The Equilibrium Effects of Asymmetric Information: Evidence from Consumer Credit Markets
Seth Zimmerman, Assistant Professor of Economics
This project combines a scaled credit information deletion policy in Chile with administrative data and machine learning techniques to create a “laboratory” in which we study how institutions that limit the information available to lenders affect the quantity of consumer credit. In 2012, Chilean credit bureaus were forced to stop reporting past defaults for 2.8 million individuals with relatively low default amounts. These individuals made up approximately 67% of borrowers in default. Using data on the universe of bank borrowers in Chile and access to the deleted registry information, we measure exposure to the deletion policy by constructing cost predictions with and without the deleted data. We then estimate the effects of exposure to changes predicted costs by comparing changes in borrowing over time for borrowers whose predicted costs rise or fall. We find that limiting credit information reduces overall borrowing, with the biggest proportional losses for poorer individuals. To illustrate how our approach might work in other settings, we simulate the effects of hypothetical deletion policies such as the elimination of data on gender. We find that information restrictions tend to reduce overall borrowing, with largest drops for lower-income individuals and women.