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Testing Core Predictions of Spatial Models: Platform Moderation and Challenger Success*

  • Brendan Pablo Montagnes and Jon C. Rogowski
Abstract

A large class of spatial models of elections converges upon a single prediction: a candidate’s vote share increases in the congruence between her platform and the median voter’s preferences. Though considerable empirical research provides support for this prediction, these studies have not adequately identified the effects of platform positioning net of other factors. In this paper, we study the impact of challenger moderation on vote shares using data from 444 US House elections from 1996 to 2006 in which successive challengers competed against a common incumbent. Our findings are largely null. We uncover no evidence that challengers increase their vote shares by adopting more moderate platform positions. This finding is robust across a wide range of model specifications and subsets of districts.

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B. Pablo Montagnes is an Assistant Professor in the Harris School of Public Policy, University of Chicago, 1130 E. 60th Street, Chicago, IL 60637 (pmontagnes@uchicago.edu). Jon C. Rogowski is an Assistant Professor in the Department of Political Science, Washington University in St. Louis, One Brookings Drive, Campus Box 1063, St. Louis, MO 63130 (jrogowsk@artsci.wustl.edu). The authors gratefully acknowledge Project Vote Smart for providing data used in this paper, and Ethan Bueno de Mesquita, Will Howell, Eric Oliver, Drew Linzer, Boris Shor, Betsy Sinclair, two anonymous reviewers, and the editor for helpful comments. Data used in this project were obtained from Project Vote Smart, the Federal Election Commission, and the Clerk of the US House of Representatives. Replication data can be accessed through the Political Science Research and Methods Dataverse. To view supplementary material for this article, please visit http://dx.doi.org/10.1017/psrm.2014.36

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