The DOI matches its Crossref record; 37 of the 39 references it cites were found in a registry or an index.
Artificial Intelligence, Automation and Work
Open accessAbstract
We summarize a framework for the study of the implications of automation and AI on the demand for labor, wages, and employment.Our task-based framework emphasizes the displacement effect that automation creates as machines and AI replace labor in tasks that it used to perform.This displacement effect tends to reduce the demand for labor and wages.But it is counteracted by a productivity effect, resulting from the cost savings generated by automation, which increase the demand for labor in non-automated tasks.The productivity effect is complemented by additional capital accumulation and the deepening of automation (improvements of existing machinery), both of which further increase the demand for labor.These countervailing effects are incomplete.Even when they are strong, automation in-creases output per worker more than wages and reduce the share of labor in national income.The more powerful countervailing force against automation is the creation of new labor-intensive tasks, which reinstates labor in new activities and tends to increase the labor share to counterbalance the impact of automation.Our framework also highlights the constraints and imperfections that slow down the adjustment of the economy and the labor market to automation and weaken the resulting productivity gains from this transformation: a mismatch between the skill requirements of new technologies, and the possibility that automation is being introduced at an excessive rate, possibly at the expense of other productivity-enhancing technologies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, research by the National Bureau of Economic Research (NBER) suggests that in the postindustrial global era, this effect will be insufficient to counterbalance current negative (un)-employment developments such as the rapid advance of automation and artificial intelligence (Acemoglu & Restrepo, 2018).”
From Common Good HRM: A paradigm shift in Sustainable HRM? (2020) · cites this paper as Acemoglu & Restrepo, 2018
Experimental — accuracy not yet measuredIn the citing paper
“According to the study of Ng et al., AI literacy can be divided into four common dimensions to build structure”
From Developing AI Literacy for Primary and Middle School Teachers in China: Based on a Structural Equation Modeling Analysis (2022) · cites this paper as [24]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1983) · Cited by 33,192
The Skill Content of Recent Technological Change: An Empirical Exploration
Autor, D. H., Levy, F., & Murnane, R. J. (2003) · The Quarterly Journal of Economics · doi:10.1162/003355303322552801 · Cited by 6,205
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
Robots and Jobs: Evidence from US Labor Markets
Acemoglu, D., & Restrepo, P. (2020) · Journal of Political Economy · doi:10.1086/705716 · Cited by 4,088
Self-citationWhy 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
Computation: Finite and Infinite Machines.
Clarke, D. A., & Minsky, M. L. (1968) · American Mathematical Monthly · doi:10.2307/2313471 · Cited by 2,618
Skills, Tasks and Technologies: Implications for Employment and Earnings
Acemoglu, D., & Autor, D. (2010) · doi:10.3386/w16082 · Cited by 2,180
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
Dong, X., & McIntyre, S. H. (2014) · Quantitative Finance · doi:10.1080/14697688.2014.946440 · Cited by 1,681
The Race between Education and Technology
GOLDIN, C., & KATZ, L. F. (2010) · doi:10.2307/j.ctvjf9x5x · Cited by 1,520
The Skill Content of Recent Technological Change: An Empirical ExplorationPreprint
Autor, D., Levy, F. S., & Murnane, R. J. (2001) · SSRN Electronic Journal · doi:10.2139/ssrn.272691 · Cited by 1,374
Flagged first, then by citations
2 of the 39 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Landes, David (1969) The Unbound Prometheus, Cambridge University Press: New York.
- Olmstead, Alan L. and Paul W. Rhode (2001) "Reshaping the Landscape: The Impact and Diffusion of the Tractor in American Agriculture, 1910-1960," The Journal of Economic History 61(3): 663-98.
Cited by
Sorted by citations
The role of artificial intelligence in achieving the Sustainable Development Goals
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020) · Nature Communications · doi:10.1038/s41467-019-14108-y · Cited by 3,294
Taxonomy of Risks posed by Language Models
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533088 · Cited by 764
Jun, L., Chang, H., Forrest, J. Y., & Yang, B. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120142 · Cited by 707
The wrong kind of AI? Artificial intelligence and the future of labour demand
Acemoglu, D., & Restrepo, P. (2020) · Cambridge Journal of Regions, Economy and Society · doi:10.1093/cjres/rsz022 · Cited by 634
Self-citationAcemoglu, D., & Restrepo, P. (2022) · The Review of Economic Studies · doi:10.1093/restud/rdab031 · Cited by 586
Self-citation
Metadata
- Authors
- Acemoglu, D., & Restrepo, P.
- Affiliations
- Boston University (US)
- Corresponding author
- Restrepo, Pascual
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2018-01
- OpenAlex
- W2783386352
- Cited by
- 962
- References
- 39
- Citations by year
- 2026: 108 · 2025: 187 · 2024: 154 · 2023: 123 · 2022: 90 · 2021: 108 · 2020: 92 · 2019: 67 · 2018: 29
- Type
- report
- Language
- en
- Open access
- green
- doi.org
- Locations
- National Bureau of Economic Research (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Alfred P. Sloan Foundation
- 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 › Economic Growth and Productivity
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Keywords
- artificial intelligence, automation, labor demand, wages, employment, displacement effects, productivity effects, capital accumulation, wage share, skills mismatch, labor market adjustment, output per worker
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
