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A Matter of Representation: Spatial Voting and Inconsistent Policy Preferences

Published online by Cambridge University Press:  12 July 2017

Lukas F. Stoetzer
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Abstract

The application of spatial voting theories to popular elections presupposes an electorate that chooses political representatives on the basis of their well-structured policy preferences. Behavioral researchers have long contended that parts of the electorate instead hold unstructured and inconsistent policy beliefs. This article proposes an extension to spatial voting theories to analyze the effect of varying consistency in policy preferences on electoral behavior. The model results in the expectation that voters with less consistent policy preferences will put less weight on policy distance when learning about candidates who should represent their political positions. The study tests this expectation for the 2008 US presidential election, and finds that for respondents with less consistent self-placements on the liberal–conservative scale, policy distance less strongly affects their voting decision. The results have implications for the quality of political representation, as certain parts of the electorate are expected to be less closely represented.

Keywords

voting behaviorspatial votingmass belief systemsrepresentation
Type
Articles
Information
British Journal of Political Science , Volume 49 , Issue 3 , July 2019 , pp. 941 - 956
Copyright
© Cambridge University Press 2017 

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Footnotes

*

University of Zurich (email: [email protected]). I thank Thomas Gschwend, Adam Berinsky, Devin Caughey, Daniel Stegmueller, Eric Dickson, Laron Williams, Steffen Zittlau, Tilko Swalve, Anita Gohdes, participants of different workshops at the University of Mannheim and three anonymous reviewers for helpful comments on the article. Support for this research was provided by the Fritz-Thyssen Foundation, financing a post-doc scholarship at the Massachusetts Institute of Technology during which most of the work was conducted. Data replication sets are available in Harvard Dataverse at: doi:10.7910/DVN/J0G6K3 Stoetzer (2017) and online appendices are available at https://doi.org/10.1017/S0007123417000102

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Supplementary material: Link

Stoetzer Dataset

Datasets

http://dx.doi.org/10.7910/DVN/J0G6K3
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Supplementary material: PDF

Stoetzer supplementary material

Appendix

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