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

Abstract

Information asymmetry in the anonymous informal finance market drives the lenders to screen the borrowers by disclosed information. Using data from a powerful online peer-to-peer lending platform, we study the effects of formal financing records on successful funding and default outcomes in the informal finance market. We find that lenders are more likely to fund borrowers with formal financing records. Borrowers with formal credit are more likely to repay the loans entirely. Co-funding with the formal financial sector is strategic, corresponding to lower default risk. Moreover, without historical success records or with a low-income level, borrowers can use the formal credit signals to mitigate information asymmetry and improve the funding probability. These results are obtained after controlling for loan-level and borrower-level information, cityby-year-month, and day-of-week fixed effects. Several tests show our findings are unlikely to be an unintentional byproduct of the formal financial signals.

Migrants and default: Evidence from China

“…A growing body of literature has investigated the predictable factors of creditworthiness and financing costs in the peer-to-peer lending market. For instance, race (Pope & Sydnor, 2011), gender (Chen et al, 2017;Chen et al, 2020;Li et al, 2020), education (Chen et al, 2018a;Xu et al, 2020), formal credit records (Li et al, 2021), credit grade (Emekter et al, 2015), appearance (Duarte et al, 2012), social capital (Freedman & Jin, 2017;Hasan et al, 2020Hasan et al, , 2021Jiang et al, 2020;Lin et al, 2013), university reputation (Li & Hu, 2019), and loan description (Chen et al, 2018b;Dorfleitner et al, 2016;Herzenstein et al, 2011) can predict borrowers' repayment behavior or interest rates.…”

“…We collect loan-level data from Renrendai, one of the leading online lending platforms in China. 2 A strand of literature has also used Renrendai as an essential data source to study the issues of the Chinese peer-to-peer lending market (Chen et al, 2018b(Chen et al, , 2020Hasan et al, 2021;Jiang et al, 2020;Li et al, 2020Li et al, , 2021Liao et al, 2021;Wang et al, 2021;Xu et al, 2020). Borrowers should add their interest rate, maturity, amount, age, gender, educational qualification, income, and work address to Renrendai.…”

“…A higher credit score is associated with higher credit quality and lower default risk. We can use the high-risk indicator (HR) to differentiate the high-risk from non-high-risk borrowers following Li et al (2021) and Liao et al (2021). After lenders fully support the requested loans, borrowers should repay the principal and interest according to the schedule.…”

“…In the baseline regressions, we use the sample of the latest loans to capture the actual credit quality, but the credit score may change during the repayment process (Li et al, 2021;Liao et al, 2021). One alternative sample Therefore, we use the borrower-by-year-level sample to examine the relationships between migration status and economic attributes and use borrower-level observations to retest the relationships between migration status and the culture gap.…”

“…Borrowers should add their interest rate, maturity, amount, age, gender, educational qualification, income, and work address to Renrendai . The platform assigns credit scores to borrowers based on their characteristics and updates monthly based on repayment behavior (Li et al, 2021; Liao et al, 2021). A higher credit score is associated with higher credit quality and lower default risk.…”