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

Author

Listed:
  • Sherin Kularathne
  • Amanda Perera
  • Namal Rathnayake
  • Upaka Rathnayake
  • Yukinobu Hoshino

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.

Suggested Citation

  • Sherin Kularathne & Amanda Perera & Namal Rathnayake & Upaka Rathnayake & Yukinobu Hoshino, 2024. "Analyzing the impact of socioeconomic indicators on gender inequality in Sri Lanka: A machine learning-based approach," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-25, December.
    • Handle: RePEc:plo:pone00:0312395
    DOI: 10.1371/journal.pone.0312395
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    References listed on IDEAS

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