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Bloc Voting for Electoral Accountability

Published online by Cambridge University Press:  28 September 2023

ALICIA DAILEY COOPERMAN*
Affiliation:
George Washington University, United States
*
Alicia Dailey Cooperman, Assistant Professor, Department of Political Science and International Affairs, George Washington University, United States, acooperman@gwu.edu.
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Abstract

How do citizens hold local politicians accountable? I argue that citizens, especially through neighborhood associations, can use bloc voting as a bottom-up, grassroots strategy to pressure politicians for public services. Politicians monitor polling station voting, and communities switch allegiance if politicians do not deliver. I measure the perceived and actual relationships between community characteristics, bloc voting, and water access—an essential resource prone to political manipulation. I analyze an original household survey and conjoint experiment merged with electoral data in rural Brazil, and qualitative interviews illustrate theoretical mechanisms. Bloc voting is more likely in communities with high trust and participation, and bloc voting improves water access for association members. However, this strategy is only worthwhile for communities that can demonstrate their vote at their polling station. In contrast to top-down explanations of bloc voting, I highlight the interaction of collective action and electoral institutions for accountability and public service provision.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
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, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of American Political Science Association
Figure 0

Figure 1. Accountability Cycle

Figure 1

Figure 2. Theory Map

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Figure 3. Participatory MapSource: Photo by research assistant, 2018.

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Figure 4. Sample Profiles

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Figure 5. Direct Effects of Community Association Features on Bloc VotingNote: Outcome reflects whether the respondent selected a community profile with that characteristic as more likely to engage in bloc voting. Results show average marginal component effects, $ n=2,478 $. Standard errors clustered by respondent: 1,239 clusters. Plot shows 95% confidence interval. No controls. Coefficients in Supplementary Table A4.

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Table 1. Coordination and Electoral Mechanisms Interact for Bloc Voting

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Figure 6. Bloc VotingNote: Model from column 2 of Table 1.

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Table 2. Water Access and Vote Concentration

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Figure 7. Voting Behavior over TimeNote: Local polynomial regression fit lines (loess) calculated using “loess” ($ n=48,465 $ section-years where previous election is 2000, 2004, 2008, or 2012). Plot shows 95% confidence intervals. Does not include controls, municipal fixed effects, or clustered standard errors.

Supplementary material: Link

Cooperman Dataset

Link
Supplementary material: PDF

Cooperman supplementary material

Online Appendix

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