Special Purpose Entities and Bank Loan Contracting

Abstract
This study examines the relation between a firm’s use of special purpose entities (SPEs) andits bank loan contracting. Although SPEs can serve many legitimate business purposes, they
have been used improperly by sponsor firms to manipulate earnings and hide losses, resulting
in higher information risk. We find that (1) the use of SPEs tends to be associated withunfavorable loan contracting terms, including higher loan rates, collateral requirements, andrestrictive covenants, and (2) the above associations are more pronounced when the borrower
firm has greater CEO pay–performance sensitivity (delta) and no prior loan relationship withthe lender.

Keywords: Special purpose entity; loan contracting; information risk; earnings
management.
JEL Code: G21; M41
Data Availability: All data are available from the sources identified in the paper.

1. Introduction
Companies have been using special purpose entities (SPEs) since the 1970s. SPEs canserve many legitimate business purposes, such as lowering financing costs, isolating financial
risk, accessing segmented capital markets, and tax planning (Feng, Gramlich, and Gupta
2009; Lemmon, Liu, Mao, and Nini 2014). However, investigations of Enron and othercorporate scandals reveal that SPEs have been improperly used by sponsor firms to hide debt,
manage earnings, and achieve other financial reporting objectives. 1 SPEs can facilitate
managerial opportunism in financial reporting for two main reasons. First, a sponsor
company can avoid consolidating its SPEs in the financial statements and achieve managers’
financial reporting goals through its transactions with the SPEs under its control. Second, thecomplex nature of SPEs can increase information uncertainty or opaqueness, whichconstrains the ability of investors and creditors to fully understand the sources of managers’
earnings management.
Recent studies show evidence that financial reporting quality plays an important rolein bank loan contracting (Beatty, Ramesh, and Weber 2002; Bharath, Sunder, and Sunder
2008; Costello and Wittenberg-Moerman 2011; Graham, Li, and Qiu 2008; Kim, Song, andZhang 2011; Kim, Tsui, and Yi 2011). More specifically, these studies find that low
reporting quality is associated with unfavorable loan terms, because it may impair lenders’ability to evaluate borrowers’ default risk prior to contract initiation and increase their postcontract monitoring and renegotiation costs. In this study, we aim to provide systematic
evidence on the effect of SPEs on the loan contracting terms of their sponsor firms.
Specifically, our analysis focuses on whether and how banks take into account a borrower’s
1

For example, the Special Investigative Committee on Enron reported that Enron’s transactions with certain
SPEs “allowed Enron to conceal from the market very large losses resulting from Enron’s merchant investments”(Powers 2002, 4).

1

use of SPEs when evaluating credit quality and/or determining both the price and non-priceterms of loan contracts.
We are motivated to examine the debt market consequences of non-financial
companies’ SPE use in the context of bank loan contracting for several reasons. First, bankloans are a major source of external financing for companies around the world.2 However,while it has examined SPEs sponsored by banks for mortgage or loan securitization, priorresearch has paid little attention to the potential impacts of SPE use on the costs of bankloans to non-financial sponsor firms. Second, banks use non-price terms in the loan contracts
to facilitate the post-contract monitoring of borrower credit quality and its changes. To
evaluate the effect of SPE use on non-price terms (in addition to the price term), our study
provides evidence on whether banks tend to impose more stringent non-price terms in
response to a borrower’s SPE use. Third, Lemmon et al. (2014) find positive abnormal stock
returns and zero bond returns to the initiation of asset securitization, a common type of SPEs,
using a sample of non-financial firms. Focusing on sponsor firms’ default (operation) risk,
this finding can be viewed as evidence suggesting that asset securitization is beneficial toequity investors without hurting bond investors. Different from Lemmon et al. (2014), theprimary purpose of our study is to investigate whether as an earnings management tool, theuse of SPEs affects sponsor firms’ private debt contracting through increasing theinformation risk faced by lenders. It should be noted, however, that compared with armslength equity and bond investors, concentrated lenders such as commercial banks are
generally more sophisticated and have privileged access to sponsors’ inside information (e.g.,
Bharath et al. 2008; Kim et al. 2011b). One may therefore argue that SPE use does not

2

In the United States, the amount of loan borrowing is much larger than the amount of equity and bond issuing(Graham, Li, and Qiu 2008). In 2013, the total volume of U.S. loans is $2,142 billion (Bloomberg 2014).

2

necessarily increase lenders’ information risk, and thus has no significant impact on loan
contracting terms. Therefore, it is an empirical question whether the use of SPEs matters tobanks when contracting with SPE sponsor firms.
Using a large sample of bank loan contracts (with 11,088 loan facilities) with public
non-financial companies from 1997 to 2008, we find that loans to the sponsor companies ofSPEs are (1) charged at higher loan rates, (2) more likely to be secured by collateral, and(3) more likely to include covenant restrictions, compared with companies that do not use
SPEs. Such findings suggest that banks and other private lenders perceive that firms with
SPEs have higher credit risk or lower credit quality. We also perform an intertemporal
analysis by examining the loans issued within a four-year window around (two years before
and two years after) the initiation of SPEs for the sample of SPE sponsor firms. This
intertemporal comparison reveals that SPE initiation increases loan rates and the likelihoodof loans being subject to collateral requirements. In addition, to address potential endogeneitywith respect to a firm’s use of SPEs, we apply the propensity score matching (PSM) method.
We find that our results are robust to the employment of the PSM method, alleviating theendogeneity concern.
Thus far, our findings suggest that for prospective private lenders such as banks, thecosts associated with SPEs dominate the benefits. As discussed in prior studies (Feng et al.
2009; Lemmon et al. 2014), SPEs can provide benefits to sponsor firms, including lowered
finance costs for SPE borrowings, tax savings, and access to segmented capital markets. In
addition, by obtaining additional external financing, SPEs can increase the cash flows
available to the sponsor firms for servicing debt repayments.3 However, at the same time,SPEs can help managers hide liability off balance sheet, if not consolidated, and may
3

Lemmon et al. (2014) find that the funds from the securitization largely pay down existing debt.

3

encourage managerial risk taking by converting safe assets, such as accounts receivable, into
cash and using cash for risky projects. Therefore, it is not a priori obvious whether SPE usage
increases or decreases default risk. However, by facilitating opportunistic earnings
management, SPE activities are likely to reduce financial reporting quality (Dechow, Myers,
and Shakespeare 2010) and consequently increase the information risk faced by lenders inestimating the sponsor firms’ default risk and/or underlying cash flows (Bharath et al. 2008).
While it is challenging to separate the two dimensions of the credit risk (i.e., default
risk and information risk) associated with borrowers’ use of SPEs, we carry out subsample
analyses to identify possible situations under which the effect of SPE usage on bank loan
terms is more likely to hinge on information risk. First, prior studies find that the delta risk ofmanagerial compensation is positively related to earnings management, which increases
information risk, but negatively related to risk taking (Coles, Daniel, and Naveen 2006). We
thus argue that for firms with higher delta risk of managerial compensation, the effect of SPE
usage on bank loan contracting is more likely to hinge on information risk. Our empiricalanalyses show that the associations between SPE use and the various terms of bank loan
contracting are more pronounced for sponsor firms with higher delta risk. Second, when SPE
sponsor firms and their lenders have prior lending relationships, the lenders face lower
information risk in the sense that they have prior knowledge about the borrowers’ operations
and SPE activities. In such a case, the lenders are more likely to overcome the information
risk associated with borrowers’ use of SPEs. The findings of our empirical analyses showthat the impact of SPE usage on loan terms is mainly driven by firms with no priorrelationship with their lenders. The above cross-sectional variations in the impact of SPE

4

usage on loan contracting terms observed across different subsamples of SPE sponsor firms
provide some direct evidence on SPEs’ role in increasing information risk.
In addition to the subsample analyses discussed above, we perform a battery ofadditional analyses aimed to test the effect of SPE usage on loan contracting terms throughthe information risk channel. First, we include Standard & Poor’s long-term issuer credit
rating in the regression model as an additional control variable.4 We find that SPE usage
remains significantly associated with unfavorable loan terms even after controlling for credit
ratings. Since rating agencies should have considered off balance sheet financing and theeffect of SPE usage on default risk in setting credit ratings, information risk is likely to be an
important driver of this significant relation observed between SPE usage and unfavorable
loan terms. Second, we find a positive association between SPE usage and financial
restatements, but no significant association between SPE usage and future credit default,suggesting that SPE usage is more likely to be associated with higher information risk thandefault risk. Lastly, we specifically test the effect of asset securitization–––a specific, butcommon type of SPEs–––on loan terms. The results show that asset securitization isassociated with unfavorable loan terms after controlling for the amount of off balance sheet
borrowings through securitizations, suggesting that asset securitization increases banks’
information risk.
Our study contributes to the literature in two ways. First, it adds to the literature
examining the economic consequences of SPE use by non-financial companies.5 A recent
study by Lemmon et al. (2014) documents zero bond market reactions and positive equity
4

Since half of the firms in our sample do not have credit ratings, our sample will lose representativeness if we
include credit ratings as a control variable in our main tests.
5
Literature on the effect of SPEs mainly focuses on financial institutions (e.g., Chen, Liu, and Ryan, 2008;
Downing, Jaffee, and Wallace 2009; Mian and Sufi 2009; Keys et al. 2010; Piskorski, Seru, and Vig 2010;
Barth, Ormazabal, and Taylor; 2011; Nadauld and Weisbach 2012).

5

market reactions to the initiation of non-financial firms’ asset securitization, suggesting thatthe use of SPEs benefits existing shareholders without hurting bondholders. Our study is oneof the few which provides large-sample, evidence on the debt market consequences of nonfinancial companies’ SPE activities in the context of loan contracting.
Second, our study contributes to the bank loan literature. We provide evidence that
SPE use is an incrementally significant factor determining credit quality, particularly the
information risk of the borrowing firms, over and beyond other borrower- and loan-specificfactors that are known to affect the price and non-price terms of loan contracting.
Finally, our study has a policy implication. In 2009, the Financial AccountingStandards Board (FASB) issued FAS 166 and 167 to amend existing standards that guide thereporting of “special purpose entities” (FASB 2009a, b). Robert Herz, the then Chairman of
the FASB claims that “[T]hese changes were proposed and considered to improve existing
standards and to address concerns about companies who were stretching the use of offbalance sheet entities to the detriment of investors. The new standards eliminate existing
exceptions, strengthen the standards relating to securitizations and special-purpose entities,
and enhance disclosure requirements. They provide better transparency for investors about acompany’s activities and risks in these areas” (emphasis added).6 Our findings suggest thatSPEs provide managers with a convenient tool for obfuscating financial reporting, which inturn increases the information risk to prospective lenders, when evaluating the default risk ofSPE sponsor firms. One way to alleviate the information risk associated with SPE usage is torequire more sufficient disclosures of SPE activities in the financial statements, consistent
with the FASB’s goal of issuing the two statements (FAS 166 and 167) to improve financial
reporting of SPEs.
6

http://www.fasb.org/cs/ContentServer?pagename=FASB/FASBContent_C/NewsPage&cid=1176156240834.

6

The remainder of the paper is structured as follows. Section 2 develops our
hypotheses. Section 3 describes our research design, including the measurement of SPE use
and hypothesis testing procedures. Section 4 first explains sample selection procedures andthen presents descriptive statistics on the major research variables. Section 5 provides theresults of our empirical analyses, including main regressions, robustness tests, and additional
tests. The final section concludes the paper.
2. Hypothesis Development
An SPE is a legally distinct entity with a limited life created by a sponsor company tocarry out limited activities or transactions as specified in the contracts (Hartgraves andBenston 2002; Soroosh and Ciesielski 2004; Feng et al. 2009). SPEs have been used invarious transactions, for example, leasing, asset securitization, throughput contracts, and joint
venture research and development (R&D) arrangements (Soroosh and Ciesielski 2004). The
formation of SPEs can bring real economic benefits to the sponsor firms for several reasons,as explained below.
First, since SPEs isolate and homogenize cash flows and business risks related to a
specific class of assets, the sponsor can obtain external financing through SPEs at a lower
cost (e.g., Soroosh and Ciesielski 2004; Dechow and Shakespeare 2009; Feng et al. 2009).
Usually SPEs are bankruptcy remote from the sponsor firms’ financial distress, and thus, thecreditors of SPEs can avoid any deadweight costs associated with the bankruptcy of thesponsor firms, which in turn lowers the financing costs of SPEs (Ayotte and Gaon 2011;
Lemmon et al. 2014).

7

Second, because SPEs are structured with very different risks from those of thesponsor firms, they can help sponsor firms access additional segments of the capital market
and acquire additional sources of capital. For example, Lemmon et al. (2014) show that SPEs
formed for securitization allow firms without the top commercial paper rating to gain accessto the commercial paper market and firms with speculative-grade ratings to have access tothe investment-grade bond market, which in turn contributes to lowering the financing costsof the sponsor firms.
Third, prior studies suggest that SPEs are usually created as flow-through entities(e.g., limited partnerships or limited liability companies), thus offering tax benefits to thesponsors (e.g., Beatty, Berger, and Magliolo 1995; Gupta and Mills 2002; Soroosh andCiesielski 2004; Feng et al. 2009). In addition, SPEs formed for joint venture R&D
arrangements allow firms with low marginal tax rates to effectively sell the benefit of theimmediate tax deductibility of R&D to firms with high marginal tax rates, realizing tax
benefits for both the sponsor firms and investors of the SPEs (Shevlin 1987; Beatty et al.
1995).
Lastly, SPE activities increase the fund available for serving debt repayment andinterest payment. For example, SPEs formed to securitize long-term receivables provide thesponsor firms with an immediate source of cash and eliminate the risk of holding thereceivables (Dechow et al. 2010). Lemmon et al. (2014) also find that funds from asset
securitization largely pay down existing debt.
On the other hand, there are also negative economic consequences of SPE formation
for the outside stakeholders of the sponsor firms, especially their creditors. First, the creditors
of SPEs have a first-priority claim on the assets transferred to the SPEs, which effectively

8subordinates existing debt with respect to the securitized assets (Lemmon et al. 2014). For
example, a sponsor firm’s retained interest in securitized receivables is available to service
on balance sheet debt only after the debt of the SPE has been paid off. Second, to lower
financing costs, the sponsor firms usually transfer fairly low risk assets to the SPEs, which
may result in more volatile assets on the balance sheets. Third, the increase in funds available
may encourage managers to take more risk in their investment decisions. For example, SPEs
formed for joint venture R&D arrangements provide the sponsor firms with more funding
sources for their innovation activities (Beatty et al. 1995).
Lemmon et al. (2014) find positive abnormal stock and zero bond returns around theinitiation of asset securitization. Their finding suggests that securitization via SPE usage
benefits existing shareholders without hurting existing bondholders. Stated another way, this
implies that the benefits of securitization dominate the associated costs to arms-length capital
suppliers such as shareholders and bondholders. However, their study does not consider
another potential cost, that is, the increase in information risk arising from SPE usage thatoutside capital suppliers, in general, and concentrated private lenders such as commercial
banks, in particular, must face when evaluating the default risk of the sponsor firms. As
shown in Duffie and Lando (2001), information risk or the transparency component of credit
risk is an important part of credit risk, along with default risk.
Although originated to serve legitimate business purposes, SPEs have been used as anaccounting tool for corporate managers to window-dress financial statements and manipulateearnings. For example, in a sale-leaseback transaction, the sponsor creates an SPE to borrowmoney and then the SPE uses the borrowed money to purchase the sponsor’s long-term fixed
asset at a price set by the sponsor. Then the sponsor leases the asset back from the SPE under9the operating lease. This series of transactions facilitates the sponsor’s earnings management.
The sponsor has control over the price of the fixed asset sold to the SPE and thus can inflate
its current-period reported earnings through the amount of gains recognized on the sale. In
addition, the sponsor can control the timing and amount of the operating lease payments forleasing back the fixed asset from the SPE, which further facilitates earnings management by
the sponsor. In a typical receivable securitization transaction, the sponsor sells a portion of
the interests in its receivables to an SPE and retains the residual interest in the remaining
receivables. The SPE sells the acquired receivables to a third party in exchange for a loan andpays the sponsor using cash proceeds from the loan. By treating securitizations as sales rather
than collateral borrowings, the sponsor’s balance sheet appears less risky. In addition, by
inflating the value of the sold and retained interest in the receivables, the sponsor canrecognize gains and manage earnings upward.
The collapse of Enron and other SPE-related corporate scandals provide evidence thatcompanies hide debt and manage earnings by not consolidating SPEs into their financial
statements (Yale, 2002; SEC, 2005). 7 Dechow and Shakespeare (2009) document thatsponsors time securitization transactions to achieve financial reporting goals, such as
lowering leverage and beating earnings thresholds. Dechow et al. (2010) find that firmmanagers use discretion within fair value accounting rules (e.g., choosing the discount rate)
to report larger gains from securitization, thus obtaining higher compensation. In addition,
the complex nature of SPEs makes it hard for investors/creditors to understand the sources of
earnings management. Dechow et al. (2010) find that even informed and independent
7

Before 2003, U.S. Generally Accepted Accounting Principles allowed a sponsor to exclude an SPE from its
financial statements if third-party residual equity investment at risk equaled at least 3% of the SPE’s total
capitalization (FASB 1990). In response to the Enron scandal, in 2003 the FASB issued FIN No. 46(R) torequire that variable interest entities be consolidated in the financial statements of the primary beneficiary(FASB 2003).

10directors do not distinguish between securitization gains and other components of earnings
when awarding CEO pay.8
In this study, we examine the impact of SPE use from the perspective of private
lenders such as banks. While the predictions for the effect of SPEs on default risk are twosided, the increase in information risk due to SPE usage should be associated withunfavorable loan contracting terms. SPEs provide firms with a tool for earnings management,
which lowers the quality of financial reporting and increases the information risk faced bycreditors in estimating a sponsor’s future cash flows and assessing its credit quality. Previous
theoretical and empirical studies have documented that lenders require compensation forborrower information risk, which is incremental to default risk (e.g., Duffie and Lando 2001;
Easley and O’Hara 2004; Francis et al. 2005; Lambert, Leuz, and Verrecchia 2007; Bharath
et al. 2008; Graham et al. 2008; Costello and Wittenberg-Moerman 2011; Kim et al. 2011b,
2011c). Bharath et al. (2008) find that banks charge lower interest rates to loans made tofirms with higher accrual quality. Graham et al. (2008) show that borrowers are charged highloan rates after their financial statements are restated. Both Kim et al. (2011b) and Costello
and Wittenberg-Moerman (2011) find that banks charge higher interest rates to borrowers
with internal control weaknesses than those without.
Drawing on the above discussions, we predict that SPE use is more likely to be
associated with higher loan interest rates, primarily due to its effect on increasing information
risk. To test this prediction, we formally hypothesize the following in alternative form.

8

By examining banks’ asset securitization, Cheng, Dhaliwal, and Neamtiu (2011) find that securitizations
increase information uncertainty, as reflected in higher bid–ask spreads and analyst forecast dispersion,compared to banks without such transactions and that information uncertainty increases with the amount of
securitized assets. Dou, Liu, Richardson and Vyas (2014) find that investors recognized the increasing risk of
securitized subprime and commercial mortgages as the recent financial crisis progressed, suggesting that therisk structure of securitization is opaque.

11H1: The use of SPEs is positively associated with a sponsor firm’s loan interest rates,
all else being equal.
Compared with equity and bond investors, banks are generally more sophisticated,have privileged access to private information about the borrower, and have stronger
information processing abilities (e.g., Bharath et al. 2008). In addition, since only a limited
number of lenders are associated with private loans, post-contract monitoring is less subject
to the free rider problem and thus more effective (Diamond 1984). This superior information
access and effective ex post monitoring could reduce the adverse selection costs and mitigate
the information uncertainty when contracting with borrowers with SPEs. One can therefore
expect that, contrary to the prediction of H1, SPE use has no impact on loan pricing. Statedanother way, the null hypothesis of no relation between SPE use and loan interest rate iscredible.
Bank loan contracts include not only price terms (i.e., interest rates), but also nonprice terms, such as collateral requirements and restrictive covenants. Commercial banks andother private lenders use non-price terms in loan contracts to monitor post-contract credit
quality changes, mitigate information problems and agency conflicts, and control lenders’risk exposure. Previous studies show that lenders are more likely to require collateral for andimpose restrictive covenants on loans to borrowers with higher default and information risks
to facilitate post-contract monitoring and renegotiation (e.g., Berger and Udell 1990; Rajan
and Winston 1995; Jimenez, Salas, and Saurina 2006; Bharath et al. 2008; Graham et al.
2008; Demiroglu and James 2010; Kim et al. 2011b, 2011c). To the extent that the use ofSPEs increases the default and information risks of the sponsor firms, we expect SPE use to
be positively associated with the likelihood of loans being secured by collateral and/or
subject to restrictive covenants. In addition, private lenders may use non-price terms as a
12

substitute for price terms when contracting with borrowers of high default/information risk.
To provide systematic evidence on the impact of SPE usage on non-price loan terms, we test
our second hypothesis in alternative form.
H2: The likelihoods of loans being secured by collateral and of loans being subject torestrictive covenants are greater for borrowers who use SPEs than for those who do
not use SPEs, all else being equal.
The predictions of H1 and H2 can be based on the effect of SPE usage on increasing
either the borrower’s default risk or information risk or both. An additional challenge is that
SPE usage may simply represent off balance sheet liability, which lenders must take into
account when evaluating borrowers’ credit quality and, thus, should be associated withunfavorable loan terms. In an attempt to provide more direct evidence of the impact of SPE
use on information risk, we identify potential situations under which the effect of SPE usage
on bank loan terms is more likely to hinge on information risk than on default risk. If weobserve significant cross-sectional differences in the SPE effect on loan terms or a stronger
SPE effect in certain situations, to some extent it helps us rule out the possibility that SPE
usage simply measures off balance sheet liability. In other words, if SPE usage only
represents off balance sheet liability, we should not be able to identify situations under which
SPE usage has no effect on loan contracting.
The finance literature suggests that higher CEO pay–performance sensitivity (delta) is
associated with less managerial risk taking and more reporting opportunism (Core and Guay
2002; Bergstresser and Philippon 2006; Burns and Kedia 2006; Coles et al. 2006; Kim, Li,
and Zhang 2011). The sensitivity of CEO wealth to stock price (delta) aligns the incentives ofmanagers with the interests of shareholders because managers share gains and losses withshareholders (Coles et al. 2006). However, because managers are undiversified with respect

13

to firm-specific wealth, they are more risk averse than diversified shareholders and a largerdelta exposes managers to more risk and reduces their incentive to take on risky projects.
Coles et al. (2006) find that a higher delta is associated with lower R&D expenditures, higher
capital expenditures, and lower leverage. Then, in the context of our study, this suggests thatthe fund generated by SPEs is more likely to be used in debt repayment or invested in safer
projects if the sponsor firms’ managers have higher pay–performance sensitivity (delta),
reducing the firms’ default risk.
Another potential effect of an increased delta is that managers may have incentive to
misreport to increase equity value. Bergstresser and Philippon (2006) find that the use of
discretionary accruals to manipulate reported earnings is more pronounced at firms where thechange in the CEO’s potential total compensation is more sensitive to stock price changes(delta). Kim et al. (2011a) show evidence that managerial equity incentive captured by delta
is positively associated with future stock price crash risk, an extreme outcome of earnings
manipulation and bad news hoarding. Burns and Kedia (2006) find a positive relation
between the CEO’s portfolio delta and accounting restatement. Therefore, we expect that for
SPE sponsor firms with greater pay–performance sensitivity, captured by delta, their lenders
face even higher information risk/uncertainty associated with SPEs because the borrowers
have stronger incentive to manipulate earnings and the SPEs provide them with a convenient
tool for misreporting. Motivated by the information risk effect, we develop our third
hypothesis in alternative form to examine the impact of SPE usage on price and non-priceloan terms conditional on managerial pay–performance sensitivity.
H3: SPEs have a stronger impact on the price and non-price terms of loans borrowedby sponsor firms with greater managerial pay–performance sensitivity (delta), allelse being equal.

14

In loan contracting, the information friction caused by adverse selection and moral
hazard can be mitigated if the leading lender has had a strong past relationship with theborrower, because borrower-specific information obtained through a prior lending
relationship is largely durable and reusable (Boot 2000; Bharath et al. 2011). Bharath et al.
(2011) find that the benefits of relationship lending in reducing information friction are more
pronounced when there is greater information asymmetry between lenders and borrowers. To
the extent that SPE usage increases information asymmetry between lenders and borrowers,
we expect prior lending relationship to alleviate the impact of SPE usage on price and nonprice loan terms. To show evidence on the impact of SPE usage on loan contracting
conditional on a prior lending relationship, we test the following hypothesis in alternativeform.
H4: SPEs have a stronger impact on the price and non-price terms of loans borrowedby sponsor firms that never had a prior lending relationship with their lenders, allelse being equal.
3. Research Design
3.1 Measurement of SPE Use
Following Feng et al. (2009), we measure a firm’s use of SPEs by counting thelimited partnerships, limited liability partnerships, limited liability companies, and trusts
included in the list of subsidiaries and affiliates in Exhibit 21 of SEC Form 10-K on Edgarfor each year. The acronyms L.P., LP, LLP, L.L.P., LLC, and L.L.C. are also included in the
search. Our measure of SPE use is NSPE, which refers to the number of a borrowing firm’s
SPEs each year and is intended to capture a sponsor firm’s intensity of SPE use.
By employing NSPE instead of the SPE proxies related to particular transactions,such as asset securitizations, we can conduct large-sample cross-sectional and intertemporal

15

analyses in the bank loan market. Before Feng et al. (2009), studies that examine nonfinancial companies’ SPEs used manually collected data for small samples to examine SPE
use in particular transactions such as asset securitizations (Dechow et al., 2008) and R&D
financing (Beatty et al., 1995). As Feng et al. (2009) argue, such small-sample or shortperiod analyses limit the ability to examine broader issues and to generalize results. By
employing the number of SPEs as the proxy for the intensity of SPE use, we can investigate
the broader issue of whether SPE use affects loan contracting terms, using a large sample.9
3.2 Tests of Hypotheses
To provide empirical evidence on the role of SPE use in loan pricing as hypothesizedin H1 and H2, we specify the following regression model:
Loan Featureikt = α0 + α1Log NSPEit–1 + α2Loan-specific Controlikt+ α3Borrower-specific Controlit–1 + α4Economy-wide Controlt+ (Loan Purpose Indicatorsikt) + (Year Indicatorsit)+ (Industry Indicatorsit-1) + errorikt,(1)

where the dependent variable, Loan Featureikt, refers to one of the following features of aloan contract for a borrower i’s facility k in year t: (i) Log AIS, (ii) DSecu, (iii) DFinCov, and(iv) DGenCov.
The variable Log AIS is used as a proxy for the interest cost of borrowing and ismeasured by the natural log of the drawn-all-in spread (plus the upfront fee and annual fee, ifany) in basis points in excess of the benchmark rate, that is, the London Interbank Borrowing
Rate (LIBOR). The variable DSecu is an indicator variable that equals one if the loan issecured with collateral and zero otherwise. The variable DFinCov is an indicator variable that
equals one if the loan includes any financial covenant and zero otherwise. The variable
9

Feng et al. (2009) have conducted several tests to show the construct validity of NSPE.

16

DGenCov is an indicator variable that equals one if the loan includes any general (nonfinancial) restrictive covenant, such as dividend restrictions and/or investment restrictions,and zero otherwise. When Log AIS is the dependent variable, we estimate Eq. (1) using
ordinary least squares (OLS) regression; when DSecu, DFinCov, or DGenCov is thedependent variable, we estimate Eq. (1) by applying probit regression procedures.10
The test variable Log NSPE is the natural log of one plus the number of SPEs for eachfirm–year.11 We merge bank loan data with Log NSPE and financial statement data for the
fiscal year before loans are initiated. The procedure ensures that our test variable, Log NSPE,
reflects an observable result of SPE use. Our hypotheses H1 and H2 translate into a positivecoefficient on Log NSPE (i.e., 1 > 0) when the dependent variable in Eq. (1) is the loan
spread (Log AIS), the indicator of collateral requirement (DSecu), or the indicators offinancial and general loan covenants (DFinCov and DGenCov, respectively). This is because
SPEs are related to earnings management and other obfuscations in financial reporting andthus increase the borrower’s information risk or deteriorate its credit quality.
Following other studies in the loan contracting literature (e.g., Bharath et al. 2008;
Graham et al. 2008; Kim et al. 2011a, 2011b; Lin et al. 2011), we include in Eq. (1) a set ofloan-level control variables: Log Maturity, Log Loan Size, Log NLenders, Performance10

We also consider the joint determination of interest rates, collateral requirements, and covenant restrictions
and re-estimate the effect of SPE use on these loan terms following the loan contracting literature (Dennis,
Nandy, and Sharpe 2000; Ivashina 2009; Bharath et al. 2011). First, prior studies suggest that non-price terms
are normally determined before setting the loan interest rate in the loan syndication process. We thus assume
that loan spread is affected by the inclusion of collateral and covenants and estimate the effect of SPE use on theloan rate, using an instrumental variable approach, and find similar results to those reported in the paper.
Second, since DSecu and DFinCov (DGenCov) are two correlated binary variables, we re-examine the effect of
SPE use on collateral requirements and covenant restrictions by estimating a bivariate probit model and findsimilar results to those reported in the paper. Lastly, to check further on the joint determination of loan terms,
we also estimate a system of equations with the loan spread, DSecu, DFinCov, and DGenCov as dependent
variables, using the seemingly unrelated regression procedure. The unreported results for this procedure are
qualitatively identical to those reported in the paper.
11
Another possible measure of SPE use is the percentage of SPEs among a firm’s reported subsidiaries.
However, we argue that it is the existence, not the percentage, of SPEs that facilitates managerial opportunism.
Nevertheless, we find qualitatively similar results using the percentage measure.

17

Pricing, and Term Loan. The variable Log Maturity is the natural log of loan maturity inmonths and Log Loan Size is measured by the natural log of the dollar amount of each loan
facility. Previous studies (e.g., Graham et al. 2008) show that lenders charge lower loan rates
for shorter-maturity loans and larger loan facilities. The variable Log NLenders is the naturallog of the number of lenders in a loan deal and Performance Pricing is an indicator variable
that equals one for loans with performance pricing provisions and zero otherwise. We expect
loan contracts involving larger numbers of lenders and performance pricing provisions tohave lower interest rates. To control for potential differences in loan terms between
transaction-based term loans and other types of non-term loans that are more relationshipbased, we include in the model an indicator variable, Term Loan, that equals one if the loan
facility is a term loan and zero otherwise. Since collateral requirements and covenant
restrictions are imposed at the deal level rather than at the facility level, we replace Log Loan
Size with Loan Concentration, which is the dollar amount of the loan deal divided by theborrower’s total liabilities (Bharath et al. 2011), with DSecu, DFinCov, or DGenCov used as
the dependent variable.
We also control for a set of borrower-specific (sponsor-specific) variables that are
known to affect credit quality and thus loan contracting terms: Size, Leverage, MB,
Profitability, Funds, Tangibility, Log IntCov, O-Score, AbsAccr, and Prior. The variables
Size and Leverage are measured by the natural log of total assets and the ratio of total debt tototal assets, respectively. We expect Size (Leverage) to be positively (negatively) related tocredit quality. The variable MB is measured by the market value of equity plus the bookvalue of debt divided by the book value of total assets. To the extent that it proxies for aborrower’s growth potential, MB is likely to be positively associated with credit quality.

18

However, growing firms often face higher risk. In such a case, MB is likely to be inversely
associated with credit quality (Billett, King, and Mauer 2007). The variable Profitability
refers to earnings before interest, taxes, depreciation, and amortization (EBITDA) divided bytotal assets and Tangibility is the ratio of property, plant, and equipment (PP&E) to total
assets. The variable Funds is a proxy for the supply of internal funds, which is measured by
the sum of cash flow from operating activities and cash flow from investing activities divided
by average total assets. The variable Log IntCov is the natural log of one plus the coverage
ratio, that is, the ratio of operating income after depreciation (before interest) to interest
expenses. We expect Profitability, Funds, Tangibility, and Log IntCov to be positively
associated with credit quality. The variable O-Score is Ohlson’s (1980) O-score, a proxy forthe borrower’s default risk. The term AbsAccr is the absolute value of abnormal accruals
obtained from the modified Jones model (Dechow et al. 1995). We predict that AbsAccr,
which is an inverse measure of accounting quality (Bharath et al. 2008), is positively
associated with the borrower’s information risk. The term Prior is an indicator variable that
equals one if the borrower has prior loan relationships with at least one of the lead banks of aloan syndicate for the current loan deal in the past five years and zero otherwise. We expect
Prior to be negatively associated with the loan spread and the likelihood of loans being
subject to collateral requirements and restrictive covenants (Bharath et al. 2011).
In addition, we include two economy-wide variables, Term Spread and Credit Spread,to control for the potential effects of macroeconomic conditions on loan contract terms. The
variable Term Spread is the difference in yield between 10- and two-year U.S. Treasury
bonds, while Credit Spread is the difference in yield between BAA- and AAA-rated
corporate bonds. Finally, we include Loan Purpose Indicators to control for potential

19differences in the price and non-price terms of loan contracts associated with different loan
purposes.12 We also include Year Indicators and Industry Indicators to control for potential
differences in SPEs and loan features across years and industries.
For the test of H3, we partition our sample into two subsamples based on the medianof borrowers’ CEO pay–performance sensitivity, that is, delta risk (Core and Guay 2002;
Coles et al. 2006), and estimate Eq. (1) for both subsamples.13 According to H3, we expect astronger impact of SPE usage on loan terms for the subsample of higher CEO delta risk. For
the test of H4, we partition our sample into two subsamples based on whether the borrower
and the lead lender have a prior loan relationship and estimate Eq. (1) for both subsamples.
According to H4, we expect a stronger impact of SPE usage on loan terms for the subsample
of borrowers that have no prior relationships with their lenders.
4. Sample Selection and Descriptive Statistics
4.1 Sample and Data
Our initial sample consists of all public companies that have bank loan data in the
Loan Pricing Corporation (LPC) DealScan database for 1997–2008. The LPC DealScan
database contains a variety of historical bank loan data and other financial arrangements
collected from SEC filings and other information self-reported by banks. The DealScan loan
data are compiled for each transaction or deal. Each deal, which is a loan contract between aborrower and bank(s) on a specific date, can have only one facility or a package of several
facilities with different price and non-price terms. We consider each facility a separate

12

The purposes of loan facilities in DealScan include corporate purposes, debt repayment, working capital,
takeover, and acquisition lines. Accordingly, Loan Purpose Indicators is a series of indicator variables for loans
with these different purposes.
13
Delta is the change in the dollar value of the CEO’s wealth associated with a 1% change in the firm’s stock
price. We are grateful to Jeffrey L. Coles, Naveen D. Daniel, and Lalitha Naveen for providing the data on CEO
delta risk.

20

observation for our sample, since many loan characteristics and loan spreads vary acrossfacilities. We require that all loan facilities in our sample be senior debts. With regard to thetypes of loans, our sample includes term loans, revolvers, and 364-day facilities but excludes
bridge loans and non–fund-based facilities such as leases and standby letters of credit. We
also exclude financial companies from our sample. We manually collect the SPE data bysearching the annual 10-K reports in Edgar for our sample firms.14 We obtain borrowers’
financial statement data from Compustat. DealScan and Compustat are matched using thelinking table originally created by Chava and Roberts (2008).15 Our final sample consists of
11,088 facility–years. Table 1 presents the sample selection procedure.
[INSERT TABLE 1 HERE]
4.2 Descriptive Statistics and Correlations
Panel A of Table 2 presents descriptive statistics for all loan-specific variables at thefacility level. The mean and median of the drawn-all-in spread over the LIBOR (i.e., AIS) arearound 185 and 175 basis points (bps), respectively, with a standard deviation of about 139
bps, suggesting that AIS is reasonably distributed. The mean (median) maturity is about 47(57) months, while the mean (median) facility size is $372 million ($150 million). On
average, the amount of a loan deal accounts for 34% of a borrower’s total liability. Panel A
of Table 2 also shows that 54% of the loan facilities in our sample require loans to be secured
by collateral, about 74% of them include at least one financial (general) covenant, and 57%

14

Since our SPE measure is lagged by one period, we use computer algorithm to collect SPE data from 10-K
filings during 1996–2007. We choose this period to fully cover the sample period (1997–2004) of Feng et al.
(2009) to compare the yearly distributions of SPE firms with those reported in their study. Untabulated results
show that the yearly distributions of our SPE data are very similar to those of Feng et al. during the common
period. The percentage of firm–years reporting at least one SPE in our test sample, 49.5%, is higher than that in
the authors’ sample, 41.9%, and the SPE firm–years in our test sample report more SPEs (18.73 for the mean
and 4.00 for the median) than those in their sample (11.63 for the mean and three for the median). That is
because the public firms covered by LPC DealScan are generally larger than the average firms in Compustat.
15
We are grateful to Michael Roberts for providing the DealScan–Compustat link file.

21

have a performance pricing provision. Most of the loan facilities in our sample are syndicatedloans that have, on average, nine participating lenders. About 29% of the loan facilities in our
sample are term loans, while the others are revolvers or 364-day facilities.
[INSERT TABLE 2 HERE]
Panel B of Table 2 reports descriptive statistics for our measures of SPEs and all
borrower-specific (lender-specific) variables used in this study. The mean and median
numbers of SPE (NSPE) sponsored by the borrowers in our sample are about 10 and zero,
respectively, with a large standard deviation of 57.88. This suggests that the distribution of
NSPE is very right skewed. The natural log transformation of NSPE (Log NSPE) slightly
mitigates the skewness. The variable Size is reasonably distributed, with a mean and medianof 6.87 and 6.79, respectively, and a standard deviation of 1.81. The mean (median) marketto-book ratio is 1.75 (1.46) and the mean (median) O-score is -6.88 (-6.93). On average,
long-term debt, EBITDA, and tangible assets (i.e., PP&E) are about 27%, 15%, and 34% of
total assets, respectively. The mean and median ratios of internal cash flows to average total
assets (Funds) are -1% and 2%, respectively. The variable Log IntCov has a mean (median)of 1.82 (1.64) and AbsAccr, a proxy for accounting quality, has a mean (median) of 0.12
(0.05). About 46% of borrowers in our sample have prior lending relationships with the samelead banks for their current loans.
Table 3 reports Pearson correlation coefficients among selected loan- and borrowerspecific variables. Inconsistent with our predictions, we find in Table 3 that Log NSPE issignificantly and negatively correlated with Log AIS (-0.11), DSecu (-0.10), DFinCov (-0.05),
and DGenCov (-0.03), suggesting that SPE use is associated with more favorable loan
contracting terms. We also find that Log NSPE has a strong correlation with Size (0.43),

22

while Size has significantly negative correlations with Log AIS (-0.54), DSecu (-0.42),
DFinCov (-0.24), and DGenCov (-0.14). In addition, Size is highly correlated with Log Loan
Size (0.81) and Log NLenders (0.60). Thus, our conjecture is that the correlations between
SPE use and favorable loan terms are driven by firm size effect. Table 3 shows that SPE use
is positively correlated with leverage and asset tangibility, while it is negatively correlatedwith the market-to-book ratio, profitability, interest coverage, and the O-score.
Among loan contracting terms, Log AIS is positively correlated with DSecu, DFinCov,
DGenCov, and Log Maturity, while it is negatively correlated with Log Loan Size andPerformance Pricing. The variable DSecu is positively correlated with both DFinCov and
DGenCov and these two covenant indicators are highly and positively correlated with eachother. These correlations are consistent with those reported in the prior loan literature.
[INSERT TABLE 3 HERE]
5. Empirical Results
5.1 Tests of H1 and H2
Table 4 reports the estimated results for Eq. (1). All reported t-statistics (z-statistics)
are based on standard errors corrected for heteroscedasticity and two-dimensional (firm andyear) clustering. As shown in column (1), we find that the coefficient on Log NSPE is
positive and statistically significant at less than the 1% level (coefficient = 0.039, t-value =3.84). This finding is consistent with H1, suggesting that the number of SPEs reported in acompany’s 10-K is positively associated with loan interest rates. Stated another way, theabove finding is consistent with the view that banks and other private lenders tend to chargehigher interest rates for loans made to firms that use SPEs because such firms are perceived
as having higher default risk and/or information risk.

23

The effect of SPE use on loan interest rate is economically significant as well. The
magnitude of the coefficient on Log NSPE (0.039) suggests that a move from no SPEs (thefirst quartile of NSPE in our sample) to four SPEs (the third quartile of NSPE in our sample)
leads to a 6.5% increase in AIS, with all other AIS determinants unchanged.16 Suppose that afirm without SPEs takes out a loan of $372 million for 47 months at the all-in spread of 185
bps (i.e., an average loan facility in our sample, as shown in Panel A of Table 2). Our results
imply that the all-in spread of a loan with the same features borrowed by a firm with fourSPEs is 197 bps, that is, 12 bps higher than that of the loan to a borrower without SPEs. In
other words, on average, a borrower with four SPEs has to pay annually $0.45 million ($372
million × 12 bps) more interest expense for about four years than a borrower without SPEs.
[INSERT TABLE 4 HERE]
In column (2), the coefficient on Log NSPE is also positive and statisticallysignificant at less than the 1% level (coefficient = 0.062, t-value = 3.47), suggesting that SPE
use increases the likelihood of loans being secured by collaterals. In columns (3) and (4), we
find that the coefficients on Log NSPE are both positive and statistically significant at lessthan the 1% level (coefficient = 0.096, t-value = 4.74 and coefficient = 0.063, t-value = 2.79,
respectively). This finding suggests that SPE use is positively associated with the likelihoodthat lenders impose financial covenants and general covenants in the loan contracts to protect
themselves from the SPE sponsor’s default risk and/or information risk. In terms of economic
significance, the results suggest that when NSPE increases from zero to four, the probabilityof its loans being secured by collaterals increases by 2.7% and the probabilities of its loans
being subject to financial and general covenants increase by 2.9% and 1.8%, respectively. As

16

The calculations are as follows: Log AISNSPE=4 – Log AISNSPE=0= 0.039 × [ln5 – ln1] = 0.0628. Thus, AISNSPE=4
÷ AISNSPE=0 = e0.0628 = 1.0648. The ratio reflects a 6.5% increase in AIS when NSPE moves from zero to four.

24

predicted by H2, banks and other private lenders tend to require collateral and use restrictive
covenants in loans made to the sponsor firms of SPEs because those firms are likely to havehidden debt financing and more flexibility in earnings management and thus their borrowers
are subject to higher default and information risks.
The results regarding our control variables are consistent with those of previous
studies on the cross-sectional determinants of price and non-price terms of loan contracts. For
brevity, we omit discussions on those control variables.
5.2 Tests of H3 and H4
Panel A of Table 5 reports the estimated results for Eq. (1) for the two subsamples
partitioned based on CEO pay–performance sensitivity, measured by delta. The results showthat the coefficients on Log NSPE are significantly positive only for the high-delta subsample
in columns 1, 2, and 4, where AIS, DSecu and DGenCov, respectively, are used as thedependent variable (coefficient = 0.062, t-value = 4.47 for AIS; coefficient = 0.090, t-value =2.74 for DSecu; coefficient = 0.087, t-value = 2.05 for DGenCov). In addition, we find thatthe coefficients on Log NSPE are significantly larger for the subsample of high-delta firms
than for the subsample of low-delta firms. Since SPE usage is more likely to be associatedwith misreporting when borrowers have greater CEO pay–performance sensitivity, ourresults provide some evidence that banks and other private lenders price the information risk
associated with SPE usage and also use non-price terms to facilitate the post-contract
monitoring of the sponsor firm’s credit quality. In addition, our results suggest that banks’superiority in accessing and processing borrowers’ private information is not sufficient toovercome the information risk resulted from SPE usage.
[INSERT TABLE 5 HERE]

25

Panel B of Table 5 reports the estimated results for Eq. (1) for the subsample of loans
made to borrowers who have prior lending relationships with the lead lenders and for the
subsample of loans made to borrowers who do not. Panel B shows that the coefficients on
Log NSPE are significantly positive only for loans made to firms with no prior lending
relationship, in columns 2 and 4, where DSecu and DGenCov, respectively, are used as thedependent variable (coefficient = 0.087, t-value = 4.22 for DSecu; coefficient = 0.103, tvalue = 3.57 for DGenCov). In addition, the coefficients on Log NSPE are significantly larger
for the subsample with no prior relationship than for the prior-relationship subsample. For
financial covenants, although the coefficients on Log NSPE are statistically significant for
both subsamples, the estimated coefficient is significantly larger for the subsample with noprior relationship than for the prior-relationship subsample. For loan rates, the coefficientsare statistically significant for both subsamples and are not statistically different from eachother. The stronger impact of SPE usage on non-price terms for the subsample with no priorrelationship suggests that the information risk associated with SPE activities does matter indetermining loan contracting terms.
Overall, the results of the subsample analyses provide some direct evidence on theimpact of SPE use on increasing information risk faced by banks and other private lenders.
Dechow et al. (2010) provide evidence that even informed and independent directors do not
distinguish between securitization gains and other components of earnings when awarding
CEO pay. The evidence shown in this study suggests that banks and other private lenders
price the information risk associated with SPEs in the form of higher loan interest rates.
The analyses also help us rule out the possibility that Log NSPE only represents offbalance sheet liability. If it is indeed no more than off balance sheet liability, we are unlikely26to observe significant differences in the impact of SPE usage on loan terms between the two
subsamples (high CEO compensation delta versus low CEO compensation delta and priorrelationships versus no prior relationships between lenders and borrowers). In particular, wefind that Log NSPE is not associated with loan interest rates when the borrowers with SPEs
have below-median CEO pay–performance sensitivity, suggesting that Log NSPE does not
simply represent off balance sheet liability.
5.3 Robustness Check
5.3.1 Selection Bias and Endogeneity
Our analyses thus far focus on how borrowers’ SPE usage impacts their loan
contracting terms. However, it is possible that some confounding factors that contribute to
the formation of SPEs also affect the loan contracting terms in the predicted direction. For
example, managerial incentives to boost reported earnings may motivate managers toestablish SPEs, which in turn causes lenders to impose unfavorable contracting terms onloans to SPE sponsor firms. To address this self-selectivity concern, we include the absolutevalue of abnormal accruals in Eq. (1) as a control variable.
In this section, we further control for potential self-selection bias and endogeneity
associated therewith. More specifically, we use the propensity score matching (PSM) method
to match each treatment firm that uses SPEs with each control firm that does not use SPEs.17
Following Feng et al. (2009), we use the following model to predict firms’ decisions to form
SPEs:
DSPEit = α0 + α1 LEVit-1 + α2 R_INTCOVit-1 + α3 BONUSPit-1 + α4 DEBTISSit-1+ α5STOCKISSit-1 + α6RISKit-1 + α7FUNDSit-1 + α8CLTDit-1
17

The PSM method has been used to alleviate potential endogeneity concerns and is supported by a stream ofrecent accounting studies (e.g., Doyle, Ge, and McVay 2007; Armstrong et al. 2010; Larcker and Rusticus 2010;
Lawrence, Minutti-Meza, and Zhang 2011; Francis et al. 2012).

27

+ α9TDUMit-1 + α10SETRit-1 + α11INTANGIBLEit-1 + α12FOREIGNit-1+ α13LMKTCAPit-1 + α14INDPERCit-1 + (Year Indicatorsit) + errorit,(2)

where DSPE is an indicator that equals one if the firm reports SPEs and zero otherwise. 18
The predictors of DSPE include leverage, defined as total debt divided by total assets (LEV);the reverse interest coverage ratio, that is, interest expense divided by operating income after
depreciation (before interest) (R_INTCOV);19 the bonus percentage, that is, the ratio of the
CEO bonus to the sum of salary and bonus (BONUSP); the issue of debt, defined as thedifference between long-term debt issuance deflated by average total assets (DEBTISS); andthe issue of stock, defined as the difference between the common and preferred stock saleand purchase deflated by average total assets (STOCKISS); stock return volatility, measured
by the decile rank score of the standard deviation of daily stock returns for the year (RISK);
internal cash flows, defined as the sum of operating cash flow and investing cash flow
deflated by average total assets (FUNDS); the costs of debt renegotiation, defined as longterm debt due within one year divided by total assets (CLTD); a tax rate dummy, which is anindicator that takes the value of one if the pretax income is positive and zero otherwise(TDUM); 20 the state effective tax rate, defined as the sum of current and deferred state
income tax expenses divided by total pretax income (SETR); intangible assets, which ismeasured by intangible assets deflated by total assets (INTANGIBLE); foreign income,defined as the ratio of foreign pretax income to total pretax income (FOREIGN); the naturallog of the sum of the market value of common shares and the book value of preferred stock
18

For Feng et al. (2009), the dependent variable is the number of SPEs. For the purpose of the PSM test, we use
the dummy variable DSPE as the dependent variable to run the first-stage probit model.
19
We use the reverse interest coverage ratio to maintain firm–year observations with zero interest expense. So
the predicted sign of the coefficient on R_INTCOV should be positive.
20
Feng et al. (2009) use a simulated marginal tax rate as the predictor of tax benefits. They also suggest using adummy variable that indicates whether firms need to pay income tax because of positive profits as areplacement.

28

and liabilities (LMKTCAP); the industry SPE percentage, defined as the percentage of firms
reporting SPEs within each industry–year, with industries classified according to the Fama–
French (1997) 48-industry classification (INDPERC); and year indicators.21
For all firms in our sample, we first compute the predicted probability (i.e.,
propensity score) of SPE use, using the estimated coefficients of the probit regression modelin Eq. (2). For each treatment firm, we then choose a matched control firm that has theclosest predicted probability of SPE use to that of the treatment firm. After applying theabove PSM procedure, we obtain a matched sample of 4,318 loan facilities consisting of
2,159 facilities for borrowing firms with SPEs and 2,159 facilities for borrowing firms
without SPEs. We first compare the distributions of the control variables in Eq. (1) between
the treatment firms with SPEs and the control firms with no SPEs. Though not tabulated forbrevity, we do not find significant differences in various firm characteristics between these
two samples, suggesting that the PSM matching is effective. We then re-estimate Eq. (1)using this PSM sample. Panel A of Table 6 reports the estimated results of the first-stage
probit model for predicting DSPE, which are similar to those reported by Feng et al. (2009).
Panel B of Table 6 reports the estimated results of Eq. (1) using the PSM sample. As shownin Panel B, the coefficients on Log NSPE are all positive and statistically significant at lessthan the 5% level when Log AIS, DSecu, or DFinCov is the dependent variable, suggestingthat our main results are unlikely to be driven by potential endogeneity associated with afirm’s voluntary decision to use SPEs.
[INSERT TABLE 6 HERE]

21

Since, as argued by Feng et al. (2009), the additional data requirement for governance variables reduces the
sample size by one-third, we do not include these governance variables in Eq. (2) to maintain our sample size.
We obtain similar but slightly weaker results if the governance variables are included in Eq. (2).

29

5.3.2 Within-Firm Analysis
In this section, we perform additional tests to examine temporal changes in loan
contracting terms before and after the launching of SPEs. To do so, we identify the first-time
filing date of SPEs in 10-K by each SPE sponsor firm. We then require that the SPE firm
have at least one loan facility within the two years before and after its first-time SPE filing
via 10-K. We choose the loan facilities initiated around the SPE firms’ first-time filing ofSPEs because it provides the most powerful setting for investigating the potential effect of
SPE use on temporal changes in loan terms. After applying the above selection procedure, weconstruct a sample of 2,343 loan facilities borrowed by 541 SPE firms over the four-year
period surrounding their first-time SPE filing dates. The test variable of this temporal
analysis is AfterSPE, an indicator variable that equals one if the loan facility is initiated after
the borrower files SPEs for the first time and zero if initiated before.
Using this reduced sample, we regress loan terms on AfterSPE and a set of control
variables. If banks perceive the use of SPEs as a credit-risk increasing factor and, thus, adjust
various loan terms accordingly, we expect to observe a significant coefficient on AfterSPE.
The estimated results are reported in Panel C of Table 6. Columns (1) and (2) show that theestimated coefficient on AfterSPE is significantly and positively associated with Log AIS and
DSecu, suggesting that loans issued after borrowers start using SPEs tend to have higher
interest rates and are more likely to have collateral requirements, compared with loans issued
before the use of SPEs. We find, however, that the coefficients on AfterSPE are insignificant
when DFinCov or DGenCov is used as the dependent variable.
5.4 Additional Analyses

30

5.4.1 Controlling for Borrower Credit Ratings
When credit rating agencies and bond analysts evaluate firm credit risk, they evaluate
both on and off balance sheet liabilities (e.g., Moody’s 2006; Standard & Poor’s 2006; Kraft
2009) and, thus, credit ratings should reflect the evaluation of the off balance sheet liability
and default risk of the rated firms. In this section, we include Standard & Poor’s long-term
issuer credit rating (Rating)22 in Eq. (1) to better control for the default risk and off balance
sheet liability of SPE sponsor firms. To some extent, the inclusion of Rating allows us toseparate the effect of SPE usage on information risk from its effect on default risk. Table 7
presents the estimated results for Eq. (1), with Rating included as an additional control
variable. Note here that our sample size drops by about a half. Table 7 shows that thecoefficient on our measure of SPE usage remains positive and significant at conventional
levels, even after controlling default risk proxied by the sponsor firms’ credit ratings. This
result lends further support to the view that banks and other private lenders perceive SPE
usage to be a factor that increases information risk.
[INSERT TABLE 7 HERE]
5.4.2 Effect of SPE Usage on Earnings Restatement and Future Default
As discussed earlier, SPE usage may affect both the default and information risks ofsponsor firms. SPE usage facilitates managerial opportunism in financial reporting andreduces the quality of financial reports. As a result, outside capital suppliers are likely to facehigher information risk when evaluating the default risk of SPE sponsor firms and, thus,
charge higher loan rates and/or impose more stringent non-price terms to make up for bearing
the information risk. As for default risk, SPE usage may lower or increase it, depending on

22

Credit ratings are transformed into a numerical value (Rating) ranging from one to 22. A smaller value for
Rating indicates a better credit rating (e.g., one for AAA and two for AA+).

31

whether the economic benefits of SPE usage dominate or are dominated by the associatedcosts. Lemmon et al. (2014) find no evidence of any deterioration in credit quality after the
initiation of securitization programs.
To further examine the mechanisms through which SPE usage affects loan
contracting, we test whether SPEs are associated with opaque financial reporting represented
by accounting restatement and future credit default. Following Dechow et al. (2011), we run
a probit regression to estimate the effect of SPE usage on the likelihood of future accounting
restatements. Panel A of Table 8 reports the estimated results.23 As shown in Panel A, wefind that the coefficient on SPE usage in the probit regression is positive and highly
significant at less than the 1% level. This finding suggests that SPE use is positively
associated with the likelihood of future misstatements of financial reports, suggesting that
SPE usage increases the opacity of financial reports. This result lends further support to ourargument that SPEs are used as a tool for opportunistic earnings management.
[INSERT TABLE 8 HERE]
Second, we examine whether our measure of SPE usage can predict the actual
occurrence of credit defaults observed for our sample firms. If SPE usage is only associatedwith the sponsor’s default risk, it should be a significant predictor of actual defaults orbankruptcies. Following the recent bankruptcy prediction literature (Chava and Jarrow 2004;
Chava, Livdan, and Purnanandam 2009), we estimate the Cox (1972) proportional hazard
model to assess the predictive power of SPE usage with respect to actual defaults for thesponsor firms in our sample. The data on actual default events are obtained from Moody’s
Corporate Default Risk Service. Panel B of Table 8 reports the estimated results for the23

We use Accounting and Auditing Enforcement Releases issued by the SEC against the company to identify
firms that misstate their earnings.

32

hazard model of default prediction. We find that the coefficient on Log NSPE is insignificant.
The finding suggests that SPE use does not have the ability to predict the occurrence ofactual defaults or default risk.
In short, the use of SPEs can predict the likelihood of financial restatement in the
future, but it does not have the predictive ability with respect to actual defaults. The results
reported in both Panels A and B of Table 8, taken together, suggest that the use of SPEs is
associated more closely with information risk of the sponsor firm rather than its default risk.
5.4.3 Asset Securitization
Following Feng et al. (2009), our main measure of SPE usage is a count of the limited
partnerships, limited liability partnerships, limited liability companies, and trusts included in
the list of subsidiaries and affiliates reported in Exhibit 21 of 10Ks. The merit of this measureis that it incorporates all types of SPEs. However, it is also possible that this measure does
not capture the real underlying SPE activities carried out by the firms. In this section, we
examine whether and how the existence of a specific and common type of SPE, that is, asset
securitization, affects loan contracting, using the data made available by Lemmon et al. (2014)
from the sample period 1996–2009. 24
Specifically, in Eq. (1), we replace our SPE measure (Log NSPE) with ABSDUM, that
is, an indicator variable that equals one if the firm reports an on-going asset securitization
program in its 10K and zero otherwise. At the same time, we adjust the leverage ratio byadding the amount of SPE borrowings disclosed in the 10K to both the numerator anddenominator if the SPE is unconsolidated, 25 essentially treating the securitization as

24

We thank the authors Lemmon, Liu, Mao, and Nini for providing the data on their website.
By including a consolidation dummy in Eq. (1), we also find that the negative consequence of securitization
on loan interest rates and collateral requirements is mitigated if the SPE is consolidated. The consolidation
dummy takes the value of one if the borrower mentions in the 10K that the SPE is consolidated and zero

25

33

collateralized borrowings instead of sales of receivables.26 To address potential endogeneitywith respect to asset securitization, in the first stage we run a probit model to estimate thepropensity score of a firm’s decision to initiate a securitization program, using the same setof predictor variables as Lemmon et al. (2014). Then we match each treatment firm (withsecuritization) with the control firm (without securitization) that has the closest propensity
score.
Panel A of Table 9 reports the estimated results for the first-stage probit regression.
Panel A shows that similar to the results reported in Lemmon et al. (2014), the likelihood of a
firm engaging in asset securitization is higher for larger firms, firms with larger accountreceivables (AR) or higher leverage (Leverage), and firms with BB+ credit rating or below(BB). Panels B and C present the estimated results for Eq. (1), using the full sample and thePSM sample, respectively. As shown in Panel B, the full-sample results reveal that asset
securitization is positively associated with loan spread, collateral requirements, and financial
covenant restrictions at less than the 1% level. The results using the PSM sample in Panel C
show essentially the same results as those reported in Panel B, which buttresses our fullsample results.
[INSERT TABLE 9 HERE]
6. Conclusions
In this study, we examine the effect of SPE usage on various terms of loan contracts.
Often SPEs are established to serve the economic purposes of isolating financial risk,
accessing segmented capital markets, reducing finance costs, and maximizing tax benefits.
otherwise. However, such information is not very reliable. Out of 523 firms that reported securitization in their
10Ks, only 34 of them mentioned that the SPE was consolidated. In addition, due to accounting loopholes, firms
can easily avoid consolidating their SPEs, making the consolidation decision a self-selected choice.
26
Prior studies suggest that off-balance-sheet debt related to securitizations is treated as secured borrowings byequity market participants (Landsman, Peasnell, and Shakespeare 2008; Niu and Richardson 2010).

34

Since the collapse of Enron, the public has begun to pay close attention to the dark side ofSPEs. The Enron investigation revealed that SPEs were one of the main tools used by Enron
executives to hide losses and unfavorable performance. Nevertheless, there is a lack ofgeneral evidence on the broader issue of whether and how SPE use is perceived by market
participants when contracting with the sponsor firms that use SPEs. A recent study byLemmon et al. (2014) shows that the equity market reacts positively to the initiation of asset
securitization and the bond market does not generate a significant reaction to such events.
Their findings suggest that asset securitization provides a beneficial form of externalfinancing to equity holders without hurting debt holders. Our study aims to provide largesample, systematic evidence on the effect of SPEs on both the price and non-price terms ofbank loan contracts. Specifically, our study focuses on examining the consequences of
information risk associated with SPE usage, which is not considered by Lemmon et al.
(2014).
We empirically show that banks and other private lenders perceive SPEs as asignificant factor that increases credit risk. Our results show that firms with SPEs tend to pay
higher loan interest rates and are more likely to have their loans secured by collateral or
subject to restrictive covenants. In an attempt to show some direct evidence on the impact of
SPE usage on information risk, we further examine the relation between SPE usage and loan
contracting terms conditional on CEO pay–performance sensitivity and the existence of aprior lending relationship. We find that the negative effect of SPE usage on bank loan terms
is more pronounced for SPE sponsor firms with higher CEO pay–performance sensitivity andno prior lending relationship with the lenders. This finding suggests that SPE usage is35

associated with higher information risk, which in turn leads to higher loan interest rates andunfavorable non-price terms.
One of the limitations of our study lies in the challenge of distinguishing the effect of
SPE usage on default risk from that on information risk. In an effort to provide direct
evidence on SPE usage as an information risk-increasing factor, besides the subsample
analyses, we also include the credit rating variable as an additional control variable in the
regressions. Moreover, we show evidence that SPE usage is linked to future accounting
restatements, but not future credit default. We admit, however, that none of these tests canperfectly separate the effect of SPE use on information risk from that on default risk. The
other limitation is that, to better evaluate the economic benefits and costs of using SPEs, weshould also examine the financing costs of the entity in its totality, that is, the sponsor plus
the SPE. However, due to data unavailability, we can only show that SPE usage increases
loan contracting costs for the sponsor firms.

36

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Appendix. Variable Definitions
Test variables
NSPE
Log NSPE
AfterSPEThe number of SPEs used by the borrower.
The natural log of one plus NSPE.
An indicator variable that equals one when the loan facility is initiated after
the borrower starts using SPEs and zero otherwise.
Loan-specific variables
The drawn-all-in spread charged by the bank over the LIBOR for the drawn
AIS
portion of the loan facility, obtained from the DealScan database.
The natural log of AIS.
Log AISThe maturity of the loan in months.
Maturity
The natural log of Maturity.
Log Maturity
The amount of the loan facility in millions of dollars.
Loan Size
Log Loan Size The natural log of Loan Size.
Deal Size divided by the sum of Deal Size and the borrower’s total
Loan
Concentration liabilities.
The dollar amount of the loan deal in millions of dollars.
Deal Size
Log Deal Size The natural log of DealSize.
The number of banks in the loan deal.
NLenders
NDomLenders The number of domestic banks in the loan syndicate.
The number of foreign banks in the loan syndicate.
NForLenders
Log NLenders The natural log of NLenders.
An indicator variable that equals one if the loan contract includes
Performance
performance pricing provisions and zero otherwise.
Pricing
An indicator variable that equals one if the loan facility is a term loan andTerm Loanzero otherwise
An indicator variable that equals one if the loan is secured with collateralDSecu
and zero otherwise.
An indicator variable that equals one if the loan contract includes any
DFinCov
financial covenants and zero otherwise.
An indicator variable that equals one if the loan contract includes any
DGenCov
general covenants and zero otherwise.
An indicator variable that equals one if at least one of the lead arrangers for
Top Leadthe loan was a top 25 U.S. lead arranger (in terms of loan volume) in theyear of loan initiation, based on loan data from DealScan, and zerootherwise.
Loan Purpose A series of indicator variables for the purposes of loan facilities in
DealScan, including corporate purposes, debt repayment, working capital,
Indicators
CP backup, takeover, acquisition lines, and leverage buyout offers.
The natural log of the average AIS for loans initiated over the past six
Log AvgAIS
months.
Borrower-specific variables
Firm size, which is the natural log of total assets in millions of dollars.
Size
Leverage ratio, defined as long-term debt divided by total assets.
Leverage43

Market-to-book ratio, measured as the market value of equity plus the bookvalue of debt divided by total assets.
EBITDA divided by average total assets.
Profitability
Supply of internal funds, which is measured by the sum of cash flows from
Funds
operating activities and cash flows from investing activities divided byaverage total assets.
Net PP&E divided by total assets.
Tangibility
Interest coverage ratio, which is measured by the ratio of operating income
IntCov
after depreciation (before interest) to interest expenses.
The natural log of one plus IntCov.
Log IntCov
Ohlson’s (1980) O-score, where a larger O-Score implies a higher default
O-Scorerisk: O-score = –1.32 – 0.407 × log(total assets) + 6.03 × (total
liabilities/total assets) – 1.43 × (working capital/total assets) + 0.076 ×(current liabilities/current assets) – 1.72 × (1 if total liabilities > total assets,
0 otherwise) – 2.37 × (net income/total assets) – 1.83 × (operating income
before depreciation/total liabilities) + 0.285 × (1 if net income is negative
for the last two years, 0 otherwise) – 0.521 × ((net incomet – net incomet-1)/(|net incomet| + |net incomet-1|)).
An indicator variable that equals one if the borrower had a prior loan
Prior
relationship with at least one of the lead banks for the current loan deal in
the past five years and zero otherwise.
The absolute value of abnormal accruals obtained from the modified Jones
AbsAccrmodel (Dechow et al. 1995), considering accounting conservatism (Balland Shivakumar 2006).
Numerical transformation from Standard and Poor’s domestic long term
Ratingissuer credit rating, ranging from one to 22. A smaller value of Rating
indicates a better credit rating.
Macroeconomic variables
Difference in the yield between 10- and two-year U.S. Treasury bonds
Term Spreadmeasured one month before the loan becomes active, obtained from theFederal Reserve Board of Governors.
Credit Spread Difference in yield between BAA- and AAA-rated corporate bonds
measured one month before the loan becomes active, obtained from theFederal Reserve Board of Governors.
MB

44

Table 1: Sample Selection
Firms

Loan
facilities

6,292

32,108(1,593)(10,687)(567)(2,851)Loans to public companies available in DealScan from 1997
to 2008
Less:
Loans borrowed by companies without an identifiable
Exhibit 21 in their 10K filings
Loans borrowed by companies in the financial industry
Non-senior debts, bridge loans, bonds, letters of credit,
and other non–fund-based facilities
Observations missing necessary data items for tests(82)(1,520)(1,384)(5,962)

Total observations

2,666

11,088Notes: This table reports the sample selection procedure for our data during the period 1997–
2008.

45

Table 2: Descriptive Statistics
Panel A: Loan Characteristics
Variables
AIS (bps)
Maturity (months)
Loan Size (millions $)DSecu
Loan Concentration
DFinCov
DGenCov
Performance Pricing
NLenders
Term Loan

Mean
184.980
46.873371.965
0.542
0.340
0.735
0.740
0.569
9.506
0.285

1st quartile
75.00031.00050.000
0.000
0.163
0.000
0.000
0.000
3.000
0.000

Median175.000
57.000150.000
1.000
0.322
1.000
1.000
1.000
7.000
0.000

3rd quartile
250.000
60.000390.000
1.000
0.478
1.000
1.000
1.000
13.0001.000

Std. deviation
139.152
23.057840.927
0.498
0.210
0.441
0.439
0.495
10.117
0.451

3rd quartile
4.000
1.609
8.016
0.382
1.980
0.191
0.070
0.493
2.417
-5.8500.117
1.000

Std. deviation
57.876
1.300
1.810
0.220
1.132
0.082
0.164
0.235
1.274
2.134
1.962
0.499

Panel B: Borrowing Firm Characteristics
Variables
NSPE
Log NSPE
Size
LeverageMB
Profitability
Funds
Tangibility
Log IntCov
O-ScoreAbsAccrPrior

Mean
10.608
0.976
6.867
0.270
1.754
0.153
-0.0120.335
1.819-6.8820.122
0.463

1st quartile
0.000
0.000
5.613
0.109
1.152
0.099
-0.0530.144
1.086-8.0700.020
0.000Median
0.000
0.000
6.794
0.240
1.457
0.139
0.017
0.277
1.638
-6.9320.051
0.000

Notes: This table presents the descriptive statistics of the major variables. The sample period isfrom 1997 to 2008 and the sample consists of 11,088 observations of loan facilities. All variables
are as defined in the Appendix.

46

Table 3: Pearson Correlation Matrix
A

B

C

D

E

F

G

Log NSPEA

1.00

Log AIS

B

-0.11

1.00

DSecu

C

-0.10

0.59

1.00

DFinCov

D

-0.05

0.20

0.38

1.00

DGenCov

E

-0.03

0.17

0.38

0.82

1.00

Log Maturity

F

0.04

0.19

0.24

0.16

0.18

1.00

Log Loan Size

G

0.37

-0.49

-0.33

-0.13

-0.03

0.06

1.00

H

I

J

K

L

M

N

O

P

Q

R

Log NLenders

H

0.26

-0.31

-0.20

0.05

0.13

0.14

0.66

1.00

Performance Pricing

I

-0.02

-0.08

0.12

0.56

0.60

0.19

0.11

0.22

1.00

Size

J

0.43

-0.54

-0.42

-0.24

-0.14

-0.10

0.81

0.60

-0.02

1

Leverage

K

0.09

0.23

0.14

0.00

0.03

0.12

0.09

0.14

-0.04

0.09

1

MB

L

-0.04

-0.22

-0.10

-0.02

-0.02

-0.03

0.05

0.00

0.00

-0.01

-0.11

1

Profitability

M

-0.07

-0.23

-0.10

0.03

0.02

0.03

0.06

0.04

0.08

-0.07

-0.10

0.53

1

Funds

N

0.01

-0.15

-0.12

-0.03

-0.03

-0.04

0.03

0.00

-0.03

0.08

-0.15

0.08

0.14

1

Tangibility

O

0.06

-0.05

-0.07

-0.04

-0.03

-0.04

0.15

0.09

-0.01

0.18

0.23

-0.12

0.06

-0.06

1

Log IntCov

P

-0.03

-0.35

-0.21

0.01

-0.00

-0.01

0.06

0.01

0.10

-0.02

-0.52

0.40

0.58

0.13

-0.19

1

O-Score

Q

-0.05

0.38

0.22

0.01

0.01

-0.01

-0.17

-0.10

-0.12

-0.15

0.56

-0.23

-0.36

-0.09

0.11

-0.67

1

AbsAccr

R

-0.01

0.03

0.02

0.01

0.01

0.01

-0.02

-0.00

-0.02

-0.01

-0.01

0.00

-0.01

0.03

-0.02

-0.02

0.02

1

Prior

S

0.11

-0.16

-0.13

-0.07

-0.05

-0.05

0.24

0.22

0.01

0.25

0.05

0.00

0.00

-0.04

0.05

-0.02

-0.02

-0.01

S

1.00

Notes: This table reports the Pearson correlation matrix for the major variables used in our empirical tests. The sample period is from 1997 to 2008. All variables
are as defined in the Appendix. The correlations in boldface indicate significance at less than the 10% level in a two-tailed test.

47

Table 4: Number of SPEs and Loan Contracting
VARIABLES
Log NSPE
Log Maturity
Log Loan Size

(1)
Log AIS

(2)
DSecu

(3)
DFinCov(4)
DGenCov

0.039***(3.84)
0.111***(5.88)-0.054***(-6.39)0.062***(3.47)
0.185***(4.80)

0.096***(4.74)-0.100**(-2.09)0.063***(2.79)-0.094(-1.59)1.102***(4.76)-0.106**(-2.55)0.441***(10.17)0.405***(6.84)-0.262***(-5.53)0.521***(3.71)-0.035(-1.00)-0.792**(-2.51)-0.275**(-1.99)-0.330**(-2.23)-0.203***(-6.94)0.048***(2.97)
0.618***(3.65)-0.100***(-2.99)-0.010(-0.18)0.081(0.85)
1.410***(2.86)
Yes
Yes
Yes
11,0880.31-0.050(-0.30)0.214***(5.52)
1.863***(27.02)0.449***(9.59)-0.254***(-7.55)-0.088(-0.68)0.006(0.16)-0.118(-0.27)0.231(1.09)
0.106(0.65)-0.041(-1.50)0.009(0.75)
0.217(1.61)-0.105*(-1.65)-0.014(-0.18)0.119(1.05)
0.845*(1.95)
Yes
Yes
Yes
11,0880.42

0.466***(2.89)
0.226***(5.04)
2.043***(18.17)0.468***(9.00)-0.103***(-2.95)0.051(0.28)
0.027(0.81)-0.175(-0.39)0.309*(1.94)
0.023(0.14)-0.079***(-3.21)0.017(1.17)
0.508***(3.31)-0.097**(-2.27)-0.025(-0.39)-0.204(-1.44)-0.029(-0.07)
Yes
Yes
Yes
11,0880.44

Loan Concentration
Log NLenders
Performance Pricing
Term Loan
Size
LeverageMB
Profitability
Funds
Tangibility
Log IntCov
O-ScoreAbsAccrPrior
Term Spread
Credit Spread
Intercept
Loan Purpose Indicators
Year Indicators
Industry Indicators
No. of observations
Adj./Pseudo R2

0.003(0.20)-0.056***(-3.05)0.368***(15.08)-0.217***(-11.29)0.348***(5.52)-0.052***(-2.64)-0.688***(-3.31)-0.317***(-5.86)-0.254***(-3.96)-0.114***(-7.17)0.024***(3.59)
0.008***(7.68)-0.034(-1.48)0.086(1.54)
0.249**(2.52)
6.275***(43.71)
Yes
Yes
Yes
11,0880.60

Notes: This table presents the estimated results of the effect of SPE use on loan contracting terms. Column(1) is for an OLS regression and columns (2)–(4) are for probit regressions. All variables are as defined in
the Appendix. The t-statistics (z-statistics), in parentheses, are based on standard errors corrected forheteroscedasticity and firm- and year-level (two-dimensional) clustering. The superscripts *, **, and ***denote significance at the 10%, 5%, and 1% levels, respectively, in a two-tailed test.

48

Table 5: Number of SPEs and Contracting: Subsample Analyses
Panel A: CEO Delta Risk
Variable

Log NSPE

(1)
AIS
Low
(I)
0.014
(0.92)

Diff.
(II)-(I)
Control Variables
Loan Purpose Indicators
Year Indicators
Industry Indicators
N

Yes
Yes
Yes
Yes
2,767

(2)
DSecu
High
(II)

Low
(I)

High
(II)

Low
(I)

High
(II)

(4)
DGenCov
Low
High
(I)
(II)

0.062***(4.47)
0.048***(3.06)-0.008(-0.21)0.090***(2.74)
0.098**(2.00)

0.073**(2.43)

0.088**(2.21)

0.014(0.31)

Yes
Yes
Yes
Yes
2,779

Yes
Yes
Yes
Yes
2,767

Yes
Yes
Yes
Yes
2,779

Yes
Yes
Yes
Yes
2,767

Yes
Yes
Yes
Yes
2,779

Yes
Yes
Yes
Yes
2,767

Yes
Yes
Yes
Yes
2,779

Yes
(I)
0.036
(1.30)

No(II)
0.087***(4.22)
0.051**(1.95)

Yes
No
(I)
(II)
0.054***
0.137***(2.69)(4.16)
0.083**(2.39)

Yes
(I)
0.024
(0.77)

No(II)
0.103***(3.57)
0.079**(2.36)

Yes
Yes
Yes
Yes
5,131

Yes
Yes
Yes
Yes
5,957

Yes
Yes
Yes
Yes
5,131

Yes
Yes
Yes
Yes
5,131

Yes
Yes
Yes
Yes
5,957

Panel B: Prior Lending Relationship
Yes
No
(I)
(II)
Log NSPE
0.028*
0.045***(1.93)(5.03)
0.017
Diff.
(1.16)(II)-(I)
Control Variables
Loan Purpose Indicators
Year Indicators
Industry Indicators
N

Yes
Yes
Yes
Yes
5,131

Yes
Yes
Yes
Yes
5,957

(3)
DFinCov

0.015(0.34)

Yes
Yes
Yes
Yes
5,957

0.087**(2.05)
0.073*(1.81)

Notes: This table presents the estimated results of the effect of SPE use on loan contracting terms partitioned based on the median of CEO delta risk and whether
there is a prior lending relationship between the borrower and lender. Column (1) is for an OLS regression and columns (2)–(4) are for probit regressions. Allvariables are as defined in the Appendix. The t-statistics (z-statistics), in parentheses, are based on standard errors corrected for heteroscedasticity and firm- andyear-level (two-dimensional) clustering. The superscripts *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively, in a two-tailed test.

49

Table 6: Number of SPEs and Loan Contracting: PSM sample
Panel A: First Stage Probit Regression for PSM Method
Pr (DSPE= 1)
VARIABLES
Coefficients
LEV
0.707***
R_INTCOV
0.145***
BONUSP
0.126
DEBTISS
0.853***STOCKISS-0.687***
RISK-0.391***
FUNDS-0.004
CLTD-0.959***
TDUM
0.103
SETR
0.198
INTANGIBLE
1.856***FOREIGN
0.011
LMKTCAP
0.371***
INDPERC
3.280***
Constant-5.057***
Year Indicators
No. of observations
Pseudo R2
Panel B: PSM Sample
VARIABLES
Log NSPE

Control Variables
Loan Purpose Indicators
Year Indicators
Industry Indicators
No. of observations
Adj./Pseudo-R2

t-statistics(5.70)(5.83)(0.97)(4.02)(-3.59)(-2.82)(-0.02)(-2.69)(1.37)(0.61)(11.25)(0.18)(16.72)(10.03)(-7.20)Yes
22,747
0.207(1)
Log AIS0.030***(3.33)(2)DSecu
0.058**(2.21)(3)
DFinCov
0.110**(2.41)(4)
DGenCov
0.062(1.25)

Yes
Yes
Yes
Yes
4,318
0.68

Yes
Yes
Yes
Yes
4,318
0.37

Yes
Yes
Yes
Yes
4,318
0.53

Yes
Yes
Yes
Yes
4,318
0.61

(1)
Log AIS0.076***(2.64)(21.28)(2)
DSecu
0.204**(2.16)(4.90)(3)
DFinCov-0.181(-1.29)(0.51)(4)
DGenCov-0.116(-0.97)(1.98)

Yes
Yes
Yes
Yes
2,343
0.67

Yes
Yes
Yes
Yes
2,343
0.43

Yes
Yes
Yes
Yes
2,343
0.52

Yes
Yes
Yes
Yes
2,343
0.60

Panel C: Within Firm Analyses
VARIABLES
AfterSPE

Control Variables
Loan Purpose Indicators
Year Indicators
Industry Indicators
No. of observations
Adj./Pseudo-R2

Notes: This table presents the results of the effect of SPE use on loan contracting terms using the PSM
sample. Panel A reports the first-stage probit regression for predicting the likelihood of SPE formation.
Panel B reports the estimated results of the effect of SPE use on loan contracting terms using the PSM
sample. Panel C presents the estimated results of within-firm analysis using a sample of loan facilities
borrowed by SPE firms around their first-time filing of SPEs in a 10-K. The test variable AfterSPE is an50

indicator variable that equals one when the loan facility is initiated after the borrower starts using SPEs andzero otherwise. For Panels B and C, Column (1) is for an OLS regression and columns (2)–(4) are forprobit regressions. All variables are as defined in the Appendix. The t-statistics (z-statistics), in parentheses,
are based on standard errors corrected for heteroscedasticity and firm- and year-level (two-dimensional)
clustering. The superscripts *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively,
in a two-tailed test.

51

Table 7: Number of SPEs and Loan Contracting: Controlling for Credit Ratings
VARIABLES
Log NSPE
RatingControls
Loan Purpose Indicators
Year Indicators
Industry Indicators
No. of observations
Adj./Pseudo R2(1)
Log AIS

(2)
DSecu

(3)
DFinCov(4)
DGenCov

0.030***(3.25)
0.158***(27.41)0.039*(1.87)
0.303***(10.85)0.086***(4.24)
0.085***(6.49)

0.051**(1.99)
0.094***(6.97)

Yes
Yes
Yes
Yes
5,803
0.77

Yes
Yes
Yes
Yes
5,803
0.49

Yes
Yes
Yes
Yes
5,803
0.50

Yes
Yes
Yes
Yes
5,803
0.58

Notes: This table presents the estimated results of the effect of SPE use on loan contracting terms after
controlling for credit ratings. Column (1) is for an OLS regression and columns (2)–(4) are for probit
regressions. All variables are as defined in the Appendix. The t-statistics (z-statistics), in parentheses, arebased on standard errors corrected for heteroscedasticity and firm- and year-level (two-dimensional)
clustering. The superscripts *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively,
in a two-tailed test.

52

Table 8: The Impact of SPE Use on Accounting Restatement and Future Credit Default
Panel A: Accounting Restatement
VARIABLE
Log NSPEt
DARt
DROAt
DINVTtRRSTtISSUEtDCSALEt
PPE_ATt
Constant

Pr (MISSTATE = 1)
Estimator
0.362***
1.422*-0.911
3.195***-0.1720.699
0.218***-2.440***-5.092***

Year Indicators
No. of Observations
Pseudo R-Square
Panel B: Observed Defaults
VARIABLE
Log NSPEt
Log MktCap
LEVERAGE
Profitability
Z-ScoreTerm Spread
Credit Spreadz-statistics(5.31)(1.91)(-1.53)(3.94)(-0.61)(1.58)(3.33)(-5.31)(-10.68)
Yes
27,0790.067

Pr (DEFAULT = 1)
Estimator
0.039-0.091*1.534***-4.927***-0.047**-0.172
0.544

Year Indicators
Industry Indicators
No. of Observations
Pseudo R-Squarez-statistics(0.46)(-1.91)(6.18)(-2.80)(-2.14)(-0.93)(1.24)
Yes
Yes
6,676
0.082

Notes: This table reports the logistic regression results of the impact of SPE usage on accounting
misstatement and credit default. The sample period is from 1996 to 2007. In Panel A, MISSTATE is anindicator that takes the value of 1 for misstatement firm years and 0 otherwise; DAR is the change inaccounts receivable deflated by average total assets; DROA is the change in return on assets measured asincome before extraordinary item deflated by average total assets; DINVT is the change in inventory
deflated by average total assets; RRST is an accrual measure defined as the change in working capital plus
the change in noncash net operating assets and the change of net financial assets deflated by average total
assets; ISSUE is an indicator variable coded 1 if the firm issued securities during the year and 0 otherwise;
DCSALE is the percentage change in cash sales; PPE_AT is PPE deflated by total assets. In Panel B,
DEFAULT is an indicator that takes value of 1 if the firm experiences bankruptcy or defaults that do not
result in a bankruptcy filing and zero otherwise; Log MktCap is the natural log value of market
capitalization; Z-score is Altman’s (1968) Z-score. All the other variables are as defined in Appendix I. The
t-statistics reported in parentheses are based on standard errors clustered by both firm and time. The
superscripts *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively, in a two-tailed
test.

53

Table 9: Effect of Asset-backed Securitization on Loan Contracting
Panel A: First-Stage Probit Model
VARIABLES
Size
AR
Leverage
Leverage2
Rated
InvestGradeBB
DowngradeConstant

Pr(ABSDum = 1)
Coefficients
0.370***
1.316***
0.712*-0.3000.091
0.081
0.372***-0.057-4.525***

Year Indicators
Industry Indicators
No. of observations
Pseudo R2

z-statistics(9.35)(2.61)(1.79)(-0.79)(0.69)(0.53)(2.95)(-0.63)(-7.20)
Yes
Yes
6,255
0.23

Panel B: Full Sample
VARIABLES
ABSDumAdj. LeverageControls
Loan Purpose Indicators
Year Indicators
Industry Indicators
No. of observations
Adj./Pseudo R2(1)
Log AIS

(2)
DSecu

(3)
DFinCov(4)
DGenCov

0.165***(4.69)
0.357***(6.13)

0.207***(2.59)
0.540***(3.61)

0.204***(2.88)-0.073(-0.58)-0.003(-0.04)0.047(0.25)

Yes
Yes
Yes
Yes
11,088
0.60

Yes
Yes
Yes
Yes
11,088
0.31

Yes
Yes
Yes
Yes
11,088
0.42

Yes
Yes
Yes
Yes
11,088
0.44(1)
Log AIS

(2)
DSecu

(3)
DFinCov(4)
DGenCov

0.161***(4.26)
0.276**(2.07)

0.202*(1.76)
1.626***(2.68)

0.206**(2.26)
0.157(0.34)-0.061(-0.42)0.359(0.67)

Yes
Yes
Yes
Yes
1,390
0.70

Yes
Yes
Yes
Yes
1,390
0.46

Yes
Yes
Yes
Yes
1,390
0.58

Yes
Yes
Yes
Yes
1,390
0.66

Panel C: PSM Sample
VARIABLES
ABSDumAdj. LeverageControls
Loan Purpose Indicators
Year Indicators
Industry Indicators
No. of observations
Adj./Pseudo R2

54

Notes: This table presents the estimated results of the effect of asset securitization on loan contracting
terms. Panel A reports the estimated results of the first stage probit regression for predicting securitization.
Panel B reports the estimated results of Eq. (1) using full sample. Panel C reports the estimated results ofEq. (1) using the PSM sample. In panels B and C, column (1) is for an OLS regression and columns (2)–(4)are for probit regressions. ABSDum is an indicator that equals one if the borrower reports securitization inits 10K and zero otherwise. The determinants used in the first stage probit model to predict securitization
include: AR: account receivable divided by total assets; Rated: indicator variable that equals one if theborrower has an S&P Domestic Long-Term Issuer Credit Rating, and zero otherwise; InvestGrade:indicator variable that equals one if the borrower has an investment-grade S&P Domestic Long-TermIssuer Credit Rating (i.e., BBB- and above), and zero otherwise; BB: indicator variable that equals one if
the borrower’s S&P Domestic Long-Term Issuer Credit Rating is BB+, BB, or BB-, and zero otherwise;
Downgrade: indicator variable if the borrower’s current credit rating is worse than that last year. All othervariables are as defined in the Appendix. The t-statistics (z-statistics), in parentheses, are based on standard
errors corrected for heteroscedasticity and firm- and year-level (two-dimensional) clustering. The
superscripts *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively, in a two-tailed
test.

55