UNU-MERIT Working Papers Series
LLM Meets Job Advertisements: Unmasking Skill Premia in the UK
Sidharth Rony #2026-010Rapid advances in technology and events such as COVID-19 have significantly transformed the modern workplace, potentially altering the skills demanded in jobs. This study examines the evolving demand and posted-wage premia for Information and Communication Technology (ICT), interpersonal, and Artificial Intelligence (AI) skills in the UK labour market. Using a comprehensive dataset of online job advertisements (2016 to 2022), skills are extracted and categorised via GPT-4 zero-shot learning. Cross-sectional log-wage regressions, incorporating occupation and regional fixed effects with three-way Cameron-Gelbach-Miller clustered standard errors, reveal divergent trends in skill compensation. While interpersonal skills are ubiquitously demanded (approximately 90% of listings), they yield no significant posted-wage premium, likely reflecting their near-universal baseline requirement across postings. In contrast, ICT skills, demanded in approximately 55% of postings, carry a postedwage premium of approximately 7%. AI skills, mentioned in approximately 3% of postings, carry a posted-wage premium of approximately 9% within the ICT-mentioning subsample. These findings document robust associational posted-wage premia for technical competencies amidst recent pandemic-induced and technological labour market shifts.
Keywords: Skills, Wage premium, Machine-assisted mixed methods, Big data, Large Language Model (LLM), COVID-19, AI
JEL Classification: J24, C45, O33
DOI: https://doi.org/10.53330/QGGU3545
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