the highest fertility rate was France, with 1.64 children per woman. On the other hand, Ukraine had the lowest fertility rate, averaging around one child per woman.

The average for 2022 based on 192 countries was 2.51 births per woman. The highest value was in Niger: 6.75 births per woman and the lowest value was in Hong Kong: 0.7 births per woman. The indicator is available from 1960 to 2022. Below is a chart for all countries where data are available.

With an average of *** births per woman, Afghanistan had the highest fertility rate throughout the Asia-Pacific region in 2024. Pakistan and Papua New Guinea followed with the second and third-highest fertility rates, respectively. In contrast, South Korea and Hong Kong had the lowest fertility rates across the region. Contraception usage Fertility rates among women in the Asia-Pacific region have fallen throughout recent years. A likely reason is an increase in contraception use. However, contraception usage varies greatly throughout the Asia-Pacific region. Although contraception prevalence is set to increase across South Asia by 2030, women in both East Asia and Southeast Asia had higher contraception usage compared to South Asia in 2019. Women in APAC With the rise of feminism and the advancement of human rights, attitudes towards the role of women have changed in the Asia-Pacific region. Achieving gender equality has become a vital necessity for both men and women throughout the region. Alongside changes in traditional gender roles, women in certain Asia-Pacific countries, such as New Zealand, have become more inclined to marry later in life. Furthermore, the focus for younger women appears to be with having stability in their lives and securing an enjoyable job. This was displayed when female high school students in Japan were questioned about their future life aspirations.

This horizontal bar chart displays birth rate (per 1,000 people) by country full name using the aggregation average, weighted by population in Czech Republic. The data is about countries per year.

This horizontal bar chart displays birth rate (per 1,000 people) by country full name using the aggregation average, weighted by population in San Marino. The data is about countries per year.

A flexible model to reconstruct education-specific fertility rates: Sub-saharan Africa case study

The fertility rates are consistent with the United Nation World Population Prospects (UN WPP) 2022 fertility rates.

The Bayesian model developed to reconstruct the fertility rates using Demographic and Health Surveys and the UN WPP is published in a working paper.

Abstract

The future world population growth and size will be largely determined by the pace of fertility decline in sub-Saharan Africa. Correct estimates of education-specific fertility rates are crucial for projecting the future population. Yet, consistent cross-country comparable estimates of education-specific fertility for sub-Saharan African countries are still lacking. We propose a flexible Bayesian hierarchical model to reconstruct education-specific fertility rates by using the patchy Demographic and Health Surveys (DHS) data and the United Nations’ (UN) reliable estimates of total fertility rates (TFR). Our model produces estimates that match the UN TFR to different extents (in other words, estimates of varying levels of consistency with the UN). We present three model specifications: consistent but not identical with the UN, fully-consistent (nearly identical) with the UN, and consistent with the DHS. Further, we provide a full time series of education-specific TFR estimates covering five-year periods between 1980 and 2014 for 36 sub-Saharan African countries. The results show that the DHS-consistent estimates are usually higher than the UN-fully-consistent ones. The differences between the three model estimates vary substantially in size across countries, yielding 1980-2014 fertility trends that differ from each other mostly in level only but in some cases also in direction.

Funding

The data set are part of the BayesEdu Project at Wittgenstein Centre for Demography and Global Human Capital (IIASA, OeAW, University of Vienna) funded from the “Innovation Fund Research, Science and Society” by the Austrian Academy of Sciences (ÖAW).

We provide education-specific total fertility rates (ESTFR) from three model specifications: (1) estimated TFR consistent but not identical with the TFR estimated by the UN (“Main model (UN-consistent)”; (2) estimated TFR fully consistent (nearly identical) with the TFR estimated by the UN ( “UN-fully -consistent”, and (3) estimated TFR consistent only with the TFR estimated by the DHS ( “DHS-consistent”).

For education- and age-specific fertility rates that are UN-fully consistent, please see https://doi.org/10.5281/zenodo.8182960

Variables

Country: Country names

Education: Four education levels, No Education, Primary Education, Secondary Education and Higher Education.

Year: Five-year periods between 1980 and 2015.

ESTFR: Median education-specific total fertility rate estimate

sd: Standard deviation

Upp50: 50% Upper Credible Interval

Lwr50: 50% Lower Credible Interval

Upp80: 80% Upper Credible Interval

Lwr80: 80% Lower Credible Interval

Model: Three model specifications as explained above and in the working paper. DHS-consistent, Main model (UN-consistent) and UN-fully consistent.

List of countries:

Angola, Benin, Burkina Faso, Burundi, Cote D'Ivoire, Cameroon, Central African Republic, Chad, Comoros, Congo, Democratic Republic of Congo, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Tanzania, Togo, Uganda, Zambia, Zimbabwe

For most of the past two centuries, falling birth rates have been associated with societal progress. During the demographic transition, where pre-industrial societies modernize in terms of fertility and mortality, falling death rates, especially among infants and children, are the first major change. In response, as more children survive into adulthood, women have fewer children as the need to compensate for child mortality declines. This transition has happened at different times across the world and is an ongoing process, with early industrial countries being the first to transition, and Sub-Saharan African countries being the most recent to do so. Additionally, some Asian countries (particularly China through government policy) have gone through their demographic transitions at a much faster pace than those deemed more developed. Today, in countries such as Japan, Italy, and Germany, birth rates have fallen well below death rates; this is no longer considered a positive demographic trend, as it leads to natural population decline, and may create an over-aged population that could place a burden on healthcare systems.

This horizontal bar chart displays birth rate (per 1,000 people) by country full name using the aggregation average, weighted by population in South America. The data is about countries per year.

This horizontal bar chart displays birth rate (per 1,000 people) by country full name using the aggregation average, weighted by population in Comoros. The data is about countries per year.

Education- and age-specific fertility rates for 50 African and Latin American countries between 1970 and 2020.

The fertility rates are consistent with the United Nation's World Population Prospects (UN WPP) 2022 fertility rates.

The Bayesian model developed to reconstruct the fertility rates using Demographic and Health Surveys and the UN WPP is published in a working paper.

Abstract:

Consistent and reliable time series of education- and age-specific fertility rates for the past are difficult to obtain in developing countries, although they are needed to evaluate the impact of women’s education on fertility along periods and cohorts. In this paper, we propose a Bayesian framework to reconstruct age-specific fertility rates by level of education using prior information from the birth history module of the Demographic and Health Surveys (DHS) and the UN World Population Prospects. In our case study regions, we reconstruct age- and education-specific fertility rates which are consistent with the UN age specific fertility rates by four levels of education for 50 African and Latin American countries from 1970 to 2020 in five-year steps. Our results show that the Bayesian approach allows for estimating reliable education- and age-specific fertility rates using multiple rounds of the DHS surveys. The time series obtained confirm the main findings of the literature on fertility trends, and age and education specific differentials.

Funding:

These data sets are part of the BayesEdu Project at Wittgenstein Centre for Demography and Global Human Capital (IIASA, OeAW, University of Vienna) funded from the “Innovation Fund Research, Science and Society” by the Austrian Academy of Sciences (ÖAW).

Variables:

Country: Country names

Education: Four education levels, No Education, Primary Education, Secondary Education and Higher Education.

Age group: Five-year age groups between 15-19 and 45-49.

Year: Five-year periods between 1970 and 2020.

Median: Median education and age-specific fertility rate estimate

Upper_CI: 95% Upper Credible Interval

Lower_CI: 95% Lower Credible Interval

List of countries:

Angola

Benin

Brazil

Burkina Faso

Burundi

Cameroon

Central African Republic

Chad

Colombia

Comoros

Congo

Côte D'Ivoire

DR Congo

Ecuador

Egypt

Eswatini

Ethiopia

Gabon

Gambia

Ghana

Guatemala

Guinea

Honduras

Kenya

Lesotho

Liberia

Madagascar

Malawi

Mali

Mexico

Morocco

Mozambique

Namibia

Nicaragua

Niger

Nigeria

Paraguay

Peru

Rwanda

Sao Tome and Principe

Senegal

Sierra Leone

South Africa

Sudan

Tanzania

Togo

Tunisia

Uganda

Zambia

Zimbabwe

This horizontal bar chart displays birth rate (per 1,000 people) by country full name using the aggregation average, weighted by population in Hungary. The data is about countries per year.

The crude birth rate in Montenegro was estimated to be **** live births per 1,000 population in 2024, the highest among countries in Europe. By contrast, Ukraine had the lowest crude birth rate in Europe, at *** live births per 1,000 population.

CN: Population: Birth Rate: Shanxi data was reported at 0.694 % in 2024. This records an increase from the previous number of 0.613 % for 2023. CN: Population: Birth Rate: Shanxi data is updated yearly, averaging 1.132 % from Dec 1990 (Median) to 2024, with 35 observations. The data reached an all-time high of 2.254 % in 1990 and a record low of 0.613 % in 2023. CN: Population: Birth Rate: Shanxi data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Birth Rate: By Region.

Native Hawaiian and Pacific Islander women had the highest fertility rate of any ethnicity in the United States in 2022, with about 2,237.5 births per 1,000 women. The fertility rate for all ethnicities in the U.S. was 1,656.5 births per 1,000 women. What is the total fertility rate? The total fertility rate is an estimation of the number of children who would theoretically be born per 1,000 women through their childbearing years (generally considered to be between the ages of 15 and 44) according to age-specific fertility rates. The fertility rate is different from the birth rate, in that the birth rate is the number of births in relation to the population over a specific period of time. Fertility rates around the world Fertility rates around the world differ on a country-by-country basis, and more industrialized countries tend to see lower fertility rates. For example, Niger topped the list of the countries with the highest fertility rates, and Taiwan had the lowest fertility rate.

This horizontal bar chart displays birth rate (per 1,000 people) by country full name using the aggregation average, weighted by population in Russia. The data is about countries per year.

Historical chart and dataset showing Africa fertility rate by year from 1950 to 2025.

This horizontal bar chart displays birth rate (per 1,000 people) by country full name using the aggregation average, weighted by population in Tajikistan. The data is about countries per year.

This horizontal bar chart displays birth rate (per 1,000 people) by country full name using the aggregation average, weighted by population in Eastern Asia. The data is about countries.