Ethnic inequality has been argued to have numerous pernicious effects. Among other things, scholars have argued that it breeds political violence, destabilizes democracy, and impedes economic development. While the arguments developed by these literatures implicitly assume that ethnic inequality increases the degree to which individuals identify with their ethnicity, this assumption has yet to be tested empirically at the individual-level. This paper argues and empirically demonstrates that between-ethnic group inequality does strengthen ethnic identities. However, we also find that the magnitude of its effect weakens as inequality within ethnic groups increases. That is, individuals identify most strongly with their ethnic identity when ethnicity is reinforced by economic inequality. Using the Afrobarometer, we provide the first cross-national empirical test of the effect of ethnic inequality on the strength of ethnic identities at the individual-level. Our dataset covers 21 sub-Saharan African countries and 85 ethnic groups. Results strongly support our hypothesis.
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Replication data and codes are available on the journal’s Dataverse page (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KGUTGA).
It must be noted that these authors—with the exception of Kuhn and Weidmann (2015)—focus on the direct effect of WGI, not on whether WGI conditions the effect of BGI. Therefore, the question they address is fundamentally different than ours.
It remains possible that WGI concentrates resources among political entrepreneurs, which may enable them to mobilize their co-ethnics and strengthen ethnic identities. Which effect dominates is ultimately an empirical question. The evidence provided below suggests that WGI weakens the effect of BGI.
This also implies that, for example, groups with high (or intermediate) WGI and low (or intermediate) BGI should only weakly identify with their ethnicity. Under such conditions, there is little difference between members of different groups and members from the same group lack cohesiveness. Individuals should identify with their social class rather than their ethnicity. As shown in the right panel of Fig. 2, our results support this hypothesis.
See Table A1 and Fig. A1.
See Table A2 and Fig. A2.
This argument is essentially an extension of the arguments of Boix (2003) and Acemoglu and Robinson (2006) about the effect of inter-group inequality on preferences over economic policies to a situation in which groups are ethnic groups (rather than social classes). Acemoglu and Robinson (2006, pp. 109–113) extend their model to include between- and within-ethnic group inequality and arrive to conclusions similar to ours.
See Fig. A3.
Moreover, respondents may be reluctant to openly state to an interviewer that they do not support helping the poor. This would reduce the estimated effect of BGI on hostility toward pro-poor policies, and could explain why the magnitude of the relationship is weaker for members of rich groups (who are more likely to oppose such policies both according to our argument and the empirical findings).
Also, we find no evidence that the effect of BGI on the strength of ethnic identities is stronger among poor groups (model 1 of Table A7, Online Appendix).
Horowitz (1971) employs the terms horizontal and vertical ethnic arrangements instead.
Of the 20 ethnic groups that are coded as politically excluded in our dataset (based on the Ethnic Power Relations dataset), 10 are poor and 10 are rich.
The countries are listed in Table A5 of the Online Appendix.
However, results are unchanged when we include data from all three rounds of the South Africa surveys (Table A14, Online Appendix).
Summary statistics for all variables are available in Table A4 of the Online Appendix.
The models that use this variable are reported in Table A6 (Online Appendix).
The distributions of BGI and WGI are shown in Figs. A4 and A5 (online appendix), respectively. Figures A11 and A12 show the variation in BGI and WGI within groups over time, respectively (they only include groups for which there is more than one survey).
BGI is significant in other models of Table A9. However, in all models reported in Table 1 adding the interaction term increases the fit of the model: model 2 (Chi square = 65.69, p value <0.0001); model 3 (Chi square = 29.34, p value <0.0001); and model 4 (Chi square = 49.59, p value <0.0001).
Figures A6–A8 (Online Appendix) redo Fig. 2 separately for the three other values of the Strength of Ethnic Identities variable. As expected, BGI reduces the likelihood that an individual identifies more with his/her nationality than his/her ethnicity when WGI is low.
Figure A9 (Online Appendix) shows the effect of BGI on the predicted probability that a respondent mostly identifies with ethnicity at low and high WGI levels. The relationship follows a similar pattern as in Fig. 2.
Figure A10 (online appendix) redoes Fig. 1 based on model 4.
See Table A10.
See Fig. A4.
See Tables A8 and A16, respectively.
See Tables A11–A13.
The variable Electoral Distance takes the value 0 if there is an election in the same year as the survey, 1 if there were an election the year before the survey or the year after the survey, and so on. Since Eifert et al. (2010) argue that the effect of electoral distance is conditional on electoral competitiveness, we rerun the analysis with an interaction term between Electoral Margin and Electoral Distance (Table A12).
See the notes below the tables for more information on how the variables have been constructed.
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Higashijima, M., Houle, C. Ethnic Inequality and the Strength of Ethnic Identities in Sub-Saharan Africa. Polit Behav 40, 909–932 (2018). https://doi.org/10.1007/s11109-017-9430-8
- Ethnic inequality
- Ethnic identification
- Economic inequality
- Identity politics
- Social cleavages
- Sub-Saharan Africa