Tropical Geography ›› 2022, Vol. 42 ›› Issue (6): 928-938.doi: 10.13284/j.cnki.rddl.003489

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Spatial Evolution and Underlying Factors of the Urban Financial Network in China

Jie Zhang(), Kerong Sheng(), Chuanyang Wang   

  1. School of Economics, Shandong University of Technology, Zibo 255012, China
  • Received:2021-05-19 Revised:2021-07-05 Online:2022-06-05 Published:2022-06-29
  • Contact: Kerong Sheng;


Since the implementation of the reform and opening-up policy, China has experienced rapid development of the financial industry, with a large number of financial enterprise groups being established over the past 40 years. Meanwhile, the distribution of branches of financial enterprises has expanded rapidly, which has accelerated the integration of the financial market in China. Against this background, financial service relationships have played important roles in strengthening the linkages between cities, providing an important perspective for the study of city networks. This study aimed to analyze the spatial patterns, influencing factors, and mechanisms of the key factors in the financial network in China. First, data on the headquarter and branch locations of financial enterprises in China were subjected to the interlocking network model to approximate the financial network, resulting in a 285 × 285 valued urban network, and its spatial patterns were described from the three aspects of centrality, linkages, and core-periphery structure. Then, by using the Quadratic Assignment Procedure, an econometric analysis was conducted to identify the influencing factors, and the micro processes in the spatial growth of the urban network were examined. Finally, by combining theories of information hinterland and resource dependence, a conceptual framework for comprehensively understanding the mechanisms driving financial network growth in China was suggested for further discussion. This study has three main findings: First, the financial network presents a significant concentrated multi-dimensional core-periphery structure. The spatial distribution of centrality exhibits obvious spatial orientation and path dependence characteristics. The cities well-positioned in the network are mainly the core cities in China's major metropolises, such as Beijing and Tianjin in the Beijing-Tianjin area; Shanghai, Suzhou, and Hangzhou in the Yangtze Delta area; and Shenzhen, Guangzhou, and Foshan in the Pearl Delta area. The connectivity of city linkage exhibits enhanced relevance and hierarchical structure characteristics, which promotes the emergence of a "core-periphery" mode in financial network structure. Second, vital resources possessed by cities, such as market potential, political rank, knowledge base, and economic openness, are important factors underlying the formation of China's financial network. Links are more likely to occur between cities with large market potential, abundant political resources, intensive knowledge capital, and high economic openness. Geographical distance, location condition, and historical basis also have a profound influence on the spatial patterns of the financial network. Third, preferred linkage, geographical proximity, and spatial agglomeration are the dynamic mechanisms underlying the development of the financial network. Preferred linkage and geographical proximity can be interpreted as the observable results of sharing vital resources and reducing transportation costs in accessing valuable information flows. The spatial agglomeration mechanism, stemming from the agglomeration economy in the location selection of financial enterprises, tends to strengthen the financial network structure formed historically. In the network environment, the policy of urbanization in China needs to be adjusted accordingly. The Chinese government should support cities to choose differentiated development paths in the financial network, give full play to the supply and guidance function of the financial network to urban economic growth, and promote network cooperation between cities on a larger spatial scale.

Key words: urban network, interlocking network model, QAP regression, information hinterland, China

CLC Number: 

  • F299.2