Multicutoff RD designs with observations located at each cutoff: problems and solutions

Margherita Fort, Andrea Ichino, Enrico Rettore and Giulio Zanella

No 2022-01, FBK-IRVAPP Working Papers from Research Institute for the Evaluation of Public Policies (IRVAPP), Bruno Kessler Foundation

Abstract: In RD designs with multiple cutoffs, the identification of an average causal effect across cutoffs may be problematic if a marginally exposed subject is located exactly at each cutoff. This occurs whenever a fixed number of treatment slots is allocated starting from the subject with the highest (or lowest) value of the score, until exhaustion. Exploiting the “within” variability at each cutoff is the safest and likely efficient option. Alternative strategies exist, but they do not always guarantee identification of a meaningful causal effect and are less precise. To illustrate our findings, we revisit the study of Pop-Eleches and Urquiola (2013).

Keywords: Regression Discontinuity; Multiple Cutoffs; Normalizing and Pooling (search for similar items in EconPapers)
JEL-codes: C01 (search for similar items in EconPapers)
Date: 2022-02
New Economics Papers: this item is included in nep-ecm and nep-his
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Citations: View citations in EconPapers (8)

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Related works:
Working Paper: Multi-cutoff RD designs with observations located at each cutoff: problems and solutions (2022)
Working Paper: Multi-Cutoff RD Designs with Observations Located at Each Cutoff: Problems and Solutions (2022)
Working Paper: Multi-cutoff RD designs with observations located at each cutoff: problems and solutions (2022)
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