In the section | Surveys |
Title of the article | Spatial Modeling of Voter Choice: The Survey of Theoretical and Empirical Approach |
Pages | 127-164 |
Author |
Lada Evgenyevna Kuletskaya Postgraduate Student Post-graduate student of the Graduate school of Economics (Department of applied Economics), National Research University Higher School of Economics 20 Myasnitskaya St., Moscow, 101000, Russian Federation This email address is being protected from spambots. You need JavaScript enabled to view it. ORCID: 0000-0003-2069-9800 |
Abstract | 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 |
Code | 51-77 |
JEL | C21, C31, D72 |
DOI | https://dx.doi.org/10.14530/se.2021.2.127-164 |
Keywords | contextual effects ♦ neighborhood effects ♦ spatial voting theory ♦ spatial econometrics ♦ political positions of voters ♦ electoral choice |
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For citation | Kuletskaya L.E. Spatial Modeling of Voter Choice: The Survey of Theoretical and Empirical Approach. Prostranstvennaya Ekonomika = Spatial Economics, 2021, vol. 17, no. 2, pp. 127–164. https://dx.doi.org/10.14530/se.2021.2.127-164 (In Russian) |
References | 1. Akhremenko A.S. Spatial Modeling of Electoral Choice: Development, Modern Problems and Prospects (II). Polis. Politicheskie Issledovaniya = Polis. Political Studies, 2007, no. 2, pp. 165–179. 2. Aleskerov F.T., Borodine A.D., Kaspe S.I., Marshakov V.A., Salmin A.M. Analysis of Electoral Preferences in Russia in 1993–2003: Polarization Index Dynamics. Ekonomicheskiy Zhurnal VSHE = HSE Economic Journal, 2005, vol. 9, no. 2, pp. 173–184. (In Russian). 3. Anselin L. Local Indicators of Spatial Association – LISA. Geographical Analysis, 1995, vol. 27, issue 2, pp. 93–115. 4. Anselin L. Spatial Econometrics: Methods and Models. Studies in Operational Regional Science, 1988, vol. 4, 284 p. 5. Anselin L. The Moran Scatterplot as an ESDA Tool to Assess Local Instability in Spatial. Spatial Analytical Perspectives on GIS. London: Routledge, 1993, pp. 111–126. 6. Arbia G., Dickson M.M., Espa G., Giuliani D. Dirty Spatial Econometrics. The Annals of Regional Science, 2016, vol. 56, issue 1, pp. 177–189. 7. Arzheimer K., Evans J. Geolocation and Voting: Candidate-Voter Distance Effects on Party Choice in the 2010 UK General Election in England. Political Geography, 2012, vol. 31, issue 5, pp. 301–310. 8. Austen-Smith D., Banks J.S. Positive Political Theory II: Strategy and Structure. University of Michigan Press, 2005, 472 p. 9. Balzer W., Dreier V. The Structure of the Spatial Theory of Elections. British Journal for the Philosophy of Science, 1999, vol. 50, no. 4, pp. 613–638. 10. Bauman D., Drouet T., Fortin M. J., Dray S. Optimizing the Choice of a Spatial Weighting Matrix in Eigenvector-Based Methods. Ecology, 2018, vol. 99, issue 10, pp. 2159–2166. 11. Beck N., Gleditsch K.S., Beardsley K. Space Is More than Geography: Using Spatial Econometrics in the Study of Political Economy. International Studies Quarterly, 2006, vol. 50, issue 1, pp. 27–44. 12. Belotti F., Hughes G., Mortari A.P. Spatial Panel-Data Models Using Stata. The Stata Journal: Promoting Communications on Statistics and Stata, 2017, vol. 17, issue 1, pp. 139–180. 13. Berry F.S., Berry W.D. State Lottery Adoptions as Policy Innovations: An Event History Analysis. American Political Science Review, 1990, vol. 84, issue 2, pp. 395–415. 14. Black D. On the Rationale of Group Decision-Making. Journal of Political Economy, 1948, vol. 56, issue 1, pp. 23–34. 15. Blais A., Nadeau R., Gidengil E., Nevitte N. The Formation of Party Preferences: Testing the Proximity and Directional Models. European Journal of Political Research, 2001, vol. 40, issue 1, pp. 81–91. 16. Borodin A.D. Social Conformity in the Behavior of Russian Voters. Ekonomicheskiy Zhurnal VSHE = HSE Economic Journal, 2005, vol. 9, no. 1, pp. 74–81. (In Russian). 17. Burbank M.J. Explaining Contextual Effects on Vote Choice. Political Behavior, 1997, vol. 19, issue 2, pp. 113–132. 18. Burnett J.W., Lacombe D.J. Accounting for Spatial Autocorrelation in the 2004 Presidential Popular Vote: A Reassessment of the Evidence. The Review of Regional Studies, 2012, vol. 42, issue 1, pp. 75–89. 19. Cho S., Endersby J.W. Issues, the Spatial Theory of Voting, and British General Elections: A Comparison of Proximity and Directional Models. Public Choice, 2003, vol. 114, issue 3–4, pp. 275–293. 20. Coleman S. Popular Delusions: How Social Conformity Molds Society and Politics. New York: Cambria Press, 2007, 324 p. 21. Coleman S. The Effect of Social Conformity on Collective Voting Behavior. Political Analysis, 2004, vol. 12, issue 1, pp. 76–96. 22. Coleman S. Voting and Conformity: Russia, 1993–2016. Mathematical Social Sciences, 2018, vol. 94, pp. 87–95. 23. Cox K.R. Suburbia and Voting Behavior in the London Metropolitan Area. Annals of the Association of American Geographers, 1968, vol. 58, issue 1, pp. 111–127. 24. Cox K.R. The Voting Decision in a Spatial Context. Progress in Geography, 1969, vol. 1, pp. 81–117. 25. Cutts D., Webber D., Widdop P., Johnston R., Pattie C. With a Little Help from my Neighbours: A Spatial Analysis of the Impact of Local Campaigns at the 2010 British General Election. Electoral Studies, 2014, vol. 34, pp. 216–231. 26. Darmofal D. Spatial Econometrics and Political Scienc, 40 p. Available at: 27. Davis O.A., Hinich M.J., Ordeshook P.C. An Expository Development of a Mathematical Model of the Electoral Process. American Political Science Review, 1970, vol. 64, issue 2, pp. 426–448. 28. Downs A. An Economic Theory of Political Action in a Democracy. Journal of Political Economy, 1957, vol. 65, issue 2, pp. 135–150. 29. Duggan J. A Survey of Equilibrium Analysis in Spatial Models of Elections, 2005, 36 p. Available at: 30. Durlauf S.N. Neighborhood Effects. Handbook of Regional and Urban Economics. Vol. 4. Cities and Geography. Elsevier, 2004, pp. 2173–2242. 31. Elhorst J.P. Spatial Panel Data Analysis. Encyclopedia of GIS. Editeds by S. Shekhar, H. Xiong, X. Zhou. Cham: Springer International Publishing, 2017, pp. 2050–2058. 32. Elhorst J.P. Spatial Panel Data Models. Handbook of Applied Spatial Analysis. Berlin, Heidelberg: Springer, 2010, pp. 377–407. 33. Enelow J.M., Hinich M.J. A General Probabilistic Spatial Theory of Elections. Public Choice, 1989, vol. 61, issue 2, pp. 101–113. 34. Ethington P.J., McDaniel J.A. Political Places and Institutional Spaces: The Intersection of Political Science and Political Geography. Annual Review of Political Science, 2007, vol. 10, pp. 127–142. 35. Eulau H., Rothenberg L. Life Space and Social Networks as Political Contexts. Political Behavior, 1986, vol. 8, issue 2, pp. 130–157. 36. Florax R.J.G.M., Folmer H., Rey S.J. Specification Searches in Spatial Econometrics: The Relevance of Hendry’s Methodology. Regional Science and Urban Economics, 2003, vol. 33, issue 5, pp. 557–579. 37. Foladare I.S. The Effect of Neighborhood on Voting Behavior. Political Science Quarterly, 1968, vol. 83, issue 4, pp. 516–529. 38. Forbes J., Cook D., Hyndman R.J. Spatial Modelling of the Two-Party Preferred Vote in Australian Federal Elections: 2001–2016. Australian and New Zealand Journal of Statistics, 2020, vol. 62, issue 2, pp. 168–185. 39. Franzese R.J., Hays J.C. Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data. Political Analysis, 2007, vol. 15, issue 2, pp. 140–164. 40. Glaeser E.L., Sacerdote B.I., Scheinkman J.A. The Social Multiplier. Journal of the European Economic Association, 2003, vol. 1, issue 2–3, pp. 345–353. 41. Gorecki M.A., Marsh M. Not Just ‘Friends and Neighbours’: Canvassing, Geographic Proximity and Voter Choice. European Journal of Political Research, 2012, vol. 51, issue 5, pp. 563–582. 42. Griffith D.A. Spatial Autocorrelation and Spatial Filtering: Gaining Understanding Through Theory and Scientific Visualization. Springer-Verlag Berlin Heidelberg, 2003, 250 p. 43. Heywood A. Global Politics. Macmillan International Higher Education. Red Globe Press, 2014, 616 p. 44. Hinich M.J., Pollard W. A New Approach to the Spatial Theory of Electoral Competition. American Journal of Political Science, 1981, vol. 25, issue 2, pp. 323–341. 45. Horowitz D.L. Ethnic Groups in Conflict. Berkeley: University of California Press, 1985, 697 p. 46. Hotelling H. Stability in Competition. Economic Journal, 1929, vol. 39, no. 153, pp. 41–57. 47. Huckfeldt R. Politics in Context: Assimilation and Conflict in Urban Neighborhoods. New York: Agathon Press, 1986, 191 p. 48. Huckfeldt R., Sprague J. Discussant Effects on Vote Choice: Intimacy, Structure, and Interdependence. The Journal of Politics, 1991, vol. 53, no. 1, pp. 122–158. 49. Huckfeldt R., Sprague J. Networks in Context: The Social Flow of Political Information. The American Political Science Review, 1987, vol. 81, issue 4, pp. 1197–1216. 50. Iversen T. Political Leadership and Representation in West European Democracies: A Test of Three Models of Voting. American Journal of Political Science, 1994a, vol. 38, no. 1, pp. 45–74. 51. Iversen T. The Logics of Electoral Politics: Spatial, Directional, and Mobilizational Effects. Comparative Political Studies, 1994b, vol. 27, issue 2, pp. 155–189. 52. Jensen C.D., Lacombe D.J., McIntyre S.G. A Bayesian Spatial Econometric Analysis of the 2010 UK General Election. Papers in Regional Science, 2013, vol. 92, issue 3, pp. 651–666. 53. Jessee S.A. Spatial Voting in the 2004 Presidential Election. American Political Science Review, 2009, vol. 103, issue 1, pp. 59–81. 54. Johnson M., Phillips Shively W., Stein R.M. Contextual Data and the Study of Elections and Voting Behavior: Connecting Individuals to Environments. Electoral Studies, 2002, vol. 21, issue 2, pp. 219–233. 55. Kapoor M., Kelejian H.H., Prucha I.R. Panel Data Models with Spatially Correlated Error Components. Journal of Econometrics, 2007, vol. 140, issue 1, pp. 97–130. 56. Kelejian H.H., Piras G. Estimation of Spatial Models with Endogenous Weighting Matrices, and an Application to a Demand Model for Cigarettes. Regional Science and Urban Economics, 2014, vol. 46, pp. 140–149. 57. Kelejian H.H., Prucha I.R. 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, 1998, vol. 17, issue 1, pp. 99–121. 58. Kelley J., McAllister I. Social Context and Electoral Behavior in Britain. American Journal of Political Science, 1985, vol. 29, no. 3, pp. 564–586. 59. Kim J., Elliott E., Wang D. A Spatial Analysis of County-Level Outcomes in US Presidential Elections: 1988–2000. Electoral Studies, 2003, vol. 22, issue 4, pp. 741–761. 60. Lacombe D.J., Shaughnessy T.M. Accounting for Spatial Error Correlation in the 2004 Presidential Popular Vote. Public Finance Review, 2007, vol. 35, issue 4, pp. 480–499. 61. Lacy D., Paolino P. Testing Proximity Versus Directional Voting Using Experiments. Electoral Studies, 2010, vol. 29, issue 3, pp. 460–471. 62. Leenders R.T.A.J. Modeling Social Influence Through Network Autocorrelation: Constructing the Weight Matrix. Social Networks, 2002, vol. 24, issue 1, pp. 21–47. 63. LeSage J.P. An Introduction to Spatial Econometrics. Revue d’Economie Industrielle, 2008, no. 123, pp. 19–44. 64. LeSage J.P. Bayesian Estimation of Spatial Autoregressive Models. International Regional Science Review, 1997, vol. 20, issue 1–2, pp. 113–129. 65. Macdonald S.E., Listhaug O., Rabinowitz G. Issues and Party Support in Multiparty Systems. American Political Science Review, 1991, vol. 85, issue 4, pp. 1107–1131. 66. Millo G., Piras G. Splm: Spatial Panel Data Models in R. Journal of Statistical Software, 2012, vol. 47, issue 1. 67. Nwankwo C.F. The Spatial Pattern of Voter Choice Homogeneity in the Nigerian Presidential Elections of the Fourth Republic. Bulletin of Geography, 2019, vol. 43, issue 1, pp. 143–165. 68. Ord K. Estimation Methods for Models of Spatial Interaction. Journal of the American Statistical Association, 1975, vol. 70, issue 349, pp. 120–126. 69. Ordeshook P.C. The Spatial Analysis of Elections and Committees: Four Decades of Research. Perspectives on Public Choice: A Handbook. Edited by D.C. Mueller. Cambridge University Press, 1993, pp. 247–270. 70. Pattie C., Johnston R. ‘People Who Talk Together Vote Together’: An Exploration of Contextual Effects in Great Britain. Annals of the Association of American Geographers, 2000, vol. 90, issue 1, pp. 41–66. 71. Podkolzina E.A., Demidova O.A., Kuletskaya L.E. Spatial Modeling of Voting Preferences in Russian Federation. Prostranstvennaya Ekonomika = Spatial Economics, 2020, vol. 16, no. 2, pp. 70–100. https://dx.doi.org/10.14530/se.2020.2.070-100 (In Russian). 72. Poole K.T. Spatial Models of Parliamentary Voting. Spatial Models of Parliamentary Voting. Cambridge University Press, 2005, 248 p. 73. Poole K.T., Rosenthal H. U.S. Presidential Elections 1968–80: A Spatial Analysis. American Journal of Political Science, 1984, vol. 28, no. 2, pp. 282–312. 74. Posner E.A. Controlling Agencies with Cost-Benefit Analysis: A Positive Political Theory Perspective. The University of Chicago Law Review, 2001, vol. 68, no. 4, pp. 1137–1199. 75. Rabinowitz G., Macdonald S.E. A Directional Theory of Issue Voting. American Political Science Review, 1989, vol. 83, issue 1, pp. 93–121. 76. Riker W., Ordeshook P.C. An Introduction to Positive Political Theory. Englewood Cliffs, 1973, 387 p. 77. Roemer J.E. Modeling Party Competition in General Elections. Cowles Foundation Discussion Papers 1488, 2004, 36 p. 78. Tanner T. An Analysis of Voter Predictive Dimensions and Recovery of the Underlying Issue Space. Public Choice, 1994, vol. 93, pp. 315–334. 79. Vakulenko E.S.С. Introduction to Spatial Econometrics. Moscow: National Research University Higher School of Economics, 2013, 33 р. Available at: 80. Wuhs S., McLaughlin E. Explaining Germany’s Electoral Geography: Evidence from the Eastern States. German Politics and Society, 2019, vol. 37, issue 1, pp. 1–23. |
Submitted | 07.03.2021 |
Revised | 24.05.2021 |
Published online | 30.06.2021 |