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A Categorization Theory of Spatial Voting: How the Center Divides the Political Space

Published online by Cambridge University Press:  19 January 2016

Abstract

This article presents a categorization theory of spatial voting, which postulates that voters perceive political stances through coarse classifications. Because voters think in terms of categories defined by the ideological center, their behavior deviates from standard models of utility maximization along ideological continua. Their preferences are characterized by discontinuities, rewarding parties on their side of the ideological space more than existing spatial models would predict. While this study concurs with prior studies suggesting that voters tend to use a proximity rule, it argues that this rule mainly serves to distinguish among parties of the same side. Overall, the results suggest that voters’ party evaluations are characterized by a nontrivial identity component, generating in-group biases not captured by the existing spatial models of voting.

Type
Articles
Copyright
© Cambridge University Press 2016 

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Footnotes

*

Center for Comparative and International Studies, Swiss Federal Institute of Technology Zurich (email: joergen.boelstad@eup.gess.ethz.ch); Department of Politics and International Relations, University of Oxford (email: elias.dinas@politics.ox.ac.uk). The authors would like to thank James P. Cross, Spyros Kosmidis, Jonathan Nagler, Trajche Panov, Pedro Riera, Piero Stanig, Alex Trechsel, Pablo Fernandez-Vazquez, Till Weber, Thomas Winzen, the members of their panels at the 2nd Annual General Conference of the European Political Science Association, Berlin, 21–23 June 2012, and the Annual Elections, Public Opinion and Parties Conference at the University of Exeter, 9–11 September 2011, as well the attendees of relevant sessions of the ETH Colloquium on European Politics and the EUI Colloquium on Political Behavior. Replication codes are available at http://thedata.harvard.edu/dvn/dv/BJPolS and online appendices are available at http://dx.doi.org/doi:10.1017/S0007123415000393. All data used in this article are publicly available.

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