Does credit evaluation inhibit the regional and education preferences in peer-to-peer lending?


(1.Internet Finance and the National School of Development, Peking University 100871)

【Abstract】Regional and education preferences always exist in the loan market, and peer-to-peer lending in China today is developing in full swing. Based on the transaction data of Renrendai platform, this paper studied the impacts of lenders’ regional and education preferences on the success rate, interest rate, the number of bidders and bid time of loan order, and verified the existence of such preferences in peer-to-peer lending and the inhibiting role of credit evaluation mechanisms. The research results show that subject to incomplete credit evaluation mechanisms, the lenders certainly have regional and education preferences when making investment decisions; however, such preferences become non-significant with the credit evaluation mechanisms improving. Specifically speaking, with credit ratings rising, the inhibition on regional and education preferences is increasingly more prominent. The research holds that measures such as encouraging competition and innovation in peer-to-peer lending, regulating credit evaluation mechanisms and decreasing information asymmetry can help effectively solve the problem of preferences concerning labeled information such as region and education, and enhance the matching efficiency in peer-to-peer lending.

【Keywords】 credit evaluation; peer-to-peer lending; regional and education preferences; order;


【Funds】 National Social Science Foundation of China (14BJY194)

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(Translated by GENG Qingyou)


    [1]. ① At present, the Renrendai platform mainly discloses loan orders and a series of detailed information about the owners of such orders so that the lenders can refer to them in making investment decisions, but does not release information on lenders. Therefore, this paper does not show the features of lenders and their relationship with the features of borrowers. In addition, in the design of cross section, to make the research theme clearer, this paper focuses on two variables—the regions that borrowers come from and their educational attainment, and does not study other features of borrowers in detail. [^Back]

    [2]. ① In accordance with the State Council [2000] No. 33 document, this paper divides the 31 provinces, municipalities and autonomous regions in Chinese mainland (excluding Hong Kong, Macau and Taiwan) into the four classes by development level. The first-class areas include Beijing, Shanghai, Guangdong, Tianjin, Jiangsu, Zhejiang, Fujian, Shandong and Hainan in the eastern coastal region. The second-class areas include Liaoning, Jilin, and Heilongjiang in the northeastern region. The third-class areas include Hebei, Shanxi, Henan, Hubei, Hunan, Jiangxi and Anhui in the central region. The fourth-class areas include Chongqing, Sichuan, Shaanxi, Inner Mongolia, Yunnan, Guizhou, Guangxi, Xinjiang, Gansu, Ningxia, Qinghai and Tibet in the western region. [^Back]

    [3]. ① Subject to space limitations, Tables 8–15 just list the impacts of core variables. [^Back]


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This Article


CN: 11-1166/F

Vol 37, No. 07, Pages 147-161

July 2016


Article Outline


  • 1 Introduction
  • 2 Theoretical analysis
  • 3 Research variable, descriptive statistics, and models
  • 4 Research process
  • 5 Conclusion and implication
  • Footnote