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Elections and the Regression Discontinuity Design: Lessons from Close U.S. House Races, 1942–2008

  • Devin Caughey (a1) and Jasjeet S. Sekhon (a2)

Following David Lee's pioneering work, numerous scholars have applied the regression discontinuity (RD) design to popular elections. Contrary to the assumptions of RD, however, we show that bare winners and bare losers in U.S. House elections (1942–2008) differ markedly on pretreatment covariates. Bare winners possess large ex ante financial, experience, and incumbency advantages over their opponents and are usually the candidates predicted to win by Congressional Quarterly's pre-election ratings. Covariate imbalance actually worsens in the closest House elections. National partisan tides help explain these patterns. Previous works have missed this imbalance because they rely excessively on model-based extrapolation. We present evidence suggesting that sorting in close House elections is due mainly to activities on or before Election Day rather than postelection recounts or other manipulation. The sorting is so strong that it is impossible to achieve covariate balance between matched treated and control observations, making covariate adjustment a dubious enterprise. Although RD is problematic for postwar House elections, this example does highlight the design's advantages over alternatives: RD's assumptions are clear and weaker than model-based alternatives, and their implications are empirically testable.

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Authors' note: An appendix and supplementary materials for this article are available on the Political Analysis Web site. We thank David Lee for generously providing replication files for Lee (2008). We are grateful to Scott Adler, David Brady, Gary Jacobson, Keith Poole, and Jonathan Wand for sharing their data with us. We thank the editors, the anonymous reviewers, Henry Brady, Andy Eggers, Andrew Gelman, Don Green, Jens Hainmueller, Luke Keele, Winston Lin, Walter Mebane, Jr., Eric Schickler, Laura Stoker, Dan Tokaji, Rocío Titiunik, and Rob Van Houweling for providing helpful comments, and Peter Ryan for helping to shape the project in its formative stages. Willa Caughey, Mona Fang, Julia Gettle, and Sarah Weiner provided excellent research assistance.

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