TY  - RPRT
        AU  - Fort, Margherita
AU  - Ichino, Andrea
AU  - Rettore, Enrico
AU  - Zanella, Giulio
        TI  - Multi-Cutoff RD Designs with Observations Located at Each Cutoff: Problems and Solutions
        PY  - 2022/Jan/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 15051
        UR  - https://www.iza.org/index.php/publications/dp15051
        AB  - 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).
        KW  - regression discontinuity
KW  - multiple cutoffs
KW  - normalizing-and-pooling
        ER  -