Stylized facts in Brazilian vote distributions

TL;DR

This paper analyzes Brazilian election vote distributions over several decades, revealing non-universal power-law behaviors that evolve over time and proposing a model linking these patterns to electorate feedback mechanisms.

Contribution

It introduces a multi-species nonlinear differential equation model to explain the non-universal power-law exponents in vote distributions and their temporal evolution.

Findings

Vote distributions follow a scale-free power-law regime.

The power-law exponent varies with time and is characteristic of the electorate.

The model links the exponent to the degree of feedback among voters.

Abstract

Elections, specially in countries such as Brazil with an electorate of the order of 100 million people, yield large-scale data-sets embodying valuable information on the dynamics through which individuals influence each other and make choices. In this work we perform an extensive analysis of data sets available for Brazilian proportional elections of legislators and city councillors throughout the period 1970-2012, which embraces two distinct political regimes: a military dictatorship and a democratic phase. Through the distribution $P(v)$ of the number of candidates receiving $v$ votes, we perform a comparative analysis of different elections in the same calendar and as a function of time. The distributions $P(v)$ present a scale-free regime with a power-law exponent $\alpha$ which is not universal and appears to be characteristic of the electorate. Moreover, we observe that $\alpha$ typically increases with time. We propose a multi-species model consisting in a system of nonlinear differential equations with stochastic parameters that allows to understand the empirical observations. We conclude that the power-law exponent $\alpha$ constitutes a measure of the degree of feedback of the electorate interactions. To know the interactivity of the population is relevant beyond the context of elections, since a similar feedback may occur in other social contagion processes.