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

Aug 5, 2026

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This paper uses unsupervised machine-learning methods on nominal and industrial-sector production variables to evaluate convergence among European Monetary Union (EMU) member countries, emphasizing macroeconomic and structural homogeneity. Results identify distinct country clusters driven by macroeconomic stability and sectoral characteristics, including a core northern group and a secondary peripheral group concentrated mainly in Southern Europe. Differences between clusters, especially in real factors, indicate EMU fragility under large or asymmetric shocks and motivate structural reforms and careful risk-sensitive analysis.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Introduction

The intended benefits and OCA motivation

Research design and contribution

Theoretical framework and Eurozone construction

Mundell’s monetary area concept

Clustering methodology (Section 3)

Data description (Section 4)

Results and preliminary findings (Section 5)

Policy implications and conclusion

Contents lists available at Vilnius University Press | Ekonomika ISSN 1392-1258 eISSN 2424-6166
2024, vol. 103(4), pp. 61–80 DOI: [https://doi.org/10.15388/Ekon.2024.103.4.4](https://doi.org/10.15388/Ekon.2024.103.4.4) | | --- | | Fragile Union: A Machine Learning Analysis of Structural Heterogeneity and Divergence within the EMU
León Padilla*
Universidad de las Américas, Ecuador Email: [leon.padilla@udla.edu.ec](leon.padilla@udla.edu.ec)
ORCID: [https://orcid.org/0000-0001-9455-0158](https://orcid.org/0000-0001-9455-0158)
Sarah J. Carrington
Pontificia Universidad Católica del Ecuador [Email: sjcarring@gmail.com](Email: sjcarring@gmail.com)
ORCID: [https://orcid.org/0000-0002-9260-5028](https://orcid.org/0000-0002-9260-5028)
Eduardo Marín Nicolalde
Tary Analytics, Madrid, Spain Email: [eduardo@tary.io](eduardo@tary.io) | | 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.
Keywords: Optimum currency areas, Monetary unions, Eurozone, Cluster analysis. | 1. Introduction After a prolonged period of economic integration in Europe, the euro was introduced in January 2002. Initially, the founding countries of the eurozone included eleven member states: Germany, Austria, Belgium, Spain, Finland, France, Ireland, Italy, Luxembourg, the Netherlands, and Portugal. Over the following years, nine additional countries adopted the * Correspondent author. Received: 12/07/2024 . Revised: 20/08/2024 . Accepted: 26/10/2024 Copyright © 2024 León Padilla, Sarah J. Carrington, Eduardo Marín. Published by Vilnius University Press This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2024, vol. 103(4) euro: Greece, Slovenia, Cyprus, Malta, Slovakia, Estonia, Latvia, Lithuania, and Croatia. The euro has effectively acted as a constant force for deepening and broadening European integration since the early 2000s. However, the euro crisis has called into question the integrity of the Eurozone, whose structural and institutional fault lines have been revealed by the financial crisis (Pegkas et al., 2020) . While there have been challenges concerning the monetary union in Europe, its formation was initially intended to foster economic prosperity by enhancing currency stability, reducing transaction costs, and promoting trade integration, while also curbing competitive devaluations (Covi, 2021; Frieden, 2002; Glick and Rose, 2016), fostering political integration to strengthen the European (Sadeh and Verdun, 2009; Van Oudenaren, 2005), facilitating financial market integration to enhance efficiency and competition (Stavarek et al., 2011), and promoting sustainable fiscal policy to reduce the risk of sovereign debt escalation (Allard et al., 2013; McNamara, 1999) . Many potential benefits of monetary unification depend on the similarities of member states’ economic structures, macroeconomic objectives, and the correlation of externa

FAQs

What machine-learning approach does the paper apply to EMU member countries?

The study applies unsupervised machine-learning techniques to nominal and industrial-sector production variables to assess convergence and similarity of economic dynamics.

How does the clustering results characterize European Monetary Union members?

Clustering reveals distinct groups based on macroeconomic stability and industrial-sector characteristics, including a core northern group and a mainly Southern peripheral group.

Why does the paper argue the EMU is fragile?

The paper shows substantial differences between clusters, particularly when real factors are considered, suggesting vulnerability to large or asymmetric shocks unless structural reforms and careful analysis are pursued.

Aug 5, 2026

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