Analyzing the impact of socioeconomic indicators on gender inequality in Sri Lanka: A machine learning-based approach.

Journal: PloS one
PMID: 39724101

Abstract

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.

Authors

  • Sherin Kularathne
    Faculty of Graduate Studies and Research, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka.
  • Amanda Perera
    Department of Business Economics, Faculty of Management Studies and Commerce, University of Sri Jayewardenepura, Gangodawila, Nugegoda, Sri Lanka.
  • Namal Rathnayake
    River and Environmental Engineering Laboratory, Graduate School of Engineering, The University of Tokyo, Bunkyo City, Tokyo, Japan.
  • Upaka Rathnayake
    Department of Civil Engineering and Construction, Faculty of Engineering and Design, Atlantic Technological University, Sligo, Ireland.
  • Yukinobu Hoshino
    School of Systems Engineering, Kochi University of Technology, Kami, Kochi, Japan.

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