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Comparing Treatments across Labor Markets: An Assessment of Nonexperimental Multiple-Treatment Strategies

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  • Carlos A. Flores

    (Department of Economics, University of Miami)

  • Oscar A. Mitnik

    (Department of Economics, University of Miami)

Abstract

We consider the problem of using data from several programs, each implemented at a different location, to compare what their effect would be if they were implemented at a specific location. In particular, we study the effectiveness of nonexperimental strategies in adjusting for differences across comparison groups arising from two sources. First, we adjust for differences in the distribution of individual characteristics simultaneously across all locations by using unconfoundedness-based and conditional difference-in-difference methods for multiple treatments. Second, we explicitly adjust for differences in local economic conditions. We stress the importance of analyzing the overlap of, and adjusting for, local economic conditions after program participation. Our results suggest that the strategies studied are valuable econometric tools for the problem we consider, as long as we adjust for a rich set of individual characteristics and have sufficient overlap across locations for both individual and local labor market characteristics. Our results show that the overlap analysis of these two sets of variables is critical for identifying non-comparable groups and they illustrate the difficulty of adjusting for local economic conditions that differ greatly across locations.

Suggested Citation

  • Carlos A. Flores & Oscar A. Mitnik, 2011. "Comparing Treatments across Labor Markets: An Assessment of Nonexperimental Multiple-Treatment Strategies," Working Papers 2011-10, University of Miami, Department of Economics.
    • Handle: RePEc:mia:wpaper:2011-10
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    More about this item

    Keywords

    Multiple treatments; Generalized propensity score; Local economic conditions;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

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