Liu Daphne H, Raftery Adrian E
Department of Statistics, University of Washington.
Department of Statistics and Department of Sociology, University of Washington.
Ann Appl Stat. 2024 Mar;18(1):375-403. doi: 10.1214/23-aoas1793. Epub 2024 Jan 31.
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. Quantifying the potential accelerating effect of education and family planning policies on fertility decline in a probabilistic way is of interest to policymakers, particularly in high-fertility countries. We propose a conditional Bayesian hierarchical model for projecting fertility given education and family planning policy interventions. 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.
女性的教育程度和避孕普及率是已被确定为对生育率下降具有加速作用的两个机制，并且可直接受到政策的影响。以概率方式量化教育和计划生育政策对生育率下降的潜在加速作用，这对政策制定者来说很有意义，尤其是在高生育率国家。我们提出了一个条件贝叶斯分层模型，用于在考虑教育和计划生育政策干预的情况下预测生育率。为了说明政策变化可能对未来生育率产生的影响，我们根据诸如到2030年实现普及中等教育和普及计划生育的可持续发展目标（SDGs）等情景，创建生育率的概率预测。
