Quantifying Barriers to Women's Career Advancement in Evolving Policy Contexts using Logistic Regression Analysis

Abstract

Despite decades of legislative interventions and corporate diversity initiatives, the vertical segregation of women in professional hierarchies remains a persistent sociological and economic challenge. This paper investigates the structural and procedural barriers impeding women's career advancement, specifically focusing on the efficacy of recent policy evolutions designed to mitigate gender bias. By employing a binary logistic regression framework, we analyze a longitudinal dataset comprising five thousand mid-level professionals across the financial and technology sectors. The study aims to quantify the probability of promotion while controlling for human capital variables such as tenure, education, and performance ratings, alongside interaction effects representing policy implementation periods. Our findings suggest that while overt discrimination has diminished, subtle structural barriers persist, often manifesting as reduced returns on human capital investment for female employees compared to their male counterparts. Furthermore, the analysis reveals that certain flexible work policies, while well-intentioned, may inadvertently reduce promotion odds by signaling lower commitment in high-performance cultures. This research contributes to the literature by offering a rigorous statistical evaluation of the glass ceiling hypothesis within contemporary regulatory frameworks.