在产品层面绘制全球价值链
Mapping Global Value Chains at the Product Level
AI总结：
本文提出一种结合机器学习与贸易理论的方法，利用300多个区域和1200多种产品的细粒度贸易数据推断产品层面的全球价值链关系，为物流、贸易和可持续发展研究提供近似绘图工具。
摘要：
价值链数据对于应对经济扰动至关重要，例如由COVID-19疫情和乌克兰战争所引发的扰动。然而，尽管其重要性不言而喻，公开可用的价值链数据集，如“World Input-Output Database”、“Inter-Country Input-Output Tables”、“EXIOBASE”或“EORA”，缺乏关于产品（例如无线电接收器、电话、电容器、液晶显示器等）的详细信息，而是依赖于更聚合的产业部门（例如电气设备、电信）。在此，我们引入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汇总300多个世界区域（例如美国的州、日本的县等）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，我们使用比例分配来分配区域和国家之间的贸易流。这项工作提供了一种近似方法，用于在具有相关贸易流的产品层面绘制价值链数据，这应当会引起从事物流、贸易和可持续发展工作的人们的兴趣。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
