The Research of the impact of gender and industry on income gap based on multiple linear regression analysis and multilevel linear model

Abstract

Income disparities across industries are a persistent global issue, influenced by structural factors such as industry characteristics, market demand, and technological advancements. Understanding the mechanisms driving these disparities, particularly the role of gender and industry type, is essential for addressing income inequality. This study aims to explore the effects of gender and industry type on income disparities. Through multiple linear regression (MLR) and multilevel linear models (MLM), the study reveals significant findings. MLR results indicate that industries such as manufacturing/construction, insurance, real estate, and professional services offer significantly higher income levels, while gender disparities persist, with men earning significantly more than women (p < 0.01). MLM analysis uncovers interaction effects, showing that gender income disparities vary across industries: men earn more in sectors like finance and construction, while women have higher earnings in fields like services and education. The findings demonstrate that 69% of income variation can be explained by gender and industry factors (R^2 = 0.69). This research provides theoretical insights into income distribution mechanisms and offers practical guidance for policymakers to address gender-based and industry-specific income disparities, contributing to more equitable economic development.