The total fertility rate of the world has dropped from around 5 children per woman in 1950, to 2.2 children per woman in 2025, which means that women today are having fewer than half the number of children that women did 75 years ago. Replacement level fertility 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. Regional variations When broken down by continent, Africa is the only region with a fertility rate above the global average, and, alongside Oceania, it is the only region with a fertility rate above replacement level. Until the 1980s, the average woman in Africa could expect to have 6-7 children over the course of their lifetime, and there are still several countries in Africa where women can still expect to have 5 or more children in 2025. Historically, Europe has had the lowest fertility rates 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.

Graph and download economic data for Crude Birth Rate for the United States (SPDYNCBRTINUSA) from 1960 to 2023 about birth, crude, rate, and USA.

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.

Crude birth rates, age-specific fertility rates and total fertility rates (live births), 2000 to most recent year.

Births rates across Lake County, Illinois by ZIP Code. Explanation of field attributes: LBW - Low birth weight is defined as a birth where the baby weighs less than 2,500 grams. This is a percent. Preterm - Preterm birth is defined as a birth that occur before 37 weeks of pregnancy. This is a percent. Teen Birth – Teen births are defined as women aged 15 to 19 years who give birth. This is a rate. Birth Rate – Birth rate is defined as the number of live births per 1,000 populations. 1st Trimester of Care – 1st Trimester of care refers to the doctor’s visits and care provided during the first 13 weeks of pregnancy. This is a percent.

Graph and download economic data for Fertility Rate, Total for the United States (SPDYNTFRTINUSA) from 1960 to 2023 about fertility, rate, and USA.

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.

These data contain the Crude Colorado Census Tract Low Weight Birth Rate which equals the total number of low weight births (singleton low weight births) divided by the denominator of all singleton births (2015-2019). Low weight births are defined as infants weighing 5 pounds, 8 ounces or less (under 2,500 grams) at birth. These data are from the Colorado Department of Public Health and Environment's Vital Records Birth Dataset and are published annually by the Colorado Department of Public Health and Environment for use in its Health Equity/Environmental Justice Collaborative activities.

BackgroundKawasaki disease (KD) is a common cause of acquired paediatric heart disease in developed countries. KD was first identified in the 1960s in Japan, and has been steadily increasing since it was first reported. The aetiology of KD has not been defined, but is assumed to be infection-related. The present study sought to identify the factor(s) that mediate the geographical variation and chronological increase of KD in Japan.Methods and FindingsBased upon data reported between 1979 and 2010 from all 47 prefectures in Japan, the incidence and mean patient age at the onset of KD were estimated. Using spatial and time-series analyses, incidence and mean age were regressed against climatic/socioeconomic variables. Both incidence and mean age of KD were inversely correlated with the total fertility rate (TFR; i.e., the number of children that would be born to one woman). The extrapolation of a time-series regressive model suggested that KD emerged in the 1960s because of a dramatic decrease in TFR in the 1940s through the 1950s.ConclusionsMean patient age is an inverse surrogate for the hazard of contracting the aetiologic agent. Therefore, the observed negative correlation between mean patient age and TFR suggests that a higher TFR is associated with KD transmission. This relationship may be because a higher TFR facilitates sibling-to-sibling transmission. Additionally, the observed inverse correlation between incidence and TFR implies a paradoxical “negative” correlation between the incidence and the hazard of contracting the aetiologic agent. It was hypothesized that a decreasing TFR resulted in a reduced hazard of contracting the agent for KD, thereby increasing KD incidence.

In 2023, there were around *** live births per 1,000 inhabitants in Japan, down from about *** in the previous year. The total number of live births in the nation amounted to approximately ******* in 2023. Japan’s super aging society Directly after the end of WWII, the live birth rate in Japan was over ** 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 increased, reaching about **** years for women and **** years for men in 2022. Due to the combination of both factors, Japan has developed into one of the most rapidly aging societies in the world. Almost ** 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 2023, the number of marriages per 1,000 Japanese citizens was ***, less than half compared to that in the early *****. The average age of first marriage has also risen for both men and women. This trend can be partially attributed to the increasing number of employed and therefore financially and socially independent women in the past two decades. The employment rate of women in Japan exceeded ** percent for the first time in history in ****.

From 1950 to 1955, the worldwide crude birth rate was just under 37 births per thousand people, which means that 3.7 percent of the population, who were alive during this time had been born in this five year period. Between this five year period, and the time between 2015 and 2020, the crude birth rate has dropped to 18.5 births per thousand people, which is fifty percent of what the birth rate was seventy years ago. This change has come as a result of increased access and reliability of contraception, a huge reduction in infant and child mortality rate, and increased educational and vocational opportunities for women. The continents that have felt the greatest change over this seventy year period are Asia and Latin America, which fell below the global average in the 1990s and early 2000s, and are estimated to have fallen below the crude birth rate of Oceania in the current five-year period. Europe has consistently had the lowest crude birth rate of all continents during the past seventy years, particularly in the 1990s and 2000s, when it fell to just over ten births per thousand, as the end of communism in Europe caused sweeping demographic change across Europe. The only continent that still remains above the global average is Africa, whose crude birth rate is fifteen births per thousand more than the world average, although the rate of decrease is higher than it was in previous decades.

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

Vital Statistics: Birth Rate: per 1000 Population: Manipur data was reported at 13.300 NA in 2020. This records a decrease from the previous number of 13.600 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Manipur data is updated yearly, averaging 14.600 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 19.000 NA in 1998 and a record low of 12.900 NA in 2016. Vital Statistics: Birth Rate: per 1000 Population: Manipur data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States.

About this dataset

Context

The dataset tabulates the data for the Indiana population pyramid, which represents the Indiana 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 Indiana, is 30.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Indiana, is 25.5.
• Total dependency ratio for Indiana is 55.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Indiana is 3.9.

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 Indiana population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Indiana for the selected age group is shown in the following column.
• Population (Female): The female population in the Indiana for the selected age group is shown in the following column.
• Total Population: The total population of the Indiana 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 Indiana Population by Age. You can refer the same here

In 2024, the average number of children born per 1,000 people in China ranged at ****. The birth rate has dropped considerably since 2016, and the number of births fell below the number of deaths in 2022 for the first time in decades, leading to a negative population growth rate. Recent development of the birth rate Similar to most East-Asian countries and territories, demographics in China today are characterized by a very low fertility rate. As low fertility in the long-term limits economic growth and leads to heavy strains on the pension and health systems, the Chinese government decided to support childbirth by gradually relaxing strict birth control measures, that had been in place for three decades. However, the effect of this policy change was considerably smaller than expected. The birth rate increased from **** births per 1,000 inhabitants in 2010 to ***** births in 2012 and remained on a higher level for a couple of years, but then dropped again to a new low in 2018. This illustrates that other factors constrain the number of births today. These factors are most probably similar to those experienced in other developed countries as well: women preferring career opportunities over maternity, high costs for bringing up children, and changed social norms, to name only the most important ones. Future demographic prospects Between 2020 and 2023, the birth rate in China dropped to formerly unknown lows, most probably influenced by the coronavirus pandemic. As all COVID-19 restrictions were lifted by the end of 2022, births figures showed a catch-up effect in 2024. However, the scope of the rebound might be limited. A population breakdown by five-year age groups indicates that the drop in the number of births is also related to a shrinking number of people with child-bearing age. The age groups between 15 and 29 years today are considerably smaller than those between 30 and 44, leaving less space for the birth rate to increase. This effect is exacerbated by a considerable gender gap within younger age groups in China, with the number of females being much lower than that of males.

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

About this dataset

Context

The dataset tabulates the data for the Port Jefferson, NY population pyramid, which represents the Port Jefferson 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 Port Jefferson, NY, is 25.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Port Jefferson, NY, is 37.2.
• Total dependency ratio for Port Jefferson, NY is 63.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Port Jefferson, NY is 2.7.

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 Port Jefferson population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Port Jefferson for the selected age group is shown in the following column.
• Population (Female): The female population in the Port Jefferson for the selected age group is shown in the following column.
• Total Population: The total population of the Port Jefferson 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 Port Jefferson Population by Age. You can refer the same here

The total population of Germany was estimated at over 84.4 million inhabitants in 2025, although it is projected to drop in the coming years and fall below 80 million in 2043. Germany is the most populous country located entirely in Europe, and is third largest when Russia and Turkey are included. Germany's prosperous economy makes it a popular destination for immigrants of all backgrounds, which has kept its population above 80 million for several decades. Population growth and stability has depended on immigration In every year since 1972, Germany has had a higher death rate than its birth rate, meaning its population is in natural decline. However, Germany's population has rarely dropped below its 1972 figure of 78.6 million, and, in fact, peaked at 84.7 million in 2024, all due to its high net immigration rate. Over the past 75 years, the periods that saw the highest population growth rates were; the 1960s, due to the second wave of the post-WWII baby boom; the 1990s, due to post-reunification immigration; and since the 2010s, due to high arrivals of refugees from conflict zones in Afghanistan, Syria, and Ukraine. Does falling population = economic decline? Current projections predict that Germany's population will fall to almost 70 million by the next century. Germany's fertility rate currently sits around 1.5 births per woman, which is well below the repacement rate of 2.1 births per woman. Population aging and decline present a major challenge economies, as more resources must be invested in elderly care, while the workforce shrinks and there are fewer taxpayers contributing to social security. Countries such as Germany have introduced more generous child benefits and family friendly policies, although these are yet to prove effective in creating a cultural shift. Instead, labor shortages are being combatted via automation and immigration, however, both these solutions are met with resistance among large sections of the population and have become defining political issues of our time.

In the United Kingdom, the crude birth rate in 1800 was 37 live births per thousand people, meaning that 3.7 percent of the population had been born in that year. From 1800 until 1830, the crude birth rate jumped between 35 and 45, before plateauing between 35 and 37 until the 1880s. From 1880 until the Second World War, the crude birth rate dropped to just under fifteen births per one thousand people, with the only increase coming directly after World War One. After WWII, the United Kingdom experienced a baby boom, as many soldiers returned home and the economy recovered, however this boom stopped in the late 1960s and the crude birth rate went into decline again. From the late 1970s until today, the crude birth rate has remained between eleven and fourteen, and is expected to be 11.5 in 2020.

BackgroundBirth weights have continued to decline in Japan in recent years. However, secular trend changes such as the birth weight relative to the week of gestation remain to be explored. This study aimed to determine the trends over time in mean birth weight and small for gestational age (SGA) rate for each gestational week.MethodsWe used a large dataset of 27,015,792 births obtained from birth certificates between 1997 and 2021. Births from 22 to 41 weeks of gestation were evaluated in six groups (22–24, 25–27, 28–31, 32–33, 34–36, and 37–41 weeks of gestational age). For each group, secular trend changes in the z-scores calculated from standard birth weight values were assessed. Time trends in the proportion of SGA and mean birth weight z-scores were evaluated using the Cochran–Armitage trend test and linear regression analysis. Binomial logistic regression was performed to ascertain the effects of gestational age, sex, primiparity, number of births, and maternal age on the likelihood of SGA.ResultsThe mean birth weight of preterm infants continued to decrease, and the z-score for mean birth weight decreased linearly, falling to −0.7 at 25–27 weeks of gestation from 1997–2001 (first period) to 2017–2021 (final period). Maternal age continued to increase from the first period to the last period for all weeks of gestation. There was a linear increase in the SGA rate in preterm infants born at