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Effect of the proportionality degree of electoral systems on corruption

Author

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  • Maria Rosaria Alfano
  • Anna Laura Baraldi
  • Erasmo Papagni

Abstract

This work makes two major innovations within the empirical literature on the effect of electoral systems on corruption. First, in order to distinguish electoral systems, we propose to measure their degree of proportionality, instead of using dummy variables. A measure of the proportionality degree seems the appropriate way to identify electoral systems in an empirical setting because it allows all their variants to be considered. Second, alongside a parametric panel data approach, we provide a new (in this field) semi-parametric technique to estimate a non-linear relationship between the proportionality degree of electoral systems and corruption. Our results show that, when we increase plurality elements into proportional, both voters’ and opponents’ monitoring power induce politicians to avoid corrupt behaviour. But it is beneficial only up to certain proportionality degrees, after which this effect stops. Moreover, data show that the ‘best’ range of proportionality degrees is most likely found under mixed electoral rules. For governors the choice of the proportionality degree therefore becomes fundamental.

Suggested Citation

  • Maria Rosaria Alfano & Anna Laura Baraldi & Erasmo Papagni, 2016. "Effect of the proportionality degree of electoral systems on corruption," Environment and Planning C, , vol. 34(8), pages 1895-1916, December.
  • Handle: RePEc:sae:envirc:v:34:y:2016:i:8:p:1895-1916
    DOI: 10.1177/0263774X16642771
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    More about this item

    Keywords

    Electoral systems; corruption; proportionality degree; semi-parametric estimation;
    All these keywords.

    JEL classification:

    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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