标题： 在全球价值链的产品层面进行映射 显示英文标题

标题： Mapping Global Value Chains at the Product Level

摘要： 价值链数据对于应对经济中断至关重要，例如由新冠疫情和乌克兰战争引起的中断。 尽管其重要性，但公开的价值链数据集，如“世界投入产出数据库”、“国家间投入产出表”、“EXIOBASE”或“EORA”，缺乏关于产品的详细信息（例如收音机、电话、电容器、液晶显示器等），而是依赖更广泛的工业部门（例如电气设备、电信）。 在此，我们介绍一种基于机器学习和贸易理论的方法，从细粒度的国际贸易数据中推断产品层面的价值链关系。 我们将该方法应用于总结300多个世界地区（例如美国的各州、日本的各都道府县等）和1200多种产品的进出口数据，以推断其贸易模式中隐含的价值链信息。 此外，我们使用比例分配方法在地区和国家之间分配贸易流量。 这项工作提供了一种近似的方法，在产品层面映射具有相关贸易流量的价值链数据，应会引起从事物流、贸易和可持续发展工作的人员的兴趣。 显示英文摘要

摘要： Value chain data is crucial to navigate economic disruptions, such as those caused by the COVID-19 pandemic and the war in Ukraine. Yet, despite its importance, publicly available value chain datasets, such as the ``World Input-Output Database'', ``Inter-Country Input-Output Tables'', ``EXIOBASE'' or the ``EORA'', lack detailed information about products (e.g. Radio Receivers, Telephones, Electrical Capacitors, LCDs, etc.) and rely instead on more aggregate industrial sectors (e.g. Electrical Equipment, Telecommunications). Here, we introduce a method based on machine learning and trade theory to infer product-level value chain relationships from fine-grained international trade data. We apply our method to data summarizing the exports and imports of 300+ world regions (e.g. states in the U.S., prefectures in Japan, etc.) and 1200+ products to infer value chain information implicit in their trade patterns. Furthermore, we use proportional allocation to assign the trade flow between regions and countries. This work provides an approximate method to map value chain data at the product level with a relevant trade flow, that should be of interest to people working in logistics, trade, and sustainable development.