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Going beyond LATE: Bounding Average Treatment Effects of Job Corps Training

Author

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  • Chen, Xuan
  • Flores, Carlos A.
  • Flores-Lagunes, Alfonso

Abstract

We derive nonparametric sharp bounds on average treatment effects with an instrumental variable (IV) and use them to evaluate the effectiveness of the Job Corps training program for disadvantaged youth. We focus on the population average treatment effect (ATE) and the average treatment effect on the treated (ATT), which are parameters not point identified with an IV under heterogeneous treatment effects. The main assumptions employed to bound the ATE and ATT are monotonicity in the treatment of the average outcomes of specified subpopulations, and mean dominance assumptions across the potential outcomes of these subpopulations. Importantly, the direction of the mean dominance assumptions can be informed from data, and some of our bounds do not require an outcome with bounded support. We employ these bounds to assess the effectiveness of Job Corps using data from a randomized social experiment with non-compliance (a common feature of social experiments). Our empirical results indicate that the effect of Job Corps on eligible applicants (the target population) four years after randomization is to increase weekly earnings and employment by at least $24:61 and 4:3 percentage points, respectively, and to decrease yearly dependence on public welfare benefits by at least $84:29. Furthermore, the effect of Job Corps on participants (the treated population) is to increase weekly earnings by between $28:67 and $43:47, increase employment by between 4:9 and 9:3 percentage points, and decrease public benefits received by between $108:72 and $140:29. Finally, some of our results point to positive average effects of Job Corps on the labor market outcomes of those individuals who decide not to enroll in Job Corps regardless of their treatment assignment (the so-called never takers), suggesting that these individuals would benefit from participating in Job Corps.

Suggested Citation

  • Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2017. "Going beyond LATE: Bounding Average Treatment Effects of Job Corps Training," GLO Discussion Paper Series 93, Global Labor Organization (GLO).
  • Handle: RePEc:zbw:glodps:93
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    Cited by:

    1. Vitor Possebom, 2019. "Sharp Bounds for the Marginal Treatment Effect with Sample Selection," Papers 1904.08522, arXiv.org.
    2. Anthony Strittmatter, 2019. "Heterogeneous Earnings Effects of the Job Corps by Gender Earnings: A Translated Quantile Approach," Papers 1908.08721, arXiv.org.
    3. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    4. Domenico Depalo & Santiago Pereda-Fernández, 2020. "Consistent estimates of the public/private wage gap," Empirical Economics, Springer, vol. 58(6), pages 2937-2947, June.
    5. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    6. Possebom, Vitor, 2018. "Sharp bounds on the MTE with sample selection," MPRA Paper 89785, University Library of Munich, Germany.
    7. Strittmatter, Anthony, 2019. "Heterogeneous earnings effects of the job corps by gender: A translated quantile approach," Labour Economics, Elsevier, vol. 61(C).

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    More about this item

    Keywords

    Training programs; Program evaluation; Average treatment effects; Bounds; Instrumental variables;
    All these keywords.

    JEL classification:

    • J30 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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