Conditional Probabilistic Projections of Fertility Given Policy Intervention Scenarios

Daphne Liu, University of Washington Department of Statistics
Vladimira Kantorova, United Nations
Mark Wheldon, United Nations Population Division
Patrick Gerland, United Nations Population Division

The United Nations Sustainable Development Goals (SDGs) include targets for universal secondary education and universal access to sexual and reproductive health-care services, including family planning. Due to the accelerating effect of women's educational attainment and contraceptive prevalence on fertility decline in high-fertility settings, achievement of these SDG targets is likely to have an impact on future fertility and population size for countries that are currently undergoing the fertility transition. Policymakers in high-fertility countries may therefore be interested in quantifying the potential effect of different policy interventions related to education and family planning on the future fertility and population of their countries. We propose a conditional Bayesian hierarchical model for projections of fertility that incorporates women's educational attainment, contraceptive prevalence, and GDP per capita as covariates and create annual probabilistic projections of fertility given a range of policy intervention scenarios targeting expansion of education and access to family planning.

Keywords: Population projections, forecasts, and estimations, Bayesian methods , Population Policies

See extended abstract.