Deciphering the global production network from cross-border firm transactions

TL;DR

This paper leverages extensive cross-border firm transaction data to map global supply chains, revealing product clusters, key country roles, and the predictive power of supply chain linkages for country diversification.

Contribution

It introduces a novel method to infer detailed global supply chain networks from firm transaction data, providing insights into product clustering and country roles.

Findings

Products cluster into textiles, chemicals, food, machinery, and metals.

European nations and China dominate critical intermediate products.

Supply chain linkages predict country-product diversification patterns.

Abstract

Critical for policy-making and business operations, the study of global supply chains has been severely hampered by a lack of detailed data. Here we harness international firm-level transaction data covering 20m global firms, and 1 billion cross-border transactions, to infer key inputs for over 1200 products. Transforming this data to a directed network, we find that products are clustered into three large groups including textiles, chemicals and food, and machinery and metals. European industrial nations and China dominate critical intermediate products such as metals, common components and tools, while industrial complexity is highly correlated with embeddedness in densely connected supply chains. We find structural similarities with AIPNET, a product network generated via LLM queries, and strong linkages between products identified in manually-mapped electric vehicle battery and semiconductor supply chains. Finally, both forward and backward linkages are predictive of country-product diversification patterns, with stronger overall evidence for backward (upstream) linkages.