Abstract
To what extent does voting for anti-immigrant parties relate to long-term changes in ethnic composition within states? Four theoretical models are developed, based on studies of interethnic attitudes, housing segregation, racial violence, and hate crime in the United States. Each model is tested with the data on ethnic composition of the Russian Federation from 1989 to 2002 and voting for the extreme nationalist Zhirinovsky Bloc in the 2003 parliamentary election, using multiple regression and ecological inference methods. Most consistently supported is the “defended nationhood” model derived from the sociology of neighborhood vigilantism and the psychology of the security dilemma. Non-trivial, counterintuitive findings are: (1) xenophobic voting was responsive to changes in the proportion of some ethnic groups more so than others and not necessarily those that were more numerous or more widely disliked at the time of the vote (Chechens), but those that raised more uncertainty about the future ethnic composition and identity of the state (Asians); (2) levels of change, but not the rapidity of change in the ethnic composition of the population related significantly to xenophobic voting; and (3) greater percentage of the nation’s dominant ethnic group in a region reduced xenophobic voting by members of that dominant group (the highest share of Slavs voted for Zhirinovsky in the ethnically mixed Volga-Urals area).
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Notes
However, once we go to lower levels of aggregation—city districts, boroughs, quarters—the relationship between xenophobic voting and ethnic composition becomes less evident. Mayer (1996), for example, found that the number of the foreign born in Paris by city quarter had no effect on the Front National vote. Similarly, Perrineau (1985) and Rey and Roy (1986) found no relationship between the same variables in Grenoble and the Seine-Saint-Denis region. Green, Strolovitch, and Wong (1998), however, established a sophisticated linkage between ethnic composition and hate crime rates in New York boroughs.
The Caucasus groups (based on the 1989 and 2002 census lists) are: Abkhaz, Avar, Agul, Adyg, Azeri, Armenian, Balkar, Georgian (including Adzhar and Ingiloi), Dargin, Ingush, Kabarda, Karachai, Kumyk, Kurd, Lak, Lezgin, Nogai, Ossetian, Rutul, Tabassaran, Turk, Tsakhur, Chechen, Cherkess. The East Asian groups are Chinese, Japanese, Korean, and Vietnamese.
These regions are Kaliningrad, Leningrad (oblast), Pskov, Stavropol, Astrakhan, Volgograd, Orenburg, Samara, Saratov, Kurgan, Tyumen, Cheliabinsk, the Republics of Altai and Buryatia, Altai krai, Novosibirsk, Omsk, Chita, Amur, Primorskii krai, Khabarovskii krai, Sakhalin, and the Jewish Autonomous District.
The Republic of Adygea was part of Krasnodar krai; Karachaevo-Cherkessiia—of Stavropol krai; the Altai Republic—of the Altai krai; Khakassia, Taymyr and Evenk republics—of Krasnoyarsk krai; Khanty-Mansi and Yamal-Nenets—of the Tyumen Oblast; Komi-Permyak District—of the Komi Autonomous Republic; Aga-Buryat Republic—of the Buryat Autonomous Republic; the Jewish Autonomous District—of the Khabarovskii krai; Chukotka—of the Magadan Oblast; and Koryak Autonomous District—of the Kamchatka Oblast.
Whereas the exclusion of ΔNon-Slavs is tempting, given that it is already part of the interaction term with Slavs in Equation 1, it would pose grave methodological problems. As Golder (2003: 436) points out, the exclusion of one of the linear components of an interactive term from the equation would (1) make an unrealistic assumption that neither of the interaction term components affect the dependent variable in the absence of the other component and (2) make the variance of the interaction term indeterminate (since it can only be interpreted in conjunction with the variance of its linear components).
For Tests 2–4 I also ran additional regressions that included variables capturing the size of constituent non-Slavic populations (Chechens, Caucasus, and East Asians) in 2002. Their effects were insignificant (sig. = .234, .286, and .986 respectively) and did not affect the findings presented in Table 2. The size of each group strongly correlated with each group’s proportion change (R = −.403, .924, and .600 respectively, and p < .001 for all of them).
The marginal effects are estimated as
y÷
x = β1 + β3Z, where Z is the other component element of the interaction term, also called the modifying variable. For example, in this study the marginal effect of the percentage Slav population in 1989 on the LDPR 2003 vote modified by changes in the percentage of non-Slav population = .032 + .008 ΔNon-Slavs.Doing so still does not eliminate the attenuation bias. This is clearly the pattern if one compares the EzI estimates with the hypothetical vote based on survey results assuming that 90% of the LDPR vote, on average, came from ethnic Slavs (see Appendix A).
I considered as ethnically mixed regions where the proportion of ethnic Slavs was between about one thirds and two thirds of the population. These 12 regions are Bashkortostan, Chuvashia, Kabardino-Balkaria, Kalmykia, Komi, Mari El, Mordovia, N. Ossetia, Sakha-Yakutia, Tatarstan, Tyva, and Udmurtia.
To reduce attenuation bias, these calculations are based on the upper 95% confidence interval estimates for Slavs and the lower 95% confidence interval estimates for non-Slavs.
A comparison of means for the statistically significant predictors of the LDPR vote in the regression tests between the same 12 mixed regions and 60 predominantly Slavic regions confirms the decisive impact of ethnic composition. The effects of other variables most likely cancel out each other. For example, among the predominantly Slav regions higher education levels are higher and unemployment levels are lower than in the mixed regions, but also more regions are on the contested borders and had a higher share of the “against all” vote. The former two factors would make the LDPR vote less likely, but the latter two would make the LDPR vote more likely in the Slav regions.
It is not advisable, however, to use EI point estimates in second-stage regression—if anything, due to the tendency of standard errors for the EI-estimated dependent variable to be correlated with their true values (Cho & Gaines, 2004; Herron & Shotts, 2003). Solutions offered by Adolph, King, Herron, and Shotts (2003) are unlikely to help, since resolving the aggregation bias makes EI estimates inconsistent with regression findings. However, assuming that none of these concerns applied, I ran second-stage regressions using the upper-bound EzI estimates and the estimates based on the survey findings that ethnic Slavs account for approximately 90% of the LDPR vote. These tests yielded nearly identical results to those reported in Table 2. The only major difference was, as expected, that the sign of the coefficient for Slavs changed from plus to minus. The coefficients for the non-Slav variables and interaction terms in all models retained their signs and significance.
y÷
x = β1 + β3Z, where Z is the other component element of the interaction term, also called the modifying variable. For example, in this study the marginal effect of the percentage Slav population in 1989 on the LDPR 2003 vote modified by changes in the percentage of non-Slav population = .032 + .008 ΔNon-Slavs.References
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Acknowledgments
Research reported in this article was made possible, in part, by the grants of the National Science Foundation (SES-0452557) and the John D. and Catherine T. MacArthur Foundation’s Program on Global Security and Sustainability. The author thanks Nikolay Petrov and Alexei Titkov of the Center for Political-Geographic Research in Moscow for providing the demographic and voting data broken down by constituent regions and republics of the Russian Federation. The article incorporates valuable methodological and theoretical suggestions by Jason Wittenberg and Stuart Kaufman and comments by Volodymyr Dubovik, for which the author is grateful. The author would also like to thank the anonymous reviewers of the manuscript for substantively rich and methodologically sophisticated suggestions, as well as for valuable references to additional literature.
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Alexseev, M. Ballot-Box Vigilantism? Ethnic Population Shifts and Xenophobic Voting in Post-Soviet Russia. Polit Behav 28, 211–240 (2006). https://doi.org/10.1007/s11109-006-9009-2
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DOI: https://doi.org/10.1007/s11109-006-9009-2