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Spatial Econometric Approach to Modeling Election Results in Russia: Municipal Level
 
PIIS042473880024435-7-1
DOI10.31857/S042473880024435-7
Publication type Article
Status Published
Authors
Lada Kuletskaya 
Occupation: -
Affiliation: National Research University Higher School of Economics
Address: Pokrovsky bulvar 11
Olga Demidova
Occupation:  Associate Professor
Affiliation: National Research University Higher School of Economics
Address: Pokrovsky bul.11, office S 534
Elena Semerikova
Occupation: Senior lecturer
Affiliation: National Research University Higher School of Economics
Address: Pokrovsky bulvar 11
Journal nameEkonomika i matematicheskie metody
EditionVolume 59 No. 3
Pages137-148
Abstract

In this article we assess the role of mutual influence of voters living in neighboring territories and the influence of socio-economic factors on the example of voting results for the main candidate in the 2018 elections in Russia. We claim that spatial factors (neighboring of municipalities, regions and belonging of municipalities to the same region) significantly affect the results of voting for the main candidate in each municipality. To confirm this hypothesis, we evaluated several different specifications of the Durbin model, which include dummy variables for the region and other spatial factors, and compared the results with the specifications of the model without taking into account spatial factors. We confirmed main hypothesis: the results of voting depend on the region in which the municipality is included, and, in addition, there is a positive spatial autocorrelation (the results of voting in neighboring municipalities depend on each other). The absence of consideration of spatial factors reduces the quality of regression fitting, there coefficient estimates are biased, and the qualitative picture of the results obtained is distorted. We also showed that the economic situation of the region also affects the results of the voting: economically stronger the municipality received higher share of votes for the main candidate.

KeywordsSpatial autocorrelation, Electoral preferences, The role of socio-economic conditions, Spatial Durbin model, Presidential elections in Russia 2018, Analysis of municipal data, Spatial influence of territories on each other
Received16.02.2023
Publication date19.09.2023
Number of characters37708
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1. Aleskerov F. T., Borodin A. D., Kaspeh, S. I., Marshakov V. A., Salmin A. M. (2005). Analiz ehlektoral'nykh predpochtenij v Rossii v 1993–2003 gg.: dinamika indeksa polyarizovannosti // Ehkonomicheskij Zhurnal Vysshej Shkoly Ehkonomiki. T. 9. № 2. S. 173–184. [Aleskerov F.T., Borodin A.D., Kaspe S.I., Marshakov V.A., Salmin A.M. (2005). Analysis of electoral preferences in Russia in 1993–2003: Polarization Index dynamics. Economic Journal Higher School of Economics, 9, 2, 173-184 (in Russian).]
2. Aleskerov F. T., Golubenko M. A. (2003). Ob otsenke simmetrichnosti politicheskikh vzglyadov i polyarizovannosti obschestva. Preprint GU Vysshaya Shkola Ehkonomiki, WP7/2003/04, Moskva. [Aleskerov F.T., Golubenko M.A. (2003). On the evaluation of a symmetry of political views and polarization of society. Working paper WP7/2003/04 — Moscow: State University — Higher School of Economics, 2003. — 24 p. (in Russian).]
3. Akhremenko A. S. (2007). Prostranstvennoe modelirovanie ehlektoral'nogo vybora: razvitie, sovremennye problemy i perspektivy (II) // Polis. Politicheskie Issledovaniya. T. 2. S. 165–179. [Akhremenko A.S. (2007). Spatial Modeling of Electoral Choice: Development, Modern Problems and Prospects (II). Polis. Political Studies, 2, 165–179 (In Russian).]
4. Akhremenko A. S. (2009). Prostranstvennyj ehlektoral'nyj analiz: kharakteristika metoda, vozmozhnosti krossnatsional'nykh sravnitel'nykh issledovanij // Politicheskaya Nauka. T. 1. S. 32–59. [Akhremenko A.S. (2009). Spatial electoral analysis: characteristics of the method, possibilities of cross-national comparative studies. Political Science, 1, 32-59 (In Russian).]
5. Borodin A. D. (2005). Soglasovannost' kollektivnykh dejstvij v povedenii rossijskikh izbiratelej // Ehkonomicheskij Zhurnal Vysshej Shkoly Ehkonomiki. T. 9. № 1. S. 74-81. [Borodin A. D. (2005). Social Conformity in the Behavior of Russian Voters. Economic Journal Higher School of Economics, 9,1, 74–81 (in Russian).]
6. Kuletskaya L. E. (2021). Prostranstvennye modeli ehlektoral'nogo vybora: obzor teoreticheskikh i ehmpiricheskikh podkhodov // Prostranstvennaya Ehkonomika. T. 17. № 2. S. 127–164. [Kuletskaya L.E. (2021). Spatial Modeling of Voter Choice: The Survey of Theoretical and Empirical Approach. Spatial Economics, 17, 2, 127–164 (in Russian).]
7. Podkolzina E. A., Demidova O. A., Kuletskaya L. E. (2020). Prostranstvennoe modelirovanie ehlektoral'nykh predpochtenij v Rossijskoj Federatsii // Prostranstvennaya Ehkonomika. T. 16. № 2. S. 70-100. [Podkolzina E. A., Demidova O. A., Kuletskaya L. E. (2020). Spatial Modeling of Voting Preferences in Russian Federation. Spatial Economics, 16, 2, 70–100 (in Russian).]
8. Arzheimer K., Evans J. (2012). Geolocation and voting: Candidate-voter distance effects on party choice in the 2010 UK general election in England. Political Geography, 31(5), 301–310. https://doi.org/10.1016/j.polgeo.2012.04.006 
9. Blank R. H. (1974). Socio-economic determinism of voting turnout: A challenge. The Journal of Politics, 36(3), 731–752.
10. Bloise F., Chironi D., Pianta M. (2021). Inequality and voting in Italy’s regions. Territory, Politics, Governance, 9(3), 365–390.
11. Burbank M. J. (1997). Explaining contextual effects on vote choice. Political Behavior, 19(2), 113–132. https://doi.org/10.1023/A:1024806024732 
12. Cho S., Endersby J. W. (2003). Issues, the spatial theory of voting, and British general elections: A comparison of proximity and directional models. Public Choice, 114(3–4), 275–293. https://doi.org/10.1023/A:1022616323373 
13. Clinton J. D. (2006). Representation in congress: Constituents and roll calls in the 106th House. Journal of Politics. https://doi.org/10.1111/j.1468-2508.2006.00415.x 
14. Coleman S. (2004). The effect of social conformity on collective voting behavior. Political Analysis, 12(1), 76–96. https://doi.org/10.1093/pan/mpg015 
15. Coleman S. (2007). Popular delusions: How social conformity molds society and politics. Cambria Press. https://doi.org/10.5860/choice.45-6399 
16. Coleman S. (2018). Voting and conformity: Russia, 1993 – 2016. Mathematical Social Sciences, 94, 87–95. https://doi.org/https://doi.org/10.1016/j.mathsocsci.2017.10.005 
17. Cox K. R. (1968). Suburbia and Voting Behavior in the London Metropolitan Area. Annals of the Association of American Geographers, 58(1), 111–127. https://doi.org/10.1111/j.1467-8306.1968.tb01639.x 
18. Cutts D., Webber D., Widdop P., Johnston R., Pattie C. (2014). With a little help from my neighbours : A spatial analysis of the impact of local campaigns at the 2010 British general election. Electoral Studies, 34, 216–231. https://doi.org/10.1016/j.electstud.2013.12.001 
19. Davis O. A., Hinich M. J., Ordeshook P. C. (1970). An Expository Development of a Mathematical Model of the Electoral Process. The American Political Science Review, 64(2), 426–448. https://doi.org/10.2307/1953842 
20. de Dominicis L., Dijkstra L., Pontarollo N. (2022). Why are cities less opposed to European integration than rural areas? Factors affecting the Eurosceptic vote by degree of urbanization. Cities, 130, 103937.
21. Downs, A. (1957). An economic theory of political action in a democracy. Journal of Political Economy, 65(2), 135–150. https://doi.org/10.1111/j.1467-6435.1986.tb01254.x 
22. Duggan J. (2005). A survey of equilibrium analysis in spatial models of elections. Unpublished Manuscript.
23. Durlauf S. N. (2004). Neighborhood Effects. In Handbook of regional and urban economics (pp. 2173–2242). Elsevier. https://doi.org/10.1016/B978-008044910-4.00480-6 
24. Elinder M. (2010). Local economies and general elections: The influence of municipal and regional economic conditions on voting in Sweden 1985–2002. European Journal of Political Economy, 26(2), 279–292. https://doi.org/https://doi.org/10.1016/j.ejpoleco.2010.01.003 
25. Enelow J. M., Hinich M. J. (1989). A general probabilistic spatial theory of elections. Public Choice, 61(2), 101–113. https://doi.org/10.1007/bf00115657 
26. Erikson R. S., Romero D. W. (1990). Candidate Equilibrium and the Behavioral Model of the Vote. American Political Science Review, 84(4), 1103–1126. https://doi.org/10.2307/1963255 
27. Ethington P. J., McDaniel J. A. (2007). Political places and institutional spaces: The intersection of political science and political geography. Annual Review of Political Science, 10, 127–142. https://doi.org/10.1146/annurev.polisci.10.080505.100522 
28. Gerber A. S., Rogers T. (2004). Descriptive social norms and motivation to vote: Everybody’s voting and so should you. The Journal of Politics, 71(1), 178–191. https://doi.org/10.1017/S0022381608090117 
29. Hinich M. J., Pollard W. (1981). A New Approach to the Spatial Theory of Electoral Competition. American Journal of Political Science, 25(2), 323–341. https://doi.org/10.2307/2110856 
30. Huckfeldt R., Sprague J. (1991). Discussant effects on vote choice: Intimacy, structure, and interdependence. The Journal of Politics, 53(1), 122–158.
31. Iversen T. (1994a). Political leadership and representation in West European democracies: A test of three models of voting. American Journal of Political Science, 45–74. https://doi.org/10.2307/2111335 
32. Iversen T. (1994b). The logics of electoral politics: Spatial, directional, and mobilizational effects. Comparative Political Studies, 27(2), 155–189. https://doi.org/10.1177/0010414094027002001 
33. Jensen C. D., Lacombe D. J., McIntyre S. G. (2013). A Bayesian spatial econometric analysis of the 2010 UK General Election. Papers in Regional Science, 92(3), 651–666. https://doi.org/https://doi.org/10.1111/j.1435-5957.2012.00415.x 
34. Jessee S. A. (2010). Partisan bias, political information and spatial voting in the 2008 presidential election. Journal of Politics, 72(2), 327–340. https://doi.org/10.1017/S0022381609990764 
35. Johnson M., Shively W.P., Stein R. M. (2002). Contextual data and the study of elections and voting behavior: Connecting individuals to environments. Electoral Studies, 21(2), 219–233. https://doi.org/10.1016/S0261-3794 (01)00019-1
36. Kelejian H. H., Prucha I. R. (1998). A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. The Journal of Real Estate Finance and Economics, 17(1), 99–121. https://doi.org/10.1177/1536867x1301300201 
37. Kim J., Elliott E., Wang D. (2003). A spatial analysis of county-level outcomes in US Presidential elections : 1988 – 2000. Electoral Studies, 22, 741–761. https://doi.org/10.1016/S0261-3794 (02)00008-2
38. Lacombe D. J., Shaughnessy T. M. (2007). Accounting for Spatial Error Correlation in the 2004 Presidential Popular Vote. Public Finance Review, 35(4), 480–499. https://doi.org/10.1177/1091142106295768 
39. Leenders R. T. A. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21–47. https://doi.org/10.1016/S0378-8733 (01)00049-1
40. LeSage J. P., Pace R. K. (2009). Introduction to spatial econometrics. Boca Raton, US: CRC Press Taylor, Francis Group. 340 p.
41. Macdonald S. E., Listhaug O., Rabinowitz G. (1991). Issues and party support in multiparty systems. The American Political Science Review, 1107–1131. https://doi.org/10.2307/1963938 
42. Moraski B., Reisinger W. M. (2010). Spatial Contagion in Regional Machine Strength: Evidence from Voting in Russia’s Federal Elections. In APSA 2010 Annual Meeting Paper (p. 48).
43. Nieuwbeerta P., Flap H. (2000). Crosscutting social circles and political choice Effects of personal network composition on voting behavior in The Netherlands. Social Networks, 22, 313–335.
44. Nwankwo C. F. (2019). The spatial pattern of voter choice homogeneity in the Nigerian presidential elections of the fourth republic. Bulletin of Geography, 43(1), 143–165. https://doi.org/10.2478/bog-2019-0010 
45. Pattie C., Johnston R. (2000). “People who talk together vote together”: An exploration of contextual effects in Great Britain. Annals of the Association of American Geographers, 90(1), 41–66. https://doi.org/10.1111/0004-5608.00183 
46. Podkolzina E., Kuletskaya L., Demidova O. (2022). Spatial modelling of voting preferences: The “Mystery” of the Republic of Tatarstan. Applied Econometrics, 67, 74–96.
47. Poole K. T. (2005). Spatial models of parliamentary voting. Spatial Models of Parliamentary Voting. https://doi.org/10.1017/CBO9780511614644 
48. Poole K. T., Rosenthal H. (1984). U.S. Presidential Elections 1968-80: A Spatial Analysis. American Journal of Political Science, 28(2), 282–312. https://doi.org/10.2307/2110874 
49. Rabinowitz G., Macdonald S. E. (1989). A directional theory of issue voting. The American Political Science Review, 93–121. https://doi.org/10.2307/1956436 
50. Reisinger W. M., Moraski B. J. (2009). Regional Voting in Russia’ s Federal Elections and Changing Regional Deference to the Kremlin. In Annual National Conference of the Midwest Political Science Association, 67th (Chicago, Illinois) (p. 42). https://doi.org/https://doi.org/10.17077/g02k-n8zq 
51. Reitan T. C. (2003). Too sick to vote? Public health and voter turnout in Russia during the 1990s. Communist and Post-Communist Studies, 36(1), 49–68.
52. Sharafutdinova G., Turovsky R. (2017). The politics of federal transfers in Putin’s Russia: regional competition, lobbying, and federal priorities. Post-Soviet Affairs, 33(2), 161–175.
53. Stekhoven D. J., Bühlmann P. (2012). MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112-118.
54. Stoetzer L. F. (2017). A Matter of Representation: Spatial Voting and Inconsistent Policy Preferences. British Journal of Political Science, 49(3), 941–956. https://doi.org/10.1017/S0007123417000102 
55. Tucker J. A., Tucker J. L., Martin R. A. (2006). Regional Economic Voting: Russia, Poland, Hungary, Slovakia, and the Czech Republic, 1990-1999. Cambridge University Press.
56. Turovsky R., Gaivoronsky Y. (2017). Russia’s regions as winners and losers: Political motives and outcomes in the distribution of federal government transfers. European Politics and Society, 18(4), 529–551.
57. Wuhs S., McLaughlin E. (2019). Explaining Germany’s electoral geography: Evidence from the eastern states. German Politics and Society, 37(1), 1–23. https://doi.org/10.3167/gps.2018.370101

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