Ethnicity and voters’ evaluations of political leadership: “lab-in-the-field” experiments in Russian regions

ABSTRACT

This paper presents an experimental test of ethnic-based bias in citizens’ evaluation of a political leader’s decision-making across three multi-ethnic regions of the Russian Federation (Sakha, Buryatia, and Tatarstan) and one mono-ethnic region (Arkhangel’sk Oblast). With variations in the intensity of ethnic loyalties, degree of titular ethnic group assimilation, and differences in an ethnic division of labor, Russian regions present an interesting site for the investigation of these conjectures. The experiment participants read a vignette describing a decision by a local mayor and assess the decision. We randomly vary the mayor’s ethnicity and policy choice. Our findings suggest that policy substance is the primary influence and dominates ethnicity when citizens evaluate public officials in three of the four regions. In the fourth region, Sakha in Northern Siberia, we provide analyses to explain the greater salience of ethnicity.

KEYWORDS:

• Russia
• gender and ethnic bias
• experimental survey

Acknowledgments

We would like to thank our research partners in the Russian regions for their help conducting the surveys: Marina Kalinina, Vyacheslav Kozlov, Erzhena Gylykova, Lyudmila Sandakova, Tatyana Titova, Dekabrina Vinokurova, and Yury Zhegusov, as well as our large team of research assistants in each region.

Supplementary material

Supplemental data for this article can be accessed here

Notes

1. Titular ethnic group refers to the dominant ethnic group in the region, which gave its name to its respective republic.

2. The average group size per experimental session was approximately 25 people. As a manipulation check during debriefing, we would ask how many evaluated a female mayor and how many evaluated a male mayor. Consistently people split by nearly half (as they should). We would then describe the purpose and design of the experiment.

3. It should be noted that our unweighted results are not different from the weighted results.

4. We ran ordered logit analyses to test for robustness; those results are consistent with the OLS results.

5. As noted, the greatest sampling difficulty was enticing elderly respondents to participate. Census data show 11% of the population of Arkhangel’sk Oblast is 70 years or older. Yet under 2% of those who came out to our sites were 70 or older. Therefore, we cut off the sample at 70. We believe our samples do a good job of reflecting the population from 18 to 69, but are too thin for estimates of the oldest Russians.

6. We use the Model 3 equation and assume the mayor decides to invest in the school in all cases. Control variables are set to the sample mean.

7. Tatars are 0.8% of the population in Arkhangel’sk Oblast (All Russia Population Census 2010); they are 1.4% of respondents in our sample (11 of 772 respondents).

Additional information

Funding

This research is supported by the National Science Foundation Grant (Grant ARC #0756122).