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Geographic Boundaries as Regression Discontinuities

  • Luke J. Keele (a1) and Rocío Titiunik (a2)


Political scientists often turn to natural experiments to draw causal inferences with observational data. Recently, the regression discontinuity design (RD) has become a popular type of natural experiment due to its relatively weak assumptions. We study a special type of regression discontinuity design where the discontinuity in treatment assignment is geographic. In this design, which we call the Geographic Regression Discontinuity (GRD) design, a geographic or administrative boundary splits units into treated and control areas, and analysts make the case that the division into treated and control areas occurs in an as-if random fashion. We show how this design is equivalent to a standard RD with two running variables, but we also clarify several methodological differences that arise in geographical contexts. We also offer a method for estimation of geographically located treatment effects that can also be used to validate the identification assumptions using observable pretreatment characteristics. We illustrate our methodological framework with a re-examination of the effects of political advertisements on voter turnout during a presidential campaign, exploiting the exogenous variation in the volume of presidential ads that is created by media market boundaries.


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Authors' note: Authors are in alphabetical order. We thank the Associate Editor Betsy Sinclair, two anonymous referees, Lisa Blaydes, Matias Cattaneo, Thad Dunning, Don Green, Justin Grimmer, Danny Hidalgo, Simon Jackman, Marc Meredith, Clayton Nall, Ellie Powell, Randy Stevenson, Wendy Tam Cho, Jonathan Wand, Teppei Yamamoto, and seminar participants at the University of Michigan, Stanford University, Yale University, Duke University, the London School of Hygiene and Tropical Medicine, and Penn State University for valuable comments and discussion. Titiunik gratefully acknowledges financial support from the National Science Foundation (SES 1357561). An earlier version of this article was the winner of a 2010 Atlantic Causal Inference Conference Thomas R. Ten Have Citation for “exceptionally creative or skillful research on causal inference.” Parts of this manuscript were previously circulated in a working paper entitled “Geography as a Causal Variable.” Replication files available at Political Analysis Dataverse (Keele, Luke, and Titiunik, Rocio, 2014, Replication data for: Geographic boundaries as regression discontinuities, IQSS Dataverse Network [Distributor] V1 [Version]).



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Geographic Boundaries as Regression Discontinuities

  • Luke J. Keele (a1) and Rocío Titiunik (a2)


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