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On Omitted Variable Bias and Measurement Error in Returns to Schooling Estimates

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  • Mellander, Erik

    (The Research Institute of Industrial Economics)

Abstract

Lam and Schoeni (1993) consider an equation where earnings are explained by schooling and ability. They assume that ability data are lacking and that schooling is measured with error. The estimate obtained by regressing earnings on schooling thus contains omitted variable bias (OVB), which is positive, and measurement error bias (MEB), which is negative. Adding a family background variable to proxy for ability is claimed to: i) decrease the OVB towards, but not below, zero and ii) make the MEB even more negative. This note claims that while ii) is true, even in the context of multiple family background variables, i) is in general incorrect. The OVB may increase in magnitude and/or change sign. Conditions are provided under which i) holds. A simulation procedure is suggested that will yield consistent estimates of the total bias and its components, conditional upon values on the true return and the measurement error variance.

Suggested Citation

  • Mellander, Erik, 1998. "On Omitted Variable Bias and Measurement Error in Returns to Schooling Estimates," Working Paper Series 494, Research Institute of Industrial Economics.
    • Handle: RePEc:hhs:iuiwop:0494
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    References listed on IDEAS

    as
    1. Lam, David & Schoeni, Robert F, 1993. "Effects of Family Background on Earnings and Returns to Schooling: Evidence from Brazil," Journal of Political Economy, University of Chicago Press, vol. 101(4), pages 710-740, August.
    2. Welch, Finis, 1975. "Human Capital Theory: Education, Discrimination, and Life Cycles," American Economic Review, American Economic Association, vol. 65(2), pages 63-73, May.
    3. Griliches, Zvi, 1977. "Estimating the Returns to Schooling: Some Econometric Problems," Econometrica, Econometric Society, vol. 45(1), pages 1-22, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Missing data; Proxy variables; Measurement error; Partial correlations; Simulations;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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