Mapping Global Value Chains at the Product Level

TL;DR

This paper presents a machine learning-based method to infer detailed product-level global value chains from international trade data, enhancing understanding of supply networks crucial for economic resilience and policy-making.

Contribution

It introduces a novel approach combining trade theory and machine learning to map product-specific value chains from coarse trade data, filling a significant data gap.

Findings

Successfully applied to data from over 300 regions and 1200 products.

Provides an approximate but detailed mapping of product-level trade flows.

Enhances the granularity of global value chain data for policy and logistics use.

Abstract

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.