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The Activation of Prejudice and Presidential Voting: Panel Evidence from the 2016 U.S. Election

Abstract

Divisions between Whites and Blacks have long influenced voting. Yet given America’s growing Latino population, will Whites’ attitudes toward Blacks continue to predict their voting behavior? Might anti-Latino prejudice join or supplant them? These questions took on newfound importance after the 2016 campaign, in which the Republican candidate’s rhetoric targeted immigrants from Mexico and elsewhere. We examine the relationship between Whites’ prejudices, immigration attitudes, and voting behavior using a population-based panel spanning 9 years. Donald Trump’s candidacy activated anti-Black but not anti-Latino prejudice, while other GOP candidates had no such effect. This and other evidence suggests that Whites’ prejudice against Blacks is potentially activated even when salient political rhetoric does not target them exclusively. These results shed light on the continued political impact of anti-Black prejudice while deepening our understanding of the mobilization of prejudice and the associated psychological mechanisms.

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Notes

  1. 1.

    Throughout, “Whites” refers to non-Hispanic white Americans.

  2. 2.

    To be sure, Trump rose to political prominence in part through his public antagonism to Obama—including by promoting false claims about Obama’s birthplace—so he may well have activated anti-Black attitudes. That said, the Obama Presidency had already activated Whites’ anti-Black attitudes (Tesler 2016a), meaning that ceiling effects may have limited any further priming of views about Blacks.

  3. 3.

    Replication data and code are available at https://doi.org/10.7910/DVN/BGTAGA.

  4. 4.

    While the data employed below does not include measures of anti-Muslim attitudes, readers should instead consult Lajevardi and Abrajano (2019).

  5. 5.

    Specifically, the weighted sampling procedure used to invite 2012 respondents from 2008 respondents sought to produce a population whose marginal distributions on age, racial/ethnic background, and education mirrored those of the target U.S. population. Due to the paucity of available panelists with certain demographic profiles (e.g. non-white, older, with lower educational attainment), this sampling procedure had the unintended effect of under-representing older whites with high levels of educational attainment. According to the Census Bureau, 31% of White respondents over 50 had at least a bachelor’s degree in 2016, but the comparable number in our panel’s final 2016 wave is 7%.

  6. 6.

    These stereotype assessments took place in a randomized order, and were separated by blocks of unrelated questions. We assessed stereotypes of Blacks in waves 4, 5, 6, 7, 10, 11, and 12 and of Latinos in waves 6, 7, 10, and 11.

  7. 7.

    Early panel waves included a third item about groups’ perceived intelligence which ran from “intelligent” (0) to “unintelligent” (100). However, analyses undertaken in preparation for administering the 2016 waves indicated that stereotype measures were virtually identical without that item, leading us to remove it from the questionnaire due to space constraints. Specifically, in the October 2012 wave, the correlation between anti-Latino prejudice scales measured with and without the intelligence stereotypes is 0.97. For anti-Black prejudice, the correlation is 0.98. The intelligence item is thus not used in any analyses, but the results employing it for earlier waves are substantively identical.

  8. 8.

    See also SI Fig. 3.

  9. 9.

    We estimated a linear model parallel to those below with October 2016 immigration attitudes regressed on several lagged demographics as well as 2012 anti-Black and anti-Latino prejudice. The coefficient for anti-Latino prejudice is − 1.48 (SE = 0.51), 49% larger than the − 0.99 (SE = 0.50) estimated for anti-Black prejudice.

  10. 10.

    In October 2014, respondents were asked about their support for their House of Representatives race rather than for president. This estimate serves as an alternative point of comparison for 2016.

  11. 11.

    Those factors include respondents’ prior candidate preference, 2008 income, lagged partisanship measured via six indicator variables, union membership in 2007, self-identification as Catholic or Protestant in 2007, and educational levels, age, and gender reported in 2012.

  12. 12.

    Due to the small numbers of respondents who chose third-party candidates, we group them with those who decline to state a preference or plan not to vote.

  13. 13.

    Note that the reference category for these models is respondents not stating a major-party preference, a fact which may partially explain shifting coefficient estimates across waves. Those interested in the OLS coefficients for other independent variables should instead consult Tables including 18 and 19.

  14. 14.

    Our primary models depend on various lagged measures, meaning that we cannot estimate comparable models for 2008. However, when we specify roughly similar models for that year which omit anti-Latino prejudice and which use measures of prior partisanship and vote choice measured at various times, we still find that the effect of anti-Black prejudice in 2016 exceeds that in 2008. See SI Table 15.

  15. 15.

    See the SI for estimation details.

  16. 16.

    In a similar model in which we instead include the four immigration attitudes measured separately, the coefficient for anti-Black prejudice is 0.35 (SE = 0.15).

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Acknowledgements

This paper previously circulated under the title “Prejudice, Priming, and Presidential Voting: Panel Evidence from the 2016 U.S. Election.” The author gratefully acknowledges helpful feedback or advice from Phil Jones, Michael Jones-Correa, Zoltan Hajnal, Mirya Holman, Leonie Huddy, Cheryl Kaiser, Thomas Leeper, Yph Lelkes, Matt Levendusky, Helen Marrow, Marc Meredith, Diana Mutz, Fabian Neuner, Efrén Pérez, John Sides, and Paul Sniderman as well as seminar participants at Arizona State University’s 2017 Kopf Conference “Diverse Perspectives toward Immigration and Ethnic/Racial Minorities,” the Rubin Lecture Series at the University of Michigan, the Mershon Center’s 2016 Presidential Election Conference at The Ohio State University, and seminars at George Washington, Princeton Universities and ETH Zurich and the University of Zurich. David Azizi, Tiger Brown, Isaiah Gaines, Sydney Loh, Thomas Munson, and Samantha Washington provided insightful research assistance. The survey described herein was reviewed by the University of Pennsylvania Institutional Review Board (824036). This research was supported by a Russell Sage Foundation Grant (Award 94-17-01).

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Hopkins, D.J. The Activation of Prejudice and Presidential Voting: Panel Evidence from the 2016 U.S. Election. Polit Behav 43, 663–686 (2021). https://doi.org/10.1007/s11109-019-09567-4

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Keywords

  • 2016 election
  • Voting behavior
  • Activation
  • Donald Trump
  • Racial prejudice