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

Author

Listed:
  • Bollinger, Christopher R.

    (University of Kentucky)

  • Hirsch, Barry

    (Georgia State University)

Abstract
This paper examines alternative forms of match bias arising from earnings imputation. Wage equation parameters are estimated based on mixed samples of workers who do and do not report earnings, the latter group being assigned earnings of donors who share some but not all the attributes of the recipients. Regressions that include attributes not used as imputation match criteria (e.g., union status) are severely biased. Related forms of match bias arise with respect to attributes used as match criteria, but matched imperfectly. For example, an imperfect match on schooling creates bias that flattens estimated earnings profiles within low, middle, and high education groups, while creating large jumps in returns across groups. The same pattern arises in wage-age profiles. The paper provides a general analytic expression to correct match bias in regression coefficients under the assumption of conditional mean missing at random. The full sample correction approach is compared to the alternative of omitting imputed earners from the sample, with and without reweighting. Additional problems considered are bias in longitudinal analysis and the presence of dated donors.

Suggested Citation

  • Bollinger, Christopher R. & Hirsch, Barry, 2005. "Match Bias from Earnings Imputation in the Current Population Survey: The Case of Imperfect Matching," IZA Discussion Papers 1846, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp1846
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    match bias; imputation; CPS; wage equations; measurement error;
    All these keywords.

    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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