Interchange Fees and Credit Card Issuing An Empirical Investigation by ps0001


									                      Interchange Fees and Credit Card Issuing:
                                   An Empirical Investigation∗
                                                Niyati Ahuja†
                                             New York University

                                                 January, 2009


          This paper develops a framework for the empirical analysis of the role of interchange fees in the
       issuer–consumer relationship. In the credit card market, the interchange fee is the fraction of each
       transaction that is retained by the issuing bank. Under a market environment where merchants
       typically do not surcharge credit card users, these fees have important competitive consequences
       and their collective setting by card networks raises policy concerns. Although the theory literature
       in this context is very rich, empirical work on the competitive effects of interchange is relatively
       behind. I propose a step in addressing this gap. I consider the issuer–consumer side of this market
       and build a structural model with heterogeneous consumers and competing issuers. The primary
       goal of this paper is to measure the extent to which issuers pass on changes in interchange fees
       via interest rates to consumers and the degree of consumer sensitivity to issuers’ policies. Using
       a combination of micro and macro data, I estimate demand and supply parameters for general
       purpose credit cards and analyze equilibrium for a given level of interchange fees. A comparison of
       equilibrium values across different levels of interchange then allows predictions on how consumers’
       use of cards and issuer policies respond to changes in these fees. The results indicate that reducing
       the fee down by about a third increases interest rates on average by less than 2.5% of their original
       levels and consequently, leads to a 2.9% decline in credit card debt and a 2.7% fall in transactions.
       The paper also sheds new light on the nature of competition in the market by generating cross–
       elasticities for competing issuers.

    Preliminary and Incomplete. Please do not cite.
    Department of Economics, New York University, New York 10012. Email: I am indebted to my
advisory committee – Ariel Pakes, Michael L Katz and Daniel (Yi) Xu, for their constant guidance and encouragement. I
would also like to thank Chaim Fershtman, Fumiko Hayashi, Marc Rysman, Jonathan Zinman and seminar participants
at NYU for valuable input. Errors are all mine.

1    Introduction
Each time a credit card transaction takes place through a network such as Visa or MasterCard, the
merchant whose location the card was used at pays a proportion of the transaction value as fees to the
bank that processes its accounts (the acquiring bank). After retaining a small markup, the acquirer
passes on this fee to the consumer’s bank (the issuer) in the form of interchange fees. The levels
and structure of the interchange fee are usually determined by the networks (Visa or MasterCard),
as opposed to being negotiated between thousands of issuers and acquirers. On the one hand,
the interchange fee may offer an instrument for networks to balance card usage among consumers
and card acceptance among merchants. On the other hand, this fee may impose externalities on
agents not involved in the transaction because merchants typically charge the same price to all their
customers and are likely to pass on the fees as higher prices to non-card users as well. It is due to
these concerns that the credit card industry in general and the interchange fee in particular, have
been a subject of much attention from economists and policy makers.
    This paper builds an empirical framework to analyze the competitive effects of changes in inter-
change on the consumer-issuer side of the market under the assumption that the merchant-acquirer
side does not respond. The primary goal is to present a model that enables one to estimate de-
mand and supply parameters for general purpose credit cards and use it to measure the extent of
issuer pass-through of the fees via interest rates. I develop a structural model that captures two
important aspects of this industry, namely, heterogeneity of consumers and differentiation among
cards. Consumers choose cards to maximize their utility which depends on card characteristics and
their own attributes. Competing issuers choose interest rates to maximize profits given demand and
exogenously set interchange fees. The empirical model and estimation procedure fit three quantity
variables (credit card accounts, debt and transactions) to their counterparts observed in the data.
The model provides a way to tie these three variables together and presents a channel for interchange
fees to affect equilibrium interest rates. Counterfactual experiments on the estimated model then
allow me to predict the effect of changes in the fees on issuer profits, consumers’ choice of cards and
their credit card balances.
    I find that regulating the interchange fee down by about a third increases interest rates on an
average by less than 2.5% of their original levels, which implies that the mean APR would increase
from 15.4% to 15.8%. Although this does lead to a drop in credit card usage (accounts, debt and
transactions), the change is not dramatic. Even when interchange fee is reduced by nearly 50%, the
impact on the key quantity variables is close only to 5%. These findings are in line with the observed
impact of regulation in Australia, where among other reforms, the interchange fee was reduced by
about 50% in late 2003. Based on market data trends, Chang et. al. (2005)[10] and Gans (2007) [13]
report that even after several years, there was no dramatic effect of reforms over a broad range of
indicators. The analysis presented here also points to the possible reason. Interchange fee accounts

for about one-fourth of issuer profit, however, the rest is mainly interest income. The sensitivity of
the estimated demand to interest rates prevents issuers from raising them substantially. This also
suggests that the decline in interest rates in recent years may have had a lot to do with demand

1.1      Industry Background

The credit card industry has seen tremendous growth in the last couple of decades. In 2005, credit
cards accounted for 25.2% of the total volume of consumer payments in the U.S., up from 14.5% in
1990.1 According to the 2004 Survey of Consumer Finances, 74.9% of all the families held atleast one
credit card and 58.0% of these carried balances on them at the time of the survey, the mean balance
being $5,100.2 This industry has a complex, yet fascinating, organizational structure. Payment
systems are two-sided markets because for them to work consumers must use them and merchants
must accept them. A credit card network needs to bring both sides of end-users on board for it to run
successfully. In the United States credit card market, Visa and MasterCard are the two largest card
networks collectively accounting for over 70% of all transactions.3 These are commonly categorised
as Open-Loop networks or Four-party systems, implying that they do not deal with card holders and
merchants directly. They consist of a number of member banks, those that issue cards to consumers
(issuing banks or issuers) and those that settle accounts with sellers (acquiring banks or acquirers).
The issuer sets all the terms and conditions for the contract with the consumer such as the credit line,
APRs, annual fees, reward features, late fees etc., and the consumer settles her monthly statement
with the issuer directly. The acquirers deal with the merchant side of the market. They set up the
terminal equipment, keep track of transactions and transfer funds to settle accounts on a day to day
basis. The network itself is an association of these banks that is responsible for the collective setting
of rules and regulations, for maintaining the infrastructure and advertising among other functions.
       Figure 1 illustrates a simplified example of how a transaction in this system works. It begins with
the consumer swiping her credit card at the merchant location. The card information is read through
the magnetic strip and relayed along with the transaction amount to the acquirer who then forwards
it to the network for authorization. Upon approval, the issuer credits the acquirer the full amount
of the transaction minus an interchange fee and the acquirer credits the merchant the transaction
amount minus a merchant service fee, usually within a 24-hour period. At the end of the billing
cycle, the issuer sends the bill to the consumer who subsequently pays for the goods bought along
with any other fees due.4 In this example, the transaction value is $100, the interchange fee is $1.50
     The Nilson Report, Issues 656 and 869
     Bucks, Kennickell and Moore (2006) [5]
     American Express and Discover form most of the remaining 30%. For 2005, the Nilson Report (Issuer 851) estimates
the following market shares for these four networks: Visa-42.7%, MasterCard-29.8%, American Express-22.3% and
     This system is in contrast to the Closed-Loop network, like Diner’s Club and until recently, American Express and

                                     CREDIT CARD NETWORK
                                           Eg Visa or MasterCard

                          ISSUING                                          ACQUIRING
                            BANK                                             BANK
                          Eg Citibank                                         Eg First Data

                       CONSUMER                                            MERCHANT

                     Figure 1: Example of a Transaction in an Open–Loop Network

and the merchant discount fee is 50 cents. Acquiring is known to be a rather competitive business,
so the merchant fee is usually not much higher than the interchange fee. In most cases, the network
receives a small percentage as well.
    The interchange fee structure is a point of interest here. The fees are collectively set by the
network and they differ across the two networks and by the type of merchant or the type of card.
Supermarkets and convenience store fees are low, hotels and internet sellers incur higher fees. Bigger
merchants may be able to bargain for lower fees. Premium cards have higher fees and merchants
are required to accept these under their agreements with the network. In the U.S., the Wall Street
Journal estimated in 2006 that card issuers earned over $30 billion in interchange fees, up by 117%
since 2001 and that the average card fee was about 1.75%. The complete fee structure is mainly
hidden from the public because Visa and MasterCard do not disclose details on monthly statements
and they keep merchants from disclosing them on receipts. The card networks and issuers argue that
the fees are essential for maintaining a balance over the two sides of the market and that collective
setting saves negotiation costs for the large numbers of issuers and acquirers involved.
    Policy makers are concerned that under No-Surcharging (which means that merchants do not
Discover, which operate as a single entity and deal with the card holder and the merchant directly. In a closed network,
a transaction follows a very similar route except the network independently handles all issuing and acquiring functions
and therefore, there is no need for an interchange fee. Visa and MasterCard had previously enforced exclusionary rules
that prohibited member banks from issuing rival network credit cards. The U.S. Department of Justice brought an
antitrust lawsuit against these networks in 1998 and after a trail and a failed appeal in 2004, banks such as MBNA,
Citibank and Bank of America are now able to issue American Express credit cards. (United States of America v. Visa
U.S.A. Inc., Visa International Corp., and MasterCard International Incorporated, Complaint for Equitable Relief for
Violations of 15 U.S.C. §1, October 7, 1998)

charge higher prices to credit card users5 ), the marginal cost to society of using a payment system
is not reflected to consumers and this may lead to usage externalities. Higher interchange fees are
passed on by acquirers to merchants as higher merchant discount fee. This makes card acceptance
costly for merchants6 and they pass on the fee (at least in part) to all their consumers including
people who pay by checks or cash. At the issuer’s end, if the higher interchange fee is passed on to
consumers as lower fees and higher rewards, it is likely to distort consumer choice of payment system
in favor of credit cards. This raises distributional concerns because it implies that non-card users
(and partly merchants) end up cross-subsidizing card users. This is important because people who
do not possess credit cards on average have lower income than those who do. Also, merchant groups
have increasingly expressed dissatisfaction over the fees and there are several ongoing lawsuits filed
by retailers in this regard.
       It is due to these concerns that Australian authorities regulated their interchange fees in 2003
and other countries are considering the same.7 In the U.S., direct regulatory action has not been
undertaken yet but the debate is ongoing.8 The latest piece of legislation being considered is the
Credit Card Fair Fee Act of 2008, which will allow large and small businesses to negotiate directly
with credit card companies in an effort to reduce interchange fees.9

1.2      Research Question & Limitations

The role of interchange fee in the credit card market has been explored extensively in the literature.
However, the majority of this research is theoretical. Not surprisingly, there is lack of consensus10 in
the theory, emerging primarily due to different modeling assumptions. Many of the theoretical find-
ings may be empirically testable. For instance, whether issuers do indeed pass on higher interchange
fees to consumers and if this alters consumer choice is largely an empirical question. Nevertheless,
empirical work in this area is challenging because of the complexity of the market and the difficulty
of obtaining comprehensive data. I circumvent these difficulties here partly by using data that is
      In the U.S., Visa had imposed this as a contractual condition for a long time before relaxing it recently. MasterCard
still prohibits surcharging, but does allow discounts for cash users. However, in practice U.S. merchants rarely offer
discounts for cash payments.
      It is interesting to examine why merchants accept credit cards in the first place if they are costly for them. Rochet
and Tirole (2002) [31] use a Hotelling model to show that merchants accept cards for strategic reasons, to attract
card-users from rivals who do not accept cards. In equilibrium, merchants do not benefit from accepting cards and end
up in a Prisoner’s dilemma kind of situation.
      See Reserve Bank of Australia (2002) [28] and Katz (2001) [21] for details. See Weiner and Wright (2005) [39] for
interchange fees related developments in various countries.
      See Federal Reserve Bank of New York Conference “Antitrust Activity in Card-Based Payment Systems: Causes
and Consequences” (Sept 2005) and Federal Reserve Bank of Kansas City Conference “Interchange Fees in Debit and
Credit Card Industries: What Role for Public Authorities?” (May 2005).
      See Evans and Schmalensee (2005) [12] for the history of regulatory action in the U.S. and for in depth information
about this industry.
      See Section 1.3 for a rather brief look at the literature, Katz (2005) [22] for a summary of key findings and
Chakravorti (2003) [8], Rochet (2003) [30], Schmalensee (2003) [34] or Hayashi and Wiener (2006) [19] for detailed

easily accessible to researchers and partly by focusing on a narrower research problem.
   A comprehensive empirical investigation of interchange would need to explore both the consumer
and the merchant sides of the market. It would investigate how both issuers and acquirers adjust
their pricing in response to changes in interchange fees and how consumers and merchants, in turn,
change their acceptance decisions. The acquirer-side pricing problem in this market is relatively
simple, but issuer-pricing is not. A credit card is a product that can be differentiated along several
dimensions such as interest rates, rewards and a menu of fees that include annual fees, late fees,
minimum finance charges etc. Some of these features may be personalized based on consumer credit-
worthiness. In addition, a complete analysis would also build in competing networks and allow for
substitution to other modes of payment. Due to data limitations, such a detailed study is beyond
the scope of this paper. Instead, it confines itself to the following narrower questions:

      In response to changes in interchange fees in the open-loop network:

        1. How do issuers adjust their base interest rates? To what extent do they pass on the change
             in interchange via interest rates to consumers?
        2. Assuming that merchants do not change their acceptance decisions and given the changes
             in base interest rates by the issuers, how do consumers adjust their demand for cards and
             credit card debt?

This paper focuses the issuer-consumer relationship, assuming that the acquirer-merchant side does
not adjust. Essentially, my analysis boils down to considering credit cards as a one–sided market. The
underlying assumption is that the cost of not accepting credit cards is relatively high for merchants
and hence, small changes in interchange will not alter merchant acceptance. Also, if merchants
change overall prices in response to interchange fees, I assume that consumers cannot foresee this
and hence, do not take it into account in their decision making process. In addition, this paper
confines itself to analyzing the response of interest rates to changes in interchange fee, assuming
that other characteristics such as reward features and annual fees are left unchanged. These other
features could, by all means, be sensitive to interchange fees and could respond to changes in the
fee structure. This would mean that the results presented here over-estimates the actual extent of
pass-through. It must also be kept in mind that these results are based on issuer-level data. As
with any structural empirical problem, the results would be sharper if micro-level choice data were
   This approach comes with a price. It implies that my analysis is unable to directly address
the broader policy question. Nevertheless, it does offer a starting point and a meaningful addition
to the existing empirical literature on credit cards. The focus here is on issuer pass-through via
interest rates, which is a key component in determining the competitive effects of interchange fees.
It is believed that in the United States, acquirers pass through a higher percentage than issuers and

issuers’ pricing more involved. I propose a modeling framework that builds together heterogeneous
consumers and competing issuers. The model offers a way to estimate demand and cost parameters
and cross-price elasticities among competing issuers. The paper generates an initial estimate of the
extent of pass-through and presents a new look at the factors that influence demand.

1.3   Related Literature

The theory literature concerning interchange fee focuses mainly on establishing a distinction (or
lack thereof) between privately and socially optimal fees and exploring their welfare implications.
This work began with Baxter’s (1983) [2] normative analysis who derived the socially optimal fee
in a model where agents do not interact strategically (single merchant, single consumer and perfect
competition among banks) and a payment transaction is considered a joint-service to consumers and
merchants. The paper emphasized that the fees are necessary to balance costs on both sides and
that bilateral negotiation of interchange fee would be costly.
   The work following Baxter considered implications of competition between networks, issuers and
acquirers and variations in consumer and merchant demand characteristics. Katz (2001) presented
a model with perfectly competitive issuers and acquirers and no surcharging by merchants. He con-
cludes that inefficient use of cards (termed therein as economically excessive use of cards) may result
under the absence of surcharging because merchants cost differences represent social cost differences,
but the card holder does not bear the costs imposed on merchants. Scwartz and Vincent (2006) [35]
similarly conclude that under no surcharging, when rebates to card users are feasible, the networks
raise interchange fee which leads to higher total consumer surplus but hurts cash users. Rochet and
Tirole (2002) [31] presented the industry as a two-sided market and considered platform competition
for a few different governance structures. With competing networks, one of the factors that influences
the optimal interchange fee is whether consumers single-home or multi-home, that is, whether they
hold cards from a single or multiple networks. For instance, Guthrie and Wright (2007) [17] show
that when some consumers multihome, merchants reject the more expensive card, causing competing
schemes to lower the interchange fee in an attempt to attract merchants.
   The literature so far (notably Schamlensee (2002) [33], Gans and King (2003) [14] and Chakravorti
and Roson (2004) [9] among several others) highlights that the welfare consequences of interchange
fee depend crucially on the underlying market structure - the extent of competition among suppliers
and the nature of demand by consumers and merchants. This paper follows the literature in assuming
that the extent of pass-through of the fee is crucial and that the degree of pass-through depends on
underlying demand conditions and competition among issuers.
   On the empirical front, a seminal article that examines profitability and pricing in the credit
card market is Ausubel (1991) [1]. This article provides evidence for an the apparent puzzle in
this market during the 1980s - the credit card interest rates were sticky and profits high, despite

the fact that the market structure appears rather competitive. Ausubel offered adverse selection,
search costs and switching costs as explanations. Calem and Mester (1995) [6] provided empirical
evidence using the 1989 Survey of Consumer Finances that all these three factors may be important
in explaining Ausubel’s puzzle. Rysman (2007) [32] comes closest to a comprehensive empirical study
of both sides of the market. He exploits a unique data set and shows that consumers concentrate
their spending on a single payment network (single-homing), although many maintain unused cards
from multiple networks (multi-homing). The paper also establishes a correlation between consumer
usage and merchant acceptance and suggests that there exists a positive feedback loop between
consumer usage and merchant acceptance. Other empirical work has focused on several different
aspects namely, determinants of the demand for credit cards and debt,11 price setting behavior by
issuers12 and costs of payment systems.13 This paper draws from all three of the existing empirical
literature on the credit card market in so far as it attempts to analyze profit, costs and demand under
a unified framework. It also closely follows the the empirical antitrust literature using methodologies
set forth in Berry [4] and used by several authors in the context of the banking industry.14
         The rest of the paper is organized as follows. Section 2 describes data sources and summarizes key
trends observed in the data. Sections 3 and 4 outline the empirical model and estimation strategy.
Sections 5 and 6 discuss the main findings and results from the policy experiment.

2         Data Description
2.1        Data Sources

The data used in this paper has been compiled from a number of different sources. Product-level data
for credit card issuers is pooled together from the Nilson Report and the Terms of Credit Card Plans
(TCCP). Consumer-level micro data comes from the Survey of Consumer Finances (SCF). These
three sources combined form the core data set used and are supplemented by additional information
about issuing banks and network interchange fees.
         The quantity data comes from the periodical Nilson Report, which is one of the leading trade
publications for consumer payment systems. They provide detailed information on a variety of
aspects of credit cards and other payment media worldwide. Every year they publish a list of the
U.S. credit card industry’s top 150 (or more) issuers providing three key quantity variables used
in this paper: number of active accounts, total outstanding debt and total transaction volume per
issuer. This information is matched with pricing and credit card characteristics data from the Terms
    Castronova and Hagstrom (2004) [7], Hayashi and Klee (2003) [18], Gross and Souleles (2002a) [16], Kim and
DeVaney (2001) [23]
    Knittel and Stango (2003) [25], Park (1997) [29], Stango (2002) [36] and (2003) [37]
    Garcia-Swartz, Hahn and Layne-Farrar (2004) [15]
    Dick (2008) [11], Ishii (2004) [20] and Zhou (2007) [40]

of Credit Card Plans. The TCCP is a semiannual survey of roughly 150 issuers conducted by the
Board of Governors of the Federal Reserve. The survey is sent to 25 of the largest credit cards issuers
and at least 125 additional institutions including regional issuers. Each respondent provides details
about its credit card plan that has the largest outstanding number of cards. They report the annual
percentage rate (APR), annual fees, grace period, balance computation method, finance charges, late
fees and reward features among other characteristics. For each issuer, I use the second TCCP entry
in the year (the July data instead of the January) wherever available.
       I derive additional issuer-level data from the Federal Reserve’s National Information Center and
from the Call Reports filed by the issuing banks. The former is a repository of financial data and
institutional characteristics maintained by the Federal Reserve. From here I get information on
the type of institution that the issuer is (such as National Bank, Savings Bank etc.) and also the
identifying RSSD-ID numbers for the issuers that help match data under TCCP to that in Nilson.
The Call Reports are used to obtain some key cost variables that are used as instruments in the
estimation.15 The reports also help determine whether an issuer is a monoline credit card bank. If
75% or more of the bank’s reported total assets are held in credit card balances, I regard it as a
monoline issuer. Finally, on the issuers’ side, the interchange fees used are the default retail store
non-supermarket rates.16
       Lastly, the paper uses household-level data from the 1998, 2001 and 2004 Surveys of Consumer
Finances. This is a triennial survey of approximately 4,500 respondents17 conducted by NORC at
the University of Chicago and sponsored by the Federal Reserve Board. It is the most comprehensive
publicly-available source of data on household financial and credit characteristics. It has a few useful
questions regarding credit attitudes and the use of payment cards. The SCF has been used in several
studies relating to payment cards, especially credit cards.18 Unfortunately, this data does not identify
credit card issuers by name. Hence, I cannot match this data with the quantity and price data above.
Nevertheless, as outlined in section 4 this data plays a crucial role in the estimation.

2.2      Data Summary

2.2.1      Issuer-level Quantities and Card Characteristics

The data includes nationally-issued Visa and MasterCard credit cards. I construct a panel data
set spanning ten years from 1996 to 2005 by matching Visa and MasterCard national credit card
issuers from the Nilson Report and TCCP. After choosing institutions that are common to both and
eliminating missing observations, I am left with a total of 300 issuers. As indicated in Table (1),
     See section 4 for a description of these.
     I would like to thank Fumiko Hayashi for granting me access to these data.
     For the years relevant here, this is not a panel data set. The last panel SCF was conducted during the years
1983-1989. Since then it has only had a different cross-section of households every three years.
     Calem and Mester (1995) [6], Klee (2006) [24], Stavins (2001) [38] and Zinman (2006) [41] to name a few

this isn’t a balanced panel. In some years the data has more than forty issuers, while in most there
are less than thirty. However, the market concentration in this industry during this period is high
and the data includes the biggest issuers, so it captures 70 to 85 percent of the market for most
years. Market shares have been calculated using aggregate market quantities which are also reported
in Nilson. The mean share of the inside good in the data ranges from 1.39% to 4.37%. The C(1)
measure reported in Table (1) shows a general upwards trend over time, somewhat reversed in the
last year (2005).
    Tables (2) and (3) provide definitions and summary statistics for the price and quantity data.
The average number of active accounts per issuer is 5.63 million and the mean credit card debt and
transactions are $13.85 billion and $25.21 billion respectively. The standard deviation for debt and
transactions is very high which again points to the concentrated nature of this market. From the
TCCP pricing data, I obtain APR, annual fee, rewards, affiliated network (Visa/MasterCard), type
of APR (Fixed or Variable) and length of grace period.19 Note that under TCCP the issuers are
required to report the plan that had the largest number of cards outstanding and that was available
to new customers as of the report date. I drop other reported characteristics such as late fee, over
limit fee, cash advance fees etc., because these are not reported by issuers in a uniform manner (some
report dollar values and others report them as percentage of outstanding balance) and in preliminary
regressions these did not turn out to be significant determinants of demand. In the profit equation,
these fees will be absorbed into the marginal cost of transaction.
    The mean APR in the data is 15.43%, which is a little more than double the mean prime rate
during this ten year period of 6.85%. The average annual fee is $11.48 and 64% of all issuers report
their annual fees as zero. The variable rewards is a summary measure of enhancements offered under
the credit card plan. In the TCCP data, unfortunately, the issuers do not report details about their
reward programs. They simply report a zero or one value for reward features rebates on purchases,
purchase protection, travel discounts etc. and these cannot be used to assign a dollar value to the
reward features. I construct a summary measure by taking the sum of the enhancements reported.
The maximum possible value for this variable is 10 and the average is 1.6. The majority (63%) of
the credit cards in the data are Visa cards. Table (3) does not show year-wise statistics, but it is
interesting to note that in this dataset the proportion of Visa plans reduces over time. Fixed APR
is offered much less frequently than variable APR and among the variable APR plans, most are tied
to the prime rate (73.3% of all cards). The average grace period is about 25 days.
    Turning now to issuer classification, national banks are the biggest group in the data. They make
up 61% of all issuers followed by state member banks (12.3%) and savings banks (6.3%). A third
     The grace period is the interest-free period at the end of the billing cycle (float period). Typically, people that
have previous outstanding balances (revolvers) do not enjoy a float period since they start accruing interest charges
immediately. This is an important distinction between credit cards and debit cards. Credit cards provide an option to
carry debt and a float period if you choose not to carry debt. Zinman (2006) explores how this difference influences
use of debit cards.


                                                                  Visa $1,000 IF
                                                                  MC $1,000 IF
                                                                  Avg CC Interest Rate (%)
                                                                  Prime Rate (%)




                       1996   1997   1998   1999   2000    2001   2002    2003     2004      2005

                      Figure 2: Interchange Fee and Credit Card Interest Rates

of all plans here are offered by institutions that specialize in credit card issuing, known as monoline
issuers. Notable examples are MBNA and Capital One. 26% of the plans in the data were offered
by issuers that are known to consider risky (subprime) borrowers. Providian and Household bank
were major subprime lenders during this period. As mentioned earlier, this is a rather concentrated
market and the group of issuers included are very diverse. I include a dummy variable that takes the
value one if an issuer was one of the top 25 issuers listed in Nilson (based on outstandings) during
the year before. In this data, 55.3% of all issuers fall under this Big Issuer category. This variable is
intended to capture the fact that a lot of these issuers are very well know around the country while
others may be issuing national credit card plans but have branches in certain regions and are better
known only locally. For example, Citibank, Bank of America and Chase are consistently among the
biggest issuers. This is similar to the approach taken by previous authors working with demand
estimation for the banking industry who classified banks as big, medium or small (Dick (2008) [11]
and Zhou (2007) [40]) based on total assets. The advantage of defining the variable this way is that
it is predetermined and it captures the reputation effect.
   Figure (2) depicts the average interest rates observed in the data for each year against the Visa
and MasterCard interchange fees for that year. The mean interchange fee during this entire period
was 1.46% and Visa’s fee was usually lower than MasterCard’s, though both rose steadily. The

graph appears to depict an inverse relationship between mean interest rates and interchange fees.
This occurs simultaneously with a positive movement between interest rates and the prime rate.

2.2.2    Household Attributes

From the Survey of Consumer Finances, I obtain information on the subsample of credit card hold-
ers. This includes household demographic and financial characteristics, credit worthiness and credit
attitude indicators as well as some details regarding credit use. The data used is summarized in
tables (4) and (5). The average number of cards per household is 2.4 and the mean interest rate
reported is 13.38%.20 The mean credit card debt over all card holders is $2,395 and that amongst
revolvers is $4,375. The differences between convenience users and revolvers are interesting to note
here. Households that carry debt on their credit cards on average have lower income, financial assets
and non-financial assets. Their credit worthiness indicators point towards higher risk as compared
to convenience users. In particular, a higher percentage are subprime21 (16.58% compared to 7.8%).
Fewer of them own their homes and have checking accounts. 18.24% of households that carry credit
card debt report being refused credit in the past or not applying due to the fear of getting rejected.
This proportion is only 4.5% among those that pay off their balances regularly. Revolvers also re-
port attitude indicators that support an inclication towards borrowing behavior. For instance, when
asked if they think its alright for someone like them to borrow for financing a vacation 19.3% answer
affirmatively as compared to 10.9% of convenience users. The demographic characteritics of the two
groups differ as well. Among revolving households, the head of the household reports a comparatively
lower age, lesser educational attainment and a higher probability of having children.

3     Empirical Model
The empirical model and estimation procedure fit three quantity variables – credit card accounts,
debt and transactions – to their counterparts observed in the data. The model presents a way
to tie these three variables together. The primary equation is the demand for active accounts.
Heterogeneous consumers choose an active account from a menu of differentiated credit cards. Card
holders use their accounts for day-to-day transactions and carry some amount of debt on them. The
model predicts each issuers total accounts, debt and transactions by aggregating over individual
account holders. The issuers’ price setting problem provides a channel for interchange fees to affect
equilibrium interest rates. Interchange fees earned on transactions is one source of revenue for the
issuers. As the interest rate changes, quantity demanded for credit card transactions adjusts and the
     For households that have multiple cards, the interest rate reported in the SCF is the rate paid on the card with
the largest balance. If balance is zero on all cards, it is the rate on the card obtained most recently.
     A household is considered as subprime if either their annual income is below the poverty line or they have filed for
bankruptcy previously.

revenue from interchange fee changes. Hence, an issuer’s profit maximizing interest rate is a function
of the exogenously determined interchange fees. This enables me to back out the impact of changes
in interchange fee on the equilibrium outcomes.

3.1       Consumer Behavior

Credit cards are differentiated products. They differ along several dimensions - APRs, reward fea-
tures, annual fees, late fees etc. The financial institutions that issue them also differ substantially in
their size, functions, services and scope. Hence, a model of credit card demand must accommodate
product differentiation. In addition to this, credit card holders differ as well. A key distinction comes
in the use of cards. Some people regularly borrow on cards (termed as revolvers) while others pay off
their balances in full at each billing cycle (convenience users). Consumers also differ in their credit
worthiness which is an indicator of how likely they are to pay off their credit card debt. All of these
factors drive people’s choice of cards, transactions and debt.
       I follow the discrete choice literature and model demand for credit cards using a random coef-
ficients discrete choice model. Heterogeneous consumers are assumed to pick a single product from
an menu of horizontally differentiated credit cards. Their utility from a given card depends on its
characteristics and their own attributes such as income and whether they are regular borrowers. The
central modeling assumption here is that individuals pick a single active credit card account. It is
well known that on an average people carry more than one credit card in their wallets. In the SCF
data average person carries 2.4 credit cards. The assumption here is not that people choose one
credit card but that they choose one active card, which is the card that they use most of the times.
Rysman (2007) [32] finds that although people maintain unused credit cards from multiple networks,
they concentrate their spending on a single network. Here I am extending this observation to issuers
within the Visa and MasterCard networks. I assume that a card-holder chooses a single active credit
card from the available Visa and MasterCard credit cards and accordingly, the data I use is that for
number of active accounts per issuer.
       This brings us to the model. Let j = 0, 1, . . . J be the set of credit cards available in a particular
year. Consumer i’s indirect utility from credit card j is a function of both consumer attributes and
card characteristics. It is assumed to take the following form:22

                                        Uij   = U (xj , rj , hi , ξj ; θ)
                                              = λxj + βhi rj + ξj +         ij                                    (1)

where xj and ξj represent observed & unobserved card characteristics, rj is the card’s APR, hi are
consumer attributes (including expected credit card debt and an unobservable consumer character-
    The data is a panel data set for 10 yrs. Each year represents a separate market. For simplicity, the year (market)
subscript is dropped here.

istic, among others) and       ij   captures idiosyncratic taste. The parameters to be estimated are the λs
and the βs. This specification is adapted from Berry, Levinsohn and Pakes (1995) (hereafter BLP).
Note that it includes interaction terms between the APR and individual characteristics. The hi s rep-
resent factors that generate preference differences among heterogeneous consumers. They account for
the fact that people’s sensitivity toward credit card interest rates depends on their attributes such as
income and how much debt they carry on their cards and the β coefficients determine the impact of
these preferences on utility. The paper also presents results from a simplified Logit model that does
not include these interaction terms. An implication of the logit model is that substitution patterns
are a function of product shares alone. Although the BLP model is less convenient to estimate than
the Logit model, it relaxes these restrictions by killing the IIA problem and produces more realistic
cross-price effects.23
    Now, each individual chooses the card that maximizes her utility. Assuming there are no ties,
the subset of h that choose card j is:

                                          Aj (θ) = {hi : Uij > Uik , ∀k}

Let δj = λxj + ξj be referred to as the mean utility levels. It is assumed that                 ij   follows the Type
I extreme value distribution. This implies that we can analytically integrate over                      ij   to get this
expression for the probability that individual i chooses card j conditional on h:

                                                             exp(δj + βhi rj )
                                    Pij (δ, r, h; θ) =                                                               (2)
                                                          1 + k exp(δk + βhi rk )

We obtain the predicted share of issuer j in Active Accounts (sCCA) by aggregating this probability
over the population, assuming that household characteristics h have a distribution g(h):

                               sCCAj       =         Pij (δ, r, h; θ)g(h)dh
                                                        exp(δj + βhi rj )
                                           =                                 g(h)dh                                  (3)
                                                 h   1 + k exp(δk + βhi rk )

Let M be the size of the market. Then, the total number of active accounts for issuer j are:

                                      CCAj     = M×             Pij (δ, r, h; θ)g(h)dh                               (4)

Next, we need a model for credit card debt and transactions for the issuers. Let ECCDij denote the
amount of credit card debt individual i is expected to carry on card j conditional on obtaining this
card. Then, issuer j can expect the amount ECCDij Pij in credit card debt from individual i, where
     See BLP (1995) [4] for further detail. Ishii (2004) [20], Dick (2008) [11] and Zhou (2007) [40] also use this model
for demand estimation in the context of the banking industry.

Pij is the probability that i gets card j. Hence, the model’s prediction for issuer j’s total credit card
debt is obtained by aggregating each individual’s expected debt over the population:

                           CCDj     = M×          ECCDij Pij (δ, r, h; θ)g(h)dh                      (5)

For credit card transactions, assume that individual i spends a proportion αj of her income Yi as
transactions on her active account, i.e. CCTij = αj Yi . As with debt, issuer j can expect the
amount αj Yi Pij in credit card transactions from individual i and his total transactions are obtained
by aggregating individual transactions over the population of interest:

                             CCTj     = M×            αj Yi Pij (δ, r, h; θ)g(h)dh                   (6)

This completes the description of the consumer’s behavioral model. The underlying hypothesis here
is that people choose credit card accounts and then they choose how much debt and transactions
to put on each account. The model generates predictions on an issuers expected share in accounts,
debt and transactions by aggregating over individual choices.

3.2   Firm Behavior

Issuer profit provides the channel through which interchange fees affects equilibrium in this model.
Issuers are assumed to maximize profit conditional on demand and given interchange fee. Interchange
fees are determined exogenously and so are card characteristics other than interest rates. There are
three sources of profit for issuers. Firstly, issuers earn interest income on credit card debt. Their
costs on debt consist of cost of funds and the losses they incur in the form of unrecoverable loans
or chargeoffs. Secondly, they earn interchange revenue along with any miscellaneous fees such as
late fees, cash advance fees, over-limit penalty etc., on their credit card transactions. There is a
marginal cost for each transaction which includes the cost of reward features, data processing and
fraud related losses. Thirdly, they collect annual fees (if positive) on their accounts and there is a
marginal cost for servicing accounts. The expression for issuer j’s variable profit is:

          Πj   = (rj − cf )CCDj + (˜ − mctj )CCTj + (AFj − mcaj )CCAj − Chargeoffsj
                                   a                                                                 (7)

where CCDj , CCTj and CCAj are credit card debt, transactions and accounts respectively, rj
denotes the interest rate on balances, a the interchange fee, AFj is the annual fee, cf is the cost of
funds, mctj is the marginal cost of a transaction (net of miscellaneous fees) and mcaj is the cost of
servicing an account. The parameters to be estimated here are the two marginal costs and chargeoffs.
To impose more structure on chargeoffs, it is assumed that each issuer loses a proportion γj of his
total credit card debt holdings as chargeoffs, that is Chargeoffsj = γj CCDj . Consequently, issuer j’s

variable profit simplifies to:

               Πj    = (rj − cf − γj )CCDj + (˜ − mctj )CCTj + (AFj − mcaj )CCAj
                                              a                                                   (8)

The issuer chooses APR rj to maximize profit. I assume that there exists an interior Nash equilibrium
in interest rates. The first order condition for profit maximization is:

      ∂Πj                          ∂CCDj                      ∂CCTj                 ∂CCAj
             = (rj − cf − γj )           + CCDj + (˜ − mctj )
                                                   a                + (AFj − mcaj )
      ∂rj                           ∂rj                        ∂rj                   ∂rj
             = 0                                                                                  (9)

Interchange fees do not directly affect demand for credit cards. In response to an exogenous change in
interchange fee, issuers recompute profits and choose new interest rates and subsequently, consumers
adjust quantities demanded.

4    Estimation
Estimation follows a generalized method of moments approach. For each issuer, I observe three
quantity variables in the data - active accounts, credit card debt and transactions. The model’s
predicted accounts, debt, transactions and issuer’s first-order condition generate moment conditions
that are matched to the data. This section outlines how these moment conditions are set up and
    The data confines the market to that for credit cards issued under the Visa or MasterCard
logo and the population of interest is all credit card holders. As mentioned in section 2, I use
issuer-level data on quantities and card characteristics (from Nilson and TCCP) and household-level
data (from the SCF). The BLP demand model provides a convenient platform for bringing together
these two sources of data. Consumer utility is a function of both card characteristics and consumer

                    Uij   = U (xj , rj , hi , ξj ; θ)
                          = λxj + βhi rj + ξj +         ij

                          = δj + β1 rj ECCDij + β2 rj Yi−1 + β3 rj IiBC + β4 rj ϑi +   ij       (10)

The consumer characteristics hi used here are the amount of credit card debt that the person expects
to hold at interest rate rj (denoted as ECCDij ), the inverse of household income Yi−1 , an indicator
for low credit worthiness I BC (where BC stands for Bad Credit) and an unobservable consumer
characteristic ϑi . Yi and I BC are observed directly in the SCF data. I BC is defined such that it
takes the value one if the household either self reports credit problems or is classified as subprime

(poor or bankrupt). The random coefficient ϑi is drawn from a standard normal distribution. This
is intended to capture unknown sources of consumer heterogeneity.
       For expected credit card debt, I estimate credit card debt from as a function of household charac-
teristics and interest rate from the SCF data and use this estimated function to predict the expected
credit card debt at each issuer’s rj . The SCF has detailed household data including several demo-
graphic, financial and credit worthiness related characteristics. Since a large number of households
report zero credit card debt, I use the Type-I tobit censored regression model for the same. In this
model the decisions to borrow and the amount to borrow are not estimated separately, which is
appropriate here because the objective is to predict the expected amount of debt per household. All
households are assumed to borrow, the expected value of debt is a lot more for some than others.
One drawback with using the SCF data is that the interest rate reported is the rate on the card
which has the largest balance whereas the debt is the total over all cards. To circumvent this issue,
we could confine ourselves to the subsample of households that hold exactly one card. However,
this approach would introduce a serious sample selection problem as pointed out by Zinman (2006)
[41]. Note that I do not explicitly model credit limits here, but the estimated equation does include
several variables that control for credit worthiness which largely determines credit limits.
       Now, the predicted credit card debt, other household attributes and card characteristics are
plugged into equation (2) to obtain the probability that individual i chooses credit card j. The
structural disturbance term ξj is intended to capture issuer-specific factors that may influence demand
but are not observed directly. This term eliminates the over-fitting problem that arises when the
only difference between the predicted and actual market shares is the sampling error. However, it
leads to an endogeneity problem because if issuers and consumers both observe it, then rj and ξj
are determined simultaneously and ξj cannot be assumed to be exogenous. Hence, we must use
instrumental variables to fix this problem. Given a set of demand-side instruments zj , this gives us
our first moment condition - at the true parameter value θ0 , the disturbance term ξj is independent
of variables zj :

                                                    E(ξ(θ0 )zj ) = 0                                (11)

I follow Berry (1994) [3] and BLP and use a contraction mapping to extract out the ξj s for setting
up this moment condition.24
       Next, the quantities of credit card accounts, debt and transactions per issuer in equations (3),
(4), (5) and (6) need to be computed. The integral over households in these equations does not have
a tractable solution. Following BLP, these are simulated by taking ns random draws from the SCF
dataset and aggregating over these. For each of the 10 years I take a new set of 1000 draws from
the closest SCF survey. The SCF has an oversampling of wealthy households but the survey does
       See Berry et. al. (1995) [4] or Nevo (1998) [27] for detail

supply sampling weights to correct this problem.25 The simulated expression for market shares is:

                                              sCCAj        =               Pij (δ, r, h; θ)                   (12)
                                                                 ns   ns

Credit Card debt and transactions for each issuer are computed in a similar fashion. These are
required for the interest rate setting equation. Given a set of demand parameters, total credit card
debt for issuer j from equation (5) is obtained simply as a product of the predicted debt per individual
and the probability of choosing card j summed across individuals. The estimated debt from SCF is
household debt. I use a scaling factor α1 to convert this to expected individual debt. This leads to
a second moment condition:

                    CCDj      =           M×    1
                                                ns       ns α
                                                                1 CCD
                                                                        ij Pij (δ, r, h; θ)     = CCDObsvd
                        νj        =       M      1
                                              × ns       ns α
                                                                1 CCD
                                                                        ij Pij (δ, r, h; θ)    −   CCDObsvd
                                                        1       D
                                                     E(νj (θ0 )zj ) = 0                                       (13)

The only new parameter to be estimated from this equation is the scaling factor α1 . However,
this equation does involve the demand parameters that enter into Pij (δ, r, h; θ) and adding it to
the estimation improves their precision, as does the transactions equation. In the expression for
transactions, the proportion of income spent as credit card transactions αj is unknown and needs to
be estimated. I assume αj to be a linear function of selected card and bank characteristics. In the
data, the transactions for each issuer are observed. The corresponding moment condition is set up
equating the predicted transactions to those that are observed:

                      CCTj            =   M×        1
                                                           ns αj Yi Pij (δ, r, h; θ)          = CCT Obsvd
                             νj       =       M      1
                                                  × ns         2
                                                           ns αj Yi Pij (δ, r, h; θ)      −      CCT Obsvd
                                                        2       D
                                                     E(νj (θ0 )zj ) = 0                                       (14)

       Further, we need an estimation equation for the cost parameters. Equation (9) represents the
issuer’s price setting condition. The proportion of debt lost as chargeoffs γj , marginal cost of a
transaction mctj and that of servicing an account mcaj are the parameters to be estimated here.
For simplicity, I assume that mcaj is constant across all issuers and that mctj is a function of reward
features plus a constant. For cost of funds cf , I use the annual average of the prime rate. It is
also assumed that the chargeoffs proportion γj is constant across firms, except for an firm level
    The SCF uses a multiple imputation technique. Here I have used the first impute from each of the three SCF

disturbance term:

                                             mctj    = τ1 RWj + τ2                                             (15)
                                                γj   = γ + ωj                                                  (16)

where γ and τ2 are constants, ωj is an unobservable source of differences in cost of chargeoffs among
issuers and RWj is summary measure of reward features on the card as observed in the TCCP data.
The resulting interest rate setting condition is:

                      ∂Πj                           ∂CCDj
                              = (rj − cf − γ − ωj )         + CCDj
                      ∂rj                              ∂rj
                                                      ∂CCTj               ∂CCAj
                                  +(˜ − τ1 RWj − τ2 )
                                    a                       + (AFj − mca)                                      (17)
                                                        ∂rj                ∂rj

This expression is inverted to recover ωj . The identifying assumption here is that ωj is uncorrelated
with suitable instruments zj and the corresponding moment condition is:

                                               E(ω(θ0 )zj ) = 0                                                (18)

Finally, the four moment conditions in equations (11), (13), (17) and (18) are estimated jointly. They
are stacked together as follows:
                                                                                 
                                                                       ξj (θ)zj
                                                               J  1      D
                                                       1          νj (θ)zj       
                                  GJ (θ) = E[mj (θ)] =           
                                                                  2      D
                                                             j=1  νj (θ)zj
                                                                   ωj (θ)zj

It is assumed that at the true parameter value G(θ) = 0.
       This brings us to a discussion of the instruments. As demand-side instruments, I use card
characteristics other than APR and bank characteristics. Following BLP, sums of these variables
over competing issuers in the same market are also valid instruments. In addition, cost shifters
provide a good source of demand instruments. I obtain two of these from the Call Reports filed by
the banks - expenses on premises & equipment and wages, both normalized by assets.26 Since these
are exogenous cost shifters, they are also valid cost instruments and so are the exogenous card and
bank characteristics. Estimation relies on the validity of these instruments and exogenous variation
across issuers and consumers (both within and across markets) for identification. The appendix
summarizes the steps involved in estimation and outlines how standard errors are computed. As
   Other costs reported in the Call Reports, such as provisions for loans and leases, are not included because these
may not be exogenous with respect to consumer demand.

a reference point, I also estimate demand for active accounts using a restricted form of the utility
function wherein consumer heterogeneity enters preferences only through the idiosyncratic term      ij .
This leads to the logit demand model of McFadden (1974) [26].

5    Results
The estimation procedure involves several equations and a few sets of parameters. In this section, I
discuss the results and their implications in order, starting with the demand side.

Credit Card Debt Equation (SCF)
The first step in estimation is computing the demand for credit card debt from the SCF data as a
function of interest rate and household characteristics. This is used to predict the amount of debt
an individual i is expected to carry on card j at the interest rate rate offered by the issuer, rj . The
predicted debt along with a few more household attributes is then plugged into the utility function
for further estimation. I fit a censored regression (Type I Tobit) model with credit card balances as
the dependent variable and interest rate and household observables as explanatory variables. From
the SCF data, households that reported negative incomes or annual incomes over one million were
dropped. A small percentage of households report credit card debt exceeding their credit limits
were also omitted. The resulting sample has 8,397 observations. Table (6) shows the results from
this step. Credit attitude, credit worthiness, demographic and financial factors all turn out to be
important in explaining household balances. Firstly, the interest rate coefficient is significant. It
implies a mean elasticity of -0.39 when computed at the interest rates offered by the issuers. The
other coefficients reflect the trends observed in the average statistics and it is interesting to note
their relative importance. Debt holding increases with age, marriage and children and is lesser at
both the lowest and the highest levels of education. Households that self report credit problems
and an inclination toward borrowing are significantly more likely to carry higher credit card debt.
Households that save and those with higher income and assets carry less debt on their cards.

Demand for Accounts, Debt and Transactions
The empirical model and estimation procedure fits three quantity variables to their counterparts
observed in the data. The primary equation is the demand for active accounts. Credit Card holders
are expected to use their active accounts by charging some amount of debt on their cards and for
conducting day to day transactions. I match the model’s predictions regarding shares in active
accounts, total outstanding debt and total transactions with those observed in the data.
    The demand side estimation follows the BLP framework. For comparison, the first equation
(demand for active accounts) was also fit using the logit model which is a simplified version of the
full BLP model. The dependent variable for the logit model is ln(sj ) − ln(s0 ) and the explanatory

variables are card and issuer characteristics. Table (7) presents the results from two different specifica-
tions of this equation. The first was estimated without instrumenting for interest rate (OLS version).
The second includes exogenous card and issuer characteristics as instruments (IV Logit version). Af-
ter instrumenting for APR, its coefficient increases about ten-fold from -0.017 to -0.147. This pro-
vides strong evidence for the endogeneity of the price variable. Correlation between the unobservable
and APR would tend to bias the coefficient toward zero. The instruments correct this bias. The logit
model also indicates that demand for active accounts is elastic. For instance in 2005, the predicted
mean elasticity with respect to APR for the logit model was -1.92. Among the other explanatory
variables, the dummy variables that indicate the type of bank seem to be more important (both in
terms of their absolute levels and their significance) than the other card characteristics such as type
of APR and grace period etc. This trend is preserved in the full BLP model. The R-squared for
the IV logit specification is 0.68 which indicates that the majority of the variance in mean utility is
explained by these independent variables.
   The full model is an extension of the simple logit model in that it allows for consumer hetero-
geneity to be built into the demand analysis. I include interactions between consumer attributes and
APR to allow for people’s sensitivity to APR to depend on factors such as their income, their credit
worthiness and the amount of debt they are expected to carry on their cards. Table (8) shows the
results from this specification. The linear APR coefficient is -0.16, higher than that under the logit
model. Among the interaction variables, that between predicted debt CCDij and APR is significant
and positive. This indicates that people with higher than average credit card debt care less about
interest rates than others. This may seem surprising at first but it has an explanation. Credit card
interest rates are usually higher than rates on consumer loans and installment loans available from
other sources. People who carry large amounts of credit card debt possibly do so for the convenience
rather than being motivated by interest rates. The other interaction terms in the utility function
were not estimated with sufficient precision.
   Among the linear variables, just as in the logit model, the coefficients of the issuer classification
variables are large in magnitude and significant when compared with card characteristics other than
APR. The coefficient for savings bank is negative implying that people prefer credit cards from
national banks or state member banks over these. (Non-member banks is the omitted category
here.) MasterCard credit cards generate higher utility than Visa. Although Visa is the bigger of
the two networks, the share of MasterCard during this period was rising and some of the large
issuers such as Citibank were aggressively promoting MasterCard credit cards over Visa. Annual
fees, rewards and grace period all have the expected signs though their magnitudes are low.
   The second and third equations match the predicted credit card debt and transactions for the
issuers to those observed in the data. The new parameters estimated here are first, the scaling factor
that is intended to transform household-level credit card debt to individual-level debt (α1 in the

model) and second, the proportion of income spent as credit card transactions (αj ). The scaling
factor is estimated at 61.4%, implying that on average there are less than two active account holders
per household. The mean proportion of income spent using credit cards as the method of payment
was estimated at 5.4%. This proportion is estimated as a function of card and issuer charateristics.
It reduces with APR and is lower for monoline banks and subprime lenders. It is higher for national
banks and big issuers and rises with reward features. Several of the estimated coefficients in this
equation are significant at the 1% level.
      Tables (9), (10) and (11) present a sample from year 2004 of the own and cross semi-elasticities
for all three of these quantity variables with respect to interest rates. Table (12) shows the mean and
weighted mean of the own price elasticities across all issuers, with initial shares as the weights. It
is interesting to note that demand for all three variables is on average elastic. The mean own price
elasticities indicate that a 1% increase in an issuer’s APR would lead to a 2.05% decline in accounts,
2.06% decline in transactions and 1.87% fall in debt on average. The weighted means are somewhat
lower. As can be seen from tables (9)-(11), the larger issuers tend to have lower elasticities. For
instance, in table (9) First National, Cross-Country bank, Direct Merchant’s and Merrick Bank all
have elasticities exceeding 2% in absolute value, whereas Chase and Bank of America, which are
among the top ten issuers, have inelastic demand.

Costs, Revenue and Profits
Turning now to the supply side, I estimate issuer chargeoffs, the marginal cost of transactions and the
cost of servicing an account from the issuer’s price setting condition. These estimates are presented
in table (13). The estimated chargeoffs are 5.13% of credit card debt outstandings. This figure is
well within the range of chargeoffs reported by the banks in their call reports. The Federal Reserve’s
report on chargeoff and delinquency rates27 for the 100 largest banks shows that the chargeoff rate on
consumer credit card loans during this time period varied between 3.97% and 8.08% with a quarterly
average of 4.93%. The mean marginal cost of a transaction (net of miscellaneous fees) is estimated
to be about 0.57 cents. This cost increases with reward features, but the coefficient on rewards is
rather small. The estimated cost of servicing an account is $4.24, but this parameter has a high
standard error of $15.8.
      These parameter estimates allow predictions on the composition of issuer costs, revenues and
profits. Figure (3) depicts this composition. Debt heavily outweighs transactions and accounts as
the source of both costs and revenue. Cost of funds and chargeoffs collectively account for 90.1% of
the issuer costs, whereas transaction costs and account servicing are just 8.3% and 1.6% respectively.
Evans and Schmalensee (2005) [12] also report that the largest chunk of costs and revenue are
attributable to credit card debt. Annual fees form 2.6% of profits and account servicing is 1.6% of

                    Figure 3: Composition of Estimated Profits, Costs and Revenue

costs. Income earned as interest on debt is 83.6% of all revenue. Interchange fee contributes 14.4%
of the total revenue. A quarter of the profits come from these fees, but the majority (72%) are net
earnings on debt. The average credit card profitability on debt was estimated to be 3.09%. This is
in line with the return on assets reported by the Federal Reserve for a sample of large credit card
banks, which was ranging between 2.14% and 3.66% for the years 1996-2005.28 Although this rate
of return has fallen as compared to the late 1980s and the early 1990s, credit cards still remain more
profitable than most other commercial bank activities.


6         Counterfactual Experiment
Given the parameters estimated from the structural model, a policy experiment to analyse the effect
of changes in interchange fees is relatively straight-forward. Interchange fees are exogenously set
to a new level and a new equilibrium in interest rates is computed numerically, starting from the
observed interest rates. The issuer behavior follows a Nash pricing assumption, so that the new
vector of interest rates is such that no firm can gain profits by deviating.29 I consider three new
levels of interchange - 1%, 0.75% and 0.5%. The average interchange fee in the data used in this
paper was 1.5% and its mean contribution to issuer revenue was about 14.4%. In Australia, the
fee was regulated in 2003 and brought down to 0.5% which is about half of its pre-regulation level.
In most countries, possible regulation of the fee is unlikely to reduce it to levels lower than 0.5%.
For each new level of interchange fee considered, Table (14) presents percentage changes in the key
quantity variables and profit under the new equilibrium.
         The results indicate that regulating the interchange fee down by a third increases interest rates
on an average by less than 2.5% of their original levels, which implies that the mean APR would
increase from 15.4% to 15.8%. This causes credit card debt to fall by 2.9% and transactions to
decline by 2.7%. Even when interchange fee is reduced by 50%, the impact on the quantity variables
is close only to 5%. It would take a decline in the fee of the order of a dollar for every hundred for
the impact on the equilibrium quantities to be over 7%. This impact is not significant especially
when considering that annual fees, late fees etc. are exogenous to this model. Demand is expected to
be relatively inelastic with respect to a few of these fees and issuers might change them in response
to changes in interchange fee to partially recover their losses. This would imply that the figures
presented here over-estimate the increase interest rates. Thus, it is possible that the actual impact
might be even less.
         Demand elasticity also explains why issuers are not able to increase interest rates substantially.
The elasticities with respect to APR reported in Tables (9)-(11) are high for most banks and partic-
ularly so for smaller issuers. Some of the large issuers face inelastic demand at the original APRs,
but their ability to raise interest rates is also curtailed by the possibility of losing market share to
the smaller banks. The increase in interest rates is not completely able to compensate for the decline
in interchange fee revenue and issuer profits suffer in the process. The average decline in profits is
6.3% when interchange fee is reduced to one percent. The loss in profits is higher for larger issuers30
and so is the change in their debt, transactions and accounts. In all three cases considered, large
issuers raise interest rates more than smaller ones do. Due to this difference in response of large
and small issuers, market concentration reduces following a drop in the interchange fee. The C(4)
ratio drops in almost all years. The share of the largest issuer falls on average by 8.5% of its original
         I restrict the search to APRs between 4% and 35% but neither of these limits turn out to be binding.
         Those with market share greater than 5%

level. These changes indicate that even though aggregate quanitites may not change drastically in
response to regulation of interchange fee, the market does undergo some reorganization.

7    Conclusion
This paper outlines an empirical model and estimation strategy for the evaluation of the role of
interchange fee in credit card issuing. The motivation for this research stems from the recent attention
that the credit card industry has received from economic theorists and regulators. One of the central
themes explored in the literature is interchange fees – the determination of optimal interchange
fee, the competitive effect of its collective setting and the justification, if any, for intervention by
regulatory authorities. The theoretical underpinnings of interchange fees have been investigated
pretty thoroughly, but there is a lack of consensus in the theory and it raises several empirically
testable questions. The question explored in this paper is: given the nature of competition in the
market, to what extent are issuers likely to pass on decreases in interchange fees to consumers via
higher interest rates?
    I propose a model that builds together consumers and credit cards issuers in one framework
and estimates optimal policies by combining data from several sources. Counterfactual experiments
allow me to predict the impact of changes in interchange fees on consumer usage and issuer profit,
assuming that merchant behavior is invariant. I find that competition in the market is intense and
demand is elastic with respect to interest rates. This prevents issuers from raising interest rates
substantially in response to a fall in interchange fees and since interchange is an important part of
their revenue, issuer profits suffer. Although aggregate quantities of transactions and debt do not
seem to fall significantly, there is some amount of restructuring involved as market shares readjust and
larger issuers suffer higher losses. A comprehensive evaluation of this subject requires accounting
for changes in other card characteristics (such as reward features) and allowing for acquirers and
merchants to respond as well. That, however, remains open for further research.

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A. Summary of Estimation Procedure

The empirical model involves four equations that are estimated together. The objective function for
estimation is set up as follows:

   • Step 1: Estimate Credit Card Debt as a function of interest rate r from the SCF Data using
     a censored regression model. The household’s desired stock of debt is:

                                     CCD∗ (r) = max {0, ρH + ρr + η}

     where H are household demographic, financial and credit characteristics. Predict CCD∗ (r) at
     interest rate rj for each issuer to obtain CCDij .

   • Step 2: Take simulation draws from the SCF dataset. Plug in the estimated debt along with
     other individual characteristics into Uij and predict the probability that individual i chooses
     card j as:

                                                   exp(δj + βhi rj )
                         Pij (δ, r, h; θ) =
                                              1+     k exp(δk + βhi rk )
                                                 exp(δj + β1 rj CCDij + β hi rj )
                                              1 + k exp(δk + β1 rk CCDik + β hi rk )

   • Step 3: Sum over the simulation draws to obtain the predicted active accounts, credit card
     debt and transactions for issuer j:

                               CCAj     = M×                  Pij (δ, r, h; θ)
                                                   ns    ns
                              CCDj      = M×                  α1 CCDij Pij (δ, r, h; θ)
                                                   ns    ns
                                             1                 2
                               CCTj     = M×                  αj Yi Pij (δ, r, h; θ)
                                             ns          ns

                                                                  ∂CCAj ∂CCDj                ∂CCTj
     and compute expressions for their first derivatives:           ∂rj , ∂rj           and    ∂rj .

   • Step 4: Recover the two disturbance terms - ξj using the contraction mapping and ωj from
     the first order condition. Compute the difference between the predicted and the observed debt
                        1      2
     and transactions, νj and νj respectively.

• Step 5: Set up the sample analogue of the moment condition:
                                                                             
                                                                   ξj (θ)zj
                                        J                  J    1      D
                                    1                  1        νj (θ)zj     
                           GJ (θ) =           mj (θ) =         
                                                                2      D
                                    J                  J
                                                           j=1  νj (θ)zj
                                        j=1                                   
                                                                 ωj (θ)zj

• Step 6: Minimize GN (θ)     where A is a weight matrix, to obtain the parameter estimates.
  The estimated parameters are N −consistent and asymptotically normal (under certain con-
  ditions) with the following variance-covariance matrix is given by the GMM formula:

                                   (Γ AΓ)−1 Γ AV AΓ(Γ AΓ)−1

  where Γ = ∂G(θ)/∂θ is the gradient of the objective function and V = E[m(θ0 )m(θ0 ) ] plus two
  other terms that adjust variance in the moment condition due to simulation error and prediction
  error. See BLP (1995) for detail. These standard errors allow for arbitrary heteroskedasticity.

            Table 1: Market Dataset Summary

Year   N           Share        Share    Share   C(1)
                   Mean         S.Dev.   Sum

1996   49          1.39         2.29     68.09   11.69
1997   45          1.60         2.66     72.13   10.82
1998   35          2.37         4.08     82.98   16.59
1999   28          2.89         4.29     81.02   16.58
2000   27          3.07         4.45     82.85   18.28
2001   25          3.39         4.25     84.67   14.68
2002   27          3.15         4.54     85.16   16.78
2003   25          3.35         5.16     83.78   20.86
2004   22          3.71         5.98     81.64   20.35
2005   17          4.37         6.88     74.25   19.17

               Table 2: Data Definitions for Quantities and Characteristics Dataset

Variable                 Source          Definition

APR                      TCCP            Annual Percentage Rate
Annual Fee               TCCP            Membership fee paid annually
Rewards                  TCCP            The issuer reports 1 or 0 for the following enhance-
                                         ments offered: Rebates on purchases, extension of war-
                                         ranty, purchase protection, travel accident insurance,
                                         travel discounts, automobile rental insurance, discounts
                                         on purchases other than travel discounts, card registra-
                                         tion and other. This summary measure is the sum of all
                                         reported enhancements. Max value = 10
Visa                     TCCP/Nilson     Equals 1 if Network name mentioned under TCCP
                                         credit card plan name is Visa or if no name was men-
                                         tioned, if Number of Visa cards > Number of Master-
                                         Card Cards in Nilson
Fixed APR                TCCP            If the credit card plan APR is fixed
Variable APR (Prime)     TCCP            If the credit card plan APR is variable, tied to the prime
                                         rate. Variable APRs tied to some other rate such as T-
                                         bill rates are the omitted category.
Grace Period             TCCP            The period of time from the end of the billing cycle in
                                         which credit extended for purchases during that billing
                                         cycle may be repaid without incurring a finance charge
National Bank            Fed NIC         Federal Reserve’s Bank Classification
State Member Bank        Fed NIC         Federal Reserve’s Bank Classification
Savings Bank             Fed NIC         Federal Reserve’s Bank Classification
Monoline CC Bank         Call Reports    If 75% or more of the bank’s reported total assets are
                                         held in credit card balances
Subprime Lender          Nilson          If the issuer offers credit cards to risky individuals
Big Issuer               Nilson          If the issuer was one of the top 25 issuers reported in
                                         Nilson during the previous year

Note:                                    Under TCCP, issuers report the plan that had the
                                         largest number of cards outstanding AND that was
                                         available to new customers as of the report date

 Table 3: Summary Statistics: Quantity and Characteristics

Variable                            Mean          Std.Dev.

Outstandings ($ Bil.)              13.8504          25.3862
Transaction Volume ($ Bil.)        25.2087          49.8536
Active Accounts (Mil.)              5.6304           9.5368

Card Features
APR (%)                            15.4270           3.9316
Annual Fee ($)                     11.4839          18.6335
Zero Annual Fee                     0.6400           0.4808
Rewards                             1.6967           1.9709
Visa                                0.6300           0.4836
Fixed APR                           0.1900           0.3930
Variable APR (Prime)                0.7333           0.4430
Grace Period                       25.7833           5.0006

Bank Type
National Bank                       0.6100           0.4886
State Member Bank                   0.1233           0.3294
Savings Bank                        0.0633           0.2440
Monoline CC Bank                    0.3367           0.4734
Subprime Lender                     0.2600           0.4394
Big Issuer                          0.5533           0.4980

Interchange Fee (%)                 1.4591           0.1757

                                Table 4: Data Definitions for SCF Dataset

 Variable                          Definition

 Married, Age, etc.                Refer to the Head of the Household.a
 Home: Regular                     Home type is other than farm, ranch, mobile home or RV
 OK to borrow for vacation         The respondent feels that it is all right for someone like herself to
                                   borrow money for a vacation
 OK to borrow for luxury           The respondent feels that it is all right for someone like herself to
                                   borrow money for a fur coat or jewelry
 Savings Dummy                     Indicator of whether the household saved over the past 12 months
 Have Professional Advice          Household seeks advice from an Accountant, Banker, Broker, Lawyer
                                   or Financial Planner when making decisions about credit or borrow-
 Income                            Household income for previous calendar year
 Financial Assets                  Total value of financial assets held by household
 Non Financial Assets              Total value of nonfinancial assets held by household
 Other Debt                        Total value of debt held by the household excluding installment loans
                                   and credit card balances
 Last yr Income high               Last yr income higher than what they would expect in a normal yr
 Last yr Spending high             Last yr spending higher than what they would expect in a normal
 Number of Institutions            The number of financial institutions where the household has ac-
                                   counts or loans, or does regular personal financial business with
 Home Ownership Dummy              Whether the household owns their home
 No Checking Account               Household has no checking account, ownership includes checking ac-
                                   counts with a zero balance.
 Credit Problems                   In the last 5 yrs, have the respondent or partner been turned down
                                   for credit or not given as much credit (even after reapplying) or not
                                   applied for credit due to the fear of being rejected
 Late Payments                     Of all loan and mortgage payments, the household has been behind
                                   in any by over two months
 Poor                              Income lower than the Federal poverty line
 Bankrupt                          The household has filed for bankruptcy previously
 Subprime                          Poor or Bankrupt, as above
 Number of Cards                   Number of general purpose credit cards: Visa, MasterCard, Discover
                                   or Optima
 Credit Limit                      Total credit limit on all general purpose credit cards
 Amount of Debt                    The balance still owed on these accounts after the last payments
 Utilization Rate                  Amount of Debt/Credit Limit
 Interest Rate                     The interest rate paid on the card with the largest balance. If balance
                                   is zero on all cards, the rate on the card obtained most recently

    The respondent may not be the head of the household. This variable has been suitably adjusted using the
’switch’ variable x8000.

                  Table 5: Descriptive Statistics for Credit Card Holders from the SCF Dataset

                                      All Card Holders             Convenience Users                Revolvers
Variable                              Meana   Std.Dev.             Mean     Std.Dev.             Mean   Std.Dev.

Married                                 0.6564         0.4749          0.6711           0.4699   0.6443          0.4788
Single Female                           0.2171         0.4123          0.1927           0.3945   0.2372          0.4254
Age (yrs.)                               48.74          15.98           53.67            16.75    44.66           14.06
White                                   0.8239         0.3809          0.8933           0.3088   0.7666          0.4231
Any Children Dummy                      0.4317         0.4954          0.3471           0.4761   0.5016          0.5001
Education: No High School               0.0784         0.2688          0.0761           0.2651   0.0803          0.2719
Education: College                      0.3903         0.4878          0.4726           0.4993   0.3222          0.4674
Home: Regular                           0.9375         0.2420          0.9422           0.2333   0.9337          0.2489

Credit Attitude
OK to borrow for vacation               0.1548         0.3617          0.1085           0.3110   0.1931          0.3948
OK to borrow for luxury                 0.0673         0.2506          0.0536           0.2253   0.0787          0.2693
Savings Dummy                           0.6422         0.4794          0.7480           0.4342   0.5547          0.4971
Have Professional Advice                0.4630         0.4987          0.5016           0.5001   0.4311          0.4953

Financial Indicators
Incomeb                                  76.72         165.07           96.82           235.65    60.11           57.18
Financial Assets                        227.88        1258.33          398.35          1807.05    86.87          387.00
Non Financial Assets                    340.83        1784.04          516.82          2551.95   195.37          620.52
Other Debt                               74.14         140.35           76.33           178.27    72.33           98.56
Last yr Income high                     0.1061         0.3079          0.1087           0.3114   0.1038          0.3051
Last yr Spending high                   0.1667         0.3727          0.0991           0.2989   0.2225          0.4160

Credit Worthiness
Number of Institutions                  2.7317         1.6875          2.9023           1.8084   2.5907          1.5670
Home Ownership Dummy                    0.7752         0.4175          0.8530           0.3541   0.7109          0.4534
No Checking Account                     0.0290         0.1679          0.0130           0.1133   0.0423          0.2013
Credit Problems                         0.1202         0.3252          0.0449           0.2071   0.1824          0.3862
Late Payments                           0.0915         0.2883          0.0363           0.1870   0.1371          0.3440
Subprime: Poor or Bankrupt              0.1261         0.3320          0.0780           0.2683   0.1658          0.3720

Credit Use
Number of Cards                         2.3837         1.6615          2.1844           1.5315   2.5485          1.7448
Credit Card Limit                        20.16          47.77           22.78            63.77    18.00           28.20
Amount of Debt                            2.40           6.00               0                0     4.38            7.56
Utilization Rate                        0.2101         0.6037               0                0   0.3838          0.7740
Interest Rate                            13.38           5.69           13.35             5.37    13.40            5.95

N                                             10198                             5987                      4211

     These are weighted means.
     All monetary values in are in ’000s and deflated to 2001 dollar.

Table 6: Demand for Credit Card Debt: Censored Regression Model

Variable                                      Coeff.                  Std.Err.

Married                                      1.2998**                 0.4170
Single Female                                0.7307                   0.4606
Age                                          0.4673**                 0.0584
Age Squared                                 -0.0056**                 0.0006
White                                       -0.7842*                  0.3163
Any Kids Dummy                               0.9056**                 0.3140
Education: No High School                   -1.1403**                 0.4256
Education: College                          -1.5153**                 0.3443
Home: Regular                                0.0735                   0.4801

Credit Attitude
OK to borrow for vacation                    2.7792**                 0.4288
OK to borrow for luxury                      1.7121**                 0.6016
Savings Dummy                               -2.4464**                 0.3582
Have Professional Advice                    -0.4511                   0.2909

Credit Worthiness
Number of Institutions                       0.0834                   0.0878
Home Ownership Dummy                        -1.2163**                 0.3615
No Checking Account                         -0.6814                   0.5583
Credit Problems                              3.9645**                 0.5018
Subprime: Poor or Bankrupt                   1.3114**                 0.3357

Last yr Income high                         -0.1605                   0.4556
Last yr Spending high                        2.6111**                 0.4254
Income                                      -0.0132**                 0.0023
Financial Assets                            -0.0005                   0.0003
Non Financial Assets                        -0.0001                   0.0001
Other Debt                                   0.0048**                 0.0012

Interest Rate                               -0.1371**                 0.0304
1998 SCF                                     0.0190                   0.3167
2004 SCF                                     0.6754*                  0.3176
Constant                                    -7.5806**                 1.5819
Sigma                                       10.3182                   0.5366

N                                                       8397

The dependent variable is household credit card balances in ’000s.
Significance at 1% and 5% levels is indicated by ** and * respectively
                 Table 7: Demand for Credit Card Accounts: Logit Model

                                                     OLS                   IV LOGIT
Variable                                       Coeff.   Std.Err.          Coeff.  Std.Err.

Card Features
APR                                       -0.0167       0.0185          -0.1466**   0.0204
Annual Fee                                -0.0085*      0.0037          -0.0099*    0.0040
Rewards                                    0.0075       0.0321           0.0068     0.0353
Visa                                      -0.2094       0.1305          -0.2256     0.1436
Fixed APR                                  0.3999       0.2511           0.4159     0.2763
Variable APR (Prime)                       0.1988       0.2212           0.1985     0.2434
Grace Period                              -0.0181       0.0139           0.0095     0.0153

Bank Type
National Bank                              0.7762**     0.1615           0.6367**   0.1777
State Member Bank                          0.7920**     0.2106           0.6323**   0.2317
Savings Bank                              -0.3459       0.2729          -0.7170*    0.3002
Monoline CC Bank                           0.3270*      0.1446           0.5135**   0.1592
Subprime Lender                            0.4072*      0.1643           0.5280**   0.1808
Big Issuer                                 2.0403**     0.1300           1.9331**   0.1430

Constant                                  -4.2927**     0.5263          -2.8611**   0.5791

R-squared                                          0.6766                      0.6811
N                                                    300                         300
Time Trend Included                                 Yes                         Yes

The dependent variable is ln(sj ) − ln(s0 ).
The IV Logit specification uses instruments for the price variable APR
Significance at 1% and 5% levels is indicated by ** and * respectively

       Table 8: Demand Parameter Estimates : Full Model

Variable                                      Coeff.              Std.Err.

Demand for Active Accounts:
Linear Coefficients
APR                                         -0.16396**           0.01807
Annual Fee                                  -0.00001             0.00019
Rewards                                      0.00022**           0.00007
Visa                                        -0.29837*            0.13352
Fixed APR                                    0.47155             0.26488
Variable APR (Prime)                         0.40551             0.23692
Grace Period                                 0.01211             0.01410
National Bank                                0.70866**           0.17209
State Member Bank                            0.64716**           0.22525
Savings Bank                                -0.61458*            0.29165
Monoline CC Bank                             0.45920**           0.15153
Subprime Lender                              0.46055**           0.16787
Big Issuer                                   2.04231**           0.13347
Constant                                    -3.43897**           0.52130
Time Trend Included                             Yes
Interactions with APR
N (0, 1)                                     0.00392             0.03355
ECCD                                         0.01428**           0.00484
Bad Credit                                  -0.05002             0.04185
Income Inverse                              -0.00004             0.00006

CCD Scaling Factor                          0.61483**            0.01328

Proportion of Income spent as CCT
APR                            -0.00016**                        0.00004
Rewards                         0.00089                          0.00081
National Bank                   0.00970*                         0.00399
State Member Bank               0.01931**                        0.00526
Savings Bank                    0.00224                          0.00692
Monoline CC Bank               -0.01892**                        0.00353
Subprime Lender                -0.01706**                        0.00383
Big Issuer                      0.01484**                        0.00320
Constant                        0.04956**                        0.00512
Mean(%)                         5.443

N                                                         300

Significance at 1% and 5% levels is indicated by ** and * respectively

                                        Table 9: Own and Cross Semi-Elasticities of Credit Card Accounts wrt APR
                                                                Sample from year 2004

                                       Prov.      Nat’l       Citi     JPM        First     BofA      MBNA        Capital     Direct      Cross-    Merrick
                                                  City                Chase       Nat’l                             One       Merch.       C’try     Bank

     Providian                        -1.1759    0.0608     0.1207    0.0720     0.0052     0.0535      0.0902      0.1487      0.1026     0.0152      0.0049

     National City Bank               0.1377    -1.2444     0.1207    0.0722     0.0055     0.0539      0.0999      0.1499      0.1028     0.0158      0.0055

     Citigroup                        0.1407     0.0621    -1.0524    0.0738     0.0056     0.0551      0.1021      0.1531      0.1051     0.0162      0.0057

     JP Morgan Chase                  0.1463     0.0646     0.1282    -0.8227    0.0058     0.0572      0.1062      0.1592      0.1092     0.0168      0.0059

     First National Bank              0.1038     0.0458     0.0910    0.0544    -2.7779     0.0406      0.0753      0.1130      0.0775     0.0119      0.0042

     Bank of America                  0.1438     0.0635     0.1260    0.0754     0.0057    -0.9617      0.1044      0.1565      0.1074     0.0165      0.0058

     MBNA                             0.1280     0.0565     0.1121    0.0671     0.0051     0.0501     -1.6374      0.1393      0.0955     0.0147      0.0051

     Capital One                      0.1283     0.0566     0.1124    0.0672     0.0051     0.0502      0.0931     -1.5776      0.0958     0.0147      0.0052

     Direct Merchant’s Bank           0.1103     0.0487     0.0967    0.0578     0.0044     0.0432      0.0801      0.1201     -2.3833     0.0127      0.0044

     Cross Country Bank               0.0889     0.0393     0.0779    0.0466     0.0035     0.0348      0.0646      0.0968      0.0664    -3.4573      0.0036

     Merrick Bank                     0.1150     0.0508     0.1007    0.0603     0.0046     0.0450      0.0835      0.1251      0.0858     0.0132     -2.2871

     Cell entries (i, j), where i indexes row and j indexes column, give the percentage change in the accounts of issuer i with a 1% change in issuer j’s APR
                                      Table 10: Own and Cross Semi-Elasticities of Credit Card Transactions wrt APR
                                                                Sample from year 2004

                                       Prov.      Nat’l       Citi     JPM        First     BofA      MBNA        Capital     Direct      Cross-       Merrick
                                                  City                Chase       Nat’l                             One       Merch.       C’try        Bank

     Providian                        -1.2049    0.0473     0.0790     0.0577    0.0015     0.0446      0.0523      0.0679      0.0289     0.0044         0.0004

     National City Bank               0.0369    -1.2746     0.0464     0.0339    0.0009     0.0262      0.0307      0.0399      0.0170     0.0026         0.0002

     Citigroup                        0.0447     0.0336    -1.0788     0.0410    0.0011     0.0317      0.0372      0.0482      0.0206     0.0031         0.0003

     JP Morgan Chase                  0.0378     0.0285     0.0475    -0.8442    0.0009     0.0268      0.0315      0.0408      0.0174     0.0026         0.0002

     First National Bank              0.0773     0.0582     0.0971     0.0709    -2.5474    0.0548      0.0643      0.0835      0.0356     0.0054         0.0005

     Bank of America                  0.0360     0.0271     0.0452     0.0330    0.0009    -0.9862      0.0300      0.0389      0.0166     0.0025         0.0002

     MBNA                             0.0515     0.0387     0.0646     0.0472    0.0012     0.0365     -1.6744      0.0556      0.0237     0.0036         0.0003

     Capital One                      0.0596     0.0448     0.0748     0.0546    0.0014     0.0422      0.0496     -1.6141      0.0274     0.0042         0.0004

     Direct Merchant’s Bank           0.0810     0.0609     0.1018     0.0743    0.0019     0.0575      0.0674      0.0875     -2.1635     0.0057         0.0005

     Cross Country Bank               0.0688     0.0517     0.0864     0.0631    0.0016     0.0488      0.0572      0.0742      0.0316    -3.2013         0.0004

     Merrick Bank                     0.0836     0.0629     0.1050     0.0767    0.0020     0.0593      0.0695      0.0903      0.0385     0.0058        -2.0807

     Cell entries (i, j), where i indexes row and j indexes column, give the percentage change in the transactions of issuer i with a 1% change in issuer j’s APR
                                          Table 11: Own and Cross Semi-Elasticities of Credit Card Debt wrt APR
                                                                Sample from year 2004

                                       Prov.      Nat’l       Citi     JPM        First     BofA      MBNA        Capital     Direct      Cross-     Merrick
                                                  City                Chase       Nat’l                             One       Merch.       C’try      Bank

     Providian                        -1.0491    0.0939     0.1892     0.1158    0.0071     0.0848      0.1469      0.2209      0.1394     0.0191      0.0069

     National City Bank               0.2125    -1.1120     0.1892     0.1167    0.0072     0.0859      0.1471      0.2210      0.1396     0.0194      0.0077

     Citigroup                        0.2160     0.0954    -0.9376     0.1186    0.0073     0.0873      0.1495      0.2246      0.1418     0.0197      0.0078

     JP Morgan Chase                  0.2223     0.0981     0.1978    -0.7307    0.0076     0.0899      0.1539      0.2310      0.1459     0.0203      0.0080

     First National Bank              0.1713     0.0757     0.1525     0.0941   -2.5474     0.0693      0.1186      0.1781      0.1125     0.0157      0.0062

     Bank of America                  0.2195     0.0969     0.1954     0.1206    0.0075    -0.8557      0.1520      0.2282      0.1442     0.0201      0.0079

     MBNA                             0.2011     0.0888     0.1790     0.1104    0.0068     0.0813     -1.4709      0.2090      0.1320     0.0184      0.0073

     Capital One                      0.2014     0.0889     0.1793     0.1106    0.0069     0.0814      0.1395     -1.4143      0.1323     0.0184      0.0073

     Direct Merchant’s Bank           0.1795     0.0793     0.1598     0.0986    0.0061     0.0726      0.1243      0.1866     -2.1635     0.0164      0.0065

     Cross Country Bank               0.1523     0.0673     0.1356     0.0837    0.0052     0.0616      0.1055      0.1584      0.1000    -3.2013      0.0055

     Merrick Bank                     0.1853     0.0818     0.1649     0.1017    0.0063     0.0749      0.1283      0.1926      0.1216     0.0169      -2.0807

     Cell entries (i, j), where i indexes row and j indexes column, give the percentage change in the debt of issuer i with a 1% change in issuer j’s APR
             Table 12: Mean Own-Price Semi-Elasticities

       Quantity                       Mean                 Wtd.
       Variable                                            Mean

       Accounts                      -2.0502              -1.9027

       Transactions                  -2.0647              -1.9340

       Debt                          -1.8657              -1.7259

       Initial shares are used as weights to compute the weighted mean.

          Table 13: Cost Parameter Estimates : Full Model

Variable                                         Coeff.              Std.Err.

Chargeoffs Proportion: (%)                      5.13**               0.1300

Marginal Transaction Cost:
(Net of Misc. Fees)
Rewards                                        0.00019**            0.00004
Constant                                       0.00563**            0.00035
Mean (cents)                                   0.569

Account Servicing Cost ($)                     4.24776              15.7704

Significance at 1% and 5% levels is indicated by ** and * respectively

           Table 14: Counterfactual Results: Average
             Percentage Changes in Key Variables

Variable                 IF = 0.5%       IF = 0.75%      IF = 1.0%

All Issuers
Profits                       -12.518          -9.437         -6.277
Debt                          -7.926          -5.593         -2.888
Transactions                  -7.412          -5.229         -2.682
Accounts                      -7.025          -4.957         -2.538
Interest Rates                +6.652          +4.656         +2.434

Large Issuers
Profits                       -13.839          -10.810        -7.615
Debt                          -7.990           -5.957        -3.051
Transactions                  -7.550           -5.516        -2.802
Accounts                      -7.166           -5.231        -2.651
Interest Rates                +6.667           +4.875        +2.564

Small Issuers
Profits                       -11.802          -8.694         -5.552
Debt                          -7.892          -5.397         -2.800
Transactions                  -7.337          -5.074         -2.617
Accounts                      -6.948          -4.808         -2.477
Interest Rates                +6.644          +4.537         +2.364

Large issuers are those with initial market shares more than 5%.


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