Comparative Advantage in AI: Positioning the EU amongst its trade partners
N.H. Menghrajani (TU Delft - Technology, Policy and Management)
C.P. van Beers – Graduation committee member (TU Delft - Technology, Policy and Management)
R. Stöllinger – Mentor (TU Delft - Technology, Policy and Management)
T. Chatzivasileiadis – Mentor (TU Delft - Technology, Policy and Management)
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Abstract
This thesis examines how the accumulation of AI-related knowledge has shaped the comparative advantage of countries in global trade since 2010 and assesses the implications for the technological sovereignty goals of the EU. Using an extended Heckscher Ohlin Vanek (HOV) framework with factor-content corrections proposed by Trefler and Zhu (2010), AI and non-AI patent stocks are integrated alongside traditional labour and capital stock endowments to analyse trade patterns across 45 countries from 2010 to 2021. The empirical analysis reveals strong support for the HOV framework, with sign test achieving 90% success rate and patents performing better than traditional factors of labour and capital in predicting trade patterns. The results demonstrate that as of 2021, the US, China, and Korea hold a comparative advantage in AI patents, while the EU does not. Within the EU, only the Netherlands, Sweden, and Finland attempt to maintain a comparative advantage, whereas major economies, including Germany, France, and Italy, lag. The relative factor abundance in AI patents for the EU deteriorated between 2012 and 2019, coinciding with the rise of China and reflecting internal fragmentation among member states. These findings indicate that the lack of comparative advantage in AI for the EU creates strategic vulnerabilities that undermine its technological sovereignty goals, particularly as AI becomes embedded in critical infrastructures. For the EU to achieve genuine strategic autonomy, regulatory leadership must be complemented by strengthened capabilities in AI-enabling hardware, reduced dependence on foreign AI intellectual property, improved translation of research into commercial innovation, and cohesion-oriented policies addressing internal capability divergence. The research contributes methodologically by demonstrating that accumulated knowledge stocks measured through patents constitute formable factors that shape comparative advantage, and empirically by providing the first comprehensive HOV analysis of AI-related patent endowments across major trading economies.