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

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