我国信用环境的区域差异及影响因素研究Research on regional differences and influencing factors of China’s credit environment
朱月月,张福浩,刘晓东,杨鸿海,赵习枝
摘要(Abstract):
为了深入了解我国各地区的信用环境现状,研究影响信用环境的社会经济因素,对有针对性地提升城市信用环境水平,吸引人才和投资,促进经济发展具有重要意义。该文利用局部空间自相关分析我国城市商业信用环境指数的空间分布格局,其高高值区域主要集中在环渤海地区和长江三角洲地区,低低值区域主要集中在中部地区和西南地区。基于城市商业信用环境指数分析我国信用环境的区域分布规律,利用极端梯度提升算法建立城市商业信用环境指数与社会经济指标的回归模型,模型的决定系数R~2为0.857 1。特征重要性分析结果表明,住户存款余额、年末金融机构人民币各项存款余额是影响我国信用环境的重要因子。引入TreeSHAP解释模型从微观角度分析因子对信用环境的影响,总体上金融体系的发展水平、政府行政管理以及区域经济的发展越好,城市信用环境越好。
关键词(KeyWords): 信用环境;XGBoost;TreeSHAP;区域差异;影响因素
基金项目(Foundation): 国家重点研发计划项目(2018YFB1403002);; 兰州交通大学优秀平台支持项目(201806)
作者(Author): 朱月月,张福浩,刘晓东,杨鸿海,赵习枝
DOI: 10.16251/j.cnki.1009-2307.2021.12.026
参考文献(References):
- [1]熊发登,王婷婷.改善贫困地区信用环境的对策建议[J].当代县域经济,2017(6):91-92.(XIONG Fadeng,WANG Tingting.Suggestions on improving the credit environment in poor areas[J].Contemporary County Economy,2017(6):91-92.)
- [2]张勇.县域信用环境与经济增长关系研究:以江苏、浙江两省为例[D].南京:南京审计大学,2019:1-59.(ZHANG Yong.Research on the relationship between County credit environment and County economic growth:an example of Jiangsu and Zhejiang provinces[D].Nanjing:Nanjing Audit University,2019:1-59.)
- [3]张原,陈玉菲,高革,等.基于因子分析的陕西省区域信用环境评价研究[J].北京交通大学学报(社会科学版),2015,14(2):13-22.(ZHANG Yuan,CHEN Yufei,GAO Ge,et al.A research on the evaluation of regional credit environment of Shaanxi Province based on factor analysis[J].Journal of Beijing Jiaotong University(Social Sciences Edition),2015,14(2):13-22.)
- [4]刘凤委,李琳,薛云奎.信任、交易成本与商业信用模式[J].经济研究,2009,44(8):60-72.(LIU Fengwei,LILin,XUE Yunkui.Trust,transaction cost and mode of trade credit[J].Economic Research Journal,2009,44(8):60-72.)
- [5]张凯.信用环境的空间异质性、驱动因素及其对经济增长质量的影响[D].杭州:浙江财经大学,2019:1-58.(ZHANG Kai.Spatial heterogeneity and driving factors of credit environment and its impact on the quality of economic growth[D].Hangzhou:Zhejiang University of Finance&Economics,2019:1-58.)
- [6]陈海盛,陈哲,王宁江,等.浙江省商业信用环境影响因素的空间计量估计[J].征信,2017,35(11):27-30.(CHEN Haisheng,CHEN Zhe,WANG Ningjiang,et al.Spatial econometrics assessment of the affecting factors of Zhejiang commercial credit environment[J].Credit Reference,2017,35(11):27-30.)
- [7]林钧跃.中国城市商业信用环境指数研制与分析[J].财贸经济,2012(2):89-97.(LIN Junyue.Development and analysis of China’s urban commercial credit environment index[J].Finance and Trade Economics,2012(2):89-97.)
- [8]刘成.城市综合社会信用环境评价及其应用研究[D].北京:北方工业大学,2019:1-78.(LIU Cheng.Evaluation and application of urban comprehensive social credit environment[D].Beijing:North China University of Technology,2019:1-78.)
- [9]谭燕芝,王超,李国锋.信用环境的经济绩效及其影响因素:基于CEI指数及中国省级、地级市的数据[J].经济经纬,2014,31(4):144-149.(TAN Yanzhi,WANGChao,LI Guofeng.Economic performance and influencing factors of the credit environment:empirical study based on CEI and Chinese provincial and prefecture-level city data[J].Economic Survey,2014,31(4):144-149.)
- [10]周庆岸.基于遗传XGBoost模型的个人网贷信用评估研究[D].南昌:江西财经大学,2019:1-74.(ZHOUQingan.A research of personal credit rating evaluation based on genetic XGBoost model[D].Nanchang:Jiangxi University of Finance and Economics,2019:1-74.)
- [11]田德琥.基于XGBoost-LR综合模型的现金贷借款人信用评价研究[D].武汉:武汉理工大学,2019:1-91.(TIAN Dehu.Research on credit evaluation of cash loan borrowers based on XGBoost-LR comprehensive model[D].Wuhan:Wuhan University of Technology,2019:1-91.)
- [12]王嘉豪.基于XGBoost的还款概率预测模型分析与优化[D].西安:西安电子科技大学,2019:1-94.(WANGJiahao.Analysis and optimization of repayment probability prediction model based on XGBoost[D].Xi’an:Xidian University,2019:1-94.)
- [13]丁浩.基于XGBoost多模型融合强化技术的个人信用评估研究[D].南京:南京信息工程大学,2019:1-60.(DING Hao.Research on personal credit evaluation based on enhanced technology of XGBoost multi-model fusion[D].Nanjing:Nanjing University of Information Science&Technology,2019:1-60.)
- [14]陈耀飞,陈逸杰,李铭.基于XGBoost的信用评分预测模型[C]//2017年(第五届)全国大学生统计建模大赛获奖论文集.北京:中国统计教育学会,2017:16.(CHEN Yaofei,CHEN Yijie,LI Ming.Credit score prediction model based on XGBoost[C]//2017(Fifth)National College Student Statistical Modeling Contest Winning Paper Selection.Beijing:China Statistical Education Society,2017:16.
- [15]TORLAY L,PERRONE-BERTOLOTTI M,THOMASE,et al.Machine learning-XGBoost analysis of language networks to classify patients with epilepsy[J].Brain Informatics,2017,4(3):159-169.
- [16]SHAPLEY L S.A value for n-person games[J].Contributions to the Theory of Games,1953,2(28):307-317.
- [17]陈贵,林钧跃,尚伟龙.2017中国城市商业信用环境指数(CEI)蓝皮书[C/OL].[2020-12-23].https://xueshu.baidu.com/usercenter/paper/show?paperid=1m620t406y2m0210w74d0ap0yv620772.(CHEN Gui,LIN Junyue,SHANG Weilong.2017China city business credit environment index(CEI)blue book[C/OL].[2020-12-23].https://xueshu.baidu.com/usercenter/paper/show?paperid=1m620t406y2m0210w74d0ap0yv620772.)
- [18]姚小义,钟心岑,杨凯.中国信用环境评价:基于2006-2010年的省际数据[J].财经理论与实践,2013,34(3):12-18.(YAO Xiaoyi,ZHONG Xincen,YANG Kai.The evaluation of the operating environment of China’s credit system:based on provincial statistics from 2006-2010[J].The Theory and Practice of Finance and Economics,2013,34(3):12-18.)
- [19]叶陈毅,陈依萍,谢丽莉,等.基于因子分析的京津冀社会信用环境评价研究[J].财会通讯,2019(26):66-70.(YE Chenyi,CHEN Yiping,XIE Lili,et al.Research on Beijing-Tianjin-Hebei social credit environment evaluation based on factor analysis[J].Communication of Finance and Accounting,2019(26):66-70.)
- [20]罗能生,吴枭宇.社会信用的区域差异及影响因素的空间计量分析[J].财经科学,2016(4):101-112.(LUONengsheng,WU Xiaoyu.Spatial econometrics analysis of regional differences and affecting factors of China social credit[J].Finance&Economics,2016(4):101-112.)
- [21]ANSELIN L.Local indicators of spatial association-LISA[J].Geographical Analysis,1995,27(2):93-115.
- [22]郑梦柳,杨红磊,彭军还,等.市域尺度货物运输碳排放时空变化及因素分析[J].测绘科学,2019,44(5):76-84.(ZHENG Mengliu,YANG Honglei,PENGJunhuan,et al.Spatiotemporal variations and potential variables of greenhouse gas emissions based on city scale[J].Science of Surveying and Mapping,2019,44(5):76-84.)
- [23]李文慧,韩惠.兰州市商品住宅价格的空间分异规律[J].测绘科学,2018,43(2):45-50.(LI Wenhui,HANHui.Characteristics of commercial residential price spatial differentiation in Lanzhou city[J].Science of Surveying and Mapping,2018,43(2):45-50.)
- [24]REN Xudie,GUO Haonan,LI Shenghong,et al.Anovel image classification method with CNN-XGBoost model[J].Lecture Notes in Computer Science,2017,10431:378-390.
- [25]周琪.类别不平衡数据的个人信用风险评估算法研究[D].保定:河北大学,2020:1-63.(ZHOU Qi.Research on individual credit risk assessment for imbalanced data[D].Baoding:Hebei University,2020:1-63.)
- [26]陈圣灵,沈思淇,李东升.基于样本权重更新的不平衡数据集成学习方法[J].计算机科学,2018,45(7):31-37.(CHEN Shengling,SHEN Siqi,LI Dongsheng.Ensemble learning method for imbalanced data based on sample weight updating[J].Computer Science,2018,45(7):31-37.)
- [27]曹婷婷.气象不均衡数据分类算法研究[D].兰州:兰州交通大学,2020:1-64.(CAO Tingting.Research on classification algorithm of meteorological imbalanced data[D].Lanzhou:Lanzhou Jiatong University,2020:1-64.)
- [28]LUNDBERG S M,LEE S I.Consistent feature attribution for tree ensembles[C]//2017ICML Workshop on Human Interpretability in Machine Learning.Sydney,NSW,Australia:[s.n.],2017.
- [29]李锐,沈雨奇,蒋捷,等.公共地图服务中访问热点区域的时空规律挖掘[J].武汉大学学报(信息科学版),2018,43(9):1408-1415.(LI Rui,SHEN Yuqi,JIANGJie,et al.Temporal and spatial characteristics of hotspots in public map service[J].Geomatics and Information Science of Wuhan University,2018,43(9):1408-1415.)
- [30]李善同,侯永志.中国大陆:划分8大社会经济区域[J].理论参考,2004,(7):10-12,22.(LI Shantong,HOUYongzhi.China's mainland:division of 8social economic Regions[J].Theoretical Reference,2004,(7):10-12,22.)