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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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    Cited by:

    1. Larrimore, Jeff & Splinter, David, 2019. "How much does health insurance cost? Comparison of premiums in administrative and survey data," Economics Letters, Elsevier, vol. 174(C), pages 132-135.
    2. 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.
    3. 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.
    4. 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," AEI Economics Working Papers 1018925, American Enterprise Institute.
    5. 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.
    6. 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.
    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. Ameri, Mason & Ali, Mohammad & Schur, Lisa & Kruse, Douglas L., 2019. "Disability and the Unionized Workplace," IZA Discussion Papers 12258, Institute of Labor Economics (IZA).
    9. 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, June.
    10. 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.

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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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