Abstract
Individuals are members of multiple social groups (race, class, religion, etc.). Intersectionality theory contends we cannot understand the influence of a group in isolation because group identities interact to influence outcomes collectively. This assertion challenges the typical approach in the political behavior literature, which assumes the effects of group memberships are additive. In this paper, I correct this shortcoming. I test how racial and ethnic group memberships condition the impact of gender, religion, region, and social class on policy attitudes, partisanship, and vote choice. Using pooled ANES (2000–2016) and the 2016 CMPS data, I show that these group memberships’ effects are conditional upon race and ethnicity. They shape Whites, Blacks, Latinos, and Asians’ political attitudes in diverse and, in some cases, opposite ways. The results imply that behavioral studies must be careful not to assume that group identities are additive instead of interactive. I conclude that quantitative scholars should account for the interactions of group identities in theoretical and empirical models.
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Data Availability
The data and code necessary to replicate the following analysis are available from Political Behavior’s Dataverse page. See: https://doi.org/10.7910/DVN/SRYVKV.
Notes
The CMPS sample is somewhat different. It is drawn from a sample of people that are registered to vote and a sample that are not registered to vote. Presumably this sample of non-registered people includes some non-citizens. I have replicated the CMPS analysis on the registered voter subsample of the data and the results are broadly comparable to the full sample analysis presented in the appendix.
I discuss the coding of the CMPS variables in the online appendix. The coding schemes are broadly similar, however, there are some important differences between the two surveys.
The set of questions available in the ANES varies from year to year. However, I took several steps to ensure that the results of the factor analysis are comparable from year to year. Please see the supplemental appendix for more details.
These summary statistics are weighted using the ANES and CMPS survey weights, designed to make the sample more closely resemble the underlying population.
One note about the incorporation of religious group memberships as independent variables: I only interact “Black Protestant” with the African American dummy variable since there are so few members of other groups that identify as members of traditionally Black Protestant denominations.
I use Nicholas Winter’s (2020) “combomarignsplot” package in STATA to produce the figures.
It is possible to back out these indirect effects in a variety of ways. One way would be to use a series of equations as part of a structural equation framework. Another way would be to estimate a model that withheld policy orientations and partisanship variables and then compare the biased coefficients to the coefficients in Fig. 4. The difference between the biased and correct coefficients is the indirect effect of group memberships on vote choice that travels through partisanship and policy orientations.
Again, keeping in mind this is controlling for many other variables that are also associated with these group memberships.
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Acknowledgements
The author would like to thank Jeff Davis for his invaluable research assistance with this project. The author would also like to thank Kevin Banda and three anonymous reviewers for this help and suggestions.
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Zingher, J.N. How Social Group Memberships Interact to Shape Partisanship, Policy Orientations, and Vote Choice. Polit Behav (2021). https://doi.org/10.1007/s11109-021-09725-7
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DOI: https://doi.org/10.1007/s11109-021-09725-7
Keywords
- Group identity
- Intersectionality
- Partisanship
- Policy orientations
- Social groups