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Proxying ability by family background in returns to schooling estimations is generally a bad idea

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  • Mellander, Erik

    (IFAU - Institute for Labour Market Policy Evaluation)

  • Sandgren-Massih, Sofia

    (Department of Economonics, Uppsala University)

Abstract

A regression model is considered where earnings are explained by schooling and ability. It is assumed that schooling is measured with error and that there are no data on ability. Regressing earnings on observed schooling then yields an estimate of the return to schooling that is subject to positive omitted variable bias (OVB) and negative measurement error bias (MEB). The effects on the OVB and the MEB from using family background variables as proxies for ability are investigated theoretically and empirically. The theoretical analysis demonstrates that the impact on the OVB is uncertain, while the MEB invariably increases in magnitude. The empirical analysis shows that the MEB generally dominates the OVB. As the measurement error increases and/or more family background variables are added, the total bias rapidly becomes negative, driving the estimated return further and further away from the true value.

Suggested Citation

  • Mellander, Erik & Sandgren-Massih, Sofia, 2008. "Proxying ability by family background in returns to schooling estimations is generally a bad idea," Working Paper Series 2008:22, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    • Handle: RePEc:hhs:ifauwp:2008_022
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    References listed on IDEAS

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    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. Hansen, Karsten T. & Heckman, James J. & Mullen, K.J.Kathleen J., 2004. "The effect of schooling and ability on achievement test scores," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 39-98.
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    7. Griliches, Zvi, 1977. "Estimating the Returns to Schooling: Some Econometric Problems," Econometrica, Econometric Society, vol. 45(1), pages 1-22, January.
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    Cited by:

    1. Erik Mellander & Sofia Sandgren‐Massih, 2008. "Proxying Ability by Family Background in Returns to Schooling Estimations is Generally a Bad Idea," Scandinavian Journal of Economics, Wiley Blackwell, vol. 110(4), pages 853-875, December.
    2. Mellander, Erik, 2014. "Transparency of human resource policy," Working Paper Series 2014:24, IFAU - Institute for Evaluation of Labour Market and Education Policy.

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    More about this item

    Keywords

    Missing data; proxy variables; measurement error; consistent estimates of omitted variable bias and measurement error bias;
    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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