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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
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).
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.
Given the number of different samples we consider in this paper, we include a table in Online Appendix A with full breakdowns of each.
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.
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.
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.
As noted above, we also ask about ideology, assessed on a 7-point scale. Those results are included in Online Appendix C.
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).
Details of both samples are included in the Online Appendix A.
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).
Results using Ideological Assessments of the Candidate and the Policy are presented in Online Appendices E and F, respectively.
That said, these cues may work in subliminal ways, and we thus include results based on the entire sample in Online Appendix D.
All photos are shown in the Appendix.
Again, results based on the entire sample are included in Online Appendix D.
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.
Ahler, D. J., & Sood, G. (2018). The parties in our heads: Misperceptions about party composition and their consequences. The Journal of Politics, 80(3), 964–981. https://doi.org/10.1086/697253
Ahler, D. J., Citrin, J., Dougal, M. C., & Lenz, G. S. (2017). Face value? Experimental evidence that candidate appearance influences electoral choice. Political Behavior, 39(1), 77–102. https://doi.org/10.1007/s11109-016-9348-6
Arceneaux, K., & Kolodny, R. (2009). Educating the least informed: Group endorsements in a grassroots campaign. American Journal of Political Science, 53(4), 755–770. https://doi.org/10.1111/j.1540-5907.2009.00399.x
Banducci, S. A., Karp, J. A., Thrasher, M., & Rallings, C. (2008). Ballot photographs as cues in low-information elections. Political Psychology, 29(6), 903–917. https://doi.org/10.1111/j.1467-9221.2008.00672.x
Bishop, B., & Cushing, R. G. (2008). The big sort: Why the Clustering of Like-Minded America is Tearing us Apart. New York NY: Houghton Mifflin Harcourt.
Brusattin, L. (2012). Candidate visual appearance as a shortcut for both sophisticated and unsophisticated voters: Evidence from a Spanish online study. International Journal of Public Opinion Research, 24(1), 1–20.
Carpinella, C. M., & Johnson, K. L. (2013). Politics of the face: The role of sex-typicality in trait assessments of politicians. Social Cognition, 31(6), 770–779.
Carpinella, C. M., & Johnson, K. L. (2016). Visual political communication: the impact of facial cues from social constituencies to personal pocketbooks. Social and Personality Psychology Compass, 10(5), 281–297. https://doi.org/10.1111/spc3.12249
Carpinella, C. M., Hehman, E., Freeman, J. B., & Johnson, K. L. (2016). The gendered face of partisan politics: Consequences of facial sex typicality for vote choice. Political Communication, 33(1), 21–38. https://doi.org/10.1080/10584609.2014.958260
Casler, K., Bickel, L., & Hackett, E. (2013). Separate but equal? A comparison of participants and data gathered via Amazon’s MTurk, social media, and face-to-face behavioral testing. Computers in Human Behavior, 29(6), 2156–2160. https://doi.org/10.1016/j.chb.2013.05.009
Cichocka, A., Bilewicz, M., Jost, J. T., Marrouch, N., & Witkowska, M. (2016). On the grammar of politics—or why conservatives prefer nouns. Political Psychology, 37(6), 799–815. https://doi.org/10.1111/pops.12327
Coleman, R. (2010). Exploring the framing and agenda-setting effects of visual images. In P. D’Angelo & J. A. Kuypers (Eds.), Doing News Framing Analysis: Empirical and Theoretical Perspectives (pp. 233–262). Routledge.
Dancey, L., & Sheagley, G. (2013). Heuristics behaving badly: Party cues and voter knowledge. American Journal of Political Science, 57(2), 312–325. https://doi.org/10.1111/j.1540-5907.2012.00621.x
DellaPosta, D., Shi, Y., & Macy, M. (2015). Why do liberals drink lattes? American Journal of Sociology, 120(5), 1473–1511. https://doi.org/10.1086/681254
Domke, D., Perlmutter, D., & Spratt, M. (2002). The primes of our times? An examination of the ‘power’of visual images. Journalism, 3(2), 131–159. https://doi.org/10.1177/146488490200300211
Dumitrescu, D. (2016). Nonverbal communication in politics: A review of research developments, 2005–2015. American Behavioral Scientist, 60(14), 1656–1675. https://doi.org/10.1177/0002764216678280
Dumitrescu, D., & Popa, S. A. (2016). Showing their true colors? How EU flag display affects perceptions of party elites’ European attachment. American Behavioral Scientist, 60(14), 1698–1718. https://doi.org/10.1177/0002764216676248
Fellows, I. (2018). Wordcloud. R package. version 2.6
Fischer, C. S., & Mattson, G. (2009). Is America fragmenting? Annual Review of Sociology, 35, 435-455.
Frank, T. (2004). What’s the matter with Kansas. New York, NY: Henry Holt.
Geise, S., & Baden, C. (2015). Putting the image back into the frame: Modeling the linkage between visual communication and frame-processing theory. Communication Theory, 25(1), 46–69. https://doi.org/10.1111/comt.12048
Gibson, R., & Zillmann, D. (2000). Reading between the photographs: The influence of incidental pictorial information on issue perception. Journalism & Mass Communication Quarterly, 77(2), 355–366. https://doi.org/10.1177/107769900007700209
Graham, J., Nosek, B. A., & Haidt, J. (2012). The moral stereotypes of liberals and conservatives: Exaggeration of differences across the political spectrum. PLoS ONE, 7(12), e50092. https://doi.org/10.1371/journal.pone.0050092
Green, D. P., Palmquist, B., & Schickler, E. (2004). Partisan hearts and minds: Political parties and the social identities of voters. Yale University Press.
Grinberg, E. (2016, November 08). 'Pantsuit Nation' suits up for Election Day. Retrieved from https://www.cnn.com/2016/11/06/politics/pantsuit-nation-trnd/index.html.
Griner, D. (2018). Why you won't hear 'Dilly Dilly' in bud light's newest dilly dilly ad. Retrieved from https://www.adweek.com/creativity/why-you-wont-hear-dilly-dilly-in-bud-lights-newest-dilly-dilly-ad/.
Hayes, D. (2005). Candidate qualities through a partisan lens: A theory of trait ownership. American Journal of Political Science, 49(4), 908–923. https://doi.org/10.1111/j.1540-5907.2005.00163.x
Hayes, D. (2011). When gender and party collide: Stereotyping in candidate trait attribution. Politics & Gender, 7(02), 133–165. https://doi.org/10.1017/S1743923X11000055
Hetherington, M., & Weiler, J. (2018). Prius Or Pickup?: How the Answers to Four Simple Questions Explain America’s Great Divide. New York NY: Houghton Mifflin Harcourt.
Hurwitz, J., & Peffley, M. (2005). Playing the race card in the post–Willie Horton Era the impact of racialized code words on support for punitive crime policy. Public Opinion Quarterly, 69(1), 99–112. https://doi.org/10.1093/poq/nfi004
Iyengar, S., Sood, G., & Lelkes, Y. (2012). Affect, not ideology a social identity perspective on polarization. Public Opinion Quarterly, 76(3), 405–431. https://doi.org/10.1093/poq/nfs038
Jost, J. T., Federico, C. M., & Napier, J. L. (2009). Political ideology: Its structure, functions, and elective affinities. Annual Review of Psychology, 60, 307–337. https://doi.org/10.1146/annurev.psych.60.110707.163600
Kalmoe, N. P., & Gross, K. (2016). Cueing patriotism, prejudice, and partisanship in the age of Obama: Experimental tests of US flag imagery effects in presidential elections. Political Psychology, 37(6), 883–899. https://doi.org/10.1111/pops.12305
Lau, R. R., & Redlawsk, D. P. (2001). Advantages and disadvantages of cognitive heuristics in political decision making. American Journal of Political Science, 45(4), 951–971. https://doi.org/10.2307/2669334
Laustsen, L., & Petersen, M. B. (2016). Winning faces vary by ideology: How nonverbal source cues influence election and communication success in politics. Political Communication, 33(2), 188–211. https://doi.org/10.1080/10584609.2015.1050565
Lev-On, A., & Waismel-Manor, I. (2016). Looks that matter: The effect of physical attractiveness in low-and high-information elections. American Behavioral Scientist, 60(14), 1756–1771. https://doi.org/10.1177/0002764216676249
Lupia, A. (1994). Shortcuts versus encyclopedias: Information and voting behavior in California insurance reform elections. American Political Science Review, 88(1), 63–76. https://doi.org/10.2307/2944882
Lupia, A., & McCubbins, M. D. (1998). The democratic dilemma: Can citizens learn what they need to know? Cambridge University Press.
Margolis, M. F., & Sances, M. W. (2017). Partisan differences in nonpartisan activity: The case of charitable giving. Political Behavior, 39, 839–864. https://doi.org/10.1007/s11109-016-9382-4
McDermott, M. L. (1997). Voting cues in low-information elections: Candidate gender as a social information variable in contemporary United States elections. American Journal of Political Science, 41(1), 270–283. https://doi.org/10.2307/2111716
McDermott, M. L. (1998). Race and gender cues in low-information elections. Political Research Quarterly, 51(4), 895–918. https://doi.org/10.1177/106591299805100403
McDermott, M. L. (2005). Candidate occupations and voter information shortcuts. The Journal of Politics, 67(1), 201–219. https://doi.org/10.1111/j.1468-2508.2005.00314.x
Mendelberg, T. (2001). The race card: Campaign strategy, implicit messages, and the norm of equality. Princeton University Press.
Messaris, P., & Abraham, L. (2001). The role of images in framing news stories. In S. D. Reese, O. H. Gandy, & A. E. Grant (Eds.), Framing public life: Perspectives on media and our under- standing of the social world (pp. 215–226). Routledge.
Moehler, D., & Conroy-Krutz, J. (2016). Eyes on the ballot: Priming effects and ethnic voting in the developing world. Electoral Studies, 42, 99–113. https://doi.org/10.1016/j.electstud.2016.01.010
Mondak, J. J. (1993). Public opinion and heuristic processing of source cues. Political Behavior, 15(2), 167–192. https://doi.org/10.1007/BF00993852
Munoz, C. L., & Towner, T. L. (2017). The image is the message: Instagram marketing and the 2016 presidential primary season. Journal of Political Marketing, 16(3–4), 290–318. https://doi.org/10.1080/15377857.2017.1334254
Nunberg, G. (2007). Talking right: How conservatives turned liberalism into a tax-raising, latte-drinking, sushi-eating, Volvo-driving New York Times-Reading, Body-Piercing, Hollywood-Loving, Left-Wing Freak Show. Public Affairs.
Oliver, J. E., Wood, T., & Bass, A. (2016). Liberellas versus konservatives: social status, ideology, and birth names in the United States. Political Behavior, 38(1), 55–81. https://doi.org/10.1007/s11109-015-9306-8
Olivola, C. Y., Sussman, A. B., Tsetsos, K., Kang, O. E., & Todorov, A. (2012). Republicans prefer Republican-looking leaders: Political facial stereotypes predict candidate electoral success among right-leaning voters. Social Psychological and Personality Science, 3(5), 605–613. https://doi.org/10.1177/1948550611432770
Rule, N. O., & Ambady, N. (2010). Democrats and Republicans can be differentiated from their faces. PLoS ONE, 5(1), e8733. https://doi.org/10.1371/journal.pone.0008733
Settle, J. E. (2018). Frenemies: How social media polarizes America. Cambridge: Cambridge University Press.
Schatz, R. T., & Lavine, H. (2007). Waving the flag: National symbolism, social identity, and political engagement. Political Psychology, 28(3), 329–355. https://doi.org/10.1111/j.1467-9221.2007.00571.x
Simon, B. (2011). Not going to Starbucks: Boycotts and the out-scouring of politics in the branded world. Journal of Consumer Culture, 11(2), 145–167. https://doi.org/10.1177/1469540511402448
Skitka, L. J. (2005). Patriotism or Nationalism? Understanding Post-September 11, 2001, Flag-Display Behavior. Journal of Applied Social Psychology, 35(10), 1995–2011. https://doi.org/10.1111/j.1559-1816.2005.tb02206.x
Soroka, S., Loewen, P., Fournier, P., & Rubenson, D. (2016). The impact of news photos on support for military action. Political Communication, 33(4), 563–582. https://doi.org/10.1080/10584609.2015.1133745
Swigger, N. (2012). What you see is what you get: Drawing inferences from campaign imagery. Political Communication, 29(4), 367–386.
Valentino, N. A., Hutchings, V. L., & White, I. K. (2002). Cues that matter: How political ads prime racial attitudes during campaigns. American Political Science Review, 96(01), 75–90. https://doi.org/10.1017/S0003055402004240
Wood, M. L., Stoltz, D. S., Van Ness, J., & Taylor, M. A. (2018). Schemas and frames. Sociological Theory, 36(3), 244–261. https://doi.org/10.1177/0735275118794981
Wilson, R. (2014, January 08). What your beer says about your politics, in one chart. Retrieved from https://www.washingtonpost.com/blogs/govbeat/wp/2014/01/08/what-your-beer-says-about-your-politics-in-one-chart/?utm_term=.b24da7c9e558.
Winter, N. J. (2010). Masculine republicans and feminine democrats: Gender and Americans’ explicit and implicit images of the political parties. Political Behavior, 32(4), 587–618. https://doi.org/10.1007/s11109-010-9131-z
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.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Below is the link to the electronic supplementary material.
About this article
Cite this article
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
- Candidate evaluation
- Non-verbal communication
- Cultural divide