I’m using IPUMS CPS microdata to conduct an econometric analysis focused on transit behavior and related factors in major metropolitan areas — specifically the Bay Area, Los Angeles, Washington DC, New York, and Chicago — from January 2018 through December 2024.
I’m particularly interested in incorporating variables related to highway usage, biking, and income by month and metro area. I’ve already assembled a dataset using IPUMS CPS and would be happy to share my R code to illustrate my process. However, I’m running into issues with the income data: when I try to compute average income values, the results don’t seem to make logical sense based on my expectations.
I would be very grateful for any guidance on which income variables or methods would be most appropriate for my use case, or if there’s a better approach you’d recommend to get consistent monthly metro-level income estimates.
Thank you so much for your time and help!
While I cannot provide code review of your analysis, I can provide some conceptual feedback and guidance.
Note that the CPS is most often used to produce monthly labor market estimates and may not match income data reported using other data sources. This is especially true of estimates for substate areas (i.e., large metropolitan areas, metropolitan divisions, and principal cities), which the Census Bureau has characterized as having a high degree of sampling error due to the unavailability of independent population controls and a lack of demographic coverage. The Census Bureau notes that data users may be better served by substate area data from the annual American Community Survey — see IPUMS USA for microdata and IPUMS NHGIS for geographically aggregated summary statistics from the ACS.
I reviewed your recent data extract and observed that you’ve included a few different income variables: EARNWEEK2 and FAMINC, which are available in the monthly samples, and ADJGINC, which is only available annually in the Annual Social and Economic Supplement (ASEC). Because you have indicated you are interested in monthly estimates, I am only sharing details about EARNWEEK2 and FAMINC.
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FAMINC is a monthly variable that reports the (intervalled) income of all members of the householder’s family; this value is assigned to all persons in the household (including those who are not part of the householder’s family). To obtain household-level estimates using FAMINC, it is necessary to restrict analysis to a single individual in each household (typically to those with PERNUM = 1) and weight observations using HWTFINL.
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EARNWEEK2 is a monthly variable in the outgoing rotation group (ORG) questions that is asked of all respondents in their fourth and eighth month-in-sample (MISH). It reports a rounded value for the respondent’s weekly salary/wage income. As noted in this variable’s universe tab, it excludes self-employed persons (identified in CLASSWKR). Additionally, it only reports wage/salary income earned at the respondent’s current job (i.e., it does not include all other types of income such as income from interest, rents, disability, etc). EARNWEEK2 is a person -level variable in the ORG questions, it should be weighted using the person-level weight EARNWT.
If you are interested in capturing monthly family income, FAMINC is most appropriate. However, if you are interested in person-level income, EARNWEEK2 may be preferable. If you are interested in annual income instead of monthly income, I recommend exploring FTOTVAL as well as ADJINC; the former includes all income earned by family members while the latter excludes non-taxable income.