Predicting employment outcomes for youth with disabilities in economic empowerment programs: a machine learning approach

Abstract

Youth with disabilities face significant barriers to employment, including discrimination, limited access to education, and inaccessible workplaces which contribute to high unemployment rates and social exclusion. To improve the effectiveness of economic empowerment programs for this demographic, this study developed a machine learning model to predict employment outcomes for youth with disabilities. Drawing from data of 895 youth with disabilities from the Bunyoro subregion of Uganda who participated in economic empowerment programs, encompassing demographic data, disability types, intervention details, and employment status at follow‐up, we trained and evaluated several machine learning models. Among these were ensemble methods such as Random Forest, XGBoost, Gradient Boosting, and Stacking Ensemble. The Stacking Ensemble achieved the best performance with an accuracy of 97.21%, a precision of 92.73%, a recall of 98.08%, and an F1‐score of 95.22% in predicting improved employment status. The key factors driving employment success were soft skills training, the provision of start‐up kits, and the duration of the interventions. This research addresses the critical need to improve the effectiveness of economic empowerment initiatives developed to support youth with disabilities. The findings can inform other programs with similar contexts, contributing to broader development efforts and potentially inspiring the adoption of predictive modeling in other social programs targeting marginalized groups.