This study presents a multidimensional analysis of socioeconomic disparities across European regions between 2019 and 2023, focusing on key Sustainable Development Goal (SDG) indicators related to poverty (SDG 1), quality education (SDG 4), gender equality in employment (SDG 5), and decent work and economic growth (SDG 8). Using a dataset at the NUTS-2 regional level, the research adopts a three-dimensional analytical framework to examine how these regions vary across multiple variables over different periods. The study’s core methodology is the Tucker 3 Clustering model, specifically designed to manage complex multidimensional datasets. An in-depth analysis of the T3Clus clusters highlights shared features and regional differences, emphasizing the key drivers of socioeconomic inequalities. The study contributes to policy discussions by shedding light on the interconnectedness of poverty, education, and employment conditions across Europe. It provides valuable insights into howsocioeconomic conditions have evolved, identifying 2020 as a turning point year, and pinpoints areas in need of intervention to supportmore equitable development across Europe. In fact, the analysis reveals significant regional disparities in socioeconomic conditions across European NUTS-2 regions, with Southern Italy, Greece, and parts of Eastern Europe exhibiting the highest levels of poverty and employment challenges, while regions in theNetherlands, Switzerland, and Scandinavia demonstrate stronger socioeconomic conditions. In particular, policies promoting labor market integration, gender equality, and educational access are essential to support vulnerable NUTS-2 regions, while high-performing NUTS-2 regions can serve as benchmarks for best practices.

Uncovering socioeconomic disparities in European regions: a Tucker 3 clustering approach / Bottazzi Schenone, Mariaelena; Tomaselli, Venera; Vichi, Maurizio. - In: QUALITY & QUANTITY. - ISSN 0033-5177. - (2025), p. 1. [10.1007/s11135-025-02133-7]

Uncovering socioeconomic disparities in European regions: a Tucker 3 clustering approach

Mariaelena Bottazzi Schenone
;
Venera Tomaselli;Maurizio Vichi
2025

Abstract

This study presents a multidimensional analysis of socioeconomic disparities across European regions between 2019 and 2023, focusing on key Sustainable Development Goal (SDG) indicators related to poverty (SDG 1), quality education (SDG 4), gender equality in employment (SDG 5), and decent work and economic growth (SDG 8). Using a dataset at the NUTS-2 regional level, the research adopts a three-dimensional analytical framework to examine how these regions vary across multiple variables over different periods. The study’s core methodology is the Tucker 3 Clustering model, specifically designed to manage complex multidimensional datasets. An in-depth analysis of the T3Clus clusters highlights shared features and regional differences, emphasizing the key drivers of socioeconomic inequalities. The study contributes to policy discussions by shedding light on the interconnectedness of poverty, education, and employment conditions across Europe. It provides valuable insights into howsocioeconomic conditions have evolved, identifying 2020 as a turning point year, and pinpoints areas in need of intervention to supportmore equitable development across Europe. In fact, the analysis reveals significant regional disparities in socioeconomic conditions across European NUTS-2 regions, with Southern Italy, Greece, and parts of Eastern Europe exhibiting the highest levels of poverty and employment challenges, while regions in theNetherlands, Switzerland, and Scandinavia demonstrate stronger socioeconomic conditions. In particular, policies promoting labor market integration, gender equality, and educational access are essential to support vulnerable NUTS-2 regions, while high-performing NUTS-2 regions can serve as benchmarks for best practices.
2025
socioeconomic disparities; sustainable development goals (SDGs); Tucker 3 clustering (T3Clus); NUTS-2 regions; multidimensional analysis
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Uncovering socioeconomic disparities in European regions: a Tucker 3 clustering approach / Bottazzi Schenone, Mariaelena; Tomaselli, Venera; Vichi, Maurizio. - In: QUALITY & QUANTITY. - ISSN 0033-5177. - (2025), p. 1. [10.1007/s11135-025-02133-7]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1736395
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