Another Plea for Caution When Using Survey Income Data From the Far-Left Tail
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Another Plea for Caution When Using Survey Income Data From the Far-Left Tail James X. Sullivan 1 Accepted: 30 October 2020 / Published online: 19 November 2020 # Population Association of America 2020
Introduction As an original reviewer of Brady and Parolin’s article (published in this issue of Demography), I expressed concerns about measurement error given that the study relies on extreme outliers in the distribution of survey income. Recognizing the importance of this issue, the editorial team at Demography invited me to share these concerns in a comment. In my comment (published in this issue of Demography), I highlight evidence from a growing literature indicating that income is significantly underreported in household surveys. This underreporting is evident across many surveys in the United States and internationally, has worsened noticeably over time, and appears to be most problematic for values at the very bottom of the reported income distribution. Given this evidence, one should be very cautious about drawing strong conclusions based only on data from extremely low values of income reported in surveys. In their response to my comment, Brady and Parolin argue that the concerns I raise are not problematic for their analyses. They also present results from additional robustness exercises intended to address measurement error concerns and argue that these results confirm their conclusions. In this response, I note that the evidence that Brady and Parolin present in their response does not address the key concerns in my comment. My goal here is to clarify the key arguments and findings from the relevant literature. Brady and Parolin offer additional analyses in their response, noting that their main conclusions do not change qualitatively after they make small changes to their approach. These robustness checks, however, do not address the primary issues raised in the literature. A major recent development in this literature is the launch of the Comprehensive Income Dataset (CID) by researchers internal and external to the U.S. Census Bureau. The CID links data from several national surveys—including the Current Population Survey (CPS), which Brady and Parolin use—to administrative data on many sources of income,
* James X. Sullivan [email protected]
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Department of Economics and the Wilson Sheehan Lab for Economic Opportunities (LEO), University of Notre Dame, Notre Dame, IN 46556, USA
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J.X. Sullivan
including earnings, several cash and in-kind transfer programs, and retirement income (Medalia et al. 2019). A primary goal of the CID is to improve the accuracy of income data in large surveys. A key feature of the CID is that it can be used not only to confirm earlier studies finding that many income sources are significantly underreported at the bottom but also to correct survey-based estimates that rely heavily on these poorly measured sources. Making such corrections shows that Brady and Parolin’s estimates are biased significantly upward. For $2/day poverty, the bias is more than 120%.
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