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<reference>
The impact of Artificial Intelligence on the labour market: What do we know so far?

Downloadable! This literature review takes stock of what is known about the impact of artificial intelligence on the labour market, including the impact on employment and wages, how AI will transform jobs and skill needs, and the impact on the work environment. The purpose is to identify gaps in the evidence base and inform future OECD research on AI and the labour market.

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The impact of Artificial Intelligence on the labour market: What do we know so far?

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Marguerita Lane

Anne Saint-Martin

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Abstract
This literature review takes stock of what is known about the impact of artificial intelligence on the labour market, including the impact on employment and wages, how AI will transform jobs and skill needs, and the impact on the work environment. The purpose is to identify gaps in the evidence base and inform future OECD research on AI and the labour market.

Suggested Citation

Marguerita Lane & Anne Saint-Martin, 2021.
"
The impact of Artificial Intelligence on the labour market: What do we know so far?
,"

OECD Social, Employment and Migration Working Papers

256, OECD Publishing.

Handle:
RePEc:oec:elsaab:256-en

DOI: 10.1787/7c895724-en

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File URL:

https://doi.org/10.1787/7c895724-en
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no

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https://libkey.io/10.1787/7c895724-en?utm_source=ideas

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Cited by:
Oschinski, Matthias, 2023.
"
Assessing the Impact of Artificial Intelligence on Germany's Labor Market: Insights from a ChatGPT Analysis
,"

MPRA Paper

118300, University Library of Munich, Germany.

Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023.
"
Artificial intelligence and firm-level productivity
,"

Journal of Economic Behavior & Organization
, Elsevier, vol. 211(C), pages 188-205.

Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2022.
"
Artificial intelligence and firm-level productivity
,"

ZEW Discussion Papers

22-005, ZEW - Leibniz Centre for European Economic Research.

Dirk Czarnitzki & Gastón P Fernández & Christian Rammer, 2022.
"
Artificial Intelligence and Firm-level Productivity
,"

Working Papers of Department of Management, Strategy and Innovation, Leuven

690486, KU Leuven, Faculty of Economics and Business (FEB), Department of Management, Strategy and Innovation, Leuven.

Pawel Gmyrek & Janine Berg & David Bescond, 2025.
"
Generative AI and Jobs: An Analysis of Potential Effects on Global Employment
,"

Gospodarka Narodowa. The Polish Journal of Economics
, Warsaw School of Economics, issue 3, pages 6-30.

Cirillo, Valeria & Mina, Andrea & Ricci, Andrea, 2024.
"
Digital technologies, labor market flows and training: Evidence from Italian employer-employee data
,"

Technological Forecasting and Social Change
, Elsevier, vol. 209(C).

Valeria Cirillo & Andrea Mina & Andrea Ricci, 2024.
"
Digital Technologies, Labor market flows and Training: Evidence from Italian employer-employee data
,"

LEM Papers Series

2024/22, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

Minniti, Antonio & Prettner, Klaus & Venturini, Francesco, 2025.
"
AI innovation and the labor share in European regions
,"

European Economic Review
, Elsevier, vol. 177(C).

Oscar Molina & Florian Butollo & Csaba MakÃ³ & Alejandro Godino & Ursula Holtgrewe & Anna Illsoe & Sander Junte & Trine Pernille Larsen & MiklÃ³s IllÃ©sy & JÃ³szef Pap & Philip Wotschack, 2023.
"
It takes two to code: a comparative analysis of collective bargaining and artificial intelligence
,"

Transfer: European Review of Labour and Research
, , vol. 29(1), pages 87-104, February.

Stan Metcalfe, 2024.
"
Joseph Schumpeter, Alfred Marshall and the nature of restless capitalism
,"

MIOIR Working Paper Series

2024-02, The Manchester Institute of Innovation Research (MIoIR), The University of Manchester.

Francesco Carbonero & Sergio Scicchitano, 2025.
"
Labour and technology at the time of COVID-19: can artificial intelligence mitigate the need for proximity?
,"

Eurasian Business Review
, Springer;Eurasia Business and Economics Society, vol. 15(4), pages 1167-1203, December.

Carbonero, Francesco & Scicchitano, Sergio, 2021.
"
Labour and technology at the time of Covid-19. Can artificial intelligence mitigate the need for proximity?
,"

GLO Discussion Paper Series

765, Global Labor Organization (GLO).

Engberg, Erik & Koch, Michael & Lodefalk, Magnus & Schroeder, Sarah, 2025.
"
Artificial intelligence, tasks, skills, and wages: Worker-level evidence from Germany
,"

Research Policy
, Elsevier, vol. 54(8).

Engberg, Erik & Koch, Michael & Lodefalk, Magnus & Schroeder, Sarah, 2023.
"
Artificial Intelligence, Tasks, Skills and Wages: Worker-Level Evidence from Germany
,"

Working Papers

2023:12, Örebro University, School of Business.

Engberg, Erik & Koch, Michael & Lodefalk, Magnus & Schroeder, Sarah, 2023.
"
Artificial Intelligence, Tasks, Skills and Wages: Worker-Level Evidence from Germany
,"

Ratio Working Papers

371, The Ratio Institute.

Kateryna Tkach & Alberto Marzucchi & Ugo Rizzo & Michela Borghesi, 2026.
"
The relationship between green and digital skill supply and industrial dynamics
,"

SEEDS Working Papers

0726, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Feb 2026.

repec:aou:nszioz:y:2025:i:4:p:57-72 is not listed on IDEAS

Chankook Park & Minkyu Kim, 2026.
"
Utilization and challenges of artificial intelligence in the energy sector
,"

Energy & Environment
, , vol. 37(3), pages 1242-1261, May.

Fossen, Frank M. & Sorgner, Alina, 2022.
"
New digital technologies and heterogeneous wage and employment dynamics in the United States: Evidence from individual-level data
,"

Technological Forecasting and Social Change
, Elsevier, vol. 175(C).

Parteka, Aleksandra & Wolszczak-Derlacz, Joanna & Nikulin, Dagmara, 2024.
"
How digital technology affects working conditions in globally fragmented production chains: Evidence from Europe
,"

Technological Forecasting and Social Change
, Elsevier, vol. 198(C).

Aleksandra Parteka & Joanna Wolszczak-Derlacz & Dagmara Nikulin, 2021.
"
How digital technology affects working conditions in globally fragmented production chains: evidence from Europe
,"

GUT FME Working Paper Series A

66, Faculty of Management and Economics, Gdansk University of Technology.

Silvia Massini & Mabel Sanchez Barrioluengo & Xiaoxiao Yu & Reza Salehnejad, 2024.
"
Digital transformation in firms: determinants of technology adoption and implications for performance
,"

MIOIR Working Paper Series

2024-01, The Manchester Institute of Innovation Research (MIoIR), The University of Manchester.

Oliver Giering & Stefan Kirchner, 2025.
"
Artificial intelligence and autonomy at work: empirical insights from Germany
,"

Journal for Labour Market Research
, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 59(1), pages 1-15, December.

Jean-Philippe Deranty & Thomas Corbin, 2022.
"
Artificial Intelligence and work: a critical review of recent research from the social sciences
,"

Papers

2204.00419, arXiv.org.

More about this item

Keywords
AI
;
Artificial intelligence
;
Future of Work
;
Litterature review
;
Technology
;
All these keywords
JEL
classification:

J20
- Labor and Demographic Economics - - Demand and Supply of Labor - - - General

J81
- Labor and Demographic Economics - - Labor Standards - - - Working Conditions

J31
- Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

O14
- Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology

O33
- Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

NEP fields

This paper has been announced in the following
NEP Reports
:

NEP-CWA-2021-02-01
(Central and Western Asia)

NEP-LMA-2021-02-01
(Labor Markets - Supply, Demand, and Wages)

NEP-TID-2021-02-01
(Technology and Industrial Dynamics)

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</reference>

<statements>
1. Rather than generating uniform technological unemployment, AI is driving job transformation, task reconfiguration, and organizational restructuring, with heterogeneous outcomes shaped by sectoral conditions, firm strategies, and institutional frameworks.
2. The literature converges on the importance of proactive policy responses—targeted upskilling, inclusive AI governance, and social protection reforms—to steer AI-driven restructuring toward more inclusive outcomes.
3. Theoretical ambiguity around aggregate employment impacts is a recurring theme: while AI-facilitated automation reduces labor demand in exposed tasks, productivity gains and new tasks can offset these losses under certain conditions.
4. Aggregate impacts of AI-labor substitution on employment and wage growth remain small and difficult to detect at this stage, but restructuring at the establishment and occupation levels is clear.
5. This shift challenges the traditional assumption that high-skilled workers are uniformly insulated from automation and complicates conventional skill-biased technological change narratives.
6. These roles often command wage premia, reflecting scarcity of relevant skills and the strategic importance of AI capabilities for firms.
7. The literature concludes that AI’s employment effects are not predetermined by the technology itself but are shaped by implementation choices, worker participation, and institutional constraints, reinforcing the importance of governance and social dialogue.
8. The distributional consequences of AI adoption are therefore contingent on institutional settings: where collective bargaining, minimum wage regulations, and social protections are strong, productivity gains are more likely to translate into broad-based income growth; where such institutions are weak, inequality tends to widen.
9. Some analyses of European regions and other contexts suggest that AI innovation may reduce the labor share of income, particularly where complementary policies to strengthen worker bargaining power and skill development are absent.
10. Emerging and developing economies, in contrast, often exhibit greater adjustment constraints, including limited digital infrastructure, weaker education and training systems, and larger informal sectors, which can magnify displacement risks and restrict access to high-quality AI-complementary employment.
11. Reviews of high-quality journal articles between 2020 and 2025 emphasize that technological progress is not inherently inclusive; its distributional effects depend on how AI is integrated into organizational and social frameworks and whether workers and their representatives have a voice in shaping implementation.
12. Where such institutions are absent or weak, AI adoption can exacerbate precariousness and inequality.
13. Skills mismatches and education system inertia are recurring challenges, particularly in countries where curricula have not kept pace with rapid AI diffusion or where training opportunities are unevenly distributed.
14. In advanced economies, policy debates revolve around how to rebalance taxation, social insurance, and regulatory frameworks to address the rise of superstar firms, platform-based work, and algorithmic management.
15. Systematic reviews emphasize that a majority of empirical studies report net positive or mixed employment effects, with relatively few documenting large-scale technological unemployment.
16. Collective bargaining and social dialogue around AI adoption can help ensure that productivity gains are shared and that job redesign favors augmentation over displacement.
17. Partnerships between governments, firms, and educational institutions can support more responsive training ecosystems aligned with evolving AI technologies.
18. Third, aggregate employment impacts appear modest so far, but distributional consequences in terms of inequality and polarization are substantial and likely to intensify without supportive institutions and policies.
19. As AI continues to evolve as a central driver of the Fourth Industrial Revolution, understanding and shaping its labor market restructuring dynamics remains a critical research and policy frontier.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.