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数字普惠金融发展的动态演进及趋势预测研究--以珠三角城市群为例 被引量:3

Dynamic Evolution and Trend Prediction of Digital Inclusive Finance--Empirical Research Based on the Pearl River Delta Urban Agglomeration
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摘要 综合使用Kernel密度估计、传统马尔科夫链和空间马尔科夫链分析法对珠三角城市群数字普惠金融发展的动态研究规律和特征进行了分析,并重点预测其未来发展趋势。结果发现:(1)数字普惠金融发展呈递增趋势,不具有多极化特征和明显的右拖尾分布,发展不均衡问题依然存在。(2)数字普惠金融发展差距有所缩小,且城市群内部之间不存在发展的"马太效应",但短时间内想实现跨越式发展存在一定难度。(3)数字普惠金融的动态转移具有一定的空间依赖性,发展的低水平趋同现象较为稳定。因此,有必要加快推进数字普惠金融发展体制机制创新,驱动珠三角城市群数字普惠金融协同发展。 This paper comprehensively used Kernel density estimation,traditional Markov chain and spatial Markov chain method to analyze the dynamic research laws and characteristics of the development of digital inclusive finance in the Pearl River Delta urban agglomeration,and predicted its development trend.The results showed that:①The development of digital inclusive finance showed an increasing trend,and its de-velopment did not have the characteristics of multi-polarity and obvious right-tailing distribution,and the problems of uneven development still exists.②The gap in the development of digital inclusive finance had narrowed,and there was no“Matthew Effect”in the development of urban agglomerations,but it was difficult to achieve leapfrog development in a short period of time.③The dynamic transfer of digital inclusive finance had a certain spatial dependence,and the low-level convergence of development was relatively stable.Therefore,it was of great signifi-cance to drive the development of digital inclusive finance in urban agglomerations in the Pearl River Delta through coordinated promotion of mechanism innovation for the development of digital inclusive finance.
作者 程风雨 CHENG Feng-yu(Institute of Area Development,Guangzhou Academy of Social Sciences,Guangdong 510410,China)
出处 《资源开发与市场》 CAS 北大核心 2021年第9期1086-1091,共6页 Resource Development & Market
基金 广东省财政厅课题(编号:Z202107) 广州市哲学社会科学规划课题(编号:2021GZQN06) 国家自然科学基金青年项目(编号:71804099) 教育部青年基金项目(编号:18YJC790236)。
关键词 数字普惠金融 Kernel密度估计 传统马尔科夫链 空间马尔科夫链 digital inclusive finance Kernel density estimation traditional Markov chain space Markov chain
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