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

De Lombaerde, Philippe (2021). Regional Integration Clusters and Optimum Customs Unions: A Machine Learning Approach. UNU-CRIS Working Paper. UNU Insitute on Comparative Regional Integration Studies.

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  • Subtitle Working Paper
    Sub-type Working paper
    Author De Lombaerde, Philippe
    Title Regional Integration Clusters and Optimum Customs Unions: A Machine Learning Approach
    Series Title UNU-CRIS Working Paper
    Volume/Issue No. 2021/4
    Publication Date 2021
    Place of Publication Bruges
    Publisher UNU Insitute on Comparative Regional Integration Studies
    Pages 24
    Language eng
    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 a given, our method uses a network clustering algorithm adapted from the machine learning literature to identify, in a data-driven way, those groups of neighbouring 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. 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.
    Copyright Holder UNU Institute on Comparative Regional Integration Studies
    Copyright Year 2021
    Copyright type All rights reserved
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    Created: Thu, 22 Feb 2024, 17:23:15 JST by Masovic, Ajsela on behalf of UNU CRIS