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

Author

Listed:
  • Xuan Chen
  • Carlos A. Flores
  • Alfonso Flores-Lagunes

Abstract

We derive bounds on the population average treatment effect (ATE) and the average treatment effect on the treated (ATT) with an instrumental variable and employ them to evaluate the effectiveness of the Job Corps (JC) training program using data from a randomized evaluation with noncompliance. We find positive effects of JC on earnings and employment, and negative effects on public benefits dependence for eligible applicants (ATE) and participants (ATT). Some of our results also point to positive average effects on the labor market outcomes of “never-takers” (individuals who never enroll in JC regardless of their treatment assignment).

Suggested Citation

  • Xuan Chen & Carlos A. Flores & Alfonso Flores-Lagunes, 2018. "Going beyond LATE: Bounding Average Treatment Effects of Job Corps Training," Journal of Human Resources, University of Wisconsin Press, vol. 53(4), pages 1050-1099.
    • Handle: RePEc:uwp:jhriss:v:53:y:2018:i:4:p:1050-1099
    Note: DOI: 10.3368/jhr.53.4.1015-7483R1
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    File URL: http://jhr.uwpress.org/cgi/reprint/53/4/1050
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    Citations

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    Cited by:

    1. Xintong Wang & Carlos A. Flores & Alfonso Flores-Lagunes, 2020. "The Effects of Vietnam-Era Military Service on the Long-Term Health of Veterans: A Bounds Analysis," Center for Policy Research Working Papers 234, Center for Policy Research, Maxwell School, Syracuse University.
    2. Tommasi, Denni & Zhang, Lina, 2024. "Bounding program benefits when participation is misreported," Journal of Econometrics, Elsevier, vol. 238(1).
    3. Lixiong Li & D'esir'e K'edagni & Ismael Mourifi'e, 2020. "Discordant Relaxations of Misspecified Models," Papers 2012.11679, arXiv.org, revised Dec 2022.
    4. Xintong Wang & Alfonso Flores-Lagunes, 2022. "Conscription and Military Service: Do They Result in Future Violent and Nonviolent Incarcerations and Recidivism?," Journal of Human Resources, University of Wisconsin Press, vol. 57(5), pages 1715-1757.
    5. Christelis, Dimitris & Messina, Julián, 2019. "Partial Identification of Population Average and Quantile Treatment Effects in Observational Data under Sample Selection," IDB Publications (Working Papers) 9520, Inter-American Development Bank.
    6. Michela Bia & German Blanco & Marie Valentova, 2021. "The Causal Impact of Taking Parental Leave on Wages: Evidence from 2005 to 2015," LISER Working Paper Series 2021-08, Luxembourg Institute of Socio-Economic Research (LISER).
    7. German Blanco & Xuan Chen & Carlos A. Flores & Alfonso Flores-Lagunes, 2020. "Bounds on Average and Quantile Treatment Effects on Duration Outcomes Under Censoring, Selection, and Noncompliance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 901-920, October.
    8. Das, Tirthatanmoy & Polachek, Solomon, 2019. "A New Strategy to Identify Causal Relationships: Estimating a Binding Average Treatment Effect," IZA Discussion Papers 12766, Institute of Labor Economics (IZA).
    9. Kédagni, Désiré, 2023. "Identifying treatment effects in the presence of confounded types," Journal of Econometrics, Elsevier, vol. 234(2), pages 479-511.

    More about this item

    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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