The DOI matches its Crossref record; 75 of the 77 references it cites were found in a registry or an index.
Economics of Artificial Intelligence: Implications for the Future of Work
Open accessAbstract
The current wave of technological change based on advancements in artificial intelligence (AI) has created widespread fear of job loss and further rises in inequality. This paper discusses the rationale for these fears, highlighting the specific nature of AI and comparing previous waves of automation and robotization with the current advancements made possible by a widespread adoption of AI. It argues that large opportunities in terms of increases in productivity can ensue, including for developing countries, given the vastly reduced costs of capital that some applications have demonstrated and the potential for productivity increases, especially among the low skilled. At the same time, risks in the form of further increases in inequality need to be addressed if the benefits from AI-based technological progress are to be broadly shared. For this, skills policies are necessary but not sufficient. In addition, new forms of regulating the digital economy are called for that prevent further rises in market concentration, ensure proper data protection and privacy, and help share the benefits of productivity growth through the combination of profit sharing, (digital) capital taxation, and a reduction in working time. The paper calls for a moderately optimistic outlook on the opportunities and risks from AI, provided that policymakers and social partners take the particular characteristics of these new technologies into account.
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“In an important review of the literature, which combines a historical approach with a task-focused one, Ernst et al. (2019) conclude that AI in fact is likely to play out in ways different from previous waves, by increasing productivity and potentially creating more inclusive growth, provided correct educational measures are taken in countries that are part of globalised labour chains.”
From Artificial intelligence and work: a critical review of recent research from the social sciences (2024) · cites this paper as Ernst et al. 2019
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“In low- and middle-income countries (LMICs), the risk of job loss due to AI is even higher than in advanced economies, with risks being 69 % in India, 72 % in Thailand, 77 % in China, and 85 % in Ethiopia (Ernst et al., 2019).”
From Impacts of generative artificial intelligence on the future of labor market: a systematic review (2025) · cites this paper as Ernst et al., 2019
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Metadata
- Authors
- Ernst, E., Merola, R., & Samaan, D.
- Affiliations
- Research Department, International Labour Organisation , Route des Morillons 4, 1211 Geneva , Switzerland
- International Labour Organization (CH)
- ORCID
- Samaan, Daniel
- Venue
- IZA Journal of Labor Policy
- Publisher
- Walter de Gruyter GmbH
- Published
- 2019-06-01
- Volume
- 9
- Issue
- 1
- ISSN
- 2193-9004 (ISSN-L)
- OpenAlex
- W2969615692
- Cited by
- 405
- References
- 77
- Citations by year
- 2026: 39 · 2025: 84 · 2024: 96 · 2023: 61 · 2022: 52 · 2021: 36 · 2020: 30 · 2019: 7
- FWCI
- 39.01
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IZA Journal of Labor Policy (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Econstor (Econstor) (submittedVersion, cc-by)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence, future of work, job displacement, inequality, productivity growth, automation, robotization, low-skilled workers, skills policy, digital economy regulation, market concentration, data protection, profit sharing, working time reduction, developing countries, 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 ›
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