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Replication Data for: Regression Discontinuity with Multiple Running Variables Allowing Partial Effects

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Title Replication Data for: Regression Discontinuity with Multiple Running Variables Allowing Partial Effects
 
Identifier https://doi.org/10.7910/DVN/9YCVJC
 
Creator Choi, Jin-Young
Lee, Myoung-Jae
 
Publisher Harvard Dataverse
 
Description In regression discontinuity (RD), a running variable (or “score”) crossing a cutoff de-
termines a treatment that affects the mean regression function. This paper generalizes this usual ‘one-score mean RD ’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 identi…cation needing the partial effects to be separated, and (iii) informative quantile/mode regression functions. We establish identi…cation for ‘multiple-score RD (MRD)’, and propose simple estimators that become ‘local difference in differences (DD) ’in case of double scores. We also provide an empirical illustration where partial effects exist.
 
Subject Social Sciences
regression discontinuity, multiple running variables, partial effect, difference in differences
 
Contributor Choi, Jin-Young