Abstract The quality of education in sub-Saharan Africa (SSA) continues to be low, with existing research indicating an ongoing ‘learning crisis’. Some key dimensions of the issue of education quality in SSA relate to the availability and the (mis-)allocation of teachers within countries. Drawing on the literature on the role of Information, Communication and Technology in improving public service delivery and promoting transparency, this study uses machine-learning techniques to assess the distributional effects of various teacher allocation mechanisms on students' learning outcomes in primary education in Senegal. Our results suggest that the average performance of students (measured by the mean of student scores) improves in all 12 simulations tested, but at the expense of equity (measured by the standard deviation of student scores). These results highlight a trade-off between quality and equity, which should be further explored and considered in the search for an ‘optimal’ teacher allocation mechanism in primary education in Senegal. Furthermore, a comparison across the 12 simulations based on a ratio defined as the equity cost for a one-unit improvement in quality suggests that regional-level teacher allocation mechanisms are more effective than the national-level ones, and assigning more experienced teachers to low-performing classrooms at regional level works best in reconciling quality improvement with the need to minimise educational inequality in primary education system in Senegal.
Koussihouèdé et al. (Thu,) studied this question.