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RCEP国家贸易网络结构特征及其影响因素分析——基于社会网络分析法 被引量:1

Analysis on RCEP Countries'Trade Network Structure Characteristics and Its Impact Factors——Based on Social Network Analysis
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摘要 通过社会网络分析法研究RCEP国家贸易网络结构特征及演变趋势。结果表明:RCEP国家贸易网络整体密度大、强度高;整体上各国点度中心度水平逐年上升,紧密中心度、中介中心度逐年下降;在凝聚子群分析方面,中国、泰国表现较好,在各个小团体中参与度较高。研究也表明该贸易网络存在核心-边缘结构,且结构稳定。澳大利亚、中国、新加坡、韩国、日本等十国处于核心地位,老挝、柬埔寨、缅甸、文莱、菲律宾处于边缘位置。在使用QAP回归分析研究了RCEP国家贸易网络的影响因素后发现:经济距离差异、创新水平差异对RCEP国家贸易有显著正向影响;环境水平差异、互联网普及水平差异对RCEP国家贸易有显著负向影响。 This paper studies the structural characteristics and evolution trend of trade network among RCEP member countries through social network analysis.The results show that the trade network of RCEP countries has high density and intensity.On the whole,the point centrality of each country increased year by year,while the close centrality and intermediate centrality decreased year by year.In terms of cohesive subgroups analysis,China and Thailand performed better,with higher participation in each clique.The research also shows that the trade network has a core-edge structure and the structure is stable.Ten countries,including Australia,China,Singapore,the Re-public of Korea,Japan and others,are at the core;while Laos,Cambodia,Myanmar,Brunei and the Philippines are at the periphery.After using QAP regression analysis to study the influencing factors of the trade network of RCEP countries,it is found that the differences of economic distance and innovation level have significant positive effects on the trade of RCEP countries.Differences in environmental level and Intermet penetration level have a significant negative impact on the trade of RCEP countries.This provides a certain direction guidance for the development lay-out of Chinese enterprises in this region.
作者 廖佳 王雪淞 LIAO Jia;WANG Xuesong(School of International business,Shanghai University of International Business and Economics,Shanghai 201620,China)
出处 《金融教育研究》 2023年第5期31-44,共14页 Research of Finance and Education
基金 国家社会科学基金项目“全球价值链重构背景下RCEP对中国贸易结构转型升级的影响及对策研究”(21BGJ039)。
关键词 RCEP 社会网络分析法 核心-边缘结构 QAP回归 RCEP Social network analysis Core-edge structure QAP regression
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