Retirement Age Men in the United States increased to 66.83 Years in 2025 from 66.67 Years in 2024. This dataset provides - United States Retirement Age Men - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides values for RETIREMENT AGE MEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Graph and download economic data for Not in Labor Force - With No Disability, 65 Years and over (LNU05075379) from Jun 2008 to Aug 2025 about 65 years +, disability, labor force, labor, household survey, and USA.

During the pandemic, the share of retirees in the U.S. population rose much faster than its normal pace. Typically, an increase in this share is driven by more people transitioning from employment to retirement. However, we show that the recent increase was instead driven by fewer people transitioning from retirement back into employment, likely due to pandemic-related health risks. More retirees may rejoin the workforce as these health risks fade, but the retirement share is unlikely to return to a normal level for some time.

This dataset provides values for RETIREMENT AGE WOMEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

United States CSI: Savings: Adequate Retirement Income Probability: Don’t Know data was reported at 0.000 % in May 2018. This records a decrease from the previous number of 1.000 % for Apr 2018. United States CSI: Savings: Adequate Retirement Income Probability: Don’t Know data is updated monthly, averaging 2.000 % from Dec 1997 (Median) to May 2018, with 246 observations. The data reached an all-time high of 7.000 % in Feb 2001 and a record low of 0.000 % in May 2018. United States CSI: Savings: Adequate Retirement Income Probability: Don’t Know data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H026: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the chances are that when you retire, your income from Social Security and job pensions will be adequate to maintain your living standards?

Retirement Age Women in the United States increased to 66.83 Years in 2025 from 66.67 Years in 2024. This dataset provides - United States Retirement Age Women - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Population ages 65 and above for the United States (SPPOP65UPTOZSUSA) from 1960 to 2024 about 65-years +, population, and USA.

About this dataset

Context

The dataset tabulates the Holladay 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 Holladay. 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 17,437 (55.82% 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 Holladay population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Holladay is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Holladay 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 Holladay Population by Age. You can refer the same here

United States CSI: Savings: Adequate Retirement Income Probability: 50% data was reported at 11.000 % in May 2018. This records an increase from the previous number of 10.000 % for Apr 2018. United States CSI: Savings: Adequate Retirement Income Probability: 50% data is updated monthly, averaging 15.000 % from Dec 1997 (Median) to May 2018, with 246 observations. The data reached an all-time high of 21.000 % in Jan 2004 and a record low of 10.000 % in Apr 2018. United States CSI: Savings: Adequate Retirement Income Probability: 50% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H026: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the chances are that when you retire, your income from Social Security and job pensions will be adequate to maintain your living standards?

This dataset sets forth the Police Retirement System holdings (both equity and fixed income) of the identified pension/retirement system as of the close of the fiscal year.

The 2015 American Housing Survey marks the first release of a newly integrated national sample and independent metropolitan area samples. The 2015 release features many variable name revisions, as well as the integration of an AHS Codebook Interactive Tool available on the U.S. Census Bureau We site. This data collection provides information on the characteristics of a national sample of housing units in 2015, including apartments, single-family homes, mobile homes, and vacant housing units. Data from the 15 largest metropolitan areas in the United States are included in the national sample survey (the AHS 2015 Metropolitan Data are also available as ICPSR 36805). The data are presented in three separate parts: Part 1, Household Record (Main Record), Part 2, Person Record, and Part 3, Project Record. Household Record data includes questions about household occupancy and tenure, household exterior and interior structural features, household equipment and appliances, housing problems, housing costs, home improvement, neighborhood features, recent moving information, income, and basic demographic information. The household record data also features four rotating topical modules: Arts and Culture, Food Security, Housing Counseling, and Healthy Homes. Person Record data includes questions about personal disabilities, income, and basic demographic information. Finally, the Project Record data includes questions about home improvement projects. Specific questions were asked about the types of projects, costs, funding sources, and year of completion.

United States CSI: Savings: Adequate Retirement Income Probability: 75-99% data was reported at 14.000 % in May 2018. This records a decrease from the previous number of 17.000 % for Apr 2018. United States CSI: Savings: Adequate Retirement Income Probability: 75-99% data is updated monthly, averaging 12.000 % from Dec 1997 (Median) to May 2018, with 246 observations. The data reached an all-time high of 21.000 % in Mar 2017 and a record low of 8.000 % in Sep 2010. United States CSI: Savings: Adequate Retirement Income Probability: 75-99% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H026: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the chances are that when you retire, your income from Social Security and job pensions will be adequate to maintain your living standards?

Retirement Notice: This item is in mature support as of June 2024 and will retire in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer presents the USA 2020 Census State boundaries of the United States in the 50 states and the District of Columbia. The geography is sourced from US Census Bureau 2020 TIGER FGDB (National Sub-State) and edited using TIGER Hydrology to add a detailed coastline for cartographic purposes.Attribute fields include 2020 total population from the US Census PL94 data.The boundaries are sourced from US Census TIGER files. These were published in May 2023. Other administrative boundaries for this country are also available:CountryCountyZIP CodeTractBlockGroup

Graph and download economic data for Age Dependency Ratio: Older Dependents to Working-Age Population for the United States (SPPOPDPNDOLUSA) from 1960 to 2024 about 64 years +, working-age, ratio, population, and USA.

Time series data for the statistic The age at which men and women can retire with partial pension benefits is the same (1=yes; 0=no) and country United States. Indicator Definition:The indicator measures measures whether the age at which men and women can retire and receive partial pension benefits is the same, or if the age at which men and women can retire and receive partial benefits is not mandated. Partial pension benefits refer to a reduced or proportional minimum old-age pension payable to workers who did not accumulate enough work experience or contributions or have not reached the statutory age to qualify for a minimum old-age pension. If transitional provisions gradually increase, decrease or equalize the statutory retirement age, the answer reflect the age according to the report's data collection cycle, even if the law provides for changes over time.

Key Table Information.Table Title.Retirement Income in the Past 12 Months for Households.Table ID.ACSDT1Y2024.B19059.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, a...

United States CSI: Savings: Change in Comfortable Retirement: 5Yrs Ago: Gone Up data was reported at 30.000 % in May 2018. This records an increase from the previous number of 29.000 % for Apr 2018. United States CSI: Savings: Change in Comfortable Retirement: 5Yrs Ago: Gone Up data is updated monthly, averaging 22.000 % from Dec 1997 (Median) to May 2018, with 246 observations. The data reached an all-time high of 38.000 % in Nov 2000 and a record low of 7.000 % in May 2009. United States CSI: Savings: Change in Comfortable Retirement: 5Yrs Ago: Gone Up data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H026: Consumer Sentiment Index: Savings & Retirement. Table 19: Change in Likelihood of Comfortable Retirement The question was: Compared with 5 years ago, do you think the chances that you (and your husband/wife) will have a comfortable retirement have gone up, gone down, or remained about the same?

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

About this dataset

Context

The dataset tabulates the May township 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 May township. 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 527 (57.03% 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 May township population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in May township is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the May township 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 May township Population by Age. You can refer the same here