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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (12): 148-157    DOI: 10.11925/infotech.2096-3467.2020.0320
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Evaluating SMEs-Supporting Policies During COVID-19 Pandemic with K-Means Clustering
Zhao Zheng1,Huang Qianqian1,2,Tong Nannan1,2()
1Department of Big Data Development, State Information Center, Beijing 100045, China
2School of Information Resource Management, Renmin University of China, Beijing 100872, China
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Abstract  

[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 “Level Ⅰ”, while the degree of deviation in Hunan, Henan, and Yunnan is “Level Ⅲ”. Therefore, more SMEs-supporting policies need to be added in the “Level Ⅲ” provinces. [Conclusions] The proposed method could effectively evaluate the enterprise supporting policies in each Chinese province.

Key wordsCoronavirus Disease      Benefit Enterprise Policy      Policy Deviation     
Received: 16 April 2020      Published: 20 January 2022
ZTFLH:  TP393  
Fund:National Social Science Fund of China(18CSH018)
Corresponding Authors: Tong Nannan,ORCID:0000-0001-6709-9031     E-mail: tongnn@sic.gov.cn

Cite this article:

Zhao Zheng, Huang Qianqian, Tong Nannan. Evaluating SMEs-Supporting Policies During COVID-19 Pandemic with K-Means Clustering. Data Analysis and Knowledge Discovery, 2021, 5(12): 148-157.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0320     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I12/148

Overall Thought
一级指标 二级指标 三级指标
政策惠企程度综合评价C 政策完备度评价C1 财政支持措施完备度C11、税收支持措施完备度C12、社保支持措施完备度C13、金融支持措施C14、稳岗就业支持措施C15、降本减负支持措施C16、科创支持措施C17、外贸支持措施C18、营商环境优化措施C19
政策强力度评价C2 财政专项资金补贴力度C21 、税收专项资金补贴力度C22、社保专项资金补贴力度C23 、就业专项资金补贴力度C24、金融补贴力度C25(含贴息额度、提供专项贷款、贷款利率有实质性下调等)、降本力度C26(含提供租房、资源要素等成本资金补贴等)、科创/科技/技改专项资金补贴力度C27、外贸企业专项资金补贴力度C28
政策精细度评价C3 财政支持措施精细度C31、税收支持措施精细度C32、金融支持措施精细度C33、社保支持措施精细度C34、就业支持措施精细度C35、降本减负支持措施精细度C36、科创支持措施精细度C37、外贸支持措施精细度C38、营商环境优化措施精细度C39
Benefit Enterprise Policy Evaluation Index System
特征分类 参数名 变量名 变量内容
发文特征变量 F1 DOC_NUMS 省政府/省直部门发布文件数M
F2~F10 INT1 ~ INT9 政策完备度三级指标C11~C19
F11 STREN 政策强力度二级指标C2
F12 DET 政策精细度二级指标C3
受灾特征变量 F13 PER_NUMS 实际确诊人数P(截至2020年2月10日0时)
F14 HB_RELATION 与湖北的经济联系度T
产业结构特征 F15 NUM_COMP 当前企业存量N
F16 PRO_SMALL 中小企业占比W5
F17 PRO_ESTATE 房地产业占比W1
F18 PRO_TOUR 旅游业占比W2
F19 PRO_ANI 畜牧业占比W3
F20 PRO_ACCOM 住宿餐饮业占比W4
Variable Selection for Cluster Feature
省市分布 金融
信贷
财政
支持
降本减负 稳岗
就业
优化营商环境 其他
税收
优惠
社保
减免
社保
缓缴
租金/
物业费减免
水、电、
气收费下调
已发布相关政策的省份数量 21 15 22 2 13 17 6 18 16 8
已发布相关政策的城市数量 102 69 99 19 85 93 34 99 81 30
Statistics on Benefit Enterprise Policy
排名 政策完备度 政策强力度 政策精细度 综合排名
1 安徽 北京 北京 北京
2 上海 上海 福建 福建
3 浙江 福建 广东 上海
4 北京 四川 河北 安徽
5 山东 安徽 上海 广东
6 福建 山东 辽宁 天津
7 天津 天津 海南 河北
8 重庆 湖北 安徽 山东
9 广东 吉林 天津 湖北
10 湖北 江苏 吉林 四川
Top 10 Regions in Evaluation Index of Benefit Enterprise Policy
The Number of Confirmed Cases and the Degree of Economic Impact
Cluster Analysis Results of Benefit Enterprise Policy Deviation
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