Why do Firms Issue Guaranteed Bonds?

Fang Chen, Jing-Zhi Huang, Zhenzhen Sun, Tong Yu∗

October 4, 2017

∗Chen is at the College of Business, University of New Haven. Huang is at the Smeal College of Business,Pennsylvania State University. Sun is at the School of Business, University of Massachusetts at Dartmouth.Yu is at the Car H. Lindner School of Business, University of Cincinnati. Emails: fchen@newhaven.edu,jxh56@psu.edu, zsun@umassd.edu, and tong.yu@uc.edu. We appreciate the comments from Van Son Lai,Mike Eriksen, and the participants of the International Finance and Banking Society 2015 Conference, the2015 Midwestern Finance Conference, and the 2017 Financial Management Association Meetings. All errorsare our own.

Why do Firms Issue Guaranteed Bonds?

Abstract

Corporates typically use affiliated firms as guarantors to issue guaranteed bonds, combining

external financing with “internal” credit enhancements. Our finding shows that issuers with

fewer tangible assets or lower credit rating are more likely to issue guaranteed bonds. More-

over, firms with more pronounced debt overhang problems and greater managerial agency

problems tend to issue guaranteed bonds. While on average the use of guarantees improves

bond ratings, it has little impact on bond initial yield spreads.

Keywords: Guarantee, Credit Enhancement, Corporate Bonds, Credit Rating, Corporate

Investments

1 Introduction

One of the latest developments in the corporate bond market is the emergence of guaranteed

corporate bonds, also known as credit enhanced corporate bonds. Guaranteed corporate

bonds accounted for about fourteen percent of newly issued corporate bonds in terms of the

issuance amount in 1993-2012. With the guarantee arrangement, guarantors make payments

to bond investors in case of issuer defaults. Partly owing to the recent financial crisis, the use

of credit enhancement has received substantial attention (e.g., Arora, Gandhi and Longstaff,

2012; Demyanyk and Van Hemert, 2011). The spotlight is nevertheless on the packaging of

credit enhancement with commercial loans, mortgage- and asset-backed securities, municipal

bonds, and sophisticated structured products.1 Little is known about the use of guarantees

in the corporate bond market.

Guarantees used in the corporate bond market are an interesting hybrid. Guaranteed

bonds are typically insured by third party guarantors, making them different from traditional

internal credit enhancement devices which improve bond rating by pledging collaterals, im-

posing covenants to restrict issuers’ activities or offering senior securities. Even so, since

guarantors, the third party “insuring” guaranteed bonds, are either parents or subsidiaries

of issuers, guarantees are neither an external arrangement to bond issuance.

Motivated by the fact that guaranteed bonds are typically issued by affiliated firms with

a trivial explicit guarantee cost, we introduce hypotheses that associate guaranteed bond

issuance with debt overhang and agency problem. It is well noted that financial constraints

limit corporates’ ability to borrow debt from investors.2 Guarantees improve the bond

creditworthiness and corporate financing ability. However, among variety of ways to enhance

the bond creditworthiness, it is not clear which factors were the driving force for firms to

choose guarantee. Given the unique feature of internal-arrangement insurance and trivial

1On the applications of credit enhancements in mortgage backed securities, see Ashcraft and Santos,2009; Griffin and Tang (2012); Arora, Gandhi and Longstaff (2012). Related to the applications of creditenhancement in municipal bonds, refer to Braswell, Nosari and Browning (1982), Kidwell, Sorensen andWachowicz (1987), Nanda and Singh (2004).

2For the literature regarding the effect of financial constraints, see Kaplan and Zingales (1997), Lamont,Polk and Saaa-Requejo (2001), and Baker, Stein and Wurgler (2003).

1

cost, we propose that the motivation of using guarantees in overcoming financial constraints

is in two folders. On one hand, under the assumption that the use of guarantees lowers

corporate required debt payments, we show that guaranteed bond issuance alleviates financial

constraints due to debt overhang by increasing firm equityholder value. On the other hand, in

a parent-subsidiaries context, agency problem stemming from misaligned incentives between

parent firms and the subsidiaries is regarded as one of the main reasons for the inefficiency

(Bolton and Scrafstein, 1998; Scharfstein and Stein, 2000; Ozbas and Scharfstein, 2010;

Holod, 2012). In an inefficient internal market, the capital allocation may not obey the rule

on project NPVs. Rather, firms undertake projects benefiting issuing companies but harmful

to affiliated guarantors.

The simple story leads to clear empirical predictions. First, firms with greater finan-

cial constraints are more likely to issue guaranteed bonds. Second, holding the firm rating

constant, firms with more pronounced debt overhang problem are more inclined to use guar-

antee. Third, firms with greater agency problems rising from the misaligned incentives

between the parent firms and the subsidiaries have higher odds of issuing guaranteed bonds

after considering the firm rating factor.

Empirical findings offer supports to the above predictions. While we do not find evidence

that the likelihood of using guarantee is significantly related to three conventionally used

financial constraint proxies, we show firms with poorer credit rating or less collateral are

more likely to use guarantee on bonds. This is in line with the financial constraint expla-

nation. Moreover, we find that firms with greater debt overhang problem are more likely

to issue guaranteed bonds. That is, firms’ debt overhang, measured by the current value of

bondholders’ rights to recoveries in default (Hennessy, 2004; Hennessy et al., 2007), is found

to be significantly positively associated with the odds of issuing guaranteed bonds. Further,

we present the evidence that guaranteed bond issuance may be a consequence of empire-

building of the parent firms or “corporate socialism” among the subsidiaries. Specifically,

firms with more severe agency problems featured with higher free high cash flow and lower

growth opportunities are more inclined to issue guarantee bonds.

2

We perform two sets of additional analyses regarding guaranteed bond issuance for ro-

bustness. In the first, we differentiate between firms issuing guaranteed bonds only and

firms alternatively issuing guaranteed and non-guaranteed bonds within a financial year.

The result of multinominal logistic analysis shows that debt overhang is the main driver

for issuing guaranteed bonds only as well as rotating between guarantee and non-guarantee

bonds. Interestingly, the agency problem plays a significant role in the firms’ odds in firms

issuing guaranteed bonds only, not in firms issuing both guaranteed and non-guaranteed

bonds. In the second analysis, we address the concern that COMPUSTAT data reflects the

consolidated financial information of corporates with a parent-subsidiary structure. We use

the minority interest to estimate the financial variables at the parent firm level and then

rerun the determinants analysis. Using the estimated income at parent firm level, we distin-

guish the operating firms from the financial holding firms. The result of the regressions with

estimated variables at parent firm level and the dummy variable for the operating firms is

consistent with that of the regression with the consolidated financial data.

Given the fact that guaranteed bonds are issued with alternative drives, one may wonder

whether the rating agency and the market have captured the incentives. We shed light on

this question by performing further analysis to test the sequential rating and yield effects

of guaranteed bond issuances. The result reveals that on average guarantee improves bond

ratings but has no effect on initial yield spreads. More interestingly, when we respectively

examine the individual effects of issuers’ financial constraints, debt overhang, and managerial

agency problems on the relation between guarantee use and bond yield spreads, we find that

guarantees used by bond issuers facing greater financial constraints or with more pronounced

debt overhang indeed reduce yield spreads, while guarantees used by bond issuers with more

severe managerial agency problems increase yield spreads. The offset of two effects provides

a plausible explanation for no effect on yield spreads from guarantees overall.

To summarize, guarantees enhance corporate debt capacity by allowing issuers to reach

collateral resources beyond corporate borders. Such resources, however, are from affiliated

firms. Our study focuses on the hybrid nature of guaranteed bonds. We show that, acting

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like a double-edged sword, guaranteed bonds have the advantage to expand corporate debt

capacity and alleviate debt overhang but its use may aggravate the agency conflict between

parent companies and subsidiaries. The findings add to the stream of research on the effi-

ciency of the internal capital market (Bolton and Scharfstein, 1998; Scharfstein and Stein,

2000; Ozbas and Scharftein, 2010).

The remainder of the paper is organized as follows. Section 2 provides the institutional

background of guarantees used in the corporate bond market and reviews the relevant liter-

ature. In Section 3, we introduce the hypotheses. Section 4 describes our sample and data

used in the empirical analysis. Section 5 presents empirical findings. Section 6 concludes.

2 Background

We begin this subsection with an example of a guaranteed corporate bond. On September

12, 2000, MGM Mirage issued a 10-year 8.5% senior bond using all its wholly owned do-

mestic subsidiaries to provide a guarantee. The aggregate par value of the bond issuance is

850,000,000 USD. The guarantee is an unsecured senior obligation of the guarantor. At the

time of bond issuance, MGM Mirage has a rating right below the investment grade: Ba1 by

Moody’s3 while the newly issued bond is rated at an investment grade, BBB- by S&P and

Baa3 by Moody’s.

The use of guarantees on newly issued bonds has increased substantially since its inception

in the early 1990s. Reported in Table 1, based on par value, the market share of the

guaranteed bonds in all US corporate bonds issued in a year rises from 1.4% in 1993 to 18%

in 2012. Guaranteed bond issues peaked in 2009, with 37% of US corporate bonds being

guaranteed. The aggregate par value of US corporate bonds during the period 1993-2012 is

roughly $17 trillion, of which 14% (i.e., $2.3 trillion) were issued with guarantees.

Based on the Mergent Fixed Income Securities database (Mergent FISD), guarantees

3Issuer ratings are assessed based on issuers’ ability to honor senior unsecured financial obligations andcontracts. Issuer ratings share the same scheme as corporate bonds. Based on Moody’s rating scheme, Baa3is the lowest investment grade.

4

are one of three types of credit enhancements for corporate bonds, in addition to bond

insurance and letters of credit. In terms of issuance amount, guaranteed bonds account

for over 96% of corporate bonds with credit enhancement in the period from 1993 to 2012.

Corporate bonds issued with bond insurance and letters of credit merely account for 3% and

less than 1%, respectively, of the market. Documented in Nanda and Singh (2004), bond

insurance is frequently used in the municipal bond market; roughly 50% of municipal bonds

are packaged with bond insurance. Nanda and Singh (2004) suggest that the tax-exempt

benefit of municipal bonds can be extended through third-party insurance, and is the main

reason for the frequent use of bond insurance in municipal bonds. Corporate bonds are not

eligible for tax exemption, thus tax benefit does not provide incentive to corporates to use

guarantee on bonds.4

The super majority of guarantors of guaranteed corporate bonds are either parent com-

panies or subsidiaries of bond issuers. A clear advantage of using internal resources from

affiliated firms to guarantee a corporate bond is its efficiency – making use of available corpo-

rate resources. While a parent firm and its subsidiaries are separate legal entities, guarantee

bounds their risk together. For example, without guarantees, if subsidiaries are in default,

subsidiary bondholders do not have any recourse to the parent companies unless the parent

companies are involved in some wrong-doing (Thomson, 1991). With a guarantee from the

parent company, the subsidiary debtholders have recourse to its parent guarantors should

the subsidiary default. In practice, most of the guarantees to the public issuers are provided

jointly by most or all of their domestic subsidiaries. The guarantees are normally senior

obligations of the guarantors, ranked equally with all other existing and future senior debt

of the guarantors in right of payment. It is also a common practice to contain the covenants

in the indenture to limit the payments of the guarantors, such as dividend payout, share

repurchase or making principle payment prior to the schedule, among other arrangements.

Consequently, this arrangement helps to reduce credit risk of guaranteed bonds.

Alternative types of credit enhancements differ in issuance expenses. In early years the

4Our sample only contains 4 bond issues backed by bank letter of credits, out of 11,226 corporate bondsissued with credit enhancements. As a result, we focus our comparison on bond guarantees and insurance.

5

parent/subsidiary guarantee was treated as an internal arrangement which requires a filing

to the Securities and Exchange Commission (SEC) (no such filing requirement for insurers

or banks when they offer credit enhancements). Nevertheless, the SEC filing expense for

guaranteed bonds was as trivial as a few thousand dollars. The requirement of filing was

abandoned by the SEC in 2003, thus no more filing expense is associated with guarantees.

The other direct floatation cost is the guarantee fee. As the internally arranged guarantees

are considered as an arm’s-length transaction, parents/subsidiaries guarantee fee is seldom

included in the contract. In contrast, the insurance fee is explicit and ranges from 0.5 to

2.0 percent of total debt (Cole and Officer, 1981). In summary, as an internal arrangement,

guarantees to corporate bonds virtually have no direct cost. This is different from bond

insurance and letters of credit, which typically involve explicit expenses. Nevertheless, as

indicated in the later analysis, the use of guarantees is not costless – indirect costs may arise.

Like the example discussed in the beginning of the subsection, credit ratings of guaranteed

bonds differ from those of bonds with insurance or letters of credit. At the time when

bonds are issued, bond insurers and letters of credit providers typically have an AAA rating.

Therefore bonds backed up by insurance or letters of credit have an AAA rating. By contrast,

guarantors and guaranteed bonds have their ratings varying from AAA as the highest to D

as the lowest.

Moreover, the interconnection between issuers and guarantors has a potential effect on

credit ratings of bonds. For example, if a parent rating is ‘CCC+’ or lower while a subsidiary

has a stand-alone credit rating of ‘B-’ or higher, the subsidiary’s final credit rating could

be lowered accordingly due to the concern of extraordinary negative intervention from the

parent firms. Guarantee strengthens the interconnection. When the subsidiary, acting as a

guarantor, is downgraded, the rating agency would subsequently reevaluate the bond and

lower the rating of the guaranteed bond. The liability entails the default risk to the guarantor.

For example, Vitro, the largest manufacturer of glass containers and flat glass in Mexico,

suffered a dramatic decline in operating income as a result of the global economic and

financial crisis that began in 2008. The decline in its operating income caused Vitro to

6

default on certain financial obligations, including $1.216 billion in outstanding notes. Those

notes were unsecured, but guaranteed by certain subsidiaries of Vitro, including some located

in the United States. The default caused the involuntary bankruptcy of its U.S. subsidiaries

(Porzecanshi, 2011).

3 Hypothesis Development

When firms have free access to capital, guarantees and credit enhancements could play a

little role in the corporate bond market and they would not be introduced in the first place.

The real market, nonetheless, is far from being perfect. As noted in the existing literature,

financial constraints (Kaplan and Zingales, 1997; Lamont, Polk and Saaa-Requejo, 2001;

and Baker, Stein and Wurgler, 2003) and debt overhang (e.g., Hennessy, 2004; Hennessy

et al., 2007) are major detriments to corporate financing activities and their investments.

By issuing guaranteed bonds, firms internalize external funding activities. Therefore, the

choices between guaranteed and non-guaranteed bonds, therefore, reflect corporate status

quo prior to bond issuance.

First consider the effect of financial constraints on the issuance of guaranteed bonds.

Consider a financially constrained firm has a positive NPV project. The project’s NPV is q

(> 0) before adjusting the funding cost. Owing to financial constraints, the firm relies on

capital infusion to materialize the investment opportunity. There is a wedge between the

cost of internal capital and that of external capital, ci and ce respectively. The value of the

project, q, is between ci and ce. That is, ci < q < ce. Clearly, the firm would invest in the

project when it has internal capital but it has to forgo the opportunities when it has to raise

capital externally.

Using subsidiaries as guarantors, corporates issue guaranteed bonds to lower the external

capital cost thus undertake the positive NPV project. Consequently, we expect that the

greater financial constraints, the stronger incentive of the firm to use guarantees. This gives

rise to the first hypothesis.

7

H1. Firms with more financial constraints are more likely to issue bonds with guarantees,

all else being equal.

An alternative driver for a firm to forgo a positive NPV project is the so-called debt

overhang problem. First suggested by Myers (1977), equityholders could forgo the positive

NPV project if the return of such investment goes to bondholders. Note that debt overhang

is harmful to both bondholders and equityholders because when rationally expecting equi-

tyholders to have the underinvestment incentive, potential bondholders would be reluctant

to lend their money or charge a high cost of debt at issuance. Myers (1977) suggests various

remedies, such as including protective covenants to restrict equityholders’ activities in order

to alleviate such a debt overhang problem. In spite of variations, the essential purpose of

the proposed remedies renders equityholders less likely to forgo positive investment projects.

Aligned with this idea, Stulz and Johnson (1985) suggest that firms may issue secured bonds

to reduce the debt overhang problem in the sense that because secured debt has collateral,

secured creditors rely less on the new investments than unsecured creditors when firms are

at default, thus equityholders are less likely to forgo the investment opportunity. Alterna-

tively, Mayers and Smith (1987) suggest to include a bond covenant that requires the issuer

to purchase protective coverage. The protection pays off the loss or makes up the cash flow

shortfall, reducing the expected downside loss of the project. This results in greater incentive

for equityholders to invest in the positive NPV project.

Issuing guaranteed bonds potentially combines the benefits of secured bond issuance and

protective coverage. When the parent defaults, the bond guarantors hold the responsibility to

make promised payments – This makes a guaranteed bond like a “secured” bond. Guarantees

from affiliated firms also act as if a standby insurance to the bond issuer. Thus, we expect

that firms issue guaranteed bonds to address the debt over hang problem. This gives rise to

the second hypothesis.

H2. Firms with more pronounced debt overhang problem are more likely to use guarantees

on bonds, all else being equal.

8

Despite that there is no or little explicit fee for the the guarantee provided by affiliated

firms, the use of guaranteed bonds to overcome financial constraints is not completely free of

costs, such as that the internal arrangement of guaranteed bonds may restrict guarantors to

issue new bonds. Especially in the parent-subsidiary context of guarantee, the incentive of a

parent firm may be misaligned with that of subsidiaries, regardless of the cost of guarantee

issuance to subsidiaries. As a result, the allocation of internal resources in the form of a

guarantee may not follow the investment opportunities. Instead, the parent company could

use the guarantee for an empire-building purpose5.

When there are financial constraints for conventional bond issuance and the implicit cost

of guarantee is not the concern of the parent company due to the agency conflict, guarantee is

a preferred way to raise capital even when the benefit from the new investment opportunity

is negative. This reasoning results in a link between corporate agency conflicts and incentive

to issue guaranteed bonds. This leads to our third hypothesis.

H3. Firms with greater agency problem are more likely to issue guaranteed bonds, all else

being equal.

Finally we propose a hypothesis related to yield spreads of guaranteed bonds. For firms

facing financial constraints from default risk, as the use of internal guarantees reduces the

default risk and thus mitigates constraints, yield spreads of guaranteed bonds are naturally

lower than those of non-guaranteed bonds, all else being equal.

H4a. Guaranteed bonds have a lower yield spread than non-guaranteed bonds when issuers

face financial constraints, all else being equal.

The debt overhang is an internal constraints to the investment due to the conflict be-

5For example, Bolton and Scharfstein (1998) point out that allocating capital to divisions with oppor-tunities aligns with parents’ empire-building preference. Scharfstein and Stein (2000) argue that a two-tieragency problem, stemming from misaligned incentives at parents and at divisions, is necessary for “corporatesocialism” in internal capital allocation. Ozbas and Scharfstein (2010) find that the ownership stakes of topmanagement have a positive relation with the extent of Q-sensitivity differences, suggesting that agencyproblem leads to the inefficient capital market.

9

tween bondholders and equityholders. Guarantees alleviate the debt overhang problem by

increasing the equityholders value and therefore increase the chance of investing on positive

NPV projects. The higher cost from debt overhang, the higher benefit from guarantee and

the lower yield spread is required by rational bond investors.

H4b. Guaranteed bonds have a lower yield spread than non-guaranteed bonds when issuers

have more debt overhang, all else being equal.

However, under the agency conflict scenario, the purpose of issuing guarantee bonds to

overcome the financial constraints is mainly for empire building. A rational bond investor

may require higher yield due to the risk from the misalignment of the growth opportunities

and guarantee bond issuance.

H4c. Guaranteed bonds have a higher yield spread than non-guaranteed bonds when is-

suers have greater agency problem, all else being equal.

Taken together, the combined effect of guarantees on debt value can be positive or neg-

ative which remains as an empirical question.

4 Data

4.1 Sample

This study utilizes the Mergent FISD database which provides issues and issuers information

for bonds issued by both public and private firms. We select U.S. corporate bonds including

U.S. corporate debenture, corporate midterm notes (MTNs), asset-backed securities and

other corporate bonds issued by U.S. firms. Mergent FISD contains information on whether

corporate bonds were issued with any type of credit enhancements: (i) guarantees, (ii) letters

of credit (LOC) and (iii) bond insurance.

Our sample ranges from 1993 to 2012. Among a total 123,034 corporate bonds, 11,226

corporate bonds were issued with credit enhancements. Specifically, there were 11,551 guar-

10

anteed bond issues, 441 issues with bond insurance, and 4 issues backed by bank LOC. We

identify the guarantors of the issues with guarantees via two ways. First, Mergent FISD

directly lists most guarantors as “Subsidiaries”. Second, the database lists the parent of the

issuers. If a parent’s identification number matches a guarantor’s identification number, the

bond was guaranteed by the parent firm of the issuer. If guaranteed bonds have no infor-

mation on guarantors, we manually search issuers’ SEC filings. The guarantors’ information

is disclosed in the 424B, S-4, 8-k, 10-Q or 10-K. This study investigates the public issuers

only. We restrict our sample to the issuers that are listed at the time of bond issuance by

matching the CUSIP of the issuers in FISD with that in the Center for Research in Security

Prices (CRSP) database.

Consistent with prior studies, e.g., Custdio, Ferreira, and Laureano (2013) and Becker

and Josephson (2016), we exclude financial firms (SIC codes 6000-6999) and regulated utility

firms (SIC codes 4900-4999) because they have significantly different capital structures and

are subject to different regulations. We further exclude bonds issued with bond insurance or

letters of credit (LOCs) to focus on the use of guarantee. There are 8,797 corporate bonds

issued by public firms, of which 825 are guaranteed bonds.

To facilitate the analysis, we remove bonds when issuers’ total assets, firm ratings and

bond ratings are not available. We measure firm ratings using the most recent S&P long

term issuer credit ratings right before bond issuance (Frating(AAA=26,D=1)) in Compustat,

also in line with prior studies such as Norden and Kampen (2013), Alp (2013), and Baghai,

Servaes and Tamayo (2014). We obtain bond ratings at issuance from Mergent FISD. As

there are ratings from multiple rating agencies, we use the S&P rating if it is available for a

specific bond. In case the S&P rating is missing in Mergent FISD, we alternatively use the

Moody’s rating. If both S&P and Moody’s ratings are missing, we use the Fitch’s rating.

The highest rating number is 26 (equivalent to S&P’s AAA) and the lowest rating number

is 1. Our treatments result in a final sample consisting of 7,201 corporate bonds, of which

731 bonds with guarantees (see Figure 2).

Since all guaranteed bonds are guaranteed by either the subsidiaries or the parent firms,

11

to run a fair comparison, we require non-guaranteed bonds in our sample to hold similar

corporate structures. We use Capital IQ to search relevant information manually. Capital

IQ lists the parent firms and the subsidiaries in its corporate tree section. In the final

sample, 5,949 bonds were issued by firms which have parent firms or subsidiaries, of which

647 bonds were packed with guarantees. This is the subsample used in the regressions of

the determinants of guarantee use. Figure 2 provides a detailed description of the number

of bonds with guarantee issued by publicly listed companies in the sample.

We obtain accounting data from Compustat, stock return data from CRSP, and bond

issuance data from Mergent FISD. From FISD, we obtain the bond issuance information

such as time to maturity, initial bond yield spread, bond ratings, the indicators for callable

bonds, putable bonds, and secured bonds, and so on. Then we merge sample bonds with

the Compustat data by the issuer ID. We obtain firm variables from the Compustat data

including firm rating, total assets, operating income, tangible assets, sales, age, dividend,

debt, cash flow and free cash flow. The accounting variables are at the fiscal year end before

the debt issuance.

As Compustat provides the financial data from the consolidated statements, the financial

variables are measured at the group level except the issuer’s firm rating from S&P. When

S&P assesses the credit rating of a firm within a parent-subsidiary structure, S&P considers

the firm’s stand-alone credit profile and the support or intervention from other firms within

the group (see Standard & Poor’s, 2013). We consider having no financial variables available

at the parent firms level as a limit of our data when we run the regressions on the parent

firms.6 In order to examine the validation of our test, we use the minority interest method to

approximately estimate the financial variables in the parent firms only and run a robustness

check.

6We also checked FactSet, one database available to academics that includes information for both parentcompanies and subsidiaries. Unfortunately, FactSet has very little financial data on subsidiaries available.We find that data are available only for those subsidiaries that used to public and only for the period whenthey were public.

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4.2 Summary Statistics

In Table 1, we present the distribution of guaranteed bonds by both public and private firms

in Mergent FISD. It shows that the fraction of guaranteed bonds in Mergent FISD is about

14% (9%) in terms of the aggregate par value of bonds (the number of bonds). Focusing on

the public firms, the percentage of guaranteed bonds issued by public firms is 9% (8%) in

terms of the aggregate par value (number) of bonds. The fraction of aggregate par value of

bonds issued by public firms ranges between 1.19% in 1994 and 24% in 2009.

Figure 3 presents the percentage of guaranteed bonds in terms of aggregate issuance

amount over time. Shown in Panel A, the percentage of guaranteed bonds issued by all firms

increases over time and peaks in 2009. Panel B shows that the percentage of guaranteed

bonds issued by public firms varies and peaks in 2009 as well. This finding is consistent with

that in Table 1.

In Table 2, we provide the summary statistics of guaranteed bonds and non-guaranteed

bonds as well as their issuers’ characteristics. In both panels A (for non-guaranteed bonds)

and B (for guaranteed bonds) of the table, the first three columns report the distributions

(mean, median and standard deviation) of bond issuer and issue attributes, under the heading

“All”. The average firm’s credit rating (16.13, corresponding to S&P’s BB+, relative to 26

for an AAA rating) of guaranteed bonds is lower than that of non-guaranteed bonds (17.80,

corresponding to S&P’s BBB). Given the fact that the threshold point between investment

grade bonds and speculative grade bonds is BBB- by S&P (the numeric rating of 17.00),

the mean firm rating for guaranteed bonds is right below the threshold and the mean firm

rating for non-guaranteed bonds is right above. Not tabulated, we find the correlation

between rating numbers and a dummy variable of whether a guaranteed is used to be -0.15,

suggesting that, without considering issuer and bond characteristics, the average rating of

guaranteed bonds is unconditionally lower than that of non-guaranteed bonds.

Besides issuer rating, it is also shown in the table that the guaranteed bond issuers have

slightly lower Tobin’s Q (1.97) than non-guaranteed bond issuers (2.07). Guaranteed bond

issuers also make less profit, borrow more debt, and hold fewer assets than non-guaranteed

13

bond issuers. Taken together, the comparisons of issuer characteristics reveal that guaranteed

bond issuers unconditionally have a poorer growth prospect and worse business condition

than non-guaranteed bond issuers.

The comparisons of bond characteristics are also reported in Table 2. The average yield

spread of guaranteed bonds (313.62 basis points) is much larger than that of non-guaranteed

bonds (215.83 basis points). Interestingly, guaranteed bonds typically have larger par value

and shorter time to maturity than non-guaranteed bonds.

Next, we take a close look of the distribution of guaranteed and non-guaranteed bonds

by dividing them into eight sub-groups based on the issuers’ ratings: AAA, AA, A, BBB,

BB, B, CCC and others. We compute the percentage of the aggregate par value in each

issuer’s rating for guaranteed bonds and non-guaranteed bonds respectively. This is plotted

in Figure 4 – the majority of guaranteed bonds were issued by firms with the ratings of BBB

or BB while most of the non-guaranteed bonds were issued by firms with the ratings of A or

BBB.

We also report, in Table 2, the result of univariate comparisons for the variables between

guaranteed bonds and non-guaranteed bonds in the above four firm rating groups. The

comparison of all variables between guaranteed bonds (issuers) and non-guaranteed bond

(issuers) in each rating group is consistent with the overall comparison reported in the first

three columns. Nevertheless, the magnitude of the difference between the guaranteed bonds

and the non-guaranteed bonds is dynamic in each firm rating group. The mean Tobin’s Q

of guaranteed bond issuers is 2.09 and that of non-guaranteed bond issuers is 2.49 in the

AAA-A group, while it is 1.78 for both guaranteed bond issuers and non-guaranteed bond

issuers in the B-CC group. In the AAA-A group, the mean yield spread of guaranteed bond

issuers is 162.52 basis points (bps) and that of non-guaranteed bonds issuers is 113.19 bps ,

while in the B-CC group, the mean yield spread is 589.50 bps for guaranteed bond issuers

and 479.39 bps for non-guaranteed bond issuers.

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4.3 Regression Variables

Now, we discuss all the variables to be used in hypothesis tests conducted in Section 5. To

be specific, in order to examine the effect of financial constraints on corporate guarantee

use (the first hypothesis), we include the following variables in panel regressions. The main

dependent variable is GT, an indicator variable equal to one if it is issued with a guarantee

and zero otherwise. The main independent variables include financial constraint measures,

firm characteristics and bond characteristics. Financial constraint measures are: the KZ

index (KZ ), the WW index (WW ), the SA index (SA), Tangible (Tangible) and issuer

credit rating (Frating). We follow Lamont, Polk, and Sa-Requejo (2001) to construct the

KZ index (Kaplan and Zingales, 1997) as follows:

KZ = −1.001909 ∗ Cash Flow/K + 0.2826389 ∗ Tobin’s Q (1)

+3.139193 ∗ Debt/Total Capital − 39.3678 ∗ Dividends/K

−1.314759 ∗ Cash/K

where Cash Flow is computed as (Income Before Extraordinary Item + Total Depreciation

and Amortization), K as PP&E, Tobins’ Q as (Market Capitalization + Total Shareholder’s

Equity - Book Value of Common Equity - Deferred Tax) / Total Asset, Debt as (Total Long

Term Debt + Current Portion of Long Term Debt), Total Capital as (Total Long Term Debt

+ Current Portion of Long Term Debt + Total Shareholder’s Equity), Dividends as (Total

Cash Dividends Paid), Cash as (Cash + Short-Term Investment).

Following Whited and Wu (2006), the WW index is computed as

WW = −0.091 ∗ CF − 0.062 ∗ DIVPOS + 0.021 ∗ TLTD (2)

−0.044 ∗ LNTA + 0.102 ∗ ISG − 0.035 ∗ SG

where CF is the ratio of cash flow to total assets, DIVPOS is an indicator that equals one

if a firm pays cash dividend and 0 otherwise, TLTD is the ratio of long-term debt to total

assets, LNTA is the natural logarithm of total assets, ISG is a firm’s industry sales growth,

and SG is a firm’s sales growth.

15

The SA index is computed using the following formula as in Hadlock and Pierce (2010):

SA = −0.737 ∗ Size + 0.043 ∗ Size2 − 0.040 ∗ Age (3)

where Size is the natural logarithm of inflation-adjusted total assets, Age is the number of

years the firm is listed in Compustat.

Another financial constraint proxy, Tangible is measured by the ratio of a firm’s property,

plant and equipment to its total assets. It is a proxy for collateral to increase the credit-

worthiness of bonds. Finally, Frating is the most recent S&P long term issuer rating before

bond issuance. We set a numerical score 26 for AAA rated bonds and 1 for bonds receicing

a rating D.

Firms with higher KZ, higher WW, higher SA, lower Tangible and lower Frating are more

likely to have financial constraints than firms with lower KZ, lower WW, lower SA, higher

Tangible and higher Frating under tightened financial conditions. In unreported results, we

find that five constraint measures are highly correlated with each other. For example, the

rank correlation between size and the WW index, SA Index are -0.25 and -0.46 respectively.

Although these proxies may be picking up similar information, it nonetheless appears that

each measure picks up some unique information as well. KZ, WW and SA place a heavy

weight on the firms’ ability of internal funding capacity while Tangible and Frating primarily

focus on the creditworthiness. We thus use each of the alternative measures as financial

constraints.

Firms’ characteristics variables include Size, Profit, FCF and Dividend and bond char-

acteristics variables include MktYld, Term, Par, Call, and Put. MktYld is the Moody’s yield

spread of corporate bonds (the average monthly yield of Moody’s AAA and Baa bonds) over

corresponding treasuries before issuance. It is used to control for the timing of bond issuance

by a firm. The lower of the average yield of in the bond market (i.e., lower debt financing

cost), the more likely firms are to use guarantees to increase their debt capacity. Term is

the logarithm of the bond’s time to maturity in years. Par is measured as the logarithm of

the total offering amount in millions of dollars. Call (Put) is the indicator for a bond equal

to one if the bond has a call (put) provision. A larger par (Par), a longer time to maturity

16

(Term) or a call option (Call) increase the risk for investors and the need for guarantee as

credit enhancement. A put option (Put) decreases the risk for investors.

The under-investment hypothesis (the second hypothesis) implies that the debt overhang

problem is an obstruction to new debt issuance and leads to under-investment. Guarantee

can be used to mitigate the under-investment problem. The main independent variable

used in the test of the effect of under-investments on corporate guarantee uses is DOH.

We construct DOH by following the approach of Hennessy (2004) and Hennessy, Levy, and

Whited (2007).

DOH = Leveraget ∗Recovery ∗

[20∑s=1

ρt+s[1 − 0.05(s− 1)](1 + r)−s

](4)

where Leverage is the ratio of total debt to total assets. Recovery by SIC is based on Altman

and Kishore (1996). ρt+s denotes the probability of default in period t+s based on debt rating

on date t. We use the average default rates by ratings from Moody’s. Similar to Hennessy,

Levy, and Whited (2007), we ignore short-term debt and assume that long-term debt mature

in a straight-line fashion, with 5% of the original debt maturing each year. r is the yield to

maturity for zero coupon bonds with maturity of 1 to 20 years. The yield data are obtained

from Gurkaynak, Sack, and Wright (2007).

Firms have higher tendency for over-investment when agency problem is more serious. It

has been well noted in the literature that the lower growth opportunities and the higher free

cash are associated with higher agency problem (Jenson, 1986; Opler and Titman, 1993).

Following Kolasinski (2009) and Custdio, Ferreira and Laureano (2013), we use a firm’s

Tobin’s Q to measure growth opportunities. We sort firms into three equal numbered groups

based on the issuers’ Q and their free cash flow (FCF ) respectively. Q is the ratio of market

value to book value computed as the total assets minus total book value of equity plus market

capitalization and then divided by total assets. FCF is computed as the EBITDA minus the

sum of XINT, TXT, DVC, and DVP and then divided by total assets. The main independent

variable used in the test of the effect of over-investments on corporate guarantee uses (the

third hypothesis) is LQ*HFCF. LQ*HFCF equals one if the issuer is simultaneously in the

bottom Q tercile group and top FCF tercile group and zero otherwise.

17

5 Empirical Results

In this section, we empirically test the impact of financial constraints, debt overhand and

agency problem on the odds of issuing guaranteed bonds. Further, we examine the effect of

guarantees on bond ratings and yield spreads at issuance.

5.1 Determinants of Guarantee Use

In this subsection, we explore the determinants for firms to issue guaranteed bonds. Specifi-

cally, we examine a set of factors in a logistic function that models the probability of a bond

issuer to use the guarantee. In the logistic regression, the dependent variable is the guarantee

dummy equal to one if a newly issued bond is a guaranteed bond and zero otherwise, and

the independent variables are a set of variables explaining bond issuers’ propensity to use a

guarantee.

5.1.1 Effect of Financial Constraints

We test the first hypothesis that the high level of firms’ financial constraints drives guarantee

uses. The main independent variables are five financial constraint measures (WW, SA, KZ,

Tangible and Frating). The control variables include firm characteristic variables (Size,

Profit, FCF, and Dividend) and bond characteristic variables (Term, Par, Call, and Put).

To control for time-varying macroeconomic factors and industry specific factors, we also

include the yield spreads of Moody’s corporate bonds over corresponding treasuries before

issuance (MktYld), the fixed year effect and fixed industry effect. All variables are defined

in Section 4 and Appendix B.

The results are reported in Columns 1-5 of Table 3. In all the columns, the standardized

beta of the independent variables is reported. We first test the effect of three financial con-

straint indexes on the guarantee. Owing to the high correlations between three indexes (WW,

SA, KZ ) and firm characteristic variables (Size, Profit, FCF, and Dividend), we include three

financial constraints indexes and the firm characteristic variables in the separate regression

18

specifications. In Columns 1 to 3, the coefficients of WW, SA and KZ are insignificant.

In Columns 4 and 5, we use two other proxies of the financial constraint: Tangible

and Frating. In Column 4, we first include Tangible in the regression with firm and bond

characteristic variables as control variables. The standardized beta of Tangible is -0.129 and

significant at the 1% level. In Column 5, we include Frating(AAA=26,D=1) and Tangible

in the regression with the same set of control variables in Column 4. The standardized betas

of Frating and Tangible are -0.463 and -0.19, respectively. Both are significant at the 1%

level. The result shows that firms with less tangible assets and lower credit rating are more

likely to use guarantee on bonds.

In terms of economics significance, one standard deviation increase of Frating results in

is associated with 0.463 standard deviations decrease in the log odds of using guarantees.

In other words, one standard deviation increase in Frating is associated with % probability

decrease of using guarantees.

In sum, Table 3 shows the effect of five financial constraints measures on the guarantee

use. Although the three financial constraints indexes that lean more on the internal funding

capacity of firms are insignificant in the regressions, Frating(AAA=26,D=1) and Tangible

that weight more on the creditworthiness of firms are statistically and economically signifi-

cant. The result supports the first hypothesis that financial constraints is one of the main

driver of the guarantee use by firms.

5.1.2 Effects of Debt Overhang and Managerial Agency Conflict

In this subsection, we present the analysis to evaluate the effects of debt overhang and agency

problem on corporate use of guarantees.

The results are reported in Columns 6 and 7 of Table 3. In Column 6, we include

DOH, Tangible and Frating and firm and bond characteristic variables. The standardized

beta coefficients on Tangible and Frating(AAA=26,D=1) are significantly negative. This is

consistent with the result of the effect of the financial constraints on guarantee use.

The standardized beta of DOH is 0.089 and significant at the 5% level. One standard

19

deviation decrease in DOH increases the odds of using guarantee by 0.088 standard devi-

ations. The marginal R2 of adding DOH is 0.006 by comparing specifications with and

without DOH. The result suggests that firms with more debt overhang are more likely to use

the guarantee on bonds. The implication is that with guarantee on bonds, firms mitigate

the debt overhang problem and thus reduce the under-investment. The result supports the

second hypothesis that firms with more pronounced debt overhang problem are more likely

to issue guaranteed bonds.

In Column 7, we use LQ*HFCF to proxy the agency problem. Other independent vari-

ables include DOH, Tangible, Frating, firm and bond characteristic variables. The standard-

ized betas on DOH, Tangible and Frating is 0.088, -0.173 and -0.543, respectively and all are

statistically significant.

The standardized beta of LQ*HFCF is 0.053 and significant at the 5% level. One stan-

dard deviation decrease in LQ*HFCF increases the odds of using guarantee by 0.053 standard

deviations.The marginal R2 of adding LQ*HFCF is 0.003 by comparing specifications with

and without LQ*HFCF. The result indicates that firms with greater agency problem are

more likely to issue the guaranteed bonds. In other words, firms with fewer growth op-

portunities have more tendency to use guarantee to issue bonds and thus may induce the

over-investment. The result supports the third hypothesis that firms with greater agency

problem are more inclined to issue guaranteed bonds.

Column 7 reveals some interesting findings with all independent variables in the regres-

sion. First, larger firms and less profitable firms are more likely to use guaranteed bonds.

Second, putable bonds and shorter maturity bonds are less likely to be guaranteed bonds.

Third, when the Moody’s corporate bond yield spread is higher, firms are more likely to

use a guarantee. Among all the significant independent variables, Frating has the largest

marginal R2 which is 0.014. Tangible has the second largest marginal R2 which is 0.007.

The marginal R2 for DOH is 0.006 and for LQ*HFCF is 0.003. Frating also has the largest

standardized beta.

20

5.1.3 Time Variation of Guarantee Use

The above evidence suggests that guarantee use is a consequence of pronounced debt over-

hang or greater agency problem. However, guarantee use in our sample is not constant over

time. In this subsection, we explore the time variation of guarantee use. In the logistic

regression, we include the year dummy. Other independent variables for the test are the

same as those in Column 7 of Table 3.

The results are reported in Table 4. The standardized betas of 20 Year Dummy are

presented. From 1993 to 2005, five out of thirteen Year Dummy are positive and statistically

significant. The significant standardized beta of Year Dummy ranges from 0.117 to 0.167.

From 2006 to 2012, all the Year Dummy are positive and statistically significant in a row.

Moreover, overall the standardized betas have a large increase since year 2006 comparing

to the years before. The impact of Year Dummy on the guarantee use starts to have a big

jump in year 2006 and peaks at year 2010. Specifically, the Year Dummy for year 2006,

2007, 2008, 2009, 2010, 2011 and 2012 is 0.225, 0.197, 0.193, 0.295, 0.318, 0.244 and 0.262,

respectively. It shows that the use of guarantee is dynamic along the time.

5.2 Robustness Check of the Determinants of Guarantee Use

5.2.1 Multinomial Logistic Regression on Guarantee Use

It is not unusual for a firm to issue multiple bonds in a year. Some firms purely issued either

non-guaranteed bonds or guaranteed bonds while other firms alternatively switched between

them. In this subsection, we investigate the determinants of the firms’ choice of among three

issuances strategies: non-guaranteed bonds only strategy, guaranteed bonds only strategy

and switching strategy between guaranteed bonds and non-guaranteed bonds. If a specific

set of determinants drives corporate to use guarantee on bonds, it is interesting to examine

whether the determinants play a role solely in issuing guaranteed bonds or issuing mixed

bonds or both.

The multinomial logistic model is used and it is specified as follows:

21

Log

(P (Ym = 1, 2)

P (Yi = 0)

)= αm +

K∑k=1

βmkXik + ε (5)

The three categories of the dependent variable (Y ) are:

0: if the firm issues non-guaranteed bonds only in a year.

1: if the firm issues both non-guaranteed bonds and guaranteed bonds in a year.

2: if the firm issues guaranteed bonds only in a year.

In the analysis, the baseline group is Yi = 0. X denotes a vector of independent variables:

Tangible, Frating, DOH, LQ*HFCF, Size, Profit, FCF, Dividend, MktYld, Term, Par, Call,

Put.

The result is reported in Table 5. The regression result in Column 1 is for Yi = 1 in

which the dependent variable is the log odds of issuing mixed bonds to purely issuing non-

guaranteed bonds. The regression result in Column 2 is Yi = 2 in which the dependent

variable is the log odds of purely issuing guaranteed bonds to purely issuing non-guaranteed

bonds.

The coefficient of Frating is -0.403 in the mixed strategy group while it is -0.317 in the

pure guarantee strategy group. That is, holding other variables constant, with a one-unit

decrease in the firm rating, the odds of using the mixed strategy (i = 1) to issuing non-

guaranteed bonds only (i = 0) increases 0.496, and the odds of purely using the guarantee

strategy (i = 2) over purely using non-guaranteed bonds (i = 0) increases 0.373. For Tangible,

the coefficient is -0.216 in the mixed strategy regression and -0.072 in the purely guaranteed

bonds strategy regression. The result shows that firms with lower rating and less tangible

assets prefer to issue either mixed bonds or guaranteed bonds to non-guaranteed bonds.

The coefficient of DOH is significant at the 1% level in both regressions. Specifically,

the coefficient is 0.224 in the mixed bond group regression and 0.131 in the purely issuing

guaranteed bonds regression. Holding other variables constant, with a one-unit increase in

DOH, the odds of using the mixed strategy (i = 1) to issuing non-guaranteed bonds only (i

= 0) increases 0.251, and the odds of purely using the guarantee strategy (i = 2) over purely

using non-guaranteed bonds (i = 0) increases 0.139. The result indicates that firms with

22

greater debt overhang are more likely to issue either guaranteed bonds only or mixed bonds

rather than issue non-guaranteed bonds.

It is noteworthy that the coefficient of LQ*HFCF is not significant in the mixed bonds

regression. Thus there is no evidence that agency problem increases the odds of issuing mixed

bonds to issuing non-guaranteed bonds. However, in the purely issuing guaranteed bonds

regression, the coefficient of LQ*HFCF is 0.366 and significant at the 5% level. Holding

other variables constant, with a one-unit increase in LQ*HFCF, the odds of purely using

the guarantee strategy (i = 2) over issuing non-guaranteed bonds only (i = 0) increases

0.251. The result indicates that firms with greater agency problem are more inclined to issue

guaranteed bonds only than issuing non-guaranteed bonds.

The result also shows that larger firm (Size), lower market corporate yield spread (Mk-

tYld) and longer maturity (Term) increase the log odds of purely issuing guaranteed bonds

over purely issuing non-guaranteed bonds while having no effect on issuing mixed bonds.

Callable bonds (Call) have higher log odds of issuing either pure guaranteed bonds or mixed

bonds than non-guarantee bonds. In contrast, putable bonds (Put) have higher odds of

issuing non-guarantee bonds than issuing either pure guaranteed bonds or mixed bonds.

5.2.2 Analysis with Estimated Variables at Parent Firm Level

Given the fact that guaranteed bonds in our sample are typically insured by the subsidiaries,

a natural concern is whether the firm-specific variables used in the test are measured for

parent companies or the consolidated level. A related concern is whether the parent firms

are financial holding firms or operational firms. Financial holding firms have most of the

operations in the subsidiaries and thus may have more incentive to use the subsidiaries’

guarantee than operational firms. We address these concerns in three folds. First, we use

the minority interest in the Balance Sheet to estimate relevant firm variables at the parent

firms level. When firms own more than 50% but less than 100% of their subsidiaries, firms

include the subsidiaries’ assets in the consolidated balance sheet using the equity method

and report the non-parent-owned assets as the minority interest. To begin with, we take an

23

average 75% of parent-owned assets and 25% of the minority interest for all firms. We then

deduct parent-owned assets at the subsidiaries from assets in the consolidated Balance Sheet

and calculate the ratio of the parent firm level assets to the consolidated assets. Then we

apply the ratio to the consolidated Balance Sheet to calculate the relevant variables at the

parent firm level. With the same method, we use the minority interest in Income Statement

to calculate the relevant variables at the parent firm level.

Second, with the ratio of the firm-level assets to the consolidated assets, we classify a

firm as an operational firm if its firm-level income is more than 50% of the consolidated

income and financial holding firm otherwise. In this way, we distinguish operational firms

and financial holding firms.

Third, we manually check the Exhibit 21 of the 10-k for the number of subsidiaries.

Exhibit 21 provides the number of the subsidiaries but no financial data.

Because firms do not report minority interest when they have 100% ownership of the

subsidiaries, the observations of the subsample using the above method is smaller than the

original sample. However, it provides the robust check for our test when we run the regression

with the firm-level variables, the indicator of operational or financial holding firms and the

number of subsidiaries. The results are presented in Table 6.

The variables in the regressions are the same as those in Table 3 except two variables: the

logarithm of the number of the subsidiaries (SubNum) and the indicator of operational firm

(OperFirm). Tangible, DOH, LQ*HFCF, Size and Profit are calculated with the firm-level

variables under the previous assumption. As explained in Section 4, Frating(AAA=26,D=1)

is at the firm level based on the rating agency’s methodology.

In Columns 1 through 4 of Table 6, we rerun the same regressions as those in Table

3 with the estimated firm-level variables. The standardized betas of DOH,LQ*HFCF and

Call are all positive and statistically significant. The standardized betas of Tangible, Frating,

Dividend are all negative and statistically significant. In other words, firms with more debt

overhang, greater agency problem, less tangible assets, lower ratings, less dividend, issuing

callable bond or issuing bonds with larger par are more likely to issue guaranteed bonds.

24

The results are consistent with the regression results obtained with the consolidated firm

variables reported in Table 3 and confirm the robustness of the results in Table 3.

In Column 5, we add the dummy for operational firm (OperFirm). The standardized

beta is -0.154 and marginally significant at the 10% level. The result shows that operational

firms are less likely than financial holding firms to use guarantees. In Column 6, we add the

logarithm of the number of the subsidiaries (SubNum) to the regression. The standardized

beta is not significant and the result shows that the number of subsidiaries is not one of the

main reasons for firms to use guarantee. In an unreported regression, we include SubNum

and exclude Size. The standardized beta of SubNum is positive and significant. However,

after adding Size, the standardized beta of SubNum is not significant. The result shows that

the effect of the number of subsidiaries is subsumed by the effect of size.

Alternatively, we redo the above analysis using 40%, 30% and 20% as the threshold for

operational firms, and the results do not change.

5.3 Effect of Guarantee Use on Bond Ratings

A key premise of the study is that the guarantee use reduces default risk. In this subsection,

we conduct the tests to examine whether the guarantee use indeed improves bond ratings.

The dependent variable is the rating of a specific bond at its issuance. As we discuss in the

sample section, we primarily rely on the S&P ratings when there are multiple ratings from

different rating agencies. We use Moody’s ratings or Fitch’s ratings (in this order) when

S&P ratings are unavailable. The highest bond rating (e.g., S&P’s AAA) is assigned with a

rating number of 26 and the lowest bond rating number is 1. Both guaranteed bonds and

non-guaranteed bonds are included in the analysis. The regression specifications are shown

as follows.

Bond Rating = α0 + α1GT + α2Controls + ε (6)

where GT is the indicator variable equal to one if the bond is a guaranteed bond and zero oth-

erwise. We include control variables of firm and bond characteristics, following prior studies

25

on bond ratings and credit spreads, Campbell and Taksler (2003), for example. Specifically,

firm characteristic variables include firm rating (Frating), investment grade (InvGrade), firm

size (Size), profit (Profit) and leverage (Leverage) to capture any variation in firms’ perfor-

mance and creditworthiness. Bond characteristic variables include time to maturity (Term),

bond issuance amount (Par), the dummy variable for secured bonds (Secure), the dummy

variable for callable bonds (Call), and the dummy variable for putable bonds (Put). Finally,

we control for the fixed industry and the fixed year effect. Industries are classified based on

the first two digits of firms’ Standardized Industry (SIC) Codes.

The regression results are reported in Table 7. In Column 1, we include the guarantee

dummy variable (GT ) only. R-Squared of the regression is 0.15. The standardized beta

of GT is significantly negative, consistent with our earlier finding that, unconditionally,

guaranteed bond issuers have a poorer rating than non-guaranteed bond issuers.

Next, we control for the influence of issuers’ ratings to see the impact of GT on bond

ratings. In Column 2, we include two additional variables related to issuer ratings: Frat-

ing(AAA=26,D=1) and InvGrade. The result shows the standardized beta of the guarantee

dummy turns to be positive, significant at the 1% level. The finding states that, conditional

on issuer ratings, the use of guarantees improve bond ratings. It also is worth noting that

the R2 of the regression reported in Column 2 is 0.939, suggesting that bond ratings are

primarily driven by issuer ratings.

Further we include a set of firm characteristic variables in the analysis reported in Column

3 and include both firm and bond characteristic variables in Column 4. This is to isolate the

GT effect from the potential effects of firm and bond characteristics. In both columns, the

standardized betas of GT are positive and significant at the 5% level, while the increases

in R2 from that of Column 2 is virtually non-existent, further confirming that the primary

role of issuer ratings in bond rating determinations. Despite so, our analysis reveals that

the guaranteed use has a robust and significant role in determining bond ratings – after we

control for firm and bond characteristics, the use of guarantee on average increases bond

rating by 0.11 of a rating notch.

26

5.4 Effect of Guarantee Use on Yield Spreads

We further examine if the guarantee use affects initial yield spreads of individual bonds. We

adopt the following regression:

Yield Spread = α0 + α1GT + α2Controls + ε (7)

where a bond’s yield spread at issuance comes from Mergent FISD. It is the difference

between the yield of the benchmark treasury issue and the bond’s offering yield expressed

in basis points.

The regression results are reported in Table 8. Similar to Table 7, the first column solely

involves the guarantee dummy variable (GT ). The standardized beta on GT is significantly

positive. That is, unconditionally, guaranteed bonds have a higher yield spreads than non-

guaranteed bonds, a finding consistent with the rating analysis.

Interestingly, after we add two variables related to the firm rating: Frating and InvGrade

in Column 2, the standardized beta of GT is no longer significant. We continue to obtain

an insignificant standardized beta of GT after including both firms and bond characteristic

variables, which is reported in Column 3. The result shows that, conditional on issuer ratings

and issuer and bond characteristics, the guarantee use no longer has impact on bond yield

spreads, a result different from the rating analysis reported in Table 7.

To understand on the results reported in Columns 2 and 3, we look into the specific

roles played by financial constraints, debt overhang, and managerial agency problem in

the impact of guarantee use on bond yield spreads. We respectively interact the dummy

variable GT with the dummy variable LPPE (i.e., low tangible assets) for more financial

constraints, the dummy variable HDOH (i.e., high DOH) for more pronounced debt overhang

and the dummy variable LQ*HFCF (i.e., Low Q*high FCF) for greater agency problem. We

separately include one of the three interaction terms in the regressions while excluding GT

from the regression.

In Column 4, we augment regression setup in Column 3 by additionally including GT*LPPE.

We find that the standardized beta of GT*LPPE is negative and it is only marginally sig-

27

nificant (at the 10% level). Thus, there is some weak support to the Hypothesis 4a. Next,

in Column 5, we alternatively include GT*HDOH and find that the standardized beta of

GT*HDOH is negative and significant at the 5% level. The use of guarantees lowers bond

yield spreads and increases the bond value after we have controlled for other factors. The

evidence support to the Hypothesis 4b. In Column 6, we include GT*LQ*HFCF and the

standardized beta of GT*LQ*HFCF is significantly positive. The result shows that the

guarantee use in firms with greater agency problem increases the yield spread and thus de-

creases the bond value. The evidence support to the Hypothesis 4c. Finally, in Column 7,

we include the three interaction terms together in one regression. The result in the column

is similar to those reported in earlier columns.

Putting all together, the analysis in Sections 5.3 and 5.4 shows that on average guarantee

improves bond ratings while such use has little impact on initial yield spreads. However,

guarantees used by bond issuers facing greater financial constraints or with more pronounced

debt overhang indeed reduce yield spreads, while guarantees used by bond issuers with more

severe managerial agency problems increase yield spreads. These two inverse effects cancel

out in a sample where all bond issues are pooled together.

6 Conclusions

This paper examines U.S. firms’ issuance of guaranteed bonds. We focus on a unique feature

of guarantee on corporate bonds – the guarantors of these bonds are typically affiliated firms.

The key advantage of using internal guarantee for credit enhancement is its relatively trivial

explicit issuance cost. A simple tradeoff motivate firms to issue guaranteed bonds. On

one hand, the use of guarantees potentially reduces corporate financial constraints and debt

overhang and increases firms’ ability in investing in favorable growth opportunities. Thus,

firms with greater financial constraints and stronger debt overhang problems are more likely

to issue guaranteed bonds. On the other hand, as the explicit cost of guarantee issuance is

low, firms with greater agency conflicts between the parent companies and subsidiaries are

28

also more likely to issue such bonds for the purpose of empire building.

Empirical findings support our predictions. Guaranteed bonds are predominantly issued

by low creditworthiness firms featured with low rating and less tangible assets. We also find

that firms with more pronounced debt overhang problem tend to issue guaranteed bonds

to alleviate the problems, and that firms with poorer investment opportunities but greater

agency conflicts are more inclined to use guaranteed bonds. Moreover, we find that on

average guarantee improves bond rating but has little effect on the initial bond yield spread.

Further analysis suggests that the overall decreasing yield spread effect from guarantee by

firms with more pronounced debt overhang is offset by the overall increasing yield spread

effect from guarantee by firms with more severe agency problem.

29

Appendix A: Main Variables Used in the Analysis

Variable Definition

GT Dummy variable equal to one if the bond is issued with a guarantee and zero otherwise.

Financial Constraints MeasuresFrating(AAA=26,D=1) The most recent S&P long term issuer rating before a bond issuance.Tangible The ratio of the property, plant and equipment to total assets.WW The WW index of Whited and Wu (2006). Calculation method is provided at Section 4.3.SA The SA index of Hadlock and Pierce (2010). Calculation method is provided at Section 4.3.KZ The KZ index of Kaplan and Zingales (1997). Calculation method is provided at Section 4.3.LPPE All firms are sorted into three groups based on the issuers’ property, plant and equipment (PPE).

It is a dummy variable that equals to one if a firm is in the lowest PPE group and zero otherwise.

Debt Overhang MeasuresDOH The measure of a firm’s debt overhang. It is calculated using the approach by Hennessy (2004)

and Hennessy, Levy, and Whited (2007).HDOH All firms are sorted into three groups based on the debt overhang measure. It is a dummy

variable that equals to one if a firm is in the highest overhang group and zero otherwise.

Agency Problem MeasureLQ*HFCF The measure of a firm’s agency problem. All firms are sorted into three equal numbered

groups based on the issuers’ Q and their free cash flow respectively. It is a dummy variable thatequals to one if the issuer is simultaneously in the bottom Q tercile group and top free cash flowtercile group and zero otherwise.

Firm CharacteristicsSize Logarithm of total book value of assets.Profit Ratio of the operating income before depreciation to total assets.Dividend Dummy variable for a firm equal to one if the firm paid dividend and zero otherwise.Leverage Ratio of total debt to total assets.Q Ratio of market value to book value computed as the total assets minus total book.

value of equity plus market capitalization and then divided by total assets.FCF Free cash flow is computed as the EBITDA minus the sum of XINT, TXT, DVC, and DVP and

then divided by total assets.InvGrade Dummy variable equal to one if a firm has a investment grade rating and zero otherwise.

Bond CharacteristicsYield Spread The yield difference between a bond and the corresponding treasury bond with matching maturity

at the time of issuance.Term Logarithm of time to maturity in years.Par Logarithm of par value in billion dollars.Call Dummy variable equal to one if a bond has a Call provision and zero otherwise.Put Dummy variable equal to one if a bond has a Put provision and zero otherwise.MktYld The market yield difference between Moody’s corporate bonds and corresponding Treasury bonds.

30

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32

Table 1: Statistics for Guaranteed Bonds

This table reports the total number and the aggregate par value of both newly issued corporate bonds (All)and newly issued guaranteed corporate bonds (GT) included in the Fixed Income Securities Database (FISD)from 1993 to 2012. Par value is in billion dollars. The statistics are reported separately for public firms andall firms (including both public and private firms).

Public Firms All Firms

Number of Issues Par Value Number of Issues Par Value

Year All GT All GT All GT All GT

1993 820 75 125.92 2.77 3,253 221 299.21 4.221994 302 8 50.76 0.60 2,788 71 166.14 2.281995 503 20 79.84 1.36 4,396 103 250.25 4.251996 585 28 114.96 6.96 3,109 84 285.51 15.411997 735 36 150.62 5.22 3,703 201 394.95 38.381998 897 44 224.08 10.00 4,153 289 624.12 60.371999 624 37 219.33 9.19 4,053 235 691.79 64.622000 524 26 241.94 9.65 3,539 189 824.65 101.612001 783 40 373.33 15.23 4,049 305 989.22 83.542002 615 38 244.39 16.64 4,312 412 760.04 63.902003 923 42 318.78 16.19 6,507 401 919.22 73.622004 730 47 255.89 13.37 6,955 592 903.26 62.772005 624 27 239.38 10.46 6,790 896 891.38 69.992006 749 71 343.33 37.45 7,601 1,137 1,443.00 128.572007 1,104 59 420.16 26.68 10,414 1,489 1,420.38 119.562008 626 49 286.74 37.14 9,249 1,338 1,136.49 222.742009 725 125 396.92 96.82 6,387 1,093 1,572.62 575.792010 681 108 376.58 53.89 9,285 859 1,078.16 206.112011 642 63 390.67 35.84 10,365 687 1,053.16 186.222012 841 116 468.36 65.40 12,126 624 1,305.98 231.28Total 14,033 1,059 5,322.00 470.87 123,034 11,226 17,009.55 2,315.23

33

Table 2: Summary Statistics

This table reports the cross-sectional mean, median, and standard deviation of the main variables used in the analysis for non-guaranteedbonds (in Panel A) and guaranteed bonds (in Panel B) respectively. The main variables include the ratio of the operating income beforedepreciation to total assets (Profit), the ratio of total debt to total assets (Leverage), total assets in billion dollars (Size), the most recentS&P long term issuer rating before issuance (Frating(AAA=26,D=1)), firm Tobin’s Q (Q), yield spread at issuance (Spread, in basis points),the bonds’s time to maturity in years (Time to Maturity), and par value in million dollars (Par Value). The definitions of the variables areprovided in Appendix A. We then sort sample bonds into four rating groups (AAA-A, BBB, BB, B-CC) and report the mean and medianof the variables in each group. AAA-A group include all bonds with the ratings from AAA to A. BBB (BB) groups include bonds with theratings of BBB (BB). B-CC group includes bonds with the ratings from B to CC. The sample period is from 1993 to 2012.

Panel A: Non-Guaranteed Bonds

All AAA-A BBB BB B-CC

Mean Median Std dev Mean Median Mean Median Mean Median Mean Median

Profit 0.14 0.14 0.08 0.18 0.17 0.14 0.14 0.13 0.12 0.08 0.09Leverage 0.35 0.31 0.21 0.26 0.24 0.31 0.31 0.42 0.41 0.55 0.51Size 19.85 6.14 44.01 40.69 17.49 13.73 7.11 4.40 2.18 3.74 1.23Frating(AAA=26,D=1) 17.80 18.00 3.52 - - - - - - - -Q 2.07 1.72 1.24 2.49 2.19 1.88 1.62 1.74 1.45 1.78 1.45Spread 215.83 150.00 183.63 113.19 90.00 177.94 150 341.09 325.5 479.39 476.5Term 12.49 10.00 10.93 14.19 10.00 13.54 10.00 10.13 10.00 9.19 8.00Par 448.58 300.00 439.34 600.60 450.00 409.06 300.00 333.77 250.00 322.31 225.00

Panel B: Guaranteed Bonds

All AAA-A BBB BB B-CC

Mean Median Std dev Mean Median Mean Median Mean Median Mean Median

Profit 0.13 0.13 0.09 0.18 0.18 0.15 0.15 0.13 0.12 0.09 0.09Leverage 0.37 0.36 0.19 0.23 0.21 0.31 0.30 0.39 0.39 0.53 0.49Size 20.30 6.70 35.01 53.26 22.83 31.13 15.46 6.33 4.04 5.53 2.09Frating(AAA=26,D=1) 16.13 16.00 3.03 - - - - - - - -Q 1.97 1.63 1.59 2.09 1.93 2.35 1.76 1.75 1.51 1.78 1.33Spread 313.62 245.00 208.96 162.52 136.25 216.38 183 382.89 354.5 589.5 566Term 11.57 10.00 7.90 12.29 10.00 13.76 10.00 10.37 10.00 9.02 8.00Par 522.54 400.00 437.25 784.77 500.00 635.86 500.00 420.70 350.00 326.48 290.00

34

Table 3: The Effect of Financial Constraints, Debt Overhang and Agency Problem onCorporate Guarantee Uses

This table shows the results of logistic regressions using the sample bonds issued by public firms withsubsidiaries. The dependent variable is GT, an indicator variable equal to one if it is issued with guaran-tee and zero otherwise. The main independent variables include five financial constraint measures (WW,SA, KZ, Tangible and Frating(AAA=26,D=1)), debt overhang measure (DOH), agency problem measure(LQ*HFCF). The control variables are Size, Profit, Free Cash Flow (FCF), Dividend, MktYld, Term, Par,Call, and Put. All variables are defined in Section 4. All specifications include industry fixed effect and yearfixed effect. Standardized betas are reported and p-value are presented in parentheses. ***, **, and * meansignificant at the 1%, 5%, and 10% level, respectively.

(1) (2) (3) (4) (5) (6) (7)

WW -0.009(0.75)

SA 0.035(0.176)

KZ -0.061(0.280)

Tangible -0.129*** -0.191*** -0.172*** -0.173***(0.001) (0.001) (0.001) (0.001)

Frating(AAA=26,D=1) -0.463*** -0.546*** -0.543***(0.001) (0.001) (0.001)

DOH 0.089** 0.088**(0.012) (0.013)

LQ*HFCF 0.053**(0.038)

Size -0.057 0.236*** 0.305 0.295***(0.125) (0.001) (0.001) (0.001)

Profit -0.106*** 0.043 -0.067* -0.078**(0.005) (0.191) (0.073) (0.037)

FCF 0.037 0.046 0.022 -0.001(0.194) (0.116) (0.499) (0.969)

Dividend -0.086*** -0.028 -0.019 -0.016(0.001) (0.293) (0.524) (0.590)

MktYld 0.824 0.094 0.068 0.081 0.116* 0.153* 0.153*(0.205) (0.144) (0.321) (0.212) (0.079) (0.054) (0.056)

Term 0.089*** 0.097*** 0.113*** 0.104*** 0.151*** 0.139*** 0.139***(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Par 0.001 0.015 0.018 0.008 0.047 0.001 0.003(0.970) (0.589) (0.555) (0.767) (0.189) (0.978) (0.941)

Call -0.010 -0.010 0.008 0.006 -0.001 0.044 0.045(0.720) (0.716) (0.779) (0.839) (0.983) (0.213) (0.204)

Put -0.037* -0.038 -0.052* -0.047* -0.075** -0.081** -0.079**(0.196) (0.177) (0.096) (0.099) (0.013) (0.016) (0.018)

Fixed industry Y Y Y Y Y Y YFixed year Y Y Y Y Y Y Y# of Obs. 5814 5949 5154 5767 5767 5767 5767Likelihood Ratio 348.83 351.28 336.0839 404.57 534.03 465.92 470.12Pseudo R2 0.058 0.057 0.059 0.067 0.081 0.087 0.090

35

Table 4: Time Variation of Guarantee Use

This table shows the result of logistic regression on the time variation of guarantee use. The dependentvariable is GT, an indicator variable equal to one if it is issued with guarantee and zero otherwise. Themain independent variables include five financial constraint measures (WW, SA, KZ, Tangible and Frat-ing(AAA=26,D=1)), debt overhang measure (DOH ), agency problem measure (LQ*HFCF ). The controlvariables are Size, Profit, Free Cash Flow (FCF), Dividend, MktYld, Term, Par, Call, and Put. All variablesare defined in Section 4. The year dummy are reported. All specifications include industry fixed effect.Standardized betas are reported and p-value are presented in parentheses. ***, **, and * mean significantat the 1%, 5%, and 10% level, respectively.

Standardized Estimate p-value

Tangible -0.173*** (0.001)Frating(AAA=26,D=1) -0.543*** (0.001)DOH 0.088** (0.013)LQ*HFCF 0.053** (0.038)Size 0.295*** (0.001)Profit -0.078** (0.037)FCF -0.001 (0.969)Dividend -0.016 (0.590)MktYld 0.153* (0.056)Term 0.139*** (0.001)Par 0.003 (0.941)Call 0.045 (0.204)Put -0.079** (0.018)1994 -0.013 (0.837)1995 -0.064 (0.432)1996 0.128** (0.025)1997 0.056 (0.406)1998 0.138* (0.070)1999 0.101 (0.152)2000 0.117** (0.041)2001 0.111 (0.141)2002 0.104 (0.112)2003 0.156* (0.056)2004 0.167** (0.025)2005 0.105 (0.141)2006 0.225*** (0.001)2007 0.197*** (0.007)2008 0.193*** (0.002)2009 0.295*** (0.002)2010 0.318*** (0.001)2011 0.244** (0.005)2012 0.262*** (0.003)Fixed industry Y# of Obs. 5767Likelihood Ratio 470.12Pseudo R2 0.090

36

Table 5: Multinomial Logistic Regressions for Guarantee Use

The choice of guaranteed bonds and non-guaranteed bonds is modeled as the outcome of a variable GTthat takes the value of zero when the firm issues non-guaranteed bonds only in a year, one when the firmissues both guaranteed and non-guaranteed bonds in a year, two when the firm issues guaranteed bonds onlyin a year, and is estimated as a multinomial logistic regression. All data are in the fiscal year before thedebt issuance. The baseline group is non-guaranteed bonds only. The main independent variables includefinancial constraint measures Tangible and Frating(AAA=26,D=1), debt overhang measure (DOH), agencyproblem measure (LQ*HFCF). The control variables are Size, Profit, Free Cash Flow (FCF), Dividend,MktYld, Term, Par, Call, and Put. All variables are defined in Section 4. All specifications include industryfixed effect and year fixed effect. p-value are presented in parentheses. ***, **, and * mean significant atthe 1%, 5%, and 10% level, respectively.

Mixed GT&Non-GT GT Only

(1) (2)

Intercept 5.572*** 0.451(0.001) (0.621)

Tangible -0.216** -0.072*(0.015) (0.067)

Frating(AAA=26,D=1) -0.403*** -0.317***(0.001) (0.001)

DOH 0.224*** 0.131***(0.001) (0.003)

LQ*HFCF 0.232 0.366**(0.271) (0.042)

Size 0.199** 0.303***(0.017) (0.001)

Profit -1.526** -1.298*(0.041) (0.064)

FCF 0.934* 0.798(0.067) (0.164)

Dividend 0.152 0.001(0.251) (0.996)

MktYld -0.113 -0.264***(0.138) (0.001)

Term -0.223 0.282**(0.194) (0.048)

Par -0.005 0.072(0.969) (0.583)

Call 1.114*** 0.772***(0.001) (0.001)

Put -0.603* -1.337***(0.054) (0.002)

Fixed industry Y YFixed year Y Y# of Obs. 3239Pseudo R2 0.172

37

Table 6: Analysis of Determinants of Guaranteed Bond Issuance Using Estimated ParentFirm Level Variables

This table shows the results of logistic regressions using the estimated parent firm level variables. Parentfirm level variables Tangible, DOH, LQ*HFCF, Size, Profit, FCF are measured at the parent firm level usingthe estimation from the minority interest method. Frating(AAA=26,D=1) is at the parent firm level basedon the rating agency’s rating methodology. The dependent variable is GT, an indicator variable equal toone if it is issued with guarantee and zero otherwise. The main independent variables include the financialconstraint measures (Tangible and Frating(AAA=26,D=1)), debt overhang measure (DOH ), agency prob-lem measure (LQ*HFCF ). The control variables are Size, Profit, Free Cash Flow (FCF), Dividend, MktYld,Term, Par, Call, and Put. All variables are defined in Section 4. All specifications include industry fixedeffect and year fixed effect. Standardized betas are reported and p-value are presented in parentheses. ***,**, and * mean significant at the 1%, 5%, and 10% level, respectively.

(1) (2) (3) (4) (5) (6)

Tangible -0.313*** -0.380*** -0.398*** -0.381*** -0.401*** -0.385***(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Frating(AAA=26,D=1) -0.404*** -0.348*** -0.321*** -0.373*** -0.335***(0.001) (0.001) (0.001) (0.001) (0.001)

DOH 0.088** 0.063** 0.111** 0.081**(0.042) (0.042) (0.038) (0.038)

LQ*HFCF 0.153** 0.176*** 0.378***(0.021) (0.001) (0.001)

OperFirm -0.154* -0.143*(0.054) (0.052)

SubNum -0.058(0.388)

Size 0.156* 0.392*** 0.405*** 0.316*** 0.328*** 0.320***(0.093) (0.001) (0.001) (0.007) (0.005) (0.007)

Profit 0.089 0.109 0.132* -0.118 -0.188** -0.133*(0.181) (0.109) (0.059) (0.104) (0.015) (0.076)

FCF 0.079 0.161* 0.177** 0.101 0.075 0.097(0.291) (0.049) (0.049) (0.266) (0.421) (0.285)

Dividend -0.389*** -0.298*** -0.301*** -0.285*** -0.284** -0.291***(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

MktYld 0.209 0.225 -0.085 09.984 -0.084 -0.081(0.184) (0.159) (0.535) (0.547) (0.543) (0.554)

Term -0.005 0.010 -0.027 -0.035 -0.032 -0.036(0.937) (0.881) (0.705) (0.633) (0.668) (0.631)

Par 0.141 0.121 0.103 0.147 0.142 0.155(0.122) (0.158) (0.244) (0.117) (0.122) (0.191)

Call 0.158** 0.179** 0.175** 0.176** 0.178** 0.180**(0.039) (0.021) (0.037) (0.036) (0.034) (0.032)

Put -0.053 -0.043 -0.029 -0.026 -0.021 -0.030(0.552) (0.634) (0.748) (0.778) (0.822) (0.742)

Fixed industry Y Y Y Y Y YFixed year Y Y Y Y Y Y# of Obs. 1304 1304 1304 1304 1304 1304Likelihood Ratio 200.68 221.26 213.67 218.88 222.41 222.54Pseudo R2 0.136 0.149 0.151 0.155 0.157 0.157

38

Table 7: Effect of Guarantees on Bond Rating

This table reports the results of the impact of guarantees on bond ratings. The dependent variable isthe bond rating at issuance. The independent variables include GT,Frating(AAA=26,D=1),InvGrade, Size,Profit, Leverage, Tangible, Par, Term, Secure, Call and Put. All variables are defined in Section 4. Allspecifications include industry fixed effect and year fixed effect. All data are in the fiscal year before thedebt issuance. Standardized betas are reported and p-value are presented in parentheses. ***, **, and *mean significant at the 1%, 5%, and 10% level, respectively.

Bond Rating

(1) (2) (3) (4) )

GT -0.126*** 0.150** 0.110** 0.111**(0.001) (0.002) (0.028) (0.020)

Frating(AAA=26,D=1) 0.964*** 0.910*** 0.919***(0.001) (0.001) (0.001)

InvGrade 0.789*** 0.841*** 0.840***(0.001) (0.001) (0.001)

Size 0.155*** 0.179***(0.001) (0.001)

Tangible 0.002 -0.022(0.958) (0.636)

Profit 1.065** 1.223***(0.002) (0.001)

Leverage -0.239** -0.298***(0.035) (0.007)

Par -0.084**(0.019)

Term 0.016(0.363)

Secure 1.113***(0.001)

Call -0.029(0.358)

Put -0.122*(0.067)

Fixed ind. effect Yes Yes Yes YesFixed year effect Yes Yes Yes Yes# of Obs. 6366 6366 6366 6366Adjusted R2 0.145 0.939 0.941 0.943

39

Table 8: Effect of Guarantees on Yield Spread

This table reports the results of the impact of guarantees on yield spread at issuance. The dependent vari-able is the yield spread. The independent variables include GT, Frating(AAA=26,D=1),InvGrade, Size,Profit, Leverage, Par, Term, Secure, Call, Put, GT*LPPE, GT*HDOH and GT*LQ*HFCF. GT*LPPE isthe interaction term between GT and the dummy variable LPPE for more financial constraints. GT*HDOHis the interaction term between GT and the dummy variable HDOH for more pronounced debt overhang.GT*LQ*HFCF is the interaction term between GT and the variable LQ*HFCF for greater agency problem.HDOH and LPPE are defined in Appendix B and other variables are defined in Section 4. All specificationsinclude industry fixed effect and year fixed effect. All data are in the fiscal year before the debt issuance.Standardized betas are reported and p-value are presented in parentheses. ***, **, and * mean significantat the 1%, 5%, and 10% level, respectively.

Yield Spread

(1) (2) (3) (4) (5) (6) (7) )

GT 0.076*** -0.002 -0.002(0.001) (0.854) (0.978)

GT*LPPE -0.016* -0.016*(0.069) (0.096)

GT*HDOH -0.014** -0.014**(0.046) (0.042)

GT*LQ*HFCF 0.021** 0.027**(0.043) (0.024)

Frating(AAA=26,D=1) -0.452*** -0.356*** -0.357*** -0.356*** -0.354*** -0.354***(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

InvGrade -0.299*** -0.285*** -0.284*** -0.284*** -0.285*** -0.284***(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Size -0.129*** -0.129*** -0.128*** -0.132*** -0.131***(0.001) (0.001) (0.001) (0.001) (0.001)

Profit -0.069*** -0.071*** -0.069*** -0.069*** -0.069***(0.001) (0.001) (0.001) (0.001) (0.001)

Leverage 0.012 0.011 0.011 0.013 0.013(0.330) (0.340) (0.367) (0.263) (0.290)

Par 0.054*** 0.054*** 0.054*** 0.054*** 0.055***(0.001) (0.001) (0.001) (0.001) (0.001)

Term 0.062*** 0.062*** 0.062*** 0.062*** 0.062***(0.001) (0.001) (0.001) (0.001) (0.001)

Secure 0.098*** 0.097*** 0.097*** 0.097*** 0.097***(0.001) (0.001) (0.001) (0.001) (0.001)

Call 0.046*** 0.046*** 0.045*** 0.046*** 0.046***(0.001) (0.001) (0.001) (0.001) (0.001)

Put -0.031*** -0.031*** -0.031*** -0.031*** -0.031***(0.001) (0.001) (0.001) (0.001) (0.001)

Fixed industry effect Yes Yes Yes Yes Yes Yes YesFixed year effect Yes Yes Yes Yes Yes Yes Yes# of Obs. 5316 5316 5316 5316 5316 5316 5316Adjusted R2 0.223 0.631 0.651 0.671 0.673 0.667 0.679

40

Figure 1: Guarantors of Guaranteed Corporate Bonds

This figure shows the number of guaranteed corporate bonds in our sample and the number of guaranteedbonds with different types of guarantors. The two main types of guarantors are external and internalguarantors. Further, the internal guarantors are either parent firms or subsidiaries.

The number of Guaranteed (GT) Bonds: 731

GT Bonds with internal guarantors: 647

GT bonds with external guarantors: 84

GT bonds using subsidiaries as guarantors: 621 GT bonds using parents as guarantors: 26

41

Figure 2: Percentage of Guaranteed Corporate Bonds in All Corporate Bond Issues

This figure shows the the percentage of guaranteed corporate bonds issued in terms of the amount issued.Issuers are either public firms or private firms. Panel A presents the percentage for public and private firmstogether. Panel B presents the percentage for public firms. The sample period is from 1993 to 2012.

1995 2000 2005 20100

20%

40%Panel B: Public Firm Sample

1995 2000 2005 20100

20%

40%Panel A: All Firm Sample

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Figure 3: Distribution of Corporate Bond Issuers

This figure plots the distributions of corporate bond issues across different rating groups. Panel A is for theissuers of guaranteed bonds (credit enhanced bonds) and Panel B is for the issuers of non-guaranteed bonds.The AAA group includes issuers whose firm rating is AAA. The AA group includes issuers whose firm ratingsare AA+, AA, and AA-. The A group includes issuers whose firm ratings are A+, A, and A-. The BBBgroup includes issuers whose firm ratings are BBB+, BBB, and BBB-. The BB group includes bond issuerswhose firm ratings are BB+, BB, and BB-. The B group includes bond issuers whose firm ratings are B+,B, and B-. The CCC group includes bond issuers whose firm ratings are CCC+, CCC, and CCC-. TheCC group includes bond issuers whose firm ratings are CC, C, and D. The sample period is from 1993 to 2012.

AAA AA A BBB BB B CCC Others0

10%

30%

Panel A: Guaranteed Bond Issuers

AAA AA A BBB BB B CCC Others0

10%

30%

Panel B: Non-Guaranteed Bond Issuers

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