Pigeonholing Partisans: Stereotypes of Party Supporters and Partisan Polarization


What comes to mind when people think about rank-and-file party supporters? What stereotypes do people hold regarding ordinary partisans, and are these views politically consequential? We utilize open-ended survey items and structural topic modeling to document stereotypes about rank-and-file Democrats and Republicans. Many subjects report stereotypes consistent with the parties’ actual composition, but individual differences in political knowledge, interest, and partisan affiliation predict their specific content. Respondents varied in their tendency to characterize partisans in terms of group memberships, issue preferences, or individual traits, lending support to both ideological and identity-based conceptions of partisanship. Most importantly, we show that partisan stereotype content is politically significant: individuals who think of partisans in a predominantly trait-based manner—that is, in a way consistent with partisanship as a social identity—display dramatically higher levels of both affective and ideological polarization.

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Fig. 1
Fig. 2


  1. 1.

    By this, we mean based on increased dislike for the opposing party rather than explicit policy or ideological disagreements.

  2. 2.

    In addition, for many of the ideas our subjects report, it is difficult, if not impossible, to assess accuracy. For that reason, we do not directly explore accuracy.

  3. 3.

    The undergraduate sample was collected between March and April 2016, the MTurk sample was collected in April 2016, and the national sample was collected in early August 2016.

  4. 4.

    We use the “stm” package in R for all such analyses (Roberts et al. 2017). For guidance in using this package, we have relied heavily on the information provided by Roberts et al. (2014) and Roberts et al. (2016).

  5. 5.

    In our case, each participant’s responses are combined into a single document, and the STM then enables us to make sense of those responses taken as a whole.

  6. 6.

    We note that, because these calculations occur outside of the STM package (due to limitations on the analyses in STM), this procedure introduces additional uncertainty into the succeeding analyses. This suggests that caution should be taken when interpreting results on the edges of conventional levels of significance.

  7. 7.

    Before constructing the summary tables that appear below and in the Online Appendix, we first recoded the free responses to group together synonyms and words with highly similar or related meanings (e.g., “rich” and “wealthy” were combined into a single category). More details on this process can be found in the Online Appendix. While we used recoded data in the following Tables (1, A.2, A.3, A.4, A.5) for presentational purposes, all analyses after this section were conducted using raw, non-recoded data.

  8. 8.

    The inclusion of our convenience samples in addition to our nationally diverse sample jibes with prior work on stereotype content, which makes frequent use of student samples (Katz and Braly 1933; Devine and Elliot 1995; Madon et al. 2001), MTurk samples (Scherer et al. 2015), and other convenience samples (Graham et al. 2012). We also note that research on these kinds of convenience samples demonstrates the utility of both student (Druckman and Kam 2011) and MTurk samples (Mullinix et al. 2015; Levay et al. 2016) for making generalizable inferences. Furthermore, the STM procedures explicitly incorporate differences between samples when generating topics, allowing people from different samples to use the topics in different amounts. What this means in practical terms is, for example, if our national sample uses a particular topic frequently but the student sample does not (or vice versa), that topic will still appear in the final output.

  9. 9.

    While the appropriate number of topics cannot be objectively determined by the structural topic model, it provides a number of metrics that researchers may use to determine a sensible body of topics. See Roberts et al. (2014) and the Online Appendix of this paper for more details.

  10. 10.

    Instead of reporting the most frequent vocabulary terms found in each topic, we follow Roberts et al. (2014) in reporting words with the simplified frequency-exclusivity (FREX) scores. FREX words are summarized using the harmonic mean of the semantic coherence and exclusivity of a word within a given topic. Additionally, note that the words in these tables have been stemmed—that is, trailing characters (such as -ing or -ed) have been removed. This procedure is standard in any kind of textual analysis.

  11. 11.

    Here again we refer to FREX words.

  12. 12.

    We use strict topic categorization standards, erring on the side of labeling a topic ambiguous any time the appropriate placement seemed unclear. As a robustness check, we also noted whether each ambiguous topic “leaned” toward traits or groups/issues; if we group these topics with the corresponding “unambiguous” topics when performing the analyses that follow, our substantive results remain largely unchanged.

  13. 13.

    Note that frequency is not simply a function of the number of topics in each categorization. There was a great amount of variation in the usage of each topic, and a higher number of topics in one category (i.e., traits vs. issues and groups) does not necessarily imply a higher usage by subjects.

  14. 14.

    Another way to consider this point is to look at the correlation between the trait topics and the issues/groups topics. If subjects used only one or the other, we would expect a correlation near to -1. However, what we observe in our data is a correlation of -0.07 (p=0.02) using our strict topic groupings, 0.22 (p=0.000) using broader groupings.

  15. 15.

    In these models, we include sample fixed effects (that is, dummy variables for each data collection) in order to account for baseline differences between our samples. The OLS results presented here are also robust to alternative specifications (most notably, beta regression), but we prefer OLS owing to its simplicity of interpretation and general familiarity. Results from alternative models are available from the authors upon request.

  16. 16.

    In Fig. 1, we only include traits, issues/groups, and “don’t know” topics as dependent variables, omitting results for ambiguous topics. This is due to the fact that we did not have prior expectations about the ambiguous topics - a parallel figure containing these results can be found in Fig. A.1 of the Online Appendix. We also include parallel analyses predicting use of each individual topic within the STM package, but here we have little theoretical guidance on what to expect. These can be found in Table A.7, which reports when various respondent characteristics are related to specific topics. In Table A.6 and Table A.7, all independent and dependent variables are rescaled from 0-1 for ease of interpretation.

  17. 17.

    In addition to the model in Table A.6 depicting the relationships between these characteristics and use of different broad categories of topics, Table A.7 in the Online Appendix summarizes significant correlations with the use of specific topics.

  18. 18.

    In the interest of graphical clarity, we do not report coefficients for the ambiguous topics in Fig. 2. These (largely non-significant) results can be found in the Online Appendix.

  19. 19.

    In each case, we use the absolute value of the difference between these items.

  20. 20.

    We note that the proportion of ambiguous topics also relates to stronger respondent ideology, as well as greater perceived ideological distance between party supporters and between congresspersons. If we use a less stringent standard for designating predominantly trait-based and group/issue-based topics, the association with ambiguous topics disappears—but the consistent effect of using trait-dominant topics on all of our outcomes remains.

  21. 21.

    We obtain similar results when including subjects from our student sample. Unfortunately, we lack these outcome measures for the Mechanical Turk sample.

  22. 22.

    The possibility remains that these results are an artifact of the particular way we chose to categorize our topics. Given the apparent convergence of partisanship with other identities (see Mason 2016), it may be appropriate to put trait- and group-based topics together, with issue-based topics standing alone. We explored an alternative grouping of stereotype topics along these lines. In that case, the use of trait/group topics positively relates to all the forms of ideological and affective polarization described previously. The use of issue topics positively correlates with only one of these variables: perceptions of ideological differences in Congress. Thus, however we choose to place stereotypes based on social groups—with political issues or with traits—the relationship between trait-based stereotyping and various forms of polarization holds.

  23. 23.

    To conduct this robustness check, we recoded the individual words provided by subjects as either traits, issues/groups, or other/ambiguous, and used the proportion of each as regressors. For this recoding procedure, we relied on lists of political issues, social groups associated with the parties, and personality traits generated by subjects themselves in another portion of our data collection (not reported here) as our recoding dictionaries. In brief, we obtain results that accord with those shown above: only traits consistently exerted a statistically significant impact on these measures of polarization (with the sole exception being perceived ideological distance between Democrats and Republicans in Congress); in each case, the effect was large and positive.

  24. 24.

    We also examined the possibility that an interaction between stereotype content and valence relates to our polarization variables. However, interacting the use of trait-based topics with outparty valence reveals little evidence of such a relationship; the positive correlation between trait content and polarization remains constant across all our dependent variables, except for the extremity of respondents’ self-rated ideology. In that case, as outparty valence increases, the relationship between trait content and more extreme ideology diminishes as outparty valence becomes more positive. We find no evidence of an interaction between the use of group/issue topics and outparty valence.


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We are grateful to Jamie Druckman, Doug Ahler, participants in the Druckman political science research lab, participants in the Thursday group at Brigham Young University, three anonymous reviewers, and discussants at MPSA and WPSA for insightful feedback and suggestions. We also thank Brandon Stewart and Matthew Lacombe for their helpful methodological advice. All errors are our own. Financial support for this research came from the Political Science Department at Northwestern University. This research was approved by the Institutional Review Board at Northwestern University. The authors contributed equally to this work. Data and replication code for the analyses presented in this paper can be accessed at https://doi.org/10.7910/DVN/U23L09.

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Correspondence to Richard M. Shafranek.

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Rothschild, J.E., Howat, A.J., Shafranek, R.M. et al. Pigeonholing Partisans: Stereotypes of Party Supporters and Partisan Polarization. Polit Behav 41, 423–443 (2019). https://doi.org/10.1007/s11109-018-9457-5

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  • Partisanship
  • Political parties
  • Partisan polarization
  • Social identity
  • Stereotypes