Debt Structure and Debt Specialization
Transcript of Debt Structure and Debt Specialization
Debt Structure and Debt Specialization*
Paolo Colla Università Bocconi†
Filippo Ippolito
Università Bocconi‡
Kai Li University of British Columbia§
This version: March, 2010 First version: October, 2009
Abstract This paper provides the first large sample evidence on the patterns and determinants of debt structure using a new database of publicly listed U.S. firms. Within what is generally referred to as debt financing, we are able to distinguish between commercial paper, revolving credit facilities, term loans, senior and subordinated bonds, and capital leases. We show that small and unrated firms rely exclusively on either capital leases or bank debt for financing. In contrast, financing through multiple types of debt is only observed for large firms with very high credit quality. Most sample firms concentrate their borrowing in only one of these debt types, and this debt specialization persists over time. Finally, we show that firm characteristics that are known to be associated with leverage, such as profitability and tangibility of assets, have very different effects on different types of debt. Our paper suggests that debt structure is an important part of capital structure decisions. Keywords: debt specialization, debt structure, revolving credit facilities, senior bonds, term loans JEL classification: G32
* We thank Miguel Ferreira, Mark Flannery, Michael Meloche, and seminar participants at the New University of Lisbon (Nova), and Universitat Pompeu Fabra for helpful comments. We thank Milka Dimitrova and Huasheng Gao for excellent research assistance. Li acknowledges the financial support from the Social Sciences and Humanities Research Council of Canada. All remaining errors are our own. † Department of Finance-2-D2-08, Università Bocconi, Via G. Röntgen, 20136 Milano, Italy, (+39) 02.5836.5346, [email protected]. ‡ Department of Finance-2-D2-02, Università Bocconi, Via G. Röntgen, 20136 Milano, Italy, (+39) 02.5836.5918, [email protected]. § Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver, BC V6T 1Z2, 604.822.8353, [email protected].
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Debt Structure and Debt Specialization
Abstract
This paper provides the first large sample evidence on the patterns and determinants of debt structure using a new database of publicly listed U.S. firms. Within what is generally referred to as debt financing, we are able to distinguish between commercial paper, revolving credit facilities, term loans, senior and subordinated bonds, and capital leases. We show that small and unrated firms rely exclusively on either capital leases or bank debt for financing. In contrast, financing through multiple types of debt is only observed for large firms with very high credit quality. Most sample firms concentrate their borrowing in only one of these debt types, and this debt specialization persists over time. Finally, we show that firm characteristics that are known to be associated with leverage, such as profitability and tangibility of assets, have very different effects on different types of debt. Our paper suggests that debt structure is an important part of capital structure decisions. Keywords: debt specialization, debt structure, revolving credit facilities, senior bonds, term loans JEL classification: G32
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I. Introduction
Much attention has been devoted to the issues of why firms choose between equity and debt, and
how optimal capital structure is designed to minimize the cost of financing (see the survey by
Frank and Goyal (2007) of the voluminous literature on capital structure). In this paper, we focus
on a much less studied topic in capital structure, namely debt structure. Our goals are to explore
the types of debt commonly employed by U.S. public companies, and to examine their
determinants. To our knowledge, our paper is the first to provide large sample evidence on the
subject.
The existing literature suggests a number of important and as of yet unanswered
questions concerning the patterns and determinants of debt structure: How are different types of
debt used in practice to meet corporate funding needs? Do firms tend to specialize in one or two
debt instruments, or do they borrow simultaneously from a variety of sources? How do these
choices vary with firm characteristics, including their access to the public bond markets and their
funding needs?
To answer these questions, we take advantage of a new database available through
Capital IQ, an affiliate of Standard and Poor’s, to examine debt structure of publicly listed U.S.
firms.1 Within what is generally referred to as debt financing, we are able to distinguish between
commercial paper, revolving credit facilities, term loans, senior and subordinated bonds, and
capital leases. After merging the Capital IQ database with the Compustat database, we end up
with a large panel data set that comprises 14,242 firm-year observations involving 3,332 unique
firms for the period 2001-2007. In addition to information on debt structure, the sample also
contains leverage and other firm characteristics (e.g., firm size and profitability).
Our first main finding is that firms specialize in borrowing: Most firms concentrate their
borrowing in only one of the above types of debt. As primarily an exploratory exercise, we use
1 The SEC mandated electronic submission of SEC filings in 1996. Capital IQ has been collecting information about debt structure since then. The coverage has much improved since 2001 and hence the starting point of our sample period.
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cluster analysis to search for patterns of debt structure. We identify seven different groups of
firms: Six of them are users of a single type of debt, while only one group relies simultaneously
on more than one debt instrument. This last group is mainly composed of the largest firms with
the highest credit ratings. The evidence is suggestive of the idea that an average U.S. public firm
specializes in borrowing one type of debt to meet its funding needs.
To further corroborate the above finding, we analyze conditional debt structure. We first
require firms to allocate a significant fraction of their debt to a given type of debt, and we then
examine the composition of their debt structure under this condition. We find that the majority of
firms rely overwhelmingly on only one type of debt; specifically, the one which we have
conditioned upon. For example, conditioning for firms to have more than 30% of their debt in
term loans, we find that among the selected firms term loans represent approximately 70% of
their debt. We show that this result is robust to different specifications of the conditioning
threshold, including using the debt structure in the prior year. For example, over 70% of the
firms that have at least 30% of their debt in term loans in any given year continue to have at least
30% of their debt in term loans in the following year. This finding supports the idea that debt
specialization is not only a widespread phenomenon, but also a persistent one.
Second, we show that a key factor for understanding debt structure is credit quality. We
find that debt structure varies substantially between not only unrated and rated firms but also
across firms with different credit ratings: Large and high credit quality firms tend to have access
to different sources of financing, while small and unrated firms rely exclusively on either capital
leases or bank debt for financing. Faulkender and Petersen (2006) show that firms that do not
have access to the public debt markets, as measured by not having a debt rating, tend to have
lower debt ratios. Our finding highlights that the actual credit rating, a comprehensive measure
of firm credit worthiness, affects firm access to different sources of financing.
Third, we find that there are asymmetric changes in debt structure in response to rating
downgrades versus upgrades. In particular, downgrades are associated with decreases in bank
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debt and increases in senior bonds, while upgrades are only associated with increases in
revolving credit facilities. Sufi (2009) finds that firms with high cash flows are more likely to
obtain bank lines of credit. Our finding further shows that firms with improved credit quality
increase their usage of revolving credit facilities, suggesting that credit ratings are an important
metric in banks’ lending decisions.
Finally, we address the question of how choices of different types of debt vary with firm
characteristics, including their access to the public bond markets and their funding needs. We
rely on some recent papers in capital structure (see for example, Fama and French (2002) and
Lemmon, Roberts and Zender (2008)) to identify firm characteristics that are known to be
associated with cross-sectional variations in leverage, including profitability, asset tangibility,
market to book ratios, firm size, cash flow volatility, and the dividend payer dummy variable.
We find that the effects these firm characteristics have on leverage and on types of debt can vary
substantially. For example, the previous literature has shown profitability to be negatively and
significantly associated with leverage as predicted by the pecking order theory. Our analysis
corroborates this finding, and further shows that this negative association is mainly driven by
two types of debt: senior bonds and capital leases. In contrast, profitability is positively and
significantly associated with revolving credit and term loans. These results indicate that using a
gross measure of leverage such as total debt can be misleading, as it hides heterogeneity across
various types of debt. Further, there are some significant non-linear relations between actual
credit ratings and types of debt beyond the usual categorization of firms being rated or not. For
example, the amount of senior bonds is increasing in credit quality, peaks at the rating of A, and
then is decreasing in credit quality as the latter further improves. Importantly, we show that the
presence of a financing gap is mainly met by firms issuing senior and subordinated bonds,
complementing prior work by Frank and Goyal (2003) and Lemmon and Zender (2009) that
focuses on the role of financing gap in testing capital structure theories. We conclude that the
choice of using different types of debt instruments is also an important financing decision.
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Our paper is most related to Rauh and Sufi (2010) who examine types, sources, and
priorities of debt using a sample of 305 rated public firms. Our work differs from theirs in the
following aspects. First, our much larger sample allows us to examine the financing patterns of
unrated firms as well, while the Rauh and Sufi sample is limited to rated firms. Second, as a
result of the broader sample, we are able to uncover the phenomenon of debt specialization in
firm financing behavior, which is otherwise unobservable. Indeed, Rauh and Sufi conclude that
financing through multiple sources of debt is the norm among large and rated firms in their
sample, which we confirm only among a subsample of our firms. We are also able to show that
smaller firms with no or poor credit ratings focus on only one type of debt. Finally, we present
new evidence on how the presence of a financing gap affects choices of debt instruments.
The outline of the paper is as follows. Section II reviews the related literature that
motivates the current study. Section III describes our data and provides an overview of debt
structure. Section IV presents evidence that suggests specialization in debt structure. Section V
provides our explanations for the observed financing pattern. Section VI carries out various
robustness checks on our main findings and some additional investigations. Finally, Section VII
summarizes our findings and concludes.
II. Literature Review
The literature on capital structure is vast and we only selectively review empirical papers
that primarily examine the structure of debt rather than its level.2
The first strand of the literature examines the role of bank relationships, sources of debt,
and growth opportunities in capital structure decisions. Houston and James (1996) show that
reliance on bank borrowing depends on firm size, the importance of growth opportunities and
intangible assets, leverage, the number of bank relationships, and the firm’s access to public debt
2 As such, we also bypass a strand of the literature that examines debt maturity (e.g., Barclay, and Smith (1995), Guedes and Opler (1996), Stohs and Mauer (1996), and Billett, King and Mauer (2007)).
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markets. They find that reliance on bank borrowing is decreasing in firm size and leverage,
suggesting that the banks specialize in lending to smaller, less risky firms. They also find that the
relation between bank borrowing and the importance of growth opportunities is negative for
firms with a single bank relationship, and positive for firms with multiple banking relationships
and public debt.
Johnson (1997) focuses on the relation between sources of debt (bank debt, non-bank
private debt, and public debt) and firm characteristics. He finds that firms use more public debt if
they have lower information and monitoring costs, lower likelihood and costs of inefficient
liquidation, and weaker incentive to harm the lenders.
Goyal, Lehn, and Racic (2002) examine the relation between growth opportunities and
corporate debt policy using the case of the U.S. defense industry during the late 1980s to early
1990s as a natural experiment. They show that when growth opportunities decline, firms increase
their debt level, lengthen debt maturity, decrease private debt relative to public debt, and
decrease senior debt.
Using a sample of 305 randomly selected non-financial firms for the period 1996-2006
(2,453 firm-year observations), Rauh and Sufi (2010) examine the determinants of debt structure.
They first show that almost three quarters of their sample firm-year observations have more than
two types of debt, and that a quarter of the sample firms has no significant one-year change in
their total debt but significant change in their composition in debt. Further, high credit quality
firms (BBB and higher) primarily use two tiers of capital: senior unsecured debt and equity. Low
credit quality firms (BB and lower) tend to use several tiers of debt including secured, senior
unsecured, and subordinated issues. Finally, they establish the causal relation between changes in
credit quality and choices of different debt instruments by focusing on a sample of “fallen
angels.”
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The second strand of the literature focuses on the new issue decisions. Hadlock and
James (2002) ask why some firms borrow from public sources while others borrow from banks.
They find that undervalued firms tend to borrow from banks, because banks have the ability to
accurately price financial claims and thus alleviate any information asymmetry problem. Also,
the sensitivity of choosing bank debt to solve information problem is greater for firms with no
public debt, which is the authors’ proxy for the low contracting costs of bank debt.
Denis and Mihov (2003) examine the choice among bank debt, non-bank private debt,
and public debt using evidence from new corporate borrowings. They find that new debt choices
are linked with prior financing decisions: Firms with public debt outstanding are likely to issue
public debt, while firms with no reputation in the credit markets resort predominantly to bank
debt. Controlling for the existing mix of debt claims, they show that firms with the highest credit
quality use public debt; firms with medium credit quality borrow from banks; and firms with the
lowest credit quality use non-bank private debt.
Gomes and Phillips (2009) examine why public firms issue different types of securities.
They show that asymmetric information is a major determinant of security issuance decisions
within private and public markets as well as across markets. Further, firms that switch from
issuing public securities to private equity and convertibles experience increases in the extent of
asymmetric information, while firms that switch from issuing private securities to public equity
experience decreases in the extent of asymmetric information.
In summary, most prior studies have examined the cross-sectional determinants of
different sources of debt financing in a piecemeal fashion: choices between public versus private
debt (including differentiating between bank and non-bank private debt), choices between public
versus private issues, with Rauh and Sufi (2010) as the notable exception. Our paper focuses on
the patterns and determinants of debt structure using a more detailed and comprehensive dataset
on types of debt, thus complementing prior studies of the determinants of new security issues.
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III. Data Overview
A. Sample Description
We start with U.S. public firms traded on AMEX, NASDAQ, and NYSE covered by both
Capital IQ (CIQ) and Compustat from 2001 to 2007. CIQ compiles capital structure details by
going through financial footnotes contained in firms’ 10K SEC filings.3 We remove utilities (SIC
codes 4900-4949) and financials firms (SIC codes 6000-6999) and end up with 34,923 firm-year
observations. We further remove 1) firm-years with missing data for any of the Compustat
variables listed in Table A1 (21,857 observations remaining); 2) firm-years with market or book
leverage outside the unit interval (as in Lemmon, Roberts and Zender (2008), 21,674
observations remaining); 3) firm-years with zero total debt (17,232 observations remaining); 4)
firm-years with missing data for any of the CIQ variables listed in Table A2 (15,717
observations remaining); and 5) firm-years for which the difference between total debt as
reported in Compustat and the sum of debt types as reported in CIQ exceeds 5% of total debt.
Our final sample has 14,242 firm-year observations involving 3,332 unique firms.
In constructing our variables we use the same definitions as in Lemmon, Roberts and
Zender (2008). The appendix provides a detailed description of the variables used in our
analysis. Total assets, book value (BV) equity, and total debt are expressed in millions of 2001
dollars deflated by the consumer price index. All variables are winsorized at the upper and lower
0.5th percentiles to mitigate the effect of outliers and data errors.
Table 1 presents descriptive statistics. Panel A contains means and medians of key firm
characteristics aggregated across all years for our sample firms (columns (1) and (2)). We show
that over the sample period, the mean (median) market leverage as measured by the ratio of total
3 Regulation S-X requires firms to detail their long-term debt instruments. Regulation S-K requires firms to discuss their liquidity, capital resources, and operating results. As a result of these regulations, firms detail their long-term debt issues and bank revolving credit facilities. Firms often also provide information on notes payable within a year (Rauh and Sufi (2009)).
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debt to the sum of total debt and market value of equity is 0.215 (0.159). In Rauh and Sufi
(2010), the sample mean (median) market leverage is 0.263 (0.238) for a sample of 305 firms
with credit ratings in at least one year from 1996 to 2006. Using a sample of non-financial, non-
utility firms from Compustat over the period 1986-2000, Faulkender and Petersen (2006) report
that the mean (median) market leverage is 0.222 (0.183) for leveraged firms. These comparisons
suggest that the leverage ratio of our sample firms is similar to that reported in prior studies.
In comparison to the sample in Rauh and Sufi, firms in our sample are less profitable, (as
measured by operating income before depreciation over assets), have less tangible assets as a
percentage of total assets, and have a similar market to book ratio of assets. The sample mean
(median) firm size in terms of total assets is $2.35 billion ($392.2 million) in 2001 constant
dollars. In contrast, the sample mean (median) firm size is $6.19 billion ($1.31 billion) in 1996
constant dollars for the Rauh and Sufi sample. About 30% of our sample firms pay out dividends
in any given year, in contrast with the evidence presented in Fama and French (2001) where the
proportion of firms paying cash dividends in 1999 is only 20.8%.
Panel A of Table 1 also presents means and medians for key firm characteristics for the
entire Compustat population (columns (3)–(4)), and for Compustat leveraged firms, i.e., firms
with positive debt, (columns (7)–(8)) over the same period of our sample. The two Compustat
samples are formed by imposing similar filters as to our sample except filters 4) and 5). Our
sample on average covers two-thirds of the Compustat population, and close to 85% of the
Compustat leveraged firms.
Columns (5)–(6), and (9)–(10) test whether our sample is different from the Compustat
population, and from the Compustat leveraged firms, respectively. We show that our sample
firms are larger, more leveraged, more profitable, have more tangible assets, and pay out
dividends more often, while firms in the Compustat population have higher market-to-book
ratios and higher cash flow volatility. Compared to the Compustat leveraged firms, our sample
firms are significantly more profitable and more likely to make dividend payments, although the
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economic significance of these differences is small. We conclude that our sample is
representative of the Compustat leveraged firms.
Table 1 Panel B presents summary statistics of key firm characteristics over time. Our
sample firms experience a gradual decline in their market leverage, a small, gradual increase in
their size, and a gradual increase in their propensity to pay dividends. Overall, however, we find
that most firm characteristics have little variation over the sample period.
B. Debt Structure Overview
CIQ decomposes total debt into seven mutually exclusive debt types: commercial paper,
revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt.4
Our appendix provides an example illustrating how CIQ collects and constructs the various debt
types. Table 2 provides detailed summary statistics for debt types (normalized by total debt).
We first show that the majority of sample firms rely on senior bonds for financing. The
sample mean (median) ratio of senior bonds to total debt is 0.361 (0.130). Second, the median
ratios of both revolving credit and term loans to total debt are zero, while the 75th percentiles are
above zero, suggesting that between a quarter and a half of the sample firms rely on revolving
credit facilities or term loans, both provided by banks. When adding up both debt types to obtain
total bank debt, we find that more than half of the sample firms employ bank debt, with the
sample mean (median) at 0.390 (0.229) (untabulated). Third, more than a quarter of sample firms
have capital leases, and less than a quarter of the sample firms use subordinated bonds. Lastly,
we show that the 95th percentile of commercial paper is zero, suggesting that less than 5% of the
sample firms use commercial paper for financing.
Total adjustment is the difference between the total debt variable obtained from
Compustat and the sum of CIQ seven debt types. When forming our sample, we have imposed
4 Other debt mostly consists of short-term borrowings. Occasionally, it takes other forms such as deferred credits, fair value adjustments related to hedging contracts, or trust-preferred securities.
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the filter that the total adjustment for firms in the sample be less than 5% of total debt. After this
filter, there is little discrepancy between the sum of debt types from CIQ and total debt from
Compustat: Both mean and median values of total adjustment are zero, and only the 99th
percentile of this variable is slightly above 3% of total debt. This result, together with our wide
coverage of Compustat leveraged firms, is reassuring about the CIQ’s data quality.
It is interesting to compare the composition of debt in our sample firms with that reported
by Rauh and Sufi (2010). Defining bank debt as the sum of revolving credit and term loans, they
report a mean total debt to capital ratio of 0.502 for their sample firms, with a mean bank debt to
total debt ratio at 0.263, and a mean bonds (the sum of senior and subordinated bonds) to total
debt ratio at 0.382. About two thirds of their sample firms use bonds and bank debt. Given that
Rauh and Sufi have a sample of larger firms with better ratings than ours, bank debt appears to
be less important for their sample than for our own. About 15% of their firms use commercial
paper, a third uses capital leases, and about a quarter uses convertible debt. As we discuss in
more detail below, it appears that our sample firms employ fewer types of debt instruments at
any point in time, as compared to the larger firms in the Rauh and Sufi sample.
In summary, although there are many different types of debt, for our sample of firms,
senior bonds are the most commonly employed debt instrument, followed by revolving credit
and term loans. In the rest of the paper, we provide a more detailed investigation of patterns and
determinants of debt structure.
C. Credit Ratings and Debt Structure
The literature has previously examined the relation between credit ratings and leverage.
Diamond (1991), Chemmanurs and Fulghieri (1994), and Bolton and Freixas (2000) have shown
that credit quality is the primary source of variation driving a firm’s optimal choices of different
types of debt instruments. Faulkender and Petersen (2006) examine the role of the source of
capital in firms’ financing decisions. Using a dummy variable for being rated as a proxy for firm
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access to public bond markets, they find that firms with access have substantially more debt.
Kisgen (2006) finds that firm credit ratings affect capital structure decisions: Firms near a credit
rating upgrade or downgrade issue less debt. Lemmon and Zender (2009) use the likelihood of
being rated as a proxy for debt capacity. They show that after accounting for debt capacity, the
pecking order appears to be a good description of a firm’s financing behavior. Rauh and Sufi
(2010) find that high credit quality firms (BBB and higher) rely almost exclusively on two tiers
of capital—senior unsecured debt and equity—while lower credit quality firms (BB and lower)
use multiple tiers of debt including secured, senior unsecured, and subordinated issues.
Table 3 presents an overview of the relation between credit ratings and debt structure. We
consider a firm-year to be rated if the firm has at least one monthly Standard & Poor’s long-term
issuer rating, as recorded in Compustat (data item 280). About a third of our sample firms are
rated. In untabulated analysis, we find that there is little temporal variation in the fraction of
firms being rated over time.5
Panel A presents differences in the use of various debt types between unrated and rated
firms in our sample. We show that revolving credit facilities and term loans together, on average,
account for about half of unrated firms’ total debt, while senior bonds account for about 30% of
their total debt. Unrated firms are also the heaviest users of capital leases. Overall, unrated firms
use significantly less commercial paper and senior and subordinated bonds, and significantly
more revolving credit and capital leases than their rated counterparts, suggesting that both banks
and lessors have a comparative advantage in dealing with information asymmetry associated
with unrated firms. At the bottom of Panel A, we also present sample mean (median) market
leverage of unrated and rated firms. We show that consistent with Faulkender and Petersen
5 Using Compustat firms over the period 1986-2000, Faulkender and Petersen (2006) show that only 19% (21%) of firms (with positive debt) have debt ratings. They conclude that public debt is uncommon. In unreported analysis, we find that our sample firms are larger (both in terms of sales and book value of assets), and have a higher book leverage ratio than those in Faulkender and Petersen (2006). This is consistent with Lemmon and Zender’s (2009) finding that large firms with high leverage are more likely to be rated. Focusing on 305 randomly chosen Compustat firms with a long-term issuer rating in at least one year from 1996-2006, Rauh and Sufi (2009) show that three-quarters of their firm-year observations are rated.
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(2006), unrated firms tend to employ less debt with a mean (median) market leverage ratio of
17% (11%) than rated firms with a mean (median) ratio of 31% (26%).
To examine the relation between credit ratings and debt structure, we first assign to each
monthly S&P letter rating class an integer number ranging from 1 (AAA) to 22 (D). Then, for
each rated firm-year we round the average monthly rating to the nearest integer, and refer to it as
the firm rating in a given year. In unreported analysis, we find that 16.7% of our sample firms
have a credit rating of A and higher. Close to 43% of our sample firms have investment grade
ratings (equal to or higher than BBB-).6
Panel B provides differences in the use of various debt types (as a share of total debt)
across a broad rating spectrum. We first show that there is a non-linear relation between credit
quality and the amount of senior bonds used by our sample firms: The amount of senior bonds is
increasing in credit quality, peaks at the rating of A, and then is decreasing in credit quality.
Second, term loans and subordinated bonds are most heavily used by speculative grade (equal to
or lower than BB) firms. Third, commercial paper is used almost exclusively by investment
grade firms, especially AAA- and AA-rated firms.
We conclude that credit quality affects both the composition and the level of debt, as
shown by Faulkender and Petersen (2006) and Kisgen (2006) for the latter. Later in our
multivariate analysis, we will include a dummy for each different rating class and for firms that
are not rated to control for the complex relation between credit ratings and debt structure.7
IV. Debt Specialization and Persistence
A. Cluster Analysis
6 Using Compustat firms from 1986-2001, Kisgen (2006) shows that 44.2% of his sample firms have a credit rating of A and higher, and 69.1% of his sample have investment grade ratings. Rauh and Sufi (2009) report 21.7% of their firms have a credit rating of A and higher, and close to half has investment grade ratings. The difference in rating distributions between the Kisgen’s sample and our sample is probably due to the fact that his sample includes financial and utilities which tend to have better ratings than industrial firms. 7 We assign an integer equal to 23 to the variable “Rating” for an unrated firm-year observation.
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Our first piece of evidence on specialization in borrowing comes from cluster analysis,
which is commonly used to discover unknown structures in data by maximizing variance (in
terms of the Euclidean distance) between clusters and minimizing it within clusters. Once we
separate data in clusters, we effectively remove much of the variance in any of the debt types.
Table 4 Panel A presents summary statistics for debt structure and key firm characteristics across
the identified clusters using firm-year observations, sorted according to ascending median firm
size.8
Cluster 1 includes the smallest firms in our sample. These firms have much lower
profitability and asset tangibility, but much higher M/B ratios and cash flow volatility than the
average firm in our sample. They are also much less likely to pay dividends and are essentially
unrated. These firms tend to use predominantly capital leases for financing. The group mean
(median) capital leases to total debt ratio is 0.945 (1.000). It is worth noting that this group of
firms has the lowest leverage among the sample firms. The group mean (median) market
leverage is 0.034 (0.003), compared to the full sample mean (median) at 0.215 (0.159).
Cluster 2 includes the second smallest firms in our sample. These firms are less likely to
make dividend payments and to be rated as compared to the average firm in our sample. They
tend to use mostly term loans. The group mean (median) term loans to total debt ratio is 0.819
(0.875).
Cluster 3 has the smallest number of observations. The group median firm size suggests
that firms in this cluster actually are quite small in size. These firms tend to use predominantly
other debt (see footnote 4 for a description). The group mean (median) other debt to total debt
ratio is 0.913 (1.000). This group of firms has the second lowest leverage among the sample
firms. The group mean (median) market leverage is 0.130 (0.044).
8 Firm characteristics are measured contemporaneously. Using lagged measures gives qualitatively the same results except that sample size is slightly smaller.
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Cluster 4 is the set of firms that have higher profitability and lower M/B ratios than the
average firm in our sample. Less than a tenth of them are rated as compared to a third of our
sample firms that are, and the credit quality of these firms is slightly better than that of cluster 1
firms. These firms tend to use mostly revolving credit. The group mean (median) revolving
credit to total debt ratio is 0.829 (0.884).
Firms in cluster 5 are considerably bigger: The median firm size is more than three times
larger than that of the firms in cluster 4. These firms are significantly less likely to make
dividend payments as compared to the average firm in the sample: Only 13.4% of them pay
dividends. More than half of them are rated. These firms tend to use mostly subordinated bonds.
The group mean (median) subordinated notes and bonds to total debt ratio is 0.799 (0.858). This
set of firms appears to have the second highest leverage among the sample firms. The group
mean (median) market leverage is 0.291 (0.241) as compared to an average mean (median)
market leverage of 0.215 (0.159).
Cluster 6 includes the second largest firms in the sample. They are more likely to make
dividend payments as compared to the average firm in the sample. Close to half of them are
rated, and they appear to have slightly better credit quality than the average firm in the sample.
These firms tend to use predominantly senior bonds. The group mean (median) senior bonds to
total debt ratio is 0.925 (0.965).
Finally, cluster 7 includes the largest firms in the sample. These firms are more
profitable, and have more tangible assets and lower cash flow volatility than the average firm in
the sample. They are also more likely to make dividend payments. More than half of them are
rated, and they have much better credit quality than the rest of the sample firms. These firms tend
to use a mix of senior bonds, revolving credit, and term loans. The group mean (median) senior
notes and bonds, revolving credit, and term loans to total debt ratio is 0.542 (0.565), 0.173
(0.118), and 0.116 (0.002), respectively. It is worth noting that this group of firms has the highest
leverage among the sample firms.
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In summary, the evidence from cluster analysis suggests that firms specialize in
borrowing from one type of debt. The smallest firms exclusively use capital leases for financing.
This is probably due to the fact that they have no other source of capital available. Larger firms
predominantly rely on only one major source of financing, which may be term loans, revolving
credit, subordinated bonds, or senior bonds. Only the largest and least risky firms simultaneously
employ multiple types of debt.
Our findings on debt specialization are in stark contrast to Rauh and Sufi (2010), who
show that their average sample firm employs multiple types of debt simultaneously. We attribute
the difference in findings to the different samples examined in their paper and ours. They focus
primarily on large and rated firms, while in our sample only a third of the firms are rated. From
our findings we conclude that across public firms, specialization—not diversity—in types of debt
is the dominant phenomenon.
B. Conditional Debt Structure
Our second piece of evidence on debt specialization comes from examining conditional
debt structures. Table 5 Panel A presents the shares of firm-year observations conditional on a
particular debt type exceeding 30% of total debt (significant user). Looking across the rows in
Panel A, we find that significant users of one debt type are rarely significant users of any other
debt types. This is true with the exception of significant users of commercial paper which are all
very large firms that simultaneously employ a significant amount of senior bonds. In all other
cases, the table indicates that if a firm’s use of a particular type of debt exceeds 30% of its debt,
that type is likely to be its only source of debt financing. Furthermore, the last column shows that
close to 45% of the sample firms are significant users of senior bonds, and about a quarter of the
sample are significant users of either revolving credit or term loans. These results provide further
support to the idea that firms specialize in borrowing from one type of debt. If firms were
16
simultaneously employing multiple types of debt, we would have observed few firms exceeding
30% of their debt from a single source of financing.
Panel B presents both the mean and median ratios of each debt type to total debt
conditional on a particular debt type exceeding 30% of total debt. Specifically, we first impose
the condition that a firm’s use of a particular debt type exceeds 30% of its debt, thus identifying
a subset of firms. Then, for this subset we compute mean and median ratios of all debt types to
total debt and test the null hypothesis that the mean (median) ratio is less than 30%. We also
report the number of firm-year observations whose particular debt type is strictly greater than
30% of total debt. For example, in the first row we require that the amount of commercial paper
exceeds 30% of debt. This leaves us with 129 observations. For these observations the mean
(median) ratio of commercial paper to total debt is 0.413 (0.453), the mean (median) ratio of
revolving credit to total debt is 0.033 (0.000), and so on for all other types.
Examining the numbers in bold face along the diagonal line of the panel, we show that
the ratio of a given debt type to total debt is between 70% and 80%, conditional on the ratio of
that particular type of debt to total debt exceeding the threshold of 30% (again with the notable
exception of commercial paper). Further, the t- and median tests strongly reject the null that the
mean and median ratios of various debt types are below 30%. The off-diagonal numbers reveal
that significant reliance on more than one debt type is rarely observed: The exception is that the
significant users of commercial paper are also significant users of senior bonds. Indeed, these
results highlight the general pattern that very few firms use other sources of debt over and
beyond the one which we condition upon. This is strong evidence of firms borrowing primarily
from a single type of debt.
We conclude that once firms decide to employ a significant portion of a particular type of
debt, they tend to overwhelmingly rely on that particular type of debt for financing. The next
natural question to ask is whether specialization in borrowing is persistent for firms across time.
17
C. Persistence in Debt Specialization
Debt structure is likely to be influenced by firm characteristics, for example, both
Johnson (1997), and Goyal, Lehn, and Racic (2002) show that firm size is positively related to
public debt and negatively related to bank debt. Insofar as these firm characteristics are persistent
over time (as shown in Table 1 Panel B), we expect to observe persistence in debt specialization.
We examine this issue in Tables 6 and 7.
Table 6 replicates the analysis in Table 5 with conditions imposed on debt types in the
previous year. For example, in the first row of Table 6 Panel A we examine the percentage of
firm-year observations for which the ratio of each debt type to total debt is strictly greater than
30%, conditional on the ratio of that particular debt type to total debt being strictly greater than
30% in the previous fiscal year. As we are now conditioning on previous year debt, the number
of firms in each row is smaller than the parallel row of Table 5 Panel A.
Table 6 Panel A shows that, with the exception of commercial paper, firms exhibit
persistence in debt structure. For example, 73.3% of the firms with their ratio of revolving credit
to total debt greater than 30% in the previous year have the same ratio greater than 30% in the
current year. Panel B reports summary statistics for debt structure conditional on the ratio of a
particular debt type to total debt being greater than 30% in the previous year. The first row shows
that firms use commercial paper as a transition before resorting to senior bonds: 77.8% (=
81/104) of the firms that specialize in commercial paper borrowing in the previous year now
have more than 50% of debt in senior bonds in the current year. In all other cases, the two
statistical tests that we perform indicate significant persistence in firm debt structure: Only along
the diagonal line of Panel B are the mean and median ratios of various debt types to total debt far
greater than the threshold of 30%.
18
Having established persistence in debt specialization, we then examine the possible
explanations for persistence in a multivariate setting by estimating the following regression
model:
t,i
t,iDt,iVt,iSt,iMB
t,iTt,iPt,it,iCt,i
FEYearFERatingFEIndustryPayerDividendVolatilityCFSizeB/M
yTangibilitityProfitabilTypeDebtTypeDebt
εββββ
βββα
++++
++++
+++=
−−−−
−−−
1111
111 (1)
The explanatory variables include known determinants of leverage such as profitability, asset
tangibility, M/B, firm size, cash flow volatility, and the dividend payer dummy variable (for
example, see Rajan and Zingales (1995), and Faulkender and Petersen (2006)), all measured in
the previous year, together with the lagged measure of the particular debt type being examined.
We also include industry (measured at the 2-digit SIC) fixed effects,9 rating fixed effects,10 and
year fixed effects, and standard errors are clustered at the firm level following Sufi (2009) and
Lemmon and Zender (2009). Equation (1) takes the lead-lag specification, and as a result, the
sample size is slightly different from that for the summary statistics.
Table 7 provides our multivariate results on persistence in specialization. Panel A reports
OLS regression results, while Panel B reports probit regression results, where the dependent
variable takes a value of one if the ratio of that particular debt type to total debt is strictly greater
than 30% and zero otherwise.11 We use probit to account for the large number of zeros across
different debt types (see Table 2).
The main result of Table 7 is that debt structure persists over time: The coefficients on
the lagged measures of each debt type are all positive and statistically significant at the 1% level.
The results of the probit regressions are consistent with those of the OLS specification. Denis
9 In the literature, both the 4-digit SIC and 2-digit SIC are used to capture the industry effects. Our results are robust for both choices. 10 As we have shown in Table 3 Panel B, conditional on a particular rating class, there is substantial heterogeneity in debt structure. Therefore, we opt here for rating class dummies that are finer than the investment/speculative/unrated grid previously used in the literature (for example, Guedes and Opler (1996), and Billett et al. (2007)). 11 The sample sizes differ from Panel A due to the fact that some of our independent variables explain the binary dependent variable perfectly and Stata automatically drops them.
19
and Mihov (2003) show that firms exhibit persistence in the type of securities to issue in new
corporate borrowings. We further confirm that there is persistence in borrowing from one type of
debt measured at the capital stock level.
V. Explaining Debt Structure
A. Rating Changes and Debt Structure
In this section we examine more closely the relation between ratings and debt structure.
Previously we have established the fact that ratings are a significant determinant of how firms
raise debt capital. Firms with better rating are able to tap more diversified types of debt, while
firms that are not rated rely more on bank debt. We now explore how ratings affect debt choices
in a dynamic setting, as shown by Kisgen (2006) for capital structure.
Table 8 examines the relation between changes in rating and changes in debt structure for
the three year period centered on the year of a rating change.12 As a benchmark, in the first
column of each table, we present the relation between changes in rating and changes in market
leverage. A downgrade (upgrade) means that the rating of the firm has decreased (increased)
during the current year of observation.13 With downgrade(–1) and upgrade(–1) we refer to
variables measured one year before the rating change. Similarly, downgrade(+1) and
upgrade(+1) refers to variables measured one year after the rating change. We only consider
events that are not contaminated by any other rating change in the opposite direction during the
event window examined. We therefore identify 334 downgrades and 277 upgrades in our sample.
Panel A shows that leverage increases significantly in the year prior to the downgrade
and decreases significantly both at the time of the downgrade and in the year after. This suggests
that credit quality deteriorates following an increase in leverage. Further, in the year prior to the
12 Rating changes that take place either during 2001 or 2007 automatically drop out of the analysis as we do not have data for changes in debt types for 2000 and 2008. 13 The addition of a plus or a minus modifier to the rating class, e.g., from A to A+, is considered as a rating change.
20
downgrade, commercial paper experiences a significant decline, a trend that continues over the
subsequent two years. In contrast, senior bonds significantly rise in the year before and the year
of the rating change. These findings indicate that as their rating worsens firms issue less
commercial paper, as it requires high credit quality, and shift to bond financing. Both Graham
and Harvey (2001) and Kisgen (2006) observe that corporate managers place a high priority on
maintaining their existing credit rating. We offer one explanation for the observed pattern:
Downgrades limit firms’ access to multiple sources of financing.
In terms of upgrades, we find that leverage decreases significantly in the year prior to and
during the upgrade. Consistent with our findings about downgrades, a reduction in market
leverage leads to higher credit quality and an upgrade. Accordingly, in the year prior to the
upgrade, subordinated bonds experience a moderate decline that continues in the next two years.
This shift is accompanied by an increase in senior bonds, thus suggesting a reallocation of debt
from subordinated to senior bonds. We also observe a significant increase in the use of
commercial paper. In summary, an upgrade is associated with a decrease in leverage, a decrease
in the use of subordinated bonds in favor of senior bonds, and a contemporaneous increase in the
use of commercial paper.
To examine whether and how changes in credit quality affect a firm’s debt structure, we
run the following regression on our samples of downgrades and upgrades:
t,i
t,iAt,iCt,iBt,i
FEYearFERatingFEIndustryAfterYearYearCurrentBeforeYearTypeDebt
εβββα
++++
+++= (2)
The coefficients of interest are βB, βC, and βA, which represent the change in the dependent
variable for the year before, at, and after the rating change, respectively.
Table 8 Panel B presents the downgrade regression results. There is a significant increase
in market leverage in the year before the downgrade, which is consistent with the univariate
evidence in Panel A. Moreover, we document that firms reduce bank debt (including both
revolving credit and term loans) and increase senior bonds in the year after the downgrade.
21
Panel C presents the upgrade regression results. We show that there is a significant
decrease in market leverage before the upgrade, again in line with evidence in Panel A. There is
some evidence of a significant drop in term loans in the year before the upgrade, and a
significant increase in revolving credit after the upgrade. Sufi (2009) finds that firms with high
cash flows are more likely to obtain bank lines of credit. We further show that firms with
improved credit quality increase their usage of revolving credit facilities.
We conclude that downgrades are associated with significantly reduced use of bank debt,
and increased use of senior bonds. Upgrades are associated with significantly increased use of
revolving credit. Our results confirm some of the findings in Rauh and Sufi (2010), and further
highlight how debt structure reacts asymmetrically with respect to downgrades versus upgrades.
B. Determinants of Capital and Debt Structures
So far, we have focused on the composition of debt and accordingly we have expressed
debt types as a percentage of total debt. We now compare and contrast the determinants of
capital and debt structures. To this end we divide each debt type by total capital, i.e. the sum of
book value of debt and market value of equity, which is typically used to compute market
leverage (see Table A1 in the appendix). Scaling debt types by total capital enables us to
decompose market leverage into its constituents.
To examine determinants of capital and debt structures, we estimate the following
regression model:
t,i
t,iDt,iVt,iSt,iMB
t,iTt,iPt,i
FEYearFERatingFEIndustryPayerDividendVolatilityCFSizeB/M
yTangibilitityProfitabilTypeDebt
εββββ
ββα
++++
++++
++=
−−−−
−−
1111
11 (3)
The regressors largely overlap with those in Equation (1). To account for the fact that the
decisions of employing different types of debt are not independent, we employ the Seemingly
Unrelated Regression (SUR) specification when estimating all debt types. We further restrict the
22
sum of coefficients on each control variable in the debt type regressions to be equal to the
coefficient on the same control variable in the market leverage regression due to the fact that the
sum of all debt types equals market leverage.
Table 9 presents the regression results. To establish a benchmark, the first column
presents estimation results when market leverage is the dependent variable. We find that
consistent with prior research such as Frank and Goyal (2007) and Lemmon, Roberts, and
Zender (2008), profitability, M/B, cash flow volatility, and the dividend payer dummy are
negatively and significantly associated with market leverage, while asset tangibility and firm size
are positively and significantly associated with market leverage.
More importantly, we show that some of the significant relations between the above firm
characteristics and leverage are driven by certain debt types but definitely not all. For example,
the negative association between profitability and leverage is mainly driven by senior bonds,
capital leases, and other debt. In contrast, profitability is positively and significantly associated
with revolving credit and term loans. This suggests that banks are relatively more risk averse
than other investors, thus confirming results previously shown by Houston and James (1996).
The positive association between tangibility and leverage is mainly driven by revolving credit,
term loans, senior bonds and capital leases, whereas there is a negative association between
tangibility and subordinated bonds. These findings support the idea that subordinated bonds are
likely to be unsecured, while term loans and senior bonds are generally secured. M/B ratios,
which proxy for growth opportunities, are negatively and significantly associated with revolving
credit and term loans, consistent with findings in Johnson (1997) but opposite to findings in
Goyal, Lehn, and Racic (2002). Overall, our evidence suggests that banks specialize in lending to
small and less risky firms.
Finally, we demonstrate that to understand the exact relation between firm characteristics
and debt types, it is important to separate bank debt into its two components: revolving credit
and term loans; and bond debt into its two components: senior and subordinate bonds. For
23
example, the effect of tangibility on term loans is twice the size of that on revolving credit,
suggesting that banks put much more emphasis on collateral when providing long-term
financing. Within bond financing, tangibility is negatively and significantly associated with
subordinated bonds, while it is positively associated with senior bonds.
Overall, our results capture several salient features of the choices of different types of
debt within a firm’s capital structure. Small, profitable, low growth, and low risk firms tend to
borrow primarily from banks. Large, low growth, and low risk firms use mainly senior bonds for
financing. Smaller, less profitable firms with a more tangible asset base use capital leases.
A direct comparison of our results with Rauh and Sufi (2010) is not possible, because our
debt types do not exactly overlap with the Rauh and Sufi’s classification. In addition, in our
regressions we control for more firm characteristics than they do. However, in general terms our
results are consistent with Rauh and Sufi in that our findings show that profitability is positively
and significantly associated with bank debt, while growth opportunities and firm size are
negatively and significantly associated with bank debt. Compared to Rauh and Sufi, we find a
more robust association between some firm characteristics and debt types, as shown by the high
adjusted R-squares of our regressions.
C. Financing Gap and Debt Structure
One long standing debate in the capital structure literature is whether the trade-off or the
pecking order theory is better at explaining financing decisions (see for example, Frank and
Goyal (2003), and Lemmon and Zender (2009)). Following this strand of literature, we explore
how the financing gap affects the composition of debt. As Frank and Goyal (2003) suggest, we
define the financing gap as the sum of dividends, investment, and change in working capital;
minus the cash flow after interest and taxes; and then we scale this sum by lagged total capital.
This measure of financing gap is intended to capture a firm’s funding need.
24
To examine the effect of financing gap on changes of debt structure we estimate the
following regression:
iiDEFiS
iMBiTiPi
GAPSizeB/MyTangibilitityProfitabilTypeDebt
εβ∆β∆β∆β∆βα∆
++++++= (4)
∆ denotes the change in debt type scaled by lagged total capital. GAP is the financing gap,
defined as above. We employ the SUR specification and restrict the regression coefficients
accordingly as in Equation (3) when estimating changes to debt types.
Table 10 presents the regression results. We first show that the change in profitability is
negatively and significantly associated with the change in leverage, while the changes in asset
tangibility and firm size are positively and significantly associated with the change in leverage.
These findings are consistent with Frank and Goyal (2003). Second, we document that the role of
the financing gap in capital structure does not support predictions of the pecking order theory:
The coefficient in front of the financing gap in the leverage regression is significantly different
from unity, again consistent with Frank and Goyal (2003). Third and more importantly, we show
that the financing gap is positively and significantly associated with senior and subordinated
bonds. The economic interpretation of this result is that for every dollar of the financing gap,
about 6 (3) cents are met by issuing senior (subordinated) bonds. Park (2000) predicts that senior
debt holders have the strongest monitoring incentives given their priority to receive payoffs from
their monitoring effort. Our evidence supports that prediction by showing that when firms are
under financial duress and hence require external financing, senior debt is most frequently used
because of their holders’ strong incentives to monitor.
VI. Additional Investigation
In this section, we implement various robustness checks on our main results and some
additional analyses. First, to mitigate the fact that in our unbalanced panel some firms with
25
complete time series observations from 2000 to 2007 receive more weight than other firms with
fewer observations, we implement the cluster analysis by using the time series average of each
debt type and hence firm-level observations. Table 11 Panel A presents the result. We still
observe seven distinct clusters of firms with six of them engaged in debt specialization, even
though the extent of specialization is somehow weakened based on firm-level observations as
compared to that based on firm-year observations. This drop in the degree of specialization is
expected if debt specialization is indeed a persistent phenomenon (as shown in Tables 6 and 7),
while the time series averaging (as reported in Panel A) ignores the temporal persistence in debt
specialization.
Second, we also re-examine conditional debt structures and show that our results are
robust to a different threshold. In Panels B and C we use 10% as the threshold. For example, in
the first row of the table, we require firm-year observations with the ratio of commercial paper to
total debt exceeding 10%. We find that the patterns of specialization in borrowing are
qualitatively similar to what we observe in Table 5 Panels A and B where we use 30% as the
threshold.
Third, we further assess persistence in debt structures by replicating the analysis in Table
5 with conditions imposed on debt types observed three-years prior. Panels D and E contain the
results. For example, in the first row of Panel D we report the percentage of firm-year
observations for which the ratio of the amount of a particular debt type to total debt is strictly
greater than 30%, conditional on the ratio of the amount of that debt type to total debt being
strictly greater than 30% three fiscal-years ago. Not surprisingly, the number of firms in each
row is much smaller than the parallel row of Table 5 Panel A once we go back by three years.
Looking across the diagonal line of Panel E, we still observe persistence among five debt types:
revolving credit, term loans, senior and subordinated bonds, and capital leases.
Finally, we examine the effect of drastic changes in credit ratings on debt structure. Table
12 presents changes in debt structure associated with a major downgrade or an upgrade. We use
26
the term fallen angel to refer to a downgrade from investment grade to speculative grade, and the
term rising star to refer to an upgrade from speculative grade to investment grade following
Rauh and Sufi (2010). As these major changes in rating are relatively rare, the sample size
becomes relatively small.
Differing from what we observe in the broader downgrade sample, fallen angels do not
experience a reduction in their use of commercial paper, although they do increase term loans
and capital leases in the year after the downgrade. Similar to the upgrade sample, rising stars
significantly reduce leverage in the year before the upgrade, but not during the year of the rating
change. Further differing from the upgrade sample, rising stars have increased their use of
revolving credit in the year of the upgrade. For comparison, using a sample of fallen angels Rauh
and Sufi (2010) find that after a downgrade, bank debt increases, senior unsecured debt
(including program debt, bank debt, and bonds) decreases, and subordinated debt (including
bonds and convertible) increases.
VII. Conclusions
This paper provides a comprehensive analysis of the patterns and determinants of debt
structure. We first show that debt structure varies substantially between unrated and rated firms
and across a wide spectrum of credit ratings. Large and high credit quality firms tend to have
access to different sources of financing, while small and unrated firms rely exclusively on either
capital leases or bank debt for financing.
We then present fresh evidence on firms’ specialization in borrowing. Cluster analysis
identifies seven distinct groups of firms, six of which concentrate their borrowing through one
type of debt. Conditional on borrowing a significant fraction of their debt through a particular
type of debt instrument, firms are found to specialize in borrowing by limiting themselves to a
few debt instruments.
27
We further show that debt structure exhibits a substantial degree of persistence:
Controlling for standard firm characteristics known to affect capital structure, we find that each
debt type is positively and significantly related to its prior level. Moreover, common factors that
are known to explain capital structure, such as profitability, growth opportunities, and credit
ratings, also tend to have very different influences on different types of debt.
We also show that asymmetric changes in debt structure occur in response to rating
downgrades and upgrades. Specifically, downgrades are significantly associated with reduced
use of bank debt, and increased use of senior bonds. In contrast, upgrades are significantly
associated with increased use of revolving credit. Finally, we find that bonds are most frequently
used to meet the financing gap.
Based on these results, we conclude that the choice of debt structure is an important part
of capital structure decisions.
28
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30
APPENDIX I. Variable Definitions Table A1. Description of Compustat Variables
Variable Construction Firm Size Logarithm of Book Value of Total Assets (6) Preferred Stock Max[Preferred Stock Liquidating Value (10), Preferred Stock Redemption Value (56), Preferred Stock Carrying Value (130)] BV Equity Total Assets (6) – Total Liabilities (181) – Deferred Taxes and Investment Tax Credit (35) – Preferred Stock Total Debt Debt in Current Liabilities (34) + Long-Term Debt (9) Book Leverage Total Debt / Total Assets (6) MV Equity Stock Price (199) × Common Shares Used to Calculate EPS (54) Market Leverage Total Debt / (Total Debt + Market Value of Equity) Profitability Operating Income Before Depreciation (13) / Total Assets (6) Tangibility Net Property, Plant, and Equipment (8) / Total Assets (6) M/B (Market Value of Equity + Total Debt + Preferred Stock Liquidating Value (10) – Deferred Taxes and Investment Tax Credit (35)) / Total Assets (6) CF Volatility Standard Deviation of Operating Income (13) over Previous 12 Quarters Scaled by Total Assets (6) Dividend Payer A dummy variable that takes the value of one if common stock dividends (21) are positive, and zero otherwise Rated A dummy variable that takes the value of one if the firm is rated by the S&P, and zero otherwise Rating Monthly S&P ratings (280)
Table A2. Description of Capital IQ Variables
Variable Construction CP Commercial Paper RC Drawn Revolving Credit TL Term Loans SBN Senior Bonds SUB Subordinated Bonds CL Capital Leases Other Other Debt TPrinDue = CP + RC + TL + SBN + SUB + CL +
Other Total Principal Due = Commercial Paper + Revolving Credit + Term Loans + Senior Bonds +
Subordinated Bonds + Capital Leases + Other Debt TAdj = total_debt – TPrinDue Total Adjustment = Total Debt – Total Principal Due
31
II. Example: CIQ’s Classification of Debt Types Using Form 10K
AMR Corporation, Form 10K for the fiscal year ended December 31, 2003.
Avalable at:
http://www.sec.gov/Archives/edgar/data/6201/000095013404002668/d12953e10vk.htm
The following table illustrates how CIQ calculates each item (in millions of USD):
CIQ Source Calculation
Capital Structure Data
Total Debt 13,930 10K Item 8 Long-term debt, less current maturities (11,901) + Obligations under capital leases, less current obligations (1,225) + Current maturities of long-term debt (603) + Current obligations under capital leases (201) = 13,930
Total Equity 46 10K Item 8 Stockholders’ equity (46)
Total Capital 13,976 10K Item 8 Total debt + Stockholders’ equity
Debt Summary Data
Total Revolving Credit 834 10K Item 8 Credit facility agreement due through 2005 (834)
Total Term Loans 0 10K Item 8 Total Senior Bonds and Notes
11,668 10K Item 8 Secured variable and fixed rate indebtedness due through 2021 (6,041) + Enhanced equipment trust certificates due through 2011 (3,747) + Special facility revenue bonds due through 2036 (947) + Debentures due through 2021 (330) + Notes due through 2039 (303) + Senior convertible notes due through 2023 (300)
Total Capital Leases 1,426 10K Item 8 Obligations under capital leases, less current obligations (1,225) + Current obligations under capital leases (201)
Other Borrowings 2 10K Item 8 Other (2)
Additional Totals
Total Senior Debt 13,930 10K Item 8 = Total debt
Total Convertible Debt 300 10K Item 8 Senior convertible notes due through 2023 (300)
Curr. Port. of Long-Term Debt/Capital Leases
804 10K Item 8 Current maturities of long-term debt (603) + Current obligations under capital leases (201)
Long-Term Debt (Incl. Capital Leases)
13,126 10K Item 8 Long-term debt, less current maturities (11,901) + Obligations under capital leases, less current obligations (1,225)
Total Bank Debt 834 10K Item 8 Credit facility agreement due through 2005 (834)
Total Secured Debt 11,214 10K Item 8 Secured variable and fixed rate indebtedness due through 2021 (6,041) + Enhanced equipment trust certificates due through 2011 (3,747) + Obligations under capital leases, less current obligations (1,225) + Current obligations under capital leases (201)
Total Unsecured Debt 2,716 10K Item 8 Special facility revenue bonds due through 2036 (947) + Credit facility agreement due through 2005 (834) + Debentures due through 2021 (330) + Notes due through 2039 (303) + Senior convertible notes due through 2023 (300) + Other (2)
32
Table 1. Sample Overview The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. We have removed 1) firm-years with missing data for any of the Compustat variables listed in Table A1, 2) firm-years with market or book leverage outside the unit interval, 3) firm-years with zero total debt, 4) firm-years with missing data for any of the CIQ variables listed in Table A2, and 5) firm-years whose difference between total debt reported in Compustat and the sum of debt types reported in CIQ exceeds 5% of total debt. After the above filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. Applying the same filters (except the data discrepancy filter #5) to the Compustat population over the same years as our sample, we obtain 21,674 firms of which 17,232 have positive leverage. See Table A1 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Total Assets are expressed in millions of 2001 dollars deflated by the consumer price index. Panel A presents means and medians aggregated across all years for our sample, the Compustat population, and Compustat leveraged firms. We also test for difference between our sample and the two Compustat samples using the t-test and the two-sample Wilcoxon rank-sum (Mann-Whitney) test. Panel B presents key firm characteristics year by year. Panel A: Comparing Our Sample with Compustat Firms
Our Sample
Compustat All Firms
Test of Difference Comp. All Firms vs.
Our Sample
Compustat Leveraged Firms
Test of Difference Comp. Leveraged
Firms vs. Our Sample (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Mean Median Mean Median t-test
(p-value) MW-test (p-value)
Mean Median t-test (p-value)
MW-test (p-value)
Mkt Leverage 0.215 0.159 0.171 0.093 -19.884 (0.000)
-34.265 (0.000)
0.215 0.155 -0.071 (0.944)
-1.586 (0.056)
Profitability 0.071 0.111 0.043 0.102 -11.634 (0.000)
-10.228 (0.000)
0.061 0.107 -4.137 (0.000)
-4.184 (0.000)
Tangibility 0.272 0.200 0.242 0.164 -12.340 (0.000)
-16.111 (0.000)
0.271 0.197 -0.380 (0.704)
-1.219 (0.111)
M/B 1.678 1.250 1.878 1.322 11.356 (0.000)
8.431 (0.000)
1.679 1.236 0.073 (0.942)
-1.936 (0.026)
BV Assets 2347.9 392.2 1936.4 239.3 -5.767 (0.000)
-18.397 (0.000)
2329.6 341.5 -0.236 (0.814)
-5.048 (0.000)
CF Volatility 0.024 0.012 0.028 0.014 4.792 (0.000)
14.048 (0.000)
0.025 0.012 1.489 (0.136)
3.880 (0.000)
Dividend Payer 0.307 0.000 0.262 0.000 -9.100 (0.000)
-9.179 (0.000)
0.289 0.000 -3.359 (0.001)
-3.364 (0.000)
Panel B: Firm Characteristics Over Time
Mkt Leverage
Profitability Tangibility M/B BV Assets CF Volatility
Dividend Payer
# Obs.
Mn Md Mn Md Mn Md Mn Md Mn Md Mn Md Mn Md 2001 0.256 0.054 0.295 1.680 2390.9 0.025 0.273 1654
0.186 0.103 0.228 1.174 355.5 0.012 0.000 2002 0.269 0.065 0.290 1.284 2233.6 0.029 0.265 2080
0.208 0.105 0.221 0.973 331.1 0.014 0.000
2003 0.210 0.077 0.275 1.782 2217.3 0.034 0.288 2045
0.159 0.107 0.204 1.272 360.7 0.013 0.000 2004 0.191 0.084 0.268 1.788 2311.3 0.022 0.319 2127
0.142 0.114 0.194 1.337 389.8 0.012 0.000 2005 0.189 0.087 0.260 1.754 2277.3 0.019 0.333 2143
0.138 0.118 0.190 1.331 412.8 0.011 0.000
2006 0.186 0.072 0.263 1.760 2457.3 0.019 0.334 2136 0.137 0.115 0.187 1.363 454.9 0.011 0.000
2007 0.216 0.057 0.263 1.693 2556.6 0.020 0.326 2057 0.161 0.109 0.177 1.272 470.3 0.011 0.000
33
Table 2. Summary Statistics
The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Table A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Total adjustment is calculated as the difference between total debt and the sum of the individual debt types. When total adjustment is zero, the sum of the debt types collected by Capital IQ is identical to total debt obtained from Compustat. We present mean, median, 75th, 95th, and 99th percentiles across all firm-years.
Share of Total Debt
Mean Median 75th Percentile
95th Percentile
99th Percentile
Debt Types Commercial Paper 0.008 0.000 0.000 0.000 0.285 Revolving Credit 0.203 0.000 0.300 0.993 1.000 Term Loans 0.187 0.000 0.250 0.997 1.000 Senior Bonds 0.361 0.130 0.783 1.000 1.000 Sub. Bonds 0.102 0.000 0.000 0.876 1.000 Capital Leases 0.092 0.000 0.015 1.000 1.000 Other Debt 0.046 0.000 0.001 0.238 1.000 Total Adjustment 0.000 0.000 0.000 0.006 0.031 # Obs. 14242
34
Table 3. Credit Ratings and Debt Structure
The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Table A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Data on ratings ranging from AAA to D are from Compustat (280). Panel A presents debt structure in terms of mean and median debt ratios across unrated and rated groups. Panel B presents debt structure in terms of mean and median debt ratios across different rating classes. We test for difference between rated and unrated using the t-test and the two-sample Wilcoxon rank-sum (Mann-Whitney) test. Panel A: Being Rated and Debt Structure Unrated
Rated Test of Difference
Unrated/Rated (1) (2) (3) (4) (5) (6) Mean Median Mean Median t-test
(p-value) MW-test (p-value)
Share of Total Debt Debt Types Commercial Paper 0.001 0.000 0.022 0.000 -18.882
(0.000) -33.999 (0.000)
Revolving Credit 0.257 0.000 0.093 0.000 35.494 (0.000)
12.896 (0.000)
Term Loans 0.209 0.000 0.144 0.000 12.362 (0.000)
-0.559 (0.288)
Senior Bonds 0.280 0.005 0.523 0.624 -35.444 (0.000)
-33.763 (0.000)
Sub. Bonds 0.073 0.000 0.160 0.000 -17.800 (0.000)
-26.813 (0.000)
Capital Leases 0.128 0.000 0.019 0.000 33.104 (0.000)
12.448 (0.000)
Other Debt 0.051 0.000 0.035 0.000 5.768 (0.000)
-32.061 (0.000)
Market Leverage 0.170 0.106 0.306 0.264 -37.630
(0.000) -42.619 (0.000)
# Obs. 9510 4732
35
Panel B: Credit Ratings and Debt Structure Debt Types CP RC TL SBN SUB CL Other # Obs.
AAA 0.143 0.000 0.067 0.532 0.067 0.017 0.165 49 0.104 0.000 0.003 0.557 0.000 0.000 0.099 AA +/– 0.144 0.020 0.032 0.674 0.000 0.016 0.087 103 0.109 0.000 0.000 0.719 0.000 0.000 0.037 A +/– 0.087 0.034 0.029 0.745 0.020 0.008 0.065 636 0.000 0.000 0.000 0.820 0.000 0.000 0.006 BBB +/– 0.023 0.115 0.061 0.704 0.042 0.017 0.036 1225 0.000 0.003 0.000 0.809 0.000 0.000 0.002 BB +/– 0.000 0.123 0.201 0.374 0.251 0.022 0.029 1604 0.000 0.010 0.007 0.254 0.000 0.000 0.000 B +/– 0.000 0.073 0.234 0.391 0.261 0.024 0.018 1044 0.000 0.000 0.018 0.265 0.000 0.000 0.000 CCC+ or below 0.000 0.051 0.245 0.494 0.187 0.028 0.003 71 0.000 0.000 0.133 0.627 0.000 0.000 0.000 Unrated 0.001 0.257 0.209 0.280 0.073 0.128 0.051 9510
0.000 0.000 0.000 0.006 0.000 0.000 0.000
36
Table 4. Cluster Analysis The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Tables A1 and A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. For the cluster analysis we employ the Stata command cluster kmeans. We use the Euclidean Distance measure and run kmeans with up to 15 clusters. Using the stopping rule based on the Calinski/Harabasz index, we obtain seven clusters. We present the sample mean and median of debt types and key firm characteristics for the seven clusters sorted by ascending median firm size, using firm-year observations.
Debt Types
Cluster CP RC TL SBN SUB CL Other Mkt
Lev. Profitab
ility Tangibi
lity M/B Firm Size CF Vol.
Dividend Payer Rated Rating # Obs.
1 0.000 0.012 0.014 0.019 0.002 0.945 0.008 0.034 -0.036 0.165 2.499 261.71 0.042 0.091 0.033 22.666 1074 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.003 0.058 0.109 1.852 104.79 0.023 0.000 0.000 23.000
2 0.001 0.068 0.819 0.042 0.034 0.026 0.009 0.220 0.066 0.268 1.660 807.27 0.022 0.218 0.221 20.823 2549 0.000 0.000 0.875 0.000 0.000 0.000 0.000 0.150 0.107 0.201 1.245 179.70 0.013 0.000 0.000 23.000
3 0.001 0.021 0.011 0.027 0.005 0.022 0.913 0.130 0.051 0.230 1.869 2393.62 0.025 0.243 0.143 21.061 490 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.044 0.101 0.151 1.400 189.28 0.014 0.000 0.000 23.000
4 0.001 0.829 0.056 0.054 0.023 0.026 0.011 0.206 0.094 0.269 1.482 514.65 0.022 0.268 0.091 21.972 2623 0.000 0.884 0.000 0.000 0.000 0.000 0.000 0.144 0.114 0.186 1.125 204.45 0.013 0.000 0.000 23.000
5 0.001 0.058 0.073 0.050 0.799 0.016 0.008 0.291 0.076 0.229 1.524 1195.01 0.023 0.134 0.530 17.674 1465 0.000 0.000 0.000 0.000 0.858 0.000 0.000 0.241 0.101 0.143 1.257 651.69 0.012 0.000 1.000 16.000
6 0.009 0.018 0.016 0.925 0.005 0.013 0.013 0.209 0.070 0.297 1.764 4419.45 0.026 0.453 0.495 16.496 3943 0.000 0.000 0.000 0.965 0.000 0.000 0.000 0.160 0.118 0.242 1.297 880.67 0.011 0.000 0.000 23.000
7 0.036 0.173 0.116 0.542 0.053 0.036 0.037 0.295 0.109 0.332 1.424 4480.68 0.017 0.434 0.523 16.243 2098 0.000 0.118 0.002 0.565 0.000 0.000 0.000 0.246 0.121 0.280 1.111 1172.52 0.008 0.000 1.000 16.000
Total 0.008 0.203 0.187 0.361 0.102 0.092 0.046 0.215 0.071 0.272 1.678 2347.89 0.024 0.307 0.332 18.985 14242 0.000 0.000 0.000 0.130 0.000 0.000 0.000 0.159 0.111 0.200 1.250 392.238 0.012 0.000 0.000 23.000
37
Table 5. Conditional Debt Structure
The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Table A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Panel A presents the shares of observations with significant amounts of various debt types conditional on a particular debt type (given in the first column of the table) exceeding 30% of total debt. Panel B presents the mean and median of each debt type as a fraction of total debt conditional on a particular debt type (given in the first column of the table) exceeding 30% of total debt. We report both t-tests on means and sign tests on medians being strictly greater than 30% (one-sided), as well as the number of observations for which a particular debt type is strictly greater than 30%. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A: Shares of Observations Conditional on Significant Amounts of Debt Types
Debt Types
CP RC TL SBN SUB CL Other # Obs.
Share of Sample
Debt Types
CP>30% 1.000 0.016 0.039 0.643 0.000 0.008 0.016 129 0.009
RC>30% 0.001 1.000 0.128 0.190 0.056 0.017 0.011 3562 0.250
TL>30% 0.002 0.138 1.000 0.131 0.090 0.027 0.008 3302 0.232
SBN>30% 0.013 0.107 0.069 1.000 0.042 0.019 0.016 6289 0.442
SUB>30% 0.000 0.105 0.157 0.140 1.000 0.009 0.006 1895 0.133
CL>30% 0.001 0.047 0.067 0.089 0.014 1.000 0.023 1310 0.092
Other>30% 0.003 0.061 0.042 0.152 0.019 0.047 1.000 643 0.045
38
Panel B: Debt Structure Conditional on Significant Amounts of Debt Types
Debt Types
CP RC TL SBN SUB CL Other # Obs.
Debt Types CP>30% 0.413*** 0.033 0.031 0.352*** 0.001 0.011 0.028 129 0.453*** 0.000 0.000 0.398*** 0.000 0.000 0.000 129 2 5 83 0 1 2
RC>30% 0.001 0.718*** 0.085 0.122 0.036 0.025 0.013 3562 0.000 0.741*** 0.000 0.000 0.000 0.000 0.000 2 3562 455 676 199 61 39
TL>30% 0.001 0.095 0.722*** 0.091 0.055 0.026 0.010 3302 0.000 0.000 0.744*** 0.000 0.000 0.000 0.000 5 455 3302 431 298 88 27
SBN>30% 0.015 0.080 0.055 0.779*** 0.028 0.022 0.022 6289 0.000 0.000 0.000 0.840*** 0.000 0.000 0.000 83 676 431 6289 266 117 98
SUB>30% 0.001 0.080 0.104 0.089 0.705*** 0.016 0.010 1895 0.000 0.000 0.000 0.000 0.704*** 0.000 0.000 0 199 298 266 1895 18 12
CL>30% 0.000 0.032 0.044 0.056 0.008 0.845*** 0.014 1310 0.000 0.000 0.000 0.000 0.000 1.000*** 0.000 1 61 88 117 18 1310 30
Other>30% 0.004 0.042 0.031 0.092 0.011 0.032 0.788*** 643 0.000 0.000 0.000 0.000 0.000 0.000 0.975***
2 39 27 98 12 30 643
39
Table 6. Conditional Debt Structure on Lagged Debt Types
The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Table A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Panel A presents the shares of observations with significant amounts of various debt types conditional on a particular lagged debt type (given in the first column of the table) exceeding 30% of total debt. Panel B presents the mean and median of each debt type as a fraction of total debt conditional on a particular lagged debt type (given in the first column of the table) exceeding 30% of total debt. We report both t-tests on means and sign tests on medians being strictly greater than 30% (one-sided), as well as the number of observations for which a particular debt type is strictly greater than 30%. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A: Shares of Observations Conditional on Significant Amounts of Lagged Debt Types Debt Types
CP RC TL SBN SUB CL Other # Obs.
Share of Sample
Lagged Debt Types
CP>30% 0.538 0.019 0.038 0.779 0.000 0.000 0.010 104 0.007
RC>30% 0.004 0.733 0.176 0.241 0.077 0.043 0.022 2563 0.180
TL>30% 0.001 0.198 0.762 0.180 0.103 0.040 0.026 2321 0.163
SBN>30% 0.013 0.131 0.092 0.883 0.054 0.026 0.022 4654 0.327
SUB>30% 0.000 0.124 0.173 0.205 0.816 0.016 0.009 1487 0.104
CL>30% 0.000 0.101 0.116 0.136 0.028 0.754 0.041 852 0.060
Other>30% 0.013 0.180 0.151 0.234 0.040 0.066 0.550 471 0.033
40
Panel B: Debt Structure Conditional on Significant Amounts of Lagged Debt Types
Debt Types
CP RC TL SBN SUB CL Other # Obs.
Lagged Debt Types
CP>30% 0.281 0.037 0.037 0.526*** 0.001 0.007 0.027 104 0.326 0.000 0.000 0.556*** 0.000 0.000 0.000
56 2 4 81 0 0 1
RC>30% 0.002 0.569*** 0.128 0.177 0.054 0.047 0.023 2563 0.000 0.613*** 0.000 0.007 0.000 0.000 0.000
10 1878 452 618 197 110 57
TL>30% 0.001 0.146 0.585*** 0.136 0.065 0.040 0.026 2321 0.000 0.000 0.623*** 0.000 0.000 0.000 0.000
3 460 1769 418 239 92 60
SBN>30% 0.015 0.103 0.075 0.710*** 0.037 0.031 0.027 4654 0.000 0.000 0.000 0.799*** 0.000 0.000 0.000
62 611 426 4108 250 120 101
SUB>30% 0.001 0.095 0.127 0.143 0.602*** 0.021 0.014 1487 0.000 0.000 0.000 0.000 0.612*** 0.000 0.000
0 184 257 305 1214 24 14
CL>30% 0.000 0.076 0.091 0.105 0.020 0.676*** 0.032 852 0.000 0.000 0.000 0.000 0.000 0.976*** 0.000
0 86 99 116 24 642 35
Other>30% 0.008 0.139 0.121 0.177 0.032 0.063 0.459*** 471 0.000 0.000 0.000 0.000 0.000 0.000 0.366**
6 85 71 110 19 31 259
41
Table 7. Explaining Persistence in Debt Structure The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Tables A1 and A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Each regression includes the lagged values of the dependent variable, profitability, asset tangibility, market-to-book ratio (M/B), size, cash flow volatility, and dividend payer. Industry fixed effects are based on two-digit SIC codes. Rating fixed effects are based on 22 rating dummies and the unrated dummy. Panel A reports the OLS regression results where the dependent variable is measured as a fraction of total debt. Panel B reports the probit regression results where the dependent variable takes a value of one if the particular debt type exceeds 30% of total debt, and zero otherwise. Standard errors clustered at the firm level are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A: OLS Regressions
Debt Types Lagged Regressors Mkt Lev CP RC TL SBN SUB CL Other Mkt. Leverage 0.787***
(0.009)
Debt Type 0.584*** 0.681*** 0.729*** 0.759*** 0.814*** 0.776*** 0.547***
(0.042) (0.011) (0.011) (0.009) (0.011) (0.015) (0.025)
Profitability -0.020*** 0.002 0.059*** 0.034** -0.058*** -0.008 -0.010 -0.001
(0.006) (0.002) (0.013) (0.014) (0.016) (0.007) (0.013) (0.007)
Tangibility 0.012 -0.001 -0.025* 0.033** 0.035** -0.011 -0.020** -0.008
(0.008) (0.002) (0.014) (0.014) (0.014) (0.007) (0.008) (0.009)
M/B 0.000 0.000 -0.004** -0.002 0.004* -0.002*** 0.004** -0.001
(0.001) (0.000) (0.002) (0.002) (0.002) (0.001) (0.002) (0.001)
Size 0.005*** -0.000 -0.005** -0.011*** 0.012*** 0.003** -0.002* 0.002
(0.001) (0.000) (0.002) (0.002) (0.002) (0.001) (0.001) (0.001)
CF Volatility -0.025** 0.001 -0.019 -0.026 0.002 -0.005 0.035 0.002
(0.012) (0.001) (0.021) (0.016) (0.037) (0.014) (0.037) (0.009)
Dividend Payer 0.003 0.000 0.008 -0.004 0.012** -0.010*** -0.002 0.004
(0.003) (0.001) (0.006) (0.005) (0.006) (0.003) (0.003) (0.003)
Constant 0.018 0.056** 0.101 0.109*** -0.003 -0.018 0.011 0.084
(0.018) (0.027) (0.064) (0.036) (0.039) (0.016) (0.016) (0.057)
Ind. FE Yes Yes Yes Yes Yes Yes Yes Yes
Rating FE Yes Yes Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes Yes Yes
Obs. 10265 10265 10265 10265 10265 10265 10265 10265
Adj. R2 0.760 0.539 0.546 0.561 0.672 0.720 0.624 0.349
42
Panel B: Probit Regressions (Debt Type>30%) Debt Types Lagged Regressors Mkt Lev CP RC TL SBN SUB CL Other Mkt. Leverage 1.813***
(0.046)
CIQ Type 1.861*** 1.823*** 2.050*** 2.254*** 2.680*** 2.440*** 2.171***
(0.202) (0.040) (0.041) (0.040) (0.056) (0.065) (0.076)
Profitability -0.574*** 2.359*** 0.449*** 0.342*** -0.473*** -0.105 -0.055 -0.112
(0.127) (0.896) (0.135) (0.118) (0.096) (0.147) (0.123) (0.148)
Tangibility 0.418*** -0.215 -0.142 0.081 0.290*** -0.307** -0.581*** -0.269
(0.113) (0.428) (0.110) (0.103) (0.106) (0.154) (0.181) (0.199)
M/B -0.249*** 0.078 -0.045*** -0.027* 0.009 -0.074*** 0.042*** -0.014
(0.028) (0.053) (0.016) (0.014) (0.013) (0.025) (0.015) (0.020)
Size 0.051*** 0.028 -0.054*** -0.094*** 0.089*** 0.067*** -0.056*** 0.001
(0.016) (0.051) (0.014) (0.014) (0.014) (0.020) (0.020) (0.024)
CF Volatility -1.380 -9.556 -1.008* -1.215** 0.041 -0.116 0.201 -0.339
(0.878) (7.577) (0.541) (0.582) (0.224) (0.513) (0.153) (0.387)
Dividend Payer -0.081* 0.268 0.035 -0.053 0.076* -0.272*** -0.034 0.067
(0.045) (0.205) (0.044) (0.044) (0.043) (0.065) (0.073) (0.071)
Constant -2.090*** -5.265 -6.165*** -0.792 -1.731*** -6.973*** -10.019*** -2.213***
(0.454) (0.000) (0.419) (0.487) (0.395) (0.330) (0.899) (0.470)
Ind. FE Yes Yes Yes Yes Yes Yes Yes Yes
Rating FE Yes Yes Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes Yes Yes
Obs. 10644 5916 10124 10050 10219 10007 9108 9519
Adj. R2 0.476 0.525 0.391 0.417 0.538 0.612 0.519 0.369
43
Table 8. Rating Changes and Debt Structure
The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Table A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Profitability, asset tangibility, market to book ratio (M/B), size, cash flow volatility, and dividend payer are lagged one year. Industry fixed effects are based on two-digit SIC codes. Rating fixed effects are based on 22 rating dummies and the unrated dummy. Data on ratings ranging from AAA to D are from Compustat (280). Downgrade(–1) and Upgrade(–1) indicate respectively a change in debt one year before the change in rating occurs. Downgrade and Upgrade indicate respectively a decrease and an increase in rating during the year in which the change in debt occurs. Downgrade(+1) and Upgrade(+1) indicate respectively a change in debt one year after the change in rating occurs. Panel A reports the effect of a change in rating on the change in each debt type both for mean (first row) and median (second row) values. T-tests (for means) and sign tests (for medians) are based on the null hypothesis that the change in debt type is zero. Panel B reports regression results to explain changes in debt structure due to downgrades. Panel C reports regression results to explain changes in debt structure due to upgrades. Standard errors clustered at the firm level are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A: Changes in Debt Structure Due to Rating Downgrades and Upgrades
Debt Types ∆Mkt Lev ∆CP ∆RC ∆TL ∆SBN ∆SUB ∆CL ∆Other # Obs
0.021*** -0.007** 0.002 -0.007 0.015 0.000 -0.001 -0.002 334 Downgrade(–1) 0.013** 0.000** 0.000 0.000 0.001*** 0.000 0.000*** 0.000***
-0.026*** -0.009*** -0.006 0.015* 0.019* -0.010* 0.002 -0.006 334 Downgrade -0.024*** 0.000*** 0.000 0.000 0.001*** 0.000 0.000** 0.000
-0.015** -0.005* 0.001 0.007 0.005 -0.008 0.003 0.000 334 Downgrade(+1) -0.013*** 0.000 0.000 0.000 0.000 0.000 0.000*** 0.000
-0.055*** 0.000 -0.008 -0.002 0.033*** -0.025*** 0.004 -0.004 277 Upgrade(–1) -0.040*** 0.000 0.000 0.000** 0.000** 0.000 0.000 0.000*
-0.041*** 0.009** 0.006 0.007 0.005 -0.027** 0.001 -0.004 277 Upgrade -0.028*** 0.000** 0.000 0.000 0.000 0.000* 0.000** 0.000**
0.001 0.006 0.024* -0.005 -0.002 -0.022* -0.004 0.001 277 Upgrade(+1)
-0.002 0.000 0.000 0.000 0.000 0.000** 0.000 0.000**
44
Panel B: Explaining Changes in Debt Structure Due to Downgrades
Panel C: Explaining Changes in Debt Structure Due to Upgrades
Debt Types Mkt Lev CP RC TL SBN SUB CL Other
Year Before 0.050*** 0.012** 0.019 0.001 -0.003 -0.010 -0.014 -0.008 (0.018) (0.006) (0.015) (0.017) (0.033) (0.027) (0.009) (0.014) Current Year 0.010 0.008 -0.013 0.008 0.018 -0.029** 0.001 0.002 (0.010) (0.005) (0.010) (0.013) (0.020) (0.014) (0.004) (0.007) Year After -0.001 0.003 -0.030** -0.035* 0.074** -0.014 -0.002 0.004 (0.017) (0.006) (0.014) (0.019) (0.031) (0.024) (0.005) (0.015) Constant 0.224*** 0.074*** -0.056* 0.309*** 0.721*** -0.178*** 0.017 0.110*** (0.039) (0.016) (0.031) (0.049) (0.083) (0.057) (0.017) (0.040) Ind. FE Yes Yes Yes Yes Yes Yes Yes Yes Rating FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs. 774 774 774 774 774 774 774 774 Adj. R2 0.552 0.391 0.136 0.181 0.312 0.321 0.196 0.125
Debt Types Mkt Lev CP RC TL SBN SUB CL Other
Year Before -0.060*** -0.008 0.018 -0.053* 0.006 0.003 0.002 0.037** (0.015) (0.007) (0.025) (0.031) (0.046) (0.031) (0.007) (0.017) Current Year -0.012 -0.002 -0.024 0.024 0.005 -0.015 0.009 0.000 (0.008) (0.003) (0.017) (0.021) (0.023) (0.018) (0.006) (0.008) Year After -0.018 -0.000 0.052** -0.024 -0.054 0.012 -0.000 0.013 (0.013) (0.005) (0.025) (0.027) (0.044) (0.028) (0.008) (0.016) Constant 0.228*** -0.004 0.448*** -0.100 0.158 0.749*** -0.118*** -0.150** (0.038) (0.042) (0.074) (0.141) (0.211) (0.067) (0.043) (0.058) Ind. FE Yes Yes Yes Yes Yes Yes Yes Yes Rating FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs. 606 606 606 606 606 606 606 606 Adj. R2 0.440 0.197 0.140 0.258 0.281 0.194 0.074 0.098
45
Table 9. Leverage and Debt Structure Regressions The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Tables A1 and A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of (Total Debt + Market Value of Equity) (the same denominator used for computing market leverage, see Table A1). Profitability, asset tangibility, market to book ratio (M/B), size, cash flow volatility, and dividend payer, are lagged one year. Industry fixed effects are based on two-digit SIC codes. Rating fixed effects are based on 22 rating dummies and the unrated dummy. The estimation is via the Seemingly Unrelated Regression (SUR) specification across the system of equations. Standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Mkt Debt Types Scaled by Total Debt + Market Value of Equity Lagged Regressors Leverage CP RC TL SBN SUB CL Other
Profitability -0.069*** -0.000 0.008* 0.012** -0.073*** -0.006 -0.005*** -0.005***
(0.008) (0.000) (0.005) (0.005) (0.006) (0.004) (0.001) (0.001)
Tangibility 0.135*** -0.000 0.017*** 0.034*** 0.099*** -0.022*** 0.010*** -0.002
(0.009) (0.000) (0.005) (0.005) (0.007) (0.005) (0.001) (0.002)
M/B -0.030*** -0.000 -0.009*** -0.007*** -0.009*** -0.003*** -0.001*** -0.001***
(0.001) (0.000) (0.001) (0.001) (0.001) (0.001) (0.000) (0.000)
Size 0.012*** 0.000*** -0.003*** -0.004*** 0.014*** 0.006*** -0.001*** 0.001***
(0.001) (0.000) (0.001) (0.001) (0.001) (0.001) (0.000) (0.000)
CF Volatility -0.044*** 0.000 -0.026*** -0.018** -0.001 0.002 -0.002 -0.000
(0.016) (0.001) (0.009) (0.009) (0.012) (0.008) (0.002) (0.003)
Dividend Payer -0.023*** 0.000** -0.004* -0.004* 0.003 -0.019*** -0.002*** 0.002**
(0.004) (0.000) (0.002) (0.002) (0.003) (0.002) (0.000) (0.001)
Constant 0.000 0.000 0.080*** 0.070*** 0.000 -0.050** 0.000 0.000
(0.000) (0.000) (0.026) (0.027) (0.000) (0.024) (0.000) (0.000)
Ind. FE Yes Yes Yes Yes Yes Yes Yes Yes
Rating FE Yes Yes Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes Yes Yes
Obs. 10737 10737 10737 10737 10737 10737 10737 10737
Adj. R2 0.385 0.244 0.152 0.151 0.293 0.184 0.101 0.048
46
Table 10: Financing Gap and Debt Structure The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Tables A1 and A2 for variables definitions. Financing gap is defined as the sum of dividends, investment, change in working capital, minus the cash flow after interest and taxes, and scaled by lagged total capital. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of (Total Debt + Market Value of Equity) (the same denominator used for computing market leverage, see Table A1). Data on ratings ranging from AAA to D are from Compustat (280). Industry fixed effects are based on two-digit SIC codes. Rating fixed effects are based on rating dummies that take values ranging from 1 (rating class AAA) to 23 (not rated). The estimation is via the Seemingly Unrelated Regression (SUR) specification across the system of equations. Standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Debt Types ∆Mkt Lev. ∆CP ∆RC ∆TL ∆SBN ∆SUB ∆CL ∆Other
∆Profitability -0.204*** -0.001 -0.050*** -0.065*** -0.063*** -0.022*** -0.004*** 0.001
(0.009) (0.001) (0.006) (0.007) (0.007) (0.005) (0.001) (0.002) ∆Tangibility 0.172*** -0.001 0.062*** 0.071*** 0.009 0.012 0.013*** 0.006
(0.019) (0.001) (0.013) (0.014) (0.014) (0.010) (0.002) (0.005) ∆M/B 0.006*** 0.000 0.001 0.004*** -0.000 0.000 0.000* 0.001
(0.001) (0.000) (0.001) (0.001) (0.001) (0.001) (0.000) (0.000) ∆Size 0.264*** 0.001** 0.059*** 0.088*** 0.079*** 0.027*** 0.004*** 0.006***
(0.005) (0.000) (0.003) (0.004) (0.004) (0.003) (0.001) (0.001) Financing Gap 0.008*** 0.000 0.001 -0.001 0.006*** 0.003** -0.000 -0.001*
(0.003) (0.000) (0.002) (0.002) (0.002) (0.001) (0.000) (0.001) Constant 0.028 0.001 0.016 0.017 -0.005 0.004 -0.000 -0.002
(0.032) (0.002) (0.020) (0.022) (0.023) (0.015) (0.003) (0.008) Ind. FE Yes Yes Yes Yes Yes Yes Yes Yes Rating FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs. 10265 10265 10265 10265 10265 10265 10265 10265 Adj. R2 0.360 0.015 0.073 0.103 0.111 0.036 0.023 0.010
47
Table 11. Robustness Checks
The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Table A2 for variable definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Panel A presents the sample mean and median of debt types and key firm characteristics for the seven clusters sorted by ascending median firm size, using firm-level observations obtained by time series average. Panel B presents the shares of observations with significant amounts of various debt types conditional on a particular debt type (given in the first column of the table) exceeding 10% of total debt. Panel C presents the mean and median of each debt type as a fraction of total debt conditional on a particular debt type (given in the first column of the table) exceeding 10% of total debt. We report both t-tests on means and sign tests on medians being strictly greater than 10% (one-sided), as well as the number of observations for which a particular debt type is strictly greater than 10%. Panel D presents the shares of observations with significant amounts of various debt types conditional on a particular three-year lagged debt type (given in the first column of the table) exceeding 30% of total debt. Panel E presents the mean and median of each debt type as a fraction of total debt conditional on a particular three-year lagged debt type (given in the first column of the table) exceeding 30% of total debt. We report both t-tests on means and sign tests on medians being strictly greater than 30% (one-sided), as well as the number of observations for which a particular debt type is strictly greater than 30%. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A: Cluster Analysis Using Firm-Level Observations Obtained by Time Series Average
Debt Types
Cluster CP RC TL SBN SUB CL Other Mkt
Lev. Profitab
ility Tangibi
lity M/B Firm Size CF Vol.
Dividend Payer Rated Rating # Obs.
1 0.000 0.043 0.046 0.054 0.004 0.837 0.015 0.034 -0.058 0.143 2.582 232.30 0.043 0.073 0.026 22.752 319 0.000 0.000 0.000 0.000 0.000 0.977 0.000 0.007 0.034 0.087 2.009 91.30 0.028 0.000 0.000 23.000
2 0.003 0.102 0.087 0.081 0.021 0.084 0.621 0.113 0.032 0.212 1.934 2046.31 0.026 0.211 0.132 21.313 158 0.000 0.000 0.000 0.000 0.000 0.000 0.552 0.055 0.098 0.141 1.508 154.76 0.015 0.000 0.000 23.000
3 0.001 0.099 0.763 0.055 0.034 0.033 0.016 0.199 0.047 0.252 1.869 570.50 0.025 0.213 0.182 21.254 579 0.000 0.008 0.750 0.000 0.000 0.000 0.000 0.132 0.103 0.171 1.396 164.04 0.015 0.000 0.000 23.000
4 0.001 0.757 0.087 0.071 0.032 0.037 0.015 0.199 0.086 0.273 1.546 438.70 0.024 0.251 0.071 22.226 537 0.000 0.746 0.000 0.003 0.000 0.000 0.000 0.159 0.112 0.189 1.184 193.84 0.015 0.000 0.000 23.000
5 0.012 0.181 0.164 0.490 0.058 0.062 0.033 0.252 0.063 0.305 1.585 2743.16 0.027 0.276 0.350 18.933 618 0.000 0.157 0.138 0.510 0.000 0.002 0.000 0.208 0.102 0.240 1.264 443.58 0.013 0.000 0.000 23.000
6 0.001 0.076 0.107 0.101 0.674 0.033 0.013 0.284 0.073 0.219 1.616 1086.64 0.026 0.119 0.481 18.233 331 0.000 0.014 0.018 0.011 0.655 0.000 0.000 0.242 0.105 0.129 1.339 617.74 0.015 0.000 0.500 18.667
7 0.015 0.038 0.025 0.875 0.010 0.015 0.020 0.214 0.044 0.289 1.891 4734.52 0.030 0.447 0.500 16.336 790 0.000 0.000 0.000 0.888 0.000 0.000 0.000 0.171 0.115 0.232 1.344 1007.69 0.012 0.143 0.500 18.310
Total 0.006 0.198 0.202 0.338 0.092 0.114 0.048 0.201 0.047 0.258 1.816 2028.38 0.028 0.264 0.283 19.660 3332 0.000 0.044 0.031 0.202 0.000 0.000 0.000 0.148 0.104 0.182 1.358 308.25 0.015 0.000 0.000 23.000
48
Panel B: Shares of Observations Conditional on Significant Amounts of Debt Types Debt Types
CP RC TL SBN SUB CL Other # Obs. Share of Sample Debt Types
CP>10% 1.000 0.103 0.091 0.888 0.019 0.022 0.160 418 0.029
RC>10% 0.009 1.000 0.278 0.415 0.134 0.080 0.042 4958 0.348
TL>10% 0.008 0.306 1.000 0.351 0.181 0.085 0.040 4495 0.316
SBN>10% 0.051 0.281 0.216 1.000 0.102 0.073 0.069 7321 0.514
SUB>10% 0.003 0.276 0.338 0.312 1.000 0.046 0.032 2401 0.169
CL>10% 0.004 0.197 0.191 0.267 0.055 1.000 0.044 2010 0.141
Other>10% 0.059 0.182 0.158 0.443 0.067 0.077 1.000 1136 0.080
Panel C: Debt Structure Conditional on Significant Amounts of Debt Types
Debt Types
CP RC TL SBN SUB CL Other # Obs.
Debt Types CP>10% 0.252*** 0.032 0.032 0.576*** 0.009 0.010 0.047 418
0.210*** 0.000 0.000 0.658*** 0.000 0.000 0.007 418 43 38 371 8 9 67
RC>10% 0.003 0.570*** 0.121*** 0.204*** 0.058 0.028 0.016 4958 0.000 0.541*** 0.000 0.024 0.000 0.000 0.000 43 4958 1376 2057 662 395 207
TL>10% 0.002 0.128*** 0.582*** 0.165*** 0.078 0.031 0.014 4495 0.000 0.000 0.559*** 0.001 0.000 0.000 0.000 38 1376 4495 1579 812 383 179
SBN>10% 0.014 0.115*** 0.084 0.696*** 0.041 0.027 0.024 7321 0.000 0.000 0.000 0.770*** 0.000 0.000 0.000 371 2057 1579 7321 750 536 503
SUB>10% 0.001 0.110*** 0.132*** 0.135*** 0.598*** 0.017 0.012 2401 0.000 0.000 0.000 0.002 0.561*** 0.000 0.000 8 662 812 750 2401 111 76
CL>10% 0.001 0.100 0.093 0.148*** 0.027 0.613*** 0.019 2010 0.000 0.000 0.000 0.000 0.000 0.644*** 0.000 9 395 383 536 111 2010 88
Other>10% 0.016 0.084 0.068 0.249*** 0.028 0.034 0.520*** 1136 0.000 0.000 0.000 0.004 0.000 0.000 0.374*** 67 207 179 503 76 88 1136
49
Panel D: Shares of Observations Conditional on Significant Amounts of Three-Year Lagged Debt Types
Panel E: Debt Structure Conditional on Significant Amounts of Three-Year Lagged Debt Types
Debt Types
CP RC TL SBN SUB CL Other # Obs.
Share of Sample
Lagged Debt Types
CP>30% 0.538 0.019 0.038 0.779 0.000 0.000 0.010 76 0.005
RC>30% 0.004 0.733 0.176 0.241 0.077 0.043 0.022 1468 0.103
TL>30% 0.001 0.198 0.762 0.180 0.103 0.040 0.026 1180 0.083
SBN>30% 0.013 0.131 0.092 0.883 0.054 0.026 0.022 2625 0.184
SUB>30% 0.000 0.124 0.173 0.205 0.816 0.016 0.009 864 0.061
CL>30% 0.000 0.101 0.116 0.136 0.028 0.754 0.041 418 0.029
Other>30% 0.013 0.180 0.151 0.234 0.040 0.066 0.550 261 0.018
Debt Types
CP RC TL SBN SUB CL Other # Obs.
Lagged Debt Types
CP>30% 0.196 0.054 0.052 0.610*** 0.001 0.007 0.035 76 0.154 0.000 0.000 0.641*** 0.000 0.000 0.001
25 4 6 65 0 0 2
RC>30% 0.002 0.443*** 0.161 0.238 0.073 0.061 0.021 1468 0.000 0.396*** 0.000 0.017 0.000 0.000 0.000
5 805 301 454 146 83 26
TL>30% 0.002 0.201 0.445*** 0.183 0.073 0.065 0.029 1180 0.000 0.000 0.409*** 0.000 0.000 0.000 0.000
2 318 670 274 122 72 32
SBN>30% 0.017 0.123 0.104 0.638*** 0.047 0.038 0.031 2625 0.000 0.000 0.000 0.738*** 0.000 0.000 0.000
38 399 332 2047 162 92 72
SUB>30% 0.000 0.120 0.171 0.237 0.411*** 0.041 0.020 864 0.000 0.000 0.000 0.008 0.361*** 0.000 0.000
0 129 176 268 470 31 12
CL>30% 0.000 0.124 0.185 0.185 0.048 0.420*** 0.037 418 0.000 0.000 0.000 0.000 0.000 0.236 0.000
0 64 93 91 25 186 19
Other>30% 0.014 0.122 0.186 0.256 0.075 0.079 0.264 261 0.000 0.000 0.000 0.000 0.000 0.000 0.000
4 38 61 90 26 22 79
50
Table 12. Fallen Angels and Rising Stars The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Table A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Fallen angel refers to a downgrade from investment grade to speculative grade. Rising star refers to an upgrade from speculative grade to investment grade. We report the effect of a change in rating on the change in each debt type, both for mean (first row) and median (second row) values. T-tests (for means) and sign tests (for medians) are based on the null hypothesis that the change in debt type is zero. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Debt Types ∆Mkt Lev ∆CP ∆RC ∆TL ∆SBN ∆SUB ∆CL ∆Other # Obs
Fallen angels(-1) 0.046** -0.014 -0.006 -0.020 0.027 0.014 -0.001 0.000 32 0.029 0.000* 0.000 0.000* 0.014 0.000 0.000** 0.000
Fallen angels -0.040 -0.006 -0.034 0.000 -0.009 0.036 0.020 -0.007 32
-0.044 0.000 0.000 0.000 0.007 0.000 0.000 0.000 Fallen angels(1) -0.025 -0.002 0.005 0.037** -0.023 -0.024 0.003* 0.002 32
-0.037** 0.000 0.000 0.000** 0.000 0.000 0.000 0.000
Rising stars(-1) -0.078*** 0.000 -0.028* -0.035 0.036 -0.020 0.042 0.003 21 -0.067*** 0.000 0.000 0.000 0.000 0.000 0.000 0.000
-0.015 0.000 0.132* -0.008 0.057 -0.132** -0.045 -0.004 21 Rising stars -0.005 0.000 0.000 0.000 0.043* 0.000 0.000 0.000
Rising stars(1) 0.025* 0.000 0.011 0.073 -0.034 -0.004 -0.001 -0.044 21 0.027 0.000 0.000 0.000 0.000 0.000 0.000 0.000