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... Appendix A.2, we show that without the permanent component of earnings, the model fails to account for credit among high income consumers. 17 Figure 6 shows that the benchmark model also accounts for 2004 levels of credit statistics by age. The model captures the hump-shaped profile of credit, increasing credit limits, flat credit card interest rates, and increasing population with credit cards over the life cycle. ...
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... The gains to consumers amount to 73 percent of the value of credit access. * The rate cap analysis in this paper stems from a rate cap experiment performed in a previous version of Raveendranathan and Stefanidis (2022). We thank anonymous referees for suggesting that we drop the experiment in that paper and analyze rate caps more rigorously in a separate paper. ...
... We perform our analysis in a model of revolving credit lines from Raveendranathan and Stefanidis (2022), which builds on prior work by Drozd and Nosal (2008), Mateos-Planas and Ríos-Rull (2013), and Raveendranathan (2020). In our model, lenders matched with consumers have market power. ...
... Our model economy uses the framework of Raveendranathan and Stefanidis (2022), which builds on prior work by Drozd and Nosal (2008), Mateos-Planas and Ríos-Rull (2013), and Raveendranathan (2020). ...
We show how a rate cap can be designed to improve both consumer and lender welfare in the credit card market. We analyze transition paths resulting from different rate caps in a model with revolving credit lines, search frictions, and lender market power. Our analysis shows that if a rate cap only applies to new credit card issuance and not existing accounts, it can improve lender welfare. Incumbent lenders benefit because they can retain their customers for a longer time. New issuers are not affected as long as the posting of credit offers is competitive (zero expected profits in equilibrium). Consumers benefit because of lower interest rates. The rate cap that maximizes consumer welfare leads to gains to consumers and lenders that are equivalent to a onetime transfer worth 0.44 percent of disposable income. The gains to consumers amount to 73 percent of the value of credit access. * The rate cap analysis in this paper stems from a rate cap experiment performed in a previous version of Raveendranathan and Stefanidis (2022). We thank anonymous referees for suggesting that we drop the experiment in that paper and analyze rate caps more rigorously in a separate paper. We also thank SSHRC for Insight Development Grant 430-2021-00075. † McMaster University.
Do cognitive biases call for regulation to limit the use of credit? We incorporate over-optimistic and rational borrowers into an incomplete markets model with consumer bankruptcy. Over-optimists face worse income risk but incorrectly believe they are rational. Thus, both types behave identically. Lenders price loans forming beliefs-type scores-about borrower types. This gives rise to a tractable theory of type scoring. As lenders cannot screen types, borrowers are partially pooled. Over-optimists face cross-subsidized interest rates but make financial mistakes: borrowing too much and defaulting too little. In equilibrium, the welfare losses from mistakes are more than compensated by cross-subsidization. We calibrate the model to the US and quantitatively evaluate policies to address these frictions: financial literacy education, reducing default cost, increasing borrowing costs, and debt limits. While some policies lower debt and filings, only reducing default costs and financial literacy education improve welfare. However, financial literacy education benefits only rationals at the expense of over-optimists. Score-dependent borrowing limits can reduce financial mistakes but lower welfare.
I propose a model of revolving credit lines and targeted search to analyze what accounts for the profitability of the U.S. credit card industry. My analyses lead to two main findings. First, the search friction has minimal impact on the level of profitability of the credit card industry. Most of the profitability is a result of the lender choosing the terms of contract. Second, improved information about consumers accounts for the fall in profitability since the 1980s. Consistent with the data, lenders respond to more information by increasing credit card limits and lowering markups.
I propose a model of revolving credit lines and targeted search where credit card firms issue long-term contracts specified by a credit limit and interest rate to heterogeneous consumers. The model accounts for income and life cycle profiles of various credit card market variables. Using the model to adjust for default risk, I show that consumers with lower earnings pay higher spreads because they face tighter credit limits (imposes a quantity restriction) and receive credit offers with lower probabilities. I also quantify the role of improvements in information and improved matching on spreads across consumers between 1983 and 2004.
