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Bounds on Average and Quantile Treatment Effects of Job Corps Training on Wages

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

Abstract

We review and extend nonparametric partial identification results for average and quantile treatment effects in the presence of sample selection. These methods are applied to assessing the wage effects of Job Corps, United States’ largest job-training program targeting disadvantaged youth. Excluding Hispanics, our estimates suggest positive program effects on wages both at the mean and throughout the wage distribution. Across the demographic groups analyzed, the statistically significant estimated average and quantile treatment effects are bounded between 4.6 and 12 percent, and 2.7 and 14 percent, respectively. We also document that the program’s wage effects vary across quantiles and demographic groups.

Suggested Citation

  • 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.
    • Handle: RePEc:uwp:jhriss:v:48:y:2013:iii:1:p:659-701
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    1. Carlos Dobkin & Reza Shabani, 2009. "The Health Effects Of Military Service: Evidence From The Vietnam Draft," Economic Inquiry, Western Economic Association International, vol. 47(1), pages 69-80, January.
    2. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
    3. Angrist, Joshua D. & Chen, Stacey H. & Frandsen, Brigham R., 2010. "Did Vietnam veterans get sicker in the 1990s? The complicated effects of military service on self-reported health," Journal of Public Economics, Elsevier, vol. 94(11-12), pages 824-837, December.
    4. Peter Z. Schochet & John Burghardt & Steven Glazerman, 2001. "National Job Corps Study: The Impacts of Job Corps on Participants' Employment and Related Outcomes," Mathematica Policy Research Reports db6c4204b8e1408bb0c6289ec, Mathematica Policy Research.
    5. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    6. 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.
    7. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
    8. Angrist, Joshua D. & Krueger, Alan B., 1999. "Empirical strategies in labor economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 23, pages 1277-1366, Elsevier.
    9. Zhang, Junni L. & Rubin, Donald B. & Mealli, Fabrizia, 2009. "Likelihood-Based Analysis of Causal Effects of Job-Training Programs Using Principal Stratification," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 166-176.
    10. Sims,Christopher A. (ed.), 1994. "Advances in Econometrics," Cambridge Books, Cambridge University Press, number 9780521444606.
    11. Richard Blundell & Amanda Gosling & Hidehiko Ichimura & Costas Meghir, 2007. "Changes in the Distribution of Male and Female Wages Accounting for Employment Composition Using Bounds," Econometrica, Econometric Society, vol. 75(2), pages 323-363, March.
    12. Molinari, Francesca, 2008. "Partial identification of probability distributions with misclassified data," Journal of Econometrics, Elsevier, vol. 144(1), pages 81-117, May.
    13. Guido W. Imbens & Charles F. Manski, 2004. "Confidence Intervals for Partially Identified Parameters," Econometrica, Econometric Society, vol. 72(6), pages 1845-1857, November.
    14. Michael Lechner & Blaise Melly, 2010. "Partial Idendification of Wage Effects of Training Programs," Working Papers 2010-8, Brown University, Department of Economics.
    15. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    16. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    17. Carlos A. Flores & Alfonso Flores-Lagunes, 2010. "Nonparametric Partial Identification of Causal Net and Mechanism Average Treatment Effects," Working Papers 2010-25, University of Miami, Department of Economics.
    18. Jay Bhattacharya & Azeem M. Shaikh & Edward Vytlacil, 2008. "Treatment Effect Bounds under Monotonicity Assumptions: An Application to Swan-Ganz Catheterization," American Economic Review, American Economic Association, vol. 98(2), pages 351-356, May.
    19. Joshua Angrist & Alan Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Working Papers 834, Princeton University, Department of Economics, Industrial Relations Section..
    20. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    21. Bollinger, Christopher R., 1996. "Bounding mean regressions when a binary regressor is mismeasured," Journal of Econometrics, Elsevier, vol. 73(2), pages 387-399, August.
    22. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-318, May.
    23. repec:mpr:mprres:2949 is not listed on IDEAS
    24. Sims,Christopher A. (ed.), 1994. "Advances in Econometrics," Cambridge Books, Cambridge University Press, number 9780521444590.
    25. repec:fth:prinin:455 is not listed on IDEAS
    26. Heckman, James J & Smith, Jeffrey A, 1999. "The Pre-programme Earnings Dip and the Determinants of Participation in a Social Programme. Implications for Simple Programme Evaluation Strategies," Economic Journal, Royal Economic Society, vol. 109(457), pages 313-348, July.
    27. Eisenberg Daniel & Rowe Brian, 2009. "The Effect of Smoking in Young Adulthood on Smoking Later in Life: Evidence based on the Vietnam Era Draft Lottery," Forum for Health Economics & Policy, De Gruyter, vol. 12(2), pages 1-34, June.
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    Cited by:

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    2. Das, Tirthatanmoy & Polachek, Solomon, 2017. "Micro Foundations of Earnings Differences," IZA Discussion Papers 10922, Institute of Labor Economics (IZA).
    3. Possebom, Vitor, 2018. "Sharp bounds on the MTE with sample selection," MPRA Paper 89785, University Library of Munich, Germany.
    4. 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.
    5. Bartalotti, Otávio & Kédagni, Désiré & Possebom, Vitor, 2023. "Identifying marginal treatment effects in the presence of sample selection," Journal of Econometrics, Elsevier, vol. 234(2), pages 565-584.
    6. María laura Alzúa & Guillermo Cruces & Carolina Lopez, 2016. "Long-Run Effects Of Youth Training Programs: Experimental Evidence From Argentina," Economic Inquiry, Western Economic Association International, vol. 54(4), pages 1839-1859, October.
    7. Lang, Julia & Dauth, Christine, 2017. "Should the unemployed care for the elderly? The effect of subsidized occupational and further training in geriatric care," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168130, Verein für Socialpolitik / German Economic Association.
    8. Amin, Vikesh & Flores, Carlos A. & Flores-Lagunes, Alfonso & Parisian, Daniel J., 2016. "The effect of degree attainment on arrests: Evidence from a randomized social experiment," Economics of Education Review, Elsevier, vol. 54(C), pages 259-273.
    9. Damian Clarke & Manuel Llorca Jaña & Daniel Pailañir, 2023. "The use of quantile methods in economic history," Historical Methods: A Journal of Quantitative and Interdisciplinary History, Taylor & Francis Journals, vol. 56(2), pages 115-132, April.
    10. 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.
    11. Strittmatter, Anthony, 2019. "Heterogeneous earnings effects of the job corps by gender: A translated quantile approach," Labour Economics, Elsevier, vol. 61(C).
    12. Valente, Christine, 2019. "Primary education expansion and quality of schooling," Economics of Education Review, Elsevier, vol. 73(C).
    13. Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2015. "Going Beyond LATE: Bounding Average Treatment Effects of Job Corps Training," IZA Discussion Papers 9511, Institute of Labor Economics (IZA).
    14. 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).
    15. 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).
    16. Dauth, Christine & Lang, Julia, 2017. "Should the unemployed care for the elderly? : The effect of subsidized occupational and further training in elderly care," IAB-Discussion Paper 201713, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    17. María Laura Alzúa & Guillermo Cruces & Carolina Lopez, 2015. "Youth Training Programs Beyond Employment. Experimental Evidence from Argentina," CEDLAS, Working Papers 0177, CEDLAS, Universidad Nacional de La Plata.

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    More about this item

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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