The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Changes in Relative Wages, 1963-1987: Supply and Demand Factors
Katz, L. F., & Murphy, K. M. (1992) · The Quarterly Journal of Economics · doi:10.2307/2118323 · Cited by 4,793
The China Syndrome: Local Labor Market Effects of Import Competition in the United States
Autor, D. H., Dorn, D., & Hanson, G. H. (2013) · American Economic Review · doi:10.1257/aer.103.6.2121 · Cited by 4,459
Why Are There Still So Many Jobs? The History and Future of Workplace Automation
Autor, D. H. (2015) · Journal of Economic Perspectives · doi:10.1257/jep.29.3.3 · Cited by 3,917
The growth of low-skill service jobs and the polarization of the US labor market
Autor, D. H., & Dorn, D. (2013) · American Economic Review · doi:10.1257/aer.103.5.1553 · Cited by 3,824
Acemoglu, D., & Restrepo, P. (2018) · American Economic Review · doi:10.1257/aer.20160696 · Cited by 3,121
Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach
DiNardo, J., Fortin, N. M., & Lemieux, T. (1996) · Econometrica · doi:10.2307/2171954 · Cited by 2,917
Trends in U.S. Wage Inequality: Revising the Revisionists
Autor, D., Katz, L. F., & Kearney, M. S. (2008) · The Review of Economics and Statistics · doi:10.1162/rest.90.2.300 · Cited by 2,381
Lousy and Lovely Jobs: The Rising Polarization of Work in Britain
Goos, M., & Manning, A. (2007) · The Review of Economics and Statistics · doi:10.1162/rest.89.1.118 · Cited by 2,287
Explaining Job Polarization: Routine-Biased Technological Change and Offshoring
Goos, M., Manning, A., & Salomons, A. (2014) · American Economic Review · doi:10.1257/aer.104.8.2509 · Cited by 2,111
Technical change, inequality, and the labor market
Acemoglu, D. (2002) · Journal of Economic Literature · doi:10.1257/jel.40.1.7 · Cited by 1,767
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
Sorted by citations
Items 401–432 of 432
STEMming from Where? Spatial and Demographic Income Disparities in the U.S. Technology Workforce
Xie, S., & Connor, D. S. (2026) · Annals of the American Association of Geographers · doi:10.1080/24694452.2026.2648332
Compound Economic Fragility: A Mutual Threshold Saturation Analysis of U.S. Domestic ResiliencePreprint
Green, R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6642278
The specifics of non‐routine task changes: A granular approach
Scotese, C. (2026) · Contemporary Economic Policy · doi:10.1111/coep.70041
He, H., Wang, Q., Huang, W., Yang, M., Ma, H. F., & Pang, H. (2026) · Sustainability · doi:10.3390/su18104785
Malliaros, P., & Pacheco-Jaramillo, W. A. (2026) · F1000Research · doi:10.12688/f1000research.168512.3
Digital Economy, Digital Industry Agglomeration, and Labor Income Inequality
Zhang, Q., & Yuan, K. (2025) · International Conference on Big Data Economy and Information Management (BDEIM) · doi:10.1145/3800000.3800061
Generative AI and the Future of High-Skill vs. Low-Skill Work: A Sector-Wise Forecasting Model
Bansal, O., Rajput, N., Kumar, B., & Tripathi, M. (2026) · International Conference on Intelligent Systems in Engineering, Secured Systems and Cybersecurity (ICISESSC) · doi:10.1109/icisessc68634.2026.11542754
New Technologies and Increase in Employment
Koşar, M. A. (2026) · İzmir İktisat Dergisi · doi:10.24988/ije.1657819
Mızrahı, R., Uran, H. A., Karakul, A. K., & Akyol, E. M. (2026) · Journal of Business Innovation and Governance · doi:10.54472/jobig.1831437
Capital Deepening, Employment Decline, and Wage Inequality: Firm-Level Evidence from ChinaPreprint
Wu, Y., Kim, Y., Ye, J., & Cao, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6998042
The Human-AI Collaboration Imperative
Herath, S. K., & Yoo, J. K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5816-1.ch007
The Economics of Rural Development in the United States
Hill, A. E., Low, S. A., & Taylor, K. (2026) · Annual Review of Resource Economics · doi:10.1146/annurev-resource-120324-050230
Cheng, D., & Rasool, Z. (2026) · Economics of Innovation and New Technology · doi:10.1080/10438599.2026.2704968
The Economic Returns to Skills: A Review
Dickerson, A., & Rossi, G. (2026) · Oxford University Press eBooks · doi:10.1093/9780197851418.003.1054
The Cohort Incidence of General Purpose Technology TransitionsPreprint
Morimoto, K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6893782
Precarious employment: a study of non-standard forms of employment in India
Padhan, B. (2026) · SN Business & Economics · doi:10.1007/s43546-026-01288-1
Spatial productivity disparities in Great Britain
Distefano, M., Donnat, H., Overman, H. G., & Shah, K. (2026) · Economica · doi:10.1111/ecca.70068
Li, M., Wu, Y., & Zhou, C. (2026) · Applied Economics · doi:10.1080/00036846.2026.2721547
Lue, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7372037
Hazak, A., Liuhanen, J., Kantojärvi, K., Sulkava, S., Salomaa, V. V., Koskinen, S., Perola, M., & Paunio, T. (2026) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-026-03183-4
Duy, P. K., & Hoang, P. N. (2026) · Journal of Managerial Psychology · doi:10.1108/jmp-03-2026-0262
Блохина, У. С. (2026) · Хроноэкономика · doi:10.62832/a3849-4056-6795-f
Carbon Taxation, Firm Performance, and Labor DemandPreprint
Karlsson, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7465840
Barnabas, M., & Moses, J. G. (2026) · Journal of Employment Counseling · doi:10.1002/joec.70028
Firms Diverged Faster than Households: Market-Capitalization Concentration in the S&P 500
Pattnaik, S., Jain, C., & Pinsky, E. (2026) · Journal of risk and financial management · doi:10.3390/jrfm19100747
Is Europe a laggard? Comparing the EU and the US using an interdisciplinary well-being framework
Jansen, A., Wang, R.-R., Neve, J. D., Behrens, P., & Hoekstra, R. (2027) · Ecological Economics · doi:10.1016/j.ecolecon.2026.109238
Pattnaik, S., Jain, C., & Pinsky, E. (2026) · Journal of Risk and Financial Management · doi:10.3390/jrfm19100785
THE GROWING INFLUENCE OF AI IN THE JOB MARKET AND WORKFORCE REQUIREMENTS IN MANUFACTURING INDUSTRIESPreprint
Gupta, R., & Deshpande, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7608003
Optimal taxation of robotic capital with a binding minimum wage
Attar, M. A., & Neyaptı, B. (2026) · International Tax and Public Finance · doi:10.1007/s10797-026-10011-7
Damerla, S. C. R. (2026) · International Journal of Sustainability and Innovation in Engineering · doi:10.56830/ijsie202608
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review