Abstract

With the deepening of reform and opening-up, China’s economy has been further developed, but there is still a problem of uneven development. It is of great significance to completely construct China’s economic spatial correlation network, to clarify the role and status of each province in the whole network, and to study the influencing factors of the national spatial economic network. In this paper, we employ the network analysis method to analyze China’s economic development in the past 20 years. Based on the modified gravity model, we construct China’s spatial economic network and explore the network structure from three aspects: the whole network structure feature, characteristics of individual provinces in the network, and block model analysis. The results show that (1) China’s spatial economic network has strong internal cohesion, and the hierarchy of the network is becoming less and less obvious. However, the network density is low, and the overall network relationship still needs to be strengthened. (2) The different levels in economic development illustrate the obvious economic unbalance among provinces. (3) The block model analysis results demonstrated that coastal areas are more attractive to other provinces and are playing an important role in driving China’s economy. Finally, we employ Quadratic Assignment Procedure (QAP) regression analysis to analyze the influential factors on spatial economic network. Numerical results show that the geographic proximity and the differences in six factors (industrial structure, level of economic development, degree of opening to the outside world, medical level, size of labor market, and infrastructure) have significant impact on the spatial economic network. Moreover, the influence of these factors on the economic relation among provinces has been gradually strengthened in recent years.

Highlights

  • With the process of reform and opening-up, China’s economic development has made great achievements

  • We investigate the characteristics of overall network structure and its internal structure by the network analysis method

  • The network density is still low, indicating that the economic relationship between provinces needs to be strengthened. (2) e economic spillover and benefit of provinces present the unbalanced characteristics of economic development between provinces. e data in 2019 show that there are only seven beneficiary provinces, most of which are located in coastal areas, including Shanghai, Jiangsu, Beijing, Tianjing, Inner Mongolia, Shandong, and Zhejiang. e economic spillover provinces are mainly located in the central and western regions, including Qinghai, Ningxia, Guizhou, Yunnan, and Guangzi, which belong to the regions with relatively backward economy

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Summary

Introduction

With the process of reform and opening-up, China’s economic development has made great achievements. The QAP regression method is considered to study the influencing factors of spatial economic network structure of China. It has been widely used in trade network to study the influencing factors of network structure [9,10,11,12] In recent years, this method has been gradually used to study the economic network of individual provinces or some urban agglomerations in China, but there are few literatures used to study the entire structure of China’s spatial economic network. Based on the above literature, this paper will revise the gravity model and construct the network to observe the structural characteristics of China’s spatial economic network. E influence factors of spatial economic network are analyzed by QAP regression, which is expected to provide decision basis for regional coordinated development in China Based on the above literature, this paper will revise the gravity model and construct the network to observe the structural characteristics of China’s spatial economic network. e influence factors of spatial economic network are analyzed by QAP regression, which is expected to provide decision basis for regional coordinated development in China

Study Objects and Data Sources
Construction of Gravity Model
Structural Analysis of Spatial Economic Network
Findings
Conclusion
Full Text
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