Economics

Exploring socioeconomic similarity-inequality: a regional perspective

M. L. Mouronte-lópez and J. S. Ceres

Discover how Mary Luz Mouronte-López and Juana Savall Ceres explore economic and social inequalities across global regions using machine learning and time-series data. Their innovative approach reveals critical relationships among key socioeconomic variables, aiming to influence policies that foster equality and sustainable development.

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Playback language: English

Index

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

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