Estimating education indicators for small geographic areas, such as specific districts, poses challenges due to the limited sample sizes available in these regions. These limitations can result in high variance among direct estimators and create data gaps that impede the development of targeted educational policies. This study employs the Small Area Estimation (SAE) method to provide accurate estimates of educational attainment at the district level in South Africa. We utilised a unit-level logit mixed model to predict the prevalence of different education levels, incorporating data from the 2023 General Household Survey alongside auxiliary variables from the 2022 Population Census. Our methodology applies Empirical Best Predictors (EBPs) within a random effects framework to refine estimates for districts with small sample sizes. Our analysis reveals significant disparities in educational levels between metropolitan and non-metropolitan districts. For instance, districts DC24, DC33, DC15, and DC27 exhibit the highest prevalence of individuals with no education. In contrast, urban districts such as NMA, CPT, EKU, ETH, JHB, and BUF demonstrate the lowest prevalence, all below 11%. These robust, model-based estimates provide policymakers with a reliable foundation for resource allocation and planning, highlighting that SAE is an essential tool for transforming limited survey data into actionable insights for monitoring educational equity and advancing national development goals.
Yegnanew A. Shiferaw (2026) studied this question.