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

Kendall D. Funk, Hannah L. Paul, Andrew Q. Philips

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1-19
Number of pages19
JournalPolitical Science Research and Methods
StateAccepted/In press - 2021


  • critical mass
  • machine learning
  • Non- and semiparametric models
  • pooled cross-section time series models
  • women's representation

ASJC Scopus subject areas

  • Sociology and Political Science
  • Political Science and International Relations


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