Working paper

Exposure to generative artificial intelligence in the European labour market

Women, highly educated and younger workers are more exposed to generative AI. Policy can intervene on both the labour supply and labour demand side

Publishing date
07 March 2024

We apply two sets of generative artificial intelligence (GenAI) occupational exposure scores – one task-based, one ability-based – to the European Labour Force Survey. While using different methodologies, our findings reveal consistent demographic patterns across the two approaches: jobs held by women, highly educated and younger workers are more exposed to GenAI technology in Europe. We also review the literature on the recent productivity impact of GenAI. Within the same occupations, less-experienced or  
less-skilled workers consistently get the largest productivity gains from GenAI support.

We argue that a task-based analysis is more fruitful than an ability-based one, both for guiding GenAI adoption in organisations and their workplaces, and for assessing the employment and job quality impact on workers.

Finally, we provide policy recommendations that can help workers (ie the labour supply) adjust to technological disruption, such as providing training and social safety nets. But we go further by also suggesting policy interventions that could redirect future labour demand towards better jobs, by promoting job redesign and organisational agility. Monitoring GenAI’s employment effects and researching the ‘jagged technological frontier’ is necessary to further build our understanding of the employment impact of this transformational technology.

About the authors

  • Laura Nurski

    Laura Nurski is a Research Expert at the Centre of Expertise for Labour Market Monitoring at the Faculty of Business and Economics of KU Leuven. She leads the development of an integrated labour market prediction model that identifies future skill needs in the Flemish labour market.

    Laura is currently a non-resident Fellow at Bruegel. While residing at Bruegel in the past, she led the Future of Work and Inclusive Growth project which analyses the impact of technology on the nature, quantity and quality of work, welfare systems and inclusive growth.

    Before joining Bruegel, she investigated the impact of job design and organisation design on wellbeing and productivity at work. This inherently multidisciplinary domain has left her with a broad social science background, encompassing psychology, sociology and economics. As a former data scientist in the financial and retail sector, Laura is passionate about data and technology. She is also a skilled statistical programmer, survey developer and open-source aficionado.

    Laura holds a Ph.D. in Industrial Organization, a M.Sc. in Economics and a M.A. in Business Engineering from KU Leuven.

  • Nina Ruer

    Nina works at Bruegel as a research assistant. She holds a Master's of Research (MRes) in Analysis and Policy in Economics from the Paris School of Economics (PSE). Her master's thesis, titled "The Gender Pay Gap in Student Employment in France," was a comprehensive study that delved into income disparities among university students in France. Prior to that, she earned a B.Sc. in Economics with a final year in "Magistère" from Université Paris 1 Panthéon-Sorbonne.

    Prior to joining Bruegel, she was a research assistant on a series of projects funded by PSE where she gained hands-on experience in finding and cleaning replication datasets for Randomized Control Trials (RCTs). She also developed multiple surrogate index functions for long-term forecasting. Another set of projects focused on collecting subjective forecasts, where she assessed the calibration of various groups for forecast accuracy.

    Nina is a dual Dutch and French citizen and is a French native speaker, fluent in Dutch and English.

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