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Causal decomposition analysis in disparities research: investigating the effect of self-efficacy on the gender gap in STEM

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  • Rafael Quintana

    (University of Kansas)

  • Soojin Park

    (University of California-Riverside)

  • Suyeon Kang

    (University of Central Florida)

Abstract

The underrepresentation of women in science, technology, engineering and mathematics (STEM) fields has been a subject of extensive research and policy debate. However, there is limited clarity regarding the specific mechanisms that generate these disparities, and which interventions are most effective in reducing the gap. In this study, we use causal decomposition analysis to estimate how the gender gap in STEM participation would change if we were to intervene on women’s self-efficacy beliefs in mathematics. Women tend to underestimate their abilities in math-related fields, which can affect their educational and career choices. The question we ask is to what extent the gender gap in individuals’ enrollment in STEM majors and identification with mathematics would be reduced if self-efficacy in mathematics were set to be equal across gender categories. The results suggest that equalizing this target factor will reduce the observed disparities in math identity by 53%, and in the enrollment of STEM majors by 2.5%. The modest influence of self-efficacy on enrollment disparities suggests that it is not the predominant factor. We discuss the implications of our empirical findings, as well as how causal decomposition analysis can benefit social and behavioral disparities research.

Suggested Citation

  • Rafael Quintana & Soojin Park & Suyeon Kang, 2025. "Causal decomposition analysis in disparities research: investigating the effect of self-efficacy on the gender gap in STEM," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(4), pages 3327-3351, August.
    • Handle: RePEc:spr:qualqt:v:59:y:2025:i:4:d:10.1007_s11135-025-02110-0
    DOI: 10.1007/s11135-025-02110-0
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