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Marginalization and Mobilization: The Roots of Female Legislators’ Collaborative Advantage in the States

Published online by Cambridge University Press:  19 April 2021

Clint S. Swift
Affiliation:
VoteShield, US
Kathryn VanderMolen*
Affiliation:
Political Science and International Studies University of Tampa, Tampa, FL, USA
*
Corresponding Author: Kathryn VanderMolen, University of Tampa, Tampa, FL, USA. Email: kvandermolen@ut.edu
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Abstract

Scholars have argued that female legislators are more prone to collaborate than their male counterparts. Though collaboration may be more or less evident in particular situations, we seek to more clearly establish the mechanism behind women’s collaborative activity using the framework of marginalization. In this paper, we use cosponsorship data from 74 state legislative chambers from 2011–2014 to analyze collaborative patterns and mobilizing institutions. We find female legislators are more collaborative than men, and that their collaborative advantage strengthens in chambers where women are systematically excluded from leadership positions. The advantage also extends to bipartisan collaboration, but only in less polarized settings with women’s caucuses. Furthermore, our findings imply that as women are integrated into leadership collaboration will actually decline, especially within their own party. We believe these results are important for understanding both the roots of collaborative behavior among female legislators and consequences of chambers that marginalize women from leadership positions.

Information

Type
Original Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the American Political Science Association
Figure 0

Table 1. Full set of expectations for women’s collaborative advantage (H1c and H2a)

Figure 1

Figure 1. Distribution of within-gender cosponsorship scores by chamber for the 2011/2012 legislative session.

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Figure 2. Distribution of bipartisan, within-gender cosponsorship scores by chamber for the 2011/2012 legislative session.

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Table 2. Predictors of within-gender collaboration in 74 state chambers (additive model only)

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Table 3. Predictors of within-gender collaboration in 74 state chambers: The interactive effects of marginalization and mobilization

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Figure 3. The marginal effect of being a female legislator on within-gender cosponsorship scores at different levels of female marginalization.Note. Values are derived from Model 3a in Table 3. Graphs are produced in Stata using the margins and marginsplot commands. Gray area represents the 95% confidence interval.

Figure 6

Table 4. The marginal effect of being a female legislator on within-gender cosponsorship scores in chambers with and without a women’s caucus

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Figure 4. The marginal effect of being a female legislator on within-gender cosponsorship scores at different levels of female marginalization for chambers with and without a women’s caucus.Note. Values are derived from Model 3c in Table 3. Graphs are produced in Stata using the margins and marginsplot commands. Gray area represents the 95% confidence interval.

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Table 5. Predictors of bipartisan, within-gender collaboration in 74 state chambers

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Figure 5. The marginal effect of being a female legislator on bipartisan, within-gender cosponsorship scores at different levels of female marginalization for chambers with and without women’s caucuses.Note. Values are derived from Model 5b and estimated in Stata using the margins and marginsplot commands. Gray area represents the 95% confidence interval.

Figure 10

Table 6. Predictors of bipartisan, within-gender collaboration in 74 state chambers: Conditional effects of caucuses and polarization

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Figure 6. The marginal effect of being a female legislator on bipartisan, within-gender cosponsorship scores at different levels of polarization for chambers with and without women’s caucuses.Note. Values are derived from the model in Table 6 and estimated in Stata using the margins command. Gray area represents the 95% confidence interval. The bold line indicates the area over which the effects for caucus states are statistically different from those without caucuses and also statistically different from zero.

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Table A1. Control variables: Measures and sources