66 datasets found
  1. U.S. seniors as a percentage of the total population 1950-2050

    • statista.com
    • ai-chatbox.pro
    Updated Jun 16, 2025
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    Statista (2025). U.S. seniors as a percentage of the total population 1950-2050 [Dataset]. https://www.statista.com/statistics/457822/share-of-old-age-population-in-the-total-us-population/
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    Dataset updated
    Jun 16, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In 2023, about 17.7 percent of the American population was 65 years old or over; an increase from the last few years and a figure which is expected to reach 22.8 percent by 2050. This is a significant increase from 1950, when only eight percent of the population was 65 or over. A rapidly aging population In recent years, the aging population of the United States has come into focus as a cause for concern, as the nature of work and retirement is expected to change to keep up. If a population is expected to live longer than the generations before, the economy will have to change as well to fulfill the needs of the citizens. In addition, the birth rate in the U.S. has been falling over the last 20 years, meaning that there are not as many young people to replace the individuals leaving the workforce. The future population It’s not only the American population that is aging -- the global population is, too. By 2025, the median age of the global workforce is expected to be 39.6 years, up from 33.8 years in 1990. Additionally, it is projected that there will be over three million people worldwide aged 100 years and over by 2050.

  2. Senior population of the U.S. by state 2023

    • statista.com
    Updated Jun 16, 2025
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    Statista (2025). Senior population of the U.S. by state 2023 [Dataset]. https://www.statista.com/statistics/736211/senior-population-of-the-us-by-state/
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    Dataset updated
    Jun 16, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    In 2023, there were ********* adults aged 65 and older living in California, the most out of all U.S. states, followed by Florida with over *** million adults aged 65 and older. Both California and Florida have some of the highest resident population figures in the United States.

  3. n

    Aging Population

    • linc.osbm.nc.gov
    • ncosbm.opendatasoft.com
    csv, excel, geojson +1
    Updated Mar 16, 2020
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    (2020). Aging Population [Dataset]. https://linc.osbm.nc.gov/explore/dataset/aging-population/
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    geojson, json, excel, csvAvailable download formats
    Dataset updated
    Mar 16, 2020
    Description

    Population 65+ by selected living arrangements. Estimates from US Census Bureau - American Community Survey 5-Year Estimates, 2014-2018.

  4. C

    Data associated with: Overview of Aging and Dependency in Latin America and...

    • data.iadb.org
    xlsx
    Updated Apr 10, 2025
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    IDB Datasets (2025). Data associated with: Overview of Aging and Dependency in Latin America and the Caribbean [Dataset]. http://doi.org/10.60966/aadt-2641
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    xlsx(195605)Available download formats
    Dataset updated
    Apr 10, 2025
    Dataset provided by
    IDB Datasets
    License

    Attribution-NonCommercial-NoDerivs 3.0 (CC BY-NC-ND 3.0)https://creativecommons.org/licenses/by-nc-nd/3.0/
    License information was derived automatically

    Time period covered
    Jan 1, 2015 - Jan 1, 2050
    Area covered
    Latin America, Caribbean
    Description

    This dataset was created to support the 2016 DIA (Related publication only available in Spanish). The accelerated aging process that countries in Latin America and the Caribbean are undergoing imposes unprecedented pressures on the long-term care sector. In this context, the growing demand for care from the elderly population occurs alongside a reduction in the availability of informal care. Governments in the region must prepare to address these pressures by supporting the provision of care services to alleviate social exclusion in old age. The Inter-American Development Bank has created an Observatory on Aging and Care — the focus of this policy brief — aimed at providing decision-makers with information to design policies based on available empirical evidence. In this initial phase, the Observatory seeks to document the demographic situation of countries in the region, the health of their elderly population, their limitations and dependency status, as well as their main socioeconomic characteristics. The goal is to estimate the care needs countries in the region will face. This brief summarizes the key findings from an initial analysis of the data. The results highlight the scale of the problem. The figures speak for themselves: in the region, 11% of the population aged 60 and older is dependent. Both the magnitude and intensity of dependency increase with age. Women are the most affected across all age groups. This policy brief is part of a series of studies on dependency care, including works by Caruso, Galiani, and Ibarrarán (2017); Medellín et al. (2018); López-Ortega (2018); and Aranco and Sorio (2018).

  5. f

    Measuring the Speed of Aging across Population Subgroups

    • plos.figshare.com
    • figshare.com
    xlsx
    Updated Jun 2, 2023
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    Warren C. Sanderson; Sergei Scherbov (2023). Measuring the Speed of Aging across Population Subgroups [Dataset]. http://doi.org/10.1371/journal.pone.0096289
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    xlsxAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Warren C. Sanderson; Sergei Scherbov
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    People in different subgroups age at different rates. Surveys containing biomarkers can be used to assess these subgroup differences. We illustrate this using hand-grip strength to produce an easily interpretable, physical-based measure that allows us to compare characteristic-based ages across educational subgroups in the United States. Hand-grip strength has been shown to be a good predictor of future mortality and morbidity, and therefore a useful indicator of population aging. Data from the Health and Retirement Survey (HRS) were used. Two education subgroups were distinguished, those with less than a high school diploma and those with more education. Regressions on hand-grip strength were run for each sex and race using age and education, their interactions and other covariates as independent variables. Ages of identical mean hand-grip strength across education groups were compared for people in the age range 60 to 80. The hand-grip strength of 65 year old white males with less education was the equivalent to that of 69.6 (68.2, 70.9) year old white men with more education, indicating that the more educated men had aged more slowly. This is a constant characteristic age, as defined in the Sanderson and Scherbov article “The characteristics approach to the measurement of population aging” published 2013 in Population and Development Review. Sixty-five year old white females with less education had the same average hand-grip strength as 69.4 (68.2, 70.7) year old white women with more education. African-American women at ages 60 and 65 with more education also aged more slowly than their less educated counterparts. African American men with more education aged at about the same rate as those with less education. This paper expands the toolkit of those interested in population aging by showing how survey data can be used to measure the differential extent of aging across subpopulations.

  6. Median age of U.S. population by state 2022

    • ai-chatbox.pro
    • statista.com
    Updated Aug 6, 2024
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    Statista (2024). Median age of U.S. population by state 2022 [Dataset]. https://www.ai-chatbox.pro/?_=%2Fstatistics%2F208048%2Fmedian-age-of-population-in-the-usa-by-state%2F%23XgboDwS6a1rKoGJjSPEePEUG%2FVFd%2Bik%3D
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    Dataset updated
    Aug 6, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    United States
    Description

    In 2022, the state with the highest median age of its population was Maine at 45.1 years. Utah had the lowest median age at 32.1 years. View the distribution of the U.S. population by ethnicity here.

    Additional information on the aging population in the United States

    High birth rates during the so-called baby boom years that followed World War II followed by lower fertility and morality rates have left the United States with a serious challenge in the 21st Century. However, the issue of an aging population is certainly not an issue unique to the United States. The age distribution of the global population shows that other parts of the world face a similar issue.

    Within the United States, the uneven distribution of populations aged 65 years and over among states offers both major challenges and potential solutions. On the one hand, federal action over the issue may be contentious as other states are set to harbor the costs of elderly care in states such as California and Florida. That said, domestic migration from comparably younger states may help to fill gaps in the workforce left by retirees in others.

    Nonetheless, aging population issues are set to gain further prominence in the political and economic decisions made by policymakers regardless of the eventual distribution of America’s elderly. Analysis of the financial concerns of Americans by age shows many young people still decades from retirement hold strong concern over their eventual financial position.

  7. Replication data for: The Determinants of the Macroeconomic Implications of...

    • openicpsr.org
    Updated May 1, 2014
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    Louise Sheiner (2014). Replication data for: The Determinants of the Macroeconomic Implications of Aging [Dataset]. http://doi.org/10.3886/E112780V1
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    Dataset updated
    May 1, 2014
    Dataset provided by
    American Economic Associationhttp://www.aeaweb.org/
    Authors
    Louise Sheiner
    Description

    The aging of the US population undoubtedly will be associated with macroeconomic changes. In particular, some combination of lower consumption growth and increased labor input will ultimately be required. But, the timing of these changes can have important effects on variables like the rate of return to capital and wages. If the adjustment to consumption is slow, which would be the case if budget deficits were allowed to rise significantly as the population ages, then aging is likely to be associated with an increase in the return to capital and a reduction in wages.

  8. United States Microdata Samples Extract File, 1940-1980: Demographics of...

    • icpsr.umich.edu
    • archive.ciser.cornell.edu
    ascii, sas, spss +1
    Updated Nov 4, 2005
    + more versions
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    Inter-university Consortium for Political and Social Research (2005). United States Microdata Samples Extract File, 1940-1980: Demographics of Aging [Dataset]. http://doi.org/10.3886/ICPSR08353.v2
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    sas, stata, ascii, spssAvailable download formats
    Dataset updated
    Nov 4, 2005
    Dataset authored and provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/8353/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/8353/terms

    Time period covered
    1940 - 1980
    Area covered
    United States
    Description

    This is an extract of the decennial Public Use Microdata Sample (PUMS) released by the Bureau of the Census. Because the complete PUMS files contain several hundred thousand records, ICPSR has constructed this subset to allow for easier and less costly analysis. The collection of data at ten year increments allows the user to follow various age cohorts through the life-cycle. Data include information on the household and its occupants such as size and value of dwelling, utility costs, number of people in the household, and their relationship to the respondent. More detailed information was collected on the respondent, the head of household, and the spouse, if present. Variables include education, marital status, occupation and income.

  9. c

    Elderly Population

    • data.clevelandohio.gov
    • hub.arcgis.com
    Updated Aug 21, 2023
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    Cleveland | GIS (2023). Elderly Population [Dataset]. https://data.clevelandohio.gov/maps/ClevelandGIS::elderly-population
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    Dataset updated
    Aug 21, 2023
    Dataset authored and provided by
    Cleveland | GIS
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Area covered
    Description
    This layer shows demographic context for senior well-being work. 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 layer is symbolized to show the percentage of population aged 65 and up (senior population). 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: 2019-2023
    ACS Table(s): B01001, B09021, B17020, B18101, B23027, B25072, B25093, B27010, B28005, C27001B-I

    The United States Census Bureau's American Community Survey (ACS):
    This 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 2022 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 Rico
    • Census 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.

  10. N

    Baltimore County, MD Population Pyramid Dataset: Age Groups, Male and Female...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
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    Neilsberg Research (2025). Baltimore County, MD Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/52399653-f122-11ef-8c1b-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Maryland, Baltimore County
    Variables measured
    Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Total Population for Age Groups, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, and 9 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the three variables, namely (a) male population, (b) female population and (b) total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the data for the Baltimore County, MD population pyramid, which represents the Baltimore County 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 Baltimore County, MD, is 28.2.
    • Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Baltimore County, MD, is 27.7.
    • Total dependency ratio for Baltimore County, MD is 55.9.
    • Potential support ratio, which is the number of youth (working age population) per elderly, for Baltimore County, MD is 3.6.
    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 Baltimore County population analysis. Total expected values are 18 and are define above in the age groups section.
    • Population (Male): The male population in the Baltimore County for the selected age group is shown in the following column.
    • Population (Female): The female population in the Baltimore County for the selected age group is shown in the following column.
    • Total Population: The total population of the Baltimore 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 Baltimore County Population by Age. You can refer the same here

  11. T

    United States - Age Dependency Ratio (% Of Working-age Population)

    • tradingeconomics.com
    csv, excel, json, xml
    Updated May 28, 2017
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    TRADING ECONOMICS (2017). United States - Age Dependency Ratio (% Of Working-age Population) [Dataset]. https://tradingeconomics.com/united-states/age-dependency-ratio-percent-of-working-age-population-wb-data.html
    Explore at:
    xml, json, excel, csvAvailable download formats
    Dataset updated
    May 28, 2017
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    United States
    Description

    Age dependency ratio (% of working-age population) in United States was reported at 54.45 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Age dependency ratio (% of working-age population) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

  12. Healthcare coverage share among U.S. elderly population in 2022, by race and...

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Healthcare coverage share among U.S. elderly population in 2022, by race and coverage [Dataset]. https://www.statista.com/statistics/1399409/elderly-population-with-health-insurance-by-race-and-coverage-in-the-us/
    Explore at:
    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2021
    Area covered
    United States
    Description

    In 2022, among people aged 65 years and above who had health coverage, **** percent non-Hispanic White Americans had private health insurance, while a further ** percent had Medicare Advantage. The majority of older adults in the U.S. were privately insured (with or without Medicare). This statistic illustrates the distribution of health insurance coverage among adults aged 65 and above in the U.S. in 2022, by race and coverage type.

  13. Older populations (age 60) and wildfire risk in 2020 for project areas,...

    • agdatacommons.nal.usda.gov
    bin
    Updated Apr 26, 2025
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    Richelle L. Winkler; Miranda H. Mockrin; Gregory K. Dillon; Cody R. Evers (2025). Older populations (age 60) and wildfire risk in 2020 for project areas, firesheds, counties, and states [Dataset]. http://doi.org/10.2737/RDS-2024-0077
    Explore at:
    binAvailable download formats
    Dataset updated
    Apr 26, 2025
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    Richelle L. Winkler; Miranda H. Mockrin; Gregory K. Dillon; Cody R. Evers
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    United States
    Description

    These data combine information on population numbers and older age population from the decennial Census 2020, with information on wildfire risk to quantify the number of older adults, and proportion of population that are older adults (age 60 and older), living in areas with wildfire risk (low, moderate, or high). Data were combined at census block (2020) geographies and then summarized to the state and county. Data are also summarized to fireshed and project areas, where firesheds are a broad scale geographic unit of prioritization used to plan wildfire risk reduction activities that are approximately 100,000 hectares in size, and project areas are smaller geographies within firesheds. Project areas are often used to help prioritize areas for forest management or hazardous fuel treatments and are approximately 10,000 hectares in area.Wildfire risk and losses are increasing in the United States. At the same time, the average age of the population in the United States has been increasing. The older population (age 60 and older) has grown while the proportion of younger people has contracted (i.e., population aging). Older people face a greater relative risk of dying in a wildfire and need different kinds of resources and programs tailored to their unique needs to mitigate the risk from wildfire. Information about the combination of aging and wildfire risk across U.S. states, counties, and fire management areas (firesheds, project areas) can help identify where older people are living with wildfire risk and aid implementation of wildfire risk reduction programs.For more information about this study and these data, see Winkler and Mockrin (2025).

    These data were published on 02/11/2025. Metadata updated on 03/04/2025 to update reference to newly published report.

  14. N

    Nevada County, CA Population Pyramid Dataset: Age Groups, Male and Female...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
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    Neilsberg Research (2025). Nevada County, CA Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/nevada-county-ca-population-by-age/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Nevada County, California
    Variables measured
    Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Total Population for Age Groups, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, and 9 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the three variables, namely (a) male population, (b) female population and (b) total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the data for the Nevada County, CA population pyramid, which represents the Nevada County 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 Nevada County, CA, is 24.4.
    • Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Nevada County, CA, is 50.5.
    • Total dependency ratio for Nevada County, CA is 74.9.
    • Potential support ratio, which is the number of youth (working age population) per elderly, for Nevada County, CA is 2.0.
    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 Nevada County population analysis. Total expected values are 18 and are define above in the age groups section.
    • Population (Male): The male population in the Nevada County for the selected age group is shown in the following column.
    • Population (Female): The female population in the Nevada County for the selected age group is shown in the following column.
    • Total Population: The total population of the Nevada 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 Nevada County Population by Age. You can refer the same here

  15. n

    Census Microdata Samples Project

    • neuinfo.org
    • scicrunch.org
    • +2more
    Updated Jan 29, 2022
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    (2022). Census Microdata Samples Project [Dataset]. http://identifiers.org/RRID:SCR_008902
    Explore at:
    Dataset updated
    Jan 29, 2022
    Description

    A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219

  16. N

    Valley Stream, NY Age Cohorts Dataset: Children, Working Adults, and Seniors...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
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    Neilsberg Research (2025). Valley Stream, NY Age Cohorts Dataset: Children, Working Adults, and Seniors in Valley Stream - Population and Percentage Analysis // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/valley-stream-ny-population-by-age/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Valley Stream, New York
    Variables measured
    Population Over 65 Years, Population Under 18 Years, Population Between 18 and 64 Years, Percent of Total Population for Age Groups
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age cohorts. For age cohorts we divided it into three buckets Children ( Under the age of 18 years), working population ( Between 18 and 64 years) and senior population ( Over 65 years). For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Valley Stream population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Valley Stream. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

    Key observations

    The largest age group was 18 to 64 years with a poulation of 25,551 (63.44% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

    Content

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

    Age cohorts:

    • Under 18 years
    • 18 to 64 years
    • 65 years and over

    Variables / Data Columns

    • Age Group: This column displays the age cohort for the Valley Stream population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
    • Population: The population for the age cohort in Valley Stream is shown in the following column.
    • Percent of Total Population: The population as a percent of total population of the Valley Stream 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 Valley Stream Population by Age. You can refer the same here

  17. U

    United States Senior Living Market Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Mar 7, 2025
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    Data Insights Market (2025). United States Senior Living Market Report [Dataset]. https://www.datainsightsmarket.com/reports/united-states-senior-living-market-17191
    Explore at:
    doc, pdf, pptAvailable download formats
    Dataset updated
    Mar 7, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    United States
    Variables measured
    Market Size
    Description

    The United States senior living market, valued at $112.93 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 5.86% from 2025 to 2033. This expansion is fueled by several key drivers. The aging population, particularly the baby boomer generation, is a significant factor, creating an increasing demand for assisted living, independent living, memory care, and nursing care facilities. Furthermore, rising disposable incomes and increasing awareness of the benefits of senior living communities contribute to market growth. Technological advancements in senior care, such as telehealth and remote monitoring, are also enhancing the quality of life for residents and boosting market appeal. However, the market faces some restraints, including the rising costs of healthcare and senior care services, potentially limiting accessibility for some segments of the population. Furthermore, staffing shortages within the industry represent a significant challenge. The market is segmented by property type, with assisted living, independent living, and memory care facilities representing the largest segments. Key states driving market growth include New York, Illinois, California, North Carolina, and Washington, reflecting higher concentrations of the senior population and higher disposable incomes. Major players in the market such as Ensign Group Inc, Sunrise Senior Living, Brookdale Senior Living Inc, and Atria Senior Living Inc, compete fiercely, driving innovation and service improvements. The forecast period (2025-2033) anticipates continued growth, driven by the ongoing demographic shifts and increased demand for high-quality senior care options. Strategic partnerships, acquisitions, and investments in technology are likely to shape the competitive landscape in the coming years. The industry will continue to adapt to meet the evolving needs of the aging population, focusing on personalized care, innovative technologies, and cost-effective solutions. This comprehensive report provides an in-depth analysis of the booming United States senior living market, covering the period from 2019 to 2033. With a base year of 2025 and a forecast period spanning 2025-2033, this report is an invaluable resource for investors, industry professionals, and anyone seeking to understand the dynamics of this rapidly evolving sector. The report leverages extensive data analysis to provide insightful projections and uncover key trends shaping the future of senior care in the US. Expect detailed breakdowns of key segments, including assisted living, independent living, memory care, and nursing care, across major states like California, New York, Illinois, North Carolina, and Washington. Recent developments include: July 2023: Spring Cypress senior living site expansion is set to open at the end of 2024 and will consist of three phases. The first phase of the expansion will include 19 independent-living, two-bedroom cottages. The second phase will include 24 townhomes. The third phase will feature 95 apartments. The final phase will feature a resort with several luxury amenities., Apr 2023: For seniors looking for innovative, high-quality care, Avista Senior Living is transitioning away from its SafelyYou partnership to empower safer, more personalized dementia care with real-time, AI video and remote clinical experts 24/7.. Key drivers for this market are: 4., Increase in Aging Population Driving the Market4.; Healthcare and Long-term Care Needs Driving the Market. Potential restraints include: 4., High Affordability and Cost of Care Affecting the Market4.; Staffing and Workforce Challenges Affecting the Market. Notable trends are: Senior Housing Witnessing Increased Demand.

  18. D

    Retirement Communities Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
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    Dataintelo (2025). Retirement Communities Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/retirement-communities-market
    Explore at:
    csv, pptx, pdfAvailable download formats
    Dataset updated
    Jan 7, 2025
    Authors
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Retirement Communities Market Outlook



    The global retirement communities market size was valued at approximately USD 250 billion in 2023 and is projected to reach around USD 400 billion by 2032, growing at a CAGR of about 5%. This growth is primarily driven by the aging global population, an increase in life expectancy, and changing lifestyle preferences among seniors. The shift towards comprehensive care and the integration of health and wellness services within retirement communities have further fueled this market's expansion. As societies worldwide continue to experience demographic shifts, the demand for retirement communities that offer a blend of healthcare, hospitality, and recreational amenities is expected to surge, underpinning the robust growth trajectory of the sector.



    The burgeoning aging population is one of the primary growth factors for the retirement communities market. As advances in healthcare continue to improve life expectancy, a significant proportion of the global population is projected to fall within the senior age bracket, necessitating adequate living solutions for them. This demographic shift is particularly pronounced in developed regions such as North America and Europe, where a considerable percentage of the population is transitioning into retirement age. Additionally, emerging economies in Asia Pacific are also witnessing an increase in the elderly population, driven by improved healthcare infrastructure and living standards. This demographic evolution necessitates the development of retirement communities equipped with facilities that cater to both the healthcare and lifestyle needs of seniors.



    Another significant growth factor is the increased financial independence and spending power among seniors. With many from the baby boomer generation having accrued substantial savings and investments, there is a growing willingness to spend on quality living environments that provide comfort, security, and access to healthcare and recreational activities. This financial capability, coupled with the desire for a community living environment that offers social interaction and reduces isolation, is a key driver for the retirement communities market. Furthermore, these communities are increasingly incorporating technology to enhance the quality of life for residents, with features such as telemedicine, smart home technologies, and digital health monitoring, which are appealing to the tech-savvy senior demographic.



    Moreover, the changing societal norms and lifestyle preferences among the elderly are also contributing to the market's growth. TodayÂ’s seniors are more active and health-conscious than ever before, seeking retirement communities that offer wellness programs, fitness centers, and social activities that align with their lifestyle choices. The emphasis on holistic well-being has led to a rise in integrated community models that provide a continuum of care, from independent living to assisted living and nursing care, allowing seniors to age in place with dignity and peace of mind. This trend is expected to intensify in the coming years, further propelling the growth of the retirement communities market globally.



    In recent years, the concept of Smart Communities has emerged as a transformative force within the retirement sector. These communities leverage advanced technologies to create interconnected environments that enhance the quality of life for residents. By integrating smart home devices, IoT solutions, and data-driven services, Smart Communities offer personalized and efficient living experiences. This technological integration not only improves safety and convenience for seniors but also promotes sustainable living practices. As the demand for tech-savvy solutions grows, retirement communities are increasingly adopting smart technologies to meet the evolving expectations of their residents, positioning themselves at the forefront of innovation in senior living.



    Regionally, North America currently holds the largest share of the retirement communities market, driven by a well-established infrastructure, high disposable incomes, and a significant aging population. Europe follows closely, benefiting from similar demographic trends and a strong emphasis on social welfare programs for the elderly. Meanwhile, the Asia Pacific region is anticipated to exhibit the highest growth rate over the forecast period, fueled by rapid urbanization, economic growth, and increasing healthcare investments. Countries such as China, Japan, and India are at the forefront of this expansion, as they adapt to th

  19. f

    Variable definitions and descriptive statistics.

    • plos.figshare.com
    xls
    Updated Jun 6, 2024
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    Hongen Song; Changyi Jiang; Zhaoming Sun (2024). Variable definitions and descriptive statistics. [Dataset]. http://doi.org/10.1371/journal.pone.0300124.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 6, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Hongen Song; Changyi Jiang; Zhaoming Sun
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    IntroductionAccording to the seventh national population census in China, the proportion of people aged 65 and above in the population reached 13.5%. The aging trend is more pronounced in rural areas, indicating that China has entered an aging society. This article focuses on agricultural carbon emissions in the context of aging, studying the impact of rural population aging on agricultural carbon emissions.Study objectivesUnder the background of deepening population aging, let us discuss how to maintain the green and sustainable development of agriculture in China.MethodologyFixed effects and mediating effects models are used. Technical efficiency is used as a mediating variable to discuss the relationship between rural population ageing, technical efficiency and agricultural carbon emissions.ResultsThis paper adopts the classical carbon emission calculation theory of IPCC to measure agricultural carbon emissions from 2010 to 2019, and China’s plantation carbon emissions show an "inverted U-shaped" trend, reaching a high level in 2015 and then starting to decline. In addition, the fixed-effects benchmark regression found that the aging of the rural population promotes agricultural carbon emissions, and the technical efficiency of agriculture suppresses agricultural carbon emissions. Finally, the mediating effect model is applied to explore the relationship between the three. Using technical efficiency as the mediating variable, it is found that under the masking effect, rural population aging will weaken agricultural carbon emissions through technical efficiency, thus achieving the suppression of agricultural carbon emissions.Policy recommendationThe formulation and modification of agricultural carbon-reducing policy row policies should take full account of the broader context of rural population ageing; increase the interconnectedness and interaction between rural population ageing and agricultural production technology, and actively play a positive role in promoting the efficiency of agricultural technology as a result of rural population ageing; and, in accordance with the actual situation of agricultural research, appropriately increase the strength of financial support for agriculture to improve agricultural technology and promote low-carbon development in agriculture.

  20. g

    Census of Population and Housing, 2000 [United States]: Special Tabulation...

    • search.gesis.org
    Updated Oct 1, 2013
    + more versions
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    ICPSR - Interuniversity Consortium for Political and Social Research (2013). Census of Population and Housing, 2000 [United States]: Special Tabulation on Aging - Archival Version [Dataset]. http://doi.org/10.3886/ICPSR13577
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    Dataset updated
    Oct 1, 2013
    Dataset provided by
    GESIS search
    ICPSR - Interuniversity Consortium for Political and Social Research
    License

    https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de446555https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de446555

    Area covered
    United States
    Description

    Abstract (en): The Census 2000: Special Tabulation on Aging provides information for each of the 50 states along with the District of Columbia and Puerto Rico with a special focus on persons age 60 and older. Population topics (Tables P001 through P116 for each state and state equivalent file) include basic population totals, age, sex, race, Hispanic or Latino origin, households and families, group quarters, marital status, grandparents as caregivers, ability to speak English, place of birth, citizenship status, migration, educational attainment, veteran status, disability, employment status, income, and poverty status. Household topics (tables H01 through H69) include tenure (owner occupied or renter occupied), household size, units in structure, year structure was built, availability of plumbing and kitchen facilities, and whether meals are included in the rent and value of home. Both the population and housing subjects may be cross tabulated. Files are organized according to the ten regions as defined by the Administration on Aging. Each table provides information at a number of geographical levels: United States (50 states + DC), state, Planning and Service Area (PSA -- the geographic area served by a single area agency on aging), county, county subdivision in 12 states with a population of 2,500 or more, places with a population of 2,500 or more, and census tract, as well as American Indian and Alaska Native areas. Also, the urban and rural components of states and PSAs are shown. The data are in the form of Excel tables. The technical documentation provides extensive details about such topics as the tabulation specifications, the geographical levels shown, how to use the statistical tables, and the measures used to protect confidentiality. All persons and housing units in the United States and Puerto Rico. 2013-10-01 This study was previously distributed on DVD only. The contents of the DVD are now available for public download from ICPSR as three zipped packages. Funding insitution(s): United States Department of Commerce. Bureau of the Census. United States Department of Health and Human Services. Administration on Aging.

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Statista (2025). U.S. seniors as a percentage of the total population 1950-2050 [Dataset]. https://www.statista.com/statistics/457822/share-of-old-age-population-in-the-total-us-population/
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U.S. seniors as a percentage of the total population 1950-2050

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69 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jun 16, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
United States
Description

In 2023, about 17.7 percent of the American population was 65 years old or over; an increase from the last few years and a figure which is expected to reach 22.8 percent by 2050. This is a significant increase from 1950, when only eight percent of the population was 65 or over. A rapidly aging population In recent years, the aging population of the United States has come into focus as a cause for concern, as the nature of work and retirement is expected to change to keep up. If a population is expected to live longer than the generations before, the economy will have to change as well to fulfill the needs of the citizens. In addition, the birth rate in the U.S. has been falling over the last 20 years, meaning that there are not as many young people to replace the individuals leaving the workforce. The future population It’s not only the American population that is aging -- the global population is, too. By 2025, the median age of the global workforce is expected to be 39.6 years, up from 33.8 years in 1990. Additionally, it is projected that there will be over three million people worldwide aged 100 years and over by 2050.

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