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Evaluating the Effect of Training on Wages in the Presence of Noncompliance, Nonemployment, and Missing Outcome Data

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  • Paolo Frumento
  • Fabrizia Mealli
  • Barbara Pacini
  • Donald B. Rubin

Abstract

The effects of a job training program, Job Corps, on both employment and wages are evaluated using data from a randomized study. Principal stratification is used to address, simultaneously, the complications of noncompliance, wages that are only partially defined because of nonemployment, and unintended missing outcomes. The first two complications are of substantive interest, whereas the third is a nuisance. The objective is to find a parsimonious model that can be used to inform public policy. We conduct a likelihood-based analysis using finite mixture models estimated by the expectation-maximization (EM) algorithm. We maintain an exclusion restriction assumption for the effect of assignment on employment and wages for noncompliers, but not on missingness. We provide estimates under the “missing at random” assumption, and assess the robustness of our results to deviations from it. The plausibility of meaningful restrictions is investigated by means of scaled log-likelihood ratio statistics. Substantive conclusions include the following. For compliers, the effect on employment is negative in the short term; it becomes positive in the long term, but these effects are small at best. For always employed compliers, that is, compliers who are employed whether trained or not trained, positive effects on wages are found at all time periods. Our analysis reveals that background characteristics of individuals differ markedly across the principal strata. We found evidence that the program should have been better targeted, in the sense of being designed differently for different groups of people, and specific suggestions are offered. Previous analyses of this dataset, which did not address all complications in a principled manner, led to less nuanced conclusions about Job Corps.

Suggested Citation

  • Paolo Frumento & Fabrizia Mealli & Barbara Pacini & Donald B. Rubin, 2012. "Evaluating the Effect of Training on Wages in the Presence of Noncompliance, Nonemployment, and Missing Outcome Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 450-466, June.
  • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:498:p:450-466
    DOI: 10.1080/01621459.2011.643719
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    References listed on IDEAS

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    1. Peter Z. Schochet, 2001. "National Job Corps Study: Methodological Appendixes on the Impact Analysis," Mathematica Policy Research Reports c3abb7b819cd4bc5a09a865d6, Mathematica Policy Research.
    2. repec:mpr:mprres:2949 is not listed on IDEAS
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    Cited by:

    1. Zhichao Jiang & Peng Ding & Zhi Geng, 2016. "Principal causal effect identification and surrogate end point evaluation by multiple trials," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 829-848, September.
    2. repec:bla:jorssb:v:79:y:2017:i:3:p:757-777 is not listed on IDEAS
    3. Mealli Fabrizia & Mattei Alessandra, 2012. "A Refreshing Account of Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-19, April.
    4. Fan Yang & Dylan S. Small, 2016. "Using post-outcome measurement information in censoring-by-death problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 299-318, January.
    5. 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 for the Study of Labor (IZA).
    6. 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.
    7. repec:bla:jorssa:v:180:y:2017:i:3:p:817-839 is not listed on IDEAS
    8. Alessandra Mattei & Fabrizia Mealli & Barbara Pacini, 2014. "Identification of causal effects in the presence of nonignorable missing outcome values," Biometrics, The International Biometric Society, vol. 70(2), pages 278-288, June.

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