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Total revolving credit and average credit card limits (a) Total outstanding revolving credit (b) (Cross-sectional) average credit card limit
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... particular, we focus on outstanding revolving credit (mainly credit card debt). The left panel of Figure 1 plots the time series for this variable as a percent of total U.S. disposable income. Three major patterns emerge from the time series. ...
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... normalize the (cross-sectional) average credit card limit in our sample by the disposable income per capita. The right panel of Figure 1 plots the time series for this variable. It increased from 35 percent to more than 55 percent of disposable income per capita between 1989 and 2007. ...
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... is, deep sub-prime borrowers are more likely to have failed other underwriting tests. For super-prime borrowers in 2016, more than 15 percent of rejected applications were due to Ability to Pay (Figure 14, page 129, 2017 report). The same statistic for deep sub-prime borrowers was less than 3 percent. ...
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... the left panel of Figure 10, we plot the average transfers by income quintile resulting from ATP in the benchmark model (red bars). The policy leads to ubiquitous welfare losses. ...
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... is consistent with implications discussed above where middlehigh income consumers were the most affected by ATP in terms of credit. The right panel of Figure 10 plots average transfers by age resulting from ATP (red line). It shows that young-middle age consumers are hurt the most. ...
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... the losses from ATP are equivalent to 53 percent of losses of entirely shutting down the credit card market. Notes: Figure 10 plots welfare implications of introducing Ability to Pay by income quintile (left) and age (right). Red bars/lines refer to the benchmark model. ...
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... this feature, the model misses the fact that Ability to Pay impacts the current card holder when poached by a new lender. We quantify the importance of these two features by re-calibrating the model without them to match the set of moments in Table 2. Without these two features the losses due to the policy change are almost zero (blue bars in the left panel and blue lines in the right panel of Figure 10). ...
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... quantified the welfare losses resulting from ATP, we decompose the sources in Figure 11. In particular, we focus on the level and volatility of consumption and the probability of default. ...
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... first depict the transition dynamics in the ratio of average consumption in the model with ATP to the same variable in the model without ATP. The left panel of Figure 11 plots this statistic for cohorts aged 20 and 30 when ATP comes into effect. The middle panel plots the analogous ratio for the coefficient of variation of consumption. ...
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... re-calibrate the benchmark model to match the set of target moments in Table 2 for every β. Figure 12 plots the total welfare implications. Even though the magnitudes are smaller, Ability to Pay still leads to losses even with a hyperbolic discount factor as low as β = 0.7. ...
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... focus on the effect of the cap on credit card interest rates, credit card limits, and probabilities of credit offers. Figure 13 illustrates the average values of these variables in our model for different cap levels. A rate cap of 10 implies that the maximum spread over the risk-free savings interest rate is 10 percentage points. ...
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... we go from right to left, the cap becomes tighter. As the cap becomes tighter, the left panel of Figure 13 shows that the spread decreases. This is the main benefit of the cap. ...
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... is the main benefit of the cap. The middle panel of Figure 13 shows that the population with credit cards decreases with a tighter cap. Less profits for the credit card firms lead to fewer credit offers. ...
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... credit access is one of the costs of the cap. The right panel of Figure 13 plots the average limit as a function of the cap. The resulting limit is not monotone. ...
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... depending on the tightness of the cap, the credit limits may increase or decrease. Figure 14 plots the total welfare gains (losses) for different levels of a rate cap in the benchmark model. The welfare implications are not monotone. ...
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... welfare implications are not monotone. As the cap becomes Figure 14: Total welfare gains (losses) of an interest rate cap in benchmark model tighter (right to left), consumers benefit. However, when the cap becomes too tight, consumers are hurt. ...
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... the gains from the optimal rate cap are worth 25 percent of losses consumers would face if we were to shutdown the credit card market. Figure 15 plots the welfare implications by income and age at the optimal rate cap. With respect to income, although low income consumers pay higher interest rates, the high income consumers benefit the most. ...
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... respect to age, young consumers benefit the most. Figure 16 plots the optimal rate cap (left panel) and total welfare gains (right panel) in the re-calibrated models with quasi-hyperbolic discounting. The optimal rate cap hardly changes for β between 0.8 and 1. ...
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... left panel of Figure 17 plots average credit by income quintile for the pre-policy model. In this case, the model does not even qualitatively account for credit by income. ...
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... right panel of Figure 17 plots the change in average credit by income quintile after imposing commitment for interest rates. In this case, the model leads to lower credit. ...
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... their model, the consumer picks the term of the contract at issuance and the lender picks the terms of the contract in subsequent periods. left panel of Figure 18 plots the percent of commitment and no commitment credit card contracts by income quintile. For consumers with higher income levels, lenders prefer to offer credit cards with commitment to the interest rate. ...
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... right panel of Figure 18 illustrates the key mechanism. We plot the interest rate chosen by the incumbent lender for any given value of current debt for two values of persistent earnings. ...
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... right panel of Figure 18 also shows that the lender charges heavily indebted low earnings consumers a lower interest rate. This is because a consumer with low earnings is more likely to default. ...
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... our benchmark model, we analyze a transition with a permanent drop in average earnings of 7.5 percent. Figure 19 plots the transition path for credit to income and average credit to disposable income per capita. We see that both credit and credit card limits (normalized by income) increase in the short run. ...
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Citations
... 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.
