Analyzing Income Inequalities across Italian regions: Instrumental Variable Panel Data, K-Means Clustering and Machine Learning Algorithms

Antonicelli, Margareth and Drago, Carlo and Costantiello, Alberto and Leogrande, Angelo (2025): Analyzing Income Inequalities across Italian regions: Instrumental Variable Panel Data, K-Means Clustering and Machine Learning Algorithms.

Abstract

This study examines income inequality across Italian regions by integrating instrumental variable panel data models, k-means clustering, and machine learning algorithms. Using econometric techniques, we address endogeneity and identify causal relationships influencing regional disparities. K-means clustering, optimized with the elbow method, classifies Italian regions based on income inequality patterns, while machine-learning models, including random forest, support vector machines, and decision tree regression, predict inequality trends and key determinants. Informal employment, temporary employment, and overeducation also play a major role in influencing inequality. Clustering results confirm a permanent North-South economic divide and the most disadvantaged regions are Campania, Calabria, and Sicily. Among the machine learning models, the highest income disparities prediction accuracy comes with the use of Random Forest Regression. The findings emphasize the necessity of education-focused and digitally based policies and reforms of the labor market in an effort to enhance economic convergence. The study portrays the use of a combination of econometric and machine learning methods in the analysis of regional disparities and proposes a solid framework of policy-making with the intention of curbing economic disparities in Italy.

• Item Type: MPRA Paper
• Language: English
• Keywords: Income Inequality, Regional Disparities, Machine Learning, Labor Market, Digital Divide.
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C38 - Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O15 - Human Resources ; Human Development ; Income Distribution ; Migration; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R11 - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R5 - Regional Government Analysis > R58 - Regional Development Planning and Policy

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