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Point break: using machine learning to uncover a critical mass in women's representation

Published online by Cambridge University Press:  20 September 2021

Kendall D. Funk
Affiliation:
School of Social and Behavioral Sciences, Arizona State University, Tempe, AZ, USA
Hannah L. Paul*
Affiliation:
Department of Political Science, University of Colorado Boulder, Boulder, CO, USA
Andrew Q. Philips
Affiliation:
Department of Political Science, University of Colorado Boulder, Boulder, CO, USA
*
*Corresponding author. Email: hannah.paul@colorado.edu

Abstract

Decades of research has debated whether women first need to reach a “critical mass” in the legislature before they can effectively influence legislative outcomes. This study contributes to the debate using supervised tree-based machine learning to study the relationship between increasing variation in women's legislative representation and the allocation of government expenditures in three policy areas: education, healthcare, and defense. We find that women's representation predicts spending in all three areas. We also find evidence of critical mass effects as the relationships between women's representation and government spending are nonlinear. However, beyond critical mass, our research points to a potential critical mass interval or critical limit point in women's representation. We offer guidance on how these results can inform future research using standard parametric models.

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

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Footnotes

Previous versions of this paper were presented at the Conference on Gender, Politics, and Quantitative Methods held at Texas A&M University in March 2019 and at the 2019 Meeting of the Midwest Political Science Association. We thank participants of these events and three anonymous reviewers for their insightful comments and suggestions. Of course, any remaining errors are our own.

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