Artificial Intelligence, Skills, and Labor Mobility: Understanding the Transformation of Work
David Marguerit · Open Repository and Bibliography (University of Luxembourg) · 16 Jun 2026
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Where this sits in the debate
- Will AI destroy more jobs than it creates? · its finding goes against the claim (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Key takeaway AI-written
A PhD dissertation on AI and labor markets. Using Stack Overflow-based exposure measures and an instrument based on lagged computer-science research, it finds in the U.S. (2015-2022) that augmentation AI creates new work while automation AI raises employment but depresses wages, hurting low-skilled workers; a second chapter analyzes 75 million European job postings for shifts in skill demand.
Abstract
The first three chapters of the PhD dissertation analyze the labor-market consequences of AI in the United States and Europe. Chapter four investigates the predictors of cross-border employment and its labor-market impact on returnees. Chapter one examines the effects of AI on the U.S. labor market from 2015 to 2022, distinguishing between automation AI—technologies that substitute for labor—and augmentation AI—technologies that enhance workers' output. I construct novel longitudinal measures of occupational exposure to each type of AI by mapping developer activity on Stack Overflow to occupational descriptions. I also introduce a new measure of emerging work based on successive updates of job titles in O*NET. Using an instrumental-variables strategy based on lagged computer science research intensity, I find that augmentation AI stimulates the creation of new work. Automation AI increases employment but depresses wages. These effects vary across skill groups: automation AI harms low-skilled workers, while augmentation AI raises wages and generates new work primarily for high-skilled occupations. In chapter two, we use 75 million online job postings from four European countries over the 2018–2023 period to investigate how skill and occupational demand have shifted in response to AI. Our analysis leverages data science methods to extract and classify skills from job postings across multiple languages. Instrumenting AI exposure with lagged research intensity in computer science, we document significant growth in demand for AI, Data, and Prediction skills within exposed occupations, alongside a decline in Social skills. Guided by our theoretical framework, the results are consistent with the interpretation that AI, Data, and Prediction are AI-exposed skills, while Judgment, Decision-Making, and Leadership are complementary, and Social skills are substituted. We further document an increased co-occurrence of AI-exposed and complementary skill bundles. Our findings indic
About this publication
Academic journal. Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth.
- Published
- 2026 (4 months ago)
- Citations
- 0 (OpenAlex)
0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison.
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