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Mapping Election Polarization and Competitiveness using Election Results

Carlos Navarrete, Mariana Macedo, Viktor Stojkoski, Marcela Parada-Contzen, Christopher A Martínez

TL;DR

Election Polarization (EP) and Election Competitiveness (EC) are introduced as agnostic, data-driven metrics computed purely from observed vote data to map citizen divisions on Election Day. EP aggregates Within-Antagonism to capture geographic dispersion of votes, while EC aggregates Between-Antagonism to measure candidate closeness and competitiveness, with definitions $EP=\sum_{i=1}^{N} {\text{Within-A}_{i}}$ and $EC=\sum_{i=1}^{N} {\text{Between-A}_{i}}$, each ranging from 0 to 1. Analyses across 14 countries and multiple election types show that EP correlates with traditional measures of political polarization and that EC correlates with turnout, with robustness across aggregation levels and data limitations. The framework provides scalable polarization proxies for contexts with limited survey data, enabling regional and cross-national polarization studies especially in lower- and middle-income settings.

Abstract

The simplified hypothesis that an election is polarized as an explanation of recent electoral outcomes worldwide is centered on perceptions of voting patterns rather than ideological data from the electorate. While the literature focuses on measuring polarization using ideological-like data from electoral studies-which are limited to economically advantageous countries and are representative mostly to national scales-we argue that, in fact, voting patterns can lead to mapping effective proxies of citizen divisions on election day. This paper perspectives two complementary concepts, Election Polarization (EP) and Election Competitiveness (EC), as a means to understand voting patterns on Election Day. We present an agnostic approach that relies solely on election data and validate it using synthetic and real-world election data across 13 countries in the Eurozone, North America, Latin America, and New Zealand. Overall, we find that we can label and distinguish expectations of polarized and competitive elections in these countries, and we report that EP positively correlates with a metric of political polarization in the U.S., unlocking opportunities for studies of polarization at the regional level and for lower/middle-income countries where electoral studies are available, but surveys are limited.

Mapping Election Polarization and Competitiveness using Election Results

TL;DR

Election Polarization (EP) and Election Competitiveness (EC) are introduced as agnostic, data-driven metrics computed purely from observed vote data to map citizen divisions on Election Day. EP aggregates Within-Antagonism to capture geographic dispersion of votes, while EC aggregates Between-Antagonism to measure candidate closeness and competitiveness, with definitions and , each ranging from 0 to 1. Analyses across 14 countries and multiple election types show that EP correlates with traditional measures of political polarization and that EC correlates with turnout, with robustness across aggregation levels and data limitations. The framework provides scalable polarization proxies for contexts with limited survey data, enabling regional and cross-national polarization studies especially in lower- and middle-income settings.

Abstract

The simplified hypothesis that an election is polarized as an explanation of recent electoral outcomes worldwide is centered on perceptions of voting patterns rather than ideological data from the electorate. While the literature focuses on measuring polarization using ideological-like data from electoral studies-which are limited to economically advantageous countries and are representative mostly to national scales-we argue that, in fact, voting patterns can lead to mapping effective proxies of citizen divisions on election day. This paper perspectives two complementary concepts, Election Polarization (EP) and Election Competitiveness (EC), as a means to understand voting patterns on Election Day. We present an agnostic approach that relies solely on election data and validate it using synthetic and real-world election data across 13 countries in the Eurozone, North America, Latin America, and New Zealand. Overall, we find that we can label and distinguish expectations of polarized and competitive elections in these countries, and we report that EP positively correlates with a metric of political polarization in the U.S., unlocking opportunities for studies of polarization at the regional level and for lower/middle-income countries where electoral studies are available, but surveys are limited.
Paper Structure (15 sections, 5 equations, 3 figures, 2 tables)

This paper contains 15 sections, 5 equations, 3 figures, 2 tables.

Figures (3)

  • Figure 1: Edge-cases of eight fictional elections for two (a-c) and three (d-f) candidates. Each row represents a precinct, and each color represents a candidate.
  • Figure 2: Caption
  • Figure 3: Robustness across aggregation levels using real data. Robustness by aggregation level in the (a) United States (2020) and (b) France (2022). Robustness by election type--State-level EP of senate vs. presidential--in the United States in (c) 2016 and (d) 2020. Robustness by including abstentions and spoilt votes as candidates in (e) France (2022) and (f) Chile (2021). Convergence of EP values in (g) Chile (2013-2021) and (h) France (2002-2022). The value in the top-left corner is the Pearson correlation. The dashed line in (g-h) represents the correlation equal to 0.8. The variables have been normalized by subtracting the average and dividing by the standard deviation before running the regressions. Note: *p$<$0.1; **p$<$0.05; ***p$<$0.01