Likelihood-Based Analysis of Causal Effects of Job-Training Programs Using Principal Stratification

By Junni L. Zhang, Donald B. Rubin, Fabrizia Mealli. 2009

133 citations recorded by the source corpus.

Government-sponsored job-training programs must be subject to evaluation to assess whether their effectiveness justifies their cost to the public. The evaluation usually focuses on employment and total earnings, although the effect on wages is also of interest, because this effect reflects the increase in human capital due to the training program, whereas the effect on total earnings may be simply reflecting the increased likelihood of employment without any effect on wage rates. Estimating the effects of training programs on wages is complicated by the fact that, even in a randomized experiment, wages are “truncated” (or less accurately “censored”) by nonemployment, that is, they are only observed and well-defined for individuals who are employed. In this article, we develop a likelihood-based approach to estimate the wage effect of the US federally-funded Job Corps training program using “Principal Stratification”. Our estimands are formulated in terms of: (1) the effect of the training program on wages for those who would be employed whether they were trained or not, also called the survivor average causal effect (SACE), and the proportion of people in this category; (2) the wages when trained for those who would be employed only when trained, and the proportion of people in this category; (3) the wages when not trained for those who would be employed only when not trained, and the proportion of people in this category; (4) the proportion of people who would be not employed whether trained or not. We conduct likelihood-based analysis using the EM algorithm, and investigate the plausibility of important submodels with scaled log-likelihood ratio statistics. We also conduct a sensitivity analysis with respect to specific parametric assumptions. Our results suggest that all four types of people [(1)–(4) previously] exist, which is impossible under the usual monotonicity assumptions made in traditional econometric evaluation methods.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2de5a3454b80ff0f5a310ea50663fcfbc52ab233

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Citations in the corpus

Transparent Parametrizations of Models for Potential Outcomes (2012, background): “In contrast, Zhang et al. (2009) analyze partially-identified direct e⇤ects in a jobtraining program.”
Principal causal effect identification and surrogate end point evaluation by multiple trials (2015, methodology): “Without the exclusion restriction assumption, Zhang et al. (2009) used Gaussian mixture models to identify causal effects within principal strata.”
Education and Human Capital Externalities: Evidence from Colonial Benin (2015, background): “For causal interpretation, we may only compare the always takers, those who would have been born regardless of treatment status (Horowitz and Manski 2000; Zhang and Rubin 2003; Lee 2009; Zhang, Rubin, and Mealli 2009).”
Compared to what? Variation in the impacts of early childhood education by alternative care type (2016, unknown): “CARE TYPE1”
Partial Identification of Local Average Treatment Effects With an Invalid Instrument (2013, background): “Zhang, Rubin, and Mealli (2009) and Frumento et al. (2012) point identified wage effects for certain principal strata, with the latter article also addressing the issues of noncompliance and missing outcomes, and analyzing the average characteristics and outcomes of the different strata.”
Identifiability and Estimation of Causal Effects by Principal Stratification With Outcomes Truncated by Death (2011, methodology): “…assumptions and the assumption that there exists a pretreatment covariate whose conditional distributions among two principal strata are not the same, but our approach does not need the assumption of a mixture normal distribution for outcomes as required by Zhang, Rubin, and Mealli (2009).”
Augmented designs to assess principal strata direct effects (2011, background): “…effects have been developed (e.g. Barnard et al. (2003), Cheng and Small (2006), Cheng et al. (2009), Frangakis et al. (2007), Gallop et al. (2009), Imai (2008), Lee (2009), Lynch et al. (2008), Mattei and Mealli (2007), Sjölander et al. (2009), Zhang and Rubin (2003) and Zhang et al. (2009)).”
Principal stratification analysis using principal scores (2016, methodology): “Some exceptions (e.g. Zhang et al. (2009), Ding et al. (2011) and Frumento et al. (2012)) rely on either strong modelling assumptions or additional information.”