IDEAS home Printed from https://ideas.repec.org/a/ris/integr/0827.html
   My bibliography  Save this article

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

Author

Listed:
  • De Lombaerde, Philippe

    (Neoma Business School, United Nations University Institute on Comparative Regional Integration Studies)

  • Naeher, Dominik

    (University College Dublin)

  • Saber, Takfarinas

    (Dublin City University)

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.

Suggested Citation

  • De Lombaerde, Philippe & Naeher, Dominik & Saber, Takfarinas, 2021. "Regional Integration Clusters and Optimum Customs Unions: A Machine-Learning Approach," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 36(2), pages 262-281.
    • Handle: RePEc:ris:integr:0827
    as

    Download full text from publisher

    File URL: https://www.e-jei.org
    File Function: Full text
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Takfarinas Saber & Dominik Naeher & Philippe Lombaerde, 2023. "On the Optimal Size and Composition of Customs Unions: An Evolutionary Approach," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1457-1479, December.
    2. Dominik Naeher & Philippe Lombaerde & Takfarinas Saber, 2025. "Evaluating accession decisions in customs unions: a dynamic machine learning approach," International Economics and Economic Policy, Springer, vol. 22(1), pages 1-27, February.

    More about this item

    Keywords

    Regional Integration; Customs Union; Machine Learning;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • F13 - International Economics - - Trade - - - Trade Policy; International Trade Organizations
    • F15 - International Economics - - Trade - - - Economic Integration
    • F60 - International Economics - - Economic Impacts of Globalization - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ris:integr:0827. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Yunhoe Kim (email available below). General contact details of provider: https://edirc.repec.org/data/desejkr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.