Skip to main content

Separated by Politics? Disentangling the Dimensions of Discrimination

“Partisan identity is now stronger and more meaningful for many Americans than race, ethnicity or religious denomination – and a more legitimate justification for discrimination ... It is the return of ‘No Irish need apply,’ but with Republicans or Democrats replacing the Irish.” – Jonah Goldberg , Los Angeles Times

Abstract

How rampant is political discrimination in the United States, and how does it compare to other sources of bias in apolitical interactions? We employ a conjoint experiment to juxtapose the discriminatory effects of salient social categories across a range of contexts. The conjoint framework enables identification of social groups’ distinct causal effects, ceteris paribus, and minimizes ‘cheap talk,’ social desirability bias, and spurious conclusions from statistical discrimination. We find pronounced discrimination along the lines of party and ideology, as well as politicized identities such as religion and sexual orientation. We also find desire for homophily along more dimensions, as well as specific out-group negativity. We also find important differences between Democrats and Republicans, with discrimination by partisans often focusing on other groups with political relevance of their own. Perhaps most striking, though, is how much discrimination emerges along political lines – both partisan and ideological. Yet, counter-stereotypic ideological labels can counter, and even erase, the discriminatory consequences of party.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Notes

  1. While terms like “bias” and “discrimination” often carry negative connotations, our focus here is simply on the application of characteristics in making choices rather than assessment of a choice’s normative legitimacy.

  2. Evidence of parental transmission (Jennings et al., 2009) and even heritability (Alford et al., 2005) notwithstanding. However, our point here is not whether this is an individual choice, rather that most people believe that it is.

  3. According to a 2013 Gallup Survey, 56% of Democrats believe sexual orientation is something one is born with, while 48% of Independents and 35% of Republicans believe the same. Overall, 47% of Americans believe sexual orientation is something one is born with (Jones, 2013).

  4. Though this conscious suppression of group prejudice may not reflect automatic attitudes (Devine, 1989)

  5. At the extreme, this might raise the concern that respondents will engage in satisficing. However, Bansak et al., (2018) demonstrate that only modest increases in satisficing occur, even when using far more factors than we use in this experiment.

  6. This is also true of more traditional vignette-based survey experiments, but to a lesser extent, because respondents can often deduce the dimension of interest when there are fewer moving parts. Well-crafted vignette experiments can avoid some of the pitfalls described earlier, including those highlighted by Dafoe et al., (2018), by providing a rich set of information to respondents, even if not all information is randomized.

  7. According to Miller (1956), five unique pieces of information are enough to alter an individual’s cognitive processing capabilities, and the ability to engage in deliberate decision making decreases even further when individuals are asked to compare two different stimuli that differ in multiple ways.

  8. To help assuage concerns that interactive associations like these could still be operating, even with other information present, we can explicitly model them for some interactions. For example, Fig. 6 in the main text shows the interaction of partisanship and ideology and Figure III in the Appendix shows the interaction of partisanship and race. Additionally, Figures VII, VIII, IX, and X in the Appendix display all interactions with target partisanship, as well as effects of all factor levels when partisanship is omitted from the information displayed to respondents. To be clear, while our study is designed to minimize statistical discrimination along the dimensions we assign, we are not attempting to establish the presence of taste-based discrimination in the standard economic sense (Becke,r 1957). We do not, therefore, consider whether these choices are made by respondents at the expense of some measure of utility and remain agnostic as to whether the discrimination we observe is generally taste-based or statistical in nature. It is possible, for example, that respondents are using party or some other piece of information we present as a proxy for a characteristic we do not manipulate, such as likeability. Put more generally, the measurement concern is not precisely that observed discrimination may be statistical. Rather, the concern is that statistical discrimination may lead to a spurious finding of discrimination along one theorized dimension when the real culprit is some other associated identity.

  9. These levels were fully randomized without any excluded combinations. Factor levels were intentionally chosen to ensure that fully implausible combinations would appear.

  10. If we were estimating Average Marginal Component Effects (AMCE), we would need to leave one factor level out as a reference category. While AMCEs are reported in the Online Appendix, all main-text results report marginal means with this “Not Asked” category included. These marginal means are estimated from a full model with all experimental variables included, and represent the choice probability when that factor level is presented, controlling for all other experimental manipulations. Variation in the marginal probabilities for the “Not Asked” category is itself interesting, as it suggests respondents are making an inference given the absence of a particular factor.

  11. This low level of 15% was chosen to minimize the number of profiles that had a large number of factors missing, as it would reduce realism for respondents.

  12. See, for example, a variety of studies summarized in Baert (2018) that find discrimination on many of these factors.

  13. It also provides a more realistic scenario of partial ideological deviation from the party, rather than presenting a liberal Republican or conservative Democrat.

  14. Figure I in the Appendix roughly locates each of our contexts along these two dimensions. Our goal is not to pinpoint these contexts on these two dimensions, merely to highlight that there is important variation across the tasks. Some of these contexts, particularly jury service, loan, and charity, could all be construed as somewhat political, depending on their interpretation.

  15. In keeping with the suggestions of Miratrix, Sekhon et al., (2018), the experimental analyses presented here do not use sampling weights, as the sample is already broadly representative of a national sample, and the incorporation of survey weights would decrease precision. Characteristics of this sample, particularly those relevant to the affinity analyses, are contained in Table I in the Appendix.

  16. Given assumptions of weak (non-crossover) interactions between factors, the statistical power of our design is a simple function of the expected cell size for each factor level and expected effect size. While some ancillary characteristics, like career, have lower statistical power owing to their large number of potential levels, most key factors only have a small number of factor levels, across which these 26,540 target individuals are divided. For example, even religion, which has seven factor levels excluding the “Not Asked” category, results in over 3000 target persons for each religious category, providing ample power to detect differences between religious categories. Even if we assume difference between tasks, which we discuss in the next section, this is still over 600 target persons, per religion, per task. A formal power analysis for the religion factor, with a presumed effect size of 0.05, or a 5% change in probability of choice based on this factor, reveals a power level of 0.98 For all factors except career, there are substantially greater numbers of observed profiles per factor level, resulting in power levels of 0.99 or greater. Even career, with 16 factor levels, has a power level of 0.87.

  17. There are several potential ways of assessing this, given the wide array of factors and factor levels and how effects might differ across task. To assess it systematically, we estimate Average Marginal Interaction Effect (AMIEs) for every single factor level with every single task (Egami & Imai, 2018). This results in 300 two-way interaction estimates, given the 60 unique factor levels (including “Not Asked”) by the five separate tasks. Of these, 36, or 12% are statistically significant, exceeding the 5% we would expect by chance. However, if we exclude the 80 career interactions, we only find 14/220, or 6.4%, are statistically significant. Therefore we see roughly the amount of interaction between factor levels and task type we would expect to see by chance alone, suggesting few systematic differences, excluding the career factor, particularly with respect to the loan task.

  18. All analyses contained in the main text of this paper report marginal means, estimated with OLS regression with standard errors clustered at the level of the respondent (Leeper et al., 2020; Leeper, 2020). This recovers a non-parametric probability estimate for each factor level, showing the probability (0-1) a profile with that factor level was chosen, controlling for all other experimentally-manipulated information. Estimating Average Marginal Component Effects (AMCEs) reveals substantively similar conclusions.

  19. Figure II in the Appendix displays the same figure broken out by political interest, split at the median. While some effects are larger among the high-interest individuals, they are still present among individuals with low political interest in both parties, suggesting these choices are a function of partisanship itself, not just interest (or lack thereof) in politics.

  20. We do not believe our results should reasonably be interpreted to suggest minimal effects of race and ethnicity in the real world. As we note above, reading that someone is Black or Latino in a table may be very different than encountering an actual person. And, by controlling for other dimensions, we may be substantially reducing statistical discrimination along racial and ethnic lines. That is, if we simply told a respondent that a target is Black, Latino or Asian, they might infer class, religion, partisanship and other things. We are filling in those blanks. But, we note once again that the design of our study does not provide respondents an effective mechanism for attempting to convince us they are not racist. If respondents were trying to appear non-racist, the only strategy for doing that would be to always pick the Black or Latino target. This sort of strategy would be abundantly clear in our results. We see only very minor evidence of a preference for Black targets, nothing like what you would expect if there were widespread efforts to mask such discrimination.

  21. That is, while the characteristics of the target individual is randomized, a respondent’s partisanship is correlated with many other characteristics and identities the respondent may hold.

  22. In reality, ideology and party are highly correlated today, with fewer than 5% of 2016 ANES Republican respondents identified as “liberal” and fewer than 10% of Democrats identified as “conservative.” So, these dimensions are especially difficult to disentangle with observational data.

  23. If average marginal interactive effects (AMIE) are estimated between all levels of partisanship and ideology, none reach conventional levels of statistical significance (\(p < 0.05\)) (Egami & Imai, 2018).

  24. Some of these gaps could also be due to variation in the meaning of what respondents believe it means to be “socially” or “fiscally” liberal or conservative. While these words have commonly accepted meanings, there is no extant literature to our knowledge that examines exactly what types of issues voters believe to exist in each of these clusters.

  25. This is not to say these factors interact in a statistical sense. Rather, their effects are additive, and can counteract one another.

  26. If average marginal interactive effects (AMIE) are estimated between all levels of partisanship and race, none reach conventional levels of statistical significance (\(p < 0.05\)) (Egami & Imai, 2018).

  27. Figures VII, VIII, IX, and X in the Appendix display the marginal means of all factor levels separately for each level of target partisanship.

  28. While it is possible that respondents have in mind particular politicians when answering the choices posed in this study, the context of each choice task presents real-world scenarios in which one is not likely to encounter a politician.

References

  • Abramowitz, Alan I. (2011). The disappearing center: Engaged citizens, polarization, and American democracy. New Haven, CT: Yale University Press.

    Google Scholar 

  • Abramowitz, Alan I. (2018). The great alignment: Race, party transformation, and the rise of Donald Trump. New Haven, CT: Yale University Press.

    Book  Google Scholar 

  • Abramowitz, Alan I., & Webster, Steven. (2016). The rise of negative partisanship and the nationalization of US elections in the 21st century. Electoral Studies, 41, 12–22.

    Article  Google Scholar 

  • Achen, Christopher H., & Bartels, Larry M. (2016). Democracy for realists: Why elections do not produce responsive government. Princeton, NJ: Princeton University Press.

    Book  Google Scholar 

  • Ahler, Douglas J., & Sood, Gaurav. (2018). The parties in our heads: Misperceptions about party composition and their consequences. The Journal of Politics, 80(3), 000–000.

    Article  Google Scholar 

  • Alford, J. R., Funk, C. L., & Hibbing, J. R. (2005). Are political orientations genetically transmitted?American Political Science Review, 99(02), 153–167.

    Article  Google Scholar 

  • Amber Hye-Yon, Lee, Lelkes, Yphtach, Hawkins, Carlee B., & Theodoridis, Alexander G. (2022). Negative partisanship is not more prevalent than positive partisanship Nature Human Behaviour.

  • Baert, Stijn. (2018). Hiring discrimination: an overview of (almost) all correspondence experiments since 2005 (pp. 63–77). In Audit Studies: Behind the Scenes with Theory, Method, and Nuance. Springer.

  • Banda, Kevin K., Carsey, Thomas M., & Severenchuk, Serge. (2020). Evidence of conflict extension in partisans’ evaluations of people and inanimate objects. American Politics Research, 48(2), 275–285.

    Article  Google Scholar 

  • Bansak, Kirk, Hainmueller, Jens, Hopkins, Daniel J., & Yamamoto, Teppei. (2018). The number of choice tasks and survey satisficing in conjoint experiments. Political Analysis, 26(1), 112–119.

    Article  Google Scholar 

  • Bargh, John A., Chen, Mark, & Burrows, Lara. (1996). Automaticity of social behavior: Direct effects of trait construct and stereotype ativation on action. Journal of Personality and Social Psychology, 71(2), 230–244.

    Article  Google Scholar 

  • Bartels, Larry M. (2002). Beyond the running tally: Partisan bias in political perceptions. Political Behavior, 24(2), 117–150.

    Article  Google Scholar 

  • Becker, Gary S. (1957). The economics of discrimination. USA: University of Chicago Press.

    Google Scholar 

  • Bishop, Bill. (2008). The big sort: Why the clustering of like-minded America is tearing us apart. Houghton Mifflin.

  • Bolsen, Toby, Druckman, James N., & Cook, Fay Lomax. (2014). The influence of partisan motivated reasoning on public opinion. Political Behavior, 36(2), 235–262.

    Article  Google Scholar 

  • Broockman, David E. (2016). Approaches to studying policy representation. Legislative Studies Quarterly, 41(1), 181–215.

    Article  Google Scholar 

  • Budge, Ian. (2015). Issue emphases, saliency theory and issue ownership: a historical and conceptual analysis. West European Politics, 38(4), 761–777.

    Article  Google Scholar 

  • Campbell, Angus, Converse, Philip E., Miller, William E., & Stokes, Donald E. (1960). The American voter. New York: Wiley.

    Google Scholar 

  • Campbell, David E., Green, John C., & Layman, Geoffrey C. (2011). The party faithful: Partisan images, candidate religion, and the electoral impact of party identification. American Journal of Political Science, 55(1), 42–58.

    Article  Google Scholar 

  • Carlin, Ryan E., & Love, Gregory J. (2018). Political competition, partisanship and interpersonal trust in electoral democracies. British Journal of Political Science, 48(1), 115.

    Article  Google Scholar 

  • Clifford, Scott. (2017). Individual differences in group loyalty predict partisan strength. Political Behavior, 39(3), 531–552.

    Article  Google Scholar 

  • Converse, P. E. (1964). The nature of belief systems in mass publics. In E. David (Ed.), Ideology and Discontent (pp. 75–169). Apter. New York: Free Press.

    Google Scholar 

  • Crandall, Christian S., Eshleman, Amy, & Laurie O’brien. (2002). Social norms and the expression and suppression of prejudice: the struggle for internalization. Journal of personality and social psychology, 82(3), 359.

  • Dafoe, Allan, Zhang, Baobao, & Caughey, Devin. (2018). Information equivalence in survey experiments. Political Analysis, 26(4), 399–416.

    Article  Google Scholar 

  • Devine, Patricia G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56(1), 5.

    Article  Google Scholar 

  • Druckman, James N., & Levendusky, Matthew S. (2019). What do we measure when we measure affective polarization? Public Opinion Quarterly, 83(1), 114–122.

    Article  Google Scholar 

  • Druckman, James N., Gubitz, S. R., Lloyd, Ashley M., & Levendusky, Matthew S. (2019). How incivility on partisan media (de) polarizes the electorate. The Journal of Politics, 81(1), 291–295.

    Article  Google Scholar 

  • Druckman, James N., & Bolsen, Toby. (2011). Framing, motivated reasoning, and opinions about emergent technologies. Journal of Communication, 61(4), 659–688.

    Article  Google Scholar 

  • Dunton, Bridget C., & Fazio, Russell H. (1997). An individual difference measure of motivation to control prejudiced reactions. Personality and Social Psychology Bulletin, 23(3), 316–326.

    Article  Google Scholar 

  • Feldman, Stanley, & Johnston, Christopher. (2014). Understanding the determinants of political ideology: Implications of structural complexity. Political Psychology, 35(3), 337–358.

    Article  Google Scholar 

  • Fowler, James H., & Kam, Cindy D. (2007). Beyond the self: Social identity, altruism, and political participation. The Journal of Politics, 69(3), 813–827.

    Article  Google Scholar 

  • Goggin, Stephen N., & Theodoridis, Alexander G. (2017). Disputed ownership: Parties, issues, and traits in the minds of voters. Political Behavior, 39(3), 675–702.

    Article  Google Scholar 

  • Goggin, Stephen N., Henderson, John A., & Theodoridis, Alexander G. (2020). What goes with red and blue? Mapping partisan and ideological associations in the minds of voters. Political Behavior, 42(4), 985–1013.

    Article  Google Scholar 

  • Green, Donald P. (2002). Bradley palmquist and eric schickler. Partisan hearts and minds: Political parties and the social identities of voters. Yale University Press.

  • Greene, Steven. (1999). Understanding party identification: A social identity approach. Political Psychology, 20(2), 393–403.

    Article  Google Scholar 

  • Greene, Steven. (2000). The psychological sources of partisan-leaning independence. American Politics Quarterly, 28(4), 511–537.

    Article  Google Scholar 

  • Greene, Steven. (2004). Social identity theory and party identification. Social Science Quarterly, 85(1), 136–153.

    Article  Google Scholar 

  • Hainmueller, Jens, & Hopkins, Daniel J. (2015). The hidden American immigration consensus: A conjoint analysis of attitudes toward immigrants. American Journal of Political Science, 59(3), 529–548.

    Article  Google Scholar 

  • Hainmueller, Jens, Hopkins, Daniel J., & Yamamoto, Teppei. (2013). Causal inference in conjoint analysis: Understanding multidimensional choices via stated preference experiments. Political Analysis, 22(1), 1–30.

    Article  Google Scholar 

  • Hainmueller, Jens, Hangartner, Dominik, & Yamamoto, Teppei. (2015). Validating vignette and conjoint survey experiments against real-world behavior. Proceedings of the National Academy of Sciences, 112(8), 2395–2400.

    Article  Google Scholar 

  • Heit, Evan, & Stephen P. Nicholson. (2016). Missing the party: Political categorization and reasoning in the absence of party label cues. Topics in Cognitive Science, 8(3), 697–714.

    Article  Google Scholar 

  • Henderson, John A., & Theodoridis, Alexander G. (2017). Seeing spots: Partisanship, negativity and the conditional receipt of campaign advertisements. Political Behavior .

  • Henderson, John A., Sheagley, Geoffrey, Goggin, Stephen N., Dancey, Logan, & Theodoridis, Alexander G. (2022). Primary divisions: How voters evaluate policy and group differences in intra-party contests. The Journal of Politics, 84(3), 1760–1776.

    Article  Google Scholar 

  • Hetherington, Marc J., & Weiler, Jonathan D. (2009). Authoritarianism and polarization in American politics. USA: Cambridge University Press.

    Book  Google Scholar 

  • Hetherington, Marc J., Weiler, Jonathan D. (2018). Prius or pickup?: How the answers to four simple questions explain America’s great divide. Houghton Mifflin Harcourt.

  • Hopkins, Daniel J. (2018). The increasingly United States: How and Why American political behavior nationalized. Chicago, IL: University of Chicago Press.

    Book  Google Scholar 

  • Huber, Gregory A., & Malhotra, Neil. (2017). Political homophily in social relationships: Evidence from online dating behavior. The Journal of Politics, 79(1), 269–283.

    Article  Google Scholar 

  • Huddy, Leonie, Mason, Lilliana, & Aarøe, Lene. (2015). Expressive partisanship: Campaign involvement, political emotion, and partisan identity. American Political Science Review, 109(1), 1–17.

    Article  Google Scholar 

  • Iyengar, Shanto, Sood, Gaurav, & Lelkes, Yphtach. (2012). Affect, not ideology: A social identity perspective on polarization. Public Opinion Quarterly, 76(3), 405–431.

    Article  Google Scholar 

  • Iyengar, Shanto, & Westwood, Sean J. (2015). Fear and loathing across party lines: New evidence on group polarization. American Journal of Political Science, 59(3), 690–707.

    Article  Google Scholar 

  • Jonah, Goldberg. (2018). Partisan identity is becoming a justification for discrimination. USA: Los Angeles Times.

  • Jennings, M. Kent., Stoker, Laura, & Bowers, Jake. (2009). Politics across Generations: Family transmission reexamined. The Journal of Politics, 71(3), 782–799.

    Article  Google Scholar 

  • Jerit, Jennifer, & Barabas, Jason. (2012). Partisan perceptual bias and the information environment. The Journal of Politics, 74(3), 672–684.

    Article  Google Scholar 

  • Jones, Jeffrey M. (2013). More Americans see gay, Lesbian orientation as birth factor. http://news.gallup.com/poll/162569/americans-gay-lesbian-orientation-birth-factor.aspx.

  • Karpowitz, Christopher F., Quin Monson, J., & Patterson, Kelly D. (2016). Who’s in and Who’s out: The politics of religious norms. Politics and Religion, 9, 508–536.

    Article  Google Scholar 

  • Kevin, Arceneaux, Wielen, Vander, & Ryan, J. (2017). Taming intuition: How reflection minimizes Partisan reasoning and promotes democratic accountability. UK: Cambridge University Press.

    Google Scholar 

  • Klar, Samara. (2014). A multidimensional study of ideological preferences and priorities among the American public. Public Opinion Quarterly, 78(S1), 344–359.

    Article  Google Scholar 

  • Klar, Samara, Krupnikov, Yanna, & Ryan, John Barry. (2018). Affective polarization or Partisan disdain? Untangling a dislike for the opposing party from a dislike of partisanship. Public Opinion Quarterly, 82(2), 379–390.

    Article  Google Scholar 

  • Leeper, Thomas J. (2020). Cregg: Simple conjoint analyses and visualization. R package version 0.3.6.

  • Leeper, Thomas J., & Robison, Joshua. (2020). More important, but for what exactly? The insignificant role of subjective issue importance in vote decisions. Political Behavior, 42(1), 239–259.

    Article  Google Scholar 

  • Leeper, Thomas J., Hobolt, Sara B., & Tilley, James. (2020). Measuring subgroup preferences in conjoint experiments. Political Analysis, 28(2), 207–221.

    Article  Google Scholar 

  • Lelkes, Yphtach, Sood, Gaurav, & Iyengar, Shanto. (2017). The hostile audience: The effect of access to broadband internet on partisan affect. American Journal of Political Science, 61(1), 5–20.

    Article  Google Scholar 

  • Levendusky, Matthew. (2009). The Partisan sort: How liberals became democrats and conservatives became republicans. Chicago, IL: University of Chicago Press.

    Book  Google Scholar 

  • Levendusky, Matthew, & Malhotra, Neil. (2016). Does media coverage of partisan polarization affect political attitudes? Political Communication, 33(2), 283–301.

    Article  Google Scholar 

  • Levendusky, Matthew S. (2018). Americans, not Partisans: Can priming American national identity reduce affective polarization? The Journal of Politics, 80(1), 59–70.

    Article  Google Scholar 

  • Malka, Ariel, & Lelkes, Yphtach. (2010). More than ideology: Conservative-liberal identity and receptivity to political cues. Social Justice Research, 23(2), 156–188.

    Article  Google Scholar 

  • Mason, Lilliana. (2015). I disrespectfully agree: The differential effects of partisan sorting on social and issue polarization. American Journal of Political Science, 59(1), 128–145.

    Article  Google Scholar 

  • Mason, Lilliana. (2016). A cross-cutting calm: How social sorting drives affective polarization. Public Opinion Quarterly, 80(S1), 351–377.

    Article  Google Scholar 

  • Mason, Lilliana. (2018). Uncivil agreement: How politics became our identity. USA: University of Chicago Press.

    Book  Google Scholar 

  • Mason, Lilliana, & Wronski, Julie. (2018). One tribe to bind them all: How our social group attachments strengthen partisanship. Political Psychology, 39(S1), 257–277.

    Article  Google Scholar 

  • McConnell, Christopher, Margalit, Yotam, Malhotra, Neil, & Levendusky, Matthew. (2018). The economic consequences of partisanship in a polarized era. American Journal of Political Science, 62(1), 5–18.

    Article  Google Scholar 

  • Miller, George A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81.

    Article  Google Scholar 

  • Miratrix, Luke W., Sekhon, Jasjeet S., Theodoridis, Alexander G., & Campos, Luis F. (2018). Worth weighting? How to think about and use weights in survey experiments. Political Analysis 1–17.

  • Naoki, Egami, Imai, Kosuke. (2018). Causal interaction in factorial experiments: Application to conjoint analysis. Journal of the American Statistical Association.

  • Nicholson, Stephen P. (2012). Polarizing cues. American Journal of Political Science, 56(1), 52–66.

    Article  Google Scholar 

  • Nicholson, Stephen P., Chelsea M. Coe, Jason Emory, & Anna V. Song. (2016). The politics of beauty: The effects of partisan bias on physical attractiveness. Political Behavior, 38(4), 883–898.

    Article  Google Scholar 

  • Orr, Lilla V., & Huber, Gregory A. (2020). The policy basis of measured partisan animosity in the United States. American Journal of Political Science, 64(3), 569–586.

    Article  Google Scholar 

  • Petrocik, J. R. (1996). Issue ownership in presidential elections, with a 1980 case study. American Journal of Political Science, 40(3), 825–850.

    Article  Google Scholar 

  • Phelps, Edmund S. (1972). The statistical theory of racism and sexism. The American Economic Review, 62(4), 659–661.

    Google Scholar 

  • Roush, Carolyn E. (2017). It’s not me, It’s you: How Americans’ animosity toward their opponents drives modern politics PhD thesis Vanderbilt university.

  • Sunstein, Cass R. (2015). Partyism. University of Chicago Legal Forum .

  • Theodoridis, Alexander G. (2017). Me, Myself, and (I), (D), or (R)? Partisanship and political cognition through the lens of implicit identity. The Journal of Politics, 79(4), 1253–1267.

    Article  Google Scholar 

  • Theodoridis, Alexander George. (2013). Implicit political identity. PS: Political Science & Politics 46(03):545–549.

  • Triandis, Harry C., & Triandis, Leigh M. (1960). Race, social class, religion, and nationality as determinants of social distance. The Journal of Abnormal and Social Psychology, 61(1), 110.

    Article  Google Scholar 

  • Walgrave, Stefaan, Tresch, Anke, & Lefevere, Jonas. (2015). The conceptualisation and measurement of issue ownership. West European Politics, 38(4), 778–796.

    Article  Google Scholar 

  • Wallander, Lisa. (2009). 25 years of factorial surveys in sociology: A review. Social Science Research, 38(3), 505–520.

    Article  Google Scholar 

  • Yusaku, Horiuchi, Markovich, Zachary, Yamamoto, Teppei. (2021). Does conjoint analysis mitigate social desirability bias? Political Analysis 1–15.

  • Zaller, John R. (1992). The nature and origin of mass opinion. Cambridge, New York, Oakleigh: Cambridge University Press.

    Book  Google Scholar 

  • Zingher, Joshua N. (2018). Polarization, demographic change, and white flight from the democratic party. The Journal of Politics, 80(3), 860–872.

    Article  Google Scholar 

Download references

Acknowledgements

Author names appear in reverse alphabetical order. We thank Doug Ahler, Larry Bartels, Henry Brady, Charlotte Cavaillé, Jack Citrin, Amanda Clayton, Jamie Druckman, Marc Hetherington, Cindy Kam, Matt Levendusky, Neil Malhotra, Kristin Michelitch, Steve Nicholson, Efren Perez, Emily Ritter, Carrie Roush, Eric Schickler, Gaurav Sood, Kim Twist, Rob Van Houweling, Liz Zechmeister, and seminar participants at the University of California, Irvine, Arizona State University, the University of Oregon, the London School of Economics and Political Science, Vanderbilt University, and the University of Massachusetts Amherst for helpful comments. This research was supported by generous funding from the University of California, Merced. We thank the Vanderbilt University Center for the Study of Democratic Institutions, and Research on Individuals, Politics and Society Lab. Replication data available here: https://doi.org/10.7910/DVN/IFDM4B.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexander G. Theodoridis.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 204 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Theodoridis, A.G., Goggin, S.N. & Deichert, M. Separated by Politics? Disentangling the Dimensions of Discrimination. Polit Behav (2022). https://doi.org/10.1007/s11109-022-09809-y

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11109-022-09809-y

Keywords

  • Party Identity
  • Partisanship
  • Identity
  • Discrimination
  • Religion
  • Sexual Orientation
  • Conjoint
  • Experiment