empirical analysis of uk credit card pricing
TRANSCRIPT
Rev Ind OrganDOI 10.1007/s11151-010-9258-4
An Empirical Analysis of UK Credit Card Pricing
Kevin Amess · Leigh Drake · Helen J. Knight
© Springer Science+Business Media, LLC. 2010
Abstract Previous studies show that the credit card market is imperfectlycompetitive. Using a reduced form hedonic model, the current paper demonstratesa relationship between credit card interest rates and product differentiation character-istics. The characteristics capture issuers’ attempts to: (1) screen/separate customerswith different default risk characteristics and (2) better meet heterogeneous cus-tomer preferences. The results are consistent with risk-based pricing and monopolisticcompetition in the credit card market.
Keywords Credit cards · Pricing · Hedonic regression
JEL Classification D21 · L8
1 Introduction
The credit card industry continues to attract interest from economists (e.g., Agarwalet al. 2010) seeking to explain the nature of its imperfections. Underlying this inter-est is the question of why the price (interest rate) for an apparently homogeneouscommodity such as money in a seemingly competitive industry does not conform
K. Amess (B) · L. DrakeNottingham University Business School, Nottingham NG8 1BB, UKe-mail: [email protected]
L. Drakee-mail: [email protected]
H. J. KnightNottingham Business School, Nottingham Trent University, Nottingham NG1 4BU, UKe-mail: [email protected]
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to the predictions of the competitive model. Explanations have highlighted customerheterogeneity (Ausubel 1991; Calem and Mester 1995; Agarwal et al. 2010). Thecurrent paper argues that issuing banks offer credit cards with differentiated charac-teristics in order to both sort and attract heterogeneous customers and demonstratesthe relationship between interest rates and card characteristics.1
Adverse selection theories based on switch and search costs have sought to explainimperfections in the credit card market (Ausubel 1991; Calem and Mester 1995; Stango2002; Berlin and Mester 2004; Calem et al. 2006; Agarwal et al. 2010). These theoriesare based on the notion that low-risk customers are less likely to search and high-riskcustomers are more likely to switch. Whilst these theories seek to explain price sticki-ness and disincentives for issuers unilaterally to lower their interest rates, there is littleresearch that examines risk-based pricing. Calem et al. (2006) are a notable exception,finding evidence of higher-default-risk customers’ paying higher interest rates.
Credit card attributes have been given scant attention in the literature, althoughStavins (1996) explores their implications on the demand for credit card loans. Thecurrent paper seeks to offer insight on two aspects of product differentiation: First,issuers supply some card attributes to deal with adverse selection and sort customerson the basis of risk characteristics. The paper therefore seeks to add to our understand-ing of risk-based pricing in the credit card market. Second, issuers offer a variety ofproduct attributes that consumers with heterogeneous preferences value differently.The paper demonstrates how such attributes affect credit card interest rates, particu-larly those attributes that attempt to induce switching and those that attempt to reduceswitching.
The paper uses data from the UK credit card industry. Both foreign and domesticissuers offer a range of credit cards. There are no dominant issuers in terms of marketshare with the largest three issuers (Royal Bank of Scotland/National Westminster,Barclays, and HSBC) each having no more than 15% of the market in outstandingbalances. The largest foreign issuer is MBNA with a 9% market share in outstandingbalances (European Payment Review 2004–2005).
This paper is organised in the following way: Sect. 2 outlines theoretical issuesthat relate to the economics of credit cards and card attributes. Section 3 explains themodelling strategy. Section 4 describes the data set. The empirical results and theirimplications are reported in Sect. 5, and conclusions are in Sect. 6.
2 The Economics of Credit Cards
This section outlines the economics of the credit card market. Particular emphasis isgiven to explaining imperfections in the market and how they affect credit card inter-est rates. In addition, supply- and demand-side factors in the credit card market arediscussed with reference to card attributes.
1 An issuing bank (referred to as the issuer) maintains the customer’s credit card account and is responsiblefor reimbursing the merchant’s (or retailer’s) account (usually via an acquiring bank) when a credit cardpurchase is made. The issuing bank then bills the customer for the debt. Typically, an issuer will provide arange of credit cards that have different characteristics. These characteristics are discussed in later sectionsof the paper.
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2.1 Search Costs
Issuers are able to charge high credit card rates because customers find it difficult toobtain information in order to make comparisons. If search costs could be reduced,there would be greater price competition (Berlin and Mester 2004). In 2003, UKcredit issuers introduced the summary box with marketing material in order to facili-tate comparison between cards. Summary boxes are also provided on monthly creditcard statements. The summary box provides key information to consumers includ-ing, inter alia, annual percentage interest rate (APR), minimum payment, when theminimum payment is due, fees, and default charges (i.e., late fees). The summarybox therefore reduces search costs. However, Berlin and Mester (2004) do not findevidence consistent with this type of search costs argument.
Ausubel (1991) suggests that there are low default risk borrowers that becomeunintentional borrowers. When they borrow, they expect it to be on a short-term basis.Therefore, they do not search for cards that offer low rates. Those customers that dosearch for rates are higher risk, and any issuer that did lower its rates below that ofits rivals would attract higher risk customers. This adverse selection problem wouldinhibit issuers from lowering their rates and provides a rationale for downward pricestickiness.
Ausubel (1991) theory provides the context for card issuers to provide loyaltyschemes (i.e., ‘cash back’, points, and discounts). Low-risk customers who do notaccumulate outstanding balances and use cards as a convenient method of paymentare not as profitable as are customers who accumulate balances (and who eventuallymake full payment). Loyalty schemes induce increased card usage and might thereforelead to customers’ becoming unintentional borrowers. Inducing indebtedness amongstlow-risk customers is more profitable than is offering lower rates that are attractive tohigh-risk customers that are already in debt.
2.2 Switching Costs
Calem and Mester (1995) suggest that switching costs can induce an adverse selec-tion problem for issuers in the credit card market in two ways: First, when customersestablish a good reputation with their current card issuer they are granted favourablecredit limits compared to those offered by a rival issuer. Second, customers with highoutstanding balances find it more difficult to switch because issuers are concernedabout default risk and are therefore more likely to reject their applications. Calemet al. (2006) find evidence that is consistent with these propositions. Therefore, cus-tomer indebtedness is an impediment to obtaining a card with lower rates. The concernfor issuers is that if they unilaterally lower their rates, it is the high-risk customers thatare likely to want to switch.
Previous studies have not considered the impact of reward and loyalty schemes oncard rates. Points and “air miles” loyalty schemes create switching costs becausethe reward is non-transferable between cards. Customer lock-in can be exploitedby charging higher prices (Klemperer 1995). Therefore, if points and air milesschemes create lock-in, these card attributes will be associated with higher rates. Their
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Table 1 Typical terms of different card types
Card type Average inter-est rate (%)
Minimumrequirements
Interest-freeperiod (days)
Minimumrepayments
Credit limits
Age Income (£) % £ Min (£) Max (£)
Platinum 12.98 25 25,000 56 2 5 5,000 50,000
Gold 15.48 21 15,000 56 2.25 5 500 10,000
Standard 15.81 18 0 56 2.25 5 None 50,000
Student 18.58 18 0 56 2.25 5 200 500
Starter 29.17 18 0 55 3.5 5 225 2500
effectiveness at creating customer lock-in, however, is limited by the fact thatcustomers can be multiple card holders. Such schemes are also likely to be a featureof product differentiation and monopolistic competition and are therefore discussedin section 2.4.
Chen (1997) suggests that the presence of switching costs can explain the practiceof paying customers to switch. Introductory offers on balance transfers and new pur-chases for a fixed time period (typically 6-12 months) are effectively devices thatpay customers to switch. After the discount period ends the interest rate reverts tothe cards’ standard variable rate. Most providers charge fees for a balance transfer,typically 2-3% of the value that is being transferred. Both types of offer are attemptsby issuers to increase their market share in outstanding balances. This might be dueto the maturity of the UK credit card market where growth is more likely to comefrom appropriating rivals’ market share than from market expansion. Indeed, morethan 66% of the adult population in the UK have a credit card with an average of 2.4cards per cardholder (APACS 2007).
2.3 Further Analysis of Adverse Selection and Risk
In order to attenuate the adverse selection problem that issuers face regarding custom-ers’ likelihood of default, issuers use a variety of practices in order to sort customersinto different card types or reject their applications. These practices include: creditscoring, gathering information on existing card balances, and determining employmentstatus. We do not have this information; however, we do observe cards of differenttypes that customers are sorted into on the basis of their risk characteristics.
From Table 1 it can be seen that interest rates vary widely depending on the cardtype. The different APRs, to an extent, reflect the default risk that issuers place oncustomers sorted into these different card types. Age and income are typically usedby issuers as important determinants of default risk. Thus in the context of risk basedpricing, the APR available to particular customers will tend to be a function of theirage and income. In addition, it can be seen that the minimum payment as a percentageof the outstanding balance and the minimum and maximum credit limits are relatedto credit card type.
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The minimum payment amount might also be used to attenuate adverse selection.Customers less able to afford the minimum payment or those with higher default riskwill self-select towards a card with a lower minimum payment. Therefore, an issuerhas no incentive unilaterally to lower its minimum payment. Causality could equallybe in the reverse direction with riskier borrowers offered cards with a higher rate andhigher minimum payment.
Zywicki (2000) suggests that late payment and over-limit charges are principalpredictors of eventual default and that these “hidden fees” are targeted almost exclu-sively at high default risk customers. The default fee, therefore, can be viewed as arisk characteristic. Those cards including a higher risk premium in their rate wouldalso be charging higher annual fees as a deterrent to late payment and default.
In April 2006 the UK Office of Fair Trading announced that default charges weregenerating in excess of £300 million a year for the industry and were significantlyhigher than what is legally fair. However, if issuers are concerned about lower interestrates attracting higher risk customers, they might seek to counteract this by charginga higher default rate. Eliminating or lowering the default charge would therefore leadto higher card rates.
2.4 Product Differentiation
Credit cards have a variety of characteristics from which customers can choose. There-fore, customers facing a budget constraint will choose products with characteristicsthat maximise their utility in characteristics space. Customers choose from differen-tiated products containing bundles of characteristics with product prices determinedby those characteristics (Lancaster 1996). Product differentiation can give consumersmore choice and a greater variety of products with differentiating bundles of char-acteristics allowing consumers to choose the bundle that is located closest to theirideal bundle. Nevertheless, product differentiation can be used by sellers that seek todampen price competition in the face of heterogeneous consumer preferences (Shakedand Sutton 1982).
The previous section considers card type from an issuer’s perspective in terms ofdealing with adverse selection. Card types (e.g., gold and platinum cards) are alsopoints of product differentiation and are potentially attractive to customers becauseof the prestige in obtaining one. Their prestige depends on their availability. Table 1shows that the average minimum income requirement for a gold card is £15,000whilst it is £25,000 for platinum cards. If there is prestige attached to such cards,customers would be willing to pay higher interest rates for the prestige of having agold card than a standard card. In turn, customers would be willing to pay more for aplatinum card than a gold card.2
As mentioned in section 2.2 points and air miles loyalty schemes might be a featureof product differentiation. Other loyalty schemes include discounts on selected
2 From an issuer’s perspective we argue that gold and platinum card types will be associated with lowerinterest rates, but from a consumer‘s perspective we argue that they will be associated with higher inter-est rates. Unfortunately, due to data limitations, we are unable to estimate separate issuer and consumerequations in order to explore fully these theoretical predictions.
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products and ‘cash back’. Customers with heterogeneous preferences will have dif-ferent willingnesses to pay for different loyalty schemes. Consequently, customerswill pay higher rates for those attributes that they value most highly. The provisionof loyalty schemes is also costly to issuers, and so we would expect such costs to bepassed on to customers. Cards that offer ‘cash back’ on purchases might be favoredby consumers who use cards more for their payment services than for borrowing.Cards with this attribute might therefore carry a higher rate to compensate for smallerrevolving balances.
Affinity and co-branded cards are a mechanism to generate loyalty, which helps sus-tain a stable market share, and encourage customers to build large outstanding balances.This might not lead to higher rates. Alternatively, they are also a characteristic thatallows issuers to differentiate their products from those of their rivals. Sometimes cus-tomers receive benefits from the partner organization, and sometimes customers mightactually be demonstrating their loyalty to the partner organization—e.g., by using acard with a football team logo on it. These arrangements are conducted at the issuerlevel, and issuer fixed effects in a regression context can capture these characteristics.
Affinity and co-branded cards make charity donations in two ways. First, an amountis given to the charity when the account is opened. Second, an amount donated per£100 of expenditure using the card. Both these types of donation are observed andincluded in the empirical model that is presented below. If the issuer seeks to main-tain its profit margin, it will pass the cost of donation on to the cardholder via higherinterest rates.
Cards can be differentiated on the basis of the interest-free period they offer: theperiod between a credit card transaction and the due date of the minimum monthlypayment. Credit cards allow customers to carry interest-free balances for up to twomonths, as the cardholder is able to carry the balance interest-free not only duringthe credit cycle, but for a number of days, typically around 25, after the initial creditperiod has ended. Given that it is costly for issuers to offer credit, we suggest that alonger interest-free period will be associated with higher interest rates.
Credit cards can also be differentiated on whether they charge fixed or variable rates.In the UK, fixed rate credit cards are typically fixed for a period of five years. Stango(2000) suggests that this is a feature of the credit market that will force issuers to changetheir prices asynchronously where variable rate cards will change quickly in responseto the prime rate, which is the rate banks provide to their most creditworthy customers,whereas fixed rate cards will only change their interest rate after the fixed rate periodends. If LIBOR rises (falls) unexpectedly, then the price of fixed rate cards will be below(above) that of variable rate rivals that change their interest rates in line with LIBOR.
2.5 Network Effects
A credit card connects customers and businesses via a payment system through whichelectronic transfers of money are made.3 Issuers differentiate their credit cards within
3 In a credit card transaction, the customer’s bank that issues the credit card is called the issuer andthe merchant’s bank is called the acquirer. In order for a credit card transaction to take place the issuer and
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the payment systems to which they offer customers access. There are three paymentssystems in use in the UK: Visa, MasterCard, and American Express (Amex). Custom-ers gain greater network benefits from being part of the larger payment systems suchas Visa or MasterCard rather than Amex. Therefore, if customers value the networkeffects of a credit card, they will be prepared to pay a higher price for them.
Typically, an issuer bundles a credit card with one of the payment networks. Thereare a small number of cards, however, where issuers provide customers with a choiceof MasterCard or Visa at the point of application; henceforth, these are referred to asMasterCard/Visa.
3 Modelling Strategy
Following the seminal work of Rosen (1974), hedonic regression models have becomean established way of examining how price dispersion is determined by different prod-uct attributes. In order to determine and quantify how various card attributes affect theprice of the ith credit card in period t we employ a hedonic regression of the followingform:
Pit = α + βXit + γ LIBORit−1 + fi + εit, (1)
where P is the typical APR, X is a vector of card characteristics, LIBOR is theLondon Inter-Bank Offer Rate, fi are issuer fixed effects that capture unobserved issuercharacteristics that are constant over time, ε is a stochastic error term, α is a constantterm, and the vector β and γ are unknown coefficients to be estimated.
The LIBOR is the rate that banks quote each other for overnight deposits and loans;it represents the opportunity cost of an issuing bank’s assets. We lag the LIBOR by oneperiod (one month) and would expect that γ would be equal to one in a perfectly com-petitive market; i.e., a change in the level of the LIBOR would rapidly pass throughinto the card rate. Heffernan (2002) argues that this might not occur due to ‘menucosts’: administrative costs that are associated with informing customers. It mightalso not occur due to issuers’ exploiting product differentiation and risk attributes inthe vector X. The constant term will capture an interest rate mark-up; the fixed effects,fi , capture differences in issuer-level mark-ups from the base group of issuers’ cards.
In the vector X we are able to include a variety of card characteristics. Dummy vari-ables are included for platinum, gold, student, and initial credit cards with the basecard type being the standard/classic card. We predict that a risk premium is attachedto the offer of student and initial cards. Screening allows lower risk customers to beoffered platinum and gold cards at lower rates compared to standard cards.
Dummy variables are included for the MasterCard and Amex payment systemswith Visa being used as the base payment system. A separate dummy variable isalso included where customers have a choice between using the MasterCard or Visa
Footnote 3 continuedacquirer need to be connected by an electronic payment system, such as that provided by Visa, MasterCard,and Amex. When a credit card transaction takes place and the issuer and acquirer are different, the acquirerpays the issuer an interchange fee. The interchange fee on a transaction is akin to payment for access to anelectronic payments network. Detailed explanations of the economics of payment systems can be found inSchmalensee (2002) and Rochet and Tirole (2002).
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payment system when they apply for a card. If the payment systems follow each oth-ers’ prices, these variables will not be significant. We identify four different typesof loyalty schemes with dummy variables: cash back, points for using the card, airmiles, and discounts received with the card. The base group against which the loyaltyschemes are compared is the group of cards that offer no loyalty scheme.
Two variables that capture issuers’ investments in market share are the introductoryoffer periods (in months) on balance transfers and new purchases. Predicated on thenotion that it is costly to issuers to make introductory offers and that cardholders willnot switch when the offer period ends, the greater is the offer period, the higher is theprice that is charged for the offer period. It should be noted, however, that becauseintroductory offers are devices that are used to steal market share from rivals and are notavailable to all holders of a credit card, it might not be ‘priced’ in the usual way withina hedonic regression. Indeed, a curious feature of these offers is that long-standingcustomers with outstanding balances pay the higher interest rates on these cards butdo not get to take advantage of the introductory offers for new customers.
Also included in X is the interest-free period (measured in days), which is expectedto be positively priced. The default charge will be negatively associated with the APRif it is used to support a lower APR than would be the case without a default charge.If, however, the default fee is a risk characteristic targeted at high risk customers, itwill be positively priced. Whilst the reduced form model can inform as to which effectdominates on the default fee, it is unable to provide information on the underlyingstructural relationship that the contrasting predictions imply. A dummy variable thatis equal to one (zero otherwise) is included for cards that charge a fixed rate.
It is important to note important limitations in the modelling framework adopted.Equation 1 is a reduced form from two structural equations: an equation for issuersand an equation for potential and actual card holders. A more sophisticated modelmight also provide for different motivations for product differentiation—e.g., bettermeeting customer preferences and serving as screening devices. This would allow theidentification of economic primitives with respect to issuers’ costs and customers’preferences, which is not possible in a reduced form model (Pakes 2003). Moreover,it would provide a better treatment of the arguments that were outlined in Sect. 2.Unfortunately, data limitations prevent the use of this type of modelling approach.
4 Data
The data set contains an unbalanced panel of 283 credit cards, which are observedover a seven-month period between April 2006 and October 2006, inclusive. The dataset contains a total of 1880 observations with each card being observed for a minimumof two months and a maximum of seven months. The sample attempts to reflect themarket characteristics of the UK credit card market. Cards were chosen on the basisof being issued by one of the top 15 credit card issuers and on the basis of data avail-ability. The top 15 credit card issuers in the UK account for approximately 90 percentof the market in the terms of customer share.
The typical APR and card characteristics were collected from individual credit cardissuers’ websites and summary boxes. The summary box provides customers withconsistent and succinct summaries of the key features of a credit card, thus enabling
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customers to compare different credit cards more easily (APACS 2006). All integralfeatures of the credit card product, such as the interest-free period and introductoryrates are included in the summary box. Pre-contract, the summary box should appearpredominantly on or within any application form or promotional material with theexception of television or radio promotional campaigns.
With respect to the Internet, a “click-through” to a page containing the summarybox is available. Information on free-standing or optional product features such asloyalty programmes and payment protection insurance are not shown in the summarybox and were sourced from card providers’ websites.
The variables used in the empirical analysis are reported in Table 2 along with theirsummary statistics.
5 Results
5.1 Findings
The regression results for estimating Eq. 1 are presented in Table 3. Columns (1)–(4)report models that include issuer fixed effects. The issuer fixed effects are not jointlysignificant at conventional probability levels in the model reported in column (4) andso they are dropped in the model reported in column (5). For the models in columns(1)–(4) we also conduct tests to determine if the coefficients on the issuer dummyvariables are equal. In columns (1)–(3) they are found not to be equal, though weaklyat the 8% level for column (3). This suggests that differences in unobserved organi-zational characteristics are not associated with differences in interest rates. The issuerdummy variables, however, are found to be equal in the results reported in column (4).
Column (1) presents results estimated using OLS. In column (2) we report theresults of two-stage GMM estimation of an instrumental variables model assumingthat the platinum card type, gold card type, and the minimum percentage payment areendogenous. A difference in Sargan test statistic rejects the null hypothesis of exoge-neity, providing support for our IV approach. The instrument set contains the excludedinstruments: minimum income, minimum age (and their squared terms), and the firstlag of the base rate. A Hansen/Sargan test of instrument validity is conducted, and therejection of the null hypothesis indicates the validity of the instrument set employed.
GMM estimation appears to affect coefficient estimates; however, many of the vari-ables estimated by OLS are still significant using GMM. Our results provide supportfor the argument that issuers sort customers into different card types according torisk characteristics and charge customers different rates as a consequence. Customerswith platinum cards pay a typical APR that is 3.51% (351 basis points) lower thancustomers with a standard card. Starter card customers, by contrast, pay a rate that is6.29% (629 basis points) higher than those customers with a standard card.4
4 Following Rosen (1974), a coefficient estimate is often interpreted as the price of that characteristic, whichis equal to its marginal cost under competitive conditions. Pakes (2003) argues that the hedonic functionis the expectation of the marginal costs and the mark-up on ‘own product’ attributes. Once allowing formark-ups, the hedonic regression is a ‘reduced form’ that estimates correlations that arise from underlyingcost and demand parameters, interacting with market structure, with no obvious interpretation with respectto any underlying structural relationships.
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Table 2 Summary statistics and variable definitions
Variables Definitions Mean Standard deviation
Price Typical APR 15.31 2.882
Fixed Interest rate type (0 ifvariable, 1 if fixed)
0.065 0.246
Initial Initial credit card dummy variable 0.007 0.086
Gold Gold credit card dummy variable 0.049 0.216
Platinum Platinum credit card dummyvariable
0.224 0.417
Standard Standard/classic credit carddummy variable
0.707 0.455
Student Student credit card dummy 0.013 0.114
Amex AMEX payment networkdummy variable
0.033 0.178
Master MasterCard payment networkdummy variable
0.318 0.466
MasterCard/Visa MasterCard or Visa paymentnetwork dummy variable
0.024 0.152
Visa Visa payment networkdummy variable
0.625 0.484
Purchase offer Length of introductory offeron purchases (months)
2.948 3.088
Balance transfer offer Length of introductory offer onbalance transfers (months)
6.981 3.799
Min. payment Minimum monthly payment (%) 2.249 0.354
Interest-free period Interest-free period (days) 54.347 6.363
Default charge Average default charge (£) 16.336 6.064
Points Points scheme dummy variable 0.095 0.293
Cash back Annual cash back received onpurchases (%)
0.045 0.270
Airmiles Air miles dummy variable 0.026 0.159
Discount Discount scheme dummyvariable
0.116 0.321
Donation when accountopened
Amount given to affinitypartner when accountopened (£)
3.238 6.268
Donations on spending Amount given to affinitypartner per £100 spenton card (£)
0.062 0.128
The results for the different payment systems provide some support for customerspaying a premium for accessing larger networks and enjoying network externalities.Whilst cards using the Visa and MasterCard payment networks are similarly priced(cards using the MasterCard and MasterCard/Visa networks have rates that are 36 and
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Credit Card Pricing
Tabl
e3
Res
ults
(dep
ende
ntva
riab
le=
APR
)
Var
iabl
es(1
)(2
)(3
)(4
)(5
)O
LS
IV-G
MM
Ran
dom
effe
cts
Ran
dom
effe
cts-
IVR
ando
mef
fect
s-IV
Gol
d0.
33(1
.40)
−0.3
7(–
0.42
)0.
15(0
.28)
−6.7
2∗(–
1.78
)−0
.95
(–0.
64)
Plat
inum
−1.3
9∗∗ (
–7.7
1)−3
.51∗
∗ (–1
1.07
)−1
.43∗
∗ (–4
.66)
−4.6
9∗∗ (
–4.2
8)−2
.96∗
∗ (–3
.92)
Star
ter
13.8
7∗∗ (
12.6
5)6.
29∗ (
1.65
)16
.92∗
∗ (12
.26)
23.5
3∗∗∗
(2.3
6)19
.63∗
∗ (2.
82)
Stud
ent
3.60
∗∗(6
.82)
1.38
(1.2
9)2.
66∗∗
(3.1
6)2.
19(1
.13)
3.30
∗∗∗ (
2.11
)
Am
ex−1
.07∗
∗ (–2
.85)
−1.6
5∗∗ (
–3.6
4)−0
.04
(–0.
03)
−2.8
6(–
0.96
)−2
.08
(–1.
07)
Mas
ter
−0.3
8∗∗ (
–3.5
2)−0
.36∗
∗ (–2
.60)
−0.1
9(–
0.67
)−0
.50
(–0.
91)
0.26
(0.4
6)
Mas
terC
ard/
Vis
a−0
.98∗
∗ (–3
.72)
−0.9
3∗∗ (
–3.1
1)−0
.83
(–1.
20)
−0.4
3(–
0.33
)−0
.75
(–0.
60)
Bal
ance
tran
sfer
offe
r0.
15∗∗
(3.9
6)0.
26∗∗
(3.1
9)0.
08∗∗
(5.3
3)0.
10∗∗
∗ (2.
51)
0.07
∗∗(3
.60)
Purc
hase
offe
r0.
00(0
.09)
−0.1
0(–
1.30
)−0
.16∗
∗ (–1
0.46
)−0
.15∗
∗ (–6
.19)
−0.1
6∗∗ (
–10.
28)
Min
.Pay
men
t0.
90∗ (
1.81
)7.
00∗∗
∗ (1.
98)
−1.6
2∗∗ (
–8.7
6)−9
.22
(–1.
07)
−3.0
4(–
0.89
)
Don
atio
nw
hen
acco
unt
open
ed0.
06∗∗
(5.1
6)0.
08∗∗
(4.0
9)0.
01(0
.62)
0.00
(0.0
3)−0
.00
(–0.
00)
Don
atio
nson
spen
ding
1.88
∗∗(4
.20)
1.15
∗ (1.
83)
3.22
∗∗(2
.95)
2.62
(1.4
5)4.
13∗∗
(2.7
0)
Fixe
d−4
.96∗
∗ (–1
3.98
)−3
.44∗
∗ (–9
.79)
−6.3
1∗∗ (
–12.
08)
−4.1
2∗∗ (
–3.7
9)−5
.09∗
∗ (–5
.43)
Inte
rest
-fre
epe
riod
0.03
(1.6
4)0.
09∗∗
(5.7
2)0.
04∗∗
∗ (2.
03)
0.07
∗ (1.
68)
0.06
∗∗∗ (
2.20
)
Def
ault
char
ge0.
01(1
.32)
0.01
(0.5
5)−0
.01∗
∗∗(–
2.25
)−0
.01
(–1.
51)
−0.0
1∗∗∗
(–2.
50)
Poin
ts0.
73∗∗
(5.7
4)0.
14(0
.77)
0.04
(0.3
5)−0
.02
(–0.
09)
−0.0
1(–
0.06
)
Cas
hba
ck0.
36∗ (
1.65
)0.
85∗∗
∗ (2.
29)
−0.6
5∗∗∗
(–2.
24)
−0.3
9(–
0.69
)−0
.70
(–1.
31)
Dis
coun
t0.
66∗∗
(5.6
0)0.
37∗∗
∗ (2.
07)
−0.0
2(–
0.07
)3.
64(0
.82)
0.45
(0.1
6)
Air
mile
s1.
41∗∗
(4.3
0)1.
71∗∗
(3.2
9)1.
01(1
.44)
0.53
(0.4
4)1.
58(1
.42)
lnL
IBO
Rt−
1−0
.03
(–0.
07)
−0.1
4(–
0.30
)0.
34∗∗
(2.7
9)0.
34∗ (
1.94
)0.
36∗∗
(3.0
8)
Con
stan
t10
.91∗
∗ (4.
31)
−4.7
3(–
0.49
)17
.00∗
∗ (11
.99)
35.4
9(1
.60)
17.5
0∗∗∗
(2.4
7)
R2
0.68
0.49
0.62
0.32
0.52
123
K. Amess et al.
Tabl
e3
cont
inue
d
Var
iabl
es(1
)(2
)(3
)(4
)(5
)O
LS
IV-G
MM
Ran
dom
effe
cts
Ran
dom
effe
cts-
IVR
ando
mef
fect
s-IV
Issu
erdu
mm
yva
riab
les
6.40
566
.87
27.0
417
.64
–
[Pro
b][0
.00]
[0.0
0][0
.02]
[0.2
2]–
Issu
ers
equa
l5.
4463
.72
20.6
413
.59
–
[Pro
b][0
.00]
[0.0
0][0
.08]
[0.4
0]–
End
ogen
eity
–12
7.50
–12
.12
25.6
7
[Pro
b]–
[0.0
0]–
[0.9
9][0
.14]
Han
sen-
Sarg
an–
3.28
e-05
–0.
588
0.03
06
[Pro
b]–
[0.9
9li]
–0.
443
0.86
1
Not
es:
(1)
∗ p<
0.10
,∗∗
p<
0.01
,∗∗
∗ p<
0.05
;(2
)it
t-st
atis
tics
repo
rted
inpa
rent
hese
s,th
eyar
ero
bust
toge
nera
lfo
rms
ofhe
tero
sced
astic
ityin
colu
mns
(1)
and
(2);
(3)
Issu
erdu
mm
yva
riab
les
isa
test
stat
istic
[pro
babi
lity
leve
l]of
the
join
tsi
gnif
ican
ceof
the
issu
erdu
mm
yva
riab
les;
(4)
Issu
ereq
ual
isa
test
stat
istic
[pro
babi
lity
leve
l]of
the
equa
lity
ofth
eis
suer
dum
my
vari
able
s;E
ndog
enei
tyis
ate
stst
atis
tic[p
roba
bilit
yle
vel]
tode
term
ine
whe
ther
the
endo
geno
usva
riab
les
are
actu
ally
exog
enou
s;(5
)H
anse
n-Sa
rgan
isa
Han
sen/
Sarg
ante
stst
atis
tic[p
roba
bilit
yle
vel]
ofin
stru
men
tval
idity
123
Credit Card Pricing
93 basis points lower than the Visa network, respectively), Amex cards have a rate thatis 1.65% (165 basis points) lower than Visa. This is consistent with the Amex paymentsystem being smaller than its rivals and therefore offering a lower rate to compensatefor its smaller network effects.
Issuers will require a higher minimum payment from high-risk customers. Thus,the minimum payment variable is capturing a risk attribute. The coefficient on thisvariable in column (2) indicates that each percentage point increase in the minimumpayment is associated with the APR being 7% (700 basis points) higher. This mag-nitude seems quite high; however, the variation in the minimum payment percentageis not very high, and the APR could be sensitive to small differences in minimumpayment. For instance, if the minimum payment is one standard deviation higher thanthe mean, the APR is 2.45% (245 basis points) higher than the average card.
Each £1 given to charity when an account is opened is associated with the APRbeing 8 basis points higher. In addition each £1 given to charity per £100 spent on thecard is associated with a 1.15% (115 basis points) higher APR. This is only significantat the 10% level and has stronger significance in the OLS model. Nevertheless, ourresults generally support the notion that issuers’ affinity to charity organizations is astrategy by which issuers can extract a price premium.
The OLS estimates indicate that all of the loyalty schemes that are captured in ourmodel are associated with a price premium. Points schemes are not significant, how-ever, in the IV-GMM model. This model indicates that loyalty schemes are associatedwith card rates that are between 0.37% and 1.71% points (37 and 171 basis points)higher than cards with no loyalty scheme. Air miles is the scheme associated withthe highest price premium, suggesting that at least some customers have a greaterwillingness to pay for this scheme than for the other types of loyalty schemes.
To counteract loyalty schemes and invest in market share issuers make introductoryoffers on balance transfers and to new customers’ outstanding balances on purchases.Both columns (1) and (2) indicate that the purchase offer is not significant. In con-trast, the balance transfer offer is significant, and column (2) indicates it is associatedwith the APR being 0.26% points (26 basis points) higher. Customers are not lockedin after the offer period, and so they can switch to another card with another issuerthat has an introductory offer. Inert customers that do not switch will pay the pricepremium for the introductory offer on balance transfers.5
The coefficient on the interest-free period in column (2) indicates that each dayadded to the interest-free period is associated with a card rate that is 9 basis pointshigher. Cards offering fixed rates are associated with an APR that is 3.44% points (344basis points) lower than variable rate cards. Depending on when the rate was fixed, iteither reflects that variable rates increased after the fixed rate was set, or it capturesissuers’ expectations of APRs declining in the future.
Whilst the results reported in columns (1) and (2) control for unobserved issuercharacteristics, they do not control for unobserved card characteristics. Additional
5 It should be noted that customers are to some extent restricted from behaving like what has becomeknown as a ‘credit card tart’ and switching between cards with introductory offers because issuers sharethis information with credit bureaus, and a high number of card applications increases the risk of a cardapplication being rejected.
123
K. Amess et al.
models are estimated controlling for unobserved card characteristics in order to exam-ine the effect on coefficient estimates. The error term in equation (1) will thereforebecome:
εij t = ωj + υijt , (2)
where ωj is a random effect for the j th card that is assumed to be constant over timeand υijt is the residual. A random effects model is required because card fixed effectswould be perfectly correlated with the issuer fixed effects.
The models reported in columns (3), (4), and (5) employ the error structureexpressed in Eq. 2. The results in column (3) are estimated using GLS, and theinstrumental variables models in columns (4) and (5) are estimated using general-ized two-stage least squares. Tests of endogeneity, however, reject the instrumentalvariables random effects approach. The random effects models are generally support-ive of the different card types (platinum, gold, standard, starter, and student) beingused to sort customers according to different risk characteristics. There is also supportfor the previous findings on affinity via donations, fixed-rate cards, and the inter-est-free period; however, loyalty schemes are not significant in the random effectsmodels.
Although the random effects models provide some evidence of LIBOR pass-throughto the card rate, the coefficient is relatively small, which indicates a relatively weakrelationship. Moreover, other models do not find that the LIBOR is statistically signif-icant.6 This is consistent with ‘menu costs’ inhibiting changes in card rates becauseissuers have to change their rates on both advertising material and credit card state-ments.
5.2 Implications
On active credit cards over the sample period, the average outstanding balance isabout £1,504.7 With the use of this figure and estimates from column (2), the costsand savings to certain significant card attributes are calculated. A customer with aplatinum card type will pay about £53 per annum less in servicing the debt of theaverage outstanding balance than will a customer with a standard card. In contrast,a customer with a starter card type pays about £95 per annum more in servicing theaverage outstanding balance compared to the standard card type.
Each percentage point increase in the minimum monthly payment is associatedwith an additional payment of about £105 per annum on the average outstandingbalance. As previously mentioned, minimum monthly payments are quite small,and a one percentage point increase would be well above one standard deviationfrom the mean. A one standard deviation increase in the minimum monthly payment(0.034%, or 3.4 basis points) is associated with about a £0.5 per annum increase inpayments on the average outstanding balance. Each additional day on the interest-free period costs customers about £1 in interest charges on the average outstandingbalance.
6 Experimentation with longer lag periods also yielded results that were not significant.7 This figure was calculated using data from the British Bankers Association.
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Credit Card Pricing
Customers with cards that use the Amex, MasterCard, and MasterCard/Visa pay-ment systems would pay about £25, £5, and £14 (respectively) per annum less ininterest payments on the average outstanding balance compared to customers withcards using the Visa payments system. During the sample period, customers withcards that had fixed rates would have paid about £52 per annum less on the averageoutstanding balance.
Loyalty schemes are used to differentiate products and dampen price competition.They are therefore costly to customers. Air miles is a well established loyalty-basedscheme, and the premium attached to it is consistent with a switch cost (becausethey are irredeemable) but might also reflect customers’ willingness to pay for thescheme. On the average outstanding balance, air miles costs customers about £26 perannum. Cash back and discount schemes cost the average customer about £13 and £6,respectively.
Issuer charity donations are a credit card affinity that is a point of product differ-entiation that some customers might select and have a willingness to pay for. Issuersmake donations to charity on new purchases and/or when a new account is opened.A one pence increase in charity donations per £100 spent on new purchases will costa customer about £17 per annum in additional interest payments on the average out-standing balance. A £1 increase in charity donations when an account is opened willcost about £1.20 in additional interest payments on the average outstanding balance.
In order to induce customers to switch, issuers make introductory offers. Eachmonth increase in the length of the introductory offer on balance transfers is associ-ated with payments on the average outstanding balance that are about £4 per annumhigher. Existing customers pay the higher fees on the cards that have the introductoryoffer on balance transfers, but these existing customers do not receive the benefit.We offer three reasons why customers might find themselves paying for this attri-bute: First, they regard the £4 average as a fairly trivial amount of money to warrantswitching to a card without the introductory offer attribute. Second, the customerswith outstanding balances are unintentional borrowers and do not search for the bestinterest rates (Ausubel 1991). Third, customers with outstanding balances find it moredifficult to switch (Calem and Mester 1995; Stango 2000).
6 Conclusions
The results demonstrate a relationship between risk characteristics and credit cardinterest rates. Indeed, we find evidence that is consistent with issuers’ using screeningdevices to sort customers into different card types. Lower-risk customers who obtainplatinum cards pay lower rates than do higher-risk customers who have been sortedinto starter cards. Customers pay higher interest rates for credit cards with a longerinterest-free period; this might be because it is attractive to higher-risk customers whoare accumulating debt. Issuers require that higher-risk customers make higher min-imum monthly payments as a percentage of outstanding balances. Therefore, cardswith higher minimum monthly payments are associated with higher prices.
Product differentiation also occurs in order to better meet customer preferences.The results demonstrate that some customers have a willingness to pay for cash back,
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K. Amess et al.
discount, air miles, and charity affinity schemes. Whilst it might appear that suchattributes are used to dampen price competition, this is counteracted by issuers’ usingintroductory offers on balance transfers to capture market share from rivals. Suchintroductory offers are associated with higher card rates, however.
Whilst the paper demonstrates which credit card attributes affect credit card interestrates, it is important to recognise that the paper is limited in the insights offered due tothe use of a reduced form model. This modelling strategy reflects our data limitations.Better quality data would allow the estimation of the underlying structural equationsthat would explicitly explore the role of supply- and demand-side factors in the creditcard industry.
Acknowledgements We are especially grateful to Lawrence White and two anonymous referees forproviding extensive and detailed comments. We are also grateful to Sourafel Girma, Steve Thompsonand participants at the 6th Annual International Industrial Organization Conference, Washington, DC, forhelpful comments.
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