This study explores exposure to artificial intelligence (AI) technologies and employment patterns in Europe. First, we provide a thorough mapping of European regions focusing on the structural factors—such as sectoral specialisation, R&D capacity, productivity and workforce skills—that may shape diffusion as well as economic and employment effects of AI. To capture these differences, we conduct a cluster analysis which group EU regions in four distinct clusters: high-tech service and capital centres, advanced manufacturing core, southern and eastern periphery. We then discuss potential employment implications of AI in these regions, arguing that while regions with strong innovation systems may experience employment gains as AI complements existing capabilities and production systems, others are likely to face structural barriers that could eventually exacerbate regional disparities in the EU, with peripheral areas losing further ground.

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Since Leontief’s (Leontief 1953) seminal work on the factor content of trade, the validity of the Heckscher-Ohlin-model has been judged not only on the basis of formal tests of the theory but also tested against prior expectation. In this vein, this paper uses the Heckscher-Ohlin-Vanek (HOV) approach to investigate whether supposed US leadership in the digital domain can be traced back to digital task endowments embodied in labour services. In a comparison between EU member states and the US, we find that the latter is more intensive in digital tasks than the EU and that this difference is explained by both an intensity-effect (US occupations being more digital-task intensive) and a structural component (relatively more digital-task intensive occupations). Viewed through the lens of the HOV theorem we find that the US is abundant in digital tasks relative to non-digital tasks, while the opposite is true for the EU. The standard tests for the predictive power of the HOV theorem are high and in line with the results for labour in previous literature.

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This paper revisits the Heckscher-Ohlin-Vanek (HOV) theorem and investigates its fit for digital tasks and ICT capital, which both represent endowment factors that are expected to shape the digital transformation. We use a theory-consistent methodology for calculating the measured net factor content of trade and apply it to a unique dataset on digital and non-digital tasks performed in detailed occupations, as well as recent data on ICT capital stocks. Equipped with these data, we provide new evidence on the factor-based trade patterns for 25 EU countries and use it to test the HOV theorem. Overall, the performance of the sign test and the rank test is good, if not impressive. In 83% of the cases, countries are net exporters of those factors with which they are abundantly endowed, with a higher score achieved for digital tasks than for ICT capital. The relevance of digital tasks and ICT capital is corroborated by a gravity model, which indicates that higher endowments with these factors foster bilateral exports.@en