| 摘要: |
| 发展普惠金融是实现全面建成小康社会的有效途径,从金融服务便捷度、金融服务参与度、金融服务质量三个方面选取11个指标构建我国普惠金融发展水平的评价体系,采用因子分析模型评价我国30个省(市、自治区,港、澳、台及西藏除外)普惠金融发展水平。结果表明,我国普惠金融发展总体水平不高,并存在较强的空间异质性。东部地区普惠金融发展水平相对较高,其中,北京、上海普惠金融发展水平优势明显,而中西部地区普惠金融发展相对较低；进一步通过聚类分析将30个省(市、自治区)按照发展水平划分为普惠金融发展较高、一般、较低等三类地区。我国应从完善金融服务、加大扶持力度、鼓励服务创新等方面促进普惠金融的发展。 |
| 关键词: 普惠金融 因子分析 聚类分析 |
| DOI：10.13216/j.cnki.upcjess.2019.04.0003 |
| 分类号:F062.9 |
| 基金项目:教育部人文社会科学研究基金青年项目(19YJCZH058);中国博士后基金面上项目 (2018M632271);安徽省哲学社会科学规划基金国家社科孵化项目(AHSKF2018D56) |
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| A Synthetic Evaluation on the Development Level of Inclusive Finance in China |
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HUANG Dunping1, CHEN Siyu2, GAO Fei3
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(1.School of Economics,Anhui University of Finance and Economics, Bengbu, Anhui 233030, China;2.School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, Anhui 233030, China;3.School of Management, University of Shanghai for Science and Technology, Shanghai 200093, China)
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| Abstract: |
| The development of inclusive finance is an effective way for building a well-off society in an all-round way.In this article,11 indicators are selected to build an evaluation system of inclusive finance development level from three aspects, i.e., financial service convenience, financial service participation and financial service quality, and factor analysis is used to evaluate the development level of inclusive finance in 30 provinces. The results show that the level of inclusive finance development in China is low and there is a strong spatial heterogeneity.The level in the eastern regions is relatively high, and the level in Beijing and Shanghai has obvious advantages, while the level in the western regions is relatively low. Further, through clustering analysis, the provinces in China are divided into three categories in the development level of inclusive finance:high level, middle level and low level. Finally, some suggestions are put forward from the aspects of improving financial services, increasing support and encouraging service innovation, etc.. |
| Key words: inclusive finance factor analysis clustering analysis |
