Information asymmetries are known in theory to lead to inefficiently low credit provision, yet empirical estimates of the resulting welfare losses are scarce. This paper leverages a randomized experiment conducted by a large fintech lender to estimate welfare losses arising from asymmetric information in the market for online consumer credit. Building on methods from the insurance literature, we show how exogenous variation in interest rates can be used to estimate borrower demand and lender cost curves and recover implied welfare losses. While asymmetric information generates large equilibrium price distortions, we find only small overall welfare losses, particularly for high-credit-score borrowers.

理论上，众所周知，信息不对称会导致低效的信贷供给，但对由此产生的福利损失的经验估计却很少。本文利用一家大型金融科技贷方进行的随机实验来估计在线消费信贷市场中信息不对称引起的福利损失。基于保险文献中的方法，我们展示了如何使用利率的外生变化来估计借款人需求和贷款人成本曲线并恢复隐含的福利损失。虽然信息不对称会产生较大的均衡价格扭曲，但我们发现整体福利损失很小，尤其是对高信用评分借款人而言。