Daphne Liu, University of Washington Department of Statistics
Adrian Raftery, University of Washington, Seattle
Women's educational attainment and contraceptive prevalence are two mechanisms identified as having an accelerating effect on fertility decline and that can be directly impacted by policy. We propose a conditional Bayesian hierarchical model to translate these effects into projections of fertility given particular education and family planning policy initiatives. To illustrate the effect policy changes could have on future fertility, we create probabilistic projections of fertility that condition on scenarios such as achieving the Sustainable Development Goals (SDGs) for universal secondary education and universal access to family planning by 2030. By considering potential policy outcomes within probabilistic fertility projection models, we can provide an informative tool for policymakers in high-fertility countries.
Keywords: Fertility and childbirth, Bayesian methods / estimation, Population projections, forecasts, and estimations
Presented in Session 88. Demographic Trends: Estimates and Projections