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Replication Data for: Electoral Dividends from Programmatic Policies: A Theoretical Proposal Based on the Brazilian Case

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Electoral Dividends from Programmatic Policies: A Theoretical Proposal Based on the Brazilian Case
 
Identifier https://doi.org/10.7910/DVN/G4RRPA
 
Creator Simoni Jr., Sérgio
 
Publisher Harvard Dataverse
 
Description Recent controversies in the literature on the electoral effects of cash transfer programs reveal the limitations of traditional models associating public policies to voting. Why would beneficiaries reward parties for programmatic policies when the government has no control over the distribution of the benefits? Are they guided by short-term retrospective voting in favor of the incumbent without forming durable links? By studying the Brazilian case, based on the Bolsa Escola-Program, formulated by the PSDB, and the Bolsa-Família Program, formulated by the PT, I test different theories and propose an alternative approach. My thesis is that the long-term electoral effect of programmatic policies is a consequence of credit-claiming efforts made by the competing parties, resulting in reciprocity from voters. My argument is built on the examination of parliamentary deliberations throughout the formulation of these programs and on the analysis of public opinion surveys conducted in 2005, a transitional moment when there were beneficiaries of both programs, and in 2018, the year of the first presidential election with the Bolsa-Família Program being implemented with the PT as an opposition party. The data show that party leaders came to the defense of these social programs to varying degrees and in different moments of their terms in office and that these different mobilization strategies reflected on voting behavior.
 
Subject Social Sciences
Bolsa-Família Program
elections
cash transfer programs
parliamentary debates
public policies
 
Contributor Martins, Debora