Regional Integration Clusters and Optimum Customs Unions: A Machine-Learning Approach |
Philippe De Lombaerde, 1,2 Dominik Naeher, 3 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: dominik.naeher@ucd.ie |
Copyright ©2021 The Journal of Economic Integration |
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.
JEL Classification
C60: General F13: Trade Policy; International Trade Organizations F15: Economic Integration F60: General |
Keywords:
Regional Integration | Customs Union | Machine Learning
|
|
|
|
|