How Lending Experience and Borrower Credit Influence Rational Herding Behavior in Peer-to-Peer Microloan Platform Markets

ABSTRACT

This paper analyzes the herding behavior that characterizes lenders’ lending decisions on a microloan platform and explains how rational herding behavior can resolve the information-asymmetry problem, which is a well-known reason for the failure of online microloan platforms. Using a set of panel data on individual lending decisions acquired from Paipaidai.com (PPDai), an online microloan platform, we examine the influence of the lending decisions of prominent, experienced lenders on novice lenders to identify rational herding behavior. Our empirical analysis demonstrates that rational herding behavior can in fact efficiently reduce lender loss from borrower defaults caused by limited information. Although it is typically assumed that herding behavior is irrational, we find that it can be rational in this context and can thus shed light on why PPDai has succeeded while most other microloan platforms have failed. Accordingly, we make three key contributions: 1) we use heterogeneous herding effects to empirically determine whether lenders’ herding behavior on PPDai is rational based on observational learning; 2) we investigate the moderating effect of borrower credit and novice-lender experience on herding, and we leverage this heterogeneity in lender experience to better explain loan results; and 3) because PPDai publicly provides potential lenders with a transparent credit score—in contrast to platforms like Prosper.com, which leverage hidden proprietary credit information from Experian—we further analyze the credit composition of prominent lenders to better understand the crucial determinants of rational herding. In fact, our follow-up survival simulations indicate that without rational herding, the total number of successful PPDai loans would have decreased by around 46 percent during the study period—a finding that further underlines the crucial influence of rational herding and the unique contextual factors of PPDai that have fostered it.

KEYWORDS:

• Peer-to-peer transactions
• microloans
• herding behavior
• information asymmetry
• online platforms
• microloan platforms
• rational herding

Disclosure statement

No potential conflict of interest was reported by the author(s).

SUPPLEMENTARY MATERIAL

Supplemental data for this article can be accessed online at https://doi.org/10.1080/07421222.2023.2229128

Notes

¹ Recall that between 2011 and 2018, over 84 percent of the Chinese P2P loan platforms failed [40] and that approximately 700,000 small investors collectively lost approximately RMB 120 billion (USD 18.77 billion) [71]. Thus, at the end of 2019, the Chinese government revamped the regulation of this industry and, within two years, caused the platforms to become small loan providers with heavier oversight [86].

² FinVolution Group was originally established as “Shanghai Paipaidai Financial Information Services Co. Ltd (PPDai)” but changed its name to FinVolution Group in November 2019.

³ Data source: http://map.invest.ppdai.com/.

⁴ In Prosper.com’s earlier stages (before December 2010), lenders had to bid on the actual interest rate, and the lenders who offered the lowest interest rates won the investment opportunity. However, after December 2010, the platform switched to a fixed-interest model in which there is no auction process for the interest rate charged for borrowers.

⁵ The authors found that unlike less experienced lenders, super-experienced lenders earn positive average profits and bid below the expected value conditional on winning the item, thus avoiding the “winner’s curse.” However, they earn below (risk-neutral) Nash equilibrium profits, which is slightly less than 50 percent of predicted profits.

⁶ Because we focus on lenders’ decision-making process, we omit the process for creating a listing. In that stage, borrowers first post a microloan listing on the platform, which provides the borrowers’ credit history and project information, such as interest and terms. However, the borrower is the only player who knows with certainty whether they will finally default, and the platform cannot provide assurances regarding default risks; therefore, this creates information asymmetry at this stage [37].

⁷ found that borrowers can even infer the creditworthiness of the lenders within the same credit rating category based on their standard-banking variables, such as the debt-to-income ratio, the number of current delinquencies, or the number of credit inquiries, which were unavailable for our study.

⁸ See Figure 2 for details about the lender’s credit calculation.

⁹ Again, PPDai, which launched in 2007, is the largest and most successful P2P loan platform in China. As of April 14, 2022, the platform had over 135 million cumulative registered users, had facilitated over 63 million loans, and had a total transaction volume of about RMB 191.8 billion. PPDai’s innovative processes and credit- and lender-evaluation schemes allowed it to reduce information asymmetry.

¹⁰ A possible reason for such a low default rate is the higher savings propensity in China relative to Western countries. A large portion of PPDai microloans are for individuals’ consumer goods purchases; thus, the default rates strongly depend on individuals’ income and savings propensity. The China Statistical Yearbook 2013 shows that the annual per capita income in China was RMB 28,752 in 2012. The average size of a PPDai loan in this year was RMB 5,706; thus, the loan size was relatively small with respect to income. By contrast, the savings rate was over 30 percent of net income in China in 2012, compared to 10 percent in the US in that year.

¹¹ We conducted a robustness check on the threshold value. The results remained the same regardless of whether 75th- or 90th-percentile cutoffs were used.

¹² Payoff externalities refers to other lenders’ investments having positive effects on a lender’s payoff because a listing with a higher cumulative investment amount is likely to materialize and thus lower the risk of incurring an opportunity cost of time and investment once the listing fails to materialize.

¹³ For details on the Cox hazard model’s survival-probability expression and subsequent simulations, refer to [62, p. 754].

Additional information

Notes on contributors

Paul Benjamin Lowry

Paul Benjamin Lowry ([email protected]) is an Eminent Scholar and the Suzanne Parker Thornhill Chair Professor in Business Information Technology (BIT) at the Pamplin College of Business at Virginia Tech where he serves as the BIT Ph.D. and Graduate Programs Director. He received his Ph.D. in Management Information Systems from the University of Arizona. Dr. Lowry’s research interests include: organizational and behavioral security and privacy; online deviance, online harassment, and computer ethics; human-computer interaction, social media, and gamification; and business analytics, decision sciences, innovation, and supply chains. He has over 270 publications, including more than 150 journal articles in the Journal of Management Information Systems (JMIS), Information Systems Research (ISR), MIS Quarterly, and others. He is a member of the Editorial Board of JMIS. He is also a senior editor of Journal of the AIS and associate editor of ISR.

Junji Xiao

Junji Xiao ([email protected]) is an Associate Professor in Economics at Lingnan University, China. He received his Ph.D. in Economics from the University of Toronto. He is an economist with research interest in the fields of empirical industrial organization, environmental economics, and China economy. He was awarded German DFG Fellowship and Hong Kong RGC grant. Dr. Xiao’s papers have been published in such journals as Journal of Management Information Systems, International Economic Review, Review of Economics and Statistics, Journal of Industrial Economics, Journal of Economics & Management Strategy, Journal of Economic Behavior and Organization, and others. He has also worked on commissioned projects for the State Administration for Market Regulation, China.

Jia Yuan

Jia Yuan ([email protected]) is an Associate Professor of Business Economics at the Faculty of Business Administration, University of Macau. He received his Ph.D. in Economics from the University of Minnesota. His research interests include applied economics, empirical industrial organization, and behavioral economics. Dr. Yuan has published in such journals as Journal of Management Information Systems, Journal of Comparative Economics, Journal of Economics Behavior and Organization, Economics Inquiry, Review of Industrial Organization, and others.