Accounting-based covenants and credit market access
Hans B. Christensen and Valeri V. Nikolaev
The University of Chicago
Booth School of Business
5807 South Woodlawn Avenue
Chicago, IL 60637
Abstract: We study how the use of accounting information in private credit agreements affects
credit market access. We measure contracting value of accounting information by its ability to
explain credit risk and examine how the contracting value influences contractual use of
accounting benchmarks (covenants). We argue that profitability-based covenants, which control
borrowing by reference to profitability ratios, are employed when accounting information is
relatively informative of credit quality. In contrast, capital structure covenants, which limit
future borrowing by reference to shareholders’ capital, are expected to be a more robust
mechanism to control debt-related agency problems when accounting information has lower
contracting value. Our results support these conjectures. Further, we use contracting value
proxies as instruments to study the effect of accounting use on the debt levels. We find that
profitability covenants promote while capital structure covenants limit the use of debt. Our
evidence indicates that accounting used in contracting has a large economic effect on credit
market access.
Keywords: accounting-based covenants, private debt, contracting value of accounting
JEL Classification: M40
First draft: November 2009
This version: January 2010

We thank Phil Berger and Laurence van Lent for helpful comments. Financial support from the University of
Chicago Booth School of Business is gratefully acknowledged.
1
1. Introduction
A fundamental question in accounting is whether the reliance on accounting information
in debt contracts facilitates firms’ access to (or use of) debt financing. The widespread and
continued use of accounting data in credit agreements (e.g., Leftwich 1983; Christensen and
Nikolaev 2009) is testimony to the usefulness of accounting information in reducing contracting
frictions and improving contracting efficiency (Watts and Zimmerman 1986). The focus in
much of prior empirical research, however, is on testing whether accounting choice aims at
avoiding covenant violations (see Fields et al. 2001, for review).
While the scope for
manipulations reduces the usefulness of accounting for contracting, there is little, if any, direct
evidence on the role of accounting information in creating (or deterring) firms’ access to credit
markets.
In particular, to what extent would lenders be willing to provide financing to
companies if the use of accounting ratios was not an option or if accounting information had
little use in describing credit risk?
There are several channels through which the use of accounting ratios in debt contracts
facilitates access to debt markets. First, accounting-based covenants reduce the agency costs of
debt (Jensen and Meckling 1976; Smith and Warner 1979), which make borrowing cheaper and
thus can lead to more lending in equilibrium. Second, accounting-based covenants can alleviate
credit rationing, which arises in loan markets with imperfect information (Jaffee and Russell
1976; Stiglitz and Weiss 1981). Due to adverse selection and moral hazard problems, some
borrowers may not be able to borrow even if they are willing to pay higher than equilibrium
interest, while others may not afford debt financing. Contractual requirements to maintain
accounting ratios at certain levels can helps banks in ex ante screening borrowers and thus can
reduce the amount of credit rationing. Third, accounting covenants can also serve as tripwires
2
that give lenders an option to renegotiate in response to deteriorating economic conditions
(Berlin and Mester 1992). Their use can alleviate ‘hold up’ problems associated with short-term
debt (Sharpe 1990; Rajan 1992) and improve lenders’ incentives to monitor the loan (Rajan and
Winton 1995), thus making debt financing more accessible.
The theory that explains the use of covenants, however, commonly assumes that
accounting information is available for contracting and portrays the underlying credit risk.
Deviations from these assumptions are rarely analyzed in the literature. The literature does not
consider how deviations from these assumptions can affect debt contract design, specifically,
ways in which companies contract on accounting information, and different channels, i.e., types
of accounting variables used in contracting. This raises two empirical questions: How does the
ability of accounting information to measure credit risk affects debt contracts’ reliance on
accounting ratios or benchmarks in contracts? And, to what extent does the use of accounting
ratios or benchmarks in debt contracts affects the amount of debt raised by firms?
The answers to these questions are not obvious.
On one hand, when accounting
information reflects credit quality, lenders should be more willing to contract. If so, we are
interested in understanding to what extent this is the case. On the other hand, the role of
accounting covenants could mainly be not in promoting the ex ante use of debt but in limiting its
ex post levels. For example, lenders may still be willing to lend if covenants were not employed,
however, they should charge a lower risk premium if a company restricts future borrowing (by
means of covenants).
In this study, we attempt to address the questions stated above by
performing two sets of analyses.
We first examine how the degree to which accounting
information reflects credit quality affects the choice of covenants. Second, we examine how the
use of covenants in credit agreements is related to the level of leverage.
3
We argue that in relation to controlling the level of debt, accounting benchmarks
(covenants) divide into two groups (occurrence of these groups in credit agreements is negatively
correlated). The first group we refer to as “capital structure covenants”. These covenants only
rely on the balance sheet information and effectively control the ratio of debt to equity. The
second group is “profitability covenants” which always rely on income statement information
that in some cases is scaled by a balance sheet variable (e.g., minimum ratio of earnings to
interest expense or debt). While both covenant types limit debt-related agency problems, we
argue that they accomplish this in different ways. Capital structure covenants directly restrict the
mix of debt and equity and thus ensure that shareholder’s wealth is sensitive to adverse
managerial actions (e.g., overinvestment, or underinvestment in positive NPV projects). This is
done without direct reference to profitability (which for instance could be zero or negative in
some periods) and a firm can usually avoid violation of these covenants by contributing
additional equity or cutting back on dividends. In contrast, we argue that profitability covenants
limit leverage indirectly by becoming binding upon earnings deterioration or increases in interest
rates. The key difference between capital structure and profitability covenants is that the later
generally can detect deteriorations in credit quality sooner than capital structure covenants. For
instance, profitability covenants require a decrease in earnings to become binding whereas
capital structure covenants generally require a loss or even a series of losses.
We hypothesize that profitability covenants are employed when earnings are informative
of credit quality.
However, capital structure covenants are expected to be a more robust
mechanism that limits debt-related agency problems when accounting earnings is a poor
predictor of a firm’s credit worthiness. In turn, capital structure covenants are expected to
4
exercise stricter control over the use of debt, while profitability covenants are expected to
postpone restrictions on debt until economic performance deteriorates.
We use Standard & Poor’s long-term credit ratings to estimate the contracting value of
accounting, i.e., its ability to portray the underlying credit risk. Credit ratings are forwardlooking proxies for credit risk that capture the ability and willingness of a corporation to meet its
financial obligations. We follow Ball, Bushman and Vasvari (2008) and measure the contracting
value of accounting information at industry level.1 While our approach is similar to that in Ball
et al., it has several important distinctions (discussed in Section 3.2 in more detail). Intuitively,
our proxies measure the inherent degree to which accounting ratios are a sufficient statistic in
describing the credit risk within industries. Thus, contracting value proxies are defined as Rsquareds from industry level regressions of credit ratings on accounting variables commonly
used in contracts. We use Dealscan as the source of information on private lending agreements
and limit to contracts for which the covenants information is available. We measure firms’
reliance on accounting information in the contracting process by the number of accounting-based
ratios (covenant benchmarks of the two types) employed in lending agreements.
We document that profitability and capital structure covenants act as substitutes: they are
negatively correlated and exhibit associations of opposite sign with most firm/contract
characteristics. The results imply that the two types of covenants are used in different situations.
A direct implication for future research is that pooling covenants of these types together to form
a single covenant index is not innocuous. More importantly, we find that the use of profitability
covenants is increasing, while the use of capital structure covenants is decreasing, in the debt
1
These measures are subsequently used in firm-level regressions as explanatory variables of instruments. Data
limitations generally preclude measuring contracting value at firm-level. Nevertheless, measurement at industry
level fits well with our analysis. While the use of covenants can differ with firm’s characteristics, lenders are likely
to rely at their industry experience to assess the ability of accounting information to capture credit quality.
5
contracting value of accounting information. The evidence suggests that profitability covenants
are chosen over capital structure covenants when accounting does well in describing firm’s credit
quality, in line with our predictions. The documented effects hold when we control for a number
of commonly used determinants of covenant use.
Our second set of results shows that profitability covenants exhibit strong positive
association with the level of long-term debt following debt issuance whereas the opposite is true
for capital structure covenants. One accounting-based covenant on average is associated with
0.04 higher leverage. The result holds despite the presence of a comprehensive set of
determinants of leverage used in the literature. Consistent with this result, we find that the
fraction of profitability covenants is positively associated with leverage, controlling for total
number of covenants. Endogeniety in the relation between covenants and the level of debt is an
important issue that complicates the interpretation of our results (we discuss this issue more
extensively in Sections 3.1 and 6). To what extent can the results be attributed to the use of
accounting benchmarks per se, rather than to credit risk increasing in leverage and hence the
need for additional covenants? To address this issue we instrument for covenants using our
proxies for the debt contracting value of accounting information. The reliance on covenants is
expected to shift with the contracting value of accounting information. At the same time, unless
via the use of covenants, the contracting value of information should not have a direct effect on
leverage (supply of debt) and thus the exclusion restriction can be applied (we also use an
alternative set of instruments computed by looking at the use of covenants by other companies
with the same lead arranger, see section 6.3). The results of this analysis suggest that contractual
use of accounting information affects the supply of debt financing and that the effect is due to the
use of profitability covenants. The economic magnitude of these results is substantial. As we
6
discuss later in the paper, we are able to identify the effect of covenant mix on the level of debt.
Contracts with 100% use of profitability covenants exhibit 0.21 higher level of leverage as
compared to 100% balance sheet covenants. Because our study is conditional on covenants
being used, more research is needed to understand the ex ante effect covenant inclusion on the
supply of credit.
Overall, the results imply that credit agreements can either use capital structure covenants
that ex ante limit debt market access or, when accounting provides a good description of credit
quality, opt for profitability covenants that postpone the point at which financing is restricted to a
future adverse event. Indeed a recent study by Roberts and Sufi (2009) documents significant
decline in debt use/issuance following covenant violations. Our results complement theirs and
indicate that profitability covenants allow companies to borrow more ex ante. Overall the results
are consistent with accounting having a large effect on debt market access.
The remainder of our paper is structured as follows: Section 2 discusses the roles of
accounting in credit agreements and develops hypotheses; Section 3 explains our empirical
strategy and the research design; Section 4 outlines the sample selection method and provides
descriptive statistics; Section 5 presents the results on covenant choices and the contracting value
of accounting; Section 6 examines accounting-based covenants affect on credit market access;
and Section 7 concludes our study.
2. Background and hypotheses
In the presence of agency and/or information problems between borrowers and lenders,
firms may not be able to raise the level of debt they would prefer to raise in a frictionless market.
Agency problems can lead to asset substitution (Jensen and Meckling 1976), debt overhang
7
(Myers 1977), or claim dilution problems (Smith and Warner 1979). All these problems become
more severe as leverage increases and, therefore, can make lending less attractive (or
substantially increase its costs), i.e., restrict credit market access. Additionally, the presence of
information asymmetry in the lending market opens up the scope for adverse selection and moral
hazard problems and thus can lead to credit rationing (Stiglitz and Weiss 1981), which arises
when firm’s credit quality cannot be easily evaluated by the lender while increasing borrowing
costs attract more risky companies. Thus, irrespective the amount of interest a borrower is
willing to pay, banks may not be willing to lend funds to some borrowers. Covenants are a
contracting mechanism that limits both agency costs and adverse selection and thus is expected
to influence the supply of debt capital ceteris paribus. Reliance on covenants reduces the agency
problems because they limit managers’ ability to take opportunistic actions that hurt debtholders
(e.g., Smith and Warner 1979). In addition covenants may serve as a signaling/screening device
that help lenders learn information about the borrower, which in turn alleviates credit rationing.
The main role of accounting in debt markets is to provide contractible information used
to detect financial distress and contractually transfer control rights to debtholders at times when
the value of their stakes is at risk. Depending on the industry a company operates in (i.e., type of
business, informational environment, growth opportunities, nature of assets and liabilities, etc.)
the extent to which accounting information reflects the credit quality is expected to vary. In turn,
reliance on covenants as a mechanism that limits contracting frictions is expected to vary with
the contracting value of accounting information. Furthermore, as covenants cannot control the
underlying agency problems in the same manner when the contracting value of accounting
information is poor this is expected to affect the supply of debt.
8
2.1. Capital structure versus profitability covenants
We classify covenants into two categories: capital structure covenants and profitability
covenants. Capital structure covenants typically restrict on the maximum portion of debt (or
minimum amount of equity), for instance, by requiring a leverage ratio or net worth maintained.
Shareholders may contribute additional equity capital or cut back on dividends to meet the
capital structure covenants. In contrast, profitability covenants place no direct restrictions on
capital but require a minimum level of earnings-to-interest (or earnings-to-debt) and are more
difficult to relax by additional equity contributions or dividend cuts.
We argue that, while both groups of covenants limit agency problems, they accomplish
this in different ways. Capital structure covenants effectively force shareholders to participate
with a minimum fraction of their own capital in projects the firm invests in. This ensures that
shareholders' wealth is sensitive to actions that decrease firm value. This also encourages
shareholders to monitor managements' actions and aligns interests of shareholders with those of
debtholders. In other words, capital structure covenants reduce agency problems by requiring a
higher level of equity than shareholders may otherwise prefer. In contrast, provided a firm is
sufficiently profitable, profitability covenants do not directly require shareholders to maintain
any minimum level of shareholders' equity. Instead, profitability covenants transfer control
rights to lenders when the firm performs poorly and, consequently, agency conflicts become
more severe. By transferring control rights upon deteriorations in credit quality, profitability
covenants can limit the increased potential for debtholders’ value expropriation, which is a rather
different way to deal with agency costs.
Another distinction between the two types of covenants that we identify is the timeliness
of control transfers.
Capital structure covenants are based on the cumulative amount of
9
profitability plus shareholders' net contribution of capital. Profitability covenants, however, are
functions of current earnings only. This generally means that, for capital structure covenants to
become binding, a company needs a loss or even a series of losses. In contrast, profitability
covenants will generally become binding in the period when a company experiences a substantial
decline in profitability (which need not be a loss). This suggests that profitability covenants are
likely to detect economic distress and transfer decision rights to debtholders earlier than capital
structure covenants.
Finally, profitability covenants require that accounting information reflects firm’s credit
quality. If this is not the case, profitability covenants will be ineffective at limiting agency
problems because they will not transfer control rights to lenders when agency conflicts become
more sever. Capital structure covenants do not have as strong of a requirement for accounting
information to explain credit quality. In part this is because capital structure covenants reduce
agency costs by aligning shareholders’ and debtholders’ interests rather than by identifying the
timing of credit quality changes. And, in part, because accounting noise components in earnings
reverse over time and, therefore, have little long-term impact on the cumulative measures that
capital structure covenants rely on.
The above arguments imply that, when accounting information portrays credit quality
reasonably well, profitability covenants can detect deteriorations in credit quality before capital
structure covenants and thus will be used as a contracting mechanism. Early detection of credit
quality deterioration is important because shareholders have stronger incentives to expropriate
debtholders' wealth when a company moves closer to default. However, when contracting value
of accounting information is relatively poor, reliance on profitability covenants is likely to be
ineffective and costly and thus we predict lenders to resort to the less flexible but arguably more
10
robust mechanism to control the agency problems – capital structure covenants. Thus, our first
hypothesis is:
H1: The number of profitability (capital structure) covenants in credit agreements is
positively (negatively) associated with the extent accounting earnings’ reflect credit
quality.
2.2. Covenants and credit market access
It is often argued in the capital structure literature that firms appear to be underleveraged
and thus to forego an opportunity to increase firm value by exploiting the tax advantage of debt
financing (Graham 2000). Limited access to credit markets has been argued as one explanation
for this result (Faulkender and Petersen 2006). Frictions that arise in credit markets, such as
agency and/or information problems (that give rise to capital rationing or agency costs), are
likely to be key in explaining the limited use of debt financing. The contractual reliance on
accounting information serves the purpose of reducing these market frictions, and thus promotes
the access to debt financing. Hence we hypothesize:
H2: The use of accounting ratios in debt contracts increase access to debt financing.
It is not obvious, however, that accounting increases the use of debt financing. At loan
origination, provided that accounting is suitable for contracting purposes, lenders may be willing
to lend more when covenants are used. However, the very purpose of many covenants is
ultimately to limit the use of debt. Limitations on indebtedness is the mechanism that controls
the contracting frictions discussed earlier. Thus, empirically, one can find either a positive or a
negative association between leverage and covenants. Which of the two effects dominates in
practice is an empirical question and likely depends on the type of covenants at hand. Because
capital structure covenants reduce agency costs by limiting debt relative to equity (and thereby
increase the sensitivity of shareholder’s wealth to changes in the market value of the firm) they
11
are more likely to serve as limitations on the level of debt. Profitability covenants, on the other
hand, are more likely to control leverage ex post, that is, by detecting when a firm performs
poorly and promptly reallocating decision rights to debthoders. Profitability covenants do not
require a maximum level of debt as long as the firm is sufficiently profitable. For example,
increase in profitability implies greater latitude in issuing debt under profitability covenants then
under capital structure covenants.
Provided a good accounting measure of default risk is
available, profitability covenants are more likely to promote debt market access as long as the
firm is not at risk of violating covenant thresholds. Our third hypothesis is:
H3: Firms that rely on profitability covenants more than on capital structure covenants
have increased access to credit markets.
Notice that hypotheses H1 and H3 are closely connected. When accounting does well in
reflecting companies’ credit quality, a company is expected to use profitability covenants that
postpone the point at which credit market access is restricted (if an adverse event occurs in the
future) and thereby can afford higher levels of debt.
3. Empirical strategy and research design
3.1. Identification.
Theoretical arguments presented earlier suggest that the use of accounting information in
debt contracts helps controlling the contracting frictions present in credit markets. Firms are thus
expected to enjoy increased access to debt financing when accounting information is available
for contracting purposes. The use of accounting information in the contracting process is likely
to depend on its ability to reflect the firm’s underlying credit risk. The empirical strategy
discussed in this section seeks to identify the effect of accounting information use in contracts on
the level of debt financing obtained by firms. We start by specifying a basic regression model
12
and discuss the identification issues that arise when estimating this model. We consider firms’
leverage to be a function of firms’ access to debt markets (Faulkender and Petersen 2006) and
the number of accounting-based benchmarks that a lending agreement employs.2 The model we
are interested in is the following:
Leverageit 1    1  ACovenantsit   k X kit 1   it 1
(1)
where Leverage is the ratio of long-term debt to total assets, ACovenants is a proxy for reliance
on accounting-based benchmarks of a certain type, and Xk represents a comprehensive set of
determinants of leverage (discussed later in the paper). The coefficient, 1 , is predicted to be
positive if covenants indeed facilitate access to debt financing, but its OLS estimates cannot be
interpreted as causal effects since covenants are endogenous to leverage. Specifically, increases
in leverage will be accompanied by increasing default risk and are, therefore, expected to
increase the use of covenants. However, to the extent the increase in the use of covenants makes
the increase in leverage possible, this result is still useful. While it does not bear a causal
interpretation, the association is still relevant as it suggests that covenants are a necessary,
although may not be a sufficient, condition to raise debt. Would firms be able to choose the
same level of debt if the use of covenants was not an option? Their strong association with
leverage implies that, at best such a choice would be more expensive, if at all feasible (e.g., due
to credit rationing). The problem may arise when some companies willing to use covenants are
not able to borrow (i.e., we do not observe the counterfactual). The question of whether a
2
There is a difference between access to debt financing and actual use of debt (e.g., zero leverage companies can
have access to debt financing). We assume that use of debt is a function of the access to debt financing. Therefore,
increased access to debt financing, ceteris paribus, should lead to higher level of debt, while higher levels of debt
should be indicative of credit market access.
13
random firm that is willing to rely on more covenants is able to borrow more is more difficult to
answer, as we discuss next.
Identification of the causal effect, 1 , requires a set of instrumental variables that do not
directly influence leverage, but that are capable of inducing variation in the use of accounting
covenants.
We construct our set of instruments by measuring properties of accounting
information. These properties, while theoretically having no direct relation to leverage, are
expected to exhibit association with the use of covenants. Specifically, the extent to which
accounting information reflects credit risk makes the use of accounting-based covenants, and
profitability covenants in particular, a more attractive contracting mechanism. Proxies for the
contracting value of accounting information, discussed next, are thus suitable instruments.
3.2. Measuring the contracting value of accounting information.
To quantify the ability of accounting information to capture companies’ default risk we
construct a number of “contracting value proxies”. We follow Ball et al. (2008) and base our
contracting value proxies on industry-level regressions of (quantified) long-term debt ratings on
quarterly accounting variables commonly used in lending agreements (i.e., earnings, cash flows,
interest coverage, etc).3 As the objective of credit ratings constructed by S&P is to describe
companies’ credit risk profile, we use R-squares from these regressions to proxy for the ability of
accounting information to explain default risk. Debt ratings are constructed by S&P based on the
comprehensive analysis of both financial and non-financial information, as well as information
about other companies in the industry. Thus low explanatory power of accounting variables in
explaining credit ratings indicates the presence of a substantial information component not
3
Quarterly data measurement is justified by its use for quarterly compliance with accounting-based covenants
common for private credit agreements.
14
reflected in the accounting information that credit agreements rely on (but which needs to be
taken into account in assessing credit risk). In contrast, high R-squared implies that accounting
benchmarks are sufficient statistics for determining credit risk within a particular industry. The
regressions are estimated at the industry level over the period 1988-2008.
Our approach differs from that in Ball et al. who measure the association of changes in
accounting earnings with changes in credit ratings (downgrades) for the following reasons. First,
our objective is different. We attempt to measure whether accounting information sufficiently
explains credit risk, or whether there is a significant non-accounting (orthogonal) information
component (residuals) in credit ratings.
Changes in credit ratings are rare and for some
companies credit ratings do not change over many years. Given this, it is difficult to evaluate
whether accounting information explains (as a sufficient statistic) credit quality by examining
changes in credit ratings.4 Second, we cannot directly observe credit rating downgrades.5 Next,
we describe the definitions of the specific contracting value proxies our analysis rely on.
CV1: Our first proxy, CV1, for the debt contracting value of accounting information, is
based on the following industry-level regression:
Ratingit  0  1Eit  2 Eit 1  3Eit 2  4 Eit 3  5 Eit 4   it
(2)
where Ratingit is constructed by assigning “1” to companies with the highest credit rating
following quarter t, “2” to companies with second highest credit rating, and so on, and
subsequently taking natural logarithm of this variable.
Eit  s is earnings before extraordinary
4
As an extreme example, consider firm/industry with no changes in credit ratings. Accounting information can still
do a good job indicating credit quality.
5
For example, suppose we observe credit rating as of February 1, 2000 and, subsequently, on April 1, 2002 a lower
rating. This can either mean that company was downgraded right before April 1, or alternatively that there was an
interruption in coverage.
15
items in quarter t  s divided by average total assets over that quarter. CV1 equals R-squared
from model (2).
CV2: Our second contracting value proxy, CV2, is R-squared from the following
industry-level regression:
Ratingit  0  1Eit
  2 Eit 1
 3 Eit 2
  4 Eit 3
 5 Eit 4
 6CFOit  7CFOit 1  8CFOit 2  9CFOit 3  10CFOit 4   it
(3)
where CFOit s is quarter t  s cash flow from operations scaled by average total assets in that
quarter; other variables are defined previously.
CV3: Our third proxy, CV3, is R-squared from the following industry-level regression:
Ratingit  0  1Coverit  2Coverit 1  3Coverit 2  4Coverit 3  5Coverit 4   it (4)
where Coverit s is quarter t  s coverage of interest; other variables are defined previously.
CV4: Our fourth contracting value proxy, CV4, is R-squared from the following industrylevel regression:
Ratingit  0  1EDit  2 EDit  3EDit 1  4 EDit 2  5 EDit 3  6 EDit 4   it
(5)
where EDit s is quarter t  s ratio of earnings before extraordinary items to average total
liabilities; other variables are defined previously.
CV5: Our fifth, all-in-one, contracting value proxy, CV5, is based on R-squared the
following industry level regression:
Ratingit  0  1Eit  2CFOit  3Coverit  4 DEit  5 Debtit   it
(6)
where Debt the ratio of total liabilities to assets in quarter t; the other variables are defined
previously.
16
Appendix A provides pooled sample estimates for models (2) – (6). In addition to the
individual contracting value proxies discussed above we construct a composite measure, CVALL,
by taking the average of all five contracting value proxies and use this measure as an alternative.
3.3. Proxies for timely loss recognition.
Timely loss recognition, or conditional conservatism, plays an important role in debt
markets (Watts 2003).
In particular, timely loss recognition is expected to improve the
contracting value of accounting-based covenants by facilitating transfers of control to
debtholders when a company approaches financial distress (Ball and Shivakumar 2005). We
thus measure timely loss recognition and use it to validate our measures for the contracting value
of accounting.6
Timely loss recognition, TLR1, is measured by the coefficient  3 from the following
model based on Basu (1997):
Et / Pt 1  0  1D( Rt  0)  2 Rt  3D( Rt  0) Rt   t
(8)
where Et / Pt 1 is defined as a ratio of annual earnings before extraordinary items (scaled by
beginning of period market value of common stock, Rt is stock return from CRSP compounded
over twelve months starting three months after the beginning of the fiscal year (to exclude prior
earnings announcement effects), and D(.) is an indicator function. This regression is estimated at
the industry level over the period 1988-2008.
As scaling by price can confound inferences (Patatoukas and Thomas 2009), we also use
earnings before extraordinary items scaled by average total assets as the dependent variable in
6
Ball et al. (2008) also use timeliness, R-squared from regression of stock returns on accounting earnings and their
changes. They find, however, that timeliness exhibits negative correlation with two other contracting proxies.
Given this, and that contracting benefit of higher return-earnings association is unclear, we do not include this
measure.
17
regression (8). The coefficient  3 from this regression is defined as our second proxy for timely
loss recognition, TLR2.
4. Sample and summary statistics
4.1 Sample selection
Our data comes from several sources.
We use Dealscan to measure reliance on
accounting-based covenants and other loan characteristics. Accounting information variables
and firm characteristics are constructed based on data from Compustat. We merge loan contracts
from Dealscan to fiscal years in which they are issued on Compustat, based on the link
constructed (and maintained) by Chava and Roberts (2008).7 If a deal package has more than
one credit facility, we randomly take one of the facilities; however, we leave out all facilities
with maturities of one year or less (as accounting covenants are less of a concern for short term
debt). Contracts for which covenant information is not available are excluded from the analysis.8
The classification of accounting-based covenants into profitability covenants (PRCovenants) and
capital structure covenants (CSCovenants) is described in Appendix B.
To construct the proxies for contracting value of accounting information we link the S&P
Credit Ratings Database to the Compustat quarterly database and use S&P’s local currency
firms’ long-term credit ratings.
Each credit rating observation is linked to accounting
information from the preceding fiscal quarter. If S&P did not update credit rating data during a
particular quarter, we use the most recent long-term credit rating. S&P Credit Ratings dates back
7
We thank the authors for generously sharing the link information.
8
Approximately, 50% of credit agreements in Dealscan are coded as having no covenants. It is unlikely that these
credit agreements do not employ covenants given that we know that almost all private credit agreements’ rely on
covenants (e.g., Christensen and Nikolaev 2009). Thus, we exclude contracts with no covenant information rather
than set this number to zero.
18
to the 1920s, however, coverage is sparse before 1986.
As we further require cash flow
statement data, in the estimation of the contracting value proxies, we limit the sample to the
period 1988-2008. Over this period S&P rated over 5,500 companies and on average released
credit rating (or economic outlook) information 1,900 times per year (this number ranges from
about 500 in 1988 to 3,300 in 2008). On average it takes more than a year for S&P to updates
the information about long-term credit of a given company.
Contracting value is measured based on SIC industry classification: 3-digit SIC is used in
cases where more than twenty five companies and two hundred fifty quarterly observations are
available; SIC codes that do not meet this requirement are combined and considered at the 2digit level. We further exclude resulting industry groups with less than twenty five companies or
two hundred and fifty quarterly observations to improve the reliability of estimates and avoid
over-fitting that may occur in small samples. This procedure results in 50 industry groups.
We employ analogous industry classification (and data requirements) when measuring
timely loss recognition. These properties are estimated using the intersection of CRSP and
annual Compustat data. 1% of observations for CRSP and Compustat variables used in this
study are left out at each tail and the sample is restricted to non-negative EBITDA (necessary to
compute coverage of interest).9 All variables are defined in Appendix C.
The final sample size varies from 5,000 to 7,000 debt contracts depending on data
availability in the specific regressions.
4.2. Summary statistics.
Table 1 presents summary statistics for variables used in the subsequent analysis. With a
mean (median) market value of assets at $3,780 ($923) millions our sample is represented by
9
The results are not sensitive to this choice.
19
relatively large firms.
Average book-to-market is 0.62 which is typical for an average
Compustat firm. Leverage, as measured by long-term debt divided by book (market) value of
assets is 27% (20%). This is substantially higher than the population of Compustat firms for
which long-term debt constitutes 17% (12%) of book (market) value of assets. Turning to
contracting value proxies, their averages range from 17% to 29%, which is the portion of
variance in credit ratings explained by accounting information variables.
Table 2 provides correlations among contracting value proxies as well as their
correlations with timely loss recognition proxies and industry level cash flow variability. All CV
proxies exhibit high positive correlations. CV proxies also exhibit positive and significant
correlations with TLR proxies. The correlation between TLR1 (TLR2) and CVALL is 28% (31%).
This result is in line with timely loss recognition being a desirable accounting income property in
debt markets, and serves as a validity check for our contracting value proxies.
Table 3 provides Pearson correlations among firm- and contract- level variables as well
as their correlation with the composite contracting value proxy, CVALL. Notably, CVALL
exhibits the highest correlations with PRCovenants (0.22) and CSCovenants (-0.19).
Profitability covenants (PRCovenants) and capital structure-based covenants (CSCovenants)
exhibit a significantly negative correlation of -0.44. This suggests that indeed these two types of
covenants serve as substitutes rather than complements, an issue we study in more detail next.
5. The contracting value of accounting information and covenants.
This section studies the determinants of the covenant mix. Subsequently, we examine the
relation between contracting value proxies and the choice between profitability and capital
structure covenants (i.e., provide evidence on hypothesis H1).
20
5.1. Determinants of profitability and capital structure covenants.
The correlation matrix in Table 3 suggests that profitability and capital structure
covenants are used in different situations. To establish how different profitability and capital
structure covenants are we explain their use by the most common variables shown by prior
research to determine the use of covenants in general (Nash et al. 2003; Billett et al. 2007). We
do not include leverage as an explanatory variable as the relation between covenants and
leverage is the focus of our analysis later in the paper (including instrumental variable tests).
Table 4 presents the estimated coefficients for regressions of covenants of the two types
on their determinants. Model (1) explains the determinants of profitability covenants, while
Model (2) explains the use of capital structure covenants. Interestingly, except for ROA which
has a significantly positive coefficient in both of these models, the majority of determinants load
significantly but with the opposite signs in the models. Specifically, Size, book-to-market (B/M),
R&D, dividends (Div), tangibility (Tang), and Age are negatively related to the use of
profitability covenants but are positively related to capital structure covenants. The opposite is
true in the case of deal size (DealAmount), and loan maturity (Maturity). Model (3) analyses the
determinants of covenant mix, defined as the ratio of profitability covenants to the sum of all
covenants. The estimates indicate that the mix of covenants itself is non-random and exhibit
predictable associations with most firm- and contract-level characteristics.
The results above indicate that pooling all covenants is rarely meaningful, which has
direct implications for empirical research that combines covenants into one proxy to measure
their use. The documented relationship between the two classes of covenants or their mix and
other firm/contract characteristics is intuitive in many cases. For example, firms that are larger,
older, and have higher levels of tangible assets are leaning towards capital structure covenants,
21
which can be explained by timely accounting information being lees important in monitoring
credit risk for these firms. In contrast, large loans and loans with longer maturities are more
likely to rely on profitability covenants.
Interestingly, R&D intensive firms rely less on
profitability covenants and more on capital structure covenants, which is consistent with lower
contracting value of earnings for these firms.
Overall, the evidence in this sub-section confirms our prior that the two types of
covenants will be used in different situations and act as substitutes. More research is needed,
however, to enhance our understanding of the differential role of the two classes of covenants.
5.3. The choice of covenants and the contracting value of accounting information.
In this subsection, we examine the relation between the proxies for debt contracting value
of accounting information and the use of covenants, and test the first hypothesis (H1).
According to H1, the use of profitability (capital structure) covenants is expected to increase
(decrease) in the CV proxies. We start with separate univariate analysis of both types of
covenants and subsequently employ multiple regression to explain their mix.
Profitability covenants. Table 5 presents the univariate regression results of profitability
covenants (PRCovenants) on CV proxies as well as TLR proxies. The estimates on all individual
proxies CV1 – CV5, as well as their composite measure, CVALL, are positive and statistically
significant. In addition, the coefficients on proxies for timely loss recognition, TLR1 and TLR2
are also positive and statistically significant. R-squares in many of these regressions are around
5%.
The results are consistent with H1 and imply that the use of profitability covenants
increases with the contracting value of accounting information. Such relation is intuitive as
profitability covenants become more effective in reducing the underlying contracting frictions,
when they can promptly transfer control to debtholders following a decline in firm’s credit
22
quality, i.e., when accounting information has contracting value. Next, we examine the capital
structure covenants.
Capital structure covenants.
Table 6 presents the univariate regressions of capital
structure covenants on CV and TLR proxies. The findings closely mirror the evidence for
profitability covenants as all the contracting proxies, CV1 – CV5, as well as TLR1 and TLR2
exhibit a negative and significant association with the use of capital structure covenants. This
evidence is also in line with H1 and confirms prior evidence that the two types of covenants act
as substitutes. The results broadly suggest that companies retreat to capital structure covenants
in situations, where accounting information does not reflect the underlying default risk. As we
argued earlier, in such situations the capital structure covenants are expected to be more robust.
Covenant mix. Finally, Table 7 presents the univariate regressions of the mix of
covenants (CovenantMix = PRCovenants/(PRCovenants + CSCovenants)) on CV and TLR
proxies.
As one would expect based on prior tests, CovenantMix exhibits a statistically
significant positive association with the contracting value proxies. The explanatory power in case
of CV1–CV4 exceeds 5%.
To isolate the confounding effects of other relevant firm- and contract-specific
characteristics on the relationship between covenants and contracting value proxies, we regress
the mix of covenants on the contracting value proxies and a wide range of control variables.
Table 8 presents the estimates for this model. Despite the presence of control variables, the
CovenantMix exhibits a significantly positive association with the CV and TLR proxies. This
corroborates prior evidence and implies that the mix of covenants is a function of inherent
properties of accounting information, and more specifically its ability to measure the underlying
default risk and its changes.
23
5.4. Robustness checks.
Following Ball et al. (2008) we cluster the standard errors at firm level. One may argue
that standard errors should be clustered at industry level. The econometric argument is not
straightforward as measuring a right hand side variable at industry level does not in itself
introduce non-independence to the error term in regression. Specifically, when industry level
variables are measured without error, non-independence of the error term across different
companies within the same industry does not arise.
Thus, industry clustering is likely to
overstate standard errors. When measurement error in industry level variables is present, the
non-independence of the error term, however, is of secondary importance as its presence
confounds the identification. We require a substantial number of observations within industries
to measure industry-level proxies more precisely (see Section 4) and thus report errors clustered
at firm level. To demonstrate the robustness of our results, we also verify that results in Tables 5
– 8 remain statistically significant when errors are clustered at industry level. We do not tabulate
these results to preserve space, however.
6. Accounting-based covenants and credit market access
This section examines our main question of interest, namely, whether the use of
accounting information in debt contracts facilitates firms’ access to debt financing. We begin
with OLS analysis of leverage as a function of its determinants known in the literature and the
number of accounting-based covenants.
Since OLS precludes causal interpretations of our
results we proceed by performing IV analysis.
24
6.1. Covenants and leverage: Ordinary least squares.
Table 9 presents the results of OLS regressions of leverage on covenants and their mix.
We employ both book- and market-based proxies for leverage: levbook and levmkt, respectively.
Columns (1) and (2) suggest that leverage has a statistically significant relation to the total
number of accounting based benchmarks used in lending agreements (TotalCovenants =
PRCovenants + CSCovenants), which is consistent with H2 but confounded by endogeneity of
covenants. However, when we separate the covenants into two classes, as suggested by models
(3) and (4), profitability covenants have a statistically significantly positive association with
leverage, while the opposite holds for capital structure covenants. On average, one profitability
covenants is associated with 5.4% (4.0%) higher level of levbook (levmkt), while one capital
structure covenant is associated with 2.6% (1.8%) lower levbook (levmkt).
The economic
magnitudes of these estimates are large, especially, taking into account that we control for most
commonly used determinants of leverage. These associations are consistent with profitability
covenants facilitating access to debt financing, and capital structure covenants, in contrast,
limiting the level of debt, as we discuss in Section 2 and put forward in hypothesis H3. An
alternative interpretation of these results is that leverage increases default risk which in turn leads
to more heavy use of covenants, i.e., covenants may be a necessary, but not sufficient, condition
for increased borrowing. In this case, however, we would expect leverage to exhibit positive
associations with both types of covenants.
The models (5) and (6) of Table 9 examine the effect of the mix between profitability and
capital structure covenants on book- and market-leverage, respectively. Controlling, for the total
number of covenants, the mix of covenants has a statistically significant and economically
important association with leverage. Firms that use 100% profitability covenants have 12% (9%)
25
higher level of levbook (levmkt). Although the evidence from this analysis cannot be interpreted
as causal, it does highlight the key role of accounting-based covenants in determining firms’
capital structure.
6.2. Covenants and leverage: Instrumental variable analysis with industry level instruments.
Our identification strategy relies on correlations between the properties of accounting
information (CV1 – CV5) and the use of accounting-based covenants. The limitation of these
instruments is that, although they are useful in explaining the mix of profitability and capital
structure covenants, they cannot explain their independent inclusion into the contract. In other
words, as contracting value increase, we observe substitution of capital structure covenants with
profitability covenants. To test H2, however, we need instruments predicting each class of
covenants, while holding the other class unchanged (i.e., holding everything else constant). This
generally precludes the tests of H2 using instrument variables, however, enables the test of H3.
That is, we are able to determine whether relatively more heavy reliance on profitability
covenants affects the amount of leverage.
While prior studies employed industry-level
instruments when examining debt market access (e.g., Faulkender and Petersen 2006), a caveat
applies. While conceptually appealing, industry based instruments may not be fully exogeneous,
and one should bear this in mind when interpreting the results. To alleviate this concern, we
perform the analysis using an alternative set of instruments as a robustness check in subsection
Table 10 presents 2SLS analysis, where CV1 – CV5 are used as instruments. Across four
models, the partial R-squared is in the range 4.9-5.9%, which indicates a considerable
incremental explanatory power of the instruments in the first stage regressions (cluster adjusted
F-tests are highly significant throughout the four models). As other loan-characteristics are
generally also endogenous, and since we do not have a set of instruments to deal with this issue,
26
we show specifications with and without main loan characteristics.
The results show that
CovenantMix has a statistically significant effect on both leverage proxies. The economic effect
is also large. Specifically, when loan characteristics are included, firms that rely fully on
profitability covenants reach 30% (21%) higher levels of book (market) leverage as compared to
firms that fully rely on capital structure covenants. The corresponding effects are similar if the
loan characteristics are excluded.
6.3. Robustness check: Instrumental variable analysis with lender level instruments.
We also examine an alternative set of instruments to alleviate concerns with validity of
the instruments. Specifically, we measure lead arranger reliance on covenants (and their mix)
and use these as instruments in our analysis. Specifically, for each loan, we calculate the average
number of profitability and capital structure covenants across all contracts originated by a
particular lead arranger over the five years preceding a particular loan, excluding the current
borrower company. We find substantial variation with respect to covenants use across lenders.
In spirit, our procedure to construct instruments is similar to Christensen and Nikolaev (2009),
Faulkender and Petersen (2006), and Ivashina (2009), although their context differs.
The rationale for our instruments is the following: the lender-level averages aim at
determining a standard (boilerplate) contract offered by the lead arranger to various borrowers,
that is expected to vary with lender’s preferences with respect to the use of accounting basedcovenants (and may in part be due to lender’s specialization). Some lenders are known to rely on
covenants more than others (while others price protect instead). Thus, the use of covenants in a
particular loan will be correlated with lead lender preferences, while at the same time the lead
lender’s preference for covenant use is not a direct determinant of firms’ leverage (unless via the
use of covenants, controlling for other characteristics).
In other words, lender-level
27
preferences/specialization conceptually is an appropriate instrument. Partial R-squareds
indicating explanatory power of the instruments vary between 3.0 and 5.7%.
The results of this analysis are presented in Table 11. The results are similar to those in
Table 10 and even indicate a higher effect of CovenantMix on the level of debt financing.
Overall, the results of our analysis provide evidence on the importance of accounting for
debt contracting. The ability to use accounting-based benchmarks as an input into covenant
provisions has a large economic effect on firms’ ability to access credit markets.
7. Conclusion
In this paper we examine the effect of reliance on accounting-based benchmarks in
private lending agreements on the level of debt. We argue that the use of accounting ratios in the
definitions of financial covenants increases firms’ access to credit markets. Accounting-based
covenants alleviate agency and information problems that arise in credit markets and thus
facilitate borrowing. The ability of accounting information to reflect firms’ credit risk is shown
to be an important determinant of covenants and their mix. Specifically, firms substitute to more
timely profitability covenants from capital structure covenants as the contracting value of
accounting information increases. This, as shown by our instrumental variable tests, leads to
increases in firms’ debt capacity.
More research is needed, however, to understand the effect reliance on accounting based
covenants on the level of debt as we are only able to identify the effect of covenant mix on
leverage.
28
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30
Appendix A: Estimation of CV proxies: Pooled regressions
This appendix presents parameter estimates for models (2) – (6) based on pooled sample. Ratingit
is constructed by assigning “1” to companies with the highest credit rating following quarter t,
“2” to companies with second highest credit rating, and so on, and subsequently taking natural
logarithm of this variable. Et-s is earnings before extraordinary items scaled by average total
assets in quarter t  s divided by average total assets over that quarter. CFOt-s is quarter t  s
cash flow from operations scaled by average total assets in that quarter; Covert-s is quarter t  s
coverage of interest; EDt-s is quarter t  s ratio of earnings before extraordinary items to average
total liabilities; Debt the ratio of total liabilities to assets in quarter t.
VARIABLES
Et
Et-1
Et-2
Et-3
Et-4
CFOt
CFOt-1
CFOt-2
CFOt-3
CFOt-4
Covert
Covert -1
Covert -2
Covert -3
Covert -4
EDt
EDt -1
EDt -2
EDt -3
EDt -4
Debtt
(1)
Rating
(2)
Rating
-18.19***
(-5.910)
-17.58***
(-19.68)
-19.94***
(-21.31)
-19.72***
(-21.66)
-18.53***
(-20.05)
-16.36***
(-5.592)
-15.88***
(-18.38)
-17.81***
(-19.77)
-17.48***
(-19.42)
-16.59***
(-18.16)
1.391***
(2.782)
-2.556***
(-6.461)
-2.312***
(-5.600)
-1.039***
(-2.589)
-4.087***
(-9.474)
(3)
Rating
(4)
Rating
(5)
Rating
-29.55***
(-4.339)
-4.562***
(-6.554)
-0.299***
(-7.192)
-0.255***
(-10.33)
-0.318***
(-15.10)
-0.345***
(-15.18)
-0.434***
(-11.37)
-0.501***
(-5.540)
-9.324***
(-3.569)
-7.074***
(-12.36)
-7.965***
(-13.75)
-7.429***
(-12.88)
-6.351***
(-10.57)
12.02***
(3.533)
7.260***
31
Constant
10.32***
(124.0)
10.70***
(101.4)
11.90***
(67.07)
10.14***
(99.78)
(17.88)
8.207***
(33.64)
Observations
59532
56554
50577
59796
60610
R-squared
0.148
0.162
0.178
0.101
0.254
Robust t-statistics adjusted for clustering at firm level are in parentheses, *** p<0.01, ** p<0.05, * p<0.1
Appendix B: Covenant classification.
Profitability-based covenant benchmarks:
(1) Cash interest coverage ratio; (2) Debt service coverage ratio; (3) Level of EBITDA; (4) Fixed
charge coverage ratio; (5) Interest coverage ratio; (6) Debt to EBITDA; (7) Senior debt to EBITDA.
Capital structure-based covenant benchmarks:
(1) Quick ratio; (2) Current ratio; (3) Debt-to-equity ratio; (4) Loan-to-value ratio; (5) Debt-totangible-net-worth ratio; (6) Leverage ratio; (7) Senior leverage ratio.
Appendix C: Variable definitions.
Levbook
= ratio of long-term debt (Compustat item TLDT) divided by total assets (Compustat item
AT).
Levmkt
= ratio of long-term debt (Compustat item TLDT) divided by market value of total assets
(Compustat: AT – SEQ + PRCC_F×CSHO).
Size
= natural logarithm of “market value” of total assets (Compustat: log(AT – SEQ +
PRCC_F×CSHO)).
Margin
= total revenue divided by- cost of goods sold (Compustat items REVT/COGS).
B/M
= book-to-market ratio (Compustat: SEQ/(PRCC_F×CSHO)).
R&D
= R&D expense divided by total revenue (Compustat: XRD/REVT).
expense is replaced with zeros.
Adv
= Advertizing expense divided by total revenue (Compustat: XADV/REVT).
SG&A
= SG&A expense divided by total revenue (Compustat: XSGA/REVT).
Capex
= Capital expenditures divided by total assets net of capital expenditures (Compustat:
CAPX/(AT – CAPEX)).
ROA
= Return on assets defined as a ratio of income before extraordinary items divided by total
assets.
Div
= Dividend yield computed as a ratio of common dividends to market value of equity
(Compustat: DVC/(PRCC_F×CSHO)).
Tangible
= Asset tangibility defined as a ratio of net value of property plant and equipment to total
assets (Compustat: PPENT/AT).
Missing R&D
Marginal tax = Marginal tax rate based on method described in Graham (1996). See also Graham and
Mills (2009).
32
Age
= Natural logarithm of the number of years on CRSP.
StdRet
= Natural logarithm of the standard deviation of daily returns over the fiscal year.
Maturity
= Years to maturity.
DealAmt
= Natural logarithm of total deal amount (all facilities included).
Secured
= An indicator variable that takes value of one if debt is secured, and zero otherwise.
LendFreq
= Lending frequency computed as a number of loan deals a company have had over the
prior five years.
Revolver
= An indicator variable that takes value of one if revolving facility exists in the deal
package, and zero otherwise.
DivRestrict = An indicator variable that takes value of one when restriction on dividend payments is
included.
PRCovenants = Number of profitability-based covenants (See Appendix B for classification).
CSCovenants = Number of capital structure-based covenants (See Appendix B for classification).
CovenantMix = PRCovenants/(PRCovenants + CSCovenants).
CV1 – CV5
= Debt contracting value of accounting information proxies, 1 through 6. Measure the
extent to which accounting information explains S&P’s entity-level long-term credit rating.
See Section 4 for details.
TLR1–TLR2 = Proxies for timely loss recognition based on Basu (1997). See section 4 for details.
IndSize
= Natural logarithm of the number of observations within a particular SIC industry (see
Section 3 for industry definitions).
StdROA
= SIC industry-level standard deviation of ROA (see Section 3 for industry definitions).
StdCFO
= SIC industry-level standard deviation of cash flow from operations (see Section 3 for
industry definitions).
33
Table 1: Summary Statistics
Table 1 presents summary statistics for the variables used in the analysis in Tables 4 - 11. All variables are defined
in Appendix C. We obtain loan characteristics from Dealscan, credit ratings from S&P Credit Ratings Database,
accounting and firm characteristics from Compustat, and return data from CRSP. Contracts without covenant
information are excluded and if a deal package has multiple facilities we randomly select one. The sample sizes for
individual variables varies depending on data availability.
VARIABLES
N
Mean
Std.Dev.
p25
p50
p75
Size
Levbook
7324
7289
3780
0.27
9947
0.19
260
0.11
923
0.25
2956
0.39
Levmkt
CV1
7245
5938
0.20
0.17
0.16
0.14
0.06
0.04
0.17
0.14
0.30
0.28
CV2
5938
0.19
0.14
0.05
0.16
0.31
CV3
CV4
5938
5938
0.31
0.18
0.17
0.15
0.16
0.07
0.30
0.13
0.40
0.31
CV5
CVALL
5938
5938
0.29
0.23
0.15
0.14
0.18
0.11
0.26
0.20
0.39
0.35
TLR1
TLR2
7332
7332
0.21
7.63
0.10
4.40
0.15
4.74
0.21
6.78
0.27
9.23
StdCfo
7332
0.12
0.05
0.10
0.12
0.15
B/M
Adv
7292
7275
0.62
0.01
0.55
0.02
0.31
0
0.51
0
0.78
0.00
Rnd
Margin
7329
7310
0.02
1.76
0.10
0.96
0
1.27
0
1.47
0.01
1.84
ROA
7323
0.02
0.11
0.00
0.04
0.07
Div
Marginal Tax
7287
7332
0.01
0.30
0.02
0.08
0
0.3
0
0.34
0.01
0.35
Tangible
Age
7059
7332
0.32
2.29
0.25
1.03
0.12
1.61
0.25
2.30
0.48
3.14
StdRet
PRCovenants
7309
7332
0.12
1.60
0.06
0.98
0.07
1
0.11
2
0.15
2
ISCovenants
7332
0.54
0.66
0
0
1
CovenantMix
DealAmount
7205
7332
0.73
18.64
0.34
1.58
0.5
17.62
1
18.83
1
19.8
Maturity
DivRestrict
7332
7332
4.02
0.75
1.70
0.44
3
0
4.00
1
5.00
1
LendFreq
7332
2.36
2.20
1
2
3
Revolver
7332
0.88
0.33
1
1
1
34
Table 2: Pearson Correlation among Contracting Value proxies
Table 2 presents Pearson correlations among the Contracting Value (CV) proxies and Timely Loss Recognition
(TLR) variables. All variables are defined in Section 4. We obtain loan characteristics from Dealscan, credit ratings
from S&P Credit Ratings Database, accounting and firm characteristics from Compustat, and return data from
CRSP. Contracts without covenant information are excluded and if a deal package has multiple facilities we
randomly select one. P-values are provided below the correlations.
VARIABLES
CV1
CV2
CV3
CV4
CV5
CVALL
TLR1
TLR2
CV2
0.99
1
CV3
0.84
0.84
1
CV4
0.91
0.89
0.89
1
CV5
0.83
0.84
0.96
0.83
1
CVALL
0.95
0.96
0.96
0.95
0.94
1
TLR1
0.28
0.26
0.28
0.2
0.31
0.28
1
TLR2
0.35
0.34
0.25
0.33
0.23
0.31
0.06
1
35
Table 3: Pearson Correlations
Table 3 presents Pearson correlations among the none-contract-value variables used in the analysis in Tables 4 - 11. Profitability covenants (PRCovenants) and capital
structure covenants (CSCovenants) are defined in Appendix A, all other variables are defined in Appendix C. We obtain loan characteristics from Dealscan, credit ratings
from S&P Credit Ratings Database, accounting and firm characteristics from Compustat, and return data from CRSP. Contracts without covenant information are excluded
and if a deal package has multiple facilities we randomly select one. P-values are provided below the correlations.
(1)
(1) CVALL
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
0.09
1
(3) Levmkt
0.05
0.89
1
(4) Size
-0.09
0.12
0.02
1
(5) B/M
-0.06 -0.03
0.25
-0.3
(14)
(15)
(16)
(17)
(18)
0.18 -0.04 -0.07
(7) R&D
0.02 -0.12 -0.14 -0.07 -0.08 -0.01
0.05 -0.05
1
-0.04
0.02 -0.05
0.06 -0.12
0.11
0.09
1
(9) ROA
-0.03 -0.08 -0.12
0.23 -0.12
0.02 -0.28
0.06
1
-0.13
0.15
0.24
0.05 -0.03 -0.08 -0.01
0.07
1
0 -0.03
0.2
-0.1
0.28
0.12
0.11
0.01
0.12 -0.03
0.02 -0.06 -0.06
0.12
0.23
0
0.11
(12) Tangible
-0.04
(13) Age
-0.06 -0.04 -0.04
0.25
(14) StdRet
0.01 -0.04
(15) PRCovenants
0.22
0.28
0.24
0.08
0 -0.42
0.01
-0.1 -0.02
0.04 -0.06 -0.12
0.31 -0.04
0.16 -0.04
0.23 -0.04 -0.01
(16) CSCovenants -0.19 -0.17 -0.13 -0.17
0.02
0.05 -0.07
0.12
0 -0.32
-0.1 -0.02
0.08
1
0.08
1
0.03
1
-0.3 -0.18 -0.09 -0.26
0.04 -0.11
0.01 -0.09 -0.14
0.02 -0.01
0.04
0
0.06 -0.02
1
0.03
1
0.07 -0.44
1
(17) Dealamount
-0.02
0.28
0.21
0.82 -0.15
0.04 -0.16
0.02
0.19
0.19
0.17
0.11
0.25 -0.36
0.13 -0.27
1
(18) Loanmaturity
0.12
0.19
0.12
0.25 -0.11
0.02 -0.05
0
0.09
0.01
0.08
0.09
0.05
-0.2
0.12 -0.17
0.33
1
0.02
0.08
0.07
-0.2 -0.04 -0.03 -0.08
0.12
0.01
-0.07
0.2
0.2
(20) LendFreq
(21) Revolver
(20)
1
(8) Margin
(19) DivRestrict
(19)
1
(6) Adv
(11) Marginal tax
(13)
1
(2) Levbook
(10) Div
(12)
0.01 -0.04 -0.03
-0.1
0.02 -0.04 -0.02 -0.01 -0.06
0.41 -0.04 -0.01
0.07
0
-0.1 -0.02
0.06
0.09
0.07
0.08
0.02 -0.11 -0.03
0.07
0.02
0.02 -0.06
0.24 -0.13
0
0.15 -0.16 -0.02 -0.05
0.38
0.06 -0.04
0.18 -0.22
0.06 -0.09
1
0.08 -0.03
0.06
1
0
36
Table 4: Determinants of mix of covenants
Table 4 presents estimates from three regressions of the number of profitability covenants, capital structure covenants,
and covenant mix on firm-specific variables in columns (1) - (3), respectively. Profitability covenants (PRCovenants)
and capital structure covenants (CSCovenants) are defined in Appendix B, all other variables are defined in Appendix C.
We obtain loan characteristics from Dealscan, credit ratings from S&P Credit Ratings Database, accounting and firm
characteristics from Compustat, and return data from CRSP. Contracts without covenant information are excluded and if
a deal package has multiple facilities we randomly select one.
VARIABLES
Size
B/M
Adv
R&D
Margin
ROA
Div
Tangible
Age
StdRet
LendFreq
DealAmount
Maturity
Revolver
Constant
(1)
PRCovenants
(2)
CSCovenants
(3)
CovenantMix
-0.243***
[-12.16]
-0.140***
[-4.349]
0.456
[0.565]
-0.669***
[-4.877]
-0.0261
[-1.581]
0.285**
[2.109]
-7.462***
[-7.212]
-0.380***
[-5.482]
-0.134***
[-8.566]
-0.375
[-1.426]
-0.00139
[-0.174]
0.285***
[14.28]
0.0734***
[7.010]
0.0397
[0.955]
-2.413***
[-9.063]
0.0704***
[6.951]
0.0647***
[3.727]
-1.165**
[-2.303]
0.321***
[3.204]
0.0236**
[2.384]
0.288***
[3.328]
2.689***
[3.370]
0.173***
[4.033]
0.0431***
[4.284]
0.00478
[0.0288]
0.00494
[1.036]
-0.147***
[-13.89]
-0.0517***
[-7.734]
-0.101***
[-3.717]
2.965***
[14.07]
-0.0741***
[-10.96]
-0.0444***
[-4.863]
0.753***
[2.721]
-0.297***
[-5.149]
-0.0163***
[-2.692]
0.0211
[0.417]
-2.235***
[-5.065]
-0.129***
[-5.111]
-0.0325***
[-5.837]
0.0185
[0.212]
-0.00369
[-1.317]
0.0943***
[14.40]
0.0232***
[7.065]
0.0309**
[2.202]
-0.757***
[-8.212]
6882
0.204
6762
0.201
Observations
6882
R-squared
0.184
Robust t-statistics in brackets, *** p<0.01, ** p<0.05, * p<0.1
37
Table 5: Contracting Value and Profitability Covenants
Table 5 presents estimates from univariate regressions of the number of Profitability Covenants on Contracting Value (CV) proxies and Timely Loss Recognition
(TLR) variables. Profitability Covenants (PRCovenants) are defined in Appendix B and CV proxies are defined in Section 4. We obtain loan characteristics from
Dealscan, credit ratings from S&P Credit Ratings Database, accounting and firm characteristics from Compustat, and return data from CRSP. Contracts without
covenant information are excluded and if a deal package has multiple facilities we randomly select one.
VARIABLES
CV1
(1)
PRCovenants
(2)
PRCovenants
(3)
PRCovenants
(4)
PRCovenants
(5)
PRCovenants
(6)
PRCovenants
(7)
PRCovenants
1.617***
(12.43)
CV2
1.539***
(11.97)
CV3
1.190***
(10.65)
CV4
1.470***
(11.59)
CV5
1.226***
(9.505)
CVALL
1.541***
(11.75)
TLR1
1.035***
(6.437)
TLR2
Constant
(8)
PRCovenants
1.317***
(45.86)
1.305***
(42.95)
1.230***
(31.16)
1.326***
(44.55)
1.236***
(28.88)
Observations
5938
5938
5938
5938
5938
R-squared
0.053
0.049
0.043
0.049
0.035
Robust t-statistics adjusted for clustering at firm level are in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
1.243***
(35.26)
1.381***
(35.26)
0.0123***
(2.696)
1.510***
(41.76)
5938
0.050
7332
0.012
7332
0.003
38
Table 6: Contracting value proxies and Capital Structure Covenants
Table 6 presents estimates from univariate regressions of the number of Capital Structure covenants on Contracting Value (CV) proxies and Timely Loss
Recognition (TLR) variables. Capital Structure Covenants (CSCovenants) are defined in Appendix B and CV proxies are defined in Section 4. We obtain loan
characteristics from Dealscan, credit ratings from S&P Credit Ratings Database, accounting and firm characteristics from Compustat, and return data from CRSP.
Contracts without covenant information are excluded and if a deal package has multiple facilities we randomly select one.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
VARIABLES
CSCovenants
CSCovenants
CSCovenants
CSCovenants
CSCovenants
CSCovenants
CSCovenants
CSCovenants
CV1
-0.851***
(-10.91)
CV2
-0.856***
(-11.05)
CV3
-0.672***
(-10.85)
CV4
-0.831***
(-11.43)
CV5
-0.678***
(-9.481)
CVALL
-0.853***
(-11.33)
TLR1
-0.367***
(-3.750)
TLR2
Constant
0.705***
(39.84)
0.720***
(38.62)
0.764***
(34.06)
0.710***
(40.33)
0.757***
(30.94)
Observations
5938
5938
5938
5938
5938
R-squared
0.033
0.034
0.031
0.035
0.024
Robust t-statistics adjusted for clustering at firm level are in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
0.753***
(36.13)
0.623***
(25.75)
-0.0120***
(-5.332)
0.636***
(30.84)
5938
0.035
7332
0.003
7332
0.006
39
Table 7: Contracting value proxies and Mix of Covenants
Table 7 presents estimates from univariate regressions of CovenantMix on Contracting Value (CV) proxies and Timely Loss Recognition (TLR) variables.
CovenantMix the fraction of PRCovenants in total number of covenants (CovenantMix =PRCovenants/(PRCovenants + CSCovenants)). CV proxies are defined in
Section 4, and other variables are defined in Appendix B. We obtain loan characteristics from Dealscan, credit ratings from S&P Credit Ratings Database,
accounting and firm characteristics from Compustat, and return data from CRSP. Contracts without covenant information are excluded and if a deal package has
multiple facilities we randomly select one.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
VARIABLES
CovenantMix
CovenantMix
CovenantMix
CovenantMix
CovenantMix
CovenantMix
CovenantMix
CovenantMix
CV1
0.565***
(13.01)
CV2
0.561***
(13.00)
CV3
0.452***
(12.39)
CV4
0.542***
(13.10)
CV5
0.450***
(10.98)
CVALL
0.564***
(13.21)
TLR1
0.329***
(5.679)
TLR2
Constant
0.619***
(57.08)
0.610***
(53.80)
0.578***
(40.42)
0.617***
(56.03)
0.584***
(38.94)
Observations
5843
5843
5843
5843
5843
R-squared
0.053
0.053
0.051
0.054
0.039
Robust t-statistics adjusted for clustering at firm level are in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
0.587***
(45.49)
0.654***
(44.71)
0.00461***
(3.248)
0.690***
(56.35)
5843
0.055
7205
0.010
7205
0.003
40
Table 8: Mix of Covenants and Contracting Value Proxies: Controlling for Firm and Contract characteristics
Table 8 presents estimates from regressions of the covenant mix on Contracting Value (CV) proxies and Timely Loss Recognition (TLR) variables and control
variables. The covenant mix is defined as PRCovenants/(PRCovenants + CSCovenants), where PRCovenants and CSCovenants are Profitability- and Capital
Structure Covenants, respectively, defined in Appendix B. CV and TLR proxies are defined in Section 4 and all control variables are defined in Appendix C. We
obtain loan characteristics from Dealscan, credit ratings from S&P Credit Ratings Database, accounting and firm characteristics from Compustat, and return data
from CRSP. Contracts without covenant information are excluded and if a deal package has multiple facilities we randomly select one.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
VARIABLES
CovenantMix
CovenantMix
CovenantMix
CovenantMix
CovenantMix
CovenantMix
CovenantMix
CovenantMix
CV1
0.410***
(9.817)
CV2
0.411***
(9.731)
CV3
0.289***
(8.576)
CV4
0.381***
(9.525)
CV5
0.282***
(7.428)
CVALL
0.393***
(9.609)
TLR1
0.283***
(3.754)
TLR2
Size
B/M
Adv
R&D
Margin
ROA
Div
-0.0659***
(-9.300)
-0.0351***
(-3.553)
0.123
(0.411)
-0.310***
(-5.514)
-0.0144**
(-2.249)
0.0345
(0.637)
-1.826***
(-3.797)
-0.0658***
(-9.269)
-0.0351***
(-3.549)
0.166
(0.557)
-0.313***
(-5.508)
-0.0147**
(-2.288)
0.0335
(0.617)
-1.850***
(-3.828)
-0.0639***
(-9.066)
-0.0373***
(-3.764)
0.348
(1.135)
-0.304***
(-5.239)
-0.0161**
(-2.526)
0.0279
(0.522)
-1.801***
(-3.716)
-0.0647***
(-9.148)
-0.0353***
(-3.578)
0.0631
(0.206)
-0.295***
(-5.224)
-0.0131**
(-2.044)
0.0304
(0.567)
-1.695***
(-3.532)
-0.0657***
(-9.241)
-0.0390***
(-3.899)
0.397
(1.286)
-0.301***
(-5.337)
-0.0170***
(-2.661)
0.0286
(0.531)
-1.754***
(-3.633)
-0.0646***
(-9.157)
-0.0359***
(-3.632)
0.190
(0.627)
-0.305***
(-5.380)
-0.0150**
(-2.350)
0.0325
(0.605)
-1.787***
(-3.692)
-0.0663***
(-10.14)
-0.0373***
(-4.226)
0.520*
(1.880)
-0.313***
(-5.201)
-0.0152**
(-2.515)
0.0134
(0.266)
-1.863***
(-4.319)
0.00209*
(1.647)
-0.0682***
(-10.42)
-0.0401***
(-4.518)
0.446
(1.615)
-0.323***
(-5.339)
-0.0144**
(-2.380)
0.0150
(0.299)
-1.768***
(-4.103)
41
Tangible
Age
StdRet
LendFreq
DealAmt
Maturity
Revolver
StdCFO
IndSize
Constant
-0.120***
(-4.221)
-0.0309***
(-5.030)
0.0176
(0.186)
-0.00161
(-0.532)
0.0889***
(12.56)
0.0204***
(5.991)
0.0241
(1.599)
0.555***
(3.295)
-0.0232***
(-3.039)
-0.779***
(-6.594)
-0.122***
(-4.289)
-0.0312***
(-5.093)
0.0192
(0.203)
-0.00139
(-0.460)
0.0889***
(12.53)
0.0203***
(5.956)
0.0240
(1.588)
0.566***
(3.352)
-0.0208***
(-2.683)
-0.808***
(-6.863)
-0.104***
(-3.604)
-0.0335***
(-5.442)
-0.0221
(-0.232)
-0.00171
(-0.568)
0.0867***
(12.41)
0.0207***
(6.068)
0.0263*
(1.727)
0.635***
(3.792)
-0.0256***
(-3.346)
-0.728***
(-6.511)
-0.108***
(-3.830)
-0.0313***
(-5.060)
0.00319
(0.0336)
-0.00144
(-0.479)
0.0877***
(12.48)
0.0207***
(6.078)
0.0253*
(1.671)
0.750***
(4.565)
-0.0231***
(-3.045)
-0.782***
(-6.885)
-0.113***
(-3.888)
-0.0331***
(-5.351)
-0.0186
(-0.196)
-0.00194
(-0.636)
0.0879***
(12.47)
0.0214***
(6.253)
0.0261*
(1.692)
0.623***
(3.631)
-0.0301***
(-3.918)
-0.708***
(-6.163)
Observations
5444
5444
5444
5444
5444
R-squared
0.249
0.249
0.242
0.248
0.238
Robust t-statistics adjusted for clustering at firm level are in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
-0.116***
(-4.080)
-0.0319***
(-5.179)
0.00409
(0.0432)
-0.00155
(-0.515)
0.0876***
(12.49)
0.0203***
(5.975)
0.0248
(1.637)
0.574***
(3.423)
-0.0220***
(-2.874)
-0.774***
(-6.773)
-0.0710***
(-2.763)
-0.0316***
(-5.740)
0.0716
(0.831)
-0.00403
(-1.459)
0.0904***
(14.09)
0.0212***
(6.602)
0.0314**
(2.274)
0.518***
(2.792)
-0.0479***
(-7.184)
-0.609***
(-5.646)
-0.0644**
(-2.504)
-0.0318***
(-5.736)
0.0365
(0.426)
-0.00384
(-1.361)
0.0906***
(14.06)
0.0213***
(6.618)
0.0318**
(2.299)
0.996***
(6.803)
-0.0465***
(-7.046)
-0.626***
(-5.791)
5444
0.247
6762
0.220
6762
0.217
42
Table 9: Leverage and Covenants: Ordinary Least Squares
Table 9 presents estimates from regressions of leverage on covenant characteristics and control variables. The
covenants characteristics are: acc_covenants that count all accounting-based covenants, PRCovenants counts
profitability covenants, CSCovenants counts capital structure covenants, and mix_covenants is defined as
PRCovenants/(PRCovenants + CSCovenants), where PRCovenants and CSCovenants are defined in Appendix B.
All other variables are defined in Appendix C. We obtain loan characteristics from Dealscan, credit ratings from
S&P Credit Ratings Database, accounting and firm characteristics from Compustat, and return data from CRSP.
Contracts without covenant information are excluded and if a deal package has multiple facilities we randomly
select one.
VARIABLES
TotalCovenants
(1)
Levbook
(2)
Levmkt
0.0398***
[12.11]
0.0302***
[10.99]
PRCovenants
(3)
Levbook
0.0535***
[16.43]
-0.0262***
[-5.858]
CSCovenants
(4)
Levmkt
B/M
Adv
R&D
Margin
ROA
Div
Marginal tax
Tangible
Age
StdRet
Constant
0.0218***
[11.23]
-0.00853
[-1.236]
-0.158
[-1.055]
-0.212***
[-4.495]
0.000167
[0.0500]
-0.285***
[-6.665]
0.395*
[1.843]
-0.0337
[-0.949]
0.173***
[12.74]
-0.00738**
[-2.361]
-0.0208
[-0.365]
-0.0604
[-1.443]
0.0150***
[9.116]
0.0733***
[10.49]
-0.281***
[-2.634]
-0.180***
[-4.749]
-0.00565**
[-2.376]
-0.244***
[-6.858]
0.576***
[3.194]
-0.0279
[-0.859]
0.136***
[12.15]
-0.00344
[-1.363]
-0.0351
[-0.725]
-0.0658**
[-2.003]
0.0185***
[9.824]
-0.00698
[-1.067]
-0.240*
[-1.676]
-0.161***
[-4.149]
0.00213
[0.656]
-0.266***
[-6.706]
0.713***
[3.326]
-0.0275
[-0.820]
0.188***
[14.02]
-0.00205
[-0.689]
0.0206
[0.376]
-0.00607
[-0.157]
(6)
Levmkt
0.0345***
[9.954]
0.0258***
[8.913]
0.121***
[15.13]
0.0210***
[11.04]
-0.00726
[-1.058]
-0.256*
[-1.746]
-0.165***
[-3.952]
0.00274
[0.823]
-0.306***
[-6.998]
0.685***
[3.264]
-0.0196
[-0.560]
0.185***
[13.68]
-0.00342
[-1.122]
-0.00741
[-0.131]
-0.0978***
[-2.971]
0.0869***
[13.10]
0.0144***
[8.877]
0.0765***
[10.96]
-0.352***
[-3.299]
-0.147***
[-4.317]
-0.00368
[-1.559]
-0.263***
[-7.166]
0.777***
[4.278]
-0.0170
[-0.530]
0.143***
[12.84]
-0.000628
[-0.253]
-0.0217
[-0.450]
-0.0930***
[-3.464]
0.0402***
[14.68]
-0.0180***
[-4.702]
CovenantMix
Size
(5)
Levbook
0.0127***
[7.832]
0.0753***
[11.13]
-0.342***
[-3.295]
-0.142***
[-4.479]
-0.00419*
[-1.818]
-0.230***
[-6.862]
0.807***
[4.349]
-0.0217
[-0.705]
0.146***
[13.25]
0.000392
[0.160]
-0.00360
[-0.0767]
-0.0276
[-0.897]
Observations
6854
6824
6854
6824
R-squared
0.164
0.210
0.227
0.257
Robust cluster adjusted at firm level t-statistics in brackets, *** p<0.01, ** p<0.05, * p<0.1
6734
0.205
6704
0.243
43
Table 10: Leverage and Covenants: Instrumental variable tests
Table 10 presents estimates from regressions of leverage on covenant mix and control variables using instrumental
variables for covenant mix. Covenants mix is defined as PRCovenants/(PRCovenants + CSCovenants), where
PRCovenants and CSCovenants are Profitability- and Capital Structure Covenants, respectively, defined in
Appendix A. Contracting Value (CV) proxies, defined in Section 4, are used as instruments. All other variables are
defined in Appendix C. We obtain loan characteristics from Dealscan, credit ratings from S&P Credit Ratings
Database, accounting and firm characteristics from Compustat, and return data from CRSP. Contracts without
covenant information are excluded and if a deal package has multiple facilities we randomly select one.
VARIABLES
CovenantMix
Size
B/M
Adv
R&D
Margin
ROA
Div
Marginal tax
Tangible
Age
StdRet
(1)
Levbook
(2)
Levbook
(3)
Levmkt
(4)
Levmkt
0.332***
[6.926]
0.0187***
[8.002]
-0.00402
[-0.479]
-0.334*
[-1.878]
-0.111**
[-2.515]
0.00495
[1.297]
-0.296***
[-6.785]
1.127***
[3.838]
0.0102
[0.275]
0.207***
[11.55]
-0.000693
[-0.179]
-0.0255
[-0.391]
0.289***
[5.621]
-0.0128*
[-1.859]
-0.0196**
[-2.252]
-0.230
[-1.400]
-0.0784**
[-2.220]
0.00551
[1.571]
-0.263***
[-6.287]
1.346***
[5.014]
-0.00705
[-0.198]
0.183***
[10.28]
-0.00202
[-0.553]
0.0162
[0.263]
0.0320***
[4.250]
0.00534**
[2.510]
0.0140*
[1.653]
0.0118***
[6.984]
-0.0413***
[-4.656]
-0.514***
[-6.040]
0.244***
[5.989]
0.0130***
[6.659]
0.0814***
[9.685]
-0.408***
[-2.896]
-0.102***
[-2.882]
-0.00249
[-0.907]
-0.251***
[-6.758]
1.189***
[4.759]
-0.00276
[-0.0830]
0.157***
[10.55]
0.00189
[0.591]
-0.0111
[-0.202]
0.213***
[4.849]
-0.0140**
[-2.423]
0.0683***
[8.237]
-0.318**
[-2.475]
-0.0696**
[-2.543]
-0.00176
[-0.701]
-0.222***
[-6.293]
1.382***
[6.021]
-0.0145
[-0.461]
0.138***
[9.230]
0.000766
[0.257]
0.0202
[0.391]
0.0271***
[4.250]
0.00251
[1.381]
0.0112
[1.604]
0.0114***
[8.009]
-0.0323***
[-4.411]
-0.460***
[-6.262]
DealAmount
Maturity
DivRestrict
LendFreq
Revolver
Constant
-0.0982**
[-2.474]
-0.0957***
[-2.968]
Observations
5420
5420
5392
R-squared
0.086
0.182
0.144
Robust cluster adjusted at firm level z-statistics in brackets, *** p<0.01, ** p<0.05, * p<0.1
5392
0.232
44
Table 11: Leverage and Covenants: Alternative Instruments
Table 11 presents estimates from regressions of leverage on covenant mix and control variables using instrumental
variables for covenant mix. Covenants mix is defined as PRCovenants/(PRCovenants + CSCovenants), where
PRCovenants and CSCovenants are Profitability- and Capital Structure covenants, respectively, defined in Appendix
B. Lender-level covenant characteristics are used as instruments. All other variables are defined in Appendix C. We
obtain loan characteristics from Dealscan, credit ratings from S&P Credit Ratings Database, accounting and firm
characteristics from Compustat, and return data from CRSP. Contracts without covenant information are excluded
and if a deal package has multiple facilities we randomly select one.
VARIABLES
CovenantMix
Size
B/M
Adv
R&D
Margin
ROA
Div
Marginal tax
Tangible
Age
StdRet
(1)
Levbook
(2)
Levbook
(3)
Levmkt
(4)
Levmkt
0.459***
[11.02]
0.0241***
[9.089]
-0.00925
[-1.127]
-0.584***
[-3.005]
-0.0715*
[-1.704]
0.00788*
[1.755]
-0.420***
[-8.368]
1.335***
[4.172]
-0.0296
[-0.729]
0.225***
[11.74]
0.00482
[1.157]
0.0675
[0.966]
0.362***
[6.674]
-0.00997
[-1.429]
-0.0271***
[-3.224]
-0.459***
[-2.600]
-0.0731*
[-1.925]
0.00704*
[1.772]
-0.382***
[-7.838]
1.333***
[4.598]
-0.0429
[-1.137]
0.196***
[10.85]
0.000683
[0.169]
0.0897
[1.373]
0.0330***
[4.423]
0.00609***
[2.615]
0.00122
[0.128]
0.0131***
[7.644]
-0.0485***
[-4.823]
-0.700***
[-8.458]
0.358***
[10.69]
0.0173***
[7.764]
0.0785***
[9.880]
-0.637***
[-4.320]
-0.0787**
[-2.181]
0.000798
[0.244]
-0.371***
[-9.026]
1.307***
[4.701]
-0.0168
[-0.481]
0.172***
[11.17]
0.00734**
[2.176]
0.0520
[0.909]
0.289***
[6.566]
-0.0102*
[-1.788]
0.0641***
[8.233]
-0.543***
[-4.050]
-0.0749**
[-2.326]
0.000447
[0.154]
-0.338***
[-8.482]
1.332***
[5.222]
-0.0253
[-0.772]
0.149***
[10.16]
0.00408
[1.250]
0.0639
[1.183]
0.0258***
[4.262]
0.00273
[1.442]
-0.000180
[-0.0231]
0.0127***
[8.844]
-0.0398***
[-4.893]
-0.594***
[-8.805]
DealAmount
Maturity
DivRestrict
LendFreq
Revolver
Constant
-0.418***
[-6.652]
-0.379***
[-7.343]
Observations
5252
5252
5230
R-squared
0.086
0.104
0.135
Robust cluster adjusted at firm level z-statistics in brackets, *** p<0.01, ** p<0.05, * p<0.1
5230
0.142
45