Cifras e votos: uma relação democrática: análise das propostas de reforma do sistema de financiamentode campanhas eleitorais no Brasil à luz da teoria da democracia deliberativa-participativa

Abstract

The research concerns the political and academic debates on electoral campaign financing in Brazil, and it aims to answer the following research question: which financing model would be able to improve democratic participation from different and divergent perspectives, values, and interests, and yet, to collaborate on reducing corrupt practices? According with the theoretical perspective on deliberative-participatory model of democracy, it was demonstrated that a democratic financial system for electoral campaigns must contribute to improve participation from different perspectives and increase transparency and control. In order to investigate whether participation in political-electoral process is equal, data from reports of general elections of 2010 were analyzed and compared with the proposals in the political reform acts PL 1.210/2007 and APJ 02/2011. Regarding the link between campaigns financing and corruption, the transparency and control mechanisms from the actual system were analyzed and measured against those from the political reform acts proposals. In accordance with the theoretical reference adopted, the conclusions are that a democratic model of financing electoral campaign should: guarantee the minor parties participation, indicating counter majority criteria for distribution of public resources, especially for distribution of radio and television time; establish nominal limits to minimize inequality among private donors; furnish qualified transparency mechanisms capable of anticipating debates about financing that do not operate only afterwards but also during the campaign. Finally, the dissertation presents the research´s contributions and limitations, revealing the need to expand the studies about transparency and control of elections, verifying judicial process and jurisprudences, and also including qualitative data.