Fragile Union: A Machine Learning Analysis of Structural Heterogeneity and Divergence within the EMU

Abstract

This paper applies unsupervised machine-learning techniques to a set of nominal and industrial sector production variables to examine the convergence of European Monetary Union (EMU) member countries, focusing on macroeconomic and structural homogeneity. Our findings reveal distinct clusters of countries based on macroeconomic stability and industrial sector characteristics, highlighting a central group of core Northern European countries and a secondary group of peripheral, mainly Southern European economies. The significant differences between these clusters, particularly when considering real factors, underscore the fragility of the EMU in the face of large or asymmetric shocks. The study emphasized the need for structural reforms and careful analysis of economic characteristics to mitigate potential risks associated with expansion, ensuring the long-term stability and resilience of the union.