A Bayesian model for estimating Sustainable Development Goal indicator 4.1.2: School completion rates

Abstract: Estimating school completion is crucial for monitoring Sustainable Development Goal (SDG) 4 on education. The recently introduced SDG indicator 4.1.2, defined as the percentage of children aged 3–5 years above the expected completion age of a given level of education that have completed the respective level, differs from enrolment indicators in that it relies primarily on household surveys. This introduces a number of challenges including gaps between survey waves, conflicting estimates, age misreporting and delayed completion. We introduce the Adjusted Bayesian Completion Rates (ABCR) model to address these challenges and produce the first complete and consistent time series for SDG indicator 4.1.2, by school level and sex, for 164 countries. Validation exercises indicate that the model appears well-calibrated and offers a meaningful improvement over simpler approaches in predictive performance. The ABCR model is now used by the United Nations to monitor completion rates for all countries with available survey data.

The paper can be accessed here. Supporting code and model documentation can be found in the ABC Github repository.