Abstract
To understand rising income inequality in the United States, we use data from the March Current Population Survey (CPS) to decompose income inequality into components attributable to five personal traits: sex, race, education, occupation, and industry of work. By quantifying how income differences across these traits contribute to total inequality, and how those contributions have changed over time, we vet competing hypotheses for the rising gap. In performing this analysis, we correct for data censorship (“Top-coding”) within the CPS by fitting the upper tail of the income distribution and imputing the hidden observations; this represents an extension to previous studies that instead truncate the top several percentiles of income data. Our findings suggest that changes in the returns to education played an important role in driving the observed rise in inequality.
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Notes
Recent authors have also sought to overcome the censorship issue by combining data from the CPS with other sources, such as tax data; this approach has been taken by various US statistical agencies, including the Congressional Budget Office (Habib and Steele 2021) and the Bureau of Economic Analysis (Gindelsky 2021). See Bartels and Waldenström (2022) for a summary.
Due to its lack of scale invariance, the sample variance is not frequently used as an income inequality measure.
The March supplement of the CPS is also known as the Annual Social and Economic Supplement (ASEC). A number of versions of the CPS/ASEC are publicly available; we employ the Integrated Public Use Microdata Series, or IPUMS CPS (Flood et al. 2020) for this study due its harmonized occupation and employment variables, and its ease of use.
Note that we classify farm and business income under labor income; this is consistent with Smith et al. (2019), who find that the majority of pass-through business income can be attributed to labor activities (e.g., the work of a doctor running a small medical practice).
These figures are sourced from the online Online Appendix, https://gabriel-zucman.eu/usdina/, Tables II, distributional series, pre-tax estimates Tables 8 and 9, individual estimates.
There is also a smaller spike in 1992–1993, apparently originating from changes to the Census Department’s data collection procedures; this increase is apparent in both the amended CPS and the Burkhauser results, but absent in our imputations. For more details, see Burkhauser et al. (2012) section “Explaining the differences in trends in the share of the top 1%’
The Atkinson index, another metric popularized by Anthony Barnes Atkinson, is also based on the GEI. Additionally, some authors refer to the I0 as the Theil-L and I1 as the Theil-T.
The race and ethnicity variables in the CPS have evolved over time, with only “white”, “black”, and “other”being available since the start of the sample, and “Hispanic”available since 1971. For the purposes of this study, we defined four categories: non-Hispanic white, Hispanic white, black and other, with Hispanics classified under “white”until 1971.
Gray et al. (2003) uncovered similar results with Canadian data.
The variables ind1950 and occ1950, respectively.
For example, “Professional and Technical Services”, and “Agriculture”; see https://cps.ipums.org/cps-action/variables/OCC1950 and https://cps.ipums.org/cps-action/variables/IND1950 for full breakdown.
Post-grads, College Graduates, 1–2 Years of College, High School Only.
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Special thanks to Ira Gang for his comments on our manuscript.
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He, Z., Jiang, Y. Decomposing income inequality in the United States: 1968–2018. Empir Econ 65, 2751–2778 (2023). https://doi.org/10.1007/s00181-023-02434-6
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DOI: https://doi.org/10.1007/s00181-023-02434-6