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Journal of Economic Integration 2021 June;36(2) :262-281.
Regional Integration Clusters and Optimum Customs Unions: A Machine-Learning Approach

Philippe De Lombaerde1,2 Dominik Naeher3 Takfarinas Saber 4

1,2Neoma Business School, France
United Nations University Institute on Comparative Regional Integration Studies, Belgium
3University College Dublin, Ireland
4Dublin City University, Ireland
Corresponding Author: Dominik Naeher ,Email:
Copyright ©2021 Journal of Economic Integration
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.

JEL Classification
C60: General
F13: Trade Policy; International Trade Organizations
F15: Economic Integration
F60: General
Keywords: Regional Integration | Customs Union | Machine Learning
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Regional Integration of Stock Markets in Latin America  2002 April;17(4)
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