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Lower-skilled occupations face greater upskilling pressure in U.S. job ads

Di Tong, Lingfei Wu, James A. Evans · Nature Communications · 31 Dec 2025

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Abstract

Substantial scholarship has estimated the susceptibility of jobs to automation, but little has examined how job contents evolve in the information age as new technologies substitute for tasks, shifting required skills rather than eliminating entire jobs. Here we explore patterns of occupational skill change and characterize occupations and workers subject to the greatest re-skilling requirements in the United States. Recent work found that changing skill requirements are greatest for STEM occupations in the 2010s. Nevertheless, analyzing 167 million online job posts covering 721 occupations, we find that when accounting for distance between skills, skill change is greater for lower-skilled occupations: those with fewer skills, lower wages, and less educational requirements. We further investigate the differences in skill change across employer and market size, as well as social demographic groups. We find that jobs from small employers and markets experienced larger skill upgrades to catch up with the skill demands of their large employers and markets. While these varied skill changes could create uneven reskilling pressures across workers, they may also lead to a narrowing of gaps in job quality and prospects. We conclude by showcasing our model's potential to chart job evolution directions using skill embedding spaces.

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
2025 (10 months ago)
Citations
3 (OpenAlex)

Citations in context

median 10 this paper: 3 0 591

3 citations — 30th percentile among the 33 indexed papers from 2025 in this collection. Citation counts favor older papers; this compares only same-year papers here, not the whole literature.

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□ added to this collection in 2026.

author affiliation matches seed institutions massachusetts institute of technology, university of chicago — part of why this paper is in the collection.

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