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Which electoral systems succeed at providing proportionality and concentration? Promising designs and risky tools

Published online by Cambridge University Press:  17 January 2017

Johannes Raabe*
Affiliation:
Department of Political Science, University of Kiel, Kiel, Germany
Eric Linhart
Affiliation:
Professor of Political Science, Chemnitz University of Technologies, Chemnitz, Germany
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Abstract

Electoral systems are typically faced with the problem of being asked to provide both proportional representation and party system concentration leading to accountable government. Which electoral system designs are able to successfully deliver on both these challenges and thus optimize the representativeness – accountability trade-off? This paper investigates the performance of different general electoral system designs as well as their specific technical details (such as legal threshold, tier linkages, and compensation mechanisms) based on a data set of 590 elections in 57 countries. The key results are that both proportional representation systems with moderate district magnitudes and mixed-member proportional systems are able to optimize performance. Going to the level of details confirms these results and deepens our understanding further: while different technical changes are able to improve the chances of reaching the best of both worlds, some of these (e.g. raising the legal threshold) also increase the risk of ending up with the worst.

Information

Type
Research Article
Copyright
© European Consortium for Political Research 2017 
Figure 0

Figure 1 Promises and pitfalls of aiming for a superior balance.

Figure 1

Table 1 Technical details of electoral systems

Figure 2

Figure 2 Performance of system types in separate dimensions. LSI=least squares index; ENP=effective number of parties; PR=proportional representation; dm=district magnitudes; MMP=mixed-member proportional; MMM=mixed-member majoritarian.

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Figure 3 Overall performance of different general designs. LSI=least squares index; ENP=effective number of parties; PR=proportional representation; dm=district magnitude; MMP=mixed-member proportional; MMM=mixed-member majoritarian.

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Table 2 General designs and their performance

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Figure 4 Predicted probabilities of performing well in both dimensions. PR=proportional representation; dm=district magnitudes; MMP=mixed-member proportional; MMM=mixed-member majoritarian.

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Table 3 Ordinary least squares regression models

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Table 4 Logit regression models

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Table 5 Marginal effects of changes in technical details

Supplementary material: File

Raabe and Linhart supplementary material

Appendix A-B

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