Education Premium in the Online Peer-to-Peer Lending Marketplace: Evidence From the Big Data in China

Abstract

We study the education premiums in the online peer-to-peer (P2P) lending marketplace in which individuals bid on unsecured microloans applied by individual borrowers. Using more than 100,000 consummated and failed listings from the largest online P2P lending marketplace in China — Paipaidai.com, we examine whether higher education level lead to lower interest rates and lower risk of default. We find that controlling for other characteristics of borrowers, borrowing rates of borrowers with bachelor’s degrees is 0.141 percent higher than that of borrowers with associate’s degrees, and that female borrowers’ education premiums were higher than their male counterparts. With regard to loan performance, borrowers with bachelor’s degrees are 13% less likely to default than the borrowers with associate’s degrees. Therefore, the education premiums in the P2P lending marketplace are rational.

Access to the Internet and Access to Finance: Theory and Evidence

“…Our paper, therefore, contributes to the growing literature on Internet finance as well as the broader literature on crowdfunding. Recent investigations include [10][11][12]14,15,[17][18][19][20][21][22][23][24][25][26][27]. Given that the explosive penetration of the Internet and Mobile Internet that has laid a sound foundation in China, which has been rapidly skyrocketing (China has greater Internet and mobile Internet development among emerging market economies with penetration ratios of 45% and 37.1%, respectively, in 2013, reported by Lei [19]), our study has important and timely implications not only for academics and practitioners, but for policy makers as well.…”

Borrower-lender Information Fusion for P2P Lending: A Nonparametric Approach

“…Duarte et al [8] studied the role of appearance in peer-to-peer lending and argued that borrowers who appear more trustworthy have higher probabilities of having their loans funded. Lin and Viswanathan [9] found evidence that home bias exists in P2P lending market and showed that rationality-based explanations cannot fully explain such behavior Chen et al [10] examined higher education level will lead to lower interest rates and lower risk of default in P2P lending platform. Dorfleitner et al [11] concluded that the soft information (spelling errors, text length etc.)…”

“…Next, for each given loan in DataI, we compute the default likelihood distances and between and each loan in DataH. Then, we compute the borrower kernel weights and the investor composition kernel weight based on , and the optimized bandwidth hb and hc using Equations (8) and (10). Furthermore, we compute multi-kernel weights using Equation 14based on , , correlation coefficients and using Equations (12) and (13).…”

Strategic co-funding in informal finance market: evidence from China

“…In the online peer-to-peer lending domain, the existing literature mainly focuses on the predictable factors, including description (Chen, Huang, & Ye, 2018;Dorfleitner, Priberny, Schuster, Stoiber, Weber, de Castro, & Kammler, 2016;Herzenstein, Sonenshein, & Dholakia, 2011;Larrimore, Jiang, Larrimore, Markowitz, & Gorski, 2011), appearance (Duarte, Siegel, & Young, 2012), social capital (Freedman & Jin, 2017;Lin, Prabhala, & Viswanathan, 2013), gender (Chen, Huang, & Ye, 2020), race (Pope & Sydnor, 2011), credit grade (Emekter, Tu, Jirasakuldech, & Lu, 2015;Han, Chen, Liu, Luo, & Fan, 2018), location (Burtch, Ghose, & Wattal, 2014;Lin & Viswanathan, 2016;Wang, Zhao, & Shen, 2021), education (Chen, Zhang, & Yin, 2018), debt to income ratio (Emekter, Tu, Jirasakuldech, & Lu, 2015;Iyer, Khwaja, Luttmer, & Shue, 2016), etc., on the funding probability and default risk. We add to the literature by investigating formal financial signals' effects on successful funding and default risk in the peer-to-peer lending market.…”