Interpreting Regression Discontinuity Designs with Multiple Cutoffs
By: Researcher
Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).
| Authors | Matias D. Cattaneo, Luke Keele, Rocío Titiunik, Gonzalo Vázquez-Baré |
| Journal/Conference Name | THE JOURNAL OF POLITICS |
| Paper Category | Social Sciences |
| Paper Abstract | We consider a regression discontinuity (RD) design where the treatment is received if a score is above a cutoff, but the cutoff may vary for each unit in the sample instead of being equal for all units. This multi-cutoff regression discontinuity design is very common in empirical work, and researchers often normalize the score variable and use the zero cutoff on the normalized score for all observations to estimate a pooled RD treatment effect. We formally derive the form that this pooled parameter takes and discuss its interpretation under different assumptions. We show that this normalizing-and-pooling strategy so commonly employed in practice may not fully exploit all the information available in a multi-cutoff RD setup. We illustrate our methodological results with three empirical examples based on vote shares, population, and test scores. |
| Date of publication | 2016 |
| Code Programming Language | R |
| Comment |
Copyright Researcher 2022