Spatial Modeling of Voting Preferences in Russian Federation
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 spat...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | Russian |
| Published: |
Economic Research Institute of the Far East Branch of the Russian Academy of Sciences
2020-07-01
|
| Series: | Prostranstvennaâ Èkonomika |
| Subjects: | |
| Online Access: | http://www.spatial-economics.com/images/spatial-econimics/2020_2/SE.2020.2.070-100.Podkolzina.pdf |
| _version_ | 1818083192722161664 |
|---|---|
| author | Elena Anatolyevna Podkolzina Olga Anatolyevna Demidova Lada Evgenyevna Kuletskaya |
| author_facet | Elena Anatolyevna Podkolzina Olga Anatolyevna Demidova Lada Evgenyevna Kuletskaya |
| author_sort | Elena Anatolyevna Podkolzina |
| collection | DOAJ |
| description | 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 |
| first_indexed | 2024-12-10T19:34:05Z |
| format | Article |
| id | doaj.art-a6ffdf69287b458fa8cd8e4947ab9df4 |
| institution | Directory Open Access Journal |
| issn | 1815-9834 2587-5957 |
| language | Russian |
| last_indexed | 2024-12-10T19:34:05Z |
| publishDate | 2020-07-01 |
| publisher | Economic Research Institute of the Far East Branch of the Russian Academy of Sciences |
| record_format | Article |
| series | Prostranstvennaâ Èkonomika |
| spelling | doaj.art-a6ffdf69287b458fa8cd8e4947ab9df42022-12-22T01:36:10ZrusEconomic Research Institute of the Far East Branch of the Russian Academy of SciencesProstranstvennaâ Èkonomika1815-98342587-59572020-07-011627010010.14530/se.2020.2.070-100Spatial Modeling of Voting Preferences in Russian FederationElena Anatolyevna Podkolzina0https://orcid.org/0000-0002-8363-6711Olga Anatolyevna Demidova1https://orcid.org/0000-0001-5201-3207Lada Evgenyevna Kuletskaya2https://orcid.org/0000-0003-2069-9800National Research University Higher School of EconomicsNational Research University Higher School of EconomicsNational Research University Higher School of EconomicsThe 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 resultshttp://www.spatial-economics.com/images/spatial-econimics/2020_2/SE.2020.2.070-100.Podkolzina.pdfspatial autocorrelationelectoral preferencesglobal and local indices of spatial autocorrelation |
| spellingShingle | Elena Anatolyevna Podkolzina Olga Anatolyevna Demidova Lada Evgenyevna Kuletskaya Spatial Modeling of Voting Preferences in Russian Federation Prostranstvennaâ Èkonomika spatial autocorrelation electoral preferences global and local indices of spatial autocorrelation |
| title | Spatial Modeling of Voting Preferences in Russian Federation |
| title_full | Spatial Modeling of Voting Preferences in Russian Federation |
| title_fullStr | Spatial Modeling of Voting Preferences in Russian Federation |
| title_full_unstemmed | Spatial Modeling of Voting Preferences in Russian Federation |
| title_short | Spatial Modeling of Voting Preferences in Russian Federation |
| title_sort | spatial modeling of voting preferences in russian federation |
| topic | spatial autocorrelation electoral preferences global and local indices of spatial autocorrelation |
| url | http://www.spatial-economics.com/images/spatial-econimics/2020_2/SE.2020.2.070-100.Podkolzina.pdf |
| work_keys_str_mv | AT elenaanatolyevnapodkolzina spatialmodelingofvotingpreferencesinrussianfederation AT olgaanatolyevnademidova spatialmodelingofvotingpreferencesinrussianfederation AT ladaevgenyevnakuletskaya spatialmodelingofvotingpreferencesinrussianfederation |