The Financial Review 39 (2004) 79--99
When Are Commercial Loans Secured?
John S. Gonas
Belmont University
Michael J. Highfield∗
Louisiana Tech University
Donald J. Mullineaux
University of Kentucky
Abstract
We analyze the factors that influence the decision to secure a commercial loan. We find
evidence that variables reflecting adverse selection, moral hazard, and the prospects for default
all affect the likelihood a loan will be collateralized. We find no evidence in favor of the
predictions of certain theoretical models that high-quality firms signal by providing collateral.
Our results also show that lenders with less risk protection in the form of equity capital are
more likely to require collateral, but that banks themselves are less likely to secure loans than
nonbanks. Certain loan characteristics also influence the collateralization decision.
Keywords: secured loans, collateral, credit risk, information asymmetry, moral hazard
JEL Classifications: G20, G21, G28
∗ Corresponding author: Department of Economics and Finance, College of Administration and Business,
Louisiana Tech University, P.O. Box 10318, Ruston, LA 71272; Phone: (318) 257-2112; Fax: (318) 2574253; E-Mail: [email protected].
The authors thank an anonymous referee, Brent Ambrose, Dan Bradley, Mark Carey, Marcia Cornett, Steve
Dennis, Larry Wall, and seminar participants at Louisiana Tech University’s Research and Policy Forum,
the 2002 Midwest Finance Association annual meeting, the 2002 Financial Management Association
annual meeting, and the Symposium on Financial Institutions at the 2003 Eastern Finance Association
annual meeting for helpful comments on earlier drafts of the paper. All errors remain ours. A previous
version of this paper was presented under the title “The Determinants of Secured Loans.”
79
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1. Introduction
While there is a significant amount of research addressing the effect of collateral
on credit risk premiums on bonds and bank loans, there is little empirical work on the
factors that affect decisions to secure loans. Theoreticians have argued that collateral
can play a multitude of roles, such as facilitating signaling, controlling information
asymmetry problems, mitigating moral hazard problems, and providing respite against
default and bankruptcy loss. Our objective in this paper is to examine whether one
of these rationales is dominant in the collateralization decision or if each plays an
independent role. We employ a large sample of transaction-specific loans from the
Loan Pricing Corporation’s DealScan database over the period December 1988 to
January 2001 in the empirical analysis.
We find evidence that collateral is more likely to be pledged in the presence
of significant information asymmetries between borrowers and lenders. We use data
that reflect whether the borrower is rated or exchange-listed as proxy variables for
the quantity and quality of information about the borrower. We also take account
of whether a borrower is domiciled outside the United States and argue that such
firms are more information-problematic to lenders based in countries other than the
borrower’s. Moral hazard is a relatively more difficult phenomenon to investigate.
We suggest that loan maturity can serve as a rough proxy for moral hazard and we
observe that longer term loans are more likely to be collateralized.
When we limit our sample to rated borrowers we can evaluate the impact of
default risk on collateralization by using the borrower’s senior debt rating as a measure
of credit risk. The evidence indicates that riskier loans are much more likely to be
secured. Indeed, loans to high-risk, non-investment grade borrowers are almost always
secured.
We also examine whether banks are more likely to require collateral than nonbank
lenders like finance companies, investment banks, and insurance companies. We find
that nonbank lenders are more likely to require collateral than banks. This could reflect
the fact that nonbanks assume riskier credits, on average, than banks. We also examine
whether lenders that are less well protected against risk themselves are more likely
to require borrowers to pledge assets in a loan agreement. We find that banks with
lower equity capital ratios are indeed more likely to seek collateral to secure their
loans. This is consistent with evidence in the literature showing that lower-quality
banks charge higher rates on loans. Throughout the empirical analysis, we control for
certain loan characteristics, such as loan purpose, industry effects, and time-related
influences.
2. Theories and evidence
The literature provides three primary rationalizations for why some bank loans
are secured: (1) information asymmetry and adverse selection problems, (2) moral
hazard problems, and (3) borrower credit risk. Since credit risk could be higher in
the presence of information asymmetry and/or moral hazard, the explanations are not
J. S. Gonas et al./The Financial Review 39 (2004) 79–99
81
mutually exclusive. Leeth and Scott (1989) identify other factors that can affect the
collateralization decision such as the effect of security on the lender’s monitoring and
administrative costs, the costs associated with restricting borrower asset usage, and
the prospects for limiting the dilution of legal claims in bankruptcy.
2.1. Adverse selection and information asymmetry
In an asymmetric information setting, collateral can convey valuable information
to the lender. Besanko and Thakor (1987) and Chan and Thakor (1987) develop models
demonstrating that, within a class of borrowers that appear equally risky, a borrower’s
willingness to provide collateral will be inversely related to the default risk on the loan.
Consequently, banks can induce borrowers to reveal their characteristics by offering
two loan contracts. The first involves a lower interest rate, but requires collateral. The
second does not require security, but involves a higher borrowing rate. This leads to a
separating equilibrium in which less risky borrowers will choose the contract requiring
collateral, since offering security is relatively less onerous, while riskier borrowers
will prefer the unsecured loan. In this signaling context, high quality firms are more
likely to pledge collateral than low-quality firms. Similarly, Chan and Kanatas (1985)
argue that securing debt enables high quality firms to signal their creditworthiness,
and the theoretical models of Townsend (1975) and Bester (1985) also predict that
collateral will be associated with higher-quality borrowers.
In a more general setting, Boot, Thakor, and Udell (1991) emphasize the relevance of precontract, private information in loan contracting. In this case the lender
is unaware of some exogenous parameter that influences the borrower’s payoff distribution. The results reveal that private information unambiguously increases the
use of collateral in loan contracts, but has uncertain effects on the relation between
collateral and borrower risk under moral hazard. While the authors carry out some
empirical tests, none of the variables in the model capture information-related factors.
Furthermore, Dennis, Nandy, and Sharpe (2000) also find evidence that collateral is
more likely in the presence of information asymmetries.
2.2. Moral hazard
Moral hazard occurs when borrowers face incentives to take large risks during the
life of the loan or when they have bargained in bad faith. Finance theory predicts that
securing a loan reduces the probability that borrowers will engage in underinvestment,
asset substitution, or provide an inadequate supply of effort. As noted above, Boot,
Thakor, and Udell (1991) demonstrate that collateral serves to mitigate moral hazard
in loan contracting, but the extent of the relation varies with the extent of private
information.
Myers (1977) demonstrates how the use of collateral eliminates underinvestment
in profitable projects and reduces the probability of bankruptcy. Igawa and Kanatas
(1990) examine a Myers-type model that shows how pledging collateral allows a
high quality firm to optimize the net benefits gained from “over-collateralizing”
(the value of pledged collateral exceeds the value of the loan), while simultaneously
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J. S. Gonas et al./The Financial Review 39 (2004) 79–99
underinvesting in the maintenance of such collateral. Similarly, Stulz and Johnson
(1985) show that secured debt enhances firm value because it reduces the incentive
to underinvest that results when a firm relies on equity or unsecured debt. In a study
focused on moral hazard associated with asset usage and managerial effort, Smith
and Warner (1979) predict that collateral prevents a borrower from “consuming” a
loan or engaging in costly asset substitution.
2.3. Credit risk
Collateral protects the lender against loss by granting title to specific assets in
the event of default. Scott (1977) asserts that because secured claims have priority,
collateralized debt can limit the degree of loss in the event of bankruptcy. He also
demonstrates how issuing secured debt can increase the value of the firm. Critical
to Scott’s results is the fact that certain claimants (e.g., litigants and tax authorities)
are disadvantaged in bankruptcy by the use of collateral, but are unable to extract
compensation for such in ex ante contracting. There are a number of theoretical
studies demonstrating that credit riskier firms are more likely to pledge collateral
(Swary and Udell, 1988; Boot, Thakor, and Udell, 1991; Black and deMeza, 1992).
Empirical research by Morsman (1986) and Hempel, Coleman, and Simsonson
(1986) shows that the due diligence efforts of banks often require observably more
risky borrowers to pledge collateral. Orgler (1970) compiles individual loan data
categorizing borrowers as either “good” or “bad,” based on the opinions of bank
examiners. He finds a significant, positive relation between the presence of collateral
and loans that were categorized “bad.” Hester (1979) regresses a secured/unsecured
dummy variable on six accounting variables that are proxies for firm risk. He likewise
finds that riskier firms are more likely to pledge collateral. Similarly, Leeth and Scott
(1989) find that more collateral is pledged with loans to riskier, small businesses, and
Berger and Udell (1990) find that riskier firms are more likely to borrow on a secured
basis and that the average secured loan in their sample is riskier than the average
unsecured loan.
3. Research design
Building on the previous theoretical and empirical research on the determinants
of secured loans, in this section we develop testable hypotheses regarding information
asymmetry, moral hazard, and credit risk problems. We then introduce our sample
and sketch the empirical models we use to test these hypotheses.
3.1. Hypotheses
3.1.1. Collateralization is more likely in the presence of information
asymmetry problems
There are several ways to proxy information asymmetry problems. First, if the
borrower has a senior credit rating, we argue there is less information asymmetry
J. S. Gonas et al./The Financial Review 39 (2004) 79–99
83
between borrowers and lenders. Firms must submit significant documentation and
undergo a detailed evaluation process to obtain a rating. Rating agencies have access
to “inside” information, including internal forecasts of earnings and cash flows. Consequently, rated firms are more easily monitored and pose fewer adverse selection
problems to lenders, implying that rated firms are less likely to secure loans than their
nonrated counterparts. Signaling by higher-quality firms, on the other hand, would
imply that listed firms are more likely to secure their loans.
Given the listing requirements of stock exchanges and the SEC’s associated
reporting requirements, traded firms present more transparent information to lenders
and investors than nontraded firms. Therefore, we expect that exchange-listed firms
will involve relatively fewer adverse selection problems. We hypothesize that publicly
traded firms will be less likely to pledge collateral if adverse selection motivates
lenders to seek collateral.
Likewise, larger firms pose relatively fewer information asymmetry problems to
lenders than their smaller counterparts. Larger companies generally enjoy increased
product and brand recognition. In addition, a larger firm is likely to be better known
given its relatively large workforce, enhanced line of products, and increased community presence. Thus, we assume that a firm’s revenues can proxy for its size and
hypothesize that adverse selection problems decline as firms grow. Adverse selection
would imply an inverse relation between borrower size and collateralization, while
signaling could suggest an opposite relation.
We also argue that lenders find it more difficult to gather information and monitor
firms headquartered outside the United States. Such loans should involve higher
prospects for information asymmetry. In addition, foreign borrowers are exposed to
idiosyncratic forms of country risk, involving unpredictable changes in economic and
political conditions, along with exchange rate risk. We hypothesize that banks making
loans to borrowers outside the United States will be more likely to be secured.
Finally, prior relationships can attenuate information asymmetry problems. In
fact, Berger and Udell (1995) find evidence that the character of borrower-lender
relationships influences loan contract terms. Consequently, we suggest that repeat
borrowers pose fewer information asymmetry problems and should be less likely to
secure loans.
3.1.2. Collateralization is more likely in the presence of moral
hazard problems
Although many theoretical models emphasize the relevance of moral hazard in
debt contracting, empirical tests are difficult to implement. In this paper, we assume
that moral hazard problems like underinvestment and asset substitution can be proxied by loan maturity.1 We contend that asset substitution and underinvestment do not
1 Maturity might also be related to both credit risk and the presence of information asymmetries; however,
we argue that loan maturity provides an effective proxy for moral hazard after controlling for other forms
of information asymmetry and credit risk.
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J. S. Gonas et al./The Financial Review 39 (2004) 79–99
occur overnight, so agency problems are less likely to surface with a six-month than
a six-year loan. If moral hazard is more prevalent over longer contracting periods,
longer-term loans should be more likely to be secured than short-term loans.2 Empirically, Dennis, Nandy, and Sharpe (2000) find a significantly positive relationship
between the duration of a revolving credit agreement and its secured status. Boot,
Thakor, and Udell (1991) find the opposite result.
3.1.3. Collateralization is more likely in the presence of credit risk
We also examine a sample that includes only firms with credit ratings and hypothesize that firms with low default risk are less likely to secure loans than high-risk
borrowers. John, Lynch, and Puri (2002) found such a result for public bond issues.
This suggests that higher quality firms should find that the costs of securing loans,
particularly in the form of loss of asset control, outweigh the benefits. Collateral is
especially valuable in the event of bankruptcy since secured lenders hold a priority
claim. For example, Moody’s (1998) reports a recovery rate of 87% on senior secured
bank loans in bankruptcy over the period from 1986 through 1997, versus 79% on
senior unsecured loans over the same period. We hypothesize that the prospect a loan
will be secured increases with credit risk and that loans to investment grade firms
will be collateralized less often than loans to non-investment grade borrowers.
3.2. Research methods
3.2.1. Sample selection
We collect a sample of 7,619 commercial loans that closed between December
1988 and January 2001. The sample was obtained from the Loan Pricing Corporation
(LPC) DealScan database, and we restrict the sample to loans with complete and
confirmed information. DealScan contains information on individual loan transactions, including borrower information (name, credit rating, location, and annual sales),
lender information (name, location, and role), and loan contract information (secured
status, loan size, maturity, loan purpose, and rate). We also obtain the lender’s asset
and equity information from the Federal Reserve Bank of Chicago Commercial Bank
2 While
our model specification assumes that the explanatory variables are exogenous, we recognize that
loan contract terms, such as maturity and collateralization, could be determined simultaneously. Thus,
due to this endogeneity, the estimate of the maturity coefficient in our model could be biased and/or
inconsistent. However, assuming joint distribution conditional probability modeling, our results suggest
that loan collateralization and maturity are associated, but we cannot draw any inferences about the direction
of causality. If we wanted to say that there is a directional relationship we must acknowledge three possible
directions. First, a longer maturity can cause collateralization. Second, collateralization can cause a longer
maturity. Third, there is an omitted variable that underlies both collateralization and maturity such that
an increase in the error term due to an increase in this omitted variable causes both collateralization and
maturity to increase.
J. S. Gonas et al./The Financial Review 39 (2004) 79–99
85
and Holding Company Database for the years 1988 through 2000.3 Lender capital
ratios are then matched to the LPC sample by lender name, lender location, and year
of loan origination.
In Table 1, we present summary statistics for the explanatory variables. Just
over 73% of the loans are secured. About 26% of the loans went to borrowers with
a senior S&P rating, and a little over 12% of the borrowers had an investment grade
S&P rating. About 21% of the loans went to borrowers with a Moody’s rating, and
approximately 10% of these borrowers had an investment grade rating. About 69%
of the loans involve borrowers listed on a U.S. stock exchange. While the average
borrower reports yearly sales of approximately $8.64 billion, the median is $257.9
million. Almost 4% of the borrowers are based outside the United States and obtained
a loan from a lender located outside their home country, and some 38% of the loans in
the sample were made to repeat borrowers. The average loan maturity is approximately
48 months. About 89% of the sample loans were made by banks, and of the 3,177
cases where lender information could be matched to the loans, banks maintained an
average equity capital ratio of 9.3%.
About 21% of the loans were used to refinance debt (PREF), while 26% were
used for effecting changes in corporate control (PCC), such as acquisitions, leveraged
buyouts, or employee stock option plans. Only 6% of the loans were made to finance
fixed asset purchases, and 19% were used for general corporate purposes (PGCP).4
The other 28% of the loans in the sample were listed as “other purpose” or they did
not fall into one of the previous four categories.
3.2.2. The full sample logit model
To test the hypotheses outlined above, we first estimate a model with the entire
sample. This model is as follows:
SECURED = β0 + β1 RATED + β2 EXCHANGE + β3 LNSALES + β4 FOREIGN
+ β5 REPEAT + β6 LNMATURITY + β7 (BANK or BANKCAR)
+ β PURPOSE CONTROLS + β INDUSTRY CONTROLS
+ β YEAR CONTROLS + ε
(1)
SECURED is a binary variable equal to one for secured loan agreements and zero
for an unsecured loan agreement. We use RATED, a variable representing either an
S&P rating (SPRATE) or a Moody’s rating (MRATE), to test the hypothesis that
3 The
Federal Reserve Bank of Chicago Commercial Bank and Holding Company Database is publicly
available at http://www.chicagofed.org/economicresearchanddata/data/bhcdatabase/index.cfm. For syndicated loans, we take the lead lender’s capital ratio.
4 General
corporate purposes (PGCP) includes loans with “general corporate purposes” as their stated
purpose as well as loans for working capital and trade finance.
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J. S. Gonas et al./The Financial Review 39 (2004) 79–99
Table 1
Descriptive statistics
The sample contains 7,619 commercial loan arrangements closed between December 1988 and January
2001. The descriptive statistics for selected variables are presented below. SECURED is a binary variable
representing secured loans, SPRATED (MRATED) is a binary variable representing firms with an S&P
(Moody’s) rating available, SPINVEST (MINVEST) is a binary variable representing firms with a investment grade S&P (Moody’s) rating, SPHIGHYLD (MHIGHYLD) is a binary variable for firms with a
high-yield grade S&P (Moody’s) rating, SPNR (MNR) is a binary variable representing a firm without an
S&P (Moody’s) rating, and SPORDERRATE (MORDERRATE) is a variable representing S&P (Moody’s)
ratings ranked from one (D, D) to ten (AAA, Aaa). EXCHANGE is a binary variable representing firms
listed on an stock exchange at loan origination, SALES is the annual sales amount of the borrower in
$billions, LNSALES is the natural log of sales size (SALES). FOREIGN is a binary variable for borrowers located outside the United States, and REPEAT is a binary variable for a repeat loan matching
borrower to lender on a previous date in the sample. MATURITY is the term of the loan in months, and
LNMATURITY is the natural log of the loan’s maturity in months. BANK is binary variable identifying
the lending institution as a bank, and BANKCAR is the capital asset ratio, defined as the ratio of bank
equity to bank assets, of the lending institution. Also included are a set of binary variables for the purpose
of the loan including refinancing, corporate control, fixed asset backing, general corporate purposes, or
other purposes not listed above (PREF, PCC, PFAB, PGCP, POTH, respectively), a set of a set of binary
variables for the industry of the borrower based on SIC codes (SIC0–SIC9, not presented), and a set of
binary variables for the year of issue (YR1988–YR2001, not presented).
Variable
SECURED
SPRATED
SPINVEST
SPHIGHYLD
SPNR
SPORDERRATE
MRATED
MINVEST
MHIGHYLD
MNR
MORDERRATE
EXCHANGE
SALES
LNSALES
FOREIGN
REPEAT
MATURITY
LNMATURITY
BANK
BANKCARATIO
PREF
PCC
PFAB
PGCP
POTH
N
Mean
Std. dev.
7619
7619
7619
7619
7619
1958
7619
7619
7619
7619
1644
7619
7619
7619
7619
7619
7619
7619
7619
3177
7619
7619
7619
7619
7619
0.7323796
0.2569891
0.1224570
0.1345321
0.7430109
6.2865169
0.2157763
0.0983069
0.1174695
0.7842237
6.2718978
0.6933981
8.6495361
19.5340380
0.0366190
0.3769524
47.7088857
3.6641936
0.8905368
0.0927809
0.2086888
0.2560704
0.0637879
0.1890012
0.2824518
0.4427476
0.4370020
0.3278344
0.3412455
0.4370020
1.3997343
0.4113868
0.2977487
0.3220000
0.4113868
1.2940854
0.4611129
1.5136329
1.9571471
0.1878368
0.4846546
25.5627354
0.7166013
0.3122400
0.0311705
0.4063982
0.4364898
0.2443908
0.3915353
0.6034559
J. S. Gonas et al./The Financial Review 39 (2004) 79–99
87
rated borrowers present fewer information asymmetry and adverse selection problems
to lenders. The coefficient of the rating binary variables should be negative in our
estimations if adverse selection drives the collateralization decision, but positive if
firms signal their quality by pledging assets.
We also use listed status and firm size as alternative measures of information
asymmetry problems. EXCHANGE is a binary variable equal to one if the borrower
is a listed firm and zero otherwise. Again, listed firms involve more transparent
information, so the coefficient of this variable should be negative. We use the log
of the borrower’s annual sales (LNSALES) as a proxy for firm size, and we assume
it is less costly to acquire information about large firms relative to small firms. A
borrower with larger sales figures should be less likely to secure a loan, other things
equal, implying a negative relation between borrower sales and the probability that a
loan is secured. Again, signaling by high quality firms could imply an opposite sign.
Foreign borrowers are likely to be more information problematic to lenders.
Therefore, we expect a positive relation between the variable FOREIGN, a binary
variable for firms located outside of the United States, and the probability that a loan
is secured.5 We also include REPEAT, a binary variable reflecting cases in which the
borrower uses the same lender more than once during the sample period, as a proxy
for the extent of the relationship between the contracting parties. Repeat borrowers
should pose fewer information asymmetry problems to the lender, other things equal,
so we hypothesize a negative sign for this coefficient.
We include LNMATURITY, the natural logarithm of term to maturity of the
loan, as a proxy for moral hazard problems. Maturity captures the length of the
contractual relationship, and we argue that borrowers are more likely to engage in
exploitative behavior in longer-term relationships. The coefficient of this variable
should be positively signed, since banks are more likely to seek collateral in the
presence of moral hazard.
Although most of the loans in our sample are made by banks, our sample also
includes nonbank lenders such as finance companies, insurance companies, and investment banks. Thus, we include the binary variable BANK, a binary variable equal
to one if the lender is a bank and zero otherwise. Since the quality of bank loans is
evaluated by bank examiners based on collectibility, we might anticipate that banks
are more likely to secure loans than nonbanks. However, Staten, Gilley, and Umbeck
(1990) suggests that nonbanks are willing to deal with relatively riskier borrowers,
since they do not rely on insured deposits and hence are not subject to examination.
This could imply that nonbanks would be more likely to seek collateral than banks
giving a negative coefficient on this variable.
5 While
most of the lenders are U.S. banks, when the lender is domiciled in a country different from the
borrower (e.g., a loan by a Canadian bank to a Mexican firm), we code the variable as one, since the same
arguments apply in such cases.
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J. S. Gonas et al./The Financial Review 39 (2004) 79–99
Hubbard, Kuttner, and Palia (2002) show that lower quality banks tend to charge
higher prices on loans than high quality banks. We accordingly control for bank quality
in our estimations by incorporating the lender’s ratio of equity capital to assets in place
of the variable BANK. We expect that a loan will be more likely to be secured as
the lending bank’s capital position deteriorates, so we posit a negative coefficient on
BANKCAR.
The loan’s purpose could influence the bank’s decision to require collateral
since certain projects are inherently riskier than others. Loans to finance leveraged
buyouts, for example, are quite risky and hence more likely to be secured. Conversely,
if a loan’s purpose is to purchase highly marketable fixed assets, we hypothesize the
opposite effect. We also suggest that loans for refinancings carry more repayment risk
and are more likely to warrant collateral. Like Kleimeier and Megginson (2000), we
organize the various loan purposes into five broad categories and we include four of
these purposes as binary variables in the model: bank refinancing (PREF), corporate
control (PCC), fixed asset backing (PFAB), and general corporate purposes (POTH).
All other loan purposes represent the excluded category (POTH).
We also include a set of industry dummies based on one-digit SIC codes (SIC0–
SIC9) since industries differ in their susceptibility to macroeconomic shocks. Some
industries, like electric utilities, are highly regulated, possibly affecting their credit
risk and the attendant use of collateral. Financial services firms offer another example, since they engage extensively in off-balance-sheet activities, and these types of
contingent assets or liabilities pose more asymmetric information and moral hazard
problems to lenders.
Lastly, the loan market cycles over a period as long as a decade. A loan that might
not be secured in 1990 might well be secured in 1997 simply because of changing
market conditions. We control for such differences using dummy variables for the
year of the loan commitment (YR1988–YR2001).6
3.2.3. The logit models for rated firms
To test the hypothesis that collateralization is more likely in the presence of
credit risk, we isolate the sample borrowers with an S&P rating. We then separate
this subsample into investment grade (AAA, AA, A, and BBB) and high-yield (BB,
B, CCC, CC, C, and D) borrowers. We do the same for firms rated by Moody’s, and
in both cases the control group is high-yield borrowers. Under a credit risk argument we should find that the investment grade dummies have negative coefficients,
indicating that such firms are less likely to offer collateral than their high-yield
counterparts. Under the signaling hypothesis, however, the high quality, investment
grade firms should collateralize more often, giving a positive coefficient on the
6 The
industry and year control variables are omitted from the tables to conserve space.
J. S. Gonas et al./The Financial Review 39 (2004) 79–99
89
investment grade variable. The remainder of the model remains unaltered and is as
follows:
SECURED = β0 + β1 INVEST + β2 EXCHANGE + β3 LNSALES + β4 FOREIGN
+ β5 REPEAT + β6 LNMATURITY + β7 (BANK or BANKCAR)
+ β PURPOSE CONTROLS + β INSDUSTRY CONTROLS
+ β YEAR CONTROLS + ε
(2)
Lastly, we also run a regression using the credit ratings ranked ordinally from
one through ten as S&P (Moody’s) credit ratings increase from D (D) to AAA (Aaa).
Again, as credit risk increases, the likelihood of collateral should rise and we should
observe a negative coefficient on the ordinal variable. Signaling again implies a
positive coefficient. Since this specification involves a linearity constraint on the
impact of credit risk on collateral, we also include a squared term in the model to
evaluate whether the true relation is nonlinear. The remainder of the model remains
unaltered and is as follows:
SECURED = β0 + β1 ORDERRATE + β2 ORDERRATE2 + β3 EXCHANGE
+ β4 LNSALES + β5 FOREIGN + β6 REPEAT
+ β7 LNMATURITY + β8 (BANK or BANKCAR)
+ β PURPOSE CONTROLS + β INSDUSTRY CONTROLS
+ β YEAR CONTROLS + ε
(3)
4. Results
4.1. Basic data analysis
In Table 2, we examine the default risk characteristics of the sample loans.
Panel A shows the distribution of loan agreements by secured status across S&P
senior debt ratings. When the borrower is rated AAA by S&P, only 11% are secured.
Similar results are found for AA and A rated loans. A little more than a fifth of BBB
rated loans are secured, but over 66% of the loans made to BB-rated borrowers and
95% of loans to B-rated borrowers were secured. All the loans made to CCC-, CC-,
and D-rated borrowers were secured. These results imply lenders engage in credit
rationing based on perceived default risk and the willingness/capacity of borrowers
to provide collateral. Investment grade borrowers obtain the bulk of unsecured credit,
while higher risk, non-investment grade borrowers must always provide collateral to
qualify for loans.
Looking at the Moody’s ratings in Panel B of Table 2 we find very similar results.
None of the loans to Aaa-rated borrowers were secured, and only 21% of the loans
to Baa-rated borrowers were secured. Turning to high-yield bonds, the similarities
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J. S. Gonas et al./The Financial Review 39 (2004) 79–99
Table 2
Distribution of commercial loans by secured status across senior debt ratings and borrower’s geographical location
The sample contains 7,619 commercial loan arrangements closed between December 1988 and January
2001. Panel A of this table shows the distribution of the loans by S&P senior debt rating and collateral
classification, and Panel B shows the distribution of loans by Moody’s senior debt rating and collateral
classification.
Panel A: Standard and Poor’s senior debt rating
Classification
S&P senior debt rating
Secured
(Percent)
Unsecured
(Percent)
Total
AAA
AA
A
BBB
BB
B
CCC
CC
C
D
NR
1
7
23
116
212
582
72
5
0
18
4,544
11.11%
8.86
7.64
21.32
66.04
95.57
100.00
100.00
—
100.00
80.27
8
72
278
428
109
27
0
0
0
0
1,117
88.89%
91.14
92.36
78.68
33.96
4.43
0.00
0.00
—
0.00
19.73
9
79
301
544
321
609
72
5
0
18
5,661
Total
5,580
73.23
2,039
26.77
7,619
Panel B: Moody’s senior debt rating
Classification
Moody’s senior debt rating
Secured
(Percent)
Unsecured
(Percent)
Total
Aaa
Aa
A
Baa
Ba
B
Caa
Ca
C
D
NR
0
3
19
92
246
428
83
9
4
0
4,696
0%
7.32
7.12
21.05
70.86
95.11
97.65
100.00
100.00
—
78.59
4
38
248
345
101
22
2
0
0
0
1,279
100.00%
92.68
92.88
78.95
29.14
4.89
2.35
0.00
0.00
—
21.41
4
41
267
437
347
450
85
9
4
0
5,975
Total
5,580
73.23
2,039
26.77
7,619
continue, with over 66% of the loans to Ba-rated borrowers and 95% of loans to
B-rated borrowers being collateralized.
Table 3 provides the correlation coefficients for selected independent variables. None of the independent variables are highly correlated and there appears
to be little multicollinearity in the explanatory variable set. As an additional test for
BANKCAR
BANK
LNMATURITY
REPEAT
FOREIGN
LNSALES
EXCHANGE
0.18777
(0.0001)
−0.08253
(0.0001)
0.01325
(0.2475)
−0.10351
(0.0001)
0.05406
(0.0001)
−0.00943
(0.5952)
1.0000
EXCHANGE
0.08089
(0.0001)
0.03790
(0.0009)
−0.02383
(0.0375)
0.03857
(0.0008)
0.15067
(0.0001)
1.0000
LNSALES
−0.1899
(0.0974)
−0.00564
(0.6223)
0.00345
(0.7635)
−0.00867
(0.6252)
1.0000
FOREIGN
0.14108
(0.0001)
0.03589
(0.0017)
0.01216
(0.4931)
1.0000
REPEAT
−0.08770
(0.0001)
0.02981
(0.0929)
1.0000
LNMATURITY
0.00288
(0.8709)
1.0000
BANK
1.0000
BANKCAR
Pearson correlation coefficients
The sample contains 7,619 commercial loan arrangements closed between December 1988, and January 2001. Introduction of bank capital asset ratios reduces
the sample size to 3,177 observations. This table provides the Pearson correlation coefficients and the probability that absolute value of the correlation coefficient
is greater than zero for selected variables. EXCHANGE is a binary variable representing firms listed on a stock exchange at loan origination. LNSALES is
the natural log of sales size. FOREIGN is a binary variable for borrowers located outside the United States, and REPEAT is a binary variable for a repeat loan
matching borrower to lender on a previous date in the sample. MATURITY is the term of the loan in months, and LNMATURITY is the natural log of the loan’s
maturity in months. BANK is a binary variable identifying the lending institution as a bank, and BANKCAR is the capital asset ratio, defined as the ratio of bank
equity to bank assets, of the lending institution.
Table 3
J. S. Gonas et al./The Financial Review 39 (2004) 79–99
91
92
J. S. Gonas et al./The Financial Review 39 (2004) 79–99
multicollinearity and ill-conditioned data, however, the matrix condition number is
provided for each logit model estimated.7
4.2. Results: The full sample logit model
We initially use the entire sample of rated and nonrated borrowers to estimate
logit model (1), predicting the probability a loan will be secured. The sample contains
over 7,600 loans, but the subsample of loans in which we can observe the lender’s
capital-asset ratio consists of 3,177 observations. These results are presented in
Table 4.
With respect to information transparency, we find that borrowers with either S&P
or Moody’s ratings are less likely to secure loans. Consistent with the adverse selection
hypothesis, banks appear to require collateral when information asymmetries are more
severe. The evidence does not support the theoretical argument that high-quality firms
signal using collateral.
Likewise, firms listed on a U.S. stock exchange are less likely to secure loans
than unlisted firms. Larger firms likewise are less likely to secure loans, providing
additional evidence of the role of information asymmetries and adverse selection.
We also hypothesize that repeat borrowings would enhance the quality of borrowerlender relationships and reduce the prospect of collateralization. While the coefficient
is negative, the result is not significant. Nor is the result for the foreign borrower
variable significant in the full sample. In general, these findings provide evidence
that potential adverse selection problems lead lenders to require collateral.
We argued above that longer-term loans allow borrowers more opportunities
to engage in underinvestment and asset substitution, and, as shown in Table 4, we
find that the probability of securing a loan increases significantly with loan maturity.
This supports the hypothesis that collateralization increases in the presence of moral
hazard problems.
Somewhat surprisingly, we find that loans by banks are less likely to be collateralized than loans by nonbanks. Loans by finance companies and investment banks
tend to have higher credit risk than bank loans, however, and this could account for
our result. Finally, we find that the purpose of the loan affects the decision to secure
a loan. Loans involving refinancings, changes in corporate control, and the purchase
of fixed assets are more likely to be secured. The latter result is not surprising, since
the fixed asset acquired can readily serve as collateral.
The second column in each panel of Table 4 presents the results for the subsample
that includes only bank lenders where we could observe the equity capital ratio. We
7 The
condition index of the normalized data matrix is a vector composed of the ratio of the individual singular values to the minimum element. This index summarizes the ill-conditioning of the matrix.
The square-root of the largest element in the condition index, the condition number, measures the illconditioning for the variable with the smallest contribution of independent information. Therefore, the
condition number proves a convenient scalar measure of multicollinearity. Condition numbers greater than
30 imply the potential of disruptive ill-conditioning, while values over 50 indicate serious problems. For
a detailed discussion of this point, see Chapter 3 in Belsley, Kuh, and Welsh (1980).
93
J. S. Gonas et al./The Financial Review 39 (2004) 79–99
Table 4
The full sample logit model
The sample contains 7,619 commercial loan arrangements closed between December 1988 and January
2001. Introduction of bank capital asset ratios reduces the sample size to 3,177 observations. A binary
variable representing a secured loan (SECURED) is regressed on a binary variable for firms rated at the
close of the loan by S&P (SPRATED) in Panel A, or Moody’s (MRATED) in Panel B, a binary variable
for firms listed on a U.S. stock exchange (EXCHANGE), the natural logarithm of the annual sales of
the borrower (LNSALES), a binary variable for foreign firms (FOREIGN), a binary variable for a repeat
loan matching borrower to lender on a previous date in the sample (REPEAT), the natural logarithm for
the maturity of the loan in months (LNMATURITY), a binary variable for loans underwritten by a bank
(BANK), the lending bank’s capital asset ratio (BANKCAR), a set of binary variables for the purpose of
the loan including refinancing, corporate control, fixed asset backing, general corporate purposes (PREF,
PCC, PFAB, PGCP), a set of binary variables for the industry of the borrower based on SIC codes (not
presented), and a set of binary variables for the year of issue (not presented). Please note that the control
variable for rated firms (SPRATED or MRATED) is nonrated firms (SPNR or MNR, respectively); the
control variable for the purpose binary variables is other purposes (POTH). The expected sign, based on
our hypotheses in Section 2 of the paper, is provided for each coefficient. The standard errors (S.E.) of
the estimates are reported in the parentheses immediately to the right of the estimate and Wald chi-square
statistical significance is displayed by the use of one (10%), two (5%), or three (1%) stars. The number of
observations, likelihood ratios, Wald statistics, Hosmer and Lemeshow goodness-of-fit statistics, and the
matrix condition number are reported at the bottom of the table.
Panel A: S&P ratings
Regression
Variable
Expected sign
INTERCEPT
SPRATED
EXCHANGE
LNSALES
FOREIGN
REPEAT
LNMATURITY
BANK
BANKCAR
PREF
PCC
PFAB
PGCP
OBSERVATIONS
LIKELIHOOD RATIO
WALD STATISTIC
HOSMER AND LEMESHOW
MATRIX CONDITION NUMBER
−
−
−
+
−
+
−
−
+
+
−
−
Estimate
S.E.
7.312∗∗∗
−0.678∗∗∗
−0.239∗∗∗
−0.369∗∗∗
−0.017
−0.096
0.423∗∗∗
−1.144∗∗∗
(1.940)
(0.070)
(0.070)
(0.019)
(0.169)
(0.095)
(0.041)
(0.126)
0.413∗∗∗
0.445∗∗∗
0.515∗∗∗
−0.012
(0.100)
(0.077)
(0.137)
(0.081)
7,619.00
1,641.37∗∗∗
1,215.64∗∗∗
32.61∗∗∗
14.93
Estimate
S.E.
7.243∗∗∗
−0.452∗∗∗
−0.192∗
−0.408∗∗∗
−0.273
−0.117
0.470∗∗∗
(0.898)
(0.116)
(0.109)
(0.030)
(0.495)
(0.143)
(0.065)
−6.341∗∗∗
0.459∗∗∗
0.701∗∗∗
0.532∗∗
−0.074
(1.432)
(0.168)
(0.130)
(0.209)
(0.119)
3,177.00
670.41∗∗∗
495.51∗∗∗
12.12
14.97
(continued )
find that the financial position of the bank lender affects loan structure, and banks
with higher equity capital ratios are less likely to require collateral, other things
equal. These results are consistent with those presented by Hubbard, Kuttner, and Palia
(2002), who find that lower-quality banks tend to charge higher loan rates, suggesting
94
J. S. Gonas et al./The Financial Review 39 (2004) 79–99
Table 4 (continued )
The full sample logit model
Panel B: Moody’s ratings
Regression
Variable
Expected sign
INTERCEPT
SPRATED
EXCHANGE
LNSALES
FOREIGN
REPEAT
LNMATURITY
BANK
BANKCAR
PREF
PCC
PFAB
PGCP
OBSERVATIONS
LIKELIHOOD RATIO
WALD STATISTIC
HOSMER AND LEMESHOW
MATRIX CONDITION NUMBER
−
−
−
+
−
+
−
−
+
+
−
−
Estimate
S.E.
7.744∗∗∗
−0.577∗∗∗
−0.210∗∗∗
−0.392∗∗∗
−0.010
−0.089
0.419∗∗∗
−1.186∗∗∗
(1.972)
(0.074)
(0.070)
(0.018)
(0.169)
(0.095)
(0.041)
(0.126)
0.418∗∗∗
0.456∗∗∗
0.531∗∗∗
−0.010
(0.100)
(0.077)
(0.137)
(0.081)
7,619.00
1,610.11∗∗∗
1,190.64∗∗∗
37.52∗∗∗
14.70
Estimate
S.E.
7.494∗∗∗
−0.300∗∗
−0.163
−0.428∗∗∗
−0.271
−0.118
0.473∗∗∗
(0.895)
(0.126)
(0.109)
(0.029)
(0.496)
(0.143)
(0.065)
−6.484∗∗∗
0.475∗∗∗
0.694∗∗∗
0.543∗∗∗
−0.065
(1.432)
(0.168)
(0.129)
(0.209)
(0.118)
3,177.00
663.06∗∗∗
487.03∗∗∗
41.49∗∗∗
14.77
∗∗∗
Indicates statistical difference from zero at the 0.01 level.
Indicates statistical difference from zero at the 0.05 level.
∗ Indicates statistical difference from zero at the 0.10 level.
∗∗
that such banks make riskier loans. The rest of the coefficients are quite similar to
those in the full sample model, save for the coefficient on the listed/unlisted firm
dummy, which declines in significance for the S&P results in Panel A but becomes
insignificant for the Moody’s results in Panel B.
4.3. Results: The logit models for rated firms
We next estimate a logit model for a sample that takes into account the borrower’s
credit risk as reflected in the firm’s senior debt rating. We first isolate the firms with
S&P ratings; this reduces the sample size to 1,958 observations. If we additionally
match this group to cases in which we can observe the lender’s capital-asset ratio, the
sample size declines to 652. Sampling borrowers with Moody’s ratings, the sample
size declines to 1,644 and 519 borrowers, respectively. The results of the estimation
are presented in Table 5.
95
J. S. Gonas et al./The Financial Review 39 (2004) 79–99
Table 5
The logit models for rated firms
The sample contains 7,619 commercial loan arrangements closed between December 1988 and January
2001. After eliminating observations without an S&P (Moody’s) rating, the testable sample size is reduced
to 1,958 (1,644) observations. A binary variable representing a secured loan (SECURED), is regressed
on a binary variable for firms rated as investment grade at the close of the loan by S&P (SPINVEST),
in Panel A, or Moody’s (MINVEST) in Panel B, or a ranked variable and its quadratic for S&P ratings
(SPORDERRATE) in Panel C, or a ranked variable and its quadratic for Moody’s ratings (MORDERRATE)
in Panel D, a binary variable for firms listed on a U.S. stock exchange (EXCHANGE), the natural logarithm
of the annual sales of the borrower (LNSALES), a binary variable for foreign firms (FOREIGN), a binary
variable for a repeat loan matching borrower to lender on a previous date in the sample (REPEAT),
the natural logarithm for the maturity of the loan in months (LNMATURITY), a binary variable for
loans underwritten by a bank (BANK), the lending bank’s capital asset ratio (BANKCAR), a set of
binary variables for the purpose of the loan including refinancing, corporate control, fixed asset backing,
general corporate purposes (PREF, PCC, PFAB, PGCP), a set of binary variables for the industry of the
borrower based on SIC codes (not presented), and a set of binary variables for the year of issue (not
presented). Please note that the control variable for rated firms (SPRATED or MRATED) is nonrated
firms (SPNR or MNR, respectively); the control variable for the purpose binary variables is other purposes
(POTH). The expected sign, based on our hypotheses in Section 2 of the paper, is provided for each
coefficient. The standard errors (S.E.) of the estimates are reported in the parentheses immediately to the
right of the estimate and Wald chi-square statistical significance is displayed by the use of one (10%),
two (5%), or three (1%) stars. The number of observations, likelihood ratios, Wald statistics, Hosmer and
Lemeshow goodness-of-fit statistics, and the matrix condition number are reported at the bottom of the
table.
Panel A: Categorized S&P ratings
Regression
Variable
INTERCEPT
SPINVEST
EXCHANGE
LNSALES
FOREIGN
REPEAT
LNMATURITY
BANK
BANKCAR
PREF
PCC
PFAB
PGCP
OBSERVATIONS
LIKELIHOOD RATIO
WALD STATISTIC
HOSMER AND LEMESHOW
MATRIX CONDITION NUMBER
Expected sign
−
−
−
+
−
+
−
−
+
+
−
−
Estimate
S.E.
6.255∗∗∗
−3.127∗∗∗
−0.134
−0.278∗∗∗
1.399∗∗∗
−0.379∗
0.484∗∗∗
−0.727∗∗∗
(1.288)
(0.151)
(0.177)
(0.041)
(0.427)
(0.211)
(0.098)
(0.269)
0.280
0.682∗∗∗
0.207
0.033
(0.216)
(0.171)
(0.402)
(0.216)
1,958.00
1,282.73∗∗∗
675.65∗∗∗
21.03∗∗∗
15.86
Estimate
S.E.
3.476∗∗
−3.012∗∗∗
0.065
−0.254∗∗∗
2.789∗∗
−0.396
0.758∗∗∗
(1.743)
(0.265)
(0.282)
(0.063)
(1.348)
(0.343)
(0.169)
−6.914∗∗
1.220∗∗∗
1.146∗∗∗
0.581
0.030
(3.471)
(0.386)
(0.299)
(0.607)
(0.350)
652.00
389.55∗∗∗
199.55∗∗∗
13.06
14.69
(continued )
96
J. S. Gonas et al./The Financial Review 39 (2004) 79–99
Table 5 (continued )
The logit models for rated firms
Panel B: Categorized Moody’s ratings
Regression
Variable
INTERCEPT
MINVEST
EXCHANGE
LNSALES
FOREIGN
REPEAT
LNMATURITY
BANK
BANKCAR
PREF
PCC
PFAB
PGCP
OBSERVATIONS
LIKELIHOOD RATIO
WALD STATISTIC
HOSMER AND LEMESHOW
MATRIX CONDITION NUMBER
Expected sign
Estimate
6.005∗∗∗
−
−
−
+
−
+
−
−
+
+
−
−
S.E.
−3.174∗∗∗
0.052
−0.244∗∗∗
1.236∗∗∗
−0.299
0.528∗∗∗
−0.627∗∗
(1.384)
(0.167)
(0.210)
(0.043)
(0.419)
(0.220)
(0.106)
(0.288)
0.075
0.094
0.335
−0.287
(0.220)
(0.181)
(0.446)
(0.257)
1,644.00
1,074.44∗∗∗
566.41∗∗∗
16.24∗∗
15.44
Estimate
S.E.
5.241∗∗
−3.397∗∗∗
−0.177
−0.301∗∗∗
2.489
−0.235
0.683∗∗∗
(2.235)
(0.313)
(0.392)
(0.075)
(1.592)
(0.373)
(0.200)
−5.933∗∗
1.044∗
0.640
0.428
−0.391
(4.257)
(0.431)
(0.350)
(0.865)
(0.438)
519.00
355.29∗∗∗
164.11∗∗∗
4.81
14.48
(continued )
We analyze the role of credit risk in two ways. First, in Panel A we create a
dummy variable equal to one for investment grade borrowers and zero otherwise for
S&P ratings. Panel B repeats the analysis for Moody’s investment grade borrowers.
Second, in Panel C we create a variable that reflects the borrower’s credit on an ordinal
scale of one (D) to ten (AAA) for the S&P ratings, and the analysis of ordinal Moody’s
ratings is presented in Panel D. For both S&P and Moody’s we also include the square
of this ordinal term, since John, Lynch, and Puri (2002) provide evidence that there is
a nonlinear relation between credit risk premiums and default risk prospects. In both
cases, we find a strong relation between default prospects and loan collateralization.
We also confirm that the relation is highly nonlinear. Thus, the results of the univariate
analysis reported in Table 2 continue to hold in a multivariate setting. The probability a
loan will be secured increases significantly as the borrower’s credit rating deteriorates
and borrowers migrate to non-investment grade states, other things equal.
We also observe that the results relating to information proxies remain relatively
robust in Table 5. Although EXCHANGE becomes insignificant when we consider
credit risk, firm size continues to have the hypothesized effect. We also find limited evidence that international loans are more likely to be secured. Again, repeat
customers are less likely to pledge collateral, but the result is not systematically
significant.
97
J. S. Gonas et al./The Financial Review 39 (2004) 79–99
Table 5 (continued )
The logit models for rated firms
Panel C: Ordered S&P ratings
Regression
Variable
INTERCEPT
SPORDERRATE
SPORDERRATE2
EXCHANGE
LNSALES
FOREIGN
REPEAT
LNMATURITY
BANK
BANKCAR
PREF
PCC
PFAB
PGCP
Expected sign
−
+
−
−
+
−
+
−
−
+
+
−
−
OBSERVATIONS
LIKELIHOOD RATIO
WALD STATISTIC
HOSMER AND LEMESHOW
MATRIX CONDITION NUMBER
Estimate
S.E.
29.227∗∗∗
−6.744∗∗∗
0.383∗∗∗
0.060
−0.144∗∗∗
1.150∗∗∗
−0.307
0.392∗∗∗
−0.578∗
(2.928)
(0.805)
(0.059)
(0.198)
(0.043)
(0.338)
(0.213)
(0.104)
(0.305)
0.172
0.703∗∗∗
0.301
−0.015
(0.219)
(0.178)
(0.382)
(0.234)
1,958.00
1,422.25∗∗∗
548.46∗∗∗
59.08∗∗∗
20.84
Estimate
S.E.
32.297∗∗∗
−8.117∗∗∗
0.493∗∗∗
0.231
−0.158∗∗
3.041∗∗
−0.390
0.620∗∗∗
(4.257)
(1.169)
(0.084)
(0.316)
(0.065)
(1.512)
(0.367)
(0.183)
−8.175∗∗
1.162∗∗∗
1.128∗∗∗
0.156
0.051
(4.068)
(0.404)
(0.321)
(0.651)
(0.379)
652.00
460.01∗∗∗
179.27∗∗∗
8.89
19.88
(continued )
Supporting the moral hazard hypothesis and suggesting that asymmetric information and moral hazard play roles independent of credit risk, longer term loans
likewise continue to be associated with a higher probability of collateral. The remaining results are quite similar to the full sample findings, although the loan purpose
dummies are generally less significant when we explicitly account for credit risk.
This suggests that the purpose of the loan can proxy for default risk in the absence of
credit rating information.
In the second column of each panel we present the model including the lender’s
capacity to absorb risk. Again, we generally observe that banks with lower capital
ratios are more likely to require collateral. The remaining coefficients are generally
robust to the addition of this variable.
5. Summary and conclusion
This paper examines the factors that determine when commercial loans will be
secured. We examine 7,619 loans closed between December 1988 and January 2001.
Overall, our findings suggest that information asymmetry, moral hazard problems,
and credit risk all play significant roles, but we find no evidence in favor of the
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J. S. Gonas et al./The Financial Review 39 (2004) 79–99
Table 5 (continued )
The logit models for rated firms
Panel D: Ordered Moody’s ratings
Regression
Variable
INTERCEPT
MORDERRATE
MORDERRATE2
EXCHANGE
LNSALES
FOREIGN
REPEAT
LNMATURITY
BANK
BANKCAR
PREF
PCC
PFAB
PGCP
OBSERVATIONS
LIKELIHOOD RATIO
WALD STATISTIC
HOSMER AND LEMESHOW
MATRIX CONDITION NUMBER
Expected sign
−
+
−
−
+
−
+
−
−
+
+
−
−
Estimate
S.E.
20.422∗∗∗
−3.966∗∗∗
0.169∗∗
0.154
−0.127∗∗∗
1.574∗∗∗
−0.209
0.478∗∗∗
−0.534∗
(3.708)
(1.088)
(0.083)
(0.228)
(0.043)
(0.441)
(0.217)
(0.109)
(0.309)
−0.030
0.181
0.321
−0.301
(0.226)
(0.184)
(0.414)
(0.278)
1,644.00
1,150.87∗∗∗
459.48∗∗∗
31.43∗∗∗
21.43
Estimate
S.E.
35.343∗∗∗
−8.100∗∗∗
0.457∗∗∗
0.045
−0.232∗∗∗
2.523
−0.293
0.505∗∗
(6.965)
(1.928)
(0.141)
(0.442)
(0.078)
(1.801)
(0.396)
(0.208)
−3.916∗
1.056∗∗
0.871∗∗
0.514
−0.451
(2.961)
(0.473)
(0.368)
(0.798)
(0.493)
519.00
402.30∗∗∗
130.41∗∗∗
2.86
20.41
∗∗∗
Indicates statistical difference from zero at the 0.01 level.
Indicates statistical difference from zero at the 0.05 level.
∗ Indicates statistical difference from zero at the 0.10 level.
∗∗
predictions of certain theoretical models that high-quality firms signal by providing
collateral.
Specifically, we find that firms with a credit rating are less likely to secure
loans than unrated firms, implying that lenders are more likely to require collateral
in the face of potential adverse selection problems. Likewise, the results indicate that
a borrower’s sales figure is negatively related to the probability a loan is collateralized. This supports the hypothesis that larger firms pose relatively fewer information
asymmetry problems. Similarly, after controlling for default risk, we find some evidence that international loans are more likely to be collateralized than domestic loans.
Overall, the results are again consistent with a role for information asymmetries.
Supporting our hypothesis that collateral can address moral hazard problems
like underinvestment and asset substitution, we find that longer-term loans are more
likely to be collateralized. We also find that credit risk independently influences the
decision to secure loans. Somewhat surprisingly, we find that loans by banks are
less likely to be collateralized than loans by nonbanks. Lastly, lending banks that
themselves have less protection against risk are more likely to require collateral.
J. S. Gonas et al./The Financial Review 39 (2004) 79–99
99
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