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Spatial modelling of voting preferences: The “Mystery” of the Republic of Tatarstan

Author

Listed:
  • Podkolzina, Elena

    (HSE University, Moscow.)

  • Kuletskaya, Lada

    (HSE University, Moscow.)

  • Demidova, Olga

    (HSE University, Moscow.)

Abstract

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.

Suggested Citation

  • 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.
    • Handle: RePEc:ris:apltrx:0453
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    More about this item

    Keywords

    spatial autocorrelation; electoral preferences; local economic conditions; GMM; 2-step OLS; Russian 2018 presidential elections;
    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
    • R50 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - General

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