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Testing for Treatment Effect Heterogeneity in Regression Discontinuity Design

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Abstract

Treatment effect heterogeneity is frequently studied in regression discontinuity (RD) applications. This paper is the first to propose tests for treatment effect heterogeneity under the RD setup. The proposed tests study whether a policy treatment is 1) beneficial for at least some subpopulations defined by covariate values, 2) has any impact on at least some subpopulations, and 3) has a heterogeneous impact across subpopulations. Compared with other methods currently adopted in applied RD studies, such as the subsample regression method and the interaction term method, our tests have the advantage of being fully nonparametric, robust to weak inference and powerful. Monte Carlo simulations show that our tests perform very well in small samples. We apply the tests to study the impact of attending a better high school and discover interesting patterns of treatment e ect heterogeneity that were neglected by classic mean RD analyses. JEL Classification: C21, C31

Suggested Citation

  • Yu-Chin Hsu & Shu Shen, 2016. "Testing for Treatment Effect Heterogeneity in Regression Discontinuity Design," IEAS Working Paper : academic research 16-A005, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    • Handle: RePEc:sin:wpaper:16-a005
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    Cited by:

    1. Yoichi Arai & Yu‐Chin Hsu & Toru Kitagawa & Ismael Mourifié & Yuanyuan Wan, 2022. "Testing identifying assumptions in fuzzy regression discontinuity designs," Quantitative Economics, Econometric Society, vol. 13(1), pages 1-28, January.
    2. Kantorowicz, Jarosław & Köppl–Turyna, Monika, 2019. "Disentangling the fiscal effects of local constitutions," Journal of Economic Behavior & Organization, Elsevier, vol. 163(C), pages 63-87.
    3. Pomeranz, Dina & Gerardino, Maria Paula & Litschig, Stephan, 2017. "Distortion by Audit: Evidence from Public Procurement," CEPR Discussion Papers 12529, C.E.P.R. Discussion Papers.
    4. Yu-Chin Hsu & Chung-Ming Kuan & Giorgio Teng-Yu Lo, 2017. "Quantile Treatment Effects in Regression Discontinuity Designs with Covariates," IEAS Working Paper : academic research 17-A009, Institute of Economics, Academia Sinica, Taipei, Taiwan.

    More about this item

    Keywords

    Sharp regression discontinuity; fuzzy regression discontinuity; treatment effect heterogeneity.;
    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

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