Data and Welfare in Credit Markets
We show how to measure the welfare effects arising from increased data availability. When lenders have more data on prospective borrower costs, they can charge prices that are more aligned with these costs. This increases total social welfare, and transfers surplus from borrowers to lenders. We show that the magnitudes of the welfare changes can be estimated using only quantity data and variation in prices. We apply the methodology on bankruptcy flag removals, and find that removing prior bankruptcy information increases the surplus of previously bankrupt consumers substantially, at the cost of decreasing total social welfare modestly, suggesting that flag removals have low efficiency costs for redistributing surplus to previously bankrupt borrowers.
We are grateful to Jacelly Cespedes, Tony Cookson, Eduardo Davila, Anthony DeFusco, Andreas Fuster, Andra Ghent, Zhiguo He, Kyle Herkenhoff, Ray Kluender, Christian Leuz, Sanjog Misra, Matt Notowidigdo, Nathan Seegert, Alp Simsek, Huan Tang, Ansgar Walther, Jialan Wang, and Wanran Zhao for thoughtful discussions and comments, as well as seminar participants at the University of Chicago, Yale School of Management, Cambridge University Judge School of Business, HEC Paris, INSEAD, Washington University in St. Louis, the University of Kentucky Gatton College of Business, the Swiss Finance Institute, the Swiss Federal Institute of Technology Lausanne, Durham University Business School, SAIF, HEC Lausanne, FIRS, the London Business School Summer Finance Symposium, the Swiss Society for Financial Market Research Conference, the Midwest Finance Association Conference, and the Minnesota Corporate Finance Conference. Greg Tracey provided superb research assistance. Calculated (or derived) based on credit data provided by TransUnion, a global information solutions company, through a relationship with the Kilts Center for Marketing at the University of Chicago Booth School of Business. TransUnion (the data provider) has the right to review the research before dissemination to ensure that it accurately describes TransUnion data, does not disclose confidential information, and does not contain material it deems to be misleading or false regarding TransUnion, TransUnion’s partners, affiliates or customer base, or the consumer lending industry. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.