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Is Earnings Nonresponse Ignorable?

Author

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  • Bollinger, Christopher R.

    () (University of Kentucky)

  • Hirsch, Barry

    () (Georgia State University)

Abstract

Earnings nonresponse in the Current Population Survey is roughly 30% in the monthly surveys and 20% in the annual March survey. Even if nonresponse is random, severe bias attaches to wage equation coefficient estimates on attributes not matched in the earnings imputation hot deck. If nonresponse is ignorable, unbiased estimates can be achieved by omitting imputed earners, yet little is known about whether or not CPS nonresponse is ignorable. Using sample frame measures to identify selection, we find clear-cut evidence among men but limited evidence among women for negative selection into response. Wage equation slope coefficients are affected little by selection but because of intercept shifts, wages for men and to a lesser extent women are understated, as are gender wage gaps. Selection is less severe among household heads/co-heads than among other household members.

Suggested Citation

  • Bollinger, Christopher R. & Hirsch, Barry, 2010. "Is Earnings Nonresponse Ignorable?," IZA Discussion Papers 5347, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp5347
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    References listed on IDEAS

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    1. Cheti Nicoletti & Franco Peracchi, 2005. "Survey response and survey characteristics: microlevel evidence from the European Community Household Panel," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(4), pages 763-781.
    2. James J. Heckman & Paul A. LaFontaine, 2006. "Bias-Corrected Estimates of GED Returns," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 661-700, July.
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    4. 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.
    5. Hamermesh, Daniel S. & Donald, Stephen G., 2008. "The effect of college curriculum on earnings: An affinity identifier for non-ignorable non-response bias," Journal of Econometrics, Elsevier, vol. 144(2), pages 479-491, June.
    6. Korinek, Anton & Mistiaen, Johan A. & Ravallion, Martin, 2007. "An econometric method of correcting for unit nonresponse bias in surveys," Journal of Econometrics, Elsevier, vol. 136(1), pages 213-235, January.
    7. 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.
    8. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    9. Giuseppe De Luca & Franco Peracchi, 2007. "A sample selection model for unit and item nonresponse in cross-sectional surveys," CEIS Research Paper 95, Tor Vergata University, CEIS.
    10. 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.
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    Citations

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    Cited by:

    1. Ziliak, James P. & Hardy, Bradley & Bollinger, Christopher, 2011. "Earnings volatility in America: Evidence from matched CPS," Labour Economics, Elsevier, vol. 18(6), pages 742-754.
    2. Fertig, Michael & Görlitz, Katja, 2013. "Missing wages: How to test for biased estimates in wage functions?," Economics Letters, Elsevier, vol. 118(2), pages 269-271.
    3. Jon Jellema & Nora Lustig & Astrid Haas & Sebastian Wolf, 2016. "The Impact of Taxes, Transfers, and Subsidies on Inequality and Poverty in Uganda," Commitment to Equity (CEQ) Working Paper Series 1353, Tulane University, Department of Economics.
    4. Barry T. Hirsch & John V. Winters, 2014. "An Anatomy Of Racial and Ethnic Trends in Male Earnings in the U.S," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(4), pages 930-947, December.
    5. Jon Jellema & Nora Lustig & Astrid Haas & Sebastian Wolf, 2016. "The Impact of Taxes, Transfers, and Subsidies on Inequality and Poverty in Uganda," Commitment to Equity (CEQ) Working Paper Series 53, Tulane University, Department of Economics.
    6. Hirsch, Barry & Manzella, Julia, 2014. "Who Cares – and Does It Matter? Measuring Wage Penalties for Caring Work," IZA Discussion Papers 8388, Institute for the Study of Labor (IZA).
    7. 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.
    8. repec:eee:econom:v:199:y:2017:i:2:p:117-130 is not listed on IDEAS
    9. Drechsler, Jörg & Kiesl, Hans, 2014. "Beat the heap - an imputation strategy for valid inferences from rounded income data," IAB Discussion Paper 201402, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

    More about this item

    Keywords

    response bias; earnings nonresponse; gender gap; imputation; CPS;

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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