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.

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

The fertility rate of a country is the average number of children that women from that country will have throughout their reproductive years. In the United States in 1800, the average woman of childbearing age would have seven children over the course of their lifetime. As factors such as technology, hygiene, medicine and education improved, women were having fewer children than before, reaching just two children per woman in 1940. This changed quite dramatically in the aftermath of the Second World War, rising sharply to over 3.5 children per woman in 1960 (children born between 1946 and 1964 are nowadays known as the 'Baby Boomer' generation, and they make up roughly twenty percent of todays US population). Due to the end of the baby boom and increased access to contraception, fertility reached it's lowest point in the US in 1980, where it was just 1.77. It did however rise to over two children per woman between 1995 and 2010, although it is expected to drop again by 2020, to just 1.78.

United States US: Fertility Rate: Total: Births per Woman data was reported at 1.800 Ratio in 2016. This records a decrease from the previous number of 1.843 Ratio for 2015. United States US: Fertility Rate: Total: Births per Woman data is updated yearly, averaging 2.002 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 3.654 Ratio in 1960 and a record low of 1.738 Ratio in 1976. United States US: Fertility Rate: Total: Births per Woman data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average; Relevance to gender indicator: it can indicate the status of women within households and a woman’s decision about the number and spacing of children.

In 2021, the birth rate in the United States was highest in families that had under 10,000 U.S. dollars in income per year, at 62.75 births per 1,000 women. As the income scale increases, the birth rate decreases, with families making 200,000 U.S. dollars or more per year having the second-lowest birth rate, at 47.57 births per 1,000 women. Income and the birth rate Income and high birth rates are strongly linked, not just in the United States, but around the world. Women in lower income brackets tend to have higher birth rates across the board. There are many factors at play in birth rates, such as the education level of the mother, ethnicity of the mother, and even where someone lives. The fertility rate in the United States The fertility rate in the United States has declined in recent years, and it seems that more and more women are waiting longer to begin having children. Studies have shown that the average age of the mother at the birth of their first child in the United States was 27.4 years old, although this figure varies for different ethnic origins.

This statistic shows the 50 metropolitan areas with the highest birth rate in the United States in 2023. Birth rate is the total number of live births per 1,000 of a population in a particular year. The Hinesville metro area in Georgia was ranked first with 18.69 births per 1,000 residents in 2023.

In 2025, there are six countries, all in Sub-Saharan Africa, where the average woman of childbearing age can expect to have between 5-6 children throughout their lifetime. In fact, of the 20 countries in the world with the highest fertility rates, Afghanistan and Yemen are the only countries not found in Sub-Saharan Africa. High fertility rates in Africa With a fertility rate of almost six children per woman, Chad is the country with the highest fertility rate in the world. Population growth in Chad is among the highest in the world. Lack of healthcare access, as well as food instability, political instability, and climate change, are all exacerbating conditions that keep Chad's infant mortality rates high, which is generally the driver behind high fertility rates. This situation is common across much of the continent, and, although there has been considerable progress in recent decades, development in Sub-Saharan Africa is not moving as quickly as it did in other regions. Demographic transition While these countries have the highest fertility rates in the world, their rates are all on a generally downward trajectory due to a phenomenon known as the demographic transition. The third stage (of five) of this transition sees birth rates drop in response to decreased infant and child mortality, as families no longer feel the need to compensate for lost children. Eventually, fertility rates fall below replacement level (approximately 2.1 children per woman), which eventually leads to natural population decline once life expectancy plateaus. In some of the most developed countries today, low fertility rates are creating severe econoic and societal challenges as workforces are shrinking while aging populations are placin a greater burden on both public and personal resources.

This is the code replication archive for the paper, "The COVID-19 Baby Bump in the United States," published in the Proceedings of the National Academy of Sciences. The underlying natality microdata are restricted, so this archive contains only the code to replicate our analysis.We use natality microdata covering the universe of U.S. births for 2015-2021 and California births from 2015 through February 2023 to examine childbearing responses to the COVID-19 pandemic. We find that 60% of the 2020 decline in U.S. fertility rates was driven by sharp reductions in births to foreign-born mothers although births to this group comprised only 22% of all U.S. births in 2019. This decline started in January 2020. In contrast, the COVID-19 recession resulted in an overall “baby bump” among U.S.-born mothers which marked the first reversal in declining fertility rates since the Great Recession. Births to U.S.-born mothers fell by 31,000 in 2020 relative to a pre-pandemic trend but increased by 71,000 in 2021. The data for California suggest that U.S. births remained elevated through February 2023. The baby bump was most pronounced for first births and women under age 25, suggesting that the pandemic led some women to start families earlier. Above age 25, the baby bump was most pronounced for women ages 30-34 and women with a college education. The 2021-2022 baby bump is especially remarkable given the large declines in fertility rates that would have been projected by standard statistical models.

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows fertility in past 12 months by age of mother. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. The calculated percentages are slightly different from traditional age-specific fertility rates in that the total number of live births (due to twins or higher-order multiple births) is not available in this table. Note: Data are not available for all geographies within this layer due to data suppression done by the American Community Survey. Since there was a data collection error in the 2011 and 2012 surveys, this impacts the 2010-2014 5-year estimates. To learn more and to see a list of the affected geographies, visit this page about Errata 119.This layer is symbolized to show the percent of women age 15 to 50 who had a birth in the past 12 months. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B13016 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 11, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The average for 2022 based on 12 countries was 29.29 percent. The highest value was in Uruguay: 80.87 percent and the lowest value was in Suriname: 0.45 percent. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

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.

This layer shows fertility in past 12 months by age of mother. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. The calculated percentages are slightly different from traditional age-specific fertility rates in that the total number of live births (due to twins or higher-order multiple births) is not available in this table. This layer is symbolized to show the percent of women age 15 to 50 who had a birth in the past 12 months. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B13016 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The fertility clinics have experienced robust growth in recent years, driven by shifting demographics and technological advancements. A notable climb in birth rates among women over 30 and diverse family structures, including single and same-sex couples, has expanded the client base. Clinics have responded by investing in cutting-edge reproductive technologies to meet the needs of older and more varied clients. High success rates in assisted reproductive technology (ART) and personalized treatment approaches have enhanced patient confidence and satisfaction. The sector's focus on individualized care and advanced treatment options underscores the growing demand for fertility services. Meanwhile, artificial intelligence integration transforms diagnostic and treatment planning, improving the quality of clinical outcomes and efficiency. Industry-wide revenue has grown at a CAGR of 6.5% over the past five years and is expected to total $9.0 billion in 2025, when revenue will jump by an estimated 1.5%. Over the past five years, clinics have benefited from strategic partnerships, such as collaborations with hospitals, to enhance service offerings while sharing resources. Despite rising wage pressures because of labor shortages, profitability remains a strong point, as patients prioritize success over price. Clinics maintain manageable purchase and rent expenses, focusing financial resources on human capital and advanced technologies instead. Marketing expenditures take a backseat with the imbalanced supply and demand for these services, allowing budgets to be directed towards operational improvements. This efficiency highlights a strategic shift in resource allocation that addresses the evolving landscape while maintaining quality care. The next five years promise continued evolution within the fertility sector, driven by technological innovation and regulatory changes. Expected insurance coverage expansion in several states could provide treatment access for more potential parents, supporting industry growth. Meanwhile, complicated legal requirements pose potential challenges, requiring clinics to adapt strategically to new regulations and possibly expand networks to compliant regions. The infusion of private equity investments aims to propel technological advancements, offering opportunities for service refinement. As clinics embrace virtual consultations and AI-driven diagnostics, improved outcomes are likely to attract a broader clientele. By focusing on advancements and patient satisfaction, the industry stands poised for sustained growth and success in an ever-changing environment. Industry revenue is forecast to grow at a CAGR of 2.7% over the five years through 2030 to total $10.3 billion.

Introduction: Though awareness of climate change rose globally with the release of former Vice President Al Gore’s movie and book An Inconvenient Truth in 2006, there has seemingly never been a connection drawn between Gore’s works and subsequent fertility trends in the United States, particularly along political lines. Objectives: The primary objective of this project is to determine whether the release of the movie and book An Inconvenient Truth in 2006 sparked an inflection point within a year or two in the United States for birth rates, and whether those rates differ between red and blue states. The secondary objective is to determine whether there was a drop in birth rates after that inflection point. Methods: This project used natality data – birth rates per state per year from 2003-2020 – from the Centers for Disease Control and Prevention, joined with state political party data from the 2020 Presidential election from Wisevoter. Data were cleaned using Excel and analyzed using Tableau visualizations. Results: The year 2007 was indeed an inflection point in the United States for birth rates, as both red and blue states recorded their highest birth rates at this point in the 2003-2020 span. The birth rate in red states was higher than that of blue states throughout the span but both rates had a positive correlation, running parallel throughout the span. Conclusions: The United States birth rate declined after 2007 in both red and blue states, but it is unclear whether the release of An Inconvenient Truth influenced this decline.

The 1965 National Fertility Survey was the first of three surveys that succeeded the Growth of American Families surveys (1955 and 1960) aimed at examining marital fertility and family planning in the United States. Currently married women were queried on the following main topics: residence history, marital history, education, income and employment, family background, religiosity, attitudes toward contraception and sterilization, birth control pill use and other methods of contraception, fecundity, family size, fertility expectations and intentions, abortion, and world population growth. Respondents were asked about their residence history, including what state they grew up in, whether they had lived with both of their parents at the age of 14, and whether they had spent any time living on a farm. Respondents were also asked a series of questions about their marital history. Specifically, they were asked about the duration of their current marriage, whether their current marriage was their first marriage, total number of times they had been married, how previous marriages ended, length of engagement, and whether their husband had children from a previous marriage. Respondents were asked what was the highest grade of school that they had completed, whether they had attended a co-ed college, and to give the same information about their husbands. Respondents were asked about their 1965 income, both individual and combined, their occupation, whether they had been employed since marriage, if and when they stopped working, and whether they were self-employed. They were also asked about their husband's recent employment status. With respect to family background, respondents were asked about their parents' and their husband's parents' nationalities, education, religious preferences, and total number children born alive to their mother and mother-in-law, respectively. In addition, respondents were asked about their, and their husband's, religious practices including their religious preferences, whether they had ever received any Catholic education, how religious-minded they perceived themselves to be, how often they prayed at home, and how often they went to see a minister, rabbi, or priest. Respondents were asked to give their opinions with respect to contraception and sterilization. They were asked whether they approved or disapproved of contraception in general, as well as specific forms of contraception, whether information about birth control should be available to married and unmarried couples, and whether the federal government should support birth control programs in the United States and in other countries. They were also asked whether they approved or disapproved of sterilization operations for men and women and whether they thought such a surgery would impair a man's sexual ability. Respondents were asked about their own knowledge and use of birth control pills. They were asked if they had ever used birth control pills and when they first began using them. They were then asked to give a detailed account of their use of birth control pills between 1960 and 1965. Respondents were also asked to explain when they discontinued use of birth control pills and what the motivation was for doing so. Respondents were also asked about their reproductive cycle, the most fertile days in their cycle, the regularity of their cycle, and whether there were any known reasons why they could not have or would have problems having children. Respondents were asked about their ideal number of children, whether they had their ideal number of children or if they really wanted fewer children, as well as whether their husbands wanted more or less children than they did. Respondents were then asked how many additional births they expected, how many total births they expected, when they expected their next child, and at what age they expected to have their last child. Respondents were asked how they felt about interrupting a pregnancy and whether they approved of abortion given different circumstances such as if the pregnancy endangered the woman's health, if the woman was not married, if the couple could not afford another child, if the couple did not want another child, if the woman thought the child would be deformed, or if the woman had been raped. Respondents were also asked to share their opinions with respect to world population growth. T

In 2023, the total fertility rate in children per woman in the United States stood at 1.62. Between 1960 and 2023, the figure dropped by 2.03, though the decline followed an uneven course rather than a steady trajectory.

While the standard image of the nuclear family with two parents and 2.5 children has persisted in the American imagination, the number of births in the U.S. has steadily been decreasing since 1990, with about 3.6 million babies born in 2023. In 1990, this figure was 4.16 million. Birth and replacement rates A country’s birth rate is defined as the number of live births per 1,000 inhabitants, and it is this particularly important number that has been decreasing over the past few decades. The declining birth rate is not solely an American problem, with EU member states showing comparable rates to the U.S. Additionally, each country has what is called a “replacement rate.” The replacement rate is the rate of fertility needed to keep a population stable when compared with the death rate. In the U.S., the fertility rate needed to keep the population stable is around 2.1 children per woman, but this figure was at 1.67 in 2022. Falling birth rates Currently, there is much discussion as to what exactly is causing the birth rate to decrease in the United States. There seem to be several factors in play, including longer life expectancies, financial concerns (such as the economic crisis of 2008), and an increased focus on careers, all of which are causing people to wait longer to start a family. How international governments will handle falling populations remains to be seen, but what is clear is that the declining birth rate is a multifaceted problem without an easy solution.

To understand marriage patterns, homogamy, and fertility of women of European ancestry in the United States from an evolutionary perspective, we investigated if a prevalence of ancestral homogamy exists, the factors influencing a female preference for an ancestral homogamous vs. heterogamous marriage, and if ancestral homogamous vs. heterogamous marriages have an impact on fertility. Furthermore, we aim to determine the heritability of homogamous vs. heterogamous marriage behavior. We used the census data of 369,121 women in the United States married only once and aged between 46 and 60 years, provided by IPUMS USA (https://usa.ipums.org/usa/). We used linear mixed models to determine the association between the probability of a homogamous vs. heterogamous marriage and the individual fertility of women. We aimed to estimate the heritability (genetics and parental environment) of marriage behavior using a linear mixed model. We found that ancestral heterogamous marriages are more frequent compared to homogamous marriages, but only if all ancestry groups are included. If ancestry is aggregated, homogamous marriages are more frequent compared to heterogamous marriages. Most of the variance (up to 27%) in inter-ancestry marriage and fertility (up to 12%) is explained by ancestry per se, followed by the ratio of individuals of a certain ancestral background in a county (∼6%), indicating a frequency depending selection into marriage: the more individuals of a certain ancestry live in a county, the lower is the tendency to marry someone of a different ancestral background. Furthermore, we found that about 12% (depending to some extent on the clustering) of the marriage behavior is heritable. Being in a homogamous marriage and the income of the spouse are both significantly positively associated with the number of children women have and the probability that women have at least one child, albeit explaining only a very low proportion of the overall variance. The most important factor (in terms of variance explained) for being in an ancestral homogamous vs. heterogamous marriage, for the number of children, and for childlessness is the ancestry of the women. Most children are born to women of Irish, French, and Norwegian ancestry (Irish X̄: 3.24, French X̄: 3.21, and Norwegian X̄: 3.18), the lowest number of children is to women of Latvian, Rumanian, and Russian ancestry (Latvian X̄: 2.26, Rumanian X̄: 2.19, and Russian X̄: 2.35). Albeit, we are not able to distinguish the genetic and social heritability on the basis of our data, only a small heritability for in-group vs. out-group marriage behavior is indicated (∼12% of variance explained).

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Foreign born excludes people born outside the United States to a parent who is a U.S. citizen..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Population pyramids provide a way to visualize the age and sex composition of a geographic region, such as a nation, state, or county. A standard population pyramid divides sex into two bar charts or histograms, one for the male population and one for the female population. The two charts mirror each other and are divide age into 5-year cohorts. The shape of a population pyramid provides insights into a region’s fertility, mortality, and migration patterns. When a region has high fertility and mortality, but low migration the visualization will look like a pyramid, with the youngest age cohort (0-4 years) representing the largest percent of the population and each older cohort representing a progressively smaller percent of the population.

In many regions fertility and mortality have decreased significantly since 1970, as people live longer and women have fewer children. With lower fertility and mortality, population pyramids are shaped more like a pillar.

While population pyramids can be made for any geographic region, when interpreting population pyramids for smaller areas (like counties) the most important force that shapes the pyramid is often in- and out-migration (Wang and vom Hofe, 2006, p. 65). For smaller regions, population pyramids can have unique shapes.

This data archive provides the resources needed to generate population pyramids for the United States, individual states, and any county within the United States. Population pyramids usually require significant data cleaning and graph making skills to generate one pyramid. With this data archive the data cleaning has been completed and the R script provides reusable code to quickly generate graphs. The final output is an image file with six graphs on one page. The final layout makes it easy to compare changes in population age and sex composition for any state and any county in the US for 1970, 1980, 1990, 2000, 2010, and 2017.