Published online by Cambridge University Press: 23 July 2020
Research on clientelism emphasizes the use of brokers to mobilize voters. To utilize these agents efficiently, politicians must learn about brokers’ relative abilities and allocate scarce resources accordingly. Drawing upon a hand-coded dataset based on the archives of Gustavo Capanema, a powerful mid-twentieth-century congressman from Minas Gerais, Brazil, this paper offers the first direct evidence of such learning dynamics. The analysis concentrates on Brazil’s pre-secret ballot era, a time when measuring broker performance was particularly straightforward. Consistent with theories of political learning, the data demonstrate that resource flows to local machines were contingent on the deviation between actual and expected votes received in previous elections. Moreover, given politicians’ ability to discern mobilization capacity, payments to brokers were highly effective in bringing out the vote.
The author would like to thank Danilo Medeiros and Mariana Brazão for excellent research assistance on this article. Thanks for comments and suggestions go to Shan Aman-Rana, Ernesto Calvo, Jose Antonio Cheibub, Anderson Frey, John Gerring, Michael Gilbert, Yamile Guibert, Deborah Hellman, Gabrielle Kruks-Wisner, Fabrice Lehoucq, Raul Madrid, Scott Mainwaring, Ken Roberts, David Singerman, Sandip Sukhtankar, Dawn Teele, Silvia Tidey, Jan Vogler, participants in the panel “Comparative Democratization” at the 2019 Annual Meeting of the American Political Science Association, participants in the panel “Party Systems and Dynamics” at the 2019 Congress of the Latin American Studies Association, seminar participants at Cornell University and the University of Virginia, the Editors, and three anonymous referees. Funding for this research was provided by the Corruption Laboratory for Ethics, Accountability and the Rule of Law (CLEAR) of the Democracy Initiative at the University of Virginia and a SPRINT grant from the University of Virginia. Replication files are available at the American Political Science Review Dataverse https://doi.org/10.7910/DVN/QIWQWL.
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