Artificial Intelligence and service sector in OECD countries
DOI:
https://doi.org/10.18559/rielf.2026.1.4486Keywords:
service sector, artificial intelligence, OECD, GMM regressionAbstract
Purpose: The emergence of artificial intelligence has profoundly transformed the labor market, particularly in the service sector, where many occupations have been redefined or displaced. This sector is now characterised by an increasing concentration of professions requiring skills in artificial intelligence. This article analyses the impact of artificial intelligence on employment in the service sector across the Organization for Economic Co-operation and Development (OECD).
Design/methodology/approach: This article used the Generalised Method of Moments (GMM) on annual secondary data covering the period 2012–2024 for 36 OECD countries, using STATA 17.0.
Findings: The findings suggest a replacement of jobs across the service sector, where the result of the GMM model shows that employment in the service sector decreases by 4.8% for each 1 point of the AI adoption index. Therefore, it is essential to bridge the intersectoral gap to ensure a smooth transition in the labor market.
Originality/value: This study introduces a novel measure of AI constructed using principal component analysis (PCA) based on patents filed by residents and nonresidents across OECD countries. The findings reveal a negative impact of AI on employment in the service sector, underscoring the need to rethink labor market policies and implement tailored upskilling and reskilling strategies to support service sector workers in adapting to emerging employment trends.
JEL Classification
Econometrics (C01)
Estimation: General (C13)
Forecasting and Prediction Methods • Simulation Methods (C53)
Labor Force and Employment, Size, and Structure (J21)
Technological Change: Choices and Consequences • Diffusion Processes (O33)
Downloads
References
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30.
View in Google Scholar
DOI: https://doi.org/10.1257/jep.33.2.3
Acemoglu, D., & Restrepo, P. (2020, May). Unpacking skill bias: Automation and new tasks. AEA Papers and Proceedings, 110, 356–361. https://doi.org/10.1257/pandp.20201063
View in Google Scholar
DOI: https://doi.org/10.1257/pandp.20201063
Aghion, P., & Howitt, P. (2023). The creative destruction approach to growth economics. European Review, 31(4), 312–325. https://doi.org/10.1017/S1062798723000212
View in Google Scholar
DOI: https://doi.org/10.1017/S1062798723000212
Albanesi, S., Dias da Silva, A., Jimeno, J. F., Lamo, A., & Wabitsch, A. (2025). New technologies and jobs in Europe. NBER Working Paper, 31357. https://doi.org/10.3386/w31357
View in Google Scholar
DOI: https://doi.org/10.21034/iwp.105
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.
View in Google Scholar
DOI: https://doi.org/10.2307/2297968
Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51.
View in Google Scholar
DOI: https://doi.org/10.1016/0304-4076(94)01642-D
Astivia, O. L. O., & Zumbo, B. D. (2019). Heteroskedasticity in multiple regression analysis: What it is, how to detect it and how to solve it with applications in R and SPSS. Practical Assessment, Research & Evaluation, 24(1), n1.
View in Google Scholar
Baltagi, B. H. (2021). Heteroskedasticity and serial correlation in the error component model. In B. Baltagi, Econometric analysis of panel data (pp. 109–147). Springer
View in Google Scholar
DOI: https://doi.org/10.1007/978-3-030-53953-5_5
Benoit, K. (2011). Linear regression models with logarithmic transformations. London School of Economics, 22(1), 23–36.
View in Google Scholar
Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.
View in Google Scholar
DOI: https://doi.org/10.1016/S0304-4076(98)00009-8
Blyton, P. (2018). Working population and employment. In R. Bean (Ed.), International labour statistics (pp. 18–49). Routledge.
View in Google Scholar
DOI: https://doi.org/10.4324/9780429021336-2
Brioscú, A., Lauringson, A., Saint-Martin, A., & Xenogiani, T. (2024). A new dawn for public employment services: Service delivery in the age of artificial intelligence. OECD Publishing. https://doi.org/10.1787/5dc3eb8e-en
View in Google Scholar
DOI: https://doi.org/10.1787/5dc3eb8e-en
Broecke, S. (2023). Artificial intelligence and the labour market: Introduction. OECD Employment Outlook, 93. https://doi.org/10.1787/08785bba-en
View in Google Scholar
DOI: https://doi.org/10.1787/08785bba-en
Calvino, F., Dernis, H., Samek, L., & Ughi, A. (2024). A sectoral taxonomy of ai intensity. OECD Publishing.
View in Google Scholar
DOI: https://doi.org/10.1787/1f6377b5-en
Daemen, J., & Rijmen, V. (2020). Correlation matrices. In J. Daemen & V. Rijmen, The design of Rijndael: The advanced encryption standard (AES) (pp. 91–113). Springer.
View in Google Scholar
DOI: https://doi.org/10.1007/978-3-662-60769-5_7
Ding, C., Song, X., Xing, Y., & Wang, Y. (2023). Bilateral effects of the digital economy on manufacturing employment: substitution effect or creation effect? Sustainability, 15(19), 14647. https://doi.org/10.3390/su151914647
View in Google Scholar
DOI: https://doi.org/10.3390/su151914647
Doraszelski, U., & Jaumandreu, J. (2018). Measuring the bias of technological change. Journal of Political Economy, 126(3), 1027–1084.
View in Google Scholar
DOI: https://doi.org/10.1086/697204
Green, A., & Lamby, L. (2023). The supply, demand and characteristics of the AI workforce across OECD countries. OECD Social, Employment, and Migration Working Papers, (287), 1–62. https://doi.org/10.1787/bb17314a-en
View in Google Scholar
DOI: https://doi.org/10.1787/bb17314a-en
Growiec, J. (2019). The hardware-software model: A new conceptual framework of production, R&D, and growth with AI. SGH KAE Working Papers, 2019/042. https://doi.org/10.33119/kaewps2019042
View in Google Scholar
DOI: https://doi.org/10.33119/kaewps2019042
Gu, T. T., Zhang, S. F., & Cai, R. (2022). Can artificial intelligence boost employment in service industries? Empirical analysis based on China. Applied Artificial Intelligence, 36(1), 2080336. https://doi.org/10.1080/08839514.2022.2080336
View in Google Scholar
DOI: https://doi.org/10.1080/08839514.2022.2080336
Guarascio, D., & Reljic, J. (2025). AI and employment in Europe. Economics Letters, 247, 112183. https://doi.org/10.1016/j.econlet.2025.112183
View in Google Scholar
DOI: https://doi.org/10.1016/j.econlet.2025.112183
Hadi, N. U., Abdullah, N., & Sentosa, I. (2016). An easy approach to exploratory factor analysis: Marketing perspective. Journal of Educational and Social Research, 6(1), 215–223.
View in Google Scholar
Hansen, L. P. (2010). Generalized method of moments estimation. In S. N. Durlauf & L. E. Blume (Eds.), Macroeconometrica and time series analysis (pp. 105–118). Palgrave Macmillan.
View in Google Scholar
DOI: https://doi.org/10.1057/9780230280830_13
İşcan, E. (2021). An old problem in the new era: Effects of artificial intelligence to unemployment on the way to industry 5.0. Yaşar Üniversitesi E-Dergisi, 16(61), 77–94. https://doi.org/10.19168/jyasar.781167
View in Google Scholar
DOI: https://doi.org/10.19168/jyasar.781167
Jafarzadegan, M., Safi-Esfahani, F., & Beheshti, Z. (2019). Combining hierarchical clustering approaches using the PCA method. Expert Systems with Applications, 137, 1–10.
View in Google Scholar
DOI: https://doi.org/10.1016/j.eswa.2019.06.064
Jula, D., & Jula, N. M. (2017). Foreign direct investments and employment. Structural analysis. Romanian Journal of Economic Forecasting, 20(2), 29–44.
View in Google Scholar
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36.
View in Google Scholar
DOI: https://doi.org/10.1007/BF02291575
Kaur, P., Stoltzfus, J., & Yellapu, V. (2018). Descriptive statistics. International Journal of Academic Medicine, 4(1), 60-63.
View in Google Scholar
DOI: https://doi.org/10.4103/IJAM.IJAM_7_18
Lin, S., Tuvd, D., & Buljinsuren, O. (2026). Empirical analysis of the impact of AI on the employment of employees in service enterprises. Norwegian Journal of Development of the International Science, (150), 45–53.
View in Google Scholar
Ma, H., Gao, Q., Li, X., & Zhang, Y. (2022). AI development and employment skill structure: A case study of China. Economic Analysis and Policy, 73, 242–254. https://doi.org/10.1016/j.eap.2021.11.007
View in Google Scholar
DOI: https://doi.org/10.1016/j.eap.2021.11.007
Mutascu, M. (2021). Artificial intelligence and unemployment: New insights. Economic Analysis and Policy, 69, 653–667. https://doi.org/10.1016/j.eap.2021.01.012
View in Google Scholar
DOI: https://doi.org/10.1016/j.eap.2021.01.012
OECD. (2023). AI and the future of skills, vol. 2: Methods for evaluating AI capabilities. OECD Publishing. https://doi.org/10.1787/a9fe53cb-en
View in Google Scholar
DOI: https://doi.org/10.1787/a9fe53cb-en
OECD.AI. (2025). Tracking Europe’s progress on AI: Insights from the implementation of the EU Coordinated Plan on Artificial Intelligence. OECD. https://oecd.ai/en/wonk/tracking-europes-progress-on-ai-insights-from-the-implementation-of-the-eu-coordinated-plan-on-artificial-intelligence
View in Google Scholar
O’Reilly, J., Ranft, F., Neufeind, M., Gregory, S. S., Zierahn, U., Went, R., & Holts, K. (2018). Work in the digital age: Challenges of the fourth industrial revolution. Rowman & Littlefield.
View in Google Scholar
Osabohien, R., Oluwalayomi, D. A., Itua, O. Q., & Elomien, E. (2020). Foreign direct investment inflow and employment in Nigeria. Investment Management & Financial Innovations, 17(1), 77.
View in Google Scholar
DOI: https://doi.org/10.21511/imfi.17(1).2020.07
Rasulov, J. (2022). Technological innovation and unemployment across Sweden: An analysis based on patent counts [master’s thesis]. Linnaeus University. https://lnu.diva-portal.org/smash/record.jsf?pid=diva2:1664857
View in Google Scholar
Rojas-Valverde, D., Pino-Ortega, J., Gómez-Carmona, C. D., & Rico-González, M. (2020). A systematic review of methods and criteria standard proposal for the use of principal component analysis in team’s sports science. International Journal of Environmental Research and Public Health, 17(23), 8712.
View in Google Scholar
DOI: https://doi.org/10.3390/ijerph17238712
Romer, P. M. (1989). What determines the rate of growth and technological change? World Bank Publications.
View in Google Scholar
DOI: https://doi.org/10.3386/w3210
Sargan, J. D. (1958). The estimation of economic relationships using instrumental variables. Econometrica, 26(3), 393–415.
View in Google Scholar
DOI: https://doi.org/10.2307/1907619
Schumpeter, J. (1942). Creative destruction. Capitalism, Socialism and Democracy, 825, 82–85.
View in Google Scholar
Shabbir, A., Kousar, S., Kousar, F., Adeel, A., & Jafar, R. A. (2019). Investigating the effect of governance on unemployment: A case of South Asian countries. International Journal of Management and Economics, 55(2), 160–181.
View in Google Scholar
DOI: https://doi.org/10.2478/ijme-2019-0012
Shao, S., Shi, Z., & Shi, Y. (2022). Impact of AI on employment in manufacturing industry. International Journal of Financial Engineering, 9(3), 2141013. https://doi.org/10.1142/S2424786321410139
View in Google Scholar
DOI: https://doi.org/10.1142/S2424786321410139
Singla, A., Sukhovetsky, A., Yee, L., & Chui, M. (2024). The state of AI in 2025: Agents, innovation, and transformation. McKinsey & Company.
View in Google Scholar
Uctu, R., Tuluce, N. S. H., & Aykac, M. (2024). Creative destruction and artificial intelligence: The transformation of industries during the sixth wave. Journal of Economy and Technology, 2, 296–309. https://doi.org/10.1016/j.ject.2024.09.004
View in Google Scholar
DOI: https://doi.org/10.1016/j.ject.2024.09.004
Ullah, S., Akhtar, P., & Zaefarian, G. (2018). Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data. Industrial Marketing Management, 71, 69–78.
View in Google Scholar
DOI: https://doi.org/10.1016/j.indmarman.2017.11.010
Wooldridge, J. M. (2016). Introductory econometrics a modern approach. Cengage Learning.
View in Google Scholar
World Bank. (2024). DataBank. World Bank Indicators. https://databank.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG/1ff4a498/Popular-Indicators
View in Google Scholar
Wu, L., & Kane, G. C. (2021). Network-biased technical change: How modern digital collaboration tools overcome some biases but exacerbate others. Organization Science, 32(2), 273–292. https://doi.org/10.1287/orsc.2020.1368
View in Google Scholar
DOI: https://doi.org/10.1287/orsc.2020.1368
Young, R., & Johnson, D. R. (2015). Handling missing values in longitudinal panel data with multiple imputation. Journal of Marriage and Family, 77(1), 277–294.
View in Google Scholar
DOI: https://doi.org/10.1111/jomf.12144
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Samia Al Kaakour
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
