Heaping Bias

Heaping Bias - NBER WORKING PAPER SERIES HEAPING-INDUCED...

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Unformatted text preview: NBER WORKING PAPER SERIES HEAPING-INDUCED BIAS IN REGRESSION-DISCONTINUITY DESIGNS Alan I. Barreca Jason M. Lindo Glen R. Waddell Working Paper 17408 http://www.nber.org/papers/w17408 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 September 2011 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. NBER working papers are circulated for discussion and comment purposes. They have not been peer- reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2011 by Alan I. Barreca, Jason M. Lindo, and Glen R. Waddell. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Heaping-Induced Bias in Regression-Discontinuity Designs Alan I. Barreca, Jason M. Lindo, and Glen R. Waddell NBER Working Paper No. 17408 September 2011 JEL No. C14,C21,I12 ABSTRACT 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. Alan I. Barreca 206 Tilton Hall Tulane University New Orleans, LA 70118 [email protected] Jason M. Lindo Department of Economics University of Oregon 1285 University of Oregon Eugene, OR 97403 and NBER [email protected] Glen R. Waddell Department of Economics University of Oregon 1285 University of Oregon Eugene, OR 97403 [email protected] 1 Introduction Empirical researchers have witnessed a resurgence in the use of regression-discontinuity (RD) designs since the late 1990s. This approach to evaluating causal effects is often character- ized as superior to all other non-experimental identification strategies (Cook 2008; Lee and Lemieux 2010) as RD designs usually entail perfect knowledge of the selection process and...
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Heaping Bias - NBER WORKING PAPER SERIES HEAPING-INDUCED...

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