Аннотация. Основной задачей данной работы является оценка влияния индивидов, проживающих в соседних территориальных областях, друг на друга в принятии решений на примере выборов Президента России в 2018 г. c использованием данных по 2718 территориальным избирательным комиссиям (ТИКам). Рассчитанные авторами локальные и глобальные показатели пространственной автокорреляции (индексы Морана, Гири, Гетиса -Орда) дают эмпирическое подтверждение глобальной положительной автокорреляции (т. е. в целом по стране избиратели в каждом ТИКе голосуют сходным образом с соседями). Были выявлены ТИКи, входящие в локальные кластеры (где избиратели голосуют аналогично), и ТИКи -выбросы (outlier), т. е. окруженные ТИКами, где голосуют противоположным образом. На примере Татарстана, региона, где встречалось больше всего и ТИКов, образующих локаль-
As for today, political elections are the key form of people’s participation in the formation of the state in all democratic countries, which is why theoretical works in the field of spatial modeling of voter choice appeared relatively long ago and played a major role in the development of both further theoretical and empirical research in this area. In this survey we firstly give a brief overview of the history of the formation of spatial modeling in relation to election results and political preferences of individuals from the point of view of research methodology, based on the classical theoretical ‘proximity model’ and ‘directional model’, where rational individuals determine their political positions and compare them with the positions of candidates. Secondly, we explain the appearance of the studies of the mutual influence of voters living in neighboring territories on each other as one of the factors that determine the voters’ political positions and, accordingly, the final choice of a candidate. We also point out the authors’ different explanations of the reasons for the appearance of such mutual influence of voters and other factors affecting voters living in neighboring territories (also called as ‘contextual effects’) and emphasize the importance of taking them into account in the studies of electoral preferences. A separate chapter in this paper presents the systematization and description of the main empirical approaches to spatial modeling of electoral choice: at the beginning, we present the basic econometric spatial models (used by the authors regardless of the subject of the study), and then we describe the empirical work in the field of voter choice, depending on the hypotheses, focusing on the research methodology and the data used. In conclusion, we define the main directions for the research development and the vector of further practical work in this area. This paper will help researchers understand existing fundamental works, evaluate current approaches to the modeling of electoral choice, and improve theoretical or empirical spatial analysis
We argue neighbors play a crucial role in voting behavior for the main candidate in Russia. Moreover, the official status of the region and connectedness with the ruling party matter. The neighborhood effects we explain with the idea that voters base on public choices and illustrate it on the example of Privolzhskiy federal district regions with an emphasis on Tatarstan and its effect on voting on the municipal level. The Republic of Tatarstan is an interesting case also because it is the republic in Russia that has reference to sovereignty in its constitution and at the same time is loyal to the Kremlin. This paper presents a detailed spatial analysis of voters’ responses at the municipal level covering Russian presidential elections in 2018 year using the example of the Republic of Tatarstan and its surrounding regions. The preferred 2-step OLS specification with instruments shows that Tatarstan had a strong positive effect on neighboring regions in terms of voting for the main candidate, while surrounding regions voted differently and negatively affected each other. Municipalities with better economic conditions had a negative impact on the share of votes for the main candidate and positive for the opposite.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.