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Matching Estimators and the Data from the National Supported Work Demonstration Again

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  • Zhao, Zhong

    () (Renmin University of China)

Abstract

We use the data from the National Supported Work Demonstration to study performance of non-propensity-score-matching estimators, and to compare them with propensity score matching. We find that all matching estimators we studied here are sensitive to the choice of data set. Propensity score methods are sensitive to smoothing parameters, and they usually have larger standard error. Difference-in-differences and bias-corrected matching improve the performance of the matching estimators considered here. Our results suggest that the 1974 earnings are important for Dehejia and Wahba’s PSID data but not for their CPS data in replicating experiment results. After decomposing the selection bias, we find that a sizable selection bias on unobservables is present in all data sets.

Suggested Citation

  • Zhao, Zhong, 2006. "Matching Estimators and the Data from the National Supported Work Demonstration Again," IZA Discussion Papers 2375, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp2375
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    References listed on IDEAS

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    1. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
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    Cited by:

    1. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, Institute for the Study of Labor (IZA).
    2. Asadul Islam, 2011. "Medium- and Long-Term Participation in Microcredit: An Evaluation Using a New Panel Dataset from Bangladesh," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(3), pages 843-862.
    3. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.

    More about this item

    Keywords

    treatment effect; matching estimators; NSW data; selection bias;

    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
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

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