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Rethinking Women's Interests: An Inductive and Intersectional Approach to Defining Women's Policy Priorities

Published online by Cambridge University Press:  25 August 2021

Tevfik Murat Yildirim*
Affiliation:
Department of Media and Social Sciences, University of Stavanger, Stavanger, Norway
*
Corresponding author. E-mail: murat.yildirim@uis.no
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Abstract

Much of the vast literature on the substantive representation of women takes as its point of departure important a priori assumptions about the nature of women as a group. Calling for a rethink of many of those assumptions, a recent body of work recommends an inductive approach to defining women's interests. In line with this view, this article draws on a recently constructed dataset that codes nearly a million Americans' policy priorities over the past 75 years to explore what constitutes women's interests and whether gender differences in priorities cut across partisan and racial divisions. The results suggest there are consistent gender gaps across a large number of policy categories, with women showing particular concern for policy areas traditionally associated with issues of ‘women's interests’. While in many policy areas women were more likely to share policy priorities with other women than with their male counterparts of the same race or partisan background, the results also document considerable heterogeneity among women in various policy areas, which has major policy implications for the representation of women's interests.

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Type
Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press
Figure 0

Figure 1. Gender, partisan and racial composition of the data over timeNote: the bottom portion of the figure shows the mean sample size (by year) over time.

Figure 1

Table 1. Examples of open-ended MIP responses and Singer (2011) topic categories

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Table 2. Descriptive statistics of key variables

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Figure 2. Gender differences in policy prioritization (individual policy categories)Note: the figures display coefficients on the variable Gender obtained from thirty-six linear probability models. The figure on the left (right) shows the list of policy categories that men (women) were more likely to prioritize, compared to women (men). The models account for various sociodemographic and political factors including partisan identification, respondent's residence (region: the South), race, age, formal education, income level (quartiles), and the party of the president at the time of the survey. Additionally, year fixed-effects are employed.

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Table 3. Substantive impact of gender on policy priorities (logistic regressions)

Figure 5

Figure 3. Interactive effects of gender, race and partisanship on policy priorities

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Yildirim Dataset

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