Using state administrative data to measure program performance

• 成果类型: Article
• 署名作者: Mueser, Peter R.; Troske, Kenneth R.; Gorislavsky, Alexey
• 署名单位: University of Missouri System; University of Missouri Columbia; IZA Institute Labor Economics; University of Kentucky
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest.89.4.761
• 发表日期: 2007-11
• 页码: 761-783
• 关键词: training-programs; propensity-score; sample properties; social program; BIAS; earnings; subclassification; IMPACT

摘要

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.