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Measuring the Competitiveness of Elections

Published online by Cambridge University Press:  15 July 2019

Gary W. Cox
Affiliation:
Professor, Department of Political Science, Stanford University, 100 Encina Hall West, 616 Serra St., Stanford, CA 94305, USA. Email: gwcox@stanford.edu
Jon H. Fiva
Affiliation:
Professor, Department of Economics, BI Norwegian Business School, 0442 Oslo, Norway. Email: jon.h.fiva@bi.no
Daniel M. Smith*
Affiliation:
Associate Professor, Department of Government, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138, USA. Email: danielmsmith@fas.harvard.edu

Abstract

The concept of electoral competition plays a central role in many subfields of political science, but no consensus exists on how to measure it. One key challenge is how to conceptualize and measure electoral competitiveness at the district level across alternative electoral systems. Recent efforts to meet this challenge have introduced general measures of competitiveness which rest on explicit calculations about how votes translate into seats, but also implicit assumptions about how effort maps into votes (and how costly effort is). We investigate how assumptions about the effort-to-votes mapping affect the units in which competitiveness is best measured, arguing in favor of vote-share-denominated measures and against vote-share-per-seat measures. Whether elections under multimember proportional representation systems are judged more or less competitive than single-member plurality or runoff elections depends directly on the units in which competitiveness is assessed (and hence on assumptions about how effort maps into votes).

Type
Articles
Copyright
Copyright © The Author(s) 2019. Published by Cambridge University Press on behalf of the Society for Political Methodology.

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Footnotes

Authors’ note: We thank Georgina Evans for research assistance, Royce Carroll, Olle Folke, Torben Iversen, Mark Kayser, Vincent Pons, Chi-lin Tsai, Janne Tukiainen, referees, and audience participants at the European Political Science Association meeting, Southern Political Science Association meeting, and UCSD for helpful comments, and Peter Selb for kindly sharing his replication data and codes. Replication materials for this article are available at the Political Analysis Dataverse as Cox, Fiva, and Smith (2019b).

Contributing Editor: Lonna Atkeson

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