Women in Leadership, Skilled Workforce, and Firm Performance in Bangladesh: A Machine Learning Analysis on Enterprise Survey Data

Abstract

This study examines the influence of women’s representation in top management positions and availability of skilled human capital on firm performance in Bangladesh, a critical aspect that remains underexplored in the existing literature. We leverage a fresh and comprehensive dataset of 824 firms released by the World Bank Enterprise Survey in 2023 and employ both traditional ordinary least squares (OLS) regression and machine learning algorithms to analyze the relationship. While the presence of female leadership alone may not substantially boost firm performance, our findings underscore a notable positive influence of women in leadership roles, contingent upon the presence of a skilled workforce. Notably, factors such as labor and electricity costs, access to financial resources, and international quality certification consistently show positive associations with firm performance. Our findings offer valuable guidance to policymakers and corporate decision-makers, highlighting the importance of supporting women in leadership roles while simultaneously investing in a skilled labor force to unlock firm productivity and success.

Keywords

• women leadership
• skilled workforce
• firm performance
• machine learning algorithms