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Using State Administrative Data to Measure Program Performance

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Abstract

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.

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  • Peter R. Mueser & Kenneth R. Troske & Alexey Gorislavsky, 2007. "Using State Administrative Data to Measure Program Performance," Working Papers 0702, Department of Economics, University of Missouri.
    • Handle: RePEc:umc:wpaper:0702
    Note: Updated from WP 05-20 (no longer available)
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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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