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The Financial Review 39 (2004) 79--99

When Are Commercial Loans Secured?John S. GonasBelmont University

Michael J. Highfield∗Louisiana Tech University

Donald J. MullineauxUniversity of Kentucky

Abstract

We analyze the factors that influence the decision to secure a commercial loan. We findevidence that variables reflecting adverse selection, moral hazard, and the prospects for defaultall affect the likelihood a loan will be collateralized. We find no evidence in favor of thepredictions 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 aremore likely to require collateral, but that banks themselves are less likely to secure loans thannonbanks. 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) 257-4253; E-Mail: [email protected].

The authors thank an anonymous referee, Brent Ambrose, Dan Bradley, Mark Carey, Marcia Cornett, SteveDennis, 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 Associationannual meeting, and the Symposium on Financial Institutions at the 2003 Eastern Finance Associationannual meeting for helpful comments on earlier drafts of the paper. All errors remain ours. A previousversion of this paper was presented under the title “The Determinants of Secured Loans.”

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80 J. S. Gonas et al./The Financial Review 39 (2004) 79–99

1. Introduction

While there is a significant amount of research addressing the effect of collateralon credit risk premiums on bonds and bank loans, there is little empirical work on thefactors that affect decisions to secure loans. Theoreticians have argued that collateralcan play a multitude of roles, such as facilitating signaling, controlling informationasymmetry problems, mitigating moral hazard problems, and providing respite againstdefault and bankruptcy loss. Our objective in this paper is to examine whether oneof these rationales is dominant in the collateralization decision or if each plays anindependent role. We employ a large sample of transaction-specific loans from theLoan Pricing Corporation’s DealScan database over the period December 1988 toJanuary 2001 in the empirical analysis.

We find evidence that collateral is more likely to be pledged in the presenceof significant information asymmetries between borrowers and lenders. We use datathat reflect whether the borrower is rated or exchange-listed as proxy variables forthe quantity and quality of information about the borrower. We also take accountof whether a borrower is domiciled outside the United States and argue that suchfirms are more information-problematic to lenders based in countries other than theborrower’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 weobserve that longer term loans are more likely to be collateralized.

When we limit our sample to rated borrowers we can evaluate the impact ofdefault risk on collateralization by using the borrower’s senior debt rating as a measureof credit risk. The evidence indicates that riskier loans are much more likely to besecured. Indeed, loans to high-risk, non-investment grade borrowers are almost alwayssecured.

We also examine whether banks are more likely to require collateral than nonbanklenders like finance companies, investment banks, and insurance companies. We findthat nonbank lenders are more likely to require collateral than banks. This could reflectthe fact that nonbanks assume riskier credits, on average, than banks. We also examinewhether lenders that are less well protected against risk themselves are more likelyto require borrowers to pledge assets in a loan agreement. We find that banks withlower equity capital ratios are indeed more likely to seek collateral to secure theirloans. This is consistent with evidence in the literature showing that lower-qualitybanks charge higher rates on loans. Throughout the empirical analysis, we control forcertain loan characteristics, such as loan purpose, industry effects, and time-relatedinfluences.

2. Theories and evidence

The literature provides three primary rationalizations for why some bank loansare secured: (1) information asymmetry and adverse selection problems, (2) moralhazard problems, and (3) borrower credit risk. Since credit risk could be higher inthe 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 thecollateralization decision such as the effect of security on the lender’s monitoring andadministrative costs, the costs associated with restricting borrower asset usage, andthe 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 informationto the lender. Besanko and Thakor (1987) and Chan and Thakor (1987) develop modelsdemonstrating that, within a class of borrowers that appear equally risky, a borrower’swillingness to provide collateral will be inversely related to the default risk on the loan.Consequently, banks can induce borrowers to reveal their characteristics by offeringtwo loan contracts. The first involves a lower interest rate, but requires collateral. Thesecond does not require security, but involves a higher borrowing rate. This leads to aseparating equilibrium in which less risky borrowers will choose the contract requiringcollateral, since offering security is relatively less onerous, while riskier borrowerswill prefer the unsecured loan. In this signaling context, high quality firms are morelikely 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 thatcollateral will be associated with higher-quality borrowers.

In a more general setting, Boot, Thakor, and Udell (1991) emphasize the rele-vance of precontract, private information in loan contracting. In this case the lenderis unaware of some exogenous parameter that influences the borrower’s payoff dis-tribution. The results reveal that private information unambiguously increases theuse of collateral in loan contracts, but has uncertain effects on the relation betweencollateral and borrower risk under moral hazard. While the authors carry out someempirical tests, none of the variables in the model capture information-related factors.Furthermore, Dennis, Nandy, and Sharpe (2000) also find evidence that collateral ismore likely in the presence of information asymmetries.

2.2. Moral hazard

Moral hazard occurs when borrowers face incentives to take large risks during thelife of the loan or when they have bargained in bad faith. Finance theory predicts thatsecuring 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 hazardin loan contracting, but the extent of the relation varies with the extent of privateinformation.

Myers (1977) demonstrates how the use of collateral eliminates underinvestmentin profitable projects and reduces the probability of bankruptcy. Igawa and Kanatas(1990) examine a Myers-type model that shows how pledging collateral allows ahigh quality firm to optimize the net benefits gained from “over-collateralizing”(the value of pledged collateral exceeds the value of the loan), while simultaneously


underinvesting in the maintenance of such collateral. Similarly, Stulz and Johnson(1985) show that secured debt enhances firm value because it reduces the incentiveto underinvest that results when a firm relies on equity or unsecured debt. In a studyfocused on moral hazard associated with asset usage and managerial effort, Smithand Warner (1979) predict that collateral prevents a borrower from “consuming” aloan or engaging in costly asset substitution.

2.3. Credit risk

Collateral protects the lender against loss by granting title to specific assets inthe 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 alsodemonstrates how issuing secured debt can increase the value of the firm. Criticalto 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 extractcompensation for such in ex ante contracting. There are a number of theoreticalstudies 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 morerisky borrowers to pledge collateral. Orgler (1970) compiles individual loan datacategorizing borrowers as either “good” or “bad,” based on the opinions of bankexaminers. He finds a significant, positive relation between the presence of collateraland loans that were categorized “bad.” Hester (1979) regresses a secured/unsecureddummy variable on six accounting variables that are proxies for firm risk. He likewisefinds 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, andBerger and Udell (1990) find that riskier firms are more likely to borrow on a securedbasis and that the average secured loan in their sample is riskier than the averageunsecured loan.

3. Research design

Building on the previous theoretical and empirical research on the determinantsof secured loans, in this section we develop testable hypotheses regarding informationasymmetry, moral hazard, and credit risk problems. We then introduce our sampleand sketch the empirical models we use to test these hypotheses.

3.1. Hypotheses

3.1.1. Collateralization is more likely in the presence of informationasymmetry problems

There are several ways to proxy information asymmetry problems. First, if theborrower has a senior credit rating, we argue there is less information asymmetry


between borrowers and lenders. Firms must submit significant documentation andundergo a detailed evaluation process to obtain a rating. Rating agencies have accessto “inside” information, including internal forecasts of earnings and cash flows. Con-sequently, rated firms are more easily monitored and pose fewer adverse selectionproblems to lenders, implying that rated firms are less likely to secure loans than theirnonrated counterparts. Signaling by higher-quality firms, on the other hand, wouldimply that listed firms are more likely to secure their loans.

Given the listing requirements of stock exchanges and the SEC’s associatedreporting requirements, traded firms present more transparent information to lendersand investors than nontraded firms. Therefore, we expect that exchange-listed firmswill involve relatively fewer adverse selection problems. We hypothesize that publiclytraded firms will be less likely to pledge collateral if adverse selection motivateslenders to seek collateral.

Likewise, larger firms pose relatively fewer information asymmetry problems tolenders than their smaller counterparts. Larger companies generally enjoy increasedproduct and brand recognition. In addition, a larger firm is likely to be better knowngiven its relatively large workforce, enhanced line of products, and increased com-munity presence. Thus, we assume that a firm’s revenues can proxy for its size andhypothesize that adverse selection problems decline as firms grow. Adverse selectionwould imply an inverse relation between borrower size and collateralization, whilesignaling could suggest an opposite relation.

We also argue that lenders find it more difficult to gather information and monitorfirms headquartered outside the United States. Such loans should involve higherprospects for information asymmetry. In addition, foreign borrowers are exposed toidiosyncratic forms of country risk, involving unpredictable changes in economic andpolitical conditions, along with exchange rate risk. We hypothesize that banks makingloans to borrowers outside the United States will be more likely to be secured.

Finally, prior relationships can attenuate information asymmetry problems. Infact, Berger and Udell (1995) find evidence that the character of borrower-lenderrelationships influences loan contract terms. Consequently, we suggest that repeatborrowers pose fewer information asymmetry problems and should be less likely tosecure loans.

3.1.2. Collateralization is more likely in the presence of moralhazard problems

Although many theoretical models emphasize the relevance of moral hazard indebt contracting, empirical tests are difficult to implement. In this paper, we assumethat moral hazard problems like underinvestment and asset substitution can be prox-ied 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 formsof information asymmetry and credit risk.


occur overnight, so agency problems are less likely to surface with a six-month thana 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 Em-pirically, Dennis, Nandy, and Sharpe (2000) find a significantly positive relationshipbetween 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 hy-pothesize that firms with low default risk are less likely to secure loans than high-riskborrowers. 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 isespecially valuable in the event of bankruptcy since secured lenders hold a priorityclaim. For example, Moody’s (1998) reports a recovery rate of 87% on senior securedbank loans in bankruptcy over the period from 1986 through 1997, versus 79% onsenior unsecured loans over the same period. We hypothesize that the prospect a loanwill be secured increases with credit risk and that loans to investment grade firmswill 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 December1988 and January 2001. The sample was obtained from the Loan Pricing Corporation(LPC) DealScan database, and we restrict the sample to loans with complete andconfirmed information. DealScan contains information on individual loan transac-tions, including borrower information (name, credit rating, location, and annual sales),lender information (name, location, and role), and loan contract information (securedstatus, loan size, maturity, loan purpose, and rate). We also obtain the lender’s assetand 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 thatloan 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/orinconsistent. However, assuming joint distribution conditional probability modeling, our results suggestthat loan collateralization and maturity are associated, but we cannot draw any inferences about the directionof causality. If we wanted to say that there is a directional relationship we must acknowledge three possibledirections. First, a longer maturity can cause collateralization. Second, collateralization can cause a longermaturity. Third, there is an omitted variable that underlies both collateralization and maturity such thatan increase in the error term due to an increase in this omitted variable causes both collateralization andmaturity to increase.


and Holding Company Database for the years 1988 through 2000.3 Lender capitalratios are then matched to the LPC sample by lender name, lender location, and yearof loan origination.

In Table 1, we present summary statistics for the explanatory variables. Justover 73% of the loans are secured. About 26% of the loans went to borrowers witha senior S&P rating, and a little over 12% of the borrowers had an investment gradeS&P rating. About 21% of the loans went to borrowers with a Moody’s rating, andapproximately 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 averageborrower reports yearly sales of approximately $8.64 billion, the median is $257.9million. Almost 4% of the borrowers are based outside the United States and obtaineda loan from a lender located outside their home country, and some 38% of the loans inthe sample were made to repeat borrowers. The average loan maturity is approximately48 months. About 89% of the sample loans were made by banks, and of the 3,177cases where lender information could be matched to the loans, banks maintained anaverage equity capital ratio of 9.3%.

About 21% of the loans were used to refinance debt (PREF), while 26% wereused for effecting changes in corporate control (PCC), such as acquisitions, leveragedbuyouts, or employee stock option plans. Only 6% of the loans were made to financefixed 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 didnot 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 entiresample. 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 zerofor an unsecured loan agreement. We use RATED, a variable representing either anS&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 publiclyavailable at http://www.chicagofed.org/economicresearchanddata/data/bhcdatabase/index.cfm. For syndi-cated loans, we take the lead lender’s capital ratio.

4 General corporate purposes (PGCP) includes loans with “general corporate purposes” as their statedpurpose as well as loans for working capital and trade finance.


Table 1

Descriptive statistics

The sample contains 7,619 commercial loan arrangements closed between December 1988 and January2001. The descriptive statistics for selected variables are presented below. SECURED is a binary variablerepresenting 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 in-vestment grade S&P (Moody’s) rating, SPHIGHYLD (MHIGHYLD) is a binary variable for firms with ahigh-yield grade S&P (Moody’s) rating, SPNR (MNR) is a binary variable representing a firm without anS&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 firmslisted 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 bor-rowers located outside the United States, and REPEAT is a binary variable for a repeat loan matchingborrower to lender on a previous date in the sample. MATURITY is the term of the loan in months, andLNMATURITY is the natural log of the loan’s maturity in months. BANK is binary variable identifyingthe lending institution as a bank, and BANKCAR is the capital asset ratio, defined as the ratio of bankequity to bank assets, of the lending institution. Also included are a set of binary variables for the purposeof the loan including refinancing, corporate control, fixed asset backing, general corporate purposes, orother purposes not listed above (PREF, PCC, PFAB, PGCP, POTH, respectively), a set of a set of binaryvariables for the industry of the borrower based on SIC codes (SIC0–SIC9, not presented), and a set ofbinary variables for the year of issue (YR1988–YR2001, not presented).

Variable N Mean Std. dev.

SECURED 7619 0.7323796 0.4427476SPRATED 7619 0.2569891 0.4370020SPINVEST 7619 0.1224570 0.3278344SPHIGHYLD 7619 0.1345321 0.3412455SPNR 7619 0.7430109 0.4370020SPORDERRATE 1958 6.2865169 1.3997343MRATED 7619 0.2157763 0.4113868MINVEST 7619 0.0983069 0.2977487MHIGHYLD 7619 0.1174695 0.3220000MNR 7619 0.7842237 0.4113868MORDERRATE 1644 6.2718978 1.2940854EXCHANGE 7619 0.6933981 0.4611129SALES 7619 8.6495361 1.5136329LNSALES 7619 19.5340380 1.9571471FOREIGN 7619 0.0366190 0.1878368REPEAT 7619 0.3769524 0.4846546MATURITY 7619 47.7088857 25.5627354LNMATURITY 7619 3.6641936 0.7166013BANK 7619 0.8905368 0.3122400BANKCARATIO 3177 0.0927809 0.0311705PREF 7619 0.2086888 0.4063982PCC 7619 0.2560704 0.4364898PFAB 7619 0.0637879 0.2443908PGCP 7619 0.1890012 0.3915353POTH 7619 0.2824518 0.6034559


rated borrowers present fewer information asymmetry and adverse selection problemsto lenders. The coefficient of the rating binary variables should be negative in ourestimations if adverse selection drives the collateralization decision, but positive iffirms signal their quality by pledging assets.

We also use listed status and firm size as alternative measures of informationasymmetry problems. EXCHANGE is a binary variable equal to one if the borroweris a listed firm and zero otherwise. Again, listed firms involve more transparentinformation, so the coefficient of this variable should be negative. We use the logof the borrower’s annual sales (LNSALES) as a proxy for firm size, and we assumeit is less costly to acquire information about large firms relative to small firms. Aborrower with larger sales figures should be less likely to secure a loan, other thingsequal, implying a negative relation between borrower sales and the probability that aloan 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 binaryvariable for firms located outside of the United States, and the probability that a loanis secured.5 We also include REPEAT, a binary variable reflecting cases in which theborrower uses the same lender more than once during the sample period, as a proxyfor the extent of the relationship between the contracting parties. Repeat borrowersshould 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 theloan, as a proxy for moral hazard problems. Maturity captures the length of thecontractual relationship, and we argue that borrowers are more likely to engage inexploitative behavior in longer-term relationships. The coefficient of this variableshould be positively signed, since banks are more likely to seek collateral in thepresence of moral hazard.

Although most of the loans in our sample are made by banks, our sample alsoincludes nonbank lenders such as finance companies, insurance companies, and in-vestment banks. Thus, we include the binary variable BANK, a binary variable equalto one if the lender is a bank and zero otherwise. Since the quality of bank loans isevaluated by bank examiners based on collectibility, we might anticipate that banksare 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 banksgiving 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 theborrower (e.g., a loan by a Canadian bank to a Mexican firm), we code the variable as one, since the samearguments apply in such cases.


Hubbard, Kuttner, and Palia (2002) show that lower quality banks tend to chargehigher prices on loans than high quality banks. We accordingly control for bank qualityin our estimations by incorporating the lender’s ratio of equity capital to assets in placeof the variable BANK. We expect that a loan will be more likely to be secured asthe lending bank’s capital position deteriorates, so we posit a negative coefficient onBANKCAR.

The loan’s purpose could influence the bank’s decision to require collateralsince certain projects are inherently riskier than others. Loans to finance leveragedbuyouts, 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 theopposite effect. We also suggest that loans for refinancings carry more repayment riskand are more likely to warrant collateral. Like Kleimeier and Megginson (2000), weorganize the various loan purposes into five broad categories and we include four ofthese purposes as binary variables in the model: bank refinancing (PREF), corporatecontrol (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. Someindustries, like electric utilities, are highly regulated, possibly affecting their creditrisk and the attendant use of collateral. Financial services firms offer another exam-ple, since they engage extensively in off-balance-sheet activities, and these types ofcontingent assets or liabilities pose more asymmetric information and moral hazardproblems to lenders.

Lastly, the loan market cycles over a period as long as a decade. A loan that mightnot be secured in 1990 might well be secured in 1997 simply because of changingmarket conditions. We control for such differences using dummy variables for theyear 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 ofcredit risk, we isolate the sample borrowers with an S&P rating. We then separatethis 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, andin both cases the control group is high-yield borrowers. Under a credit risk argu-ment we should find that the investment grade dummies have negative coefficients,indicating that such firms are less likely to offer collateral than their high-yieldcounterparts. Under the signaling hypothesis, however, the high quality, investmentgrade 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.


investment grade variable. The remainder of the model remains unaltered and is asfollows:

SECURED = β0 + β1 INVEST + β2 EXCHANGE + β3 LNSALES + β4 FOREIGN

+ β5 REPEAT + β6 LNMATURITY + β7 (BANK or BANKCAR)

+ β PURPOSE CONTROLS + β INSDUSTRY CONTROLS


Lastly, we also run a regression using the credit ratings ranked ordinally fromone 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 shouldobserve a negative coefficient on the ordinal variable. Signaling again implies apositive coefficient. Since this specification involves a linearity constraint on theimpact of credit risk on collateral, we also include a squared term in the model toevaluate whether the true relation is nonlinear. The remainder of the model remainsunaltered 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


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&Psenior 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 BBBrated loans are secured, but over 66% of the loans made to BB-rated borrowers and95% 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 creditrationing based on perceived default risk and the willingness/capacity of borrowersto provide collateral. Investment grade borrowers obtain the bulk of unsecured credit,while higher risk, non-investment grade borrowers must always provide collateral toqualify 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 loansto Baa-rated borrowers were secured. Turning to high-yield bonds, the similarities


Table 2

Distribution of commercial loans by secured status across senior debt ratings and borrower’s geo-graphical location

The sample contains 7,619 commercial loan arrangements closed between December 1988 and January2001. Panel A of this table shows the distribution of the loans by S&P senior debt rating and collateralclassification, and Panel B shows the distribution of loans by Moody’s senior debt rating and collateralclassification.

Panel A: Standard and Poor’s senior debt rating

Classification

S&P senior debt rating Secured (Percent) Unsecured (Percent) Total

AAA 1 11.11% 8 88.89% 9AA 7 8.86 72 91.14 79A 23 7.64 278 92.36 301BBB 116 21.32 428 78.68 544BB 212 66.04 109 33.96 321B 582 95.57 27 4.43 609CCC 72 100.00 0 0.00 72CC 5 100.00 0 0.00 5C 0 — 0 — 0D 18 100.00 0 0.00 18NR 4,544 80.27 1,117 19.73 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 0 0% 4 100.00% 4Aa 3 7.32 38 92.68 41A 19 7.12 248 92.88 267Baa 92 21.05 345 78.95 437Ba 246 70.86 101 29.14 347B 428 95.11 22 4.89 450Caa 83 97.65 2 2.35 85Ca 9 100.00 0 0.00 9C 4 100.00 0 0.00 4D 0 — 0 — 0NR 4,696 78.59 1,279 21.41 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 toB-rated borrowers being collateralized.

Table 3 provides the correlation coefficients for selected independent vari-ables. None of the independent variables are highly correlated and there appearsto be little multicollinearity in the explanatory variable set. As an additional test for


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multicollinearity and ill-conditioned data, however, the matrix condition number isprovided 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 estimatelogit model (1), predicting the probability a loan will be secured. The sample containsover 7,600 loans, but the subsample of loans in which we can observe the lender’scapital-asset ratio consists of 3,177 observations. These results are presented inTable 4.

With respect to information transparency, we find that borrowers with either S&Por Moody’s ratings are less likely to secure loans. Consistent with the adverse selectionhypothesis, banks appear to require collateral when information asymmetries are moresevere. The evidence does not support the theoretical argument that high-quality firmssignal using collateral.

Likewise, firms listed on a U.S. stock exchange are less likely to secure loansthan unlisted firms. Larger firms likewise are less likely to secure loans, providingadditional evidence of the role of information asymmetries and adverse selection.We also hypothesize that repeat borrowings would enhance the quality of borrower-lender relationships and reduce the prospect of collateralization. While the coefficientis negative, the result is not significant. Nor is the result for the foreign borrowervariable significant in the full sample. In general, these findings provide evidencethat potential adverse selection problems lead lenders to require collateral.

We argued above that longer-term loans allow borrowers more opportunitiesto engage in underinvestment and asset substitution, and, as shown in Table 4, wefind that the probability of securing a loan increases significantly with loan maturity.This supports the hypothesis that collateralization increases in the presence of moralhazard problems.

Somewhat surprisingly, we find that loans by banks are less likely to be collat-eralized than loans by nonbanks. Loans by finance companies and investment bankstend to have higher credit risk than bank loans, however, and this could account forour result. Finally, we find that the purpose of the loan affects the decision to securea loan. Loans involving refinancings, changes in corporate control, and the purchaseof fixed assets are more likely to be secured. The latter result is not surprising, sincethe fixed asset acquired can readily serve as collateral.

The second column in each panel of Table 4 presents the results for the subsamplethat 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 individ-ual 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 ill-conditioning for the variable with the smallest contribution of independent information. Therefore, thecondition number proves a convenient scalar measure of multicollinearity. Condition numbers greater than30 imply the potential of disruptive ill-conditioning, while values over 50 indicate serious problems. Fora detailed discussion of this point, see Chapter 3 in Belsley, Kuh, and Welsh (1980).


Table 4

The full sample logit model

The sample contains 7,619 commercial loan arrangements closed between December 1988 and January2001. Introduction of bank capital asset ratios reduces the sample size to 3,177 observations. A binaryvariable representing a secured loan (SECURED) is regressed on a binary variable for firms rated at theclose of the loan by S&P (SPRATED) in Panel A, or Moody’s (MRATED) in Panel B, a binary variablefor firms listed on a U.S. stock exchange (EXCHANGE), the natural logarithm of the annual sales ofthe borrower (LNSALES), a binary variable for foreign firms (FOREIGN), a binary variable for a repeatloan matching borrower to lender on a previous date in the sample (REPEAT), the natural logarithm forthe 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 ofthe 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 (notpresented), and a set of binary variables for the year of issue (not presented). Please note that the controlvariable for rated firms (SPRATED or MRATED) is nonrated firms (SPNR or MNR, respectively); thecontrol variable for the purpose binary variables is other purposes (POTH). The expected sign, based onour hypotheses in Section 2 of the paper, is provided for each coefficient. The standard errors (S.E.) ofthe estimates are reported in the parentheses immediately to the right of the estimate and Wald chi-squarestatistical significance is displayed by the use of one (10%), two (5%), or three (1%) stars. The number ofobservations, likelihood ratios, Wald statistics, Hosmer and Lemeshow goodness-of-fit statistics, and thematrix condition number are reported at the bottom of the table.

Panel A: S&P ratings

Regression

Variable Expected sign Estimate S.E. Estimate S.E.

INTERCEPT 7.312∗∗∗ (1.940) 7.243∗∗∗ (0.898)SPRATED − −0.678∗∗∗ (0.070) −0.452∗∗∗ (0.116)EXCHANGE − −0.239∗∗∗ (0.070) −0.192∗ (0.109)LNSALES − −0.369∗∗∗ (0.019) −0.408∗∗∗ (0.030)FOREIGN + −0.017 (0.169) −0.273 (0.495)REPEAT − −0.096 (0.095) −0.117 (0.143)LNMATURITY + 0.423∗∗∗ (0.041) 0.470∗∗∗ (0.065)BANK − −1.144∗∗∗ (0.126)BANKCAR − −6.341∗∗∗ (1.432)PREF + 0.413∗∗∗ (0.100) 0.459∗∗∗ (0.168)PCC + 0.445∗∗∗ (0.077) 0.701∗∗∗ (0.130)PFAB − 0.515∗∗∗ (0.137) 0.532∗∗ (0.209)PGCP − −0.012 (0.081) −0.074 (0.119)

OBSERVATIONS 7,619.00 3,177.00LIKELIHOOD RATIO 1,641.37∗∗∗ 670.41∗∗∗WALD STATISTIC 1,215.64∗∗∗ 495.51∗∗∗HOSMER AND LEMESHOW 32.61∗∗∗ 12.12MATRIX CONDITION NUMBER 14.93 14.97

(continued )

find that the financial position of the bank lender affects loan structure, and bankswith higher equity capital ratios are less likely to require collateral, other thingsequal. 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


Table 4 (continued )

The full sample logit model

Panel B: Moody’s ratings

Regression


INTERCEPT 7.744∗∗∗ (1.972) 7.494∗∗∗ (0.895)SPRATED − −0.577∗∗∗ (0.074) −0.300∗∗ (0.126)EXCHANGE − −0.210∗∗∗ (0.070) −0.163 (0.109)LNSALES − −0.392∗∗∗ (0.018) −0.428∗∗∗ (0.029)FOREIGN + −0.010 (0.169) −0.271 (0.496)REPEAT − −0.089 (0.095) −0.118 (0.143)LNMATURITY + 0.419∗∗∗ (0.041) 0.473∗∗∗ (0.065)BANK − −1.186∗∗∗ (0.126)BANKCAR − −6.484∗∗∗ (1.432)PREF + 0.418∗∗∗ (0.100) 0.475∗∗∗ (0.168)PCC + 0.456∗∗∗ (0.077) 0.694∗∗∗ (0.129)PFAB − 0.531∗∗∗ (0.137) 0.543∗∗∗ (0.209)PGCP − −0.010 (0.081) −0.065 (0.118)

OBSERVATIONS 7,619.00 3,177.00LIKELIHOOD RATIO 1,610.11∗∗∗ 663.06∗∗∗WALD STATISTIC 1,190.64∗∗∗ 487.03∗∗∗HOSMER AND LEMESHOW 37.52∗∗∗ 41.49∗∗∗MATRIX CONDITION NUMBER 14.70 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 tothose in the full sample model, save for the coefficient on the listed/unlisted firmdummy, which declines in significance for the S&P results in Panel A but becomesinsignificant 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’scredit risk as reflected in the firm’s senior debt rating. We first isolate the firms withS&P ratings; this reduces the sample size to 1,958 observations. If we additionallymatch this group to cases in which we can observe the lender’s capital-asset ratio, thesample size declines to 652. Sampling borrowers with Moody’s ratings, the samplesize declines to 1,644 and 519 borrowers, respectively. The results of the estimationare presented in Table 5.


Table 5

The logit models for rated firms

The sample contains 7,619 commercial loan arrangements closed between December 1988 and January2001. After eliminating observations without an S&P (Moody’s) rating, the testable sample size is reducedto 1,958 (1,644) observations. A binary variable representing a secured loan (SECURED), is regressedon 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 logarithmof the annual sales of the borrower (LNSALES), a binary variable for foreign firms (FOREIGN), a binaryvariable 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 forloans underwritten by a bank (BANK), the lending bank’s capital asset ratio (BANKCAR), a set ofbinary 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 theborrower based on SIC codes (not presented), and a set of binary variables for the year of issue (notpresented). Please note that the control variable for rated firms (SPRATED or MRATED) is nonratedfirms (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 eachcoefficient. The standard errors (S.E.) of the estimates are reported in the parentheses immediately to theright 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 andLemeshow goodness-of-fit statistics, and the matrix condition number are reported at the bottom of thetable.

Panel A: Categorized S&P ratings

Regression


INTERCEPT 6.255∗∗∗ (1.288) 3.476∗∗ (1.743)SPINVEST − −3.127∗∗∗ (0.151) −3.012∗∗∗ (0.265)EXCHANGE − −0.134 (0.177) 0.065 (0.282)LNSALES − −0.278∗∗∗ (0.041) −0.254∗∗∗ (0.063)FOREIGN + 1.399∗∗∗ (0.427) 2.789∗∗ (1.348)REPEAT − −0.379∗ (0.211) −0.396 (0.343)LNMATURITY + 0.484∗∗∗ (0.098) 0.758∗∗∗ (0.169)BANK − −0.727∗∗∗ (0.269)BANKCAR − −6.914∗∗ (3.471)PREF + 0.280 (0.216) 1.220∗∗∗ (0.386)PCC + 0.682∗∗∗ (0.171) 1.146∗∗∗ (0.299)PFAB − 0.207 (0.402) 0.581 (0.607)PGCP − 0.033 (0.216) 0.030 (0.350)

OBSERVATIONS 1,958.00 652.00LIKELIHOOD RATIO 1,282.73∗∗∗ 389.55∗∗∗WALD STATISTIC 675.65∗∗∗ 199.55∗∗∗HOSMER AND LEMESHOW 21.03∗∗∗ 13.06MATRIX CONDITION NUMBER 15.86 14.69

(continued )




Panel B: Categorized Moody’s ratings

Regression


INTERCEPT 6.005∗∗∗ (1.384) 5.241∗∗ (2.235)MINVEST − −3.174∗∗∗ (0.167) −3.397∗∗∗ (0.313)EXCHANGE − 0.052 (0.210) −0.177 (0.392)LNSALES − −0.244∗∗∗ (0.043) −0.301∗∗∗ (0.075)FOREIGN + 1.236∗∗∗ (0.419) 2.489 (1.592)REPEAT − −0.299 (0.220) −0.235 (0.373)LNMATURITY + 0.528∗∗∗ (0.106) 0.683∗∗∗ (0.200)BANK − −0.627∗∗ (0.288)BANKCAR − −5.933∗∗ (4.257)PREF + 0.075 (0.220) 1.044∗ (0.431)PCC + 0.094 (0.181) 0.640 (0.350)PFAB − 0.335 (0.446) 0.428 (0.865)PGCP − −0.287 (0.257) −0.391 (0.438)

OBSERVATIONS 1,644.00 519.00LIKELIHOOD RATIO 1,074.44∗∗∗ 355.29∗∗∗WALD STATISTIC 566.41∗∗∗ 164.11∗∗∗HOSMER AND LEMESHOW 16.24∗∗ 4.81MATRIX CONDITION NUMBER 15.44 14.48

(continued )

We analyze the role of credit risk in two ways. First, in Panel A we create adummy variable equal to one for investment grade borrowers and zero otherwise forS&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 ordinalscale of one (D) to ten (AAA) for the S&P ratings, and the analysis of ordinal Moody’sratings is presented in Panel D. For both S&P and Moody’s we also include the squareof this ordinal term, since John, Lynch, and Puri (2002) provide evidence that there isa nonlinear relation between credit risk premiums and default risk prospects. In bothcases, 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 univariateanalysis reported in Table 2 continue to hold in a multivariate setting. The probability aloan will be secured increases significantly as the borrower’s credit rating deterioratesand borrowers migrate to non-investment grade states, other things equal.

We also observe that the results relating to information proxies remain relativelyrobust in Table 5. Although EXCHANGE becomes insignificant when we considercredit risk, firm size continues to have the hypothesized effect. We also find lim-ited evidence that international loans are more likely to be secured. Again, repeatcustomers are less likely to pledge collateral, but the result is not systematicallysignificant.




Panel C: Ordered S&P ratings

Regression


INTERCEPT 29.227∗∗∗ (2.928) 32.297∗∗∗ (4.257)SPORDERRATE − −6.744∗∗∗ (0.805) −8.117∗∗∗ (1.169)SPORDERRATE2 + 0.383∗∗∗ (0.059) 0.493∗∗∗ (0.084)EXCHANGE − 0.060 (0.198) 0.231 (0.316)LNSALES − −0.144∗∗∗ (0.043) −0.158∗∗ (0.065)FOREIGN + 1.150∗∗∗ (0.338) 3.041∗∗ (1.512)REPEAT − −0.307 (0.213) −0.390 (0.367)LNMATURITY + 0.392∗∗∗ (0.104) 0.620∗∗∗ (0.183)BANK − −0.578∗ (0.305)BANKCAR − −8.175∗∗ (4.068)PREF + 0.172 (0.219) 1.162∗∗∗ (0.404)PCC + 0.703∗∗∗ (0.178) 1.128∗∗∗ (0.321)PFAB − 0.301 (0.382) 0.156 (0.651)PGCP − −0.015 (0.234) 0.051 (0.379)


(continued )

Supporting the moral hazard hypothesis and suggesting that asymmetric infor-mation and moral hazard play roles independent of credit risk, longer term loanslikewise continue to be associated with a higher probability of collateral. The remain-ing results are quite similar to the full sample findings, although the loan purposedummies 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 ofcredit rating information.

In the second column of each panel we present the model including the lender’scapacity to absorb risk. Again, we generally observe that banks with lower capitalratios are more likely to require collateral. The remaining coefficients are generallyrobust to the addition of this variable.

5. Summary and conclusion

This paper examines the factors that determine when commercial loans will besecured. 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




Panel D: Ordered Moody’s ratings

Regression


INTERCEPT 20.422∗∗∗ (3.708) 35.343∗∗∗ (6.965)MORDERRATE − −3.966∗∗∗ (1.088) −8.100∗∗∗ (1.928)MORDERRATE2 + 0.169∗∗ (0.083) 0.457∗∗∗ (0.141)EXCHANGE − 0.154 (0.228) 0.045 (0.442)LNSALES − −0.127∗∗∗ (0.043) −0.232∗∗∗ (0.078)FOREIGN + 1.574∗∗∗ (0.441) 2.523 (1.801)REPEAT − −0.209 (0.217) −0.293 (0.396)LNMATURITY + 0.478∗∗∗ (0.109) 0.505∗∗ (0.208)BANK − −0.534∗ (0.309)BANKCAR − −3.916∗ (2.961)PREF + −0.030 (0.226) 1.056∗∗ (0.473)PCC + 0.181 (0.184) 0.871∗∗ (0.368)PFAB − 0.321 (0.414) 0.514 (0.798)PGCP − −0.301 (0.278) −0.451 (0.493)


∗∗∗ 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 providingcollateral.

Specifically, we find that firms with a credit rating are less likely to secureloans than unrated firms, implying that lenders are more likely to require collateralin the face of potential adverse selection problems. Likewise, the results indicate thata borrower’s sales figure is negatively related to the probability a loan is collateral-ized. This supports the hypothesis that larger firms pose relatively fewer informationasymmetry problems. Similarly, after controlling for default risk, we find some evi-dence 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 problemslike underinvestment and asset substitution, we find that longer-term loans are morelikely to be collateralized. We also find that credit risk independently influences thedecision to secure loans. Somewhat surprisingly, we find that loans by banks areless likely to be collateralized than loans by nonbanks. Lastly, lending banks thatthemselves have less protection against risk are more likely to require collateral.


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