Heaping-Induced Bias in Regression-Discontinuity Designs
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 are poorly suited to identifying this type of problem, we provide alternatives. We also demonstrate how the magnitude and direction of the bias varies with bandwidth choice and the location of the data heaps relative to the treatment threshold. Finally, we discuss approaches to correcting for this type of problem before considering these issues in several non-simulated environments.
The authors thank Josh Angrist, Bob Breunig, David Card, Janet Currie, Todd Elder, Bill Evans, David Figlio, Melanie Guldi, Hilary Hoynes, Wilbert van der Klaauw, Thomas Lemieux, Justin McCrary, Doug Miller, Marianne Page, Heather Royer, Larry Singell, Ann Huff Stevens, Jim Ziliak, seminar participants at the University of Kentucky, and conference participants at the 2011 Public Policy and Economics of the Family Conference at Mount Holyoke College, the 2011 SOLE Meetings, the 2011 NBER's Children's Program Meetings, and the 2011 Labour Econometrics Workshop at the University of Sydney for their comments and suggestions. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Alan I. Barreca & Jason M. Lindo & Glen R. Waddell, 2016. "Heaping-Induced Bias In Regression-Discontinuity Designs," Economic Inquiry, Western Economic Association International, vol. 54(1), pages 268-293, 01. citation courtesy of