Abstract
Are female candidates disproportionately punished for relying on negative campaign ads? While scholars agree that sponsoring negativity works against traditional gender stereotypes, it is less clear how relying on negativity affects voter evaluations of female candidates. In this manuscript we reconsider the relationship between candidate gender and negativity. Relying on theories of conditional stereotype use, we argue that negative ads translate to significantly poorer evaluations for the female candidate when two conditions are met: (1) the female candidate is perceived as the instigator of negativity and (2) she is of a different party than the voter. We test our predictions using an experiment and show that female candidates only face a disproportionate punishment for relying on negativity under our two specific conditions. In contrast, voters are much more forgiving when they believe that a female candidate simply followed her opponent’s lead in using negative ads or when negativity is used to promote the voter’s party. While our research suggests that—compared to their male counterparts—female candidates do face some added constraints, our findings have broader implications. Not only are voters more or less likely to use gender stereotypes under certain conditions, but these conditions are highly dependent on the campaign context.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Other work also considers negativity and gender. Kahn (1993) and Nelson (2008) find few differences in negative ad use for male and female candidates. Dinzes et al. (1994) show that target gender does not affect response; Fridkin et al. (2009) show that negative ads lower evaluations of male targets. Other scholars find that female candidates are most successful with negative ads on women’s issues (Woodall 2005; Herrnson and Lucas 2006).
Bailey’s name was previously used in Brooks (2011). We conducted a separate pre-test to ensure Larson’s name did not produce any confounding effects.
We acknowledge that there are different definitions of negativity, which consider not only the tone of ads, but also the content (see Lau and Rovner 2009 for a review). We focus on Geer’s definition as previous experimental research on negativity has relied on this approach.
Our sample was recruited following Berinsky et al. (2012). Using this method, Berinsky et al. (2012) replicated many canonical psychology experiments, concluding that this approach is an effective and innovative way to recruit subjects and conduct online studies. This method relies on recruiting through an innovative new platform to Amazon’s Mechanical Turk (MTurk). Berinsky et al. (2012) have conducted extensive testing on MTurk and have found that the samples obtained through MTurk are more representative of the general population than undergraduate samples and other Internet-based opt-in samples. In our sample, 23 were excluded for either failing a simple attention check or refusing to answer various questions. Inclusion or exclusion of those who failed the check does not shift our results, though the results presented here do exclude them.
Power of 0.90, α = 0.05; differences in power calculation based on pilot study.
We also conduct a randomization check to ensure that there are no systematic differences within our groups (on similar covariates). Using a multinomial logit we show that these factors do not jointly predict assignment to our groups (χ 2 = 78.41, p = 0.680). We also use a logit to consider whether these factors predict assignment to either a control or treatment version of our manipulations; they do not suggesting that there are no systematic differences on these covariates within our groups which disrupt the randomization process. Finally, we use an ANOVA and a Bonferroni test to compare the levels across the covariates to ensure balance. No pair-wise Bonferroni comparisons reach significance, reinforcing the idea that there are no group imbalances.
Preferences for the candidate of the opposing party at baseline (ANOVA): F = 1.20 (p = 0.310); preferences for the candidate of the subject’s party at baseline (ANOVA): F = 1.16 (p = 0.328).
Furthermore these control results are similar to previous research that has considered the baseline interaction between gender and partisanship for hypothetical candidates (see for example Dolan (2010) on willingness to vote for a female President of a certain party).
We replicate a similar pattern by estimating a model that predict the likelihood of having a reason to vote against a candidate and uses the following controls: gender of subject, interest in politics, strength of partisanship, region of residence and income. The model is as follows: Reasons to Vote Against Candidate = f(Order, Gender, PID (relative to subject), Order × Gender, Gender × PID, Order × PID, Gender × Order × PID, Control for Opponent, Controls) This model show that while going negative first uniformly increases the likelihood of a subject having a reason to vote against the candidate, this is largest and significant when we consider a female candidate of the opposing party. Specifically, when we consider the change in likelihood of having a reason to vote against the candidate as a function of going negative first we obtain the following results (pooling opponent gender for ease of interpretation): Woman, Different Party + 0.24 (significant at p < 0.01), Woman, Same Party + 0.03, Man, Different Party, +0.08, Man, Same Party +0.06. In sum, much like Table 3 suggests, the punishment is highest and reaches significance only under predicted conditions. To obtain these results controls were set to mode (categorical).
We coded this index in two ways. First, we maintain the same order coding for both male and female candidates, meaning that for a female candidate a lower score means more counter-stereotypic judgments, but for a male candidate a higher score means a more counter-stereotypic judgments. Second, we also reverse-code the scale for a male candidate, meaning that for all candidates a lower score means more counter-stereotypic evaluations. These changes in coding do not have any effect on our analysis.
For this variable, a positive score for a female candidate would mean counter-stereotypic perceptions, much like a negative score would mean counter-stereotypic perceptions of a male candidate. Again, we also create a reverse-code measure such that a positive score means counter-stereotypic evaluations for both female and male candidates and again, this has no effect on our results.
Our results indicate that for the female instigator of the opposing party the ACME is significant at the 95 % level. We present the 90 % confidence intervals to reinforce the fact that the ACME is not distinguishable from 0 for other cases.
We ease the assumption of a non-interactive relationship between the treatment and mediator.
We also consider which of our stereotype index components are most effective at shifting evaluations. To do so, we follow Huddy and Terkildsen (1993) and separate the index into “warmth and expressiveness” factors and “instrumental” factors. We find evidence that the instrumental factors—the masculine stereotypes—have the stronger mediational effect on the evaluations of the female candidate of the opposing party. This offers more evidence for our theoretic approach: it is not enough to simply be less feminine, but a woman has to be more fully counter-stereotypic and display more masculine characteristics.
References
Ansolabehere, S., & Iyengar, S. (1995). Going negative: How campaign advertising shrinks and polarizes the electorate. New York: The Free Press.
Banjo, S. (2011). The time a softer approach. The Wall Street Journal, A17.
Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Using mechanical turk as a subject recruitment tool for experimental research. Political Analysis, 20, 351–368.
Brader, T. (2005). Striking a responsive chord: How political ads motivate and persuade voters by appealing to emotions. American Journal of Political Science, 49, 388–405.
Brooks, D. J. (2011). Testing the double standard for candidate emotionality: Voter reactions to the tears and anger of male and female politicians. Journal of Politics, 73, 597–615.
Dinzes, D., Cozzens, M. D., & George, C. M. (1994). The role of gender in ‘attack ads’: Revisiting negative political advertising. Communication Research Reports, 11, 67–75.
Dolan, K. (2010). The impact of gender stereotyped evaluations on support for women candidate. Political Behavior, 32, 69–88.
Dolan, K., & Kira, S. (2011). Candidate gender and experimental political science. In J. N. Druckman, D. P. Green, J. H. Kuklinski, & A. Lupia (Eds.), Cambridge handbook of experimental political science. New York: Cambridge University Press.
Eagly, A. H., & Karau, S. J. (2002). Role congruity theory of prejudice toward female leaders. Psychological Review, 109, 573–598.
Fowler, E. F., & Travis, N. R. (2010). Advertising trends in 2010. The Forum, 8(4), 1–16.
Fridkin, K. L., Kenney, P. J., & Woodall, G. S. (2009). Bad for men, better for women: The impact of stereotypes during political campaigns. Political Behavior, 31, 53–77.
Geer, J. G. (2006). In defense of negativity: Attack ads in presidential campaigns. Chicago: The University of Chicago Press.
Geer, J. G. (2012). The news media and the rise of negativity in presidential campaigns. PS: Political Science and Politics, 45, 422–434.
Gilbert, D. T., & Hixon, J. G. (1991). The trouble of thinking: Activation and application of stereotypic beliefs. Journal of Personality and Social Psychology, 60, 509–517.
Gordon, A., Shafie, D. M., & Crigler, A. N. (2003). Is negative advertising effective for female candidates?: An experiment in voters’ uses of gender stereotypes. The Harvard International Journal of Press/Politics, 8, 35–53.
Hayes, D. (2011). When gender and party collide: Stereotyping in candidate trait attribution. Politics & Gender, 7, 133–165.
Herrnson, P. S., Celeste Lay, J., & Stokes, A. K. (2003). Women running as women: Candidate gender, campaign issues, and voter-targeting strategies. The Journal of Politics, 65, 244–255.
Herrnson, P. S., & Lucas, J. C. (2006). The fairer sex? Gender and negative campaigning in U.S. elections. American Politics Research, 34, 69–94.
Hitchon, J. C., & Chang, C. (1995). Effects of gender schematic processing on the reception of political commercials for men and women candidates. Communication Research, 22, 430–458.
Hitchon, J. C., Chang, C., & Harris, R. (1997). Should women emote? Perceptual bias and opinion change in response to political ads for candidates of different genders. Political Communication, 14, 49–69.
Holloway, R., & Johnston, L. (2006). Evaluating the evaluators: Perceptions of interviewers by rejected job applicants as a function of interviewer and applicant sex. Journal of Applied Social Psychology, 36, 2635–2648.
Houston, D. A., & Doan, K. (1999). Can you back that up? Media Psychology, 1, 191–206.
Huddy, L., & Terkildsen, N. (1993). Gender stereotypes and the perception of male and female candidates. American Journal of Political Science, 37, 119–147.
Imai, K., Keele, L., Tingley, D., & Yamamoto, T. (2011). Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studies. American Political Science Review, 105, 765–789.
Imai, K. & Teppei, Y. (2012). Identification and sensitivity analysis for multiple causal mechanisms: Revisiting evidence from framing experiments. Working Paper.
Johnston, A., & White, A. B. (1994). Communication styles and female candidates: A study of the political advertising during the 1986 senate elections. Journalism Quarterly, 71, 321–329.
Kahn, K. F. (1993). Gender differences in campaign messages: The political advertisements of men and women candidates for the U.S. senate. Political Research Quarterly, 46, 481–502.
Koch, J. W. (2000). Do citizens apply gender stereotypes to infer candidates’ ideological orientations? The Journal of Politics, 62, 414–429.
Kunda, Z., & Sinclar, L. (1999). Motivated reasoning with stereotypes: Activation, application, and inhibition. Psychological Inquiry, 10, 12–22.
Kunda, Z., & Spencer, S. (2003). When do stereotypes come to mind and when do they color judgment? A goal-based theoretical framework for stereotype activation and application. Psychological Bulletin, 129, 522–544.
Lau, R. R. (1982). Negativity in political perception. Political Behavior, 4, 353–378.
Lau, R. R., & Rovner, I. B. (2009). Negative campaigning. Annual Review of Political Science, 12, 285–306.
Lau, R. R., Sigelman, L., & Rovner, I. B. (2007). The effects of negative political campaigns: A meta-analytic reassessment. The Journal of Politics, 69, 1176–1209.
Maxwell, S. E., & Delaney, H. D. (2004). Designing experiments and analyzing data. New York: Routledge Academic.
McDermott, M. L. (1998). Voting cues in low-information elections: candidate gender as a social information variable in contemporary United States elections. American Journal of Political Science, 41, 270–283.
Mezey, S. G. (1978). Does sex make a difference: A case study of women in politics. Western Political Quarterly, 31, 492–501.
Nelson, K. (2008). If you can’t say something nice: The gender dynamics of negative advertising. Boston, MA: American Political Science Association, Annual Meeting.
Panagopoulos, C. (2004). Boy talk/girl talk gender differences in campaign communication strategies. Women & Politics, 34, 131–155.
Peterson, D. A. M., & Djupe, P. A. (2005). When primary campaigns go negative: The determinants of campaign negativity. Political Research Quarterly, 58, 45–54.
Rudman, L. A. (1998). Self-promotion as a risk factor for women: The costs and benefits of counterstereotypical impression management. Journal of Personality and Social Psychology, 74, 629–645.
Rudman, L. A., Greenwald, A. G., & McGhee, D. E. (2001). Implicit self-concept and evaluative implicit gender stereotypes: Self and ingroup share desirable traits. Personality and Social Psychology Bulletin, 27, 1164–1178.
Schneider, M. C., & Bos, A. L. (forthcoming). Measuring Stereotypes of Female Politicians. Political Psychology.
Sinclair, L., & Kunda, Z. (2000). Motivated stereotyping of women: She’s fine if she praised me but incompetent if she criticized me. Personality and Social Psychology Bulletin, 26, 1329–1342.
Skaperdas, S., & Grofman, B. (1995). Modeling negative campaigning. American Political Science Review, 89, 49–61.
Trent, J., & Sabourin, T. (1993). Sex still counts: Women’s use of televised advertising during the decade of the 80’s. Journal of Applied Communication Research, 21, 21–40.
West, D. (2005). Air wars: Television advertising in election campaigns, 1952–2004 (4th ed.). Washington, DC: CQ Press.
Woodall, G. S. (2005). Playing hardball in a dress? An examination of gender differences in the effectiveness of negative campaigns. Dissertation. Arizona State University.
Acknowledgments
We would like to thank Nathaniel Birkhead, Beth Easter, Adam Seth Levine, Mary Beth Lombardo and Spencer Piston for helpful comments on various drafts of this manuscript. We are also grateful to the editors of Political Behavior and the three anonymous reviewers whose comments greatly improved this manuscript.
Author information
Authors and Affiliations
Corresponding author
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Krupnikov, Y., Bauer, N.M. The Relationship Between Campaign Negativity, Gender and Campaign Context. Polit Behav 36, 167–188 (2014). https://doi.org/10.1007/s11109-013-9221-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11109-013-9221-9