Automation and employment over the technology life cycle: Evidence from European regions
Florencia Jaccoud, Fabien Petit, Tommaso Ciarli, María Savona · Research Policy · 18 Sep 2026
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Where this sits in the debate
- Will AI destroy more jobs than it creates? · its finding: it depends (why studies differ)
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Abstract
This paper examines how the labor market effects of automation and digital technologies vary across their technology life cycles. Using data for 158 European regions from 1995 to 2017, we study the phase-specific impacts of robots, information and communication technologies (ICT), and software and databases (SDB) on employment and wages. We identify distinct technology life cycles and their early and mature adoption phases by applying a structural break methodology to investment dynamics. To address endogeneity in regional exposure, we employ a shift-share instrumental-variable strategy that leverages US investment patterns interacted with pre-determined regional industrial structures. We find that labor market effects differ systematically across both technologies and life-cycle phases. Early adoption is skill-biased for intangible digital technologies: exposure to SDB during early diffusion is associated with declining employment and rising wages. In contrast, early ICT adoption is characterized by employment expansion concentrated among lower-paid workers, consistent with productivity-driven demand growth, while robots exhibit largely skill-neutral effects in early diffusion. For tangible technologies, displacement effects emerge in mature adoption phases. Across technologies, labor market adjustment operates mainly through the services sector, even when adoption occurs in manufacturing (robots). Our findings demonstrate that aggregate estimates of ICT and robots often mask opposing life cycle phase- and technology-specific dynamics.
About this publication
Leading field journal. A leading journal in economics or a neighbouring field (on the Tinbergen Institute’s A list or near the top of the RePEc ranking), or a top-rated computer-science venue.
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- Published
- 2026 (recently)
- Citations
- 0 (OpenAlex)
0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison.
author affiliation matches seed institution university college london — part of why this paper is in the collection.
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