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全球年龄别生育率和总生育率变化的简约特征描述。

A parsimonious characterization of change in global age-specific and total fertility rates.

作者信息

Pantazis Athena, Clark Samuel J

机构信息

Department of Sociology, The University of Washington, Seattle, United States of America.

Department of Sociology, The Ohio State University, Columbus, United States of America.

出版信息

PLoS One. 2018 Jan 29;13(1):e0190574. doi: 10.1371/journal.pone.0190574. eCollection 2018.

DOI:10.1371/journal.pone.0190574
PMID:29377899
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5788345/
Abstract

This study aims to understand trends in global fertility from 1950-2010 though the analysis of age-specific fertility rates. This approach incorporates both the overall level, as when the total fertility rate is modeled, and different patterns of age-specific fertility to examine the relationship between changes in age-specific fertility and fertility decline. Singular value decomposition is used to capture the variation in age-specific fertility curves while reducing the number of dimensions, allowing curves to be described nearly fully with three parameters. Regional patterns and trends over time are evident in parameter values, suggesting this method provides a useful tool for considering fertility decline globally. The second and third parameters were analyzed using model-based clustering to examine patterns of age-specific fertility over time and place; four clusters were obtained. A country's demographic transition can be traced through time by membership in the different clusters, and regional patterns in the trajectories through time and with fertility decline are identified.

摘要

本研究旨在通过分析特定年龄生育率来了解1950年至2010年全球生育率的趋势。这种方法既纳入了总体水平(如在对总和生育率进行建模时),也纳入了特定年龄生育率的不同模式,以研究特定年龄生育率变化与生育率下降之间的关系。奇异值分解用于捕捉特定年龄生育率曲线的变化,同时减少维度数量,使得曲线几乎可以用三个参数完全描述。参数值随时间呈现出明显的区域模式和趋势,这表明该方法为全球生育率下降的研究提供了一个有用的工具。使用基于模型的聚类分析对第二和第三个参数进行分析,以研究特定年龄生育率随时间和地点的模式;共得到四个聚类。一个国家的人口转变可以通过其在不同聚类中的成员身份随时间进行追踪,并且可以识别出随时间和生育率下降的轨迹中的区域模式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/741a/5788345/1f35048eb54f/pone.0190574.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/741a/5788345/c16c9d887f35/pone.0190574.g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/741a/5788345/f3dc1cc4d760/pone.0190574.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/741a/5788345/c36fcccc705c/pone.0190574.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/741a/5788345/1f35048eb54f/pone.0190574.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/741a/5788345/c16c9d887f35/pone.0190574.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/741a/5788345/1d1eafd76941/pone.0190574.g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/741a/5788345/bd1d9c49b262/pone.0190574.g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/741a/5788345/5e4d13e4e9bc/pone.0190574.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/741a/5788345/f3dc1cc4d760/pone.0190574.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/741a/5788345/c36fcccc705c/pone.0190574.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/741a/5788345/1f35048eb54f/pone.0190574.g009.jpg

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