Working Paper: CEPR ID: DP16974
Authors: Margherita Fort; Andrea Ichino; Enrico Rettore; Giulio Zanella
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
JEL Codes: C01
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| allocation of treatment slots (C78) | outcomes of subjects located at cutoffs (C24) |
| exploiting within variability at each cutoff (C24) | precision of causal estimates (C20) |
| standard NP estimator (C51) | meaningful causal parameter (C20) |
| SFE estimator (C51) | identification of meaningful causal parameter (C20) |
| alternative strategies (dropping observations at cutoffs or redefining cutoffs) (C24) | biases (D91) |