Working Paper No. 380. Artificial Intelligence, hiring and employment: Job postings evidence from Sweden. - Ratio

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Who is afraid of AI? Who should be?

Engberg, E., Görg, H., Hellsten, M., Javed, F., Lodefalk, M., Längkvist, M., & ..

Publication year

2026

Published in

Kiel Policy Brief, 2026.

Abstract

Occupations that are highly cognitive, non-physical, and low in social interaction — typically higher-skill white-collar roles such as data analysts, software developers, and translators — turn out to be highly AI-exposed

Occupations requiring manual dexterity or intensive interpersonal contact — such as construction labourers or nursing aides — remain among the least exposed to current AI technologies

Aggregate occupational exposure to AI has risen markedly since 2010, with especially rapid gains in the late 2010s and early 2020s

Our baseline estimates show no detectable effect of AI exposure on total firm employment, while it is associated with clear skill upgrading

Engberg, E., Görg, H., Hellsten, M., Javed, F., Lodefalk, M., Längkvist, M., & .. (2026). Who is afraid of AI? Who should be?. Kiel Policy Brief, 2026.

The Impact of AI on the Labour Market: Essays on Transformative Technology, Occupations, and Firms

Publication year

2026

Published in

Örebro University.

Abstract

The topic of this thesis is the economics of transformative technology, with the impact of artificial intelligence (AI) on the labour market as the primary focus.

Analysing German data, Essay I shows that occupational AI exposure was associated with wage gains, and an increased focus on knowledge-intensive tasks. There is a clear contrast between the types of work that are exposed to AI, versus robotics.

Essay II finds that AI exposure is associated with AI adoption and increased labour demand, as measured by job vacancy postings, in Swedish establishments/workplaces.

Essay III develops a novel measure of occupational AI exposure, called Dynamic AI Occupational Exposure (DAIOE). AI exposure is shown to be associated with upskilling at the firm level in Sweden, Denmark, and Portugal.

Essay IV analyses the labour market implications of the growing social and verbal capabilities of large language models (LLMs). Analysis of occupational data from O*NET and job ads provides a map of the most important types of social work tasks. Among social tasks, verbal communication tasks have the strongest association with occupational exposure to LLMs.

Essay V is about the impact of venture capital (VC) on start-up firms. Investment from both private and governmental VCs is found to increase sales with a 2-3 year delay, driven primarily by efficiency gains, and to some extent, capital investment. Governmental VCs are more likely to make follow-on investments in non-growing firms.

Ratio Working Paper No. 388: Same Storm, Different Boats: Generative AI and the Age Gradient in Hiring

Lodefalk, M., Löthman, L., Koch, M., & Engberg, E.

Publication year

2026

Published in

Ratio Working Paper Series.

Abstract

We show that the age composition of employment within Swedish employers shifts after the arrival of generative AI, with no corresponding reduction in aggregate labour demand. Using 4.6 million job advertisements from Sweden’s largest recruitment platform, we find that the broad decline in postings since 2022 aligns with monetary tightening rather than AI, exploiting Sweden’s seven-month gap between the Riksbank’s first rate hike and the launch of ChatGPT as a timing test. We then use full-population employer–employee register data and an employer-level difference-in-differences design to estimate how AI exposure affects employment composition across six age groups. An event study documents an accelerating decline in employment of 22–25-year-olds in high-AI-exposure occupations, reaching 5.5 per cent by early 2025 relative to less exposed occupations within the same employers, while employment of workers over 50 rose by 1.3 per cent. The widening age gradient suggests that generative AI reshapes hiring composition rather than aggregate demand, with the adjustment burden falling disproportionately on entry-level workers.