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

De Lombaerde, Philippe, Naeher, Dominik and Saber, Takfarinas, (2021). Regional Integration Clusters and Optimum Customs Unions: A Machine-Learning Approach. Journal of Economic Integration, 36(2), 262-281

Document type:
Article
Collection:
UNU-CRIS Articles

Metadata
Links
Versions
Statistics
  • Sub-type Journal article
    Author De Lombaerde, Philippe
    Naeher, Dominik
    Saber, Takfarinas
    Title Regional Integration Clusters and Optimum Customs Unions: A Machine-Learning Approach
    Appearing in Journal of Economic Integration
    Volume 36
    Issue No. 2
    Publication Date 2021-06-10
    Place of Publication Online
    Publisher Journal of Economic Integration
    Start page 262
    End page 281
    Language eng
    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.
    Keyword Regional Integration
    Customs Union
    Machine learning
    Copyright Holder Journal of Economic Integration
    Copyright Year 2021
    Copyright type All rights reserved
    DOI https://doi.org/10.11130/jei.2021.36.2.262
  • Versions
    Version Filter Type
  • Citation counts
    Google Scholar Search Google Scholar
    Access Statistics: 42 Abstract Views  -  Detailed Statistics
    Created: Fri, 17 May 2024, 21:03:27 JST by Masovic, Ajsela on behalf of UNU CRIS