Analyzing multidimensional poverty in Roma settlements: A WEFE Nexus and machine learning approach

Abstract

This study aims to analyze multidimensional poverty determinants within Roma settlements in Serbia, North Macedonia, and Montenegro using logistic regression and machine learning to identify the socioeconomic and resource-based components influencing poverty within the WEFE (Water Energy Food Ecosystems) Nexus. Reliable lighting, sanitation maintenance, safe water access, consistent water supply, and energy for cooking all play a critical role in poverty alleviation. Our key findings align with SDG 1 (No Poverty), SDG 6 (Clean Water and Sanitation), and SDG 7 (Affordable and Clean Energy), underscoring the global significance and relevance of our research. Random Forest and Extra Trees perform very well when compared to logistic regression by capturing highly variable interactions that may be missed by logistic regression. Results with country-specific emphases are presented, such as digital access in Montenegro and household size in North Macedonia, to illustrate the adaptability of the WEFE framework to different regional contexts. The results advocate for resource-driven integrated policies to improve people’s access to important utilities, financial inclusion, and digital connectivity to build social sustainability and resilience. The study supports NexusNet’s plan to lead SDG-aligned poverty reduction in all sectors in the Western Balkans by focusing on WEFE resources and socio-economic supports.