Multivalued Treatments and Decomposition Analysis: An Application to the WIA Program
提出一个估计和推断框架，用于分析不同参与程度对参与者结果的影响，将结果差异分解为结构效应和组成效应，并应用于美国劳动力投资法案（WIA）计划对收入的影响评估。
This article provides a general estimation and inference framework to study how different levels of program participation affect participants’ outcomes. We decompose differences in the outcome distribution into (i) a <i>structure</i> effect, arising due to the conditional outcome distributions given covariates associated with different levels of participation; and (ii) a <i>composition</i> effect, arising due to differences in the distributions of observable characteristics. These counterfactual differences are equivalent to the multivalued treatment effects for the treated under a conditional independence assumption. We propose efficient nonparametric estimators based on propensity score weighting together with uniform inference theory. We employ our methods to study the effects of the Workforce Investment Act (WIA) programs on participants’ earnings. We find that heterogeneity in levels of program participation is an important dimension to evaluate the WIA and other social programs in which participation varies. The results of this article, both theoretically and empirically, provide rigorous assessment of intervention programs and relevant suggestions to improve their performance and cost-effectiveness. Supplementary materials for this article are available online.