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.

翻译：全球贸易的形成受到供需之外复杂因素的共同影响，包括运输成本和关税等可量化变量，以及政治经济关系等较难量化的因素。传统上，经济学家使用引力模型来建模贸易，这些模型依赖于显式协变量，可能难以捕捉这些更微妙的贸易驱动因素。在本研究中，我们采用最优传输和深度神经网络，从数据中学习一个随时间变化的成本函数，而不施加特定的函数形式。该方法在准确性上持续优于传统引力模型，并与三向引力模型性能相当，同时提供了自然的不确定性量化。将我们的框架应用于全球食品和农业贸易，我们发现全球南方国家在乌克兰战争对小麦市场的影响中遭受了不成比例的损失。我们还分析了自由贸易协定和与中国的贸易争端的影响，以及英国脱欧对英欧贸易的影响，揭示了仅靠贸易量无法发现的隐藏模式。