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Political Behavior

Erschienen in:

01.03.2014 | Original Paper

The Relationship Between Campaign Negativity, Gender and Campaign Context

verfasst von: Yanna Krupnikov, Nichole M. Bauer

Erschienen in: Political Behavior | Ausgabe 1/2014

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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.

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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.

Using the Huddy and Terkildsen (1993) measures. Percentages using the Brooks (2011)/Rudman et al. (2001) measure are substantively similar.

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.

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Titel: The Relationship Between Campaign Negativity, Gender and Campaign Context
verfasst von: Yanna Krupnikov
Nichole M. Bauer
Publikationsdatum: 01.03.2014
Verlag: Springer US
Erschienen in: Political Behavior / Ausgabe 1/2014
Print ISSN: 0190-9320
Elektronische ISSN: 1573-6687
DOI: https://doi.org/10.1007/s11109-013-9221-9

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