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Regression Discontinuity with Multiple Running Variables Allowing Partial Effects

  • Jin-young Choi (a1) and Myoung-jae Lee (a2)


In regression discontinuity (RD), a running variable (or “score”) crossing a cutoff determines a treatment that affects the mean-regression function. This paper generalizes this usual “one-score mean RD” in three ways: (i) considering multiple scores, (ii) allowing partial effects due to each score crossing its own cutoff, not just the full effect with all scores crossing all cutoffs, and (iii) accommodating quantile/mode regressions. This generalization is motivated by (i) many multiple-score RD cases, (ii) the full-effect identification needing the partial effects to be separated, and (iii) informative quantile/mode regression functions. We establish identification for multiple-score RD (MRD), and propose simple estimators that become “local difference in differences” in case of double scores. We also provide an empirical illustration where partial effects exist.


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Authors’ note: The authors are grateful to the associate editor and three anonymous reviewers for detailed comments and relevant references. The authors note that they could not accommodate all comments due to conflicts among some comments. Myoung-jae Lee’s research has been supported by a Korea University grant. Replication files for the empirical results in this paper can be found in the Political Analysis Dataverse (Choi and Lee 2018).

Contributing Editor: Jasjeet Sekhon



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Regression Discontinuity with Multiple Running Variables Allowing Partial Effects

  • Jin-young Choi (a1) and Myoung-jae Lee (a2)


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