The rise of robots and the fall of routine jobs
Open accessAbstract
This paper examines the impact of industrial robots on jobs. We combine data on robot adoption and occupations by industry in thirty-seven countries for the period from 2005 to 2015. We exploit differences across industries in technical feasibility – defined as the industry's share of tasks replaceable by robots – to identify the impact of robot usage on employment. The data allow us to differentiate effects by the routine-intensity of employment. We find that a rise in robot adoption relates significantly to a fall in the employment share of routine manual task-intensive jobs. This relation is observed in high-income countries, but not in emerging market and transition economies.
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
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?
AI technology is particularly efficient in task-oriented, routine environments where large amounts of data can be analysed to identify patterns, make decisions, and produce solutions or efficiency dividends.
“Because of this, AI technology is particularly efficient in task-oriented, routine environments where large amounts of data can be analysed to identify patterns, make decisions based on those pattens, and produce solutions or efficiency dividends, for instance within banking and finance industries (Neufeind et al. 2018;De Vries et al. 2020).”
From Artificial intelligence and work: a critical review of recent research from the social sciences (2024) · cites this paper as De Vries et al. 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 35 · Cited by 219
Research works printed but not linked: 2
- Acemoglu, D. , Restrepo, P. , 2018. Modeling automation. Am. Econ. Rev. Pap. Proc. 108, 48-53 .
- Cameron, A.C. , Gelbach, J.B. , Miller, D.L. , 2012. Robust inference with multi-way cluster- ing. J. Bus. Econ. Stat. 29 (2), 238-249 .
- Authors
- de Vries, G. J., Gentile, E., Miroudot, S., & Wacker, K. M.
- Affiliations
- Asian Development Bank (PH)
- Organisation de Coopération et de Développement Economiques (FR)
- University of Groningen (NL)
- Venue
- Labour Economics
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 66
- Pages
- 101885
- ISSN
- 0927-5371 (ISSN-L)
- OpenAlex
- W3041887358
- Cited by
- 219
- References
- 35
- Citations by year
- 2026: 26 · 2025: 48 · 2024: 67 · 2023: 34 · 2022: 26 · 2021: 15 · 2020: 2 · 2019: 1
- FWCI
- 16.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Labour Economics (publishedVersion, cc-by)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- Funders
- Asian Development Bank
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Economics, Econometrics and Finance › General Economics, Econometrics and Finance › Global trade and economics
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Keywords
- industrial robots, robot adoption, automation, employment effects, routine jobs, technical feasibility, occupational structure, high-income countries, emerging markets, transition economies
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
