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Digital Inclusive Finance, Spatial Spillover Effects and Relative Rural Poverty Alleviation: Evidence from China

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Abstract

How to govern relative rural poverty is the key and difficulty in eliminating poverty and achieving common prosperity in China. With the rapid development of digital economy, digital inclusive finance is playing an increasingly fundamental role in poverty alleviation. As an important new financial form, whether and how digital inclusive finance affects relative rural poverty is not yet known. Based on new economic geography, this paper empirically tests the direct and spatial impacts of digital financial inclusion on relative rural poverty alleviation by constructing spatial econometric models and using panel data from 31 provinces (autonomous regions and municipalities directly under the central government) in China from 2012 to 2019. The study found that there is a significant positive spatial correlation in relative rural poverty; the development of digital inclusive finance has a significant inhibitory effect on relative rural poverty. Meanwhile, the development of digital inclusive finance in the local province also has a negative spatial spillover effect on rural relative poverty in surrounding areas. Therefore, it is necessary to boost the development of digital inclusive finance, improve the coordination of inclusive finance between regions, and promote inter-regional economic cooperation in the future. Poverty alleviation remains a challenge in the world, especially in developing countries. Digital inclusive finance, which is a new form of inclusive finance and digital economy widely applied in China, could play an increasingly fundamental role in poverty alleviation in rural areas. In this study, a spatial econometric model (SAR) is constructed based on the new economic geography, and the digital financial inclusion index is integrated with macro-economic data at a provincial level in China from 2012 to 2019. The direct and spatial impacts of digital inclusive finance on poverty reduction in rural areas were accessed using the developed model. Results show that digital inclusive finance can significantly reduce relative poverty in rural areas in China. More importantly, it is indicated that there is a significant positive spatial correlation in relative rural poverty, and digital inclusive finance has a negative spatial spillover effect on relative rural poverty, which is supported by a series of endogeneity and robustness tests, such as substitution of relative poverty, replacing models, and using alternative specifications. Recommendations on implementations in poverty alleviation are proposed based on the results of this study. This paper further complements the hot research field on finance development and income inequality. Our findings offer insights into the development of inclusive financial policies for relative rural poverty alleviation in other countries, especially in developing countries with similar backgrounds to China.

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Acknowledgements

This study was supported by the Humanities and Social Sciences Research Planning Fund of the Ministry of Education (No. 20YJA790096), the Jinan Philosophy and Social Science Project (No. JNSK22B18), and the First Batch of Talent Research Project "Research on the Impact of Digital Inclusive Finance on Relative Poverty of Rural Floating Population" by Qilu University of Technology (Shandong Academy of Sciences) in 2023 (No. 2023RCKY259).

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Panpan Pei: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing-original draft, Writing-review & editing.

Shunyi Zhang: Validation, Formal analysis, Data curation, Writing-original draft, Writing-review & editing, Visualization.

Guangxia Zhou: Conceptualization, Methodology, Validation, Resources, Writing-review & editing, Supervision, Project administration, Funding acquisition.

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Correspondence to Guangxia Zhou.

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Pei, P., Zhang, S. & Zhou, G. Digital Inclusive Finance, Spatial Spillover Effects and Relative Rural Poverty Alleviation: Evidence from China. Appl. Spatial Analysis (2024). https://doi.org/10.1007/s12061-024-09580-z

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