THE JOURNAL OF INDUSTRIAL ECONOMICS
Volume LIX
June 2011
0022-1821
No. 2
CONSUMER SWITCHING COSTS AND FIRM PRICING:
EVIDENCE FROM BANK PRICING OF
DEPOSIT ACCOUNTS
Timothy H. Hannan w
Robert M. Adams z
We employ extensive information on bank deposit rates and area
migration patterns to examine pricing relationships implied by switching costs. We argue that, because of the trade-off between attracting new
customers and exploiting old ones, banks offer higher deposit rates in
areas experiencing more in-migration. Further, because greater outmigration implies that a locked-in customer will not be with the bank for
as many periods, banks will offer lower deposit rates in areas exhibiting
greater out-migration. Also, because this effect of out-migration logically depends on the extent of in-migration, an interaction effect exists.
We find evidence strongly supporting these relationships.
I.
INTRODUCTION
FOR MANY DIFFERENT PRODUCTS AND SERVICES, consumers who have purchased
from one firm incur (or perceive they incur) costs if they switch to a
competitor’s offering. Examples are the costs associated with learning to use
a new brand, the need for compatibility with existing equipment, the costs of
overcoming uncertainty about the quality of unfamiliar brands, the
psychological switching cost associated with ‘brand loyalty,’ and, of course,
the more direct costs that are sometimes incurred to change brands.
An example is the transaction costs that a depositor incurs when changing
banks. This requires not only the closing of one account and the opening
of another, but in recent years it has come to mean also the notification of
employers regarding automatic deposit of wages and notification of
potentially numerous commercial enterprises regarding authorized electronic withdrawals (automatic or otherwise).
It has long been recognized that the existence of such switching costs can
have significant implications for pricing decisions. The exact implications
The views expressed herein are those of the authors and do not necessarily reflect the views
of the Board of Governors of the Federal Reserve System or its staff. The authors would like to
thank Elizabeth Kiser, June K. Lee and Ron Borzekowski for helpful comments and Miranda
Mei, Stephanie Ramirez and Jason Scott for excellent research assistance.
w
Authors’ affiliations: Federal Reserve Board, Washington, DC 20551, U.S.A (retired).
e-mail: [email protected].
z
Federal Reserve Board, Washington, DC 20551, U.S.A.
e-mail: [email protected].
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CONSUMER SWITCHING COSTS AND FIRM PRICING
297
depend on the underlying theoretical model employed. The simple (and
naı̈ve) one-period model, wherein all customers are locked in and there are
no new customers to attract, yields monopoly pricing when switching costs
are high enough. The more commonly presented two-period model, where
customers purchase a service or commodity in the first period and are
‘locked in’ during the second period, yields high prices in the second period
but can produce prices below those associated with short-run profit
maximization in the first period. The intuition, of course, is that first period
prices are lower because firms compete for customers that they can exploit in
the second period.
A two-period model is, however, not very useful for analyzing pricing in
the general case in which new customers enter the market each period, some
customers leave the market each period, and firms do not find it profitable to
discriminate fully between new and old customers. Beggs and Klemperer
[1992] have developed a model designed to address the pricing implications
of switching costs in this context; they find that under plausible assumptions,
prices and profits are higher in the presence of switching costs.1 They also
show that prices rise as firms discount the future more, fall as consumers
discount the future more, fall as the turnover of consumers increases, and fall
as the rate of market growth increases.2
In this paper, we employ data from the banking industry and information
on banking-market migration patterns to test several pricing implications of
switching costs that can be derived from the Beggs and Klemperer [1992]
model. As noted above, substantial switching costs should be relevant to
customer behavior in the banking industry – a point well recognized by
participants in the industry. Features such as direct deposit, automated
debits, and electronic bill payments in particular are often cited as
roadblocks to switching accounts.3 Also, it appears that banks cannot fully
discriminate between new and old customers. While they are observed to
offer free checks and other fairly minor benefits to new customers,
1
These assumptions are (1) discounting of future profits, (2) the recognition by firms that
charging a higher price today will cause rivals to have more locked in customers and therefore
charge higher prices tomorrow, and (3) the recognition by customers that responding to a low
price today will mean a higher price tomorrow.
2
They also find that larger firms or firms with larger market shares charge higher prices than
smaller firms or firms with smaller market shares, but these implications derive from a
particular assumption in their model that is not likely to apply in the case of the banking
industry. See Hannan [2008].
3
Typical is a comment by Susan E. C. Riley, a senior vice president at 1st Commonwealth
Bank of Virginia, who notes that ‘People think those links are really difficult to break. They will
stay in a relationship because they think they have to.’ cited in ‘Here’s a Switch – Easing the
Customer Migration Path,’ American Banker, March 13, 2009. In response, some banks are
investing in switch kits to make the switching process easier, but, as noted by Michael Dobbins,
a senior vice president at Charter One, ‘switching banks isn’t easy. This [a switching kit] just
makes it easier,’ cited in ‘Do Account-Switching Kits Help Banks Draw Clients?’ American
Banker, April 15, 2004. See also Kiser [2002] for a more detailed discussion of the evidence.
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HANNAN AND ADAMS
differences in deposit rates between new and old customers are not observed
for most account categories, perhaps because of the arbitrage opportunities
or customer bitterness that would result from observably different rates
offered to different customers for the same account type.
Several features of the banking industry allow identification of the impact
of switching costs on prices. The most important of these features is the local
nature of bank retail deposit pricing. Despite changes that may have
broadened the geographic scope of deposit markets in recent years,
competition for many different types of deposit funds is still regarded as
local in nature, meaning that pricing of deposit accounts can vary by area
(see Amel and Starr-McCluer [2002], and Amel, Kennickell, and Moore
[2008]). We shall argue that migration into and out of local banking markets
is relevant to bank pricing when switching costs exist. Importantly,
migration both into and out of these markets vary cross-sectionally and
over time, making possible tests of the implications of switching costs not
possible for many other industries.
Another relevant characteristic of the industry is that switching costs may
have been increasing over time as a result of the increasing use of direct
deposits and arranged withdrawals. A final relevant characteristic concerns
the difference in deposit products offered by banks. Of the several different
types of deposit accounts that banks typically offer, it is possible that some
types of accounts entail higher switching costs than others. Subject to
qualifications discussed below, these differences also offer the potential to
identify the role of switching costs in firm pricing.
Results show a strong and robust relationship between bank deposit rates
and the rates of both market in-migration and out-migration. As suggested
by an application of the Beggs and Klemperer model to the banking
industry, deposit rates are found to increase with the rate of migration into a
market and decline with the rate of migration out of the market, with the rate
of in-migration having a less positive effect on deposit rates, the higher is the
rate of out-migration. These results imply that switching costs are very much
a factor in explaining bank deposit rates and that banks consider the future
profitability of locked-in depositors in choosing current deposit rates.
Expectations based on the presumed differences in switching costs across
four different types of deposit accounts receive only weak support, and
possible reasons for this are discussed below.
As indicated in the literature review below, this paper is the first to
investigate the role of switching costs in bank deposit-rate setting since the
introduction of the numerous deposit-related innovations, and it is the first
to investigate the relationship between bank deposit rates and migration
measures using a large sample of banks and deposit rates. Also of
importance, it is the first to investigate the role of out-migration in bank
deposit-rate setting, allowing for a test of intertemporal decision making
by banks.
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CONSUMER SWITCHING COSTS AND FIRM PRICING
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The paper is organized as follows: Section II reviews the relevant
literature, and section III discusses predictions regarding pricing implications of switching costs. Section IV outlines the tests to be conducted, and
section V describes the data used. Section VI presents results, and a final
section concludes.
II. THE LITERATURE
The theoretical literature on switching costs is vast, and we refer the reader
to Klemperer [1995] and Farrell and Klemperer [2006] for extensive reviews.
We simply note some of its salient aspects. A common fixture in this literature
has been the two-period model, where firms cannot commit to future prices.
Consumers that choose to purchase from a given firm in the first period are
‘locked in’ for the second period. This causes firms to exploit their market
power in the second period and price aggressively in the first. This ‘bargainsthen-rip-off’ pattern is a main theme of many two period models.
Such models can explain some instances of pricing behavior when cohorts
can be identified by the firm. For example, banks offer college students gifts
and free services to induce them to open accounts, followed in later years by
highly profitable pricing. A two-period model is, however, less useful for
analyzing competition over many periods when new customers enter the
market in every period, some old customers leave, and firms are unable to
profitably charge new and old customers different prices. Because the
empirical environment that we wish to explore contains all three of these
elements, the tests proposed and conducted in the paper are derived from the
multi-period model of competition and switching costs presented in Beggs
and Klemperer [1992]. In this model, the prices chosen by firms under these
circumstances reflect a tradeoff between attracting new customers and
exploiting old, ‘locked-in’ customers.
Empirical models of switching costs are far less numerous. Two studies of
the banking industry actually report estimates of switching costs. Using highly
aggregated data, Kim et al. [2003] estimate the magnitude of switching costs by
deriving and then estimating a first-order condition, a market-share equation
and a supply equation under the assumption of Bertrand behavior. Using data
on Norwegian bank loans, they estimate switching costs of 4.12 per cent for the
typical customer’s loan, which seems quite substantial. Shy [2002], using data
on prices and market shares, finds that the costs of switching deposits ranges
from 0 to 11 per cent for deposit customers of Finnish banks.
Some other empirical studies relevant to the banking industry have sought
to test the implications of switching costs for pricing behavior. Two previous
studies of bank pricing have noted that newly arrived customers in an area in
essence do not face switching costs if their move required that they leave their
previous bank, while existing residents could be subject to substantial
additional costs if they were to switch their accounts to a different
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HANNAN AND ADAMS
institution. If bank pricing reflects a compromise between exploiting old
customers (made possible by switching costs) and attracting new ones, then
it follows that banks located in markets with greater in-migration would
optimally offer higher deposit rates (charge lower prices), all else equal.
Sharpe [1997], the first to test this implication of switching costs, used
monthly data on six-month CD rates and on money market deposit account
(MMDA) rates offered by 222 banks from October 1983 to November 1987.
Defining local banking markets as Metropolitan Statistical Areas (MSA’s)
or non-MSA counties, Sharpe estimated the proportion of new ‘movers’ into
each market during this period by extrapolating from Census data applying
to years 1975–1980, substantially earlier than the years examined. Using a
pooled time series of 5 annual cross sections (and adjusting for time effects),
Sharpe found that, consistent with predictions, the proportion of household
migration into a market has a generally positive (pro-competitive) effect on
deposit rates, all else equal. Because of the very limited sample of banks
employed by Sharpe for a time period now 25 years old, a reexamination of
this issue, we believe, is in order.
Hannan et al. [2003] employed a measure of migration in their
investigation of ATM surcharges imposed by banks on non-depositors.
They report that banks in local markets with higher levels of in-migration
are more likely to impose a surcharge – a finding consistent with the
hypothesis that ATM surcharges, because they can attract rather than repel
new depositors,4 are more likely to be imposed in markets where a greater
proportion of the population can be more readily attracted. Because of the
availability of annual IRS data on household migration for years more
recent than those examined by Sharpe [1997], Hannan et al. [2003] used a
more direct measure of market in-migration that does not involve a
cumbersome extrapolation from migration data applying to earlier periods.
An issue that arises in the context of these analyses is whether decision
making is intertemporal. If banks do not consider the implications for future
periods of attracting new customers in the current one, then new customers
influence prices simply because they represent a source of more elastic
demand in the current period. This ‘current-period’ analysis is essentially the
one modeled and presented by Sharpe [1997]. If, however, banks consider
the gains obtainable in future periods from attracting a new customer in the
current one, then the new customer is worth considerably more to the bank,
and the extent to which new customers are present in a market should make a
greater difference to bank behavior than if only the current period is
considered. Both imply a negative relationship between in-migration and
price (a positive relationship between in-migration and deposit rates).
4
The reason is that depositors typically do not pay surcharges for the use of their own bank’s
ATM’s, making it more desirable to open an account at a bank with many ATM’s if it is
surcharging.
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CONSUMER SWITCHING COSTS AND FIRM PRICING
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This observational equivalence, however, does not apply to a measure of
out-migration. The extent of out-migration from a market may influence the
prices that banks charge or offer, but only if banks look to future periods and
realize that a newly attracted customer is less valuable, the greater is the
likelihood that that customer will migrate out of the market. Thus, outmigration measures should be positively related to price (negatively related
to deposit rates) only if the bank considers the impact of attracting new
customers during the current period on the profits obtainable in future
periods. Accordingly, measures of market out-migration will be employed in
the analysis reported below.
III.
SOME PRICING IMPLICATIONS OF SWITCHING COSTS
Switching costs may be a factor in many industries characterized by the entry
of new consumers over different time periods, the exit of old consumers over
different time periods, and the inability of firms to distinguish between new
and old customers in their pricing decisions. Under these circumstances, the
following relationships may be derived for the more typical case in which the
price (unlike the case of bank deposit rates) is paid by the consumer to the
firm:
First, an increase in the rate of in-migration results in a decrease in the
prices that firms charge their customers. The reason is that the firm finds it
optimal to charge lower prices, the larger the proportion of customers that
are new to the market and that therefore can be readily attracted.
Second, an increase in the rate of out-migration causes firms to increase
the prices that they charge their customers, all else equal. The reason for this
effect is only slightly less intuitive. It occurs only if the firm considers new
customers as exploitable in future periods after they are ‘locked in,’ rather
than just a more elastic source of product demand in the current period.
Greater out-migration deters firms from offering low prices to attract new
customers, because new customers (on average) will not remain with the firm
for as long (and therefore be exploitable) as would otherwise be the case.
A final implication is that the effect of in-migration should depend on the
level of out-migration, and vice versa. We discuss this interaction in more
detail in the context of bank deposit rates.
The proposed test of these predictions will focus on retail deposit rates of
banks. Because deposit rates are prices that are paid to the customer by the
firm, rather than paid to the firm by the customers, the predicted effects are
the opposite of those that obtain in the more common case discussed above.
In the presence of switching costs, greater in-migration into retail banking
markets should cause banks to offer higher deposit rates because of the
greater importance of attracting new customers, while greater out-migration
should cause banks to offer lower deposit rates because locked-in depositors
are less desirable. Finally, the positive effect of in-migration on deposit rates
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HANNAN AND ADAMS
should be less pronounced (less positive), the greater is out-migration.
Because these implications are quite intuitive, formal derivations are not
presented here. See Hannan [2008] for a formal derivation of these
implications in the case of deposit rate setting by banks.
IV. THE TEST
If the interaction between in-migration and out-migration can be captured
by the product of these two variables in a linear deposit-rate regression, we
can estimate:
t1
rtd;i ¼ b0 þ b1 inmigratet1
m þ b2 outmigratem
ð1Þ
t1
t1
t1
þ b3 outmigratet1
m ðinmigratem Þ þ b4 Mm þ b5 Bi
þ nt þ mi þ eit ;
t
where rd.i
denotes the retail deposit rate of bank i at time t, inmigratetm 1
denotes the rate of in-migration observed for market m at time t 1,
outmigratetm 1 denotes the rate of out-migration observed for market m at time
t 1, Mtm 1 denotes a vector of market characteristics prevailing in market m
at time t 1, Bti 1 denotes a vector of characteristics of bank i at time t 1, nt
denotes a time-specific fixed effect, mi denotes a bank-specific fixed effect, and eit
denotes an idiosyncratic error term. Explanatory variables are lagged because
of the likelihood that it takes some time for banks to set deposit rates after a
change in the characteristics that influence those rates (and because of the way
deposit rates are calculated, as discussed below).
In the absence of the interaction term, the hypothesized positive impact of
in-migration and the hypothesized negative impact of out-migration on
deposit rates imply that b1 4 0 and b2 o 0, respectively. If, however, the
product of in-migration and out-migration is included as an additional
variable, then the hypothesized positive impact of in-migration on deposit
rates implies
ð2Þ
b1 þ b3 outmigratet1
m > 0:;
while the hypothesized negative effect of out-migration implies that
ð3Þ
b2 þ b3 inmigratet1
m 0;
The hypothesized interaction between in-migration and out-migration
implies that b3 o 0, because (focusing on (2)) new customers are less
desirable if they on average do not stay as long, causing the positive impact
of in-migration on deposit rates to decline with an increase in out-migration.
With b3 o 0, it follows from (2) that b1, the coefficient of inmigrate in (1),
is positive. If the influence of the interaction between in-migration and
out-migration is adequately captured by the product of these two variables,
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CONSUMER SWITCHING COSTS AND FIRM PRICING
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then we predict (focusing on (3)) that b2 5 0, since out-migration should
have no effect on bank deposit rates if there is no in-migration.
For purposes of statistical control, both firm and year fixed effects are
used in all estimations. Only variation over time that differs across firms
needs additional statistical control.5 Market variables employed in the basic
estimations reported below include the Herfindahl-Hirschman index of
market concentration and the total real income in the market. Measures of
market population growth, market income growth, and a measure of the
importance in the market of large competitors that operate predominantly
outside the market will also be included in some regressions.
The coefficient of the Herfindahl-Hirschman index of concentration
(defined as the sum of squared market shares and denoted hhitm 1) is
predicted to be negative if, as implied by the traditional structure-conductperformance hypothesis, firms in more concentrated markets offer lower
deposit rates because of the greater exercise of market power.
Real market income, measured in natural logs and denoted ln(mktinctm 1),
plays an important role in these regressions, because it accounts for changes in
the size of the market that might otherwise be incorrectly attributed to inmigration or out-migration. A shift in market income can influence deposit
rates by increasing the supply of deposits, implying, in all likelihood, a negative
relationship between ln(mktinctm 1) and bank deposit rates.6 An increase in
market income, however, could also cause an increase in the demand for bank
loans, and this could under some circumstances increase bank demand for
deposit dollars, with a resulting increase in bank deposit rates. The natural log
of this market variable is employed because it is highly positively skewed, and it
is not reasonable to expect it to exhibit a linear relationship with deposit rates
over the large range of values observed in the data.
Some studies have found that competition with large banking organizations that operate in many different local geographic markets can influence
the deposit rate of banks operating in a given market. One reason is that
multimarket banks tend to offer the same deposit rates in many different
markets, implying that their presence in a given market may influence the
deposit rates of banks for reasons extraneous to the underlying conditions of
any one market.7 To control for this potential effect, we include in some
5
Of particular relevance in this regard are the general level of interest rates and other aspects
of the macroeconomic environment. Assuming they apply to all areas of the country, their
influence on bank deposit rates should be accounted for by the year fixed effects.
6
This result is more likely if the bank sets deposit rates such that the incremental outlay for a
unit of deposits equates with the incremental gain obtained by holding an asset whose return to
the bank is determined in a national or international market. See Klein [1971] for a classic
discussion of this issue.
7
Yet single-market banks offer very different deposit rates in those same markets, implying
that the uniform pricing observed for multimarket banks is not simply a matter of incorrect
identification of market areas. See Heitfield [1999].
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regressions reported below the share of market branches owned collectively
by large, predominantly out-of-market banks (lpombsharetm 1). Following
previous literature, this is measured as the collective branch share of banks
that have less that 30 per cent of their deposits in the market and that have
more than $1 billion in assets (measured in year 2000 dollars).8
A bank-specific variable included in regressions reported below is the
bank’s market share. Market share has sometimes been used as an indicator
of firm-specific market power. Its use in a price regression, however, raises
serious problems of endogeneity, due in part to the fact that a firm’s price is
an obvious determinant of its market share. To ameliorate endogeneity bias
when this variable is included, it will be measured as the bank’s share of
market branches (denoted branchshareti 1) rather than the share of market
deposits, and lagged values will be employed. Because this treatment may
not eliminate all sources of endogeneity, results of regressions that exclude
this variable are also reported.
An additional bank-specific measure to be included in some regressions
reported below will account for the size of the bank, measured by the natural
log of the bank’s total assets (denoted ln(bkassetsti 1)) This variable too
will be excluded from some regressions because of endogeneity concerns.
To account for difficult-to-measure differences that may exist between
urban and rural environments, a variable reflecting the extent to which a
bank operates in urban markets (denoted urbanti 1) is also included.
V. THE DATA
The data set employed in the analysis consists of observations of individual
banks observed annually from 1989 to 2006, yielding over 140,000 bankyear observations. For each bank and year, the interest rate measures were
obtained for four different types of interest-bearing retail deposit accounts
and a broader interest rate measure that incorporates service charges on
deposit accounts. The four retail deposit rates examined are the rate for
interest-bearing transactions accounts (denoted itrate), the rate for savings
deposits (denoted svrate), the rate for time deposits less than $100 thousand
(denoted smtrate), and the rate for time deposits greater than $100 thousand
(denoted lgtrate). Interest bearing transaction accounts include NOW
accounts, ATS accounts, and telephone and preauthorized transfer
accounts, while savings accounts include money market deposit accounts
and ‘other savings accounts,’ as indicated on bank income and call report
data. Interest rates for each account category were calculated by dividing
the reported annual interest expense by the average of the beginning and endof- year dollar values of the accounts held. Due primarily to reporting errors,
this procedure can produce some fairly unrealistic estimates of deposit rates.
8
See Hannan and Prager [2009].
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CONSUMER SWITCHING COSTS AND FIRM PRICING
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To reduce the impact of such errors, observations containing the largest and
smallest one per cent of values in each account category are eliminated from
the analysis.
A potentially useful alternative would be to calculate rates, net of the fees
that depositors must pay to the bank. Unfortunately, information on
deposit-related fees is not broken down by deposit category. Since a
substantial portion of such fees should be associated with transaction accounts (both interest-bearing and non-interest bearing), we calculate
the rate paid on all transaction accounts by subtracting fee income from
interest expenses on transaction accounts (denoted transnetfeerate).9 Since
the fee data used in this calculation include fees that apply to accounts
other than transaction accounts, some unavoidable measurement error
may occur.
The most original source of data employed in the analysis is that used to
measure market-specific rates of in-migration and out-migration. These
measures are calculated using the county-to-county migration data collected
and reported annually by the Internal Revenue Service (IRS). These data are
constructed from year-to-year changes in addresses shown on the
population of returns from the IRS Master File system. For each county
in the U. S., these data indicate the number of filers that immigrated into the
county during the year (identified by an address in the county at the time of
filing and an address outside the county at the time of the previous year’s
filing), the filers that emigrated out of the county during the previous year
(identified by an address outside the county at the time of the filing and an
address inside the county at the time of the previous year’s filing, and the
filers that did not migrate in or out of the county during the previous year
(identified by an address in the county at the time of both filings).
An issue associated with the use of these data concerns the choice between
the number of returns and the number of exemptions associated with the
returns, both of which are available from this data source. We employ
migration data as reflected in the number of returns, since household
migration would seem to be a better measure of bank account activity than a
measure heavily influenced by the number of family members.
Another issue concerns the treatment of multi-county markets. Following
previous studies,10 urban markets are defined as the county or the collection
of counties that make up a metropolitan area. Rural markets are defined as
labor market areas, as defined by the Bureau of Labor Statistics. These labor
market areas are typically identical to counties, but sometimes they are
larger areas formed by combining counties when 15 per cent or more of the
9
Of course, no interest expense would be registered for the transaction accounts in this
category that do not pay interest.
10
See, for example, Hannan and Prager [2004], Berger and Hannan [1989], and Calem and
Carlino [1991].
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HANNAN AND ADAMS
employed workers in one county commute to another.11 For multi-county
markets, an issue arises concerning the appropriate treatment of migration
from one county to another within the same market. For the purpose of this
paper, such migration is not counted as relevant to the pricing of banks in the
defined market. Such moves are therefore netted out in calculating
migration into and out of multi-county markets.
For these defined markets, the rate of in-migration (inmigrate) is
constructed as the number of new filers in the market, divided by the total
number of filers, where both numerator and denominator are measured for
the tax year previous to the year for which deposit rates are observed.12 The
rate of out-migration (outmigrate) is measured as the number of filers who
left the market, divided by the total number of filers, where the numerator
refers to the tax year for which deposit rates are observed, and the
denominator refers to the previous tax year. This difference in timing used to
measure the rates of in-migration and out-migration reflects the fact that the
rate of out-migration logically requires information for two consecutive
periods (the period in which filers were observed to have left and the period in
which those same filers were observed to be in the market), while the rate of
in-migration does not.
Data to calculate market shares and the Herfindahl-Hirschman index,
defined as the sum of squared market shares (measured in deposits) of all
banks and thrift institutions operating in the market, are obtained annually
from branch-specific information on institution deposits, as reported in the
Federal Deposit Insurance Corporation’s Summary of Deposits and the
Office of Thrift Supervision’s Branch Office Survey. These data are also used
to calculate the proportion of branches in each market accounted for by
large, predominantly out-of-market banks. Data on market income are
obtained from the Department of Commerce’s Regional Accounts Data,
while data on market population are obtained from the U.S. Bureau of the
Census. The assets of each banking institution are obtained from bank
balance sheet data. In the case of banks that operate in more than one local
market, all market-specific variables are calculated as weighted averages of
market values, with the share of each bank’s total deposits that are booked in
each market serving as the weights. All of these variables are lagged one year
because of a probable lag between the generation of revenue used in the
calculation of the deposit rates and the setting of a deposit rate, as well as the
possible lag between the setting of deposit rates and the observation of
market and bank characteristics.
11
See http:/www.bls.gov/lar/laugero.htm#geolma for a detailed discussion.
For simplicity, subscripts and superscripts will be dropped from here on. This measure of
inmigrate in essence indicates the importance of depositors that could be observed by banks at
the beginning of the year for which deposit rates are measured, and this seems preferable to a
measure that would apply to the end of the year.
12
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CONSUMER SWITCHING COSTS AND FIRM PRICING
307
VI. THE RESULTS
Table I defines all variables employed in the analysis, and Table II presents
mean values, by year, of the different deposit rates and two relevant ratios.
Note from Table II that the period from 1989 to 2006 was one of generally
declining deposit rates. The mean values of the rates paid on interest-bearing
transaction accounts (itrate) declined from .048 in 1989 to nearly zero by
2004, with a slight increase thereafter. The typically higher rates paid on
large and small time deposits (and to a lesser extent savings accounts)
remained substantially above zero for the entire period. The last two
columns in Table II indicate that, while the average rate of interest paid on
transaction accounts declined sharply by the end of the period, the average
of the ratio of deposit fees to transaction accounts increased somewhat over
the same period.
Table I
Variable Definitions
itrate
The interest rate offered on interest-bearing transaction accounts, calculated from
bank income and balance sheet data (see text).
svrate
The interest rate offered on savings accounts, calculated from bank income and
balance sheet data (see text).
smtrate
The interest rate offered on small time deposit accounts (less than $100 thousand),
calculated from bank income and balance sheet data (see text).
lgtrate
The interest rate offered on large time deposit accounts (greater than or equal to
$100 thousand), calculated from bank income and balance sheet data (see text).
transnetfeerate
The interest rate paid on transactions accounts net of deposit fees charged (see text).
hhi
The market Herfindahl-Hirschman index of concentration, calculated as the sum of
squared deposit shares of all banks and thrift institutions in the market.1
urban
The share of total deposits booked at branches located in markets classified as
urban.
ln(mktincome)
The natural log of total income in the market, adjusted for changes in the CPI.1
branchshare
The bank’s share of all branches of banks and savings associations in the market.1
inmigrate
The rate of migration into the market, calculated as the proportion of all IRS
personal returns filed in the market that had addresses indicating a move into the
market since the previous filing (see text).1
outmigrate
The rate of migration out of the market, calculated as the proportion of all IRS
personal returns filed in the market that had addresses indicating a move out of the
market by the subsequent filing (see text).1
outmig-decilei
A binary variable that receives the value of one if the value of outmigrate falls within
the ith decile of its range and zero otherwise.
ln(bkassets)
Natural log of the assets of the bank.
lpombshare
The collective share of branches in the market accounted for by banks that have
more than $1 billion in assets (in year 2000 dollars) and that have less than 30 per
cent of their deposits in the market.1
popgrowth
Annual rate of population growth of the market.1
incgrowth
Annual rate of income growth of the market.1
1
Banks operating in multiple markets are assigned a weighted average, with the share of the bank’s total deposits
in each market serving as the weights.
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HANNAN AND ADAMS
Table II
Means of Deposit Rates andVarious Ratios, byYear
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
itrate
svrate
smtrate
lgtrate
Trans. int./
trans. accts
Dep. fees./
trans. accts
.048
.049
.045
.031
.024
.023
.024
.023
.023
.023
.021
.023
.018
.011
.007
.006
.009
.012
.055
.056
.051
.037
.029
.029
.032
.032
.033
.033
.032
.035
.028
.017
.011
.010
.014
.021
.081
.079
.071
.053
.043
.042
.056
.056
.056
.056
.052
.057
.057
.040
.029
.025
.030
.042
.077
.075
.065
.047
.038
.039
.054
.052
.054
.054
.050
.057
.055
.035
.028
.024
.031
.043
.022
.023
.022
.016
.013
.012
.012
.011
.011
.011
.010
.011
.008
.005
.003
.003
.004
.005
.018
.019
.020
.018
.017
.017
.017
.018
.018
.017
.018
.019
.020
.021
.021
.021
.020
.021
The mean values of the rate of in-migration and out-migration (inmigrate
and outmigrate) varied from .057 to .066 over the period (not shown), and
the maximum annual values for these variables ranged from .22 to .25.
Differences in values of these variables between urban and rural markets are
small.
The correlations between the rate of in-migration and the rate of outmigration and correlations of these variables with other market characteristics are useful in providing a framework for the multivariate results
reported below. Because reported estimations (with one exception) will
include firm fixed effects, market-specific intertemporal variations are the
most relevant for this purpose. The average market-specific correlation
between the rate of in-migration and the rate of out-migration over the
1989–2006 period is .127. The equivalently measured correlation between
market population growth and the rate of in-migration is .558, while that
between market population growth and the rate of out-migration is .325.
Thus, it appears that on average, market-specific rates of in-migration and
out-migration are not highly correlated. Not surprisingly, markets with
greater population growth have greater rates of in-migration and lower rates
of out-migration.13 Correlations with another market characteristic, market
concentration, are quite low.
Table III and IV present the results of panel data estimations obtained
using the entire sample of over 13,000 banks observed annually over the
13
The mildly positive correlation between in-migration and out-migration rates implies that
population growth is not the only characteristic associated with migration flows.
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CONSUMER SWITCHING COSTS AND FIRM PRICING
309
Table III
Bank Deposit Rates and the Extent of In-Migration and Out-Migration in
Local Banking Markets,1989^2006, with Bank andYear Fixed Effects
constant
itrate
svrate
smtrate
lgtrate
.047
(41.03)
.056
(39.96)
.081
(71.01)
.077
(45.58)
transnetfeerate
.025
(4.94)
hhi
.12E-6
( 1.16)
.17E-6 þ
( 1.79)
.11E-7
(.13)
.12E-6
( .90)
.96E-7
( .34)
urban
.88E-3
( 1.55)
.0028
(4.77)
.97E-3 þ
(1.90)
.0020
(2.50)
.0010
( .43)
ln(mktincome)
branchshare
inmigrate
outmigrate
.
.
year 2000
.
year 2003
.
year 2006
R2
Number of observations
Number of banks
.18E-3
(2.23)
.63E-4
( .62)
.83E-4
(1.07)
.28E-4
( .22)
.0012
( 3.13)
.987E-3
( 1.40)
.50E-3
( .66)
.0020
( 2.84)
.43E-3
( .38)
.0055
( 2.63)
.022
(3.78)
.034
(8.86)
.040
(9.94)
.033
(4.70)
.0047
( .37)
.019
( 4.23)
.
.
.035
( 8.65)
.
.
.045
( 8.80)
.
.
.022
( 3.79)
.
.
.027
.022
( 148.83)
( 111.38)
.
.
.025
( 160.75)
.
.021
.048
( 67.60)
( 47.95)
.
.043
.045
( 336.55)
( 233.83)
.
.
.053
( 263.18)
.
.050
.024
( 159.15)
( 65.63)
.
.038
.036
( 202.90)
( 176.57)
.040
( 235.83)
.035
.023
( 127.73)
( 52.26)
.90
142,392
13,222
.89
142,392
13,222
.91
142,392
13,222
.75
142,292
13,222
.010
( .97)
.
.
.72
142,392
13,222
Note: t-statistics are presented in parentheses. The symbols þ , , and denote statistical significance at the 10, 5,
and 1 per cent levels, respectively.
period 1989 to 2006. All regressions include both bank and year fixed effects
and allow for correlation of errors across banks in the same local market.14
Table III presents results obtained for each measure of interest rates when
the measures of both the in-migration rate (inmigrate) and the out-migration
rate (outmigrate) are included as separate explanatory variables, with no
interaction terms.
As presented in Table III, the coefficients of inmigrate are positive and
significant in all four of the retail deposit-rate regressions, consistent with the
hypothesis that banks offer higher deposit rates to attract new customers,
the greater is the share of customers in the market that can be more readily
14
For banks that operate in more than one market, the market in which the bank has the
most deposits is used for this purpose. The vast majority of banks operate predominantly in one
market.
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HANNAN AND ADAMS
Table IV
Bank Deposit Rates and the Extent of In-migration and Out-migration
(including terms to capture their in interaction) in Local Banking Markets,
1989^2006, with Bank andYear Fixed Effects
Dependent Variables:
constant
itrate
.046
(38.05)
hhi
.11E-6
( 1.15)
urban
.95E-3 þ
( 1.68)
svrate
.055
(39.22)
smtrate
.080
(67.21)
lgtrate
transnetfeerate
.076
(43.78)
.023
(4.60)
.17E-6 þ
( 1.78)
.13E-7
(.16)
.12E-6
( .88)
.89E-7
( .32)
.0027
(4.64)
.79E-3
(1.58)
.0018
(2.31)
.0012
( .52)
.68E-4
( .59)
.84E-43
(1.08)
.26E-4
( .20)
.99E-3
( 1.42)
.52E-3
( .68)
.0020
( 2.86)
.45E-3
( .40)
.0055
( 2.65)
.043
(5.07)
.068
(11.30)
.081
(11.84)
.067
(6.53)
.040
(2.20)
outmigrate
.0072
( 1.03)
.016
( 3.12)
.026
( 3.88)
.0018
( .22)
.015
(.80)
inmigrateoutmig-decile2
.0086
( 1.03)
.018
( 3.12).
.026
( 5.13)
.020
( 3.33)
.017
( 1.44)
inmigrateoutmig-decile3
.017
( 2.93)
.029
( 5.55)
.038
( 6.66)
.032
( 4.71)
.041
( 3.36)
Inmigrateoutmig-decile4
.020
( 3.25)
.038
( 7.14)
.
.045
( 7.92)
.035
( 4.47)
.043
( 3.26)
.025
( 4.07)
.
.
.
.038
( 7.18)
.
.
.
.048
( 8.55)
.
.
.
.033
( 4.49)
.
.
.
.050
( 3.86)
.
.
.
.025
( 3.47)
.039
( 6.74)
.051
( 7.82)
.042
( 4.71)
.053
( 2.97)
.90
142,392
13,222
.89
142,392
13,222
.91
142,392
13,222
.75
142,392
13,222
.72
14,392
13,222
ln(mktincome)
branchshare
Inmigrate
.18E-3
(2.21)
.
inmigrateoutmig-decile5
.
.
inmigrateoutmig-decile10
R2
Number of observations
Number of banks
.0012
(-3.19)
Note: See Table III.
attracted. This finding also is generally consistent with results reported by
Sharpe [1997] and Hannan et al. [2003], who report similar findings using
different measures of bank prices and much more restrictive data sets.
Coefficient magnitudes suggest that a one standard deviation increase in the
rate of in-migration would cause deposit rates to increase by 4.8 basis points
in the case of interest-bearing transaction accounts, 7.4 basis points in the
case of savings deposits, 8.7 basis points in the case of small time deposits,
and 7.2 basis points in the case of large time deposits.
One might suspect that differences in in-migration would have a larger
effect on products for which switching costs should be more important.
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CONSUMER SWITCHING COSTS AND FIRM PRICING
311
However, the coefficients of inmigrate are actually larger in magnitude in the
case of large and small time deposit rates (smtrate and lgtrate) than in the
case of interest-bearing transactions account rates (itrate), where one would
expect switching costs to be the greatest This difference in magnitude is
attributable to the fact that, as demonstrated in Table II, the levels of itrate
(and to a lesser extent, svrate) approached zero during the later years of the
period, thus reducing disproportionately the magnitudes of their coefficients
in estimations that use data from the whole period. Reestimation using data
for the earlier half of the sample (from 1989 to 1997) yields highly significant
coefficients of inmigrate of .047 in the case of itrate and .043 and .049 in the
case of smtrate and lgtrate, respectively (not reported). Since the levels of
itrate averaged about half of the levels of smtrate and lgtrate during the
period (see Table II), this suggests that the impact of inmigrate was about
twice as large in percentage terms for interest bearing transaction accounts
than for small and large time deposits,
Nonetheless, the highly significant coefficients of inmigration (and outmigration) in the smtrate and lgtrate regressions pose a potential challenge to
the argument that they reflect switching costs. One would not expect such
results if depositors in these accounts incurred no costs to switch from one
bank to another and if their decisions regarding where to hold these accounts
were not influenced by the location of their other accounts. In regard to the first
of these conditions, the ‘hassle cost’ of closing a time deposit at one bank and
opening it at another may not be trivial. In regard to the second, if new
customers tended to open several types of accounts, then economies associated
with one-stop shopping might induce them to consider the combination of
rates offered on several types of accounts in choosing their new bank.15
A related explanation concerns the potential for existing depositors to
arbitrage across their bank’s deposit accounts. If the costs of switching from
one account to another within the same bank are negligible, such ‘within-bank’
arbitrage might cause the rates offered on some accounts to reflect the rates
offered on accounts that are more directly influenced by switching costs.
The coefficients of outmigrate are negative and significant in all four of the
retail deposit-rate regressions, consistent with the hypothesis that banks
tend to price less aggressively to attract new depositors, the less time they are
expected to remain with the bank. Because of the potential importance of
interaction terms involving this variable, results associated with it are
discussed more fully below.
The final column in Table III reports results obtained when transnetfeerate, an estimate of the rate paid on all transaction accounts net of deposit
15
Data from Survey of Consumer Finance show a strong tendency of bank customers to hold
their various deposit accounts in one institution. See Amel, Kennickell, and Moore [2008],
Table 7. This notion of a ‘cluster’ of retail banking products has been used as a product market
in banking antitrust for several decades.
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HANNAN AND ADAMS
fees, is employed as the dependent variable. In this regression, neither the
coefficient of inmigrate nor that of outmigrate is statistically significant.
We have noted that this measure may be particularly flawed because the fee
information used in its construction may apply to account types other than
that for which the rate is calculated. Despite this, we will report results below
that are more consistent with expectations when interaction terms are
included in the analysis.
The coefficients of the other explanatory variables are also of interest.
Consistent with the greater exercise of market power in more concentrated
markets, the coefficients of the Herfindahl-Hirschman index (hhi) are
negative (with one exception). However, the coefficient is statistically
significant (and then only marginally) only in the case of the rate offered for
savings deposits, svrate.
The coefficients of urban are statistically insignificant for transaction accounts
but are positive and significant for the other three deposit rates examined. Thus,
for non-transaction accounts, the deposit rate that a bank offers tends to rise, the
larger its share of deposits that come from urban markets The per unit cost of
transactions is probably greater in urban areas, and this may cause the
relationship to be more negative in the case of transaction accounts.
The natural log of real market income over time, ln(mktincome), is positive
and significant in the case of itrate, but not in the case of the three other retail
deposit rates. As noted above, this variable may capture both demand-side
and supply-side differences, and thus its net effect is not suggested, a priori.
The coefficients of branchshare are negative in all four deposit-rate
regressions and statistically significant in only one of them.
In the case of the transnetfee regression, the coefficients of both
ln(mktincome) and branchshare are negative and significant. Because this
rate includes deposit fees, these negative coefficients undoubtedly reflect the
frequently observed phenomenon of higher fees associated with banks that
are larger and that operate in larger markets.16
All reported regressions include a full set of binary variables indicating the
year, with the first year, 1989, serving as the excluded category. For reasons
of space, only the coefficients for three relatively late years in the sample,
2000, 2003, and 2006, are reported. With 1989 serving as the excluded
category, these coefficients may be interpreted as the changes in the deposit
rates occurring over the relatively long periods of 1989 to 2000, 1989 to 2003,
and 1989 to 2006 respectively, that cannot be explained by changes in the
included explanatory variables. They document the substantial decline in
interest rates that occurred over the period.
Table IV reports results obtained when terms designed to account for the
interaction between inmigrate and outmigrate are included in the analysis.
16
See Hannan [2002].
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CONSUMER SWITCHING COSTS AND FIRM PRICING
313
Interaction effects are typically accounted for by the inclusion of a single
regressor defined as the product of the two variables in question. Inclusion of
such a term in the regressions reported in Table III yields significant positive
coefficients of inmigrate and significant negative coefficients of outmigrate,
but the coefficient of the interaction term is significantly negative only in the
case of the itrate regression (not reported). Further, the significant negative
coefficients of outmigrate in this specification do not support the prediction
that out-migration has no effect in the absence of in-migration, and failure to
find significant negative coefficients of the interaction term in more than one
case does not provide convincing support for the hypothesis that the
predicted positive impact of in-migration on deposit rates is reduced by
greater out-migration (results not reported).
Because this specification imposes a linear relationship between this
interaction term and the dependent variable, we estimate and report a more
flexible functional form that allows for a nonlinear relationship. Instead of a
variable calculated as the product of inmigrate and outmigrate, each
regression includes 9 variables, each calculated as the product of inmigrate
and a binary variable indicating for each decile of the range of outmigrate
whether the observation falls into that decile (see Table IV). The results
indicate that indeed the relationship is not linear.
Consider first the results reported in column 1 for the itrate regression.
We find that the coefficient of inmigrate is significantly positive, as predicted,
and that the coefficient of outmigrate is not statistically different from zero
when interaction terms are included, consistent with the hypothesis that outmigration has no effect in the absence of in-migration.17 To understand the
meaning of the interaction terms employed, note that outmig-decile2 is
defined as a binary variable that receives the value of one if the value of
outmigrate falls within the second decile of the values of outmigrate observed
in the data, and zero otherwise. The variables outmig-decile3 through
outmig-decile10 are defined similarly for the other deciles.
The point estimate of .0086 obtained for the interaction of inmigrate
with outmig-decile 2 implies that, for observations of outmigrate in this
decile, the positive association between inmigrate and the rate paid on
interest-bearing transactions accounts (itrate) is less by .0086 compared
to observations in the first decile, which serves as the omitted category.
The coefficients of the interactions involving higher deciles are highly
significant and become more negative up to about the 5th decile, where the
value is .025, with no further reductions in coefficient values found for
interactions involving higher deciles. The coefficient of the interaction
involving the tenth decile is included to demonstrate this leveling off.
17
This follows because, with no in-migration, inmigrate 5 0. Thus, all interaction terms take
the value of zero. If the coefficient of outmigrate equals zero, then out-migration has no effect
on deposit rates under these circumstances.
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HANNAN AND ADAMS
Interactions involving deciles 6 through 9 also have coefficients of about
.025, but are not shown to save space.
These results are consistent with the hypothesis that new customers are
less attractive (as evidenced by less of a positive association between inmigration and deposit rates), the greater is out-migration. The results also
suggest that this phenomenon exhausts itself at about the fifth decile of the
values of outmigrate, since the observed decline in the coefficients of the
interaction terms does note continue beyond this decile. The use of the
product of inmigrate and outmigrate as a single interaction term would not
capture this change in coefficients as out-migrate changes.
Results obtained for the other three deposit rates examined (svrate,
smtrate, and lgtrate) are similar in several regards. The coefficients of
inmigrate are positive and highly significant in all cases, and all three
regressions provide evidence of interactions between inmigrate and outmigrate (with the predicted signs) that are exhausted at about the fouth or
fifth decile of the values of outmigrate. In the case of the svrate and smtrate
regressions, we find negative and statistically significant coefficients of
outmigrate – a finding not consistent with the hypothesis that in the absence
of in-migration, out-migration plays no role in influencing deposit rates.
These coefficients, however, are considerably smaller in absolute value when
the interaction terms are included, as may be seen by comparing
the coefficients of outmigrate reported in this table with those reported in
Table III.
The final column of Table IV presents the results of an equivalent
regression for transnetfeerate, the rate calculated for all transaction
accounts (interest-bearing and not interest-bearing) net of deposit fees.
In contrast to the results reported in Table III, and despite the potential
measurement problems noted for this calculated rate, coefficient estimates
are consistent with predictions. The coefficient of inmigrate is positive and
significant, the coefficient of outmigrate is not statistically different from
zero with the inclusion of the interaction terms, and the coefficient estimates
of the interaction terms suggest again that the positive relationship between
this calculated rate and the rate of in-migration declines with the rate of outmigration up to about the 5th decile of outmigrate.18 The coefficients of all
other variables reported in these regressions are similar to their counterparts
in the regressions presented in Table III.
In what follows, we consider the robustness of these results to various
changes in specification and sample. Most of these changes are presented
explicitly in Table V. While our discussion of the robustness of results to
various changes will focus on the results presented in Table IV, which
18
As noted, the numerator of this rate is calculated as interest expense minus deposit fee
income, Use of the interest and fee components as dependent variables in this specification
yields generally significant coefficients with predicted signs (not reported).
r 2011 The Authors
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.
.
.
.
.
.
.0097
( 1.37)
.06
( 3.11)
.0071
( 1.02)
.
.046
(4.95)
.068
(11.30)
.
.
.017
( 3.33)
.069
(11.54)
.0011
( 1.49)
.0023
( 3.27)
.043
(5.05)
.
outmigrate
inmigrate
branchshare
.20E-3
( 1.99)
.60E-4
( .60)
.18E-3
(2.19)
.0027
(4.60)
.17E-6 þ
( 1.77)
.050
(29.47)
svtrate
.93E-4
( 1.16)
.99E-3 þ
( 1.72)
.0028
(4.82)
.82E-3
( 1.46)
urban
ln(mktincome)
.11E-6
( 1.14)
.19E-6
( 2.14)
.037
(21.71)
itrate
.055
(39.21)
svrate
.15E-6 þ
( 1.64)
.046
(37.95)
itrate
hhi
constant
Dependent Variables:
Full Sample
.
.
.0051
( .73)
.038
(4.50)
.81E-3
( 1.13)
.21E-3
(2.48)
.0010 þ
( 1.70)
.89E-7
( .91)
.0082
(6.35)
itrate
.
.
.016
( 2.96)
.062
(9.90)
.31E-6
( .00)
.15E-4
(.15)
.0025
(4.13)
.16E-6
( 1.56)
.018
(12.71)
svrate
.
.
.0031
( .40)
.029
(2.49)
.61E-3
( .78)
.63E-3
(2.05)
.0014
( 1.29)
.17E-6 þ
( 1.84)
.040
(9.97)
itrate
.
.
.0076
( 1.22)
.046
(4.99)
.24E-3
( .28)
.43E-3
(1.60)
.0039
(3.30)
.12E-6
( 1.11)
.047
(13.34)
svrate
Banks Predominantly in One
Market
TableV
Bank Deposit Rates and the Extent of In-migration and Out-migration in Local Banking Markets, for Periods1989^2006,
with Additional ExplanatoryVariables, Restricted Samples, and Bank andYear Fixed Effects
CONSUMER SWITCHING COSTS AND FIRM PRICING
315
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Note: See Table III.
R2
Number of observations
Number of banks
incgrowth
popgrowth
.90
142,392
13,222
.89
142,392
13,222
.90
142,157
13,211
.93E-7
(.02)
lpompshare
.027
( 3.52)
.
.
.027
( 4.47)
itrate
.0013
(11.70)
.042
( 6.73)
.
.
.038
( 7.14)
svrate
ln(bkassets)
.026
( 3.41)
.
Inmigratoutmig-decil10
.
.
.025
( 4.07)
itrate
.
Inmigrate outmig-decile5
.
Dependent Variables:
.89
142,157
13,211
.39E-5
(1.08)
.65E-3
(6.11)
.043
( 6.88)
.
.
.040
( 7.39)
svtrate
Full Sample
TableV. (Contd.)
.88
127,781
12,430
.0023
(.80)
.21E-5
( 1.19)
.024
( 3.07)
.
.
.023
( 3.78)
itrate
.88
127,887
12,430
.0017
( .58)
.48E-5
(2.42)
.040
( 6.10)
.
.
.038
( 6.85)
svrate
.90
124,012
12,537
.015
( 1.41)
.
.
.015 þ .
( 1.67)
itrate
.89
124,012
12,537
.024
( 2.46)
.
.
.021
( 2.58)
svrate
Banks Predominantly in One
Market
316
HANNAN AND ADAMS
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CONSUMER SWITCHING COSTS AND FIRM PRICING
317
include interaction terms, all statements will apply as well to the robustness
of the results presented in Table III, which excludes them. To save space,
only coefficients of the two interaction terms associated with the 5th and 10th
deciles are reported (although all are included in the regressions), and only
results for interest-bearing transaction accounts and savings accounts are
presented.
The first two regressions presented in Table V report results of regressions
that exclude branchshare because of endogeneity concerns. As indicated, this
exclusion makes no material difference to the coefficients of inmigrate and
the variables that capture the interaction between inmigrate and outmigrate.
One difference is that the negative coefficients of hhi register higher levels of
statistical significance in these regressions.
The second pair of regressions in Table V report results obtained with the
inclusion of a measure of bank size (ln(bkassets)) and a measure of the
importance in the market of large banks that operate predominantly out of
the market (lpombshare). As indicated, this change does not alter the results
regarding in-migration and out-migration. The positive and highly
significant coefficients of ln(bkassets) are noteworthy, since a number of
previous studies have reported the opposite relationship.19 It is possible that
this result reflects reverse causation, where banks became larger by raising
their deposit rates relative to those of other banks over the period. The
coefficients of lpombshare are not significant in these regressions.
The next two regressions report results obtained by including measures of
market population growth (popgrowth) and market income growth
(incgrowth) in the analysis. These measures are included because of their
obvious association with in-migration and out-migration. This association
raises the possibility that the coefficients of the variables measuring inmigration and out-migration are obtained not for the reasons outlined
above, but simply because of unmeasured phenomena associated with
market growth. As noted above, ln(mktincome) is included in all estimations
to account for such phenomena, so the inclusion of popgrowth and incgrowth
may be considered a further check on this issue. As indicated, only the
coefficient of incgrowth in the svrate regression is statistically significant.
As also indicated, the inclusion of these variables leaves the coefficients
associated with in-migration and out-migration unchanged in sign and
statistical significance.
The final pair of regressions presented in Table V report results obtained
when the sample is restricted to banks that operate predominantly in one
19
See, for example, Hannan and Prager [2009] and Park and Pennacchi [2009]. The reason
for this difference is suggested by the fact that these studies employ cross-sectional variation,
and the coefficients of ln(bkassets) are found to be significantly negative in regressions that drop
the bank fixed effects, thus allowing for cross-section variation. These regressions are discussed
briefly below.
r 2011 The Authors
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318
HANNAN AND ADAMS
local banking market, defined in this case as banks that have at least 75 per
cent of total deposits in one market. This is designed to eliminate any
confounding of the underlying test that may result if, as might be true in the
case of large multimarket banks, in-migrants already have accounts with the
bank in their previous locations. As indicated, the coefficients of inmigrate
and the interaction terms exhibit the predicted signs and are statistically
significant, with marginal significance in the case of interaction terms in the
itrate regression. Thus, these results appear to hold for the large majority of
U.S. banks for whom migrants not needing to change banks could not be
much of a factor.
Additional robustness checks, not reported in Table V because of space,
concern the distinction between urban and rural markets and the time periods
employed. Division of the sample between banks that operate primarily in
urban markets and those that operate primarily in rural markets yields the
predicted signs for the coefficients of inmigrate and the interaction terms with
high levels of statistical significance for both subsamples. The absolute values
of these coefficient magnitudes are larger in the case of the rural subsample.
Division of the sample in half according to the years covered yields results
equivalent to those reported above for both subsamples, although coefficients
are larger in magnitude and achieve higher levels of statistical significance for
the earlier period, presumably because some interest rates approached zero
toward the end of the later period.
Finally, reestimation of the above regressions, excluding the bank fixed
effects (not reported), also yields the predicted signs for the coefficients of
inmigrate and the interaction terms, with high levels of statistical
significance. Coefficient magnitudes tend to be larger in these regressions,
and the coefficients of outmigrate are not statistically significant in either the
itrate and svrate regressions, as would be the case if out-migration has no
impact on retail deposit rates in the absence of in-migration.
VII. CONCLUSION
This paper employs extensive information on deposit rate setting by banks
to test for the existence of pricing relationships implied by the existence of
switching costs. As argued, the banking industry is one in which substantial
switching costs should be relevant to customer behavior. Furthermore, the
local nature of the markets for some types of retail deposit accounts,
together with extensive information on the rate at which new customers
enter and exit these markets, makes possible the testing of these relationships
in ways not available in other empirical settings.
While these relationships can be derived more formally, the intuition for
them can be readily stated. Because some areas experience more inmigration than others, banks, in addressing the tradeoff between attracting
new customers and exploiting old ones, should offer higher deposit rates in
r 2011 The Authors
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CONSUMER SWITCHING COSTS AND FIRM PRICING
319
areas (and during periods) characterized by greater in-migration. Further,
because out-migration implies that on average a locked-in customer will
not be with the bank as many periods, greater out-migration should change
the bank’s assessment of this trade-off such that the bank will offer lower
deposit rates in areas (and at times) exhibiting greater out-migration, all
else equal. Also, because the effects of in-migration and out-migration
logically depend on each other, an interaction effect is implied. In translating
these predictions to other industries, it is useful to note that deposit rates
are prices paid by the firm to consumers. In the more typical case of a price
that is paid by consumers to the firm, the opposite sign predictions would
apply.
Consistent with predictions, deposit rates are found to increase with the
rate of migration into a market and decline with the rate of migration out of
the market, with the rate of in-migration having a less positive effect on
deposit rates, the higher is the rate of out-migration. This interaction effect,
however, appears to hold only up to about the 5th decile of the values of the
rate of out-migration found in the data. Coefficients are robust and, in most
cases, highly significant. They imply that switching costs are very much a
factor in explaining bank deposit rates, and the negative relationship
between bank deposit rates and the rate of out-migration implies, in
particular, that banks take into account the future profitability of locked-in
depositors in choosing current deposit rates.
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