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Cued by Culture: Political Imagery and Partisan Evaluations

Abstract

There is a popular perception that politics is increasingly permeating the everyday lives of Americans. Ostensibly non-political objects and activities are becoming “partisan,” and there is accordingly talk of a cultural divide between Latte-drinking, Volvo-driving Liberals and NASCAR-watching, truck-driving Conservatives. This study examines the extent to which this perception is accurate. We first find that survey respondents have no trouble assigning partisan leaning to non-political activities and objects. We then explore whether voters use such non-political objects as heuristics in candidate evaluations. We show that exposure to images of candidates featuring such objects can affect perceptions of candidates’ partisanship, but that these cues only very rarely shift perceptions in the face of clear policy information. These findings have important implications for understanding the way that citizens evaluate politics in changing political and media environments.

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Notes

  1. 1.

    There is a rich literature on the cues taken from candidates’ appearances on our evaluations and/or support of them. Good looks and an attractive appearance provide electoral advantages (e.g., Ahler, et al., 2017; Banducci, et al., 2008; Brusattin, 2012; Lev-On & Waismel-Manor, 2016). Facial features can prime ethnic voting (Moehler & Conroy-Krutz, 2016); voters rely on candidate race and gender from photographs to make judgments about politicians’ partisanship (McDermott, 1997; 1998; Olivola, et al., 2012). Voters even draw on the sex-typicality of candidates’ faces, that is whether they appear traditionally masculine or feminine, to infer partisanship (Carpinella & Johnson, 2013; see also Carpinella, et al., 2016; Laustsen & Petersen, 2016). The latter effects have been largely attributed to gendered partisan stereotypes whereby masculine characteristics are associated with Republicans and feminine characteristics are associated with Democrats (Hayes, 2005; 2011; Rule & Ambady, 2010; Winter, 2010).

  2. 2.

    Note that this is in line with work suggesting that parties can be viewed as sociopolitical brands; see, e.g., Ahler and Sood, 2018; Green, Palmquist, and Schickler, 2004.

  3. 3.

    Replication data and code for all studies are available on the Political Behavior Dataverse at https://doi.org/10.7910/DVN/NFAPEX. All studies included informed consent and were approved by the Institutional Review Board at the University of Michigan.

  4. 4.

    Given the number of different samples we consider in this paper, we include a table in Online Appendix A with full breakdowns of each.

  5. 5.

    The comparison cloud is plotted using the wordcloud package in R (Fellows, 2018), using the following approach: Let pi,j be the rate at which word i occurs in document j, and pj be the average across documents(Σi pi,j /ndocs). The size of each word is mapped to its maximum deviation (maxi (pi,j − pj)), and its angular position is determined by the document where that maximum occurs. Note that a comparison cloud excludes the words that are common amongst both categories (here, Republican versus Democratic descriptions). See section G of the Appendix for additional analyses.

  6. 6.

    Importantly, these results also corroborate some initial findings from a pilot study we conducted in March 2016. In that early work we fielded an identical survey with a 200-person sample. The pilot was used to inform our study design, but we include results in Online Appendix B.

  7. 7.

    The details of the pre-test surveys are as follows. We first fielded a small survey using the female candidate to 200 U.S.-based MTurkers in September 2016. These respondents were presented with the NASCAR and tattoo images (see Online Appendix I), alongside two other images that we subsequently discarded because respondents could not easily identify them. We do no present results from this first round of pre-testing here. Rather, we focus on two subsequent pre-tests. First, a subsequent survey using the female candidate was fielded to 150 MTurkers in October 2016, now also including the curtain, organic food, and shooting range conditions shown in the Figure in Online Appendix I. Second, we fielded the male candidate images to 250 MTurkers in September 2018. Note that pretests included open-ended questions after the experiment asking what the respondent saw in the picture. There were only 2 respondents who commented that the images appeared edited. While we did not explicitly ask them if the photo was real, we believe this suggests good external validity of our images.

  8. 8.

    As noted above, we also ask about ideology, assessed on a 7-point scale. Those results are included in Online Appendix C.

  9. 9.

    Note that although we are reluctant to place too much value on cross-candidate comparisons, these results are in line with the expectation that the female candidate will be viewed as more liberal than the male candidate. And although we use different curtains in the control conditions, we can compare the organic food conditions for which we have evaluations for both candidates: given the identical background, the male candidate is viewed as more conservative than the female one (0.689 vs. 0.407, t = -2.899, p = 0.0046).

  10. 10.

    Details of both samples are included in the Online Appendix A.

  11. 11.

    These policy statements were also pretested using an MTurk sample fielded in October 2016 (n = 100). The Democratic policy was rated at 1.88 on the ANES 7-point ideology scale (scaled 0–6), whereas the Republican policy was rated at 4.16 (p < .001).

  12. 12.

    Results using Ideological Assessments of the Candidate and the Policy are presented in Online Appendices E and F, respectively.

  13. 13.

    That said, these cues may work in subliminal ways, and we thus include results based on the entire sample in Online Appendix D.

  14. 14.

    All photos are shown in the Appendix.

  15. 15.

    Again, results based on the entire sample are included in Online Appendix D.

  16. 16.

    We estimate bootstrapped confidence intervals as a cautious approach to evaluating variance amongst comparatively small numbers of respondents within each treatment group. We generate nonparametric confidence intervals using the basic bootstrap method and 1,000 replicates, produced using the boot package in R. Bootstrapping in these instances makes only a very marginal (mostly imperceptible) difference to the standard errors shown in Figs. 5 and 6.

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Acknowledgements

We thank members of the Political Communication Working Group at the University of Michigan for discussions in the early stages of this project. We are grateful for feedback from participants at the University of Michigan’s Interdisciplinary Workshop in American Politics, the 2019 International Communication Association meeting, and the 2019 Midwest Political Science Association meeting. We also thank our colleague Jan Van den Bulck, who served as the model for our photo stimuli. He was an excellent model, of course; and also willing to risk learning whether he looks liberal or conservative.

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Correspondence to Dan Hiaeshutter-Rice.

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Hiaeshutter-Rice, D., Neuner, F.G. & Soroka, S. Cued by Culture: Political Imagery and Partisan Evaluations. Polit Behav (2021). https://doi.org/10.1007/s11109-021-09726-6

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Keywords

  • Partisanship
  • Candidate evaluation
  • Non-verbal communication
  • Polarization
  • Heuristics
  • Cultural divide