Educational attainment is a key social determinant of maternal and child health and is a large component of human capital. However, progress towards the targets specified in Sustainable Development Goal 4 is typically measured at the national level, potentially obscuring important subnational variation. Synthesizing and precisely geo-locating educational attainment data from 503 survey, census, and administrative datasets, we employ a temporally and spatially explicit Bayesian hierarchical modeling framework to estimate both mean years of educational attainment and the proportion of men and women attaining key levels of schooling across all low- and middle-income countries between 2000 and 2017. By providing comparable estimates at the 5x5 kilometer level as well as two levels of subnational units, we are able to provide policy-makers and advocates with a robust evidence base to effectively target resources, develop equitable policy, and track accountability.
Presented in Session 134. Education Outcomes in Low- and Middle-Income Countries