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Analyzing Regression-Discontinuity Designs With Multiple Assignment Variables

Author

Listed:
  • Vivian C. Wong

    (Northwestern University)

  • Peter M. Steiner

    (University of Wisconsin)

  • Thomas D. Cook

    (Northwestern University)

Abstract

In a traditional regression-discontinuity design (RDD), units are assigned to treatment on the basis of a cutoff score and a continuous assignment variable. The treatment effect is measured at a single cutoff location along the assignment variable. This article introduces the multivariate regression-discontinuity design (MRDD), where multiple assignment variables and cutoffs may be used for treatment assignment. For an MRDD with two assignment variables, we show that the frontier average treatment effect can be decomposed into a weighted average of two univariate RDD effects. The article discusses four methods for estimating MRDD treatment effects and compares their relative performance in a Monte Carlo simulation study under different scenarios.

Suggested Citation

  • Vivian C. Wong & Peter M. Steiner & Thomas D. Cook, 2013. "Analyzing Regression-Discontinuity Designs With Multiple Assignment Variables," Journal of Educational and Behavioral Statistics, , vol. 38(2), pages 107-141, April.
    • Handle: RePEc:sae:jedbes:v:38:y:2013:i:2:p:107-141
    DOI: 10.3102/1076998611432172
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    References listed on IDEAS

    as
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