Mapping Global Value Chains at the Product Level

Hidalgo, César A. and Karbevska, Lea (2023) Mapping Global Value Chains at the Product Level. Working Paper. arXiv. DOI

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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.

Item Type:Monograph (Working Paper)
Divisions:Corvinus Institute for Advanced Studies (CIAS)
Subjects:Information economy
Logistics, production management
ID Code:9764
Deposited By: Ádám Hoffmann
Deposited On:25 Mar 2024 13:44
Last Modified:25 Mar 2024 13:44

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