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Digital inclusive financial and household fertility:Discoveries based on dual machine learning algorithm

Year: 2024

Type: article

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.

Source: International Review of Economics & Finance

DOI: 10.1016/j.iref.2024.103747

Authors: Yan Xie, Jingjun Guo, Xiaowen Ma

Institution: Lanzhou University of Finance and Economics

Language: English

Topic: Microfinance and Financial Inclusion

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDGs: Reduced inequalities, Decent work and economic growth, Good health and well-being, No poverty

Open Access status: hybrid

Estimated APC paid: $2,790