Abstract: We study how credit information sharing regimes affect credit access. Chile’s information-sharing regime in the credit-card market features banks sharing full credit histories, while retailer card issuers keep histories proprietary. Using borrower-level panel data, we document three facts. First, retailers disproportionately serve lower-income and first-time borrowers. Second, conditional on good repayment performance, retailers increase credit limits faster than banks, consistent with learning by lending when information remains private. Third, when a major retailer sells its credit card portfolio to a bank, making those histories observable to banks, other banks increase limits for those borrowers, especially for higher-income, lower-risk individuals. Taken together, our findings are consistent with a trade-off between full information sharing regimes and financial inclusion.
Discussant: Benedict Guttman-Kenney, Rice University
Abstract: This paper highlights a trade-off in credit markets between regulatory safeguards for informed consent and the informational frictions they can amplify. In our empirical setting, we find that requiring lenders to garner explicit consent prior to raising clients' credit limits induces adverse selection. We find disproportionately higher take-up among riskier borrowers, as measured by increased utilization, delinquency, and charge-offs, which worsens the risk profile of accounts that receive a credit limit increase. In response to the policy, we find that lenders decreased the size of credit limit increases, yet simultaneously gave more frequent limit increases. We develop a model of lender credit limit provision to study the role of adverse selection and learning. We show that learning from acceptance decisions can rationalize lenders' increased frequency of credit limit increases, while adverse selection can rationalize the decline in the size of credit limit increases.
Discussant: Simon Mayer, Carnegie Mellon University
Erica Xuewei Jiang, University of California-Los Angeles
Yeonjoon Lee, Federal Reserve Bank of Richmond
Quinn Maingi, University of Southern California
Abstract: We study how the organization of information production---and its response to economic and technological forces---affects informational efficiency, credit allocation, and borrower risk. Using U.S. administrative data linking mortgage applications to loan officers and subsequent loan performance, we show that underwriting facilitated by officers located close to the borrower increases approval rates without worsening ex-post performance or processing speed, but is not always deployed where it is most valuable, because lenders allocate loan-officer labor elastically with respect to local wages. These gains are especially large for observably riskier borrowers. We develop and estimate a model that combines a core information-production problem over latent borrower risk, an endogenous choice over local versus remote underwriting, and equilibrium in mortgage and labor markets. We find substantial baseline credit rationing---up to 15 percent in high-risk segments---with local officers eliminating roughly half of it while also reducing excessively risky approvals. A technology shock that raises the processing productivity of remote officers induces lenders to substitute away from local screening, lowering informational efficiency, increasing excessively risky approvals and expected defaults, and tightening rationing for marginal borrowers despite only modest reductions in interest rates.
Discussant: Adam Jorring, University of Massachusetts-Amherst
