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Regional integration clusters and optimum customs Unions: a machine-learning approach

Lombaerde, Philippe De orcid logoORCID: 0000-0002-6546-6771, Naeher, Dominik orcid logoORCID: 0000-0002-7535-6891 and Saber, Takfarinas orcid logoORCID: 0000-0003-2958-7979 (2021) Regional integration clusters and optimum customs Unions: a machine-learning approach. Journal of Economic Integration, 36 (2). pp. 262-281. ISSN 1225-651X

Abstract
This paper proposes a new method to evaluate the composition of regional arrangements focused on increasing intraregional trade and economic integration. In contrast to previous studies which 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 benchmark, we then apply our method to critically assess the composition of real-world customs unions. Our results indicate that there is considerable variation across customs unions as to their distance to the RICs emerging from the clustering algorithm, suggesting that some customs unions 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 customs unions.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Regional Integration; Customs Union
Subjects:Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > Lero: The Irish Software Engineering Research Centre
Publisher:Center for Economic Integration
Official URL:https://doi.org/10.11130%2Fjei.2021.36.2.262
Copyright Information:© 2021 Journal of Economic Integration (CC BY-NC-ND)
Funders:Science Foundation Ireland grant 13/RC/2094_P2.
ID Code:26122
Deposited On:16 Sep 2021 11:37 by Takfarinas Saber . Last Modified 16 Sep 2021 11:37
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