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AbstractOnline peer‐to‐peer (P2P) platforms both disclose and conceal borrowers' information. Minimal research has been conducted on the impact of concealing such information on borrowers' funding success. We fill this research gap by exploring a natural experiment opportunity at Prosper, an online lending marketplace where the platform enacted a policy to conceal borrowers' past failure history. We first find that the platform's decision to conceal such information can be rational because when this information was publicly available, borrowers with past failures had lower success rates than borrowers without past failures. We next investigate the effect of hiding this information on lenders' lending decisions. Our empirical results from a series of analyses demonstrate that concealing such information helps some borrowers but also surprisingly leads to an overall lower success rate, which ultimately harms the platform and runs counter to the platform's intention. These effects are strongest for low‐risk borrowers and would attenuate in the long run. We further find that the number of borrowers' past failures is associated with lower repayment ability.

ABSTRACTMany on‐demand service platforms employ state‐dependent pricing strategies to balance supply capacity and customer demand. In the context of ride‐hailing platforms, it has been observed that drivers strategically exploit the structure of such pricing policies by coordinating with each other to deactivate some drivers in order to create an artificial shortage of supply capacity and trigger so‐called surge pricing. We develop a simple and high‐level analytical framework to structurally characterize the drivers of such collusive behavior and the consequences for drivers and the platform. We find that collusive driver behavior is more likely in settings where customers exhibit moderate sensitivity to waiting time. For some of these cases, if customers continue to request service under driver collusion, the platform may benefit from the higher surge prices. For settings where driver collusion is harmful to the platform, we consider two possible mitigation strategies: a bonus payment structure to eliminate the drivers' incentives to collude, which comes at a direct cost to the platform, and a freeze period after deactivating the app during which drivers cannot reactivate. We show that with the appropriate duration, such a freeze period can effectively eliminate driver collusion without any direct costs to the platform.