# Modelling global trade with optimal transport

## TL;DR

This paper uses optimal transport and machine learning to model global trade, revealing how conflicts and political changes affect agricultural trade patterns.

## Contribution

A novel approach combining optimal transport and deep learning to infer trade frictions without assuming a specific cost function.

## Key findings

- The method outperforms traditional gravity models in predicting trade flows.
- Low-income countries faced higher trade cost increases due to the Ukraine war's impact on wheat markets.
- The model reveals hidden trade patterns from events like Brexit and trade disputes.

## 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 low income countries experienced disproportionately higher increases in trade costs due to 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.

By combining optimal transport with machine learning, this study infers trade frictions in global food markets, highlighting how conflict, trade disputes, and shifts in political-economic relations are associated with changes in agricultural supply.