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A Series of Occasional Papers in Draft Form Prepared by Members
of the Research Department for Review and Comment.
76-2
Advertising for Demand Deposits
Chayim Herzig-Marx
Federal Reserve Bank of Chicago
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Research Paper No. 76-2
ADVERTISING FOR DEMAND DEPOSITS
By
Chayim Herzig-Marx
Department of Research
Federal Reserve Bank of Chicago
The views expressed herein are solely those of the authors
and do not necessarily represent the views of the Federal
Reserve Bank of Chicago or the Federal Reserve System. The
material contained is of a preliminary nature, is circulated
to stimulate discussion, and is not to be quoted without
permission of the authors.
I.
INTRODUCTION*
This paper explores the determinants of advertising for demand
deposits.
From a theoretical point of view, the market for demand
deposits is particularly interesting.
As an institutional feature,
price competition for demand deposits is prohibited by statute and by
regulation.
Since prices cannot clear markets, some other mechanism
must be found.
Advertising could play an important role in adjusting
supply of and demand for deposits.
The lack of attention to advertising by commercial banks must be
attributed to a distinct lack of data.
Problems in defining inputs
and outputs and the level of sales in the banking industry are quite
severe, so that banks are excluded from inter-industry studies using
Internal Revenue Service data.
Most micro data until recently were
held confidential, although even Report of Income statements do not
contain a breakdown of expenses.
This basic data problem was resolved
for this study by using information obtained from the Functional Cost
Analysis program sponsored by the Federal Reserve System.
Functional
Cost data have been used extensively in the past by researchers within
the Federal Reserve System to study bank costs and production functions.
The data themselves, however, are confidential and cannot be reported.
Within the mainstream of research in industrial organization,
advertising has been found to be an interesting feature of firm conduct.
As such, this form of behavior may be conditioned by the firm’s environ
ment.
Empirical work to be reported below provides some support for
the structure-conduct hypothesis.
*Jan Gigstad and Robert Keyt supplied the extensive programming
necessary to compile the data and prepare them for analysis.
2
The outline of this paper is as follows.
Section X presents
a model in which expenditures on advertising are adjusted to equate
the supply of lendable funds to the demand for bank loans.
Section
II discusses the sample of banks used to estimate the relationship
outlined in Section I and details the construction of the variables
of the model.
Section III reports empirical results, and section IV
concludes the paper.
II.
A Model of Deposit Advertising
Each bank has outstanding at any point in time certain commitments
to extend credit to its clients.
The maximum amount of credit and the
rate at which credit will be extended are the most important character
istics of the credit line, along with the fees or compensating balances
which pay for the line.
Since the credit line can be exercised at the
discretion of the loan customer, each bank must forecast its expected
loan "takedown” and be prepared to lend however much its clients want.
The forecasting process can be thought of as follows.
The bank estimates
the probability that each credit line outstanding will be exercised.
Then the bank estimates the expected size of the loan takedown, given
that the credit line is exercised.
pected size of the loan.
The product of these two is the ex
Summing over all credit lines outstanding gives
the total expected loans by that bank at the future date.
It is assumed
that'banks forecast one year into the future.^
Notwithstanding the growing importance of non-deposit sources of
funds, expansion of deposits will continue to be a major source of
■**It does not complicate the anlysis if it is also assumed that banks
forecast some expected increase in loans from sources other than take-downs
of credit lines, since other borrowers will respond to the same economic
forces as owners of credit lines.
3
increased lendable funds for commercial banks ►
Although the shift from
demand to time deposits is certain to continue, demand deposits remain
an important source of funds for banks.
demand deposit advertising.
This paper considers only
Time deposits are considered to be suffi
ciently different in nature to justify separate treatment.2
The loan forecast indicates to the bank the quantity of lendable
funds it will need.
By forecasting deposits the bank can estimate its
expected lendable funds.
Advertising enters the model through its
effect on future deposits.
We posit that advertising can be used to
increase deposits at the bank, and that the level of advertising expen
ditures can be determined so as to equate expected supply of lendable
funds to expected loan demand.
Implicitly the optimal level of adver
tising will be a function of the determinants of expected loans and
expected deposits.
To estimate the conceptual model outlined above would require a com
prehensive treatment of bank portfolio decision, complete with an ex
plicit expectations generator.
For the limited purposes of this study,
such as undertaking is not worthwhile.
Instead, a structure-conduct
empirical model of the type commonly encountered in industrial organiza
tion research will be used.
An elementary forecasting procedure will be
used for loans and deposits.
o
Some preliminary analysis of the type reported in this paper was
conducted for time deposits with the basic result that this model had
virtually no explanatory power. This probably results from two factors.
First, most time and savings accounts are highly homogeneous commodities,
so that price is the most important factor to the virtual exclusion of
market structure or other characteristics. Second, for deposits which
exceed the insurance limit, the adequacy of the bank’s capital, or depo
sitor expectations of the probability of the bank’s failure, assume great
significance and interact strongly with the rate paid on such deposits.
Thus the present model is not adequate to explain advertising for time
deposits.
4
Four factors are important for forecasting loan demand: (A) the
scale of the bank’s operation, meaning the total number, and dollar
value, of loan commitments outstanding; (2) the probability of loan
commitments being exercised, which is a function of total financing
needs of bank clients and alternative borrowing costs; (3) the likely
size of loan takedowns, which is probably also a function of bank scale
(large customers need to deal with large banks); and (4) expected in
crease in loan demand other than from commitments.
be used to represent these four factors:
Three variables will
deposits (as a measure of bank
scale), current loan yields (net of cost of money), and the historical
growth of loans in the bank’s market.
The forecast of future deposits depends on three factors:
(1)
present deposits; (2) the expected growth rate of deposits; and (3) a
confidence interval around the growth expectation.
The second and
third factors are represented by the historical growth rate of deposits
and by the standard deviation of deposits around their historical growth
trend.
The efficacy of advertising in attracting deposits is conditioned by
the nature of the individual bank and the characteristics of the market
in which it is located.
Four individual bank characteristics will be
tested for their influence on advertising:
market share, wholesale or
retail orientation, age (years since founding), and holding company
affiliation.
The four aspects of market structure considered most impor
tant are concentration, the conditions of demand for loans and supply of
deposits (both discussed above), regulatory restrictions on branching
(availability of substitutes for advertising), and the urban or rural
nature of the market.
5
The larger the market share of a bank, the greater the proportion
of the inter-industry effects of advertising the bank can expect to
internalize; and consequently the larger its advertising budget can be
expected to be.
The relationship between market share and intra-industry
effects is much less clear.
It seems likely that firms with larger market
shares feel more susceptible to inroads from other banks’ advertising.
This may induce leading banks to spend more on advertising, as a defensive
device.
It may, on the other hand, induce leading banks to spend less
on advertising, so as not to promote advertising by other banks.
On the
whole, it is likely that inter-industry effects outweigh intra-industry
effects and that market share has a positive relationship to advertising.
Wholesale and retail banking differ significantly in terms of
the bundles of products offered.
Wholesale banking is oriented toward
large customers, with the provision of a credit line as the major product
or service provided.
The explicit or implicit price of the credit line
(commitment fees or compensating balance requirements) is likely to be
the most important means of competing for such accounts.
The proliferation
of personal banking packages in recent years attributes to the many ways
of competing for the deposits of individuals.
Non-price terms predominate
here, and advertising is likely to play a much more important role.
As
a consequence, banks which are oriented toward wholesale customers are
likely to advertise less.
Even if both wholesale and retail banks do advertise, the media
selected to carry advertising messages are unlikely to be the same for
both.
An important feature of this advertising process is that corporate
treasurers will always be actively seeking the lowest prices for credit
lines.
This search for information on the part of bank customers will
6
also lower bank advertising expenditures.
Because wholesale banks are
likely to have considerably more large accounts, we will include average
account size in the regression equation to control for the wholesale/retail
dimension.
New firms in general must advertise to make known their existence
and to attract a loyal clientele.
In commercial banking, the importance
of the customer relationship reinforces this motive to advertise and makes
it very likely that younger banks will advertise considerably more than
older ones.
In order to use a continuous variable, the age of the bank
(years, expressed in decimals, since the bank opened for business) in
reciprocal form will be entered into the regression model.
Unfortunately,
this variable is unlikely to have any statistical significance for our
sample of banks.
The data requirements for the deposit growth and de
posit variability variables forced the exclusion of any banks fewer than
eight years old.
This will probably render the age variable insignificant.
The effects of holding company affiliation on bank decision making
are still quite unclear.
Available evidence on changes in bank operations
following holding company affiliation are only tangentially related
3
to advertising.
"Other operating expenses," which include advertising,
appear to rise significantly for banks after they are acquired by holding
companies.
On the other hand, service charges on demand deposit accounts
fall slightly but not significantly.
The main effect expected is the
centralization in the parent company of the advertising function, on the
assumption that bank subsidiaries can benefit from association with the
parent name.
Such centralization can also be seen as eliminating
o
Samuel H. Talley, "The Effect of Holding Company Acquisitions on
Bank Performance," Staff Economic Studies #69, Board of Governors of the
Federal Reserve System.
7
potentially wasteful duplication of effort and taking advantage of what
ever economies of scale may exist in advertising.
On the whole, we
expect banks affiliated with holding companies to advertise somewhat
less than other banks.
As in any structure-conduct-performance test, the definition of
the market is crucial to the analysis.
bank in its local market.
In this paper, we focus on the
This viewpoint is similar to that adopted by
the Board of Governors of the Federal Reserve System in its deliberations
on mergers and holding company acquisitions and is also in close keeping
with the nature of the sample of banks (see section II).
Specifically,
the market for any bank is assumed to be exactly coterminous with the
county in which the bank or its head office is located.
While this usage
is fairly common, the restrictiveness of this assumption should not be
underestimated.
Appendix I discusses the applicability of this definition
for the sample employed.
Concentration is measured by the numbers equivalent, the reciprocal
of the Herfindahl index.
To take account of the possibility that local
market shares are distorted due to large demand deposit balances of large
firms located in other banking markets, a Herfindahl index was computed
only for accounts of less than $1,000.
This concentration index accords
better with the local nature of the banking market and consistently
yields better regression results.
All results reported below, therefore,
use the numbers equivalent based on accounts of less than $1 ,000.
The conditions of supply of lendable funds and demand for bank
loans were discussed above in conjunction with the forecasting process.
Establishing branch offices is one of the more important alterna
tives to advertising as a means -of increasing lendable funds.
Branches
8
enable banks to compete by offering locational convenience and by enabling
the bank to follow shifts of population.
With the exception of Illinois,
all states within the Seventh Federal Reserve District allow some form of
branching within the county, and some states allow branching into con
tiguous counties.
Branching restrictions in Illinois are thus consider
ably more stringent than in other district states, and this qualitative
difference will be taken into account.
The urban or rural nature of the bank’s market may be important
for three reasons.
First, there may be important cost differences
between the two types of regions.
Especially, some advertising costs
can be expected to be functions of the distance over which messages are
propagated.
Advertising will then be cheaper in urban areas since
population densities are higher.
Second, more developed transportation
systems in urban areas may reduce the economic distance between banks,
heightening competition and promoting greater advertising expenditures.
Third, the significance of location may differ considerably.
Conven
ience and accessibility are important motifs in bank advertising.
Lo
cational differences may be more important in cities, with their locationally specialized transportation networks (e.g., in most cities the
best means of transportation are those which go into the downtown area.)
Traffic congestion is another factor increasing the importance of loca
tion in urban areas.
In addition to these conflicting aspects of urban-
rural location, it is likely that different advertising media predominate
in the different areas, making cost comparisons extremely hazardous.
Thus
the net effect of the urban or rural nature of the market cannot be pre
dicted, but is likely to be insignificant.
9
Size-related bias is to be expected in any econometric relationship
estimated with micro data.
Larger banks will spend more on advertising
because their total budgets are larger.
To correct for this bias, the
dependent variable will be deflated by thousands of dollars of demand
deposits and specified as an expenditure intensity rather than as dollars
of expenditure.
The form of the estimating equation is
(1)
A/D = bn + b-LYLD + boLGR0 + b DGRO + b DVAR + b ^ + b l ,
U
1
Z
3
4
— 0—
— 6—
where D is demand deposits, LYLD is the net yield on loans, LGRO is the
historical growth rate of loans in the county, DGRO is the historical
rate of growth of deposits of the bank, DVAR is the variability of deposits
around their growth trend, S is a vector of market structure character
istics (other than LGRO), and I is a vector of individual bank charac
teristics.
III.
The Sample and Construction of Variables
The advertising relationship discussed in section I was esti
mated with micro data drawn from a sample of 160 Seventh Federal Reserve
District commercial banks which participated in the 1972 Functional Cost
Analysis program sponsored by the Federal Reserve Bank of Chicago.
Since
participation in the program is voluntary and the data are held con
fidential, disclosure of information on a bank by bank basis is not
possible.
Expenditure data are for the year 1972.
The purpose of the Functional Cost Analysis program is to assist
banks in maintaining accurate and useful cost accounting of their
operations.
For the most part, participating banks are too small to
maintain cost accounting departments of their own, but it will be seen
IQ
that a considerable range of firm size is still encompassed in the sample.
Because the program is voluntary, and because the output of the program
is of such benefit to the participating banks, we can have considerable
confidence in the overall quality of the data.
The cost accounting framework breaks down all bank operations into
separate functions, such as demand deposits, time deposits, real estate
loans, personal loans, trust services, data processing services, etc.
For each function, participating banks allocate their expenses to the
best of their abilities.
Any expenses which cannot be allocated directly
to functions are reported as a residual.
This residual, by type of
expense, is allocated to functions indirectly by the functional cost
program itself.
Since these indirectly allocated expenses tend to be
overhead or fixed costs, attribution of them to specific functions is not
completely accurate.
In this paper, the advertising costs we seek to
explain are only those which are allocated directly by participating
banks.
The sample of banks displays considerable diversity, given that
all are located within the Seventh Federal Reserve District.
Size
as measured by total deposits ranges from under $5 million to well
over $1 billion, with a mean size of $32 million and a median size
of $45 million.
Sixty-six banks are chartered in Illinois, 20 in
Indiana, 29 in Iowa, 21 in Michigan, and 24 in Wisconsin.
Ninety-four
of the banks are located in Standard Metropolitan Statistical Areas,
and twenty three are affiliated with bank holding companies.
The
oldest bank was chartered in 1848, and the youngest opened for business
in 1964.
This sample thus displays considerably more diversity of size
11
than most samples of industrial or commercial firms, yet because most
banks are rather small there is considerable homogeneity in terms of
products and services.
In addition, we are not forced to define the
firmfs market as the entire country, since information (from Reports of
Condition and Income and Dividends) is available on virtually the entire
universe of commercial banks from which to construct market structure
variables.
Net yield on loans (LYLD) is calculated by summing gross earnings
on all loan categories and substracting total expenses for all loan
categories (direct and indirect expenses).
Dividing this difference
by total loans gives gross yield on loans.
From gross yield is sub
tracted what is termed "cost of money," or an average cost of all lendable funds.
Multiplying the result by 100 gives net yield on loans.
Historical rate of growth of loans in the market (LGR0) is calculated
as the 1971 to 1968 ratio of all loans made by all banks in the county.
Historical rate of growth of deposits (DGR0) was calculated
separately for each bank in the sample.
A compound growth rate was
fitted to annual observations on total demand deposits by simple re
gression.
Deposit variability (DVAR, multiplied by 100 for scaling
purposes) was calculated as the standard error of the estimate divided
by the mean value of deposits.
Data for years 1964, 1965, 1968-1971
were used.
. All data on market shares (SHARE) were calculated using the 1970
Summary of Deposits survey.
Average account size (ACSIZ) was calculated as total demand deposits
(in thousands of dollars) divided by total number of demand deposit
accounts.
12
Age of the bank (AGE) was calculated relative to December, 19J2,
which is the reporting date for FCA data.
Years and months since the bank
opened for business, or since the bank was chartered if the opening date
was not known, were converted to years and decimals.
The multiplicative
inverse was taken to yield the age variable.
Holding company status (HC) is represented by a simple dummy variable
taking on the value of unity for any bank affiliated with a holding company,
zero otherwise.
Herfindahl indices of concentration (CONCE) were computed using market
shares based on the 1970 Summary of Deposits survey for total deposits and
for deposits in accounts of less than $1,000.
To represent branching restrictions (BRNCH) a simple dummy variable
was used, taking the value of unity for all banks located in Illinois, zero
otherwise.
Several variables were tried to represent the urban or rural nature
of the bank’s market.
A dummy variable (SMSA) taking the value of unity
if the bank was located in an SMSA and zero otherwise, population density
(PDEN) in thousands of persons per square mile, and percent of the popula
tion living in urban areas (PURB) were all tried.
The total number of Seventh District banks participating in FCA
in 1972 was 213.
Of this number, 34 banks made no direct allocation
of their advertising and were excluded from the sample on this ground.
Of the remaining banks, 19 had to be excluded for various reasons,
primarily lack of time series data on deposits for the construction of
DGRO and DVAR.
Mergers and consolidations accounted for the lack of
consistent time series data.
One or two banks were excluded because
13
no non-bank financial intermediaries were operating in the market.
Since the presence of non-bank financial intermediaries is required for
any inter-industry effects to be possible, and because so very few
potential sample banks did not fulfill this requirement, it was de
termined to drop them from the sample rather than take account of this
status with a separate independent variable.
IV.
Empirical Results
Some estimates of the advertising relationship are given in Table
1.
Equation 1 includes all variables specified above.
Signs of three
variables, DGRO, HC, and AGE, are counter to predictions, but none of
these coefficients is significant.
In fact, the only variable whose
coefficient is significantly different from zero is DVAR, deposit vari
ability.
The only other variable whose coefficient exceeds its standard
error is BENCH, the branch banking dummy.
Equation 2 deletes three individual characteristics and one market
structure variable which added virtually nothing to the explanatory power
of the model.
(LYLD, which also adds almost nothing to the model, is
retained because of its role in the forecasting process.)
Market concen
tration is now significant at the 10 percent level, and the BRNCH and LGRO
coefficients exceed their standard errors.
Most standard errors are smaller
than in equation 1, indicating a reduction in collinearity.
Equation 3 further deletes DGRO and ACSIZ, with the result that LGRO
achieves 10 percent significance and most standard errors fall.
Finally,
deleting LYLD in equation 4 raises CONGE to 5 percent significance and
BRNCH to 10 percent.
14
The
the
coefficients
four
equations.
siderably,
No
Further
4.
Each
SMSA,
of
was
continued
pected
to b e
equation
SMSA
is
of
PURB
is
PURB
high
zero.
is
SHARE,
2,
see
if
in
one
the
HC,
or more
the
are
age
con
altered.
ACSIZ,
of
on
equation
AGE,
and
them might
All
across
excluded
conducted based
equation.
although
4,
using
Table
In
PURB
population
be
these variables
variable
1,
and
PDEN
density
as
took
on
is
entered
Its
sign
but
the
coefficient
CONCE
is
alternatives
1.
addition,
entered
is
into
negative,
variables
are
specified.
is h i g h l y
measured with
other
the m o d e l
stable
collinearity with
probably
equation:
DGRO,
increase
the
specifications
variables,
1 equation
icance while most
really
collinearity with
was
to
stability
and LGRO
here,
variables
Table
equation
from
other
its
CONCE
reported
conducted
1 of
coefficient
what
reasonable
the
ex
unexpectedly
SMSA.
along with
negative,
is
to
as w a s
not
the
the
sign
significantly
switches
to
the
sign.
When
its
of
occur when
insignificant,
in Table
different
"wrong"
show
sign.
variables
of
not
fewer
BRNCH
greater
individually
Testing was
In
to
excluded
with
and
coefficients
changes
analysis,
added
significant
due
sign
the
DVAR
The
probably
variables.
of
the
less
can be
size,
The
other
coefficients
collinear
in
(equations
independent
become
from
it
PURB
Table
with
insignificant.
following
2)
regardless
(and PDEN)
variables.
emerges
the
3,
significant
problem with
error,
seen
and
2 and
high
That
Since
signif
PURB
regression
15
PURB
-
43.7 - .386 D G R O
(9.27) (..357)
- 1.62 L G R O
(3.27)
+
.820 D V A R
(2.34)
+
1.94 L Y L D
(1-81)
-
+
.846 C O N C E
0190)
2.89 B R N C H
(2.73)
+
.201 S H A R E
OQ27)
-
4.45 H C
(3.24)
+70.8 A G E +22.7 SMSA
(76.3)
(2.36)
To
judge
by
regression
PURB
and
t-statistics
equation
SMSA
the
results
and
SMSA would
tation
of
are,
is m o s t
likely
improve
results.
While
the
.973 A C S I Z
(.644)
R-square
this
in addition,
obtained.
the
from
+
equation,
including
to
CONCE,
good
affect
substitutes.
PURB
SHARE,
These
in
=
.666
the
a n d HC.
are,
including
PURB
in place
of
CONCE,
statistical
fit,
it w o u l d
confound
in
fact,
SHARE,
interpre
16
V.
Summary
and
Mixed
results
advertising
The
to
growth
and
and
whose
of
should
yield
loans
prising
described
on
is
for
loans
this m o d e l
lendable
empirical
is
interval
enter
expected
the
coefficient
confidence
obtained
process
from
net
growth
were
equate
forecasting
confirmation
in
Conclusions
in
Section
testing.
fail
to
consistently
so
strongly
result.
Further
the
in
study
I does
Both
of
the
not
forecast.
That
deposit
of
The
DVAR,
equation
strong
deposit
significance,
one
variable
representing
this
is
variability
demand.
receive
rate
is
use
loan
statistical
significant
regression
banks
expected
significant.
deposit
the
to
exhibit
only marginally
around
funds
in w h i c h
the
variable
a rather
appears
sur
to b e
order.^
The
from
the
fundamental
the m a rket
are
the
In
little
sum,
globally,
representing
condition
of v e r y
hypothesis
concentration variable
dummy variable
senting
structure-conduct
the
of
performance
however,
the
inability
demand.
assistance
and,
explaining
this
empirical
model
results
a
lesser
to b r a n c h
Individual
in
of
to
receives
bank
are
extent,
and
LGR0,
from
repre
characteristics
demand
is
support
deposit
spotty.
advertising.
Judged
sufficiently
good
2 'V/
(R
^
.25)
accounting
^In
76-3
for
this
another
individual
paper
rate
of
multicollinearity
is
a
reasonable
complex
start
such
an
As
those
market
share,
growth
poses
a
results
deposit
of
been made
Variability,"
investigation
representing market
that
has
in
phenomenon.
("Long-Run Deposit
just
banks.
finding
concentration,
especially
that
highly
on variables
important
by
consider
(forthcoming))
gressed
of
to
is
Staff Memoranda
undertaken.
structure
and
relate
to
this
variability
is
explained
holding
deposits.
substantial
company
problem
in
study,
it
the
is
is
the
quite well
affiliation,
Therefore,
DVAR
characteristics
and
clear
that
present work.
re
APPENDIX I
COUNTIES
Three broad
a
firm's market
theory must
be
classes
for
taken
effects
(3)
availability
types
of
To
firms
put
of view,
to b e
the
the
these
area
attempts
of
to
susceptible
a
to
A
the
grave
sample
Achieving
to
deal with
tively
which
tion
definition
might
large
this
a
are
less
are
less
sample
problem.
The
individuals
in
the
have
sample;
definition
It
adequate
for
not
two
are
and
is
Therefore,
therefore
in
economic
(non-bank)
not
clear,
subject
to
calculation,
market
the
definitions
preferred.
are
are
only
preferred,
In
be
geared
case
agents
therefore,
them.
is
quickly
firms,
of
which
a market
this
both
to b e
geographically
of
point
requires
transmitted
must
other
possible
are
Theory
types
certain biases
as
a practical
investigation,
and
respects.
is
from
definitions
deposits.
exhaustive
extensive
perspective
under
size
economic
definitions
the market
influence
incorporate
not
is
that
that
apparent.
practical way
which
are
addition,
since
rela
markets
aggregation
likely.
Availability
of
in
case.
the
into
one which
market
of
of
sample.
Market
is
implications
both
influences
in many
definition
included
competitive
advertising,
the
can be
important.
demand
and
the
any
different
conditions
the
effect.
geographically
biases
as
firms
influence
least
competitive
risk,
used
which
is
over which
significantly
single market
of
factors
theory
MARKETS
(1)
(2)
strongly
three
attract
purposes:
account;
comprise
substantial
type
differ
types
which
economic
and with
to
on
into
GEOGRAPHIC
considerations
empirical
profound
data
of
AS
usual
data
is,
unfortunately,
Normally,
either
the m o s t
the m a x i m u m
important
considera
sample
known
is
1-2
in
advance
and
accommodate
one
or
a
are
as
a
the
banks
local
criteria
tions
the
definition
sample,
or
few definitions
Most
kets
the
set
are
in
this
of
to
requirements
While
small,
counties
and
80
percent
originate,
service
service
(o r
some
an
is
that
size
of
its
area.
made
application
to
the
regulatory
culties
of
service
areas
a
need
any
This
and
for
the
political
and
a
simple
to
locate
feature
Another
demographic
is
other
by
only
is
geographic
other
a bank,
market
In
be
their m a r
arbitrary
banks
for
defini
defined
figure)
of
definition.
itself
only
must
provide
which
had
a merger
addition,
the
considerable,
smallness,
all
of
counties
are well-defined
decision
and
the b a n k s
especially welcome
data which
of
the b a n k
would
clear-cut
benefit
two
authorities
They
is
latter
to
or
diffi
since
boundaries.
relative
as m a r k e t s .
matter
area
sample.
variables
the
that
smallness,
Therefore,
b a n k ’s m a r k e t
centration measures.
economic
follow
exhaustiveness
relatively
market.
not
characteristics
determining
is
candidates
constructing market
Besides
desirable
be
can be met
often-used
on
acquisition would
implicitly
implies
satisfy
the
is
areas
which
relative
information
formal
the
are
In particular,
deposits
drawback
data
is m a d e
the market.
sample
circle within which
t h e b a n k ’s
the m a r k e t
exhaustiveness
possible.
The basic
other
in nature.
above,
of
counties
available
in
for
is
for
in
any
have
areas,
so
addition
that
it
particular
calculating
the w e a l t h
such
other
con
of
regions.
Against these advantages for data collection and sample determination,
one must balance the possibility that economic activity does not follow
county lines.
This appendix presents evidence that, for the Seventh Federal
Reserve District, counties are reasonable approximations to economically
relevant markets for banking activities both of individuals and of firms.
1-3
A
reasonable
business
at
supposition
a bank office
residence.
County markets
people work
in
the m e a n
force
and
sented
in
county
standard
residing
calculated
work
the
in
for
the
a person
either
near
his
are
of
county
counties
sample.
near where
that
they
then
their
of
in
These
place
suitable
state
its
the
data
transact
of
the
show
and
a
of
strong
Residing
Counties
Seventh
in
the
two
those
his
extent
table below
The
of
that
shows
the
labor
statistics
counties
tendency
Percentage
in
or ne a r
percentage
for
banking
for
are
repre
people
to
live.
Number
State
to
The
employment.
state
his
of w o r k
only
residence.
deviation by
the
all
is
District
sample
in
total
in
of
Labor
County
Force
of
Employment
sample
mean
total
s.d.
mean
s. d .
Illinois
29
58
74.6
11.9
68.6
12.3
Indiana
13
64
79.2
13.6
66.8
15.7
Iowa
19
97
83.5
9.9
81.2
8.1
Michigan
20
67
75.4
13.8
70.1
13.7
Wisconsin
17
46
77.8
11.5
76.8
9.6
SOURCE:
15
Table
(Illinois),
As
pal
89
pal
the
banking
While
this
of
firms,
by
the
all
most
same
banks
county,
relationship
survey was
•'■Robert F.
Ware
17
the
I,
data
Federal
a bank
that
and
are
firms
in
the
all
less
in
E.
direct.
A
1973
maintained
(87
of
banking
that
relation
changed
in
the
same
which
is
not
in
Survey
their
percent
bank
A
Parts
(Wisconsin).
Ohio
Cleveland^- indicates
county
Reserve
51
study
of
firms
Duro,
in O h i o , F e d e r a l
1970,
and
Bank
same
another
in Ohio,
Lorraine
Population:
(Michigan),
a principal
nearly
selected
of
24
Reserve
added
conducted
and
Census
(Iowa),
respondent
Firm-Bank Relationships
1974.
Volume
relation with
Furthermore,
in
firms
percent
banking
bank
119,
(Indiana),
for business
manufacturing
that
16
their
princi
in
1969).
chose
a
princi
county.
the D i s t r i c t
of
of M a n u f a c t u r i n g
Bank
of
Cleveland,
June
1-4
our sample and does haye somewhat different economic features from the
Seventh District (notably the lack of a money-market the size of Chicago),
it is considered that the results are representative of Seventh District
experience also.
APPENDIX II
Simple Correlation Coefficients for Independent Variables
DGRO
DVAR
DVAR
CONCE
SHARE
LGRO
LYLD
BRANCH
HC
AGE
SMSA
.684
CONCE
-.014
- .1 0 2
SHARE
-.254
-.176
-.652
LGRO
.072
-.029
-.258
.176
LYLD
.003
- .1 1 2
-.344
.409
.107
-.051
-.147
.528
-.387
.032
-.004
.016
-.024
-.062
.128
-.177
.022
-.307
-.106
- .1 1 2
.216
.036
-.167
.110
.092
.202
BRANCH
HC
ACCTSIZE
-
ACCTSIZE
AGE
.720
.504
.145
-.148
-.108
- .1 2 0
.040
.020
-.191
SMSA
.002
.047
.414
-.372
-.236
-.158
.186
.054
.154
.160
MEAN
5.496
.5256
9.784
22.72
2.065
2.249
.4125
.1438
2241.
.0212
.5875
ST. DEV.
5.158
.6366
8.881
16.62
.3510
.6658
.4923
.3508
1788.
.0217
.4923
Table 1
Advertising Intensity Regressed on Selected Variables
Standard Errors in Parentheses
EQUATION
1
2
3
4
DGRO
.008
(.010)
.005
(.008)
DVAR
.304***
(.067)
.300***
(.066)
•330***
(.047)
.328***
(.046)
CONCE
-.004
(.005)
-.006*
(.005)
-.006*
(.004)
-.007**
(.004)
SHARE
.001
(.003)
LGRO
.085
(.094)
.098
(.089)
.114*
(.088)
.113*
(.087)
LYLD
.0 1 1
(.052)
.018
(.050)
.015
(.048)
BRNCH
.10 1
(.079)
.092
(.073)
.094
(.073)
.099*
(.071)
HC
.009
(.093)
ACSIZ
-.017
(.019)
AGE
-.883
(2 .20)
SMSA
-.013
(.068)
fl f
.066
(.267)
.056
(.239)
.015
(.234)
.054
(.195)
R-sq
.280
.277
.273
.272
R-sq
.226
.244
.249
.253
F
5.23
-.013
(.017)
8.33
11.5
^Denotes significance at the 10 percent level.
**Denotes significance at the 5 percent level,
***Denotes significance at the 1 percent level.
14.4
Table 2
Advertising Intensity Regressed on Selected Variables
Standard Errors in Parentheses
EQUATION
1
2
DGRO
3
.007
(.010)
DVAR
•329***
(.046)
.335***
(.046)
CONCE
.003
(.009)
- .0 0 1
(.005)
-.0003
(.006)
-.00002
(.003)
SHARE
.100
(.088)
LGRO
.307***
(.066)
.094
(.087)
LYLD
.079
(.092)
.018
(.052)
BRNCH
.091
(.071)
.088
(.071)
.091
(.078)
HC
.031
(.092)
ACSIZ
- .0 1 1
(.018)
AGE
-.627
(2.17)
PDEN
-.052
(.044)
PURB
'1 *
R-sq
R-sq
F
.031
(.196)
.278
.255
11.9
-.004**
(.002)
-.004**
(.002)
.294
(.223)
.284
(.282)
.293
.270
12 .8
.299
.247
5.73
^Denotes significance at the 10 percent level.
**Denotes significance at the 5 percent level.
***Denotes significance at the 1 percent level.
@FedFRASER