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 principal challenge to studying grandparents and their relationships with their adult children and grandchildren is the need to link related individuals who live in different households. In most public use census and survey data, a grandparent cannot be linked to family members who are not part of the same household. The relationships between individuals who do not live together are unknown. Names or family relationship links are not provided in public data.
 

This means that researchers interested in studying grandparents using IPUMS data will usually need to study grandparents who live with their child(ren) and grandchild(ren). The household roster in most IPUMS data collections allows users to observe the age and sex of each household member. The family interrelationship variables (sometimes present in the original data, and sometimes constructed by IPUMS using linking rules) provide further information about how household members are related to each other. For example, a pointer variable reports the person number of each individual’s mother, if their mother is in the household. Users can leverage these sets of variables to determine the number of generations in a household and how each individual is related to one another. Read more about how IPUMS has constructed family interrelationship variables for IPUMS USA and IPUMS CPS.

Limiting a study to only grandparents who are co-resident with their child(ren) and grandchild(ren) has drawbacks. Sample sizes will be relatively small, and may be unusable depending on the data collection used and other restrictions placed on the study population. Grandparents who live with these family members are also a selected group; they are likely systematically different from the general population of grandparents. 
 

Researchers who wish to study co-resident grandparents can do so using most IPUMS data collections that provide microdata (i.e., not IPUMS NHIGS or IPUMS IHGIS). Some data collections are more suitable than others. See a few relevant collection-specific notes below:
 

• In recent NHIS samples, studying the relationships between household members is nearly impossible, as even basic demographic information (e.g., age and sex) is unavailable for household members other than the sample adult and sample child.
• Three variables in IPUMS USA provide information on living with and caring for grandchildren: GCHOUSE, GCRESPON, and GCMONTHS. 
• In IPUMS ATUS, users can study time spent with grandchildren, even if they do not live in the same household. Users can also study time spent on caring for grandparents, even if they do not live in the same household.
• IPUMS USA provides historical linked data from the U.S. decennial census, allowing researchers to observe individuals and households across decades. These links make it possible to identify grandparents who do not live with their grandchildren in some cases.
• A variable called MULTGEN, which reports the number of generations in each household, is available from IPUMS USA and from IPUMS CPS.

Example studies of grandparents and grandchildren using IPUMS data

• Glaser, K., Stuchbury, R., Price, D., Di Gessa, G., Ribe, E., & Tinker, A. (2018). Trends in the prevalence of grandparents living with grandchild(ren) in selected European countries and the United States. European journal of ageing, 15(3), 237-250.
• Kwon, S. J. (2024). Grandparents and parental labor supply during the COVID-19 pandemic. Review of Economics of the Household, 22(3), 935-964.

Suggested citation

Pastoor, Isabel. 2026. “Under the Same Roof: Identifying Grandparents in IPUMS Data.” University of Minnesota Life Course Center, October 28, 2026. https://lcc.umn.edu/node/2246.

The Difference between Wages and Income

Wages and income are related but distinct measures of economic well-being. Wages refer to the hourly rate or amount a worker earns per unit of time, reflecting the value of their labor on an hourly basis. Income is the total amount of money a person earns over a defined period, such as a year, and can include earnings from non-wage sources, like investments or retirement funds. This blog post focuses on wage income, the total amount of money a person earns from wages for work over a defined period. Wage income is determined by the wage rate and the number of hours a person works over the period—which depends on work status (full time or part time), seasonality, stability, and other factors. Wage income can also include other sources of earnings like overtime pay, bonuses, or additional jobs. 

Overview of Available Data Sources

The Current Population Survey and the American Community Survey provide a number of ways to measure wages and income:

1. CPS Outgoing Rotation Groups/Earner Study: Includes weekly earnings and hourly wage information (for hourly workers only). Households in the CPS are interviewed for four months, not interviewed for 8 months, and then interviewed again for 4 more months. Households that are interviewed for the fourth month or eighth month (that is, the households that are about to rotate out of interviews for eight months or indefinitely) are asked additional questions about work and earnings. 

Strengths of the CPS Outgoing Rotation Groups/Earner Study: Because the CPS includes a longitudinal panel, the Earner Study can be used to study wage change over time. The Earner Study can also be linked to the ASEC (see below) and other monthly data. 

2. CPS Annual Social and Economic Supplement (ASEC): Includes total pre-tax wage and salary income--that is, money received as an employee--for the previous calendar year and hourly wage information (for hourly workers only). There are also measures of total personal income and total family income. 

Strengths of the ASEC: Extensive variables on labor market behavior and public or private income sources. ASEC data can also be linked to monthly CPS data.

3. ACS (American Community Survey): Includes annual income data and hourly wage information (for hourly workers). There are also measures of total personal income and total family income. 

Strengths of the ACS: The ACS includes large sample sizes, which are ideal for subgroup analyses. However, it contains fewer work- and income-related covariates than CPS and does not have a longitudinal component.

Analytic Considerations

Calculating hourly wages. Both the CPS and ACS include hourly wages for workers who are paid an hourly rate. If you are interested in approximating  hourly wages for salaried workers, you must manually calculate an hourly rate. Hourly wage estimation requires identifying hours and weeks worked annually. Researchers should note assumptions about paid leave, potential reporting errors, and the challenges inherent in self-reported income. 

Skewed distribution. Because income distributions are highly right-skewed, log transformations or medians are often preferable to mean-based analyses. 

Topcoding. Topcoding presents another challenge—particularly in CPS Earner Study, where upper-income values are truncated at relatively low thresholds. The Bureau of Labor Statistics provides detailed guidance on adjusting topcoded wages.

Adjusting for inflation. All dollar amounts in the IPUMS are nominal dollars--that is, they are given as measured in the original data. However, inflation renders these dollar amounts not comparable: $1 in 2008, for example, is worth much less than $1 in 1939. The IPUMS variable CPI99 provides an easy way to adjust dollar amounts to constant dollars. This variable, constant within years, inflates or deflates dollar amounts to the amount they would have represented in 1999 (which corresponds to the 2000 census PUMS). An IPUMS data access system feature–Adjust Monetary Vaules–will also do this for you; this IPUMS blog post describes the feature in depth. Alternatively, you can manually adjust wages and income using the Consumer Price Index Calculator. 

Other considerations. Be thoughtful about any additional universe restrictions you apply to the data. For example, when using the CPS “worked last week” variable, note that every five to seven years the September reference week overlaps with Labor Day, leading many respondents to report not working that week even though they are employed. Similarly, if you restrict by occupation, remember that the sample will include individuals who were recently employed but are not currently working. Click the “universe” tab of each IPUMS variable to find its universe.

Measuring Income Among Nonworkers

In studies of aging, many respondents are partially or fully retired. The ACS includes broad categories for non-wage income, while the CPS ASEC provides detailed sources such as Social Security, pensions, annuities, and retirement account withdrawals—critical for assessing financial security in later life.

Example Studies using IPUMS Data

From Lawn Care to Home Care: Undocumented Immigration and Aging in Place

Differential sensitivity of adversity by income: Evidence from a Study of Bereavement 

The Effect of the Minimum Wage on Employer-Sponsored Insurance for Low-Income Workers and Dependents

Nurse Employment During The First Fifteen Months Of The COVID-19 Pandemic

Families’ Job Characteristics and Economic Self-Sufficiency: Differences by Income, Race-Ethnicity, and Nativity