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Abstract
This research investigates socioeconomic similarities and inequalities across global regions using time-series data and machine learning. It ranks countries based on various socioeconomic variables (health, economic, social labor protection, and gender) using standard series clustering and Ward's algorithm. Relationships between variables are identified, and a machine learning model predicts the Gini coefficient based on these variables. The study aims to inform regional and national policies promoting equality and sustainable development.
Publisher
Humanities & Social Sciences Communications
Published On
Feb 19, 2024
Authors
Mary Luz Mouronte-López, Juana Savall Ceres
Tags
socioeconomic inequalities
machine learning
Gini coefficient
sustainable development
policy-making
time-series data
clustering
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