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Spatial Modeling of Voting Preferences in Russian Federation

Author

Listed:
  • Elena Anatolyevna Podkolzina

    (National Research University Higher School of Economics)

  • Olga Anatolyevna Demidova

    (National Research University Higher School of Economics)

  • Lada Evgenyevna Kuletskaya

    (National Research University Higher School of Economics)

Abstract

The main objective of this work is to assess the influence of individuals living in neighboring territorial areas on each other in decision-making on the example of presidential election in Russia in 2018 using data on 2718 territorial election commissions (TECs). Local and global indicators of spatial autocorrelation (Moran, Geary, Getis-Ord indices) calculated by the authors provide empirical evidence of global positive autocorrelation (i.e. in the country as a whole voters in each TEC vote similar to their neighbors). We identify TECs that can be included in local clusters (where voters vote similar) or in local outliers (surrounded by such TECs where voters vote opposite. Using the example of Tatarstan, the region where both local cluster and outlier TECs were most common we analyzed which economic indicators together with spatial ones influence the support of the main and opposition candidates. It was shown that the willingness to vote for the main candidate is explained by the increase in salaries in the area, but at the same time the indicators of economic activity in that area and the potential mobility of citizens have a negative impact on the support of the main candidate. Salary changes have no effect on votes in favour of opposition candidates, while other indicators show an inverse correlation. We have also shown that spatial effect models are preferable to OLS models for analyzing voting results

Suggested Citation

  • Elena Anatolyevna Podkolzina & Olga Anatolyevna Demidova & Lada Evgenyevna Kuletskaya, 2020. "Spatial Modeling of Voting Preferences in Russian Federation," Spatial Economics=Prostranstvennaya Ekonomika, Economic Research Institute, Far Eastern Branch, Russian Academy of Sciences (Khabarovsk, Russia), issue 2, pages 70-100.
    • Handle: RePEc:far:spaeco:y:2020:i:2:p:70-100
    DOI: https://dx.doi.org/10.14530/se.2020.2.070-100
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    References listed on IDEAS

    as
    1. Burnett, Wesley & Lacombe, Donald J., 2012. "Accounting for Spatial Autocorrelation in the 2004 Presidential Popular Vote: A Reassessment of the Evidence," The Review of Regional Studies, Southern Regional Science Association, vol. 42(1), pages 75-89, Spring.
    2. Manfred M. Fischer & Arthur Getis (ed.), 2010. "Handbook of Applied Spatial Analysis," Springer Books, Springer, number 978-3-642-03647-7, October.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Podkolzina, Elena & Kuletskaya, Lada & Demidova, Olga, 2022. "Spatial modelling of voting preferences: The “Mystery” of the Republic of Tatarstan," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 67, pages 74-96.

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    More about this item

    Keywords

    spatial autocorrelation; electoral preferences; global and local indices of spatial autocorrelation;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • R5 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis

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