THE AMERICAN ECONOMIC REVIEW MARCH 1991 CredIt Card Interest Rate 10% Cost of Funds 0L⊥⊥1111⊥⊥⊥⊥⊥⊥⊥⊥ 19821983198419851988198719881989 FIGURE 1. STICKY CREDIT CARD INTEREST RATES, 1982-1989 Credit card interest rate is the quarterly Federal Reserve System series; cost of s the quarterly car Treasury bill yield plus 0. 75 percen funds. First, using the Federal Reserve se- rate is regressed on its own lagged value ries, an aggregate credit card interest rate is the lagged cost of funds egressed on its own lagged value, the lagged variable for that bank.The of the ost of funds, and a constant. Second, a linear regressions are reported in Table 3 more thorough regression can be run using Note that, in the second regression, every the author's bCCs series for the 17 individ- coefficient has a t statistic of at least 6 al banks: each banks credit card interest while inclusion of additional variables with TABLE 2-SIZE OF BANK CREDIT CARD ISSUERS FOR WHICH DATA ARE REPORTED Number of banks Number of banks Number of banks 1987 ranking with BCcs with bccs of active accounts mputation estimation of profits rate series 3 2 41 51+ 0 2 This content downloaded from 202. 120. 224.93 on Sun. 17 Dec 201707: 42: 57 UTC Allusesubjecttohttp:/about.jstor.org/terms
54 THE AMERICAN ECONOMIC REVIEW MARCH 1991 20% ....... Credit Card Interest Rate 5% _ 1982 1983 1984 1985 1988 1987 1988 10% Year FIGURE 1. STICKY CREDIT CARD INTEREST RATES, 1982-1989 Notes: Credit card interest rate is the quarterly Federal Reserve System series; cost of funds is the quarterly one-year Treasury bill yield plus 0.75 percent. funds. First, using the Federal Reserve se- ries, an aggregate credit card interest rate is regressed on its own lagged value, the lagged cost of funds, and a constant. Second, a more thorough regression can be run using the author's BCCS series for the 17 individ- ual banks: each bank's credit card interest rate is regressed on its own lagged value, the lagged cost of funds, and a dummy variable for that bank. The results of these linear regressions are reported in Table 3. Note that, in the second regression, every coefficient has a t statistic of at least 6, while inclusion of additional variables with TABLE 2-SIZE OF BANK CREDIT CARD ISSUERS FOR WHICH DATA ARE REPORTED Number of banks Number of banks Number of banks Number of banks 1987 ranking with BCCS with call with prospectuses with BCCS by number reports enabling reports enabling enabling reports of of active accounts computation computation estimation interest (1 = largest) of profits of profits of profits rate series 1-10 1 3 4 2 11-20 2 3 0 5 21-30 2 1 1 3 31-40 1 1 2 1 41-50 1 1 1 4 51+ 0 0 0 2 Total: 7 9 8 17 Sources: Author's bank credit card survey (BCCS), consolidated reports of condition and income (call reports), and prospectuses and registration statements. Ranks according to The Nilson Report, Number 406 (June 1987), pp. 6-7. This content downloaded from 202.120.224.93 on Sun, 17 Dec 2017 07:42:57 UTC All use subject to http://about.jstor.org/terms
VOL 8I NO. I AUSUBEL: CREDIT CARD MARKET TABLE 3--ORDINARY LEAST-SOUARES REGRESSION OF CREDIT CARD INTEREST RATE ON COST OF other lags tended to cause some coefficients FUNDS AND LAGGED CREDIT CARD INTEREST RATE to become insignificant. To aid in compar- QUARTERLY, 1982-1987) ing the results of the two regressions, the Fed series is used only for the period Federal Reserve 1982-1987; using 1982-1989 data yields the Variable same conclusior survey data survey data The coefficient on the cost of funds while statistically significant in each of the two (000584) .00896) regressions, is economically insignificant CREDIT CARD INTEREST Whereas a competitive-spot-market model RATE (0.0444) (0.0326) would predict a coefficient near 1, the re- gressions using aggregated and disaggre gated data yielded coefficients of only 0.042 Bank-1 dummy and 0.054, respectively. It takes many years Bank-2 dummy (0.659) for the price to adjust to changes in marginal (0.594) cost when the rate of adjustment is only on Bank-3 dummy he order of 5 percent per quarter. (0.719) Bank-4 dummy B. Nonprice Competition Bank-5 dummy (0.651) The credit card industry has defended Bank-6 dumm high interest rates in the mid-to-late 1980,s, in part, by asserting that the increased Bank-7 dummy spread between the credit card interest rate Bank-8 dumn and the cost of funds had been caused by an (0.595) ncrease in the industry's rate of bad loans Bank-9 dummy The loan-loss data from the authors bccs (0.644) Bank-10 dummy indicate that, in the period 1982-1987, the (0.634) charge-off rate actually did increase roughly Bank-1l dummy coincident with the increase in the interest (0.589) rate spread (see Table 4). However, higher Bank-12 dummy (0.595) loan losses are an explanation for the higher interest rate spreads only if one believes (0.586) that the latter are solely determined by costs. If credit card interest rates are determined (0.577) otherwise then the causation may run in the (0.636) reverse direction: an increased interest rate Bank-16 dummy spread may cause an increase in charge-offs Suppose, for example, that a bank can Bank-17 dummy select both its interest rate and the default (0.707) risk of its marginal customer. By choosing a Number of higher marginal default rate, the bank in creases its total number of loans but also its 0.937 charge-off rate(the average default rate Durbin h 0.10 Suppose that the bank first selects its inte Notes: CREDIT CARD INTEREST RATE is the de- est rate and then its marginal default rate pendent variable in each regression. COST OF FUNDS Profit maximization requires the bank to set defined as the yield on one-year Treasury bills plus 75 percent. Observe that there is no cross-firm varia ion in this variable, so that year dummy variables ded in the Federal reserve bulletin Banks included in the au t of 0.0439 on COSt UNDS,a 0.864 on CREDIT CARD standard errors NTEREST R This content downloaded from 202. 120. 224.93 on Sun. 17 Dec 201707: 42: 57 UTC Allusesubjecttohttp:/about.jstor.org/terms
VOL. 81 NO. 1 AUSUBEL: CREDIT CARD MARKET 55 TABLE 3-ORDINARY LEAST-SQUARES REGRESSION OF CREDIT CARD INTEREST RATE ON COST OF FUNDS AND LAGGED CREDIT CARD INTEREST RATE (QUARTERLY, 1982-1987) Federal Reserve Bank Board credit card Variable survey data survey data COST OF 0.0422 0.0540 FUNDS-1 (0.00584) (0.00896) CREDIT CARD INTEREST 0.895 0.685 RATE-1 (0.0444) (0.0326) Constant 1.51 (0.807) Bank-1 dummy 5.75 (0.659) Bank-2 dummy 5.11 (0.594) Bank-3 dummy 6.38 (0.719) Bank-4 dummy 5.79 (0.657) Bank-5 dummy 5.70 (0.651) Bank-6 dummy 4.18 (0.500) Bank-7 dummy 5.69 (0.645) Bank-8 dummy 5.12 (0.595) Bank-9 dummy 5.61 (0.644) Bank-10 dummy 5.44 (0.634) Bank-11 dummy 5.00 (0.589) Bank-12 dummy 5.12 (0.595) Bank-13 dummy 4.99 (0.586) Bank-14 dummy 4.88 (0.577) Bank-15 dummy 5.50 (0.636) Bank-16 dummy 5.22 (0.593) Bank-17 dummy 6.17 (0.707) Number of observations: 24 408 R 2: 0.96 0.937 Durbin h: - 0.69 0.10 Notes: CREDIT CARD INTEREST RATE is the de- pendent variable in each regression. COST OF FUNDS is defined as the yield on one-year Treasury bills plus 0.75 percent. Observe that there is no cross-firm varia- tion in this variable, so that year dummy variables cannot be included in the second regression equation. Banks included in the author's bank credit card survey were assured anonymity. Numbers in parentheses are standard errors. other lags tended to cause some coefficients to become insignificant. To aid in compar- ing the results of the two regressions, the Fed series is used only for the period 1982-1987; using 1982-1989 data yields the same conclusions.15 The coefficient on the cost of funds, while statistically significant in each of the two regressions, is economically insignificant. Whereas a competitive-spot-market model would predict a coefficient near 1, the re- gressions using aggregated and disaggre- gated data yielded coefficients of only 0.042 and 0.054, respectively. It takes many years for the price to adjust to changes in marginal cost when the rate of adjustment is only on the order of 5 percent per quarter. B. Nonprice Competition The credit card industry has defended its high interest rates in the mid-to-late 1980's, in part, by asserting that the increased spread between the credit card interest rate and the cost of funds had been caused by an increase in the industry's rate of bad loans. The loan-loss data from the author's BCCS indicate that, in the period 1982-1987, the charge-off rate actually did increase roughly coincident with the increase in the interest rate spread (see Table 4). However, higher loan losses are an explanation for the higher interest rate spreads only if one believes that the latter are solely determined by costs. If credit card interest rates are determined otherwise, then the causation may run in the reverse direction: an increased interest rate spread may cause an increase in charge-offs. Suppose, for example, that a bank can select both its interest rate and the default risk of its marginal customer. By choosing a higher marginal default rate, the bank in- creases its total number of loans but also its charge-off rate (the average default rate). Suppose that the bank first selects its inter- est rate and then its marginal default rate. Profit maximization requires the bank to set 15With the 1982-1989 Federal Reserve Bulletin se- ries, one obtains a coefficient of 0.0439 on COST OF FUNDS_ 1, a coefficient of 0.864 on CREDIT CARD INTEREST RATE_ 1, and a constant of 2.06. This content downloaded from 202.120.224.93 on Sun, 17 Dec 2017 07:42:57 UTC All use subject to http://about.jstor.org/terms
THE AMERICAN ECONOMIC REVIEW MARCH 1991 TABLE 4-LOAN LOSSES ON CREDIT CARDS OF TEN SURVEY BANKS DURING 1976-1987 Ill. The Ex Post Profitability of the Credit D THE SPREAD BETWEEN CREDIT CARD INTEREST RATES AND THE COST OF FUN As seen in Section I, the credit card m ket of the 1980,'s possessed most of the Inte orea te usual prerequisites for invoking the model Year percentage (percentage) of perfect competition. A perfectly competi- 10 run economic profits for“ marginal”frms. 10.36 Moreover, since free entry into the industry 1979 is possible and no input appears to be in scarce supply, there is no credible source of rents to distinguish“ inframarginal” firms from"marginal"firms. Thus, the competi tive model would predict that all credit card issuers earn zero long-run economic profits 11.55 Many models of imperfect competition which preserve the free-entry assumption Source: Author 's bank credit card survey (Appendix a, would also yield the zero-profit prediction Table Al, questions 7 and 1). By way of contrast, the interest rate stick iness documented in the previous section suggests that credit cards must become ex- ts marginal default rate equal to the dif- traordinarily profitable whenever the cost of ference between the interest rate it charges funds drops. Indeed, in this section, I will and the marginal cost (net of defaults) of present a rather paradoxical set of data lending funds. (The net marginal cost should which indicates that returns from the credit equal the cost of funds plus a constant that card business were several times greater is fairly stable in the short run. )Thus, the than the ordinary rate of return in banking prediction is that an optimizing bank should during the years 1983-1988 set its marginal default rate equal to the At the same time this profitability data dent reason why credit card interest rates interpreted. On do u that the above evidence interest rate spread plus a constant. will help to assure Suppose now that there is an indeper of interest rate stickiness has been correctly might have thought to ar- fail to fall when general market interest gue that price rigidity is consistent with rates decline(for example, see Section VI below ). The logic of the previous paragraph dictates that loan losses will subsequently cards, there is stickiness despite a deregulated increase. If firms do not compete and drive environment. Second, under regulation, the airlines price down toward marginal cost, they are appi Free entry is, a reasonable depiction of a credit ently competed away their profits. likely instead to compete and drive marginal card market in which 4,087 banks (and other deposit cost up toward price, o in the form of is ing cards to less credit-worthy customers institutions) already issued their own Visa card similar (largely overlapping) number issued thei Master Cards in September 1987. All of these instit ons could legally offer accounts to customers any where in the United States. Nonmember institutions LA related argu could join the Visa system by paying a fairly trivial eorge w. hat the ci rd's one thousand dollars, according to a visa official (Only price regulations, at a time when the introduction of jet the assets of the subsidiary that issues the cards, and engines reduced the fundamental cost of air trans- not the assets of the holding company are figured into portation es to compete and drive this formula. Furthermore, it would seem strained to p to price by er passengers o n requires airplane. The ar many years or tha put is in supply, given First. in the airli the deluge of cre solicitations made by banks in been caused by price regulation, whereas with credit recent years This content downloaded from 202. 120. 224.93 on Sun. 17 Dec 201707: 42: 57 UTC Allusesubjecttohttp:/about.jstor.org/terms
56 THE AMERICAN ECONOMIC REVIEW MARCH 1991 TABLE 4-LoAN LOSSES ON CREDIT CARDS OF TEN SURVEY BANKS DURING 1976-1987 (FEWER THAN NINE DURING 1976-1978 AND 1986-1987) AND THE SPREAD BETWEEN CREDIT CARD INTEREST RATES AND THE COST OF FUNDS Average charge-off Interest rate rate spread Year (percentage) (percentage) 1976 1.15 10.57 1977 0.99 10.36 1978 1.27 8.11 1979 1.44 5.78 1980 2.04 5.08 1981 1.48 2.94 1982 1.67 6.42 1983 1.32 8.79 1984 1.36 7.69 1985 1.94 9.82 1986 3.01 11.55 1987 2.60 10.43 Source: Author's bank credit card survey (Appendix A, Table Al, questions 7 and 1). its marginal default rate equal to the dif- ference between the interest rate it charges and the marginal cost (net of defaults) of lending funds. (The net marginal cost should equal the cost of funds plus a constant that is fairly stable in the short run.) Thus, the prediction is that an optimizing bank should set its marginal default rate equal to the interest rate spread plus a constant. Suppose now that there is an indepen- dent reason why credit card interest rates fail to fall when general market interest rates decline (for example, see Section VI, below). The logic of the previous paragraph dictates that loan losses will subsequently increase. If firms do not compete and drive price down toward marginal cost, they are likely instead to compete and drive marginal cost up toward price,16 in the form of issu- ing cards to less credit-worthy customers. III. The Ex Post Profitability of the Credit Card Market As seen in Section I, the credit card mar- ket of the 1980's possessed most of the usual prerequisites for invoking the model of perfect competition. A perfectly competi- tive model would at least predict zero long- run economic profits for "marginal" firms. Moreover, since free entry into the industry is possible and no input appears to be in scarce supply,17 there is no credible source of rents to distinguish "inframarginal" firms from "marginal" firms. Thus, the competi- tive model would predict that all credit card issuers earn zero long-run economic profits. Many models of imperfect competition which preserve the free-entry assumption would also yield the zero-profit prediction. By way of contrast, the interest rate stick- iness documented in the previous section suggests that credit cards must become ex- traordinarily profitable whenever the cost of funds drops. Indeed, in this section, I will present a rather paradoxical set of data which indicates that returns from the credit card business were several times greater than the ordinary rate of return in banking during the years 1983-1988. At the same time, this profitability data will help to assure that the above evidence of interest rate stickiness has been correctly interpreted. One might have thought to ar- gue that price rigidity is consistent with "A related argument was made in the context of airline regulation. George W. Douglas and James C. Miller (1974) argued that the Civil Aeronautics Board's price regulations, at a time when the introduction of jet engines reduced the fundamental cost of air trans- portation, led airlines to compete and drive their costs up to price by placing fewer passengers on a given airplane. The arguments differ in two important ways. First, in the airline industry, price rigidity may have been caused by price regulation, whereas with credit cards, there is price stickiness despite a deregulated environment. Second, under regulation, the airlines apparently competed away their profits. 17Free entry is a reasonable depiction of a credit card market in which 4,087 banks (and other deposit institutions) already issued their own Visa cards and a similar (largely overlapping) number issued their own MasterCards in September 1987. All of these institu- tions could legally offer accounts to customers any- where in the United States. Nonmember institutions could join the Visa system by paying a fairly trivial entry fee: six dollars per million dollars in assets, plus one thousand dollars, according to a Visa official. (Only the assets of the subsidiary that issues the cards, and not the assets of the holding company, are figured into this formula.) Furthermore, it would seem strained to argue either that adjustment to the "long run" requires many years or that some input is in scarce supply, given the deluge of credit card solicitations made by banks in recent years. This content downloaded from 202.120.224.93 on Sun, 17 Dec 2017 07:42:57 UTC All use subject to http://about.jstor.org/terms
VOL. 81 NO. I AUSUBEL: CREDIT CARD MARKET competitive spot markets, if unobservable Bank credit card increases in quality exactly offset reductions low-up survey n factor costs. The profitability data enable performed direct one to dismiss this possibility: profits, in of the 50 larges fact, dramatically rose at the time that the cards cost of funds dropped Call reports: Profitability data for another It is possible to object to the following nine of the extracted reported, by their very nature, represent FDIC call reports filed by the banks with the ex post profits. Perhaps(especially since the Prospectuses: Partial data on profitability for sample period is during a cyclical boom)the an additional eight large banks were ob- observed profits are merely a very favorable tained from filings with the SeC in con- realization of a random variable whose nection with the sale of credit-card ex ante returns were quite ordinary Second backed securities it might be thought that, while the credit card market was extremely profitable in Respondents to the author's survey were the years 1983-1988, the market has now promised anonymity (but details of the con quilibrated and henceforth normal returns struction are provided in Appendix A). The will be observed. Third, the profitability call reports and prospectuses are part of the figures might be derived from accounting public record. Table 2 reports the size dis- data that either are being misinterpreted or tribution of banks included in each of the are systematically misstating true economic survey, call report, and prospectus samples I consider each of these concerns else A. An Illustrative Profit Calculation where in the paper. In Section IV, I briefly discuss an additional source of evidence(the As will be detailed in the next two sub Federal Reserve Systems functional cost sections, earnings in the banking industry analysis), which, while significantly less reli- are usefully expressed as a percentage of able than the other data (in this author's assets: returns on assets are linked with opinion), gives profits over a longer period returns on equity by the banking system's that includes the previous cyclical down- capital requirements. Before reporting turn. In Section V, I introduce another in- mary profit figures for 15 and estimates for dependent set of data which examines eight of the 50 largest issuers, I will examine sale prices of credit card portfolios between in detail the components of revenues and banks and finds that they trade at large costs for one individual credit card issuer, I premia. The latter data indicate that ex ante consider here Maryland Bank, Na returns from credit cards are quite large (MBNA), the Delaware-based credit card and, since they are based on market valua- arm of MNC Financial. which is ranked tions, should help allay any fears that the seventh in Table 1. 18 This institution was accounting data are being misinterpreted. selected because more public information Finally, it should be recalled from Table 4 exists on its credit card operations than on that the interest rate spread was quite any other banks: MBNA, which is required healthy except for a brief period around to file its own call report, has credit card 1981 and that this brief spell of unprof- loans exceeding 92 percent of its assets, and ability can be attributed to banks not hav- it has also made several credit-card-backed ng yet established credit card subsidiaries securities offerings exploiting the Supreme Court's marquette ecision. This episode does not seem likely I MNC Financial is the 39th largest U.S ussed in this section originate from three National Bank. nthe the csr orate parent of Maryland The ex post profit data re and. MBna was founded in Newark. Di independent sources and were assembled by 1982, apparently to avoid Marylands usury the author also the text near footnote 9 This content downloaded from 202. 120. 224.93 on Sun. 17 Dec 201707: 42: 57 UTC Allusesubjecttohttp:/about.jstor.org/terms
VOL. 81 NO. 1 AUSUBEL: CREDIT CARD MARKET 57 competitive spot markets, if unobservable increases in quality exactly offset reductions in factor costs. The profitability data enable one to dismiss this possibility: profits, in fact, dramatically rose at the time that the cost of funds dropped. It is possible to object to the following analysis on several grounds. First, the data reported, by their very nature, represent ex post profits. Perhaps (especially since the sample period is during a cyclical boom) the observed profits are merely a very favorable realization of a random variable whose ex ante returns were quite ordinary. Second, it might be thought that, while the credit card market was extremely profitable in the years 1983-1988, the market has now equilibrated and henceforth normal returns will be observed. Third, the profitability figures might be derived from accounting data that either are being misinterpreted or are systematically misstating true economic profits. I consider each of these concerns else- where in the paper. In Section IV, I briefly discuss an additional source of evidence (the Federal Reserve System's functional cost analysis), which, while significantly less reli- able than the other data (in this author's opinion), gives profits over a longer period that includes the previous cyclical down- turn. In Section V, I introduce another in- dependent set of data which examines re- sale prices of credit card portfolios between banks and finds that they trade at large premia. The latter data indicate that ex ante returns from credit cards are quite large and, since they are based on market valua- tions, should help allay any fears that the accounting data are being misinterpreted. Finally, it should be recalled from Table 4 that the interest rate spread was quite healthy except for a brief period around 1981 and that this brief spell of unprof- itability can be attributed to banks not hav- ing yet established credit card subsidiaries exploiting the Supreme Court's Marquette decision. This episode does not seem likely to be repeated. The ex post profit data reported and dis- cussed in this section originate from three independent sources and were assembled by the author. Bank credit card survey: The author's fol- low-up survey yielded profit calculations performed directly by executives of seven of the 50 largest bank issuers of credit cards. Call reports: Profitability data for another nine of these issuers were extracted from call reports filed by the banks with the FDIC. Prospectuses: Partial data on profitability for an additional eight large banks were ob- tained from filings with the SEC in con- nection with the sale of credit-card- backed securities. Respondents to the author's survey were promised anonymity (but details of the con- struction are provided in Appendix A). The call reports and prospectuses are part of the public record. Table 2 reports the size dis- tribution of banks included in each of the survey, call report, and prospectus samples. A. An Illustrative Profit Calculation As will be detailed in the next two sub- sections, earnings in the banking industry are usefully expressed as a percentage of assets: returns on assets are linked with returns on equity by the banking system's capital requirements. Before reporting sum- mary profit figures for 15 and estimates for eight of the 50 largest issuers, I will examine in detail the components of revenues and costs for one individual credit card issuer. I consider here Maryland Bank, N.A. (MBNA), the Delaware-based credit card arm of MNC Financial, which is ranked seventh in Table 1.18 This institution was selected because more public information exists on its credit card operations than on any other bank's: MBNA, which is required to file its own call report, has credit card loans exceeding 92 percent of its assets, and it has also made several credit-card-backed securities offerings. 18MNC Financial is the 39th largest U.S. bank hold- ing company and the corporate parent of Maryland National Bank, the largest commercial bank in Mary- land. MBNA was founded in Newark, Delaware, in 1982, apparently to avoid Maryland's usury law. See also the text near footnote 9. This content downloaded from 202.120.224.93 on Sun, 17 Dec 2017 07:42:57 UTC All use subject to http://about.jstor.org/terms
THE AMERICAN ECONOMIC REVEn MARCH 1991 TABLE 5-COMPONENTS OF PROFITS FOR MARYLAND BANK, NA Finance charges 1492 13.21% 158% Other customer charges 1.42% 1.17% Total revenue. Net charge-offs 109% 1.77% 1.80% rofi urces: Consolidated reports of condition and income (call reports), prospectuses, and registration statements for Maryland Bank, N.a. MBNA's credit card operations(and their An item-by-item profit calculation for profitability) are fairly typical of major is- MBNA is displayed in Table 5. As is typical suers, with the exception that the bank has for credit card issuers, the single largest stressed the concept of " affinity credit component of revenue is the finance charge cards, whereby cards are marketed to (which, for MBNA, derives from annual members of professional organizations, fra- percentage rates of 14.5-18.9 percent, de- ternal orders, and cause- related groups (with pending on the account). Despite the fact the organizations'endorsements). As a con- that the bank provides a 25-day grace pe- sequence, its interest rates are somewhat riod during which no finance charge is as- lower and its customers are somewhat more sessed if the account balance is paid in full redit-worthy than average. Indeed it may more than 80 percent of the banks credit interest readers that, during the period when card outstanding balances do accrue inter his article was undergoing the journals re- est. The drop in finance-charge revenues iew process, MBNA's marketing agent pro- displayed in Table 5 is largely attributable posed to establish an official American Eco- to the banks decision to reduce the interest nomic Association Visa card. This card rates on some of its accounts during would have carried a $20 annual fee($40 1985-1987 for a gold card)and an 189-percent annual MBNA also derives direct customer rey. ee: the AEa would have received s1 for enues from the annual fee and other each account opened, $3 for each account tomer charges(e.g, $15 late payment, over- renewed, and $0. 25 per retail transaction. limit, and returned-check charges). Indirect MBNA S agent estimated that 1, 000 cards revenues are derived from the interchange would be issued, generating $13 in revenue fee, the portion of the merchant discount per card per year for the AEA. However, that is paid to the customer's bank. It is the AEAs executive committee, concerned worth reemphasizing that the price sched that the aea" would be viewed as endors- ing a specific credit card by entering into such a contract, voted against establishing tary to the Executive Committee. I thank Orley Ashen the affinity card program. 9 felter and C. Elton Hinshaw for providing this informa A good rule of thumb mentioned in credit card ade publications is that 90 percent of i Draft minutes of the March 23, 1990, meeting of overall outstanding balances accrue interest. See the the AEA Executive Committee: Report of the Secre- discussion in Section VI-C. This content downloaded from 202. 120. 224.93 on Sun. 17 Dec 201707: 42: 57 UTC Allusesubjecttohttp:/about.jstor.org/terms
58 THE AMERICAN ECONOMIC REVIEW MARCH 1991 TABLE 5-COMPONENTS OF PROFITS FOR MARYLAND BANK, N.A. Components 1985 1986 1987 Finance charges 16.66% 14.92% 13.21% Annual fees 1.40% 1.58% 1.29% Other customer charges 1.10% 1.42% 1.17% Interchange fees 3.06% 3.00% 2.92% Total revenue: 22.22% 20.92% 18.60% Interest expenses 9.57% 7.80% 7.13% Noninterest expenses 4.47% 4.71% 4.87% Net charge-offs 1.09% 1.77% 1.80% Total cost: 15.13% 14.28% 13.80% Return on assets (pretax profits expressed as a percentage of outstanding balances) 7.09% 6.63% 4.80% Sources: Consolidated reports of condition and income (call reports), prospectuses, and registration statements for Maryland Bank, N.A. MBNA's credit card operations (and their profitability) are fairly typical of major is- suers, with the exception that the bank has stressed the concept of "affinity credit cards," whereby cards are marketed to members of professional organizations, fra- ternal orders, and cause-related groups (with the organizations' endorsements). As a con- sequence, its interest rates are somewhat lower and its customers are somewhat more credit-worthy than average. Indeed, it may interest readers that, during the period when this article was undergoing the journal's re- view process, MBNA's marketing agent pro- posed to establish an official American Eco- nomic Association Visa card. This card would have carried a $20 annual fee ($40 for a gold card) and an 18.9-percent annual fee; the AEA would have received $1 for each account opened, $3 for each account renewed, and $0.25 per retail transaction. MBNA's agent estimated that 1,000 cards would be issued, generating $13 in revenue per card per year for the AEA. However, the AEA's executive committee, concerned that the AEA "would be viewed as endors- ing a specific credit card by entering into such a contract," voted against establishing the affinity card program.'9 An item-by-item profit calculation for MBNA is displayed in Table 5. As is typical for credit card issuers, the single largest component of revenue is the finance charge (which, for MBNA, derives from annual percentage rates of 14.5-18.9 percent, de- pending on the account). Despite the fact that the bank provides a 25-day grace pe- riod during which no finance charge is as- sessed if the account balance is paid in full, more than 80 percent of the bank's credit card outstanding balances do accrue inter- est.20 The drop in finance-charge revenues displayed in Table 5 is largely attributable to the bank's decision to reduce the interest rates on some of its accounts during 1985-1987. MBNA also derives direct customer rev- enues from the annual fee and other cus- tomer charges (e.g., $15 late payment, over- limit, and returned-check charges). Indirect revenues are derived from the interchange fee, the portion of the merchant discount that is paid to the customer's bank. It is worth reemphasizing that the price sched- 19Draft minutes of the March 23, 1990, meeting of the AEA Executive Committee; Report of the Secre- tary to the Executive Committee. I thank Orley Ashen- felter and C. Elton Hinshaw for providing this informa- tion. 20A good rule of thumb mentioned in credit card trade publications is that 90 percent of an issuer's overall outstanding balances accrue interest. See the discussion in Section VI-C. This content downloaded from 202.120.224.93 on Sun, 17 Dec 2017 07:42:57 UTC All use subject to http://about.jstor.org/terms