期刊
ECONOMIC MODELLING
卷 92, 期 -, 页码 268-276出版社
ELSEVIER
DOI: 10.1016/j.econmod.2020.01.007
关键词
Default risk; Online lending; Educational level; Signaling effect
类别
资金
- National Natural Science Foundation of China [71971192]
- Humanities and Social Science Youth Foundation of Ministry of Education of China [19C11482075]
- Natural Science Foundation of Zhejiang Province [LY19G010005]
- First Class Discipline (Class A) Planning Project of Zhejiang Province (Zhejiang University of Finance and Economics, The research on the evaluation model of individuals' credit risk in P2P online lending)
In this study, we use data from an online lending platform named Xinxindai in China to empirically study the signaling effects of education for the default risk of borrowers. Three dependent variables are created, namely, the probability of default, overdue payments and overdue amount, and probit models, count models and Tobit models are employed correspondingly. The number of universities in the 211 Project of China at the city level is employed as the instrumental variable. The empirical evidence shows that education generally plays a strong signaling role in the identification of borrowers' default risk in China. The negative marginal effect of education declines as borrowing times increase and as the marketization of regions deepens. This study helps to fill an important gap in the existing literature. Platforms and lenders can use educational level for reference in identifying the default risk of borrowers.
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