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Political Behavior

01.06.2015 | Original Paper

The Consequences of Explicit and Implicit Gender Attitudes and Candidate Quality in the Calculations of Voters

verfasst von: Cecilia Hyunjung Mo

Erschienen in: Political Behavior | Ausgabe 2/2015

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Abstract

How much does a voter’s attitude towards female versus male leadership manifest itself at the ballot box and when does information regarding candidate qualifications or the lack thereof matter in this relationship? I conduct an in-depth survey, which includes a vote choice experiment randomizing the sex of the more qualified candidate, a novel gender and leadership Implicit Association Test, and a measure of explicit gender attitudes to explore this question. I find that the propensity to pick a female candidate increases as explicit and implicit attitudes against female leadership decrease, suggesting that traditional explicit measures underestimate the effects of gender attitudes and miss a key dimension of people’s preferences. Gender attitudes in the electoral process remain consequential, but have grown subtler, which is missed when only assessing people’s self-reported explicit attitudes. Fortunately, the effects of voters’ gender attitudes can be attenuated by candidate qualification information; however, it does not rid the effects of gender on vote choice uniformly. People who explicitly state a preference for male leaders do not respond to individuating information, even if the female candidate is clearly more qualified than her male counterpart. However, people who implicitly prefer male leaders, but explicitly state being gender-equitable respond to individuating information and tend to select the more qualified candidate regardless of the candidate’s sex. The study points to the significance of dual process account of reasoning—acknowledging that individuals operate on two levels, System 1 (automatic and implicit) and System 2 (effortful and explicit)—in understanding voting behavior.

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See http://www.cbc.ca/news/interactives/map-world-womenpolitics/.

The focus of this research project is on voter-level gender biases. Certainly, biases may exist against women in the selection of candidates for viable races at the party level. According to Burrell (2008) and Sanbonmatsu (2006), political party recruitment efforts reflect gender biases; fewer women are encouraged to run in the first place.

See http://www.gallup.com/poll/155285/atheists-muslims-bias-presidential-candidates.aspx for more information on the Gallup poll.

Sample Czar is an online sampling firm (http://www.sampleczar.com) that has an extensive panel of over five million respondents in the US and another five million globally.

When identifying panelists for a study, Sample Czar enforces (1) rigid frequency of survey restrictions, so as to not bombard panel members with multiple invitations per day; and (2) unsubscribe speed-through and other bad respondents, and panelists who have been “inactive,” which is defined as those who have not accepted a survey invitation in six months. Respondents were told the following at the start of the survey: “You are invited to participate in a research study on elections. The goal of this project is to better understand people’s attitudes toward elections and political candidates. In order to do this, we will be asking you questions about your political preferences and beliefs.”

There were 438 total observations; however, observations with data duplication issues (31 subjects) were excluded from the analysis. Data duplication problems occurred due to subjects restarting the survey multiple times. Excluding subjects with duplication problems, there were 407 respondents (93 %) that successfully completed the experiment portion of the study; 393 individuals (90 %) that successfully completed the experiment, survey, and IAT components; and 390 individuals (89 %) that completed the entire study. Attrition did not vary substantially by treatment condition. Each of the 12 treatment conditions had up to seven individuals drop out of the study.

Data are from the Center for American Women and Politics, Eagleton Institute of Politics, Rutgers University (http://www.cawp.rutgers.edu).

88 % of the sample identified as white, and the average respondent had at least some college education, and was 3.86 on the political ideology scale, where 1 denotes strong democrat and 7 denotes strong republican.

Yeager et al. (2009) note that surveys of non-probability samples of people who volunteer to do surveys for money or prizes are less accurate than probability sample surveys, and hence there should be caution in presuming that non-probability samples yield data that are externally valid.

The two exceptions I am aware of are McDermott (1998), who used a California state poll, and Anderson et al. (2011), who surveyed prospective jurors in six counties in four US states.

For the 407 participants who completed the experiment component, the maximum and minimum conditions had 43 and 25 subjects, respectively, and hence, the ratio of maximum to minimum conditions is 1.72. Maximum (minimum) condition denotes the treatment condition with the largest (smallest) number of subjects. For the 390 participants who completed the entire survey, the maximum and minimum conditions had 41 and 24 subjects, respectively, resulting in a 1.71 maximum to minimum condition ratio. Based on 10,000 Monte Carlo simulations, I find that 1.71 and 1.72 correspond to the 42nd and 44th percentile, respectively.

Respondents were also asked to consider four additional races—two legislative races and two gubernatorial races—that are not included in this study, as there are no objective candidate quality measure that are divorced from party identification or policy positions.

See http://www.abanet.org/scfedjud/ratings.html for an overview of judicial nominee ratings.

Information about when candidates were initially nominated is presented to voters in the Official Voter Information Guide, and voters can thus infer the partisan affiliation of the candidates by referencing when the candidates were initially nominated. However, acquiring knowledge regarding partisan affiliations requires that voters take pro-active steps that are not necessary in executive and legislative races, which make judicial races a lower information setting. See http://www.ajs.org/selection/sel_state-select-map.asp#COLORADO for information regarding judicial selection methods by state.

Anderson et al. (2011) is one exception.

The non-gendered (male-male) contest was included within the study to enable analyses of whether respondents were able to detect the quality differences holding gender constant. I did not include a female-female contest, as the purpose of including the non-gendered contest was to conduct a manipulation check.

Candidate pictures were only of white individuals to ensure that race considerations were unnecessary. Popular “non-ethnic” names (e.g., Mary Brooks and David Smith) in the United States were selected for the same reason. Names were selected based upon the list of popular baby names, according to the US Social Security Administration (see http://www.ssa.gov/OACT/babynames/).

Pictures were included, as typical voter guides include pictures of candidates. However, literature on “thin-slices” demonstrate that pictures can impact voters’ calculations due to attractiveness, age assessments, health assessments, etc. (e.g., Biddle and Hamermesh 1998; Mobius and Rosenblat 2006). As such, for each picture, I include controls for the pictures used (see Empirical Strategy section for details on the adjustment).

In other words, individuals could have received a “both strong” race or a “strong female” race or a “strong male” race or a “mixed strength” race first. The effect of the ordering of the seven election contests did not affect results and are available upon request.

To the extent that some respondents are unable to assess the candidate quality differentials, the effect of candidate quality on the relationship between prejudice and vote choice will likely be underestimated. If individuating information does not clearly tip the scales in favor of one candidate on the dimension of candidate quality, then relative candidate quality would be less of a consideration, making it more difficult to test the validity of Hypothesis 2.

While descriptive statistics for raw measures are reported, for all analyses I recoded all survey measures to lie between 0 and 1.

Questions were based upon recommendations in Sanbonmatsu and Dolan (2007) and Sanbonmatsu (2002).

To maximize reliability, all bipolar questions had a 7-point rating scale and unipolar questions had a 5-point rating scale (Birkett 1986; Kalton et al. 1980; Lissetz and Green 1975).

See http://www.implicit.harvard.edu/implicit.

The code for creating this IAT is available upon request.

IAT D Effect$=-[(1/2)(Mean_{stage 6}-Mean_{stage 3})/\sigma _{6,3}+(1/2)(Mean_{stage 7}-Mean_{stage 4})]/\sigma _{7,4}$ (Greenwald et al. 2003). The IAT measure involves computing two mean differences and dividing each difference score by its associated “inclusive” standard deviation. The D effect is then an equal-weight average of two resulting ratios. Stage 6 and 7 are trials in which pictures of women are paired with “leadership” words and pictures of male individuals are paired with “follower” words. Stage 3 and 4 are trials in which males are paired with “leadership” words and women are paired with “follower” words. Hence, a positive score would indicate that an individual took longer to associate pictures of women with “leadership” words (Mean _{stage 6}) than pictures of men with “leadership” words (Mean _{stage 3}), and longer to associate pictures of men with “follower” words (Mean _{stage 7}) than pictures of females with “follower” words (Mean _{stage 4}). The part of the IAT D Effect that accommodates general processing speed—the fact that irrespective of their attitudes, some individuals respond faster than others on a wide range of cognitive tasks—is the “inclusive” standard deviation. Respondents are obliged to correct errors before proceeding, and latencies are measured to the occurrence of the correct response.

Critics of the IAT note that the IAT is taken in full consciousness on the part of the subjects. According to Burdein et al. (2006), “respondents are aware of the stimulus but not of how their responses have been affected” (361). As such, savvy individuals can control their responses (e.g., slowing down their responses to all trials to minimize the gap in response times between the blocks with congruent categories and the blocks with incongruent categories (e.g., Fiedler and Bluemke 2005; Steffens 2004). To the extent that respondents were aware of the IAT and the mechanics of how the IAT measure works when completing the survey, this may be a very valid criticism. However, the details of the IAT are not well-known outside of the psychology circle, and hence, an unlikely problem. The two cases in which respondents were performing too slowly, possibly due to being distracted or an intention to game the measurement—respondents with extreme outlier latencies—were removed. Additionally, if respondents “faked” the IAT, then there would be minimal predictive power of the IAT, inducing a downward bias on predictive validity.

See http://www.implicit.harvard.edu/implicit/demo/background/raceinfo.html.

See Nosek and Hansen (2008) for evidence against this interpretation.

The IAT was utilized due to its popularity as an implicit measure; however, there are a number of criticism of the IAT measure. One potential concern is the reliability of the IAT measure. However, research on implicit measures are finding that the IAT has greater reliability than other implicit measures (Nosek and Hansen 2008; Perez 2013). A second concern, according to some scholars, is that IAT scores could be subject to priming effects (e.g., Mendelberg 2008). A third issue is around the relative nature of the IAT. The relative merits of various implicit measures are outside the scope of this study. However, given the debate on the merits and weaknesses of various implicit measures, further research utilizing alternative implicit measures are warranted.

To measure general political knowledge, participants were quizzed on ten political knowledge items proposed by the Pew Research Center (PRC) in February of 2008 (Delli Carpini and Keeter 1996; Keeter and Suls 2008). Questions were drawn from http://www.pewresearch.org/newsiq/. For each question, the share of individuals who answered each question correctly, and the mean total score (out of a possible 10) are displayed in Table D.9 in the Online Appendix. Each result is based on a multiple choice question. A description of each of the knowledge measures is provided in the Online Appendix.

A series of ten questions were asked regarding political interest. Eight questions regarding the intensity to which respondents take interest in politics and being involved in politics were asked (see Table E.10 in the Online Appendix). A description of each of the interest measures is provided in the Online Appendix. To create one political interest index, each of the eight non-political affiliation questions is normalized and summed together. The additive measure is then normalized to be between 0 and 1 (Cronbach’s α = 0.80).

Codes for control variable are as follows: (1) sex (1 = female); (2) whether the respondent identifies him/herself as white; (3) age; (4) education level (1 = elementary, 2 = junior high, 3 = some high school education, 4 = high school graduate, 5 = some college, 6 = 2-year college degree, 7 = bachelor’s degree, 8 = some graduate school education, 9 = postgraduate degree); (5) religiosity (0 = atheist, 1 = agnostic, 2 = slightly religious, 3 = moderately religious, 5 = strongly religious); (6) community type (urban, suburban, or rural); (7) household income (0 = below $20,000, 1 = $20,000–40,000, 2 = $40,000–60,000, 3 = $60,000–80,000, 4 = $80,000–100,000, 5 = $100,000–120,000, 6 = $120,000–140,000, 7 = $140,000–160,000, 8 = $160,000–180,000, 9 = $180,000–200,000, 10 = above $200,000); (8) Party Identification (1 = strong Democrat, 2 = not-strong Democrat, 3 = lean Democrat, 4 = neither, 5 = lean Republican, 6 = not-strong Republican, 7 = strong Republican); (9) political knowledge score (number correct out of a possible 10); (10) political interest index (average of eight measures, with each question measured on a 5-point scale (1 = not at all important, 2 = slightly important, 3 = somewhat important, 4 = very important, 5 = extremely important); (11) WORDSUM test score (number correct out of a possible 10). All variables were coded to lie between 0 and 1.

WORDSUM is a 10-item verbal ability test employed in the General Social Survey (GSS) of the National Opinion Research Center. The 10 GSS vocabulary items were developed by Thorndike in response to the need for a very brief test of intelligence in a social survey (Thorndike and Gallup 1944). For each of the 10 WORDSUM items, GSS respondents are asked to choose the one word of five possible matches that comes closest in meaning to the following words: lift, concern, broaden, blunt, accustom, chirrup, edible, cloistered, tactility, and sedulous. Words were selected from each level of the vocabulary section of the Institute for Education Research Intelligence Scale: Completion, Arithmetic Problems, Vocabulary, and Directions (Thorndike 1942).

To verify that results are not sensitive to empirical strategy, pooled, logistic and probit analyses were all conducted. Regression results when using a probit model and linear probability model are available upon request. There are no changes in the direction and significance of coefficients across the three dichotomous choice models.

While estimates from both empirical strategies are reported in the tables, only results from the random-effects models are reported in the main text.

All reported predictive probabilities are mean predictions, estimating the likelihood that a respondent would select the female candidate over an equally qualified male candidate over the level of the attitude measure, adjusting for the overall mean of each of the other predictors (e.g., demographic characteristics).

According to Baron and Kenny (1986), for mediation to occur, an independent variable should influence the dependent variable as well as the proposed mediator. Moreover, when the effects of the independent variable and mediator on the dependent variable are simultaneously controlled for, the effect of the mediated variable should be reduced.

Note that the main effects and the interaction effects are not sensitive to the choice of explicit measure.

Note that the word “bias” should be used carefully. One could argue that gender preferences that arise from a belief regarding ideology as opposed to stereotypes regarding gender roles and/or leadership capabilities are not really “biased” or prejudiced; their gender preferences arise from a rational assessment of preferred policy rather than gender attitudes. However, my claim that bias may be a factor arises from the fact that whether non-liberal voters who believe that female candidates are more liberal are included, excluded or considered separately, implicit attitudes matter and are attenuated by individuating information regarding candidate quality. I find that candidate quality can attenuate a predisposition to choose a candidate of a particular sex among non-liberal voters who believe women leaders are more liberal, as well as non-liberal voters without the belief that women are more liberal and liberal voters. Results are available upon request.

Investigation on the interaction between gender attitudes and on-line and memory-based processing of candidate information (Kim and Garrett 2012) is a related stream of research that will also help to build a more systematic understanding of individuals’ political information processing and its implications for vote choice.

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Titel: The Consequences of Explicit and Implicit Gender Attitudes and Candidate Quality in the Calculations of Voters
verfasst von: Cecilia Hyunjung Mo
Publikationsdatum: 01.06.2015
Verlag: Springer US
Erschienen in: Political Behavior / Ausgabe 2/2015
Print ISSN: 0190-9320
Elektronische ISSN: 1573-6687
DOI: https://doi.org/10.1007/s11109-014-9274-4