This paper re-creates three of the samples from LaLonde’s famous 1986 paper that began the literature on “within-study designs” that uses experiments as benchmarks against which to assess the performance of nonexperimental identification strategies. In particular, we recreate the experimental data for the target group of women on welfare from the National Supported Work (NSW) Demonstration and two of the corresponding comparison groups drawn from the Panel Study of Income Dynamics (PSID). The loss of these data resulted in the (sizable) subsequent literature devoting its attention solely to the NSW men. In addition to repeating LaLonde’s analyses on our recreations of his files for the AFDC women, we apply (many of) the estimators from later papers by Dehejia and Wahba and by Smith and Todd to these data. Our findings support the general view in the literature that women on welfare pose a less difficult selection problem when evaluating employment and training programs. They also call into question the generalizability of some of the broad conclusions that Dehejia and Wahba and Smith and Todd draw from their analyses of the NSW men.

We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in an additive separable way, but without imposing any parametric restrictions on the underlying population regression functions. We recommend a covariate-adjustment approach that retains consistency under intuitive conditions and characterize the potential for estimation and inference improvements. We also present new covariate-adjusted mean-squared error expansions and robust bias-corrected inference procedures, with heteroskedasticity-consistent and cluster-robust standard errors. We provide an empirical illustration and an extensive simulation study. All methods are implemented in R and Stata software packages.