Conference Agenda

Over 30m US adults do not use formal consumer credit. How many of these are inefficiently excluded because they lack a credit history or have a poor credit score? We develop a framework to characterize the efficiency-maximizing system of credit histories and credit scoring, subject to the constraints imposed by the severity of adverse selection, and by the ability of credit histories to predict future risk. We find US consumer credit features a moderate amount of adverse selection and persistent consumer types. This adverse selection generates substantial welfare loss: a majority of today’s non-borrowers would be first-best efficient to lend to. Credit reporting helps alleviate the costs of adverse selection, with the current US system recovering roughly two-thirds of the welfare that would be lost in a no-credit-reporting counterfactual, relative to a full-information first-best. We find that requiring histories to be shorter – or to forget past default sooner – would induce some market unraveling but also would help non-borrowing consumers escape the “no history trap.

How do inaccurate priors about the distribution of interest rates affect search and outcomes in consumer credit markets? Consumer credit markets feature large amounts of within-borrower price dispersion in interest rates; if consumers are unaware of the extent of this price dispersion, they may shop less and take out loans at higher interest rates than they would otherwise. We conducted a randomized controlled trial with 112,063 loan seekers in Chile where we we showed treated participants a price comparison tool that we built using administrative data from Chile's financial regulator. The tool shows loan seekers a conditional distribution of interest rates based on similar loans obtained recently by similar borrowers, using data on the universe of consumer loans merged with borrower characteristics. We also cross-randomized whether we asked participants their priors about the distribution of interest rates. We find that consumers thought interest rates were lower than they actually were, and the price comparison tool caused them to increase their expectations about the interest rate they would obtain by 56%. Consumers also underestimated price dispersion, and our price comparison tool caused them to increase their estimates of dispersion by 69%. The price comparison tool did not cause people to search or apply at more institutions, but it did cause them to receive 13% more offers and 11% lower interest rates, and to be 28% more likely to negotiate with their lender and 5% more likely to take out a loan. In contrast, merely asking participants their expectations about interest rates led them to search at 4% more institutions and obtain 9% lower interest rates.

We show the effect of credit scores’ introduction on consumer credit prices. Utilizing a novel dataset of the universe of loans in Israel, we find that a decline in information asymmetry, following credit scores’ introduction, led to a decrease in loan prices for households with strong relationship banking. Prior to that, when banks held a monopoly on potential borrowers’ credit history, they charged higher interest rates all else equal, as predicted by theoretical models. We then show that these informational rents significantly decrease once credit scores are introduced, and document the resulting decline in the hold-up problem. To the best of our knowledge, this paper is the first to show the causal impact of credit scoring on households’ loan pricing. Our results highlight the importance of information sharing in consumer credit markets and have important public policy implications.