Over the past 30 years, the birth rate in the United States has been steadily declining, and in 2022, there were 11 births per 1,000 of the population. In 1990, this figure stood at 16.7 births per 1,000 of the population. Demographics have an impact The average birth rate in the U.S. may be falling, but when broken down along ethnic and economic lines, a different picture is painted: Native Hawaiian and other Pacific Islander women saw the highest birth rate in 2022 among all ethnicities, and Asian women and white women both saw the lowest birth rate. Additionally, the higher the family income, the lower the birth rate; families making between 15,000 and 24,999 U.S. dollars annually had the highest birth rate of any income bracket in the States. Life expectancy at birth In addition to the declining birth rate in the U.S., the total life expectancy at birth has also reached its lowest value in recent years. Studies have shown that the life expectancy of both men and women in the United States has declined as of 2021. Declines in life expectancy, like declines in birth rates, may indicate that there are social and economic factors negatively influencing the overall population health and well-being of the country.

This paper documents a set of facts about the dramatic decline in birth rates in the United States between 2007 and 2020 and explores possible explanations for it. The overall reduction in the birth rate reflects both very large declines within certain groups of women, including teens and Hispanic women – and smaller declines among demographic groups that comprise a large population share, including college-educated white women. We explore potential economic, policy, and social factors that might be responsible for the overall decline. We conclude from our empirical examination of possible factors that there is not a readily identifiable economic or policy factor or set of factors this is likely responsible for a substantial share of the decline. Instead, the patterns observed suggest that widespread, hard to quantify changes in preferences for having children, aspirations for life, and the nature of parenting are more likely behind the recent decline in US births. We conclude with a brief discussion about the societal consequences for a declining birth rate and what the United States might do about it.

In 2024, the birth rate in South Korea lay at 0.75 births per woman. The country has long been struggling with a declining birth rate, first dropping below one birth per woman in 2018.

The total fertility rate of the world has dropped from around five children per woman in 1950, to 2.3 children per woman in 2023, which means that women today are having fewer than half the number of children that women did 75 years ago. This change has come as a result of the global demographic transition, and is influenced by factors such as the significant reduction in infant and child mortality, reduced number of child marriages, increased educational and vocational opportunities for women, and the increased efficacy and availability of contraception. While this change has become synonymous with societal progress, it does have wide-reaching demographic impact - if the global average falls below replacement level (roughly 2.1 children per woman), as is expected to happen in the 2050s, then this will lead to long-term population decline on a global scale.

When broken down by continent, Africa is the only region with a fertility rate above the global average, while it and Oceania are the only regions with above replacement level fertility rates. Until the 1980s, women in Africa could expect to have almost seven children throughout the course of their lifetimes, and there are still eight countries in Africa where the average woman of childbearing age can still expect to have five or more children in 2023. Historically, Europe has had the lowest fertility rate in the world over the past century, falling below replacement level in 1975 - Europe's population has grown through a combination of migration and increasing life expectancy, however even high immigration rates could not prevent its population from going into decline in 2021.

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.

Chart and table of the U.S. birth rate from 1950 to 2025. United Nations projections are also included through the year 2100.

Graph and download economic data for Fertility Rate, Total for the United States (SPDYNTFRTINUSA) from 1960 to 2022 about fertility, rate, and USA.

Today, globally, women of childbearing age have an average of approximately 2.2 children over the course of their lifetime. In pre-industrial times, most women could expect to have somewhere between five and ten live births throughout their lifetime; however, the demographic transition then sees fertility rates fall significantly. Looking ahead, it is believed that the global fertility rate will fall below replacement level in the 2050s, which will eventually lead to population decline when life expectancy plateaus. Recent decades Between the 1950s and 1970s, the global fertility rate was roughly five children per woman - this was partly due to the post-WWII baby boom in many countries, on top of already-high rates in less-developed countries. The drop around 1960 can be attributed to China's "Great Leap Forward", where famine and disease in the world's most populous country saw the global fertility rate drop by roughly 0.5 children per woman. Between the 1970s and today, fertility rates fell consistently, although the rate of decline noticeably slowed as the baby boomer generation then began having their own children. Replacement level fertility Replacement level fertility, i.e. the number of children born per woman that a population needs for long-term stability, is approximately 2.1 children per woman. Populations may continue to grow naturally despite below-replacement level fertility, due to reduced mortality and increased life expectancy, however, these will plateau with time and then population decline will occur. It is believed that the global fertility rate will drop below replacement level in the mid-2050s, although improvements in healthcare and living standards will see population growth continue into the 2080s when the global population will then start falling.

In 2022, there were around 6.3 live births per 1,000 inhabitants in Japan, down from about 6.6 in the previous year. The total number of live births in the nation amounted to approximately 770.8 thousand in 2022.

Japan’s aging society Directly after the end of WWII, the live birth rate in Japan was over 30 per 1,000 of population. The rate has constantly dropped in the last decades after the second baby boom (between 1971 and 1974). Meanwhile, the life expectancy of the Japanese people has continued to increase, reaching about 87.7 years for women and 81.7 years for men in 2020. Due to the combination of both factors, Japan has developed into the most rapidly aging society in the world. Almost 30 percent of Japan’s population is currently aged 65 years and older, falling into the “super-aged nation” defined by international institutions and organizations. 

Decreasing number of marriages In Japan, the number of births outside of marriage is small. The Japanese government, therefore, considers the decreasing number of marriages as the driving factor behind the country’s fertility decline. As of 2022, the number of marriages per 1,000 Japanese citizens was 4.1, less than half compared to that in the early 1970s. The average age of first marriage has also risen for both men and women in recent years. This trend can be partially attributed to the increasing number of employed and therefore financially and socially independent women in the last two decades. The employment rate of women in Japan exceeded 50 percent for the first time in history in 2018.

The outbreak of the COVID-19 in early 2020 and the recurring epidemic in later years have disturbed China’s economy. Moreover, China’s demographic dividend has been disappearing due to its fastest aging population and declining birth rate. The birth rates in eastern provinces of China are much lower than those of the western provinces. Considering the impacts of the COVID-19 and aging population, this paper focused on the relationship between birth rate and the disposable income and tried to find effective measures to raise China’s birth rate. We discovered through regression analysis that the link between per capita disposable income and birth rate is initially "reverse J" and later "inverted J", indicating that per capita disposable income will influence the birth rate. Women’s employment rate and educational level are negatively correlated with the birth rate. To raise the fertility rate in China, it is necessary to increase the marriage rate and the willingness to have children by raising the per capita disposable income and introducing effective tax relief policies.

About this dataset

Context

The dataset tabulates the data for the Texas population pyramid, which represents the Texas population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Texas, is 32.4.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Texas, is 19.0.
• Total dependency ratio for Texas is 51.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Texas is 5.3.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Texas population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Texas for the selected age group is shown in the following column.
• Population (Female): The female population in the Texas for the selected age group is shown in the following column.
• Total Population: The total population of the Texas for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Texas Population by Age. You can refer the same here

In the United States, the crude birth rate in 1800 was 48.3 live births per thousand people, meaning that 4.8 percent of the population had been born in that year. Between 1815 and 1825 the crude birth rate jumped from 46.5 to 54.7 (possibly due to Florida becoming a part of the US, but this is unclear), but from this point until the Second World War the crude birth rate dropped gradually, reaching 19.2 in 1935. Through the 1940s, 50s and 60s the US experienced it's baby boom, and the birth rate reached 24.1 in 1955, before dropping again until 1980. From the 1980s until today the birth rate's decline has slowed, and is expected to reach twelve in 2020, meaning that just over 1 percent of the population will be born in 2020.

About this dataset

Context

The dataset tabulates the data for the Iowa, LA population pyramid, which represents the Iowa population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Iowa, LA, is 40.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Iowa, LA, is 33.7.
• Total dependency ratio for Iowa, LA is 73.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Iowa, LA is 3.0.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Iowa population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Iowa for the selected age group is shown in the following column.
• Population (Female): The female population in the Iowa for the selected age group is shown in the following column.
• Total Population: The total population of the Iowa for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Iowa Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Poplar, MT population pyramid, which represents the Poplar population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Poplar, MT, is 30.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Poplar, MT, is 16.5.
• Total dependency ratio for Poplar, MT is 47.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Poplar, MT is 6.0.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Poplar population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Poplar for the selected age group is shown in the following column.
• Population (Female): The female population in the Poplar for the selected age group is shown in the following column.
• Total Population: The total population of the Poplar for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Poplar Population by Age. You can refer the same here

Per capita disposable income of urban residents in different regions Unit: Yuan.

Chart and table of the Philippines birth rate from 1950 to 2025. United Nations projections are also included through the year 2100.

About this dataset

Context

The dataset tabulates the data for the Lower Gwynedd Township, Pennsylvania population pyramid, which represents the Lower Gwynedd township population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Lower Gwynedd Township, Pennsylvania, is 34.4.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Lower Gwynedd Township, Pennsylvania, is 52.0.
• Total dependency ratio for Lower Gwynedd Township, Pennsylvania is 86.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Lower Gwynedd Township, Pennsylvania is 1.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Lower Gwynedd township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Lower Gwynedd township for the selected age group is shown in the following column.
• Population (Female): The female population in the Lower Gwynedd township for the selected age group is shown in the following column.
• Total Population: The total population of the Lower Gwynedd township for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Lower Gwynedd township Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Orange County, CA population pyramid, which represents the Orange County population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Orange County, CA, is 26.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Orange County, CA, is 22.9.
• Total dependency ratio for Orange County, CA is 49.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Orange County, CA is 4.4.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Orange County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Orange County for the selected age group is shown in the following column.
• Population (Female): The female population in the Orange County for the selected age group is shown in the following column.
• Total Population: The total population of the Orange County for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Orange County Population by Age. You can refer the same here

The statistic shows the 20 countries with the lowest fertility rates in 2024. All figures are estimates. In 2024, the fertility rate in Taiwan was estimated to be at 1.11 children per woman, making it the lowest fertility rate worldwide. Fertility rate The fertility rate is the average number of children born per woman of child-bearing age in a country. Usually, a woman aged between 15 and 45 is considered to be in her child-bearing years. The fertility rate of a country provides an insight into its economic state, as well as the level of health and education of its population. Developing countries usually have a higher fertility rate due to lack of access to birth control and contraception, and to women usually foregoing a higher education, or even any education at all, in favor of taking care of housework. Many families in poorer countries also need their children to help provide for the family by starting to work early and/or as caretakers for their parents in old age. In developed countries, fertility rates and birth rates are usually much lower, as birth control is easier to obtain and women often choose a career before becoming a mother. Additionally, if the number of women of child-bearing age declines, so does the fertility rate of a country. As can be seen above, countries like Hong Kong are a good example for women leaving the patriarchal structures and focusing on their own career instead of becoming a mother at a young age, causing a decline of the country’s fertility rate. A look at the fertility rate per woman worldwide by income group also shows that women with a low income tend to have more children than those with a high income. The United States are neither among the countries with the lowest, nor among those with the highest fertility rate, by the way. At 2.08 children per woman, the fertility rate in the US has been continuously slightly below the global average of about 2.4 children per woman over the last decade.

Regression results after excluding the variable EL.