Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We use administrative data from Missouri to examine the sensitivity of earnings impact estimates for a job training program based on alternative nonexperimental methods. We consider regression adjustment, Mahalanobis distance matching, and various methods using propensity-score matching, examining both cross-sectional estimates and difference-in-difference estimates. Specification tests suggest that the difference-in-difference estimator may provide a better measure of program impact. We find that propensity-score matching is most effective, but the detailed implementation is not of critical importance. Our analyses demonstrate that existing data can be used to obtain useful estimates of program impact. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.