Ações afirmativas e desigualdade eleitoral: uma análise da representação racial nas eleições legislativas brasileiras

Abstract

This study investigates ADPF No. 738/2020, a policy that established the proportional allocation of the Electoral Fund based on the total number of Black candidates nominated by political parties in Brazil’s 2022 legislative elections. The analysis uses data from the Brazilian Superior Electoral Court (TSE) for the years 2018 (pre-policy) and 2022 (post-policy). A logistic regression model with individual- and structural-level covariates— such as campaign financing, incumbency, education, marital status, region, and party ideology—is employed to estimate electoral outcomes. The results indicate that, in 2018, self-declared Black candidates had 24.36% lower odds of being elected compared to white candidates. In 2022, a positive effect is observed; however, it is not statistically significant, even after the implementation of the normative framework. Three robustness checks—a temporal placebo, a falsification test using a non-target group (white candidates), and a specificity test with an irrelevant variable (gender)—support the model’s findings. Although ADPF No. 738 increased the allocation of resources to Black candidacies and the absolute number of elected Black legislators rose by 21.7%, this increase is largely driven by the growth in the number of Black candidates (+13.4%), rather than by greater electoral competitiveness, as the success rate increased by only 0.27 percentage points. Spatial analyses reveal a concentration of resources in the North and Northeast regions, while the South, Southeast, and Center-West maintained historical patterns of resource allocation and political underrepresentation. The study concludes that ADPF No. 738/2020 did not fully achieve its objectives, as it failed to dismantle structural inequalities, indicating the need for complementary policies combining vertical redistribution (resources), horizontal redistribution (state- and region-based criteria), and effective accountability mechanisms.