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Trouble in the Tails? What We Know about Earnings Nonresponse Thirty Years after Lillard, Smith, and Welch

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  • Bollinger, Christopher R.

    (University of Kentucky)

  • Hirsch, Barry

    (Georgia State University)

  • Hokayem, Charles M.

    (U.S. Census Bureau)

  • Ziliak, James P.

    (University of Kentucky)

Abstract

Earnings nonresponse in household surveys is widespread, yet there is limited knowledge of how nonresponse biases earnings measures. We examine the consequences of nonresponse on earnings gaps and inequality using Current Population Survey individual records linked to administrative earnings data. The common assumption that earnings are missing at random is rejected. Nonresponse across the earnings distribution is U-shaped, highest in the left and right tails. Inequality measures differ between household and administrative data due in part to nonresponse. Nonresponse biases earnings differentials by race, gender, and education, particularly in the tails. Flexible copula-based models can account for nonrandom nonresponse.

Suggested Citation

  • 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).
    • Handle: RePEc:iza:izadps:dp11710
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    1. Richard Burkhauser & Shuaizhang Feng & Stephen Jenkins & Jeff Larrimore, 2009. "Recent Trends in Top Income Shares in the USA: Reconciling Estimates from March CPS and IRS Tax Return Data," Working Papers 09-26, Center for Economic Studies, U.S. Census Bureau.
    2. Lillard, Lee & Smith, James P & Welch, Finis, 1986. "What Do We Really Know about Wages? The Importance of Nonreporting and Census Imputation," Journal of Political Economy, University of Chicago Press, vol. 94(3), pages 489-506, June.
    3. Alan B. Krueger & Alexandre Mas & Xiaotong Niu, 2017. "The Evolution of Rotation Group Bias: Will the Real Unemployment Rate Please Stand Up?," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 258-264, May.
    4. Patrick Kline & Andres Santos, 2013. "Sensitivity to missing data assumptions: Theory and an evaluation of the U.S. wage structure," Quantitative Economics, Econometric Society, vol. 4(2), pages 231-267, July.
    5. Christopher R. Bollinger & Barry T. Hirsch, 2013. "Is Earnings Nonresponse Ignorable?," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 407-416, May.
    6. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    7. Francine D. Blau & Lawrence M. Kahn, 2017. "The Gender Wage Gap: Extent, Trends, and Explanations," Journal of Economic Literature, American Economic Association, vol. 55(3), pages 789-865, September.
    8. Erik Hurst & Geng Li & Benjamin Pugsley, 2014. "Are Household Surveys Like Tax Forms? Evidence from Income Underreporting of the Self-Employed," The Review of Economics and Statistics, MIT Press, vol. 96(1), pages 19-33, March.
    9. Hirsch, Barry T. & Winters, John V., 2016. "Rotation group bias in measures of multiple job holding," Economics Letters, Elsevier, vol. 147(C), pages 160-163.
    10. Christopher R. Bollinger & Barry T. Hirsch, 2006. "Match Bias from Earnings Imputation in the Current Population Survey: The Case of Imperfect Matching," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 483-520, July.
    11. José Mata & José A. F. Machado, 2005. "Counterfactual decomposition of changes in wage distributions using quantile regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 445-465.
    12. Moshe Buchinsky, 1998. "The dynamics of changes in the female wage distribution in the USA: a quantile regression approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(1), pages 1-30.
    13. Peracchi, Franco & Welch, Finis, 1995. "How representative are matched cross-sections? Evidence from the Current Population Survey," Journal of Econometrics, Elsevier, vol. 68(1), pages 153-179, July.
    14. Duncan, Greg J & Hill, Daniel H, 1985. "An Investigation of the Extent and Consequences of Measurement Error in Labor-Economic Survey Data," Journal of Labor Economics, University of Chicago Press, vol. 3(4), pages 508-532, October.
    15. Barry T. Hirsch & Edward J. Schumacher, 2004. "Match Bias in Wage Gap Estimates Due to Earnings Imputation," Journal of Labor Economics, University of Chicago Press, vol. 22(3), pages 689-722, July.
    16. Bradley Hardy & James P. Ziliak, 2014. "Decomposing Trends In Income Volatility: The “Wild Ride” At The Top And Bottom," Economic Inquiry, Western Economic Association International, vol. 52(1), pages 459-476, January.
    17. 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.
    18. Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25(3), pages 513-551.
    19. Martin H. David & Christopher R. Bollinger, 2005. "I didn't tell, and I won't tell: dynamic response error in the SIPP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 563-569.
    20. Bollinger, Christopher R & David, Martin H, 2001. "Estimation with Response Error and Nonresponse: Food-Stamp Participation in the SIPP," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 129-141, April.
    21. Jeff Larrimore & Richard Burkhauser & Shuaizhang Feng & Laura Zayatz, 2008. "Consistent Cell Means for Topcoded Incomes in the Public Use March CPS (1976-2007)," Working Papers 08-06, Center for Economic Studies, U.S. Census Bureau.
    22. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    23. Mathiowetz, Nancy A & Duncan, Greg J, 1988. "Out of Work, Out of Mind: Response Errors in Retrospective Reports of Unemployment," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 221-229, April.
    24. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    25. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
    26. Thomas Lemieux, 2006. "Increasing Residual Wage Inequality: Composition Effects, Noisy Data, or Rising Demand for Skill?," American Economic Review, American Economic Association, vol. 96(3), pages 461-498, June.
    27. Jungmin Lee & Sokbae Lee, 2012. "Does it Matter WHO Responded to the Survey? Trends in the U.S. Gender Earnings Gap Revisited," ILR Review, Cornell University, ILR School, vol. 65(1), pages 148-160, January.
    28. Bollinger, Christopher R, 1998. "Measurement Error in the Current Population Survey: A Nonparametric Look," Journal of Labor Economics, University of Chicago Press, vol. 16(3), pages 576-594, July.
    29. Mellow, Wesley & Sider, Hal, 1983. "Accuracy of Response in Labor Market Surveys: Evidence and Implications," Journal of Labor Economics, University of Chicago Press, vol. 1(4), pages 331-344, October.
    30. Poterba, James M & Summers, Lawrence H, 1986. "Reporting Errors and Labor Market Dynamics," Econometrica, Econometric Society, vol. 54(6), pages 1319-1338, November.
    31. Bound, John & Brown, Charles & Duncan, Greg J & Rodgers, Willard L, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-368, July.
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    Cited by:

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    2. Bart H. H. Golsteyn & Stefa Hirsch, 2019. "Are estimates of intergenerational mobility biased by non-response? Evidence from the Netherlands," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 52(1), pages 29-63, January.
    3. Bruce D. Meyer & Derek Wu & Victoria R. Mooers & Carla Medalia, 2019. "The Use and Misuse of Income Data and Extreme Poverty in the United States," NBER Working Papers 25907, National Bureau of Economic Research, Inc.
    4. McGuinness, Seamus & Redmond, Paul, 2018. "Estimating the effect of an increase in the minimum wage on hours worked and employment in Ireland," Research Series, Economic and Social Research Institute (ESRI), number BKMNEXT354.
    5. Randall Akee & Maggie R. Jones & Sonya R. Porter, 2019. "Race Matters: Income Shares, Income Inequality, and Income Mobility for All U.S. Races," Demography, Springer;Population Association of America (PAA), vol. 56(3), pages 999-1021, June.
    6. Blundell, Richard & Joyce, Robert & Norris Keiller, Agnes & Ziliak, James P., 2018. "Income inequality and the labour market in Britain and the US," Journal of Public Economics, Elsevier, vol. 162(C), pages 48-62.
    7. 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.
    8. Joni Hersch & Jennifer Bennett Shinall, 2018. "Imputation Match Bias in Immigrant Wage Convergence," Demography, Springer;Population Association of America (PAA), vol. 55(4), pages 1475-1485, August.
    9. Pablo Gutiérrez Cubillos, 2022. "Gini and undercoverage at the upper tail: a simple approximation," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 29(2), pages 443-471, April.
    10. Ameri, Mason & Ali, Mohammad & Schur, Lisa & Kruse, Douglas L., 2019. "Disability and the Unionized Workplace," IZA Discussion Papers 12258, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    CPS ASEC; nonresponse bias; copula; measurement error; hot deck imputation; proxy reports; earnings inequality;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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