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Bounds on Quantile Treatment Effects of Job Corps on Participants' Wages

Author

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  • Blanco, German
  • Flores, Carlos A.
  • Flores-Lagunes, Alfonso

Abstract

This paper assesses the effect of the U.S. Job Corps (JC), the nation's largest and most comprehensive job training program targeting disadvantaged youths, on wages. We employ partial identification techniques and construct informative nonparametric bounds for the causal effect of interest under weaker assumptions than those conventionally used for point identification of treatment effects in the presence of sample selection. In addition, we propose and estimate bounds on quantile treatment effects of the program on participants' wages. In general, we find convincing evidence of positive impacts of JC on participants' wages. Importantly, we find that estimated impacts on lower quantiles of the distribution are higher, with the highest impact being in the 5th percentile where a positive effect on wages is bounded between 8.4 and 16.1 percent. These bounds suggest that JC results in wage compression within eligible participants.

Suggested Citation

  • Blanco, German & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2011. "Bounds on Quantile Treatment Effects of Job Corps on Participants' Wages," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103761, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea11:103761
    DOI: 10.22004/ag.econ.103761
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    References listed on IDEAS

    as
    1. Alfonso Flores‐Lagunes & Arturo Gonzalez & Todd Neumann, 2010. "Learning But Not Earning? The Impact Of Job Corps Training On Hispanic Youth," Economic Inquiry, Western Economic Association International, vol. 48(3), pages 651-667, July.
    2. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(3), pages 1071-1102.
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    Cited by:

    1. Martin Huber & Giovanni Mellace, 2015. "Sharp Bounds on Causal Effects under Sample Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 129-151, February.
    2. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.

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    Keywords

    Labor and Human Capital; Public Economics; Research Methods/ Statistical Methods;
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