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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.
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“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
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“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]
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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 ›
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