Modelling global trade with optimal transport

Thomas Gaskin, Guven Demirel, Marie-Therese Wolfram and Andrew Duncan

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: REF; fund; 2025/2026 (search for similar items in EconPapers)
JEL-codes: L81 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2026-02-19
New Economics Papers: this item is included in nep-agr, nep-cis, nep-cmp, nep-int and nep-tra
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Published in Nature Communications, 19, February, 2026, 17. ISSN: 2041-1723

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:137330

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