Modelling Global Trade with Optimal Transport

TL;DR

This paper introduces a novel optimal transport-based deep learning framework to model global trade, outperforming traditional models and revealing hidden trade patterns influenced by geopolitical events.

Contribution

It presents a data-driven, flexible approach using neural networks to learn trade costs without predefined functional forms, enhancing accuracy and interpretability.

Findings

Outperforms traditional gravity models in accuracy.

Reveals disproportionate impact of Ukraine war on the Global South.

Uncovers hidden trade pattern effects of trade agreements and geopolitical events.

Abstract

Global trade is shaped by a complex mix of factors beyond supply and demand, including tangible variables like transport costs and tariffs, as well as less quantifiable influences such as political and economic relations. Traditionally, economists model trade using gravity models, which rely on explicit covariates that might struggle to capture these subtler drivers of trade. In this work, we employ optimal transport and a deep neural network to learn a time-dependent cost function from data, without imposing a specific functional form. This approach consistently outperforms traditional gravity models in accuracy and has similar performance to three-way gravity models, while providing natural uncertainty quantification. Applying our framework to global food and agricultural trade, we show that the Global South suffered disproportionately from the war in Ukraine's impact on wheat markets. We also analyse the effects of free-trade agreements and trade disputes with China, as well as Brexit's impact on British trade with Europe, uncovering hidden patterns that trade volumes alone cannot reveal.