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 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 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.