<p>Finance plays a pivotal role in the regional economic development, making the control of financial risk and its spatial spillover critical to a country’s core competitiveness. Utilizing inter-provincial panel data from China spanning 2007 to 2022, through the entropy weight method, improved economic gravity model, social network analysis, and other methods to analyze the spatial correlation and driving factors of inter-provincial financial risk in China. The findings reveal that financial risk exhibits a wavy distribution pattern, with high levels in the eastern regions that gradually decrease toward the west, while showing no significant variation along the north-south axis. The spatial linkages of inter-provincial financial risks, representing cross-regional risk transmission channels, are densely concentrated in the eastern coastal provinces, such as Shanghai, Jiangsu, Zhejiang, and Guangdong, forming the core of the financial risk network. In contrast, provinces in the northeast and western regions, such as Heilongjiang, Liaoning, Hainan, and Ningxia, occupy periphery positions with sparse network connections. Block model analysis categorizes the network into a net-flow-out block, two two-way-spillover blocks, and an intermediate block that bridges risk spillovers across regions. QAP regression analysis highlights that spatial proximity, economic intensity differences, liquidity ratio disparities, and financial supervision differences jointly shape the structure of China’s inter-provincial financial risk spillover network. These insights underscore the spatial dimensions of financial risk and inform strategies for mitigating cross-regional financial vulnerabilities.</p>

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Spatial correlation network and driving factors of inter-provincial financial risk in China

  • Yongheng Zhang,
  • Mengkai Xing,
  • Xiaoqi Zhang

摘要

Finance plays a pivotal role in the regional economic development, making the control of financial risk and its spatial spillover critical to a country’s core competitiveness. Utilizing inter-provincial panel data from China spanning 2007 to 2022, through the entropy weight method, improved economic gravity model, social network analysis, and other methods to analyze the spatial correlation and driving factors of inter-provincial financial risk in China. The findings reveal that financial risk exhibits a wavy distribution pattern, with high levels in the eastern regions that gradually decrease toward the west, while showing no significant variation along the north-south axis. The spatial linkages of inter-provincial financial risks, representing cross-regional risk transmission channels, are densely concentrated in the eastern coastal provinces, such as Shanghai, Jiangsu, Zhejiang, and Guangdong, forming the core of the financial risk network. In contrast, provinces in the northeast and western regions, such as Heilongjiang, Liaoning, Hainan, and Ningxia, occupy periphery positions with sparse network connections. Block model analysis categorizes the network into a net-flow-out block, two two-way-spillover blocks, and an intermediate block that bridges risk spillovers across regions. QAP regression analysis highlights that spatial proximity, economic intensity differences, liquidity ratio disparities, and financial supervision differences jointly shape the structure of China’s inter-provincial financial risk spillover network. These insights underscore the spatial dimensions of financial risk and inform strategies for mitigating cross-regional financial vulnerabilities.