Comparative Advantage in AI-Intensive Industries: Evidence from US Imports

Alessandra Bonfiglioli (), Rosario Crinò, Mattia Filomena and Gino Gancia

No 11642, CESifo Working Paper Series from CESifo

Abstract: This paper investigates the determinants of comparative advantage in Artificial Intelligence (AI)-intensive industries using a comprehensive dataset of US imports from 68 countries across 79 manufacturing and service industries over the period 1999–2019. Using a novel measure of AI intensity based on the prevalence of occupations requiring expertise in machine learning and data analysis, we identify key factors influencing exports in AI-intensive industries. Our analysis reveals that countries with larger STEM graduate populations, broader Internet penetration and higher export volumes exhibit stronger export performance in AI-intensive industries. In contrast, regulatory barriers to digital trade are associated with lower AI-intensive exports. These results are robust to controlling for traditional sources of comparative advantage and addressing potential threats to identification. Our findings have implications for understanding competitiveness in the digital economy and highlight that fostering capabilities in data-driven industries may be particularly important due to their pronounced scale economies.

Keywords: artificial intelligence; international trade; digital data; comparative advantage (search for similar items in EconPapers)
JEL-codes: F10 F14 J23 (search for similar items in EconPapers)
Date: 2025
New Economics Papers: this item is included in nep-ain, nep-int and nep-lma
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_11642

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