  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.
