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Using Administrative Earnings Records to Assess Wage Data Quality in the March Current Population Survey and the Survey of Income and Program Participation

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  • Marc Roemer

Abstract

The March Current Population Survey (CPS) and the Survey of Income and Program Participation (SIPP) produce different aggregates and distributions of annual wages. An excess of high wages and shortage of low wages occurs in the March CPS. SIPP shows the opposite, an excess of low wages and shortage of high wages. Exactly-matched Detailed Earnings Records (DER) from the Social Security Administration allow comparing March CPS and SIPP people’s wages using data independent of the surveys. Findings include the following. March CPS and SIPP people differ little in their true wage characteristics. March CPS and SIPP represent a worker’s percentile rank better than the dollar amount of wages. Workers with one job and low work effort have underestimated March CPS wages. March CPS has a higher level of "underground" wages than SIPP, and increasingly so in the 1990s. March CPS has a higher level of self-employment income "misclassified" as wages than SIPP, and increasingly so in the 1990s. These trends may explain one-third of March CPS’s 6-percentage-point increase in aggregate wages relative to independent estimates from 1993 to 1995. Finally, the paper delineates March CPS occupations disproportionately likely to be absent from the administrative data entirely or to "misclassify" self-employment income as wages.

Suggested Citation

  • 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.
    • Handle: RePEc:cen:tpaper:2002-22
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    Citations

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    Cited by:

    1. Reid Giles & Zabala Felipa & Holmberg Anders, 2017. "Extending TSE to Administrative Data: A Quality Framework and Case Studies from Stats NZ," Journal of Official Statistics, Sciendo, vol. 33(2), pages 477-511, June.
    2. Fredrik Andersson & Elizabeth E. Davis & Matthew L. Freedman & Julia I. Lane & Brian P. Mccall & Kristin Sandusky, 2012. "Decomposing the Sources of Earnings Inequality: Assessing the Role of Reallocation," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 51(4), pages 779-810, October.
    3. Celik Sule & Juhn Chinhui & McCue Kristin & Thompson Jesse, 2012. "Recent Trends in Earnings Volatility: Evidence from Survey and Administrative Data," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 12(2), pages 1-26, June.
    4. Richard Bavier, 2008. "Reconciliation of income and consumption data in poverty measurement," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 27(1), pages 40-62.
    5. Emily Isenberg & Liana Christin Landivar & Esther Mezey, 2013. "A Comparison Of Person-Reported Industry To Employer-Reported Industry In Survey And Administrative Data," Working Papers 13-47, Center for Economic Studies, U.S. Census Bureau.
    6. John Abowd & John Haltiwanger & Julia Lane, 2009. "Wage Structure and Labor Mobility in the United States," NBER Chapters, in: The Structure of Wages: An International Comparison, pages 81-100, National Bureau of Economic Research, Inc.
    7. Stüber, Heiko & Grabka, Markus M. & Schnitzlein, Daniel D., 2023. "A tale of two data sets: comparing German administrative and survey data using wage inequality as an example," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 57, pages 1-8.
    8. Katharine G. Abraham & John Haltiwanger & Kristin Sandusky & James R. Spletzer, 2013. "Exploring Differences in Employment between Household and Establishment Data," Journal of Labor Economics, University of Chicago Press, vol. 31(S1), pages 129-172.
    9. Andreasch Michael & Lindner Peter, 2016. "Micro- and Macrodata: a Comparison of the Household Finance and Consumption Survey with Financial Accounts in Austria," Journal of Official Statistics, Sciendo, vol. 32(1), pages 1-28, March.
    10. James P. Ziliak & Charles Hokayem & Christopher R. Bollinger, 2022. "Trends in Earnings Volatility Using Linked Administrative and Survey Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 12-19, December.
    11. repec:mpr:mprres:6064 is not listed on IDEAS
    12. Daniel Kuehn, 2016. "An estimate of the error in self-reported college major," Applied Economics Letters, Taylor & Francis Journals, vol. 23(11), pages 757-760, July.
    13. Jonathan Heathcote & Fabrizio Perri & Giovanni L. Violante, 2010. "Unequal We Stand: An Empirical Analysis of Economic Inequality in the United States: 1967-2006," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 13(1), pages 15-51, January.
    14. John L. Czajka & James Mabli & Scott Cody, "undated". "Sample Loss and Survey Bias in Estimates of Social Security Beneficiaries: A Tale of Two Surveys," Mathematica Policy Research Reports 764acc7a0a0b462c9906514d5, Mathematica Policy Research.
    15. Alethea Abuyuan & Glenn Yago & Betsy Zeidman, 2007. "A history of emerging domestic markets," Community Development Innovation Review, Federal Reserve Bank of San Francisco, issue 1, pages 1-22.
    16. John L. Czajka & Gabrielle Denmead, "undated". "Income Data for Policy Analysis: A Comparative Assessment of Eight Surveys," Mathematica Policy Research Reports 19724257b78544bdbd55f15be, Mathematica Policy Research.
    17. John L. Czajka, 2013. "Can Administrative Records Be Used to Reduce Nonresponse Bias?," The ANNALS of the American Academy of Political and Social Science, , vol. 645(1), pages 171-184, January.
    18. repec:mpr:mprres:6195 is not listed on IDEAS
    19. Justin Falk, 2012. "Comparing Wages in the Federal Government and the Private Sector: Working Paper 2012-03," Working Papers 42922, Congressional Budget Office.
    20. John M. Abowd & Paul A. Lengermann & Kevin L. McKinney, 2002. "The Measurement of Human Capital in the U.S. Economy," Longitudinal Employer-Household Dynamics Technical Papers 2002-09, Center for Economic Studies, U.S. Census Bureau, revised Mar 2003.
    21. 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.
    22. 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.
    23. Jonathan A. Schwabish, 2006. "Earnings Inequality and High Earners: Changes During and after the Stock Market Boom of the 1990s: Working Paper 2006-06," Working Papers 17738, Congressional Budget Office.
    24. Bollinger, Christopher R. & Hirsch, Barry & Hokayem, Charles M. & Ziliak, James P., 2018. "Trouble in the Tails? What We Know about Earnings Nonresponse Thirty Years after Lillard, Smith, and Welch," IZA Discussion Papers 11710, Institute of Labor Economics (IZA).

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