Before beginning research on retirement or retirees, data users should have a clear definition of retirement and their study population. Some definitions of retirement require that an individual not be in the labor force. Some researchers may be interested in semi-retirement, e.g., individuals who receive a Social Security pension but continue to work, or have returned to work after retiring.

Researchers studying retirement or retirees will need information on one or more of the following factors:

• Retirement status: Some data collections and samples include variables that clearly indicate that an individual is retired.
• Labor force/employment status: If retirement status is not explicitly reported, users can make assumptions about who is and is not retired based on employment or labor force status.
• Social Security income receipt and amount: Many U.S. retirees receive a Social Security pension—but some individuals who are not retired (i.e., survivors of Social Security-eligible individuals, and people with disabilities) also receive Social Security income.
• Other retirement income receipt and amount: Other sources of retirement income to consider include income from IRAs, 401Ks, other private savings accounts, and employee pensions.

The table below summarizes the information available on retirement in four IPUMS data collections: IPUMS USA, IPUMS CPS, IPUMS Health Surveys (IPUMS MEPS and IPUMS NHIS), and IPUMS ATUS. 

Table 1. Information on retirement in IPUMS data collections

Example studies of retirement using IPUMS data

• Coile, C. C., & Levine, P. B. (2011). Recessions, retirement, and social security. American Economic Review, 101(3), 23-28.
• Flood, S., Moen, P., & Wang, J. (2019). SHORT-TERM US PATHS TOWARD RETIREMENT: SIMILARITIES AND DIFFERENCES ACROSS AGE-GENDER-CLASS INTERSECTIONS. Innovation in Aging, 3(Supplement_1), S805-S805.

Suggested citation

Pastoor, Isabel. 2026. “Studying Retirement with IPUMS Data.” University of Minnesota Life Course Center, November 3, 2026. https://lcc.umn.edu/node/2251.

The American Time Use Survey (ATUS) collects information on eldercare beyond what is observed directly in time diaries of ATUS respondents. From 2011-onward, information has been recorded on eldercare provision within the last three months for all ATUS respondents. Those who had provided care within the last three months gave additional information about the frequency of eldercare and their relationship with the recipient of the care. This blog post includes ATUS data from 2011-2024. 

Though the BLS defines eldercare recipients as being age 65 and older, in ATUS data, eldercare recipients can be younger. More than 98 percent of eldercare recipients in 2011-2024 ATUS data are age 50 and older, and more than 92 percent are age 60 and older. 

Women perform more family eldercare than men

Women bear the brunt of family eldercare, but gender gaps have shrunk over time. Between 2011 and 2024, 57 percent of family eldercare providers were women. The graph below (Figure 1) shows the share of men and women who provided eldercare to a family member by year. Each year, a larger share of women than men are eldercare providers within their families. Over the pooled time period, 15.6 percent of women provided eldercare to a family member, compared to 12.6 of men, meaning women were 23 percent more likely than men to provide family eldercare. 
 

The share of women who provide eldercare to a family member declined from 16 percent in 2011 to 14 percent in 2024. The share of men who provide eldercare to a family member also declined between 2011 and 2022, before increasing in 2023 and 2024. The simultaneous decline in women’s eldercare provision and the rise in men’s eldercare provision in the last two years of data have led to a near gender convergence in the rate at which U.S. adults provide eldercare to family members. 

Figure 1. Share of women and men that provide family eldercare over time

Women also provide family eldercare more frequently than men. The graph below (Figure 3) shows the percentage of male and female family eldercare providers who take care of their family member(s) daily; several times a week; about once a week; several times a month; once a month; or some other frequency that was not stated. Women are more likely than men to report providing care daily or several times a week. Women are less likely than men to report providing care at all lower frequencies.

Figure 2.  Frequency of family eldercaregiving by sex of caregiver

Caregivers are more likely to take care of women 

Family eldercare providers of both sexes are about twice as likely to care for a woman as they are to care for a man. The graph below (Figure 3) shows who male and female caregivers take care of in terms of the sex of the recipient family member(s). 

Slightly over half of family eldercare providers care for a woman (or women) only, shown by the pink bars. This is true for both male and female eldercare providers. Both men and women are much less likely to care for a man (or men) only than they are to care for a woman (or women) only. One in five male eldercare providers and 17 percent of female eldercare providers care for a man (or men) only, shown by the green bars. One in five male caregivers and one in four female caregivers care for at least one person of each sex, shown by the blue bars. 

This means that 80 percent of male caregivers and 83 percent of female caregivers take care of at least one female family member. In contrast, 40 percent of male caregivers and 43 percent of female caregivers take care of at least one male family member. In total, a family eldercare provider is roughly twice as likely to be caring for a woman as they are to be caring for a man.

These sex disparities in who family eldercare providers take care of could reflect differences in need for eldercare by sex (i.e., women may be more likely to need care), differences in receipt of needed care (i.e., men and women need care at the same rates but women are more likely to receive it), or differences in where older adults receive care (i.e., men and women are equally likely to need and to receive care, but women are more likely than men to receive it from family members).

Analytical consideration: While ATUS data on eldercare provision can tell us a lot about caregivers themselves, these data do not allow us to identify all older adults with met and unmet need for caregiving. This means it is not possible to determine—using ATUS data alone—which of the three scenarios described above is most accurate. ATUS data should be paired with other data sources to understand the need for eldercare and identify populations that are potential recipients of eldercare. Also note that when working with ATUS data on eldercare, weights are only available for the ATUS respondent themselves—that is, the caregiver. When weights are applied, ATUS respondents are representative of the U.S. population, but their care recipients are not necessarily representative. Therefore, analyses must be conducted at the level of the ATUS respondent—the caregiver—rather than at the level of the care recipient.

Figure 3. Sex of care recipient by sex of caregiver

Studies of eldercare and gender using IPUMS ATUS data

Forden, J. (2025). Elder caregiving frequency, labor force participation, and work. Journal of Aging & Social Policy, 37(5), 740-755.

Kim, S. O. (2025). Mobility of care and time poverty: the travel burdens of family caregivers in an aging America. Transportation, 1-23.

Radpour, S., Forden, J., & Ghilarducci, T. (2025). Unseen Costs: How Providing Eldercare Impacts Work and Economic Security. Generations, 49(3).

Reproduce these figures and analysis in Stata

Suggested citation

Pastoor, Isabel. 2026. “What ATUS Data Tells Us About Gender and Family Eldercare.” University of Minnesota Life Course Center, September 22, 2026. https://lcc.umn.edu/node/2161.

IPUMS Time Use consists of three data collections: IPUMS ATUS (American Time Use Survey), IPUMS AHTUS (American Heritage Time Use Study), and IPUMS MTUS (Multinational Time Use Study). Each of these data collections provides data from time diary studies, where respondents record their activities over a short period of time—usually a day—and include details about where and with whom they did the activity. IPUMS ATUS and IPUMS AHTUS provide U.S. data, while IPUMS MTUS provides data from 23 countries.

In IPUMS ATUS, time spent on community participation can be identified with the activity-level variable ACTIVITY—which codes each time use activity according to a detailed classification system—or with a number of person-level variables reporting the amount of time a respondent spent on various activities. Community participation can be captured in time spent on activities like volunteer activities, religious and spiritual activities (e.g., church services), attending events (e.g., sporting events, senior citizens meetings), and socializing (e.g., talking with friends). For each activity, users can identify with whom the respondent spent time, and where the activity took place. These variables can be used to determine, for example, how much time an older adult spent with others, or the types of places in the community older adults spend their time.

IPUMS AHTUS allows users to study time use in the U.S. for a limited range of years between 1930 and 2018. Information is available about the activity, who the respondent was with during the activity, and where the activity took place. The AHTUS data can be used to study trends in the community participation of older adults over a long range of time, albeit with less detail about the activities.

IPUMS MTUS provides time use data from many countries, allowing users to study community participation of older adults around the world and across time. Information is available about the activity, who the respondent was with during the activity, and where the activity took place. In some samples, more or less detailed information is available. Users should note that a number of samples only include respondents up to a maximum age.

In all three time use data collections from IPUMS, users can use our custom time use variable builder tools to measure older adults’ community participation. Custom time use variables report the amount of time spent on activities specified by the user, and can optionally include only time spent in certain locations and/or with certain people. For example, it is possible to create a variable that reports the amount of time each respondent spent out of their home and in the presence of at least one non-household member.

Users should consider pooling multiple samples together to ensure sample sizes are large enough to study a relatively small population (older adults) using time use data. Sample sizes can be found on the sample description page for each data collection.

Example studies of community participation using IPUMS data

• Chen, J. H. (2024). Pandemic and Older Adults’ Social Structure of Shared Time. The Journals of Gerontology: Series B, 79(2), gbad159.
• Yu, J., Huang, W., & Kahana, E. (2025). Temporal and Racial/Ethnic Patterns of Social Engagement among Older Adults: Evidence from American Time Use Survey 2019 to 2022. Journal of Applied Gerontology, 44(1), 116-125.

Suggested citation

Pastoor, Isabel. 2026. “Community Participation Among Older Adults: IPUMS Time Use Data.” University of Minnesota Life Course Center, August 25, 2026. https://lcc.umn.edu/node/2161.
 

IPUMS provides harmonized data from major national surveys that make it possible to study paid care work consistently across time and across data sources. Drawing on datasets such as the American Community Survey and the Current Population Survey, IPUMS enables researchers to identify paid care workers using detailed occupation and industry codes and to analyze their wages, hours, job stability, and demographic characteristics. By supporting longitudinal and cross-sectional analyses, IPUMS allows researchers to examine employment trends, workforce composition, and inequality within the paid care sector.

Measuring Paid Care Work

Paid care workers can be identified in datasets like the American Community Survey (ACS) and the Current Population Survey (CPS), available through IPUMS USA and IPUMS CPS. These datasets include detailed occupation codes, which are based on the Standard Occupational Classification (SOC) system, which allow researchers to examine groups such as nursing assistants, personal care aides, home health workers, and community health educators. Industry codes, which are based on the North American Industry Classification System (NAICS),  allow researchers to identify the setting in which an individual is working, such as a hospital, a physician’s office, or a home-based setting. 

By limiting one’s data file to individuals with a specific set of occupation and/or industry codes and drawing on wage, hours worked, and demographic information, researchers can analyze important dimensions of the healthcare workforce such as job quality, employment trends, and disparities across race, gender, and geography. Because the data are harmonized across time, users can trace how care work has evolved in response to demographic shifts, policy changes, and public health crises.

Analytical considerations

Change over time. An important consideration in measuring paid care work is that occupation and industry codes are revised and change over time. They are revised appropriately every ten years due to changes in the types of work that are performed and to enumerate new and emerging occupations. Consequently, to measure occupation and industry over time, researchers either need to standardize occupation codes using crosswalks or use a standardized occupation variable provided by IPUMS. 

Measuring turnover and employment transitions. A question that a lot of researchers, practitioners,and policy makers want to know about paid care workers is how many, or what percent, of workers are leaving their jobs, how many are entering, and how many are changing industries or occupations. Good news! IPUMS CPS includes a harmonized longitudinal panel that allows researchers to track individuals over a 16 month period. When selected into the CPS sample, household members are surveyed in four consecutive months, left un-enumerated during the subsequent eight months, and then resurveyed in each of another four consecutive months. New rotation groups are brought into the CPS sample each month. An employment transition can be identified when an individual was employed as a paid care worker in the prior month and then reports working in a new occupation in the subsequent month. 

Choosing the right dataset. Choosing the right IPUMS dataset to measure paid care workers depends on your research question. IPUMS USA, which includes the American Community Survey, has the largest sample sizes and best representation of workers across occupation, industry, and other factors, including geography, The IPUMS CPS has a smaller sample size, but the longitudinal panel within the CPS allows workers to better measure employment transitions or turnover (described above). Finally, the CPS March Supplement, called the Annual Social and Economic Survey (ASEC), provides more contextual data for researchers, including jobs and income held in the last year and other work characteristics, like health insurance. 

Example studies using IPUMS data

Azaroff LS, Woolhandler S, Touw S, Bor D, Himmelstein DU. Deporting Immigrants May Further Shrink the Health Care Workforce. JAMA. 2025;333(22):2018–2020. doi:10.1001/jama.2025.3544

Baughman RA, Stanley B, Smith KE. Second job holding among direct care workers and nurses: implications for COVID-19 transmission in long-term care. Medical Care Research and Review. 2022 Feb;79(1):151-60. https://doi.org/10.1177/1077558720974129

Dill JS, Frogner BK. The gender wage gap among health care workers across educational and occupational groups. Health Affairs Scholar. 2024 Jan;2(1):qxad090. https://doi.org/10.1093/haschl/qxad090