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The Role of CPS Nonresponse in the Measurement of Poverty

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  • Charles Hokayem
  • Christopher Bollinger
  • James P. Ziliak

Abstract

The Current Population Survey Annual Social and Economic Supplement (CPS ASEC) serves as the data source for official income, poverty, and inequality statistics in the United States. There is a concern that the rise in nonresponse to earnings questions could deteriorate data quality and distort estimates of these important metrics. We use a dataset of internal ASEC records matched to Social Security Detailed Earnings Records (DER) to study the impact of earnings nonresponse on estimates of poverty from 1997-2008. Our analysis does not treat the administrative data as the "truth"; instead, we rely on information from both administrative and survey data. We compare a "full response" poverty rate that assumes all ASEC respondents provided earnings data to the official poverty rate to gauge the nonresponse bias. On average, we find the nonresponse bias is about 1.0 percentage point.

Suggested Citation

  • Charles Hokayem & Christopher Bollinger & James P. Ziliak, 2015. "The Role of CPS Nonresponse in the Measurement of Poverty," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 935-945, September.
    • Handle: RePEc:taf:jnlasa:v:110:y:2015:i:511:p:935-945
    DOI: 10.1080/01621459.2015.1029576
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    References listed on IDEAS

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    1. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    2. Marc Roemer, 2002. "Using Administrative Earnings Records to Assess Wage Data Quality in the March Current Population Survey and the Survey of Income and Program Participation," Longitudinal Employer-Household Dynamics Technical Papers 2002-22, Center for Economic Studies, U.S. Census Bureau.
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    Cited by:

    1. Gopi Shah Goda & Emilie Jackson & Lauren Hersch Nicholas & Sarah See Stith, 2023. "The impact of Covid-19 on older workers’ employment and Social Security spillovers," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(2), pages 813-846, April.
    2. Meyer, Bruce D. & Mittag, Nikolas, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," IZA Discussion Papers 10943, Institute of Labor Economics (IZA).
    3. Carlos A. Piccioni & Saulo B. Bastos & Daniel O. Cajueiro, 2024. "Measuring Inequality Using Electronic Payment Data," Working Papers Series 608, Central Bank of Brazil, Research Department.
    4. David Brady & Zachary Parolin, 2020. "The Levels and Trends in Deep and Extreme Poverty in the United States, 1993–2016," Demography, Springer;Population Association of America (PAA), vol. 57(6), pages 2337-2360, December.
    5. Borgschulte, Mark & Cho, Heepyung & Lubotsky, Darren, 2022. "Partisanship and survey refusal," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 332-357.
    6. Klee, Mark A. & Chenevert, Rebecca L. & Wilkin, Kelly R., 2019. "Revisiting the shape of earnings nonresponse," Economics Letters, Elsevier, vol. 184(C).
    7. Ethan Krohn, 2024. "Earnings Through the Stages: Using Tax Data to Test for Sources of Error in CPS ASEC Earnings and Inequality Measures," Working Papers 24-52, Center for Economic Studies, U.S. Census Bureau.
    8. Bhashkar Mazumder, 2018. "Intergenerational Mobility in the United States: What We Have Learned from the PSID," The ANNALS of the American Academy of Political and Social Science, , vol. 680(1), pages 213-234, November.
    9. George L. Wehby & Dhaval M. Dave & Robert Kaestner, 2020. "Effects of the Minimum Wage on Infant Health," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 411-443, March.
    10. Margaret E. Brehm & Olga Malkova, 2023. "The Child Tax Credit over Time by Family Type: Benefit Eligibility and Poverty," National Tax Journal, University of Chicago Press, vol. 76(3), pages 707-741.
    11. Bruce Meyer & Nikolas Mittag, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," Working Papers 2017-075, Human Capital and Economic Opportunity Working Group.
    12. James X. Sullivan, 2020. "A Cautionary Tale of Using Data From the Tail," Demography, Springer;Population Association of America (PAA), vol. 57(6), pages 2361-2368, December.
    13. Marko Ledić & Ivica Rubil & Ivica Urban, 2024. "Using tax records to correct for under‐representation of top income sources in surveys," Fiscal Studies, John Wiley & Sons, vol. 45(4), pages 521-541, December.
    14. Meyer, Bruce D. & Mittag, Nikolas, 2021. "An empirical total survey error decomposition using data combination," Journal of Econometrics, Elsevier, vol. 224(2), pages 286-305.
    15. Peter Valet & Jule Adriaans & Stefan Liebig, 2019. "Comparing survey data and administrative records on gross earnings: nonreporting, misreporting, interviewer presence and earnings inequality," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(1), pages 471-491, January.
    16. Robert Bernhardt & David Munro & Erin L. Wolcott, 2024. "How does the dramatic rise of nonresponse in the Current Population Survey impact labor market indicators?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 498-512, April.
    17. Adam Bee & Joshua Mitchell & Nikolas Mittag & Jonathan Rothbaum & Carl Sanders & Lawrence Schmidt & Matthew Unrath, 2023. "National Experimental Wellbeing Statistics - Version 1," Working Papers 23-04, Center for Economic Studies, U.S. Census Bureau.

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