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.

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.

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.

Projected indicators included are derived from the published 2018-based subnational population projections for England, Wales, Scotland and Northern Ireland up to the year 2043. The indicators are the projected 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 the subnational ageing tool, which was published on July 20, 2020 (see link in Related datasets). The tool is interactive, and users can 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 data sources: England, Wales, Scotland and Northern Ireland independently publish subnational population projections and the data available here are a compilation of these datasets. The ONS publish national level data for the UK, England, Wales and England & Wales, which has been included. National level data for Scotland and Northern Ireland have been taken from their subnational population projections datasets.

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.

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.

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: 2018-2022ACS Table(s): B01001, B09021, B17020, B18101, B23027, B25072, B25093, B27010, B28005, C27001B-IData downloaded from: Census Bureau's API for American Community Survey Date of API call: December 7, 2023The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 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 RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

In 2025, 24.7 percent of the total population in Italy is estimated to be 65 years and older. According to data, the share of elderly people in the Italian society has been growing constantly since 2009. Consequently, the share of young population experienced a decrease in the last years. As a result, the average age of Italians has risen. In 2011, it was 43.6 years, whereas in 2024 it was estimated to be 46.8 years. The oldest country in Europe Italy and Portugal are the European countries with the largest percentage of elderly citizens. In 2024, 24 percent of the total population was aged 65 years and older. Bulgaria and Finland followed in the ranking, while Azerbaijan had the lowest share of elder population, less than ten percent. An increasingly longer lifespan might provide an explanation for such a high share of citizens over 65 years in Italy. The Republic ranks among the countries with the highest life expectancy worldwide. In Europe, only people in Switzerland and Spain can expect to live longer. Fewer babies than ever The share of young people is getting slimmer, not only because the elderly are living longer than ever before. In fact, Italians are having fewer children compared to previous years. The birth rate in the country has been constantly decreasing: in 2024, only 6.3 babies were born per 1,000 inhabitants, three children less than in 2010. In the south of Italy, in 2023 the birth rate stood at 6.7 infants per 1,000 inhabitants, whereas in central Italy this figure reached only 5.8, the highest and lowest rates in the country, respectively.

Population ages 65 and above (% of total population) in Portugal was reported at 24.11 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Portugal - Population ages 65 and above (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Population ages 65 and above (% of total population) in Norway was reported at 18.53 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Norway - Population ages 65 and above (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

In 2020, about 17.9 percent of the population in China had been 60 years and older. This share is growing rapidly and was estimated to reach 40 percent by 2050. China's aging population With China’s boomer generation growing old and life expectancy increasing at the same time, the number of people at an age of 60 or above nearly doubled between 2000 and 2020 and reached around 255 million. This development is even more pronounced for the age group of 80 and above, which nearly tripled and is expected to reach a size of roughly 132 million in 2050, up from only 32 million in 2020. At the same time, the share of the working-age population is forecasted to decrease gradually from 64 percent of the total population in 2020 to around 50 percent in 2050, which could pose a heavy economic strain on the social security system. The old-age dependency ratio, which denotes the relation of the old-age to the working-age population, is estimated to grow from 18.2 percent in 2020 to more than 50 percent in 2050, implying that by then, statistically, two working-age adults would have to support one elderly. Strain on the social security net During the last 15 years, China's government has successfully increased the coverage of the pension insurance and health insurance. Today, most of the people are covered by some kind of social insurance. Conditions in the pension system are generous, with a regular retirement age for males at 60 years and women at 50 or 55. With the number of retirees increasing quickly, the social insurance system is now under pressure. From an economic point of view, improving the productivity of China's economy would be the primary choice for mitigating alleged inconsistencies of the system. However, without increasing the burden on the working people while tightening payment conditions, balancing the social security net could prove to be challenging.

About this dataset

Context

The dataset tabulates the Santa Fe 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 Santa Fe. 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 50,861 (57.65% 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 Santa Fe population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Santa Fe is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Santa Fe 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 Santa Fe Population by Age. You can refer the same here

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.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

Elderly illiterate population, 65+ years, % female in Malaysia was reported at 71.45 % in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Malaysia - Elderly illiterate population, 65+ years, % female - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

About this dataset

Context

The dataset tabulates the Lake County 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 Lake County. 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 438,410 (61.47% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Lake County population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Lake County is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Lake County 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 Lake County Population by Age. You can refer the same here

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

Singapore Old-Age Support Ratio: Resident: Per Elderly >65 Years: 20-64 Years data was reported at 4.500 Per Person in 2019. This records a decrease from the previous number of 4.800 Per Person for 2018. Singapore Old-Age Support Ratio: Resident: Per Elderly >65 Years: 20-64 Years data is updated yearly, averaging 10.600 Per Person from Jun 1957 (Median) to 2019, with 63 observations. The data reached an all-time high of 21.300 Per Person in 1957 and a record low of 4.500 Per Person in 2019. Singapore Old-Age Support Ratio: Resident: Per Elderly >65 Years: 20-64 Years data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.G001: Population: Statistics.

The dataset detailing the top 10 countries with aging populations aged 65 and above, including demographic statistics and figures of aging populations.

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.

Taiwan Aging Index data was reported at 68.200 % in 2010. This records an increase from the previous number of 40.400 % for 2000. Taiwan Aging Index data is updated yearly, averaging 10.600 % from Dec 1956 (Median) to 2010, with 8 observations. The data reached an all-time high of 68.200 % in 2010 and a record low of 5.600 % in 1956. Taiwan Aging Index data remains active status in CEIC and is reported by Directorate-General of Budget, Accounting and Statistics, Executive Yuan. The data is categorized under Global Database’s Taiwan – Table TW.G002: Population: Population and Housing Census.