The microdynamics of spatial polarization: A model and an application to survey data from Ukraine

Abstract

Although spatial polarization of attitudes is extremely common around the world, we understand little about the mechanisms through which polarization on divisive issues rises and falls over time. We develop a theory that explains how political shocks can have different effects in different regions of a country depending upon local dynamics generated by the preexisting spatial distribution of attitudes and discussion networks. Where opinions were previously divided, attitudinal diversity is likely to persist after the shock. Meanwhile, where a clear precrisis majority exists on key issues, opinions should change in the direction of the predominant view. These dynamics result in greater local homogeneity in attitudes but at the same time exacerbate geographic polarization across regions and sometimes even within regions. We illustrate our theory by developing a modified version of the adaptive voter model, an adaptive network model of opinion dynamics, to study changes in attitudes toward the European Union (EU) in Ukraine in the context of the Euromaidan Revolution of 2013 to 2014. Using individual-level panel data from surveys fielded before and after the Euromaidan Revolution, we show that EU support increased in areas with high prior public support for EU integration but declined further where initial public attitudes were opposed to the EU, thereby increasing the spatial polarization of EU attitudes in Ukraine. Our tests suggest that the predictive power of both network and regression models increases significantly when we incorporate information about the geographic location of network participants, which highlights the importance of spatially rooted social networks.

Statistical Mechanics of Political Polarization

“…The model has twelve parameters: three for the groups’ respective internal cohesiveness J, six representing how they relate to each other, K, and three H. They can be estimated qualitatively, as we have done here, informed by publicly available poll data (here, we used [ 7 , 41 , 48 ]).…”

Inequality, Identity, and Partisanship: How redistribution can stem the tide of mass polarization

“…Our work focuses on affective polarization and sorting with respect to identity groups among the electorate. However attempts to prevent and reverse polarization must also take account of the mechanisms that enable elite [31,32] and ideological polarization [32,33], and must account for the role of factors such as geography [34] and population and social network structure [35][36][37] in producing mass polarization, in addition to the inter-group and economic factors studied here. We must also remain alert to the circumstances under which polarization can provide benefits [38,39] (e.g.…”

Spatial and Temporal Evolutionary Characteristics and Its Influencing Factors of Economic Spatial Polarization in the Yangtze River Delta Region

“…It also determines a variety of explanation mechanisms for spatial polarization, such as factor flow, urban prejudice, agglomeration economy, and knowledge spillover [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. Other scholars study the spatial polarization of Japanese land price evolution and Ukrainian public opinion polls with the help of the spatial variation function model, micro-dynamic model, and exploratory spatial analysis [ 42 , 43 ]. Additionally, some scholars measure and explore the level of spatial polarization in the development of urbanization, railway transportation, and scientific and technological innovation, and have found that the regional labor market, economic structure, settlement structure, population density, transportation infrastructure, etc., all affect the development of regional imbalances from different aspects [ 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ].…”