Using State Administrative Data to Measure Program Performance
利用密苏里州的行政数据，通过多种非实验方法（如回归调整、马氏距离匹配、倾向得分匹配）评估职业培训项目对收入的影响，发现倾向得分匹配最有效，且双重差分估计量可能更准确。
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.