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Partial Idendification of Wage Effects of Training Programs

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Abstract

In an evaluation of a job-training program, the influence of the program on the in-dividual wages is important, because it reflects the program effect on human capital. Esti-mating these effects is complicated because we observe wages only for employed individuals, and employment is itself an outcome of the program. Only usually implausible assumptions allow identifying these treatment effects. Therefore, we suggest weaker and more credible assumptions that bound various average and quantile effects. For these bounds, consistent, nonparametric estimators are proposed. In a reevaluation of a German training program, we find that a considerable improvement of the long-run potential wages of its participants.

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  • Michael Lechner & Blaise Melly, 2010. "Partial Idendification of Wage Effects of Training Programs," Working Papers 2010-8, Brown University, Department of Economics.
    • Handle: RePEc:bro:econwp:2010-8
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    Cited by:

    1. Huber, Martin & Melly, Blaise, 2011. "Quantile Regression in the Presence of Sample Selection," Economics Working Paper Series 1109, University of St. Gallen, School of Economics and Political Science.
    2. Possebom, Vitor, 2018. "Sharp bounds on the MTE with sample selection," MPRA Paper 89785, University Library of Munich, Germany.
    3. Kaitlin Anderson & Gema Zamarro & Jennifer Steele & Trey Miller, 2021. "Comparing Performance of Methods to Deal With Differential Attrition in Randomized Experimental Evaluations," Evaluation Review, , vol. 45(1-2), pages 70-104, February.
    4. Schünemann Benjamin & Lechner Michael & Wunsch Conny, 2015. "Do Long-Term Unemployed Workers Benefit from Targeted Wage Subsidies?," German Economic Review, De Gruyter, vol. 16(1), pages 43-64, February.
    5. Vitor Possebom, 2019. "Sharp Bounds for the Marginal Treatment Effect with Sample Selection," Papers 1904.08522, arXiv.org.
    6. Brigham R. Frandsen & Lars J. Lefgren, 2021. "Partial identification of the distribution of treatment effects with an application to the Knowledge is Power Program (KIPP)," Quantitative Economics, Econometric Society, vol. 12(1), pages 143-171, January.
    7. Maasoumi, Esfandiar & Wang, Le, 2017. "What can we learn about the racial gap in the presence of sample selection?," Journal of Econometrics, Elsevier, vol. 199(2), pages 117-130.
    8. Bampasidou, Maria & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2011. "Unbundling the Degree Effect in a Job Training Program for Disadvantaged Youth," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103619, Agricultural and Applied Economics Association.
    9. Martin Huber & Blaise Melly, 2012. "A test of the conditional independence assumption in sample selection models," Working Papers 2012-11, Brown University, Department of Economics.
    10. Giovanni Mellace & Roberto Rocci, 2011. "Principal Stratification in sample selection problems with non normal error terms," CEIS Research Paper 194, Tor Vergata University, CEIS, revised 02 May 2011.
    11. German Blanco & Carlos A. Flores & Alfonso Flores-Lagunes, 2013. "Bounds on Average and Quantile Treatment Effects of Job Corps Training on Wages," Journal of Human Resources, University of Wisconsin Press, vol. 48(3), pages 659-701.
    12. Bia, Michela & Flores-Lagunes, Alfonso & Mercatanti, Andrea, 2018. "Evaluation of Language Training Programs in Luxembourg Using Principal Stratification," IZA Discussion Papers 11973, Institute of Labor Economics (IZA).

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    Keywords

    Bounds; treatment effects; causal effects; program evaluation;
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