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Work of the Past, Work of the Future
Open accessAbstract
US cities today are vastly more educated and skill-intensive than they were five decades ago. Yet, urban non-college workers perform substantially less skilled jobs than decades earlier. This deskilling reflects the joint effects of automation and, secondarily, rising international trade, which have eliminated the bulk of non-college production, administrative support, and clerical jobs, yielding a disproportionate polarization of urban labor markets. The unwinding of the urban non-college occupational skill gradient has, I argue, abetted a secular fall in real non-college wages by: (1) shunting non-college workers out of specialized middle-skill occupations into low-wage occupations that require only generic skills; (2) diminishing the set of non-college workers that hold middle-skill jobs in high-wage cities; and (3) attenuating, to a startling degree, the steep urban wage premium for non-college workers that prevailed in earlier decades. Changes in the nature of work--many of which are technological in origin--have been more disruptive and less beneficial for non-college than college workers.
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“Some of these technologies, like personal computers, have been complementary mostly to educated workers (63, 64)”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [64]
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Flagged first, then by citations
Research works printed but not linked: 3
- 10.1257/0022051026976 · DOI · Paper page
- Autor David, 2019, The Inversion of the Rural-Urban Age Gradient in the United States, 1950
- Costa Dora L, 2000, Changes in the Locational Choice of the College Educated, 1940
Cited by
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Self-citationToward understanding the impact of artificial intelligence on labor
Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., Feldman, M., Groh, M., Lobo, J., Moro, E., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900949116 · Cited by 713
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The impact of generative artificial intelligence on socioeconomic inequalities and policy making
Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., Bonnefon, J.-F., Brañas-Garza, P., Butera, L., Douglas, K. M., et al. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae191 · Cited by 371
Metadata
- Authors
- Autor, D. H.
- Affiliations
- National Bureau of Economic Research (US)
- Corresponding author
- Autor, David H.
- ORCID
- Autor, David H.
- Venue
- AEA Papers and Proceedings
- Publisher
- American Economic Association
- Published
- 2019-05-01
- Volume
- 109
- Pages
- 1-32
- ISSN
- 2574-0768 (ISSN-L)
- 2574-0776
- OpenAlex
- W2917662199
- Cited by
- 435
- References
- 42
- Citations by year
- 2026: 63 · 2025: 79 · 2024: 79 · 2023: 43 · 2022: 53 · 2021: 56 · 2020: 48 · 2019: 11
- FWCI
- 52.43
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc
- Locations
- AEA Papers and Proceedings (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Agglomeration and Urban Systems
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Keywords
- deskilling, automation, international trade, urban wage premium, low-wage employment, technological change
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