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

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.