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Vol. 34. No. 4
ECONOMIC REVIEW
1998 Quarter 4
Beneath the Rhetoric:
Clarifying the Debate on
Mortgage Lending Discrimination
2
by Stanley D. Longhofer and Stephen R. Peters
Banking and Commerce:
How Does the United States
Compare to Other Countries?
byJoao A.C. Santos
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Vol. 34, No. 4
http://clevelandfed.org/research/review/
Economic Review 1998 Q4
1998 Quarter 4
Beneath the Rhetoric:
Clarifying the Debate on
Mortgage Lending Discrimination
2
by Stanley D. Longhofer and Stephen R. Peters
Banking and Commerce:
How Does the United States
Compare to Other Countries?
by João A.C. Santos
14
1
ECONOMIC REVIEW
1998 Quarter 4
Vol. 34, No. 4
Beneath the Rhetoric:
Clarifying the Debate on
Mortgage Lending Discrimination
2
by Stanley D. Longhofer and Stephen R. Peters
The authors’ simple model of the mortgage underwriting process provides a
framework within which to define discrimination and various notions of the
default rate. By providing those with differing views a common framework
for discussing their positions, the model clarifies and reconciles some of
the most controversial issues in the debate over mortgage discrimination. It
also shows how this theoretical framework can help in the design of practical policy responses to this vexing social problem.
Banking and Commerce:
How Does the United States
Compare to Other Countries?
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by João A.C. Santos
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2
Beneath the Rhetoric:
Clarifying the Debate
on Mortgage Lending
Discrimination
by Stanley D. Longhofer and Stephen R. Peters
Introduction
Home ownership has long been identified as
a central element of the “American Dream”;
in fact, many treat the two as synonymous.
No wonder, then, that much of federal antidiscrimination law focuses on housing and the
lending markets used to finance home purchases. For more than 30 years, bank regulators have been charged with the task of rooting
out illegal discrimination in credit markets.
The long-standing debate over whether
lenders systematically discriminate against
minorities received added momentum in 1992
with the release of the now-famous “Boston
Fed Study” (Munnell et al. [1992]), which purported to find that minority applicants in the
Boston area are roughly half again as likely as
similarly situated white applicants to be denied
mortgage loans. Although their conclusions
have been hotly debated in the ensuing years,
there can be no question that Munnell et al. put
the mortgage discrimination debate on the front
burner in both policy and academic circles.
This debate still rages today. Yet after more
than six years, we are not much closer to
knowing whether discrimination is a widespread problem in mortgage markets. Indeed,
Stanley D. Longhofer is an economist at the Federal Reserve Bank
of Cleveland and Stephen R.
Peters is a visiting assistant professor of finance at the University
of Illinois at Urbana–Champaign.
economists have yet to agree on whether it is
more effective to detect discrimination using
denial rate analyses like those employed by
Munnell et al. or by examining borrower default rates. Nor have we settled on the appropriate policy response to make if discrimination
is in fact as prevalent as Munnell et al. suggest.
A key roadblock in answering these questions has been the lack of a solid theoretical
foundation on which to conduct the debate.
Without a consistent economic theory of the
mortgage application, underwriting, and default
processes, fundamental tasks such as defining
discrimination and outlining how it might be
detected have proven slippery. As a consequence, more advanced issues such as identifying the source of discrimination and evaluating
potential policy responses have been obviated,
despite longstanding calls to “move beyond a
debate over whether discrimination exists to a
discussion of how best to eradicate it” (Galster,
[1993, p. 146]).
In this article, we outline a simple theoretical
model of the mortgage underwriting process,
originally developed by Longhofer and Peters
(1999), which provides a framework for clearly
defining discrimination and various notions of
the default rate. By giving those with differing
3
views a common framework for arguing their
positions, this model clarifies and reconciles
some of the most contentious questions at the
heart of the controversy over mortgage discrimination. We also show how this theoretical
foundation can aid in designing practical policy
responses to this vexing social problem.
The next section reviews the highlights of
the debate that ensued in the wake of the initial
study by Munnell et al. Section II introduces the
Longhofer–Peters model of mortgage underwriting and shows how it can be used to clarify
many of the definitions and assumptions that
underlie this debate. Section III reexamines the
mortgage discrimination debate in light of that
model, and section IV concludes.
I. The Mortgage
Discrimination
Debate
The debate over whether lenders systemically
discriminate against minority applicants began
in earnest with the release of Munnell et al.
(1992).1 The authors of this study found that
minority applicants in the Boston metropolitan
area were approximately 50 percent more
likely to be rejected than whites, even after
controlling for the factors that banks use to determine an applicant’s creditworthiness. “This
means that 17 percent of black or Hispanic applicants instead of 11 percent would be denied
loans, even if they had the same obligation
ratios, credit history, loan to value, and property characteristics as white applicants. In short,
the results indicate that a serious problem exists
in the market for mortgage loans, and lenders,
community groups, and regulators must work
together to ensure that minorities are treated
fairly” (Munnell et al. [1992, p. 44]).
In his Nobel Lecture, Becker (1993b) challenges these findings. Recasting his economic
theory of discrimination in the context of the
mortgage lending market, Becker argues that
bigoted lenders are willing to sacrifice the profit
they might earn by approving marginally qualified minority applicants in order to satisfy their
“tastes for discrimination.” Thus, the proper way
to detect discrimination is to do so directly, by
comparing the relative profitability of loans to
minorities and whites: “This requires examining
the default and other payback experiences of
loans, the interest rates charged, and so forth. If
banks discriminate against minority applicants,
they should earn greater profits on the loans
actually made to them than on those to whites.
The reason is that discriminating banks would
be willing to accept marginally profitable white
applicants” (Becker [1993b, p. 389]).
If banks discriminate against minority borrowers, Becker argues, we should observe
minorities defaulting less frequently than whites
on average. “[T]he theory of discrimination contains the paradox that the rate of default on
loans approved for blacks and Hispanics by
discriminatory banks should be lower, not
higher, than those on mortgage loans to whites.
The reason is that such banks only accept the
very best minority candidates” (Becker [1993a]).
Because discriminators who are following their
tastes will pass up better-qualified minority applicants for less-qualified white ones, the average creditworthiness of the minorities who
actually receive loans will be higher than that
of whites.
Macey (1994) uses Becker’s argument to refute the principal conclusions of Munnell et al.,
citing that study’s own data.2 “The default rates
for white and black mortgage loan applications
are equal across census tracts. If bankers were
discriminating by turning down marginally qualified black applicants while accepting marginally qualified white applicants, then default rates
among whites would be higher. But bankers are
accepting the same level of risk from both black
and white applicants” (Macey [1994]).
The validity of this argument regarding average default rates relies on the assumption that
white and minority credit risk is equally distributed in the borrower pool. In fact, however,
Munnell et al.’s data suggest that minority applicants (and borrowers) are less creditworthy
than whites on average (see Munnell et al.
[1996], table 1).
Galster (1993) uses this fact to argue that
minorities will default more often than whites
in the absence of discrimination. Thus, he concludes that equal average default rates across
minority and white borrower pools imply tastebased discrimination against minorities. On
these grounds, Galster suggests that default
rates cannot be considered a reliable indicator
of discrimination.3
The above arguments revolve around
whether average default rates can be used to
uncover discriminatory behavior on the part of
lenders. Others, including Calomiris et al. (1994)
■
1 Later revised and published as Munnell et al. (1996).
■
2 See also Brimelow and Spencer (1993) and Becker (1993a).
■ 3 Tootell (1993), Browne and Tootell (1995), and Munnell et al.
(1996) echo Galster’s conclusions.
4
and Ferguson and Peters (1995) focus on the
interpretation of marginal default rates. These
authors argue that, regardless of the relative distributions of creditworthiness across racial
groups, in the absence of taste-based discrimination the marginal borrower—the least creditworthy applicant to be approved for a loan—
will have the same creditworthiness across
groups. That is, if banks hold all applicants to
the same credit standard, the marginal default
rate will be the same for all racial groups. If this
is true, then comparisons of the marginal default
rate across races may enable regulators to detect
taste-based discrimination (in the manner of
Becker [1993b]).
In summary, a variety of apparently conflicting tests have been proposed for inferring
whether lenders discriminate against minority
applicants: Lower average default rates for minorities are evidence of discrimination against
minorities (for example, Becker [1993b]); equal
average default rates for minorities and whites
are evidence of discrimination against minorities (Galster [1993]); and, lower marginal minority default rates are evidence of discrimination
against minorities (Calomiris et al. [1994]).
Which of these conclusions are we to believe?
How should we go about uncovering illegal
discrimination? In the next section, we develop
a simple theoretical model that can help answer
these questions.
Let gA(θ ) represent the density of creditworthiness in the group A applicant pool and
gB (θ ) represent the corresponding density for
members of group B. It is important to note
that these two densities generally will differ
from one another.4 We assume that both gA(θ )
and gB (θ ) are known to all banks.
Based on their costs of funds and the competitive interest rate in the market, banks determine a θ ∗ defining the minimum acceptable
creditworthiness they are willing to approve. In
a full-information world, lenders would know
each individual’s true θ and would approve
only those applicants with θ ≥ θ ∗. Unfortunately, lenders cannot perfectly observe θ , but
instead observe a signal s that is correlated with
θ . This signal can be thought of as a summary
of all the information a lender collects on a loan
application, including the applicant’s current
income ratios and past credit history, the characteristics of the subject property, and so on.5
Let p (s θ ) denote the likelihood that a
lender observes signal s from an applicant of
type θ .6 Using this signal generation process
and the distribution of creditworthiness in the
applicant pool, lenders derive an estimate of an
applicant’s expected creditworthiness, given his
signal. We will often refer to this expected
creditworthiness as an applicant’s inferred
“quality,” which is denoted by
(1)
II. A Theoretical
Underpinning
for the Debate
In order to define what constitutes discrimination and determine how such behavior might
be detected, we must first understand how
mortgage applications are underwritten. To do
this, we use the mortgage underwriting model
developed in Longhofer and Peters (1999).
Consider a world in which individuals want
to buy a house, but lack sufficient funds to do
so. As a result, they must obtain loans from a
financial institution, which we will call a “bank.”
Individuals in this world are distinguished by
two characteristics. The first is the likelihood
with which they repay their loans, which we
denote by θ ∈ [0,1]. The second is their membership in one of two “groups,” A or B. For ease
of exposition, we will often refer to group A as
the “white” group and to group B as the “minority” group. It should be clear, however, that
these groups may alternatively be thought of as
distinguishing individuals according to any publicly observable characteristic over which fairlending laws might prohibit discrimination.
qi (s) = ∫ θ p(sθ )gi (θ ) dθ, i = A, B
Ti
ωi (s)
where ωi (s) = ∫T p(sθ )gi (θ )dθ, i = A, B, is the
i
density of signals received from members of
group i, and Ti is the set of group i applicants
that apply for loans: {θ gi (θ ) > 0}.
Expression (1) makes it clear that an applicant’s inferred quality is simply his expected
creditworthiness, where this expectation is taken over the lender’s (Bayesian) posterior beliefs
about the applicant’s creditworthiness. Clearly,
■ 4 Longhofer and Peters (1999) analyze how individual self-selection
can lead to endogenous differences in credit risk across groups. Alternatively, one could imagine that the distributions of credit risk in the underlying population differs across groups, leading to differences in these densities for the group applicant pools even in the absence of self-selection (see,
for example, Calem and Stutzer [1995]).
■ 5 Although an applicant’s θ is assumed to be private information,
the group to which he belongs can be costlessly observed by lenders, giving them an opportunity to discriminate on this basis if they so choose.
■ 6 Although we assume that this signal generation process is the
same for members of both groups, one could imagine a world in which it
differed. Such a difference in p (sθ ) across groups lies at the heart of the
cultural affinity hypothesis proposed by Calomiris et al. (1994).
5
F I G U R E
1
Taste-Based
Discrimination (Bigotry)
Bigoted lenders act on a taste for discrimination by holding minority applicants
to a higher creditworthiness threshold (qA* < q B* ). This implies that bigoted
lenders will hold minorities to a more stringent underwriting standard than they
do whites (sA* < sB** ).
this posterior will be determined by both the
distribution of credit risk in the overall applicant
pool and the specific signal sent by the applicant. Given these beliefs, a lender will approve
an applicant if and only if q (s) ≥ q * ≡ θ * .
Longhofer and Peters (1999) show that an
applicant’s inferred creditworthiness is increasing in his signal. That is, q ′(s) > 0, confirming
the intuition that applicants who send better signals tend to be more creditworthy. More importantly, this result assures us that there exists a
unique cutoff signal, s ∗, such that all applicants
sending signals better than s ∗ are approved
while those sending worse signals are not.
Defining
Discrimination
The most striking omission in the debate over
mortgage discrimination has been the lack of a
formal definition of what behavior actually constitutes “discrimination.” Perhaps this should
not be surprising, given that the term is not
defined in either the Equal Credit Opportunity
Act or the Fair Housing Act—the two laws that
directly prohibit discriminatory practices in the
mortgage underwriting process. Presumably,
this omission indicates Congress’ belief that
clarification would be superfluous because the
meaning of the term is well understood by all
reasonable people.
In our opinion, however, much of the controversy over how best to detect discrimination
arises precisely because of differing implicit
definitions of this central concept. To clarify the
law’s intent, we turn to Merriam Webster’s Collegiate Dictionary, 10th ed., which defines discrimination as “the act, practice, or an instance
of discriminating [making a distinction] categorically rather than individually.” In other words,
any difference in treatment across individuals
based solely on group membership—rather
than on personal characteristics specifically related to the performance of the loan—would
constitute discriminatory behavior under the
law. It is our interpretation (and the practice of
the major bank regulators) that such a definition
precludes lenders from applying group-specific
underwriting standards, even if group membership is correlated with loan performance.7
Consistent with this idea, consider the
following:
Definition: A lender is said to discriminate
against minority applicants if it
requires them to meet a more stringent underwriting standard than it
does white applicants; i.e., if sB* > sA* .
When discrimination is explicitly defined in
this way, it becomes clear that lenders’ incentives to discriminate can arise for two distinct
reasons. First, lenders may have Beckerian
“tastes for discrimination,” bigoted preferences
that would manifest themselves through differences in the q * required from members of each
group. In other words, we say that a lender
exhibits bigoted preferences if qB* > qA* . This
source of discrimination is depicted in figure 1,
which assumes that qA(s) = qB (s) for every s.8
In this case, the monotonicity of q implies that
a bigoted lender will have an incentive to discriminate against members of group B by setting sB* > sA* .
Even without such tastes for discrimination,
however, lenders may still have an incentive to
discriminate if the overall pool of minority
applicants is less creditworthy on average than
the white applicant pool. In other words, if the
minority applicant pool consists of relatively
more low-θ individuals, qA (s) will lie above
qB (s) for every s, giving banks an incentive for
statistical discrimination.9
■ 7 A noteworthy exception is the Equal Credit Opportunity Act, which
specifically allows age to be considered in credit-scoring models, but only
if it is to the advantage of older applicants.
■ 8 This requires that the distribution of credit risk, g (θ ), be the same
across the two applicant pools.
■
9 Strictly speaking, it is only necessary that qA (s) > qB (s) for
s < sB* ; Longhofer and Peters (1999) present a case in which statistical
discrimination arises and yet qA (s) < q B (s) for large s.
6
F I G U R E
2
Statistical Discrimination
Defining
Default Rates
Given the above discussion, we can now define
and analyze the different notions of default rates
that have arisen in the mortgage discrimination
controversy. As it turns out, participants in the
debate rarely use the most useful of these notions, the conditional default rate.
Definition: The conditional default rate for
members of group i is the fraction of
group i borrowers that actually default, conditional on their signal s:
di (s) ≡ 1 – qi (s), i = A, B.
If minority applicants are less creditworthy on average than whites, this will be
reflected in the lender’s assessment of an applicant’s inferred quality. Thus, qA(s)
will lie above qB (s). In this case, even if lenders hold white and minority applicants to the same credit standard, qA* = q B* , they will have an incentive to statistically discriminate against minorities by setting sA* < s B* .
Statistical discrimination is depicted by
figure 2, in which lenders set the same minimum creditworthiness standard for both white
and minority applicants. Nevertheless, because
minority applicants are less creditworthy (have
lower θ s) on average, lenders believe that a
minority applicant sending any signal s is less
creditworthy than a white applicant sending the
same signal. Put another way, given the distribution of credit risk in their applicant pools,
lenders know that a minority applicant sending
any signal s is more likely to default than a
white applicant sending the same signal. As a
result, lenders have an incentive to hold minority applicants to a higher s ∗.
Of course, these beliefs do not arise in a
vacuum. As long as lenders accurately estimate
the distribution of credit risk in their applicant
pools, gi (θ ), the probability that a group i applicant repays his loan is qi (s). Thus, lenders
that statistically discriminate find their beliefs
validated ex post; on average, minority borrowers sending signal sB* repay their loans at exactly the same rate as white borrowers sending
signal sA* , and both repay at the minimum rate
acceptable to the bank.
As discussed above, the likelihood that a
group i borrower with signal s will end up
defaulting on his loan is 1 – qi (s). If there is a
sufficiently large population of borrowers, then
the actual fraction of group i borrowers sending signal s that default will be di (s). From a
lender’s perspective, this conditional default
rate is what matters most. After all, the lender’s
underwriting decision is ultimately determined
by the likelihood that an applicant will default,
given the information on his application. This is
exactly what di (s) measures.
Most of the mortgage discrimination debate,
however, has focused on other notions of
default rates.
Definition: The average default rate is the
fraction of all borrowers who
actually default on their loans:
(2)
di ≡
∫ *di (s)
s ≥ si
ω i (s) ds, i = A, B.
ω
∫ i (s)ds
s ≥ si*
Thus, the average default rate is simply the
weighted average of the conditional default
rates of individuals who were approved, where
the weights are determined by the distribution
of signals sent by approved applicants.
Finally, Berkovec et al. (1994), Calomiris et
al. (1994), and Ferguson and Peters (1997, 1998)
all consider varying notions of the “marginal”
rate of default (or denial). Unfortunately, this
concept has often been somewhat ill-defined.10
It can be most precisely defined as follows:
Definition: The marginal default rate is the
fraction of defaulters among the borrowers sending the lowest approved
signal: d mi ≡ di (s * ) = 1 – qi (s * ),
i = A, B.
■
10 A transgression to which both of the present authors plead guilty.
7
F I G U R E
3
Default Rates under Bigotry
market. As we will argue, understanding the
root causes of the problem is essential to
designing effective policy responses.
The Default Rate
Controversy
If credit risk is identically distributed across the minority and white applicant
pools, bigoted lenders will require minority applicants to meet a more stringent
underwriting standard (a higher s* ). Because the minority applicants who are
rejected are those with the highest default risk, the average default rate will be
lower for minorities than it will be for whites.
In the context of our model, the notion of a
marginal default rate may be easily understood.
In the real world, however, it is virtually impossible for regulators to identify the unique cutoff signal s ∗ used by lenders.11 As a practical
matter, then, the marginal default rate is often
taken as either the average or the conditional
default rate among borrowers sending signals
below some arbitrary threshold. For example,
Berkovec et al. (1994) analyze conditional
default rates among FHA borrowers, under the
presumption that they are riskier than those
obtaining conventional loans; the authors further attempt to focus on “marginal” borrowers
by dividing their sample into risk quartiles,
comparing conditional default rates across
white and minority borrowers in each quartile.
III. Deciphering
the Debate
In section II, we introduced a simple theoretical
model of the mortgage underwriting process
and used it to help define discrimination and
various notions of the default rate. In the present section, we use these definitions to reconsider the various arguments outlined in section I.
In particular, we analyze the accuracy and the
consistency of these arguments and consider
why this debate has been so difficult to resolve.
Finally, we discuss how our model’s formal
structure can be used to develop a better
understanding of the underlying causes of any
discrimination that does exist in the mortgage
We begin by imagining a world where credit
risk is identically distributed in both the minority and white applicant pools; that is, gA(θ ) =
gB(θ ), ∀θ . In such a world, the only possible
source of discrimination is bigotry, which manifests itself as a higher required standard of
creditworthiness for minorities (qB* > qA* ). As
shown in figure 1, lenders with such a taste for
discrimination will respond by setting sB* > sA*.
Nevertheless, the fact that gA(θ ) = gB(θ ), ∀θ
implies that the inferred creditworthiness of a
white applicant and a minority applicant, each
of whom sends the same signal s, will be identical; that is qA(s) = qB (s), ∀s. Thus, conditional
default rates will be the same across the two
groups. But if conditional default rates are the
same across groups, the average default rates of
the two groups must diverge whenever banks
set sB* > sA*; that is, d A > d B . Put another way, if
minority and white borrowers are equally creditworthy on average, then any difference in the
average default rate across groups must arise
from lenders’ tastes for discrimination.
This idea is shown graphically in figure 3,
in which conditional default rates are identical
across the two groups. Because qB* > qA*, however, lenders set sB* > sA*, implying that white
applicants with s ∈ [sA*, sB* ] are approved, while
minority applicants sending the same signals
are not. Ex post, these borrowers are the least
creditworthy and hence the most likely to
default. Because these applicants are excluded
from the minority borrower pool, the average
default rate of minorities who are actually
approved for loans must be lower than that
of whites.
Pursuing this logic, Becker (1993a), Brimelow
and Spencer (1993), and Macey (1994) claim
that Munnell et al.’s own data refute a conclusion of discrimination. After all, they argue,
default rates across census tracts do not appear
to be systematically related to the percentage of
minorities residing in those tracts. While not
conclusive, this evidence seems inconsistent
with the notion that minorities default more frequently on average than whites.
■ 11 For a discussion of how this model relates to econometric
analyses of s* by regulators and researchers, see Craig et al. (1998).
8
F I G U R E
4
Differences in Underlying
Creditworthiness across Groups
θ
θ
θ
θ
If minority applicants are less creditworthy on average than white applicants, the
density of credit risk in the minority applicant pool will lie to the left of the density for the white applicant pool.
F I G U R E
5
Statistical Discrimination
and Default Rates
If minority applicants are less creditworthy than their white counterparts on
average, lenders will have an incentive to statistically discriminate against them,
even in the absence of bigotry. They do so by choosing sA* and sB* so as to
equalize the inferred quality of the last applicant approved from each group
[qA (sA* ) = qB* (sB* )]. This in turn implies that the marginal default rate of the two
groups will be identical. However, the relative average default rates will be
ambiguous.
Given their assumptions, Becker (1993a),
Brimelow and Spencer (1993), and Macey
(1994) are all correct in their conclusions. In
our model, however, it is clear that for a group
of borrowers, the average default rate depends
not only on the underwriting standard applied
to that group, but also on the distribution of
signals sent by those borrowers, ωi (s). This, in
turn, depends on the distribution of credit risk
in the group’s applicant pool. Thus, ex ante differences in credit risk across applicant pools
can lead to ex post differences in observed
average default rates, even in the absence of
discrimination.
This is the essential point made by Tootell
(1993) and Galster (1993). They argue that
Munnell et al.’s data verify that minority applicants are less creditworthy on average than their
white counterparts.12 This stylized fact is illustrated in figure 4, where gB (θ ) is shifted slightly
to the left of gA(θ ).13 In the context of our model, such differences in credit risk across groups
cause lenders to adjust their assessment of an
applicant’s inferred quality, so that qA (s) > qB (s),
∀s. As a result, if lenders were to hold both
groups to the same s*, the average default rate
would be lower for white applicants. On the
other hand, if lenders do set s*A < s*B , then the
relative average default rate between the two
groups will be ambiguous (see figure 5).
Thus we see that, if lenders are bigoted,
there are two counteracting effects on minority
default rates: the bigotry effect and lower average minority creditworthiness. On the one
hand, lender tastes for discrimination imply that
relatively more of the less-creditworthy white
borrowers get included when calculating dA.
On the other hand, for any given s, a minority borrower is more likely to default than a
white borrower. Which of these two effects will
dominate in practice is unclear.14 As a result,
Tootell and Galster both argue, average default
rates cannot be used to disprove a charge of
discrimination. In fact, if minority borrowers are
less creditworthy on average than their white
counterparts, a finding that both groups default
at the same rate could only be consistent with
lenders acting on bigotry against minorities.
Even though average default rates cannot
disprove the existence of discrimination, other
notions of the default rate may still be useful.
The essential problem with average default
rates is that they incorporate too much information. In contrast, conditional and marginal
default rates focus on a specific subset of borrowers, making it easier to interpret what lies
behind any differences across groups.
At its core, Becker’s argument is driven by
the fact that, if lenders hold minorities to a
more stringent underwriting standard arising
from bigotry, the “marginal” minority borrower
■
12 The Federal Reserve’s National Surveys of Consumer Finances
also support this conclusion.
■
13 Formally, one can think of the cumulative distribution function
of credit risk in the white applicant pool, GA(θ ), as being first-order stochastic dominant over GB (θ ), the cumulative distribution of credit risk in
the minority applicant pool; see Ferguson and Peters (1995).
■ 14 In addition, the distribution of signals, ω (s), will differ across
groups, further complicating this calculation.
9
T A B L E
1
Relative Default Rates
Implied by Bigotry and
Statistical Discrimination
Bigotry
Only
Conditional
default rates dA(s)=dB (s), ∀s
Marginal
default rates dA(sA* ) > dB (sB* )
Average
dA > dB
default rates
Statistical
Only
Both
dA(s) < dB (s), ∀s
dA(s) <dB (s), ∀s
dA(sA* ) = dB (sB* )
dA(sA* ) > dB (sB* )
dA ≥ ≤ dB
dA ≥ ≤ dB
(that is, the one with the lowest approved signal) will be more creditworthy than the marginal white borrower (see figure 3). In contrast,
if lenders practice statistical discrimination, they
do so precisely because they wish to ensure
that the marginal white and marginal minority
borrowers are equally creditworthy. In other
words, statistical discrimination arises when
lenders try to offset differences in the overall
applicant pools across races (see figure 5).
Thus, when we focus on marginal default rates,
we see that Becker’s argument remains valid,
even in the presence of differential creditworthiness across racial groups.15
Unfortunately, regulators and econometricians cannot precisely observe a borrower’s
actual signal, nor can they pin down a lender’s
true underwriting guidelines. Thus, identifying
the marginal borrower from each group is a
practical impossibility. One way around this
problem is to examine the default risk within a
subset of borrowers whose creditworthiness is
below some arbitrary threshold, as in Berkovec
et al. (1994). Using a sample of FHA borrowers,
they find that minorities in the highest-risk
quartile have a significantly higher conditional
default rate than do whites in the same risk
class.16 If we generically treat all borrowers in
this risk class as “marginal,” the results of
Berkovec et al. would appear to suggest that
statistical discrimination is the best explanation
for those of Munnell et al.
Table 1 summarizes the default-rate implications discussed in this section. In a world in
which members of both groups are equally
creditworthy on average, conditional default
rates will likewise be the same across groups
[dA(s) = dB (s),∀s ]. In this case, if lenders exhibit
tastes for discrimination the default rate of the
marginal minority borrower will be lower than
that of the marginal white borrower [dA(sA* ) >
dB(sB* )], causing the average default rate of
minority borrowers to be lower than that of
whites (dA > dB ).
In contrast, when lenders have an incentive
to statistically discriminate, minorities’ conditional default rate will be higher than that of
whites [dA(s) < dB(s),∀s]; average default rates
will be ambiguous. Nevertheless, lenders acting
solely on a profit motive will discriminate
against minorities only to the extent necessary
to equalize the marginal default rate of members of each group [dA(sA* ) = dB(sB* )].
Finally, when lenders act on both statistical
and taste-based motives, conditional default
rates will continue to be higher for minorities
[dA(s) < dB (s),∀s]. Interestingly, however,
lender bigotry will cause the default rate of the
marginal minority borrower to fall below that of
the marginal white borrower [dA(sA* ) > dB(sB* )],
despite the higher conditional default rates for
minority borrowers.
It is important to note that while conditional
default rates may be useful in determining
whether lenders have an incentive to discriminate, they cannot be used to determine whether
lenders actually act on this incentive. Conditional default rates across groups will diverge
whenever minority borrowers on average are
substantially less creditworthy than white borrowers. This will be true whether or not lenders
actually require minorities to meet a higher
underwriting threshold, s *. Thus, we must rely
on denial rate analyses such as Munnell et al. to
uncover discrimination and use default rate
information to help determine the underlying
source, if discrimination is determined to exist;
we discuss the importance of this issue in more
detail next.
What Constitutes
Discrimination?
Taken together, Munnell et al.’s denial rate findings and Berkovec et al.’s default rate results
pose a puzzle: How can marginal minority borrowers be significantly more likely to default
than their white counterparts, yet still be less
likely to be approved in the first place?
■
15 This point was made by Calomiris et al. (1994).
■ 16 Although Berkovec et al. do control for numerous property and
personal characteristics, an important limitation of their analysis is that
their data do not contain any information on individual credit histories.
The authors attempt to correct for any bias resulting from this omission,
and still conclude that marginal minority borrowers default at a significantly higher rate than do whites.
10
The answer is statistical discrimination. In
contrast to the taste-based discrimination considered by Becker, statistical discrimination is based
on lenders’ beliefs about applicant creditworthiness. Calomiris et al. (1994), Calem and Stutzer
(1995) and Longhofer and Peters (1999) each
show that statistical discrimination can occur if
lenders’ beliefs about creditworthiness differ
across groups.17 For example, Longhofer and
Peters (1999) show that if a lender believes that
minority applicants’ average creditworthiness is
lower than that of white applicants, then minority applicants will have to clear a higher hurdle
—that is, minorities will be required to have
better applications than whites—in order to receive a loan. Thus, although the inferred creditworthiness (the posterior assessment of creditworthiness) of white and minority borrowers is
the same at the margin, econometric tests that
use a borrower’s application information (the
signal) to proxy for his true creditworthiness will
find that minorities default more often.
Although statistical discrimination provides a
straightforward, plausible resolution of the
results of Munnell et al. and Berkovec et al.,
many economists appear to be uncomfortable
with this explanation. In fact, they seem to
operate under the assumption that statistical
discrimination is not, or should not be, illegal.
Where would such an assumption come
from? Current law permits lenders to include
most any underwriting variable that is demonstrably related to the profitability of a loan.18
For example, lenders may consider an applicant’s past credit history, even though this variable is highly correlated with race, because it is
also strongly associated with an applicant’s likelihood of repaying the loan. On the basis of
this observation, it is reasonable to conclude
that the proper scope of fair-lending law is to
prohibit discrimination that arises for motives
that are not profit-based, that is, from bigotry.
For example, Becker and Macey’s focus on
average default rates would seem rather misplaced, considering that average minority creditworthiness is lower than average white
creditworthiness. Such a focus is perfectly reasonable, however, if one believes that statistical discrimination is not, in fact, discrimination
at all. We are also struck by the lengths to
which Munnell et al. go to reject the notion
that their findings are driven by statistical
■ 17 Arrow (1972a, 1972b) and Phelps (1972) offer the first discussion of statistical discrimination; they do so in the context of labor markets.
■ 18 Such variables must pass the “disparate impact test,” as we discuss below. See the Federal Financial Institution Examination Council’s
1994 joint statement on fair-lending enforcement.
discrimination: “The dearth of any evidence that
minorities default more frequently, given their
economic fundamentals, makes a conclusion of
economically rational [statistical] discrimination
problematic” (Munnell et al., [1996, p. 45]).
As we’ve already argued, however, existing
fair-lending laws appear to prohibit both tastebased and statistical discrimination implicitly.
Indeed, if statistical discrimination were permissible, lenders could simply include race as a
variable in a statistically verified credit-scoring
model; as long as the race variable showed a
statistically significant impact on the loan’s
overall profitability, it would be permissible to
consider race under such an interpretation of
the law. Of course, this is expressly illegal. Furthermore, the “disparate impact test” in fairlending enforcement effectively precludes the
use of underwriting variables that themselves
have no information content, but rather serve
as proxies for race.
This discussion highlights why it is so important to confront issues like mortgage discrimination with a sound theoretical model. In the
context of our formal model of the mortgage
underwriting process, the definition of discrimination and the proper way of measuring it
become clear. When both statistical discrimination and bigotry can result in differences in s *
across groups, the question of whether default
rates can be used to detect discrimination
becomes largely irrelevant.
This is not to suggest, however, that default
rates are useless in the mortgage discrimination
debate. In fact, one of the most important
questions is how best to respond to any discrimination that does exist in the mortgage
market. It is to this question that we turn next.
Uncovering
the Source of
Discrimination
Although borrower default rates are poorly
suited for determining whether or not lenders
discriminate against minority applicants, they
can be useful in determining what incentives lie
at the root of any discrimination that is uncovered. In other words, default rates may provide
a means of distinguishing between statistical
discrimination and bigotry.
It is worth asking why we are interested in
identifying the source of mortgage market discrimination. After all, we have argued that this
distinction is not important under the law. Nor
is the root cause of discrimination likely to
interest an individual victim. From a policy-
11
maker’s perspective, however, it is quite important to know whether discrimination is beliefbased or preference-based. Any policy’s ability
to eradicate discrimination will depend on the
underlying source of that discrimination.19
For example, an appropriately designed
penalty/subsidy scheme might counteract a bigoted lender’s willingness to forgo profitable
opportunities for lending to minority applicants,
thereby inducing it to equalize its required qA*
and qB* .20 Alternatively (and perhaps more
effectively), policymakers may attempt to combat taste-based discrimination by promoting
competition in the mortgage market.
While these kinds of policies may work to
eliminate bigotry, they can have the opposite
effect on a statistical discriminator. Eradication
of belief-based discrimination hinges on a policy’s ability to make profit-maximizing lenders
ignore costless information (the correlation
between race and default) that, if used, would
improve their profitability. Increased competition strengthens a lender’s desire to employ
such information, effectively increasing its
incentives to statistically discriminate. Instead,
statistical discrimination is best fought by focusing directly on the underlying source of differences across groups.
Likewise, penalty/subsidy schemes (much
like the current use of the CRA as a tool for fairlending enforcement) may be less desirable
when the true source of discrimination is statistical correlation between race and creditworthiness. Although such a scheme may well
enhance minorities’ access to credit, it will do
so at a very large cost: By making mortgage
loans more attractive to less-creditworthy
minority applicants, a penalty/subsidy solution
would reduce the average creditworthiness of
the applicant pool, strengthening the correlation between race and default. As a result, even
larger subsidies or penalties would be required
to eliminate statistical discrimination.
Finally, because statistical discrimination is
motivated by a desire to maximize profits, in
order to equalize sA* and sB* banks will have to
hold whites to a higher creditworthiness standard than minorities (qA* > qB* ). That is, to eradicate statistical discrimination, regulators must
give banks a “taste for discrimination” against
white applicants so that banks will have the
incentive to pass up more profitable loans (to
whites) in order to make less profitable ones
■
19 See Longhofer (1995) for a related discussion.
■ 20 Whether this involves the bank’s raising its standard for whites
or lowering it for minorities, however, is a more complicated question.
(to minorities). Beyond its social and political
implications, such a policy may be particularly
difficult to implement.
Despite its importance from a policy perspective, uncovering the root cause of discrimination has not been the purpose of existing
research. The reason is that both the statistical
discriminator and the bigot will require a minority applicant to have a “better” application
than a white applicant in order to be granted a
loan. That is, a finding that sA* < sB* is equally
consistent with both preference-based and statistical discrimination.
Instead, the difference between taste- and
preference-based discrimination is the fact that
a bigot will require minorities to meet a higher
minimum (inferred) standard of creditworthiness than whites (that is, qA* < qB* ), while the
statistical discriminator, because he has no taste
for discrimination, is unwilling to forgo lending
to creditworthy minority applicants and so
holds minorities and whites to the same minimum credit standard (that is, qA* = qB* ).
This fact suggests that conditional and marginal default rates may help determine the
underlying source of any discrimination in the
mortgage market. In particular, if conditional
default rates are equal across groups, then statistical discrimination is an unlikely explanation
for any discrimination that is uncovered through
denial-rate analyses. On the other hand, if the
conditional default rate of marginal minority
borrowers exceeds that of marginal white borrowers, then bigotry becomes a less likely candidate for the root cause of discrimination.
Alternatively, Craig et al. (1998) propose a
method for distinguishing empirically between
statistical and preference-based discrimination
that uses denial-rate data like those employed
by Munnell et al. Unlike Munnell et al., they
can address this issue because they take advantage of the structure inherent in the underwriting model developed by Longhofer and Peters
(1999) and discussed here. Because the difference between statistical discrimination and bigotry lies in the relationship between qA* and qB*,
the empiricist must be able to reconstruct the
lender’s Bayesian updating process (the underwriting process) to arrive at each applicant’s
inferred quality, q(s). A finding that minority
applicants are more likely to be rejected than
whites, conditional on q(s), would be consistent with preference-based discrimination. On
the other hand, if the likelihood of being
rejected varies across racial groups controlling
for s, but does not vary after controlling for
each applicant’s q, statistical discrimination is
the more plausible explanation.
12
IV. Conclusion
References
We have shown here how the current mort-
Arrow, Kenneth J. “Models of Job Discrimination,” in Anthony H. Pascal, ed., Racial Discrimination in Economic Life. Lexington,
Mass.: Lexington Books, 1972a, pp. 83–102.
gage discrimination debate has suffered
because of inadequate theoretical underpinnings. Using a formal model of the underwriting process developed by Longhofer and Peters
(1999), we are able to define what is meant by
discrimination and to design tests for uncovering such behavior. Furthermore, we are able to
define several different types of default rates
precisely and to explore their implications
under different underlying discriminatory
incentives. Finally, we argue that developing
appropriate policy responses to mortgage market discrimination depends crucially on understanding its root causes. Using our theoretical
model, we are able to design some tests for
uncovering this crucial information.
The definition of discrimination that arises
out of our model implies that denial rate analyses like those performed by Munnell et al. are
the proper tool for uncovering discrimination.
This should not be interpreted, however, as
suggesting that their results prove the existence
of widespread discrimination in mortgage markets. Compelling as they are, we share some of
the well-documented concerns about the veracity and interpretation of their results.21 In the
end, more research will be required to confirm
or refute the existence of widespread discrimination and to understand its causes.
—— . “Some Mathematical Models of Race Discrimination in the Labor Market,” in Anthony
H. Pascal, ed., op. cit., 1972b, pp. 187–203.
Becker, Gary S. The Economics of Discrimination, 2d. ed. Chicago: University of Chicago
Press, 1971.
——. “The Evidence against Banks Doesn’t
Prove Bias,” Business Week, April 19, 1993a,
p. 18.
——. “Nobel Lecture: The Economic Way of
Looking at Behavior,” Journal of Political
Economy, vol. 101, no. 3 (June 1993b),
pp. 385–409.
Berkovec, James A., Glenn B. Canner, Stuart
A. Gabriel, and Timothy H. Hannan.
“Race, Redlining, and Residential Mortgage
Loan Performance,” Journal of Real Estate,
Finance, and Economics, vol. 9, no. 3
(November 1994), pp. 263–94.
Brimelow, Peter, and Leslie Spencer. “The
Hidden Clue,” Forbes, January 4, 1993, p. 48.
Browne, Lynn Elaine, and Geoffrey M.B.
Tootell. “Mortgage Lending in Boston—A
Response to the Critics,” Federal Reserve
Bank of Boston, New England Economic
Review, September/October 1995, pp. 53–78.
Calem, Paul, and Michael Stutzer. “The
Simple Analytics of Observed Discrimination
in Credit Markets,” Journal of Financial
Intermediation, vol. 4, no. 3 (July 1995),
pp. 189–212.
Calomiris, Charles W., Charles M. Kahn,
and Stanley D. Longhofer. “HousingFinance Intervention and Private Incentives:
Helping Minorities and the Poor,” Journal of
Money, Credit, and Banking, vol. 26, no. 3,
pt. 2 (August 1994), pp. 634–74.
■ 21 See, for example, Horne (1994, 1997), Yezer et al. (1994),
Hunter and Walker (1996), and Day and Leibowitz (1998). See also Browne
and Tootell (1995).
Craig, Ben R., Stanley D. Longhofer, and
Stephen R. Peters. “Measuring Creditworthiness and Discrimination in Mortgage
Lending,” Federal Reserve Bank of Cleveland, unpublished manuscript, May 1998.
13
Day, Theodore E., and Stanley J. Leibowitz.
“Mortgage Lending to Minorities: Where’s
the Bias?” Economic Inquiry, vol. 36, no. 1
(January 1998), pp. 3–28.
Federal Financial Institutions Examination
Council. “Policy Statement on Discrimination in Lending,” pt. III, Federal Register,
vol. 59, no. 73 (April 15, 1994).
Ferguson, Michael F., and Stephen R.
Peters. “What Constitutes Evidence of Discrimination in Lending?” Journal of Finance,
vol. 50, no. 2 (June 1995), pp. 739–48.
——, and ——. “Cultural Affinity and Lending
Discrimination: The Impact of Underwriting
Errors and Credit Risk Distribution on Applicant Denial Rates,” Journal of Financial
Services Research, vol. 11, nos. 1–2 (April
1997), pp. 153–67.
——, and ——. “Is Lending Discrimination
Always Costly?” University of Illinois, unpublished manuscript, September 1998.
Galster, George C. “The Facts of Lending Discrimination Cannot Be Argued Away by
Examining Default Rates,” Housing Policy
Debate, vol. 4, no. 1 (1993), pp. 141–46.
Horne, David K. “Evaluating the Role of Race
in Mortgage Lending,” FDIC Banking
Review, vol. 7, no. 1 (Spring/Summer 1994),
pp. 1–15.
—— . “Mortgage Lending, Race, and Model
Specification.” Journal of Financial Services
Research, vol. 11, nos. 1–2 (April 1997),
pp. 43–68.
Hunter, William C., and Mary Beth Walker.
“The Cultural Affinity Hypothesis and Mortgage Lending Decisions,” Journal of Real
Estate Finance and Economics, vol. 13, no. 1
(July 1996), pp. 57–70.
Longhofer, Stanley D. “Rooting Out Discrimination in Home Mortgage Lending.” Federal
Reserve Bank of Cleveland, Economic Commentary, November 1995.
——, and Stephen R. Peters. “Self-Selection
and Discrimination in Credit Markets,”
Federal Reserve Bank of Cleveland, Working Paper No. 9809, July 1998 (revised February 1999).
Macey, Jonathan R. “Banking by Quota,” Wall
Street Journal, September 7, 1994, p. A14.
Munnell, Alicia H., Lynn E. Browne, James
McEneaney, and Geoffrey M.B. Tootell.
“Mortgage Lending in Boston: Interpreting
HMDA Data,” Federal Reserve Bank of
Boston, Working Paper No. 92–7, October
1992.
——, Geoffrey M.B. Tootell, Lynn E.
Browne, and James McEneaney. “Mortgage Lending in Boston: Interpreting HMDA
Data,” American Economic Review, vol. 86,
no. 1, March 1996, pp. 25–53.
Phelps, Edmund S. “The Statistical Theory of
Racism and Sexism,” American Economic
Review, vol. 62, no. 4 (September 1972),
pp. 659–61.
Tootell, Geoffrey M.B. “Defaults, Denials, and
Discrimination in Mortgage Lending,” New
England Economic Review, (September/
October 1993), pp. 45–51.
Yezer, Anthony M.J., Robert F. Phillips, and
Robert P. Trost. “Bias in Estimates of Discrimination and Default in Mortgage Lending: The Effects of Simultaneity and SelfSelection,” Journal of Real Estate Finance
and Economics, vol. 9, no. 3 (November
1994), pp. 197–215.
14
Banking and Commerce:
How Does the United
States Compare
to Other Countries?
by João A.C. Santos
Introduction
Recent attempts to repeal the Glass-Steagall
Act, which would allow commercial banking to
mix with investment banking, have again initiated debate over current regulation prohibiting
the affiliation between banking and commerce.
Although participants in that debate have put
forward many arguments based on U.S. historical experience, international evidence, and
research results, the debate remains unsettled.
There is no consensus as to the extent of the
affiliation between banks and nonfinancial firms
throughout American history. In some cases, this
lack of consensus is the result of differing definitions of banking and commerce. In other cases,
it results from considering different ways
through which the corporate affiliation between
banks and firms can be implemented. Still in
other cases, the difference results from the use
of like terminology, “mixing banking with commerce,” to indicate two different ways to implement the affiliation—for a bank to own a stake
in a firm, or for a firm to own a stake in a bank.
As we will see, each of these two forms of affiliation has been subject to different regulations.
Therefore, using either one of them as a basis
for measuring the association between banking
and commerce will lead to different conclusions.
João A.C. Santos is an economist
at the Bank for International Settlements in Basel, Switzerland.
The author thanks Joseph
Haubrich and George Pennacchi
for their useful comments and
suggestions. Part of this research
was developed while the author
was at the Federal Reserve Bank
of Cleveland.
Any dividing line between banking and commerce is bound to be imprecise. The legal definition of banking itself has changed over time
and has been subject to many interpretations.
Throughout this article, the term banking is
used to denote commercial banking in its simplest form; therefore, a bank is defined as a firm
that accepts demand deposits and makes loans.
This allows, when necessary, a distinction between commercial banking and investment
banking, which includes activities such as brokerage and securities underwriting. Commerce
is used to denote all nonfinancial firms—that is,
commercial and industrial firms. The article
assumes that the association between banks
and firms may be implemented (1) through one
party undertaking the other party’s activity inhouse; (2) through one party’s investment in
the capital of the other party; or (3) through a
parent company, such as a bank holding company (BHC), which owns a stake in both a
bank and a firm.1
■
1 The case where an investor, rather than a corporation, owns a
stake in a bank and in a firm is also sometimes considered as a way to mix
banking with commerce. This form of association at the personal level is
not considered here. See Huertas (1986) for examples of investors in the
United States with simultaneous controlling interests in banks and in nonfinancial firms.
15
Using these definitions, the article shows
that, traditionally, U.S. banking regulations have
not allowed banks to enter commerce, though
there have been significant exceptions to this
separation at different points in time. Regulation of firms’ ownership of banks is very recent,
beginning only with the passage of the Bank
Holding Company Act (BHCA) in 1956. When
comparing current U.S. regulations on the affiliation between banks and firms to those in force
abroad, the article finds that other countries’
regulations are significantly more liberal. However, an analysis of banks’ investments in equities in these countries shows that such investments account for a small fraction of banks’
total assets.
As in the past, the most recent debate on
whether banks should be permitted to affiliate
with firms has focused on the potential implications of that affiliation for the banking sector
and the safety net.2 There has also been a
great deal of interest in the discussion of how
these implications would vary with the organizational forms adopted by banks to implement
the affiliation.3 The potential effects of that
affiliation on other segments of the economy,
such as nonfinancial firms, have been largely
ignored, yet they are of considerable importance. Despite recent research on the design of
financial systems, our understanding of the
implications associated with different financial
systems is far from complete. Nonetheless, as
we will see, there is a segment of that research
that is particularly timely for the ongoing
debate. This research has focused on the implications for borrowing firms resulting from
banks’ owning equity positions in these firms.
The paper proceeds as follows: The next
section summarizes the regulations on banks’
investments in firms and those on firms’ investments in banks throughout American history.
Section II compares the current U.S. regulations
with those in force abroad and discusses the
extent of banks’ investments in equities in several countries. Section III reviews the literature
dealing with the implications of banks’ investments in nonfinancial firms. Section IV ends the
paper with some final remarks.
■ 2 See Huertas (1988), Mester (1992), or Saunders (1994) for a
review of the arguments at the center of the debate.
■
3 Some of the issues in that discussion have also been present in
the debate over the organizational forms that banks should be allowed to
adopt to combine commercial banking with investment banking. See
Santos (1998a) for an extensive discussion of these issues in the context
of the commercial–investment banking debate and Cumming and Sweet
(1987) for a comparison of the predominant organizational forms adopted
in the G-10 countries to integrate banking with commerce.
I. Banking
and Commerce
Throughout
American History
Although the debate on the association
between banks and nonfinancial firms prior to
the National Banking Act of 1864 still continues, since then, and in particular after the passage of the BHCA in 1956, the law has dictated
the separation of banking from commerce.4 In
the past, the regulation of firms’ ownership of
stakes in banks was not as restrictive as that of
banks’ ownership of stakes in firms. With respect to the current U.S. banking regulations,
they allow firms to make limited investments in
banks, but not to control them. That regulation
also prohibits banks from making investments
in firms, but allows BHCs to make such investments within certain limits.
Nonfinancial Firms’
Investment in Banks
Restrictions on nonfinancial firms’ investment in
banks first appeared in American banking regulation in 1956 with the BHCA. Until then, any
commercial or industrial firm could be the sole
owner of a bank. The primary restrictions on
the ownership of banks before the BHCA were
not directed at firms. One of these restrictions
was defined in the National Banking Act of
1864 and prohibited a bank from owning
shares in another bank. Another restriction was
defined in section 20 of the Glass-Steagall Act
and prohibited any firm “principally engaged”
in the investment banking business from affiliating with member banks.5
There are many examples of firms owning
banks throughout American history. Firms’ ownership of banks goes as far back as 1799, when
the Manhattan Company was chartered to supply New York City with fresh water. That company entered the business of banking by creating the Bank of the Manhattan Company, which
it operated as a subsidiary. Likewise, in 1954 the
Transamerica Corporation controlled banks in
five western states and owned subsidiaries
engaged in various nonbanking activities.6
■ 4 See Shull (1983), Huertas (1986), Fein and Faber (1986), and
Blair (1994) for an analysis of the association between banking and commerce in American banking history.
■ 5 An additional constraint, though not very restrictive, of the Banking Act of 1933 was to require corporations owning more than 50 percent
of one or more member banks to apply to the Federal Reserve for a permit
to vote their stock.
16
With respect to the current restrictions on
firms’ investments in banks, they are determined by three definitions contained in the
BHCA of 1956 and its subsequent 1966 and
1970 amendments. They define a BHC, a bank,
and the powers of a BHC, respectively. When
the BHCA was enacted in 1956, it defined a
BHC as any company that controlled two or
more banks. Therefore, single BHCs were
exempted. Not surprisingly, many single BHCs
were established and their number continued
to grow until 1970, when the BHCA was
amended to end the loophole. The BHCA states
that a company controls another company if it
(1) has direct or indirect ownership, control, or
power to vote 25 percent or more of any class
of voting securities of that company; (2) is able
in any manner to elect a majority of the directors of that company; or (3) is able to exercise,
directly or indirectly, a controlling influence
over that company as determined by the Board.
That definition, therefore, allows firms to make
noncontrolling investments of up to 25 percent
of the voting shares of a bank.
As expected, the definition of a bank has also
played a key role in the regulations that separate banking from commerce. As mentioned
above, the Glass-Steagall Act prohibition against
firms “principally engaged” in investment banking owning banks applied only to member
banks—that is, all national banks and the state
banks that chose to be members of the Federal
Reserve System. The BHCA of 1956, however,
adopted a broader definition of a bank: “[A]ny
national banking association or any state bank,
savings bank or trust company” was considered
a bank. The 1966 amendment to the BHCA
redefined a bank as any institution that accepted
demand deposits. The 1970 amendment again
redefined banks, this time as any firm that “(1)
accepts deposits that the depositor has a legal
right to withdraw on demand, and (2) engages
in the business of making commercial loans.” As
a result of this last definition, any institution that
offered one service but not the other would not
be classified as a bank.7 Such firms became
known as “nonbank banks” and were owned by
many different corporations. This led to yet
another redefinition of banks in the Competitive
Equality Banking Act of 1987 as an institution
that is either insured by the Federal Deposit
Insurance Corporation or offers demand
deposits (or transaction accounts) and makes
commercial loans.8
Finally, the BHCA restricted BHCs’ powers to
the business of banking and to some bankingrelated activities. The act contained a list of several nonbanking businesses that BHCs could
offer and directed the Board to authorize any
other nonbanking activities. The 1970 amendment, however, requires these nonbanking
activities to be “closely related to banking” and
for their provision by BHCs or their subsidiaries
to produce public benefits that outweigh possible adverse effects.9
In sum, a nonfinancial firm in the United
States may make equity investments in banks
and BHCs, but they cannot control these institutions. More specifically, nonfinancial firms
cannot own more than 25 percent of a bank or
a BHC because they would themselves become
BHCs. As for the BHCs, they can be involved in
banking and banking-related business, but not
in nonfinancial activities.
Banks’ Investment in
Nonfinancial Firms
Throughout American banking history, banks
were not permitted to own nonfinancial firms.
There were, however, notable exceptions to
this rule. Shull (1983) argues that the U.S. policy of prohibiting banks from owning firms
originated in the 1694 House of Commons Act
establishing the Bank of England. That act
defined the powers of the Bank of England and
included a clause explicitly prohibiting the
Bank from dealing in merchandise.
The charters of early U.S. banks did not
always define the business of banking or bank
powers. There were, however, banks whose
charters included clauses mimicking that of the
Bank of England. Examples of such banks
include the Bank of North America, established
in 1781 as the first incorporated bank in the
United States; the Bank of New York, established in 1784; and the First and Second Banks
■ 6 See Huertas (1986) and Shull (1994) for more examples of firms
that owned banks at various times in the nineteenth and twentieth centuries.
■ 7 Some institutions, for example, continued to offer demand
deposits, but restricted their extension of commercial credit to the purchase of money-market instruments such as commercial paper. Other firms
avoided that definition of banks by offering NOW (negotiable orders of
withdraw) accounts instead of demand deposits. NOWs are similar to
demand deposits, with the difference that they require prior notice before
the customer can withdraw the money.
■ 8 The nonbank banks established at the time were grandfathered,
with the restriction that their assets could not grow more than 7 percent in
any 12-month period. See Mester (1992) and Shull (1994) for several
examples of nonbank banks.
■
9 See Pollard, Passaic, Ellis, and Daly (1988) for a detailed presentation of U.S. banking law and the list of nonbanking activities allowed
by the Board.
17
of the United States, established in 1791 and
1816, respectively.
With time, bank charters increasingly detailed
the powers of banks. One of the first definitions
of the “business of banking” appeared in 1825
in the charter of the Commercial Bank of Albany. It included a detailed list of the bank’s
powers and a clause requiring the bank to have
“no other powers whatsoever,” implicitly prohibiting the bank from dealing in merchandise.
This definition was later included in the New
York Free Banking Act of 1838, which opened
banking to the general public, and it influenced
the banks’ powers clause of the National Banking Act of 1864, which established the national
banking system.
The National Banking Act allowed national
banks to offer “all such incidental powers as
shall be necessary to exercise the business of
banking.” The act listed the explicit powers
available to banks and gave the Comptroller
of the Currency the authority to determine the
activities that were incidental to the business
of banking. Since its enactment, the act’s powers clause has been the subject of much
debate and interpretation. In litigation, it was
determined that national banks could accept
corporate stock as collateral or payment for
debt (in this case, they could hold the stock
only for a limited period of time), but they
could not deal in or purchase stock as an
investment.10 In addition, banks could not
engage in the operation of a business, even if
it had been acquired in satisfaction of a debt.
Therefore, the National Banking Act extended
in time the prohibition of the early bank charters against dealing in merchandise.
Throughout American history there were,
however, periods of time when banks were
permitted to enter commerce. For example,
there is evidence that during the second quarter
of the nineteenth century—a period that
became known as the “free” or “wildcat” banking era—banks in some states, such as Connecticut, Michigan, New Jersey, South Carolina
and Texas, received charters allowing them to
combine banking with many other nonbanking
activities. There is also evidence of banks mixing with commerce after the enactment of the
National Banking Act in 1864. When the act was
enacted, it was believed that state-chartered
banks would convert to national charters. But
while a national charter did confer greater prestige and competitive advantage in terms of note
issuance (state-chartered banks had to pay
taxes on the notes they issued), state charters
allowed broader powers. California, for example, allowed its banks to enter commerce.
Another instance in which banking mixed with
commerce occurred during the post–Civil War
period through private banks. These were
unincorporated banks that did not issue notes
and were free to pursue any activity. Although
they were referred to as banks, many states
prohibited them from using the name “bank.”
Private banks combined commercial banking
with many other activities, including brokerage,
securities underwriting, and commerce. With
the passage of the Glass-Steagall Act in 1933,
some private banks opted to terminate their
deposit-taking activities in order to continue
their nonbanking activities. Some of them went
on to become leaders in the investment banking business in the United States.11
Current regulation prohibits banks from
making investments in nonfinancial firms. As
mentioned above, the National Banking Act
of 1864 has been interpreted as prohibiting national banks from making such investments.
This prohibition has also been extended to
state member banks. State nonmember banks
are usually limited to investments that are permissible for national banks, but there are
exceptions which vary with the state in which
they are chartered.12 Finally, with respect to
BHCs, as noted before, the BHCA restricts
them to the business of banking and to some
banking-related activities. Because of this,
BHCs have only been allowed to make investments in nonfinancial firms that do not account
for more than 5 percent of the firm’s outstanding voting shares.13
In conclusion, banks in the United States in
general have not been allowed to make investments in the capital of nonfinancial firms. There
■ 10 See James (1995) for a characterization of the conditions under
which banks can accept corporate stock for payment of debt claims when
the borrower is in financial distress.
■ 11 See Fein and Faber (1986) for examples of banks that combined
banking with nonbanking activities during the free banking era, and for
examples of private banks that mixed banking with nonbanking businesses.
■
12 See Halpert (1988) for some examples of these exceptions.
■ 13 In addition to the restrictions emanating from the banking law, it
is usually argued that the legal doctrine of equitable subordination reduces
banking organizations’ incentive to make equity investments in firms. This
doctrine, which is in force in the United States and the United Kingdom,
reduces a bank’s incentive to take an equity position in a firm to which it
has extended loans, because the exercise of control rights associated with
its equity stake may lead to a loss of the bank’s legal status as a creditor in
the event of bankruptcy. See Prowse (1990) and Roe (1990) for a characterization of the doctrine and Kroszner (1998) for a discussion of its implications for banking organizations.
18
T A B L E
1
Regulations on Nonfinancial Firms’
Ownership of Commercial Banks
Complies with EC Second Banking Directive.a
Complies with the EC Second Banking Directive.a
However, the Banking and Finance Commission
examines the “fit and proper” character of those
shareholders holding at least 5 percent of the
bank’s capital.
Canada No shareholder may own more than 10 percent
of a bank’s outstanding shares.
Denmark Complies with the EC Second Banking Directive.a
However, a firm may not have an interest that
allows a decisive influence on the bank.
Finland Complies with the EC Second Banking Directive.a
However, the firm cannot vote at an annual
meeting with more than 5 percent of the total
voting rights present at the meeting.
France
Complies with the EC Second Banking Directive.a
Germany Complies with the EC Second Banking Directive.a
Greece
Complies with the EC Second Banking Directive.a
Ireland
Advance notification is required for any application of more than 5 percent of the voting rights
in a bank, and prior approval is required for any
application of 10 percent or more of the total
shares or voting rights or any holding or interest
that confers a right to appoint or remove directors.
Italy
Persons who engage in significant business activity in sectors other than banking and finance are
forbidden from acquiring an equity stake which,
when added to that already held, would result in a
holding exceeding 15 percent of the voting capital
of a bank or in the control of a bank.
Austria
Belgium
Japan
Total investment is limited to firms’ capital or
net assets. The Anti-Monopoly Law prohibits
establishment of a holding company whose
main business is to control the business activities of other domestic companies through the
holding of ownership.
Luxembourg Nonfinancial firms may legally be the majority
shareholders in banks. However, general policy discourages nonfinancial groups or private
persons from being major shareholders in
banks.
Netherlands Complies with the EC Second Banking Directive.a However, a declaration of nonobjection
from the Minister of Finance is required for an
investment exceeding 5 percent of a bank’s
capital.
Portugal
Complies with the EC Second Banking
Directive.a
Spain
Complies with the EC Second Banking Directive.a However, a nonfinancial firm cannot
hold more than 20 percent of the shares of a
new bank during the first five years of its existence. Specified shareholder thresholds
require authorization by the Bank of Spain
before additional investment.
Sweden
Ownership is limited to 50 percent except
when a bank is near insolvency and there is a
need for external capital injection. In this
case, greater ownership may be permitted
based on the suitability of new owners.
Switzerland Unrestricted.
United
Complies with EC Second Banking Directive.a
Kingdom
a. The EC Second Banking Directive subjects a “qualifying investment” (a direct or indirect holding in an undertaking equal to at least 10 percent of its capital or voting rights or permitting the exercise of significant influence over its management) to regulatory consent based only on
the suitability of shareholders.
SOURCE: Barth, Nolle, and Rice (1997).
were, however, periods of time when certain
banks were granted permission to do so and
other occasions when they were able to combine banking with commerce by developing
substitutes for banks. As for the current regulation, it prohibits banks from investing in nonfinancial firms and it only allows BHCs to make
some rather limited investments in these firms.
II. Banking and
Commerce:
International
Evidence
Not all countries regulate the association
between banking and commerce as restrictively
as the United States. The difference is particularly striking in the case of the regulations on
banks’ investment in equities of nonfinancial
firms. As this section will show, most of the
countries reviewed here allow banking and
commerce to mix. However, the data available
indicate that, on an aggregate basis, shares and
participations represent a small fraction of foreign banks’ assets.
19
T A B L E
2
Regulations on Commercial Banks’
Ownership of Nonfinancial Firms
Complies with EC Second Banking Directive.a
Single shareholdings may not exceed 10 percent of
a bank’s own funds, and such shareholdings on an
aggregate basis may not exceed 35 percent of own
funds. More restrictive than the EC Second Banking Directive during a transitional period.a
Canada
Limited to 10 percent of outstanding shares of a
nonfinancial firm, with aggregate holdings not to
exceed 70 percent of bank capital.
Denmark Complies with EC Second Banking Directive.a
However, a bank may not hold a permanent, decisive participation in a nonfinancial firm.
Finland
Complies with EC Second Banking Directive.a
France
Complies with EC Second Banking Directive.a
Germany Complies with EC Second Banking Directive.a
Greece
Complies with EC Second Banking Directive.a
Ireland
Complies with EC Second Banking Directive.a
Italy
Most banks are subject to an overall investment
limit of 15 percent of their own funds (7.5 percent
in the case of unlisted firms) and to a concentration limit of 3 percent of funds in each holding in
nonfinancial firms. Some banks, due to their size
and proven stability, are subject to less stringent
limits (overall and concentration limits of 50 percent and 6 percent, respectively, for leading banks,
and 60 percent and 15 percent for specialized
banks).
Japan
A single bank’s ownership is limited to 5 percent
of a single firm’s shares.
Austria
Belgium
Luxembourg Complies with EC Second Banking Directive.a
Netherlands Complies with EC Second Banking Directive.a
However, a declaration of nonobjection from
the Minister of Finance is required for any bank
investment exceeding 10 percent of the capital
of the firm.
Portugal
Complies with EC Second Banking Directive.a
However, a bank may not control more than 25
percent of the voting rights of a nonfinancial
firm.
Spain
Complies with EC Second Banking Directive.a
Sweden
Investments on an aggregated basis are limited
to 40 percent of a bank’s own funds. Ownership in a firm is limited to 5 percent of this base
and must not exceed 5 percent of the total voting power of the firm. These limits do not
apply when a bank must protect itself against
credit losses; in this case, the bank must sell
when market conditions are appropriate.
Switzerland A single participation is limited to the equivalent of 20 percent of the bank’s capital; however, the Swiss Banking Commission can allow
this limit to be exceeded.
United
Complies with EC Second Banking Directive.a
Kingdom
However, an ownership share of more than 20
percent requires the investment to be deducted
from the bank’s capital when calculating its
capital adequacy on a risk basis. Otherwise, the
investment is treated as a commercial loan for
the risk-based calculation.
a. The EC Second Banking Directive limits qualifying investments to no more than 15 percent of a bank’s own funds for investment in a single
firm, and to no more than 60 percent for all investment in nonfinancial firms. In exceptional circumstances, these limits may be exceeded, but
the amount by which the limits are exceeded must be covered by a bank’s own funds and these funds may not be included in the solvencyratio calculation.
SOURCE: Barth, Nolle, and Rice (1997).
The Regulations
Tables 1 and 2 summarize, for a selected group
of foreign countries, the main regulations on
firms’ ownership of banks and on banks’ ownership of firms, respectively. An analysis of
these tables indicates that, as in the United
States, the majority of countries regulate firms’
ownership of banks less restrictively than they
do banks’ ownership of firms. With respect to
firms’ investment in banks, most countries’ regulations do not impose an absolute limit on the
share of a bank’s capital that a firm can own.
Instead, their regulations give supervisory
authorities control over the suitability of banks’
shareholders. There are, however, a few countries, such as Canada, Italy, Sweden, and the
United States, that limit the share of a bank’s
capital that a firm can own.
Contrary to the United States, the foreign
countries reviewed allow for limited investments in firms by banks. Most regulations
define a limit for each individual investment
and for the total of these investments in terms
of the bank’s capital. Some countries put an
additional restriction on that investment defined
in terms of the firm’s capital. For example, in
the Netherlands banks need authorization to
own a stake larger than 10 percent. In the
United Kingdom, stakes larger than 20 percent
are deducted from the bank’s capital for the
purpose of capital adequacy. In Denmark,
banks are not permitted to hold a permanent
participation in firms. Finally, in Portugal,
Canada, and Japan, banks may not own more
than 25 percent, 10 percent, and 5 percent of
the capital of a nonfinancial firm, respectively.
20
T A B L E
Banks’ Investment
in Equities
3
Banks’ Investment in Equitiesa
Ratio of shares
and participations to:
Country
Nb
Austria
1,041d
Belgium
104f
Large banks
7f
Canada
11f
Denmark
114e
Finland
7f
France
413f
Germany
266f
Large banks
3f
Greece
18f
Large banks
4f
Italy
269g
h
139f
Japan
Large banks 11f
Luxembourg 220i
Netherlands 173j
Portugal
37f
Spain
170k
Sweden
13f
Switzerland
88f
Large banks
4f
U.K.
40f
U.S.
9,986f
Large banks 100f
Loans/
assets
Securities/
assets
Assets
0.509
0.362
0.376
0.665
0.433
0.484
0.340
0.574
0.541
0.270
0.249
0.424
0.668
0.650
0.189
0.634
0.333
0.411
0.436
0.474
0.471
0.521
0.634
0.625
0.143
0.330
0.307
0.196
0.290
0.291
0.188
0.170
0.185
0.336
0.367
0.139
0.143
0.126
0.189
0.141
0.232
0.184
0.356
0.178
0.181
0.185
0.214
0.187
0.038
0.017
0.021
—
0.042
0.052
0.028
0.048
0.063
0.043
0.049
0.020
0.046
0.060
0.003
0.006
0.031
0.041
0.029
0.049
0.052
—
—
—
Nonbank Capital
depositsc reserves
0.087
0.47
0.055
—
0.075
0.098
0.116
0.116
0.132
0.060
0.068
0.055
0.060
0.083
0.008
0.014
0.060
0.091
0.056
0.106
0.113
—
—
—
0.882
0.604
0.787
—
0.604
1.142
0.844
0.902
1.234
0.916
1.134
0.220
1.387
1.949
0.119
0.153
0.382
0.477
0.494
0.766
0.860
—
—
—
a. Data relate to December 31, 1995.
b. Number of commercial banks, except when indicated otherwise.
c. Excludes interbank deposits.
d. Includes commercial, foreign-owned, savings, and cooperative banks and
other financial institutions.
e. Includes commercial and savings banks.
f. Includes foreign-controlled banks operating in France in the form of
branches or subsidiaries. However, branches of banks with headquarters in
other EC countries are excluded.
g. Includes limited company, cooperative, and main mutual banks, central
credit institutions, and branches of foreign banks.
h. Data relate to fiscal year ending March 31, 1995.
i. Includes banks and sociétés anonymes set up under Luxembourg law and
foreign companies.
j. Includes universal banks, banks organized on a cooperative basis, savings
banks, mortgage banks, other capital market institutions, and security credit
institutions.
k. Includes foreign-owned banks.
SOURCE: Organisation for Economic Co-operation and Development.
The firm-level data necessary to study the association between banking and commerce across
countries are not readily available. The OECD,
however, publishes data that give us an idea of
the extent of banks’ investment in equities.
Table 3 presents some statistics computed with
data for the same countries, with the exception
of Ireland, that were included in tables 1 and 2.
When reading table 3, the reader should keep
in mind that the statistics were computed
using aggregated data and that the variable
“shares and participations” include the investments in equities of all corporations, not only
nonfinancial firms. In addition, the reader
should also take into account the usual caveats,
such as the differences in accounting rules
and reporting methods, when making international comparisons.
As table 3 shows, for most countries, loans
represent a larger portion of the banks’ assets
than securities (Greece and Luxembourg are the
only exceptions), and shares and participations
represent a small fraction of banks’ securities.
Banks’ limited investments in equities are also
evidenced by two other statistics: banks’ investments in shares and participations represent less
than 12 percent of their nonbanks’ deposits and
are smaller than banks’ capital and reserves
(Finland and Japan are the only two exceptions).14 Table 3, however, tells us nothing
about the extent of that investment at the bank
level. The data available allow us to study that
issue only for a given group of banks, the group
of the largest banks, and only for a subset of the
countries considered here (Belgium, Germany,
Greece, Japan, Switzerland, and the United
States). Comparing the statistics for these banks
with those for the banking sector in the same
country, it becomes clear that larger banks are
more involved in equities investment than the
rest of the banking sector in the country, though
the extent of their investments is still quite limited. For example, in Germany and Japan, the
two countries where the largest banks’ investment in equities is highest, shares and participations still represent less than 7 percent of the
banks’ assets.
Table 3 is also mute with respect to several
other important issues related to banks’ investments in equities. For example, on average,
those investments account for what stake of the
■
14 The banking sector’s limited investment in equities in 1995 evidenced in table 3 accords with the results unveiled in Langohr and Santomero (1985) based on comparable data for 1981.
21
firms’ capital? At what point in a firm’s life do
banks make these investments? Are they made
in conjunction with other services, such as the
extension of a loan or a line of credit? As we
will see in the next section, some of these
questions have already been the subject of
research, mainly using data on Japan and Germany.
III. Banks’
Investment in
Equities: A Review
of the Literature
As with previous debates on changes to the
U.S. banking regulation, the ongoing debate
over a relaxation of the barriers between banking and commerce has focused on the potential implications of this regulatory change on
banks’ stability. The consequences of allowing
banks to mix with nonfinancial firms, however,
go far beyond that impact. For example, commercial banks’ ability to make investments in
nonfinancial firms would allow them to own
stakes in firms to which they extend loans.
Such investments would influence, among
other things, banks’ relationships with their
borrowers, including the way they monitor
borrowers and the design of contracts that
banks could offer borrowers throughout their
life. These effects would, in turn, influence
borrowers’ incentives, as well as the cost and
amount of funds made available to them.
For many years, economic theory has focused on the real sector of the economy and
disregarded the financial sector, viewing it as a
veil. The justification was that in a frictionless
world, à la Arrow–Debreu, there is no room for
financial intermediaries. However, the world we
live in is quite different from that envisioned by
Arrow and Debreu, and financial intermediaries
are perceived to play a key role in it. For example, Gerschenkron (1962) argues that financial
intermediation influences long-term growth.
Mayer (1988) suggests that there are systematic
differences in performance between the socalled bank-based systems and the marketbased systems, in which banks play a lesser role
and financial markets are more prominent. King
and Levine (1993) find a strong correlation between the size of the financial system and the
level of economic development.
Research on financial system design is still
in its early stages, but it has already unveiled
some important implications of certain features of a financial system.15 For example,
Dewatripont and Maskin (1990) argue that bad
projects persist too long in bank-based systems, whereas good projects are cut off too
early in market-based systems. Sabani (1994)
suggests that market-based systems restructure
less financially distressed borrowers than
bank-based systems. Allen and Gale (1997)
argue that bank-based systems provide better
intertemporal risk sharing, whereas marketbased systems provide better cross-sectional
risk sharing. Boot and Thakor (1997) justify
the coexistence of financial intermediaries and
securities markets because each performs a
different role: the former resolve postlending
and interim moral hazard problems, while the
latter facilitate trades by informed agents and,
hence, the transmission of information.16
Research on financial institution design has
sought primarily to explain the existence of
banks.17 More recently, however, that research
has focused on the implications of mixing commercial banking with other activities, in particular with investment banking.18 With respect to
the association between banking and commerce, most of it has focused on the potential
implications of banks’ equity investments on
nonfinancial firms.19 Pozdena (1991), Kim
(1992), and John, John, and Saunders (1994),
for example, show that a borrower’s risk-taking
incentive is reduced when the financier funds
the borrower through a combination of a loan
and an equity stake, rather than through a single loan. Santos (1999) studies the implications
of owning an equity stake when funding is provided by a bank rather than a financier in the
presence of moral hazard caused by deposit insurance. He shows that allowing banks to make
equity investments in firms to which they
extend loans does not increase the moral hazard problem. Boyd, Chang, and Smith (1997),
however, reach the opposite conclusion. Key to
their findings is the assumption that banks can
■ 15 For an extensive discussion of the literature on the design of
financial systems, see Thakor (1996) and Allen and Gale (1999).
■ 16 For additional theories explaining the simultaneous existence
of securities markets and banks, see Gorton and Haubrich (1987) and
Seward (1984).
■ 17 See Bhattacharya and Thakor (1993) and Freixas and Rochet
(1997) for an extensive review of the contemporary banking literature.
■
18 See Santos (1998b) and Rajan (1996) for a review of the literature on the association between commercial and investment banking.
■
19 See Santos (1998c) for a review of the literature on the affiliation between banking and commerce.
22
benefit as equity holders of the firm, but not as
debt holders, from the borrower’s behavior
associated with the moral hazard problem
embedded in the model. Their model differs
from Santos’ model in various respects. For
instance, they focus on banks’ monitoring to
control the moral hazard problem, while Santos focuses on the incentives driven by debt
and equity contracts.
Rajan (1992) studies the impact of the financier’s equity stake on his credibility when
he underwrites the securities of firms to which
he has loans outstanding. Rajan shows that a
possible remedy would be for the financier to
commit to purchase an equity stake at market
price at the time of the new issue. In contrast,
in a model where the financier holds a claim in
firms and chooses to underwrite some of them
to liquidate his claim, Puri (1996) shows that
an equity claim gives him more incentive than
a debt claim to underwrite bad firms and retain
his equity claim in good ones. Berlin, John,
and Saunders (1996) show that having an
informed financier holding both a debt claim
and an equity stake can prevent him from colluding with the borrower to exploit the firm’s
nonequity-uninformed stakeholders when the
firm is in financial distress.
Haubrich and Santos (1998) focus on a set
of issues rather different than those addressed
by the previous literature. They use the liquidity approach to financial intermediation pioneered by Myers and Rajan (1998). Myers and
Rajan use the positive and negative aspects of
liquidity to derive banks: These institutions
emerge as a special type of conglomerate, one
combining a firm that takes in liquid deposits
with a firm making illiquid loans. Haubrich and
Santos use a similar analysis to identify the
conditions under which a broader class of conglomerates, those combining banks with nonfinancial firms, can be advantageous.
Aside from these theoretical studies, there
is already a large body of empirical literature
on various aspects of banks’ ownership of
equity stakes in nonfinancial firms. However,
nearly all of that literature relates to German
and Japanese banks. Some studies find evidence that bank equity stakes in healthy firms
reduce agency costs. Sheard (1989), for example, finds that the bank with the largest loan
share is generally one of the top five shareholders of the firm. Prowse (1990) also finds a
significant correlation between the percentage
of outstanding debt and the percentage of
outstanding equity held in the same firm by
the largest debt holder; the correlation
becomes more significant in firms where
shareholders have greater scope to engage in
opportunistic behavior at the expense of debt
holders. Flath (1993) also finds that banks in
Japan hold more stock in companies for which
the agency problems of debt are more severe,
and that stockholding induces greater borrowing. As for Germany, Cable (1985) finds that
bank voting control and bank representation
on a firm’s supervisory board are both significantly correlated with bank borrowing by the
firm.20 Chirinko and Elston (1996), however,
find that independent firms have more bank
debt than bank-influenced firms.21
Other studies find evidence that bank equity
stakes in healthy firms reduce the incentive
and informational problems, thus increasing
the availability of funding and reducing the
cost of it. Hoshi, Kashyap, and Scharfstein
(1991), for example, find that investment by
Japanese firms with a close relationship to a
bank, in the industrial organizational form of a
keiretsu, is less sensitive to their liquidity than
firms raising funds through more arms-length
transactions. Elston (1993) finds similar results
for a sample of German firms by comparing
investment by firms in which banks have an
equity stake, with investment by firms in which
banks do not have a direct equity ownership.
Weinstein and Yafeh (1998), however, find a
less favorable result in Japan. They find that
the cost of capital for unaffiliated firms is lower
than that for firms with a main bank—that is,
firms whose largest lender is also their largest
bank equity holder.
Still another set of studies looks at the impact of bank equity stakes on healthy firms’
performance. Cable (1985) finds, based on a
sample of German firms, that bank voting
control and bank representation on a firm’s
supervisory board are both significantly correlated with bank borrowing by the firm and
with the firm’s performance. Gorton and
Schmid (1995) also find that banks’ equity
ownership in firms improves the performance
of these firms. Chirinko and Elston (1996),
however, find that German banks do not have
a significant effect on the profitability of the
■ 20 Banks in Germany not only own equity stakes in nonfinancial
firms, but they also have proxy rights to vote the shares of other agents
who keep their shares on deposit at the bank. See Mülbert (1997) for a
description of the German banking law on the monitoring instruments
available to banks.
■ 21 A firm is considered to be bank-influenced if a national bank or
a national insurance company holds more than 50 percent of the firm’s outstanding shares or if these institutions hold more than 25 percent of outstanding shares and no other owner holds more than 25 percent.
23
firms with which they are associated. Wenger
and Kaserer (1997) find an even less favorable
result. They find that bank-dominated firms
(firms in which banks have at least a 10 percent equity stake), have a lower shareholder
return than non-bank-dominated firms.
Finally, some studies have looked at the impact of bank equity stakes on the performance
of financially distressed firms. Hoshi, Kashyap,
and Scharfstein (1990) find that the costs of
financial distress are lower for Japanese firms in
industrial groups than for other firms. In particular, firms in industrial groups invest and sell
more than nongroup firms in the years following the onset of financial distress. They also
find that firms that are not part of groups—but
have close ties to a main bank—invest and sell
more than firms without strong bank ties.22
James (1995) identifies the conditions under
which American banks take equity positions in
debt restructurings. He finds, among other
things, that firms in which banks take equity
positions are more cash-flow-constrained and
have poorer operating performance prior to the
restructuring. However, these firms perform
better after the restructuring than firms with no
bank stock ownership.
As this sample of research shows, bank equity positions in nonfinancial firms have many
effects that go beyond the potential implications
on banks, though the latter tend to dominate
the debate on mixing banking with commerce.
Some of that research finds conflicting results,
and the robustness of some of its results can be
questioned. Moreover, our knowledge about
many of the potential effects of banks’ equity
stakes in firms is still limited. For example, the
empirical research conducted so far focuses on
bank stakes in well-established firms. It would
also seem important to ascertain the role of
those stakes throughout a firm’s life, in particular during the earlier stages of its life. Despite
these gaps, it would seem important for policymakers to take this literature into account
when debating future U.S. regulation of banking and commerce.
■ 22 Sheard (1989) describes, for various Japanese firms in financial distress, the financial assistance measures adopted by the firm’s main
bank during restructurings.
IV. Final Remarks
Throughout American banking history, regulations have generally prohibited banks from
making investments in nonfinancial firms but
allowed these to own equity stakes in banks.
There were several occasions when banks were
allowed to own stakes in firms, but those occurred mainly before the National Banking Act
of 1864. The regulation of firms’ ownership of
banks has also become more restrictive with
time, particularly since the enactment of the
Bank Holding Company Act in 1956. As a result
of these trends, the United States currently regulates the association between banking and
commerce significantly more restrictively than
other countries.
The most recent debate over loosening the
current U.S. barriers that separate banking from
commerce, particularly those that limit banks’
and BHCs’ investments in firms, has, as in the
past, focused on the potential implications of
this regulatory change on banks and the safety
net. While undoubtedly important, these are
only part of the potential effects of that association. As the literature reviewed here shows,
banks’ investments in firms have implications
that go far beyond the banking sector. That literature has produced some conflicting results
and it has not addressed some questions that
are pertinent to the debate. Nonetheless, it
would appear important for policymakers participating in the debate to consider that literature and, through it, the potential impact of the
association between banking and commerce
on the nonfinancial sector of the economy, together with the potential impact on the banking
sector and the safety net.
24
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