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

    • statista.com
    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. Share of aging population ASEAN 2040, by country

    • statista.com
    Updated Jun 26, 2025
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    Statista (2025). Share of aging population ASEAN 2040, by country [Dataset]. https://www.statista.com/statistics/713809/asean-forecast-aging-population/
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    Dataset updated
    Jun 26, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2019
    Area covered
    Asia
    Description

    In 2040, the percentage of the population of Singapore above the age of 65 was forecasted to reach more than ** percent. Comparatively, the share of the population older than 65 in Laos was forecasted to reach about *** percent.

  3. Share of elderly population Indonesia 2013-2024

    • statista.com
    Updated Aug 12, 2025
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    Statista (2025). Share of elderly population Indonesia 2013-2024 [Dataset]. https://www.statista.com/statistics/713515/indonesia-aging-population/
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    Dataset updated
    Aug 12, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Indonesia
    Description

    In 2024, the percentage of the population of Indonesia aged 65 years or older was around *** percent. The share of the elderly population across the country has gradually increased over the past decade.

  4. Local authority ageing statistics, based on annual mid-year population...

    • cy.ons.gov.uk
    • ons.gov.uk
    csv, csvw, txt, xls
    Updated Jun 30, 2020
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    Population Statistics Division (2020). Local authority ageing statistics, based on annual mid-year population estimates [Dataset]. https://cy.ons.gov.uk/datasets/ageing-population-estimates
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    xls, txt, csv, csvwAvailable download formats
    Dataset updated
    Jun 30, 2020
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    Authors
    Population Statistics Division
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Indicators included have been derived from the published 2019 mid-year population estimates for the UK, England, Wales, Scotland and Northern Ireland. These are the number of persons and percentage of the population aged 65 years and over, 85 years and over, 0 to 15 years, 16 to 64 years, 16 years to State Pension age, State Pension age and over, median age and the Old Age Dependency Ratio (the number of people of State Pension age per 1000 of those aged 16 years to below State Pension age).

    This dataset has been produced by the Ageing Analysis Team for inclusion in a subnational ageing tool, which was published in July 2020. The tool enables users to compare latest and projected measures of ageing for up to four different areas through selection on a map or from a drop-down menu.

  5. G

    The Aging Population

    • ouvert.canada.ca
    • datasets.ai
    • +2more
    jpg, pdf
    Updated Mar 14, 2022
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    Natural Resources Canada (2022). The Aging Population [Dataset]. https://ouvert.canada.ca/data/dataset/a2c4bdd0-d79c-5395-a857-2a06b4dd717c
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    pdf, jpgAvailable download formats
    Dataset updated
    Mar 14, 2022
    Dataset provided by
    Natural Resources Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    Contained within the 5th Edition (1978 to 1995) of the National Atlas of Canada is a sheet that has 2 maps and an inset map. The first map shows proportion of total population in 65 to 74 and 75 plus age groups for each Census Division in 1986. An inset map shows the same information for the area from Windsor to Quebec. The second map of Canada shows proportion under 15 by Census Division. Population pyramids of age / sex distributions for 1961 and 1986 shown for each province, territory and for Canada.

  6. o

    Replication data for: Retirement Security in an Aging Population

    • openicpsr.org
    Updated May 1, 2014
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    James M. Poterba (2014). Replication data for: Retirement Security in an Aging Population [Dataset]. http://doi.org/10.3886/E116123V1
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    Dataset updated
    May 1, 2014
    Dataset provided by
    American Economic Association
    Authors
    James M. Poterba
    Time period covered
    2000 - 2013
    Area covered
    United Statess
    Description

    Elderly individuals exhibit wide disparities in their sources of income. For those in the bottom half of the income distribution, Social Security is the most important source of support; program changes would directly affect their well-being. Income from private pensions, assets, and earnings are relatively more important for higher-income elderly individuals, who have more diverse income sources. The trend from private sector defined benefit to defined contribution pension plans has shifted responsibility for retirement security to individuals. A significant subset of the population is unlikely to be able to sustain their standard of living in retirement without higher pre-retirement saving.

  7. 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/datasets/elderly-population/explore
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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.

  8. Projected share of aging population APAC 2023, by country

    • statista.com
    Updated Jun 26, 2025
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    Statista (2025). Projected share of aging population APAC 2023, by country [Dataset]. https://www.statista.com/statistics/1100149/apac-projected-aging-population-forecast-by-country/
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    Dataset updated
    Jun 26, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    APAC, Asia
    Description

    In 2023, the share of the population of Japan above the age of 65 was projected to amount to around ** percent. In contrast, the share of the population older than 65 in Thailand was projected to be about *** percent that year.

  9. d

    State of Aging in Allegheny County Survey

    • catalog.data.gov
    • data.wprdc.org
    • +2more
    Updated Jan 24, 2023
    + more versions
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    University of Pittsburgh (2023). State of Aging in Allegheny County Survey [Dataset]. https://catalog.data.gov/dataset/state-of-aging-in-allegheny-county-survey
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    Dataset updated
    Jan 24, 2023
    Dataset provided by
    University of Pittsburgh
    Area covered
    Allegheny County
    Description

    For more than three decades UCSUR has documented the status of older adults in the County along multiple life domains. Every decade we issue a comprehensive report on aging in Allegheny County and this report represents our most recent effort. It documents important shifts in the demographic profile of the population in the last three decades, characterizes the current status of the elderly in multiple life domains, and looks ahead to the future of aging in the County. This report is unique in that we examine not only those aged 65 and older, but also the next generation old persons, the Baby Boomers. Collaborators on this project include the Allegheny County Area Agency on Aging, the United Way of Allegheny County, and the Aging Institute of UPMC Senior Services and the University of Pittsburgh. The purpose of this report is to provide a comprehensive analysis of aging in Allegheny County. To this end, we integrate survey data collected from a representative sample of older county residents with secondary data available from Federal, State, and County agencies to characterize older individuals on multiple dimensions, including demographic change and population projections, income, work and retirement, neighborhoods and housing, health, senior service use, transportation, volunteering, happiness and life satisfaction, among others. Since baby boomers represent the future of aging in the County we include data for those aged 55-64 as well as those aged 65 and older.

  10. Data from: Senior Population

    • hub.arcgis.com
    • covid19.esriuk.com
    Updated Feb 4, 2015
    + more versions
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    Urban Observatory by Esri (2015). Senior Population [Dataset]. https://hub.arcgis.com/datasets/16ac068ca6f441648e1cafc283a96d53
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    Dataset updated
    Feb 4, 2015
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    This map shows where senior populations are found throughout the world. Areas with more than 10% seniors are highlighted with a dark red shading while a dot representation reveals the number of seniors and their distribution in bright red.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

  11. m

    ELDERLY HEALTH IN INDIA: A DISCUSSION

    • data.mendeley.com
    Updated May 26, 2022
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    Sabitri Dutta (2022). ELDERLY HEALTH IN INDIA: A DISCUSSION [Dataset]. http://doi.org/10.17632/kmvj667crv.1
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    Dataset updated
    May 26, 2022
    Authors
    Sabitri Dutta
    License

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

    Description

    The elderly population (ageing 60 above) in India is increasing and is projected to climb by 11% point between 2010 to 2050 (UNPD, 2011). Due to better living condition and improved well-being, better health care system, availability of medicines, awareness among the people the mortality rate has reduced substantially. This demography brings a new economic and social concerns afront. The present work tries to investigate the health perception, nature and status of ailment and treatment availed by this part of population in India along with their demographic profile. The database used in the study is the 71st round dataset of National Sample Survey Organisation (NSSO). The work gives a brief review of the recent policies and initiatives taken to end the health challenges faced by the ageing population. Probable policy recommendations have been made that can potentially address the health concerns of the elderly in the country.

  12. n

    Public Use Microdata Sample for the Older Population

    • neuinfo.org
    • scicrunch.org
    • +2more
    Updated Feb 1, 2001
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    (2001). Public Use Microdata Sample for the Older Population [Dataset]. http://identifiers.org/RRID:SCR_010487
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    Dataset updated
    Feb 1, 2001
    Description

    A public-use microdata sample focusing on the older population created from the 1990 census. This sample consists of 3 percent of households with at least one member aged 60 or older. Although, the highest age presented is age 90, this allows analysis of data on the very old for most states with a reasonable degree of reliability. Since data for all members in households containing a person 60 years and over will be on the file, users will be able to analyze patterns such as living arrangements and sources of household income from which older members may benefit. Additionally, users will be able to augment the PUMS-O sample with a PUMS file. The Census Bureau has issued two regular PUMS files for the entire population. One PUMS file will contain 1 percent of all households; the other PUMS file will contain 5 percent of all households. Both files have most sample data items, and differ only in geographical composition. The 1-percent file contains geographic areas that reflect metropolitan vs. non-metropolitan areas. The 5-percent file shows counties or groups of counties as well as large sub-county areas such as places of 100,000 or more. The geography on the 5-percent PUMS file matches that of the PUMS-O file. Since data for different households are present on the two files, users can merge the PUMS-O file with the 5-percent PUMS to construct an 8-percent sample. However, weighted averages must be constructed for any estimates created because each sample yields state-level estimates. Thus, it is possible to analyze substate areas even for the very old. In states where the geographic areas identified on the PUMS-O and the 5-percent PUMS are coterminous with State Planning and Service Areas (used by service providers in relation to the Older Americans Act), the Planning and Service Areas are identified. * Dates of Study: 1990-2000 Links: 1980: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08101 2000: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04204

  13. Share of population over the age of 65 in European countries 2024

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Share of population over the age of 65 in European countries 2024 [Dataset]. https://www.statista.com/statistics/1105835/share-of-elderly-population-in-europe-by-country/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    Europe
    Description

    In 2024, Italy and Portugal were the European countries with the largest share of elderly population, with ** percent of the total population aged 65 years and older. Bulgaria, Finland, and Greece were the countries with the next highest shares of elderly people in their population, while the European Union on average had **** percent of the population being elderly. Iceland, Ireland, and Luxembourg had around ** percent of their population being elderly, while Türkiye and Azerbaijan had around ** percent.

  14. C

    Cayman Islands Population: Total: Aged 65 and Above

    • ceicdata.com
    Updated Jan 17, 2024
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    CEICdata.com (2024). Cayman Islands Population: Total: Aged 65 and Above [Dataset]. https://www.ceicdata.com/en/cayman-islands/population-and-urbanization-statistics/population-total-aged-65-and-above
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    Dataset updated
    Jan 17, 2024
    Dataset provided by
    CEICdata.com
    License

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

    Time period covered
    Dec 1, 2010 - Dec 1, 2021
    Area covered
    Cayman Islands
    Description

    Cayman Islands Population: Total: Aged 65 and Above data was reported at 5,608.000 Person in 2022. This records an increase from the previous number of 5,332.000 Person for 2021. Cayman Islands Population: Total: Aged 65 and Above data is updated yearly, averaging 1,651.000 Person from Dec 1960 (Median) to 2022, with 63 observations. The data reached an all-time high of 5,608.000 Person in 2022 and a record low of 596.000 Person in 1962. Cayman Islands Population: Total: Aged 65 and Above data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cayman Islands – Table KY.World Bank.WDI: Population and Urbanization Statistics. Total population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.;World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2022 Revision.;Sum;

  15. Aging Index per year

    • ine.es
    csv, html, json +4
    Updated Jun 24, 2024
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    INE - Instituto Nacional de Estadística (2024). Aging Index per year [Dataset]. https://www.ine.es/jaxiT3/Tabla.htm?t=36709&L=1
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    txt, csv, text/pc-axis, xls, xlsx, html, jsonAvailable download formats
    Dataset updated
    Jun 24, 2024
    Dataset provided by
    National Statistics Institutehttp://www.ine.es/
    Authors
    INE - Instituto Nacional de Estadística
    License

    https://www.ine.es/aviso_legalhttps://www.ine.es/aviso_legal

    Time period covered
    Jan 1, 2024 - Jan 1, 2039
    Variables measured
    Type of data, Demographic Concepts, Autonomous Communities and Cities
    Description

    Population Projections: Aging Index per year. Annual. Autonomous Communities and Cities.

  16. n

    Cross-Sectional and Longitudinal Aging Study

    • neuinfo.org
    • scicrunch.org
    • +2more
    Updated May 13, 2025
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    (2025). Cross-Sectional and Longitudinal Aging Study [Dataset]. http://identifiers.org/RRID:SCR_008903
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    Dataset updated
    May 13, 2025
    Description

    A data set designed to provide a cross-sectional description of health, mental, and social status of the oldest-old segment of the elderly population in Israel, and to serve as a baseline for a multiple-stage research program to correlate demographic, health, and functional status with subsequent mortality, selected morbidity, and institutionalization. Study data are based on a sample of Jewish subjects aged 75+, alive and living in Israel on January 1, 1989, randomly selected from the National Population Register (NPR), a complete listing of the Israeli population maintained by the Ministry of the Interior. The NPR is updated on a routine basis with births, deaths, and in and out migration, and corrected by linkage with census data. The sample was stratified by age (five 5-year age groups: 75-79, 80-84, 85-89, 90-94, 95+), sex, and place of birth (Israel, Asia-Africa, Europe-America). One hundred subjects were randomly selected in each of the 30 strata. However, there were less than 100 individuals of each sex aged 95+ born in Israel, so all were selected for the sample. The total group included 2,891 individuals living both in the community and in institutions. A total of 1,820 (76%) of the 75-94 age group were interviewed during 1989-1992. An additional cognitive exam (Folstein) and a 24-hour dietary recall interview were added in the second round. Kibbutz Residents Sample The kibbutz is a social and economic unit based on equality among members, common property and work, collaborative consumption, and democracy in decision making. There are 250 kibbutzim in Israel, and their population constitutes about 3% of the country''s total population. All kibbutz residents in the country aged 85+, both members and parents, were selected for interviewing, of whom 80.4% (n=652) were interviewed. A matched sample aged 75-84 was selected, and 85.9% (n=674) were successfully interviewed. The original interview took approximately two hours to administer, and collected extensive information concerning the socio-demographic, physical, health, functioning, life events (including Holocaust), depression, mental status, and social network characteristics of the sample. The questionnaire used for kibbutz residents in the follow-up interview is identical to that utilized in the national random sample. Data Availability: Mortality data for both the national and kibbutz samples are available for analysis as a result of the linkage to the NPR file updated as of June 2000. The fieldwork for first follow up was completed as of September 1994 and for the second follow up as of December 2002. The data file of the three phases of the study is ready for analysis. * Dates of Study: 1989-1992 * Study Features: Longitudinal, International * Sample Size: 2,891

  17. d

    Replication Data for: Aging and the Politics of Monetary Policy in Japan

    • search.dataone.org
    Updated Nov 8, 2023
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    Yamada, Kyohei; Park, Gene (2023). Replication Data for: Aging and the Politics of Monetary Policy in Japan [Dataset]. http://doi.org/10.7910/DVN/BA15EX
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    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Yamada, Kyohei; Park, Gene
    Description

    This paper explores how Japan’s aging population impacts the politics of monetary policy. Previous research suggest that the elderly have a variety of distinct policy preferences. Given that elderly voters also have higher voting rates, the rapid greying of the population could have significant effects on distributive struggles over economic policy across much of the developed world. In Japan, aging is advancing rapidly, and the central bank has engaged in massive monetary stimulus to induce inflation, which existing work suggests the elderly should oppose. Analyzing results from three surveys, this paper has three central findings: (1) the elderly tend to have higher inflation aversion, (2) the elderly display some opposition to quantitative easing (QE), and (3) despite such policy preferences, the concentration of elderly in electoral districts has no significant effect on the preferences either of legislative incumbents or candidates. The third finding is attributable to the fact that elderly opposition to quantitative easing is moderated by their partisan identification. Elderly Liberal Democratic Party voters have systematically lower opposition to quantitative easing, likely reflecting that these voters have aligned their preferences with the LDP’s policies.

  18. f

    Descriptive statistics and results of analyses for the total population and...

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    Updated Sep 30, 2016
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    Spruijt, Ineke T.; van der Hoek, Wim; Dijkstra, Frederika; de Lange, Marit M. A.; Donker, Gé A. (2016). Descriptive statistics and results of analyses for the total population and the elderly population. [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001530259
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    Dataset updated
    Sep 30, 2016
    Authors
    Spruijt, Ineke T.; van der Hoek, Wim; Dijkstra, Frederika; de Lange, Marit M. A.; Donker, Gé A.
    Description

    Descriptive statistics and results of analyses for the total population and the elderly population.

  19. 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
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    csv, pptx, pdfAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset provided by
    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

  20. Local authority ageing statistics, older people economic activity

    • ons.gov.uk
    • cy.ons.gov.uk
    csv, csvw, txt, xls
    Updated Jun 30, 2020
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    Population Statistics Division (2020). Local authority ageing statistics, older people economic activity [Dataset]. https://www.ons.gov.uk/datasets/older-people-economic-activity
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    xls, csvw, csv, txtAvailable download formats
    Dataset updated
    Jun 30, 2020
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    Authors
    Population Statistics Division
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Indicators included are economic activity and employment rates for those aged 50-64 years, by country, region and local authority. Both economic activity and employment rates are displayed as percentages. These have been calculated from the ONS Annual Population Survey and have been extracted from NOMIS.

    https://www.nomisweb.co.uk/

    This dataset has been produced by the Ageing Analysis Team for inclusion in a subnational ageing tool, which will be published in July 2020. The tool will be interactive, and users will be able to compare latest and projected measures of ageing for up to four different areas through selection on a map or from a drop-down menu.

    Note on update frequency: NOMIS provide quarterly updates on both indicators. For consistency with other indicators presented in the subnational ageing tool, these will be updated on an annual basis.

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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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63 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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