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P
The Importance of Payments-Driven Revenues
to Franchise Value and in Estimating
Bank Performance
Tara Rice
Emerging Payments Occasional Papers Series
2003-1D
The Importance of Payments-Driven Revenues
to Franchise Value and in Estimating Bank Performance
Tara Rice
Abstract
This paper examines how the production of payment services impacts the franchise value of banks.
It also explores whether analysts are incorrectly measuring the performance of the banking sector
and failing to realize the full importance of payments-driven revenues to banks. In initial empirical
analysis, we find limited evidence to suggest that higher payments-driven revenues are associated
with higher franchise value. We find, also, that estimates of productive efficiency change
dramatically for a small number of banks heavily involved in payments services. We find evidence
to suggest that traditional efficiency estimates that exclude nontraditional bank activities
inaccurately measure the relative performance of some types of BHCs. We infer from these results
that estimation of efficiency must take into account the different mix of traditional and
nontraditional activities in which banks engage.
Economist, Federal Reserve Bank of Chicago. tara.rice@chi.frb.org. The author thanks Robert DeYoung, Cathy
Lemieux, Ed Green, Victor Stango, Sujit Chakavorti, Paul Kellog, Kristin Stanton, and seminar participants at the
Federal Reserve Bank of Cleveland for helpful comments. The views expressed are those of the author and do not
represent the views of the Federal Reserve Bank of Chicago or the Board of Governors of the Federal Reserve System.
1
1. Introduction
The objective of this paper is to explore two questions relating to the value that paymentsrelated activities may add to the bank. First, does the production of payment services impact the
franchise value of banks? We examine whether the degree of BHC involvement in paymentsrelated activities (measured by the revenue derived from the production of these services) adds
value to a bank. We first calculate Tobin’s Q, a ratio of the market value of the firm to the book
value of its assets and liabilities. We then empirically examine whether increased payments-driven
revenues contribute to the franchise value of the bank.
The second question asks whether banking sector output is being mismeasured by
underestimation of payment-related revenues. Does exclusion of payments activities from the cost
and/or profit function affect the measurement of bank performance? Do payments services offer
opportunities for revenue enhancement? We explore whether analysts are incorrectly measuring
the performance of the banking sector and failing to realize the full importance of payments-related
revenues to banks.
We first estimate productive efficiency using the traditional empirical
framework. We modify that framework to better account for payment activities at banking
companies, re-estimate productive efficiency, and compare the traditional and modified efficiency
estimates in empirical analysis.
The remainder of the paper is organized as follows. Section 2 discusses potential reasons
for a bank to produce payments services. Section 3 addresses the first question regarding the
franchise value of the bank, while section 4 addresses the second question, concerning the
profitability of banks. Section 5 concludes and offers some inferences based upon our initial
empirical analysis.
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2. Potential Reasons for BHCs to Offer Payments Services
The payments system, as defined by Hancock and Humphrey (1998), consists of a legal
framework, rules, institutions, and technical mechanisms for the transfer of money. As such, it is
an integral part of the monetary and financial system in a smoothly operating market economy
(Hancock and Humphrey, 1998).
Banks traditionally have offered payments-related services to their customers, but more
recently, nonbanks1 have entered into the industry and provide either supporting or competing
payments-related services to bank customers. Yet these firms do not provide the same set of
services that banks offer. Bradford, Davies and Weiner (2002) examine the involvement of
nonbanks in payments activities, and find that nonbanks, while pervasive in the payments system,
are not directly involved in settlement activities2.
Furthermore, banks are provided by their charters with the ability to hold specialized assets
(i.e., commercial loans) and to issue some specialized liabilities (i.e., demand deposits). The bank
charter also gives banks access to the discount window and deposit insurance. Thus, banks have
two unique features/connections with regard to the payments system:
(1) they have the ability to offer settlement activities, and,
(2) due to their ability to accept deposits, and the fact that the payments systems is heavily
reliant upon deposit-based instruments, they are in a unique position to offer paymentsbased products and services to their customers.
However, as Berger, Hancock and Marquardt (1996) note, three types of innovation,
technological, financial and regulatory, are transforming the payments system in the US. While
banks maintain a unique connection with the payments-system, the system and its players are
changing rapidly. A Boston Consulting study (February 2003) concludes that while the volume
1
Any financial services firm that is not a depository institution.
3
and value of payments are increasing around the world, banks are collecting less revenue for each
transaction. The authors conclude that competitive pressures on the banks are driving down their
revenue per transaction, and in order to gain market share, the banks must provide new products
and technologies to its customers (American Banker, March 4, 2003). Banks are providing an
increased number of products and services, due to financial innovation and deregulation, which has
eased some restrictions on bank-eligible activities. However, competition is increasing as nonbank
firms and banks outside of the traditional market, as discussed, are also able to offer competing
products and services. Banks, therefore, are pressured to keep current customers and gain new
ones.
DeYoung and Hunter (2002) discuss the switching costs to bank customers, but focus on
the effects of the particular payment service: the internet. These authors contend that, prior to
recent innovation of the internet as a payment service distribution channel, customers were
“captive”: they would not typically move their deposits to a different bank because of high
switching costs.
High switching costs resulted from the proximity of bank branches to a
customer’s home because most transactions required visits to physical bank offices. DeYoung and
Hunter (2002) state that internet distribution channel offers to customers increased convenience
(banking can be performed at home), at a reduced costs to the bank (processing these transactions
costs significantly less than paper-based transactions). With the rise in the number of banks
offering internet banking services, comes the ability to move to banks outside a narrow geographic
area. Customers have more choices and switching costs have decreased.
To counter the decrease in switching costs, banks may increase cross-selling in order to
embed the customer more firmly in the local branch network (DeYoung and Hunter, 2002).
Chakravorti and Kobor (2002) interviewed a number bank holding companies (BHCs) between
June and November 2002. Based on their interviews, these authors find, in fact, that many of the
2
Settlement is defined by Bradford, Davies and Weiner (2002) as the irrevocable transfer of funds between
parties in a payments system.
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BHCs offered payments services to retain customers. The Chakravorti and Kobor study suggests
that payment activities provide profit enhancement to the BHCs in one of two ways: either through
product bundling or as a stand-alone product. BHCs may bundle payment activities into other
products, where each product need not contribute directly to profit alone, but to the retention and
acquisition of customers. Stand-alone payment activities contribute to revenue enhancement
directly by increasing profit, or decreasing costs.
Chakravorti and Kobor (2002) also cite the “stickiness” of using some payment services as
a way of retaining customers and discuss the high cost to customers using automatic debit
payments and ACH networks of switching to another institution. Most businesses require two
weeks written notice in advance of an account change/closure which makes transferring to another
institutions difficult and time-intensive for a customer.
Furst, Lang and Nolle (1998), who conduct a survey of bank payment systems in 1997-8,
come to a similar conclusion. These authors find that many banks are concerned that they will lose
profits and market share if their competitors are better able to take advantage of low-cost delivery
channels such as ACH, internet and ATMs.
In their survey, these authors find that banks are
investing in electronic delivery channels of payment services and “counting on a payoff in the nearterm from technological improvements in their traditional delivery channels.” One of the larger
goals banks hoped to achieve through this investment was to increase their cross-selling. These
authors define cross-selling as the sale of additional products and services to a customer based on
an analysis of data about the customer’s current purchases of products and services (p. 28).
These studies argue that competitive pressure from nonbanks and other financial
institutions is leading banks to offer new products and technologies in order to retain their
customers over the long term. Thus, banks that offer or are in a position to offer a greater variety
of payments services, or are able to do so at a lower cost have a competitive advantage over their
bank competitors. Furthermore, because payments services are an intricate part of a bank’s
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activities, appropriately measuring the profitability of these activities is vital for management in
determining bank business strategies.
While a number of recent studies discuss the motivation for banks to offer payment
services, very little empirical work has attempted to test these hypotheses. Part of the problem is
the lack of consistent data and of an understanding of the volume of revenue that payment services
contribute to the bank. This paper addresses how payment services contribute to the bank franchise
value and profitability by empirically examining the effect that payments-driven revenues at large
US BHCs have on value and performance. We employ a data set, following Rice and Stanton
(2003), that measures categories of payments-driven revenues that until 2001 were immeasurable.
An added level of detail in income reporting by BHCs allows more precise measurement of
payments-related noninterest income. We, therefore, have an ability to test a number of hypotheses
regarding the effects of payments-driven revenues on franchise value and in estimating bank
performance.
Specifically, we ask whether payment activities add value to the bank. If payment services
do, in fact, add franchise value to a banking firm, then payments-driven revenues would be
positively associated with a higher degree of franchise value. Alternatively, if payment activities
do not contribute to the franchise value of a bank, but are considered a part of a banks total product
mix, then we expect that business strategy and not individual payment activities to be positively
associated with franchise value.
While most researchers agree that banks of different size or organizational structure do not
operate in the same manner, few studies identify the various bank strategies. Amel and Rhodes
(1988) test for the existence of strategic groups in banking by focusing on differences in balance
sheet composition. They find that a number of bank types based on business strategy exist, and
conclude the following. First, the groups are not based on size alone but also on portfolio choices
available to banks. Second, strategy choices rather than efficiency differences may be the
explanation for observed intraindustry differences in bank performance. Their findings suggest
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that industries or markets may generally be defined too broadly and that studies of market
performance should take in to account bank characteristics as well as general industry
characteristics.
DeYoung and Hunter (2002) consider how recent technological innovation affects bank
business strategy and describe two banking strategies, large banks, and small (community) banks.
These authors contend that while payments services have always been a primary service provided
by banks, technological innovation (namely, the internet) has potential to change the production of
payments services, away from paper-based transactions, which will, in turn, have differential
effects on banks of varying business strategies. We extend this analysis by dividing the large bank
business strategy into four strategies: conglomerates, Global Processors, credit card banks and
regional banks, and examining the effect of payments-driven revenues on each of these bank
business models. Future research will also include community banks as a business strategy.
3. Does the production of payment services impact the franchise value of banks?
Tobin’s Q is a widely-used, market-based measure of valuing intangible assets. The value
of the intangible assets is its franchise value. Franchise value is defined as the present value of a
firm’s future profits (revenues in excess of all costs, including the cost of capital). It is the market
value of the firm divided by the replacement costs of assets.
If BHCs which offer a greater amount of payments services have a competitive advantage
over their bank competitors, then the intangible value of those firms would be higher, all else held
constant, because the market price of publicly traded companies is based on the present value of
their future earnings. We calculate the franchise value for a sample of 98 BHCs and empirically
test whether increased payments revenue is associated with increased franchise value.
Calculating Tobin’s Q
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Several definitions of Tobin’s Q exist. We employ the measure used by Demsetz,
Saidenberg and Strahan (1996).
We quantify franchise value by taking the difference between a
firm’s market value and its replacement cost, where replacement cost is the expense of rebuilding
the firm today. We denote franchise value (FV) as:
FV = market value – replacement cost.
The difference between market value and replacement cost will be large when franchise
value is high, or when there are profits associated with the firm as a going concern. We cannot,
however, measure market value or replacement costs directly. Demsetz, Saidenberg and Strahan
(1996) approximate the market value of a bank holding company’s (BHC) assets by adding the
market value of its equity (shares of stock outstanding times price per share) and the book value of
its liabilities.
When a BHC purchases an asset for more than its book value, the difference between its
book value and the purchase price is accounted for on the purchaser’s books as goodwill. Because
this difference is a component of the purchaser’s franchise value,
Demsetz, Saidenberg and Strahan (1996) approximate the replacement cost of a BHC’s assets
using the book value of its assets minus goodwill. They divide the franchise value by book value
of assets (net of goodwill) as such:
FV
E + L − ( A − goodwill )
=
,
( A − goodwill )
( A − goodwill )
(1)
where E is the market value of equity, L is the book value of liabilities and A is the book value of
assets. Adding 1 and simplifying gives a proxy for the measure of “Tobin’s q”:
Q=
E+L
.
( A − goodwill )
(2)
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One caveat in using this measure: it includes both franchise value and government
guaranteed deposit insurance. For that reason, we examine the franchise value of a sample of
publicly traded BHCs, excluding nonbank financial holding companies.
In equation 2, equity (E) equals the stock price at the end-of-year 2001 times the total
number of shares outstanding. Stock prices and number of shares outstanding are obtained from
Bloomberg. The book-value data (total liabilities, total assets and goodwill) for end-of-year 2001
are obtained from the “Consolidated Financial Statements for Bank Holding Companies” or Y9
reports. Payments-related revenue and other BHC-specific financial data are taken from the Y9s.
We draw our sample of 98 BHCs from the American Banker’s list of the top 150 Bank and Thrift
Holding Companies. We exclude foreign BHCs, because the equity of the top holder BHC is
issued in another country. Also excluded are thrift holding companies. The remaining data set has
98 observations. We rely on 2001 changes to the bank and BHC reporting forms, thus cannot use
data before 2001.
Measurement of Payments-Related Revenues
Based on the approach outlined in Rice and Stanton (2003), we create measures for the five
categories of payments-related revenue listed above: Service charges on deposit accounts, foregone
interest revenue, payments-related trust revenues, payments-related credit card revenue, and
revenue from ATMs.
Briefly, these variables are defined as follows:
Service Charges on Deposit Accounts
Service charges on deposit accounts are aggregate fees charged the depositors. These
include: maintenance of the account, failure to maintain minimum balances, check-clearing fees,
actual transfer of currency, fees for drawing checks on accounts with insufficient funds, and for
issuing stop payments orders. We draw this information directly from the Y9 reports.
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Foregone Interest
To measure foregone interest revenue, we estimate the amount of interest expense that
banks would earn if they had to pay the federal funds rate to all deposit account holders. We also
estimate the amount of interest expense that banks pay to the transactions account holders.
The
difference between the two is the amount of interest that the bank “saves” by offering customers
transaction accounts. We calculate foregone interest (FI) revenue as follows:
FI = $DDA*(fed funds)+$NOW*(fed funds-NOWr)+$MMDA*(fed funds-MMDAr),
(4)
where $DDA denotes aggregate balance in dollars in demand deposit accounts, $NOW denotes the
balance in negotiated order of withdraw accounts, and $MMDA denotes the balance in money
market deposit accounts.
The subscript, r, on each of the account types denotes the average rate those accounts paid
to deposit holders. The rates for each of the account types are national averages on the last
reported date of the year 2001 obtained from the Bank Rate Monitor, a weekly publication of
deposit rates. Future research will use a local rate, rather than a national rate (i.e., state or MSA
rate). The balances on each deposit account types are obtained from the FR-Y9C reports.
Payments-Related Trust Revenues
Dependent upon the type of trust account that is managed or held by an BHC’s trust
department, the BHC earns a wide range of revenues from payment activities. We, therefore,
estimate a range of revenues earned through payments-related activities in trust accounts. We
include, on the low end, one category of trust revenue, “custody and safekeeping accounts”. On
the high end, we aggregate four of the eleven revenue categories listed in the Y9: revenues from
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employee benefit (defined contribution and defined benefit) accounts, corporate trust and agency
accounts, and custody and safekeeping accounts.
Payments-related Credit Card Revenues
We estimate the payments-related credit card revenue from on balance sheet receivables
and for payments-related securitized credit card receivables. Credit Card Management (2001)
breaks down 1999 revenues of Visa and MasterCard into six subcategories: interchange fees,
annual fees, penalty fees, cash-advance fees, enhancements, and interest. We consider interchange
fees, annual fees, and enhancements to be payments-related revenues. Based on this figure, 14
percent of total MasterCard and Visa revenues come from interchange fees, another 2 percent from
annual fees, and 1 percent from enhancements. Generalizing this result, we assume that 17 percent
of all credit card revenues are derived from payments-services. We then take total revenue from
credit cards and multiply that by 0.17 to give us an estimate of on balance sheet payments-related
credit card revenue.
We then estimate the payments-related revenue from the securitized credit cards by
converting the off balance sheet securitized credit card receivables into an on balance sheet
equivalent. Payments-related revenue from securitized credit card receivables includes the same
activities as the on balance sheet receivables, except that the revenues from these activities accrue
to the credit card master trusts. These revenues include similar fees as in the on balance sheet
credit card receivables, such as annual fees and late fees.
We first assume that the securitized credit card receivables earn the same rate of return that
the on balance sheet credit card receivables earn. This assumption implies that the portfolio
composition of the off balance sheet credit card receivables is the same as the on balance sheet
credit card receivables and that fees earned from these activities are roughly similar. We then
multiply 0.17 (our on balance sheet estimate of payments-related revenue) times a rate of return
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that the on balance sheet receivables would earn. The calculation of payments-related securitized
credit card revenue (SCC) is as follows:
on balance sheet credit card revenue
SCC = 0.17 * sec uritized credit card receivables
on balance sheet credit card receivables
(5)
ATM revenues
Beginning in 2001, the Bank Call reports require that banks report the “income and fees
from automated teller machines” (ATMs) in the category of “other noninterest income” when it
exceeds 1 percent of total revenue (defined as total interest income plus total noninterest income).
We aggregate the ATMs fees by BHC. By collecting data on all bank subsidiary ATM fees and
summing those fees for each BHC by linking each bank subsidiary with its parent BHC. One issue
with using this variable is that, because banks must only report this item when it exceeds 1 percent
of total revenue, some BHCs show ATM revenues of zero (or missing), when in fact, they are just
below the 1 percent threshold.
Aggregate Payments-Services Revenue
Finally, we include an aggregate BHC measure of payments-related revenue by summing up all
five categories of payments-related revenue. Thus, AGG_PS is the sum of SC, FIR, TR, CC, and
ATM.
Business Strategy
We include indicator variables for BHC business strategy or operations. Four BHC types
are identified: Conglomerate BHCs, Global Processing BHCs, Regional BHCs, and Credit Card
BHCs. Foreign BHCs are excluded. We define each of the BHC types as follows:
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Conglomerate BHCs are BHCs composed of affiliate companies in a variety of businesses
including, but not limited to, insurance, securities, commercial banking, and payments-processing
activities.
Global processing BHCs handle the cross-border safeguarding, settlement, and reporting of
clients' securities and cash on a worldwide basis. These global custodians execute security trades,
collect dividend and interest income on securities and cash holdings, recover taxes imposed on
such income by the local governments and notify clients of corporate actions affecting their
securities holdings. Accounting tasks include reporting all transactions, providing an accurate
listing of a fund's assets, and valuing the fund's individual assets as well as the fund itself, if so
desired by the client.
Credit Card companies are defined as either monoline companies focused on credit card
operations or banking companies that are not mono-line but have large credit card operations.
Regional BHCs focus on large and middle-market commercial lending and retail banking. These
companies have a presence in specific geographic areas of the country, i.e., the Southwest or the
Midwest.
Correlation Analysis of Franchise Value and Payments-Driven Revenues
Table 1 presents the summary statistics of the set of 98 BHCs while Table 2 presents the
summary statistics for a larger sample of 284 BHCs, used in the empirical analysis of efficiency.
Table 3 presents the correlation (_) between Tobin’s Q and the five categories of payments driven
revenue, plus the sum of those five categories. We find payments-related trust revenues to have the
highest positive correlation (0.368) among the five types of payments-driven revenues. The
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correlation between payments-related credit card revenues and franchise value is 0.103. Service
charges on deposit accounts, a measure more closely associated with traditional bank activity of
taking deposits, has the lowest correlation with franchise value (0.045). We also test, for each of
the payments-driven categories, the hypothesis H0: _=0, or that the correlation between paymentsdriven revenues and franchise value, at a given confidence level, is greater than zero. We denote in
Table 3 those correlation coefficients that are statistically significantly different from zero at the 10
percent level with a “*”. We find the correlation of payments-related trust revenues and franchise
values to be significantly different from zero. We also find the correlation (0.172) of “all”
payments-driven revenue and franchise value to be significantly different than zero. While limited,
this finding suggests that increased payments-related revenues are associated with increased
franchise value. A caveat: this table is based on end of year 2001 data for publicly-traded BHCs.
Future research will include more firms over a greater number of time periods.
4. Is Banking Sector Output Mismeasured by Underestimation/Omission of Payment-Related
Revenues?
This section explores whether exclusion of payments activities from the cost and/or profit
function affects the measurement of bank performance. If analysts are incorrectly measuring the
performance of the banking sector, we could be failing to realize the full importance of paymentsrelated revenues to banks. We first estimate productive efficiency using the traditional empirical
framework. We modify that framework to better account for payments-services activities at
banking companies and re-estimate efficiency. We compare the efficiency estimates in empirical
analysis.
Profit inefficiency (or “X-inefficiency”) is a statistics-based measure that estimates
deviations from “optimal” firm costs and profits.
Estimates of “optimal” costs and profits are
generated by constructing “best practices” cost and profit frontiers using data from all the firms in
the industry.
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Profit inefficiency is a preferred measure over cost inefficiency, because a seemingly
inefficient bank might be offsetting higher expenses with higher revenues (Sprong, Sullivan and
DeYoung, 1996). This allows us to examine the “bottom line” and to measure the degree to which
banks are managing their inputs and costs to produce the highest, or “best practice” profit.
Profit inefficiency estimates (or X-efficiencies) are defined deviations from optimal firm
behavior and are generated by estimating a minimum-cost or maximum-profit frontier and
measuring each firm's deviation from that frontier (Berger, 1993, Örs, 1999). Inefficiency
estimates include both technical inefficiency (errors that result in overuse of inputs) and allocative
inefficiency (errors in choosing an input mix consistent with relative prices), (Berger, 1993).
Since we cannot observe the optimal frontier, we use the "best-practice" firms to substitute
for the optimal firm behavior and then benchmark the performance of all other firms by deviations
from this frontier. Efficiency is overstated to the extent that the best-practice firms within the
sample fail to achieve true allocative and technical efficiency (Berger, 1993). Appendix A
describes the method for obtaining inefficiency measures.
4.1 Estimation
We estimate the profit inefficiency in three stages. The profit function is first estimated
using ordinary least squares (OLS), and the coefficients and standard deviation of the OLS
residuals are saved for the second stage. Next, the profit inefficiency ratio is estimated by
Maximum Likelihood Estimation (MLE) using the information saved from the first stage. Third,
the profit inefficiency ratio is again estimated by MLE, this time using as initial values the
coefficient estimates and standard deviation of the MLE residuals from the second-stage
estimation.
The profit efficiency of 284 BHCs is estimated using end-of-year 2001 data. The bankspecific financial data are taken from the Federal Financial Institutions Examination Council's
15
Consolidated Reports of Condition and Income (call reports). The parent BHC consolidated
financial data are collected from the Federal Reserve Board's FR Y-9C reports.
We estimate profit efficiency for the 284 BHCs using the traditional or intermediation
model specification.
Next, we identify payments-services driven revenues excluded from
traditional model specification. Where possible, we include relevant revenue measures from the
FR Y-9 report for BHCs and/or Bank Call report as additional output vectors in the efficiency
estimation. Not all payments-driven revenues have observable counterparts in the Y-9 or Call
reports, however. For these measures, we have created proxy variables for those revenues not
directly itemized in the Y-9 or Call reports. The payments-related output vectors are described
below.
4.2 Traditional Estimation of Efficiency
Mester (1987) notes two approaches to a multi-product framework for efficiency
estimation: the “production” approach and the “intermediation” approach. According to the
production approach, the BHC produces a variety of individual accounts of different sizes using
labor and capital as inputs. In the intermediation approach, the production process for a BHC
involves financial intermediation, that is, the borrowing of funds and subsequent lending of those
funds. The production approach is limited in its use, since the BHC Y-9 and Bank Call reports do
provide information on the number of accounts per BHC (Mester 1987). The only data that contain
information on the number of accounts is the Functional Cost Analysis (FCA) data. Therefore, this
approach is not frequently used. Humphrey (1985) finds that the approaches yield average costs
that are roughly consistent.
The intermediation approach measures output as the dollar value of the firm’s earning
assets. Deposits, in addition to labor and capital, are treated as inputs in the production of the
assets. Costs include both interest and operating expenses.
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The variables included in the intermediation model are as follows:
C = variable operating plus interest costs (for cost efficiency estimates),
! = variable profits (for alternative profit estimates)
w1 = the price of labor (salaries and employee benefits / number of full-time equivalent
employees),
w2 = the price core deposits,
w3 = the price of purchased funds,
y1 = securities (all non-loan financial assets),
y2 = loans and leases (book value of consumer and business loans),
z1 = physical capital (book value of fixed assets and premises),
z2 = equity capital.
Specifically, we measure the traditional cost and profit functions using the FR-Y9 items listed in
Table 4.
4.3 Modified Estimation of Efficiency
DeYoung (1993) modifies the balance sheet outputs by including fee income minus
services charges on deposit accounts (service charges on deposit accounts are included below in y3,
the transactions based fees on deposits) to account for the increasing importance of noninterest
income to commercial banks (and bank holding companies).
Accounting for payments-driven revenues, we modify the output vector (the y vectors) of
the cost and profit functions to include the outputs from banking activities other than lending. The
modified output vector includes:
(a) total loans = y1
(b) total securities = y2
(c) payments-related outputs = y3
(d) all other noninterest income = y4
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The payments-related outputs, described in Section 2 include:
(1) Service charges on deposit accounts,
(2) Foregone interest revenue,
(3) Payments-related income from fiduciary accounts (trust revenues),
(4) Payments-related fees from credit cards (from both on-balance sheet credit card
receivables and securitized credit card receivables), and
(5) Fees from ATMs (third-party fees not included in service charges on deposit accounts).
We, therefore, modify the traditional cost function accordingly, and alternate our cost and
profit function specification, where
y3 = payments-related outputs, and
y4 = noninterest income - y3.
Items included in y3 and y4 are listed in Table 2.
Sum of Payments-related revenues
We measure separately each of the payments-related revenue streams (service charges on
deposit accounts, ATM Fees, payments-related credit card revenue, foregone interest, and
payments-related trust revenues). For estimating efficiency, we aggregate these measures into one
variable and define this variable “y3 = payments services related outputs” as follows:
y3 = service charges + payments-related card revenues + ATM revenues + foregone interest +
payments-related trust revenues
We also include in the estimation:
y4
= All other noninterest income
= noninterest income – (y3).
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Business strategy
Banks which are heavily involved in payments services, or that produce a different output
mix, have a different production function than banks which produce few payments services. This
may also differ by specific payments service, i.e., banks primarily involved in credit cards or in
processing. Thus, estimation of production function must include non-intermediation outputs such
as payments-services and fee income. We include a measure of business strategy in our analysis,
and also vary the inclusion of bank by business strategy in our estimation.
4.4 Tests Of Hypotheses In Efficiency Estimation Including Payment Services.
The first hypothesis is that the payments-driven revenues omitted in the traditional
estimation are substantial and that by including additional (payments-related) output vectors in our
estimation, the efficiency estimates change substantially. The second hypothesis is that business
strategy and not specific payment activities affect efficiency. If this is the case, then we do not
expect the relative ranking, or efficiency order, of these BHCs to change for many of the BHCs in
the sample.
We conduct three tests. First, we test for a statistically significant difference in efficiency
estimated by the traditional approach and the modified approach. Next, we split the sample into
quartiles and test for statistically significant differences in efficiency between the traditional and
modified approach. Finally, we rank the BHCs by traditional efficiency, and record those rankings,
then we rank the BHCs by modified efficiency and compare the rankings, by business strategy with
the traditional efficiency rankings.
Estimated Efficiency
Tables 5 lists the mean efficiency estimates in the traditional and modified approach. Also
included are the standard deviation of those estimates and the mean Tobin’s Q. Table 5 shows that,
on average, all BHCs increase efficiency by about 20%. Global Processors, specifically, increase
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efficiency by about 50% when we include payments-driven revenues into the production function,
a statistically significantly greater increase in efficiency then the sample. This suggests that one
type of BHC may be more greatly affected by inclusion of payments-driven revenues than other
BHCs. Table 5 also lists the mean franchise value or Tobin’s Q by BHC type. Note that despite
the lower traditional efficiency estimates, the Global Processors have higher mean franchise value
than the other three BHC types. This difference is statistically significant at the 1 percent level.
This suggests that although estimates of traditional efficiency may indicate that Global Processors
are less efficient, the market values these BHCs at a higher level than other BHC types. We infer
from this result that traditional efficiency estimates which exclude nontraditional bank activities
inaccurately measure the relative performance of some types of BHCs. Further research on this
topic is warranted to identify additional activities that should be included in efficiency estimation.
Payment activities are just one of many activities excluded from traditional efficiency estimation.
Rogers (1998) discusses some of the other activities that may be included in future research.
Tests of Efficiency Differences in Quartiles
If the traditional approach to efficiency estimation significantly understates the profit
efficiency of banks that generate a large portion of their income through these activities and
overstates the profit efficiency of banks that generate only a small portion of their income through
these activities.
Because efficiency estimates are not directly comparable across frontiers, the traditional
estimates are not directly comparable to the modified estimates. We, therefore, rank the estimates
by traditional and by modified efficiency and divide the sample into quartiles.
We split the sample into quartiles and test for changes in efficiency in the lowest-efficiency
and highest-efficiency quartile between the traditional and the modified model specifications.
Efficiency is estimated for 284 BHCs using the traditional approach. The sample is split into
quartiles by efficiency and averaged by quartile. We maintain the quartile subsamples and re-
20
estimate efficiency using the modified approach. We average the efficiency by the subsample
created in the first step and test for significant changes in efficiency among subsamples. If we are
underestimating efficiency in the traditional method, then we expect to find that the lowest quartile
shows a significant increase in average efficiency when efficiency is re-estimated in the modified
method.
We find, in fact, that we are underestimating efficiency for the lowest quartile. In the
lower quartile, average efficiency goes from 21.5 in the traditional estimation to 27.6 in the
estimation that includes payment services, a 28.8 percent increase in estimated efficiency.
Moreover, three of the four Global Processors were in the bottom quartile and all four were in the
bottom half of the sample using the traditional approach.
If we rank the BHCs by modified
efficiency, we find that all four of the Global Processors move to the top half of the sample.
In the upper quartile, the most efficient BHCs ranked by traditional standards have an
average efficiency of 43.2 in the traditional estimation and 49.8 in the modified estimation, a 15.6
percent increase. We infer from this result that (1) both the lower quartile and upper quartile have
higher profit efficiency estimates when we include revenues from payment services and (2) that the
lower quartile BHCs, by traditional standards, does indeed show a significant increase in average
efficiency in the modified specification.
Further research is warranted to determine more precisely how the profit efficiency is
affected by the choice of inputs in the production function. We note that our results are limited by
data availability on payments-driven revenues. As more years of data become available, we will
include them in our sample. Future research will include a great number of firms and will use a
panel set of banks, rather than year-end 2001 only.
5. Summary and Conclusion
This study explores two issues relating to the value that payments-related activities may
add to the bank. We first explore the effect of payment-driven revenues to the franchise value of
21
BHCs. We then examine whether banking sector output is being mismeasured by underestimation
of payment-related revenues.
In exploring the first issue (franchise value), we find a small but positive correlation
between payments-driven revenues and franchise value (Table 3). We find, also, that those BHCs
with greater franchise value have a different business strategy than other BHCs (Table 5).
Conglomerates have statistically significantly lower franchise values, while Global Processors and
Credit Card banks have higher franchise values on average.
With regard to the second issue (bank efficiency), we find that business strategy affects
profit efficiency and suggest that empirical analysis must separate BHCs by activity. Profit
efficiency shows the greatest change in one type of business strategy (Global Processors) when
payments-driven revenues are included. Our estimation shows that, on average, BHCs increase
efficiency by about 20%. Global Processors, specifically, increase efficiency by about 50% when
we include payments-driven revenues into the production function. These BHCs show a dramatic
change in relative efficiency ranking; the Global Processors were in the lower half of the sample
when ranked by traditional efficiency, but all moved to the top half of the sample when ranked by
modified efficiency.
Assessing the results from these two issues of franchise value and efficiency together
(Table 5) provides additional insight. Despite lower traditional efficiency estimates, the Global
Processors have higher mean franchise values than the other three BHC types. We find evidence
to suggest that traditional efficiency estimates which exclude nontraditional bank activities
inaccurately measure the relative performance of some types of BHCs. Further research is
warranted to determine more precisely how the profit efficiency is affected by the choice of inputs
in the production function. We note that our results are limited by data availability on paymentsdriven revenues.
22
References
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Economics and Statistics 70 (4): 685-689.
Bauer, Paul and Gary Ferrier. 1996. “Scale Economies, Cost Efficiencies, and Technical Change
in Federal Reserve Payments Processing.” Journal of Money, Credit and Banking 28(4):
1004-1039.
Berger, Allen and Loretta Mester. 1997. “Inside the Black Box: What Explains Differences in the
Efficiencies of Financial Institutions?” Journal of Banking and Finance 21: 895-947.
Berger, Allen, Diana Hancock and Jeffrey Marquardt. 1996. “A Framework for Analyzing
Efficiency, Risks, Costs and Innovations in the Payments Systems.” Journal of Money,
Credit and Banking 28 (4): 696-732.
Bradford, Terri, Matt Davies and Stuart Weiner. 2002. “Nonbanks in the Payments System.”
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Chakravorti, Sujit and Emery Kobor. 2002. “Why Invest in Payment Innovations?” Federal
Reserve Bank of Chicago Working Paper.
Chakravorti, Sujit and William R. Emmons. 2001. “Who Pays for Credit Cards?” Federal Reserve
Bank of Chicago Working Paper.
Clark, Jeffrey A. 1988. “Economies of Scale and Scope at Depository Financial Institutions: A
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Demsetz, Rebecca S., Marc R. Saidenberg and Philip Strahan. 1996. “Banks with Something to
Lose: The Disciplinary Role of Franchise Value.” FRBNY Economic Policy Review
(October).
DeYoung, Robert and William C. Hunter. 2002. “Deregulation, the Internet and the Competitive
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Banking. Benton Gup (Ed.)
DeYoung, Robert. 1993. “Fee-Based Services and Cost Efficiency in Commercial Banks.”
Federal Reserve Bank of Chicago, 1994 Conference on Bank Structure and Competition
Proceedings.
Furst, Karen, William Lang and Daniel Nolle. 1998. “Technological Innovation in Banking and
Payments: Industry Trends and Implications for Banks.” Office of the Comptroller of the
Currency Quarterly Journal 17 (3): 23-31.
Green, Edward and Richard Todd. 2001. “Thoughts on the Fed’s Role in the Payments System.”
Federal Reserve Bank of Minneapolis Quarterly Review 25 (1): 12-27.
23
Instructions for Preparation of Consolidated Financial Statement for Bank Holding Companies
(Reporting form FR Y-9C), Washington DC: Board of Governors of the Federal Reserve
System.
Mester, Loretta. 1987. “Efficient Production of Financial Services: Scale and Scope Economies.”
Federal Reserve Bank of Philadelphia Business Review (January/February).
_____. 2000. “The Changing Nature of the Payments System: Should New Players Mean New
Rules? Federal Reserve Bank of Philadelphia Business Review (March/April): 3-26.
Radecki, Lawrence. July 1999. “Banks’ Payments-Driven Revenues.” Federal Reserve Bank of
New York Economic Policy Review 4, no. 2: 53-70.
Rice, Tara and Kristin Stanton. 2003. “Estimating the Volume of Banks’ Payments-Driven
Revenues.” Federal Reserve Bank of Chicago Working Paper.
24
Table 1
Summary Statistics
N=98
Definition
Tobins Q (N=98)
Service Charges on Deposit
Accounts ($mil)
Payments-related
Credit-card Revenue ($mil)
ATM Fees ($mil)
Trust Revenues – Lower Bound ($mil)
Foregone Interest Revenue ($mil)
Assets ($mil)
Mean
Std Dev
Min
Max
1.113
0.094
0.916
1.475
211.68
557.57
0
4,559.00
52.03
258.11
0
2,327.49
39.66
94.11
163.08
53,722.20
135.04
378.37
368.98
147,691.78
0.420
0
0
2,069.99
1,114.40
3,311.13
2,483.29
1,051,450.0
Table 2
Summary Statistics
N=284
Definition
Service Charges on Deposit
Accounts ($mil)
Payments-related
Credit-card Revenue ($mil)
ATM Fees ($mil)
Trust Revenues – Lower Bound ($mil)
Foregone Interest Revenue ($mil)
Assets ($mil)
Profit ($mil) (Non interest income +
interest income – noninterest expenseinterest expense)
Total Revenue ($mil)
Securities ($mil)
Loans and Leases ($mil)
Profit Efficiency,
Traditional Model (%)
Profit Efficiency,
Payment Systems (%)
Mean
Std Dev
Min
Max
80.16
339.60
0
4,559.00
18.37
152.68
0
2,327.49
16.44
32.53
62.24
21,406.41
82.94
225.20
227.51
90,638.67
0
0
0
1,005.45
1,114.40
3,311.13
2,483.29
1,051,450.0
440.05
2,017.47
1,824.0
28,119.0
1,947.51
7,095.30
10,481.52
8,552.36
37,495.35
38,702.27
56.69
44.87
118.97
111,444.0
449,973.00
408.21
31.24
10.10
14.44
100.00
37.38
12.75
18.45
100.00
25
Table 3
Correlation Analysis of Franchise Value and Payments-Driven Revenues:
2001
Franchise value is measured as Tobin’s Q. It is calculated as Q=(E+L)/(A-goodwill), where E is the market
value of equity, L is the book value of liabilities and A is the book value of assets. * denotes that correlation
is significantly different from zero at the 10 percent level. The category “All payments-driven revenues” is
the sum of the first five categories of payments-derived revenues listed below. N=98 BHCs.
Service Charges on Deposit Accounts
0.045
Payments-Driven Credit Card Revenue
0.103
ATM Fees
0.083
Payments-Related Trust Revenues
0.368*
Foregone Interest Revenue on Transaction
Accounts
All Payments-Driven Revenues (sum of rows 1-5)
0.053
0.172*
26
Variable
Table 4
Definition of Variables Included in Profit Efficiency Estimation
Definition
Calculation
P
Variable Profits
w1
w2
Price of Labor
Price of Core
Deposits
w3
The Price of
Purchased Funds
y1
Consumer and
Business Loans
Securities
y2
y3
Sum of PaymentsRelated Outputs
(See below)
y4
All Other
Noninterest Income
Physical Capital
Equity Capital
z1
z2
Interest Income + Interest Income – (Interest Expense +
Noninterest Expense) – Extraordinary Items
Salaries and Benefits / Number of Full Time Employees
Interest Expense on “Other” Deposits (All Deposits Other than
in Foreign Offices and Domestic Time Deposits, or CDs) /
[(Demand Deposits + NOW, ATS, Other Transactions
Accounts + Money Market Deposit Accounts and Other
Savings Accounts) In Commercial Bank Subsidiaries +
(NOW, ATS, Other Transactions Accounts + Money Market
Deposit Accounts and Other Savings Accounts) In Other
Depository Institutions Or Nonbank Subsidiaries]
(Expense of Federal Funds Purchased and Securities Sold
Under Agreements To Repurchase + Interest on Trading
Liabilities and Other Borrowed Money + Interest Expense on
Subordinated Notes and Debentures and on Mandatory
Convertible Securities) / Total Liabilities
Loans And Leases, Net
Interest Bearing Balances in US Offices + Interest Bearing
Balances in Foreign Offices + Securities Held-To-Maturity +
Securities Available-For-Sale + Federal Funds Sold and
Securities Purchased Under Agreement To Resell + Trading
Assets
Sum Of Payments-Related Revenues, Listed Below:
(1) Service Charges On Deposit Accounts in domestic
accounts (including demand deposits, excluding savings
deposits),
(2) Payments-Related Fees From Credit Cards (From Both
On-Balance Sheet Credit Card Receivables And Securitized
Credit Card Receivables),
(3) Fees From ATMs (Third-Party Fees Not Included In
Service Charges On Deposit Accounts), And
(4) Foregone Interest Revenue (Deposits in DDAs*(fed
funds)+ Deposits in NOW accounts*(fed funds-NOW
rate)+deposits in MMDAs*(fed funds-MMDA rate))
(5) Income From Fiduciary Accounts*.
Total noninterest income minus y3
Expense Of Bank Premises, Furniture, Etc.
Total Equity Capital
27
Table 5
Profit Efficiency
Including ALL BHCs
N=284
This table displays efficiency estimates averaged by BHC type. Standard deviations are listed in
parentheses below average efficiency estimates.
N
Tobins’ Q
Traditional
Profit
Efficiency
All BHCs
284
31.24
(10.10)
Conglomerates
6
1.12
25.65
(1.65)
Global Processors
4
1.29
23.64
(2.81)
Credit Card BHCs
4
1.23
29.06
(8.21)
Regional BHCs
270
1.10*
31.51
(10.07)
* Only 85 observations of Tobin’s Q available for Regional Banks
Modified
Profit
Efficiency
37.38
(12.75)
34.83
(8.77)
35.66
(15.27)
32.57
(12.30)
37.53
(12.43)
Percentage
Change
19.65%
35.79%
50.85%
12.08%
15.20%
28
Table 6
Difference of Means Tests for Upper and Lower Quartiles
Traditional and Modified Efficiency Estimates
Difference of Means Test - Upper Quartile
Traditional
Mean
43.2
Variance
11.2
Observations
76
t-statistic
-3.37
Percentage Change in Efficiency
Modified
49.8
13.4
76
15.65%
Difference of Means Test - Lower Quartile
Traditional
Mean
21.5
Variance
27.7
Observations
70
t-statistic
-4.81
Percentage Change in Efficiency
Modified
27.6
10.4
70
28.80%
Hypothesized mean difference in both tests is 0.00
Critical t-statistic for two-tailed standard normal distribution is 1.98
29
Appendix A
Efficiency Concepts
Alternative Profit Inefficiency
Profit inefficiency measures how close a BHC is to earning maximum profits given its
output levels. (Berger and Mester, 1997). Standard profit maximization takes output prices as
given. Alternative profit maximization takes output quantities as given. We estimate alternative
profit efficiency in initial analysis.
The alternate profit function in log form is:
ln (! + _) = f(w,y,z) + ln uc + ln _c,
(6)
where ! measures variable profits, w is the vector of quantities of prices of variable inputs,
y is the vector of quantities of variable outputs, z indicates the quantities of any fixed netputs
(inputs or outputs). A constant, _, is added to each firm's profit, so that the log is taken of a
positive number.
Stochastic Frontier Approach (SFA)
The SFA estimates a cost or profit function with a composite error structure that separates
the random error and the profit inefficiencies. The random error term, ln _, is assumed to be twosided (usually normally distributed). The inefficiency term, ln u, is assumed to be one-sided
(usually half-normally distributed).
One disadvantage of this approach is that the SFA imposes distributional assumptions on
the error term and the inefficiency. Two studies (Bauer and Hancock, 1993 and Berger, 1993) find
that the inefficiency terms, ln u, behave more like symmetric normal distributions than half-normal
distributions.
Functional Forms for the Parametric Methods
30
The functional form that we choose for our profit functions (equations 2, 3, 4) is the
Fourier-flexible functional form. This functional form is a global approximation that includes a
standard translog plus Fourier trigonometric terms (Berger and Mester, 1999). One disadvantage
of this functional form is that its assumes mutual orthogonality between the profit inefficiency
estimates and the profit function exogenous variables. This orthogonality is perfect only if the data
are evenly distributed over the [0,2!] interval (defined in equation 4 below), which is not the case
with US banking data (Berger, 1993). This functional form, however, has been shown in recent
studies to fit data for US financial institutions better than other functional forms, namely the
translog functional form (McAllister and McManus, 1993; Berger, Cummins and Weiss, 1997;
Berger and DeYoung, 1997; Mitchell and Onvural, 1996).
The profit function is specified as:
2
ln( P / w3 z 2 ) = α + ∑ β i ln(wi / w3 )
i =1
+
2
2
1
β
ln(
w
/
w
)
ln(
w
/
w
)
+
γ k ln( y k / z 2 )
∑ ∑ ij i 3
∑
j
3
2 i =1 j =1
k =1
+
1 2 2
1
γ km ln( y k / z 2 ) ln( y m / z 2 ) + δ 1 ln( z1 / z 2 ) + δ 11 ln( z1 / z 2 ) 2
∑∑
2 k =1 m=1
2
2
2
(7)
2
2
+ ∑∑ η ln( wi / w3 ) ln( y k / z 2 ) + ∑ µ i ln(wi / w3 ) ln( z1 / z 2 )
i =1 k =1
ik
i =1
2
+ ∑ θ k ln( y k / z 2 ) ln( z1 / z 2 ) + ln u + ln ε
k =1
where:
P = variable profits, plus a constant, _,
w1 = the price of labor (salaries and employee benefits / number of full-time equivalent
employees),
w2 = the price core deposits,
31
w3 = the price of purchased funds,
y1 = securities (all non-loan financial assets),
y2 = loans and leases (book value of consumer and business loans),
z1 = physical capital (book value of fixed assets and premises),
z2 = equity capital,
ln uc + ln _c = the composite error term, and
cos(_) and sin (_) are mutually orthogonal trigonometric Fourier terms included to improve
the fit of the model and span the interval [0,2!].
Inefficiency Ratio
We measure profit inefficiency as the ratio of estimated BHC costs required to produce its
given outputs if it were as efficient as the best-practice BHC producing the same exact output. The
profit inefficiency ratio (Berger and Mester, 1997) is:
∧
Alternative π EFF i =
π min
∧
π
∧
=
i
∧ max
where
uπ
∧i
{exp[ f ( wi , y i , z i )] × exp[ln uπ ]} − θ
∧
∧ max
,
(8)
{exp[ f ( wi , y i , z i )] × exp[ln uπ ]} − θ
∧i
is the maximum value of
uπ
in the sample, i indexes the individual BHCs. This
ratio can be considered as the proportion of profits that are used efficiently. Likewise, if we subtract
that ratio from one (1 – profit EFFi), then we have the proportion of costs that are used inefficiently.
For example, if a BHC profit ratio is 0.75, then that BHC is 75% efficient, or 25% inefficient. The
range of this ratio is between 0 and 1, with 1 being a wholly efficient BHC.
32
@FedFRASER