Giuseppe Molinari

Progetto di ricerca
Artificial intelligence and changes in labour market structure

It is now widely recognized that artificial intelligence can reshape work, the economy, and their future trajectories. While public debates have mainly focused on whether AI substitutes for human labour, its effects extend beyond, influencing labour market structures, working conditions, the distribution of economic power between firms and workers, and required abilities and skills. This PhD thesis adopts this broader perspective and analyses the impact of artificial intelligence on labour markets in Europe and the United States along three complementary dimensions. First, a structural “objective” dimension, concerning changes in occupational composition and employment shares associated with exposure to AI. Second, an “objective” institutional and distributive dimension, focusing on transformations in firms’ market power on the labour demand side, measured through monopsony. Third, a “subjective” dimension, centred on changes in job content—namely abilities, skills, and knowledge—and how these evolve with advances in AI. Each chapter addresses one of these dimensions through a distinct yet conceptually integrated empirical analysis.

The first chapter investigates the relationship between occupational exposure to AI and changes in the structure of the European labour market across 25 countries from 2011 to 2021, using AI exposure measures from the literature and data from the European Labour Force Survey (LFS-EU). Results show that employment shares increased in occupations more exposed to AI, suggesting a complementary rather than substitutive effect of technology during the period considered. Stratification by education reveals employment growth in low- and high-skill occupations and a decline in medium-skill jobs, consistent with theories of Routine Biased Technological Change and job polarization. Younger workers experience relative employment gains, potentially reflecting greater adaptability to AI-related skills. Country-level analyses highlight substantial heterogeneity, pointing to the role of national institutions, education systems, and the pace of AI diffusion.

The second chapter, co-authored with Michele Cantarella and Chiara Strozzi, examines the relationship between monopsony power and occupational exposure to AI in 26 European countries between 2011 and 2020. Using LFS data on wages and employment and Google Patents to construct an AI exposure index, monopsony power is measured through the wage elasticity of labour supply. Results show a marked decline in labour supply elasticity over time, indicating increasing monopsony power. This trend appears largely independent of AI exposure, which plays a limited role. Stratified analyses indicate that monopsony power is strongest in low-wage occupations, followed by high-wage ones, while middle-wage occupations exhibit weaker monopsony. A rising trend is also observed in low-education occupations.

The third chapter, co-authored with Michele Cantarella and Chiara Strozzi, focuses on how AI reshapes the subjective content of work rather than task structures. Using longitudinal O*NET data from 2011 to 2025 and two novel AI exposure measures—based on AI research topics and LLM benchmark performance—we construct exposure indices at the occupation–requirement–year level. Results show that within occupations, AI exposure is positively associated with increases in importance-weighted requirement levels across Abilities, Skills, and Knowledge. At the occupation level, however, higher AI exposure is associated with a decline in overall requirement levels, driven mainly by reductions in Abilities. This divergence suggests that AI strengthens specific complementary requirements while contributing to an erosion of average occupational requirements.   

Pubblicazioni

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