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Match Bias from Earnings Imputation in the Current Population Survey: The Case of Imperfect Matching

Author

Listed:
  • Christopher R. Bollinger

    (University of Kentucky)

  • Barry T. Hirsch

    (Trinity University)

Abstract

This article examines match bias arising from earnings imputation. Wage equation parameters are estimated from mixed samples of workers reporting and not reporting earnings, the latter assigned earnings of donors. Regressions including attributes not used as imputation match criteria (e.g., union) are severely biased. Match bias also arises with attributes used as match criteria but matched imperfectly. Imperfect matching on schooling (age) flattens earnings profiles within education (age) groups and creates jumps across groups. Assuming conditional missing at random, a general analytic expression correcting match bias is derived and compared to alternatives. Reweighting a respondent-only sample proves an attractive approach.

Suggested Citation

  • 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.
  • Handle: RePEc:ucp:jlabec:v:24:y:2006:i:3:p:483-520
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    References listed on IDEAS

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    1. Horowitz, Joel & Manski, Charles, 1997. "Nonparametric Analysis of Randomized Experiments With Missing Covariate and Outcome Data," Working Papers 97-16, University of Iowa, Department of Economics.
    2. Aigner, Dennis J., 1973. "Regression with a binary independent variable subject to errors of observation," Journal of Econometrics, Elsevier, vol. 1(1), pages 49-59, March.
    3. Melissa Clark & David Jaeger, 2006. "Natives, the foreign-born and high school equivalents: new evidence on the returns to the GED," Journal of Population Economics, Springer;European Society for Population Economics, vol. 19(4), pages 769-793, October.
    4. 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.
    5. Jacob A. Mincer, 1974. "Introduction to "Schooling, Experience, and Earnings"," NBER Chapters,in: Schooling, Experience, and Earnings, pages 1-4 National Bureau of Economic Research, Inc.
    6. Freeman, Richard B, 1984. "Longitudinal Analyses of the Effects of Trade Unions," Journal of Labor Economics, University of Chicago Press, vol. 2(1), pages 1-26, January.
    7. 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.
    8. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, pages 37-58.
    9. Jacob A. Mincer, 1974. "Schooling, Experience, and Earnings," NBER Books, National Bureau of Economic Research, Inc, number minc74-1, January.
    10. Bollinger, Christopher R., 1996. "Bounding mean regressions when a binary regressor is mismeasured," Journal of Econometrics, Elsevier, vol. 73(2), pages 387-399, August.
    11. 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.
    12. Molinari, Francesca, 2010. "Missing Treatments," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 82-95.
    13. Card, David, 1996. "The Effect of Unions on the Structure of Wages: A Longitudinal Analysis," Econometrica, Econometric Society, vol. 64(4), pages 957-979, July.
    14. Melissa A. Clark & David Jaeger, 2002. "Natives, the Foreign-Born and High School Equivalents: New Evidence on the Returns to the GED," Working Papers 841, Princeton University, Department of Economics, Industrial Relations Section..
    15. Willis, Robert J., 1987. "Wage determinants: A survey and reinterpretation of human capital earnings functions," Handbook of Labor Economics,in: O. Ashenfelter & R. Layard (ed.), Handbook of Labor Economics, edition 1, volume 1, chapter 10, pages 525-602 Elsevier.
    16. Angrist, Joshua D. & Krueger, Alan B., 1999. "Empirical strategies in labor economics," Handbook of Labor Economics,in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 23, pages 1277-1366 Elsevier.
    17. Murphy, Kevin M & Welch, Finis, 1990. "Empirical Age-Earnings Profiles," Journal of Labor Economics, University of Chicago Press, vol. 8(2), pages 202-229, April.
    18. repec:fth:prinin:462 is not listed on IDEAS
    19. Barry T. Hirsch, 2005. "Why Do Part-Time Workers Earn Less? The Role of Worker and Job Skills," ILR Review, Cornell University, ILR School, vol. 58(4), pages 525-551, July.
    20. Marco Manacorda, 2004. "Can the Scala Mobile Explain the Fall and Rise of Earnings Inequality in Italy? A Semiparametric Analysis, 19771993," Journal of Labor Economics, University of Chicago Press, vol. 22(3), pages 585-614, July.
    21. Lang Wu, 2004. "Exact and Approximate Inferences for Nonlinear Mixed-Effects Models With Missing Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 700-709, January.
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    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
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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