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But Can She Make America Great Again? Threat, Stability, and Support for Female Candidates in the United States

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Abstract

Drawing on the literature on system justification, I argue that the fate of female candidates in the U.S. is tied to whether the election is occurring in relatively good or bad times. Using an original experiment embedded in the 2016 Cooperative Congressional Election Study, I show that exposure to low vs. high threat messages led to higher evaluations of Hillary Clinton. These findings are complimented by a vote choice model that shows that the likelihood of supporting Clinton over Trump was significantly higher when perceived threat (as captured by economic assessments) was lower. A second original experiment better isolates the effects of candidate sex and generalizes these findings beyond the 2016 case. These results highlight the importance of including campaign context in studies of candidate evaluation and suggest that overt sexism is not the only hurdle facing women running for office in the U.S.

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Notes

  1. Principal Investigators Stephen Ansolabehere and Brian Schaffner. For more detail, see https://cces.gov.harvard.edu/.

  2. In the 116th Congress, men outnumber women 333 to 102 in the House and 75 to 25 in the Senate.

  3. This temporal dynamic may also shed some light on the differences between the Azavedo et al. (2017) and Weinschenk and Dawes (2019) findings, as the survey in the former was fielded prior to the survey in the latter.

  4. Data and code to replicate all analyses presented in this article are publicly available at https://doi.org/10.7910/DVN/IONQ8V.

  5. Though these treatments effectively manipulate threat, they are perhaps more mild than some of the actual rhetoric and threat messaging used in the 2016 campaign. I thank an anonymous reviewer for raising this point and return to it in the concluding discussion.

  6. The order in which the four items were shown to respondents was randomized.

  7. See Hoffman and Hurst (1990) and Jost and Kay (2005).

  8. For sample characteristics across treatments, see Online Appendix A.

  9. Nonvoters and those who reported voting for a different candidate are omitted.

  10. Manipulation checks in both studies showed that those in the instability condition were significantly less likely to view the current economy as stable.

  11. These include partisanship, ideological identification, sex, age, education, race, family income, and residency in a southern state.

  12. Modern sexism ranges from 1 to 5 (\(\stackrel{-}{X}\) = 2.27; s.d. = 1.22) and is the mean of agreement (on a 5-point scale) with the statements “Discrimination against women in the United States is no longer a problem” and “Women demanding equality are seeking special favors.” (α = .80). Traditional sexism is measured by agreement (on a 5-point scale) with the single item “It is better if a man works and a woman stays at home” (\(\stackrel{-}{X}\) = 2.15; s.d. = 1.25).

  13. The racial resentment index ranges from 1–6 (\(\stackrel{-}{X}\) = 2.16; s.d. = .97) and is the mean of agreement (on a 6-point scale) with three statements: (1) White people in the U.S. have certain advantages because of the color of their skin; (2) Racial problems in the U.S. are rare, isolated situations; and (3) I am angry that racism exists (α = .71).

  14. See the Online Appendix A for these results.

  15. Azevedo et al. (2017) report r = .52.

  16. See Online Appendix A.

  17. 17.53% of respondents are at or above the median of system justification, but below the median of modern sexism. Conversely, 14.64% of respondents are at or above the median of modern sexism but below the median of system justification. The pattern for traditional sexism is similar, as those same percentages are 21.39 and 18.88, respectively.

  18. The exception being Reny, Collingwood, and Valenzuela (2019), which includes a number of contextual economic indicators.

  19. Approval to work with human subjects in on file with the University of Houston. Details about the sample are available in Appendix B.

  20. Since the significant differences in my CCES findings were between these two treatments and not treatments and control, I omitted the control condition.

  21. Results of the pretest are available in Online Appendix B.

  22. The order in which these items were shown was also randomized.

  23. The mean ratings by treatment are available in Appendix B. These results show that across all treatments, Campbell has a slight advantage on both relative trait ratings (\(\stackrel{-}{X}\) = − .14) and vote share (50.67%). When looking across the various treatments, there does appear to be somewhat of a disconnect between trait evaluations and vote choice (i.e., Campbell receives higher ratings in 5 out of 6 cases but only has a higher vote percentage in 3 of the 6). These numbers likely reflect apparently lower levels of partisan bias in expressed vote choice. Indeed, while only 29.69% of partisans gave higher relative trait ratings to the candidate from the opposite party, 45.53% voted for the candidate from the opposite party. A full exploration of the causes of this difference is beyond the scope of this manuscript. I will, however, note that the questions were asked in different ways. Recall that vote choice was asked using wording which allows subjects to “save face” (Krupnikov et al. 2016). While this method is intended to lower social desirability pressure to support a female candidate, there may have been unintended effects on partisan bias. Such a possibility has not been tested, as the treatments used by Krupnikov et al. (2016) did not include party labels.

  24. For high system justifiers, the mean N for each of the twelve candidate X threat level groups is 54.17.

  25. Full transcript available here: https://www.politico.com/story/2016/07/full-transcript-donald-trump-nomination-acceptance-speech-at-rnc-225974.

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  1. Department of Political Science, University of Houston, 3551 Cullen Boulevard, Room 447, Philip Guthrie Hoffman Hall, Houston, TX, 77204-3011, USA

    Elizabeth N. Simas

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Simas, E.N. But Can She Make America Great Again? Threat, Stability, and Support for Female Candidates in the United States. Polit Behav 44, 1–21 (2022). https://doi.org/10.1007/s11109-020-09607-4

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