Artificial Intelligence, Disaggregated Unemployment, And Sustainable Development In South Africa: Does The Routine-Biased Technological Change Hypothesis Explain The Dynamics?
DOI:
https://doi.org/10.58423/2786-6742/2026-13-13-32Keywords:
Artificial Intelligence, Disaggregated Unemployment, Sustainable Development, South AfricaAbstract
This study examines the relationship between artificial intelligence (AI) adoption and disaggregated unemployment in determining sustainable development in South Africa, grounded in the Routine-Biased Technological Change (RBTC) framework. Using annual time-series data from 2003–2024 and the Autoregressive Distributed Lag (ARDL) modelling approach, the study simultaneously estimates short- and long-run coefficients to capture full developmental dynamics. Empirical results confirm a long-run equilibrium relationship among AI adoption, skill-disaggregated unemployment, and sustainable development. In the short run, AI adoption negatively impacts development due to adjustment costs from routine-task substitution, labour market rigidities, and skill mismatches. In the long run, AI contributes positively and significantly through productivity gains and innovation spillovers once structural adjustments are completed. Regarding skill-specific unemployment, highly educated workers' unemployment consistently impedes development across both horizons, reflecting the productivity costs of underutilised advanced human capital. Unemployment among less-educated workers shows a positive long-run relationship, suggesting structural labour reallocation effects consistent with RBTC predictions. Total unemployment proves statistically insignificant, confirming that aggregate measures mask critical distributional differences across skill groups, and thus validating the core theoretical proposition that skill-biased technological change operates through heterogeneous channels that remain invisible at the aggregate level. The findings are particularly relevant for policymakers in emerging economies facing the dual challenge of accelerating technological transformation and persistent structural unemployment. The study recommends integrated policy frameworks combining AI development incentives, labour market reform, and education strategies to ensure that technological progress translates into inclusive and sustainable development outcomes.
References
1. Acemoglu, D., & Autor, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. In D. Card & O. Ashenfelter (Eds.), Handbook of labor economics (Vol. 4, pp. 1043–1171). Elsevier. https://doi.org/10.1016/S0169-7218(11)02410-5 DOI: https://doi.org/10.1016/S0169-7218(11)02410-5
2. Acemoglu, D., & Restrepo, P. (2018). Artificial intelligence, automation and work (NBER Working Paper No. 24196). National Bureau of Economic Research. http://www.nber.org/papers/w24196 DOI: https://doi.org/10.3386/w24196
3. Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics, 118(4), 1279–1333. https://doi.org/10.1162/003355303322552801 DOI: https://doi.org/10.1162/003355303322552801
4. Bhorat, H., Hill, R., Köhler, T., Monnakgotla, J., & Steenkamp, F. (2023). Who are the robots coming for? The evolving task content of employment in South Africa (SARChI Industrial Development Working Paper No. 2023/06).
5. Giwa, F., & Ngepah, N. (2024). The relationship between artificial intelligence and low-skilled employment in South Africa. Heliyon, 10, Article e40640. https://doi.org/10.1016/j.heliyon.2024.e40640 DOI: https://doi.org/10.1016/j.heliyon.2024.e40640
6. Pieters, J., Kujundzic, A., Burger, R., & Gondwe, J. (2025). Inequality at risk of automation? Gender differences in routine tasks intensity in developing country labor markets (arXiv preprint arXiv:2504.07689). https://doi.org/10.48550/arXiv.2504.07689
7. Google Cloud. (2025). 2025 state of AI infrastructure report.
8. Jung, J., & Mercenier, J. (2014). Structural change, skill bias and employment dynamics. Journal of Macroeconomics, 41, 130–147. https://doi.org/10.1016/j.jmacro.2014.05.002 DOI: https://doi.org/10.1016/j.jmacro.2014.05.002
9. Mienye, I. D., Sun, Y., & Ileberi, E. (2024). Artificial intelligence and sustainable development in Africa: A comprehensive review. Machine Learning with Applications, 18, Article 100591. https://doi.org/10.1016/j.mlwa.2024.100591 DOI: https://doi.org/10.1016/j.mlwa.2024.100591
10. KPMG. (2025). The AI outlook: The role of AI in shaping tomorrow’s world. https://kpmg.com/in/en/insights/2025/09/ai-outlook-the-role-of-ai-in-shaping-tomorrows-world.html
11. Macpherson, W. E., Werner, A., & Mey, M. R. (2023). Industry 4.0: Emerging job categories and associated competencies in the automotive industry in South Africa. SA Journal of Human Resource Management, 20, Article a1916. https://doi.org/10.4102/sajhrm.v20i0.1916 DOI: https://doi.org/10.4102/sajhrm.v20i0.1916
12. Mabungela, M. (2023). Artificial intelligence and automation in the world of work: A threat to employees? Research in Social Sciences and Technology, 8(4), 135–146. https://doi.org/10.46303/ressat.2023.37 DOI: https://doi.org/10.46303/ressat.2023.37
13. Mondolo, J. (2022). The composite link between technological change and employment: A survey of the literature. Journal of Economic Surveys, 36(4), 1027–1068. https://doi.org/10.1111/joes.12484 DOI: https://doi.org/10.1111/joes.12469
14. Dauth, W., Findeisen, S., Südekum, J., & Woessner, N. (2021). The adjustment of labor markets to robots. Journal of the European Economic Association, 19(6), 3104–3153. https://doi.org/10.1093/jeea/jvab012 DOI: https://doi.org/10.1093/jeea/jvab012
15. Nzama, M. L., Epizitone, G. A., Moyane, S. P., Nkomo, N., & Mthalane, P. P. (2024). The influence of artificial intelligence on the manufacturing industry in South Africa. South African Journal of Economic and Management Sciences, 27(1), Article a5520. https://doi.org/10.4102/sajems.v27i1.5520 DOI: https://doi.org/10.4102/sajems.v27i1.5878
16. OECD. (2024). Digital economy outlook 2024: Embracing the technology frontier (Vol. 1). OECD Publishing. https://doi.org/10.1787/a1689dc5-en DOI: https://doi.org/10.1787/a1689dc5-en
17. Opesemowo, O. A. G., & Adekomaya, V. (2024). Harnessing artificial intelligence for advancing sustainable development goals in South Africa’s higher education system: A qualitative study. International Journal of Learning, Teaching and Educational Research, 23(3), 67–86. https://doi.org/10.26803/ijlter.23.3.4 DOI: https://doi.org/10.26803/ijlter.23.3.4
18. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.616 DOI: https://doi.org/10.1002/jae.616
19. Poisat, P., Cullen, M., & Calitz, A. P. (2024). Human resource managers’ perceptions on the impact of AI on the South African workforce. SA Journal of Human Resource Management, 22, Article a2593. https://doi.org/10.4102/sajhrm.v22i0.2593 DOI: https://doi.org/10.4102/sajhrm.v22i0.2593
20. PwC & Strategy&. (2023). Sizing the prize: What’s the real value of AI for South Africa? PwC South Africa.
21. Ramaswamy, K. V. (2018). Technological change, automation and employment: A short review of theory and evidence. International Review of Business and Economics, 2(2), 1–20. DOI: https://doi.org/10.56902/IRBE.2018.2.2.1
22. Sebastian, R., & Biagi, F. (2018). The routine-biased technical change hypothesis: A critical review. European Commission, Joint Research Centre.
23. Singh, J. P., Shehu, A., Wesson, C., & Dua, M. (2023). 2023 global artificial intelligence infrastructures report. George Mason University AI Strategies Team & Stimson Center.
24. Tinonetsana, F., Musariwa, P., & Madondo, E. (2024). Integrating AI for environmental sustainability in medium to large corporations: A case study of South Africa. International Journal of Business Ecosystem & Strategy, 6(6), 15–21. https://www.bussecon.com/ojs/index.php/ijbes DOI: https://doi.org/10.36096/ijbes.v6i6.643
25. World Bank. (2024). World development indicators.
26. Yi, M., & Choi, H. (2023). What drives the acceptance of AI technology? The role of expectations and experiences. arXiv. https://doi.org/10.48550/arXiv.2306.13670
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