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Update Newsletter: Summer 2008

Recent Developments in Consumer Credit & Payments

Since its inception in 2000, the Payment Cards Center has enjoyed a strong working relationship with the Bank's Research Department. In September 2007 the two groups co-hosted their fourth bi-annual conference on developments in consumer credit and payments. This event brought together leading researchers from academia and from within the Federal Reserve System to discuss six selected papers. This issue of Update provides only a brief synopsis of the papers, but the full documents can be found at: http://www.philadelphiafed.org/payment-cards-center/events/.

Liquidity Constraints and Imperfect Information in Subprime Lending

Jonathan Levin, of Stanford University, presented his paper on a study (with William Adams and Liran Einav) showing the economic significance of liquidity constraints, using the example of the market for subprime auto loans. In the study, Levin and his co-authors examined a sample of loan applications at a large subprime auto lender between June 2001 and December 2004. They also examined the details of the loan contracts for the applications that were accepted and the repayment history on all loans through April 2006.

Levin argued that a car-buying customer who is not liquidity constrained would care only about the present value of total loan payments. In other words, a dollar spent today to cover the down payment should have the same effect on the borrower's purchasing decision as an appropriately discounted dollar spent tomorrow to repay the loan. On the contrary, Levin and co-authors found that a $100 increase in the minimum down payment had the same effect on the probability of purchase as a $900 increase in the car price, evidence that purchase decisions were strongly affected by the customer's ability to come up with the initial cash.

The authors also attempted to uncover the underlying sources of liquidity constraints, especially those associated with the effects of adverse selection and moral hazard. The authors defined adverse selection as the tendency for borrowers who have a higher risk of default to take out larger loans, and they defined moral hazard as the tendency for borrowers with larger loans to default more often. Their results provided evidence of both effects, with moral hazard having the stronger impact.

Information Technology and the Rise of Household Bankruptcy

The second paper was presented by Borghan Narajabad from Rice University, who discussed the results of his work on the underlying causes of the increase in consumer bankruptcies in the mid-1990s. Narajabad argued that previous research had failed to adequately explain why the rise in bankruptcies coincided with other developments in consumer credit markets, including a significant rise in credit card debt and usage as well as an increase in the variation of credit terms offered to customers. For example, the often cited explanation of a decline in "stigma" as the cause of the increase in bankruptcy filings should also result in lenders' offering less credit to consumers, when, in fact, lenders offered more.

Narajabad developed a theoretical model in which he simultaneously accounted for the increased willingness of lenders to offer unsecured credit and the willingness of consumers to borrow more. He explained that the increased credit supply was a result of an improvement in lenders' screening technology that permitted them to better distinguish between high-risk and low-risk borrowers. If lenders are more certain that a consumer is low risk, they are more likely to offer him or her a higher credit limit. At the same time, however, a rise in the consumer's debt to income ratio increases the consumer's vulnerability to shocks that might eventually lead to default. Thus, better screening is consistent with a rise in borrowing and default rates among borrowers initially regarded as low risk.

On the other hand, by itself, an improvement in screening technology would cause lenders to reduce the credit available to high-risk borrowers, a pattern inconsistent with the observed data for the 1990s. Narajabad argues that this is due to an offsetting effect: the introduction of risk-based pricing by lenders. In other words, if lenders are able to charge high-risk consumers for the additional credit risk, and those consumers are willing to pay those rates to obtain credit, it is possible for the amount of credit offered to these consumers to increase rather than decrease. Again, the resulting higher debt to income ratio would also imply an increase in the default rate. Relatively speaking, the increase in borrowing among high-risk borrowers would not be as great as for low-risk borrowers because the screening effect works in the opposite direction for these two groups. At the same time, the application of risk-based pricing implies an increased difference in interest rates paid by low- and high-risk consumers. Thus, an important additional implication of Narajabad's model is that we should observe increasing differences in the quantity and price of credit offered to high- and lowrisk consumers.

Narajabad uses data on consumers' use of credit cards, pricing, borrowing, debt to income, and default behavior from the Survey of Consumer Finances in 1992 and 1998 to demonstrate that each of these patterns exists in the data. He also performs a numerical analysis, in which he alters the quality of lenders' screening technology and their pricing strategy to show that such changes can account for at least a third of the rise in defaults during this period.

Conference presenters (left to right): Borghan Narajabad, Rice University; Barry Scholnick, University of Alberta; Jonathan Levin, Stanford University; James Vickery, Federal Reserve Bank of New York; Ronel Elul, Federal Reserve Bank of Philadelphia; and John C. Driscoll, Board of Governors of the Federal Reserve System.Conference presenters (left to right): Borghan Narajabad, Rice University; Barry Scholnick, University of Alberta; Jonathan Levin, Stanford University; James Vickery, Federal Reserve Bank of New York; Ronel Elul, Federal Reserve Bank of Philadelphia; and John C. Driscoll, Board of Governors of the Federal Reserve System.

Who Makes Credit Card Mistakes?

Barry Scholnick, of the University of Alberta, discussed the results of his study (with Nadia Massoud and Anthony Saunders) of financial mistakes made by credit card holders. They examined the prevalence of certain types of mistakes, as well as the types of customers who made these mistakes. The main question motivating their study was whether mistakes were made predominantly by wealthy customers, who might make mistakes because the impact on their total wealth is trivial, or by poor and less educated customers, who might make mistakes because of a lack of financial sophistication. To answer this question, the authors developed a unique data set of Canadian consumers based on proprietary bank data combined with highly disaggregated data on demographic composition and residential real estate for the neighborhoods where these consumers live.

The authors defined four types of "mistakes" that resulted in the credit card holders' paying penalty fees despite the fact that they had adequate bank balances to cover the transactions. Interestingly, they found that a significant fraction of the transactions could have been avoided by using available bank balances and thus were deemed mistakes.

Turning to the question of who makes such mistakes, the authors found that less wealthy cardholders were more likely to make mistakes. Scholnick argued that these results were not consistent with the view that mistakes were mainly committed by wealthier customers, rationally allocating their attention to more significant financial decisions.

The Age of Reason: Financial Decisions Over the Life Cycle

The fourth paper, presented by John Driscoll, of the Federal Reserve Board, was based on research (with Sumit Agarwal, Xavier Gabaix, and David Laibson) that looked at the pattern of financial decision- making over an individual's lifetime. Driscoll and his co-authors used proprietary data sets from a national financial institution to consider financial decisions in 10 separate contexts, including a number of decisions involving home equity loans, auto loans, and credit cards. In addition to information on the financial transactions themselves, the data sets include substantial demographic information on the decisionmaking customers.

Based on the empirical findings, the authors found that the sophistication of financial decisions varies by age, with middleaged adults borrowing at lower interest rates and paying fewer fees compared with both younger and older adults. These results are consistent with the authors' hypothesis that financial sophistication follows a U-shaped pattern whereby the quality of financial decisions rises and then falls with age. According to Driscoll and his co-authors, a model in which an individual's analytic capabilities decline roughly linearly from age 20 onward, while experience with financial matters increases throughout the individual's life but at a decreasing rate over time can explain this age-of-reason effect. Specifically, improvements in financial performance occur until (roughly) age 53, after which the decline in cognitive ability dominates.

Bankruptcy: Is It Enough to Forgive or Must We Also Forget?

Many countries have laws that prevent credit bureaus from disseminating old information; for example, in the United States the Fair Credit Reporting Act of 1970 (FCRA) generally prohibits bureaus from reporting bankruptcies after 10 years. Yet using such information would be profitable for lenders. Ronel Elul, of the Federal Reserve Bank of Philadelphia, discussed his research (with Piero Gottardi, of the University of Venice) that examined why such laws might nevertheless be desirable from a social point of view.

The basic tradeoff in their model is as follows. When a borrower anticipates that his default might be "forgotten," this makes his incentives worse because it reduces the punishment for failing to repay. On the other hand, once a borrower has actually defaulted, his incentives will generally improve if the default were erased. The reason is that with a default on his record, he will already have been identified as a risky borrower by lenders, and so whether or not he repays in the future will not have much effect on his reputation. The authors derive conditions under which the second effect is stronger than the first and, thus, that society would benefit from restrictions on the use of old information. They then use their model to examine the policy debate surrounding the adoption of these laws and to understand the effects of cross-country differences in these rules.

Interest Rates and Consumer Choice in the Residential Mortgage Market

The last presenter at the conference, James Vickery, of the Federal Reserve Bank of New York, outlined the results of his research concerning the elasticity of substitution between fixed-rate mortgages (FRMs) and adjustable-rate mortgages (ARMs). Roughly speaking, the elasticity of substitution measured here is the difference in the market share of conforming and nonconforming FRMs divided by the difference in the rates between FRMs and ARMs just above and below the conforming loan limit. The Office of Federal Housing Enterprise Oversight (OFHEO) sets the conforming loan limit — the maximum loan amount — that Fannie Mae and Freddie Mac can purchase.

The regulatory cutoff for conforming mortgages — the maximum size for loans that can be purchased and insured by the government-sponsored enterprises (GSEs) — creates a discontinuity at the conforming loan limit. Vickery argued that the supply of fixed-rate mortgages falls discontinuously at the conforming loan limit because loans cannot be as easily securitized without a guarantee from the GSEs. The greater difficulty of securitizing loans affects the supply of FRMs more than the supply of ARMs because FRMs subject the lender to interest-rate risk if they are kept on the lender's balance sheet. As long as the relative demand for FRMs and ARMs is affected by their rates, but not by the conforming loan limit per se, the discontinuity permitted Vickery to identify the demand curve for FRMs.

Vickery determined how consumers respond to the price difference between FRMs and ARMs by looking at the demand curve for FRMs against the supply curves for conforming loans and nonconforming loans. Specifically, he found that a 20-basis-point increase in retail FRM interest rates (relative to ARMs) increases the probability that a household will choose an ARM by 17 percentage points. This finding represents an elasticity of substitution close to one, indicating that the demand for FRMs is sensitive to their price in comparison to the price of ARMs.