基于K-Means聚类的新冠肺炎疫情期间惠企政策偏离度研究  

Evaluating SMEs-Supporting Policies During COVID-19 Pandemic with K-Means Clustering

作　　者：赵正 黄倩倩 童楠楠[1,2] Zhao Zheng;Huang Qianqian;Tong Nannan(Department of Big Data Development,State Information Center,Beijing 100045,China;School of Information Resource Management,Renmin University of China,Beijing 100872,China)

机构地区：[1]国家信息中心大数据发展部,北京100045 [2]中国人民大学信息资源管理学院,北京100872

出　　处：《数据分析与知识发现》2021年第12期148-157,共10页Data Analysis and Knowledge Discovery

基　　金：国家社会科学基金项目(项目编号:18CSH018)的研究成果之一。

摘　　要：【目的】更好地理解和准确地把握新冠肺炎疫情期间惠企政策出台和实施的总体情况,推进政策目标的有效实现。【方法】基于新冠肺炎疫情期间所出台的政策文本数据、企业注册与投资关系数据以及新冠肺炎确诊数据等多源数据,综合考虑各省政策出台文件数、政策评价三大指标得分、受灾程度、产业结构及与湖北经济联系程度等多方面,采用K-Means聚类方法,确定各省惠企政策偏离等级。【结果】京、沪、闽等省市惠企政策偏离度等级为I级,湘、豫、云等省市偏离度等级为III级,惠企政策力度与其经济潜在受损程度不匹配,需补充采取更多惠企措施。【结论】所提方法融合了计量经济学、指标评价和机器学习算法,以实证数据为基础,实现融合多因素的政策偏离度评价,具有现实意义和可推广性。[Objective]This paper tries to better understand the overall situation of the SMEs-supporting policies during the COVID-19 pandemic,aiming to promote the effective realization of policy objectives.[Methods]First,we collected the policy texts,relationship between corporate registration and investment,as well as the COVID-19 diagnosis data.Then,we calculated the number of policies issued by each province,the scores of the three major policy evaluation metrics,the degree of disaster,the industry structure and their economic ties with Hubei Province.Finally,we used the K-means clustering method to determine the degree of deviation from the enterprise policies in each province.[Results]The degree of deviation of the policies in Beijing,Shanghai,Fujian and other provinces is“LevelⅠ”,while the degree of deviation in Hunan,Henan,and Yunnan is“LevelⅢ”.Therefore,more SMEs-supporting policies need to be added in the“LevelⅢ”provinces.[Conclusions]The proposed method could effectively evaluate the enterprise supporting policies in each Chinese province.

关 键 词：新冠肺炎 惠企政策 政策偏离度 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]