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NOVEMBER/DECEMBER 1996
L
ECONOMIC PERSPECTI
A review from the
Federal Reserve Bank
of Chicago
H o w a re s m a ll fir m s fin a n c e d ?
E v id e n c e fr o m s m a ll b u s in e s s
in v e s tm e n t c o m p a n ie s
G R A a n d f a ir le n d in g r e g u la tio n s :
R e s u ltin g tr e n d s in m o r tg a g e le n d in g
In d e x f o r 1 9 9 6
FEDERAL RESERVE BANK
OF CHICAGO
Call for
Papers
J997 Conference on
Bank Structure
& Competition
Contents
How are small firm s financed?
Evidence from small business
investm ent com panies............................................................................................2
Elijah B rew er III, Hesna Genay,
W illiam E. Jackson III, and
Paula R. W orth in g to n
This article examines the investment decisions of small
business investment companies (SBICs). The results
indicate that potential costs of contracting among SBICs,
small firms, and others may have significant effects on
how small firms are funded. For instance, projects
generating tangible assets and firms operating in industries
with few growth opportunities are more likely to be
financed with debt than nondebt.
CRA and fa ir lending regulations:
Resulting trends in m ortgage lending.............................................................. 19
Douglas D. E van off and Lew is M . Segal
This article provides background on the evolution of Community
Reinvestment Act (CRA) and fair lending regulations, summa
rizes the relevant economic literature, and evaluates the effective
ness of the regulations by analyzing recent trends in mortgage
lending activity. Are the trends in line with the intent of the
regulations? Can the trends be attributed to the regulations?
Call fo r papers..........................................................................................................47
Index fo r 1 9 9 6 .......................................................................................................... 48
ECONOMIC PHiRSPL(il IV KS
N o vem ber/D ecem ber 1996, Volum e XX, Issue 6
President
ECONOMIC PERSPECTIVES is published by
Michael H. Moskow
Senior Vice President and D ire c to r o f Research
William C. Hunter
Research Department
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Douglas Evanoff, Assistant Vice President
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Charles Evans, Assistant Vice President
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Editor
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Production
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ISSN 0164-0682
How are small firms financed?
Evidence from small business
investment companies
E lijah B re w e r III, Hesna G enay,
W illia m E. Ja ck so n III, and
Paula R. W o rth in g to n
How do firms and financial
intermediaries decide how to
finance investment projects
undertaken by a firm? Some
firms fund projects by issuing
equity, others by borrowing
from investors and/or financial intermediaries.
This issue interests researchers and practitio
ners in corporate finance, as well as public
officials whose policies influence the availabil
ity of capital and the terms on which capital is
provided to firms. Since Modiagliani and
Miller’s (1958) seminal work demonstrating
the conditions under which a firm’s value is
not affected by the choice between debt and
equity to finance its activities (capital struc
ture), research has focused on establishing the
analytical and empirical determinants of a
firm’s capital structure. Three hypotheses,
which are not mutually exclusive, are offered
to explain the relevance of capital structure.
The asymmetric information hypothesis holds
that managers and other insiders of a firm are
better informed about the current and future
prospects of the firm than outside providers of
capital. The firm’s capital structure, or financ
ing policy, is designed to convey this private
information to the capital markets and to mini
mize any underpricing of the firm’s financial
instruments due to investors’ uncertainty about
the quality of the firm. The second hypothesis
is based on the differential tax treatment of
equity and debt and implies that firms design
their financial policy to minimize taxes. In
this article, we focus on the third hypothesis,
which stems from work in contracting theory.
2
Contracting theory views a firm as a nexus of
contracts among its various stakeholders, such
as management, shareholders, creditors, suppli
ers, and customers. From this perspective, the
financing policy of a firm is designed to mini
mize total contracting costs, including potential
conflicts of interest among the parties (agency
conflicts).1 All of these hypotheses offer pre
dictions about which types of firms should
issue which types of securities. Although
numerous studies test these predictions, the
evidence is not conclusive.2
We examine the implications of contract
ing theory, using a unique, transactions-level
dataset on the investment activities of small
business investment companies (SBICs), which
are private venture capital firms licensed and
regulated by the U.S. Small Business Adminis
tration (SBA). The SBIC program was estab
lished by Congress in 1958 to encourage the
provision of long-term private sector capital,
both debt and equity, to the nation’s small
businesses. SBICs are private firms but, in
return for accepting some restrictions on the
types of investments they undertake, they are
eligible to receive government subsidies by
Elijah Brewer III, Hesna Genay, and Paula R.
Worthington are economists at the Federal
Reserve Bank of Chicago. William E. Jackson III is
an assistant professor of finance and economics at
the Kenan-Flagler Business School, University of
North Carolina at Chapel Hill. The authors would
like to thank the Small Business Administration for
providing the data, Leonard W. Fagan, Jr., for
providing detailed information on the SBIC pro
gram, Julian Zahalak for his excellent research
assistance, and David Marshall for comments.
ECONOMIC PERSPECTIVES
issuing SBA-guaranteed debentures (SBA
leverage). Our data contain information about
every financing transaction conducted by
SBICs between 1983 and 1992, including char
acteristics of the small firm receiving funds,
the type of security used (debt, equity, or some
hybrid), and other characteristics of the project
and transaction agreement. Thus, instead of
using stock data to examine the capital struc
ture question, we use flow data to consider
each financing transaction separately. This
permits us to separate the influence of firm,
industry, and project characteristics on the
decision of whether to use debt in a particular
transaction. Furthermore, the data allow us to
examine the relationship between the charac
teristics of investors (SBICs) and the types of
securities they purchase. Hence, we can offer
evidence on how the agency relationships of
SBICs with others affect their investment policy
with small firms.
Overall, our results are consistent with the
predictions of contracting theory. Our main
finding is that business projects that generate
tangible assets and allow little management
discretion tend to be funded with debt rather
than equity. This result is consistent with the
view that projects that generate tangible assets
minimize the ability of owner/managers to
shift funds to riskier projects. We also find
that smaller firms are more likely to obtain
debt than equity financing and that, over the
age range in our sample, the probability of
receiving debt financing increases with age,
though at a decreasing rate. Characteristics
of the recipient firm’s industry also matter:
Greater growth opportunities and research and
development (R&D) intensity are associated
with a higher probability of nondebt financing.
These results suggest that firms whose value
depends on growth opportunities or industryspecific information, such as R&D, are less
likely to receive debt financing because the
costs of financial distress are likely to be great
er for those firms. We also find that character
istics of the SBIC doing the funding are impor
tant: SBICs that are highly leveraged and affili
ated with nonbank organizations are more
likely to provide debt financing than other
investment companies.
In the remainder of this article, we discuss
the determinants of capital structure, describe
the data we use, and estimate an empirical
model of security choice.
FEDERAL RESERVE
BANK OF CHICAGO
The determinants of an SBIC's
security choice
What determines the type of security used
by an SBIC to finance the investment project
of a small firm? What characteristics of the
project, the small firm, and the SBIC affect
whether the SBIC makes a loan or becomes a
shareholder?
Agency conflicts
According to contracting theory, firms and
their contracts are organized such that the total
contracting costs among stakeholders are mini
mized. One of the main contracting costs is
potential conflicts of interest among stakehold
ers. In financial contracts, the significant stake
holders are the management, shareholders, and
creditors of the firm. Conflicts between manag
ers and shareholders may arise because the
managers are agents of the shareholders and do
not own 100 percent of the firm’s equity (Jensen
and Meckling, 1976; Jensen, 1986; Harris and
Raviv, 1990;Stulz, 1990). Because the manag
ers own only a fraction of the firm, they capture
only a fraction of the benefits of their effort.
Similarly, if they misuse firm assets, they only
bear a fraction of the cost. Furthermore, manag
ers may invest in projects that reduce the value
of the firm but enhance their control over its
resources. For instance, although it may be
optimal for the investors to liquidate the firm,
managers may choose to continue operations to
enhance their position.
Conflicts between shareholders and credi
tors may arise because they have different
claims on the firm. Equity contracts do not
require firms to pay fixed returns to investors
but offer a residual claim on a firm’s cash
flow. However, debt contracts typically offer
holders a fixed claim over a borrowing firm’s
cash flow. When a firm finances a project
through debt, the creditors charge an interest
rate that they believe is adequate compensation
for the risk they bear. Because their claim is
fixed, creditors are concerned about the extent
to which firms invest in excessively risky
projects. For example, after raising funds from
debtholders, the firm may shift investment
from a lower- to a higher-risk project. Equity
holders tend to prefer that the firm invest in
profitable but risky projects. If the project is
successful, the creditors will be paid and the
firm’s shareholders will benefit from its
improved profitability. If the project fails, the
3
firm will default on its debt, and shareholders
will invoke their limited liability status. In
addition to the asset substitution problem be
tween shareholders and creditors, shareholders
may choose not to invest in profitable projects
(underinvest) if they believe they would have
to share the returns with creditors.
Investors can design their contracts with
the firm to minimize these potential conflicts
of interest. To minimize the adverse effects of
asset substitution by shareholders, creditors
can require collateral or place restrictive cove
nants on the loans they make (see Berger and
Udell, 1990, 1995; and Hooks and Opler,
1994). Shareholders can limit management’s
discretion with regard to the firm’s resources
by requiring regular payments through debt
(Jensen, 1986;Stulz, 1990). Debt can also
force optimal liquidation decisions by giving
creditors the right to liquidate the firm if pay
ments are not made. Furthermore, by increas
ing the equity stake of management, debt can
better align the incentives of management and
shareholders.
Monitoring by investors can also be im
portant in mitigating agency conflicts. As
residual claimants, equity holders can become
what Jensen (1989) terms active investors by
getting involved in the day-to-day management
of firms (Hoshi, Kashyap, and Scharfstein,
1990a, 1990b, 1991; Pozdena 1991; Berlin,
John, and Saunders, 1993; dos Santos, 1995a,
1995b). Equity can also mitigate the underin
vestment problem associated with debt, since
old and new shareholders have the same incen
tives to invest in profitable projects. Accord
ing to contracting theory, the financial policy
of a small firm would depend on the types of
agency conflicts it faces. Therefore, the char
acteristics of a firm that are correlated with
agency conflicts would affect how it funds its
projects. What are those characteristics?
Characteristics o f the small firm
Risk of bankruptcy—If a firm operates in a
volatile sector and its cash flows vary a lot, the
likelihood that it may be unable to meet its debt
obligations is high. On the other hand, the firm’s
income may also be sufficiently high to earn
high returns for its shareholders. A firm with a
very volatile cash flow is more likely to finance
its projects with equity than debt.
Liquidation value—Even if a firm has a
high probability of bankruptcy, it can finance
4
its projects with debt if the costs of bankrupt
cy for creditors are small. Firms with rela
tively high levels of tangible assets or assets
that can be liquidated easily would have rela
tively low ex-post costs of bankruptcy and
ex-ante costs of issuing debt (Williamson,
1988; Schleifer and Vishny, 1992).3 Firms
with high levels of easy-to-monitor tangible
assets and few opportunities to substitute
risky assets will have less conflict between
debtholders and shareholders and a lower cost
of debt (Jensen and Meckling, 1976). As a
result, we would expect SBICs to provide
more debt to firms with high liquidation value
than to firms with low liquidation value.
Growth opportunities—For firms with
high growth opportunities, the cost of restrict
ing management’s discretion, thereby the like
lihood that the firm will not have sufficient
funds to invest in profitable projects, is relatively
high (Stulz, 1990). Conflicts between share
holders and creditors over the exercise of growth
options and the underinvestment problem are
also likely to be greater. Therefore, firms with
high growth opportunities are more likely to
finance their investments with equity than debt.
Profitability—If a firm is profitable, the
risk that it would be unable to meet its debt
obligations is smaller. Furthermore, the share
holders of profitable firms may be less likely to
substitute risky projects for safer ones after a
debt contract is written, since they have more
to lose if the project fails. Therefore, we would
expect profitable firms to finance more of their
projects with debt.4
Organizational form—Shareholders of
corporations and limited partners of firms have
limited liability against losses, whereas general
partners and owners of sole proprietorships have
unlimited liability. Consequently, shareholdercreditor conflicts are more likely among corpo
rations and limited partners than they are for
general partners and sole proprietorships. Thus,
corporations may be more likely to finance their
projects with equity.
Size—Size and the choice of financing
instrument may be related in several ways.
First, if larger firms are more diversified and
therefore less risky, we would expect them to
issue more debt. Second, recent work in cor
porate finance indicates that a positive relation
ship may exist between firm value and debt
issues (Harris and Raviv, 1990). High ex-post
ECONOMIC PERSPECTIVES
liquidation value implies high ex-ante firm
value, as well as greater likelihood of issuing
debt. As a result, to the extent that size is
related to firm value, larger firms are more
likely to issue debt.
Ease o f monitoring—If creditors can easi
ly identify the investment projects of firms,
then the likelihood that shareholders can sub
stitute risky assets, hence the cost of issuing
debt, would be low. Furthermore, if providing
equity capital to a firm allows the investor to
get involved in the management of the compa
ny (for instance, through board representation),
we would expect firms that are otherwise hard
to monitor to be financed with equity.
Characteristics o f the SBIC
In addition to the characteristics of a firm,
the characteristics of the investor are likely to
influence what type of financing is used. Because
SBICs are agents in their transactions with
investors who provide funds to them, they face
the same sort of agency conflicts with their
shareholders and creditors as small firms. There
fore, the investment policy of SBICs is likely
to be influenced by their characteristics. Although
the finance literature contains several studies
that examine how the principal-agent relation
ship between the investors and firms may af
fect firms’ financing policy, there is little evi
dence on how firms’ financing policy may be
affected by the principal-agent relationship
between the investors and their financiers.
The results in Brewer and Genay (1994) and
the statistics in table 4 (reviewed below) indi
cate that there are significant differences between
SBICs that provide debt financing and those
that provide nondebt financing. However,
because we have no structural model that exam
ines the effects of multiple agency relationships
of investors on their investment policy, we
include the characteristics of SBICs as control
variables in the following empirical analysis.
SBIC size and age—The venture capital
literature offers some evidence that the agency
relationship between venture capitalists and
their investors may affect the investment strate
gy of venture capitalists. Specifically, Gompers
(1995a) suggests that venture capitalists may
encourage a premature initial public offering
(IPO) of a firm to develop their reputation and
improve their ability to market the next venture
fund. He finds that relatively inexperienced
FEDERAL RESERVE
BANK OF CHICAGO
venture capitalists tend to bring companies to
the IPO market earlier than more experienced
venture capitalists. Similarly, Lerner (1994)
finds that experienced venture capitalists can
time the IPO market better. If experience of
venture capitalists affects how and when they
realize the returns on their investments, then
experience, as measured by age, of SBICs may
similarly affect their choice of securities.
The size of SBICs may also influence their
investment strategy. Sahlman (1990) describes
the extensive involvement of venture capital
ists in their portfolio companies. Venture
capitalists sit on the board of directors, are
actively involved in evaluating key managers
and investment and restructuring decisions,
and interact closely with firms’ suppliers and
customers. Our conversations with the manag
ers of SBICs indicate that SBICs are similarly
involved with small firms in which they hold
equity stakes. If these investments require
more investigation and industry expertise, such
activities can be carried out by larger, more
experienced investors at a lower cost (for exam
ple, due to economies of scale and ability to
attract better managers), reducing the relative
costs of equity financing. However, size is
determined by other policies of SBICs (such
as financing policy), as well as by investment
policy. Again, lacking a structural model, we
cannot determine the a priori relationship
between SBIC size and investment policy.
SBA leverage—Many SBICs fund their
activities by issuing SBA-guaranteed deben
tures, which are long-term securities. Our
previous research (Brewer, Genay, Jackson,
and Worthington, 1996) suggests that SBA
leverage is more burdensome for SBICs orient
ed toward equity investments, because lever
aged SBICs need to generate sufficient cash
flows to make payments on their SBA debt.
Similarly, the U.S. General Accounting Office
(1993) reports that the SBA leverage of SBICs
and their portfolio composition had a signifi
cant impact on the likelihood that they would
be liquidated. As a result, efficient asset man
agement implies that highly leveraged SBICs
should be more likely to make debt invest
ments than are less leveraged SBICs.
Bank-affiliation o f SBICs—The SBIC
program enlarges the investment activities of
banking organizations beyond those typically
permitted for their commercial bank and
5
venture capital units. For example, while
traditional bank-owned venture capital units
can only own up to 5 percent of a firm’s equity,
banks’ SBIC units can own up to 50 percent of
a small firm’s equity.5 By establishing an
SBIC unit, banks reveal their preferences for
making equity investments, which are likely to
complement the loans made by the banks’
credit departments and provide opportunities
for diversification. In addition, equity invest
ments may enable these firms to spread the
costs of monitoring and generating information
over several products/services, generate scale
economies in monitoring costs, and participate
in the profits of companies in which they invest,
thus providing compensation for their monitor
ing activities (Rajan, 1992; Petersen and Rajan,
1993, 1994). We expect bank-affiliated SBICs
to be more likely to make equity investments.
SBICs’ organizational form—SBICs that
are publicly owned companies or partnerships
with a predetermined lifetime need to raise
funds regularly to finance their investments.
Management of these SBICs may be particu
larly concerned about the short-term perfor
mance of the company. There is some evidence
that concerns about future ability to raise funds
affect the investment strategies of venture
capital firms (Gompers, 1995a). On the other
hand, as Barry (1994) notes, the captive venture
capital firms may face other constraints in how
they invest their funds.
Profitability of SBICs—If shareholders of
profitable SBICs are less likely to substitute
risky assets in order to transfer wealth from the
SBIC’s creditors to themselves, then we would
expect profitable SBICs to make more debt
investments, all else being equal.
Overview of SBICs and their
investments
Below, we describe our data and provide
an overview of SBICs, the types of investments
they made, and the characteristics of the firms
and projects they financed over the 1983-92
period. We use data from reports of condition
of SBICs and their investments, provided by
the SBA. The reports of condition provide
detailed balance-sheet and income statement
information for SBICs over the 1986-91 period.6
The investment files, which cover the 1983-92
period, provide the name, SIC code, total as
sets, number of employees, and location of the
6
firms being financed; the dollar amount and
type of financing provided (loans, equity, or
debt with equity features); whether there was
a put option on the equity financing, requiring
the small firm to repurchase its equity in the
future; whether the deal included debt financ
ing; the interest rate charged; the activity that
was being financed; and variables that indicate
whether the SBIC previously provided financ
ing to the firm.
We augment the SBA data with informa
tion from the COMPUSTAT database. Specif
ically, we construct variables that describe the
characteristics of the industry (two-digit SIC)
in which sample firms operate, covering the
1986-91 period. We restrict the firms sampled
from the COMPUSTAT to those with assets
less than $250 million to ensure that we are
measuring the characteristics of smaller firms.
The original files on the investments of the
SBICs have 20,159 observations; however,
many of these observations have no informa
tion on the size of the small firm. Restricting
the sample to those transactions for which we
have data from both the SBICs’ reports of
condition and the COMPUSTAT files reduces
the sample size further. Consequently, we
report results using two samples: one sample
comprises 12,182 transactions that have data
on size of the small businesses; the other com
prises 5,881 transactions that also have data on
SBIC and industry characteristics.
Figure 1, which is based on data from the
SBA’s Statistical Abstract (1995), shows the
time series of overall SBIC investments since
the program’s inception in 1958. Having grown
rapidly in the 1960s, SBIC investments declined
in the mid-1970s as SBICs failed and their
assets were liquidated. Modest recovery
followed the 1974-75 recession, and the 1980s
saw significant growth in SBIC funding as the
industry expanded again (see Gompers, 1994,
for a discussion). SBIC fundings reached their
local peak in 1988, then declined, reaching a
local trough in 1991. Thus, the period we
study, 1983-92, covers much of the recent
boom and bust cycle experienced by SBICs.
We note that SBICs were responsible for about
one-sixth of total venture capital financing
over this period.
We wish to emphasize two aspects of
our data. First, the firms receiving SBIC
funding are not a random sample of small
ECONOMIC PERSPECTIVES
recipient firm’s (stock) capital
structure. This occasionally lim
its our ability to compare some of
our results with other studies.
In the rest of this section, we
summarize our transactions data,
addressing two principal ques
tions. First, which types of firms
received SBIC funding between
1983 and 1992? Second, are
there any obvious firm or SBIC
characteristics that appear to be
related to whether a debt or non
debt security is used?
Table 1 shows the distribu
tion by type of SBIC investments
over the 1983-92 sample period
and the total dollar value of activ
ity in each investment category,
adjusted for inflation. Nondebt
securities (equity, debt with equi
ty features, and mixed issues)
firms in the United States. Rather, these are
represent a larger fraction of both the number
firms that successfully applied for SBIC
of financings and the dollar volume of activity
funding. For example, the 5,392 firms repre
than debt securities. Among nondebt securi
sented in our sample are, on average, bigger
ties, equity investments account for the largest
and more likely to be in the manufacturing or
portion of transactions and dollar amounts. On
services sectors than the firms sampled by the
average, nondebt financings are larger than
1987 National Survey of Small Business
debt financings. The average nondebt financ
Finances (NSSBF) (Elliehausen and Wolken,
ing is $271,000, while the average debt financ
1995, table 1.1). Second, though our data
ing is $ 121 ,000. Among nondebt financings,
contain excellent information on the flow of
combinations of equity and debt finance are
funds going from an SBIC to a small firm in a
larger ($570,100) than equity ($276,800) and
particular transaction, they say little about the
debt with equity features ($184,500) financ
ings. Though we recognize that
there may be important differ
TABLE 1
ences between the three catego
Summary statistics on SBIC financings, 1983-92
ries labeled nondebt in table 1,
we believe that examining the
Total amount
Number of
disbursed
Mean size
simple two-way split between
financings
($ millions)
($ thousands)
pure debt transactions and all
other transactions is a useful first
Debt
4,982
602.8
121.0
pass at considering the debtNondebt
7,200
1,951.3
271.0
versus-equity question. Thus, in
Equity
4,105
1,136.4
276.8
the
remainder of this article we
Debt with equity
consider
only the debt/nondebt
features
2,463
454.5
184.5
Equity and debt
classification.
with equity features
632
360.3
570.1
Table 2 reports the frequency
Total
12,182
2,554.1
209.7
of debt and nondebt funding, hold
ing constant firm characteristics
Notes: Sample consists of all transactions over the 1983-92 period for
which complete data are available. All dollar figures are deflated by the
such as size, age, and organiza
consumer price index for all items.
tional form.7 In broad terms, the
Source: Authors' calculations from data provided by the U.S. Small Busi
ness Administration.
table indicates that debt fundings
FEDERAL RESERVE
BANK OF CHICAGO
7
TABLE 2
Small business characteristics and security choice, 1983-92
A. Number
of employees
Debt
(% of financings)
Nondebt
(% of financings)
1-49
50-249
250-499
500 and over
47.9
28.0
13.6
17.0
52.1
72.0
86.4
83.0
B. Legal form
Debt
(% of financings)
Nondebt
(% of financings)
38.0
56.6
88.3
62.0
43.4
11.7
Debt
(% of financings)
Nondebt
(% of financings)
< 1 year
1-5 years
5-10 years
Over 10 years
33.7
35.2
42.0
60.0
66.3
64.8
58.0
40.0
1,437
5,966
2,604
2,175
Total number
of financings
4,982
7,200
12,182
Corporation
Partnership
Sole proprietorship
C. Age
Total number
of financings
8,270
3,349
381
182
Total number
of financings
11,258
350
574
Total number
of financings
Share of all
financings (%)
67.9
27.5
3.1
1.5
Share of all
financings (%)
92.4
2.9
4.7
Share of all
financings (%)
11.8
49.0
21.4
17.8
Notes: Sample consists of all transactions over the 1983-92 period for which complete data are available. Nondebt
financings include equity, debt with equity features, and combinations of equity and debt with equity features.
Source: Authors' calculations from data provided by the U.S. Small Business Administration.
by SBICs go to smaller, older firms, while
nondebt fundings go to larger, younger firms.
At first blush, the age effect seems consistent
with contracting theory, while the size effect
does not. In particular, SBIC fundings to small
firms are more likely to be debt than fundings
to large firms: 47.9 percent of SBIC financ
ings to the smallest firms, those with fewer
than 50 employees, were in the form of debt,
compared with just 17.0 percent of financings
to the largest firms (over 500 employees) (table
2, panel A). In dollar shares, the figures are
31.7 percent and 13.4 percent, respectively. In
contrast, evidence from the 1987 NSSBF indi
cates that large firms are more likely to have
loans outstanding than smaller firms (Elliehausen and Wolken, 1995, table 4.5), suggest
ing that we might have expected a higher
percentage of debt fundings going to large
firms than to small firms. We can resolve the
apparent contradiction between our findings
and contracting theory by noting that the
NSSBF also suggests that larger firms are
somewhat more likely to have other (nonSBIC) debt outstanding than small firms
8
(Elliehausen and Wolken, 1995, table 5.5).
Thus, large firms in our SBIC sample probably
do have debt in their capital structures, but
from non-SBIC sources.8
Panel C of table 2 shows how firm age
affects security choice. In general, SBIC
fundings to young firms are less likely to be
debt than are fundings to older firms. Among
firms less than one year old, 33.7 percent of
SBIC financings were in the form of debt,
while among firms over 10 years old, the debt
share was 60.0 percent; the dollar share figures
are 14.5 and 39.2 percent, respectively. For
comparison, we note that the 1987 NSSBF
(Elliehausen and Wolken, 1995, tables 1.1 and
4.5) suggests that the impact of age on loan
usage is nonmonotonic, with the youngest and
the oldest firms less likely to use loans than
middle-aged firms.
As shown in table 2, the smallest firms
accounted for over two-thirds (67.9 percent) of
all funding transactions; however, these firms
received only half (50.4 percent) the dollars
disbursed by SBICs between 1983 and 1992.
Similarly, firms less than one year old accounted
ECONOMIC PERSPECTIVES
TABLE 3
Intended use of funds and security choice, 1983-92
A. Intended use of funds, as reported
Debt
(% of financings)
Nondebt
(% of financings)
Total number
of financings
Share of all
financings {%]
Operating capital
39.9
60.1
8,957
73.5
Plant modernization
83.8
16.2
173
1.4
Acquisition of existing
business
24.7
75.3
981
8.1
Consolidation of debts
55.9
44.1
899
7.4
New building or
plant construction
78.0
22.0
100
0.8
Acquisition of
machinery/equipment
59.8
40.2
440
3.6
Land acquisition
90.6
9.4
139
1.1
9.1
90.9
121
1.0
6.8
93.2
326
2.7
39.1
60.9
46
0.4
Marketing activities
Research and development
Other
B.
Intended use of funds, by type
Debt
(% of financings)
Nondebt
(% of financings)
Operating capital
39.9
60.1
8,957
73.5
Transaction-oriented
63.7
36.3
1,751
14.4
Relationship-oriented
19.9
80.1
1,474
12.1
Total number of
financings
4,982
7,200
Total number
of financings
Share of all
financings (%)
12,182
Notes: Sample consists of all transactions over the 1983-92 period for which complete data are available. Nondebt
financings include equity, debt with equity features, and combinations of equity and debt with equity features. Transac
tion-oriented uses include plant modernization, consolidation of debts, new building or plant construction, acquisition of
m achinery/equipment, and land acquisition. Relationship-oriented uses include the acquisition of an existing business,
marketing activities, research and development, and other.
Source: Authors' calculations from data provided by the U.S. Small Business Administration.
for 11.8 percent of all SBIC fundings but 19.6
percent of all dollars invested.
Table 3 reports on the relationship be
tween the intended use of funds and security
choice. The most important category for in
tended use of funds is operating capital, which
accounted for 73.5 percent of all financings
and 56.8 percent of dollar investments. Other
important categories are acquisition of existing
businesses, debt consolidation, acquisition of
machinery, and research and development.
Transactions in which the reported uses of
funds included plant modernization, new build
ing or plant, acquisition of machinery, and land
acquisition were very likely to be financed by
debt, while those linked to the acquisition of an
existing business, marketing, or research and
development were highly unlikely to be financed
FEDERAL RESERVE
BANK OF CHICAGO
by debt. Panel B of table 3 groups the uses of
funds into three categories, operating capital,
transaction-oriented uses, and relationshiporiented uses, along lines suggested by Naka
mura (1993). Transaction-oriented uses in
clude plant modernization, new building or
plant construction, debt consolidation, machinery
acquisition, and land acquisition; relationshiporiented uses include the acquisition of exist
ing business, marketing activities, and research
and development. This grouping reflects our a
priori judgement that relationship-oriented
projects offer greater scope for insider discretion
as to how the assets (funds) are used than trans
action-oriented projects, which are likely to
require less monitoring and are less subject to
asset substitution problems. Furthermore, transaction-oriented uses may involve the purchase
9
FIGURE 2
Distribution of fundings by
sector, 1983-92
(Share of dollars disbursed)
more likely to involve less profitable, nonbankaffiliated SBICs. These patterns suggest the
need to control for intermediary characteristics
in the models we estimate in the next section.
An empirical model of SBICs'
investment decisions
Given the possible relationships we estab
lished between the type of security an SBIC
uses to fund a firm and the characteristics of
the firm and the SBIC, we relate these charac
teristics empirically to the probability that an
SBIC invests in a small firm through debt.
We estimate the following probit model of
the probability that the SBIC makes a debt
investment in a small firm:
1)
SE C C H O IC E
= F( U SE T R A N S, F IR M A G E ,
E l -4 9 , C O R P O R A T IO N , PARTN ERSHIP,
S A M ESTATE, SB IC SIZE , SB IC A G E ,
Notes: Other includes agriculture; mining; construction;
w holesale trade; finance, insurance, and real estate; and
public adm inistration. All dollar figures are deflated by the
consum er price index for all items.
Source: Authors' calculations from data provided by the U.S.
Sm all Business Adm inistration.
of assets that have some liquidation value in
the case of borrower default. As table 3 shows,
fundings for relationship-oriented uses are
unlikely to be debt, while fundings for transac
tion-oriented uses are quite likely to be debt.
We note that the sectoral and geographic
distributions of SBIC investments over the
1983-92 period were somewhat concentrated.
The manufacturing, services, and retail trade
sectors accounted for nearly three-fourths
(73.7 percent) of all SBIC investments, with
manufacturing alone accounting for 46.4 per
cent of all dollars invested under the program
(see figure 2).9 Similarly, the top five states
in SBIC fundings accounted for over half
(51.7 percent) the total dollars disbursed under
the program; these five states (California, Con
necticut, Massachusetts, New York, and Texas)
accounted for only 20.2 percent of total U.S.
employment growth between 1983 and 1992.
Table 4 offers some evidence that the
SBICs investing in debt securities differ from
those investing in nondebt securities. On aver
age, debt transactions involve smaller, older
SBICs that have significantly more SBA lever
age outstanding than SBICs involved in nondebt
transactions. Furthermore, debt transactions are
10
S B I C C O R P , S B IC B A N K , SB A PR IV ,
SB 1C R O A , IN D -L IQ , IN D -R & D , IN D -M V /B V ,
IN D -IN T A N , 1N D -R O A ,
IND-SROA) + e,
where SECCHOICE is an indicator variable
that is equal to one if the SBIC makes a debt
financing, zero otherwise; e is a mean 0, vari
ance a 2, normally distributed error term; and
all other variables are defined in table 5. Because
we do not estimate a structural model of secu
rity choice and other policies of small firms
and SBICs, we recognize that equation 1 is a
reduced-form equation and that we cannot
interpret the estimated coefficients as struc
tural ones. Instead, we interpret the coeffi
cients of equation 1 as partial correlations that
nonetheless may shed light on the theory of
security choice.
Table 5 summarizes definitions and descrip
tions of the variables in equation 1. We in
clude variables that measure ease of monitor
ing, ease of asset substitution, firm growth
opportunities, and firm risk, as well as a num
ber of control variables, such as SBIC charac
teristics, industry (of the small firm), and year
indicator variables.
Table 5 also summarizes our expectations
regarding the signs of the coefficients on the
variables in equation 1. The ease of monitor
ing the small firm and the ease of asset substi
tution by the small firm are measured by the
firm’s intended use of funds (USETRANS),
organizational form (CORPORATION and
PARTNERSHIP), proximity to its funding
ECONOMIC PERSPECTIVES
research and development vari
able, IND-R&D, and our intangi
Characteristics of SBICs, 1986-91
ble assets variable, IND-INTAN,
Nondebt
Debt
to be negative, since firms in
financings
financings
industries with high values of
these variables may be less attrac
40.48*
35.40
Total assets ( m illio n $)
tive to debt investors seeking to
12.21*
14.43
Age (ye ars)
avoid messy monitoring prob
69.55*
82.38
Corporate (% o f to ta l)
lems. Finally, we have no prior
50.02*
23.48
Bank-affiliated (% o f to ta l)
on the sign of the SAMESTATE
coefficient. If monitoring costs
SBA leverage (SBA fu n d s /
0.99*
1.94
p riv a te c a p ita l)
are fixed per financing and vary
by proximity of the SBIC and the
0.10*
0.07
Return on assets (a t m a rk e t va lu e )
small firm, and if monitoring
3,287
2,594
Number of observations
costs do not differ according to
^Indicates differences in means are significant at the 5 percent level.
whether debt or nondebt is used,
Notes: The numbers are simple means. Sample consists of all transac
tions over the 1986-91 period for which complete data are available. Bankthen the coefficient may be posi
affiliated are SBICs in which banking organizations own at least 10 percent
tive, reflecting the fact that most
of equity. Return on assets is the ratio of unrealized and realized gains to
total assets at market value.
debt financings are smaller than
Source: Authors' calculations from data provided by the U.S. Small
nondebt financings (table 1).
Business Administration.
Hence, fixed monitoring costs are
spread out over a larger size deal
SBIC (SAMESTATE), the average industry
when the security choice is nondebt as com
ratio of research and development expendi
pared to debt. However, if monitoring costs do
tures to sales (IND-R&D), and the average
differ by security type, then the coefficient on
industry ratio of intangible assets to total
SAMESTATE is ambiguous.
assets (IND-INTAN). We expect factors that
Firm risk and growth opportunities are
increase the ease of monitoring (and decrease
measured by firm age (FIRMAGE), firm size
the ease of asset substitution) to enter equation
(El--49), and average industry measures of
1 with positive coefficients, that is, to be posi
profitability (IND-ROA), income volatility
tively associated with the probability of using
(IND-SROA), liquidity (IND-LIQ), and growth
debt in a given transaction. Thus, we expect
opportunities (IND-MV/BV). We expect any
the coefficient on USETRANS to be positive.
thing that is positively correlated with risk or
Research and development, marketing, and
growth opportunities to enter equation 1 with a
acquisition of existing businesses are risky
negative coefficient, that is, to decrease the prob
activities that are difficult to monitor and allow
ability that debt is used, other things being
owners/managers a great deal of discretion
equal. For example, young firms with little
over the disbursement of funds. On the other
reputational capital may take on riskier projects
hand, plant modernization, new building or
(Diamond, 1991), and younger firms may have
plant construction, consolidation of debts,
more growth potential than older ones. Thus,
acquisition of machinery, and land acquisition
we expect the coefficient on FIRMAGE to be
are activities that generate tangible assets and
positive. Similarly, small firms are likely to be
allow little management discretion. Conse
less diversified and to have more volatile earn
quently, the agency costs of debt are likely to
ings, implying a negative coefficient on El-49.
be lower; fund suppliers can monitor owners/
Other bankruptcy risk measures are our profit
managers easily, minimizing their ability to
ability and volatility measures, IND-ROA and
shift funds to riskier projects. We expect the
IND-SROA, and financial liquidity (IND-LIQ).
coefficients on CORPORATION and PART
We expect the coefficient on IND-ROA to be
NERSHIP to be negative, since the limited
positive and that on IND-SROA to be negative.
liability feature of corporations and limited
If IND-LIQ is a measure of a firm’s short-term
partnerships tends to increase the incentives of
ability to meet its debt obligations, then we
owner/managers to substitute risky assets for
would expect it to have a positive coefficient in
safe ones, making debt less attractive to inves
equation 1. However, because firms decide the
tors. We also expect the coefficients on our
amount of financial slack as part of their other
FEDERAL RESERVE
TABLE 4
RANK OF CHICAGO
11
TABLE 5
Variable definitions and descriptions
Variable
Expected sign
in security
choice equation
Definition
Source
D e p e n d e n t V a r ia b le
SECCHOICE
In dicator =1 if debt
tran sactio n, =0 oth erw ise
N.A.
SBIC tran sactio n data
M e a s u r e s o f a s s e t s u b s t it u t io n
a n d / o r e a s e o f m o n it o r in g
U SETR A N S
In dicator =1 if intended use
of fun ds is tran sactio norie n te d , =0 oth erw ise
+
SBIC tran sactio n data
C O R PO R ATIO N
In dicator =1 if corporation ,
=0 oth erw ise
-
SBIC tran sactio n data
PA RTNERSHIP
In dicator =1 if partnership,
=0 oth erw ise
-
SBIC tran sactio n data
S A M E S TA TE
In dicator =1 if firm and SBIC
are in sam e state, =0 oth erw ise
?
SBIC tran sactio n data
IN D -IN T A N
A ve ra g e industry ratio of
in tan g ib le assets to total assets
-
C O M PUSTAT
IN D -R & D
A ve ra g e in dustry ratio
of R&D spending to sales
-
C O M PUSTAT
FIRM A G E
A ge of sm all firm , in years
+
SBIC tran sactio n data
E 1 -49
In d ic a to r ^ for firm s
w ith < 50 e m p loyees,
=0 oth erw ise
IN D -M V /B V
A ve ra g e industry ratio o f m arket
to book value o f assets
-
C O M PUSTAT
IN D -R O A
A verage industry
return on assets (ROA)
+
COM PUSTAT
IN D -S R O A
A ve ra g e industry
standard deviation of ROA
-
C O M PUSTAT
IN D -LIQ
A ve ra g e in dustry ratio of
c urrent assets to total assets
+
C O M PUSTAT
M e a s u r e s o f fir m r is k a n d / o r
g r o w t h o p p o r t u n it ie s
SBIC tran sactio n data
C o n t r o l v a r ia b le s
SBICAGE
A ge of SBIC, in years
?
SBICs' fin an cial statem ents
SBICSIZE
N atu ral lo garithm of
SBIC total assets
?
SBICs' fin an cial s tatem ents
SBICCORP
In dicator =1 if SBIC is a
corp o ra tio n , =0 oth erw ise
?
SBICs' fin an cial statem ents
SB ICBANK
lndicator=1 if at least
10% of SBIC's equity
is ow ned by banking
org a n izatio n , =0 oth erw ise
SBAPRIV
Ratio of SBIC's SBA leverage to
its private invested capital
+
SBICs' fin an cial statem ents
SBICRO A
Ratio of realized and unrealized
profits of SBIC to m arket
valu e of total assets
?
SBICs' fin an cial statem ents
SBICs' fin an cial statem ents
Notes: Unless otherwise noted SBIC transaction data cover 1983 to 1992, while SBICs' financial statements and COMPUSTAT data cover 1986 to 1991. Nondebt financings include equity, debt with equity features, and combinations of equity
and debt with equity features. COMPUSTAT industry averages are computed as unweighted means over firms with less
than $250 million in assets in a given two-digit SIC industry, using annual data over the 1986-91 period. IND-SROA is
computed as a nine-year rolling average standard deviation of IND-ROA, using data over the 1978-91 period.
Sources: Authors' calculations, U.S. Small Business Administration, and COMPUSTAT.
12
ECONOMIC PERSPECTIVES
book value of assets (IND-MV/BV), which we
expect to enter negatively, since it is a measure
of growth opportunities likely to face the small
firm.
Table 5 also lists our control variables,
which describe characteristics of the funding
SBICs, including age (SBICAGE), size
(SBICSIZE), organizational form (SBICCORP), bank ownership status (SBICBANK),
SBA leverage (SBAPRIV), and profitability
(SBICROA). We expect SBICBANK to have
a negative coefficient, reflecting bank-affiliated
SBICs’ tendency to make equity
investments. We also expect
SBAPRIV to enter equation 1 with
TABLE 6
a negative coefficient, for the assetSecurity choice using only small firm characteristics
liability matching reasons outlined
A. Full sample3
above. We have no priors on the
Standard
Marginal
signs of the other coefficients.
policies, the relationship between IND-LIQ
and the probability of using debt may depend
on factors affecting firms’ other policies. For
instance, because IND-LIQ is also a measure
of financial slack, which is most valuable to
firms that have ample profitable projects, it
may also be a measure of growth opportunities.
In that case, we would expect IND-LIQ to have
a negative coefficient in equation 1. Finally, as
suggested by Gompers (1995b), Barclay and
Smith (1995a, 1995b), and others, we include
the average industry ratio of market value to
error
prob
0.0493*
0.0030
0.0191
-0.0560'
0.0051
-0.0217
Coefficient
Empirical results
FIRMAGE
(FIRMAGE)2/ 100
0.5154*
0.0279
0.1997
CORPORATION
-0.9991*
0.0734
-0.3871
PARTNERSHIP
E1-49
-0.7373*
0.1007
-0.2857
SAMESTATE
0.2634*
0.0251
0.1021
USETRANS
0.4498*
0.0365
0.1743
Number of observations
Log likelihood
12,182
-7,008.95
B. Restricted sample
Marginal
prob
Coefficient
Standard
error
FIRMAGE
0.0545*
0.0042
0.0216
(FIRMAGE)2/ 100
-0.0578*
0.0070
-0.0229
E1-49
0.5760*
0.0396
0.2280
CORPORATION
-1.6396*
0.1844
-0.6491
PARTNERSHIP
-1.4152*
0.2086
-0.5603
SAMESTATE
0.3110*
0.0361
0.1231
USETRANS
0.4271*
0.0548
0.1691
Number of observations
Log likelihood
5,881
-3,337.37
aSample is all transactions over the 1983-92 period for which complete
data are available.
bSample is all transactions over the 1986-91 period for which complete
data are available.
I
"Indicates significance at the 5 percent level.
Notes: The "M arginal prob" column presents the marginal effects of the
right-hand-side variables (X) on the probability of debt, computed at the
mean values of X. See table 5 for variable definitions. Sector and year
indicator variables were included but are not reported in the table.
Source: Authors' calculations from data provided by the U.S. Small
Business Administration.
FEDERAL RESERVE
BANK OF CHICAGO
Tables 6-8 report the coeffi
cient estimates of the determi
nants of the probability of debt
usage using pooled cross-section
time-series data.
Small firm characteristics and
security choice
The first panel of results in
table 6 is estimated over the 1983—
92 period, using only the charac
teristics of small firms (12,182
transactions). The second panel
of results is estimated over the
period (1986-91), for which we
have data on both firm and SBIC
characteristics (5,881 transac
tions). The results in panel A of
table 6 indicate that transactionrelated projects are more likely to
be financed with debt than non
debt securities. Thus, nontransaction-oriented projects tend to in
crease the likelihood of nondebt
financing. This is consistent with
the idea that projects of firms that
involve intangible assets are more
likely to be financed with equity,
on average, than projects of firms
that produce tangible assets.
The results also suggest that
the age of the small business
positively affects the probability
13
frequencies reported in table 2 are consistent
with this: Both the largest (500 or more em
ployees) and the next largest (between 250 and
499 employees) firms report very low frequen
cies of debt financing (17.0 percent and 13.6
percent, respectively), compared with about 48
percent for firms with fewer than 50 employees.
As we discussed earlier, we believe that the
larger firms in our sample are likely to have
debt from other (non-SBIC) sources; hence, our
results are not inconsistent with theories sug
gesting that larger firms are more likely to ob
tain debt financing than smaller firms.
A firm’s organizational characteristics
have an important influence on the probability
of debt financing. Being incorporated raises
the probability of receiving nondebt financing
by 39 percentage points relative to sole propri
etorships and by about 10 percentage points
relative to partnerships. An owner/manager
firm has a greater incentive to take on risky
projects if it has limited liability. Thus, these
firms are more likely to receive
nondebt than debt financings to
TABLE 7
minimize the asset substitution
Security choice using small firm and investment
problem.
company characteristics
The results in table 6 also
Standard
Marginal
suggest that firms located in same
error
Coefficient
prob
state as the SBIC (SAMESTATE)
are more likely to be funded with
0.0044
0.0159
FIRMAGE
0.0401"
debt instruments than firms in
-0.0384"
0.0074
-0.0152
(FIRMAGE)2/ 100
other states; thus we find that
0.1854
0.0419
E1-49
0.4683"
being in the same state raises the
0.1964
-0.6161
CORPORATION
-1.5559"
probability of a debt security
0.2217
PARTNERSHIP
-1.3866"
-0.5490
being used in a given financing.
SAMESTATE
0.3187*
0.0381
0.1262
Finally, we note that the results in
0.3248"
0.0582
0.1286
USETRANS
panel
B of table 6 are broadly
0.1217"
0.0183
0.0482
SBICSIZE
consistent
with those in panel A
0.0043
SBICAGE
0.0109
0.0096
of table 6. Thus, using the small
0.0298
-0.0063
(SBICAGE)2/ 100
-0.0160
er sample does not affect the
0.0449
0.0491
0.1241"
SBICCORP
manner in which small firm char
0.2527"
0.1007
SBAPRIV
0.0216
acteristics are associated with
SBICROA
0.1029
-0.3231
-0.8160"
security choice.
SBICBANK
-0.2310"
0.0548
-0.0915
that the firm will obtain debt financing, but the
marginal impact of age declines as age rises
(positive coefficient on FIRMAGE, negative
on (FIRMAGE)2). The coefficients on the age
variables imply that the mean effect of raising
the firm’s age by one year is to raise the proba
bility of debt by about 2.0 percentage points.
This result is in line with contracting theory’s
implication that older firms are more likely to
receive debt than nondebt financing. Because
younger firms are likely to be riskier and have
greater growth opportunities than older firms,
they are more likely to be financed by non
debt securities.
The results in table 6 also indicate that the
smallest firms are more likely to obtain debt
than nondebt financing, as the simple frequen
cies in table 2 showed: For example, the proba
bility that funding will be debt is about 20.0
percentage points higher for small firms than for
large firms (50 or more employees). The simple
Number of observations
Log likelihood
Inclusion o f SBIC characteristics
5,881
-3,082.79
‘ Indicates significance at the 5 percent level.
Notes: The "Marginal prob" column presents the marginal effects of the
right-hand-side variables (X) on the probability of debt, computed at the
mean values of X. Sample consists of all transactions over the 1986-91
period for which complete data are available. See table 5 for variable
definitions. Sector and year indicator variables were included but are not
reported in the table.
Source: Authors' calculations from data provided by the U.S. Small
Business Administration.
14
Table 7 reports the empirical
results of adding the SBIC vari
ables to the specification. The
addition of SBIC-specific vari
ables has very little qualitative
impact on the estimated coeffi
cients on firm characteristics,
including age, size, organizational
ECONOMIC PERSPECTIVES
structure, intended use, and industry classifica
tion variables. Intended use of funds still has a
strong positive effect on the probability of debt
usage, with transaction-oriented uses more
likely to be debt financed than other types of
projects. Several of the SBIC-specific vari
ables have a statistically significant impact on
the probability of debt financing. For exam
ple, larger SBICs are more likely to do debt
financings than smaller ones, and SBICs with
higher SBA leverage are more likely to do debt
financings than other investment companies.
Bank-affiliated investment companies (SBICBANK) are significantly less likely to do debt
fundings (negative coefficient). Being bankaffiliated lowers the probability that an SBIC
will do a debt funding by 9 percentage points.
Being a partnership raises the probability of
providing nondebt financing by about 5 percent
age points, compared to a corporation. More
profitable investment companies (SBICROA)
tend to provide nondebt financing.
Inclusion o f COMPUSTAT variables
Table 8 reports the empirical results when
the COMPUSTAT variables are added to the
specification. The addition of industry-specific
variables has very little qualitative impact on
the estimated coefficients on small firm- and
SBIC-specific variables, most of which main
tain their significance. Firms in industries with
relatively high IND-MV/BV ratios have a
greater chance of receiving nondebt financing
than other companies. This result
is consistent with the idea that
TABLE 8
firms with more growth opportu
Security choice using small firm, investment
nities generally receive more
company, and industry characteristics
equity financing than others,
Standard
Marginal
since potential agency costs asso
Coefficient
error
prob
ciated with firms’ investment
behavior rise with growth oppor
FIRMAGE
0.0427'
0.0044
0.0169
tunities. Liquidity considerations
(FIRMAGE)2/ 100
-0.0415'
0.0074
-0.0164
are important in the choice of
E1-49
0.4725'
0.0442
0.1871
financing instruments. Firms in
CORPORATION
0.1969
-0.5977
-1.5096'
industries with relatively high
PARTNERSHIP
-1.3902'
-0.5504
0.2223
ratios
of current assets to total
SAMESTATE
0.3183'
0.0384
0.1260
assets
(IND-LIQ) tend to have a
0.2444'
USETRANS
0.0597
0.0968
lower
probability
of receiving
SBICSIZE
0.1343'
0.0185
0.0532
debt
financing,
suggesting
that it
SBICAGE
0.0056
0.0097
0.0022
may
be
measuring
the
extent
of
(SBICAGE)2/ 100
0.0033
0.0301
0.0013
growth opportunities in the indus
SBICCORP
0.1031'
0.0453
0.0408
try that is not captured by INDSBAPRIV
0.2309'
0.0219
0.0914
MV/BV. Firms in industries with
SBICROA
-0.8275'
0.1036
-0.3276
more volatile ROA (IND-SROA)
SBICBANK
0.0554
-0.2509'
-0.0993
have a lower chance of receiving
IND-R&D
-0.0135
0.0074
-0.0053
debt financing than other compa
IND-MV/BV
-0.0242'
0.0059
-0.0096
nies. This result is in line with
IND-LIQ
-1.2457*
0.1800
-0.4932
the view that there is a greater
IND-ROA
-0.0023
0.0033
-0.0009
risk of firms in industries with
IND-SROA
-0.0234'
0.0080
-0.0093
more volatile earnings being
IND-INTAN
0.6419
1.4979'
0.5930
unable to meet their debt obliga
Number of observations
5,881
tions; as a result, such firms are
-3,029.64
Log likelihood
more likely to receive nondebt
financing.
‘ Indicates significance at the 5 percent level.
Notes: The "Marginal prob" column presents the marginal effects of the
Firms in R&D intensive
right-hand-side variables (X) on the probability of debt, computed at the
industries are more likely to re
mean values of X. Sample consists of all transactions over the 1986-91
period for which complete data are available. See table 5 for variable
ceive nondebt financing than
definitions. Sector and year indicator variables were included but are not
reported in the table.
other firms. R&D intensive in
Sources: Authors' calculations from data provided by the U.S. Small
dustries are likely to accumulate
Business Administration and COMPUSTAT.
physical and intellectual capital
FEDERAL RESERVE
RANK OF CHICAGO
15
that is very industry- and firm-specific. As
asset specificity increases, so do expected
agency costs in liquidation. Hence, consistent
with the predictions of contracting theory,
firms in R&D intensive industries are more
likely to receive nondebt financing. However,
our results also indicate that firms in industries
with more intangible assets are more likely to
receive debt than nondebt financing. This
result is surprising. We believe it may be due
to the flow nature of our data: A firm’s securi
ty choice in a particular transaction may be
more closely related to the asset being funded
by that transaction than the composition of the
firm’s stock of assets.
Conclusion
In this article, we use a unique transactionslevel dataset of small business financing to
examine how firms and investment companies
decide on the types of security used to finance
firms’ investment projects. Our result shows
that there is a strong, positive association be
tween the incidence of using debt to fund a
small business and using the funds to finance a
project likely to generate tangible assets. This
relationship shows through our simple frequen
cy tables, as well as our probit analyses of
security choice. Thus, we find that business
projects that are likely to generate tangible
assets and allow little management discretion
tend to be funded with debt rather than equity.
This result is consistent with the contracting
theory view of the firm, which suggests that
the security choice of investors and firms is
designed to minimize their costs of contracting.
We also find that younger firms are more
likely to obtain nondebt than debt financing.
This effect conforms with standard theories on
capital structure choice, which suggest that
young firms with little reputational capital may
take on riskier projects and have more growth
opportunities than older ones. These agency
concerns create incentives for investment com
panies to provide nondebt rather than debt
financing to young firms. In addition, we find
that smaller firms are more likely to receive
debt financing than larger firms. Although this
result appears to conflict with the predictions
of contracting theory, it may be explained
partially by the fact that larger firms in our
16
sample may have alternative, non-SBIC sourc
es for credit. The private placement of debt
with SBICs by the smallest firms in our sample
may indicate that SBICs offer a funding oppor
tunity for these firms. The results also demon
strate that lower market to book ratios and
R&D intensities are associated with a greater
chance of receiving debt rather than nondebt
financing. This is because the agency cost of
debt is likely to be lower; and the investment
companies can monitor owner/managers easily.
Further, we find that characteristics of the
funding SBIC and the recipient firm’s industry
affect security choice. In particular, SBICs
using a higher amount of funds and guarantees
from the SBA tend to be more likely to do debt
than nondebt financing. In addition, SBICs
affiliated with banking organizations and those
organized as partnerships are more likely to
provide nondebt financings. These results
suggest that multiple agency relationships of
investors may affect how they fund firms.
We plan to extend our work in at least two
directions. The first is motivated by previous
research and certain features of our dataset.
We have information on whether each financ
ing transaction in our dataset is the first such
transaction between a particular SBIC and
small firm, or whether it is a repeat transac
tion; we can also identify transactions that
involve two or more SBICs simultaneously.
We intend to examine these transaction charac
teristics to determine whether the relationships
we identified here remain intact, since previous
research indicates that the terms and even
availability of credit for small businesses can
vary with the strength of the relationship be
tween lender and borrower (Petersen and Rajan,
1994; Berger and Udell, 1995).
The second extension of this work will be
to model the financing policy of small firms in
conjunction with their other policies. For in
stance, we find that project choice is signifi
cantly correlated with financing choice. How
ever, since a firm’s project choice is likely to
be made simultaneously with the financing
arrangements, both project choice and security
choice are likely to be endogenous. Develop
ing and testing a structural model along these
lines remains a topic for future research.
ECONOMIC PERSPECTIVES
NOTES
'Empirical evidence suggests that information asymme
tries are generally important in determining firms’ finan
cial policies. However, because firms place their debt
and/or equity securities privately with the SBICs and do
not issue them in public markets, and because SBICs tend
to get involved in the management of the companies they
finance, we focus on agency theory explanations of
security choice.
2For an excellent review of the agency theory and asym
metric information literature, see Harris and Raviv (1992).
’The liquidation value of a firm is also related to how
specific its assets are to that firm or sector. Firms with
assets that are highly industry- and firm-specific would
use less debt because the liquidation value of these assets
is substantially reduced.
4On the other hand, if the current profitability of a firm is
an indication of its investment and growth opportunities,
then more profitable firms may choose equity over debt
financing.
5For a more detailed discussion of bank- versus nonbankowned SBICs, see Brewer and Genay (1994) and Brewer,
Genay, Jackson, and Worthington (1996).
6Specifically, the financial statements pertain to the fiscal
years 1987-92.
7A similar table, with the share of dollars devoted to debt
and nondebt funding, is available on request.
"This is an example of how the flow nature of our data
forces us to be careful when comparing our numbers to
those of other studies.
9For comparison, we note that these three sectors account
ed for 71.3 percent of total U.S. nonfarm payroll employ
ment growth between 1983 and 1992, with the services
and retail trade sectors accounting for all of it: Manufac
turing employment actually fell modestly over this period.
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Brewer III, Elijah and Hesna Genay, “Funding
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___________ , “Optimal investment, monitoring,
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1988, pp. 567-591.
ECONOMIC PERSPECTIVES
CRA and fair lending regulations:
Resulting trends in mortgage lending
D o u glas D. E v a n o ff and Lew is M . Segal
In response to concerns that
banks were not adequately
serving the credit needs of
their local communities and
not treating all applicants fair
ly, during the 1960s and 1970s Congress passed
the fair lending laws and the Community Rein
vestment Act (CRA).1 These laws, aimed at
eliminating discriminatory lending practices and
encouraging lending to low-income individuals
and in low-income areas, have been controver
sial since their inception. Community advocates
argued that the acts were either inadequate or
inadequately enforced and that banks continued
to channel deposits away from local communi
ties, resulting in inadequate financing for the
areas most in need. Bankers argued that they
treated applicants fairly and the acts smacked
of credit allocation that could adversely affect
bank safety and soundness.
Although there continues to be significant
disagreement regarding these regulations, re
cently there has been a wave of positive reviews
of their effectiveness.2 The regulations have
been given credit for encouraging banks to
implement special loan programs aimed at lowerincome communities and for effectively chan
neling funds toward previously underserved
areas and minority groups. Community advo
cates argue that significant progress has been
made and continued enforcement will reap
additional benefits. Some bankers state that in
responding to the CRA they have discovered
new, profitable, previously untapped lending
opportunities. These opportunities have come
FEDERAL RESERVE
BANK OF CHICAGO
at a most convenient time as the demand in
traditional lending markets has slowed.
While the arguments for the fair lending
laws and the CRA are essentially ones of equi
ty, there may also be economic arguments for
constraining private market behavior and chan
neling funds to underserved areas. It may be
that these credit flows produce positive exter
nalities which, from a societal perspective,
generate a total return greater than that received
by the providers of the credit.1 That is, although
society reaps the full benefits of providing this
credit, the service provider (a bank in this case)
may not. While this provides economic justifi
cation for channeling credit to particular markets,
it does not necessarily warrant doing so through
the banking system.
In this article, we examine the evolution of
the fair lending regulations and the CRA. We
then summarize the economic literature that
pertains to these regulations. Finally, we evaluate
the effectiveness of the fair lending laws and the
CRA by analyzing recent trends in mortgage
lending activity and discussing whether these
trends are in line with the intent of the regulations.
We ask whether the trends can be attributed to the
regulations and whether the data suggest that the
regulations have been successful.
Douglas D. Evanoff and Lewis M. Segal are econ
omists at the Federal Reserve Bank of Chicago.
They would like to thank John Bergstrom, Raphael
Bostic, Paul Calem, Glenn Canner, and Lorrie
Woos for helpful comments on earlier drafts, and
Pat Dykes for her generous data support. They
also acknowledge the technical assistance of
Jonathan Siegel.
19
Evolution of the CRA and the fair
lending laws
Although it is common to group together
the Fair Housing Act, the Equal Credit Oppor
tunity Act (ECOA), and the CRA, they are
more accurately classified into two groups: the
fair lending laws and the CRA. The fair lend
ing laws are aimed at eliminating lending dis
crimination based on the inherent attributes of
the borrower, such as race or gender. The CRA
primarily addresses geographic discrimina
tion, that is, failing to serve the credit needs
of the local community in which the bank was
chartered. The Home Mortgage Disclosure
Act (HMDA) provides information on lend
ing to individuals and locations, supporting
the enforcement of both the fair lending laws
and the CRA.
Fair lending laws
The Fair Housing Act was approved by
Congress in 1968 as part of the Civil Rights
Act of that year. It prohibits discrimination in
residential real estate transactions based on
race or color, religion, national origin, gender,
handicap, or family status.4 The ECOA encom
passes a broader array of transactions. Passed
in 1974, it prohibits discrimination with regard
to any aspect of a credit transaction (consumer,
commercial, or real estate loan) based on race
or color, religion, ethnic origin, gender, marital
status, age, and receipt of public assistance.5
It has been argued that fair lending enforce
ment prior to the 1990s was generally unaggressive.6 The techniques employed to detect discrimi
nation (reviewing whether internal policies were
followed and performed uniformly across the
protected factors) typically detected only the
most blatant cases of discrimination. Since that
time, in response to growing public concern
about lending discrimination and well-publicized
research that reported evidence of discrimination,
regulatory agencies and the U.S. Department of
Justice have stepped up their enforcement efforts.
For example, a 1988 study of mortgage discrim
ination in Atlanta led the Justice Department to
initiate an investigation into fair lending practic
es by depositories in that market.7 The investi
gation resulted in the first major lawsuit filed by
the department against an institution for vio
lating fair lending laws.8 This is in sharp
contrast to the number of suits filed for civil
rights violations in other areas, for example,
20
housing and employment. Congress also
responded to repeated claims of lending dis
crimination by amending the Fair Housing Act
in 1988 to allow private parties to originate
mortgage discrimination lawsuits more easily.
The ECOA was amended in 1991 to require
bank regulators to refer cases to the Department
of Justice instead of handling them independently,
sending a signal that the department was going
to be more aggressive in the prosecution of such
cases. Perhaps most significantly, the 1975
HMDA was amended in 1988, 1989, and 1991
to develop a database that would provide regula
tors and the public with data to analyze deposi
tory institution lending patterns.
As originally enacted, HMDA required
depository institutions and their subsidiaries to
provide the total number and dollar value of
mortgages originated and purchased in the
local market, typically segmented by census
tract. The 1989 amendment required lenders to
report information at the loan application level
regarding race, gender, and income, along with
details on the disposition of the application
(deny/accept/withdraw, reason for denial,
etc.).9 Banks were required to make the data
publicly available. These expanded data have
enabled regulators to complement their manual
reviews of loan files with systematic statistical
analysis.10 The additional data also allow the
public to more closely scrutinize lending patterns
of depository institutions.
There have also been recent efforts by
bank regulators to help depository institutions
comply with fair lending regulation by clarify
ing the compliance requirements. While the
purpose of fair lending laws and regulations is
relatively straightforward, there have been
problems in implementation, and disagreements
have arisen between regulatory agencies and
lenders as to interpretations of the law. To
provide guidance, a 1994 interagency task
force representing the federal depository regu
lators released guidelines as to what could
constitute discriminatory lending practices."
Under these fair lending guidelines, a lender
may not, because of a prohibited factor:
■ Fail to provide information or services or
provide different information or services
regarding any aspect of the lending process,
including credit availability, application
procedures, or lending standards;
ECONOMIC PERSPECTIVES
1 Discourage or selectively encourage applicants
with respect to inquiries about or applications
for credit;
■ Refuse to extend credit or use different stan
dards in determining whether to extend credit;
* Vary the terms of credit offered, including the
amount, interest rate, duration, or type of loan;
■ Use different standards to evaluate collateral;
■ Treat a borrower differently in servicing a
loan or invoking default remedies;
■ Use different standards for pooling or pack
aging a loan in the secondary market;
■ Express, orally or in writing, a preference
based on these prohibited factors, or indicate
that it will treat applicants differently based
on these factors; or
■ Discriminate because of the characteristics
of a person associated with a credit applicant
or the prospective occupants of the area
where property to be financed is located.12
While blatant discrimination may be
obvious to most parties, there are times when
sound business practices may result in an
unintended discriminatory practice against a
protected group. To emphasize to lenders the
need to avoid unintended effects in setting
underwriting criteria, the interagency task
force also listed the forms of discrimination
that the courts had previously recognized as
illegal. These include: overt discrimination—
the lender openly discriminates; disparate
treatment—the lender treats applicants differ
ently based on one of the prohibited factors
(whether or not it is motivated by prejudice
or intent to discriminate); and disparate
impact—the lender applies a practice uni
formly to all applicants, but the practice has a
discriminatory effect and cannot be justified
by business necessity.
As a result of the increased scrutiny of
lending practices by regulators, there has been
a significant increase in the number of ECOA
violations referred to the Department of Justice
by the regulatory agencies and in the number
of suits filed by the department for violation
of the fair lending laws. Most of the suits have
been settled through well-publicized consent
agreements, which relayed the message of
stringent enforcement of the fair lending laws.
FEDERAL RESERVE
BA1NK OF CHICAGO
In evaluating the effect of the fair lending
laws on mortgage activity, therefore, one
would expect to see more of an impact on
lending patterns in the 1990s, as institutions
respond to increased regulatory pressure.13
The CRA
The major impetus for the 1977 passage of
the CRA was concern by community groups
that banks and thrifts were not responding
adequately to the credit needs of local commu
nities. Depository institutions were accused of
discriminating against individuals based on the
characteristics of their neighborhood, that is,
redlining. This was seen as having a particu
larly adverse impact on minority groups and
contributing to the deterioration of inner-city
neighborhoods. However, the emphasis of the
act was on adequately preserving communities
and not on channeling credit based on race.
Community groups argued that it was common
for banks to reinvest a relatively small portion
of deposits generated from local communities
back into those markets.14
The initial community reinvestment bill
was much more intrusive to banks than the
final act. The initial proposal argued that
banks were chartered institutions with access
to a government safety net and, as such, had a
formal responsibility to perform social func
tions in addition to pursuing the objectives of a
private enterprise. The proposal defined the
bank’s relevant local market from which it
received deposits and required it to focus on
satisfying credit demands in this market prior
to exporting funds to other areas. Banks ar
gued that such behavior would run counter to
existing safety and soundness regulation and
constituted overt credit allocation without
regard to the credit quality of applicants in
different geographic areas.
The final act omitted the explicit credit
allocation criteria. It required financial institu
tions to serve the convenience and needs of the
communities in which they were chartered with
out mandating how this was to be accomplished.
Additionally, it emphasized the need for bank
management to be conscious of community
credit needs and stressed that this was to be
done without sacrificing safety and soundness.
The mandate of the CRA, to have institu
tions serve the needs of the community in which
they are chartered, was actually already in place.
21
The 1935 Banking Act required banks to meet
the convenience and needs of their communities,
as did the 1956 Bank Holding Company Act and
the bank charter itself. The fair lending laws,
while not explicitly outlawing redlining, addressed
similar concerns. Finally, while HMDA provid
ed no mechanism for imposing sanctions on
depository institutions, the data were being
collected precisely for the purpose of monitoring
lending patterns and detecting neighborhood
redlining. The real thrust of the CRA was to
reemphasize the need for good lending practic
es, to shift the emphasis on reinvestment away
from the liability side of the balance sheet (deposit
gathering) to the asset side (credit generation), and
to put the onus squarely upon regulators to monitor
the lending patterns of financial institutions and
encourage investment in local communities.
In the early years of the CRA, regulatory
agencies required banks to specify their local
community; develop a public statement, includ
ing the local community definition and listing
the type of credit instruments the bank intend
ed to provide; post a list of consumer rights
under the CRA; and maintain a file of public
comments for public inspection. These proce
dural requirements were relatively straightfor
ward. In addition, regulators performed an
evaluation to “assess the institution's record of
meeting the credit needs of the entire communi
ty, including low- and moderate-income neigh
borhoods, consistent with the safe and sound
operation of each institution” (Regulation BB).
To assess the institution’s performance in
satisfying this requirement, the regulators devel
oped 12 assessment factors grouped into five
performance categories:15
Category A: Ascertainment of community
credit needs
1. Communication with members of the
community to ascertain credit needs; and
2.
Extent of involvement by the board of
directors in the CRA activities.
Category B: Marketing and types of credit
offered and extended
3. Marketing efforts to make the types of
credit offered known in the community;
4. The extent of loans originated in the com
munity; and
5.
The extent of participation in government
loan programs.
22
Category C: Geographic distribution and
record of opening and closing offices
6.
The geographic distribution of credit appli
cations, approvals, and denials; and
7.
The record of office openings and closings
and extent of service provided at the offices.
Category D: Discrimination and other illegal
credit practices
8.
9.
Practices to discourage credit applica
tions; and
Discriminatory or other illegal practices.
Category E: Community development
10. Participation in community development
projects or programs;
11. The institution’s ability to meet community
credit needs; and
12. Other relevant factors which could bear
upon the extent to which the institution is
helping to meet the credit needs of the
community.
For each of the assessment factors, the
examiner was to assign a grade of 1 (excep
tional) to 5 (significantly inferior), similar to
the CAMEL rating given for safety and sound
ness evaluation. Later, to avoid confusion with
safety and soundness ratings, the CRA rating
was changed to a four scale grading system:
outstanding, satisfactory, needs to improve, or
substantial noncompliance.
The regulations did not impose explicit
sanctions on institutions found not to have
adequately served the needs of their commu
nities. Instead, the regulator was to consider
the CRA rating along with other factors, such
as safety and soundness, when ruling on an
application for a geographic expansion of
facilities through a merger or acquisition,
the introduction of new branches, an office
change, etc. However, there are additional
costs from having a poor CRA rating or being
accused of poor CRA performance, even if
the application is ultimately approved. For
example, the application process can be sig
nificantly lengthened and complicated if
community groups protest the application.
In a period in which banks were aggressively
expanding geographically, the potential for
lost deals, delays in expansion, and negative
public relations could be quite burdensome.
ECONOMIC PERSPECTIVES
Mortgage Association and the Federal Home
Loan Mortgage Corporation under an affirma
tive obligation to facilitate financing of lowand moderate-income housing. It also estab
lished mortgage purchasing goals for these
agencies relating to low- and moderate-income families for affordable housing and for
the central city. Bankers continued to com
plain about the vagueness of CRA require
ments and the resulting regulatory burden.
Community groups continued to complain
that banks were inadequately serving the
credit needs of their local communities and
that regulators were inadequately enforcing
the act.
After much public and congressional debate,
new CRA regulations were issued in 1995 for
implementation over the following two years.
The new regulations stressed performance over
effort in meeting CRA requirements and intro
duced a new evaluation system, replacing the
previous 12 assessment factors with three new
tests: lending, investment, and service. For each
test a bank is assigned one of five grades from
outstanding to substantial noncompliance.
There is also an overall composite rating for
CRA compliance.17
The lending test evaluates
whether a bank has a record of
TABLE 1
meeting the credit needs of its
CRA test ratings
local community. The regulator
evaluates the number, amount,
Component test ratings are assigned to reflect
and distribution across income
the bank’s lending, investment, and services.
groups and geographic areas of
Component
mortgage, small business, small
test ratings
Lending
Investment
Service
farm, and consumer loans in the
Outstanding
12
6
6
assessment area(s) or communi
High satisfactory
4
9
4
ties.18 The regulator also consid
Low satisfactory
6
3
3
ers the innovativeness of the bank
Needs to improve
1
3
1
in addressing the credit needs of
Substantial
low- or moderate-income individ
noncompliance
0
0
0
uals or areas and in generating
community
development loans.
Preliminary composite rating is assigned by summing the three
As
illustrated
in table 1, the lend
component test ratings and referring to the chart below.
ing
test
carries
a disproportional
Points
Composite assigned rating
weight in determining the com
20 +
Outstanding
posite rating. A bank cannot
11-19
Satisfactory
receive a composite rating of
5-10
Needs to improve
satisfactory or better unless it
0-4
Substantialnoncompliance
receives a minimum of low satis
factory on the lending test.19
Note: Adjustments to prelim inary composite rating— no bank may
receive a composite assigned rating of satisfactory or higher unless
The investment test evaluates
it receives at least low satisfactory on the lending test.
how well a bank satisfies the credit
Following passage of the act, bankers
frequently complained about the vagueness of
the requirements, including the lack of a spe
cific ranking or weighting scheme for the as
sessment factors to guide the allocation of
resources. Regulatory agencies would periodi
cally issue policy statements providing guidance
to institutions as to how the assessment criteria
were scored and discussing elements of effec
tive CRA programs. Most of these statements
emphasized effort, and the documentation of
such effort, instead of performance. In the late
1980s, Congress amended the act to have the
assessments made public and increased public
scrutiny of banks and regulators.
As with the fair lending laws, enforcement
of the CRA intensified in the early 1990s.
Denials of merger or acquisition applications
based on poor CRA performance became more
common. Although the ratings were not made
public prior to 1990, evidence suggests that
regulators have tightened enforcement and have
been more strict in assigning CRA ratings.16
To stress the commitment to low-income financ
ing, Congress passed the Federal Housing
Enterprises Financial Safety and Soundness
Act in 1991. This act put the Federal National
FEDERAL RESERVE
BANK OF CHICAGO
23
needs of its local neighborhoods through quali
fied community investments that benefit the
assessment area. Again, the bank’s innovative
ness in responding to community development
needs is also taken into account.
Finally, the service test evaluates how well
the credit needs of the community are met by
the bank’s retail service delivery systems. This
includes the distribution of branches across areas
serving low- to moderate-income individuals
and geographies, as well as alternative delivery
systems, such as ATM, telephone, computer,
and mail. The delivery systems and services
should be directed at meeting the needs of the
local community, for example, low-balance
checking accounts and extended lobby hours.
Again, the innovativeness of the bank in using
these alternative delivery systems to serve the
low- and moderate-income individuals and
neighborhoods within the community is taken
into consideration.
Although it is too early to determine the
effectiveness of the revisions to the CRA, a
recent Government Accounting Office review
of the new guidelines argued that regulators
may face significant challenges in imple
menting the reforms.20 The potential prob
lems are similar to those which existed before
the reforms, namely:
* A continued need for excessive documenta
tion of effort and process;
■ Inconsistency in ratings and uncertainty
about the performance criteria;
■ Incomplete consideration of all relevant
material in determining the performance of
the institution; and
■ Dissatisfaction with regulatory enforcement
(depending on one’s perspective, too strin
gent or too lax).
To minimize potential problems, the report
recommended that significant efforts be made
to provide improved examiner training, improve
the quality of the data used in evaluating perfor
mance, and increase the use of public disclo
sure of the ratings.
Evidence of discrimination in
mortgage lending
The CRA was introduced because redlining
was believed to be a common practice by banks.
The fair lending laws were passed because there
24
was a perception that certain borrowing groups
were not being treated equitably. However,
there continues to be significant disagreement
as to the extent of these problems.
Housing and mortgage discrimination has
been a topical issue since the 1960s, when
community groups argued that neighborhoods
were deteriorating as a result of practices by
mortgage originators. The originators were
accused of using noneconomic criteria to limit
funding to non-white applicants and/or non
white neighborhoods. Research in this area has
intensified in recent years as amendments to
HMDA reporting requirements have increased
the availability of data used to compare lending
patterns across race and ethnic groups, income
groups, and geographic areas. However the
data exclude many of the more relevant vari
ables used in the credit evaluation process.21
The most meaningful studies of the role of race
and neighborhood effects in mortgage lending
incorporate information beyond HMDA data
and evaluate discrimination based either on the
neighborhood of the applicant or the character
istics of the individual applicant. These studies
are divided into four classes: neighborhood
redlining studies, application accept/reject
studies, studies of default rates, and perfor
mance of institutions specializing in loans to
low-income individuals or in low-income
neighborhoods. Below, we summarize the
studies to emphasize the ongoing controversies
in this area of research.
Redlining studies
Redlining is the practice of having the loan
decision based on, or significantly influenced
by, the location of the property without appro
priate regard for the qualifications of the appli
cant or the value of the property. As a result,
the neighborhood’s financial needs are not
adequately served and the region is unable to
develop economically. Redlining studies typi
cally take the neighborhood as the unit of obser
vation, evaluating whether the aggregate sup
ply of funds made available is related to the
racial composition of the area.
Early analysis of differences in loan origi
nations across markets found significant differ
ences based on the racial composition of the
neighborhood. However, these studies attribut
ed all market differences to the race variable.22
The findings from a number of recent studies,
ECONOMIC PERSPECTIVES
which either directly or indirectly addressed
the redlining issue and attempted to explicitly
account for market differences, are summa
rized in box A.
Although improvements have been made
in redlining studies, inherent methodological
problems remain. First, in a number of redlin
ing studies the unit of obserxmtion may be too
large. To the extent redlining occurs, it could
be for a relatively small area, such as two or
three city blocks. In larger areas, such as met
ropolitan statistical areas (MSA), redlining
may not be detectable in the aggregate data.
Additionally, assuming some lenders redline
and others do not, if borrowers eventually find
the non-redlining lender, data at the broader
level will imply that no redlining has occurred.
The unit of observation should, therefore, be
relatively small. There may also be a signifi
cant omitted variable bias. Exclusion of vari
ables correlated with race may produce a sig
nificant coefficient for race even in the absence
of discrimination. A standard criticism of
redlining studies is that they inadequately ac
count for demand factors. Thus, it is impossi
ble to attribute differences in mortgage activity
across markets to an inadequate supply of
funding (redlining) or to a lower demand from
potential borrowers. The creditworthiness of
the applicant pool is also important since the
riskiness of the loan will obviously be a deter
mining factor in the underwriting decision.
Additional variables to account for differences
in borrower credit demand and creditworthi
ness that have been included in the recent studies
are neighborhood average income, percent of
owner-occupied houses, changes in property
values, poverty and welfare rates, percent
of housing units vacant, crime rates, wealth
measures, mobility rates, average age of pop
ulation and housing stock, total housing units,
duration of residency, and the stock of conven
tional mortgages.
Typically, studies that have accounted for
these market characteristics more comprehen
sively have reported a less significant impact
of racial composition than that found in earlier
studies. For example, when Holmes and Horvitz (1994) excluded measures of risk in their
analysis of the Houston market, they found that
the flow of mortgage credit was negatively
associated with minority status, consistent with
redlining. When the risk measure was includ
FEDERAL RESERVE
BANK OF CHICAGO
ed, minority status was not found to influence
the flow of credit.23 Studies which employ a
single-equation model to explain the amount of
credit made available in a neighborhood will
be mixing elements of both supply and demand
for credit. Redlining will affect the supply
loans. However, with the single-equation
approach the supply and demand effects cannot
be separated (Yezer, Phillips, and Trost, 1994).
Arguing that the race variable represents dis
crimination requires that there be no demandside effects. As mentioned above, a number of
studies have shown this to be incorrect. Final
ly, model specification has been shown to drive
some results (Horne, 1997). Concern with
model specification argues that one should use
a relatively flexible financial form which has
the more commonly used alternative forms
nested within it.
Some researchers have argued that the
problems associated with the above credit flow
type of redlining studies are too large to over
come and, as a result, these studies cannot
adequately identify the role of racial composi
tion of the neighborhood in loan decisions.
An alternative approach, which addresses the
problem of individuals eventually finding the
non-redlining lender, is to directly survey indi
viduals who were active in the mortgage mar
ket. Benston and Horsky (1992, 1979) surveyed
home sellers and buyers to gather information
on credit difficulties encountered in attempting
to sell or purchase homes in several U.S. cities.
Instead of viewing only the mortgages approved,
the survey gathered information on individuals
who requested credit but were unable to obtain
it (for reasons such as redlining), in areas in
which charges of redlining had been made and
in control areas. If obtaining credit was a prob
lem, additional information as to the reason for
the problem was obtained—for example, unem
ployment, inadequate down payment, or loca
tion of the house. The survey explicitly asked
home buyers if either a lending institution or
real estate agent had stated or implied that
obtaining a mortgage might be difficult because
of the neighborhood in which the home was
located. In both studies, the authors were unable
to detect evidence of discrimination or unmet
demand. The bottom line appears to be that
there is little convincing evidence to suggest
that redlining explains lending patterns in lowincome neighborhoods.
25
Accept/Reject studies
Given the above criticisms of credit flow
studies, the availability of more detailed
HMD A data since 1990, and a desire to more
directly address the discrimination issue, recent
research has taken a more microeconomic
approach. Accept/reject studies take individual
application data and evaluate the determinants
of the lender’s decision. They estimate a prob
ability of rejection function based on various
risk factors and include a race variable to ac
count for discrimination.24 While these studies
can also be used to test for redlining, their
focus is on discrimination with respect to indi
vidual applicants. (For a sample of these stud
ies, see box B.)
Prior to the availability of HMDA data,
Black et al. (1978) used special survey data to
determine the economic variables important to
the lending decision and whether personal vari
ables such as race played a role. After accounting
for economic variables and terms of the loans,
they found that, although the personal charac
teristics did not significantly add to the power
of their model in explaining the accept/denial
decision, race was significant. Black applicants
had a higher probability of denial at the 90 per
cent significance level.
In a well-publicized accept/reject study,
Munnell et al. (1992) used HMDA data aug
mented with survey information about the
creditworthiness of borrowers to analyze lend
ing behavior in Boston. A variable to account
for the racial composition of the market was
not found to affect the lender’s accept/reject
decision, but applicant race was found to be
statistically related to the decision. Minorities
were rejected 56 percent more often than
equally qualified whites.
The Boston study has been criticized for a
number of reasons.25 First, as with the credit
flow studies, there is the potential for omitted
variable bias. If omitted variables are associat
ed with the race variable, the coefficient on
race will account for the true effect of race plus
that of the omitted variable(s). The Boston
study included several variables to account for
borrower risk. However, not all risk factors
could be captured, and some researchers argue
that the race coefficient is actually capturing
the riskiness of the applicant. Race would
appear significant in an analysis which fails to
account for wealth if, as has been shown else
where, minorities have lower levels of wealth.
26
There was also little consideration of the char
acteristics of the property and credit history of
the applicant. Second, the study has been
criticized for data errors. These potential data
errors include monthly incomes that are incon
sistent with annual levels, negative interest rates,
loan to value ratios exceeding one, loan to
income ratios outside reasonable ranges, the
inclusion of black applicant denials because of
over-qualification for special lending programs,
and a number of extreme outliers. Brown and
Tootell (1995) and Munnell et al. (1996) contend
that even after accounting for the data concerns,
the fundamental result remains—minorities are
more likely to be denied mortgages than similarly
qualified whites.
Other follow-up studies have shown mixed
results. Using data from Munnell et al. (1992),
Zandi (1993) found no race effect, while Carr
and Megbolugbe (1993) found the effect remains
after “cleaning the data,” as did Glennon and
Stengel (1994). Using a model similar to that
in Munnell et al. to evaluate the Boston and
Philadelphia markets, Schill and Wachter
(1993) found evidence consistent with redlin
ing and discrimination. When variables are
included to proxy for neighborhood risk, the
neighborhood racial composition became insig
nificant, although racial status still significant
ly decreased the probability of acceptance.
Stengel and Glennon (1995) also found that it
is important to use bank-specific guidelines in
the analysis to capture unique, but economically
based, underwriting criteria. Using a more
generic market model, for example, secondary
market criteria, can lead to misleading results.26
Using cleansed data from Munnell et al.
(1992), Hunter and Walker (1996) did not find
evidence of discrimination via higher underwrit
ing standards for all minorities. They contend
that race matters only in the case of marginally
qualified applications. Needless to say, there is
little uniformity of view.
Yezer (1995) and Rosenblatt (1997) argue
that fundamental problems in the use of accept/
reject models to evaluate discrimination result
from the informal prescreening of applicants.
Both applicants and lenders only want to pro
ceed with applications that appear likely to
qualify for a loan because denials are costly for
both parties. Thus, during the initial lenderborrower contact, the lender and borrower
decide whether the application warrants pursuing.
Then, the formal application takes place, and
ECONOMIC PERSPECTIVES
denials occur only in those cases in which
information not available in the initial contact
affects the decision (for example, bad credit
history). Therefore, denial may be as closely
related to communication skills and cultural
background as to economic variables. Sophis
ticated underqualified potential applicants will
not reach the formal application process because
they realize they will not be accepted, while
unsophisticated candidates will follow through
only to be denied. Thus, there is a significant
selection bias problem in the formal application
stage which may explain the race differentials.
To support this view, Rosenblatt (1997) cites
evidence that education levels are strongly pre
dictive of credit approvals. The argument, there
fore, is that the information in denial rate data
may not be what researchers perceive it to be.
Default rate studies
An alternative means of evaluating lender
discrimination is to examine the default rates
of borrowers thought to be discriminated
against relative to other borrowers. Research
ers have compared default rates across groups
based on the theory that if minorities are overt
ly discriminated against, the average minority
borrower should be of higher credit quality
than the average nonminority borrower.27 This
should be reflected in mortgage default rates
and resulting loss rates; for minority loans,
both should be lower. However, studies have
not found evidence of lower default rates for
minority holders of mortgages. In critiquing
the Boston study, Becker (1993) cited data
indicating the default rates were equal for
white and minority sections of the Boston
market, which was not consistent with overt
discrimination.28 A more recent study by
Berkovec et al. (1996) also tests for discrimi
nation using default rates. Controlling for
various loan, borrower, and property related
characteristics, the authors evaluated the default
rates and resulting losses for FHA-insured
loans and found a higher likelihood of default
on the part of black borrowers and higher loss
rates. These results suggest that lenders, per
haps as a result of regulatory pressure, may have
over-extended credit to minorities.
However, this line of research has also
been criticized. First, if discrimination occurs,
while the marginal minority borrower may be
better qualified than the marginal white appli
cant, inferences about the average borrower
FEDERAL RESERVE
BANK OF CHICAGO
cannot be made without making assumptions
about the distribution of creditworthiness
across the two groups of potential borrowers,
for example, Ferguson and Peters (1995).
The distributions could be significantly differ
ent. Additionally, minorities may also be treat
ed differently once they are in default. Default
studies typically use data on foreclosures.
Bank forbearance in defaults favoring one of
the two groups could bias the results.29
Performance studies
There are two general areas of research
relating bank performance to the CRA and fair
lending regulations. The first deals with the
profitability associated with lending in lowincome markets. If such lending is not profit
able, regulation requiring it should adversely
affect performance. The second area of re
search addresses the implications of mortgage
discrimination on bank performance. If some
banks are choosing to discriminate and forego
profitable lending opportunities, other banks
that do not discriminate should be able to exploit
these opportunities.
During the debate prior to the enactment
of the CRA, critics argued that economics was
driving lending patterns and the CRA might
either have no impact, but be costly to imple
ment, or actually generate bad loans. From the
banks’ perspective it would be a tax and, if
lending patterns did not change, it would be
without benefits. If increased lending in the
low-income market did occur, but was not as
profitable as that in alternative markets, then
the CRA would act as a tax and a credit redis
tribution mechanism. The argument in favor
of the CRA was that banks were foregoing
profitable opportunities because of discriminato
ry behavior or market failure, and performance
could be enhanced if they became more actively
involved in this market (although performance
could be adversely affected in the short run as
start-up costs were incurred).
There have been a limited number of studies
evaluating the effect on performance of lending
in the low-income market. Canner and Passmore (1996) offered a number of testable
hypotheses concerning the potential impact on
profitability and the relationship between the
extent of the bank’s activity in this market and
performance. They found no evidence of lower
profitability at banks specializing in the lowincome market, consistent with the view that,
27
once start-up cost are incurred, lending in this
market can be just as profitable as in other
markets.30 Beshouri and Glennon (1996) eval
uated the relative performance of credit unions
that specialize in the low-income market and
found that while these specialized firms have
greater return volatility, higher delinquency
rates, charge-off rates, and operating costs,
they are compensated for these differences and
generate similar rates of return. Similarly, in
analyzing the performance of low-income and
minority lending, Malmquist, Phillips-Patrick,
and Rossi (1997) found that while low-income
lending was more costly, lenders were com
pensated with higher revenues, making profits
similar for both low- and high-income lending.
Finally, Esty (1995) evaluated the performance
of Chicago’s South Shore Bank, which has been
held up as the model community development
bank with the dual objectives of making a
profit and aiding in the development of the
local community. Esty’s analysis found the
economic return of the bank to be substandard.
Shareholders, however, appeared to be willing
to trade off the lower return for the social return
received from community improvement. That
is, the shareholders’ objectives were apparently
aligned with the dual objectives of the bank.31
In interviews with shareholders and employees,
Lash and Mote (1994) found similar evidence
of a willingness to trade off economic profit to
emphasize the development objective. While
the behavior of South Shore’s management and
shareholders may be admirable, if Esty’s anal
ysis is correct, it is not obvious that this model
can be implemented across the entire industry.
The second performance-related area of
research deals with the profit implications of
discrimination. If an institution overtly discrim
inates, it will deliberately forego profitable
lending opportunities. This implies that lenders
that do not discriminate will be the beneficiaries
of this behavior. Assuming that minority-owned
banks do not discriminate against minorities,
one might expect them to outperform the dis
criminating-banks. Calomiris, Kahn, and
Longhofer (1994) developed a model of cultural
affinity to explain differences in minority deni
al rates. Their basic argument is that because
of a general lack of familiarity with the culture
of minority applicants, the typical white loan
officer may not be as accommodating with
these applicants as he would with a white
28
applicant. For the minority applicant, the loan
officer will rely more heavily on low-cost,
objective information instead of making the
extra effort, as with the white applicant, to
obtain additional information to improve the
chances of approval. There is some empirical
support for this argument (see Hunter and
Walker, 1996). Again, this implies that minorityowned banks should benefit, since they will not
lack a cultural affinity with minority applicants.32
If discriminatory banks forego profitable
opportunities, ceterus paribus, minority-owned
banks should have superior profitability, lower
minority denial rates, and lower bad loans.
However, the empirical evidence does not
support this. A number of studies have found
that minority-owned banks have lower profits
(Bates and Bradford, 1980, Boorman and Kwast,
1974, and Brimmer, 1971). There is also evi
dence of higher loan losses at minority-owned
banks (Kwast, 1981). Additionally, there is
evidence that bank ownership shifts from white
to black control result in fewer loans being
generated (Dahl, 1996). Generally, there is
evidence that minority-owned banks do not
have particularly good performance or lending
records and have relatively poor CRA ratings
(Kwast and Black, 1983, Clair, 1988, and
Black, Collins, and Cyree, 1997). This evidence
is not consistent with overt discrimination.
In summary, the findings for the various
forms of discrimination are quite mixed.
While some studies have found race to be a
factor in loan decisions, the evidence is far
from conclusive. Additionally, methodological
problems bring into question the validity of
many studies. Parties on either side of the
issue frequently draw uncritically on the stud
ies that align with their own position. Addi
tional research is needed before we can draw
meaningful conclusions about race and the
credit decision.
Recent trends in mortgage activity:
The effect of regulation
How successful has the recent enforcement
of the CRA and the fair lending laws been?
Headlines proclaiming surges in credit to minor
ity groups suggest that the stricter enforcement
of the CRA and fair lending laws during the
1990s has been successful. Most of these
claims are based on recent trends in lending to
low-income individuals or in low-income neigh
borhoods, such as those presented in figure l.33
ECONOMIC PERSPECTIVES
FIGURE 1
Mortgage originations— 1-4 family, owner-occupied
By tract income
By personal income
thousands
thousands
By race
thousands
By tract minority (percent)
’
thousands
Note: See footnote 33 for the definition of income groups and footnote 40 for an explanation of inclusion of data in the sample.
Source: Hom e M ortgage Disclosure Act data, various years.
Between 1990 and 1995 the annual number of
mortgage originations to low- and moderateincome households, in low- and moderateincome census tracts, and to minorities almost
doubled. New loans in census tracts where
minorities accounted for at least half the popu
lation also grew significantly. As figure 1
shows, there was a considerable increase in the
number of loans to individuals targeted by fair
lending and CRA regulations. Some bankers
argue that the regulatory mandate to increase
lending in low-income neighborhoods and to
low-income individuals has actually been a
blessing in disguise as it has opened up new,
lucrative, previously untapped markets. Oth
ers continue to criticize the regulations.14
A full assessment of the success of the
CRA and the fair lending programs would re
quire a comprehensive cost-benefit analysis.
Accurately quantifying the cost is difficult.35 It
is also difficult to quantify the success of these
programs because of the vagueness of the legis
lation and the regulations enforcing it. The
mandate to banks was to use the proper criteria
FEDERAL RESERVE
BANK OF CHICAGO
in making loan decisions and to reach out to
the local community, including low- and mod
erate-income neighborhoods and individuals.
Based on this mandate, success may not require
any change in lending patterns. Another prob
lem with associating recent lending patterns
with regulation is the lack of a control group.
The issue is not whether lending to the targeted
groups increased, but whether it increased as a
result of the regulations,
There are, however, a number of credit
flow measures often associated with the CRA
and fair lending laws. Concerning redlining,
one would want to analyze changes in the
volume and dollar value of loans flowing into
low-income or minority neighborhoods. The
number of applications in these areas could
also be considered if redlining resulted in ap
plications not being accepted from these areas.
Some argue that the purpose of the regulations
was to increase the flow of credit to specific
groups of borrowers (based either on income or
race), therefore, credit flows to those particular
groups could be analyzed. It has also been
29
argued that the elimination of dif
FIGURE 2
ferences in denial rates may be
Historical mortgage origination activity
desirable.36 Concerning fair lend
millions of dollars
ing, one would want to analyze
changes in lending to minority
individuals and/or denial rates.
Although data limitations
hamper the degree to which rigor
ous analysis of the regulatory impact
can be undertaken, we can evaluate
lending patterns and check for
trends consistent with what are
typically perceived to be desired
changes. We use three different
control group specifications. First,
we compare lending patterns preand post- the recent regulatory
changes. Second, we compare the
Note: Shaded areas indicate recessions.
Source: U.S. Departm ent of Housing and Urban Developm ent,
degree of lending to targeted
S u rv e y o f M o rtg a g e L e n d in g A c tiv ity , various years.
groups (minority and low-income
individuals and neighborhoods)
growth in the 1990s be attributed to regulatorywith lending to nontargeted groups. Last, we
induced changes in lending behavior or to
compare the lending behavior of more heavily
other
factors related to the aggregate demand
regulated depository institutions with that of
for
housing
credit? Three pieces of evidence
less regulated mortgage companies.
suggest
that
the latter hypothesis may be more
Below, we present evidence on these credit
accurate.
First,
there is considerable growth
flows and discuss how they align with the goals
throughout
the
entire
period, even in real terms
of the legislation. We would expect the CRA
as
indicated
by
the
colored
line depicting the
and fair lending reforms of the late 1980s and
dollar
value
of
mortgage
originations
deflated
early 1990s to have increased lending to mi
by
the
Consumer
Price
Index
for
Urban
Workers.
nority and low-income individuals and lowThe
colored
line
also
highlights
the
procyclical
income neighborhoods. We would also expect
and seasonal nature of mortgage originations.
that most of the impact would be concentrated
Second, there is a substantial decline in the
in recent years as regulatory, legal, and public
number of mortgage originations after the 1993
scrutiny intensified and the cost of failing to
peak,
which might be due to a curtailment of
satisfy the requirements increased. We analyze
refinance
activity.37 Clearly, there is no regula
two potential effects. If the regulations were
tory
explanation
for this decline. There are
successful in getting lenders to expand their
probably
a
number
of factors beyond regula
business into new markets, this might influence
tion
that
affect
the
number
of originations and
the overall level of mortgage activity. Alterna
increase
the
difficulty
of
graphically
detecting
tively, we may see distributional effects as
a
structural
break.
To
address
this,
we
use a
lenders allocated a larger share of the credit
regression
model
of
the
quarterly
growth
rate
pool toward the new markets.
of
originations,
controlling
for
the
growth
rate
To analyze the effect on the aggregate
of
gross
domestic
product
(GDP),
the
change
level of mortgage activity, we used a nominal
in mortgage rates, and the growth rate of the
dollar measure of all mortgage activity for
consumer
price index.38 Quarterly indicators
1970 through 1995, combining originations
are
included
to absorb the seasonality in the
and refinances, from the Survey of Mortgage
dependent
variable.
Table 2 displays the re
Lending Activity issued by the U.S. Department
gression
results.
In
column
1, over 50 percent
of Housing and Urban Development (HUD)
of
the
variation
in
the
growth
of originations is
(see figure 2). Figure 2 suggests considerable
explained
by
the
right-hand-side
variables.
growth in mortgage originations over the 1990s,
The
coefficients
suggest
that
an
increase
in
in particular 1993-94. Can the high rate of
30
ECONOMIC PERSPECTIVES
TABLE 2
Model of aggregate growth in U.S. mortgage activity
Model 1
Model 2
-10.01**
(4.66)
-6.98
(5.37)
-7.88
(5.35)
-6.75
(5.13)
3.18**
(1.58)
2.71*
(1.64)
2.92*
(1.62)
2.90*
(1.58)
-11.84**
(2.91)
-11.24**
(2.95)
-11.41**
(2.96)
-11.04**
(2.94)
Growth of CPI-U
-2.15
(2.11)
-3.27
(2.33)
-3.02
(2.37)
-3.53
(2.30)
Quarter 2 seasonal
35.66**
(4.19)
35.47**
(4.18)
35.52**
(4.20)
35.39**
(4.16)
Quarter 3 seasonal
16.49**
(4.08)
16.31**
(4.08)
16.37**
(4.09)
16.29**
(4.06)
Quarter 4 seasonal
1.66
(4.07)
1.47
(4.07)
1.53
(4.08)
1.46
(4.05)
Intercept
Growth of real GDP
chain weighted
Change in mortgage rates
1990 and beyond
Model 3
-4.2
(3.72)
1991 and beyond
-3.27
(4.02)
1992 and beyond
Error degrees of freedom
Model 4
-6.24
(4.21)
91.00
90.00
90.00
90.00
Adjusted R2
0.54
0.54
0.54
0.55
Durbin Watson
2.21
2.19
2.20
2.22
* * Indicates a t-statistic greater than or equal to 1.96.
* Indicates a t-statistic greater than or equal to 1.64.
Note: Standard errors are in parentheses.
mortgage rates corresponds to a decrease in the
growth of mortgage originations, while an
increase in the growth rate of GDP corresponds
to an increase in the level of originations. The
controls for seasonality, the quarterly indicator
variables, suggest faster growth in mortgage
originations in the second and third quarters
than in the first and fourth. Column 2 of the
table presents the regression model with the
addition of a binary variable to capture a struc
tural shift in the post-1990 period. The coeffi
cient of the post-1990 indicator variable is
actually negative, but is significant at only the
74 percent confidence level. Therefore, the
regression model is unable to support the hy
pothesis that mortgage originations were stron
ger in the period following the recent regulato
ry changes. Tests for structural breaks for
post-1992 and post-1993, columns 3 and 4,
produced similar results.
Although we find the recent growth in
mortgage lending is consistent with earlier
FEDERAL RESERVE
BANK OF CHICAGO
patterns, the regulations may have resulted in
distributional changes in lending patterns.39 That
is, there may be a shift in lending emphasis away
from traditional markets toward low- or moder
ate-income groups and individuals. To evaluate
this, we assembled HMDA data for depository
institutions and their affiliates over the 1982-95
period and decomposed the data by income
groups and, when possible, racial groups.40
To the extent that regulations influence
lending patterns, we have argued above that
the effect would be most evident in recent
years, because of increased scrutiny, and most
pronounced among low-income and minority
borrowers and neighborhoods. We therefore
divided total lending activity into four income
categories and evaluated growth trends in the
number of mortgage applicants, originations,
and dollar value of originations for the 1990s.
We also developed data for the number and
dollar value of originations for the 1980s by
neighborhood income levels.41 Table 3 shows
31
TABLE 3
Base mortgage lending data, 1990
Applications
Acceptances
(000s)
(000s)
1,491.35
1,276.16
Tract income/MSA
income shares
Low income
Moderate income
Medium income
High income
0.01
0.10
0.57
0.31
0.01
0.09
0.57
0.33
Applicant income/MSA
income shares
Low income
Moderate income
Medium income
High income
0.05
0.17
0.27
0.50
0.04
0.16
0.28
0.53
Race shares
White
Black
Other m inority
0.82
0.06
0.11
0.84
0.05
0.11
Total Applications
Source: HMDA data, see footnotes 33 and 40.
the 1990 levels and relative shares of mortgage
activity on which our analysis is based. The
targeted groups received a relatively small
portion of the applications, originations, and
dollars. Low- and moderate-income tracts
account for approximately 10 percent of the
applications and loans and slightly less of the
32
dollars lent. Low- and moderateincome individuals, as opposed to
home purchases in low- and mod
Dollars
erate-income tracts, represent
(b illio n )
nearly 20 percent of applications
and originations, but still less than
127.72
10 percent of the dollars lent.
Roughly 80 percent of mortgage
activity (applicants, originations,
0.01
0.08
and dollars) involved white appli
0.49
cants. These figures demonstrate
0.43
that the targeted populations are a
relatively small share of the aggre
gate, which may explain why
0.01
increases may not be observable
0.08
in the aggregate data.
0.20
0.71
The year-over-year growth
rates for the number of loans
0.82
originated by neighborhood in
0.04
come groups for the 1980s and
0.13
1990s are presented in figures 3
and 4, respectively. For the
1980s, growth in the low- and
moderate-income groups lagged
that in other areas. For four of the years in the
1980s, the low-income tracts showed the slow
est growth and the moderate-income tracts also
showed relatively slow growth. In these years,
growth in the overall market was quite robust.
Thus, for the 1980s overall, growth in loan
volume was not particularly concentrated in
the low- and moderate-income
groups. Originations to the lowand moderate-income groups
grew more than 40 percent be
tween 1985 and 1986, exceeding
growth for these groups for any
single year throughout the 1990s.
However, the 1986 growth of
mortgage originations in middleand upper-income tracts still
exceeded that of the low- and
moderate-income groups.
Things changed in the
1990s. After 1991, growth was
relatively fast in the two lowest
income groups, particularly in
the years when overall market
growth was greatest. This find
ing suggests that banks have
responded to the CRA and have
made significantly more loans
in the low- and moderate-in
come markets. The change is
ECONOMIC PERSPECTIVES
overwhelmingly statistically significant based
on a test of whether the share of loans in each
income category is constant throughout the
1990s. We conclude that the growth in mortgage
originations has not been uniform throughout
the 1990s, consistent with the 1992 through
1994 growth spurt in lending to low- and
moderate-income groups.
Figure 5 presents data for recent mortgage
applications.42 After 1991, low- and moderate-
FEDERAL RESERVE
RANK OF CHICAGO
income neighborhoods saw signifi
cantly stronger growth than oc
curred in other areas. Growth in
the middle-income group, where
the majority of mortgage activity
is occurring (see table 3), saw the
slowest increase over this period.
These trends are consistent with
the view that banks have been
making a significant effort to
encourage applications from lowerincome neighborhoods and with
statements by community groups
that progress is being made in
less affluent neighborhoods. The
test of differences across catego
ries is again highly statistically
significant.43
Figures 6 and 7 analyze mort
gage activity by the income level
of the borrower instead of the
neighborhood in which the prop
erty is located. While not quite as pronounced
as the neighborhood data, figure 6 shows
growth in mortgage activity to low- and mod
erate-income individuals, particularly for the
years in which overall growth was greatest.
The mortgage application data in figure 7 tell
a similar story. Overall, the data suggest an
increase in the growth of loan applications and
loans approved for low- and moderate-income
individuals, with much of the growth coming
after 1992. However, the differ
ences are only significant at the 46
percent and 54 percent confi
dence levels, respectively, for
originations and applications.
Thus, based on this test, lending
to low- and moderate-income
individuals was uniform through
out the 1990s. Data on applicant
income were not collected for the
1980s so we cannot compare the
two periods.
Figures 8 and 9 show loan
activity by applicant race. While
neither the CRA nor the fair lend
ing laws explicitly require lenders
to change underwriting criteria
and affirmatively pursue additional
minority mortgage business, lend
ers may believe that doing so will
help them avoid charges of dis
crimination and be looked upon
33
more favorably during their regulatory assess
ment. The growth in minority applications and
originations during the 1990s has been high
relative to that for nonminorities. The increase
in applications and originations among blacks is
even more significant.44 Figures 10 and 11
present a similar analysis based on the minority
proportion of the census tract as opposed to the
minority status of the applicant. Since 1991,
34
growth appears to have been relative
ly similar across the groups.
Several of the results in fig
ures 3-11 are consistent with
efforts by banks to target lowand moderate-income individuals
and neighborhoods in their mort
gage business. This observation
is based not on the level of loans
made but on the fact that the
growth in lending to the targeted
groups exceeded that to the nontargeted groups. One could ar
gue, however, that the improve
ments are somewhat diminished,
being from such a small base (see
table 3). Lending in low- and
moderate-income neighborhoods
constitutes approximately 10
percent of total originations and
even less of the dollar value of
loans originated. Based on in
come alone, we would expect the demand for
mortgages in these neighborhoods to be rela
tively low. Mortgage activity among lowincome individuals constitutes approximately
20 percent of the market. However, the 31
percent growth in mortgage originations in
low- and moderate-income tracts from 1993
to 1994 corresponds to nearly 35,000 loans
and approximately $2.7 billion. If all of this
change is attributed to the regula
tions, it translates to just over 100
loans and $8 million per MSA.
In addition to the number of
loans and applications, we evalu
ated denial rates for ethnic
groups. Minorities have typically
been shown to have higher denial
rates than other applicants. One
of the common debates in the
literature and popular press is
whether the differences across
racial groups can be explained
by economic characteristics.45
We present two measures of dif
ferences in denial rates. The first
is a standard odds ratio: Based on
the loan decision, we calculate the
odds of a minority applicant being
denied a loan relative to the odds
of a nonminority applicant being
ECONOMIC PERSPECTIVES
denied.46 An odds ratio of 1 corresponds to
equality of the denial odds for white and mi
nority applicants; values above 1 correspond to
more minority denials. If minority status is
associated with lower creditworthiness, we
would expect the odds ratio to be higher because
of the differences in qualifications. To partial
ly account for this, we also calculated an odds
ratio conditioned on income and loan value.47
FEDERAL RESERVE
BANK OF CHICAGO
The odds ratios are presented in
table 4.48
Interpretation of the odds
ratios as evidence of discrimina
tion is difficult due to the small
number of variables collected in
the HMDA data. Instead, we
focus on the changes in the ratio
over time. Under the assumption
of constant quality of the appli
cant pools, changes in the ratios
may be attributed to changes in
lender behavior. Cyclical econom
ic changes, however, are likely to
affect the creditworthiness of the
applicant pool. To account for
this, we also calculated the two
odds ratio measures for a sample
of independent mortgage compa
nies. These are typically thought
to be less stringently regulated
with respect to the CRA, but they
still report for HMDA purposes through HUD.
Thus, we use these as a control group that we
can contrast with the regulated banks to distin
guish the effect of regulation.
As table 4 indicates, for depository institu
tions the unconditioned odds ratio is relatively
stable during the 1990s.49 The odds of a minor
ity applicant being denied a mortgage request
are approximately twice those of a nonminority
applicant. The odds ratio and
trend estimates are statistically
different from 0 at the 99 percent
confidence level. Differences
between years are typically statis
tically significant at conventional
levels. The conditioned ratio for
banks is somewhat similar to the
unconditioned measure, but de
clines throughout the period. The
additional information embedded
in the conditioned measure does
explain part of the difference
between the two borrowing groups;
however, it leaves much unex
plained. Additional information
on the creditworthiness of the
applicant and any discriminatory
effects would be needed to explain
the remainder. The declining
trend in the odds ratio, after condi
tioning on a flexible specification
35
of loan amount and applicant income, suggests
a change in the treatment of minority appli
cants relative to nonminority applicants over
the 1990-95 period. Essentially, lenders be
came more accommodating to minorities. The
effect is more apparent in the last two columns
of the table, where we repeat the analysis using
black/white odds ratios. These results are
consistent with more stringent regulations
36
producing a change in lender
behavior, assuming that the un
observed characteristics
of the applicant pool remain
constant over time.
The odds ratios for the inde
pendent mortgage companies are
also presented in table 4. The
ratios for these less regulated
companies are much more erratic,
but display a similar downward
trend. Disparities between minor
ity and nonminority applicants
and between black and white
applicants decline over time for
both sets of institutions, suggest
ing that the change may not be
the result of the regulations.50
Another way to assess the
extent to which the supply of mort
gage credit to minorities increased
in the mid 1990s is to examine
home ownership rates over time. The relaxation
of binding credit constraints should cause
minority originations and home ownership
rates to increase. Figure 12 displays home
ownership rates from 1970 to 1994 for white,
black, and other minority households. Recent
home ownership rates are still well below the
rates observed in the early to mid-1980s. Recall
that blacks experienced the strongest growth in
mortgage originations in 1993
and 1994, yet there was little
effect on home ownership. Within
our sample, mortgage originations
to blacks increased by 23,000
loans from 1992 to 1993 and
another 4,000 from 1993 to 1994.
In 1994 there were roughly 11.3
million black households in the
U.S., implying that 113,000 new
loans would be necessary to move
the home ownership rate a single
percentage point. Viewed this way,
the 1993-94 changes appear small.
Summary and conclusions
In this article, we have pro
vided background on the evolu
tion of the CRA and fair lending
regulations, summarized the eco
nomic literature which pertains to
this type of regulation, and present
ed evidence on the effectiveness
ECONOMIC PERSPECTIVES
TABLE 4
Comparison of mortgage denial rates
Non-HUD regulated
Odds ratio
Denial rates
W hite
1990
1991
1992
1993
1994
1995
0.12
0.14
0.11
0.11
0.10
0.11
Black
0.29
0.31
0.27
0.25
0.23
0.23
M inority
Black
Conditioned
odds ratio
Minority
Black
Minority
0.22
2.98
1.98
0.25
0.22
0.22
0.20
0.20
2.78
2.85
2.72
2.62
2.42
2.05
2.20
2.25
2.13
2.00
2.68
2.52
2.58
2.39
2.22
2.02
2.06
2.09
2.18
2.15
1.98
1.84
-0.10
0.01
-0.13
-0.05
Trend
HUD regulated
Odds ratio
Denial rates
W hite
1990
1991
1992
1993
1994
0.10
0.26
0.14
0.13
0.14
1995
0.20
Black
0.26
0.36
0.26
0.24
0.23
0.26
Trend
M inority
0.20
0.28
0.22
0.20
0.19
0.23
Conditioned
odds ratio
Black
Minority
Black
3.27
1.62
2.20
2.19
1.72
1.40
2.27
1.11
1.73
1.70
1.40
1.19
3.13
1.43
2.03
2.27
1.37
1.83
1.98
1.53
1.33
1.75
1.44
1.27
-0.13
0.03
-0.11
-0.09
Minority
Source: HMDA data, see footnotes 33 and 40.
Although early studies appear to find evidence
of redlining, more recent studies do not support
this finding. Concerning disparate treatment
based on the race of the applicant,
some studies have found differenc
FIGURE 12
es in the probability of a loan
Home ownership rates
application being denied that are
not explained by economic charac
percent
teristics. However, these studies
have been criticized as having
methodological and data problems.
Our major purpose, however,
is to provide the reader with a
review of the literature and not to
take a position on the merits of
the various positions concerning
whether there is a need for the
CRA and fair lending regulations.
Regardless of one’s position, the
regulations do exist and are being
enforced. We evaluated how the
regulations may alter lending be
S ource: U.S. D ep artm en t of C o m m erce, B ureau of th e C ensus, v ario u s y ears.
havior, using a variety of measures
of these regulations by analyzing recent
trends in mortgage lending activity. The
literature review indicated mixed results.
FEDERAL RESERVE
BANK OF CHICAGO
37
of changes in lending patterns. We found that
over the 1990-95 period (particularly 1993-94),
loan applications and originations increased
significantly to groups targeted by the CRA
and fair lending legislation. Additionally, it
appears there may have been a compositional
shift toward blacks and a minor shift toward
low-income groups. These changes appear
large in terms of growth rates, but they started
from a very small base. The changes appear
smaller when measured in dollars rather than
the number of loans or applications.
It is difficult to attribute the increase solely
to the strengthening of the regulations. We
assessed the regulatory impact in three ways.
First, we used historical trends as a control
group. After accounting for economic condi
tions, we found that aggregate lending in re
cent years has not been unusually strong. Sec
ond, we considered changes in the composition
of mortgage activity by examining year-to-year
growth rates of applications and mortgage
originations. We used the nontargeted groups
as controls. The analyses presented some evi
dence of a change toward increased lending to
minority and low-income individuals and neigh
borhoods. Last, we compared recent trends in
denial disparity measures, blackAvhite and
minority/nonminority odds ratios over time
between depository institutions and mortgage
companies. Typically thought to be less strin
gently regulated, mortgage companies might
depict the market result in the absence of addi
tional regulation. Our analysis shows a decline
in the odds ratio for both depository and non
depository institutions, suggesting that the
effect may not be the result of increased regu
latory scrutiny.
Overall, our results are mixed. There is
some evidence of changes in lending patterns,
and some of the evidence is consistent with
changes related to the new regulatory envi
ronment. However, the growth rates are not
unprecedented and, if entirely attributed to the
regulations, translate to approximately 100
loans and $8 million per MSA. We would
emphasize that we have not addressed the
cost of implementing the regulations relative
to the benefits. In addition, we have not
addressed the question of whether these credit
market regulations are the most effective way
of changing the fundamental economic status
of the targeted groups. As the regulation of
credit markets continues to evolve, it remains
important to continually revisit these issues.
NOTES
'Throughout we use the term bank in a generic sense to
encompass all depository institutions.
2See, for example, Coplan (1996), Lindsey (1996), Seiberg
(1996a, 1996b), or Wilke (1996).
Tor a discussion of potential information externalities, see
Nakamura (1993) and Calem (1996).
4Gender was added as a protected category through 1974
amendments, and handicap and family status were added by
amendment in 1988.
5A number of these protected categories were added by
amendments in 1976.
6See Hula (1991) and GAO (1996).
7See Dedman (1988).
Tor a discussion and examples of increased scrutiny by
regulators in more recent years, see Garwood and Smith
(1993) and Macey and Miller (1993).
The reporting requirement now extends beyond depository
institutions to, generally, all lending institutions with assets of
more than $10 million with an office in a metropolitan statis
tical area (MSA) (for depositories) or loan activity in MSAs
(for nondepositories).
38
l0For a description of the process by which the Federal Re
serve Banks use statistical models to test for disparate treat
ment of applicants, see Bauer and Cromwell (1994) and
Avery, Beeson, and Calem (1997 forthcoming). For discus
sion of the use of statistics in detecting mortgage discrimina
tion, see Yezer (1995).
"See Interagency Regulatory Task Force (1994). Represent
ed in the interagency group were the Department of Housing
and Urban Development, Department of Justice, Comptroller
of the Currency, Office of Thrift Supervision, Federal Re
serve System, Federal Deposit Insurance Corporation, Federal
Housing Finance Board, Federal Trade Commission, and
National Credit Union Administration.
l2Again, although the fair lending laws and the CRA are
different, there is significant overlap as evidenced in the last
of these items. This is very close to an anti-redlining state
ment—the very reason for the passage of the CRA.
'Tor examples of enforcement activities by the Department of
Justice and bank regulatory agencies, see Macey and Miller
(1993) and GAO (1996). For a discussion of alternative
strategic responses by banks to the increased regulatory
pressure from fair lending and CRA regulations, see Evanoff
and Segal (1997 forthcoming).
ECONOMIC PERSPECTIVES
l4This argument was also being made at the time to contest
the liberalization of bank branching laws. The concern was
that banks would export deposits through their branch net
work to other, more lucrative, markets.
'’Regulation BB of the Code of Federal Regulations describes
the requirements of the community reinvestment regulation.
l6See Macey and Miller (1993) and U.S. Congress (1989).
17There are alternative tests for wholesale or limited purpose
banks, still another streamlined test for small banks, and
another for banks choosing to develop and be held account
able for a strategic plan which details how the banks intend to
satisfy CRA requirements.
'"Consumer loans will be considered if the bank collects data
on this activity and requests that they be considered in the
evaluation, or if regulators determine that this activity consti
tutes a substantial portion of the institution’s business.
l9The explicit weight assigned for each test and grade addresses
a criticism of the earlier rating system under which bankers
frequently complained about the uncertainty as to how they
should allocate resources to improve their CRA rating.
Emphasizing the lending test also addresses criticisms of the
earlier grading scheme by being less process-oriented and
more results-oriented.
2<’See GAO (1995).
2lFor example, HMDA data does not contain information on
credit history, wealth, and employment stability of the appli
cant, or the value or purchase price of the property. Adverse
credit history is the most common reason given in the HMDA
data for denying loans. For discussions of lending trends in
HMDA data, see Canner and Passmore (1994, 1995a, 1995b).
22A review of much of the early literature can be found in
Benston (1981) and Canner (1982).
23Canner, Gabriel, and Woolley (1991) reported similar
findings after controlling for market characteristics. The
inclusion of variables to capture risk factors, however, may
not resolve the endogeneity problem since their values may be
supply induced.
29For a discussion of these concerns see Tootell (1993) and
Ross (1996).
30The authors, however, emphasize the limitations of their
analysis. For example, information on the profitability of
low-income lending is not available. Thus, the authors are
comparing overall profit levels across banks with different
levels of low-income lending although this lending is typical
ly a relatively small portion of the overall portfolio. There
have also been concerns expressed recently about the growing
number of special lending programs to accommodate the
targeted groups and the resulting high default rates, see
Seiberg (1996b).
3'Esty also evaluated the impact South Shore had on the local
community and argued that there was no evidence of any
unique positive relative impact on the local community.
"One could make the argument that banks with a significant
number of minority loan officers could also benefit from a
cultural affinity. We do not have data to directly test for this.
33Low-income neighborhoods are defined as census tracts
where the median income is less than 50 percent of the MSA
median income. The moderate-income category corresponds
to greater than or equal to 50 and less than 80 percent, the
middle-income category corresponds to greater than or equal
to 80 percent and less than 120 percent, and the upper-income
category corresponds to at least 120 percent of the MSA
median income. Similar break points define the categories for
applicant income relative to MSA income. Tracts are also
classified by minority composition into low (less than 25
percent minority), moderate (25 percent to 50 percent),
middle (50 percent to 75 percent) and high (75 percent to 100
percent).
34See, for example, Wilke (1996), Seiberg (1996), or Lind
sey (1996).
’’Barefoot et al. (1993) attempt to quantify the compliance
cost associated with consumer protection regulations includ
ing the CRA. They note that bankers find the CRA to be one
of the most onerous regulations faced by banks.
24Some studies have addressed whether minority groups are
“discriminated” against in that they are more likely to receive
a particular type of loan which may have less favorable terms.
For example, Canner, Gabriel, and Woolley (1991) found
minorities are more likely to obtain an FHA loan than are
nonminorities.
3<’A narrowing of denial rate differentials is frequently cited in
the popular press as a measure of progress in fair lending, for
example, Coplan (1996). Lawmakers have also argued that
although some differential may be warranted based on credit
quality, the current differences are too great and imply some
discrimination is being undertaken; for example, in U.S.
Congress (1989), see Illinois Senator Dixon’s opening com
ments during hearings on the CRA.
25See Horne (1994, 1997 forthcoming), Liebowitz (1993), and
Day and Liebowitz (1996).
"Refinancing and new originations cannot be separated in the
HUD data.
26The authors find considerable differences in underwriting
standards across banks concerning threshold values for debt
ratios, loan-to-value ratios, etc. The authors realize that
unlike the more generic market model, regressions with an
emphasis on bank-specific underwriting standards will not
allow for a direct test of disparate impact.
’"Changes in mortgage rates measure the contract rate on 30year fixed rate conventional mortgage commitments reported
by the Federal Home Loan Mortgage Corporation.
27Becker (1971) is typically cited as the source of this argument.
28Although Becker actually used data from the Boston study,
it is questionable if the data are appropriate for critiquing the
Boston study based on default rates. The data were for loans
originated prior to the period discussed in the Boston study
and the analysis was a comparison of default rates across
geographic areas based on racial composition. From the data,
one cannot conclude how racial defaujt rates compare.
FEDERAL RESERVE
BANK OF CHICAGO
39For critiques of the CRA as a means of accomplishing this
redistribution, see Lacker (1995), Macey and Miller (1993),
and White (1993).
40It is important to emphasize that our sample may differ from
HMDA data reported elsewhere because, unless noted, we are
viewing lending activity only for depository institutions or
their affiliates, and we are screening out observations that
cannot be classified into income and racial subgroups which
we expect to be affected by the regulation. By analyzing this
group of institutions, we have a homogenous group through
39
time. Certain mortgage originators were only added to the
HMDA in recent years. Our sample consists of loan applica
tions for one-to-four family, owner-occupied properties where
the mortgage is valued between $1,000 and $1 million, the
applicant’s income is less than $1 million, and the loan-toincome ratio is less than five. For the 1980s these require
ments were imposed on market (census tract) averages since
individual loan data were not available on HMDA until 1990.
We only consider mortgages for properties in MSAs, and we
require complete data on the location of the property—state,
county, MSA, and census tract—to allow us to combine
HMDA data with census information concerning neighbor
hood income and composition. Reporting institutions must
report on applications for property located in an MSA where
they have an office. If the property is outside of an MSA or
in an MSA in which the institution does not have an office, it
has the option of reporting the MSA information. Thus, some
loans made in MSAs will be omitted from our sample because
the bank chose not to include the information. The MSA
information is necessary to merge the data with census
information. Finally, the data must pass the validity checks
(Board of Governors 1995).
4lThe data are not precisely comparable to that for the 1990s
because mortgage originations for purchase and refinancing
were not separated during this earlier period. However, the
role played by “refis” in the 1980s is not expected to be
nearly as erratic as in the 1990s. All analysis of the 1990s
excludes refinances.
“ Application data are not available for the 1980s.
4,We reject the hypothesis that shares by income categories
are constant over the 1990-95 period based on a chi-square
test statistic of 53 with 15 degrees of freedom.
44The increase in mortgage activity for blacks was also spread
across all income levels. The 1990-95 growth rates for
blacks in low-, moderate-, middle-, and upper-income neigh
borhoods were 157 percent, 129 percent, 99 percent, and 101
percent, respectively. In their evaluation of redlining Holmes
and Horvitz (1994) found that, after accounting for neighbor
hood characteristics including risk, more credit was being
made available in certain minority neighborhoods. They
found a systematic preference on the part of insured lenders
(the FHA) toward lending in areas of high or growing minori
ty populations. They attribute this to the pressures created by
the CRA and community groups to increase lending in these
areas. Similarly, Malmquist, Phillips-Patrick, and Rossi
(1997 forthcoming) found that while low-income lending was
more expensive, lenders were also compensated with higher
revenues making profits similar for both low- and higherincome loans. However, the authors found that profits were
inversely related to the share of mortgages originated to
blacks. They suggest this is a result of firms “bending over
backwards” to yield to regulatory pressures to make more
minority loans.
“ Minorities are defined as non-whites. Asians are an excep
tion and typically do not have high denial rates.
“ Odds are defined as the ratio of the probability of denial to
the probability of acceptance.
47We calculate this based on a logit regression which in
cludes, in a flexible functional form, applicant income, loan
size, and race.
“ The denial rates may be related to other factors, for exam
ple, geographic differences. In our analysis, we emphasize
changes through time and assume the effects from other
factors are constant across time.
49The mortgage company data should be interpreted cautiously,
since these institutions do not go through the same rigorous
editing process and resubmission of revised data as the
depository institutions.
^Alternatively, these “nonregulated” firms can be prosecuted
by the U.S. Department of Justice for fair lending violations,
so they may not be immune to this regulation.
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43
BOX A
Mortgage redlining studies Credit flows to geographic areas
ECONOM IC PE R SPE C T IV E S
Author cite
Period analyzed
Data sources
Dependent variables
Race variables
Ahlbrandt (1977)
1973-74 Pittsburgh
City Planning Dept.
and 1960-70 Census
Mortgage loans and dollar
value of mortgage loans
per occupied housing unit
in census tract
% black
Change in % black
% black is not related to loans
or dollar value of loans; author
concludes there is no evidence of
redlining. However, the change in
% black is positively associated
with dependent variable in some
specifications.
Hutchinson, Ostas &
Reed (1977)
1975 survey of 4 S&Ls in
Toledo Ohio SMSA, 123
census tracts
Local survey and
1960-70 Census
No. of mortgage loans in
owner occupied houses,
No. of insured loans,
% and no. of
conventional loans,
% and no. of home
improvement (HI)
loans accepted
% black
% black squared
Change in % black
Finds evidence consistent with
redlining. Total number of loans
unaffected by race but racially
mixed areas receive more
government insured loans and HI
loans. Lower acceptance rate for
HI loans.
Avery & Buynak (1981)
1977-79 Cleveland
(Cuyahoga county,
by 335 census tracts)
HMDA, 1970
Census of Housing and
County Auditor records
Percent of title transfers
financed by banks, S&Ls,
mortgage bankers, total
mortgages and home
equity loans (total value of
mortgage and HI loans) /
(value of owner occupied
housing)
7 categories from
largely white to
largely black
Finds no redlining with total sample
but this is due to the lending by
mortgage bankers, suggesting that
banks and S&Ls lend less in minority
areas (consistent with redlining).
Holmes & Horvitz (1994)
1988-91 Houston
Census, HMDA
No. of loans,
owner occupied units
% black, % Hispanic
No redlining for conventional loan
once risk factors are accounted for.
Perle, Lynch &
Horner (1994)
1982 Detroit
Michigan Financial
Institutions Board, Census
No. of loans/(year-round
housing units)
% black households
Using traditional method finds
evidence consistent with redlining.
It disappears when a more comprensive model is used. Authors argue
that traditional methods say little
about redlining.
Shlay (1988)
1980-83
Chicago SMSA
Census,
HMDA,
HUD
Number and dollar
value of loans in market
% Hispanic
% black
Conventional: % black and
% Hispanic negatively influence
the number of loans.
FHA: % black negatively influences
number and dollar value of loans.
Benston & Horsky
(1979,1992)
1976-82 Indianapolis,
Cincinnati, and
Nashville; 1974-76
Rochester, NY
Special Survey
Redlining conclusions
Finds no evidence of unmet demand
for mortgage credit. No evidence
of redlining.
ED ERA L RE SERV E BANK O F CHICAGO
1
BOX A (C O N T.)
1
Author cite
Period analyzed
Data sources
Dependent variables
Race variables
Redlining conclusions
Bradbury, Case
& Dunham (1989)
1982-87 Boston
Suffolk County
Registry of Deeds,
Census of Population
and Housing Survey
of Consumer Finance
Number of loans in market
per 100 housing structure
% black in the
neighborhood
statistical area (NSA)
% Other minority
in NSA
Minority status of NSA associated
with fewer loan originations.
10% difference in the black
race variable corresponds to
1.7 fewer loans per 100 structures.
Shlay (1989)
Baltimore city
and suburbs
Census, HMDA
Number and dollar value
of loans in market
% black
Lending inversely related
to the racial variable.
Canner, Gabriel &
Woolley (1991)
1983 National Sample
Survey of Consumer
Finance, 1980 Census
of Population and Housing
Binary for loan
delinquency, binary
for conventional
versus FHA loan
Black or Hispanic
applicant
% minority census
in census tract
Finds strong positive relationship
between FHA use and minority status.
However, this is shown to be driven by
economic characteristics of the appli
cant and not the racial composition of
the neighborhood. No evidence of
redlining.
Hula (1991)
1981-87
National Data
Census,
HMDA
No. and dollar value of
loans in Census tracts
Central city binary
% minority population
Little evidence of discrimination.
Megolugbe & Cho
(1993)
1991 U.S. MSAs (less
NY, LA)
Census, HMDA
Number of loans
per housing stock in
metropolitan area
% black
The race variable is positively
related to FHA volume. No
relationship is found for
conventional loan volume.
Avery, Beeson,
Sniderman (1994)
1990-91
National Data
HMDA, Census
Accept/deny
binary
Minority binary
Probability of denial is higher for all
minorities, particularly blacks.
However, the probability of denial is
not related to the racial composition
of the neighborhood.
*
U1
BOX B
0)
Microeconomic lending studies: Credit flows to individuals
ECONO M IC PE R SPE C T IV E S
Author cite
Period analyzed
Data sources
Dependent variables
Race variables
Conclusions
Black, Schweitzer,
and Mandell (1978)
1976-77
Special Survey
Accept/reject binary
Black binary
Race appears to be a determinant
of the loan decision.
Munnell et al.
(1992)
Boston 1990
HMDA, Special
Survey
Accept/reject binary
Black and Hispanic
binary
Race is found to influence the loan
decision. Alernative specification (B5)
rejects redlining hypothesis.
Carr and Megbolugbe
(1993)
Boston 1990
Munnell et al. data
Accept/reject binary
Black and Hispanic
binary
Confirms Munnell et al. results.
Schill & Wachter
(1993)
1990 Boston &
Philadelphia
HMDA, Census,
FFIEC
Accept/reject binary
Percent of households
headed by a black. By a
Hispanic. A binary for
black applicants. For
Hispanic applicants.
Evidence of redlining disappears
when risk variables are included.
Berkovec, Canner,
Gabriel & Hannan
(1994)
1986-89
HUD, Census
Default binary, % loss
in event of default
Racial binaries
Default is not related to racial compo
sition of neighborhood. There is a
higher likelihood of default for black
borrowers. Losses are greater for
loans extended to blacks and for loans
in areas with a larger proportion
of blacks.
Glennon & Stengel (1994)
Boston 1990
Munnell et al. data
Accept/reject binary
Black and Hispanic
binary
"Cleaned" Munnell et al. data and
found their results were robust.
Hunter & Walker (1996)
Boston 1990
HMDA and Survey data
Accept/reject binary
from Munnell et al.
Minority binary (also
interacted with various
borrower characteristics).
Race is important (1996) for the credit
decision, but only when the candidate
is marginally (un)qualified.
Black, Collins, Cyree
(1997)
1992-93
HMDA, Census,
Call Reports
Accept/reject binary
Black binary
HMDA data suggest that both blackand white-owned banks may utilize
applicant race in the mortgage pro
cess. However, after incorporating
neighborhood characteristics from
the census data from the call reports,
only black-owned banks appear to
utilize race in the credit decision.
C a ll fo r Papers
■ ■ I
Technology
//www:Policy.implications.for.the.future.of.financial.services/com
The Federal Reserve Bank of Chicago invites submis
sions for its 33rd annual Conference on Bank Structure
and Competition to be held at the Westin Hotel in
Chicago, Illinois, April 30-May 2, 1997. For over 30
years the conference has served as a forum for the
discussion of current policy issues related to the finan
cial services industry. In keeping with this tradition,
we welcome the submission of papers on a wide range
of issues related to public policy and financial structure.
We are especially interested in papers that
examine the impact of technology on the financial
services industry. Banks and other financial institu
tions are just beginning to tap the potential benefits
of current and projected technology. Technology
changes are altering the cost of providing financial
products and services and redefining barriers to entry.
What does this developing technology imply for
financial service delivery systems? Has the physical
bank branch become obsolete just as banks have
obtained the legal right to expand on an interstate
basis? What are the implications for marketing? For
industry entry barriers and the future industry struc
ture? For risk management? For asset valuation? For
the traditional view that banks are unique because of
the special (informational) relationship they have
with customers? Technology is also drastically alter
ing the regulatory landscape. As a result of technol
ogy, the composition of the industry may be altered to
include nontraditional providers, and the financial
services industry will be transformed into an interna
tional market. The ability of existing industry
participants and new entrants to use technological
innovations to avoid financial regulation raises ques
tions about the need to alter regulatory structures.
Industry participants disagree on how quickly
the changing technology will be incorporated and,
therefore, how different the industry will look in the
future. Will stored value cards be commonplace?
Cyber-banking on the Internet? W hat are the impli
cations for monetary policy? How willing are con
sumers to utilize the new technology? Some are
concerned that changes will happen so quickly that
banks that are making wrong decisions on technology
will quickly fade into obscurity. Others expect more
gradual changes.
These questions and related issues will be ad
dressed at the 1997 conference. We would like to
stress, however, that all scholarly research related to the
financial services sector and public policy will be given
full consideration for presentation. Additional topics
include: Optimal regulatory structures; Industry con
solidation and antitrust issues; Risk management and
derivatives; Payments system issues; Regulatory compe
tition; Credit availability; and Corporate governance
and bank behavior.
If you would like to present a paper at the
conference, please submit four copies of the paper or
abstract with your name, address, telephone number,
and e-mail address, and those of coauthors, by De
cember 18th. Preference will typically be given to
more complete papers.
Address correspondence to: Conference on
Bank Structure and Competition, Research De
partm ent, Federal Reserve Bank of Chicago, 230
South LaSalle Street, Chicago, IL 60604-1413.
For further information call Douglas Evanoff at
312-322-5814 or e-mail him at devanoff@frbchi.org.
Conference on Bank S tructure and C o m p e titio n
A pril 3 0-M a y 2, 1997
Federal Reserve Bank of Chicago
■I
1
E C O N O M IC P E R S P E C T IV E S — IN D E X F O R 1 9 9 6
A rtic le
Is s u e
I
Pages
B A N K IN G A N D F IN A N C E
Management efficiency in minority- and women-owned banks
Iftekhar Hasan and William C. Hunter
Mar/Apr
20-28
Sep/Oct
16-32
Nov/Dec
2-1 8
Nov/Dec
19-46
Sep/Oct
2-15
Jan/Feb
22-31
Performance and access to government guarantees:
The case of small business investment companies
Elijah Brewer III, Hesna Genay, William E. Jackson III,
and Paula R. Worthington
How are small firms financed? Evidence from small
business investment companies
Elijah Brewer III, Hesna Genay, William E. Jackson III,
and Paula R. Worthington
CRA and fair lending regulations: Resulting
trends in mortgage lending
Douglas D. Evanoff and Lewis M. Segal
IN T E R N A T IO N A L E C O N O M IC S
A supply-side explanation of European unemployment
Lars Ljungqvist and Thomas J. Sargent
M O N E T A R Y P O L IC Y A N D M A C R O E C O N O M IC IS S U E S
Some comments on the role of econometrics in economic theory
Martin Eichenbaum
Monetary policy shocks and long-term interest rates
Wendy Edelberg and David Marshall
Mar/Apr
2 -1 7
Identification and the liquidity effect: A case study
Lawrence J. Christiano
May/June
2-13
May/June
14-27
Soft landings on a bumpy runway
Francesca Eugeni and Charles L. Evans
R E G IO N A L IS S U E S
State-local business taxation and the benefits principle
William H. Oakland and William A. Testa
Jan/Feb
2 -1 9
Jul/Aug
3-2 7
Formal and informal financing in a Chicago ethnic neighborhood
Philip Bond and Robert Townsend
Copies of the 1996 E c o n o m ic P e r s p e c t i v e s can be obtained by contacting
Public Affairs Department
Federal Reserve Bank of Chicago
P.O. Box 834, Chicago, IL
60690-0834
Telephone: (312) 322-5111
F ax:(312)322-5515
Web Site: http://www.frbchi.org
48
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