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预测非洲按女性受教育程度划分的生育率下降情况。

Forecasting Africa's fertility decline by female education groups.

机构信息

Kinship Inequalities Group, Max Planck Institute for Demographic Research, Rostock 18057, Germany.

Asian Demographic Research Institute, Shanghai University, Shanghai 200444, China.

出版信息

Proc Natl Acad Sci U S A. 2024 Nov 12;121(46):e2320247121. doi: 10.1073/pnas.2320247121. Epub 2024 Nov 4.

DOI:10.1073/pnas.2320247121
PMID:39495915
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11572978/
Abstract

While female education has long been recognized as a key driver of fertility decline during the process of demographic transition and most population projection models consider it implicitly or explicitly in their forecasts of overall fertility, there still is need for a method to forecast education-specific fertility trends directly. Here we propose a method for projecting education-specific fertility declines for cohorts of women in Sub-Saharan Africa based on all available demographic and health surveys data for African countries (including 1.03Mio cases). We study at different levels of aggregation (sample clusters, strata, and national) the associations between ideal family size and completed cohort fertility for education groups, on the one hand, and the average level of education in those units, on the other. The consistently very strong empirical associations suggest a plausible narrative by which a higher prevalence of educated women in a spatial unit influences the fertility levels of women in all specific education categories. Empirical associations between education-specific cohort fertility trends at the national level and newly available quality-adjusted human capital data for these cohorts are then operationalized to produce education-specific population projections as they are needed for-among other uses-the shared socioeconomic pathways scenarios that are widely used in the climate change research community. Sensitivity analyses including out-of-sample projections support the validity of the proposed method which is then applied to 37 African countries.

摘要

虽然女性教育长期以来一直被认为是人口转变过程中生育率下降的关键驱动因素,而且大多数人口预测模型在预测总生育率时都隐含或明确地考虑了这一因素,但仍需要一种方法来直接预测特定教育的生育率趋势。在这里,我们提出了一种基于非洲国家所有可用的人口和健康调查数据(包括 103 万例)来预测撒哈拉以南非洲女性队列特定教育生育率下降的方法。我们在不同的聚合水平(样本聚类、层和国家)上研究了一方面教育群体的理想家庭规模与完成队列生育率之间的关系,以及这些单位的平均教育水平之间的关系。非常强的经验关联表明,有一种合理的说法是,在一个空间单元中受过教育的女性比例较高会影响所有特定教育类别的女性的生育水平。然后,将国家一级特定教育队列生育率趋势与这些队列新获得的经过质量调整的人力资本数据之间的经验关联转化为特定教育的人口预测,这些预测除其他用途外,还可用于气候变化研究界广泛使用的共享社会经济途径情景。包括样本外预测在内的敏感性分析支持了所提出方法的有效性,然后将该方法应用于 37 个非洲国家。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1c2/11572978/480ab392b9a5/pnas.2320247121fig04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1c2/11572978/246be0ce6d73/pnas.2320247121fig01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1c2/11572978/26f946f6a4a6/pnas.2320247121fig02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1c2/11572978/3f2d77adc38d/pnas.2320247121fig03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1c2/11572978/480ab392b9a5/pnas.2320247121fig04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1c2/11572978/246be0ce6d73/pnas.2320247121fig01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1c2/11572978/26f946f6a4a6/pnas.2320247121fig02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1c2/11572978/3f2d77adc38d/pnas.2320247121fig03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1c2/11572978/480ab392b9a5/pnas.2320247121fig04.jpg

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