Impact of automation on labor market polarization and job displacement in developing countries

Abstract

African economies suffer from some of the highest unemployment rates in the world, and the use of automation and robots are crowding out labour jobs in the developing world. It is, therefore, necessary to assess the impact of (the fourth wave of the industrial revolution), automation, on unemployment in developing countries in Africa. This study used panel data obtained from the World Bank database of the World Development Indicators and from the Integrated Public Use Microdata Series (IPUMS) that harmonizes census micro-data from around the world to explore the impact of automation on labour market polarization and job displacement in developing countries. The study identified ways in which African developing countries can get ahead of the curve and mitigate the damaging effects of the adoption of robot technology on employment. The analysis reveals that automation (robot adoption) positively correlates with unemployment (r = 0.302, p = 0.000) but requires other factors for a significant impact on labor outcomes. In the panel regression model, adding GDP growth, education level, trade openness, and sectoral employment shares improves the model's explanatory power to 95.9% (R Square = 0.959, p = 0.007). These additional factors, particularly education (B = -0.371, p = 0.000) and trade openness (B = -0.901, p = 0.000), appear to mitigate some of the potential unemployment effects associated with automation. The study concludes that automation in Kenya is increasing labor market polarization and job displacement, with impacts particularly pronounced in middle-skilled roles. Automation contributes to a growing divide, benefiting high-skilled and certain low-skilled jobs while displacing workers in routine tasks, leading to limited middle-income job opportunities. The findings highlight the need for Kenya to prioritize educational investments in digital and technical skills, support inclusive economic growth, and implement retraining programs for those displaced by automation. The government should incentivize responsible automation practices among companies and strengthen social safety nets to support displaced workers. Additionally, a phased approach to technology adoption, international support, and enhanced data collection on labor market dynamics are recommended to help Kenya manage automation's impact while fostering economic resilience.