Regional Integration Clusters and Optimum Customs Unions: A Machine-Learning Approach

Philippe De Lombaerde (), Dominik Naeher () and Takfarinas Saber ()
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Philippe De Lombaerde: Neoma Business School, United Nations University Institute on Comparative Regional Integration Studies, Postal: Neoma Business School, Rouen, France, and Director a.i., United Nations University Institute, on Comparative Regional Integration Studies (UNU-CRIS), Bruges, Belgium
Dominik Naeher: University College Dublin, Postal: School of Economics, University College Dublin, Dublin, Ireland
Takfarinas Saber: Dublin City University, Postal: Lero-Science Foundation Ireland Research Centre for Software, School of Computing, Dublin City, University, Dublin, Ireland

Journal of Economic Integration, 2021, vol. 36, issue 2, 262-281

Abstract: This study proposes a new method to evaluate the composition of regional arrangements focused on increasing intraregional trade and economic integration. In contrast to previous studies that take the country composition of these arrangements as given, our method uses a network clustering algorithm adapted from the machine-learning literature to identify, in a data-driven way, those groups of neighboring countries that are most integrated with each other. Using the obtained landscape of regional integration clusters (RICs) as a benchmark, we then apply our method to critically assess the composition of real-world customs unions (CUs). Our results indicate a considerable variation across CUs in terms of their distance to the RICs emerging from the clustering algorithm. This suggests that some CUs are relatively more driven by “natural” economic forces, as opposed to political considerations. Our results also point to several testable hypotheses related to the geopolitical configuration of CUs.

Keywords: Regional Integration; Customs Union; Machine Learning (search for similar items in EconPapers)
JEL-codes: C60 F13 F15 F60 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:ris:integr:0827

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