Capital versus Performance Covenants in Debt Contracts
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 the contracting role of financial covenants classified into two types. We
argue that capital covenants control agency problems by maintaining sufficient equity capital
and hence aligning debtholder-shareholder incentives, whereas performance covenants serve as
tripwires that facilitate early transfers of control and renegotiations when performance
deteriorates. We find that capital and performance covenants are negatively correlated but are
not used interchangeably. Performance covenants are strong predictors of future contract
renegotiations, while this is not the case for capital covenants. Further, restrictions on certain
managerial actions are less common in conjunction with capital covenants, in line with their
incentives alignment effect. We also study how the contracting value of accounting information
affects covenant package design. We predict and find that performance covenants are contracted
on when accounting information is descriptive of credit quality, while the opposite holds for
capital covenants. This relation implies that properties of accounting information have a sizable
effect on the design of financial contracts.
Keywords: accounting-based covenants, private debt, capital structure
JEL Classification: M40
First version: January 2010
This version: October 2010

We thank Ray Ball, Phil Berger, Richard Frankel, Laurence van Lent, Douglas Skinner, and workshop participants
at the University of Chicago, London Business School, Tilburg University, Washington University, and the Fourth
Interdisciplinary Accounting Conference in Copenhagen for helpful comments. Financial support from the
University of Chicago Booth School of Business is gratefully acknowledged.
1. Introduction
Covenants, which are designed to reduce the agency costs associated with conflicts of
interest between creditors and shareholders, are a key component of debt contracts. The classic
view on covenants expressed in Jensen and Meckling (1976) and Smith and Warner (1979)
suggests that covenants control the agency costs of debt by giving shareholders incentives to
follow a firm value-maximizing policy via restrictions on managerial behavior. More recent
theoretical literature views covenants as tripwires that give lenders an option to renegotiate loan
terms by threatening default following a decline in economic performance (Berlin and Mester
1992; Rajan and Winton 1995; Gorton and Kahn 2000; Garleanu and Zwiebel 2009). The
theoretical papers on covenants typically employ a generic definition of the information signal in
terms of which covenants are formulated, and hence largely disregard the fact that, in practice,
covenants are formulated in terms of a variety of accounting ratios (Leftwich 1983; Dichev and
Skinner 2002). This raises a number of interesting questions about the contracting role of
accounting-based covenants. First, are there important distinctions in the monitoring role of
different accounting ratios? Second, do covenants that rely on different accounting ratios reduce
contracting frictions through different mechanisms?
Third, what are the relations among
different accounting-based covenants and financial policy choices or non-accounting contract
features? And finally, how does the contractibility of accounting information influence covenant
package design?
In this paper we attempt to shed light on these questions by studying the intensity of
accounting-based covenants (so-called financial or “maintenance” covenants) used in private
1 lending agreements.1 To characterize the covenants package, we propose a simple classification
of these covenants into two broad categories. The first category comprises capital covenants,
which rely on information about sources and uses of capital and therefore balance sheet
information only. Capital covenants typically control the level of equity or debt in the capital
structure directly. The second category consists of performance covenants, which are formulated
in terms of current-period efficiency indicators.2 These covenants rely on income statement
(cash flow statement) information alone or combine it with a balance sheet amount (e.g., debt-tocash flow).
We argue that the two types of covenants limit debt-related agency problems in different
ways. Capital covenants essentially ensure that shareholders have enough money inside the
company. Such restrictions are an effective way to control the agency costs of debt (Smith and
Warner 1979), as they ensure that shareholder wealth is (sufficiently) sensitive to managerial
actions to incentivize shareholders to monitor management.
Thus, capital covenants align
shareholders' incentives with those of lenders ex ante, in which case there is less need for
debtholder interference with managerial actions ex post. In line with this argument, capital
covenants are not breached as long as management raises additional equity or cuts back on
dividends, which provides incentives to finance investments with equity rather than debt. A cost
of using these covenants, however, is that to be effective they have to place stringent constraints
1
Maintenance covenants require borrowers to maintain certain levels of financial ratios at every compliance date.
These covenants are different from so-called negative covenants that restrict managerial actions depending on the
level of accounting ratios. Such covenants are extensively considered by Smith and Warner (1979).
2
A concurrent working paper by Demerjian (2010) studies how accounting regulation has influenced the use of
covenants. The paper refers to capital covenants as "balance sheet covenants" and to performance covenants as
"income statement covenants". While such classification also seems appropriate, we do not use these labels for two
reasons. First, the classification of some covenants (e.g., debt-to-cash flow or debt-to-EBITDA) into balance sheet,
income statement, or cash flow statement groups is arbitrary. Second, the labels "capital" and "performance" are
more descriptive of the economic nature of these covenants and the underlying mechanism through which they
address agency problems (as discussed further in the paper).
2 on the amount of debt in the capital structure.
In contrast, we hypothesize that performance covenants are used as tripwires that detect
early signals of distress and hence provide lenders an early option to renegotiate the contract or
restrict managerial actions after deterioration in credit quality, i.e., ex post (Berlin and Mester
1992; Dichev and Skinner 2002; Garleanu and Zwiebel 2009). Performance covenants generally
detect deteriorations in credit quality sooner than capital covenants because they are based on
current performance (efficiency) ratios rather than on the stock of past profits and net capital
contributions. While this is another effective way to control contracting frictions, compared to
capital covenants' ex ante alignment of interests, it requires lenders to monitor the borrower more
closely.
To examine the differences in the role of the two covenant types, we develop two sets of
empirical predictions that we test using data from Dealscan. Our first set of predictions seeks to
improve our understanding of the contracting role of the two types of covenants. First, based on
our characterization of the two covenant types, we predict that the two types of covenants are not
used interchangeably as substitutes. In line with this prediction, we find that performance
covenants' and capital covenants' intensities have different determinants and exhibit associations
of opposite signs with many firm and contract characteristics. For example, consistent with the
idea that capital covenants limit the use of debt, we find that they exhibit a strong negative
association with the level of long-term debt, whereas consistent with tripwire covenants
improving banks' incentives to monitor the loan (Rajan and Winton 1995), performance
covenants are positively correlated with long-term debt.
Second, since performance covenants are used as tripwires, we predict that they are
positively related to the frequency of contract renegotiations, as a result of which covenants are
3 waived or reset conditional on firms' future performance. In contrast, capital covenants are
expected to reduce the need to renegotiate because of debtholder-shareholder incentive
alignment effects. We find that, in line with our prediction, performance (capital) covenants
exhibit a significantly positive (negative) correlation with renegotiations and the number of
covenant amendments.
These results extend to a multivariate setting where we find that
performance covenants, but not capital covenants, are significant predictors of subsequent
renegotiations.
Third, we predict that negative covenants such as restrictions on dividends, capital
expenditures, and "cash sweeps" are used in conjunction with performance but not capital
covenants. This is because when incentives are aligned through capital covenants, demand for
negative covenants should be lower. In contrast, performance covenants may occasionally fail to
transfer control in a timely fashion and hence negative covenants are needed to prevent
shareholders from expropriating wealth from lenders. Our results provide strong empirical
support for this prediction.
Our second set of predictions seeks to shed light on how the contracting value of
accounting information influences covenant package design. We argue that tripwire covenants
are only effective at reducing contracting frictions if they transfer control to lenders when credit
quality deteriorates (otherwise their use is costly).
Thus, our fourth prediction is that
performance covenants are used when accounting information is descriptive of credit risk. In
contrast, capital covenants are likely to be a more effective mechanism to limit debt-related
agency problems when accounting information is a poor indicator of credit quality, for two
reasons. First, capital covenants align the incentives of shareholders and lenders ex ante and
therefore do not rely on frequent control transfers and renegotiations. Second, aggregation of
4 profits over time, as it occurs in the balance sheet, reduces accounting noise. Using four proxies
to measure accounting information's inherent contracting value (similar to Ball et al. 2008), we
find that the use of performance (capital) covenants is increasing (decreasing) in the debt
contracting value of accounting information, and that the fraction of performance covenants is
also positively associated with our contracting value proxies. These results continue to hold
when we use timely loss recognition and earnings persistence as alternative contracting value
proxies and when we control for the investment opportunity set (Skinner 1993) and other
covenant determinants. Taken together, the evidence suggests that performance covenants are
chosen over capital covenants when accounting information is a good indicator of credit quality,
in line with our predictions.
Our fifth and final prediction is an extension of the arguments in Ball et al. (2008). Ball
et al. find that credit rating-based pricing grids substitute for accounting-based pricing grids
when accounting is uninformative about credit risk. Since the purpose of credit ratings and
performance measures is to detect changes in credit quality, in contrast to capital-based
measures, we argue that the effect documented in Ball et al. is largely due to substitution
between credit ratings and the indicators used by performance covenants but not capital
covenants. Our evidence supports this argument.
Our study contributes to three threads of the literature. First, we contribute to the
literature on creditor control rights and debt contract design (e.g., Dichev and Skinner 2002;
Nash et al. 2003; Nini et al. 2007; Chava and Roberts 2008). Smith and Warner (1979) highlight
the need to study interrelations between covenants. We propose a simple distinction between
two types of financial covenants that is conceptually appealing and turns out to be empirically
powerful. In particular, our results support the argument that performance and capital covenants
5 are not substitutes, that is, they reduce contracting frictions through different mechanisms. We
thus find that the covenant mix varies meaningfully with different firm characteristics as well as
contract characteristics such as leverage and negative covenants. Second, we contribute to the
literature on the renegotiation of financial contracts (Roberts and Sufi 2009). Our evidence
indicates that contracts with performance covenants are more frequently renegotiated than
contracts without performance covenants. This is not the case for capital covenants, however, in
line with capital covenants reducing the need for future renegotiations ex ante. Third, we
contribute to the literature on how accounting information affects the design of debt contracts
(Frankel and Litov 2007; Ball et al. 2008; Bharath et al. 2008; Nikolaev 2010; Costello and
Wittenberg-Moerman 2010; Demerjian 2007 and 2010). Specifically, we show that performance
covenants are preferred to capital covenants when accounting information provides a good
description of credit quality.
The remainder of our paper is organized as follows: Section 2 develops our hypotheses;
Section 3 discusses our measure of the contracting value of accounting information; Section 4
outlines the sample selection and provides descriptive statistics; Section 5 presents the results;
and Section 6 concludes.
2. Background and hypotheses
Accounting-based covenants limit contracting inefficiencies. There are many channels
through which covenants operate.
Covenants can reduce agency problems by restricting
managerial actions that potentially hurt debtholders as a company approaches financial distress
(Jensen and Meckling 1976; Smith and Warner 1979). Alternatively, covenants can serve as a
screening device that helps lenders obtain information about the borrower (Garleanu and Zwiebel
2009). Covenants can further alleviate “hold up” problems associated with short-term debt
6 (Sharpe 1990; Rajan 1992), improve lenders' incentives to monitor the loan (Rajan and Winton
1995), and provide a valuable option to renegotiate a contract following an adverse event (Berlin
and Mester 1992). However, there is a disconnect between theory and practice as the theory is
silent with respect to the roles of the different accounting ratios on which covenants are
formulated. In this paper we classify covenants into two types to provide early evidence on the
contracting role of the two types of accounting indicators used in practice. Before developing
our hypotheses in turn below, we begin with a discussion of the classification that we propose.
2.1. Capital versus performance covenants
To shed light on the role of accounting-based covenants, we classify covenants into two
types: capital covenants and performance covenants. Capital covenants are formulated in terms
of accounting information about sources and uses of capital, that is, balance sheet information
only. Examples include restrictions on leverage, debt-to-equity, loan-to-value, debt-to-tangible
net worth, and current ratios. In contrast, performance covenants consists of those covenants
formulated in terms of current-period performance or efficiency ratios. Such covenants include
interest coverage, fixed charge coverage, debt-to-earnings, and debt-to-cash flow ratios, as well
as earnings (EBITDA) itself (see Appendix A for a complete classification of covenants).
A notable distinction between the two types of covenants relates to the fact that capital
covenants typically place explicit restrictions on a firm's minimum equity or maximum debt
level, for instance, by requiring that the firm maintain certain leverage or net worth thresholds.
Shareholders can ensure that such thresholds are respected by contributing additional equity
capital or cutting back on dividend payments. Thus, capital covenants are not breached as long
as shareholders are willing to participate in the firm's investments with their own capital. In
contrast, performance covenants place no direct restrictions on the amount of a firm's debt;
7 instead they require that a minimum performance (profitability) level be maintained. Under
performance covenants a company can keep increasing the level of debt in its capital structure as
long as its new investments are sufficiently profitable.3 However, a breach of performance
covenants can rarely be avoided simply by contributing equity or cutting dividends.
Another distinction between the two covenant types relates to the timeliness of control
transfers. Capital covenants are based on a firm's cumulative profitability plus shareholders' net
capital contributions, that is, they measure equity holders' capital in the firm including the stock
of past undistributed profits. Performance covenants, in contrast, are functions of current-period
performance only and thus are informative about changes in the stock of equity capital (profits)
net of dividends. To become binding, capital covenants may require a series of losses, while it
simply takes a lower level of current-period performance (which need not be a loss) for
performance covenants to bind. Thus, performance covenants are usually timelier and more
forward-looking indicators of negative trends in credit quality. Further, capital covenants rely on
cumulated and aggregated accounting information from the balance sheet. Although this is
likely to reduce noise over longer time periods, this comes at the expense of timeliness as
current-period performance receives relatively small weight over longer periods.
We note that it is rather difficult to draw a sharp line between performance and capital
covenants because a firm's level of capital and performance are correlated. For example, if a
company underperforms over an extended period its capital will be depleted and control
reallocation will take place with capital covenants unless shareholders contribute new equity
capital. However, this correlation is expected to prevail only over rather long measurement
3
Capitalizing future profits is generally not allowed under GAAP and thus financing a profitable new project with
debt alone will be difficult in the presence of capital structure covenants.
8 horizons. Contracting parties should therefore find the distinction between the two types of
covenants relevant.
2.2. Role of capital and performance covenants in limiting contracting frictions
The distinctions we outlined above imply that the two types of covenants limit
contracting frictions in different ways. Capital covenants require that shareholders maintain
enough net assets in the firm (i.e., have enough "money" inside the firm). This ensures that
shareholders' wealth is sensitive to opportunistic actions that decrease firm value (i.e., an equity
option on the firm's assets is in the money), which in turn reduces shareholders' incentives to
expropriate debtholder value and encourages them to monitor management (i.e., aligns the
interests of shareholders and lenders). In contrast, instead of directly influencing the level of
shareholders' equity and hence aligning incentives, performance covenants lead to more active
monitoring by lenders by identifying deteriorations in a firm's performance and facilitating
renegotiation. Performance covenants therefore reduce agency problems by reallocating control
when debtholder-shareholder conflicts of interest become more severe.
Smith and Warner (1979) argue that one of the most effective ways to control debtholdershareholder agency conflicts is to limit the amount of debt in the capital structure to a relatively
low level. These authors distinguish four types of agency conflicts between shareholders (and
managers' actions on their behalf) and debtholders, namely, (1) conflicts over dividends, (2)
claim dilution, (3) asset substitution (Jensen and Meckling 1976), and (4) underinvestment
(Myers 1977). To see how capital covenants can reduce agency conflicts, consider each of these
problems in turn. First, by imposing a minimum equity constraint, capital covenants directly
limit excessive dividend payouts and claim dilution. Next, because asset substitution arises due
to shareholders' limited downside risk, which provides shareholders (and ultimately managers)
9 with risk-taking incentives, capital covenants' minimum equity constraint increases shareholders'
downside sensitivity and hence helps mitigate asset substitution.
Finally, because the
underinvestment problem arises when the return on new investments effectively accrues to
debtholders rather than shareholders, that is, when equity capital is thin and shareholders hold an
out-of-the-money option on the firm's assets, capital covenants' equity requirements help
preventing the underinvestment problem as well.
Performance covenants can also address the four agency problems above by acting as
tripwires that preempt managerial actions by reallocating control to debtholders at risk of
expropriation. Control transfers allow debtholders to discipline managerial actions such as
dividend payouts, capital expenditures, asset sales, and changes in the debt level (Roberts and
Sufi 2009). We argue that capital structure covenants are less able to serve as tripwires as they
do not focus on current performance, are less timely, and are easier to avoid. Thus, whereas
capital covenants provide ex ante incentives against opportunistic actions, performance
covenants limit agency conflicts by transferring control after operating performance
deterioration.
Based on the above discussion we expect that the two types of covenants are used by
companies with different characteristics, such as for example, different investment opportunity
sets (Skinner 1993). This implies that the two types of covenants are not direct substitutes.
More formally:
H1: Capital and performance covenants are used by borrowers with different investment
opportunity sets, i.e., they are not substitutes.
We state our first hypothesis in general form and discuss specific associations with firm
characteristics in the results section.
Our alternative hypothesis is that the two types of
covenants are substitutes and hence exhibit similar relations with firm characteristics.
10 2.3. The use of negative covenants
In addition to financial covenants, credit agreements often contain negative covenants,
such as dividend restrictions, capital expenditure restrictions, and cash sweeps (which require
remittance of a portion of cash proceeds from asset sales, new debt or equity issuance, etc. to the
lender and hence effectively restrict these actions).
Negative covenants are an alternative
mechanism to control excessive dividend payouts, asset substitution, and claim dilution. We
expect demand for negative covenants to be lower when managerial incentives are aligned with
those of debtholders via capital covenants. In contrast, negative covenants are vital when
performance covenants fail to transfer control in a timely manner and hence the risk of
expropriation increases. This leads to the following hypothesis:
H2: Dividend restrictions, capital expenditure restrictions, and cash sweeps are used in
conjunction with performance covenants but not with capital covenants.
2.4. Frequency of renegotiation
Lender monitoring via tripwire covenants is closely related to contract renegotiations.
When economic performance deteriorates, tripwire covenants give lenders an option to
renegotiate the terms of the loan. Garleanu and Zwiebel (2009), for example, show that under
asymmetric information it is optimal to give stronger decision rights to lenders via covenants,
which increase the frequency of subsequent renegotiation. We posit that because performance
covenants serve as tripwires, the intensity of performance covenants is significantly related to the
frequency of contract amendments that lead to covenant modifications. In contrast, because
capital covenants are less suitable for active lender monitoring, we do not expect a positive
association between capital covenants and contract amendments. Indeed, in the absence of
conditioning on performance covenants, capital covenants are likely to reduce the frequency of
renegotiation as they align managerial incentives ex ante. Our third hypothesis is thus as
11 follows:
H3: Performance covenants are positively related to the frequency of contract
amendments, whereas capital covenants do not show a positive relation with contract
amendment frequency.
2.5. Accounting-based covenants and the contracting value of accounting information
A key role of accounting information in the debt markets is to provide contractible
information. Properties of accounting information are likely to be important in determining debt
contract design and more specifically the use of accounting-based covenants (Skinner 1993;
Frankel and Litov 2007; Bharath et al. 2008; Nikolaev 2010). We argue that performance
covenants require accounting information to be a good indicator of credit quality. First, if
accounting is not descriptive of credit risk, performance covenants will not effectively control
agency problems as they will not transfer control in a timely manner (Type I error). Second,
performance covenants will be costly if they transfer control and require renegotiation at times
when doing so is unjustified (Beneish and Press 1993) (Type II error). Finally, if accounting
information does not easily map into credit scores, monitoring of covenants written in terms of
such information (which entails verifying quarterly compliance certificates, understanding
covenant violations, and possibly renegotiation) will be relatively costly.
In contrast, capital covenants do not require that accounting information explain credit
quality. First, capital covenants reduce agency conflicts not by reallocation of control but rather
by aligning shareholders' and debtholders' interests and giving shareholders incentives to control
managerial actions. Second, accounting noise present in performance measures (e.g., earnings)
reverse and wash out over time, and therefore have only a minor effect on capital covenants that
rely on cumulative numbers from the balance sheet. However, the use of capital covenants
entails a significant cost as they directly limit the use of debt as a mechanism to align incentives.
12 These arguments imply that there exists a tradeoff between capital and performance
covenants that is affected by the contracting value of accounting information: when accounting
information's ability to explain credit quality increases, performance (capital) covenants become
a more (less) appealing contracting mechanism.4 More formally:
H4: The reliance on performance (capital) covenants is positively (negatively) associated
with the ability of accounting information to explain credit risk.
2.6. Pricing grids and accounting information
Loans frequently rely on pricing grids that make interest payments contingent on
accounting indicators or credit ratings (Asquith et al. 2005). Pricing grids based on accounting
information can be classified into (1) capital-based pricing grids, which rely on accounting
indicators used by capital covenants, and (2) profitability-based grids, which rely on accounting
indicators used by performance covenants.5 Ball et al. (2008) predict and find that rating-based
pricing grids substitute for accounting-based grids when the contracting value of accounting
information is low. Extending their argument, we argue that rating-based grids substitute only
for profitability-based grids and not capital-based grids. To see this, note that ratings are likely
used for the same purpose as performance measures: to detect changes in credit quality. Capitalbased pricing grids, on the other hand, are less affected by the contracting value of accounting
information since they provide incentives to maintain a sufficient equity cushion rather than
detect changes in credit quality. Substitution of profitability-based grids for rating-based grids is
4
Note that this is different from predicting that performance (capital) covenants are used when performance
(capital) measures are more informative about credit quality. It is likely that informative performance measures will
lead to informative capital ratios and vice versa. Thus, we expect and find that these are highly positively correlated.
As a result, it is difficult to separate informativeness effects empirically.
5
Capital indicators include senior leverage and the ratio of debt to tangible net worth; profitability (performance)
indicators include the debt service coverage ratio, fixed charge coverage, interest coverage, senior debt to cash flow
(EBITDA), and total debt to cash flow (EBITDA); rating measures include commercial paper rating, subordinated
debt rating, and senior debt rating.
13 expected in cases where accounting information is a poor indicator of credit risk. As a result, we
predict that the use of profitability-based pricing grids, but not capital-based pricing grids, is
increasing in the contracting value of accounting information. This leads to our fifth hypothesis:
H5: Pricing grids are formulated in terms of performance indicators rather than capital
indicators when accounting information is more descriptive of credit risk.
3. Measuring the contracting value of accounting information
Accounting information explains credit risk to the extent that it maps into credit scores. To
quantify the ability of accounting information to measure default risk we construct four proxies
for contracting value. Following Ball et al. (2008), our contracting value proxies are based on
industry-level regressions of long-term debt ratings (transformed into numerical scores) on
accounting variables. As credit ratings summarize credit risk, R2s from these regressions capture
the relative ability of accounting information to explain default risk. A low explanatory power
would imply the presence of a large information component for the industry that is not easily
captured by accounting information (but that is taken into account when determining credit risk).
In this case the amount of contractible accounting information available to capture credit risk is
limited. In contrast, a high R2 would imply that accounting benchmarks are sufficient statistics
for determining credit scores within a particular industry. We estimate the regressions at the
industry level over the period 1988-2008, similar to Ball et al. (2008).
Our approach differs from that in Ball et al. (2008) who measure the contracting value by
looking at whether changes in accounting earnings explain credit rating downgrades. We do not
follow their procedure as our objective is different. The first reason for using levels rather than
changes in earnings is that we are interested in using variables actually used in contracts.
Contracts are written in terms of performance or capital indicator levels and thus it is interesting
14 to focus on these variables. Moreover, performance indicators already effectively inform us
about the changes in shareholder's capital (wealth) and thus differencing capital indicators brings
us to performance measures. Specifically, differencing net worth yields current period earnings
net of dividends. If the purpose of the analysis is to compare net worth vs. earnings as two
contracting alternatives, differencing is misleading. Second, we are not interested in whether
changes in accounting variables predict credit rating downgrades per se.6
Instead, we are
interested in determining whether accounting information maps into a credit score, or whether a
large amount of other information (captured in residuals) explains credit ratings. Because levels
of earnings and capital are likely to be important in determining credit risk, calculating changes
would difference out this essential information component.7 Further, the lack of a credit rating
downgrade need not necessarily imply that accounting information is uninformative about credit
quality; rather, it may simply suggest relatively stable performance.8 As a result, it is difficult to
evaluate whether accounting information explains (as a sufficient statistic) credit risk by focusing
on credit rating downgrades.9
6
Note that credit rating downgrades following changes in profitability are endogenously related to the use of
covenants, as contracts that rely on tight tripwire covenants have a higher probability of a downgrade following a
change, which complicates inferences. In contrast, examining the extent to which earnings map into credit risk
scores is a “non-causal” approach that is less subject to this concern. Note that the mapping of earnings into credit
scores is unlikely to depend on whether covenants are used or not.
7
While a specification in changes allows one to get closer to a causal relation between accounting variables and
credit rating, we are not interested in causal relations per se, but rather the strength of association.
8
Econometrics of panel data suggests that taking differences is likely to exacerbate econometric problems caused by
measurement error when there is a lack of within-subject (company) variation in the variables of interest (i.e.,
subjects are correlated over time) (Hsiao 2003, Section 10.5).
9
As a technical complication, our data do not allow us to construct a quality measure of credit rating downgrades.
For example, suppose we observe a credit rating on February 1, 2000, and on April 1, 2002 we observe a lower
rating. This could mean that the company was downgraded right before April 1, 2002, or alternatively it could mean
that there was an interruption in coverage.
15 Note that private loan covenants are usually not written in terms of credit ratings, and
hence they are suitable as benchmarks for measuring the contracting value of accounting
information (as this avoid selection issues). While it is beyond the scope of this paper to
establish why covenants are not written in terms of credit ratings, several explanations are
possible. First, the introduction of conflicts of interest between rating agencies (compensated by
the borrower) and debtholders can discourage contracting on ratings. Second, “circularity”
would arise because ratings are determined in part by firms' access to bank financing
(Standard&Poor's 2010) and therefore lenders' decisions. For example, S&P could downgrade a
company's debt in response to lenders' intention to recall the loan, in which case contracting on
ratings makes default a self-fulfilling prophesy, similar to covenants written on credit spreads. It
is worth pointing out that whereas the circularity above reduces the usefulness of ratings in
contracts, to our knowledge it should not bias the contracting value proxies used in this study.
As a robustness check, in Section 5.6 we use credit default spreads instead of credit ratings.
We now turn to the construction of our contracting value proxies.10 Our first proxy for
the debt contracting value of accounting information, CV1, is the R2 from the following industrylevel regression:
Rating it   0  1 Eit   2 Eit 1   3 Eit  2   4 Eit  3   5 Eit  4   it
(1)
where Ratingit is constructed by assigning “1” to companies with the highest credit rating
following quarter t, “2” to companies with the second-highest credit rating, and so on, and then
taking the natural logarithm. Eit  s is earnings before extraordinary items in quarter t  s divided
by average total assets over the quarter.
10
We use quarterly data as private credit agreements' compliance with accounting-based covenants is often
determined on a quarterly basis.
16 Our second contracting value proxy, CV2, is the R2 from the industry-level regression:
Ratingit  0  1Coverageit  2Coverageit 1  3Coverageit 2
 4Coverageit 3  5Coverageit 4   it
(2)
where Rating is defined as above and Coverageit  s is quarter t  s interest coverage (EBITDA
divided by total interest expense).
Notice that both CV1 and CV2 are formulated in terms of performance indicators that
lenders are interested in. One may also measure the extent to which accounting information
maps into credit quality based only on balance sheet variables, i.e., capital indicators. Doing so
allows us to contrast capital ratios with performance indicators. Our proxy based only on capital
indicators, CV3, is the R2 from the following regression:
Rating it   0  1 NetWorthit   2 Leverageit   3Tangibilityit   it
(3)
where Rating is as defined above, NetWorthit is the quarter t ratio of book value of equity divided
by total assets, Leverageit is long-term debt divided by total assets in period t, and Tangibilityit
is capital intensity defined as book value of net property, plant, and equipment divided by total
assets.11
Finally, our all-in contracting value proxy, CV4, is based on the R2 from the following
industry-level regression (here we do not include lags to keep the model parsimonious):
Rating it   0  1 Eit   2 Coverageit   3 NetWorthit   4 Leverageit   5 Tangibilityit   it
(4)
where all variables are as defined previously. Appendix B provides pooled sample estimates for
Models (1) through (4).
3.1. Alternative proxies for contracting value
11
While Tangibility is rarely used in credit agreements, it clearly represents a measure of assets-in-place, which is
important in lending decisions (Skinner 1993).
17 While there is no consensus in the accounting literature on how to measure “accounting
quality” (Dechow et al. 2009), we use two simple properties of accounting information that
likely affect contracting parties' decision on covenants: timely loss recognition and earnings
predictability. Apart from being interesting in their own right, timely loss recognition and
earnings persistence also help us validate our contracting value proxies described above.12
Timely loss recognition, or conditional conservatism, plays an important role in debt
markets (Watts 2003).
In particular, timely loss recognition improves the effectiveness of
accounting-based covenants by facilitating transfers of control to debtholders when a company
approaches financial distress (Ball and Shivakumar 2005). Thus, timely loss recognition is
important for tripwire-type covenants.
We measure timely loss recognition, TLR, as the
coefficient  3 from the following industry-level regression based on Basu (1997):
Et / Pt 1   0  1 D ( Rt  0)   2 Rt   3 D ( Rt  0) Rt   t
(5)
where Et / Pt 1 is defined as the ratio of annual earnings before extraordinary items scaled by
beginning-of-period market value of common stock, Rt is the stock return from CRSP
compounded over the 12-month period starting three months after the beginning of the fiscal
year (to exclude prior earnings announcement effects), and D(.) is an indicator function.
Earnings predictability is also often argued to be an important consideration for lenders
and thus is likely to influence the covenants decision. We measure predictability, PRED, as the
R2 from the following industry-level regression (e.g., Dichev and Tang 2009):
Eit   0  1 Eit 1   it
(6)
12
Ball et al. (2008) also use timeliness, the R2 from regressing stock returns on accounting earnings and their
changes. They find, however, that timeliness exhibits a negative correlation with timely loss recognition. This is
not unexpected given the construction of these two proxies. As a result, and given that the contracting benefit of a
higher return-earnings association is unclear, we do not include this measure in our analysis.
18 where E t is annual earnings divided by average total assets.
Regressions (5) and (6) are
estimated by industry over the period 1988-2008.
4. Sample and summary statistics
4.1. Sample selection
We use Dealscan to measure reliance on accounting-based covenants and other loan
characteristics. Accounting variables and firm characteristics are collected from Compustat. We
merge loans from Dealscan to the fiscal years in which they are issued on Compustat using the
Dealscan-Compustat link (August 2010 vintage) constructed and maintained by Michael Roberts
and WRDS (see Chava and Roberts 2008).13 If a loan package has several credit facilities, we
aggregate information at the deal (i.e., loan) level. Contracts without covenant information are
excluded from the analysis.14 We capture a contract's reliance on covenants by using covenant
intensity, which is the number of covenants in the contract. C-Covenants denotes a count of
capital covenants, and P-Covenants denotes a count of performance covenants.
The
classification of accounting-based covenants into performance covenants (P-Covenants) and
capital covenants (C-Covenants) is described in Appendix A.
To construct our contracting value proxies we link the S&P Credit Ratings Database to
the Compustat quarterly database and use S&P's entity-level long-term credit ratings. Each endof-quarter credit rating is linked to accounting information from the current and preceding
quarters. If S&P did not update a firm's credit rating during a particular quarter, we use the most
13
We thank the authors for generously sharing the link information.
14
Approximately 50% of credit agreements in Dealscan are coded as having no covenants. It is highly unlikely that
these credit agreements do not employ covenants given that almost all private credit agreements rely on covenants
(e.g., Christensen and Nikolaev 2009). The absence of covenant data indicates simply that Dealscan was unable to
obtain information on covenants. Thus, we exclude contracts with no covenant information rather than set this
number to zero.
19 recent long-term credit rating. S&P Credit Ratings data dates back to the 1920s, but coverage is
sparse before 1986. As we further require cash flow statement data, 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). In general it takes more than a year for S&P to update
information about a given company's long-term credit.
Contracting value is measured based on SIC industry classification: 3-digit SIC codes are
used in cases where more than 25 companies and 250 quarterly observations are available; 3digit SIC codes that do not meet this requirement are combined and considered at their 2-digit
level (all industries are mutually exclusive).
This procedure achieves a more balanced
distribution of companies across resulting industry groups. We further exclude industry groups
with less than 25 companies or 250 quarterly observations to improve the reliability of estimates
and avoid overfitting that occurs in small samples. This procedure results in 50 industry groups.
We employ the same industry classification procedure when measuring timely loss
recognition and earnings predictability.15 These properties are estimated using the intersection of
CRSP and annual Compustat data. We omit 1% of the observations for CRSP and Compustat
variables at each tail and restrict the sample to non-negative EBITDA (which is necessary to
compute interest coverage).16 All variables are defined in Appendix C. The final sample size
varies from approximately 6,000 to over 10,000 debt contracts depending on data availability for
the specific regression.
4.2. Summary statistics
15
The resulting set of industries is larger as we do not require S&P data to be present.
16
The results are not sensitive to this choice.
20 Table 1 presents summary statistics. With a mean (median) market value of assets at
$4,553 ($975) million, the average firm in our sample is larger than the average firm on
Compustat.
Average book-to-market is 0.63, which is similar to the Compustat average.
Leverage, as measured by long-term debt divided by book value of assets, is 27%, which is
substantially higher than the 17% that corresponds to the population of Compustat firms. The
mean contracting value proxies range from 15% in the case of CV1 to 44% in the case of CV4,
which implies that a substantial portion of the variation in credit ratings is explained by variation
in accounting variables. Half of the sample companies have at least one C-Covenant and two PCovenants.
Table 2, Panel A reports correlations among the contracting value proxies. All CV
proxies exhibit high positive correlations with each other. This result is interesting as it shows
empirically that profitability indicators' ability to explain credit risk is closely correlated with the
ability of balance sheet capital ratios to do so. It is therefore unlikely that the choice of
covenants is explained by trading off how well balance sheet vs. income statement variables map
into credit risk. In addition, the CV proxies exhibit significant positive correlations with the
timely loss recognition (TLR) and predictability (PRED) proxies. For example, the correlation
between TLR (PRED) and CV4 is 31% (50%). This result helps validate our contracting value
proxies.
Table 2, Panel B provides pairwise correlations among covenants based on a more
refined classification of the financial covenants into five main groups (detailed in the table
header).
The five main groups are given by indicator variables for covenants on debt-to-
profitability, interest coverage, liquidity (current ratio), leverage, and net worth. Note that
interest coverage and debt-to-profitability covenants belong to P-Covenants while liquidity,
21 leverage, and net worth covenants belong to C-Covenants. As expected, performance and capital
covenants exhibit positive correlations with other members of their group. However, they
exhibit significant negative correlations with members of the other group, which is consistent
with covenants across these groups being substitutes. Overall, P-Covenants and C-Covenants
exhibit a significantly negative correlation of -0.37.
Panel C of Table 2 shows the correlations between financial covenants' intensity and the
presence of negative covenants (dividend and capital expenditure restrictions, as well as cash
sweeps). We observe significantly positive (negative) correlations between negative covenants
and performance (capital) covenants, consistent with H2.
Table 3 provides Pearson correlations among firm- and contract-level variables. PCovenants and C-Covenants exhibit correlations of opposite signs with leverage (0.26 and -0.16,
respectively). This evidence is consistent with the idea that capital covenants mitigate agency
conflicts by limiting access to debt financing.
The covenants mix, P/C CovMix = C-
Covenants/(C-Covenants+P-Covenants), also exhibits significant correlations with a number of
firm characteristics, including size, leverage, and book-to-market.
5. Results
5.1. Are performance and capital covenants substitutes (H1)?
To examine whether performance and capital covenants are substitutes, we regress them
on the investment opportunity set (Skinner 1993) and other covenant determinants (Nash et al.
2003; Billett et al. 2007; Chava and Roberts 2008; Chava et al. 2010).
Table 4 presents the regression estimates. Models (1) and (2) explain the determinants of
performance covenants (P-Covenants) and capital covenants (C-Covenants), respectively.
Model (3) explains the covenant mix, P/C-CovMix. We find that only the coefficients on Size,
22 ROA, and Altman's Z-score share the same sign across performance and capital covenants. All
other firm characteristics in Models (1) and (2) exhibit opposite signs across the two types of
covenants and in many cases are significantly different from zero. Perhaps the most striking
finding in the table is that leverage exhibits a strong positive association with P-Covenants,
while it exhibits a strong negative association with C-Covenants. This finding confirms the
univariate results discussed in Section 4.2. In addition, whereas prior research documents a
positive association between the number of accounting covenants and leverage (Press and
Weintrop 1990; Duke and Hunt 1990; Skinner 1993; Bradley and Roberts 2004), this
relationship does not hold for C-Covenants, further underscoring the difference between CCovenants and P-Covenants.
We do not derive empirical predictions as to how different types of covenants should be
related to firm characteristics as the relationship is often an empirical question. However, we
offer interpretations for several coefficients of interest. We find that larger (Size) and more
mature companies (Age) rely on capital rather than performance covenants, which can be
explained by lower information asymmetry in such companies.
Consistent with this idea,
Garleanu and Zwiebel (2009) show that tripwire-type covenants are used to address contracting
frictions due to information asymmetry. We do not have a clear prediction on how growth
opportunities are related to the covenant mix. On the one hand, capital covenants allow for
greater managerial independence and flexibility, essential for high growth firms. On the other
hand, lenders may be reluctant to grant flexibility to such firms due to greater agency problems.
Existing empirical evidence on growth options and covenants is mixed. For instance, Bradley
and Roberts (2004) find that high growth firms face more covenants, Demiroglu and James
(2010) find that high growth firms get more but looser covenants, whereas Skinner (1993) finds
23 that high growth firms face fewer covenants. We find that companies with relatively high
growth opportunities (as implied by lower book-to-market, B/M) tend to rely on performance
rather than capital covenants. Our evidence complements and potentially helps reconcile the
mixed findings of prior literature. In particular, the more intense use of performance covenants
among high growth borrowers suggests more direct monitoring by lenders when investment
opportunities are greater.
We further find that companies with a larger portion of tangible assets (tangibility) rely
more on C-Covenants than P-Covenants. Skinner (1993) argues that having assets-in-place
helps overcome the asset substitution and underinvestment problems as such assets can be used
as collateral and are easier to monitor via covenants. The need for tripwire covenants is thus
likely lower. On the other hand, capital covenants are still likely to be useful as they help ensure
that the value of the collateral (assets-in-place) exceeds a minimum threshold. In addition, R&D
intensive companies (controlling for growth opportunities) and companies with low profits also
gravitate towards the use of C-Covenants. This result can be explained intuitively by the lack of
meaningful performance indicators in such companies (Demerjian 2007). For example, negative
earnings, due say to large R&D expenditures, preclude the interpretation of performance ratios.
Finally, consistent with the argument that volatile operations likely make the use of P-Covenants
costly due to unwarranted covenant breaches, the standard deviation of returns (StdRet) is
negatively associated with P-covenants but positively related to C-Covenants.
The results on the determinants of the covenant mix in Model (3) closely mimic those in
Models (1) and (2). This result makes P/C-CovMix a convenient summary measure of the type
of covenant package used. Overall, the evidence implies that capital and performance covenants
are indeed used by companies with different characteristics. This result, in combination with the
24 correlations in Tables 2 and 3, supports our hypothesis (H1), which posits that the two covenant
types are not substitutes. This result also implies that measuring covenant use by pooling
different types of covenants into a single covenant count index is not meaningful in many
circumstances. This finding has direct implications for empirical studies that use only one
summary measure to capture covenant use.
5.2 Negative covenants (H2)
In Models (4) through (6) of Table 4 we augment the prior regressions by contract
characteristics including the proxies for negative covenants.
Conditional on firm characteristics, P-Covenants (C-Covenants) exhibit a positive
(negative) association with loan Amount and Maturity. Such findings are consistent with the
coefficients on Leverage and are in line with the idea that P-Covenants (C-Covenants) promote
(limit) credit market access. Also note that tripwire covenants make debt maturity conditional on
performance and hence are likely to be more useful in contracts with longer maturity.
More importantly, we find that conditional on other variables, P-Covenants are
significantly positively related to the inclusion of dividend restrictions (DividendCov), capital
expenditure restrictions (CapexCov), and cash sweeps (Sweeps). In contrast, CapexCov and
Sweeps are significantly negatively related to C-Covenants (DividendCov is statistically
insignificant).
Additionally, P/C-CovMix exhibits positive and statistically significant
associations with all three types of negative covenants. This evidence supports our second
hypothesis (H2), which predicts that these negative covenants are used in conjunction with
performance but not capital covenants.17
17
Note that contract characteristics are chosen simultaneously with capital covenants. This type of endogeneity does
not allow us to draw inferences as to whether exogenous inclusion of negative covenants causes inclusion of
performance covenants. However, because we are interested in testing whether negative covenants and performance
25 5.3. Renegotiation and the covenants package (H3)
Under H3, we predict that performance covenants are significantly related to the
frequency of renegotiations. To construct a proxy for contract renegotiations, we count the
number of amendments to a specific lending agreement on Dealscan. In order to maximize the
likelihood that amendments are related to the renegotiation of covenants, we only include
amendments where the description field contains the words "covenant", "definition", or
"provision".18
The results of this analysis are presented in Table 5. Panel A reports univariate
correlations between amendment frequency and the two covenant types. Both the Amendment
indicator (which indicates the presence of at least one amendment for a particular deal) and the
Covenant Amendments Number exhibit a positive correlation with P-Covenants and P/C CovMix,
while they both show a negative correlation with C-Covenants. These results are in line with our
predictions.
Panel B of Table 5 presents results of a multivariate regression in which we explain
covenant amendment frequency controlling for a number of firm and contract characteristics. As
in Table 4, we present specifications with and without loan characteristics. The results suggest
that when P-Covenants and C-Covenants are considered in separation, P-Covenants are
significantly positive predictors of subsequent deal amendments, while C-Covenants are
negatively associated with deal amendments. Such evidence is in line with P-Covenants being
covenants are used in conjunction with each other (as conjectured in our hypothesis H2) rather than in causal effects,
we are not concerned with the simultaneity of these choices.
18
Note that Dealscan typically records as amendments those changes that require a positive vote from the required
lenders' percentage, such as changes to covenants, while those changes that require unanimous consent (e.g.,
changes to maturity or collateral) appear as new loans.
26 tripwires. In contrast, C-Covenants address agency problems in a different way; our evidence
suggests that reliance on these covenants reduces the need for frequent renegotiations. Further,
when we regress amendment frequency on both covenant types simultaneously, we find that PCovenants continue to be significant predictors of future renegotiations, while C-Covenants lose
statistical significance. Such evidence is also intuitive, as conditional on the use of tripwires
there should not be any incremental effect of capital covenants on the frequency of
renegotiations. The results continue to hold when the regressions are augmented with other loan
characteristics in Models (4) through (6). We find that the loan characteristics also exhibit
meaningful associations with amendment frequency. For example, as expected, the use of
negative covenants strongly predicts contract amendments.
A puzzle that remains to be explained is why prior studies report that violations of capital
covenants are more frequent than violations of performance covenants (Beneish and Press 1993;
Chen and Wei 1993; Sweeney 1994), as this seems to be at odds with the idea that P-Covenants
serve as tripwires. Several explanations are possible. First, the violations analyzed in these
studies represent serious breaches not cured as of the reporting date. In contrast, a typical
violation of performance covenants is not expected to be as serious and hence is expected to be
renegotiated quickly before the actual violation, in which case it may not be disclosed. Second,
dramatic changes took place in the market for syndicated lending over the last two decades and it
is not clear to what extent tripwire-type covenants were popular in the past. To shed some light
on this issue, we examine a subset of violations based on a recent sample constructed by Roberts
and Sufi (2009). We focus on 1,000 first-time covenant violations and search annual reports for
disclosure of the type of covenant being violated. In contrast to prior studies, we find that over
this more recent period, the first-disclosed covenant violation is twice as likely to be a result of a
27 P-Covenant violation than a C-Covenant violation.19 Note that these data are still likely to
understate the violation of P-Covenants as disclosed breaches are more likely to be material.20
Nevertheless, the evidence is in line with our arguments.
5.4. The contracting value of accounting information and the covenant package (H4)
In this subsection we provide evidence on H4, under which the tradeoff between capital
and performance covenants is a function of the debt contracting value of accounting information.
Specifically, tripwire covenants are expected to be costly and inefficient when accounting
information is not informative of credit quality. We start by conducting univariate analysis
(Table 6). We then study the covenant mix in a multivariate regression setting that controls for
various firm and industry characteristics (Table 7).
Performance covenants. Table 6, Panel A presents the results of univariate analysis in
which we regress performance covenants (P-Covenants) on the contracting value proxies. The
estimates on the individual proxies (CV1 through CV4) are all positive and statistically
significant. In addition, the coefficients on the proxies for timely loss recognition (TLR) and
predictability (PRED) are positive and statistically significant. These results imply that the use
of performance covenants increases with the contracting value of accounting information, in line
with H4. Such a relation is intuitive as performance covenants become more effective in
promptly transferring control to debtholders following distress when accounting information
provides a good description of credit quality.
19
In more than 50% of cases that we examined, the type of covenant in violation is not disclosed.
20
In recent years companies appear to be more likely to disclose a covenant violation voluntarily. Roberts and Sufi
(2009) find that over 1,600 companies report a covenant violation over the 1996-2005 period, whereas Sweeney
(1994) finds that only 300 firms report a violation over the longer 1977-1990 period. This pattern can potentially
help reconcile the evidence.
28 Capital covenants. Table 6, Panel B presents the results of univariate analysis in which
we regress C-Covenants on the contracting value proxies. The findings mirror the evidence for
performance covenants as CV1, CV2, CV3, and
CV4 as well as TLR and PRED exhibit
significantly negative associations with the intensity of C-Covenants. Notice that the extent to
which balance sheet (capital) ratios explain credit quality (proxied by CV3) is not positively
related to the use of capital covenants. This finding can be explained by the high positive
correlation of CV3 with CV1 and CV2. These results are in line with H4 and suggest that
companies retreat to capital covenants in situations in which accounting information does not
reflect a firm's underlying default risk.
Covenants mix. Panel C of Table 6 presents results of univariate specifications that
regress the covenants mix, P/C-CovMix, on the contracting value proxies. As expected from
previous tests, P/C-CovMix exhibits a statistically significant positive association with the
contracting value proxies, with the explanatory power of CV1 through CV4 as high as 5%.
Next, we account for potentially confounding effects on the relation between covenants
and the contracting value proxies. Table 7 presents results of a multivariate specification in
which we regress P/C-CovMix on the four CV proxies and a set of firm and industry control
variables. We find that P/C-CovMix continues to exhibit a significantly positive association with
the CV proxies, TLR, and PRED. This finding reinforces our univariate evidence, and implies
that covenant packages are affected by accounting information properties, particularly the ability
of accounting information to describe a firm's underlying credit risk.
Industry clustering robustness check. Following Ball et al. (2008), we cluster standard
errors at firm level and further by year. One may argue that standard errors should be clustered
at the industry level. The econometric argument is not clear, however, because measuring a
29 right-hand-side variable at the industry level does not in and of itself introduce nonindependence to the error term: assuming no cross-sectional dependencies at the firm level,
different firms' error terms should not be correlated when industry-level variables are measured
without error. Our analysis requires a substantial number of observations within industries to
measure industry-level proxies more precisely (see Section 4) and thus we do not report standard
errors clustered by industry. However, in untabulated robustness tests we verify the strength of
our results by (1) clustering standard errors at the industry level, and (2) performing regressions
with all variables aggregated at the industry level. The results are statistically significant across
all tests. 5.5. The contracting value of accounting information and performance pricing grids (H5)
Finally, we revisit the analysis in Ball et al. (2008), who show that the contracting value
of accounting information is positively associated with the contractual use of accounting-based
pricing grids. We advance their analysis by exploring the choice between pricing grids
formulated in terms of capital ratios (those used by capital covenants) and profitability ratios
(those used by performance covenants). Table 8 presents correlations among different pricing
grid types. We find that the indicators for rating-based pricing girds (Rating-Grids), profitability
ratio-based grids (P-Grids), and capital ratio-based grids (C-Grids) exhibit negative pairwise
correlations (note that the types of pricing grids are not mutually exclusive as contracts often
formulate pricing grids in terms of several variables). Further, we find high positive correlations
between the type of pricing grid and the type of covenants (which suggests their
complementarity; see Dichev et al. 2002).
The presence of rating-based pricing grids is
negatively correlated with all four CV proxies. This result further strengthens the validity of our
proxies because credit ratings are expected to be used when accounting information is a poor
30 summary measure of credit risk. Finally, notice that inclusion of a rating-based pricing grid
exhibits a sizable negative correlation of -0.4 with P-Covenants but not with C-Covenants, for
which the correlation is 0.05. To the extent that the use of rating-based pricing grids indicates
low contracting value of accounting, as suggested by Ball et al. (2008), this result suggests that
P-Covenants are used when accounting information is descriptive of credit quality, as predicted
by H4.
Table 9 presents results of a binomial probit regression that explains the choice of
performance pricing type.21 For brevity we limit the presentation of results to our all-in CV4
proxy. In line with Ball et al. (2008), we find that the probability of a rating-based pricing grid is
decreasing with the contracting value proxy. However, Models (2) and (3) reveal that this result
is due to an increase in the probability of profitability-based grids and not capital ratio-based
grids. This result is in line with H5, which predicts that credit ratings are mainly a substitute for
performance indicators, rather than capital ratios, when accounting information's ability to
describe credit risk declines.
5.6. Robustness test: Credit default swap spreads as a measure of credit risk
In a robustness test we use credit default swaps spreads provided by Markit as an
alternative to credit ratings to measure credit risk. Over the 2001-2009 period, Markit covered
around 1,700 U.S. companies. We were able to hand-match 1,200 of these firms to Compustat,
and a further 960 of these firms to Dealscan. Individual firm time series are generally limited to
two or three years of data, which precludes firm-level analysis. We therefore estimate the
contracting value proxies at the industry level, using the procedure analogous to that described in
21
We do not run a multinomial logit as pricing grid choices are not mutually exclusive. However, in an additional
test we exclude contracts where pricing grid is formulated in terms of more than one indicator (e.g., rating and
interest coverage); this eliminates close to 25% of the sample observations. The results are very close to those
presented here.
31 Section 3. Further requiring a minimum number of observations within an industry substantially
limits the number of available observations. Nevertheless, all of the results in the paper are
confirmed and remain statistically significant (although t-statistics are lower given the smaller
sample).
6. Conclusion
In this study we propose a simple classification of financial covenants into two distinct
groups: performance covenants and capital covenants. Performance covenants rely on measures
of profitability whereas capital covenants rely on information about sources and uses of capital,
i.e., balance sheet information.
We argue that capital covenants align incentives between
borrowers and lenders by limiting the amount of debt in the borrower's capital structure. In
contrast, performance covenants act as tripwires that transfer control to lenders when
performance deteriorates and hence incentive conflicts between shareholders and lenders become
more acute.
We document that performance covenants' and capital covenants' intensities are
negatively correlated and thus the two types of covenants are used by borrowers with different
characteristics, that is, the two types of covenants are not used interchangeably.
More
importantly, performance covenants are significant predictors of future contract amendments,
whereas capital covenants are not. This result is consistent with performance covenants acting as
tripwires. Performance covenants are also used in conjunction with negative covenants that
place direct restrictions on certain managerial actions, whereas again capital covenants are not.
This supports our argument that performance covenants align incentives between borrowers and
debtholders.
32 We also examine how the contractibility of accounting information affects the choice of
covenant mix. We find that performance covenants are preferred to capital covenants when
accounting information is a good summary measure of credit risk. This result is consistent with
our hypothesis that capital covenants are a more robust mechanism to address underlying agency
problems when accounting information gives a poor description of credit risk, and suggests that
the contracting value of accounting information has an important effect on the design of
covenant packages. Based on this finding, we further conjecture that the contracting value of
accounting information affects credit market access. In line with this conjecture, we observe that
the use of tripwire covenants is positively correlated with the level of debt. Identification of the
causal effect is, however, outside the scope of this paper and thus we leave it for future research.
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36 Appendix A: 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; and (7) Senior debt to EBITDA.
Capital-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; and (8) Net Worth requirement.
*Note that the classification of Current and Quick ratios as capital covenants is conceptually appropriate
as they require a company to maintain enough short-term assets to cover short-term liabilities, while, for
example, leverage ratio typically concerns both short and long-term assets and liabilities.
Appendix B: Estimation of CV proxies: Pooled regressions
This appendix presents parameter estimates for Equations (1) – (4) based on the pooled sample. Ratingit is
constructed by assigning “1” to companies with the highest credit rating following quarter t, “2” to companies with
the second-highest credit rating, and so on, and then taking the natural logarithm. Et-s is earnings before
extraordinary items scaled by average total assets in quarter t  s divided by average total assets over the quarter.
Coveraget-s is quarter t  s interest coverage; NetWorth is book value of shareholder's equity divided by total assets
in quarter t; LTD the ratio of long-term debt to assets in quarter t; and Tangibility is property, plant, and equipment
divided by total assets in quarter t.
(1)
(2)
(3)
(4)
VARIABLES
Rating
Rating
Rating
Rating
Et
Et-1
Et-2
Et-3
Et-4
-16.98***
(-5.739)
-18.83***
(-21.27)
-20.36***
(-21.77)
-20.37***
(-22.81)
-19.68***
(-21.35)
-0.288***
(-7.041)
-0.256***
(-11.25)
-0.327***
(-15.50)
-0.327***
(-14.90)
-0.415***
(-11.13)
Coveraget
Coveraget-1
Coveraget-2
Coveraget-3
Coveraget-4
NetWorth
Leverage
Tangibility
Constant
9.982***
11.43***
0.943**
(1.989)
10.97***
(22.01)
-3.086***
(-10.87)
6.633***
-2.106
(-0.800)
-3.886**
(-2.039)
-6.340***
(-3.807)
-7.341***
(-4.254)
-10.69***
(-4.551)
0.0737
(1.308)
-0.0707**
(-2.174)
-0.124***
(-4.507)
-0.130***
(-4.164)
-0.288***
(-6.144)
3.601***
(7.348)
10.12***
(17.62)
-3.129***
(-10.76)
7.058***
(123.3)
(68.04)
Observations
71096
60902
R-squared
0.140
0.153
Robust t-statistics are in parentheses; *** p<0.01, ** p<0.05, * p<0.10.
(22.59)
(17.38)
67303
0.267
54655
0.297
Appendix C: Variable definitions.
Levbook
= ratio of long-term debt (Compustat item TLDT) divided by total assets (Compustat item
AT).
Size
= natural logarithm of the 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 the ratio of income before extraordinary items to total assets.
Div
= Dividend yield computed as the ratio of common dividends to the market value of equity
(Compustat: DVC/(PRCC_F×CSHO)).
Z-Score
= Altman's Z-score based on the bankruptcy prediction model in Hillegeist, Keating, Cram,
and Lundstedt (2004).
Tangible
= Asset tangibility defined as the ratio of net value of property, plant, and equipment to
total assets (Compustat: PPENT/AT).
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 (all facilities included).
Secured
= An indicator variable that takes the value of one if debt is secured, and zero otherwise.
LendFreq
= Lending frequency computed as the number of loan deals a company has had over the
prior five years.
Revolver
= An indicator variable that takes the value of one if a revolving facility exists in the deal
package, and zero otherwise.
Secured
= Dummy variable that takes the value of one if debt is secured, and zero otherwise.
Missing R&D
38 DividendCov = An indicator variable that takes the value of one when a restriction on dividend payments
is included.
CapexCov
= An indicator variable that takes the value of one when a restriction on capital
expenditures is a part of a credit agreement.
Sweeps
= The number of sweep-type covenants as a proportion of the following three types: asset
sales sweep, debt issuance sweep, equity issuance sweep. Sweeps require part or all of the
cash flow from these activities to be used in reducing the amount of indebtedness.
P-Covenants = Number of profitability-based covenants (See Appendix A for classification).
C-Covenants = Number of capital-based covenants (See Appendix A for classification).
P/C-CovMix = Covenant mix: P-Covenants/(P-Covenants + C-Covenants).
Rating-Grid = Count of rating-based loan pricing grids. Types of rating-based grids include grids
formulated in terms of: commercial paper rating, subordinated debt rating, and senior debt
rating.
P-Grid
= Count of loan pricing grids based on performance (profitability) indicators. The
performance indicators include: debt service coverage ratio, fixed charge coverage ratio,
interest coverage ratio, senior debt to cash flow (EBITDA) ratio, and total debt to cash flow
(EBITDA) ratio.
C-Grid
= Count of loan pricing indicators formulated in terms of capital ratio-based indicators. The
list of capital indicators includes: leverage, debt to tangible net worth, and senior debt
leverage ratio.
CV1 to CV4 = Proxies for the contracting value of accounting information, which measure the extent to
which accounting information explains the S&P's entity-level long-term credit rating. See
Section 4 for details on their construction.
TLR
= Proxy for timely loss recognition based on Basu (1997). See Section 4 for details on its
construction.
PRED
= Proxy for predictability. See Section 4 for details on its construction.
IndStdCFO
= SIC industry-level standard deviation of cash flow from operations (see Section 3 for
industry definitions).
Amendment
= Indicator variable that takes the value of one if a contractual agreement was amended
prior to its maturity, and zero otherwise.
Covenant Amendment Number = Count of amendments to negative or financial covenants.
39 Table 1: Summary Statistics
Table 1 presents summary statistics for the variables used in the analysis in Tables 4 through 9. 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. Individual
variables sample size varies depending on data availability.
VARIABLES
Size
B/M
Levbook
Adv
Rnd
Margin
ROA
Div
Tangible
Age
Z-score
StdRet
CV1
CV2
CV3
CV4
TLR
PRED
IndStdCFO
P-Covenants
C-Covenants
P/C-CovMix
DealAmount
Maturity
LendFreq
Revolver
Secured
DividendCov
CapexCov
Sweeps
Amendment
Coven. Amendments Number
N
Mean
Std.Dev.
p25
p50
p75
12107
12054
12038
12038
12114
12061
12107
11967
11258
12117
12084
12071
10140
10140
10140
10140
12117
12117
10140
12117
12117
11985
12117
11955
12117
12117
12117
12117
12117
12117
12117
12117
6.958
0.630
0.269
0.007
0.021
1.773
0.018
0.011
0.313
2.278
0.063
0.118
0.154
0.265
0.369
0.443
0.218
0.405
0.154
1.533
1.020
0.605
18.63
43.66
2.368
0.818
0.570
0.715
0.179
2.499
0.258
0.244
1.820
0.556
0.194
0.019
0.094
0.977
0.113
0.020
0.252
1.047
0.047
0.064
0.133
0.169
0.144
0.155
0.102
0.113
0.133
0.976
0.899
0.341
1.62
22.98
2.237
0.386
0.495
0.451
0.383
1.041
0.437
0.504
5.693
0.318
0.109
0.000
0.000
1.262
0.005
0.000
0.106
1.609
0.027
0.071
0.052
0.137
0.266
0.336
0.137
0.327
0.052
1.000
0.000
0.333
17.62
26.00
1.000
1.000
0.000
0.000
0.000
3.000
0.000
0.000
6.978
0.520
0.256
0.000
0.000
1.476
0.033
0.000
0.239
2.303
0.055
0.104
0.124
0.211
0.370
0.414
0.213
0.401
0.124
2.000
1.000
0.667
18.83
39.82
2.000
1.000
1.000
1.000
0.000
3.000
0.000
0.000
8.212
0.796
0.400
0.001
0.007
1.874
0.063
0.014
0.482
3.091
0.085
0.149
0.244
0.360
0.460
0.548
0.270
0.495
0.244
2.000
2.000
1.000
19.81
60.00
3.000
1.000
1.000
1.000
0.000
3.000
1.000
0.000
Table 2: Pearson Correlation among Contracting Value Proxies and Covenants
Panel A: Correlation matrix for contracting value proxies
Panel A presents Pearson correlations among the contracting value (CV), timely loss recognition (TLR), and earnings
persistence (PRED) 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 aggregate information at the deal level. P-values are provided below the correlations.
VARIABLES
CV1
CV2
CV3
CV4
TLR
PRED
CV1
CV2
1
0.823
0.496
0.676
0.175
0.375
CV3
1
0.642
0.789
0.163
0.45
CV4
1
0.939
0.364
0.473
TLR
1
0.313
0.501
PRED
1
0.452
1
Panel B: Correlation matrix for financial covenants types
Panel B presents Pearson correlations among covenants types. Performance covenants (P-Covenants): (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; and (7) Senior debt to EBITDA. Capital covenants (C-Covenants): (1)
Quick ratio; (2) Current ratio; (3) Debt-to-equity ratio; (4) Loan-to-value ratio; (5) Debt-to-tangible net worth ratio;
(6) Leverage ratio; (7) Senior leverage ratio; (8) Net Worth; and (9) Tangible Net Worth.
VARIABLES
Any debt/ Any Interest Any liuqidity Any leverage
cash flow
coverage
ratio
ratio
Any Net
worth
PCovenants
Any debt to profitability
Any interest coverage
Any liquidity ratio
Any leverage ratio
Any Net worth
1
0.24
-0.12
-0.54
-0.14
1
-0.15
-0.21
-0.01
1
0.08
0.13
1
0.16
1
P-Covenants
C-Covenants
0.7
-0.43
0.64
-0.17
-0.18
0.52
-0.46
0.68
-0.09
0.7
CCovenants
1
-0.37
1
Panel C: Correlation of financial and non-financial (negative) covenants
Panel C presents pairwise correlations of performance and capital covenants with negative covenants. Negative
covenants are: indicator variable for dividend restrictions; indicator variable for capital expenditures restriction, and
count of cash sweeps. All correlations are statistically significant.
VARIABLES
P-Covenants
C-Covenants
Dividend Restriction
Capex Restriction
Sweeps
PCovenants
1
-0.369
0.227
0.185
0.287
CCovenants
1
-0.088
-0.190
-0.097
Dividend
Restriction
1
0.208
0.219
Capex
Restriction
1
0.12
Sweeps
1
Table 3: Pearson Correlations
Table 3 presents Pearson correlations among the no-contract-value variables used in the analysis in Tables 4 through 9. Performance covenants (P-Covenants) and capital
covenants (C-Covenants) 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 aggregate information at the deal level.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(1) Size
1.00
(2) B/M
-0.25
1.00
0.15
-0.04
(4) Adv
0.03
-0.05
-0.03
1.00
(5) R&D
-0.09
-0.08
-0.13
0.00
1.00
(6) Margin
0.03
-0.10
0.03
0.09
0.11
1.00
(7) ROA
0.23
-0.12
-0.06
-0.01
-0.32
0.05
1.00
(8) Div
0.24
0.06
0.20
-0.05
-0.09
0.00
0.07
1.00
(9) Tangible
0.10
0.03
0.28
-0.07
-0.13
0.07
0.02
0.14
1.00
(10) Age
0.30
-0.03
-0.04
0.02
-0.06
-0.08
0.10
0.16
0.06
1.00
(11) Z-score
-0.27
-0.01
-0.30
0.06
-0.07
-0.27
0.05
-0.23
-0.22
0.03
1.00
(12) StdRet
-0.40
0.18
-0.08
-0.01
0.14
0.01
-0.34
-0.31
-0.08
-0.21
0.16
1.00
(13) P-Covenants
-0.04
-0.02
0.26
0.03
-0.11
-0.02
0.05
-0.09
-0.06
-0.13
-0.01
0.02
1.00
(14) C-Covenants
-0.25
0.10
-0.16
-0.10
0.06
0.00
-0.03
0.04
0.00
-0.07
0.01
0.09
-0.37
1.00
(3) Leverage
(15)
(16)
(17)
(18)
(19)
1.00
(15) P/C-CovMix
0.09
-0.07
0.22
0.09
-0.10
-0.01
0.06
-0.07
-0.04
-0.03
0.03
-0.04
0.71
-0.81
1.00
(16) Amount
0.81
-0.12
0.29
0.03
-0.17
0.00
0.18
0.19
0.13
0.23
-0.19
-0.33
0.14
-0.33
0.24
1.00
(17) Maturity
0.07
-0.08
0.20
0.01
-0.06
-0.01
0.08
-0.10
0.08
0.00
-0.02
-0.11
0.26
-0.22
0.27
0.24
1.00
(18) LendFreq
0.41
-0.01
0.22
-0.02
-0.10
-0.03
0.07
0.14
0.10
0.14
-0.12
-0.16
0.00
-0.07
0.02
0.39
0.01
1.00
(19) Revolver
-0.15
0.00
-0.03
0.01
-0.05
-0.01
0.01
-0.07
-0.06
-0.04
0.11
0.04
0.13
-0.03
0.09
0.04
0.10
-0.08
1.00
Table 4: Determinants of Covenants and Their Mix
Table 4 presents estimates from regressions of the number of performance covenants, the number of capital covenants,
and the covenant mix on firm-specific characteristics and contract specific characteristics. Performance covenants (PCovenants) and capital covenants (C-Covenants) 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 aggregate information at the deal level. Compustat variables are truncated at
both tails using 1% cutoff values. Robust t-statistics clustered by company and year are in brackets; *** p<0.01, **
p<0.05, * p<0.10.
VARIABLES
Size
B/M
Leverage
Adv
R&D
Margin
ROA
Div
Tangible
Age
Z-score
StdRet
(1)
P-Covenants
(2)
C-Covenants
(3)
P/C-CovMix
(4)
P-Covenants
(5)
C-Covenants
(6)
P/C-CovMix
-0.0441**
[-2.287]
-0.0300
[-1.170]
1.650***
[14.95]
0.956
[1.146]
-0.725***
[-5.917]
-0.0388**
[-2.454]
0.696***
[4.722]
-8.846***
[-9.028]
-0.491***
[-7.524]
-0.113***
[-7.996]
-0.117
[-0.380]
-0.577*
[-1.946]
-0.0867***
[-6.712]
0.0475*
[1.906]
-1.048***
[-14.29]
-2.546***
[-5.512]
0.253**
[2.197]
0.0115
[0.988]
0.154
[1.626]
2.645**
[2.358]
0.188***
[3.493]
0.0550***
[4.763]
-0.901**
[-2.442]
0.439***
[2.592]
0.00453
[0.723]
-0.0221**
[-2.299]
0.530***
[22.95]
1.050***
[3.915]
-0.204***
[-3.692]
-0.0102**
[-1.968]
0.141***
[3.737]
-2.329***
[-4.921]
-0.130***
[-4.610]
-0.0328***
[-6.586]
0.472***
[3.125]
-0.143*
[-1.741]
1.105***
[4.325]
1.978***
[7.152]
0.315**
[2.024]
-0.119***
[-8.171]
-0.0930***
[-3.316]
0.847***
[9.010]
0.975
[1.416]
-0.452***
[-4.603]
-0.0337***
[-2.635]
0.656***
[4.884]
-5.312***
[-7.658]
-0.429***
[-7.454]
-0.0895***
[-6.927]
-0.615**
[-2.119]
-0.716***
[-2.841]
0.117***
[7.218]
0.00664***
[8.945]
-0.00973*
[-1.854]
0.134***
[4.852]
0.118***
[3.382]
0.219***
[7.558]
0.0496*
[1.863]
0.0877***
[6.514]
-0.677**
[-2.395]
-0.0326**
[-2.053]
0.0828***
[2.949]
-0.616***
[-7.233]
-2.223***
[-4.979]
0.168*
[1.774]
0.00345
[0.299]
0.209**
[2.317]
1.109
[1.061]
0.152***
[3.195]
0.0463***
[3.793]
-0.609*
[-1.762]
0.493***
[2.921]
-0.0898***
[-5.203]
-0.00403***
[-7.914]
0.0176***
[2.976]
-0.0223
[-0.870]
-0.0348
[-1.333]
0.0560
[1.416]
-0.191***
[-8.936]
-0.0431***
[-6.654]
3.142***
[11.62]
-0.0229***
[-3.311]
-0.0413***
[-3.958]
0.296***
[11.87]
1.000***
[3.895]
-0.147***
[-2.952]
-0.00790*
[-1.779]
0.120***
[3.620]
-1.400***
[-3.423]
-0.116***
[-4.576]
-0.0266***
[-5.610]
0.315**
[2.356]
-0.156*
[-1.911]
0.0437***
[6.573]
0.00208***
[11.12]
-0.00579**
[-2.145]
0.0221
[1.514]
0.0284***
[2.637]
0.0335**
[2.360]
0.0392***
[4.386]
0.0212***
[7.427]
-0.291**
[-2.328]
Amount
Maturity
LendFreq
Revolver
Secured
DividendCov
CapexCov
Sweeps
Constant
Year Dummies
Observations
R2
Yes
Yes
Yes
Yes
Yes
Yes
10,827
0.166
10,827
0.245
10,709
0.194
10,675
0.299
10,675
0.293
10,557
0.277
44 Table 5: Contract Amendments and Performance vs. Capital Covenants
Panel A: Pairwise correlations between covenant types and amendment indicators
Panel A presents correlations between covenant types and contract amendments. Amendment indicator signals the
presence of at least one contract amendment on Dealscan. We also count the number of amendments in specific lending
agreements on Dealscan; to maximize the likelihood that amendments are related to covenant renegotiation, we search
for the terms "covenant", "definition", or "provision" in the amendment description field and exclude amendments that
do not contain any of these terms. Performance covenants (P-Covenants) and capital covenants (C-Covenants) are
defined in Appendix A. * indicates statistical significance at 1% level.
VARIABLES
PCovenants
0.15*
0.15*
Amendment Indicator
Covenant Amendments Number
CCovenants
-0.14*
-0.107*
P/C CovMix
0.153*
0.126*
Panel B: Covenant amendments and performance vs. capital covenants
Panel B presents estimates from regressions of a proxy for the number of covenant amendments on performance and
capital covenants and other control variables. To construct a proxy for amendments, we count the number of
amendments in specific lending agreements on Dealscan; to maximize the likelihood that amendments are related to
covenant renegotiation, we search for the terms "covenant", "definition", or "provision" in the amendment description
field and exclude amendments that do not contain any of these terms. Performance covenants (P-Covenants) and capital
covenants (C-Covenants) 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 aggregate information at the deal level. Compustat variables are truncated at both tails using 1%
cutoff values. Robust t-statistics clustered by company and year are in brackets; *** p<0.01, ** p<0.05, * p<0.10.
VARIABLES
(1)
P-Covenants
0.0614***
[5.855]
C-Covenants
Size
B/M
Leverage
Adv
R&D
Margin
ROA
Div
Tangible
Age
Z-score
-0.00923
[-1.383]
0.0433***
[3.064]
0.146***
[3.164]
-0.308
[-1.026]
-0.0774*
[-1.889]
-0.00601
[-1.101]
-0.0795
[-1.214]
-0.853***
[-2.900]
-0.0117
[-0.766]
0.00439
[1.055]
0.359***
[2.916]
(2)
(3)
(4)
0.0265***
[2.996]
-0.0352***
[-5.647]
-0.0150**
[-2.126]
0.0432***
[3.162]
0.210***
[4.299]
-0.338
[-1.037]
-0.113**
[-2.441]
-0.00799
[-1.313]
-0.0313
[-0.468]
-1.304***
[-4.213]
-0.0352**
[-2.215]
-0.000627
[-0.133]
0.320***
[2.583]
0.0579***
[5.096]
-0.0112
[-1.605]
-0.0104
[-1.551]
0.0437***
[3.075]
0.140***
[3.115]
-0.333
[-1.104]
-0.0771*
[-1.887]
-0.00602
[-1.092]
-0.0753
[-1.169]
-0.855***
[-2.873]
-0.0113
[-0.747]
0.00461
[1.103]
0.349***
[2.864]
-0.0294***
[-4.489]
0.0248*
[1.734]
0.00494
[0.125]
-0.325
[-1.057]
-0.0154
[-0.405]
-0.00475
[-0.947]
-0.0454
[-0.643]
-0.156
[-0.570]
-0.0103
[-0.708]
0.00472
[1.062]
0.115
[1.026]
(5)
(6)
-0.00681
[-0.946]
-0.0328***
[-5.163]
0.0229
[1.595]
0.0232
[0.617]
-0.314
[-0.990]
-0.0262
[-0.679]
-0.00562
[-1.068]
-0.0266
[-0.372]
-0.289
[-1.079]
-0.0207
[-1.343]
0.00266
[0.558]
0.0942
[0.841]
0.0271***
[2.845]
0.00211
[0.267]
-0.0293***
[-4.542]
0.0247*
[1.695]
0.00575
[0.150]
-0.321
[-1.040]
-0.0155
[-0.408]
-0.00474
[-0.951]
-0.0462
[-0.665]
-0.155
[-0.566]
-0.0104
[-0.714]
0.00468
[1.049]
0.116
[1.051]
StdRet
0.592***
[4.750]
0.572***
[4.753]
0.595***
[4.807]
-0.0247
[-0.423]
0.457***
[4.095]
0.0384***
[5.142]
0.00109***
[4.172]
0.00611**
[2.504]
0.103***
[6.807]
0.0499***
[4.229]
0.0442***
[3.082]
0.129***
[3.394]
0.00270
[0.771]
-0.702***
[-5.979]
0.441***
[4.072]
0.0409***
[5.173]
0.00124***
[4.204]
0.00597**
[2.442]
0.106***
[6.710]
0.0528***
[4.419]
0.0504***
[3.531]
0.129***
[3.407]
0.00473
[1.199]
-0.699***
[-5.335]
0.456***
[4.095]
0.0385***
[5.020]
0.00109***
[4.138]
0.00608**
[2.525]
0.103***
[6.819]
0.0499***
[4.236]
0.0440***
[3.077]
0.130***
[3.450]
0.00274
[0.762]
-0.708***
[-5.502]
-0.0507
[-0.906]
0.0868
[1.599]
Yes
Yes
Yes
Yes
Yes
Yes
10,827
0.083
10,827
0.074
10,827
0.083
10,675
0.116
10,675
0.114
10,675
0.116
Amount
Maturity
Secured
LendFreq
Revolver
DividendCov
CapexCov
Sweeps
Constant
Year Dummies
Observations
R2
46 Table 6: Contracting Value and Covenants
Table 6 presents estimates from univariate regressions of the number of performance covenants on contracting value
(CV), timely loss recognition (TLR), and earnings predicatbility (PRED) variables. Performance Covenants (PCovenants) and Capital Covenants (C-Covenants) are defined in Appendix A. P/C-CovMix is the number of PCovenants over the total number of covenants (P/C-CovMix =P-Covenants/(P-Covenants + C-Covenants)). The 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 aggregate information at
the deal level. Robust t-statistics clustered by company and year are in brackets; *** p<0.01, ** p<0.05, * p<0.10.
Covenant
Types
(1)
CV1
(2)
CV2
(3)
CV3
(4)
CV4
(5)
TLR
(6)
PRED
Panel A: Univariate analysis of performance covenants: P -Covenants     * CV  
Slope coef.
t-stat
No. obs.
R2
1.249***
(9.590)
10,140
0.029
1.000***
(10.55)
10,140
0.030
1.268***
(10.86)
10,140
0.035
1.361***
(12.87)
10,140
0.046
1.147***
(3.941)
12,117
0.014
1.171***
(7.432)
12,117
0.019
Panel B: Univariate analysis of capital covenants: C -Covenants     * CV  
Slope coef.
t-stat
No. obs.
R2
-1.381***
(-8.881)
10,140
0.042
-0.972***
(-6.467)
10,140
0.033
-0.702***
(-6.160)
10,140
0.013
-0.840***
(-7.863)
10,140
0.021
-0.613***
(-2.281)
12,117
0.005
-0.904***
(-6.604)
12,117
0.013
0.444***
(3.287)
11,985
0.018
0.516***
(7.056)
11,985
0.030
Panel C: Univariate analysis of covenant mix: P /C -Mix     * CV  
Slope coef.
t-stat
No. obs.
R2
0.545***
(11.09)
10,031
0.045
0.453***
(8.615)
10,031
0.050
0.442***
(8.406)
10,031
0.034
0.497***
(10.13)
10,031
0.050
Table 7: Covenant Mix and Contracting Value Proxies: Controlling for Firm and Contract
Characteristics
Table 7 presents estimates from regressions of the covenant mix on contracting value (CV), timely loss recognition
(TLR), and earnings persistence (PRED) variables as well as control variables. The covenant mix is defined as PCovenants/(P-Covenants + C-Covenants), where P-Covenants and C-Covenants are performance and capital
covenants, respectively, as defined in Appendix A. The CV, TLR, and PRED 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 aggregate information at
the deal level. Compustat variables are truncated at both tails using 1% cutoff values. Robust t-statistics clustered by
company and year are in brackets; *** p<0.01, ** p<0.05, * p<0.10.
VARIABLES
CV1
(1)
P/C-CovMix
(2)
P/C-CovMix
(3)
P/C-CovMix
(4)
P/C-CovMix
(5)
P/C-CovMix
0.398***
(10.46)
CV2
0.311***
(8.729)
CV3
0.287***
(7.891)
CV4
0.324***
(9.966)
TLR
0.453***
(6.223)
PRED
Size
B/M
Leverage
Adv
R&D
Margin
ROA
Div
Tangible
Age
Z-score
StdRet
IndStdCFO
Amount
Maturity
(6)
P/C-CovMix
-0.0180**
(-2.498)
-0.0276**
(-2.442)
0.285***
(10.73)
0.653**
(2.499)
-0.234***
(-3.811)
-0.0105**
(-2.285)
0.140***
(3.320)
-0.958**
(-2.364)
-0.0967***
(-4.132)
-0.0263***
(-5.111)
0.280*
(1.716)
-0.173*
(-1.884)
0.699***
(3.161)
0.0413***
(5.929)
0.00187***
(8.301)
-0.0117*
(-1.680)
-0.0251**
(-2.245)
0.290***
(10.69)
0.798***
(3.101)
-0.226***
(-3.580)
-0.0104**
(-2.248)
0.122***
(2.878)
-0.945**
(-2.378)
-0.0872***
(-3.607)
-0.0289***
(-5.544)
0.325**
(2.042)
-0.217**
(-2.324)
0.706***
(3.321)
0.0366***
(5.321)
0.00184***
(8.004)
-0.0138*
(-1.893)
-0.0252**
(-2.300)
0.309***
(12.01)
1.030***
(3.805)
-0.225***
(-3.593)
-0.0125***
(-2.721)
0.125***
(2.907)
-1.083***
(-2.671)
-0.0980***
(-4.092)
-0.0282***
(-5.268)
0.426***
(2.597)
-0.232**
(-2.439)
0.440*
(1.870)
0.0383***
(5.420)
0.00193***
(8.366)
-0.0131*
(-1.841)
-0.0266**
(-2.413)
0.290***
(11.03)
0.906***
(3.398)
-0.231***
(-3.731)
-0.0109**
(-2.379)
0.118***
(2.765)
-1.020**
(-2.504)
-0.0982***
(-4.122)
-0.0282***
(-5.346)
0.380**
(2.345)
-0.201**
(-2.134)
0.443**
(1.965)
0.0379***
(5.412)
0.00187***
(8.246)
-0.0149**
(-2.232)
-0.0250**
(-2.549)
0.312***
(13.48)
0.915***
(3.821)
-0.222***
(-3.765)
-0.0167***
(-4.190)
0.129***
(3.716)
-1.154***
(-3.045)
-0.0857***
(-3.741)
-0.0259***
(-5.544)
0.354**
(2.399)
-0.169**
(-1.981)
0.0244
(0.143)
0.0407***
(5.849)
0.00202***
(10.51)
0.341***
(7.951)
-0.0152**
(-2.310)
-0.0271***
(-2.737)
0.297***
(12.17)
0.752***
(3.149)
-0.250***
(-3.958)
-0.0133***
(-3.362)
0.110***
(3.082)
-1.094***
(-2.859)
-0.0762***
(-3.352)
-0.0286***
(-6.117)
0.272*
(1.932)
-0.177*
(-1.959)
0.513***
(2.831)
0.0407***
(5.935)
0.00194***
(10.27)
Secured
LendFreq
Revolver
DividendCov
CapexCov
Sweeps
Constant
Year Dummies
Observations
R2
-0.00494*
(-1.695)
0.0171
(1.134)
0.0339***
(2.972)
0.0323**
(2.131)
0.0367***
(3.867)
0.0197***
(6.987)
-0.417***
(-2.617)
-0.00521*
(-1.783)
0.0169
(1.210)
0.0350***
(3.066)
0.0333**
(2.171)
0.0362***
(3.714)
0.0199***
(7.015)
-0.376***
(-2.580)
-0.00551*
(-1.927)
0.0148
(1.070)
0.0329***
(2.786)
0.0322**
(2.108)
0.0377***
(3.868)
0.0201***
(6.753)
-0.386**
(-2.483)
-0.00556*
(-1.915)
0.0146
(1.057)
0.0335***
(2.893)
0.0348**
(2.269)
0.0366***
(3.767)
0.0199***
(6.810)
-0.418***
(-2.701)
-0.00616**
(-2.317)
0.0226
(1.568)
0.0336***
(3.151)
0.0353**
(2.560)
0.0373***
(4.033)
0.0205***
(7.622)
-0.432***
(-3.393)
-0.00554**
(-2.056)
0.0216
(1.534)
0.0339***
(3.338)
0.0398***
(2.903)
0.0334***
(3.480)
0.0202***
(7.783)
-0.499***
(-3.857)
Yes
Yes
Yes
Yes
Yes
Yes
8,708
0.306
8,708
0.306
8,708
0.298
8,708
0.304
10,557
0.293
10,557
0.296
49 Table 8: Correlation Matrix for Performance Pricing Types, Covenant Types, and
Contracting Value Proxies
Table 8 presents Pearson correlations among performance pricing grid types, covenant types, and contracting value
proxies. Rating-Grid, P-Grid, and C-Grid are rating-, profitability-, and capital-based performance pricing grids,
respectively, and are defined in Appendix C. P-Covenants and C-Covenants are performance and capital covenants,
respectively, and are defined in Appendix A. The CV 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 aggregate information at the deal
level.
P-Covenants
C-Covenants
Rating-Grid
P-Grid
C-Grid
CV1
CV2
CV3
CV4
P-Covenants
C-Covenants
Rating-Grid
P-Grid
C-Grid
1
-0.369
-0.393
0.542
-0.151
1
0.05
-0.223
0.328
1
-0.656
-0.176
1
-0.28
1
0.170
0.173
0.187
0.215
-0.204
-0.182
-0.112
-0.144
-0.102
-0.161
-0.157
-0.150
0.258
0.273
0.211
0.244
-0.108
-0.079
-0.046
-0.046
Table 9: Rating-based Performance Pricing and Contracting Value Proxies: Multivariate
Analysis
Table 9 presents estimates from regressions of pricing grid types on the all-in contracting value proxy (CV4) and
control variables. Rating-Grid, P-Grid, and C-Grid are rating-, profitability-, and capital-based performance pricing
grids, respectively, and are defined in Appendix C. The CV4 proxy is 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 aggregate information at the deal
level. Compustat variables are truncated at both tails using 1% cutoff values. Robust t-statistics clustered by
company and year are in brackets; *** p<0.01, ** p<0.05, * p<0.10.
(1)
Rating-Grid
(2)
P-Grid
(3)
C-Grid
CV4
-0.747***
(-3.629)
1.362***
(6.510)
-0.347
(-1.304)
Size
0.696***
(9.488)
0.363***
(4.371)
0.493*
(1.883)
-4.441**
(-2.028)
-0.806
(-1.157)
-0.0115
(-0.269)
-0.344
(-0.572)
9.188***
(3.412)
0.00992
(0.0734)
0.146***
(3.828)
0.902
(0.947)
-4.032***
(-5.815)
0.0830
(1.630)
-0.00820***
(-3.350)
0.0162
(0.985)
-0.257***
(-3.449)
-0.881***
(-9.998)
-0.267***
(-4.104)
0.0125
(0.104)
-0.248***
(-5.179)
-0.212***
(-3.412)
0.696***
(3.552)
2.974*
(1.766)
1.470***
(2.772)
-0.146***
(-4.134)
2.244***
(4.276)
-8.782***
(-4.108)
-0.841***
(-6.632)
-0.134***
(-4.498)
0.00829
(0.0123)
-0.0222
(-0.0371)
-0.0689
(-1.563)
0.0113***
(6.613)
-0.0375***
(-3.159)
0.295***
(4.658)
0.304***
(5.588)
0.256***
(4.588)
0.147**
(2.040)
-0.109**
(-2.430)
0.176***
(2.638)
-0.286
(-1.087)
-3.634*
(-1.691)
1.224*
(1.810)
-0.149***
(-2.797)
1.585***
(2.646)
0.634
(0.267)
-0.145
(-0.613)
0.0192
(0.893)
-0.0513
(-0.0562)
0.988
(1.451)
-0.0491
(-0.933)
0.00185
(0.632)
0.0336**
(2.257)
-0.0672
(-0.775)
-0.242***
(-3.984)
0.0189
(0.237)
-0.247***
(-3.005)
VARIABLES
B/M
Leverage
Adv
R&D
Margin
ROA
Div
Tangible
Age
Z-score
StdRet
Security
Amount
Maturity
LendFreq
Revolver
DividendCov
CapexCov
51 Sweeps
Constant
Year Dummies
Observations
-0.0207
(-1.402)
-6.863***
(-10.86)
0.102***
(5.744)
2.675***
(4.417)
-0.0618***
(-4.487)
0.00138
(0.00155)
Yes
Yes
Yes
6,193
6,193
6,193
52