TY - JOUR
T1 - Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs
AU - Cattaneo, Matias D.
AU - Keele, Luke
AU - Titiunik, Rocío
AU - Vazquez-Bare, Gonzalo
N1 - Funding Information:
Cattaneo and Titiunik gratefully acknowledge financial support from the National Science Foundation (SES 1357561). We are very grateful to Fabio Sanchez and Tatiana Velasco for sharing the dataset used in the empirical application. We also thank Josh Angrist, Sebastian Calonico, Sebastian Galiani, Nicolas Idrobo, Xinwei Ma, Max Farrell, and seminar participants at various institutions for their comments. We also thank the co-editor, Regina Liu, an associate editor, and a reviewer for their comments.
Publisher Copyright:
© 2020 American Statistical Association.
PY - 2021
Y1 - 2021
N2 - Abstract–In nonexperimental settings, the regression discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference. However, RD treatment effect estimands are necessarily local, making statistical methods for the extrapolation of these effects a key area for development. We introduce a new method for extrapolation of RD effects that relies on the presence of multiple cutoffs, and is therefore design-based. Our approach employs an easy-to-interpret identifying assumption that mimics the idea of “common trends” in difference-in-differences designs. We illustrate our methods with data on a subsidized loan program on post-education attendance in Colombia, and offer new evidence on program effects for students with test scores away from the cutoff that determined program eligibility. Supplementary materials for this article are available online.
AB - Abstract–In nonexperimental settings, the regression discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference. However, RD treatment effect estimands are necessarily local, making statistical methods for the extrapolation of these effects a key area for development. We introduce a new method for extrapolation of RD effects that relies on the presence of multiple cutoffs, and is therefore design-based. Our approach employs an easy-to-interpret identifying assumption that mimics the idea of “common trends” in difference-in-differences designs. We illustrate our methods with data on a subsidized loan program on post-education attendance in Colombia, and offer new evidence on program effects for students with test scores away from the cutoff that determined program eligibility. Supplementary materials for this article are available online.
KW - Causal inference
KW - Extrapolation
KW - Regression discontinuity
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U2 - 10.1080/01621459.2020.1751646
DO - 10.1080/01621459.2020.1751646
M3 - Article
AN - SCOPUS:85087012347
VL - 116
SP - 1941
EP - 1952
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
SN - 0162-1459
IS - 536
ER -