Design-Based RCT Estimators and Central Limit Theorems for Baseline Subgroup and Related Analyses

There is a growing literature on design-based methods to estimate average treatment effects (ATEs) for randomized controlled trials (RCTs) for full sample analyses. This article extends these methods to estimate ATEs for discrete subgroups defined by pre-treatment variables, with an application to an RCT testing subgroup effects for a school voucher experiment in New York City. We consider ratio estimators for subgroup effects using regression methods, allowing for model covariates to improve precision, and prove a finite population central limit theorem. We discuss extensions to blocked and clustered RCT designs, and to other common estimators with random treatment-control sample sizes (or weights): post-stratification estimators, weighted estimators that adjust for data nonresponse, and estimators for Bernoulli trials. We also develop simple variance estimators that share features with robust estimators. Simulations show that the design-based subgroup estimators yield confidence interval coverage near nominal levels, even for small subgroups

Does Job Corps Work? Impact Findings from the National Job Corps Study

This paper presents findings from an experimental evaluation of Job Corps, the nation’s largest training program for disadvantaged youths. The study uses survey data collected over four years and tax data over nine years on a nationwide sample of 15,400 treatments and controls. The Job Corps model has promise; program participation increases educational attainment, reduces criminal activity, and increases earnings for several postprogram years. Based on tax data, however, the earnings gains were not sustained except for the oldest participants. Nonetheless, Job Corps is the only federal training program that has been shown to increase earnings for this population. (JEL I28, I38, J13, J24)