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Testing the Accuracy of Regression Discontinuity Analysis Using Experimental Benchmarks

Published online by Cambridge University Press:  04 January 2017

Donald P. Green ,
Terence Y. Leong ,
Holger L. Kern ,
Alan S. Gerber  and
Christopher W. Larimer
Donald P. Green
Affiliation:
Department of Political Science, Institution for Social and Policy Studies, Yale University, 77 Prospect St., New Haven, CT 06511
Terence Y. Leong
Affiliation:
Analyst Institute, 815 Sixteenth Street NW, Washington, DC 20006
Holger L. Kern*
Affiliation:
Institution for Social and Policy Studies, Yale University, 77 Prospect St., New Haven, CT 06511.
Alan S. Gerber
Affiliation:
Department of Political Science, Institution for Social and Policy Studies, Yale University, 77 Prospect St., New Haven, CT 06511
Christopher W. Larimer
Affiliation:
Department of Political Science, University of Northern Iowa, 332 Sabin Hall, Cedar Falls, IA 50614
*
e-mail: holger.kern@yale.edu (corresponding author)
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Abstract

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Regression discontinuity (RD) designs enable researchers to estimate causal effects using observational data. These causal effects are identified at the point of discontinuity that distinguishes those observations that do or do not receive the treatment. One challenge in applying RD in practice is that data may be sparse in the immediate vicinity of the discontinuity. Expanding the analysis to observations outside this immediate vicinity may improve the statistical precision with which treatment effects are estimated, but including more distant observations also increases the risk of bias. Model specification is another source of uncertainty; as the bandwidth around the cutoff point expands, linear approximations may break down, requiring more flexible functional forms. Using data from a large randomized experiment conducted by Gerber, Green, and Larimer (2008), this study attempts to recover an experimental benchmark using RD and assesses the uncertainty introduced by various aspects of model and bandwidth selection. More generally, we demonstrate how experimental benchmarks can be used to gauge and improve the reliability of RD analyses.

Type
Research Article
Information
Political Analysis , Volume 17 , Issue 4: Special Issue: Natural Experiments in Political Science , Autumn 2009 , pp. 400 - 417
Copyright
Copyright © The Author 2009. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Authors' note: The authors are grateful to Mark Grebner, who designed and implemented the mailing campaign analyzed here, and Joshua Haselkorn, Jonnah Hollander, and Celia Paris, who provided research assistance.

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