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.
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Data availability
The raw job posting data used in this study are available under restricted access from LightCast; access can be obtained through a licensing agreement with LightCast, with details at https://lightcast.io/. The processed data generated in this study, including aggregated occupation-year level skill demands and skill embeddings derived from job postings, are available at https://github.com/di-Tong/SkillPaper/tree/master/IntermediateData (archived on Zenodo under https://doi.org/10.5281/zenodo.17444902). A comprehensive variable dictionary for LightCast data is provided in the Supplementary Information and at https://github.com/di-Tong/SkillPaper/tree/master/Codes. The publicly available datasets used in this study are available as follows: the 2010 Penn State University Labor-Sheds for Regional Analysis data at https://sites.psu.edu/psucz/data/, the 2018 CPS data at https://cps.ipums.org/cps/, O*NET job zone data at https://www.onetonline.org/, 2010 and 2018 BLS Occupational Employment Statistics (OES) data at https://www.bls.gov/oes/tables.htm, and Labor Force Statistics data at https://www.bls.gov/cps/cps_aa2018.htm. For further inquiries regarding data access, researchers may contact LightCast directly or reach out to the corresponding author.
Code availability
The Python code used in the analysis is available at https://github.com/di-Tong/SkillPaper/tree/master/Codes and archived on Zenodo under https://doi.org/10.5281/zenodo.17444902.
References
Acemoglu, D. & Autor, D. Skills, tasks and technologies: Implications for employment and earnings. In Handbook of Labor Economics, vol. 4, 1043–1171 (Elsevier, 2011).
Acemoglu, D. & Restrepo, P. Robots and jobs: evidence from US Labor Markets. J. Polit. Econ. 128, 2188–2244 (2020).
Acemoglu, D. & Restrepo, P. Automation and new tasks: how technology displaces and reinstates labor. J. Econ. Perspect. 33, 3–30 (2019).
Arntz, M., Gregory, T. & Zierahn, U. Revisiting the risk of automation. Econ. Lett. 159, 157–160 (2017).
Autor, D. H. Why are there still so many jobs? The history and future of workplace automation. J. Econ. Perspect. 29, 3–30 (2015).
Frey, C. B. & Osborne, M. A. The future of employment: How susceptible are jobs to computerisation? Technol. Forecast. Soc. Change 114, 254–280 (2017).
Nedelkoska, L. & Quintini, G. Automation, Skills Use and Training. OECD Social, Employment and Migration Working Papers 202 (OECD Publishing, Paris 2018).
Pajarinen, M., Rouvinen, P., Ekeland, A. & Others Computerization threatens one-third of Finnish and Norwegian employment. Etla Brief. 34, 1–8 (2015).
Acemoglu, D., Autor, D., Hazell, J. & Restrepo, P. AI and jobs: evidence from online vacancies, https://doi.org/10.3386/w28257 (2020).
Autor, D., Chin, C., Salomons, A. M. & Seegmiller, B. New frontiers: the origins and content of new work. Q. J. Econ. 139, 1940–2018 (2022) .
Brynjolfsson, E., Mitchell, T. & Rock, D. What can machines learn and what does it mean for occupations and the economy? AEA Pap. Proc. 108, 43–47 (2018).
Shestakofsky, B. Working algorithms: software automation and the future of work. Work Occup. 44, 376–423 (2017).
Frank, M. R. et al. Toward understanding the impact of artificial intelligence on labor. Proc. Natl. Acad. Sci. USA 116, 6531–6539 (2019).
Autor, D. H., Levy, F. S. & Murnane, R. J. The skill content of recent technological change: an empirical exploration. SSRN Electron. J. https://doi.org/10.2139/ssrn.272691 (2001).
Atalay, E., Phongthiengtham, P., Sotelo, S. & Tannenbaum, D. The evolution of work in the United States. Am. Econ. J. Appl. Econ. 12, 1–34 (2020).
Hershbein, B. & Kahn, L. B. Do recessions accelerate routine-biased technological change? Evidence from vacancy postings. Am. Econ. Rev. 108, 1737–1772 (2018).
Maccrory, F., Westerman, G., Alhammadi, Y. & Brynjolfsson, E. Racing with and against the machine: changes in occupational skill composition in an era of rapid technological advance. http://k12accountability.org/resources/For-Parents/Racing_With_and_Against_the_Machine_-Changes_in_Occupational_Skill.pdf (2014).
Card, D. & DiNardo, J. E. Skill-biased technological change and rising wage inequality: Some problems and puzzles. J. Labor Econ. 20, 733–783 (2002).
Gordon Benzell, S., Brynjolfsson, E., Maccrory, F., Westerman, G. & Mit, N.†. Identifying the multiple skills in skill-biased technical change. https://ide.mit.edu/wp-content/uploads/2019/08/Identifying-the-Multiple-Skills-in-SBTC-8-2-19.pdf (2019).
Deming, D. J. & Noray, K. Earnings dynamics, changing job skills, and STEM careers. Q. J. Econ. 135, 1965–2005 (2020).
Anderson, K. A. Skill networks and measures of complex human capital. Proc. Natl. Acad. Sci. USA 114, 12720–12724 (2017).
Mikolov, T., Chen, K., Corrado, G. & Dean, J. Efficient Estimation of Word Representations in Vector Space. arXiv [cs.CL] (2013).
Frank, M. R., Sun, L., Cebrian, M., Youn, H. & Rahwan, I. Small cities face greater impact from automation. J. R. Soc. Interface 15, 20170946 (2018).
Aeppli, C. & Wilmers, N. Rapid wage growth at the bottom has offset rising US inequality. Proc. Natl. Acad. Sci. USA 119, e2204305119 (2022).
Moretti, E. The New Geography of Jobs (Houghton Mifflin Harcourt, 2012).
Berry, C. R. & Glaeser, E. L. The divergence of human capital levels across cities. Pap. Reg. Sci. 84, 407–444 (2005).
Autor, D., Dorn, D., Katz, L. F., Patterson, C. & Van Reenen, J. The fall of the labor share and the rise of superstar firms. Q. J. Econ. 135, 645–709 (2020).
Glaeser, E. L. Cities, Agglomeration, and Spatial Equilibrium (OUP Oxford, 2008).
Barth, E., Davis, J. & Freeman, R. B. Augmenting the human capital earnings equation with measures of where people work. J. Labor Econ. 36, S71–S97 (2018).
Deming, D. & Kahn, L. B. Skill requirements across firms and labor markets: evidence from job postings for professionals. J. Labor Econ. 36, S337–S369 (2018).
Glaeser, E. L. & Resseger, M. G. The complementarity between cities and skills. J. Reg. Sci. 50, 221–244 (2010).
Hannan, M. T. & Freeman, J. Structural inertia and organizational change. Am. Sociol. Rev. 49, 149–164 (1984).
Haveman, H. A. Organizational size and change: diversification in the savings and loan industry after deregulation. Adm. Sci. Q. 38, 20–50 (1993).
Davis, D. R. & Dingel, J. I. A spatial knowledge economy. Am. Econ. Rev. 109, 153–170 (2019).
Stinchcombe, A. L. Social structure and organizations. In Handbook of Organizations (ed. March, J.) (Routledge, 1965).
Gao, J., Jun, B., Pentland, A., ‘sandy’, Zhou, T. & Hidalgo, C. A. Spillovers across industries and regions in China’s regional economic diversification. Reg. Stud. 55, 1311–1326 (2021).
Neffke, F. M. H. The value of complementary co-workers. Sci. Adv. 5, eaax3370 (2019).
Shutters, S. T., Muneepeerakul, R. & Lobo, J. Quantifying urban economic resilience through labour force interdependence. Palgrave Commun. 1, 1–7 (2015).
Fowler, C. S. & Jensen, L. Bridging the gap between geographic concept and the data we have: The case of labor markets in the USA. Environ. Plan. A 52, 1395–1414 (2020).
Fowler, C. Labor-sheds for regional analysis. Penn State University https://sites.psu.edu/psucz/data/.
Kozlowski, A. C., Taddy, M. & Evans, J. A. The geometry of culture: analyzing the meanings of class through word embeddings. Am. Sociol. Rev. 84, 905–949 (2019).
Caliskan, A., Bryson, J. J. & Narayanan, A. Semantics derived automatically from language corpora contain human-like biases. Science 356, 183–186 (2017).
Steffen, S. Essays on Information Technology, Human Capital, and the Future of Work (Massachusetts Institute of Technology, 2022).
Mishel, L., Shierholz, H. & Schmitt, J. Don’t Blame the Robots. Assessing the Job Polarization Explanation of Growing Wage Inequality. EPI-CEPR Working Paper 19 (Economic Policy Institute, 2013).
Hunt, J. & Nunn, R. Has U.S. employment really polarized? A critical reappraisal. Labour Econ. 75, 102117 (2022).
Battisti, M., Dustmann, C. & Schönberg, U. Technological and organizational change and the careers of workers. J. Eur. Econ. Assoc. 21, 1551–1594 (2023).
Devereux, P. J. Occupational upgrading and the business cycle. Labour 16, 423–452 (2002).
Modestino, A. S., Shoag, D. & Ballance, J. Downskilling: changes in employer skill requirements over the business cycle. Labour Econ. 41, 333–347 (2016).
Modestino, A. S., Shoag, D. & Ballance, J. Upskilling: do employers demand greater skill when workers are plentiful? Rev. Econ. Stat. 102, 793–805 (2020).
Neffke, F. M. H., Otto, A. & Hidalgo, C. The mobility of displaced workers: how the local industry mix affects job search. J. Urban Econ. 108, 124–140 (2018).
Gathmann, C. & Schönberg, U. How general is human capital? A task-based approach. J. Labor Econ. 28, 1–49 (2010).
Macaluso, C. et al. Skill remoteness and post-layoff labor market outcomes. http://conference.nber.org/confer/2017/SI2017/EFMPL/Macaluso.pdf (2017).
Ingram, B. F. & Neumann, G. R. The returns to skill. Labour Econ. 13, 35–59 (2006).
Poletaev, M. & Robinson, C. Human capital specificity: evidence from the dictionary of occupational titles and displaced worker surveys, 1984–2000. J. Labor Econ. 26, 387–420 (2008).
Robinson, C. Occupational mobility, occupation distance, and specific human capital. J. Hum. Resour. 53, 513–551 (2018).
Neffke, F., Nedelkoska, L. & Wiederhold, S. Skill mismatch and the costs of job displacement. Res. Policy 53, 104933 (2024).
Börner, K. et al. Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy. Proc. Natl. Acad. Sci. USA 115, 12630–12637 (2018).
Alabdulkareem, A. et al. Unpacking the polarization of workplace skills. Sci Adv 4, eaao6030 (2018).
Moro, E. et al. Universal resilience patterns in labor markets. Nat. Commun. 12, 1972 (2021).
Bloom, N., Guvenen, F., Smith, B. S., Song, J. & von Wachter, T. The disappearing large-firm wage premium. AEA Pap. Proc. 108, 317–322 (2018).
Carnevale, A. P., Jayasundera, T. & Repnikov, D. Understanding Online Job Ads Data: A Technical Report. McCourt School on Public Policy, Center on Education and the Workforce. (2014).
Lancaster, V., Mahoney-Nair, D. & Ratcliff, N. J. Review of burning glass job-ad data. https://biocomplexity.virginia.edu/sites/default/files/projects/Technical%20Report%20Review%20of%20BGT%20Job-ad%20Data.pdf (2019).
Braxton, J. C. & Taska, B. Technological change and the consequences of job loss. Am. Econ. Rev. 113, 279–316 (2023).
Bianchini, M., Gori, M. & Scarselli, F. Inside PageRank. ACM Trans. Internet Technol. 5, 92–128 (2005).
Bonacich, P. Power and centrality: a family of measures. Am. J. Sociol. 92, 1170–1182 (1987).
Kim, S., Ahn, Y.-Y. & Park, J. Labor space: a unifying representation of the labor market via large language models. In Proceedings of the ACM Web Conference 2024, vol. 358, 2441–2451 (ACM, 2024).
Cheng, S. & Park, B. Flows and boundaries: a network approach to studying occupational mobility in the labor market. Am. J. Sociol. 126, 577–631 (2020).
Acknowledgements
We thank Lawrence Katz, workshop participants at MIT Institute for Work and Employment Research, and conference participants at Labor and Employment Relations Association 2024 Annual Meeting for helpful comments. We also thank Bledi Taska and the staff at Lightcast for generously sharing their data and comments. J.E. thanks the NSF SBE-1829366, AFOSR FA9550-19-1-0354 and DARPA HR00111820006 for support, and L.W. acknowledges the support of Richard King Mellon Foundation, NSF SOS:DCI-2239418, and NIH R01GM164731.
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D.T., L.W., and J.A.E. jointly conceived and designed the study, contributed to data interpretation, and drafted, revised, and edited the manuscript. D.T. led the data analysis and implemented the models.
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J.E. maintains a commercial relationship with Google, which played no role in the design, implementation, or decision to publish the study. The other authors declare no competing interests.
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Tong, D., Wu, L. & Evans, J.A. Lower-skilled occupations face greater upskilling pressure in U.S. job ads. Nat Commun (2025). https://doi.org/10.1038/s41467-025-67992-y
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DOI: https://doi.org/10.1038/s41467-025-67992-y
