Artificial Intelligence

Does it Intensify or Mitigate Unemployment in Kenya's Skilled Labor Market?

Authors

  • Powel Patrick Murunga University of Nairobi, Kenya

DOI:

https://doi.org/10.70641/ajbds.v2i1.162

Keywords:

Skill, Employment, Artificial Intelligence, ARDL, structural breaks, human capital

Abstract

The Fourth Industrial Revolution(4IR) is reconfiguring labor markets through artificial intelligence (AI). This study examines the effect of AI investment on skilled employment in Kenya in both short- and long-run dynamics and the role of structural breaks. Quarterly data from 2012Q1 to 2024Q4 were analyzed using the Autoregressive Distributed Lag (ARDL) model, incorporating Chow breakpoint tests, structural dummy variables, and Granger causality. Baseline estimates show AI investment significantly raises skilled employment in the short run (8.83%) and long run (4.81%). With structural breaks included, the short-run effect turns negative coefficient (β =–14.21), while the long-run effect remains positive (β =8.13). The error correction term (–0.65) indicates rapid adjustment to equilibrium. Wages positively affect skilled employment in both horizons (β=0.59; β=6.09) which will call for competitive compensation. Inflation has a strong persistent negative short-run effect (β =–51,250.92) and long-run effect (β =-27,852.28). GDP per capita exerts persistent negative effect (β =–0.47; β =–1.46) attributed to capital-intensive growth that limits labor absorption. The results show AI adoption initially displaces skilled labor but later increases demand for advanced skills. Macroeconomic factors amplify these dynamics: wage growth enhances employment, while inflation and GDP growth exert asymmetric effects. Granger causality tests confirm a bidirectional causal relationship between AI and skilled employment. Kenya may provide education and training programs to mitigate transitional unemployment, align training with AI-intensive sectors, expand broadband in underserved counties, integrate AI curricula in TVETs, and sustain competitive wage structures. Regulatory safeguards and social protection are essential to ensure inclusive AI-driven labor market transformation.

References

Acemoglu, D., & Restrepo, P. (2018). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488–1542. https://doi.org/10.1257/aer.20160696 DOI: https://doi.org/10.1257/aer.20160696

Adendorff, C., & Collier, D. (2015). An umbrella for the rainbow nation: Possible futures for the Republic of South Africa towards 2055. Cadar.

Amankwah Amoah, J., Khan, Z., Wood, G., & Knight, G. (2021). COVID-19 and digitalization: The great acceleration. Journal of Business Research, 136, 602–611. https://doi.org/10.1016/j.jbusres.2021.08.011 DOI: https://doi.org/10.1016/j.jbusres.2021.08.011

Atemoagbo, O. P., Abdullahi, A., & Siyan, P. (2024). Modeling economic relationships: A statistical investigation of trends and relationships. Social Science and Humanities Journal, 8(05), 3778–3796. https://doi.org/10.18535/sshj.v8i05.1039 DOI: https://doi.org/10.18535/sshj.v8i05.1039

Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30. https://doi.org/10.1257/jep.29.3.3 DOI: https://doi.org/10.1257/jep.29.3.3

Ayhan, F., & Elal, O. (2023). The impacts of technological change on employment: Evidence from OECD countries with panel data analysis. Technological Forecasting and Social Change, 190, Article 122439. https://doi.org/10.1016/j.techfore.2023.122439 DOI: https://doi.org/10.1016/j.techfore.2023.122439

Barbieri, L., Mussida, C., Piva, M., & Vivarelli, M. (2020). Testing the employment and skill impact of new technologies: A survey and some methodological issues. In K. F. Zimmermann (Ed.), Handbook of labor, human resources and population economics (pp. 1–27). Springer. https://doi.org/10.1007/978-3-319-57365-6_1-1 DOI: https://doi.org/10.1007/978-3-319-57365-6_1-1

Benzell, S. G., Brynjolfsson, E., MacCrory, F., & Westerman, G. (2019). Identifying the multiple skills in skill-biased technical change [Working paper]. https://ide.mit.edu/wp-content/uploads/2019/08/Identifying-the-Multiple-Skills-in-SBTC-8-2-19.pdf

Blockchain and AI Task Force. (2018). The Kenyan government created a Blockchain & Artificial Intelligence task force in February 2018. https://oecd.ai/en/dashboards/policy-initiatives/http:%2F%2Faipo.oecd.org%2F2021-data-policyInitiatives-26983

Caruso, L. (2018). Digital innovation and the fourth industrial revolution: Epochal social changes? AI & Society, 33(3), 379–392. https://doi.org/10.1007/s00146-017-0736-1 DOI: https://doi.org/10.1007/s00146-017-0736-1

Chen, Z., Shang, Q., & Zhang, J. (2024). Recent progress in hukou reform and labor market integration in China: 1996–2022. China Economic Review, 87, Article 102231. https://doi.org/10.1016/j.chieco.2024.102231 DOI: https://doi.org/10.1016/j.chieco.2024.102231

Cooley, T. F., Greenwood, J., & Yorukoglu, M. (1997). The replacement problem. Journal of Monetary Economics, 40(3), 457–499. https://doi.org/10.1016/S0304-3932(97)00055-X DOI: https://doi.org/10.1016/S0304-3932(97)00055-X

Dahlin, E. C. (2019). Are robots stealing our jobs? Socius: Sociological Research for a Dynamic World, 5, Article 2378023119846249. https://doi.org/10.1177/2378023119846249 DOI: https://doi.org/10.1177/2378023119846249

(Available from BYU ScholarsArchive: http://hdl.lib.byu.edu/1877/6695)

Damioli, G., Van Roy, V., Vértesy, D., & Vivarelli, M. (2024). Drivers of employment dynamics of AI innovators. Technological Forecasting and Social Change, 201, Article 123249. https://doi.org/10.1016/j.techfore.2024.123249 DOI: https://doi.org/10.1016/j.techfore.2024.123249

Dogan, E., & Inglesi Lotz, R. (2020). The impact of economic structure to the Environmental Kuznets Curve (EKC) hypothesis: Evidence from European countries. Environmental Science and Pollution Research, 27(11), 12717–12724. https://doi.org/10.1007/s11356-020-07878-2 DOI: https://doi.org/10.1007/s11356-020-07878-2

Giwa, F., & Ngepah, N. (2024). Artificial intelligence and skilled employment in South Africa: Exploring key variables. Research in Globalization, 8, Article 100231. https://doi.org/10.1016/j.resglo.2024.100231 DOI: https://doi.org/10.1016/j.resglo.2024.100231

Guliyev, H. (2023). Artificial intelligence and unemployment in high tech developed countries: New insights from dynamic panel data model. Research in Globalization, 7, Article 100140. https://doi.org/10.1016/j.resglo.2023.100140 DOI: https://doi.org/10.1016/j.resglo.2023.100140

Ha, J., Kose, M. A., & Ohnsorge, F. (2022). Global stagflation (CEPR Discussion Paper No. 17381; CAMA Working Paper No. 2022/41). Centre for Economic Policy Research & Centre for Applied Macroeconomic Analysis. https://cepr.org/publications/dp17381

Hami Saka, S. A. K. A., & Orhan, M. (2022). R&D and employment relation: Differences in low and high skilled employment in developing economies. Eurasian Journal of Business and Economics, 15(30), 63–86. https://doi.org/10.17015/ejbe.2022.030.04 DOI: https://doi.org/10.17015/ejbe.2022.030.04

Hutter, C., & Weber, E. (2021). Labour market miracle, productivity debacle: Measuring the effects of skill biased and skill neutral technical change. Economic Modelling, 102, Article 105584. https://doi.org/10.1016/j.econmod.2021.105584 DOI: https://doi.org/10.1016/j.econmod.2021.105584

Jahanger, A., Usman, M., Murshed, M., Mahmood, H., & Balsalobre Lorente, D. (2022). The linkages between natural resources, human capital, globalization, economic growth, financial development, and ecological footprint: The moderating role of technological innovations. Resources Policy, 76, Article 102569. https://doi.org/10.1016/j.resourpol.2022.102569 DOI: https://doi.org/10.1016/j.resourpol.2022.102569

KICTANet. (2023, November 16). Policy discussion on artificial intelligence in Kenya. KICTANet Thought Leadership Series. https://www.kictanet.or.ke/policy-discussion-on-artificial-intelligence-in-kenya/

Lee, J., & Strazicich, M. C. (2003). Minimum Lagrange multiplier unit root test with two structural breaks. Review of Economics and Statistics, 85(4), 1082–1089. https://doi.org/10.1162/003465303772815961 DOI: https://doi.org/10.1162/003465303772815961

Martínez, L. R. (2022). How much should we trust the dictator’s GDP growth estimates? Journal of Political Economy, 130(10), 2731–2769. https://doi.org/10.1086/720458 DOI: https://doi.org/10.1086/720458

Ministry of Information, Communications and the Digital Economy. (2025). Kenya National Artificial Intelligence (AI) Strategy 2025–2030 (Draft) for public validation [14-01-2025]. Republic of Kenya. https://ict.go.ke/sites/default/files/2025-01/Kenya%20National%20AI%20Strategy%20%28Draft%29%20for%20Public%20Validation%20%20%5B14-01-2025%5D.pdf

Neves, F., Campos, P., & Silva, S. (2019). Innovation and employment: An agent based approach. Journal of Artificial Societies and Social Simulation, 22(1), Article 8. https://jasss.soc.surrey.ac.uk/22/1/8.html DOI: https://doi.org/10.18564/jasss.3933

Nguyen, Q. P., & Vo, D. H. (2022). Artificial intelligence and unemployment: An international evidence. Structural Change and Economic Dynamics, 63, 40–55. https://doi.org/10.1016/j.strueco.2022.09.003 DOI: https://doi.org/10.1016/j.strueco.2022.09.003

Pinheiro, P., Putnik, G. D., Castro, A., Castro, H., Dal, B., & Romero, F. (2019). Industry 4.0 and industrial revolutions: An assessment based on complexity. FME Transactions, 47(4), 831–840. https://doi.org/10.5937/fmet1904831P DOI: https://doi.org/10.5937/fmet1904831P

Reiche, L., & Meyler, A. (2022). Making sense of consumer inflation expectations: The role of uncertainty (No. 2642). ECB Working Paper. European Central Bank. https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp2642~f96823e5de.en.pdf DOI: https://doi.org/10.2139/ssrn.4036097

Saba, C. S., & Ngepah, N. (2024). The impact of artificial intelligence (AI) on employment and economic growth in BRICS: Does the moderating role of governance matter? Research in Globalization, 8, Article 100213. https://doi.org/10.1016/j.resglo.2024.100213 DOI: https://doi.org/10.1016/j.resglo.2024.100213

Saba, C. S., Ngepah, N., & Ohonba, A. (2022). Employment impact of national, provincial and local government capital in South Africa: An aggregate and sectoral perspective. Cogent Economics & Finance, 10(1), Article 2046322. https://doi.org/10.1080/23322039.2022.2046322 DOI: https://doi.org/10.1080/23322039.2022.2046322

Salazar, R. M. (2022). A systematic literature review of the tradeoff between employment and inflation and how it affects the market economy (SSRN Working Paper No. 4119507). SSRN. https://doi.org/10.2139/ssrn.4119507 DOI: https://doi.org/10.2139/ssrn.4119507

Scherer, M. U. (2016). Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies. Harvard Journal of Law & Technology, 29(2), 353–400. https://doi.org/10.2139/ssrn.2609777 DOI: https://doi.org/10.2139/ssrn.2609777

Serrari Group. (2023). Artificial intelligence will add $2.4 billion to Kenya’s economy by 2030—Report. https://serrarigroup.com/artificial-intelligence-will-add-2-4-billion-to-kenyas-economy-by-2030-report/

Sheikheldin, G. H., & Mohamed, A. A. (2021). Skills for science systems in Africa: The case of ‘brain drain’. In R. Hanlin, A. D. Tigabu, & G. Sheikheldin (Eds.), Building science systems in Africa: Conceptual foundations and empirical considerations (pp. 135–161). Mkuki na Nyota Publishers & African Centre for Technology Studies. https://doi.org/10.1007/s11192-017-2573-x DOI: https://doi.org/10.1007/s11192-017-2573-x

Slatten, L. A., Bendickson, J. S., Diamond, M., & McDowell, W. C. (2021). Staffing of small nonprofit organizations: A model for retaining employees. Journal of Innovation & Knowledge, 6(1), 50–57. https://doi.org/10.1016/j.jik.2020.10.003 DOI: https://doi.org/10.1016/j.jik.2020.10.003

Tasioulas, J. (2019). First steps towards an ethics of robots and artificial intelligence. Journal of Practical Ethics, 7(1), 61–95. https://jpe.ox.ac.uk/papers/first-steps-towards-an-ethics-of-robots-and-artificial-intelligence/ DOI: https://doi.org/10.2139/ssrn.3172840

UNESCO. (2023). Shaping Kenya's AI future: UNESCO contributes to national AI strategy formulation. https://www.unesco.org/en/articles/shaping-kenyas-ai-future-unesco-contributes-national-ai-strategy-formulation

Violante, G. L. (2008). Skill biased technical change. In S. N. Durlauf & L. E. Blume (Eds.), The New Palgrave Dictionary of Economics (2nd ed., Vol. 2, pp. 10–25). Palgrave Macmillan. https://doi.org/10.1057/978-1-349-95189-5_2388 DOI: https://doi.org/10.1057/978-1-349-95189-5_2388

Wang, J., Hu, Y., & Zhang, Z. (2021). Skill biased technological change and labor market polarization in China. Economic Modelling, 100, Article 105507. https://doi.org/10.1016/j.econmod.2021.105507 DOI: https://doi.org/10.1016/j.econmod.2021.105507

Wang, X., Chen, M., & Chen, N. (2024). How artificial intelligence affects the labour force employment structure from the perspective of industrial structure optimisation. Heliyon, 10(5), Article e26686. https://doi.org/10.1016/j.heliyon.2024.e26686 DOI: https://doi.org/10.1016/j.heliyon.2024.e26686

Yildirim, D. Ç., Yildirim, S., Erdogan, S., & Kantarci, T. (2022). Innovation—Unemployment nexus: The case of EU countries. International Journal of Finance & Economics, 27(1), 1208–1219. https://doi.org/10.1002/ijfe.2209 DOI: https://doi.org/10.1002/ijfe.2209

Zarifhonarvar, A. (2024). Economics of ChatGPT: A labor market view on the occupational impact of artificial intelligence. Journal of Electronic Business & Digital Economics, 3(2), 100–116. https://doi.org/10.1108/JEBDE-10-2023-0021 DOI: https://doi.org/10.1108/JEBDE-10-2023-0021

Zhang, X., Sun, M., Liu, J., & Xu, A. (2024). The nexus between industrial robot and employment in China: The effects of technology substitution and technology creation. Technological Forecasting and Social Change, 202, Article 123341. https://doi.org/10.1016/j.techfore.2024.123341 DOI: https://doi.org/10.1016/j.techfore.2024.123341

Downloads

Published

2025-08-29

How to Cite

Murunga, P. P. (2025). Artificial Intelligence: Does it Intensify or Mitigate Unemployment in Kenya’s Skilled Labor Market?. African Journal of Business and Development Studies, 2(1), 539–553. https://doi.org/10.70641/ajbds.v2i1.162