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A Cross-National Measure of Electoral Competitiveness
Published online by Cambridge University Press: 04 January 2017
Abstract
Electoral competitiveness is a key explanatory construct across a broad swath of phenomena, finding application in diverse areas related to political incentives and behavior. Despite its frequent theoretical use, no valid measure of electoral competitiveness exists that applies across different electoral and party systems. We argue that one particular type of electoral competitiveness'electoral risk'can be estimated across institutional contexts and matters most for incumbent behavior. We propose, estimate, and make available a cross-nationally applicable measure for elections in 22 developed democracies between 1960 and 2011. Unlike extant alternatives, our measure captures vote volatility and is constructed at the party (not system) level, exogenous to most policy predictors, and congruent with the perceptions and incentives of policy-makers.
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- This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
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- © The Author(s), 2015. Published by Oxford University Press on behalf of the Society for Political Methodology
Footnotes
Authors' note: The authors gratefully acknowledge comments and suggestions from Francisco Cantu, Harold Clarke, John Curtis, David Fortunato, Jeff Gill, Bernard Grofman, Jude Hays, Simon Hix, Ellen Immergut, Drew Linzer, Michael McDonald, Shaun McGirr, Matthias Orlowski, Thomas Plümper, Ronald Rogowski, Tal Sadeh, Peter Selb, Jon Slapin, Stuart Soroka, Piero Stanig, Daniel Stegmüller, Randy Stevenson, Jack Vowles, and Robert Walker. Versions of this paper were presented at the Annual Meeting of the European Political Science Association and at the University of Essex, the University of Exeter, the Hertie School of Governance, the London School of Economics, the University of Oxford, and the University of Zurich. The authors thank Tanya Bagashka, Aaron Gallant, Patrick Lam, and Kong Joo Shin for the original data collection in 2006 and Johannes Kleibl, Arndt Leininger, Grzegorz Wolszczak, and Mathew Wong for more recent research assistance. Eric Chang, Miriam Golden, Bing Powell, Lawrence Ezrow, and Margit Tavits have kindly provided them with replication data that they have used for validity checks in various domains. Replication materials (Kayser and Lindstaedt 2015) are available on Dataverse. Loss probability data are available on author websites.
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