From Education to Employment: A Deep Learning Approach to Understanding Job Market Trends in Africa

The research addresses the complex relationship between education and job outcomes in Africa, examining it through a deep learning approach using South Africa’s most recent Quarterly Labour Force Survey (2024). In this study, we developed a Multilayer Perceptron (MLP) model to predict employment and long-term employment status, achieving an accuracy of 99.71% for employment prediction and 91% for long-term unemployed predictions. The use of Local Interpretable Model-Agnostic Explanations (LIME) also helped interpret the model, revealing education level, industry type, job-seeking behavior, and work experience as important predictors. We observed a discrepancy between educational attainment and job-market demands, noting that technical and vocational training plays a crucial role in addressing labor shortages. These findings have important implications for AI-generated employment predictions, supporting the use of data-driven research to inform labor-policy development and workforce planning. Key recommendations include expanding vocational training, aligning educational curricula with current labor-market demands, and developing upskilling programs for workers in transitional careers. Additionally, integrating Artificial Intelligence (AI) tools can improve national labor-market forecasting. This study contributes to promoting a more inclusive and data-driven transition from education to employment across Africa.