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HEAPING‐INDUCED BIAS IN REGRESSION‐DISCONTINUITY DESIGNS
Author(s) -
Barreca Alan I.,
Lindo Jason M.,
Waddell Glen R.
Publication year - 2016
Publication title -
economic inquiry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 72
eISSN - 1465-7295
pISSN - 0095-2583
DOI - 10.1111/ecin.12225
Subject(s) - regression discontinuity design , econometrics , discontinuity (linguistics) , monte carlo method , regression , omitted variable bias , regression analysis , variable (mathematics) , economics , computer science , statistics , mathematics , mathematical analysis
This study uses Monte Carlo simulations to demonstrate that regression‐discontinuity designs arrive at biased estimates when attributes related to outcomes predict heaping in the running variable. After showing that our usual diagnostics may not be well suited to identifying this type of problem, we provide alternatives, and then discuss the usefulness of different approaches to addressing the bias. We then consider these issues in multiple non‐simulated environments. ( JEL C21, C14, I12)