 Digital inclusive financial and household fertility:Discoveries based on dual machine learning algorithmYan Xie, Jingjun Guo and Xiaowen Ma International Review of Economics & Finance, 2025, vol. 97, issue C Abstract: The continuous decline in fertility rates and the postponement of childbirth represent significant social issues in China. Utilizing data from the China Household Finance Survey (CHFS) spanning 2013 to 2019, this paper employs a dual machine learning algorithm (DML) to estimate the causal effects of digital inclusive finance (DIF) on family size and the age at first childbirth, while also exploring the underlying mechanisms. The findings indicate that DIF has a modest positive impact on the household fertility rate and a notable negative effect on the age at first childbirth. Specifically, DIF has contributed to a slight increase in the number of births by reducing the opportunity cost of childbirth for households and has significantly lowered the age of first childbirth by mitigating income fluctuations. Given its positive effects, DIF should be actively promoted. Keywords: Digital inclusive finance; Family fertility; Dual machine learning; Income fluctuations; Opportunity costs (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:97:y:2025:i:c:s1059056024007391 DOI: 10.1016/j.iref.2024.103747 Access Statistics for this article International Review of Economics & Finance is currently edited by H. Beladi and C. Chen More articles in International Review of Economics & Finance from Elsevier |
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