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Mathematics literacy is crucial in many STEM fields, yet Black and Hispanic students are less likely to achieve high math proficiency. While previous literature investigated potential factors to mitigate racial or gendered disparities in mathematics literacy, few studies attended to the conditions under which the causal interpretation of the results obtained can be established. Guided by intersectionality theory and causal decomposition analysis, we examined the degree to which disparities in mathematics literacy (a) exist at the intersection of race and gender and (b) can be reduced by hypothetical interventions that equalize school socioeconomic status (SES) or opportunity to learn (OTL) across groups. We found large racial/ethnic differences in math literacy favoring Asians and whites and much smaller gender differences. We also found that equalizing school SES may reduce disparities for Black and Hispanic males and females; equalizing OTL may reduce disparities for Black and Hispanic males as well as Asian males and females; compared to white males. Our findings suggest that interventions that target specific race–gender groups are required to reduce disparities in math literacy.
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This content will become publicly available on March 22, 2026
Causal decomposition analysis in disparities research: investigating the effect of self-efficacy on the gender gap in STEM
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.
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- Award ID(s):
- 2243119
- PAR ID:
- 10609473
- Publisher / Repository:
- Quality and Quantity
- Date Published:
- Journal Name:
- Quality & Quantity
- ISSN:
- 0033-5177
- Subject(s) / Keyword(s):
- causal decomposition sensitivity analysis gender gap STEM self-efficacy
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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