Methods and Software for Small Area Estimation in Low- and Middle-Income Countries

Abstract

Demographic and health disparities in low- and middle-income countries (LMICs) persist, yet household surveys, the major data source for many indicators, lack the granularity needed for localized estimates due to data sparsity. This dissertation advances small area estimation (SAE) methods for demographic and health indicators in LMICs, focusing on Bayesian hierarchical modeling to improve precision, account for survey design complexities, and develop novel frameworks for fine-scale subnational estimates of key indicators. In Chapter 2, we incorporate urban/rural stratification into unit-level models to correct biases from urban oversampling. In Chapter 3, we propose ultimate years of schooling, a birth cohort-based measure that estimates final educational attainment while accounting for ongoing schooling trajectories and right-censoring in survey data. In Chapter 4, we develop a fertility estimation framework that integrates spatial, temporal, and maternal education effects to capture demographic trends at a subantional level. In Chapter 5, we present SurveyPrev RShiny, an interactive application that translates advanced SAE methods into an accessible tool for researchers and policymakers.