乡城流动借款人信用风险与空间收入差异决定
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摘要
文章利用独特的真实交易数据,首次从人口流动视角研究乡城流动借款人的信用风险以及流出地和流入地空间收入差异在其中的作用机制。研究发现:乡城流动借款人违约概率比城市借款人高3%左右,但比农村借款人低约1%;流入地、流出地的收入差异与乡城流动借款人的违约概率呈U形关系,随着流入地和流出地收入差异的扩大,违约概率先下降后上升,在收入约为3万元时违约概率最低;流入地、流出地收入差异对违约概率的影响主要来自于流入地收入水平。文章弥补了现有文献关于流动人口违约行为研究的欠缺,充实和拓展了理论界有关借款人信用风险的研究,并从增进信用角度对政府有关城镇化建设中有关流动人口政策制定提供参考。
Using the lending data from the end of 2015 to the beginning of 2018 on a network loan platform,this paper for the first time studies rural-urban floating borrowers' credit risks and the determining mechanism from spatial income differences between the outflow and inflow places. By analyzing the data,we find that online borrowers have distinct demographic characteristics,and borrowers from rural to urban areas account for 42% of the total borrowers.Therefore,this paper attempts to answer the following two questions:First,what are the differences between the credit risks of rural-urban floating borrowers,urban borrowers and rural borrowers,and what are the reasons behind the differences?Second,for rural-urban floating borrowers,whether the income differences between the inflow and outflow areas affect their credit risks,and what is the role of the income from the outflow and inflow areas?The study finds that:the overdue probability of rural-urban floating borrowers is about 3% higher than that of urban borrowers,but 1% or so lower than rural borrowers;the relationship between the inflow-outflow income differences and the overdue probability of rural-urban floating borrowers is a "U" shape;with the increase in the income differences between the inflow and outflow areas,the overdue probability of rural-urban floating borrowers decreases and then increases,and reaches the lowest level at about 30 000 Yuan;these effects mainly come from the income level of inflow places. This paper fills the research gap on the default behavior of floating borrowers in the literature,enriches the research on borrowers' credit risks. In the current process of promoting urbanization,the credit risks of people moving from rural to urban areas is lower than that of people remaining in rural areas. In terms of the current average regional income,the differences between the inflow and the outflow are in the "U"-type credit risk reduction area. Therefore,encouraging the flow of townships,reducing and ultimately eliminating the discrimination in employment,education,household registration can help to reduce the probability of default among those who have borrowing demand in the rural-urban migrants,which may help to improve the social integrity environment.
Using the lending data from the end of 2015 to the beginning of 2018 on a network loan platform,this paper for the first time studies rural-urban floating borrowers' credit risks and the determining mechanism from spatial income differences between the outflow and inflow places. By analyzing the data,we find that online borrowers have distinct demographic characteristics,and borrowers from rural to urban areas account for 42% of the total borrowers.Therefore,this paper attempts to answer the following two questions:First,what are the differences between the credit risks of rural-urban floating borrowers,urban borrowers and rural borrowers,and what are the reasons behind the differences?Second,for rural-urban floating borrowers,whether the income differences between the inflow and outflow areas affect their credit risks,and what is the role of the income from the outflow and inflow areas?The study finds that:the overdue probability of rural-urban floating borrowers is about 3% higher than that of urban borrowers,but 1% or so lower than rural borrowers;the relationship between the inflow-outflow income differences and the overdue probability of rural-urban floating borrowers is a "U" shape;with the increase in the income differences between the inflow and outflow areas,the overdue probability of rural-urban floating borrowers decreases and then increases,and reaches the lowest level at about 30 000 Yuan;these effects mainly come from the income level of inflow places. This paper fills the research gap on the default behavior of floating borrowers in the literature,enriches the research on borrowers' credit risks. In the current process of promoting urbanization,the credit risks of people moving from rural to urban areas is lower than that of people remaining in rural areas. In terms of the current average regional income,the differences between the inflow and the outflow are in the "U"-type credit risk reduction area. Therefore,encouraging the flow of townships,reducing and ultimately eliminating the discrimination in employment,education,household registration can help to reduce the probability of default among those who have borrowing demand in the rural-urban migrants,which may help to improve the social integrity environment.
引文
[1]方匡南,吴见彬.个人住房贷款违约预测与利率政策模拟[J].统计研究,2013,(10).
[2]方匡南,吴见彬,朱建平,等.信贷信息不对称下的信用卡信用风险研究[J].经济研究,2010,(S1).
[3]郭峰.网络昵称与P2P借贷的成功率和违约率[J].经济科学,2016,(6).
[4]何光辉,杨咸月.中国小微企业信用违约影响因素的实证检验——来自某国有银行地区分行的证据[J].上海财经大学学报,2015,(6).
[5]何光辉,杨咸月,蒲嘉杰.中国P2P网络借贷平台风险及其决定因素研究[J].数量经济技术经济研究,2017,(11).
[6]况伟大.中国住房抵押贷款拖欠风险研究[J].经济研究,2014,(1).
[7]马小红,段成荣,郭静.四类流动人口的比较研究[J].中国人口科学,2014,(5).
[8]马宇.我国个人住房抵押贷款违约风险影响因素的实证研究[J].统计研究,2009,(5).
[9]平新乔,杨慕云.信贷市场信息不对称的实证研究——来自中国国有商业银行的证据[J].金融研究,2009,(3).
[10]王福林,贾生华,邵海华.个人住房抵押贷款违约风险影响因素实证研究——以杭州市为例[J].经济学(季刊),2005,(2).
[11]王美艳.城市劳动力市场上的就业机会与工资差异——外来劳动力就业与报酬研究[J].中国社会科学,2005,(5).
[12]伍山林.农业劳动力流动对中国经济增长的贡献[J].经济研究,2016,(2).
[13]夏怡然,苏锦红,黄伟.流动人口向哪里集聚?——流入地城市特征及其变动趋势[J].人口与经济,2015,(3).
[14]杨菊华.中国流动人口的社会融入研究[J].中国社会科学,2015,(2).
[15]叶文平,李新春,陈强远.流动人口对城市创业活跃度的影响:机制与证据[J].经济研究,2018,(6).
[16]余向华,陈雪娟.中国劳动力市场的户籍分割效应及其变迁——工资差异与机会差异双重视角下的实证研究[J].经济研究,2012,(12).
[17]张海洋,蔡航.头衔的价值——来自网络借贷的证据[J].经济学(季刊),2018,(4).
[18]张皓星,黄益平.情绪、违约率与反向挤兑——来自某互金企业的证据[J].经济学(季刊),2018,(4).
[19]章莉,李实,Darity W A Jr,等.中国劳动力市场上工资收入的户籍歧视[J].管理世界,2014,(11).
[20]Adams W,Einav L,Levin J. Liquidity constraints and imperfect information in subprime lending[J].American Economic Review,2009,99(1):49–84.
[21]Agarwal S,Chomsisengphet S,Liu C L. The importance of adverse selection in the credit card market:Evidence from randomized trials of credit card solicitations[J]. Journal of Money,Credit and Banking,2010,42(4):743–754.
[22]Agarwal S,Chomsisengphet S,Liu C L. Consumer bankruptcy and default:The role of individual social capital[J]. Journal of Economic Psychology,2011,32(4):632–650.
[23]Ausubel L M. The failure of competition in the credit card market[J]. The American Economic Review,1991,81(1):50–81.
[24]Bi?ákováA. Does the good matter?Evidence on moral hazard and adverse selection from consumer credit market[J]. Giornale degli Economisti e Annali di Economia,2007,66(1):29–65.
[25]Bryan G,Karlan D,Zinman J. Referrals:Peer screening and enforcement in a consumer credit field experiment[J]. American Economic Journal:Microeconomics,2015,7(3):174–204.
[26]Buckley F H,Brinig M F. The bankruptcy puzzle[J]. The Journal of Legal Studies,1998,27(1):187–207.
[27]Dobbie W,Skiba P M. Information asymmetries in consumer credit markets:Evidence from payday lending[J]. American Economic Journal:Applied Economics,2013,5(4):256–282.
[28]Duarte J,Siegel S,Young L. Trust and credit:The role of appearance in peer-to-peer lending[J]. The Review of Financial Studies,2012,25(8):2455–2484.
[29]Gross D B,Souleles N S. An empirical analysis of personal bankruptcy and delinquency[J]. The Review of Financial Studies,2002,15(1):319–347.
[30]Hertzberg A,Liberman A,Paravisini D. Screening on loan terms:Evidence from maturity choice in consumer credit[J]. The Review of Financial Studies,2018,31(9):3532–3567.
[31]Karlan D,Zinman J. Observing unobservables:Identifying information asymmetries with a consumer credit field experiment[J]. Econometrica,2009,77(6):1993–2008.
[32]Laibson D. Golden eggs and hyperbolic discounting[J]. The Quarterly Journal of Economics,1997,112(2):443–477.
[33]Lin M F,Prabhala N R,Viswanathan S. Judging borrowers by the company they keep:Friendship networks and information asymmetry in online peer-to-peer lending[J]. Management Science,2013,59(1):17–35.
[34]Livshits I,Mac Gee J C,Tertilt M. The democratization of credit and the rise in consumer bankruptcies[J].The Review of Economic Studies,2016,83(4):1673–1710.
[35]O'Donoghue T,Rabin M. Doing it now or later[J]. American Economic Review,1999,89(1):103–124.
[36]Ohlson J A. Financial ratios and the probabilistic prediction of bankruptcy[J]. Journal of Accounting Research,1980,18(l):109–131.
[37]Pope D G,Sydnor J R. What’s in a picture?Evidence of discrimination from prosper.com[J]. Journal of Human Resources,2011,46(1):53–92.
[38]Ravina E. Love&loans:The effect of beauty and personal characteristics in credit markets[R]. Working Paper,2018.
[39]Stiglitz J E,Weiss A. Credit rationing in markets with imperfect information[J]. The American Economic Review,1981,71(3):393–410.
[40]Vissing-Jorgensen A. Consumer credit:Learning your customer’s default risk from what(s)he buys[R].Working Paper,2012.
[2]方匡南,吴见彬,朱建平,等.信贷信息不对称下的信用卡信用风险研究[J].经济研究,2010,(S1).
[3]郭峰.网络昵称与P2P借贷的成功率和违约率[J].经济科学,2016,(6).
[4]何光辉,杨咸月.中国小微企业信用违约影响因素的实证检验——来自某国有银行地区分行的证据[J].上海财经大学学报,2015,(6).
[5]何光辉,杨咸月,蒲嘉杰.中国P2P网络借贷平台风险及其决定因素研究[J].数量经济技术经济研究,2017,(11).
[6]况伟大.中国住房抵押贷款拖欠风险研究[J].经济研究,2014,(1).
[7]马小红,段成荣,郭静.四类流动人口的比较研究[J].中国人口科学,2014,(5).
[8]马宇.我国个人住房抵押贷款违约风险影响因素的实证研究[J].统计研究,2009,(5).
[9]平新乔,杨慕云.信贷市场信息不对称的实证研究——来自中国国有商业银行的证据[J].金融研究,2009,(3).
[10]王福林,贾生华,邵海华.个人住房抵押贷款违约风险影响因素实证研究——以杭州市为例[J].经济学(季刊),2005,(2).
[11]王美艳.城市劳动力市场上的就业机会与工资差异——外来劳动力就业与报酬研究[J].中国社会科学,2005,(5).
[12]伍山林.农业劳动力流动对中国经济增长的贡献[J].经济研究,2016,(2).
[13]夏怡然,苏锦红,黄伟.流动人口向哪里集聚?——流入地城市特征及其变动趋势[J].人口与经济,2015,(3).
[14]杨菊华.中国流动人口的社会融入研究[J].中国社会科学,2015,(2).
[15]叶文平,李新春,陈强远.流动人口对城市创业活跃度的影响:机制与证据[J].经济研究,2018,(6).
[16]余向华,陈雪娟.中国劳动力市场的户籍分割效应及其变迁——工资差异与机会差异双重视角下的实证研究[J].经济研究,2012,(12).
[17]张海洋,蔡航.头衔的价值——来自网络借贷的证据[J].经济学(季刊),2018,(4).
[18]张皓星,黄益平.情绪、违约率与反向挤兑——来自某互金企业的证据[J].经济学(季刊),2018,(4).
[19]章莉,李实,Darity W A Jr,等.中国劳动力市场上工资收入的户籍歧视[J].管理世界,2014,(11).
[20]Adams W,Einav L,Levin J. Liquidity constraints and imperfect information in subprime lending[J].American Economic Review,2009,99(1):49–84.
[21]Agarwal S,Chomsisengphet S,Liu C L. The importance of adverse selection in the credit card market:Evidence from randomized trials of credit card solicitations[J]. Journal of Money,Credit and Banking,2010,42(4):743–754.
[22]Agarwal S,Chomsisengphet S,Liu C L. Consumer bankruptcy and default:The role of individual social capital[J]. Journal of Economic Psychology,2011,32(4):632–650.
[23]Ausubel L M. The failure of competition in the credit card market[J]. The American Economic Review,1991,81(1):50–81.
[24]Bi?ákováA. Does the good matter?Evidence on moral hazard and adverse selection from consumer credit market[J]. Giornale degli Economisti e Annali di Economia,2007,66(1):29–65.
[25]Bryan G,Karlan D,Zinman J. Referrals:Peer screening and enforcement in a consumer credit field experiment[J]. American Economic Journal:Microeconomics,2015,7(3):174–204.
[26]Buckley F H,Brinig M F. The bankruptcy puzzle[J]. The Journal of Legal Studies,1998,27(1):187–207.
[27]Dobbie W,Skiba P M. Information asymmetries in consumer credit markets:Evidence from payday lending[J]. American Economic Journal:Applied Economics,2013,5(4):256–282.
[28]Duarte J,Siegel S,Young L. Trust and credit:The role of appearance in peer-to-peer lending[J]. The Review of Financial Studies,2012,25(8):2455–2484.
[29]Gross D B,Souleles N S. An empirical analysis of personal bankruptcy and delinquency[J]. The Review of Financial Studies,2002,15(1):319–347.
[30]Hertzberg A,Liberman A,Paravisini D. Screening on loan terms:Evidence from maturity choice in consumer credit[J]. The Review of Financial Studies,2018,31(9):3532–3567.
[31]Karlan D,Zinman J. Observing unobservables:Identifying information asymmetries with a consumer credit field experiment[J]. Econometrica,2009,77(6):1993–2008.
[32]Laibson D. Golden eggs and hyperbolic discounting[J]. The Quarterly Journal of Economics,1997,112(2):443–477.
[33]Lin M F,Prabhala N R,Viswanathan S. Judging borrowers by the company they keep:Friendship networks and information asymmetry in online peer-to-peer lending[J]. Management Science,2013,59(1):17–35.
[34]Livshits I,Mac Gee J C,Tertilt M. The democratization of credit and the rise in consumer bankruptcies[J].The Review of Economic Studies,2016,83(4):1673–1710.
[35]O'Donoghue T,Rabin M. Doing it now or later[J]. American Economic Review,1999,89(1):103–124.
[36]Ohlson J A. Financial ratios and the probabilistic prediction of bankruptcy[J]. Journal of Accounting Research,1980,18(l):109–131.
[37]Pope D G,Sydnor J R. What’s in a picture?Evidence of discrimination from prosper.com[J]. Journal of Human Resources,2011,46(1):53–92.
[38]Ravina E. Love&loans:The effect of beauty and personal characteristics in credit markets[R]. Working Paper,2018.
[39]Stiglitz J E,Weiss A. Credit rationing in markets with imperfect information[J]. The American Economic Review,1981,71(3):393–410.
[40]Vissing-Jorgensen A. Consumer credit:Learning your customer’s default risk from what(s)he buys[R].Working Paper,2012.