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RATIONAL HERDING AND THE SPATIAL CLUSTERING OF BANK
BRANCHES: AN EMPIRICAL ANALYSIS
Angela Chang, Shubham Chaudhuri, and Jith Jayaratne

Federal Reserve Bank of New York
Research Paper No. 9724

August 1997

This paper is being circulated for purposes of discussion and comment only.
The contents should be regarded as preliminary and not for citation or quotation without
permission of the author. The views expressed are those of the author and do not necessarily
reflect those of the Federal Reserve Bank of New York of the Federal Reserve System.
Single copies are available on request to:
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Federal Reserve Bank of New York
New York, NY 10045

Ration al herdin g and the spatial cluster ing of
bank branches: an empiri cal analysis*
Angela Chang
Shubham Chaudhuri
Department of Economics
and
School of International and Public Affairs,
Columbia University
Jith Jayaratne
Federal Reserve Bank of New York
August 1997

Abstract
Ba.nk branches in New York City tend to be spatially clustered. Fbr instance,

of the 221 branches that were opened in New York City between July, 1990

and June, 1995, 181 (or 82 percent) were opened in census tracts that already
had at least one other branch. A number of recent theoretical papers have
highlighted the possibility of rational herding in various arenas of economic
activity. This paper explores. empirically whether the apparent clustering of
bank branches can be at least partially attributed to rational herding by banks.
We find that even after controlling for the expected profitability of operating a
branch in an area, branch openings follow other, existing branches. Moreover,
such bandwagon behavior appears to reduce branch profits. These findings,
combined, suggest that herd behavior may be a factor in the branch location
decisions of banks.

••PRELIMINARY. PLEASE DO NOT QUOTE. Comments welcome. The views expressed

in this paper are those of the authors and do not necessarily reflect the opinions of the Federal
Reserve Bank of New York or of the Federal Reserve System. Corresponding author: Shubham

Chaudhuri, Department of Economics, Columbia University MC-3308 New York, NY 10027; e-mail:
1
sc301@columbia.edu.

1. Intro ducti on
Bank branch es in New York City (and in other metrop olitan areas)
tend to be
spatial ly clustered. For instance, of the 913 bank branches that were
in operat ion
in New York City in June, 1990, 66 percent were locate d in census
tracts where
there was at least one other branch even though 79 percent of the census
tracts had
no branches. Moreover, of the 221 branches that were opened in
New York City
between July, 1990 and June, 1995, 181 (or 82 percent) were opened
in tracts that
alread y had at least one other branch. The aim of this paper is to explor
e empirically
wheth er the appare nt clustering of bank branches can be at least partial
ly attribu ted
to rationa l herding by banks.
The term "ration al herding" has been usud to describe situati ons
in which it is
individually rationa l for agents/firms to mimic the actions of others
even though
such mimicry can potent ially lead to aggregate outcomes that are
sub-optimal. 1 A
numbe r of recent theoretical papers have highlighted the possibility
of rationa l herding in various arenas of economic activity. These models have been
used to explain
stylized facts about the.clu stering of retail stores, patter ns of techno
logy adoption,
voter choice And .even fertility decisions. Th<a idea that imitati ve
behavior can be
both individually ration al and socially inefficient has intrinsic intuiti
ve appeal. But
there .have been relatively few attemp ts to empirically test these ,model
s and formal
statist ical evidence of rationa l herding is rare. In this paper, we attemp
t such a test.
We focus on bank branch location for two reasons. First, branching
offers several
advantages as an arena in which to test for rationa l herding. As
indica ted above,
bank branches tend to be spatial ly clustered. Moreover, the branch
location decision
appear s to have many of the ingredients that theoretical models
suggest are conducive to rationa l herd behavior, suggesting that such behavior may
drive branch
location decisions. To begin with, there is considerable uncert ainty
about the profitabilit y of opening a branch in any given neighborhood and uncert
ainty about the
right course of action is a prerequisite for most types of rationa l herdin
g. Next, the
costs of setting up a branch are substa ntial, as are the costs, both direct
and indirect
of closing a branch. These costs suggest that banks are at least partial
ly locked in
to the locations they choose for their branches and this makes it
more likely that
herding, if it exists, can be detected. Third, the branch location choice
represents a
discrete action -to enter or not enter a neighb orhoo d-and the discret
eness of the
action space has been emphasized in some herding models. And lastly,
the fact that
banks generally expan d their networks of branches at different times
means that
in deciding where to locate their branches, banks have an opport
unity to observe
where other banks have locate d their branches. These features of
the branch location decision do not, of course, imply that rationa l herdin g will
occur; but they
suggest that it might.
If herding occurs in the location of bank branches, branch data are particu
larly
useful in detect ing such behavior. Any test of herding must separa
te those cases
1 Note that
this definition of herding is quite specific in that it excludes situatio
ns in which agents
act independently but similarl y as well as situations in which imitativ
e behavior is both individually
and socially efficient. We use the more neutral term "clustering"
to refer, in a purely descriptive
sense, to situatio ns where agents appear to be taking similar actions
but may or may not be herding.

2

where agents behave similarly because they receive identical public information from
the instances where agents mimic others who preceded them. This requires a great
deal of knowledge about the information available to agents. For instance, testing for
herding in financial markets may be difficult because the investigator may not know
much about what information was commonly available to all the agents involved.
But banks rely primarily on the limited information in the Census and other public
data sources when locating branches, and this information is also available to us.
Even if all banks have access to some information that we lack, and we allow for
this possibility in our empirical work, our point here is merely that relative to other
industries, we can better control for the information available to banks in the context
of branching.
A second reason we focus on bank branch location is that understa nding the
factors underlying the branch location decisions of banks is itself of policy interest.
Bank branches tend to be unevenly distributed. This unevenness has attracte d
considerable attentio n, both in the popular media and in policy circles because
community groups argue that a bricks-and-mortar branch presence is importa nt for
access to banking services. They point out, for example, that retail customers who
need.to cash checks and open savings accounts have few good substitu tes for banks. 2
As a result, banks face pressure to maintain a branch presence in "underserved
areas." For. example, .in several recent bank mergers, the acquirer promised to not
close-.existing branches in low-income neighborhoods. And, in a landmar k decision
in 1994, the Departm ent of Justice announced a consent decree with Chevy Chase
Federal Savings Bank .in Maryland (which had most of its branches i» relatively
affluent neighborhoods of Washington, D.C.), whereby that bank agreed to open
several offices in minority neighborhoods. of Washington, n:c. (Banking Policy Report (1994)).
Such interventions, whether in the form of public pressure exerted indirectly
through the regulatory process governing mergers, or direct ones of the type faced
by Chevy Chase Bank may be warranted if the existing spatial distribu tion of bank
branches reflects an underlying market failure and is therefore, in some sense, socially
sub-optimal. 3 There is, however, little agreement on whether that is the case. The
uneven distribution of branches partly reflects the uneven distribu tion of profitable
opportunities. Branches may be clustered simply because the underlying demand
for banking services is clustered.
However, the possibility that banks may discriminate against certain neighborhoods and individuals and refuse to provide banking services to such individuals and
neighborhoods ("redlining") has received considerable attentio n recently. 4 Such behavior, if it exists, could produce a distribution of bank branches that is more skewed
'For example, check-cashing outlets often charge fees of up to $9 to ca.sh a $500 payroll
check
(Caskey (1991)). Glassman (1995) provides an opposing point of view, arguing that
there are
a number of alternative servic~. providers and that "banks are .no.t. necessarily the
only-or the
best-sou rce of financial services for low-income communities.'1
3
The appropriate form of intervention would, of course, still depend on the nature of
the underlying market failure. It is also important here to distinguish between a situation
where the
distribution is sub-optimal from a purely efficiency perspect ive-the source of the inefficienc
y being
a market failure in the banking sector-an d one where the criterion for optimality
is somewhat
broader and includes equity .considerations. The two have at times been confused
in the policy
debate.
4 Tootell (1996) is
the most recent example, and contains references to previous research.

3

than the distrib ution of demographic and economic factors that
affect branc h profitability, and may justify policy intervention.
In this paper, we illustr ate the possibility that a different type
of marke t failure,
rational herding based on, for example, information extern
alities, may provide a
partia l explanation for the uneven distribution of branches.
This type of marke t
failure implies a different policy intervention to promo te a more
even distrib ution
of branches, namely a subsidy for opening branch offices in
thinly branched areas. There has been surprisingly little empirical work on the
factors underlying the
branch location decisions of banks. Nearly all studies of redlin
ing have focused on
lending. Avery (1991) is one of the few paper s we have been
able to identify that
directly examines the branch location choices of banks. Nor
has there been, to the
best of our knowledge, any discussion of alternative explanation
s such as ration al
herding for the uneven distribution of bank branches. 5 This
paper aims to fill both
these gaps.
We propose the following simple test of herd behavior. Contr
olling for the expected profitability of opera ting in a given tract, the probability
of a branc h being
opened in a tract should not, in the absence of herding, increa
se with the numb er
of existing branches in .that tract (or should depend negatively
to the exten t that
competition is tougher in neighborhoods with a large numb er
of branches). We test
this.·hypothesis using a new, extensive datase t on New York
City census tracts for
the 1990-95 period . We find that, consistent with the hypothesis
of herding, banks
are more likely to open branches in tracts where there are alread
y other branches,
ceteri,s pari,bus.
We then test the robustness of this finding and clarify its interp
retatio n. This
furthe r test is motivated by our concern that the statistically
significant positive
relationship between branch openings and the number of
existing branches that
we take as evidence of herding may be spurious, arisin g'inst
ead from unobserved
(to us, but not to the banks) determ inants of profitability.
Using deposits as a
proxy for branch profitability, we find that profits decrease when
banks follow other
banks' branches. This suggests that the observed patter n of
branch openings following existing branches canno t be explained in terms of existin
g branches proxying
for unmeasured determ inants of profitability. The fact that
average deposits per
branch decrease with the number of branches in a tract also
suggests that the herd
behavior that we document sterns from either informational extern
alities or reputa tional concerns rather than from positive locational externalities
, e.g., agglomeration
externalities due to consumer search behavior.
The next section provides some background on branch banki
ng and presents evidence on the spatia l clustering of bank branches and branch
openi ngs-t he starti ng
point for our analysis. In the following section, Section 3,
we discuss the literature on rational herding and describe how it might explain
the clustering of bank
branches. Section 4 outlines our empirical strate gy and provid
es a description of the
data we use. In Section 5, we present the evidence on herdin
g. Section 6 discusses
altern ative interp retatio ns of the evidence and their implications
for policy. Section
7 concludes.
5 An

exception is Lang and Nakamura (1993) which presents a
model (that we discuss in more
detail later) of mortgage redlining that yields herding based
on a dynamic information externality.

4

2, Prelimina ries
2.1. Branch banking
Despite the growth, in the last fifteen years, of a number of alternative mechanisms
for delivering banking services (such as ATMs, phone banking, PC banking, and centralized loan originations), banks continue to rely on traditional, brick-and-m ortar
branches. 6 The primary reason for this is that though ATMs and phone banking are
widely used, their usage is typically limited to specialized functions such as information inquiries and withdrawals. Bank customers continue to use branches to make
deposits. For example, a 1995 Master Card Survey of major retail banks found that
nearly 90 percent of all deposits are done in branches (Mead (1997)).
Anecdotal evidence suggests that banks investigate potential branch locations
carefully. They often hire market survey firms to produce site studies. Moreover,
banks appear to use a fine geographic grid when scouting for branch location sites,
suggesting that they do not believe that locating a branch just anywhere in a city
will do. A prominent market survey firm that helps banks locate branches informs
us that client banks typically define a "trade area" for a branch to consist of 20002500. households. This is not much larger than the typical New York City census
tract, which had 1253 households in 1990. New York City census tracts cover a very
smalbgeographic area, often no more than a few square blocks.
Banks may find it important to locate branches carefully for several reasons.
The first is that customers appear to value proximity to a branch. A 1996 American
Banker survey showed that the majority of bank customers who switched banks did
so because they wanted to be closer to a branch (Kutler (1996)). Not surprisingly,
the same survey found that the average ban\: customer visited a branch of her bank
at least three times a month. When customers value proximity, banks cannot locate
a branch anywhere and expect customers to use ATMs, etc. to bank over a distance.
A second reason for locating branches carefully is that branch profitability is
uncertain, and there are substantial fixed costs of opening and closing branches. A
banking market research firm informs us that banks are often unable to explain the
wide variation in the performance of their own branches, and that the research firm
is often hired by banks to determine the causes of such performance differences.
As for the fixed costs of operating a branch, anecdotal evidence suggests that such
expenses are considerable. The typical branch costs approximately $1.5 million to
set up (mostly in real estate and construction costs). 7 Fixed costs of operating
a branch (wages and maintenace costs) add approximately $1.4 million annually
(Radecki, et al). Since the typical branch carries $50 million in deposits, the cost of
setting up a branch represents a one-time addition of 300 basis points to the cost of
deposit funds, and fixed operating costs add another 280 basi; points annually.
Closing a branch is a costly process. Banks are required to submit a notice of
a proposed .closing with-regula tors no later ·than ninety·-<lays prior to the closing
date. The required notice must include a detailed statement of the reasons for the
decision to close a branch, and statistical and other information supporting the
reasons. Although banks do not need regulator approval to close an unprofitable
'Nationwide, the number of hank and thrift offices declined only slightly between 1990 and 1995
from 84,419 in 1990 to 81,875 in 1995 (Federal Deposit Insurance Corporation (1996)).
7

Set-up costs are estimates based on various industry sources.

5

branc h, they face considerable pressun, from comm unity group
s to keep branches
open. In several recent instances, banks that were party
to mergers comm itted
themselves to retain existing branches in low-income neighborho
ods.

2.2. Evide nce on branc h cluste ring
The popul ation of New York City banks encompasses a wide
range of institu tions,
from large money center banks to many small, retail banks.
Ninet y one indep enden t
banks and bank holding companies opera ted 844 branches
in New York City in
June, 1995. Of these, four large institu tions (Bank of New
York, Citiba nk, Chase
Manh attan Bank and Chemical Bank) owned 490 offices. In
this section we provide
some basic descriptive statist ics docum enting the spatia l cluste
ring of both bank
branches as well as branc h openings in New York City. The
data used to gener ate
these descriptive statist ics are described in more detail in a
later section.
Table 1 depicts the spatia l distrib ution of bank branches
at the census tract
level for two years, 1990 (top panel) and 1995 (botto m panel)
. The first column of
each panel provides a breakdown of census tracts , by the numb
er of branches in the
tract as of June of the relevant year, i.e., 1990 or 1995; the secon
d column shows the
distrib ution of branches, by the numb er of branches in the tract
in which the branc h
was. located. These numbers indica te that in both 1990 and
1995, bank branches
tende d to·be locate d in tracts where there were alread y other
branches, while many
tracts remai ned witho ut branches (and the basic patter n is
repea ted in all of the
years 1990---1995). For instance, looking at the top panel
we find that of the 913
branches in existence in New York City as of June 1990, 66
perce nt were locate d in
tracts where there was at least one other branch. Meanwhile,
79 perce nt of the 2218
census tracts had no branches. That is, all 913 branches were
conce ntrate d in only
21 perce nt of all census tracts . This patter n had not chang
ed in 1995 (as seen in
the lower panel of Table 1).8
The spatia l distrib ution of bank branches, observed in any
given year, is the
outco me of branc h location decisions made by banks over
an exten ded period of
time. As such, the appar ent clustering of existing bank branc
hes may simply be the
remna nt of clustering in the past and need not therefore sugge
st that clustering is an
ongoing phenomenon. More direct evidence on clustering can
therefore be obtain ed
from the spatia l distrib ution of branc h openings, which is depict
ed in Table 2.
The first column of the top panel of Table 2 provides a break
down of census
tracts by the numb er of branc h openings in the tract betwe
en July, 1990 and June,
1995; the second column shows the distrib ution of branc h openin
gs, by the numb er
of branc h openings in the tract in which the branc h was opene
d. There were 221
branc h openings between July, 1990 and June, 1995, and these
were conce ntrate d
in 142 (i.e., 7%) of the 2218 tracts . Of the 221 branc h openin
gs, 53 perce nt (or 117)
took place in tract where there was at foast one other branc
h opening during the
five-year period.
The botto m panel of Table 2 provides perha ps the clearest
indica tion of branc h
clustering. The first column shows the breakdown of the
census tracts in which
there were branc h openings, by the numb er of branches that
existed in the tract
8 These
figures probably under-estimate the degree of branch cluster
ing because tracts with
branches are likely to be themselves clustered and not uniform
ly distributed among the branchless
tracts.

Ii

at the beginning of the period, i.e., in June, 1990. Here we see that 75 percent of
all census tracts that experienced a branch opening between July, 1990 and June,
1995, already had a branch. More striking still is the fact that 82 percent of the 221
branches that were opened over this five-year period, opened in tracts with at least
one other branch at the beginning of the period (see the second column).
The simplest and most obvious explanation for the clustering of bank branches
documented above is that the demand for banking products and services is itself
spatially clustered. It is certainly true that not all neighborhoods in New York
City offer the same potential customer base for banks. And if the disparities across
neighborhoods in the extent of demand is sufficient to outweigh the adverse effects
of increased competition, banks-like Willie Sutton-mi ght simply be following the
money and locating in those areas with significant demand for banking services.
Table 3 reports some summary statistics that suggest that there is indeed some
basis for this explanation. Tracts with existing bank branches as of June, 1990,
as well as tracts in which branches were opened between June; 1990 and June,
1995, appear to be more affluent along a number of observable dimensions that are
plausible indicators of the demand for banking services. For instance, the tracts
. that had branches (in June, 1990) had, on average, larger populations, fewer poor
households, a better-educ ated population, higher median household income, and
more workers and were, on average, more commercial. The same is true of tracts in
which branches were opened between July, 1990 and June, 1995.
However, Table 3 also reveals that branches were more likely to be opened in
tracts that already had more existing branches. On average, there were nearly
3 existing branches (in June, 1990) in the tracts in which branches were opened
between July, 1990 e;nd June, 1995; on the other hand, the average number of
branches in the tracts in which no branches were opened during the five-year period,
was less than one. This stark contrast at least raises the possibility that the observed
clustering may be partly due to some form of rational herding.

3. Rational herding and the clustering of bank branches
A large literature on rational herding has emerged in recent years. 9 The literature
suggests several different channels through which herding can arise. At least three
of the suggested channels seem to us to be ways in which rational herding might
occur in the location of bank branches. We describe them below.
In information cascade models, the possibility of herding stems from an information externality (Banerjee (1992), Bikhchandani, Hirshleifer and Welch (1992),
Welch (1992)). The typical setup in these models has agents choosing from a set
of actions according to a predetermined sequence. Each agent receives a conditionally independen t private signal about the correct action to take and is also able to
observe the actions, but not the signals, of those who preceded her. Using both
her private information as well as the public information embodied in the choices of
others, each agent updates her priors about the profitability of alternative actions
and then chooses accordingly.
If the action space is coarse relative to the signal space, agents may not be able
to adequately tailor their chosen action to reflect both their private information as
9

Devenow & Welch (1996) and Gale (1996) provide very useful overviews of the literature.

7

well as the public information. They may rationally choos
e, then, to ignore their
own information and base their decision on the public infor
matio n-Le ., faced with a
choice between actin g upon her own private signal and imita
ting the choices of those
who acted before, it may be optimal for an agent to choos
e the latter . But in doing
so the agent ignores the fact that her private information
is lost to those who follow
her since her priva te information is not recoverable from
her publicly observable
action. This is the information exter nality at the heart
of these models. If agents
have identically distri buted signals, all subsequent agent
s face an identical situa tion
and consequently also choose to ignore their priva te inform
ation. The result is an
information cascade. And depending on the initia l patte
rn of choices, the actions of
agents may well converge on the wrong choicec-i.e., in
ration al herding.
Information cascades represent an extreme form of herdi
ng in which the actions
of early agents completely domi nate the private signa
ls of later agents. Lang and
Naka mura (1993) present a model of mortgage redlining
in which a some what weaker
form of herding takes place. The information exter nality
in their model stems from
the fact that the actions of predecessors affect the precis
ion of the information
available to subsequent agents. In their model, the precis
ion of appra isals -on which
mortg age lenders base .the size of required down payments---'<l.epends on the volum e
of previous home sales in a neighborhood. Appraisals
are based on the prices at
which !)l'evious sales were trans acted because these provi
de noisy signals of curre nt
prope rty values. The higher the numb er of previous home
sales, the more precise
the appraisals, and the lowe r'the required down paym
ents. Lower down paym ent
. requirements in turn lead to a larger numb er of approved
mortgage loans and hence,
a larger numb er ·of curre nt sales. The positive feedback
mechanism thus gener ated
raises the possibility of herding. and sub-o ptima l differ
ences in mortg age lending
activ ity across neighborhoods.
A third possible channel throu gh which ration al herding
might arise is throu gh
the reput ation al concerns of agents when the calib re/ quali
ty of agents is unknown.
In Scharfstein and Stein (1990), one of the first mode
ls of this kind, bette r (informed) managers receive informative signals abou t the
right course of action, and
the errors in these signals are correlated. Uninformative
signals, those received by
uninformed managers, are, on the other hand, uncor
related. The compensation
(futu re prospects) of a mana ger depends on his reput
ation -Le., on wheth er he is
regarded as informed or not. In this situat ion, each mana
ger has an incentive to
mimic the actions of managers who have acted before
him, because by doing so he
maximize his chances of appearing informed. If the action
results in a good outcome,
he benefits; even if the action, ex-post, yields a bad outco
me, the fact that other
managers made a similar choice shields the manager,
enabling him to 'hide in the
herd' , in effect, to argue that the decision was, ex-ante,
an informed one. On the
other hand, were the mana ger to act upon his private
signal, where such a signal
suggests a course of action different from that taken by
other managers, he would
run the risk of appea ring uninformed if the action result
ed, ex-post, in a poor outcome. Othe r examples of this type of herdi ng based on
reput ation al concerns and
relative performance are provided in Zweibel (1995) and
in DeCoster and Stran ge
(1993). The latter apply the Scharfstein and Stein (1990
) model to the siting decisions faced by real-estate developers concerned abou t their
reput ation s with banks.
The herdi ng in these models is based on two key premises:
first, that there exists an
agency problem in that the incentives of decision-makers
are not aligned with the
8

outcomes of their decisions; and second, that the compensation of agents is based
in some way on relative performance standards.
Any of the three types of models described above can plausibly be applied to
explain the clustering, (and possibly rational herding), of bank branches. These
models assume uncertainty of outcomes and (some) irreversibility of decisions. Both
conditions are observed in branching, as we noted in Section 2. All three assume that
agents act sequentially in an exogenously determined order and that the actions of
agents are publicly observable. 10 Banks appear, in general, to expand their branch
networks at different points in time based on a number of different factors that
are arguably exogenous to the branch location decision itself. And in making their
branch location decisions, banks are clearly able to observe the locations chosen by
other banks. 11
Cascade models require, in addition, a discrete action space (or at least that the
signal space is large relative to the action space). Incorrect cascades are prevented
when the action space is fine enough for the private information of firms to be recoverable from their chosen actions. Whether one views the branch location decision
as a series of binary decisions about opening or not opening a branch in each of a
number of neighborhoods,. or whether one views it as a single decision about the
best neighborhood in which to locate a branch, branch location represents a discrete
· · choice. 12 When a bank chooses not to open a branch in a neighborhood, or even
if it does, the. strength of its private information aqout the profit potential of the
neighborhood is not revealed. All that other banks are able to observe is the discrete
location choice. And because they rationally infer from this choice that the bank's
private signal was not strong enough to warrant a different course of action, these
other banks may choose to (not) locate branches in neighborhoods where the bank
chose to (not) locate its branch. In the process, banks may ignore their private information that alternative locations are equally profitable (or even more profitable)
10

A separate strand of the literature on informational externalities relaxes this assumption and

allows agents to choose when to act (Hendricks & Kovenock (1988), Caplin and Leahy (1993),
Chamley and Gale (1994), Gul and Lindholm (1995)), In these endogenous timing models, all
agents have an incentive to wait because the actions of early movers provide additional information
that can improve the quality of the decisions made by late movers. The resulting equilibrium
resembles a war of attrition in which agents try to out•wait others and this leads to sub-optimal
delays in action.
There are two main differences between these models and the ones we discussed above. The
first is that the clustering of agents' actions in endogenous timing models need not be inefficient;
the second is that the inefficiency always takes the form of 'underinvestment' because of excessive
delay. In sequential action models, on the other hand, 'overinvestment' (excessive clustering) is also
a possibility.
11
A slightly tricky point here is whether or not banks actually observe that certain locations were
rejected by other banks. To the extent that banks, in principle, consider all neighborhoods within
the city (subject to some obvious exceptions) to be potential sites for new branches, the location
choices that are actually made implicitly indicate that other sites were rejected.
12
While it is true-that· branch location--"COUld he thought ·of as a selection from a continuum of
possible sites, for this to eliminate the possibility of herding it would have to be the case that banks
gain from being "close" to the true optimal site--e.g., have a payoff function that is concave in the
action space (see Lee (1993)). This seems unlikely, especially in New York City, where fairly affluent
neighborhoods often adjoin more depressed areas and there does not appear to be any discernible
monotonicity in the geographical positioning of neighborhoods according to their level of affluence.
This suggests that banks face a payoff function similar to that in Banerjee (1992), which, because
of its "all-or-nothing" form, effectively discretizes the action space and thus allows the possibility

of herding,

9

and this can result in overclustering of branches in
some neighborhoods while other
neighborhoods remain underserved.
If banks care abou t the precision of the information
available to them abou t
the profit poten tial of alter nativ e neighborhoods,
they may also choose to locat e
branches in tract s with a larger numb er of existing
branches. The presence of one
or more existing bank branches in a neighborhood
can provide additional sources
of infor mati on-th ough branch-level profit figures
are usually not available, data on
branch-level deposits are readily obta ined -and such
information, when combined
with any priva te information that the bank has acqu
ired through, for instance, site
analysis studies can reduce the unce rtain ty surro undin
g the profitability of opening
a branch in a neighborhood.
Repu tatio nal concerns may also influence the branc
h-location decision if the
evaluation (and compensation) of managers is based
partl y on the ex-post relative
profitability of their branch-siting decisions. From
the perspective of a mana ger
responsible for making the branch location decision,
it may be much more attra ctive
to locate a branch in a neighborhood where there
are alrea dy several other existing
branches than to venture into a virgin neighborho
od, even when the latte r appe ars
to have significant poten tial. By doing so, the mana
ger avoids the possibility that
he will be blam ed for poor judgement.
The discussion above has been in largely heuristic
terms. We have not written down a specific struc tural model of bank branc
h location. Partl y this is due
to .the fact that we remain agnostic, at this point
, abou t which of these specific
models applies in the case of bank branch location.
A more impo rtant reason is
that we cann ot, at this stage, empirically distinguish
between the alter nativ e channels throu gh which herding might be occurring.
Our empirical strat egy is based,
therefore, on what might be considered the comm
on reduced form implication of
the three approaches outlined abov e-na mely , that
the branch location decisions
of banks should be directly influenced by the locat
ion decisions made earlier by
other banks, over and above any publicly observable
direct indicators of the profit
poten tial of a neighborhood. However, for purely
illustrative purposes, we present
in the Appe ndix a model of bank branch location
that yields herdi ng based on an
information exter nalit y along the lines of Lang and
Naka mura (1993). We outli ne
our empirical strat egy in more detail in the next sectio
n.

4. Emp irica l stra tegy and data
We have made a prim a facie case for the hypothesis
that ratio nal herding provides at
least a parti al explanation for the clustering of bank
branches described in Section 2.
The main competing hypothesis, which we also noted
earlier, is that the clustering
of bank branches is driven entirely by the fact that
the dema nd for banking services
is itself clustered. In this section, we outline a simp
le test of branch herding that
allows us to distinguish, empirically, between these
two comp eting hypotheses. We
also describe the data we use to implement the test.

10

4.1. A test of herding

We adopt a reduced form approach in testing for herding. Our starting point is the
expression for the process generating branch-level profits:

1r;t

= X;ta + 6N;, + e;t

(4.1)

Here 7f;t represents the profits from operating a branch in tract (or neighborhood)
j, starting in time period t.13 X;, is a vector of demographic and other factors
at time t that affect the expected profitability of operating a branch in tract j
and N;t is the number of existing branches in tract j at the beginning of period t.
These variables are assumed to be observable to us, as well as to the banks. The
disturbance term, e;t, captures any factors affecting branch-level profits that we
assume, for the moment (see the discussion in the next subsection), neither we nor
the banks observe.
In the absence of any positive locational externaliti es-Le., increased profits from
locating close to other branches- N;t simply proxies for the degree of bank competition in the tract, ceteri,s pari,bus. 14 Increased competition within a neighborhood
is·likely to decrease branch-level profits, and so we expect the coefficient on N;t to
be negative, or at least non-positive, i.e., 6 :,; 0.
· · The test of herding ·that we carry out is based on the simple proposition that,
as long as banks base their branch location decisions on the profit potential of a
neighborhood, in the absence of any herding, the reduced form expression for the
number of branch openings in a tract should mirror that for branch-level profits. In
particular, if we adequately control for the factors, X;,, that independently affect
the expected profitability of operating in tract j, the number of branch openings in
a tract should, in the absence of herding, depend negatively (if at all) on the number
of existing branches in the tract since more branches indicate stiffer competition.
This suggests estimating the following test equation:

O;t

= X;t/3 + ,N;t + Ujt

(4.2)

where the dependent variable O;t is the number of branch openings in tract j during
period t, and the other variables are as defined above. Under both the competing
hypotheses, the coefficients, /3, on the vector of profit factors, X;,, should qualitatively match those in equation (4.1). In the absence of herding behavior of the
sort described in Section 3, the effect of N;t should also match that in (4.1)-i.e. ,,
should be less than or equal to zero.
On the other hand, if banks herd (i.e., between two otherwise equally attractive
tracts, they choose the tract with more branches), the effect of N;t on subsequent
branch openings is the sum of two opposing forces. The competition effect implies
that we ought to observe relatively fewer branch openings in tracts with relatively
more branches at the beginning of the period. But a second, opposing influence,
arises from the fact that tracts with a greater number of existing branches will attract
more branches if banks herd. Although the overall effect of N;t is indetermin ate
13 As the basic
unit of analysis in this paper is a tract rather than a bank, to save on notation,
we do not explicitly incorporate the obvious heterogeneity that exists among banks.
14 We detail
in Section 6 how such positive locational externalities might arise, and discuss how
they affect the interpretation of our test of herding.

11

under the herding hypothesis, a positive correlation betwe
en the initial numb er
of branches and subsequent branch openings would sugge
st herding behavior in
branching.
Bank branc h location data offer several advantages in testin
g for herding behavior. First, because banks rely substa ntially on public censu
s data when makin g
their branch location decisions, we are better able to contro
l for the information
avrulable to banks , and hence are able to more cleanly identi
fy herding. 15 This contrasts with, for instance, the study by Grinb latt, Titma n and
Wermers (1995) of the
tradin g patter ns of 274 mutua l funds between 1975 and 1984.
They repor t small but
statist ically significant comovements (i.e., buying and selling
the same stock at the
same time) in the quarte rly stock holdings of these funds.
They do not, however,
control for any public information flows (e.g., earnings annou
ncements) that may
have driven these comovements. The "herding" that they
repor t may, therefore,
simply reflect the fact that fund managers responded independen
tly but similarly to
the arrival of common, new information. 16
A second advantage of our data is that the order in which
the firms acted is
clearly indicated. Relati ng the branch opening decision to
the spatia l distrib ution
'of existing branches provides a natur al way of examining
the influence of earlymovers on the actions of later agents. Other empirical paper
s on herding have
often had to rely instea d on a priori identification of "leaders"
and "followers". For
instance, Jain and Gupta (1987), which tests for herding in intern
ationa l lending by
U.Si banks during the 1970s, explores wheth er smaller U.S.
banks followed money
center banks when lending abroa d. They find that money
center banks ' portfolio
allocations did not Granger-cause smaller banks ' allocations,
and they conclude that
there.is no evidence of herding in intern ationa l lending. But this
conclusion relies on
the autho rs' correctly identifying ex ante the leaders and follow
ers in intern ationa l
lending. If different banks acted as leaders when lending to
different countries, the
Jain and Gupta test may not pick up herding.
Perha ps the closest in spirit to the approach we take is Calem
(1995). He conducts a test of Lang and Nakam ura (1993) by regressing mortg
age-loan approval
rates in U.S. urban counties in 1990-91 on 1989 home sales
(and other controls).
Lang and Nakam ura (1993) predic t that past home sales should
have a positive effect on curren t mortgage loan approval rates. Calem finds just
such an effect, and he
concludes that the data suppo rt the Lang and Nakam ura (1993)
model. However,
he finds this effect only in non-minority tracts , a troubling result
because the Lang
and Nakam ura (1993) model predicts that the information
extern ality is strong est
in areas with thin home sales (and minority areas have fewer
sales). Moreover, the
positive correlation between past home sales and curren t mortg
age approval rates
even in non-minority areas may be the result of serially-corr
elated dema nd shocks,
a possibility Calem is unable to control for using cross-sectio
nal data.
15 It
is, of couf'se·, ,still·possible·that despite •our-efforts·to be compre
hensive, banks have access to
information that we do not. We consider this possibility in
the next section when we discuss the
results

of our basic test of herding.
Partly this is a matter of definit ion-i.e ., how broady one
defines "herding". As we mentioned
at the outset, we follow the theoretical literatu re (see Deveno
w and Welch (1996)) and reserve
the term "herding" for situations in which the actions of agents
directly influence the actions of
other agents, and this type of imitative behavior raises the
possibility of system atically sub-optimal
outcomes. The distinction is also important in another
respect which is that "herd behavior" as
we choose to define it potentially justifies some form of govern
ment intervention.
16

12

L
4.2. Data

We estimated the test equation using data on commercial bank branch openings
in New York City census tracts between July 1, 1990 and June 30, 1995 (and to
a more limited extent, branch openings between July 1, 1980 to June 30, 1985). 17
These data were obtained from the Federal Deposit Insurance Corporatio n's (FDIC)
Summary of Deposits database, an annual series which lists the street addresses of
all bank branches as of June 30th of each year. We used these street addresses
to map each branch to a census tract and were thus able to obtain the number of
bank branches in each census tract in each of the five years. 18 If a branch address
appeared for the first time in a tract in June of a given year, we recorded that as a
branch opening some time in the preceding twelve months. 19
Our choice of a census tract as the basic geographical unit of analysis-th e area
j in the test equation-w as based largely on data considerations. But it appears
to correspond fairly well with what banks themselves use. For example, as mentioned earlier, a prominent market research and consulting firm that provides banks
with site analysis services to aid their branch-location decisions reports that banks
typically define the "trade area" of a branch to consist of a geographic area encompassing 2000-2500 customers. The average New York City census tract had 1253
households.
· As controls for the potential profitability of operating a branch in a census tract,
we ·use several population characteristics and indicators of business activity. These
variables are described .in Table 3. In addition to census tract population size,
•· median family income, poverty rate, race and education, we include the fraction of
population over the age of 65 because the supply of core deposits by the elderly
may be relatively interest insensitive, making them more profitable bank customers.
We include the fraction of renter-occupied housing units and the median value of
housing in a tract as possible correlates of the size of the home mortgage market in
an area. Median housing values may be also correlated with the real estate costs of
operating a branch. We include these variables because they are strongly correlated
with the number of branches in each census tract. 20 Moreover, market research firms
that help banks locate branches rely on similar census information when conducting
site analyses.
17

Thrifts (savings banks and savings and loans) are excluded partly because thrifts are not perfect
substitutes for commercial banks. Unlike banks, thrifts primarily make mortgage loans. Thrifts are

excluded partly for data reasonsj we are unable to get thrifts' branch locations for the early 1980s,
If banks do in fact consider thrift branch locations when locating bank branches, dropping thrifts

from the data creates a measurement error in the number of existing branches, N;t, which will bias
its coefficient toward zero.

Off-site ATMs are not included as branches in the FDIC's Summary of Deposits database. Hence,
only full-service branches are included in the data and in our analysis. Dropping ATMs should not
affect results here because 75 percent of ATMs are located inside branches (American Banker,
November 30, 1996).
18
The details of the mapping procedure are available upon request.
19
Note that this procedure yields the gross number of branch openings in a tract. We use
gross openings rather than net openings-i.e ., gross openings minus closinge--because the factors
underlying closings are often quite different (see Section 2) from those influencing openings. We
should point out also that if a branch changed hands but remained at the same street address we
did not record this event as an opening.
20 Based on
a regression of the existing number of branches (note, not openings) in a tract on
these tract-level variables. Results are available upon request.

13

As indicators of business activity in a tract we include the
numb er of people
working in a tract {provided by the Census Burea u in a custom
ized data file using the Burea u's 1990 Journey to Work data), and a dumm
y indica tor variable for
wheth er the tract is a net impor ter of workers. Typically,
we would expec t commercial areas to be net impor ters and residential areas to
be net exporters. We
also include the percentage of each tract's land area that
is devoted to commercial, indus trial, and residential purposes using data provided
by the New York City
Plann ing Depar tment . 21
The demographic and business activity variables listed above
describe the endowment of the census tracts as of 1990. We also include as contro
ls, changes in the
popul ation characteristics of each census tract between 1980 and
1990. The poten tial
future profits from opera ting in a tract depend not only on the
curren t characteristics
of the tract but also on future conditions. To the exten t that
past changes predic t
future changes, the measured changes in demographic variables
should partia lly control for expected future demographic changes in the tract.
Moreover, expec tation s
of future changes in real estate prices should be captu red
by the median housing
value variable if such expectations are capitalized into curren
t priees.

5. Evid ence on herd ing
In this section we first repor t the results of our basic test of
herding. We find that,
consistent with the hypothesis of herding, banks tend to open
branches in tracts
where there are already other branches, ceteris paribus. We
then carry out a test
of the robustness of this finding. This furthe r test is motiv ated
by our concern that
the statist ically signifi<'.ant positive relationship that we take
as evidence of herding
may be spurious, arising instea d from unobserved (to us,
but not to the banks)
determ inants of profitability.

5.1. Basic resul ts
Columns (1) and (2) of Table 4 display our basic results. 22
They show that after
controlling for census tract characteristics, the number of bank
branches in a tract
at the beginning of the period is positively correlated with
the numb er of branch
openings over the subsequent year, at least in those tracts
with low-to-moderate
numb er of branches. Column (1) report Poisson estimates, and
column (2) report s
ordered-Logit estimates. 23 The Poisson point estimates sugge
st that the numb er
of initial branches is positively correlated with subsequent
openings until the initial branch count is about fourteen. Thereafter, the two are
negatively correlated.
21

Land-use data are from the New York City Planning Depart
ment's 1995 Land Use Data Files.
This database tracks the actual uses of real estate, not what
the
area is zoned for.
22
The estima ted equation differs from (4.2) only in that we
have included a squared term (in the
number of existin g branches) to allow for possible nonline
arities.
23
Poisson estimation seems natural here since branch openin
gs are count data. Such estimates
are also relatively easy to interpret. However, the Poisson
estimator assumes that the opening of a
branch in a tract does not affect the probability of subsequent
openings. Since this is not true under
the hypothesis of herding, we also provide Ordered Logit
estima tes. The dependent variab le-the
number of branch openin gs-was top coded in the Ordere
d Logit estimation into four categories: 0
branch openings, 1 branch opening, 2 openings, and 3 or
more openings.

14

This is consistent with herding dominating openings behavior at relatively thinlybranched tracts. When there are many branches in a tract, stiff competition may
discourage further branch openings. However, only three tracts had more than fourteen branches in 1995. Hence, herding dominates in 99.5 percent of the tracts with
branches. Moreover, this "herding effect" is large. The Poisson estimates suggest
that the expected number of annual branch openings in a tract with two branches
is 33 percent greater than an otherwise identical tract with just one branch at the
beginning of the year.
Interestingly, few of the other tract-level variablP,s had a significant impact on
the number of branch openings. This may be due to the relatively small number
of branch openings and the resulting low power of the tests here. Nevertheless, the
estimates indicate that banks appear to find commercial tracts, tracts that attract
many commuters, and heavily populated tracts to be relatively more attractive.
They appear to have found residential areas and poorer areas unattractiv e. Minority
tracts also attract fewer branch openings, but this may be due to profitability factors
that are correlated with the racial composition of a tract.
Table 5 contains some robustness tests. This table summarizes -results from estimating the model.in Table 4 on several sub-samples. To save space, we report only
the coefficients on the initial branch count. A potential problem with the results
in .Table 4 arises from the fact that we pool five annual cross-sections of data when
estimating the model. The error term of the model for a given tract may well be correlated over time because there may be unmeasured tract features that change little
if any over back-to-back years. 24 We address this p oblem indirectly by re-estimating
(4.2) as a single cross-sectional regression with the dependent variable redefined as
the cumulative number of branch openings over the five-year period, 1990-1995. We
regressed this variable on the tract conditions in 1990. The results we obtained are
shown in the second panel of Table 5. The point estimates are comparable to those
in Table 4 (reproduced in the top panel of Table 5 for comparison).
We next re-estimated the model in Table 4 excluding head office locations from
the sample. The process of locating a head office may be quite different from that
of locating a regular branch. For example, the potential for deposit taking from
and lending in the particular neighborhood where the office is located may be less
important for a head office than for a regular branch. Also, head offices are more
likely to be located in a major commercial center such as midtown or downtown
Manhattan . Such clustering of headoffices in heavily-branched areas may skew our
results in favor of finding herding effects to the extent that we do not completely
control for the commercial characteristics of a tract. The middle two panels in Table
5 show our estimates of the same location model without head offices for the 1992-95
period. 25 These results are also consistent with herding.
Finally, we re-estimated the openings model using branch openings data for the
1980-85 period (bottom two panels, Table 5). We obtained the.. same qualitative
result of herding. The point estimates of the effect of the initial number of branches
is bigger than for the 1990s, but this is probably because we had fewer controls for
tract profitability for the 1980-85 period. (We do not have data on the extent of
24 Despite this
problem, we pooled the annual data because the number of openings and the
number of initial branches in a tract varies over the five-year period. Pooling ensures that we use
this information when estimating the correlation between openings and the initial branch count.
25
We could reliably identify head offices only for the 1992-95 period.

15

commercial activi ty in tracts in the 1980s, nor do we have measu
res of the changes
in the demograJJhic variables between 1970 and 1980.)

5.2. A furth er test
Tables 4 and 5 indica te an appar ent statist ically significant
positive effect of the
numb er of existing branches on subsequent branch openings.
We now invest igate
the possibility that this finding may be spurious. Our conce
rn here is that Njt
may simply be picking up the influence of tract characteristi
cs that we have not
controlled for, but banks are able to observe. Altho ugh we
have tried to be fairly
comprehensive in including an extensive array of tract charac
teristi cs that might
indep enden tly affect the profitability of opera ting in a tract,
banks may be privy
to more inform ation than is available to us. For instance, banks
often commission
target ed marke t surveys of poten tial locations in additi on
to relying on publicly
available information from sources such as the census.
More formally, we consider the possibility that there might be
unobserved (to us)
serially correl ated tract-level characteristics (represented by
Zjt) that affect branc h
profitability. Again, this is a concern only in that banks might
observe these characteristics ·whereas we do not. In other words, we imagine
that the ''true" process
gener ating branch-level profits is given by:

(5.1)
where:
(5.2)
With banks basing their decisions on profit ·considerations,
openings are then generated according to:
(5.3)
Clearly, since Njt = Oj,t-1 + Nj,t-1 -Cj,t- 1 (where Cjt is closin
gs), given that (from
(5.3)) Oj,t-1 is partly determ ined by Z;,t-1, N;t will be correl
ated with Zj,t-1• This
in turn implies that N;t will be correl ated with Zjt if, as we
have assumed in (5.2)
above, Z;t is serially correlated. Under this scenario, estim
ation of equat ion (4.2),
which does not control for Z;,, will suffer from omitt ed variab
le bias. And this may
yield a positive statist ically significant estim ate of the coeffic
ient on the existing
numb er of branches even where there is no herdin g and the
true coefficient on Nj,
in equat ion (5.3) is "( :S 0.
One possible solution for this problem is to assume that the
tract inform ation
observed by the bank but not by us (Zjt) is time invariant, and
use our panel data to
estim ate an equat ion with tract fixed effects. Unfortunately,
a fixed-effects Poisson
(or Order ed Logit) estim ation procedure will not produ ce
consistent estim ates of
the numb er of initial branches because this variable is the
sum of previous branc h
openings. (That is, the numb er of existing branches in any
given year, being a
function of lagged depen dent variables (i.e., past openings),
is pre-de termin ed but
not exogenous.)
Instea d, we pursu e an altern ative "fix" by testin g for the severi
ty of the omitted variable bias as follows: Suppo se that, in the estim ation
of (4.2), the positive
coefficient on Njt is being gener ated by the fact that N;t
is serving as a proxy

16

for unobserv ed/unmea sured demand factors. Then, because the branch-openings
equation, in the absence of herding, mirrors the equation for profits, if we were to
estimate equation (4.1), in which branch profitability is the dependen t variable, the
coefficient on Njt should suffer from a similar omitted variable bias. In other words,
estimatio n of (4.1) should also yield a spurious positive coefficient suggesting that
the profitability of branches in a tract is positively correlated with the number of
branches in the tract.
If, on the other hand, the positive correlation between branch openings and
existing branches does in part reflect herding, the coefficient on Njt in the branchprofits equation should be negative because of the increased competiti on from the
larger number of branches.
We cannot directly estimate ( 4.1) because we do not have direct measures of
branch profits. However, deposits held at a branch are a reasonable proxy for branch
profits. The principal function of bank branches is to gather deposits. Branches play
only a limited role in lending. Credit card loans are typically originated nationwide
by centralized operations of specialized credit card banks. Similarly, mortgage applications are often processed and approved at centralized mortgage lending units,
not in branches (at least in large banks).
If branches play any role in lending, it is in small business lending. Even here,
,branches are of limited importance. A 1995 survey by the Consumer Bankers'
Association showed that while a majority of banks relied on branches to supply
deposit services to small business customers, branches played a much smaller role
in lending. Although 69 percent of the seventy two large banks surveyed said that
they relied on branches for small business deposit services, only 26 percent said that
they used branches to originate loans, and only 8 percent said that their branches
underwri te loans (Allen (1995)). Anecdotal evidence confirms this pattern holds in
New York. Several banks in the area solicit, process, approve and maintain small
business loans from "loan centers" that cover a large area. 26
Table 6 shows the results from estimatin g a regression of the average deposits
per branch (in 1000s of dollars) in a census tract at time t on the number of branches
in that tract at time t (and all other tract features used as controls in Table 4). 27
The dependen t variable in the top panel in Table 6 is the value of total deposits at
the average branch in each census tract (for those tracts with at least one branch)
for 1990-95 and for 1984-85. (Data for previous years of the 1980s were unavailable.)
Not all deposits are equally profitable. Transactions deposits (checking accounts)
are typically less profitable than are non-transactions (savings and time deposits)
deposits. 28 Moreover, depositors who maintain a high balance are probably more
lucrative. We do not have data on average account size. However, we can control for
the type of deposit. The dependent variable in the bottom panel in Table 6 is the
value of non-transactions deposits (savings and time deposits) held at the average
26
Even if branches ..are· nlevaut--to ·producing small business loans, deposits .held at a branch
are likely to be positively correlated with the amount of small business loans associated with that
branch because such loans involve the borrower opening a deposit account {and this account
is
likely to be booked to the branch involved in originating the loans).
27 Deposit

data are also from the FDIC's Summary of Deposits database. Only deposits held by

individuals, partnerships and corporations are included. Government deposits are dropped because
location is assumed to play only a small part if any in the holding of government deposits.
28 Checking
accounts are costly because processing check transactions is costly. These costs are
thought to out-weigh the interest cost of savings and time deposits.

17

branch in a tract at time t for the period 1980-85. (Non-transa
ctions deposits data
are unavailable for the 1990s).
We find that the numb er of branches is negatively correlated with
average branc h
deposits in relatively thinly -branc hed tracts (up to six branc
hes in the 1990s and
four branches in the 1980s). That is, an increase in the numb
er of branches appea rs
to be associated with decreased revenues. This effect is large;
Table 6 suggests that
addin g a branch to a single branch tract over the 1990-95
period decreases total
deposits by $25 million (which amou nts to 16 percent of the
unconditional mean
total deposits at the typical branch in New York City of$15 5
million). We conclude
that the appar ent herding of bank branches is not due to unme
asured profitability
differences across census tracts , at least not for relatively thinly
branched tracts .

6. Inter preta tions and impl icatio ns
The result s reported in the previous section indicate that
the numb er of existing
branches in a tract has a positive, statist ically significant effect
on the numb er of
subsequent openings and suggest, moreover, that this effect
is not spurious. We
interp ret· this as evidence of ration al herding by banks, of
the sort described in
Section 3. But economic theory suggests at least two other catego
ries of explanations
·for.th e clustering of firms, in which the number of firms locate
d in an area direct ly
influences the location decisions of subsequent firms.
The first set of poten tial explanations has to do with the presen
ce of positive
locational externalities. 29 These axternalities may arise in .a
numb er of ways. For
instance, the clustering of firms, by reducing consumers' search
costs can increase
aggregate demand. Dudey (1990) identifies conditions for
an equilibrium where
such clustering occurs as firms tradeoff the increased comp
etition from locating
close to competitors against the increased deman d from such
agglomeration. This
expla nation for the clustering of bank branches would be plausi
ble if banking services
were "search goods "-i.e. , durable goods that are purchased
fairly infrequently;
that seems unlikely, however, given the survey findings menti
oned in Section 2,
which indica te that bank customers appea r to value proximity
to their bank mainly
because the average bank custom er visited a branch three times
a month . 30
Positive locational externalities might also arise if existing bank
branches, throug h
their lending operations, raise the deposit poten tial of a neigh
borhood. The problem
with this explanation is that to the exten t that banks enjoy
first-comer advantages,
individual banks ought to be able to internalize these dynam
ic externalities by expandi ng the scope of their operations within a neighborhood.
A second possible explanation for clustering is provided by model
s where in the
presence of exogenous restrictions on price competition, firms
comp ete for marke t
share throu gh locational choice. 31 Apart from the fact that
such models yield clustering equilibria only for certai n configurations of the spatia l
distrib ution of deman d,
29 As

Deveno w and Welch (1996) point out, the clustering of firms
because of positiv e locational
externalities (or in their terminology, payoff externalities)
can also be considered a form of rational
herding .
30 Note
though that the locational externalities that arise from reduct
ions in travel costs might
well explain why1 within a neighborhood, retail businesses
tend to locate along the main commercial
thoroughfare.
31
The first model of this kind appear ed in Hotelling (1929).

18

configurations that seem unlikely to correspond to the distribution of demand for
banking services in New York City, the assumption that locational choice is the only
dimension along which banks compete seems unreasonable. While it is true that until the early 1980s banks were subject to strict interest rate regulation-which might
be thought of as a restriction on price competition-many of these regulations were
eased during the 80s, and even in the 1970s there is ample anecdotal evidence that
banks competed through other means such as special promotional efforts.
These alternative explanations of branch clustering share a common prediction:
clustering should increase branch profitability. That is, in terms of our notation, in
the expression for branch-level profits:
1r;t

= X;10: + 6N;t + e;t

the coefficient on the number of existing branches should be positive, i.e., 6 > 0. To
the extent that deposits are an adequate proxy for profits, the results reported in
Table 6 reject this hypothesis-average branch-level deposits drop as the number of
branches in a tract goes up, at least for the range of branches found in most tracts.
Hence, we discount such explanations.
In contrast, the models of branch herding we discussed in Section 3 do predict
that.branch clustering can lower earnings. For example, information cascade models
s11ggest ..that banks may·herd and over-enter an area, driving down profits. Moreover,
this effect can persist for two reasons: first, branch closures may be costly (and in
Section 2 we provide some indirect evidence that this is the case); second, even
if profits are lower in lieavily-branched tracts, as long as banks continue to earn
positive profits, there may be little incentive to explore the possibility that other,
currently virgin tracts, may generate higher profits.

7. Conclusion
Bank branches in New York City tend to be spatially clustered. This unevenness
has attracted considerable attention, both in the popular media and in policy cirdes because community groups argue that a bricks-and-mortar branch presence
is important for access to banking services. In this paper we explored empirically
whether the apparent clustering of bank branches can be at least partially attributed
to rational herding by banks. We find that even after controlling for the expected
profitability of operating a branch in an area, branch openings follow other, existing
branches. Moreover, such bandwagon behavior appears to reduce branch profits.
These findings, combined, suggest that herd behavior may be a factor in the branch
location decisions of banks.
The primary implication of our finding that banks may be engaging in herd behavior is that the observed distribution of bank branches is potentially more skewed
than the distribution of demographic and economic factors that affect branch profitability. Some neighborhoods may have an excessive number of branches while
others remain underserved. In such a situation, there may be a possible governmental role in influencing the branch location decisions of banks. Unless the government
is itself better informed than banks about the profit potential of different neighborhoods (which seems unlikely), the obvious policy instrument would be some form
of subsidy that encourages experimentation, e.g., a subsidy to banks that open
19

branches in virgin territory. Ther e may also
be a more indirect role in encouraging the generation and dissemination of infor
mation abou t the characteristics of
different neighborhoods.

20

Referenc es
[1] Allen, James (1995), "Branches ancient history? Not in this market," American
Banker, February 13.
[2) Avery, Robert (1991), "Deregulation and the location of financial institution
offices," Economic Review, Federal Reserve Bank of Cleveland, Third Quarter.
[3) Banking Policy Report (1994), "Justice Departmen t Attacks Bank Marketing,
Branching PJ'\tterns for First Time," September, v .5: 4-6
[4) Bikhchandani, Sushi!, David Hirshleifer and Ivo Welch (1992), "A Theory of
Fads, Fashion and Cultural Change as Information Cascades," Journal of Political Economy, October, v.100: 993-1026.
[5) Banerjee, Abhijit (1992), "A Simple Model of Herd Behavior," Quarterly Journal of Economics, August, v.CVII: 797-817.
[6) Calem, Paul (1995), "Mortgage Credit Availability in Low-and-ModerateIncome Minority Neighborhoods: Are Information Externalities Critical?"
Working Paper No. 95-16, Board of Governors of the Federal Reserve System,
Washington, D.C.
[7) Caplin, Andrew and John Leahy (1993), "Miracle on Sixth Avenue: Information
:Externalities and Search," Discussion Paper 681, Departmen t of Economics,
Columbia University.
[8) Caskey, John (1991), "Check-Cashing Outlets in the U.S. Financial System,"
Economic Review, Federal Reserve Bank of Kansas City, 76(6):53-67.
[9] Chamley, C. and D. Gale (1994), "Information revelation wd strategic delay
in a model of investment," Econometrica, September.
[10) DeCoster, Gregory and William Strange (1993), "Spurious agglomeration,"
Journal of Urban Economics, v.33: 273-304.
[11) Devenow, Andrea, and Ivo Welch (1996), "Rational herding in financial economics," European Economic Review, v.40, pp.603-615.
[12) Durley, Marc (1990), "Competition by Choice: The Effect of Consumer Search
on Firm Location Choice," American Economic Review, v.81: 1092-1104.
[13) Federal Deposit Insurance Corporation (1996), Historical Statistics on Banking,
1934-1995, Washington, D.C.
[14) Gale, Douglas (1996), "What Have We Learned from Social Learning?" European Economic Review, v.40: 617-628.
[15) Glassman, Cynthia (1995), "Industry role: who should serve the financial needs
of 'underserved' communities?," Journal of Retail Banking, December.
[16] Grinblatt, Mark, Sheridan Titman and Russ Wermers (1995), "Momentum
Investment Strategies, Portfolio Performance and Herding: A Study of Mutual
Fund Behavior," American Economic Review, December, v85: 1088-1104.
21

[17] Gui, Faruk and Russell Lindholm (1995), "End
ogenous timing and the clustering of agents' decisions," Journal of Political Economy,
October.
[18] Hendricks, Kenneth and Dan Kovenock (1989
), "Asymmetric information, information externalities, and efficiency: the case of oil
exploration," Rand J ournal of Economics, Summer.
[19] Hotelling, H. (1929), "Stability in competition,"
Economic Journal, 39: 41-57.
[20] Jain, Arvind and Satyadev Gupt a (1987), "Som
e Evidence on 'Herding' Behavior of U.S. Banks," Journal of Money, Credit and
Banking, February,. v. 19:
78-89.
[21] Kutler, Jeffrey (1996), "Reports of the branch's
demise have been greatly exaggerated," American Banker, December 31.
[22] Lang, William and Leonard Nakamura (199:l),
"A Model of Redlining," Journal
of Urban Economics, v.33: 223-234.
[23] Lee, In Ho (1993), "On the convergence of infor
mational cascades," Journal of
Economic Theory, December.
[24] ...Loeb, P.;w. Cohen, and C. Johnson.(1995), "The
new redlining: loan discrimination," U.S. News and World Report, April 17.
[25] MP,ad, Wen dy (19iil), "Banks: right rr.ix of branc
hes, alternative delivery is
nearer," American Banker, March 12.
[26] Radecki, Lawrence, John Wenninger, and Daniel
Orlow, (1996) "Bank Branches
in Supermarkets," Federal Reserve Bank of New York
, Current Issues in Economics and Finance, December, v.2.
[27] Scharfstein, David and Jeremy Stein (1990), "Her
d Behavior and Investment,"
American Economic Review, June, v.80: 465-479.
[28] Tootell, Geoffrey (1996), "Redlining in Boston:
Do Mortgage Lenders Discriminate Against Neighborhoods?," Quarterly Journal
of Economics, November,
pp.1049-1080.
[29] Welch, Ivo (1992), "Sequential sales, learning,
and cascades," Journal of Finance, v.47, no.2, pp.695-732.
[30] Zweibel, Jeffrey (1995), "Corporate conservatis
m and relative compensation,"
Journal of Political Economy, v.103, no.1, 1-25.

22

Appendix

In this appendix we present a simple model of bank branch location which yields
rational herding based on an informational externality along the lines of Lang and
Nakamura (1993).
Suppose that the (true) profitability of operating a branch in tract j is given by:

where X; is a vector of observable characteristics of the tract, N; is number of
existing branches in the tract, o 2: 0 captures the possible (adverse) effect on profits
from competition among branches in the tract, and v; is an unobserved tract-specific
effect. We assume that there is an exogenously given probability, .>., that a bank,
i, will consider opening a branch in tract j. Once a bank decides to explore the
possibility of opening a branch in tract j, the bank receives a noisy private signal, w;;,
say from a site analysis it commissions, as well as N; noisy signals, Wkj, k = 1, ... , N;,
e.g., from the deposit levels of the N; existing branches, about the profitability of
operating in tract j. Let:

Wkj

= 'ffj + 'T/kj

'T/kj

~i.i.d N(O,a~)

The bank uses these,signals to update its priors about the unobserved tract-effect, v;.
We assume that the bank has unbiased priors regarding the value of vi; specifically
,,we assume that the bank's prior µi is normally distributed with mean v; and variance
2
aW
W€ assume that the bank is risk-averse and that its preferences can be represented by the exponential utility function:

The bank therefore decides to open a branch in tract j only if:

i.e., if its expected utility from opening a branch, given its prior, and given the signals
it receives, is positive. Given the assumption of normality, and the exponential
utility specification, this can be rewritten as:

where E[.] is the expectation and V[.], the variance, of 7rj, given w; and µi.
Now, the conjugacy property of the normal distribution implies that the bank's
posterior beliefs about Vj are also distributed norma1ly with mean:

where:

-v(N3,)
'

=

(N;

+ l)a~

)a 2 +a2 and ijJ,
(N,+1
J
µ
~
23

=

(NJ,

1

+ 1) Ek'T/k3'

Keeping in mind the assumption of unbiased priors, the expec
ted profitability of
opera ting in tract j can then be written:

The variance of 1rj is given by:

Thus a bank will open a branch in tract j only if:

a[Xj/ 1- 6(Nj

+ 1) + VjJ

1

,,.2,,.2

- - a 2 [(N
2
J+

l)o-,,+o
i -~2 ] + a-y(Nj)i)j > 0

and the probability of this occurring is given by:

Pr(a-y(N·)ij
3 3 > -a[X·1/3 - 6(N1

1

2 2

+ 1) + v 1 J + -a 2[ - .....:!.!J2~.l!.---])
2
(N·J + l)o-µ, + o- 2
~

In this expression the existing numb er of branches in a tract has
two opposing effects
on an entran t's branc h opening decision. The first effect is the
"competitive" effect
captu red by 6 which should encourage banks to open branc
hes in thinly branched
tracts . The ·second effect, which does not appea r in the
"true" data gener ating
process fot profits, but appears as a result of the bank' s prefer
ence for increased
precision, tilts banks away from opening branches in thinly branc
hed tracts . It is this
second effect which raises the possibility of ration al herding.
Note also, that in this
model, over-clustering due to herding is more likely to occur
in the more profitable
tracts since these tracts are the ones where there are likely
to be more branches to
begin with. Note also that the herdin g effect is strong est in
thinly branched tracts .
This is seen clearly in that the variance of expected profits
V[1r j lwi; µiJ decreases
at a decreasing rate in the numb er of branches, Nj, The reason
is that adding a
branch in a thinly branched area generates relatively more inform
ation about tract
profitability than adding a branch to an area that alread y has
many branches.

24

Table 1
Spatial distributio n of bank branches in New York City
June 1990
No. of branches
Census
in tract: June 1990 No.
0
1747
1
311
2
87
3
31
4
11
5
9
6-9
10
10-25
12
Total
2218

tracts
Frac.
0.79
0.14
0.04
0.01
0.00
0.00
0.00
0.00
1.00

Bank branches
No.
Frac.
0
0.00
311
0.34
174
0.19
93
0.10
44
0.05
45
0.05
72
0.09
174
0.09
913
1.00

June 1995
.:.-::~~

No. ofbranches
Census
in tract: June 1995 No.
0
1769
1
295
2
81
3
40
4
8
5
4
6-9
9
10-19
12
Total
2218

tracts
Frac.
0.80
0.13
0.04
0.02
0.00
0.00
0.00
0.00
1.00

Bank branches
No.
Frac.
0
o.oo
295
0.35
162
0.19
120
0.14
32
0.04
20
0.02
64
0.08
151
0.18
844
1.00

Note: Using the census definition, the following five counties make up New York

city: Bronx, Kings, Queens, New York, and Richmond
Source: FDIC Summary of Deposits database, 1990-95.
branches of all commercial banks.

25

Covers full-service

Tabl e 2
Spat ial distr ibut ion of bank bran ch open ings
in New York City
July 1990 to June 1995
No. of branch
openings in tract :
Census
July 1990-:-June 1995 No.
0
2076
1
104
2
16
3
15
4
2
5
2
6-9
3
Tota l
2218

tract s
Frac.
0.930
0.050
0.007
0.007
0.001
0.001
0.001
1.00

No. of branches
Census tract s
in tract : June 1990 No.
Frac.
0
35
0.25
1
39
0.27
2
23
0.16
3
14
0.10
4
5
0.03
5
5
0.03
6-9
9
0.06
10-25
12
0.08
Total
142
1.00

26

Bran ch openings
No.
Frac.
0
0.00
104
0.47
32
0.14
45
0.20
8
0.04
10
0.04
22
0.10
221
.1.00
Bran ch openings
No.
Frac.
40
0.18
48
0.22
26
0.12
19
0.09
8
0.04
11
0.05
21
0.09
48
0.22
221
1.00

L
Table 3
Summary statistics: New York City census tracts

Variable Means
No. of tracts
Population of tract
Fraction of population non-white
Fraction of population over age 65
Fraction of households poor
Fraction of population high-school graduates
Median household income ($)
Fraction of housing units rental
Median value of owner-occupied housing
Number of people working in tract
Indicator that tract is net-importer of workers
Fraction . of land area commercial
Fraction of land area industrial
. Fraction of land area single-family residences
Fraction of land area multi-family residences
No. of existing branches: June 1990
No. of branch openings: July ;/)')0-June 1.9:?-5

=--

.-

--

Variable
No. of tracts
Population of tract
Fraction ef population non-white
Fraction of population over age 65
Fraction of households poor
Fraction of population high-school graduates
Median household income ($)
Fraction of housing units rental
Median value of owner-occupied housing
Number of people working in tract
Indicator that tract is net-importer of workers
Fraction of land area commercial
Fraction of-land area industrial
Fraction of land area single-family residences
Fraction of land area multi-family residences
No. of existing branches: June 1990
No. of branch openings: July 1990-June 1995

27

All tracts
2218
3304
0.55
0.13
0.18
0.67
37043
0.65
164246
634
0.24
0.06
0.05
0.36
0.23
0.41
0.10

Branches: June1990
Tracts with Tracts without
branches
branches
471
1747
4361
3020
0.43
0.58
0.15
0.13
0.14
0.19
0.73
0.66
45663
34728
0.69
0.64
184130
158908
1715
333
0.46
0.18
0.13
0.04
0,05
0.05
0.26
0.38
0.25
0.22
1.94
0.00
0.46
0.00

====~=====c====

All tracts
2218
3304
0.55
0.13
0.18
0.67
37043
0.65
164246
639
0.24
0.06
0.05
0.36
0.23
0.41
0.10

Branch openings:
July 1990-June 1995
Tracts with Tracts without
branch
branch
openings
openings
142
2076
4728
3207
0.37
0.56
0.15
0.13
0.14
0.18
0.76
0.67
53602
35917
0.72
0.64
175690
163468
3692
421
0.60
0.21
0.21
0.04
0.04
0.05
0.16
0.37
0.27
0.22
2.90
0.24
1.55
0.00

Tabl e 4
Evid ence on herd ing: basic resul ts
Depe ndent variable: no. of branch openings, 1990-95
Poisson
No. of branches
No. of branches squar ed
Popu lation of tract
Fraction of popu lation non-white
Fract ion of popu lation over age 65
Fraction of households poor
Fract ion of popu lation high-school gradu ates
Mectian household income ($)
Fract ion of housing units renta l
Median value of Jwner-occupied housing
Num ber of people working in tract
Indic ator that tract is net-im porte r of workers
Fract ion of land area commercial
Fract ion of land area industrial
Fract ion of land area single-family residences
Fract ion of land area multi-family residences
No. of observations
Pseudo-R-square

0.32
(5.1)
-0.01
(-4.3)
9.9x l0- 5
(2.6)
-0.83
(-1.77)
1.0
(0.94)
-3.1 .
· (-2.4)
-1.7
(-1.58)
-8.lx l0- 6
(-0.76)
-0.32
(-0.5)
2.5x1 0- 7
(-0.43)
-2.5x 10- 5
(C.68)
0.66
(2.9)
0.26
(3.3)
-0.01
(-1.1)
-0.02
(-2.4)
0.01
(1.3)
10,075
0.32

Orde red
logit
0.41
(5.4)
-0.01
(-3.7)
1.0x1 0~ 4 .
(2.5)
-1.04
(-2.0)
1.4
(1.1)
-2.4
(-1.6)
-1.0
(-0.83)
-1.1 X 10- 5
(-0.9)
-0.54
(-0.65)
-1.7x 10- 7
(-0.26)
-1.1 x10- 5
(·0.25)
0.61
(2.6)
0.032
(3.6)
-0.02
(-1.5)
-0.017
(-2.2)
0.01
(1.3)

10,075
0.28

Notes: (1) t-stat istics appea r below coefficient estim ates.
(2) Regressions based
on pooled annual tract-level data for New York City
from 1990-95. (3) Regressions include changes in tract-level demographic variab
les between 1980 and 1990;
coefficient estim ates are not repor ted.

28

Table 5
Summary of regressions of branch openings on tract features
No. of
No. of
branches No. of
Dependent variable (sample) branches squared
obs.
No. of openings, annnually, of all branches: 1990-95
Poisson
0.32
-0.01
10075

(-4.3)
-0.01
10075
(-3.7)
Cumulative no. of openings of all branches: 1990-95
Poisson
0.31
-0.01
2015
(5.1)
(-4.3)
Ordered logit
0.58
-0.01
2015
(4.9)
(-2.2)
Ordered logit

(5.1)
0.41
(5.4)

Pseudo
R-squared

0.31
0.27

0.43
0.33

No. of openings, annually, of branches other than headoffices: 1992-95
Poisson
0.44
-0.02
6045
0.27
Ordered logit

(5.1)
0.55
(5.1)

(-4.3)
-0.02
(-3.6)

6045

0.24

·"Cumulative no. of openings of branches other than headoffices: 1992-95
Poisson
0.43
-0.02
2015
0.33

(-4.4)
-0.02
2015
(-3.1)
No. of openings, annnually, of all branches: 1980-85
Poisson
0.65
-0.02
105'(0
(9.5)
(-5.7)
Ordered logit
0.69
-0.02
10570
(8.4)
(-4.2)
Cumiiiativ,; no. of openings of ali branches: 1980-85
Poisson
0.67
-0.02
2114
(8.9)
(-5.0)
Ordered logit
0. 79
-0.02
2114
(5.9)
(-1.4)
Ordered logit

(4.9)
0.63
(4.6)

0.28

0.31
0.27

0.40
0.27

Notes: (1) t-statistics appear below coefficient estimates; (2) The regression
model used for the 1990-95 data is the same as that used in Table 4. The model
used for the 1980-85 data includes the following tract characteristics as controls:
population, race, p.mcerty .rate, .age, rental.rate,. median income -and. median rent.
(3) "Cumulative openings" equals the total number of branches that opened in a
tract over the relevant period. (4) Reliable data on head offices are available only
from 1992.

29

Tabl e 6
Furt her tests : Sum mary of regre ssion s of aver age
bran ch depo sits on
tract featu res

Number
Number
of branches
of branches
squared
Dependent variable:
(OLS)
(OLS)
N
Average total deposits per branch
Annual: 1990--95
-29,501
2381
2589
(-6.6)
(7.4)
Annual: 1984-85
-29,491
3310
954
(-4.2)
(5.3)
Average non-transactions deposits per branch
Annual: 1980--85
-21,267
2280
2961
(-5.2)
(6.1)

R-squared
0.41
0.58

0.31

Notes: (1) t-statistics appea r below coefficient estimates.
These t-stat istics are
based on heteroskedasticity-consistent stand ard errors
[White 1980]. (2) The re. · gression models used are the same as those used in
Table 5. But the dependent
variable now is the average deposit level across all the branc
hes in a tract in a given
year. Total deposits consist of all deposits held by every
one other than the government. Non-transactions deposits consist of savings
and time deposits held by
everyone other than the government. (3) Reliable data on
total non-government deposits are available only from 1984. Non-transactions data
are not available for the
1990s.(4) Sample means are as follows: Mean total depos
its at the typical branch
between 1990 and 1995 was $155 million; the mean for
1984--85 was $110 million;
mean non-transactions deposits for 1980--85 was $62 millio
n.

30

FEDERAL RESERVE BANK OF NEW YORK
RESEARCH PAPERS

1997
The following papers were written by economists at.the.Federal Reserve Bank of
New York either alone or in collaboration with outside economists. Single copies of up
to six papers are available upon request from the Public Information Department,
Federal Reserve Bank of New York, 33 Liberty Street, New York, NY 10045-0001
(212) 720-6134.

9701. Chakravarty, Sugato, and Asani Sarkar. "Traders' Broker Choice, Market Liquidity, and
·. Market Structure." January 1997.
'9702. Park, Sangkyun. "Option Value of Credit Lines as an Explanation of High Credit Card
Rates." February 1997.
9703. Antzoulatos, Angelos. "On the Determinants and Resilience of Bond Flows to LDCs,
1990 - 1995: Evidence from Argentina, Brazil, and Mexico." February 1997.
9704. Higgins, Matthew, and Carol Osler. "Asset Market Hangovers and Economic Growth."
February 1997.
9705. Chakravarty, Sugato, and Asani Sarkar. "Can Competition between Brokers Mitigate
Agency Conflicts with Their Customers?" February 1997.
9706. Fleming, Michael, and Eli Remolona. "What Moves the Bond Market?" February 1997.
9707. Laubach, Thomas, and Adam Posen. "Disciplined Discretion: The German and Swiss
Monetary Targeting Frameworks in Operation." March 1997.
9708. Bram, Jason, and Sydney Ludvigson. "Does Consumer Confidence Forecast Household
Expenditure:.A.Seutimeutlndex UorseRace.". Mai:ch.1997.
9709. Demsetz, Rebecca, Marc Saidenberg, and Philip Strahan. "Agency Problems and Risk
Taking at Banks." March 1997.

9710. Lopez, Jose. "Regulatory Evaluation of Value-at-Risk Models." March
I 997.
9711. Cantor, Richard, Frank Packer, and Kevin Cole. "Split Ratings and the
Pricing of Credit
Risk." March 1997.
9712. Ludvigson, Sydney, and Christina Paxson. "Approximation.BiasinLin
eacizedEuler
Equations." March I 997.
9713. Chakravarty, Sugato, Sarkar, Asani, and Lifan Wu. "Estimating the Advers
e Selection
Cost in Markets with Multiple Informed Traders." April 1997.
9714. Laubach, Thomas, and Adam Posen. "Some Comparative Evidence on
the Effectiveness
oflnfla tion Targeting." April 1997.
9715, Chakravarty, Sugato, and Asani Sarkar. "A General Model of Brokers'
Trading, with
Applications to Order Flow Internalization, Insider Trading and Off-Exchange Block
Sales." May 1997.
9716. Estrella, Arturo. "A New Measure of Fit for Equations with Dichotomous
Dependent
Variables." May 1997.
9717. Estrella, Arturo. "Why Do Interest Rates Predict Macro Outcomes? A Unified
Theory of
Inflation, Output, Interest and Policy." May 1997.
9718. Ishii Jun, and Kei-Mu Yi. "The Growth of World Trade." May 1997.
9719. Kambhu, John. "Interest Rate Options Dealers' Hedging in the US Dollar
Fixed Income
Market." May I 997.
9720. Kroszner, Randall, and Philip Strahan. "The Political Economy of Deregu
lation
Evidence from the Relaxation of Bank Branching Restrictions in the United States."
June 1997.
9721. Locke, Peter, Sarkar, Asani, and Lifan Wu. "Market Liquidity and Trader
Welfare in
Multiple Dealer Markets: Evidence from Dual Trading Restrictions." July 1997.
9722. Zakraj~ek, Egon. "Retail Inventories, Internal Finance, and Aggregate Fluctua
tions:
Evidence from U.S. Firm-Level Data." July 1997.

9723. Lown, Cara, and Robert Rich. "Is There An Inflation Puzzle?" August 1997.

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