The (Un)secured Debt Puzzle: Evidence from
U.S. Public Manufacturing Firms, 1994-2010∗
Kizkitza Biguri
Universitat Autonoma de Barcelona
First Version: June 2013
June 11, 2014
Abstract
The benefits of pledging (inside) collateral have been widely discussed
in the literature because collateral helps solving market imperfections that
are caused by asymmetric information and limited contract enforceability problems. However, the implicit assumption made is that the only
financial contract available is secured debt, ruling out the role of unsecured debt, which represents 64% of total debt outstanding of U.S. public
manufacturing firms from 1994 to 2010. My paper has three interesting
results. First, I show that debt structure is not solely determined by collateral, but by the interaction between collateral and financial strength,
which determines unsecured debt. In addition to this, I show that collateral only plays a role for those firms that are financially constrained.
Second, I prove that higher collateral does not increase borrowing capacity by lowering the financial frictions faced, but only secured debt borrowing capacity. This result allows for a complementary channel to the
so-called collateral channel, the unsecured channel. Moreover, I test the
pecking-order hypothesis and conclude that firms have a clear preference
for unsecured debt because it allows to minimize total costs of financing.
∗ I want to thank Filippo Ippolito (UPF), Gregory Udell (Kelley School of Business, University of Indiana), Ronald Mann (Columbia Law School), Lynn LoPucki (UCLA School of
Law), the participants of the UPF Finance Seminar and the UPF Student Seminar for useful comments. Finally, I would also want to thank my advisor Hugo Rodriguez Mendizabal
(IAE-CSIC) for amazing supervising work and unconditional support.
1
1
Introduction
The benefits of pledging (inside) collateral have been widely discussed in the
literature. Collateral helps solving market imperfections that are caused by
asymmetric information problems, such as moral hazard, adverse selection or
costly state verification. Myers (1977), claimed that granting collateral reduces
agency costs by alleviating the underinvestment problem. Similarly, Smith and
Warner (1979b) and Stulz and Johnson (1985) show that pledging collateral
may lower a firm’s total cost of debt by preventing asset substitution, reducing
foreclosure costs, limiting claim dilution and mitigating underinvestment. Finally, Smith and Warner (1979a) argue that collateral reduces adverse selection
problems under asymmetric information because creditors cannot distinguish
between good and bad borrowers1 .
The literature on limited contract enforceability also highlights the role of collateral in solving financial frictions. Papers like Bernanke, Gertler and Gilchrist
(1996), Kiyotaki and Moore (1997) or Livdan, Sapriza and Zhang (2009) contain a borrowing constraint for firms which allows them to borrow up to the
expected liquidation value of the tangible assets of the firm. That is, the collateral they hold. The intuition in this type of financial frictions models is clear:
the higher the collateral the firm can pledge, the higher the debt financing it
can achieve and therefore, the lower the probability of not undertaking some
profitable investment project due to credit constraints. Moreover, these type of
models give a predominant role to collateral in the propagation and amplification of exogenous shock to the real economy through the balance sheet channel
and the bank-lending channel, from a demand of credit perspective.
However, all these papers, implicitly focus on secured debt financing and determine that below some collateral threshold, firms will be financially constrained.
Secured debt is by definition debt that is backed with collateral of the firm
(asset-based lending). A specific set of assets are encumbered in order to sign
the debt contract, such that in the event of default, the creditor has the right
to liquidate the assets attached to the debt contract in order to satisfy debt
repayment. On the other hand, unsecured debt is not backed with collateral
of the firm (cash flow-based lending). Creditors extend credit on an unsecured
basis according to the financial strength of the firm and the cash flows that the
firm is expected to generate in the future through investment decisions2 .
1I
will base the analysis on inside collateral, the pledgeable assets of the firm, as opposed
to external collateral, which considers the personal assets of shareholders. The majority of the
papers highlighting the benefits of collateral, actually consider external collateral. However,
external collateral does not play any role in public firms that are atomistically owned, which
is the set-up that I will consider in the present work.
2 This definition of debt structure heterogeneity, the combination of secured and unsecured
debt, becomes relevant in terms of the priority structure upon default. Secured claims are
senior to unsecured and the incentives that both types of creditors face are very different.
2
So far, the literature has not focused on the role of unsecured debt3 . This
is surprising as unsecured debt is as important in the financing structure of
firms as secured debt at least quantitatively. In particular, 64% of debt outsanding of U.S. public firms is unsecured, using Standard & Poor’s4 Compustat
database, for a period covered from 1994 to 2010.
As opposed to the economics and finance literature5 , back since the late 70’s
the law literature has emphasized about the relevant role that unsecured debt
plays in the context of firm’s financial and investment policies and, on creditor’s
bargaining process upon default. They introduced a very popular concept, the
“secured debt puzzle” 6 : the fact that regardless of the benefits that pledging
collateral might offer, firms that are large and financially strong rather want to
rely on unsecured debt in order to finance their investment projects. According
to this concept, debt choice is determined by firm characteristics other than
collateral availability.
According to the results derived in the present paper, the role of unsecured
debt is relevant. If we allow for a second dimension to play a role, besides collateral availability of the firm, I show that debt structure is determined by the
interaction between collateral and financial strength. Moreover, collateral only
plays a role for firms that are financially constrained. Second, I show that in
terms of the balance sheet channel, more collateral only means more secured
debt borrowing capacity, which allows for a complementary channel: the unsecured channel. Furthermore, when collateral availability increases, firms that
are financially constrained, surprisingly, also show a preference for unsecured
debt. Finally, I test the pecking-order hypothesis and conclude that the strong
preference for unsecured relies on the lower financing costs attached.
Several papers argue that debt structure is relevant for many aspects of firm
strategic decisions such as investment and dividend policies. For example, Rauh
and Sufi (2010) show that corporate debt is different in terms of types, sources
and maturities using a sample of U.S. public firms. Additionally, they show that
3 A recent empirical paper by Giambona and Golec (2012) shows a positive correlation
between the firms’ investment opportunities, measured by Tobin’s q, and the usage of unsecured debt. They claim for the existence of a growth opportunity channel of debt structure
of firms, giving a predominant role to unsecured debt. These results are consistent with an
earlier study by Barclay and Smith (1995), which show that firms trying to exploit investment
opportunities do not rely on secured debt.
4 S&P’s hereafter.
5 The literature is extense in terms of consumers’ unsecured credit, including papers as
Chatterjee, Corbae and Nakajima (2007) or Chatterjee, Corbae and Rios-Rull (2008) and in
terms of trade credit (unsecured), with papers like Petersen and Rajan (1997), Casamatta
(2003) and Cunat (2007). Trade credit is not financial debt, but becomes relevant upon
default.
6 The strand of this literature contains Jackson and Kroman (1979), Schwartz (1981), Levmore (1982), Picker (1982), White (1984), Buckley (1986), Scott (1986), Shupack (1989),
Bowers (1991), Triantis (1992), Adler (1993), Barnes (1993), LoPucki (1994) and Mann (1995,
1996), among others.
3
firms do not significantly vary amount of debt from year to year, but instead,
adjust its composition. Colla, Ippolito and Li (2012) analyze corporate debt
structure of U.S. public firms and relate usage to demand/supply factors. They
conclude, as in Graham and Leary (2011), that credit constraints prevent firms
from having their preferred debt structure and that optimal capital structure
has to be designed in order to minimize firm’s costs of financing7 .
On the other hand, the literature has also emphasized about the empirical relationship between collateral and borrower’s risk. Berger and Udell (1990) prove
that not only secured loans are riskier (implying that unsecured loans are safer),
but also secured creditors are riskier too8 . Therefore, high-risk firms are willing to pledge higher collateral and ultimately, borrow more on a secured basis.
They called this empirical fact the “sorting-by-observed-risk paradigm” 9 .Their
study is consistent with later results by Carey, Prowse, Rea and Udell (1993) in
which they analyze the private placement market and conclude that riskier firms
borrow secured debt and stronger firms unsecured debt (with looser covenants).
This can be supporting evidence for the “(un)secured debt puzzle”, as larger
firms that are financially strong can be considered as low-risk firms and therefore, are expected to rely more on unsecured debt.
These studies also reflect that the role of collateral can be secondary for certain
debt contracts, but they do not deepen on the relevant firm characteristics that
make collateral not as important as the rest of the literature is trying to emphasize. The cited deficit in the literature is one of the additional goals of this
paper, which is related to both, the “sorting-by-private-information paradigm”
by Berger and Udell (1990) and the “(un)secured debt puzzle”.
The present paper is closely related to two papers: i) Berger and Udell (1990)
and Giambona and Golec (2012). The papers differ in many aspects. First,
Berger and Udell (1990) only consider bank loans in their analysis, while I will
be considering all types of available debt instruments. Second, they consider
all commercial and industrial loans in the Federal Reserve’s Survey of Terms of
Bank Lending and thus; they are considering both, private and public companies, while I only focus on U.S. public manufacturing companies. Finally, they
consider the spreads on debt types as a function of loan characteristics, while
this paper wants to deepen on borrower characteristics.
Similarly, this paper also differs from the Giambona and Golec (2012) paper.
7 Previous papers in the literature that recognize debt structure heterogeneity and seek to
understand the reasons for it are Diamond (1991a, 1993), Park (2000), Bolton and Freixas
(2000) and DeMarzo and Fishman (2007) among many others.
8 The authors use data from commercial and industrial loans from the Federal Reserve’s
Survey of Terms of Bank Lending, containing information on over one million business loans
for years 1977, 1981, 1983 and 1987.
9 As opposed to the “sorting-by-private-information paradigm”, which establishes a negative relationship between collateral and borrower’s risk. Literature validating this view include
Besanko and Thankor (1987a,b), Chan and Kanatas (1985) and Bester (1985).
4
First, they uniquely link the relevance of unsecured debt to high-growth firms.
Second, another important issue they omit is the importance of the success of
investment projects in generating cash flows high enough to maintain a high financial strength despite the increase in leverage. Therefore, high and stable cash
flows are the key in order to maintain a debt structure with a higher proportion
of unsecured debt. Finally, they make a crucial assumption: all short-term debt
is assumed to be unsecured, when clearly this is not the case.
The goal of this paper is to analyze the empirical relationship between 5 endogenous firm characteristics: debt and capital structure, collateral, financial
strength and size in order to derive stylized facts about the role of unsecured
debt and how unsecured debt borrowing is determined. Concretely, first, I
will empirically prove that debt structure is not solely determined by collateral
availability, financial strength plays a key role and I show that the (un)secured
debt puzzle holds. That is, I will shed light on the irrelevance of collateral for
specific debt contracts depending on firm characteristics, provided that 64%
of debt in debt structure of U.S. public manufacturing firms is unsecured and
unsecured debt does not rely on collateral, but on the financial strength of the
firm. Finally, I will provide one mechanism that allows for the stylized evidence
previously described: interest rates for unsecured debt are lower than those of
secured, consistent with findings in Berger and Udell (1990).
My paper is a purely descriptive paper about the relationship between unsecured debt and the rest of relevant firm characteristics defined, which intends
to provide an intuition about what is behind the relationship between these five
endogenous variables. Therefore, it is not the aim of the paper to provide a
theory about unsecured debt or to imply causality from firm characteristics to
debt or capital structure.
The structure of the paper is as follows. Section 2 will describe the sample and will present descriptive summary statistics. Section 3 introduces the
“(un)secured debt puzzle” and presents regression results for the determinants
of debt and capital structure and will prove the pecking-order hypothesis for
unsecured debt. Section 4, provides descriptive evidence for one possible mechanism for the strong preference for unsecured: price discrimination in unsecured
debt markets. Finally, section 5 concludes.
2
Data Overview
The key firm characteristics choice is based on the determinants of secured and
unsecured debt: secured debt depends on collateral, while unsecured debt depends on financial strength. However, the “(un)secured debt puzzle” involves
another important firm characteristic: size. According to this theory, size also
plays a predominant role in the determination of debt choice: the larger the size
5
of the firm, the higher the usage of unsecured debt10 .
Appendix A contains the specific definitions and data items in Compustat. I
define unsecured in debt structure as unsecured debt over total debt11 , i) unsecured and ii) secured in capital structure as i) unsecured and ii) secured debt
over total assets respectively. Financial strength is defined as the book-value of
equity over book-value of equity plus total debt12 . Following the usual definition
for tangibility, I define available collateral as property, plant and equipment, net
of depreciation, over total assets13 . Size is defined as the book-value of total
assets14 .
I am also interested on controlling for other firm-level characteristics: marketto-book or Tobin’s q, defined as the ratio between the market-value of assets
over the book-value of assets, which is a proxy for investment opportunities and
profitability, defined as operating profits over total assets of the firm. Both variables have been used in previous studies as determinants of the capital structure
of firms15 .
2.1
Sample Description
I will concentrate on U.S. firms traded on the AMEX, NASDAQ, and NYSE
from the manufacturing sector16 covered by S&P’s database Compustat from
1994 to 2010. Appendix A contains specific definitions and data items in Compustat. Appendix B contains a detailed description of the sample corrections
performed. My final sample for the manufacturing sector comprises 25,096 firmyear observations.
Table 1 presents summary statistics, including mean, median and standard deviation for firm-year observations in the sample. Results show that the average
(median) firm holds 64% (79%) of unsecured debt in debt structure, while holding 15% (11%) of unsecured debt in capital structure, as opposed to the 8%
10 Many papers in the literature have established a relationship between size and the probability of pledging collateral. While Leeth and Scott (1989), Altman, Haldeman and Narayanan
(1977), Smith and Warner (1979a) or Chan and Kanatas (1985) find a positive correlation
between size and the usage of unsecured debt, Jimenez et al. (2006) find the opposite result.
11 This definition includes both short-term and long-term debt as opposed to the definition
used in Giambona and Golec (2012), in which the implicit assumption is that all short-term
debt is unsecured.
12 Note that the way in which financial strength is defined implied that unsecured debt
is determined by some other unsecured source of financing in the capital structure of firms
(equity). I want to thank Jose Luis Peydro for pointing this out.
13 Evidence from papers using this definition, among many others, can be found in Rauh
and Sufi (2010) and Giambona and Golec (2012).
14 Evidence from papers using this definition can be found, among many others, in Colla,
Ippolito and Li (2012).
15 Evidence from papers using these definitions include Rajan and Zingales (1995) or Rauh
and Sufi (2010). They find that more profitable and high market-to-book firms use less debt,
while firms with higher asset tangibility are more levered.
16 SIC codes 2000 - 3999.
6
(2%) of secured debt holdings over total assets. That is, U.S. public manufacturing firms show a clear preference for unsecured debt, both in terms of debt
and capital structure.
The remaining firm characteristics highlight that the average (median) firmyear observation in the sample has equity in the capital structure of 69% (71%),
which denotes a high degree of financial strength, high collateral availability
equivalent to 26% (23%) and has high investment opportunities 1.86 (1.42). Finally, despite the remarkable heterogeneity across observations, summary statistics show large and profitable firms that undertake large investment projects on
average.
Figure 1 presents the time series evidence on U.S. public manufacturing firms’
usage of secured and unsecured debt, both in terms of debt structure and in
terms of capital structure. The top panel shows the evolution of unsecured
debt in debt structure, while the bottom panel presents a comparison between
secured and unsecured debt in capital structure. Debt structure exhibits welldefined cyclical properties; it increases during recessions (countercyclical), while
the capital structure graph shows that both secured and unsecured debt have
followed a downward trend since the late 90’s, but they peaked again at the
beginning of the 2007 recession17 .
The first relevant step for this descriptive section is to see if firms with distinct firm characteristics choose differentiated debt structures. Table 2 presents
summary statistics for firm characteristics of different debt structure definitions,
including specialized and mixed debt structures.
Columns 1 and 2 show the summary statistics for those firm-year observations
that specialize in terms of one type of debt, 100% secured and 100% unsecured
respectively18 . Columns 3-6 contain the summary statistics for those firm-year
observations with mixed debt structures. Only 27% of the sample specializes
in terms of one type of debt, from which only 13.18% choose to specialize in
secured debt. In terms of mixed debt structures, the highest concentration is
located in the interval in which firms hold more than 75% in unsecured debt
but less than 100%. The results evidence that 52% of the firm-year observations
have more than 75% of their debt structure in unsecured debt.
From the analysis of firms characteristics of specialized debt structures, we can
conclude that firms relying 100% in secured debt are on average less levered
(18.6% vs. 22.2%), have a higher financial strength (75.4% vs. 69.1%), are
17 This is the effect of a reduced proportion of equity in the capital structure because of the
losses generated in the majority of the manufacturing firms in the 2007-2009, not due to an
increase in leverage.
18 Evidence in Colla, Ippolito and Li (2012) suggests that only financially constrained firms
tend to specialize in one type of debt. However, they use a different definition for debt
structure heterogeneity.
7
much smaller in size (189.4 vs. 2166.1) and they are less profitable. Perhaps
this could be evidence for firms relying 100% in secured debt to be financially
constrained.
When focusing on mixed debt structures, as firms incorporate more unsecured
debt in their debt structure they become larger and more profitable. However,
the maximum average and median financial strength is found in firm-year observations with more than 25% and less than 50% in unsecured debt. This is
surprising, as if financial strength determines unsecured debt, we should expect
to find the highest financial strength in the last column, more than 75% in
unsecured debt. Finally, the highest average and median collateral availability
is found in firms with less than 25% in unsecured debt. This is the expected
correlation, as firms with high secured debt holdings should have, on average,
higher collateral requirements.
With the purpose of deepening on the effect of collateral availability and financial strength on debt structure and capital structure choice, Table 3 presents
summary statistics classified according to categories of tangibility and financial
strength: panel a shows summary statistics across the quartiles of the financial strength distribution, while panel b shows the same information across the
quartiles of the tangibility distribution.
The most relevant conclusion from panel a is that the data highlights the existence of nonlinearities in the relationship between the percentage of unsecured
debt in debt structure and financial strength: the maximum average (median)
unsecured debt holdings in debt structure are achieved in the second quartile of
the distribution at 71% (93%), but there is a sudden drop when the third and
fourth quartiles are considered, down to 56% (55%). Figure 2 graphs this relationship, including a local polynomial approximation to reflect the confidence
interval around the mean.
Additionally, as financial strength increases (leverage tends to zero), average
and median collateral availability, size and profitability decrease sharply. Perhaps this could be indicating that firms in the fourth quartile of the financial
strength distribution are likely to be financially constrained because of the low
average (median) collateral availability, 18% (15%).
The rationale for this is straightforward: firms with a high proportion of equity in their capital structure can be either firms with a remarkable balance
sheet quality (unconstrained)19 or, have a higher percentage of equity in the
capital structure because they have restricted access to capital markets (constrained). Appendix C uses the definitions for being financially constrained in
Almeida, Campello and Weisbach (2004) to validate the existence of both financially constrained and unconstrained firms in the upper bound of the financial
19 Consistent
with the evidence reported in Rauh and Sufi (2010).
8
strength distribution20 .
In terms of panel b, the most relevant conclusion is that financial strength
and collateral availability seem to be to some extent substitutes. As collateral availability increases, leverage, size and profitability increase sharply. Just
the opposite of what is been found in the case of financial strength, suggesting
that collateral and financial strength could be substitutes.
Moreover, the summary statistics prove the balance sheet channel mechanism:
as collateral availability increases, the degree of financial frictions faced by firms
decreases and this increases borrowing capacity. However, it increases borrowing capacity of both secured and unsecured debt. Therefore, summary statistics
seem to rule out a different mechanism for unsecured debt that could affect
investment decisions of firms.
The descriptive analysis of the sample performed so far seems to yield the following relevant conclusions: i) unsecured debt is quantitative more relevant than
secured in terms of debt and capital structure, ii) the majority of the firms show
a strong preference towards debt and capital structures with a high proportion
of unsecured debt over secured, iii) collateral availability and financial strength
appear to be substitutes and therefore, debt choice is determined by the interaction between the two determinants and iv) the descriptive evidence seems to
validate the balance sheet channel: higher collateral implies higher borrowing
capacity.
3
3.1
Results: The (Un)secured Debt Puzzle
Determinants of Debt Structure Choice
This section aims to deepen on the determinants of debt structure by means
of regression analysis in order to achieve robustness in terms of the conclusions derived in the descriptive analysis previously performed. The empirical
specification is defined as follows:
U nsecuredi,t
′
= α+θi +φt +γF inStrengthi,t +δCollaterali,t +Xi,t
β +ϵi,t (1)
T otalDebti,t
where i denotes a firm, t denotes a year, and α a constant. My focus is on the
importance and robustness of the estimates of financial strength, F inStrengthi,t
and collateral Collaterali,t . The regressions also contain a set of control vari′
ables Xi,t
, including the log of size, market-to-book and profitability. The empirical specification is estimated using simple OLS for the sample of manufacturing firms over the period 1994-2010. All the specifications are estimated
20 The definitions include dividend payouts, size, S&P Bond Rating and the Kaplan&Zingales Index (1997).
9
using firm-fixed effects, θi , to control for possible simultaneity biases from unobserved individual heterogeneity and year-fixed effects, φt , and finally including heterokedasticity-consistent errors clustered at a firm level, as in Petersen
(2009).
The hypothesis tested is γ > 0 and δ < 0. The OLS specification may not
do a good job in capturing nonlinearities present in the data because as Figure 2 showed, the percentage of unsecured in debt structure of firms exhibits a
nonlinear relationship with respect to financial strength and there is a sudden
reduction in unsecured debt holdings for firms with financial strength above
71% (the sample median). Table 4 reports the results for the determinants of
debt structure for two different samples. The first sample gathers firm-year
observations with financial strength below the median (71% ), columns (1)-(4),
while the second sample captures those above, columns (5)-(8)21 .
The estimate of 0.1750 on financial strength in column 4 (first sample) exhibits
the appropriate sign: financial strength is a determinant for unsecured debt
holdings. Firms appear to adjust their debt structure towards more unsecured
debt in response to positive changes in financial strength. Moreover, it is both
statistically and economically significant.
On the other hand, the coefficient on available collateral, -0.2753, suggests that
a 1% increase in tangibility, generates a decrease in unsecured debt equal to
0.2753%. That is, firms appear to adjust their debt structure towards more
secured debt in response to positive changes in collateral availability. These
findings are consistent with the convention regarding the role of collateral: the
higher the available collateral, the higher the secured debt holdings.
The comparison of estimated coefficients from samples 1 and 2, denotes that
sample 2 has a negative coefficient for financial strength. The interpretation
for this sign is that as firms incorporate a higher percentage of equity in the
capital structure, it evidences a higher degree of financial constraints faced and
therefore, they will exhibit a negative and very sensitive (-0.4070) reaction to
increases in financial strength.
Moreover, it is worth mentioning that the sensitivity of constrained with respect to increases in collateral is lower that than of unconstrained (-0.2144 vs.
-0.2753), again highlights the restricted access of constrained firms to capital
markets: they tend to adjust debt structure less towards more secured when
collateral availability increases. Finally, note that as firms get larger in size,
21 Appendix
D includes the regression results for the complete sample. The estimate of
-0.1483 on financial strength suggests that a 1% increase in financial strength, generates a
reduction in unsecured debt equal to 0.1483% and firms appear to adjust their debt structure
towards more secured debt in response to positive changes in financial strength. These findings
are inconsistent with the hypothesis that unsecured debt is determined by financial strength
or the quality of the balance sheet of the firm.
10
they tend to incorporate higher unsecured debt in their debt structure, consistent with the (un)secured debt puzzle.
Controlling for nonlinearities in terms of collateral availability, in addition to
the existing ones in terms of financial strength, seems coherent in order to know
if financial strength and collateral availability jointly determine debt structure
choice. Table 5 examines the relationship of financial strength and available collateral with mean and median unsecured debt holdings by means of a two-way
sorting procedure based on the quartiles of the financial strength and tangibility
distributions22 .
Two important conclusions can be derived from the analysis in Table 5. First,
those firms with the highest unsecured debt holdings in their debt structure are
located in the second quartile of the financial strength distribution (0.58-0.71)
and the tendency towards high proportions of unsecured debt is independent of
collateral availability.
Second, those firm-year observations with the lowest proportion of unsecured
debt are located in the fourth quartile of the financial strength distribution
(where the financially constrained firms are located) and in the first quartile of
the collateral availability distribution, with an average 23% in unsecured debt.
As collateral availability increases, surprisingly, firms rather prefer to incorporate more unsecured debt in their debt structure. However, if the median
holdings are considered for financially constrained firms, they exhibit no unsecured debt holdings: the median financially constrained firm does not have
access to unsecured debt markets, independent of the collateral holdings.
Summing up, the most relevant conclusions from the debt structure determinants regression are first, collateral availability and financial strength seem
substitutes. Moreover, for firms that are unconstrained, collateral is irrelevant. However, other things being equal, debt structure seems more sensitive to
changes in collateral than to changes in financial strength. Second, firms that
are financially constrained, on average, have access to unsecured debt and as
collateral availability increases, they prefer to incorporate more unsecured debt.
However, when the median is considered, firms specialize in 100% secured debt
as they have no access to unsecured debt.
3.2
Determinants of Capital Structure Choice
This section aims to deepen on the determinants of capital structure by means
of regression analysis in order to achieve robustness in terms of the conclusions derived in the descriptive analysis previously performed. The empirical
22 Note that firms along the fourth quartile of financial strength gather firm-year observations proved to be financially constrained according to the definitions provided in Almeida,
Campello and Weisbach (2004).
11
specifications are defined as follows:
DebtT ypei,t
′
= α+θi +φt +γF inStrengthi,t +δCollaterali,t +Xi,t
β+ϵi,t (2)
T otalAssetsi,t
where i denotes a firm, t denotes a year, and α a constant. DebtT ypei,t
in the dependent variable can be either total debt, secured debt or unsecured
debt. My focus is on the importance and robustness of the estimates of financial
strength, F inStrengthi,t and collateral Collaterali,t 23 .
The hypothesis being tested is δsec > 0 in the secured debt over total assets
regression and δunsec < 0 in the unsecured debt over total assets regression.
This would provide sufficient evidence for the existence of an additional channel, complementary to the balance sheet channel: the (un)secured channel 24 .
Table 6 reports the estimation results for the determinants of the capital structure regression. Column 1 reports the estimated coefficients for total debt over
total assets as the dependent variable, while columns (2)-(3) and (4)-(5) report
those for secured over total assets and unsecured over total assets respectively.
By looking at the estimated coefficients for financial strength in columns (1)-(5),
we can derive a straightforward conclusion: incorporating more equity in the
capital structure of the firm, reduces debt holdings for both secured and unsecured, however, unsecured debt is more sensitive than secured to the increases
in financial strength. A 1% increase in financial strength, reduces secured debt
by 0.23% (column 3), while unsecured debt reacts more negatively than double,
-0.48% (column 5). This is consistent with the evidence reported in Rauh and
Sufi (2010)25 .
If we focus on the results for the secured debt over total assets regressions,
column (3), we see that the results for the capital structure still validate the
convention regarding the role of collateral: more collateral availability also increases secured debt holdings in the capital structure of the firm. Concretely,
a 1% increase in collateral availability, increases secured debt holdings in the
capital structure by 0.084%.
On the other hand, the results for the unsecured debt over total assets regression,
column 9, shows that higher collateral availability does not contribute to more
unsecured debt holdings once we control for unobserved variation at a firm level
using firm-fixed effects. Firms appear to adjust their capital structure towards
23 The regressions maintains the same set of controls, X ′ , as in the debt structure regresi,t
sions: log of size, market-to-book and profitability. As well as firm-fixed effects, year-fixed
effects and clustered standard errors at a firm level (as in Petersen (2009).
24 Note that γ < 0 for any type of debt being considered. That is, if more equity is incorporated in the capital structure, necessarily the amount of leverage should be reduced.
25 Firms with more unsecured debt in their debt structure tend to have more equity in
their capital structure, and therefore, a further increase in equity will reduce more than
proportionally the holdings of unsecured debt.
12
less unsecured debt as their collateral availability increases26 . This result is very
interesting from a balance sheet channel perspective. That is, more collateral
availability decreases the degree of financial frictions faced and this increases
secured borrowing capacity of firms, but not unsecured borrowing capacity.
The above result would suggest the existence of a different mechanism, in addition to the conventional collateral channel, which would operate through unsecured debt and could generate very different dynamics in terms of investment:
the unsecured channel.
The last hypothesis that aims to be tested is whether we can confirm the existence of a pecking-order in terms of unsecured debt as suggested by Giambona
and Golec (2012). The debt structure regression results showed that collateral
seems to have a greater impact, in absolute value and other things being equal,
over unsecured debt than financial strength. Therefore, I would like to test how
the sensitivity of each type of debt changes as we vary the opposite determinant.
I define financial strength and collateral availability categories in 20% intervals27 and I perform the secured debt over total assets regression across the
financial strength categories and the unsecured debt over total assets regression across the collateral availability categories. The empirical specifications
are displayed below:
U nsecuredj,i,t
′
= Ωj +γj,1 F inStrengthj,i,t +δj,1 Collateralj,i,t +Xj,i,t
βj,1 +ϵj,i,t
T otalAssetsj,i,t
(3)
Securedk,i,t
′
= Ωk +γk,1 F inStrengthk,i,t +δk,1 Collateralk,i,t +Xk,i,t βk,1 +ϵk,i,t
T otalAssetsk,i,t
(4)
where j are the collateral categories, k are the financial strength categories and
Ωj , Ωk , gather the constant, the firm-fixed effects term and the year-fixed effects
term in the two specifications.
The above specifications will allow for two effects: effect1 will be the sensitivity of secured debt to collateral across financial strength categories, δk , while
effect2 will be the sensitivity of unsecured debt to financial strength across collateral availability categories, γj . Then, if effect 1 dominates over effect 2, other
things being equal, the pecking-order hypothesis will be proven. Figure 3 and
4 graph the estimated coefficients in both regressions (effects 1 and 2 ) and Appendix E shows all the regression results28 .
26 Note that the evidence from debt structure suggested that only firms that are financially
constrained increase unsecured debt holdings as collateral availability increases.
27 We will have 5 different financial strength categories, while only 4 will be available in
terms of collateral availability, as there are no observations for firms with tangible assets over
total assets above 80%.
28 The same procedure has been undertaken using categories defined as a function of the
quartiles of the distribution of financial strength and collateral availability and the results
13
As Figure 3 evidences, the contribution of collateral to secured debt decreases as
we move along the financial strength categories. This is consistent with the fact
that debt structure is determined by the interaction between financial strength
and collateral availability, and collateral and financial strength act as substitutes.
On the other hand, Figure 4 shows that the contribution of financial strength
to unsecured debt decreases as we increase collateral availability. However, note
that the sensitivity of secured debt to changes in financial strength is higher
than the sensitivity of unsecured debt to changes in collateral availability. That
is, there is a pecking-order for unsecured debt.
To conclude the actual section on the evidence on the (un)secured debt puzzle, first note that financial strength and collateral availability determined unsecured debt and secured debt respectively. Second, the increase in collateral
availability does not increase borrowing capacity as evidenced by the balance
sheet channel once we account for unobserved individual heterogeneity, but only
secured debt borrowing capacity. Moreover, firms relying on secured debt tend
to be more financially constrained, while those firms relying on unsecured debt
tend to be unconstrained. This implies that debt structure acts as a signaling
device of borrower’s quality and even firms that are financially constrained have
a preference to incorporate more unsecured debt as their collateral availability
increases. Finally, financial strength and collateral availability can act as substitutes but given the pecking-order evidence for unsecured, financial strength
primarily determines debt choice as collateral only plays a role when access to
unsecured debt is restricted.
4
The Mechanism: Price Discrimination in the
Unsecured Debt Market
It remains a question the mechanism behind the strong preference for unsecured
debt. According to Giambona and Golec (2012), there is a pecking order for
unsecured debt because it allows firms to maintain spare collateral capacity that
can be used for other purposes other than investment (i.e. risk management).
Rampini, Sufi and Viswanathan (2013) argue that collateral is a scarce resource
that is pledged for risk management and investment purposes. As firms become more financially constrained, they will tend to sacrifice risk management
in order to pledge the existing collateral to finance investment. However, the
evidence presented throughout the paper indicates that firms try to pledge collateral as infrequently as possible.
remain the same.
14
Moreover, the survey by Graham and Leary (2011) shows that capital structure is designed so as to optimize financing costs, while Graham and Harvey
(2001) determine that the most relevant factors affecting debt policy choice
are financial flexibility29 , credit ratings30 and interest rates. This last factor is
precisely the mechanism that I will hypothesize it is behind the preference for
unsecured: borrowing on an unsecured basis allows to minimize the total costs
of financing.
The conventional wisdom regarding interest rates is that secured debt contracts
should have a lower interest rate attached than unsecured debt contracts. This
should be the case because the ex-ante risk that unsecured debt contracts have
for financial intermediaries is so high due to the lack of collateral pledged, that
the interest rate consistent with the risk assumed would be very large. However,
in practice, financial intermediaries set the interest rate for unsecured debt such
that it is competitive.
Berger and Udell (1990) analyze the commercial and industrial loans market
in the U.S.31 and controlling for loan characteristics, as well as macroeconomic
conditions, they conclude that when risk is observable secured debt is riskier,
evidenced by a higher interest rate premium than unsecured debt contracts.
While Berger and Udell (1990) control for loan characteristics, my interest is
on interest rate and borrower characteristics at origination of the loans, to understand if there is descriptive evidence that can validate the hypothesis that
unsecured debt contracts have a lower interest rate attached as a function of
firm characteristics.
The information on the interest rates of loans comes from LPC’s Dealscan32 , a
database of loans to large firms. The data in Dealscan comes primarily from
SEC filings and includes most loans made to large publicly traded companies
(e.g. the Forbes 500) but there is very little information, however, on lending to
small and middle-market firms. This is a drawback in order to analyze interest
rates for both types of debt contracts as we are not able to cover the complete
Compustat manufacturing sample considered in the previous analysis.
Nevertheless, there is a reason why this should not be a problem and we could
still derive consistent relationships between interest rates attached to debt contracts and the associated firm characteristics. Dealscan contains loan information from the largest public firms in the U.S., which are most likely unconstrained. Therefore, there is no reason to believe that the interest rates on
secured and unsecured debt for unconstrained firms should be significantly dif29 Defined
as not having enough internal funds to finance investment.
already know from Rauh and Sufi (2010) that as firms improve their credit quality,
they tend to incorporate more senior unsecured in their debt structure and have a higher
proportion of equity in their capital structure.
31 Their sample covers each quarter from 1977 to the first half of 1988.
32 Dealscan hereafter.
30 We
15
ferent.
Table 7 shows the summary statistics, for interest rates and firm characteristics, from all debt contracts signed by U.S. public manufacturing firms during
the period 1994-2010, classified as secured and unsecured bank debt contracts.
Appendix E contains detailed information on how the the sample for debt contracts from Dealscan has been constructed.
As expected, the proportion of bank debt contracts signed on a secured basis is
much higher than that of unsecured, 70.85%33 . Surprisingly and in contradiction with the intuitive idea that unsecured debt contracts should have a higher
interest rate attached, the mean (standard deviation) basis points in addition to
the reference rates attached to secured debt contracts is 247.26 (129.04), while
those of unsecured debt contracts is 85.42 (82.932). That is, unsecured debt
contracts do have a lower interest rate attached or secured debt borrowers tend
to be riskier34 .
Furthermore, when analyzing debt and capital structure at the date of origination of secured and unsecured debt contracts, the average (standard deviation)
percentage of unsecured debt in debt structure for secured debt contracts was
46% (0.40) as opposed to the 89% (0.25) found for unsecured.
When considering secured and unsecured debt holdings in the capital structure, secured debt contracts exhibit a lower degree of specialization in terms
of debt types with 14% (0.15) over total assets in each type of debt. On the
other hand, unsecured debt contracts show that these firms tend to specialize in
terms of unsecured debt, 19.4% (0.13), while holding 1.7% (0.06) in secured debt.
The rest of the firm characteristics analyzed denote that unsecured debt contracts tend to be firms with average sizes larger (5,547 vs. 1,326), higher financial strength (0.67 vs. 0.58), higher collateral availability (0.28 vs. 0.25) and
larger investment projects (231.18 vs. 48.34).
All these results are consistent with conclusions previous derived when analyzing debt and capital structure determinants: firms that are larger, with high
financial strength and high collateral availability tend to have more unsecured
debt than secured, both in the debt structure and in the capital structure.
Therefore, the descriptive evidence provided allows to conclude that there is a
pecking-order for unsecured debt because it allows to minimize total costs of
financing and financial intermediaries are willing to offer lower interest rates for
unsecured debt contracts because, ex-ante, firms that borrow on an unsecured
33 The
evidence in Bolton and Freixas (2000) and Rauh and Sufi (2010) suggests that as
credit quality increases firms tend to substitute bank debt for nonbank debt (i.e. medium
term notes). This implies that the majority of larger firms borrow on an unsecured basis but
through medium term notes.
34 Consistent with the evidence in Berger and Udell (1990).
16
basis tend to have a better quality balance sheet and a built-in reputation of
repayment.
One could definitely argue that unsecured debt contracts tend to have lower
interest rates attached because of the timing in which they have been originated. That is, unsecured debt contracts tend to be originated at the beginning
of expansions when lending standards soften. In addition to his, the low interest rates for so long in the 2002-2006 expansion could have motivated the lower
interest rates for unsecured debt contracts.
In order to prove the above possibility, Figure 5 gathers in panel a the evolution for interest rates on unsecured and secured debt contracts (top panel),
as well as the evolution of the number of contracts signed35 for unsecured and
secured (bottom panel).
Two conclusions can be derived from the analysis of these graphs. First, interest rates on unsecured debt are systematically lower than secured debt interest
rates, except for years 1996-2000 and in 2005. In addition to this, secured debt
interest rates tend to be more sensitive to fluctuation in the business cycle, while
unsecured debt interest rates remained relatively stable over time until the 2007
recession. Second, focusing on the evolution of debt contracts originated during
the 2002-2006 expansion, it shows that the number of unsecured debt contracts
increased slightly, while secured debt contracts decreased. however, the evidence reported does not imply that unsecured debt spreads were low because
of timing of issuance: unsecured debt spreads have been low and below secured
beyond the 2002-2006 expansion.
5
Conclusions
The purpose of this paper was to, first, to empirically prove the “(un)secured
debt puzzle” by performing a descriptive analysis of the key firm characteristics
defined; debt and capital structure, financial strength, collateral and size and
to shed light on the possible mechanisms behind the puzzle, by identifying a
different mechanism that could make unsecured debt more attractive to most
productive firms. Second, to analyze the role of collateral for U.S. public manufacturing firms in order to understand when exactly collateral will be pledged
and the relevance of collateral in defining debt and capital structure. Third, to
understand the implications of credit quality on shaping debt structure and capital structure of firms. And finally, to test the pecking-order hypothesis and to
provide descriptive evidence on the mechanism behind the puzzle: interest rates.
The motivation for this paper was that, so far, the economics and finance lit35 These facilities are the mean for new loans and not the mean for loans outstanding at
that point in time, which allows for the comparison of firm characteristics at origination date
at each point in time.
17
erature has not focused on the role of unsecured debt, while a lot of attention
has been devoted to the role of collateral in solving market imperfections, implicitly focusing on secured debt borrowing, which according to the convention,
it is determined by the level of collateral the firm has. This is surprising as
unsecured debt is as important in the financing structure of firms as secured
debt, provided that 64% of total debt of U.S. public firms is unsecured, using
S& P’s Compustat database from 1994 to 2010. Oppositely, back since the late
70’s, the law literature has emphasized about the relevant role that unsecured
debt plays in the context of firm’s financial and investment policies and, on
creditor’s bargaining process upon default, by introducing the popular concept
of the “secured debt puzzle”.
According to the results derived in the present paper, the role of unsecured
is relevant and there is enough descriptive evidence to justify an unsecured
channel affecting firm investment, in addition to the collateral channel which
dates as far back as Fisher (1933). First, I show that debt structure is not
solely determined by collateral, but by the interaction between collateral and
financial strength, which determines unsecured debt. In addition to this, I show
that collateral only plays a role for those firms that are financially constrained.
Second, I prove that higher collateral does not increase borrowing capacity by
lowering the financial frictions faced, but only secured debt borrowing capacity.
This result allows for a complementary channel to the so-called collateral channel, the unsecured channel. Moreover, I test the pecking-order hypothesis and
conclude that firms have a clear preference for unsecured debt because it allows
to minimize total costs of financing.
The results regarding the importance of the role of collateral require further
comments. According to the results derived, on average, collateral is irrelevant
across debt structure and capital structure categories, except perhaps for firms
that are financially constrained. Moreover, collateral plays no role for firms satisfying the “(un)secured debt puzzle”. The second dimension, financial strength,
determines when collateral will be pledged as this is only the case when access
to unsecured debt is restricted.
If unsecured debt provides a different mechanism, then: i) all the literature
regarding optimal capital structure should be updated in order to account for
the effects and implications that unsecured debt might have in the optimal
capital structure choice (results show that there is a high positive correlation
between strong financial strength and a high percentage of unsecured debt). ii)
The sectorial composition of the economy would matter, both from a demand
perspective and from a supply perspective. iii) The reaction to new information
arrival would be different and this could have important implications for monetary policy for instance. Finally, iv) it could affect aggregate investment and
business cycle dynamics. Financial frictions literature gives collateral a central
role in the propagation and amplification of shocks through (a) the balance
sheet channel and (b) the bank-lending channel. However, financial accelerator
18
types of models will be missing some important part of the story, the unsecured
channel.
The present paper could be improved or extended in many ways. The results
could be extended to all sectors in the economy36 in order to know if sectorial
composition of the economy matters. Additionally, it would be helpful to define
an accurate measure for collateral in the selected sample, from 10ks and 10qs in
SEC filings in order to be sure about the results regarding the role of collateral.
This would allow not only to have the appropriate measure of collateral, but
also to properly define the variable for encumbered collateral.
On the other hand, it would be interesting to gather debt covenant information attached to debt contracts from the SEC filings for the selected sample in
order to determine whether covenants of unsecured debt are looser or stricter
and to quantify the impact of debt covenants attached to debt contracts on
investment policy of firms. Moreover, by incorporating interest rate information to the analysis, we could empirically prove whether price discrimination in
the unsecured debt market takes place and to what extend price discrimination
determines debt structure of U.S. public firms. Moreover, using regression discontinuity design, we could empirically prove the unsecured upgrade by defining
thresholds for both, collateral availability and financial strength.
The endogeneity problem present among all relevant firm characteristics defined also requires attention. Clearly, debt structure, capital structure and
investment policy of firms co-determine and reversed causality could be present
in any regression analysis performed regarding the defined firm characteristics.
For instance, in results estimated for the investment regression, the quantitative
impact of secured and unsecured debt could be biased. Another econometric
approach is needed in order to disentangle the real importance of financing policy in affecting investment decisions of firms: GMM-IV estimation, so as to
eliminate the endogeneity bias present.
The future research possibilities regarding unsecured debt are clearly large as
this is a new strand in the literature. For instance, the analysis in the present
paper could be also performed for private firms in order to determine whether
the results for U.S. public manufacturing firms can also be extended to private
firms. In addition to this, if unsecured debt provides a different mechanism at a
cross-sectional level, one could expect to find a similar pattern in the aggregate.
Preliminary research undertaken, shows that unsecured debt is counter-cyclical.
That is, it increases during recessions. Studying aggregate dynamics on unsecured debt and its effect over aggregate investment and over the business
cycle would prove useful in identifying an additional mechanism, besides that
of collateral, which could play a role in the propagation and amplification of
36 In fact, the “(un)secured debt puzzle” also holds when considering all sectors in the economy.
19
exogenous shocks to the real economy.
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22
6
Appendix
6.1
Appendix A: Variable Description
• Percentage of Debt Unsecured in Debt Structure: Total Debt minus Mortgages and Other Secured Debt (item 9 - item 241) over Total
Debt (item 9).
• Percentage of Debt Unsecured in Capital Structure: Total Debt
minus Mortgages and Other Secured Debt (item 9 - item 241) over Total
Assets (item 6).
• Percentage of Debt Secured in Capital Structure: Mortgages and
Other Secured Debt (item 241) over Total Assets (item 6).
• Financial Strength (book value): Equity (item 6 - item 181) over
Equity plus Total Debt (item 6 - item 181 + item 9). Equity is computes
as Total Assets minus Total Liabilities.
• Tangibility or Collateral Availability: Property, Plant and Equipment, Net (item 8) over Total Assets (item 6).
• Size: Total assets (item 6), total assets in million USD.
• Investment or Capital Expenditures: Capital Expenditures (item
128).
• Profitability: Operating income before depreciation (13) over Total assets (6).
• Market-to-Book: Market Value of Equity plus Total debt plus Preferred stock liquidating value (10) minus Deferred taxes and investment
tax credit (35) over Total assets (6).
6.2
Appendix B: Sample corrections
I start with U.S. firms traded on the AMEX, NASDAQ, and NYSE, and covered
by S&P’s database Compustat, from 1994 to 2010. First, I remove firm-year
observations whose percentage of debt unsecured is outside the unit circle37 and
end up with 89,684 firm-year observations. This adjustment becomes necessary
due to the way in which Compustat classifies debt into short-term and long-term
secured38 . I remove utilities (SIC codes 4900-4949), financial firms (SIC codes
6000-6999) and public administrations (SIC codes above 9000), to end up with
68,561 firm-year observations.
37 All
firm-year observations outside (0,1) have been ruled out.
a debt contract does not specify whether secured debt is short-term or longterm, Compustat assigns debt to long-term debt. Therefore, you find cases in which the level
of long-term secured exceeds the level of total long-term debt.
38 Whenever
23
I further remove i) firm-years with missing, negative or zero values for total
assets (68,543 observations remaining); ii) firm-years with missing, negative or
zero common equity as I am only interested on studying observations from firms
that are not financially distressed39 (61,636 observations remaining); iii) firmyears with missing, negative or zero values for total sales (60,221 observations
remaining); iv) firm-years with missing, negative or zero values for net property, plant and equipment (60,024 observations remaining) and v) firm-years
with missing, negative of zero investment (59,124 observations remaining).
Then, I rule out firm-year observations which are involved in substantial M&A
activity, by eliminating all firm-year observations with the percentage of amount
spent on acquisitions over total assets exceeding 15%. This correction eliminates
6,231 observations from my sample. Finally, I windsorize all key firm characteristics at the 1st and 99th percentiles (52,846 observations remaining).
My final sample for all sectors in the economy comprises 52,846 observations. I
then merge the resulting sample of the Compustat leveraged firms with Capital
IQ40 , which will allow the decompositions of long-term debt secured and unsecured from Compustat into a broader classification of secured and unsecured
long-term debt by instrument type. I rule out firm-years for which the difference between total debt as reported in Compustat and the sum of debt types as
reported in Capital IQ exceeds 10% of total debt (as in Colla, Ippolito and Li
(2012)). However, the present study will present results from the manufacturing sector, so I keep all firm-year observations belonging to the manufacturing
sector (SIC codes 2000-3999). My final sample for the manufacturing sector
contains 25,096 observations. Firm-level characteristic variables are from Compustat, while firm-level debt structure variables are from Capital IQ. Appendix
A provides a detailed description of the variables used in the analysis. Table
1 presents descriptive statistics for the manufacturing sample for the period
1994-2010.
6.3
Appendix C: Definitions for Financially Contrained in
Almeida, Campello and Weisbach (2004)
39 This sample correction is also related to the data assignment problem in Compustat cited
before, regarding short-term and long-term secured debt. When a firm defaults on a secured
debt payment, the contract is automatically reclassified as short-term debt. Therefore, during
recessions or when firms face financial distress, secured debt “disappears” from the secured
long-term debt item and it is reclassified as short-term debt. This represents an important
source of bias if we do not rule out firms that are financially distressed.
40 Regulation S-X of the Securities Act of 1933 requires firms to detail their long-term debt
instruments. Regulation S-K of the same act requires firms to discuss their liquidity, capital
resources, and operating results. Firms often also provide information on notes payable within
a year (Rauh and Sufi (2010)). The SEC mandated electronic submission of SEC filings in
1996. Capital IQ has been compiling detailed information on capital structure and debt
structure by going through financial footnotes contained in firms 10K SEC filings since then.
However, coverage by Capital IQ is comprehensive only from 2002 onwards.
24
# Observations
% Observations
4,731
4,568
51%
49%
2,897
6,402
31%
69%
1,444
7,855
16%
84%
8,891
0
100%
0%
1) Dividend Payout
Financially Constrained
Unconstrained
2) Total Assets
Financially Constrained
Unconstrained
3) Kaplan & Zingales Index
Financially Constrained
Unconstrained
4) Credit Rating
Financially Constrained
Unconstrained
6.4
Appendix D: Debt Structure Determinants Regression Results, Complete Manufacturing Sample
(1)
Financial Strength
Tangibility
% Unsecured over Total Debt
(2)
(3)
(4)
-0.1583**
(0.0235)
-0.2125**
(0.0483)
-0.1522**
(0.0234)
-0.1855**
(0.0484)
-0.1503**
(0.0235)
-0.1889**
(0.0487)
-0.1483**
(0.0239)
-0.1912**
(0.0489)
0.0361**
(0.0086)
0.0355**
(0.0087)
-0.0027
(0.0029)
0.0364**
(0.0088)
-0.0025
(0.0029)
-0.0187
(0.0286)
yes
yes
firm
yes
yes
firm
yes
yes
firm
yes
yes
firm
25,096
0.639
25,096
0.641
25,096
0.641
25,096
0.641
Log (Size)
Market-to-book
Profitability
Firm Fixed Effects
Year Fixed Effects
Clustered St. Errors
# Observations
R2
25
6.5
Appendix F: LPC Dealscan Sample Construction
The data on Dealscan are organized by ”Deal” and by ”Facility”. A deal defines
a contract signed between a borrower and a lender (or lenders) at a particular
date. Each deal is comprised of one or more facilities (debt contracts). During
the 1994-2010 period, there were 5,266 facilities on Dealscan. That is, 5,266
distinct debt contracts signed by manufacturing firms. Interest rate information on debt contracts is obtained from variable ”allindrawn”41 in ”Current
Facility Pricing”’. While Dealscan has very good information on loan contract
features, it has very little information about the borrower and therefore, borrower attributes (firm characteristics) come from the previous manufacturing
Compustat sample. I merge the LPC Dealscan data on debt contract and interest rates with Compustat to gather the firm characteristics the borrower had
at the date of origination of the debt contract.
41 This variable considers the basis points above reference rate for each debt contract, which
in the majority of the cases happens to be the LiBOR rate.
26
7
Tables
Table 1: Manufacturing Sample Overview
This table presents mean, median and standard deviation for key firm
characteristics for U.S. public manufacturing firms (SIC codes 2000-3999) from
1994 to 2010. Appendix A provides a detailed description of the variables used
in the analysis, while Appendix B contains a description of sample corrections.
Mean
Median
St. Dev.
% Unsecured (Total Debt)
% Unsecured (Total Assets)
% Secured (Total Assets)
0.64
0.15
0.08
0.79
0.11
0.02
0.37
0.15
0.12
Financial Strength
Tangibility
0.69
0.26
0.71
0.23
0.22
0.17
Size
Market-to-book
Profitability
1370
1.86
0.05
160
1.42
0.11
3610
1.36
0.20
Investment (Level)
# Observations
68.73
6.79
207.19
25,096
27
Table 2: Summary Statistics by Debt Structure Category
This table contains mean and [median] of key relevant firm characteristics and controls by reliance on debt types for U.S. public manufacturing
firms (SIC codes) from 1994 to 2010. The first two columns contain the 100%
secured and 100% unsecured debt structures respectively. For the rest of the
columns, column [0-25%) for instance, contains firm-year observations which
have a percentage of debt unsecured higher than zero but lower or equal to
25%. Appendix A provides a detailed description of the variables used in the
analysis, while Appendix B includes a description of sample corrections.
Specialized
100%
100%
Sec
Unsec
(1)
(2)
(0,
25%]
(3)
Mixed
(25%, (50%,
50%]
75%]
(4)
(5)
(75%,
100%)
(6)
% Unsecured
(Total Debt)
% Unsecured
(Total Assets)
% Secured
(Total Assets)
0.00
[0.00]
0.00
[0.00]
0.19
[0.14]
1.00
[1.00]
0.22
[0.21]
0.00
[0.00]
0.1
[0.09]
0.02
[0.01]
0.20
[0.18]
0.38
[0.38]
0.08
[0.06]
0.13
[0.10]
0,63
[0.62]
0.14
[0.12]
0.08
[0.07]
0,93
[0.95]
0.23
[0.22]
0.02
[0.01]
Financial
Strength
Tangibility
0.75
[0.81]
0.25
[0.22]
0.69
[0.70]
0.26
[0.23]
0.70
[0.74]
0.27
[0.24]
0.73
[0.80]
0.25
[0.22]
0.70
[0.73]
0.25
[0.22]
0.66
[0.67]
0.26
[0.22]
Size
189
[70]
1.57
[1.10]
0.04
[0.10]
903
2,166
[547]
1.58
[1.20]
0.09
[0.13]
5,947
333
[81]
1.55
[1.09]
0.05
[0.10]
4,664
430
[59]
1.79
[1.23]
0.01
[0.08]
3,398
644
[81]
1.64
[1.12]
0.03
[0.09]
3,136
2,266
[351]
1.52
[1.11]
0.06
[0.11]
7,048
Market-to-book
Profitability
# Observations
28
Table 3: Summary Statistics by Debt Choice Determinants
This table contains mean and [median] of key relevant firm characteristics and controls by quartiles of the distribution of financial strength (panel
a) and collateral availability (panel b) for U.S. public manufacturing firms
(SIC codes 2000-3999) from 1994 to 2010. Appendix A provides a detailed
description of the variables used in the analysis, while Appendix B includes a
description of sample corrections.
Panel A: Quartiles of Financial Strength
Q1
Q2
Q3
Q4
% Unsecured (Total Debt)
% Unsecured (Total Assets)
% Secured (Total Assets)
Financial Strength
Tangibility
Size
Market-to-book
Profitability
# Observations
0.65
[0.80]
0.28
[0.31]
0.15
[0.08]
0.71
[0.93]
0.19
[0.22]
0.08
[0.02]
0.65
[0.84]
0.10
[0.10]
0.05
[0.02]
0.56
[0.55]
0.02
[0.01]
0.02
[0.01]
0.38
[0.41]
0.30
[0.27]
0.63
[0.63]
0.30
[0.27]
0.80
[0.80]
0.27
[0.23]
0.95
[0.96]
0.18
[0.15]
1,780
[288]
1.29
[0.94]
0.06
[0.10]
6,261
1,869
[339]
1.31
[1.03]
0.09
[0.12]
6,295
1,218
[135]
1.58
[1.20]
0.07
[0.12]
6,285
560
[83]
2.19
[1.67]
-0.00
[0.08]
6,255
Panel B: Quartiles of Collateral Availability
Q1
Q2
Q3
Q4
% Unsecured (Total Debt)
0.65
[0.77]
0.12
[0.05]
0.05
[0.01]
0.65
[0.81]
0.14
[0.10]
0.06
[0.02]
0.64
[0.79]
0.15
[0.12]
0.08
[0.02]
0.64
[0.78]
0.17
[0.16]
0.10
[0.04]
0.76
[0.83]
0.07
[0.07]
0.71
[0.74]
0.17
[0.17]
0.67
[0.69]
0.29
[0.29]
0.63
[0.64]
0.51
[0.48]
Size 29 895
[78]
Market-to-book
1.99
[1.45]
Profitability -0.03
[0.05]
# Observations 6,259
1,299
[146]
1.68
[1.22]
0.05
[0.11]
6,296
1,477
[201]
1.47
[1.09]
0.09
[0.12]
6,288
1,758
[315]
1.22
[0.94]
0.10
[0.13]
6,253
% Unsecured (Total Assets)
% Secured (Total Assets)
Financial Strength
Tangibility
Table 4: Debt Structure Determinants Regression Results, Two
Samples
This table presents regression results to examine the relation between
debt structure, financial strength and collateral availability as determinants,
along with usual controls in the literature for U.S. public manufacturing firms
(SIC codes 2000-3999) from 1994 to 2010. Columns (1)-(4) show the results for
sample1, for financial strength below the sample median(71%), while Columns
(5)-(8) show the results for sample2, financial strength above the sample
median(71%). Definitions of the variables are provided in Appendix A, while
sample corrections are described in Appendix B. All specifications include
firm- and year-fixed effects and robust standard errors are clustered at the firm
level (as in Pertersen (2009)) and reported in parentheses. **, and * denote
statistical significance at the 5% and 1% level, respectively.
% Unsecured over Total Debt
Financial Strength ≤ 71%
Financial Strength > 71%
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Financial
Strength
Tangibility
0.194**
(0.033)
-0.282**
(0.042)
0.146**
(0.030)
-0.326**
(0.036)
0.150**
(0.029)
-0.293**
(0.036)
0.175**
(0.030)
-0.275**
(0.036)
0.072**
(0.002)
0.074**
(0.002)
0.006
(0.005)
0.080**
(0.002)
0.007
(0.005)
-0.095*
(0.057)
yes
yes
firm
yes
yes
firm
yes
yes
firm
yes
yes
firm
12548
0.74
12548
0.74
12548
0.74
12548
0.74
Log (Size)
Market-to
-book
Profitability
Firm FE
Year FE
Clustered SE
# Obs.
R2
30
-0.560**
(0.059)
-0.116**
(0.045)
-0.401**
(0.057)
-0.225**
(0.040)
-0.405**
(0.057)
-0.224**
(0.040)
-0.407**
(0.057)
-0.214**
(0.041)
0.068**
(0.002)
0.068**
(0.002)
-0.005
(0.003)
0.070**
(0.002)
-0.005
(0.003)
0.015
(0.038)
yes
yes
firm
yes
yes
firm
yes
yes
firm
yes
yes
firm
12548
0.66
12548
0.66
12548
0.66
12548
0.66
Table 5: Two-way sorting of Percentage of Debt Unsecured, by
Financial Strength and Tangibility
This table presents the relation between the percentage of debt unsecured,
financial strength and tangibility for U.S. public manufacturing firms from
1994 to 2010. Two-way sorting is carried out year by year and then aggregated
across years. Each cell in the table presents mean [median] percentage of
debt unsecured in debt structure. Definitions of the variables are provided in
Appendix A, while sample corrections are described in Appendix B.
Tangibility
Financial Strength
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
0.65
[0.91]
0.71
[0.99]
0.55
[0.77]
0.23
[0.00]
0.67
[0.88]
0.74
[0.98]
0.51
[0.60]
0.27
[0.00]
0.69
[0.90]
0.69
[0.95]
0.51
[0.62]
0.39
[0.00]
0.61
[0.75]
0.70
[0.94]
0.57
[0.76]
0.42
[0.08]
31
Table 6: Capital Structure Determinants Regression Results
This table presents regression results to examine the relation between
capital structure, financial strength and collateral availability as determinants,
along with usual controls in the literature for U.S. public manufacturing firms
from 1994 to 2010. Columns (1) shows the results for the total debt over total
assets regression, (2)-(3) shows the results for the secured debt over total assets
regression and (4)-(5) shows the results for the unsecured debt over total assets
regression. Definitions of the variables are provided in Appendix A, while
sample corrections are described in Appendix B. All specifications include
firm- and year-fixed effects and robust standard errors are clustered at the firm
level (as in Pertersen (2009)) and reported in parentheses. **, and * denote
statistical significance at the 5% and 1% level, respectively.
Total
(1)
Financial Strength
Tangibility
Log (Size)
Market-to-book
Profitability
Firm FE
Year FE
Clustered SE
# Obs.
R2
-0.706**
(0.005)
0.024**
(0.007)
% Debt over Total Assets
Secured
Unsecured
(2)
(3)
(4)
(5)
-0.228**
(0.009)
0.087**
(0.015)
0.014**
(0.001)
0.000
(0.000)
0.006
(0.004)
-0.231**
(0.009)
0.086**
(0.015)
-0.479**
(0.010)
-0.075**
(0.015)
-0.005*
(0.003)
-0.000
(0.001)
0.018*
(0.008)
-0.475**
(0.010)
-0.061**
(0.015)
0.019**
(0.003)
0.000
(0.001)
-0.012
(0.009)
yes
yes
firm
yes
yes
firm
yes
yes
firm
yes
yes
firm
yes
yes
firm
25,096
0.97
25,096
0.72
25,096
0.72
25,096
0.80
25,096
0.80
32
Table 7: Summary Statistics, Interest Rates and Firm Characteristics,
for Secured and Unsecured Debt Contracts
This table presents the comparison of spreads over reference rate and
firm characteristics of secured and unsecured debt contracts at date of origination for U.S. public manufacturing firms from 1994 to 2010. Definitions of the
variables are provided in Appendix A, sample corrections for the Compustat
sample are described in Appendix B, while Appendix F contains the procedure
to derive the LPC Dealscan sample (interest rate spreads).
Secured Bank Debt
Unsecured Bank Debt
Mean
St. Dev.
Mean
St. Dev.
247.25
129.04
85.41
82.93
% Unsecured (Total Debt)
% Unsecured (Total Assets)
% Secured (Total Assets)
.455
.147
.142
.402
.165
.150
.894
.194
.017
.251
.127
.060
Financial Strength
Tangibility
.576
.251
.265
.164
.670
.281
.199
.163
Size
Market-to-book
Profitability
Investment (Level)
# Debt Contracts
1,326.2
5,110.1
5,547.5
13,308.0
48.3
3,731
192.6
231.1
1,535
544.0
Spread
33