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Credit Cards: Facts and Theories Article  in  SSRN Electronic Journal · October 2006 DOI: 10.2139/ssrn.931179 · Source: RePEc

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Michael Haliassos

Board of Governors of the Federal Reserve System

Goethe-Universität Frankfurt am Main

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Credit Cards: Facts and Theories1 Carol C. Bertaut and Michael Haliassos# April 2005 Abstract We use data from several waves of the Survey of Consumer Finances to document credit and debit card ownership and use across US demographic groups. We then present recent theoretical and empirical contributions to the study of credit and debit card behavior. Utilization rates of credit lines and portfolios of card holders present several puzzles. Credit line increases initiated by banks lead households to restore previous utilization rates. High-interest credit card debt co-exists with substantial holdings of low-interest liquid assets and with accumulation of retirement assets. Although available evidence disputes ignorance of credit card terms by card holders, credit card rates do not respond to competition. There is a rising trend in bankruptcy and delinquency, partly attributable to an increased tendency of households to declare bankruptcy associated with reduced social stigma, ease of procedures, and financial incentives. Co-existence of credit card debt with retirement assets can be explained through self-control hyperbolic discounting. Strategic default motives contribute partly to observed co-existence of credit card debt with low-interest liquid assets. A framework of “accountant-shopper” households, in which a rational accountant tries to control an impulsive shopper, seems consistent with both types of co-existence and with observed utilization of credit lines. Key words: Credit cards, debit cards, revolving debt, consumer credit, portfolios

1 Paper prepared for the volume on The Economics of Consumer Credit, edited by Giuseppe Bertola, Richard Disney, and Charles Grant, MIT Press. The paper was written while Haliassos was visiting the Finance and Consumption Chair at the European University Institute, Florence, Italy. The authors thank Giuseppe Bertola, Richard Disney, Nick Souleles, and participants in the conference on the Microfoundations of Credit Contracts for helpful suggestions. Thanks are due to the Chair for funding and for stimulating research interactions. Haliassos also thanks the European Community's Human Potential Program for partial research support under contract HPRN-CT-2002-00235, [AGE]. The views expressed are those of the authors and do not necessarily represent the Board of Governors of the Federal Reserve. # Bertaut: International Finance Division, Board of Governors of the Federal Reserve System, Washington, DC, USA. Email: [email protected] . Haliassos: Goethe University Frankfurt, Germany. Email: [email protected]

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1. Introduction Access to consumer credit in the form of a credit card has grown rapidly to become one of the most frequently held financial instruments by households in the United States. Credit cards offer the convenience of cashless transactions and also allow for purchases over the telephone and, increasingly, via the internet. Credit cards also offer consumers the flexibility of deferring payment to a future date, and thus can allow consumers to smooth spending over temporary liquidity shortfalls. However, invoking a credit card’s revolving credit option typically results in paying high rates of interest not only on the existing balance but also on any new charges made on the card as well, and thus is a fairly costly form of credit, especially if the revolving credit feature is used frequently. This Paper documents features of credit card and debit card ownership and use, over time and across demographic groups in the U.S. population, using data from several waves of a high-quality and detailed survey of finances of U.S. households: the Federal Reserve Board’s Survey of Consumer Finances (SCF). We consider household responses from the SCF to questions about access to and attitudes towards credit and debit cards and explore portfolios of households with and without credit card balances. Our analysis of the data, presented in Sections 2-9, illustrates several puzzling features of credit card usage by US households. In Sections 10 and 11 we discuss recent theories of consumer behavior that may explain some of those puzzles. These include the choice to borrow at high rates of interest; the interplay between spending control problems, credit card borrowing, and personal bankruptcy filing; and the coexistence of credit card debt with considerable levels of liquid and retirement assets. We also explore the growing popularity of debit cards as either a supplement to or an alternative to credit card use. We offer concluding remarks in Section 12.

2. Card ownership over time Our primary source of information on the spread of credit and debit card use among U.S. households is from several waves of the Survey of Consumer Finances. The SCF has been conducted triennially since 1983, and recent waves have each

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consisted of about 3,000 households drawn from a standard representative sample, supplemented with about 1,500 high-wealth households selected on the basis of tax records. Sample weights are provided to make the data representative of the U.S. population as a whole. Each wave of the SCF provides detailed information on household-level holdings of a variety of financial assets as well as sources, terms, and uses of a wide range of consumer credit options, including credit cards. Data are also collected on household characteristics including age, education, family structure, race, and income. Finally, the SCF also asks a number of questions on attitudes towards consumer borrowing, reasons for saving, and investment decisions.1 In 1983, 65 percent of U.S. households had a credit card of some kind, including store-specific cards and gas cards (Table 1, column 1). Only 43 percent of households had a bank-type credit card such as a Visa or Mastercard (column 2); that is, a card that is accepted at a broad range of retail establishments, and after making a minimum required payment allows the consumer to revolve the balance if so desired. By 1992, 62 percent of the U.S. population had a bank-type credit card, and by 2001 that percentage had risen to almost 73. Over the same period, the percentage of households with any type of credit card increased much less, and in 2001 that percentage was 76 percent, only slightly higher than the percentage with a bank-type card. There has also been an increase in the number of bank-type credit cards owned per household: in 1983, households with a bank-type card typically held only one such type card. By 2001, one-third of card-holding households still had only one bank-type card, one-third had two, and about one-fourth had three or four. A little more than 7 percent had five or more. Opening of credit card accounts, either for the first time or as accounts in addition to pre-existing ones, is much more common than other changes in household portfolios (e.g., those associated with stockholding). Another source of data, the January 2001 Consumer Survey on Credit Cards, shows that about 20 percent of bank-type credit card holders had obtained one or more new accounts during the previous year, and most of these were additional or replacement accounts. According to the survey, 41 percent of holders held three or more bank-type credit card accounts (Durkin, 2002). In the remainder of our discussion below, we focus our attention on bank-type credit cards.

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3. Trends in card ownership by income, education, and age Bank-type credit card ownership in the United States is strongly correlated with household income and with education, and this correlation has persisted over all waves of the SCF. However, the increase in bank-card ownership over the last two decades was especially pronounced at lower income and education levels, reflecting in part improvements in industry credit scoring techniques and risk analysis: in 1983, only 21 percent of households with less than a high school education and less than 23 percent of households with incomes between $10,000 and $25,000 owned a bank-type credit card.2 By 2001, these percentages had doubled, to 42 percent and 54 percent, respectively. Table 1 reports card ownership, both for credit cards generally and for bank-type credit cards, for various demographic groups over time. Such tabulation is useful for describing ownership patterns across demographic groups, but not for identifying how each characteristic contributes to such ownership, controlling for other characteristics. To help distinguish the relative importance of age, education, and income as well as other factors that contribute to the likelihood of credit card ownership, table 2 presents results of probit regressions of the probability of card ownership using the pooled sample of the 1983, 1992, 1995, 1998, and 2001 waves of the Surveys of Consumer Finances. Columns 1-3 list results from a model where the dependent variable is the 0-1 dummy variable capturing ownership of any type of credit card (including store and gas cards). Columns 4-6 list results for ownership of at least one bank-type credit card. Higher levels of both education and income contribute significantly and importantly to the probability of ownership of either type of credit card, even controlling for other household characteristics. The difference between the coefficients on having a high school degree but no further education and having a college degree or higher3 implies an effect about as large as the difference between an income between $10,000 and $24,999 (in 2001 $) and an income of at least $50,000; both these effects are about twice those of the difference in age from less than 35 to aged 50-65. As would be expected, a higher level of financial wealth also contributes positively to card ownership, although the relative contribution of this variable is less notable than that of increased income or education.

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As these are reduced-form regressions, findings are the joint product of demand and supply considerations. On the demand side, education is likely to contribute to credit card ownership by increasing awareness of credit card instruments. Financial resources (both income and wealth) contribute in turn as scale variables determining the size of transactions, even though larger resources imply smaller needs for the borrowing feature of credit cards. Supply-side effects arise from the policy of credit card issuers to condition acceptance of applications on financial resources and to target specifically the more educated segments of the population. Supply-side effects are likely to contribute to the findings on the race variable. Non-white or Hispanic households are found to be significantly less likely to own a credit card, even after controlling for education, income, and financial wealth, and even after including the measure of whether the household reports being liquidity constrained.4 More limited targeting of credit cards to minorities by credit card issuers may be the main factor behind this result. On the demand side, if future prospects for minorities are worse than what is implied by included controls, then this would tend to discourage both current spending and assumption of debt that would be difficult to repay later on. In both regressions, age is a significant factor in predicting card ownership. Even after controlling for income and wealth, households with a head aged 35-49 are less likely to own either type of credit card than are those with a head aged 50-65 (the omitted dummy variable), and households aged under 35 are even less likely to be card owners. More limited participation in young ages is likely to arise from supplyside constraints rather than from demand considerations, as young households are more likely to want to have access to credit lines than their middle-aged counterparts. Households with a head 75 years or older are also significantly less likely to be card owners; indeed, the coefficient for age 75 or more is more than twice that of the coefficient for households aged under 35. More limited transaction needs and less familiarity with credit cards are likely to combine with less generous offers of credit cards to the elderly to produce this result. The regressions also include dummy variables for each of the survey years (with 2001 as the omitted dummy variable). The relative sizes of the coefficients on these dummy variables in the bank-type card regression indicate significant year effects consistent with the spread of bank-type card ownership over the nearly 20-year period from 1983 to 2001 that are not explained by changes in configuration of 5

already included household characteristics. The coefficients on the year dummies in the regression of the broader class of credit cards are smaller and generally are less significant, consistent with the less dramatic spread in ownership of any type of credit card. By applying the estimated coefficients from the probit models to characteristics of various “typical” households, we can explore how the probability of card ownership has changed over time for these representative households. Such calculations suggest that in particular young households and those with less education benefited from increased availability of bank-type credit cards. For example, a single, non-white female aged less than 35 with high school education and “typical” income and financial assets for that age and education bracket has only a .32 estimated probability of owning a bank-type credit card in 1983. By 1992, that estimated probability rises to .67, and by 2001 the estimated probability is .74.5 A typical young college-educated white male has a notably higher estimated probability of bank-type credit card ownership in 1983 (.60) and has a slightly smaller increase in the probability of card ownership by 2001 (to .91). For a middle-aged household, the rise in estimated probability of bank-type card ownership over time is less dramatic. For a 50-64 year old, married, college-educated household, the estimated probability of owning a bank-type card in 1983 is already .88; by 2001 the probability rises to .99.6 Similar calculations for a typical elderly household (age 75 or more) at various degrees of education also reveal a significant increase in the estimated probability of bank-type card ownership by 2001. However, especially for these older households, both year effects and cohort effects are present. For example, the typical married household aged 75 or more with some college education has an estimated probability of bank-type card ownership of .93 in 2001, an increase from .64 for elderly households in 1983. But the over-75 household in 2001 would likely have been aged 50-64 in 1983, and the estimated probability of bank-type card ownership for the household at that time would have been .73. Thus, the higher estimated ownership of elderly households by 2001 may largely reflect the continued ownership of households who had acquired cards when younger.

4. Trends in debit card use In 2001, 38 percent of households without a credit card responded that buying

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things on an instalment plan was a “bad idea” compared with 27 percent of cardowner households. Although credit cards may lead to spending control problems, debit cards— that is, cards that are linked to a specific account and when used, result in funds being withdrawn immediately—can provide the same benefits of cashless transactions with a form of self-control, as will be discussed below. Credit card ownership has grown rapidly between 1983 and 2001, but debit card use has grown even more rapidly and over a shorter time period. As of the 1992 SCF, less than 10 percent of U.S. households owned a debit card (Table 3, columns 2 and 6). By 1995, one-third of households reported using a debit card, and by 2001 close to half reported debit card use.7 As debit cards have become more widespread, households that use debit cards but not credit cards appear increasingly willing to describe borrowing on credit as a “bad idea”: in 1995, about 30 percent of households gave that response, and this fraction was about the same across credit card owners, debit card users, and non-card owners. By 2001, 40 percent of non-holders of credit cards who were debit card users gave the “bad idea” response, compared with 27 percent of credit card holders. Table 4 presents results from a probit regression of the probability of debit card use from the pooled sample of the 1992, 1995, 1998, and 2001 Surveys of Consumer Finances. 8 In contrast to the results on credit card ownership, younger households are much more likely to use debit cards than are older households, as the coefficient on households under age 35 is positive and significantly larger than that for age 35-49, which in turn is also positive and significantly different from zero. This result is likely to reflect the known tendency of banks to issue debit cards to younger households who have not yet acquired the financial resources or established the credit history needed for issuance of a credit card. Higher education is associated with an increased likelihood of debit card use, although households with a college degree are no more likely to use a debit card than those with only some college. Households with higher incomes are also significantly more likely to use debit cards, except for those with incomes over $100,000; these households are actually slightly less likely to use debit cards than are households with incomes between $50,000 and $99,999.

Greater financial asset holdings are

associated with a small but significant effect on debit card use.

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Since education and financial resources tend to encourage provision of credit cards by issuers, these findings do not arise from lack of access to credit cards. Rather, they are likely to reflect a deliberate choice of more educated and well-to-do households to benefit from the ease of using debit cards for payments, as compared to using checks that are less widely acceptable. It is noteworthy that such tendency of using debit cards is observed, despite the fact that use of credit cards for payments but not for borrowing usually contributes extra benefits, such as points or floating opportunities. We return to such issues below. Among other demographics, particularly interesting is the finding that although nonwhite/Hispanic households are significantly less likely than white households to have a credit card, they are no less likely to use a debit card. As with bank-type card ownership, the year dummies are significant, with relative sizes and signs consistent with the spread in debit card use. Performing the same calculations for various “typical” households as we did for credit cards illustrates the adoption of debit cards over the 1990s particularly by younger households, but also suggests that debit card use has not been universally or exclusively adopted by households who also are very likely to have access to a banktype credit card. For the young, nonwhite, high-school educated female, the estimated probability of having a debit card in 1992 is .22, less than the likelihood of having a bank-type card in 1992. By 2001, the estimated probability of using a debit card is .65, a sizable increase but still somewhat below that of having a bank-type card. For the single white college-educated male, the estimated probability of using a debit card is .21 in 1992 and increases to .64 in 2001, remaining well below the probability of bank-type card ownership.

For the 50-64 year old college-educated married

household, the probability of using a debit card rises from .12 in 1992 and reaches only .50 in 2001.

5. Credit card use over time and across demographic groups While the fraction of households with a bank-type card has increased, the SCF data indicate that the fraction of card holders who at any time revolve a credit card balance has changed relatively little over the past 20 years. In 1983, just over half of all bank-type credit card holders carried a balance on a card, after making the most

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recent payment, and before incurring new charges (Table 1, column 5). By 1995, the percentage rose to 56, but it declined slightly in the next two surveys, to 53 percent by 2001. In all of the SCF waves, younger households are much more likely to carry a balance than are older households. In contrast to the inverse relation between level of education and card ownership, the relation between education and carrying a credit card balance, conditional on card ownership (except for college-educated households ), is less pronounced,. Between 43 and 49 percent of card-owner households with a college degree revolve a credit card balance in each of the Survey years, generally about 15 percentage points less than households with either a high school degree or some college education. The distribution of credit card revolvers by income shows a changing pattern over the SCF waves. In earlier waves, card-holder households who fell in the lowest income ranges were less likely to carry a credit card balance than were households in the next two income ranges. In 1998 and 2001, this relationship was reversed, and a larger fraction of low-income card-holders revolved credit than did middle-income card holders. These simple statistics do not allow us to identify the reasons for the increase in low-income credit revolvers, but one likely explanation is that low-income households who nonetheless qualified for credit cards in the earlier waves were older and consequently may have had less need to borrow. Nearly half of card-holder households with incomes under $10,000 in 1983 were over 65, and less than 20 percent were under 35. By 2001, this age pattern had reversed, as households over 65 accounted for less than 30 percent of low-income card-holders, while more than a third were under 35. In all the SCF waves, a much smaller percentage of card-owner households with incomes over $100,000 than with lower incomes carried a credit card balance. These higher-income households may have had less need or incentive to revolve credit card debt, or may have had better access to other sources of borrowing, particularly through tax-advantaged home equity lines. Indeed, over 90 percent of high-income families in 2001 had home equity against which they could borrow, with the median amount equal to about $130,000.9 Nonetheless, a significant portion of relatively high-income households revolve credit: more than one-third of households in that income range were credit revolvers in all survey years.

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6. Repeated versus occasional credit revolvers Because the SCF is a cross section sample for each survey year and not a panel, we cannot observe whether a card balance for a given household was a temporary event or whether that household had carried a balance in the previous months. However, we use self-reported information to help distinguish habitual revolvers from those whose card balance is temporary or accidental. In each of the survey waves, households with credit cards were asked whether they “always or almost always” paid off the card balance in full each month, they “sometimes” paid it off in full, or whether they “hardly ever” paid it off. The surveys also collect information on the new charges made on the bank-type card after payment of the last bill. We use these new charges data to get an idea of which households who do not carry a balance on their credit cards appear to actively use their cards.10 Table 1 shows the percentages in each survey year of households who had a bank-type credit card (column 2), those who had a card but had no balance on the card and incurred no new charges in the current month (column 3), those who had no balance but did incur new charges (column 4), and those who had a balance and hardly ever paid off the balance (column 6; the complementary percentage had a balance but claimed they usually or sometimes paid off the balance each month). Bearing in mind the difference in how these variables are constructed in the 1983 and later SCF waves, it nonetheless appears that the fraction of card holders who had a card but did not actively use it has declined over time, from about 18 percent of cardholders in 1983 to 10 percent in 1992 and between 7 and 8 percent subsequently. In all survey waves, the largest percentages of card-holder households who do not use their cards are those who are over 65, have no more than a high school education, and generally are those with incomes under $25,000. It is possible that these households are passive cardholders who have been issued a card without actively seeking one. Alternatively, they may be concerned about their ability to control their spending, and prefer to consider the card for emergency use only. Additional information available only from the 1998 and 2001 Surveys indicates that households in this category were about twice as likely to have ever declared bankruptcy as card-holders who did not carry a balance but did record active card use, suggesting some role for concerns about over-spending and the social stigma of delinquency and bankruptcy. 10

A little less than 40 percent of card-holder households from the 1992-2001 waves had no outstanding balance on their credit card but did record new charges during the month (column 4). For the 1983 SCF, a comparable figure is 30 percent of cardholders who had no balance, but claimed they used their card “often” or “sometimes.” These households appear to use their credit cards for ease of transactions and perhaps to benefit from the float offered by deferring payment until the credit card bill is due. According to the 2001 survey, 96 percent of these households report that they “always or almost always” pay off their balance in full each month. In all surveys, the percentages of card-holder households that fall into this category are largest for older households and those with a college degree and at least $100,000 in income: households that presumably have less need to borrow especially at high rates of interest, which are likely to face less income variability, and are more likely to have a sufficient buffer-stock of assets to tide them over income fluctuations. In 1998 and 2001, these households were also the least likely to have declared bankruptcy in the previous 10 years. About a quarter of all card holders in 2001—and almost half of those who had a balance outstanding on their card—admitted to “hardly ever” paying off the balance each month (column 6). These fractions are relatively unchanged from earlier waves of the SCF. For the most part, this percentage is not much affected by age, education, or income, with the exception that households with incomes over $100,000 are less likely to fall into this category. The fact that this behavior cuts across many demographic and income groups suggests that frequent card revolvers may be motivated by factors other than simply a “need to borrow.” One category of households that does seem to have increased slightly over time is cardholders who claim they “always or almost always” pay off the balance in full but nonetheless had a balance outstanding at the time of the survey: that percentage has drifted upwards from less than 10 percent of cardholders in 1992 (18 percent of those with a balance) to 12 percent in 2001 (22 percent of those with a balance). These households may be accidental revolvers who typically do pay off balances but for whatever reason carried a balance in the month preceding the survey. Table 5 explores the relation between the percentage of U.S. households who have been denied credit by credit card ownership and card payment status. This “liquidity constrained” information is taken from a series of questions asked in the

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SCF on whether the household, in the previous five years, had been turned down for credit or had not received as much credit as requested (and had not received the full credit amount on reapplying), or had not applied for credit because they thought they would be turned down. Roughly one-third of households without a bank-type credit card can be classified as “liquidity constrained” according to this definition. Interestingly, as the fraction of households with at least one bank-type credit card has grown, so has the fraction of these card-holder households that can be classified as “liquidity constrained”: from 12 percent in 1983 to 17 percent in 2001. For 1992-2001, we can further distinguish the type of credit for which the household was turned down; roughly one-third of card holders apparently had requested additional credit in the form of a credit card. Households with no balance on their card but with new charges are the least likely to be credit constrained; only about 6 percent are so classified for any type of credit, and about 4 percent for credit other than a credit card. Roughly one-third of the frequent credit card revolvers (those with a balance who hardly ever pay it off in full) can be classified as “liquidity constrained” but only one fifth identify the type of credit denied as other than for a credit card. In other words, about 80 percent of frequent card revolvers do not claim that they have been denied another form of credit. Although they do not appear to be revolving credit card debt by default, they may have decided that switching to lower cost forms of credit is too costly in terms of transactions or time costs, or they may be unaware that other sources of credit, possibly at more attractive terms, are available.

7. Credit card balances, utilization, interest rates 7.1.

Median amounts charged

Table 6 shows the median card balance of households who revolve credit, by each survey year, and differentiating between households who claim to “almost always” or “sometimes” pay off the balance each month from those who admit that they “hardly ever” pay off the balance.11 Households who usually revolve credit tend, not surprisingly, to have larger balances on their credit cards than do households who indicate only occasional credit card revolving. The median amount of credit card debt outstanding for occasional revolvers increased from about $700 in 1983 to over $1,150 in 1995, but has since declined slightly, to about $1,000 in 2001. The median

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balance for credit revolvers has increased by more, and in recent years has been more than twice as large: it has grown from $1,244 in 1983 to $3,260 in 1998 and $2,800 in 2001. Credit card balances of households that are occasional revolvers show less variation by age, education, and income than do the balances of households who usually revolve credit. Among households who usually carry a balance, the median credit card balance generally has been between $2,500 and $3,000 for households aged less than 65, but only about $1,500 for older households. Although Table 1 indicates that a smaller percentage of card-holding households with college education revolve credit card debt, those that do revolve their card debt tend to carry larger balances than do households with less education. The median balance for college educated usual revolvers has increased from about $3,000 in 1992 and 1995 to $4,775 in 1998 and $4,000 in 2001. By contrast, the median balance for a credit-revolving household with high school education generally has been between $2,000 and $2,500. Similarly, although a smaller percentage of higher-income households usually choose to revolve credit than do lower-income households, those that do typically carry larger balances than do households with lower incomes.

7.2.

Credit limits, utilization rates, and interest rates

To some extent, higher card balances of college-educated and higher-income credit revolvers reflects higher credit limits available to such households. Starting with the 1995 survey, data were collected on the total bank-type card limit—that is, the maximum amount that could be charged on the all bank-type credit cards owned by the household—as well as on the interest rate charged on the card with the highest balance (or the most frequently used card, if the balance on all cards was zero). Table 6 indicates that credit limits are generally highest for households that have demonstrated that they can handle credit card accounts responsibly, and not necessarily those that have the greatest need to borrow. Credit limits tend to be highest for those that carry no balance but actively use their cards, or that carry a balance although they at least sometimes pay the balance in full. The median credit limit for these households ranges from $10,000 to $15,000, depending on the survey year. Households that either do not use their cards actively or usually revolve credit

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typically have credit limits of under $10,000 and often closer to $7,500. Credit limits are typically larger for households aged between 35 and 64 than for households under 35, and are somewhat larger than for households over 65. Credit limits also tend to be higher for households with higher levels of education and higher income. Table 6 also indicates that between 1995 and 2001, the median card limit declined for younger households, for those with less than high school education, and for those with incomes below $10,000. Multiple factors are likely to have contributed to the decline in the median card limit, but in part it may reflect the increase in card ownership by these demographic groups. The typical lower-education or lower income household who nonetheless qualified for a bank-type credit card in 1995 may have had a somewhat higher credit rating than the typical such household in 2001. Columns 8 and 12 show the median credit card utilization rates of households that revolve credit, constructed as the balance remaining on the card after the last payment plus any new charges made on the card over the current month, divided by the available credit limit.12 Households who have a balance but at least sometimes pay it off had a median card utilization rate of 15 percent in 1995; the utilization rate was just under 20 percent in 1998 and then declined a bit to 17.5 percent in 2001. Households that hardly ever pay off balances have considerably higher median utilization rates of almost 40 percent in 1995 and about 50 percent in 1998 and 2001. These higher utilization rates reflect both the higher card balances of this group as well as the somewhat lower card limits these households face. Nearly one-tenth of card holders and just under 20 percent of those who revolved credit in 2001 had a credit card utilization rate of 75 percent or more. A similar percentage of card users had high utilization rates in 1998, but only about half as many did in 1995. In all survey waves, these households were more likely to be young and to have less than college level education. Most high-utilization households “hardly ever” pay off their card balance. More than half of high-utilization households (and over 70 percent of young households) can be classified as “liquidity constrained,” compared with less than 20 percent of households with lower utilization rates and 6 percent of card users without an outstanding balance. Although the cross-section nature of the SCF prevents us from investigating the relation between current high card utilization rates and future default or bankruptcy filings—a topic we consider in more detail in Section 10—high-utilization households do appear more likely to exhibit indicators of

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financial difficulty: 18 percent of high-utilization households in 2001 indicated that in the previous year they had been two months or more behind in any type of loan payment, compared with only about 5 percent for all households.13

7.3.

Average interest rates, new charges, and expenses of revolvers

Although low introductory or “teaser” interest rates of 1 to 5 percent can make the interest costs of carrying a balance on a credit card credit negligible, Table 6 indicates that most habitual credit card revolvers pay relatively high rates of interest. For the typical household who sometimes paid off the balance in full, the median interest rate charged ranged from 13 to 14.8 percent, depending on the survey year. For households that usually revolve debt, the typical interest rate was 15 to 16 percent, implying an annual interest rate cost of about $400, if the balance during the survey month and new charges recorded are representative of the normal monthly balance and charges. In 2001, less than 4 percent of frequent revolvers had interest rates of 5 percent or less on the bank-type card with the largest balance; almost 19 percent faced interest rates above 20 percent.

8. Asset holdings by card payment patterns and demographic groups In this section we explore asset holdings of card owners and credit revolvers to highlight the puzzles of simultaneous accumulation of assets with high-cost credit card debt. In all survey years, the highest levels of median liquid assets (defined as amounts held in checking accounts, savings accounts, money market deposit accounts, and call accounts at brokerages), median financial assets, and median total net worth are for those households that used their bank-type credit card to make new charges, but did not have a balance outstanding. This relative ranking holds for all survey years, and for virtually all demographic subgroups, and in fact has become more pronounced over time. In 2001 dollars, median financial assets of households in this category in 2001 were $125,000, more than double the financial assets of such households in 1983, and median net worth at nearly $320,000 was about 50 percent higher. This increase in wealth can be explained in large part by the rise in the equity market over the 1990s and increased ownership of equities by these households: in 1983, less than

15

half of households in this category were stockholders, but by 2001 that fraction was 75 percent. The next highest median asset levels are held by those who have a card but did not use it to make new charges. On average, their median asset holdings are about one-third to one-half as large as those of active card users without a balance. Households who have a balance but at least sometimes pay their balance off have asset levels a bit lower than those of card owners but non-users, indicating that these households are able to accumulate financial assets. Households that hardly ever pay the card balance off have notably lower wealth levels, with median wealth averaging about half as large as for “sometimes” revolvers, and about one-fifth as large as for those who use cards but do not carry a balance. In all survey years, households without bank-type credit cards have the lowest amount of assets. The decline in median net worth of these households between 1983 and 2001 reflects the previously noted spread of card ownership to households with lower incomes.

9. Coexistence of low-interest liquid assets and high-interest card debt Gross and Souleles (2002a) point out that over 90 percent of households with credit card debt in the 1995 Survey of Consumer Finances have some very liquid assets in checking and savings accounts, which usually yield at most 1–2 percent. One-third of credit card borrowers have more than one month’s worth of gross total household income in liquid assets. Such large holdings of low-interest liquid assets are difficult to explain on the basis of transaction needs, and arbitrage considerations would call for them to be used to pay down, if not completely pay off, high-interest credit card debt. 14 In our tabulations here, we will take a more conservative stance that probably understates the puzzle. Tables 7 and 8 shows median card balances, liquid assets, financial assets, and net worth for all households and for those that carried a balance, differentiating between households that had liquid assets no larger than the credit card balance, and those that had liquid assets greater than the credit card balance (and at least $1,000 and at least half of total monthly income). Households that carry a credit card balance but appear to have more than enough liquid financial assets to pay off the balance in full are remarkably numerous. In 1995, 39 percent of credit card revolvers

16

fell into this category; about 45 percent can be so classified in 1998 and 2001. In all years, the typical household that was a high-liquid-asset revolver had an unpaid banktype credit card balance of about $1,000, while median liquid assets were six to eight times larger. These households also have fairly substantial holdings of total financial assets and net worth. Although some of these households may be accidental credit revolvers in the survey month, the majority claims only to “sometimes” pay of the balance in full, and about one-third admit to “hardly ever” paying off their card balance. These households could potentially have greater liquid asset needs than do other households, but this seems unlikely. In comparison with assets held by other survey households on Table 7, their liquid asset holdings appear somewhat larger than those who have a card but do not actively use it, but generally somewhat smaller than those of households that use cards but do not carry a balance. If the balance carried in the survey month is indicative of the balance carried throughout the year and the new charges recorded are indicative of the normal monthly charges, then the estimated annual interest cost paid by these households by not paying off the balance is on the order of $100 to $200.

10. Theories of credit card behavior Before reviewing theories of credit card behavior, it is useful to examine whether puzzling observed tendencies can be attributed imply to ignorance or limited understanding of the terms and conditions of credit card accounts. If so, it should be possible to restore optimal behavior through better information.

10.1. Are households unaware of credit card terms? Luckily, survey data make it possible to seek an answer to this question. In January 2000, the Credit Research Center sponsored a survey of nearly 500 households (representative of the forty eight contiguous US states) that investigated consumers’ attitudes towards credit cards. A more recent such survey was conducted in 2001, and their main findings are reported in Durkin (2000 for the older Survey; 2002 for the newer). Durkin (2000) also contrasts them with findings from earlier Surveys of Consumer Finances in 1970 and 1977. We report Durkin’s main findings 17

in this Section. Which terms of credit card agreements are regarded as important by consumers when opening a new or replacement card account? The January 2001 Survey found that cost items predominate, mainly annual percentage rates and finance charges, as indicated by responses of about two thirds of consumers. This percentage is not influenced by whether respondents did or did not possess a bank-type credit card. Three fifths of those without cards thought that these were the most important terms, compared to slightly more than half of cardholders. The latter assign higher importance than do non-holders to annual fees, fixed versus variable rates, and frequent flier miles. Respondents in the 2000 Survey are “aware” of the annual percentage rates (APR’s) charged on their revolving credit card debt. If we consider as “unaware” only those who state explicitly that they do not know the rate, then 91 percent of holders of bank-type credit cards are aware of their APR. If we also eliminate those who say that they know their rate but report too low an APR (i.e., an APR below 7.9 percent in 2000), then the proportion of aware holders falls to 85 percent.15 Although awareness varies slightly across demographic groups, it exceeds 80 percent for all groups using either definition. Among groups with highest awareness of APR’s were those with more than $1,500 in revolving debt and those reporting that they hardly ever pay off their balance in full. A major factor promoting awareness was the introduction of the Truth in Lending Act of 1969, which requires credit card companies to provide customers with written statements of credit costs, both at the opening of the account and on each monthly bill. After its introduction, awareness jumped from 27 percent of card holders to 63 percent in 1970 and to 71 percent in 1977. Not only are holders of bank-type credit cards aware of the terms, but two thirds of them report that information about credit terms is easy to obtain; only 7 percent think that it is very difficult. Despite such responses, slightly less than half of bank-type card holders in 2000 agree that card issuers give holders enough information to enable them to use their credit cards wisely. Part of the additional information the rest ask for is already provided on the statements. Interestingly, but perhaps not surprisingly, households are much more willing to declare negative attitudes regarding the use of credit cards made by others than by themselves. Holders of bank-type credit cards declared in 2000 that “other consumers” are confused about credit card practices, but approximately ninety percent 18

of them declare satisfaction with their own card companies, and say that it is easy to get another card if they are not treated fairly. In the 2001 Survey, two thirds respond that useful information on credit terms was very easy or somewhat easy to obtain for themselves, but fewer than half say so for others. The same percentages apply also to the question of whether credit card companies provided sufficient information to use credit cards wisely. All in all, these findings suggest that credit card holders are well informed about the terms they face, especially if they revolve credit card debt, though they do not give much credit to their card issuers for providing the information and they have little faith that others are equally well informed.

10.2. Stickiness of credit card interest rates In his seminal 1991 paper, Ausubel documents considerable stickiness of credit card rates despite extensive competition in the credit card market. This is all the more puzzling in view of the evidence presented above that credit card holders are generally aware of annual percentage rates, and they consider them very important. He points to the low concentration and considerable breadth of the industry, its freedom

from

interstate

banking

and

branch

banking

restrictions,

the

nonresponsiveness of interest rates to fluctuations in the cost of funds to the banks, and to his finding that returns from the credit card business were several times higher than the ordinary rate of return in banking during the period he examines (19831988). 16 Ausubel considers search and switch costs that can make it difficult for consumers to move to different, lower-cost providers of credit cards.17 He bases his adverse-selection theory on a class of consumers who do not intend to revolve credit card debt but find themselves doing so; and on another class of consumers that fully intend to borrow but are bad credit risks. In such a world, good customers exhibit some irrationality and are not particularly responsive to lower interest rates. Banks, on the other hand, do not want to lower interest rates, fearing that they will draw disproportionate numbers of bad risks. Thus, interest rates end up being sticky.18 Brito and Hartley (1995) argue that observed revolving of credit card debt need not be attributed to consumer irrationality, but to the ease of borrowing on the credit card compared to transactions costs involved in other types of loans. They construct a model in which relatively small costs of arranging for other types of loans

19

can induce rational individuals to borrow on high-interest credit cards. Calem and Mester (1995) use data from the 1989 Survey of Consumer Finances to test for the presence of search and switch costs. Controlling for demand and for access to credit, they find that the level of credit card debt is greater among consumers who tend not to shop around for the best terms on loans or deposits. This tendency not to shop around can be attributed perhaps to an irrational belief that debt revolving is likely to be temporary, but it can also arise simply from higher search costs. Calem and Mester also find that households with higher outstanding balances are more likely to be denied credit and to have experienced payment problems. Thus, customers with high balances face greater costs of switching to a provider that offers more attractive credit terms, because providers are likely to interpret their high balances as a signal of lack of creditworthiness. There may also be good credit risks who have been granted privileges by their existing credit card providers, such as large credit lines, and who therefore face switch costs of a different kind. More recent studies corroborate the view that the size of credit card debt influences the probability of declaring bankruptcy or delinquency. Domowitz and Sartain (1999) find that households with more credit card debt are more likely to file for bankruptcy. Gross and Souleles (2002b), who do not use survey data but an administrative set of credit card accounts, find that, even after controlling for account credit scores used by the credit card companies, accounts with larger balances and purchases, or smaller payments, are more likely to default. Based on these findings, credit card issuers would be justified to regard high balances and purchases as bad signals, even after taking credit scores into account, despite the potential to earn more on consumers revolving large amounts of debt.19 In the presence of search or switch costs, issuers would find that lowering interest rates does not attract many consumers who revolve credit card debt but are good credit risks, and this could contribute to stickiness of interest rates. Clearly, understanding the reasons and motives underlying bankruptcy and delinquency is central to understanding credit card behavior. It is to this that we now turn.

10.3. Bankruptcy, delinquency, and strategic default In the late 1990s, there has been a dramatic increase in the number of personal

20

bankruptcy filings in the United States, as well as in delinquency rates on credit cards. The former rose by about 75 percent, and the latter almost as sharply (Federal Reserve Bank of Cleveland, 1998). Personal bankruptcy filings rose from 0.3 percent of households per year in 1984 to around 1.35 percent in 1998 and 1999, while lenders lost about $39 billion in 1998 because of personal bankruptcy filings (Fay, Hurst, White, 2002). In this Section, we first examine this phenomenon in some detail and we then ask whether strategic default motives could justify observed portfolio behavior of debt revolvers.

10.3.1.

Bankruptcy and delinquency in credit cards

An important question is whether the recent increase in bankruptcy and delinquency rates signals an increased tendency of households to engage in such activities, controlling for their characteristics, economic conditions, and factors governing credit supply; or whether it simply reflects a worsening of the risk pool due to extension of credit to less credit-worthy individuals. Gross and Souleles (2002b) provide an in-depth study of this issue, using an administrative panel of thousands of individual credit card accounts from several different card issuers.20 One of the major advantages of this data set is that it includes thousands of observations of lowprobability events such as bankruptcy and delinquency, and it encompasses data observed by credit card issuers. The latter feature allows the authors to control for changes in credit supply and risk composition that were observable by the issuers, including increases in credit lines. The authors find some role for lower credit scores, larger balances and purchases, smaller payments, unemployment, weak house prices, and lack of health insurance in accounting for higher bankruptcy or delinquency rates, but only for a small part of the observed change in the late 1990s. Somewhat surprisingly, increases in credit lines were not found to contribute to the phenomenon of default, suggesting that these were extended to less risky accounts. Even controlling for all of these factors, the propensity to default increased significantly between 1995 and 1997.21 Interestingly, the size of the increase in propensity to default goes up with the number of people in one’s state who have previously filed for bankruptcy.

21

Fay, Hurst, and White (2002) use retrospective questions on bankruptcy contained in the 1996 PSID and also find that, controlling for state and time fixed effects, households are more likely to file for bankruptcy if they live in districts with higher aggregate filing rates. In addition to social stigma, they cite evidence reported in Braucher (1993) and in Sullivan et al. (1989) that the administration and practice of bankruptcy law by lawyers and judges varies across bankruptcy districts, in a way that can create differential incentives to file for bankruptcy across districts. Such findings are consistent with a role for increased acceptability and ease of filing in determining the incidence of bankruptcy, though it is difficult to make the case conclusive. Fay et al. find little support for the idea that households file for bankruptcy when adverse events reduce their ability to repay. 22 Instead, they find that households are more likely to file when bankruptcy yields higher financial benefits: the authors find that the value of the debt discharged in bankruptcy, but not the value of non-exempt assets, plays a significant role in bankruptcy decision.23 Fay et al. interpret their findings as evidence in favor of strategic behavior in bankruptcy filings, but perhaps a safer conclusion is that bankruptcy law provisions can encourage bankruptcy, controlling for the overall situation of the household. It should also be noted that the findings here, unlike those of Gross and Souleles (2002b), are subject to limitations imposed by the small number of bankruptcy observations in a survey representative of the entire population. More recently, Dunn and Kim (2004) utilize Ohio data in the late 1990s from the Ohio Survey Research Center.24 When the number of missed minimum credit card payments in the last six months is regressed on household financial and socioeconomic variables, three financial variables have a significant positive effect: the ratio of the total amount of required minimum payments on credit cards to household income; the number of credit cards on which the consumer has exhausted the credit limit; and the credit card utilization rate, measured as a percentage of the sum total of credit lines available to the consumer. Interestingly, education, income, and homeownership status are not found to influence default in the presence of these three financial variables. Such findings seem to provide support to the notion that ability to repay is an important factor behind delinquencies. The sample is then divided into “convenience users” who pay off the balance each month, borrowers with no default history, and borrowers with default history. Using tabulations, the authors find that the

22

number of credit cards held increases on average from 2.5 to 4.6 as we move from convenience users to default borrowers, while the total credit line per card halves, from about $10,000 to about $5,000. The sum total of credit lines also drops from about $21,000 to about $18,000. Although no conclusive case can be made yet, these tabulations are consistent with “Ponzi scheme” practices of obtaining additional cards with small credit lines in order to pay off old credit card debt. The overall conclusion from findings on bankruptcy is that this phenomenon has recently become more frequent, mostly for demand reasons and much less because of a worsening of the risk pool or increased readiness of credit card companies to provide larger credit lines. Households seem to be encouraged to declare bankruptcy by existing financial incentives for doing so, as well as by the prevalence of bankruptcies and by the ease with which bankruptcies are handled by judges and lawyers in their geographical localities. Evidence that default occurs also because of difficulties in meeting minimum payments and in borrowing additional funds is less clear, but need for funds cannot be ruled out as a source of household bankruptcy, especially in view of the role it seems to be playing in delinquencies.

10.3.2.

Strategic default as an explanation for debt revolving

The widespread co-existence of credit card debt with substantial liquid assets in Survey of Consumer Finances data could derive, at least for some households, from strategic bankruptcy motives. If a household holds liquid assets and declares bankruptcy, it can take advantage of bankruptcy law provisions that exempt some assets from seizure, up to an exemption level. Thus, households who plan to declare bankruptcy have no incentive to pay off credit card debt with liquid assets. As pointed out by Lehnert and Maki (2001), a household can discharge a large part of unsecured debt based on chapter 7 bankruptcy laws, and may convert liquid assets to a bankruptcy-exempt asset category in its state of residence, like housing for example. Lopes (2003) calibrates and solves a life-cycle model with uncollateralized borrowing and default, and finds that some consumers borrow with the intention of defaulting in the near future. Education matters for the incidence of default, because it affects the slope and level of the earnings profile, and hence the value attached to the loss of credit availability and stigma associated with bankruptcy in the model. Because of

23

the exemption limit, savings can co-exist with borrowing. Average simulated savings for those who borrow are higher in cases where the probability of default is higher (e.g., they monotonically drop with education). As to empirical evidence, Lehnert and Maki find in Consumer Expenditure Survey data that households living in states with high bankruptcy exemption levels are 1 to 4.5 percentage points more likely to have both liquid assets and total unsecured debt in excess of a threshold ranging between $2,000 and $5,000 (in 1996 dollars). Lopes regresses liquid financial wealth for debt revolvers on exemption level for the household’s region and on demographics, and finds a positive and significant coefficient on exemption level in the 1998 SCF. There is also evidence that links bankruptcy law, and its application, to the incidence of default. Fay et al. (2002) found that state fixed effects are significant for the incidence of default. Indeed, they found that, even after controlling for state fixed effects, households are more likely to file for bankruptcy if they live in a district with higher aggregate bankruptcy rates, or with more lawyers per capita. Gross and Souleles (2002b) similarly found evidence that the tendency to declare bankruptcy is greater for households living in states with greater numbers of people who have previously declared bankruptcy. While a strategic bankruptcy motive can explain the behavior of some households, it is hard to believe that it does so for the majority of households with substantial liquid assets. For one thing, the phenomenon of portfolio co-existence seems too widespread relative to the still limited incidence of bankruptcies in the population. To suggest that all of these households, across all demographic groups, are motivated in their behavior by strategic bankruptcy motives even though a miniscule portion of them actually default, and some of them not even strategically, seems unwarranted. Moreover, as pointed out by Gross and Souleles (2002a), even if strategic default motives were so widespread, strategic defaulters do not need to pay the interest costs of revolving high-rate balances and holding low-rate assets before they declare bankruptcy. They should instead run up their debts and buy exempt assets right before filing.

10.4. Debt levels and utilization rates of credit lines Gross and Souleles (2002a) use the same proprietary administrative data set of

24

individual credit card accounts from different card issuers described above to estimate responses of credit card debt levels and utilization rates of credit lines to exogenous increases in credit lines and to changes in interest rates. By exogenous increases, they mean credit line increases initiated by the credit card providers themselves, and not by card holders.25 They find that, over the year following an exogenous line increase, each extra $1,000 of liquidity (i.e. credit line) generates on average a $130 increase in credit card debt. Thus, liquidity matters, unlike what is implied by standard permanent income models. Estimates of this “Marginal Propensity to Consume” (MPC) are significantly larger for accounts exhibiting greater utilization of credit lines, rising to about 50 percent for accounts with more than 90 percent utilization. The average long run elasticity of debt to the interest rate on the account is estimated to be approximately -1.3, with less than half of this representing balance-shifting across credit cards. The elasticity is larger than average for interest rate declines, providing a possible justification for the popularity of low introductory (“teaser”) interest rates. It is also smaller among accounts with high utilization rates than among those with utilization rates between 50 and 90 percent. The authors uncover a remarkable response of credit card utilization rates to increases in credit lines initiated by banks. Regardless of the credit line utilization rate, the long-run cumulative response of utilization rates to an exogenous line increase is quite small, implying a return of utilization near to its initial level in about five months following the line increase. Although such behavior would be easier to understand had households themselves requested the line increase, it is less straightforward to interpret given that the initiative came from the banks themselves. As the authors suggest, such behavior can be justified in the context of buffer stock models of asset accumulation. In such models, households face nondiversifiable income risk and choose, as a result, to hold a precautionary buffer of assets so as to be able to shield future consumption levels from shocks to their financial resources.26 The same logic applies to available credit lines. Although these are not assets per se, they perform a similar function as a means to maintain consumption in the face of income shocks. Thus, households facing income uncertainty choose not to utilize their credit lines fully, but to leave a portion unused, adopting target utilization rates for credit cards.

25

10.5. Self-control explanations of credit card debt Alternative explanations of portfolio behavior by credit card holders depart from the standard framework by incorporating self-control problems. These models are part of a much broader literature based on Psychology and Marketing insights (for an excellent overview, see Shane et al., 2002). Existing approaches differ, but all assume that the separation of consumption from payment made possible by credit leads to excessive expenditures, and that moderating these tendencies is possible, if costly, through the coexistence of revolving credit card debt and low-interest liquid assets and/or retirement. The types of co-existence that can be justified and the technical complications in solving such models depend crucially on the specific framework, as will be seen below.

10.5.1.

Impulsive behavior and costly self control

The idea that self-control matters for credit card behavior is not foreign to either the general public or to professionals in Psychology and in Marketing. Durkin (2000) reports that public opinion regarding credit cards seems more polarized in 2000 than in 1970, with the majority (51 percent) of all families declaring in 2000 that use of credit cards is “bad”. Among credit card holders, such negative attitudes are more prevalent among those who typically revolve credit card debt. Households are much more willing to declare negative attitudes regarding the use of credit cards made by others than by themselves. Durkin (2001) reports that holders of bank-type credit cards declared in 2000 that too much credit is available, and that “others” have difficulty getting out of credit card debt, while ninety percent of them recognized that overspending is the fault of “other consumers” and not of credit card companies. In the 2001 Survey of Consumers, only ten percent of banktype credit card holders responded that credit cards made managing finances more difficult for them, citing overspending and overextending financial resources as the main reasons. However, forty percent felt that managing finances was made more difficult for “others”, mainly because of overspending, too much debt, and a continuing cycle of debt (Durkin, 2002). Among researchers in marketing and in consumer psychology, self-control problems are known to occur when the benefits of consumption come earlier than the

26

costs (Hoch and Loewenstein, 1991). Credit cards do separate purchases and payments, and there is evidence that liquidity, of the type provided by the acceptability of credit cards, both makes it more likely that the consumer will buy a given item, and increases the amount that the consumer is willing to pay for the item conditional on purchase (see Shefrin and Thaler, 1988; Prelec and Simester, 2001; Wertenbroch, 2003). Indeed, this may be a reason why sellers accept credit card payments despite the service charges this entails. Imposing self-control is possible, if costly, and there is ample anecdotal evidence on precommitment and self-rationing strategies (see, for example, Hoch and Loewenstein, 1991; Schelling, 1992; Thaler and Shefrin, 1981, and Wertenbroch, 2003). A telling example refers to deadlines that various people, including academics, impose on themselves to avoid procrastination even when missing them entails substantial costs (Thaler, 1980; Ariely and Wertenbroch, 2002). Another one refers to smokers who prefer to purchase small and more expensive packs of cigarettes rather than cartons, so as to discourage themselves from smoking too much. Ausubel (1991) cites the anecdotal example of card holders who immerse their credit cards in trays of water and place them in the freezer, in an effort to avoid impulsive purchases. Unfortunately, serious self-control problems are difficult to observe under controlled conditions, and therefore controlled empirical evidence on self-rationing is only now beginning to emerge (see, Wertenbroch, 1998; Soman and Cheema, 2002). Finally, while it is obviously awkward to ask survey participants directly whether they have self-control problems, some survey questions hint at impulsive behavior and other such problems. For example, respondents are sometimes asked whether they find it difficult to plan ahead, or to control their purchases, or whether they smoke, or whether they find it acceptable to borrow in order to buy frivolous luxury items. Still, such variables are not many and their interpretation is not always straightforward.

10.5.2.

Hyperbolic discounting and time inconsistency

Laibson, Repetto and Tobacman (2003) study the co-existence of revolving credit card debt with substantial accumulation of assets for retirement in a calibrated model of a household with access to liquid and illiquid assets, and to borrowing through credit cards. 27 They show that a single rate of time preference cannot

27

simultaneously match the level of accumulated assets upon transition to retirement and the observed level of revolving credit card debt at younger ages. Households appear to act impatiently with respect to short-term objectives facilitated by credit card borrowing, and much more patiently with respect to longer-term objectives regarding retirement planning. The authors propose hyperbolic discounting, under which the household should no longer be thought as a single entity, but as a sequence of temporally separated “selves” with possibly conflicting plans regarding future actions. When viewing two successive periods in the distant future, the two selves discount the second relative to the first differently. The current self is more patient with respect to longer run objectives than he is with respect to current objectives, and also more patient than what he knows his future self will be close to the relevant date. The current self tries to “tie the hands” of future selves and to force them to accumulate more than what they are likely to do on their own. The instrument of selfcontrol is irreversible investment in illiquid assets. The current self simultaneously borrows on the credit card to satisfy short-term objectives, and accumulates illiquid assets that the future self cannot liquidate to ensure that the household will have enough assets at retirement. Hence the observed co-existence of credit card debt and accumulation of retirement assets. As recognized by the authors, this elegant model of temporally separated selves cannot also account for the observed co-existence of high-interest credit card debt and low-interest liquid assets. Specifically, the model does not imply that the current self should ignore arbitrage opportunities regarding current assets and debts. We now turn to a different model that incorporates self-control considerations between contemporaneous selves.

10.5.3.

Accountant-shopper households

Bertaut and Haliassos (2002) propose an “accountant-shopper” model that can account for co-existence of high-interest credit card debt with substantial holdings of low-interest liquid assets. Haliassos and Reiter (2005) develop the underlying computational model of accountant and shopper interaction and show that the model can also account for observed co-existence of credit card debt with considerable accumulation of retirement assets, as well as for target utilization rates of credit card

28

lines found by Gross and Souleles (2002a) and discussed above. This framework splits each household into two units, which can either represent two distinct partners or two selves. In the case of a single person, it is a model of self-control, while in the other case it should be thought of as a model of “partner-control”. In either case, it is a model of contemporaneous self-control, unlike hyperbolic discounting in which the current self builds up illiquid retirement assets to control future selves. The accountant decides the size of payment into the credit card account each month, as well as the overall household portfolio. The accountant is assumed to be fully rational and to solve a standard intertemporal expected utility maximization problem, taking into account all available information, the full implications of current actions for future outcomes, as well as the behavior of the shopper. The shopper goes to stores, with credit card in hand, and determines household consumption. The shopper’s selfcontrol problem is manifested in greater impatience compared to the accountant and in more limited understanding of the process governing future payments into the credit card account, which are ultimately influenced by the evolution of household assets and debts. Faced with uncertainty about future payments, the shopper typically refrains from exhausting the entire credit card line but maintains a buffer, consistent with the Gross and Souleles (2002a) empirical finding of target utilization rates. Even under shopper behavior that is fully predictable by the accountant, it is optimal for the accountant to leave part of the credit card balance unpaid so as to restrain the shopper. In equilibrium, the accountant brings about the desired consumption level but pays the interest cost of self-control, namely the cost of not using low-interest assets to pay off high-interest debt.28 Since credit card debt is used as an instrument of self-control in addition to its traditional role of smoothing resources intertemporally, revolving debt does not conflict with holdings of either low-interest assets or retirement assets. Both types of assets are held for the usual precautionary and smoothing reasons associated with intertemporal maximization under uncertainty. Had the accountant decided to use some of these assets to lower the credit card balance, the shopper would have responded by charging more on the credit card, frustrating the accountant’s attempt. Finally, although the model does not invoke hyperbolic discounting and control of temporally separated selves to justify portfolio co-existence, it seems flexible enough to be combined with intertemporal self-control considerations, if this is desired.

29

11. The choice between debit and credit cards As we saw in the data Section , debit cards are a more recent medium than credit cards, but their use is spreading fast, and they are overtaking credit cards as the most prevalent form of electronic payment at the point of sale. Part of the usual motivation for debit cards is that they limit the potential for overspending associated with credit cards. Debit card transactions can either be made online, using a PIN, or off-line using a signature and a process very similar to credit cards. Off-line debit transactions have been aided by the Visa and Mastercard logo, and it is not an exaggeration that debit and credit cards enjoy comparable levels of acceptability today. Use of debit cards is not allowed only for items such as car rentals and some on-line purchases over the internet. Moreover, debit and credit cards now offer essentially identical fraud protection (see also Zinman, 2004). A major advantage of debit cards is that they do not allow over-borrowing, as funds are immediately withdrawn from the account linked to the debit card (or withdrawn within three days in the case of offline purchases). Debit cards appear to be a natural way of solving self-control problems of relatively impatient and impulsive shoppers. It seems possible to impose discipline on a shopper by replacing the credit card with a debit card and limiting the funds available in the linked account. Indeed, observed usage of debit cards seems to reinforce this idea.29 Still, use of debit cards is not a costless way of coping with a self-control problem. Debit card users forego the free-float offered by credit cards, since funds are (almost) immediately withdrawn from the linked account. Interest costs are not limited to those implied by absence of free floating, but also include the cost of keeping available balances in low-interest linked checking accounts, instead of in higher-rate accounts and withdrawing funds only to cover the monthly payment on a credit card. This process can be quite complicated, especially if the debit card holder is not flush with liquid financial resources and tries to avoid overdraft costs and penalties associated with the linked account. Very often, credit card issuers offer additional rewards to credit card users but not to debit card users, such as frequent flier miles and other bonuses. Thus, using debit cards as instruments of self control is costly, although probably less so than revolving credit card debt to reduce the available credit line.30 30

Zinman (2004) questions the usual motivation for use of credit cards based on self-control considerations. He investigates whether choice of debit versus credit cards at the point of sale is in fact consistent with the relative cost of charging an extra dollar to the credit card relative to paying with the debit card. A key factor determining such relative costs is whether the consumer already revolves credit card debt, in which case new purchases cannot benefit from the grace period and are thus subject to high interest rates. Zinman formulates three testable hypotheses generated by a “canonical” model of consumer choice without self-control considerations. First, credit card debt revolvers should be more likely to use debit than those who do not, as they cannot take advantage of the grace period for new purchases. Second, revolvers who face binding credit constraints should be more likely to use debit than credit, e.g. because they are likely to be close to full utilization of the credit card line. Third, nonrevolving bank card holders should be less likely to use debit than those without bankcards. The main rationale for this third prediction is increased likelihood that card holders will want to take advantage of the free float. Using data from the 2001 and other Surveys of Consumer Finances, Zinman finds economically and statistically significant effects on debit use of revolving status and of credit limit constraints in particular, supporting mainly the first two predictions of the canonical model. However, these results and some stylized facts about debit card use may also be consistent with behavioral models. For example, results also seem consistent with the accountant-shopper model described above. Since credit card debt is revolved mainly as an instrument of self-control in that model, debt revolvers are more likely to exhibit self-control problems and to use debit cards as an additional measure to discipline impulsive shoppers. The same holds a fortiori for those with nearly binding credit card limits. To the extent that these arise from a desire to limit the resources available to the shopper, they will also be associated with a greater likelihood of encouraging the shopper to use a debit card for purchases. Zinman illustrates problems of distinguishing between standard and behavioral explanations of debit card use using the Prelec and Loewenstein (1998) model of mental accounting. In that model, the act of paying produces cognitive transactions costs and incentives to decouple payments from consumption. The optimal decoupling strategy tends to favor delayed payment for durables, but prepayment for 31

instantaneous consumption. Credit cards serve as a decoupling device, because they delay payment and they also lump payments together. If there are convexities over losses associated with each distinct payment, both features attenuate “payment pain”. Debit provides relatively instantaneous payment and thus less decoupling than credit. This additional decoupling motive in credit versus debit card use could rationalize, for example, the finding of Reda (2003) that debit cards tend to be used for smaller transactions involving instantaneous consumption, while credit cards are used for larger transactions of more durable items. While it may be difficult to distinguish between traditional and behavioral models of credit versus debit card use by using solely data on choices at the point of sale, distinctions can be facilitated by reference to portfolios of credit card debt revolvers. Traditional models fail to explain co-existence of high-interest credit card debt with often substantial holdings of low-interest liquid assets. The existence of such “arbitrage” opportunities goes against the logic of models that stress rational calculation of interest and other transactions costs: if consumers are so careful about comparing costs of using debit versus credit for each purchase, how do they miss the interest cost of not paying off their outstanding balances? And if debit card use is motivated by nearly binding credit card limits, how is it optimal to keep enough money in the low-interest linked account to finance purchases rather than using these funds to make more of the credit line available to the shopper and to take advantage of points, miles and other advantages of credit card purchases? All in all, it seems that the shortcomings of standard models become apparent when these models are confronted with portfolios of credit card revolvers rather than simply with the payment margin between credit and debit cards.

12. Concluding remarks This paper documented trends in credit card and debit card access and usage in the United States using data from successive waves of the Survey of Consumer Finances between 1983 and 2001. We documented the spread of access and usage of such cards, examined trends exhibited by different demographic groups, and studied the widespread practice of revolving high-interest credit card debt. The general picture is one of spreading access and usage, but of a fairly stable proportion of bank

32

card holders who revolve credit card debt. Debt revolvers tend to exhibit partial utilization of credit card lines, and they often combine credit card debt with substantial holdings of low-interest liquid assets and with accumulation of retirement assets. We then presented an overview of some of the most important recent theoretical and empirical contributions to the study of credit and debit card behavior. Drawing on recent research, we dismissed the possibility that there is widespread ignorance among credit card holders of the terms governing their credit cards, including annual percentage rates. Despite lack of ignorance, there is considerable evidence that credit card interest rates do not respond to competition in the credit card market. This arises because consumers tend to be unresponsive to changes in interest rates, probably as a result of search and switching cost. There is a rising trend in bankruptcy and delinquency in credit cards, partly attributable to an increased tendency of households to declare bankruptcy, controlling for the quality of the card holder pool and for supply-side factors. To the extent that bankruptcy is now more widespread, and presumably more socially acceptable, it can influence portfolio behavior. Strategic default motives may contribute to the observed co-existence of credit card debt with low-interest liquid assets, though we doubt that this mechanism alone is sufficient to account for the widespread incidence of the phenomenon. Recent research on the determinants of the level of credit card debt and of the extent of utilization of credit lines has found that credit line increases initiated by banks themselves do contribute to increases in the amount of debt revolved, such that credit line utilization returns in about 5 months or so to its rate prior to the line increase. Credit card debt revolvers appear to have target utilization rates of their credit lines, and it is possible to justify such “buffer-stock” behavior in the context of modern computational models of credit card behavior. Credit and debit cards provide a natural means of testing the relevance of emerging self-control models of consumer preferences. A considerable fraction of card holders believe that credit cards create problems of self-control, mainly because of the probability of overspending, at least by others if not by themselves. Debit cards are widely regarded as instruments for self-control that reduce this possibility. Although the choice of debit versus credit cards at the point of sale can be largely justified by the relative costs of these two modes of transacting, portfolio co-existence 33

of credit card debt with liquid and with retirement assets seems to require departures from the standard framework. Hyperbolic discounting has been shown to account for the first type of co-existence. An alternative framework of “accountant-shopper” households, in which a fully rational accountant tries to control an impulsive shopper, has been shown to be consistent with both types of co-existence and with buffer-stock utilization behavior. Based on this survey of facts and of existing literature, we are led to the conclusion that credit cards provide a most fertile ground for analyzing consumption behavior, payment and repayment choice (including bankruptcy and delinquency), portfolio selection regarding both assets and debts, and the elusive nature of consumer preferences.

34

References Ariely, Dan and Klaus Wertenbroch (2002). “Procrastination, Deadlines, and Performance: Self-Control by Precommitment”, Psychological Science, 13, 219-24. Ausubel, Lawrence (1991). “The Failure of Competition in the Credit Card Market”, American Economic Review, 81, 50-81. Aizcorbe, Ana M., Arthur B. Kennickell, and Kevin B. Moore (2003). “Recent Changes in U.S. Family Finances: Evidence from the 1998 and 2001 Survey of Consumer Finances”, Federal Reserve Bulletin, January 2003, 1-32. Bertaut, Carol C. and Michael Haliassos (2002). “Debt Revolvers for Self-Control”, mimeo. Braucher, Jean (1993). “Lawyers and Consumer Bankruptcy: One Code, Many Cultures”, American Bankruptcy Law Journal, 67, 501-83. Brito, Dagobert L. and Peter R. Hartley (1995). “Consumer Rationality and Credit Cards”, Journal of Political Economy, 103, 400-33. Calem, Paul S. and Loretta J. Mester (1995). “Consumer Behavior and the Stickiness of Credit-Card Interest Rates”, American Economic Review, 85, 1327-36. Domowitz, Ian and R. Sartain (1999). “Determinants of the Consumer Bankruptcy Decision”, Journal of Finance, 54, 403-20. Dunn, Lucia F. and Tae-Hyung Kim (2004). “An Empirical Investigation of Credit Card Default: Ponzi Schemes and Other Behaviors”, Ohio State University Working Paper. Durkin, Thomas (2000). “Credit Cards: Use and Consumer Attitudes, 1970-2000”, Federal Reserve Bulletin, September, 623-34. Durkin, Thomas (2002). “Consumers and Credit Disclosures: Credit Cards and Credit Insurance”, Federal Reserve Bulletin, April, 201-13. Fay, Scott, Eric Hurst, and Michelle White (2002). “The Household Bankruptcy Decision”, American Economic Review, 92, 708-18. Federal Reserve Bank of Cleveland (1998). Economic Trends, July.

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Gross, David B. and Nicholas S. Souleles (2002a). “Do Liquidity Constraints and Interest Rates Matter for Consumer Behavior? Evidence from Credit Card Data”, Quarterly Journal of Economics, 149-85. Gross, David B. and Nicholas S. Souleles (2002b). “An Empirical Analysis of Personal Bankruptcy and Delinquency”, The Review of Financial Studies, 15, 319-47. Haliassos, Michael and Michael Reiter (2005). “Credit Card Debt Puzzles”, mimeo. Hoch, Stephen J. and George F. Loewenstein (1991). “Time-Inconsistent Preferences and Consumer Self-Control”, Journal of Consumer Research, 17(4), 492-507. Laibson, David, Andrea Repetto, and Jeremy Tobacman (2003). “A Debt Puzzle”, in Philippe Aghion, Roman Frydman, Joseph Stiglitz, and Michael Woodford (Eds.), Knowledge, Information, and Expectations inModern Economics: In Honor of Edmund S. Phelps, Princeton University Press. Lehnert, Andreas and Dean M. Maki (2001). “Consumption, Debt, and Portfolio Choice: Testing the Effect of Bankruptcy Law”, mimeo. Lopes, Paula (2003). “Credit Card Debt and Default over the Life Cycle”, Working Paper, Financial Markets Group, London School of Economics. Moss, David A. and Gibbs A. Johnson (1999). “The Rise of Consumer Bankruptcy: Evolution, Revolution, or Both?,” American Bankruptcy Law Journal 73, 311351 Prelec, Drazen and Duncan Simester (2001). “Always Leave Home Without It: A Further Investigation of the Credit-Card Effect on Willingness to Pay”, Marketing Letters, 12, 5-12. Prelec, Drazen and George Loewenstein (1998). “The Red and the Black: Mental Accounting of Savings and Debt”, Marketing Science, 17, 4-28. Reda, Susan (2003). “2003 Consumer Credit Survey”, STORES Magazine, November. Schelling, Thomas C. (1992). “Self-Command: A New Discipline”, in Loewenstein, G. and J. Elster (eds), Choice Over Time, New York: Russell Sage Foundation, 167-76.

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Shane, Frederick, George Loewenstein, and Ted O'Donoghue (2002). “Time Discounting and Time Preference: A Critical Review”, Journal of Economic Literature, 40, 351-401. Shefrin, Hersch M. and Richard H. Thaler (1988). “The Behavioral Life-Cycle Hypothesis”, Economic Inquiry, 26, 609-43. Soman, Dilip and Amar Cheema (2002). “The Effect of Credit on Spending Decisions: The Role of the Credit Limit and Credibility”, Marketing Science, 21, 32-53. Sullivan, Teresa A., Elizabeth Warren, and Jay Lawrence Westbrook (1989). As We Forgive Our Debtors: Bankruptcy and Consumer Credit in America, New York: Oxford University Press. Thaler, Richard H. (1980). “Toward a Positive Theory of Consumer Choice”, Journal of Economic Behavior and Organization, 1, 39-60. Thaler, Richard H. and Hersch M. Shefrin (1981). “An Economic Theory of SelfControl”, Journal of Political Economy, 89, 392-406. Wertenbroch, Klaus (1998). “Consumption Self-Control via Purchase Quantity Rationing of Virtue and Vice”, Marketing Science, 17, 317-37. Wertenbroch, Klaus (2003). “Self-Rationing: Self-Control in Consumer Choice,” in George Loewenstein, Roy Baumeister, and Daniel Read (Eds), Time and Decision: Economic and Psychological Perspectives on Intertemporal Choice, New York, NY: Russell Sage Foundation, 491-516. Zinman, Jonathan (2004). “Why Use Debit Instead of Credit? Consumer Choice in a Trillion Dollar Market”, mimeo, Federal Reserve Bank of New York.

37

Table 1: Percentage of Households by Bank-Type Credit Card Payment Pattern Among of those with a bank-type card: No balance

No balance

Has any

Has Bank-

and no new

but has new

Carries

credit card

Type card

charges

charges

balance

(1)

(2)

(3)

(4)

Hardly ever pays it off (5)

(6)

1983

65.4

43.0

18.3

30.5

51.1

43.9

1992

72.0

62.3

10.7

36.8

52.6

48.9

1995

74.6

66.5

7.3

36.7

56.0

47.3

1998

72.2

67.2

8.3

36.9

54.8

47.6

2001

76.2

72.6

7.2

39.4

53.4

45.9

1983 by age: lt 35

57.2

34.0

14.9

24.5

60.6

49.2

35-54

73.7

52.0

14.4

24.8

60.8

43.0

55-64

75.0

53.1

21.0

35.6

43.4

42.4

65 +

55.9

33.3

31.7

50.2

18.1

28.4

by education less than high school

41.5

21.4

24.6

28.4

47.0

46.9

high school

65.1

39.2

22.7

22.9

54.5

45.9

some college

73.1

49.4

18.4

28.9

52.7

46.4

college degree +

89.1

69.8

12.7

38.1

49.2

39.7 75.2

by income lt $10,000

20.2

5.3

38.0

20.8

41.2

10,000-24999

43.2

22.4

26.1

28.0

45.9

43.0

25000-49999

70.8

41.3

19.8

25.2

55.0

46.8

50000-99999

88.8

68.0

15.5

28.7

55.8

43.9

100,000 +

97.2

83.4

15.0

49.5

35.5

32.4

by marital status Unmarried

51.7

29.9

20.5

32.4

47.0

44.1

married or partner

74.3

51.6

17.5

29.8

52.7

43.9

by race white nonhispanic

70.3

46.8

19.3

32.5

48.2

42.4

Black

41.9

23.3

12.1

13.9

73.9

54.1

Hispanic

38.9

26.3

3.3

5.9

90.7

47.5

Other

60.7

46.3

11.1

28.4

60.4

55.6

1992 by age: lt 35

67.3

56.1

5.8

27.3

66.9

50.1

35-54

74.9

67.2

7.0

33.6

59.4

51.2

55-64

75.8

67.3

16.0

42.8

41.2

43.7

65 +

70.1

57.9

20.1

49.9

30.0

41.9

by education less than high school

39.6

27.5

22.5

28.0

49.6

70.7

high school

69.2

56.5

12.1

29.2

58.7

53.5

some college

78.9

70.0

10.2

33.5

56.3

37.1

college degree +

91.5

85.6

7.6

44.8

47.6

47.3 70.4

by income lt $10,000

28.5

18.9

23.1

25.0

51.9

10,000-24999

60.6

47.4

18.1

31.1

50.8

54.0

25000-49999

77.7

66.1

10.2

29.2

60.7

49.0

38

50000-99999

91.0

84.3

6.9

38.1

54.9

47.9

100,000 +

95.9

93.5

7.0

59.1

33.9

34.4

by marital status Unmarried

58.7

49.0

13.1

35.5

51.4

50.7

Married or partner

81.9

72.2

9.4

37.4

53.2

48.1

by race white nonhispanic

78.9

69.7

10.8

39.4

49.8

47.6

Black

47.0

35.5

9.6

13.1

77.4

47.7

Hispanic

43.6

33.7

9.5

16.0

74.5

77.8

Other

74.4

61.8

11.5

43.6

44.9

37.5

by age: lt 35

67.6 78.2

58.8 71.9

6.0 5.1

25.2 28.7

68.8 66.2

49.5 46.5

35-54

78.7

72.0

6.3

45.9

47.8

43.5

55-64

73.4

62.0

13.9

60.6

25.6

49.9

by education

48.5

34.8

15.0

31.9

53.1

56.0

less than high school

70.8

61.8

9.0

29.7

61.4

48.0

high school

79.3

71.2

7.2

28.8

63.6

49.2

some college

91.3

87.7

4.2

47.0

48.7

42.8

1995

65 +

college degree + by income

33.6

25.7

17.8

31.6

50.6

46.4

lt $10,000

61.3

49.1

8.9

32.2

58.9

57.4

10,000-24999

81.6

73.1

6.8

35.0

58.2

50.3

25000-49999

95.3

90.0

6.6

34.3

59.1

42.2

50000-99999

98.9

96.9

3.6

55.6

40.8

33.2

100,000 + by marital status

63.2

54.8

8.6

35.6

55.9

51.1

Unmarried

82.6

74.8

6.6

37.4

56.0

45.3

by race

79.4

71.9

7.6

40.2

52.2

46.4

white nonhispanic

51.7

41.0

6.2

12.2

81.5

54.5

Black

60.1

49.7

4.0

10.2

85.7

51.4

Hispanic

75.5

67.6

5.2

40.9

53.9

37.1

by age:

62.9

57.9

5.2

23.3

71.4

52.4

lt 35

76.7

72.6

6.9

31.8

61.2

50.3

35-54

79.6

75.4

9.0

41.4

49.6

38.7

55-64

69.0

61.6

14.1

59.3

26.8

31.4

by education

42.5

34.7

14.8

26.0

59.2

53.7

less than high school

68.9

62.8

10.9

31.9

57.1

51.8

high school

76.6

73.3

7.4

29.0

63.5

50.1

some college

91.6

88.2

5.4

46.4

48.2

41.0

married or partner

Other 1998

65 +

college degree + by income

29.4

23.5

8.6

29.7

61.7

51.1

lt $10,000

54.8

47.7

14.1

30.4

55.4

44.4

10,000-24999

77.0

71.0

9.4

32.8

57.8

52.6

25000-49999

91.0

87.7

6.5

34.7

58.8

47.5

50000-99999

98.9

98.0

3.6

59.1

37.3

35.3

100,000 +

39

by marital status

59.3

54.9

10.7

35.1

54.2

46.5

Unmarried

81.5

76.1

7.0

37.8

55.1

48.1

by race

77.9

73.5

8.2

39.8

52.0

46.5

white nonhispanic

48.2

39.7

10.3

15.6

74.2

57.6

Black

54.2

48.4

7.7

15.6

76.6

48.1

Hispanic

66.5

60.2

5.9

43.7

50.5

40.7

married or partner

Other

40

Table 2 Probit Estimation of Credit Card Ownership in the United States from the 1983, 1992, 1995, 1998, and 2001 Surveys of Consumer Finances Has a bank-type credit card

Has at least one credit card Intercept Married Single female Number of children Nonwhite/Hispanic Age < 35 Age 35-49 Age 65-74 Age 75+ Less than HS diploma High School diploma or equiv. College degree or higher Income < $10,000 Income $10,000-$24,999 Income $50,000-$99,9999 Income $100,000 + ln (financial assets) Self employed Not currently working Saver Liquidity constrained Has checking account d1983 d1992 d1995 d1998

number of observations -2 log likelihood

Coefficient -0.007 0.412 0.267 -0.039 -0.167 -0.210 -0.120 -0.022 -0.469 -0.584

Standard Error 0.074 0.036 0.039 0.009 0.030 0.038 0.037 0.047 0.049 0.040

Significance 0.920 <.0001 <.0001 <.0001 <.0001 <.0001 0.001 0.641 <.0001 <.0001

Coefficient -0.136 0.289 0.154 -0.036 -0.174 -0.153 -0.061 -0.071 -0.551 -0.608

Standard Error 0.072 0.035 0.038 0.009 0.030 0.036 0.033 0.043 0.047 0.038

Significance 0.059 <.0001 <.0001 <.0001 <.0001 <.0001 0.065 0.097 <.0001 <.0001

-0.257 0.269 -0.646 -0.293 0.256 0.380 0.108 0.094 -0.240 0.056 -0.324 0.317 -0.436 0.014 0.088 -0.079

0.035 0.039 0.044 0.033 0.037 0.054 0.005 0.039 0.054 0.027 0.030 0.063 0.065 0.040 0.039 0.039

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.016 <.0001 0.036 <.0001 <.0001 <.0001 0.731 0.026 0.040

-0.261 0.237 -0.615 -0.237 0.263 0.357 0.116 0.059 -0.192 0.028 -0.394 0.313 -1.111 -0.209 -0.068 -0.114

0.033 0.035 0.046 0.032 0.033 0.044 0.005 0.034 0.055 0.025 0.029 0.075 0.077 0.037 0.037 0.037

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.084 0.001 0.252 <.0001 <.0001 <.0001 <.0001 0.067 0.002

21,055 13,706.625

21,055 15,772.164

41

3. Bank-type credit card and debit card use, by age, education, and income 1992, 1995, 1998, 2001 Surveys of Consumer Finance

1992 All by age: lt 35 35-54 55-64 65 + by ed lt hs Hs some college college degree + by inc lt $10,000 10,000-24999 25000-49999 50000-99999 100,000 + 1995 all by age: lt 35 35-54 55-64 65 + by ed lt hs hs some college college degree + by inc lt $10,000 10,000-24999 25000-49999 50000-99999 100,000 + 1998 all by age: lt 35 35-54 55-64 65 + by ed lt hs hs some college college degree + by inc lt $10,000 10,000-24999 25000-49999 50000-99999 100,000 + 2001 all by age: lt 35 35-54

Neither (1)

Credit card Debit card but but no debit no credit card card (2) (3)

credit card, no debit card, no balance (4)

credit card, no debit card, has balance (5)

Both debit and Both debit and credit cards, credit cards no balance (6) (7)

Both debit and credit cards, has balance (8)

36.3

1.4

54.6

25.8

28.8

7.8

3.8

4.0

41.8 31.0 32.1 41.7

2.1 1.8 0.6 0.5

47.5 57.6 59.9 54.3

15.5 23.1 34.0 37.6

32.0 34.5 25.9 16.7

8.5 9.7 7.5 3.5

3.0 4.2 5.6 2.9

5.5 5.5 1.9 0.6

72.5 42.5 26.9 12.6

0.0 1.0 3.1 1.8

26.5 52.0 60.1 71.9

13.3 22.3 26.3 36.8

13.2 29.7 33.8 35.1

1.0 4.6 10.0 13.8

0.6 1.1 4.3 8.1

0.4 3.5 5.7 5.7

79.9 51.5 32.5 13.8 5.1

1.3 1.1 1.4 1.9 1.4

16.5 43.7 59.5 72.7 74.8

7.8 21.0 24.0 32.5 50.4

8.7 22.7 35.5 40.2 24.4

2.4 3.8 6.6 11.6 18.7

1.3 2.4 2.0 5.5 11.4

1.1 1.4 4.6 6.1 7.3

30.4

3.1

52.0

23.8

28.2

14.6

5.5

9.1

35.5 24.7 26.5 37.4

5.7 3.4 1.5 0.6

40.8 55.1 60.0 54.2

13.4 18.7 32.1 40.4

27.4 36.4 27.9 13.8

18.0 16.8 12.0 7.7

5.0 5.6 5.4 5.7

13.0 11.2 6.6 2.0

61.8 35.4 24.6 9.8

3.4 2.9 4.1 2.5

30.0 51.3 53.8 65.0

14.6 20.4 20.6 35.0

15.4 30.9 33.2 30.0

4.9 10.5 17.5 22.8

1.8 3.5 5.1 10.0

3.1 7.0 12.4 12.8

71.7 48.0 22.7 6.7 2.9

2.7 2.9 4.3 3.3 0.2

22.5 40.6 57.3 67.2 73.5

12.1 16.9 25.6 28.4 44.4

10.4 23.7 31.7 38.8 29.1

3.2 8.5 15.8 22.9 23.5

0.6 3.3 5.0 8.5 13.0

2.6 5.2 10.8 14.4 10.5

25.7

7.2

40.7

20.8

19.9

26.5

9.6

16.9

30.5 20.4 18.1 35.3

11.6 6.9 6.5 3.2

23.9 41.8 52.6 49.7

7.7 17.5 27.6 37.5

16.2 24.3 25.0 12.2

33.9 30.8 22.8 11.9

8.8 10.6 10.4 7.7

25.1 20.2 12.4 4.2

58.1 27.6 17.8 7.7

7.2 9.6 8.9 4.1

25.3 41.6 42.0 49.1

11.2 20.1 18.3 29.0

14.1 21.5 23.7 20.1

9.3 21.2 31.4 39.2

2.9 6.8 8.5 16.8

6.4 14.4 22.9 22.4

68.4 43.3 20.1 6.7 1.2

8.1 9.1 9.0 5.7 0.8

15.4 32.6 45.4 47.4 57.1

6.5 15.9 21.5 22.7 41.0

8.9 16.7 23.9 24.7 16.1

8.1 14.9 25.5 40.3 41.0

2.5 5.3 8.4 13.4 20.5

5.6 9.6 17.1 26.9 20.5

18.2

9.2

33.8

18.9

14.9

38.8

15.9

22.9

20.8 12.0

15.1 9.7

17.4 34.3

6.1 15.3

11.3 19.0

46.7 44.1

14.3 16.3

32.4 27.8

42

55-64 65 + by ed lt hs hs some college college degree + by inc lt $10,000 10,000-24999 25000-49999 50000-99999 100,000 +

17.3 28.5

6.6 3.4

42.0 50.4

23.8 37.0

18.2 13.4

34.1 17.7

15.6 12.5

18.5 5.2

43.6 20.8 11.5 4.4

14.9 9.0 10.0 5.4

24.6 37.3 34.0 39.5

12.0 16.6 15.3 26.9

12.6 20.7 18.7 12.6

16.8 32.9 44.6 50.8

6.0 9.9 15.2 24.3

10.8 23.0 29.4 26.5

59.1 32.1 13.0 6.1 1.8

12.5 14.2 10.4 6.2 2.5

20.6 30.7 38.0 36.1 42.3

7.1 15.7 18.9 19.0 31.7

13.5 15.0 19.1 17.1 10.6

7.8 23.1 38.6 51.6 53.5

2.1 8.0 10.7 21.8 29.7

5.7 15.1 27.9 29.8 23.8

Variable definitions: debit card: in 1992; household owns a debit card. In 1995, 1998, and 2001: households uses a debit card. has a balance: in all years, household has a bank-type credit card and had remaining balance after last bill was paid. hs=high school, lt hs= did not finish high school.

43

Table 4: Probit Estimation of Debit Card Use in the United States from the 1992, 1995, 1998, and 2001 Surveys of Consumer Finances Uses a debit card* Intercept Married Single female Number of children Nonwhite/Hispanic Age < 35 Age 35-49 Age 65-74 Age 75+ Less than HS diploma High School diploma or equiv. College degree or higher Income < $10,000 Income $10,000-$24,999 Income $50,000-$99,9999 Income $100,000 + ln J20(financial assets) Self employed Not currently working Saver Liquidity constrained Has checking account Buying on credit usually bad idea d1983 d1992 d1995 d1998

Coefficient -0.739 0.037 0.037 -0.012 -0.008 0.510 0.307 -0.209 -0.658 -0.327 -0.229 0.001 -0.346 -0.149 0.127 0.036 0.017 -0.240 -0.130 0.462 -0.042 0.213 0.083 -1.155 -0.843 -0.357 -0.079

* for 1992, dependent variable is debit card ownership number of observations 16,952 -2 log likelihood 16,470.584

44

Standard Error 0.078 0.037 0.042 0.011 0.032 0.037 0.031 0.042 0.056 0.046 0.036 0.032 0.057 0.040 0.035 0.041 0.005 0.029 0.058 0.034 0.026 0.031 0.024 0.035 0.031 0.029 0.039

Significance <.0001 0.318 0.388 0.290 0.806 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.983 <.0001 0.000 0.000 0.382 0.001 <.0001 0.024 <.0001 0.113 <.0001 0.001 <.0001 <.0001 <.0001 0.040

Table 5: Percentage of Liquidity Constrained Households, by Bank-Type Credit Card Payment Pattern 1983, 1992, 1995, 1998, and 2001 Surveys of Consumer Finances 1983

1992

1995

1998

2001

Percent with no bank-type credit card 57.0 of which: percent liquidity constrained 27.9 of which: percent liquidity constrained for credit other than credit card

37.7 34.0 25.6

33.5 39.3 30.6

32.8 34.0 27.2

27.4 34.3 27.3

Percent with bank-type credit card 43.0 of which: percent liquidity constrained 12.4 of which: percent liquidity constrained for credit other than credit card

62.3 14.8 10.0

66.5 15.0 10.1

67.2 16.9 10.5

72.6 17.0 11.2

Percent that has Bank-Type Card but no balance and no 18.3 new charges: of which: percent liquidity constrained 6.9 of which: percent liquidity constrained for credit other than credit card

10.7

7.3

8.3

7.2

9.7 6.6

10.7 8.2

8.8 6.6

7.5 6.7

Percent that has Bank-Type Card; no balance but has new 30.5 charges of which: percent liquidity constrained 4.4 of which: percent liquidity constrained for credit other than credit card

36.8

36.7

36.9

39.4

5.2 3.4

5.6 3.9

6.1 3.9

6.4 4.3

51.1

52.7

52.4

54.1

17.7 11.5

14.7 10.1

18.0 11.7

18.5 13.6

48.9

47.3

47.6

45.9

27.5 19.0

29.7 19.3

33.6 19.9

35.2 20.7

56.1 Percent with Bank-Type Card; balance but at least sometimes pays balance in full of which: percent liquidity constrained 10.3 of which: percent liquidity constrained for credit other than credit card Percent with Bank-Type Card; balance and hardly ever 43.9 pays balance in full of which: percent liquidity constrained 16.0 of which: percent liquidity constrained for credit other than credit card

45

Table 6: Credit card limits and interest rates charged on card with the highest balance 1995, 1998, 2001 Surveys of Consumer Finance All Figures in 2001 Dollars No balance and no No balance but has Carries balance and at least new Charges new charges sometimes pays it off in full Median credit limit interest credit limit interest balance credit limit interes card on rate on bank- rate on on t utlizatio bank-type type bank-type bank-type rate n credit credit cards cards credit rate Cards cards 1995 6,679 15.0 13,359 16.0 1,162 13,359 14.0 15.1 1998 7,620 14.0 10,885 15.0 1,087 10,885 14.8 19.8 2001 7,500 16.0 15,000 15.0 1,000 10,000 13.0 17.5 1995

Carries balance and hardly ever pays it off Median balance credit limit interest card on bank- on rate utlization type bank-type rate cards credit cards 2,905 3,260 2,800

9,351 8,708 7,500

15.0 16.0 16.0

38.3 50.6 50.0

by age: less than 35 35-54 55-64 65 +

2,672 6,679 6,679 6,679

14.0 13.9 14.0 16.0

10,153 13,359 13,359 12,023

15.3 16.0 15.0 17.0

1,045 1,510 1,742 325

9,351 13,359 13,359 12,023

14.8 14.0 13.5 14.4

15.9 17.4 15.4 6.7

2,903 3,136 2,671 1,161

6,679 10,153 8,015 9,351

14.0 16.0 14.7 15.9

44.4 39.8 35.3 16.8

by education: less than high school high school some college college degree +

6,679 6,011 6,679 6,679

15.0 15.0 14.5 14.5

8,015 10,019 12,824 14,694

17.0 16.9 16.9 16.5

592 1,045 1,161 1,510

11,622 9,351 13,359 16,030

15.9 14.0 14.5 14.0

10.9 17.4 14.5 14.6

1,510 2,439 2,323 3,252

6,679 7,080 9,351 13,359

15.0 16.0 14.0 15.0

27.3 39.8 43.6 36.9

by income: lt $10,000 $10,000-$24,999 $2,5000-$49,999 $50,000-$99,999 $100,000 +

3,473 4,008 6,679 6,679 6,679

15.0 17.0 14.5 14.5 12.0

6,679 9,351 11,355 13,349 21,379

18.0 17.9 16.9 14.0 16.5

290 813 1,278 1,278 2,324

8,015 7,247 11,622 14,695 26,717

13.9 16.5 14.0 14.0 14.0

9.9 20.6 17.4 12.7 16.8

1,743 2,208 2,324 3,486 6,043

6,679 6,679 6,679 12,023 26,717

15.9 16.0 15.0 14.7 15.0

57.8 46.9 41.7 30.2 37.3

by age: less than 35 35-54 55-64 65 +

3,810 9,361 10,885 6,531

12.9 13.5 14.0 15.0

10,885 16,328 13,062 10,885

14.0 14.9 14.9 15.0

1,042 1,194 1,086 869

6,531 10,885 14,151 8,164

15.0 14.0 15.0 15.0

28.9 18.6 14.4 14.4

2,498 3,258 3,801 1,629

5,769 10,885 10,885 10,885

16.0 15.9 18.0 15.7

60.9 45.4 57.7 38.9

by education: less than high school high school some college college degree +

5,443 7,293 10,885 10,885

15.0 13.0 14.9 14.0

10,885 8,708 10,885 16,328

15.0 15.0 14.0 15.0

760 869 1,194 1,303

6,096 8,164 8,708 13,062

17.0 15.0 14.5 14.0

30.4 17.6 24.9 16.1

2,172 2,715 3,475 4,778

4,898 7,946 7,620 10,885

18.0 16.0 17.0 15.0

56.9 46.8 47.4 53.3

by income: lt $10,000 $10,000-$24,999 $2,5000-$49,999 $50,000-$99,999 $100,000 +

2,286 5,443 7,620 13,062 10,855

12.0 15.9 12.5 14.0 13.0

5,443 9,797 10,885 13,062 21,770

14.0 15.1 15.0 14.8 15.0

554 652 1,087 1,087 2,173

5,443 5,443 8,164 11,974 20,682

15.0 15.0 14.5 14.0 14.0

29.9 23.2 22.4 15.4 20.0

2,390 1,630 3,260 3,803 5,433

2,939 4,898 7,620 11,974 16,328

18.0 18.0 16.9 14.0 15.9

66.5 53.3 50.9 44.5 51.9

by age: less than 35 35-54 55-64 65 +

5,000 8,700 9,600 8,000

14.9 14.0 17.0 17.0

10,000 15,000 20,000 15,000

14.0 15.0 15.6 16.0

1,000 1,200 1,000 600

7,000 13,000 10,000 10,000

14.0 13.0 12.0 14.9

31.0 17.3 11.5 8.8

3,000 3,000 3,000 1,500

6,000 8,000 8,000 9,000

16.0 16.0 18.0 14.9

65.5 47.8 48.5 35.4

by education: less than high school high school some college college degree +

6,000 7,700 5,000 7,500

16.3 16.0 14.9 15.0

10,000 10,000 10,000 20,000

16.7 16.0 14.9 15.5

500 900 1,000 1,500

5,500 10,000 10,000 15,000

15.0 14.0 13.5 12.5

20.8 15.5 23.3 16.4

1,200 2,000 3,000 4,000

5,000 6,000 7,500 11,000

17.0 16.9 17.0 14.0

47.0 53.0 59.1 49.5

by income: lt $10,000 $10,000-$24,999 $2,5000-$49,999 $50,000-$99,999 $100,000 +

8,500 5,000 5,800 7,700 20,000

15.0 17.0 15.0 17.0 14.0

5,000 6,500 11,000 15,000 25,000

15.0 17.0 15.0 14.0 16.7

710 500 800 1,500 2,300

2,500 5,000 9,000 15,000 20,000

18.0 14.5 14.9 11.0 13.0

40.0 21.2 20.0 14.7 17.0

1,000 1,800 2,700 4,000 4,000

2,500 4,500 6,000 12,000 15,000

18.9 16.0 16.9 14.0 15.0

50.0 60.0 60.0 50.0 31.9

1998

2001

46

Table 7 Median Liquid Assets, Total Financial Assets, and Bank-Type Credit Card Balances of U.S. Households by Credit Card Payment Patterns 1983, 1992, 1995, 1998, and 2001 Surveys of Consumer Finance All Figures in 2001 Dollars Card holders with no balance Cardholders who carry balance but and no new charges but made new "almost always" or "sometimes" pay hardly ever pay off in full charges off in full Net Net Net Net Total Total Total Total Net Total Total Median Total Total Median Total Total Financial Worth Liquid Financial Worth liquid Financial Worth Liquid Financial Worth balance on Liquid balance Liquid Financial Worth` Assets Assets assets assets Assets Assets bank type Assets Assets on Assets Assets cards bank-type cards

Non-card holders

1983 1992 1995 1998 2001 1983 by age: lt 35 35-54 55-64 65 + by education less than high school high school some college college degree + by income lt $10,000 10,000-24999 25000-49999 50000-99999 100,000 + 1992 by age: lt 35 35-54 55-64 65 + by education less than high school high school some college college degree + by income lt $10,000 10,000-24999 25000-49999 50000-99999 100,000 +

889 252 277 435 400

2,488 884 1,158 1,401 1,300

27,929 13,330 15,603 13,661 12,200

7,247 3,787 3,699 4,278 5,000

23,902 25,246 22,538 33,961 25,400

133,136 133,425 111,246 117,776 116,000

12,706 11,108 8,668 10,885 13,020

54,941 76,407 73,393 96,223 125,300

214,701 220,524 211,280 244,477 319,250

711 800 1,162 1,087 1,000

3,821 3,042 2,427 4,071 4,000

15,994 18,871 21,960 30,478 32,500

101,096 95,910 80,079 97,682 101,260

1,244 1,800 2,905 3,260 2,800

2,133 1,515 1,618 2,177 1,500

8,928 7,485 10,460 14,151 8,550

56,782 43,297 48,763 42,452 39,430

480 967 2,388 1,777

968 3,199 6,719 7,748

6,140 48,506 71,448 62,035

5,247 7,002 7,642 11,995

10,766 20,081 43,786 57,756

29,850 138,611 184,441 187,007

5,420 12,440 17,327 19,193

15,822 50,114 114,737 115,898

48,915 217,342 300,742 301,790

533 711 711 576

2,133 4,414 3,821 5,198

7,508 21,325 22,392 34,241

44,108 114,543 156,627 110,534

1,066 1,244 889 970

1,777 3,021 1,422 613

6,131 10,041 9,392 613

34,510 75,177 106,507 53,578

467 1,022 1,066 2,799

1,155 3,554 2,844 8,886

22,082 32,119 17,423 57,228

3,554 6,398 8,352 10,307

12,316 23,902 22,557 38,422

101,270 128,858 119,011 154,125

7,338 8,886 11,516 18,660

31,878 32,992 52,139 80,503

139,574 189,262 210,879 272,114

533 551 711 800

1,822 3,243 4,002 6,131

5,893 9,774 26,577 23,509

100,956 78,070 123,186 117,806

1,066 1,155 1,422 1,244

1,066 1,654 2,088 3,465

6,780 8,379 5,047 11,409

39,461 60,750 53,594 67,278

44 467 1,422 3,377 20,661

203 1,066 3,764 19,255 106,760

3,643 10,570 35,761 111,089 345,287

* 3,879 7,528 8,175 38,826

* 8,823 22,557 27,719 159,531

* 86,165 93,280 138,220 382,833

* 3,865 7,286 13,506 34,654

* 19,815 23,328 57,223 267,458

* 87,098 120,978 196,465 850,652

* 622 800 889 800

* 1,550 2,342 4,530 14,750

* 4,883 8,352 23,591 57,409

* 38,604 71,536 117,806 324,230

* 889 1,244 1,066 4,442

* 613 1,599 2,843 8,886

* 1,955 5,278 12,664 26,138

* 22,658 41,501 81,864 115,208

88 189 252 1,262

379 732 884 4,418

2,714 11,234 27,543 53,080

3,534 3,534 2,525 5,302

9,846 22,216 31,558 34,208

33,200 112,850 167,255 148,825

5,239 10,565 13,822 15,148

23,416 92,779 131,910 94,420

66,662 236,138 332,237 268,807

1,010 1,010 884 884

2,121 4,418 3,320 3,282

5,491 25,498 63,999 19,793

24,668 109,631 181,064 138,790

1,389 2,525 2,525 1,641

1,250 1,856 2,272 947

3,724 10,351 10,351 2,525

12,232 64,125 95,938 64,213

126 379 417 985

265 1,010 1,893 4,355

10,704 12,143 16,284 31,558

2,146 4,166 3,030 5,807

13,254 21,068 19,882 35,445

89,245 134,877 103,698 151,615

6,943 9,467 10,730 11,992

40,520 61,348 81,671 93,789

164,478 200,581 235,545 247,663

1,262 631 884 1,010

1,389 2,525 2,777 4,166

11,840 15,148 13,254 28,528

108,659 83,249 88,614 108,798

1,262 2,020 1,893 3,157

568 1,452 1,553 2,146

1,262 6,059 6,690 11,361

44,054 35,269 39,384 58,482

13 153 631 2,146 12,623

126 631 2,903 13,393 30,926

1,893 10,843 23,100 70,891 177,984

947 3,787 3,661 6,690 7,826

4,671 15,021 22,216 39,131 96,541

38,412 103,130 133,425 138,853 257,295

2,777 4,393 7,952 10,730 27,771

17,925 21,459 54,304 72,230 266,598

23,605 89,484 179,499 210,388 746,273

1,010 947 757 1,010 1,262

631 1,264 2,272 3,787 16,284

3,408 4,418 11,752 25,498 81,545

54,001 35,029 74,072 108,621 280,736

1,010 1,262 1,893 2,651 5,049

454 619 1,262 2,651 11,234

871 2,083 5,945 13,393 56,804

16,991 32,264 30,030 71,875 206,108

47

Table 7 Median Liquid Assets, Total Financial Assets, and Bank-Type Credit Card Balances of U.S. Households by Credit Card Payment Patterns

(cont'd)

1983, 1992, 1995, 1998, and 2001 Surveys of Consumer Finance All Figures in 2001 Dollars Non-card holders

Card holders with no balance and no new charges

Card holders with no balance but made new charges

Cardholders who carry balance but Cardholders who carry balance but "almost always" or "sometimes" pay hardly ever pay off in full off in full Median Median balance on Total Total balance on Total Total bank-type liquid Financial Net bank-type liquid Financial Net cards assets assets Worth cards assets assets Worth

Total liquid assets

Total Financial assets

Net Worth

Total liquid assets

Total Financial assets

Net Worth

Total liquid assets

Total Financial assets

Net Worth

58 231 347 925

474 1,791 705 2,196

4,843 17,164 26,237 55,733

1,040 4,392 9,246 5,548

8,356 27,369 41,840 23,232

30,282 128,063 150,138 131,414

4,623 8,091 9,246 12,483

23,347 78,363 123,566 86,801

59,177 211,280 319,579 251,502

1,045 1,510 1,742 325

1,618 3,340 2,543 4,623

8,830 31,345 24,561 36,754

31,785 119,105 142,367 123,416

2,903 3,136 2,671 1,161

1,214 1,734 1,502 1,849

5,005 12,251 13,349 10,749

11,523 64,089 77,670 79,866

59 347 254 1,156

347 1,422 1,156 7,513

12,275 18,493 9,189 31,022

4,623 2,485 4,276 3,999

21,036 22,885 27,369 23,116

121,914 110,541 102,404 103,213

4,623 6,935 8,669 10,402

21,960 61,489 64,759 108,622

134,951 193,193 205,964 255,432

592 1,045 1,161 1,734

2,312 1,734 2,312 3,571

11,015 14,898 18,435 31,149

87,517 92,741 83,911 90,233

1,510 2,439 2,323 3,252

982 1,156 1,803 2,219

3,814 6,785 9,709 19,140

36,662 43,805 41,678 58,368

0 347 578 2,219 5,652

69 1,156 3,467 18,527 34,096

2,520 13,338 26,930 59,061 112,979

2,312 3,467 2,890 3,699 22,307

8,091 14,101 34,443 21,960 96,278

76,664 102,404 131,414 110,541 415,048

1,734 3,930 6,588 11,026 26,583

7,166 23,232 54,091 95,931 310,332

20,920 111,766 185,275 244,452 724,455

290 813 1,278 1,278 2,324

1,040 1,248 1,907 3,363 10,402

3,930 4,219 18,493 37,564 85,529

29,253 48,312 67,984 113,731 268,550

1,743 2,208 2,324 3,486 6,043

566 647 1,271 2,658 6,184

1,075 2,890 8,322 21,209 58,541

4,300 33,981 35,830 61,812 169,336

174 327 631 1,306

620 1,306 2,057 5,029

3,374 15,587 28,911 72,930

2,286 6,313 6,531 4,354

12,344 35,071 64,222 34,832

29,368 117,231 139,219 146,948

4,354 10,885 13,062 12,953

23,610 93,938 138,240 130,337

54,654 258,519 316,862 278,656

1,042 1,194 1,086 869

2,395 4,898 5,225 3,472

8,545 38,206 47,415 24,165

31,828 118,973 148,744 114,510

2,498 3,258 3,801 1,629

1,252 2,286 3,048 2,068

4,572 23,839 23,457 9,579

8,675 66,594 74,203 85,382

109 545 653 1,089

490 2,188 1,961 10,450

8,708 16,491 13,693 46,098

2,286 2,634 10,493 6,096

30,228 13,269 37,009 85,121

91,358 111,125 134,854 187,331

7,837 9,361 9,034 12,300

40,819 83,325 72,385 141,505

152,259 206,869 214,761 294,004

760 869 1,194 1,303

1,850 2,482 4,354 5,987

6,967 21,389 30,478 53,337

67,552 78,274 97,040 125,221

2,172 2,715 3,475 4,778

1,089 1,742 2,068 3,113

11,974 9,426 13,334 25,374

31,817 43,257 41,766 50,561

1 327 980 1,959 12,572

131 795 5,660 16,437 291,577

1,959 6,640 29,477 58,942 378,852

762 2,286 4,191 8,164 17,971

1,056 15,892 29,934 47,132 174,444

91,358 80,549 100,501 176,555 279,702

2,504 3,461 8,817 11,429 27,212

8,708 40,819 42,854 114,510 350,323

92,991 152,259 173,126 265,812 756,834

554 652 1,087 1,087 2,173

1,143 1,089 2,504 5,606 12,017

2,612 2,743 19,038 53,380 124,415

21,639 30,707 79,069 142,463 287,689

2,390 1,630 3,260 3,803 5,433

218 1,034 1,306 3,277 6,966

958 1,948 9,165 31,131 115,381

8,588 11,135 28,192 76,739 204,856

150 300 560 1,500

500 1,200 2,000 4,400

4,140 10,430 40,800 66,000

2,700 4,000 8,000 10,901

10,900 22,700 40,000 25,400

78,030 155,600 174,900 114,840

6,000 12,700 17,000 17,200

32,505 114,500 283,300 179,600

69,550 297,000 542,002 405,300

1,000 1,200 1,000 600

2,190 4,350 5,360 7,500

7,590 45,500 40,740 26,700

19,390 133,100 177,379 147,700

3,000 3,000 3,000 1,500

1,420 2,000 1,500 1,110

4,350 13,320 13,150 4,150

7,700 53,620 76,580 74,000

100 500 660 1,600

300 1,400 2,300 12,670

8,400 15,100 10,250 41,500

4,500 6,000 4,000 8,000

17,500 22,000 22,700 43,900

112,500 90,700 155,600 179,000

11,100 7,000 12,400 17,200

46,000 69,700 122,000 218,300

196,820 197,800 304,600 465,300

500 900 1,000 1,500

2,000 3,000 3,700 5,820

9,000 23,330 19,320 60,950

59,200 87,800 71,070 178,001

1,200 2,000 3,000 4,000

1,000 1,200 1,520 2,400

2,800 6,800 9,200 22,460

23,110 34,800 34,050 69,500

10 230 1,000 1,600 4,950

40 700 4,610 15,000 98,200

1,600 7,800 23,300 70,750 249,100

1,100 3,400 7,000 6,200 21,100

1,110 10,901 31,000 32,400 127,420

60,390 79,190 110,600 193,400 467,270

2,000 4,400 7,300 12,200 38,500

14,540 37,900 63,820 118,900 452,200

67,000 137,400 197,420 304,100 980,500

710 500 800 1,500 2,300

645 1,500 2,500 5,500 10,300

800 4,500 13,650 49,000 148,300

14,900 41,280 49,700 130,000 382,129

1,000 1,800 2,700 4,000 4,000

250 720 1,400 2,950 6,430

650 1,400 6,590 29,000 79,300

6,300 9,975 24,100 74,240 189,700

1995 by age: lt 35 35-54 55-64 65 + by education less than high school high school some college college degree + by income lt $10,000 10,000-24999 25000-49999 50000-99999 100,000 + 1998 by age: lt 35 35-54 55-64 65 + by education less than high school high school some college college degree + by income lt $10,000 10,000-24999 25000-49999 50000-99999 100,000 + 2001 by age: lt 35 35-54 55-64 65 + by education less than high school high school some college college degree + by income lt $10,000 10,000-24999 25000-49999 50000-99999 100,000 +

49

Table 8: Median Liquid Assets, Total Financial Assets, and Bank-Type Credit Card Balances of U.S. Households that Carried a Credit Card Balance, by High Liquid Asset Holding 1995, 1998, and 2001 Surveys of Consumer Finances All Figures in 2001 Dollars Households with Bank-type Card Balance Greater than Liquid Assets Median balance on bank-type cards 2,440 3,260 2,500

Liquid assets 982 1,089 1,000

67.7 60.3 58.2 48.2

2,324 3,138 2,324 1,162

867 1,156 809 670

3,699 11,292 7,165 5,779

12,367 68,851 88,176 79,866

by education: less than high school high school some college college degree +

60.2 67.3 60.5 55.6

1,743 2,092 2,324 3,486

462 751 982 1,387

1,676 5,328 6,704 16,990

by income: lt $10,000 $10,000-$24,999 $2,5000-$49,999 $50,000-$99,999 $100,000 +

70.2 67.2 63.8 57.4 45.0

1,511 2,092 2,557 3,138 5,810

347 462 925 1,503 3,514

1998 by age: less than 35 35-54 55-64 65 +

41.7 38.0 33.1 21.0

2,608 3,803 3,803 3,368

by education: less than high school high school some college college degree +

61.8 56.8 53.9 46.9

by income: lt $10,000 $10,000-$24,999 $2,5000-$49,999 $50,000-$99,999 $100,000 +

Households with Liquid Assets Greater than Bank-type Card Balance (and at least $1,000 and at least one-half monthly income) Median balance on bank-type cards 1,162 1,087 1,000

Liquid assets 6,588 7,837 8,000

32.3 39.7 41.8 51.8

1,081 1,394 1,743 383

4,635 7,975 6,993 6,599

18,435 42,649 44,787 49,410

45,423 123,093 159,385 137,829

35.5 34.9 35.8 36.1

22,249 45,585 40,834 60,471

39.8 32.7 39.5 44.4

825 1,162 1,046 1,511

3,930 6,761 5,779 8,091

13,292 37,564 36,986 42,996

87,517 113,765 95,226 119,510

43.3 31.1 41.1 31.2

774 1,167 7,166 16,644 47,041

6,068 15,846 36,489 72,550 169,336

29.8 32.8 36.2 42.6 55.0

151 709 1,162 1,511 2,440

3,583 4,276 4,623 7,859 21,960

8,333 13,292 24,561 51,433 134,304

27,589 80,328 83,067 134,951 337,378

30.2 38.8 37.7 34.7 23.4

1,001 1,306 1,393 577

3,314 18,221 15,326 2,112

8,675 51,160 94,808 64,232

58.3 62.0 66.9 79.0

1,087 1,304 1,195 760

4,441 8,817 8,599 6,749

11,538 57,146 72,821 35,921

31,512 127,790 192,621 130,903

40.1 36.6 24.3 21.0

2,282 3,260 3,477 4,781

435 1,089 1,143 1,524

2,112 66,966 12,137 20,028

31,022 39,077 34,299 47,676

38.2 43.2 46.1 53.1

761 761 1,195 1,521

5,443 4,844 7,717 9,666

22,314 27,757 37,009 63,133

98,455 96,267 104,768 125,221

41.7 36.3 34.8 29.1

72.3 57.1 57.4 48.1 40.7

1,956 1,956 3,585 4,346 6,084

218 381 925 2,068 4,572

675 925 6,390 26,668 90,346

9,285 5,693 30,141 67,106 208,557

27.7 42.9 42.6 51.9 59.3

272 652 1,087 1,304 2,173

2,558 3,048 5,769 9,002 17,416

5,769 9,579 23,457 61,609 155,797

15,402 48,003 81,692 150,344 303,006

16.5 34.7 36.9 34.3 24.7

2001 by age: less than 35 35-54 55-64 65 +

66.6 55.5 44.1 41.4

2,500 2,600 3,080 2,000

950 1,200 600 900

2,850 11,500 6,100 1,880

7,180 53,960 43,260 76,500

33.4 44.5 55.9 58.6

1,000 1,500 1,200 400

5,000 8,000 10,000 8,000

19,420 52,400 75,720 37,550

38,350 146,800 196,730 147,700

35.5 31.2 30.2 22.8

by education: less than high school high school some college college degree +

60.6 60.0 59.7 46.7

1,000 2,000 2,900 4,000

500 910 1,000 1,500

1,100 5,030 5,750 16,560

17,350 36,800 22,840 54,850

39.4 40.0 40.3 53.3

700 700 800 1,700

4,600 7,100 8,000 9,700

12,300 33,630 27,950 74,300

80,750 96,720 121,250 195,910

14.7 28.8 22.3 34.2

by income: lt $10,000 $10,000-$24,999 $2,5000-$49,999 $50,000-$99,999 $100,000 +

87.3 60.0 58.1 51.7 40.4

1,000 1,700 2,900 4,000 4,000

245 500 870 2,000 4,000

510 1,000 3,300 22,910 79,300

6,410 9,975 17,350 69,400 223,500

12.7 40.0 41.9 48.3 59.6

480 510 700 1,200 2,500

1,580 3,300 5,300 9,300 17,200

6,100 10,150 25,360 58,600 165,000

61,850 5,010 81,860 169,150 399,050

13.8 29.4 41.2 28.1 19.5

1995 1998 2001 1995 by age: less than 35 35-54 55-64 65 +

Percent of card-holders with balance 61.0 53.0 55.5

Total financial Total net assets worth 7,478 48,140 10,341 38,391 6,100 36,295

49

Percent of card-holders with balance 39.0 47.0 44.5

percent that Total "hardly ever financial Total net pay off" in assets worth full 36,986 104,854 35.5 37,009 107,587 33.8 40,710 127,400 30.8

Endnotes 1

We use information from the 1983, 1992, 1995, 1998, and 2001 waves of the SCF. For a more complete discussion of features of the SCF as well as findings from the 2001 wave, see Aizcorbe, Kennickell, and Moore (2003). 2 For ease of comparison, all dollar figures are converted to 2001 prices using the urban consumers all items Consumer Price Index. 3 The omitted dummy variable is some college but not a four-year degree. 4 The probit regressions also include a number of other explanatory variables that have been found to be significant in explaining credit card ownership, including marital status, number of children, whether the household head is self-employed or not currently employed, whether the household can be considered “liquidity constrained” because it has been turned down for credit or discouraged from applying for credit, whether the household over the past year spent less than its income, and whether the household had a checking account. 5 In comparison, the estimated probability that this same individual would have any type of credit card rises from .63 in 1983 to .78 in 2001. 6 In estimating the probabilities of card ownership, income and financial assets were assigned using the median values for the age-education range under consideration. For a typical less-than35 year old with high school education, the median income was slightly over $30,000 (in 2001 $) with about $1,900 in financial assets. For the same aged household with a college degree, the median income is just under $45,000 and median financial assets were just under $10,000. For a household aged 50-64 with a college education, median income was just under $82,000 and median financial assets were slightly over $111,000. All representative households were assumed to be “savers,” all were assumed to have a checking account, and none were assumed to be liquidity constrained. 7 In the 1992 SCF, the question was about debit card ownership. In subsequent surveys, the question was rephrased to elicit a response explicitly on debit card usage. Thus, the 1992 figure is an upper bound to actual debit card use in 1992. 8 For observations from the wave of the 1992 SCF, the dependent variable is debit card ownership instead of debit card use as in the 1995-2001 waves. However, the including the 1992 sample provides a useful base from which to measure the spread of debit card use, and size and significance of coefficients on the explanatory variables are little affected if the regression is run instead only on the 1995-2001 subset. 9 In contrast, about two-thirds of all U.S. households had home equity, and slightly over half of households with incomes under $50,000. 10 Information on new charges was not asked of households in 1983. We use instead a question that asked about frequency of use of the card in question. We consider households who had no balance on their bank-type card and answered that the card was used “hardly ever” or “never” to be non-active card users. 11 As noted by Gross and Souleles (2002a, p. 151n) and others, SCF data are subject to the limitation that households substantially underreport their bankcard debt. As an example, Gross and Souleles compare the average credit card debt (including retail-store debt) across households with credit cards given by the 1995 SCF to that given by aggregate data on revolving consumer credit collected by the Board of Governors of the Federal Reserve System. The figures are around $ 2,000 and $5,000, respectively. 12 Since utilization rates are based on reported credit card balances, they are subject to the same underreporting that we noted in footnote 6 above for credit card balances. Gross and Souleles (2002a) tend to find greater utilization rates based on administrative account data than those reported here, but their unit is an account rather than a household, and they have less information on demographics and on the overall household portfolio.

50

13

Less than 1 percent of card holder households without a card balance were behind two months or more on any type of loan payment. 14 This relationship between the size of the credit card balance to available liquid assets holds even accounting for an understatement of credit card debt in the SCF as indicated in Gross and Souleles. Indeed, readily available resources to pay off the balance are somewhat understated, as the SCF does not collect data on cash held by respondents. 15 Note that some holders classified as “unaware” under the stricter criterion may be actually facing very low (‘teaser’) rates. 16 For example, Ausubel estimated that, while the ordinary pre-tax return on equity in banking for the 50 largest issuers of credit cards was on the order of 20 percent per year, these issuers earned annual returns of 60 to 100 percent or more on their credit card business during the period. 17 Examples include costs of locating other providers and of filling in new applications, and the perception that credit ratings and credit limits are functions of the length of time during which a particular credit card account is held, but he doubts that they are sufficiently important by themselves. 18 Although banks do not alter interest rates, they can be generous to credit-worthy customers by providing grace periods, small or no annual fees, and points or miles; and strict with bad customers by imposing heavy penalties on those who miss their payments or exceed their limits. 19 As Brito and Hartley (1995) put it, ‘[t]he most desirable customers are those who borrow a substantial amount on their cards and yet remain well within their credit limits and therefore are unlikely to default’ (p. 409). 20 Previous empirical studies of bankruptcy using household-level data include Moss and Johnson (1999), and Domowitz and Sartain (1999). The first uses data from the Survey of Consumer Finances, which does not include observations on bankruptcy filings. The second combines SCF data with a separate data set on a relatively limited set of bankruptcy petitions under Chapter 7 in the early 1980’s, and finds that households with more credit card debt were more likely to file for bankruptcy. 21 The probability of an average credit card holder to declare bankruptcy rose by 1 percentage point, and that to declare delinquency by 3 percentage points over the period. 22 Although income variables turn out to be significant in predicting bankruptcy, they may also be interpreted as reflecting unmeasured changes in wealth for the years in which the PSID did not collect wealth data. More direct measures of financial need in the data turn out to be insignificant or marginally significant, but of course this may be because better proxies for need are not available and not because need does not play a role. 23 Quantitatively, their model predicts that an increase of $1,000 in households’ financial benefit from bankruptcy would result in a 7-percent increase in the number of bankruptcy filings. 24 This is a random household telephone survey conducted monthly by the Ohio State University Survey Research Center, and it includes variables unavailable in other surveys. The period is February 1998 through May 1999, with additional data from September 1999. 25 It is obvious that debt levels would rise more in response to any given increase if the customer had requested it with a specific expenditure in mind than if the bank initiated it. 26 Households exhibit such behavior when they are characterized by “prudence”, usually identified with a positive third derivative of the felicity function. 27 The household has a finite lifetime of uncertain length, an effective size that varies over its lifecycle, and a bequest motive. It is faced with non-diversifiable income risk and age-income profile determined by its education level and estimated from PSID data. 28 This desired consumption level will not be the same in general as the level that would prevail in the absence of the problem of self-control. This is because the household bears costs in the effort to restrain the behavior of the shopper.

51

29

Reda (2003), quoted in Zinman (2004), reports that debit cards tend to be used for smaller transactions involving instantaneous consumption, while credit cards are used for larger transactions of more durable items. 30 Haliassos and Reiter (2005) compare simulated costs of revolving credit card debt and of using a debit card to cope with a self-control problem. They find that, even if we abstract from bonuses and fraud protection offered by credit cards, the benefits from switching to debit cards are small for a household with a self-control problem. These can plausibly be eliminated by such additional benefits offered by credit cards and by any differential transaction fees or informational requirements in acquiring the newer instrument, debit cards.

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