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Cited by 962Labor market dynamics and wage inequalityEconomic Growth and ProductivityDigital Economy and Work Transformation

Artificial Intelligence, Automation and Work

Open access
Acemoglu, Daron, Restrepo, Pascual·2018·National Bureau of Economic Research
DOI 10.3386/w24196
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Abstract

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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  • In the postindustrial global era, technological advancement's job-creating effect will be insufficient to counterbalance negative employment developments such as automation and artificial intelligence.

    “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

    The Introduction and Concluding Remarks say that productivity-related countervailing effects alone are generally insufficient to balance automation. But the paper identifies creation of new tasks as a further job-creating force that can balance automation; its False Dichotomy section treats a negative outcome as a possibility, not a prediction that job creation will be insufficient. (LLM, conf=0.87)

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  • AI literacy can be divided into four common dimensions: understanding fundamental AI concepts, applying AI concepts in practical contexts, critically evaluating and engaging with AI technologies, and understanding the ethical implications of AI applications.

    “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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2 of the 39 references couldn’t be matched to a known paper.

Research works printed but not linked: 2

  1. Landes, David (1969) The Unbound Prometheus, Cambridge University Press: New York.
  2. 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.

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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
DOI
10.3386/w24196
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 · 2017: 3 · 2015: 1
Type
report
Language
en
Open access
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

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