AI-Generated Production Networks: Measurement and Applications to Global Trade

Thiemo Fetzer, Peter John Lambert, Bennet Feld and Prashant Garg

No 19708, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: This paper leverages generative AI to build a network structure over 5,000 product nodes, where directed edges represent input-output relationships in production. We layout a two-step 'build-prune' approach using an ensemble of prompt-tuned generative AI classifications. The 'build' step provides an initial distribution of edge-predictions, the 'prune' step then re-evaluates all edges. With our AI-generated Production Network (AIPNET) in toe, we document a host of shifts in the network position of products and countries during the 21st century. Finally, we study production network spillovers using the natural experiment presented by the 2017 blockade of Qatar. We find strong evidence of such spill-overs, suggestive of on-shoring of critical production. This descriptive and causal evidence demonstrates some of the many research possibilities opened up by our granular measurement of product linkages, including studies of on-shoring, industrial policy, and other recent shifts in global trade.

Keywords: Large Language Models; Trade wars (search for similar items in EconPapers)
JEL-codes: C81 F14 F52 N74 O25 (search for similar items in EconPapers)
Date: 2024-11
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