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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. 

Causal decomposition analysis aims to identify risk factors (referred to as “mediators”) that contribute to social disparities in an outcome. Despite promising developments in causal decomposition analysis, current methods are limited to addressing a time-fixed mediator and outcome only, which has restricted our understanding of the causal mechanisms underlying social disparities. In particular, existing approaches largely overlook individual characteristics when designing (hypothetical) interventions to reduce disparities. To address this issue, we extend current longitudinal mediation approaches to the context of disparities research. Specifically, we develop a novel decomposition analysis method that addresses individual characteristics by (a) using optimal dynamic treatment regimes (DTRs) and (b) conditioning on a selective set of individual characteristics. Incorporating optimal DTRs into the design of interventions can be used to strike a balance between equity (reducing disparities) and excellence (improving individuals’ outcomes). We illustrate the proposed method using the High School Longitudinal Study data.