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The Effects of Auction‐based Pricing Mechanisms and Social Characteristics on Microloan Performance
Author(s) -
Luo Sirong,
Mookerjee Radha,
Liu Dengpan
Publication year - 2021
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.13338
Subject(s) - loan , credit score , business , context (archaeology) , listing (finance) , probability of default , default , actuarial science , microeconomics , economics , monetary economics , credit risk , finance , paleontology , biology
We empirically investigate the effects of a borrower’s choice of auction‐based pricing mechanisms and her social characteristics on listing outcomes and loan performance in online lending. Analyzing data from a leading online lending platform, we find that the borrower’s choice of borrower pricing mechanism and being affiliated with a group are both associated with higher funding probability; thus, the lenders may interpret them as positive signals of creditworthiness of the borrower. However, these signals are misleading to the lenders as they are actually both associated with higher default risk and greater credit loss (in case of default). In addition, we find that the positive effect of the borrower’s social characteristics on funding probability is smaller when she chooses the borrower pricing mechanism. Moreover, we find that although exerting a positive (negative) effect on loan performance, the number of friends (group affiliation) of the borrower aggravates (reduces) the effect of the group affiliation (number of friends) on loan default risk. Another interesting finding is that, despite having an adverse effect on loan performance, a borrower’s group affiliation, can mitigate the negative effects of the borrower’s requested amount on listing outcomes and loan performance. Our analysis also shows that, in the context of online microloan lending, a borrower’s choice of auction‐based pricing mechanisms is a better predictor of loan performance than some other traditional credit‐related information.