On the Optimal Size and Composition of Customs Unions: An Evolutionary Approach

Author:

Saber TakfarinasORCID,Naeher Dominik,De Lombaerde Philippe

Abstract

AbstractCustoms unions enable countries to freely access each other’s markets, which is thought to increase intra-regional trade and economic growth. However, accession to a customs union also comes with the condition that all members need to consent to a common external trade policy. Especially if countries feature different economic structures, this may act as a force against the creation of large customs unions. In this paper, we propose a new mathematical approach to model the optimal size and composition of customs unions in the form of a bi-objective combinatorial non-linear problem. We also use a multi-objective evolutionary algorithm (NSGA-II) to search for the best (non-dominated) configurations using data on the trade flows and economic characteristics of 200 countries. Our algorithm identifies 445 different configurations that are strictly preferable, from a global perspective, to the real-world landscape of customs unions. However, many of these non-dominated configurations have the feature that they improve outcomes for the world as a whole, on average, but not for all individual countries. The best configurations tend to favour the creation of a few large customs unions and several smaller ones.

Funder

Science Foundation Ireland

National University Ireland, Galway

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Economics, Econometrics and Finance (miscellaneous)

Reference26 articles.

1. AfDB. Africa Regional Integration Index: Report 2016. African Development Bank, African Union, and United Nations Economic Commission for Africa, Addis Ababa.

2. Baldwin, R. E., & Venables, A. J. (1995). Regional economic integration. Handbook of International Economics, 3, 1597–1644.

3. Borchert, I., & Magntorn, J. (2018) What can the UK learn from existing customs unions? https://blogs.sussex.ac.uk/uktpo/2018/03/29/what-can-the-uk-learn-from-existing-customs-unions/. Last accessed December 2021

4. De Lombaerde, P., Naeher, D., & Saber, T. (2021). Regional integration clusters and optimum customs unions: A machine learning approach. Journal of Economic Integration, 36(2), 262–281.

5. De Lombaerde, P., & Ulyanov, I. (2020). The Turkish FTA puzzle. Estey Journal of International Law and Trade Policy, 21(2), 87–95.

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