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



  • 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.

publication date

  • September 20, 2021

Date in CU Experts

  • September 28, 2021 1:22 AM

Full Author List

  • Funk KD; Paul HL; Philips AQ

author count

  • 3

Other Profiles

International Standard Serial Number (ISSN)

  • 2049-8470

Electronic International Standard Serial Number (EISSN)

  • 2049-8489

Additional Document Info

start page

  • 1

end page

  • 19