Point break: using machine learning to uncover a critical mass in women's representation. Issue 2 (20th April 2022)

• Record Type: Journal Article
• Authors: Funk, Kendall D.; Paul, Hannah L.; Philips, Andrew Q.
• Is Part Of: Political science research and methods. Volume 10:Issue 2(2022)
• Page Start: 372
• Page End: 390
• Publication Date: 2022-04-20
• Subjects: Non- and semiparametric models -- pooled cross-section time series models -- machine learning -- women's representation -- critical mass; Political science -- Periodicals; 320
• DOI: 10.1017/psrm.2021.51
• Languages: English
• ISSNs: 2049-8470

Abstract

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.