广西师范大学学报（哲学社会科学版） ›› 2024, Vol. 60 ›› Issue (3): 68-85.doi: 10.16088/j.issn.1001-6597.2024.03.007

• 经济与管理 • 上一篇    下一篇

农村普惠金融发展的空间关联网络及驱动因素研究——兼论农村普惠金融高质量发展的推进路径

王小华1, 杨玉琪2   

  1. 1.西南大学 普惠金融与农业农村发展研究中心, 重庆 400715;
    2.山东大学 经济学院,山东济南 250100
  • 收稿日期:2023-05-15 出版日期:2024-05-25 发布日期:2024-05-17
  • 作者简介:王小华(1986—),男,重庆人,西南大学经济管理学院教授,西南大学普惠金融与农业农村发展研究中心研究员,研究领域:农村金融与普惠金融、数字金融与金融科技。
  • 基金资助:
    国家社会科学基金重大项目“实现巩固拓展脱贫攻坚成果同乡村振兴有效衔接研究”(21ZDA062);国家社会科学基金一般项目“金融科技增强金融普惠性的理论逻辑与路径优化研究”(21BJL086);国家社会科学基金一般项目“数字普惠金融背景下我国农村金融消费者保护测度、影响及其提升研究”(21BJY042)

A Study on the Spatial Correlation Network and Driving Factors of Rural Inclusive Finance Development —On the Promotion Path of High Quality Development of Rural Inclusive Finance

WANG Xiao-hua1, YANG Yu-qi2   

  1. 1. Research Center for Inclusive Finance and Agricultural and Rural Development, Southwest University, Chongqing 400715;
    2. School of economics, Shandong University, Jinan 250100, China
  • Received:2023-05-15 Online:2024-05-25 Published:2024-05-17

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摘要： 推动农村普惠金融高质量发展,为“中小微弱”提供有效的金融服务,创造良好环境促使农村金融高质量服务实体经济,是解决我国经济发展不平衡不充分问题的关键环节,更是实现包容性增长、促进社会和谐稳定的应有之义。通过构建农村普惠金融评价指标体系,并基于2008—2018年中国30个省(自治区、直辖市)的数据测算农村普惠金融指数,同时利用社会网络分析(SNA)、空间杜宾模型(SDM)分析农村普惠金融发展的空间关联网络特征及其驱动因素,研究发现:(1)农村普惠金融空间关联网络的关联关系数及网络密度均呈现先递增后下降的趋势,并于2011年达到峰值,整体关联网络通达性好,同时又存在“等级森严”的网络结构,所有网络特征指标均较为稳定。(2)上海、江苏、北京、浙江、山东、广东6个省份的度数中心度、接近中心度、中间中心度均最高,一直处于空间关联网络的核心区,掌控和支配能力更强,且对其他省份金融资源具有极强的虹吸作用,导致农村普惠金融的发展仍然处于集聚阶段。(3)经济发展水平、人口密度、政府干预程度、产业结构、银行业竞争是影响农村普惠金融发展的五个重要变量。从直接效应看,经济发展、高人口密度、银行业竞争均促进本地农村普惠金融的发展;从间接效应看,某地人口密集、第一产业占比过高、激烈的银行竞争均会抑制周边地区农村普惠金融的发展,而政府加大对某地的财政支出也可以对周边地区产生正向的溢出效应。

关键词: 农村金融, 普惠金融高质量发展, 空间关联网络, 社会网络分析

Abstract: Promoting the high-quality development of inclusive finance in rural areas so as to provide effective financial services for small and medium-sized enterprises and enhance the high-quality service of rural finance to the real economy is a key link in solving the problem of imbalanced and insufficient economic development in China. It is also necessary to achieve inclusive growth and promote social harmony and stability. By constructing an evaluation index system for rural inclusive finance and calculating the rural inclusive finance index based on data from 30 provinces (autonomous regions, municipalities directly under the central government) in China from 2008 to 2018, and employing social network analysis (SNA) and spatial Durbin model (SDM) to analyze the spatial correlation network characteristics and driving factors of rural inclusive finance development, the study finds that: (1) The number of correlation relationships and network density in the spatial correlation network of rural inclusive finance show a shift from rising to declining, and the peak is available in 2011. The overall connectivity of the correlation network is good with a “hierarchical” network structure, and all network characteristic indicators are relatively stable. (2) The degree centrality, proximity centrality, and intermediate centrality in six regions in Shanghai, Jiangsu, Beijing, Zhejiang, Shandong, and Guangdong stay at the top and continuously in the core area of the spatial correlation network with stronger control and dominance abilities, thus bearing a strong siphon effect on financial resources in other regions, resulting in the agglomeration of rural inclusive finance. (3) The level of economic development, population density, degree of government intervention, industrial structure, and banking competition are five important variables that affect the development of rural inclusive finance. From the perspective of direct effects, economic development, high population density, and moderate banking competition all promote the development of rural inclusive finance; from the perspective of indirect effects, population agglomeration, a high proportion of the primary industry, and intense banking competition all inhibit the development. In addition, increasing government fiscal expenditure on a certain area also bears a positive spillover effect on its surrounding areas.

Key words: rural finance, high quality development of inclusive finance, spatial correlation network, social network analysis

中图分类号:  F832.35

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