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. We find that requiring banks to garner explicit consent prior to raising clients’ credit limits leadsto riskier borrowers disproportionately consenting. This introduces a new form of adverse selection. In response, we find banks decreased the size of the average credit limit increase and simultaneously gave more frequent limit increases. We develop a precautionary savings model with endogenous credit limits to study the role of learning and adverse selection in markets with incomplete information. We show that learning from acceptance decisions can rationalize our empirical results. Our model suggests that requiring consumer consent reduced lender profits but had negligible effects for consumers. Our counterfactuals demonstrate that under contractionary monetary policy requiring consumer consent would decrease both the frequency and size of 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 technological advances that change the relative efficiency of local and remote screening affect information production, credit rationing, and allocative efficiency. Using administrative data linking loan officers to applications and loan outcomes, we document that informational frictions are first-order; local and remote loan officers differ sharply in screening precision and processing speed; and lenders’ labor-allocation decisions respond strongly to local wage differentials, generating systematic spatial misalignment between mortgage demand and local underwriting capacity. Motivated by these patterns, we develop and estimate a structural model in which lenders compete in mortgage pricing and in labor markets for heterogeneous loan officers, borrowers with different unobserved default risks sort on prices, and screening precision varies with officer type. The model implies substantial baseline credit rationing—up to 15 percent in high-risk segments—with local officers eliminating roughly half while also reducing false approvals. A technology shock that increases the physical efficiency of remote work induces lenders to substitute away from local screening, reducing informational efficiency, raising pooled origination and expected defaults, and tightening rationing for marginal borrowers despite only modest reductions in rates.
Discussant: Adam Jorring, University of Massachusetts-Amherst
