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A General Model of Abstention Under Compulsory Voting*

Published online by Cambridge University Press:  15 November 2016

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Abstract

Invalid voting and absenteeism are alternative sources of abstention under compulsory voting. Previous research failed to systematically study the mechanisms behind each form of non-voting and the relationships between them. We develop an analytical framework and an empirical strategy to jointly examine invalid voting and absenteeism in Brazil, the world’s largest democracy with mandatory voting. Using Bayesian inferential methods and analyzing both individual and district-level data, we show that less educated and politically knowledgeable citizens are less likely to vote and, when they do, they are typically unable to successfully complete their ballot. Unlike absenteeism, invalid voting also has a political dimension reflecting voters’ disenchantment with elections and democratic performance. Both sources of abstention coexist and, together, undermine electoral participation.

Information

Type
Original Articles
Copyright
© The European Political Science Association 2016 
Figure 0

Fig. 1 Efficiency of the state electoral courts as a proxy for enforcement of compulsory voting Note: Box plots summarize the distribution—median and interquartile range—of clearance rates across electoral courts between 1986 and 2014. Values above (below) 1 indicate that the court is clearing the docket rapidly (building up a backlog). The dashed horizontal line gives the mean clearance rate across all district-years. TRE=Tribunal Regional Eleitoral.

Figure 1

Fig. 2 Invalid voting and absenteeism in Brazilian lower house elections Note: Dots represent the percentage of illegal abstainers and invalid votes in each state across elections. Lines correspond to locally weighted regression curves fitted to the data, with 90 percent confidence intervals given by the shaded areas.

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Fig. 3 Expected change in the probabilities of non-voting associated with a change in the correlates of CC, CP, E, pB and the efficiency of the TREs Note: The figure plots the expected change in $${\rm Pr}\left( {Y_{{i,j,t}} \,{\equals}\,Y_{{i,j,t}}^{s} } \right),{\rm }s\,{\equals}\,A,I$$, associated with a change in each predictor. Solid circles represent point estimates; horizontal lines give the 90 percent highest posterior density intervals. See also Table A.6 in the Online Appendix. TRE=Tribunal Regional Eleitoral.

Figure 3

Fig. 4 Expected change in the probabilities of non-voting associated with a change in the covariates capturing exemptions to the legal obligation to vote Note: The figure plots the expected change in $${\rm Pr}\left( {Y_{{i,j,t}} \,{\equals}\,Y_{{i,j,t}}^{s} } \right),{\rm }s\,{\equals}\,A,I$$, associated with a change in each predictor. Solid circles represent point estimates; horizontal lines give the 90 percent highest posterior density intervals.

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Table 1 Posterior Summaries for the Individual-Level Model

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Table 2 Posterior Summaries for the District-Level Models

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Fig. 5 Joint impact of socio-demographic, electoral and political protest variables on the average probabilities of non-voting Note: Probabilities computed from Table 1; results based on district-level estimates are displayed in the Online Appendix (Figure A.9).

Supplementary material: Link

Katz and Levin Dataset

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Supplementary material: File

Katz and Levin supplementary material

Online Appendix

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