Kularathne Sherin, Perera Amanda, Rathnayake Namal, Rathnayake Upaka, Hoshino Yukinobu
Faculty of Graduate Studies and Research, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka.
Department of Business Economics, Faculty of Management Studies and Commerce, University of Sri Jayewardenepura, Gangodawila, Nugegoda, Sri Lanka.
PLoS One. 2024 Dec 26;19(12):e0312395. doi: 10.1371/journal.pone.0312395. eCollection 2024.
This study conducts a comprehensive analysis of gender inequality in Sri Lanka, focusing on the relationship between key socioeconomic factors and the Gender Inequality Index (GII) from 1990 to 2022. By applying machine learning techniques, including Decision Trees and Ensemble methods, the study investigates the influence of economic indicators such as GDP per capita, government expenditure, government revenue, and unemployment rates on gender disparities. The analysis reveals that higher GDP and government revenues are associated with reduced gender inequality, while greater unemployment rates exacerbate disparities. Explainable AI techniques (SHAP) further highlight the critical role of government policies and economic development in shaping gender equality. These findings offer specific insights for policymakers to design targeted interventions aimed at reducing gender gaps in Sri Lanka, particularly by prioritizing economic growth and inclusive public spending.
本研究对斯里兰卡的性别不平等进行了全面分析，重点关注1990年至2022年关键社会经济因素与性别不平等指数（GII）之间的关系。通过应用包括决策树和集成方法在内的机器学习技术，该研究调查了人均国内生产总值、政府支出、政府收入和失业率等经济指标对性别差距的影响。分析表明，较高的国内生产总值和政府收入与性别不平等的减少相关，而较高的失业率则加剧了差距。可解释人工智能技术（SHAP）进一步凸显了政府政策和经济发展在塑造性别平等方面的关键作用。这些发现为政策制定者提供了具体见解，以便他们设计有针对性的干预措施，旨在缩小斯里兰卡的性别差距，特别是通过优先考虑经济增长和包容性公共支出。
