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中国城市金融竞争力的统计测度与比较 被引量:2

Statistical Measurement and Comparison of Urban Financial Competitiveness in China
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摘要 探究城市金融竞争力水平对于推动金融服务实体经济发展具有重要现实意义。文章基于2011—2020年中国30个中心城市的数据,采用主成分分析法对中国城市金融竞争力水平进行统计测度。研究结果表明,中国城市金融竞争力差异明显,经济发展水平越高的城市,金融竞争力越强、增速越慢;经济发展水平越低的城市,金融竞争力越弱、增速越快。分维度探究发现,北京、上海、深圳在金融服务水平、金融发展水平、城市基础设施水平、经济持续水平四个金融竞争力子维度中始终保持领先优势,杭州、石家庄等城市则呈快速追赶态势。根据上述研究结果,研究认为应完善金融服务体系建设、强化金融创新能力、提升城市金融基础设施建设,以此不断强化中国城市金融竞争力。 Exploring the level of urban financial competitiveness is of great practical significance for promoting the development of financial services for the real economy. Based on the data of 30 central cities in China from 2011 to 2020, this paper uses the principal component analysis method to statistically measure the level of financial competitiveness of Chinese cities. The results show that there are obvious differences in urban financial competitiveness in China. The higher the level of economic development, the stronger the urban financial competitiveness and the lower the growth rate. The lower the level of economic development, the weaker the financial competitiveness and the higher the growth rate. The sub-dimension research found that Beijing, Shanghai, and Shenzhen have always maintained a leading edge in the four financial competitiveness sub-dimensions of financial service level, financial development level,urban infrastructure level, and economic sustainability level, while Hangzhou, Shijiazhuang and other cities have shown a rapid catch-up trend. It is proposed to improve the construction of the financial service system, strengthen the financial innovation ability, and improve the construction of urban financial infrastructure, so as to continuously strengthen the financial competitiveness of Chinese cities.
作者 朱玮玮 ZHU Wei-wei(School of Finance,Zhengzhou University of Science and Technology,Zhengzhou Henan 450001,China)
出处 《技术经济与管理研究》 北大核心 2022年第4期67-72,共6页 Journal of Technical Economics & Management
基金 河南省软科学会计专项课题(212400410531)。
关键词 城市竞争力 金融竞争力 主成分分析法 统计测度 City competitiveness Financial competitiveness Principal component analysis Statistical measure
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