The Ties That Double Bind: Social Roles and Women's Underrepresentation in Politics

Abstract

This paper theorizes three forms of bias that might limit women's representation: outright hostility, double standards, and a double bind whereby desired traits present bigger burdens for women than men. We examine these forms of bias using conjoint experiments derived from several original surveys—a population survey of American voters and two rounds of surveys of American public officials. We find no evidence of outright discrimination or of double standards. All else equal, most groups of respondents prefer female candidates, and evaluate men and women with identical profiles similarly. But on closer inspection, all is not equal. Across the board, elites and voters prefer candidates with traditional household profiles such as being married and having children, resulting in a double bind for many women. So long as social expectations about women's familial commitments cut against the demands of a full-time political career, women are likely to remain underrepresented in politics.

Measuring Subgroup Preferences in Conjoint Experiments

“…We demonstrate this with examples drawn from the published political science literature (namely experiments by Hainmueller, Hopkins, and Yamamoto 2014;Bechtel and Scheve 2013;Teele, Kalla, and Rosenbluth 2018). The paper then provides suggestions for improved conjoint reporting and interpretation based around two quantities of interest drawn from the factorial experimentation literature: (a) unadjusted marginal means, a quantity measuring favorability toward a given feature, and (b) an omnibus F-test, measuring differences therein.…”

“…For fully randomized designs, the AMCE proposed by Hainmueller, Hopkins, and Yamamoto (2014) is equivalent to the average marginal effect of each feature level for a model where each feature is converted into a matrix of indicator variables with one level left out as a reference category. This is no different from any other regression context wherein one level of any categorical variable must be omitted from the design matrix in order to avoid perfect multicandidate feature examined by Teele, Kalla, and Rosenbluth (2018) or Hainmueller, Hopkins, and Yamamoto (2014) in their conjoints on candidate choice. In such cases, the probability of co-occurrence is 1 2 * 1 2 = 0.25 bounding the AMCE for female (as opposed to male) candidates to the range (−0.5, 0.5) if both candidates can have the same sex.…”

“…This kind of nested model comparison test can also be used to assess heterogeneity across conjoint features (see also Egami and Imai, 2018). For example, Teele, Kalla, and Rosenbluth (2018) report just such a test for how effects of features other than candidate sex may differ between male and female candidates, finding no such heterogeneity (8-9). Fortunately, the original analysis accurately detected an absence of subgroup differences, yet a subtly different set of analytic decisions about reference categories (as shown in Figure 4) Hopkins, and Yamamoto case), but ultimately there is no guarantee that they do in any particular analysis.…”

“…While survey experiments have traditionally examined just one or two factors that might shape outcomes (see, for reviews, Gaines, Kuklinski, and Quirk, 2007;Sniderman, 2011), conjoint designs allow researchers to study the independent effects on preferences of many features of complex, multidimensional objects. These include many different types of phenomena, such as political candidates (Campbell et al, 2016;Teele, Kalla, and Rosenbluth, 2018), immigrant admissions (Hainmueller and Hopkins, 2015;Bansak, Hainmueller, and Hangartner, 2016;Wright, Levy, and Citrin, 2016), and public policies (Gallego and Marx, 2017;Hankinson, 2018). Factorial designs of this sort have a long history, but the driving force behind this use of conjoint analysis has been the introduction by Hainmueller, Hopkins, and Yamamoto (2014) of a small-sample, fully randomized conjoint design.…”

“…Other examples abound. Ballard-Rosa, Martin, and Scheve (2016) compare preferences over tax policies across a number of subgroups defined by demographics and political orientations; Bechtel and Scheve (2013) compare AMCEs on climate agreements across four different countries, and across subgroups of respondents; and Teele, Kalla, and Rosenbluth (2018) compare AMCEs for features of male and female political candidates among male and female respondents. Most of these comparisons are visual or informal.…”