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Universal yet local: Estimating county-level fertility ideals and intentions in China

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Název: Universal yet local: Estimating county-level fertility ideals and intentions in China
Autoři: Donghui Wang, Yongai Jin, Tao Liu
Zdroj: Demographic Research, Vol 53, Iss 18, Pp 525-568 (2025)
Informace o vydavateli: Max Planck Institute for Demographic Research, 2025.
Rok vydání: 2025
Sbírka: LCC:Demography. Population. Vital events
Témata: China, fertility intention, multilevel model, poststratification, small area estimation, Demography. Population. Vital events, HB848-3697
Popis: BACKGROUND: Understanding China’s persistent low fertility requires detailed information regarding fertility attitudes at a finer geographic scale. However, data on fertility preferences at appropriate spatial resolutions are often unavailable. OBJECTIVE: This study aims to estimate county-level fertility ideals and intentions in China. METHODS: This study employs the multilevel regression and post-stratification method to estimate county-level fertility ideals and intentions. Fertility ideals and intentions data are drawn from a large national fertility survey, while post-stratification data come from the 2020 population census. The estimates are internally validated using a split sample approach and externally validated against independent national and regional surveys. RESULTS: The estimates reveal that the county-level average ideal number of children for women of reproductive age is 1.98 (ranging from 1.29 to 3.78), while the average for the intended number of children is 1.81, with a broader range (1.02 to 3.96). The spatial distribution of fertility ideals exhibits a north–south contrast, suggesting cultural influences on family norms. Fertility intentions show coastal–inland disparities, underscoring socioeconomic conditions. Within-province variations are no less than between-province variations. CONTRIBUTION: These findings highlight the complexity of the fertility attitudes landscape in China. The estimates also serve as an important data source for predicting future fertility and designing place-based policies.
Druh dokumentu: article
Popis souboru: electronic resource
Jazyk: English
ISSN: 1435-9871
Relation: https://www.demographic-research.org/articles/volume/53/18; https://www.demographic-research.org/volumes/vol53/18/53-18.pdf; https://doaj.org/toc/1435-9871
DOI: 10.4054/DemRes.2025.53.18
Přístupová URL adresa: https://doaj.org/article/0940f040614b4a988931629ea7aa1897
Přístupové číslo: edsdoj.0940f040614b4a988931629ea7aa1897
Databáze: Directory of Open Access Journals
Popis
Abstrakt:BACKGROUND: Understanding China’s persistent low fertility requires detailed information regarding fertility attitudes at a finer geographic scale. However, data on fertility preferences at appropriate spatial resolutions are often unavailable. OBJECTIVE: This study aims to estimate county-level fertility ideals and intentions in China. METHODS: This study employs the multilevel regression and post-stratification method to estimate county-level fertility ideals and intentions. Fertility ideals and intentions data are drawn from a large national fertility survey, while post-stratification data come from the 2020 population census. The estimates are internally validated using a split sample approach and externally validated against independent national and regional surveys. RESULTS: The estimates reveal that the county-level average ideal number of children for women of reproductive age is 1.98 (ranging from 1.29 to 3.78), while the average for the intended number of children is 1.81, with a broader range (1.02 to 3.96). The spatial distribution of fertility ideals exhibits a north–south contrast, suggesting cultural influences on family norms. Fertility intentions show coastal–inland disparities, underscoring socioeconomic conditions. Within-province variations are no less than between-province variations. CONTRIBUTION: These findings highlight the complexity of the fertility attitudes landscape in China. The estimates also serve as an important data source for predicting future fertility and designing place-based policies.
ISSN:14359871
DOI:10.4054/DemRes.2025.53.18