Race and Competitiveness in Brazilian Elections: Evaluating the Chances of Black and Brown Candidates through Quantile Regression Analysis of Brazil's 2014 Congressional Elections

Abstract

Although the proportion of black, brown and indigenous electoral candidates in Brazil is close to the proportion of blacks, browns and indigenous in the general population, the proportion elected to the country's Federal Congress is significantly lower. Statistical techniques such as linear or logistic regression are typically used to estimate the effect of a particular variable such as color/race or gender on a candidate's electoral performance. However, in Brazilian elections, characterized by substantive, asymmetrical differences such as extreme variations in campaign finance distribution, the efficacy of these types of regression models is limited. Such being the case in Brazil's open list proportional representation system, we propose quantile regression as the most suitable means for estimating the relationship between voting and other variables such as race/color, because it enables us to estimate relationships between the variables of interest across several distribution quantiles. Quantile regression models show that black and brown candidates get as many as 40% fewer votes than white candidates in higher vote distribution quantiles. Furthermore, analysis of access to campaign financing finds that black and brown candidates on average garner only 75% of the funds available to white candidates at quantile 80 of campaign finance distribution. This drops to 65% at quantile 90.