Accounting-based covenants and credit market access

Hans B. Christensen and Valeri V. Nikolaev

The University of Chicago

Booth School of Business

5807 South Woodlawn Avenue

Chicago, IL 60637

Abstract: We study how the use of accounting information in private credit agreements affects

credit market access. We measure contracting value of accounting information by its ability to

explain credit risk and examine how the contracting value influences contractual use of

accounting benchmarks (covenants). We argue that profitability-based covenants, which control

borrowing by reference to profitability ratios, are employed when accounting information is

relatively informative of credit quality. In contrast, capital structure covenants, which limit

future borrowing by reference to shareholders‟ capital, are expected to be a more robust

mechanism to control debt-related agency problems when accounting information has lower

contracting value. Our results support these conjectures. Further, we use contracting value

proxies as instruments to study the effect of accounting use on the debt levels. We find that

profitability covenants promote while capital structure covenants limit the use of debt. Our

evidence indicates that accounting used in contracting has a large economic effect on credit

market access.

Keywords: accounting-based covenants, private debt, contracting value of accounting

JEL Classification: M40

Preliminary draft, comments are welcome.

This version: January 2010

We thank Phil Berger, Laurence van Lent, Douglas Skinner, and workshop participants at the University of

Chicago for helpful comments. Financial support from the University of Chicago Booth School of Business is

gratefully acknowledged.

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1. Introduction

A fundamental question in accounting is whether the reliance on accounting information

in debt contracts facilitates firms‟ access to (or use of) debt financing. The widespread and

continued use of accounting data in credit agreements (e.g., Leftwich 1983, Dichev and Skinner

2002) is testimony to the usefulness of accounting information in reducing contracting frictions

and improving contracting efficiency (Watts and Zimmerman 1986). The focus in much of prior

empirical research, however, is on testing whether accounting choice aims at avoiding covenant

violations (see Fields et al. 2001, for review). While the scope for manipulations reduces the

usefulness of accounting for contracting, there is little, if any, direct evidence on the role of

accounting information in creating (or deterring) firms‟ access to credit markets. In particular, to

what extent would lenders be willing to provide financing to companies if the use of accounting

ratios was not an option or if accounting information had little use in describing credit risk?

There are several channels through which the use of accounting ratios in debt contracts

facilitates access to debt markets. First, accounting-based covenants reduce the agency costs of

debt (Jensen and Meckling 1976; Smith and Warner 1979), which make borrowing cheaper and

thus can lead to more lending in equilibrium. Second, accounting-based covenants can alleviate

credit rationing, which arises in loan markets with imperfect information (Jaffee and Russell

1976; Stiglitz and Weiss 1981). Due to adverse selection and moral hazard problems, some

borrowers may not be able to borrow even if they are willing to pay higher than equilibrium

interest, while others may not afford debt financing. Contractual requirements to maintain

accounting ratios at certain levels helps banks in ex ante screening borrowers and thus can

reduce the amount of credit rationing. Third, accounting covenants can also serve as tripwires

that give lenders an option to renegotiate in response to deteriorating economic conditions

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(Berlin and Mester 1992). Their use can alleviate „hold up‟ problems associated with short-term

debt (Sharpe 1990; Rajan 1992) and improve lenders‟ incentives to monitor the loan (Rajan and

Winton 1995), thus making debt financing more accessible.

The theory that explains the use of covenants, however, commonly assumes that reliable

accounting information is available for contracting and portrays the underlying credit risk.

Deviations from these assumptions are rarely analyzed in the literature. The literature does not

consider how deviations from these assumptions can affect debt contract design, specifically,

ways in which companies contract on accounting information, and different channels, i.e., types

of accounting variables used in contracting. This raises two empirical questions: How does the

ability of accounting information to measure credit risk affect debt contracts‟ reliance on

accounting benchmarks? And, to what extent does the use of accounting ratios or benchmarks in

debt contracts affects the amount of debt raised by firms?

The answers to these questions are not obvious. On one hand, when accounting provides

contractible information that reflects credit quality, lenders should be more willing to contract.

We are interested in understanding to what extent this is the case. On the other hand, the role of

accounting covenants could mainly be not in promoting the ex ante access to debt but in limiting

its ex post levels. For example, lenders may be willing to lend without covenants, however, they

will charge a lower risk premium if a company voluntarily restricts future borrowing via

covenants. In this case, the function of covenants is in reducing the borrowing cost via

restricting credit market access. In this study, we attempt to address the questions stated above

by performing two sets of analyses. We first examine how the degree to which accounting

information reflects credit quality affects the choice of covenants. Second, we examine how the

3

use of covenants in credit agreements is related to the level of leverage within an instrumental

variable framework.

We argue that in relation to controlling the level of debt, accounting benchmarks

(covenants) divide into two groups. The first group we refer to as “capital structure covenants”.

These covenants only rely on balance sheet information and effectively control the ratio of debt

to equity. The second group is “profitability covenants” which always rely on income statement

information that in some cases is scaled by a balance sheet variable (e.g., minimum ratio of

earnings to interest expense or debt).

While both covenant types limit debt-related agency problems, we argue that they

accomplish this in different ways. Capital structure covenants directly restrict the mix of debt

and equity and thus ensure that shareholders‟ have enough money inside the company. This

implies that shareholders‟ wealth is sensitive to adverse managerial actions (e.g., asset

substitution, overinvestment, or underinvestment in positive NPV projects), which ensures that

shareholders, besides lenders, have incentives to monitor the management. This is achieved

without direct reference to profitability (which for instance could be zero or negative in some

periods) and a firm can usually avoid violation of these covenants by raising additional equity or

cutting back on dividends. In contrast, we argue that profitability covenants limit leverage

indirectly by becoming binding upon earnings or cash flow deteriorations and giving lenders an

option to accelerate the loan or limit the commitment amount. Therefore, we expect profitability

covenants to fulfill the purpose of tripwires and further improve banks‟ incentives to monitor the

loan (Rajan and Winton 1995). A clear distinction between capital structure and profitability

covenants is that the latter generally can detect deteriorations in credit quality sooner than capital

structure covenants because profitability covenants require a decrease in earnings to become

4

binding whereas capital structure covenants generally require a loss or even a series of losses.

Thus, the lenders using profitability covenants are expected to be more involved in monitoring

managerial actions.

We hypothesize that profitability covenants are effective only when earnings are

informative of credit quality as otherwise their function as tripwires is ineffective (the lenders

will have difficulty exercising control in bad states of the world). However, capital structure

covenants are expected to be a more robust mechanism to limit debt-related agency problems

when accounting earnings is a poor predictor of a firm‟s credit worthiness by giving shareholders

incentives to monitor managerial actions. In turn, capital structure covenants are expected to

exercise stricter control over the use of debt, while profitability covenants are expected to

postpone restrictions on debt until economic performance deteriorates.

We use Standard & Poor‟s long-term credit ratings to estimate the contracting value of

accounting, i.e., its ability to portray the underlying credit risk. Credit ratings are forward-

looking proxies for credit risk that capture the ability and willingness of a corporation to meet its

financial obligations. We follow Ball, Bushman and Vasvari (2008) and measure the contracting

value of accounting information at industry level.1 While our approach is similar to that in Ball

et al., it has several important distinctions (discussed in Section 3.2 in more detail). Intuitively,

our proxies measure the inherent degree to which accounting ratios are a sufficient statistic in

describing the credit risk within industries. Thus, contracting value proxies are defined as R-

squareds from industry level regressions of credit ratings on accounting variables commonly

used in contracts. We use Dealscan as the source of information on private lending agreements

1 These measures are subsequently used in firm-level regressions as explanatory variables of instruments. Data

limitations generally preclude measuring contracting value at firm-level. Nevertheless, measurement at industry

level fits well with our analysis. While the use of covenants can differ with firm‟s characteristics, lenders are likely

to rely at their industry experience to assess the ability of accounting information to capture credit quality.

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and limit to contracts for which the covenants information is available. We measure firms‟

reliance on accounting information in the contracting process by the number of accounting-based

ratios (covenant benchmarks of the two types) employed in lending agreements.

We document that profitability and capital structure covenants act as substitutes: they are

negatively correlated and exhibit associations of opposite sign with most firm/contract

characteristics. The results imply that the two types of covenants are used in different situations.

A direct implication for future research is that pooling covenants of these types together to form

a single covenant index is not innocuous. More importantly, we find that the use of profitability

covenants is increasing, while the use of capital structure covenants is decreasing, in the debt

contracting value of accounting information. The evidence suggests that profitability covenants

are chosen over capital structure covenants when accounting does well in describing firm‟s credit

quality, in line with our predictions. The documented effects hold when we control for

investment opportunities set (Skinner 1992) and a number of commonly used determinants of

covenant use.

Our second set of results shows that profitability covenants exhibit strong positive

association with the level of long-term debt following debt issuance whereas the opposite is true

for capital structure covenants. One accounting-based covenant on average is associated with

0.04 higher leverage. The result holds despite the presence of a comprehensive set of

determinants of leverage used in the literature. Consistent with this result, we find that the

fraction of profitability covenants is positively associated with leverage, controlling for the total

number of covenants. Endogeniety in the relation between covenants and the level of debt is an

important issue that complicates the interpretation of our results (we discuss this issue more

extensively in Sections 3.1 and 6). To what extent can the results be attributed to the use of

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accounting benchmarks per se, rather than to credit risk increasing in leverage and hence the

need for additional covenants? To address this issue we instrument for covenants using our

proxies for the debt contracting value of accounting information. The reliance on covenants is

expected to shift with the contracting value of accounting information. At the same time, unless

via the use of covenants, the contracting value of information should not have a direct effect on

leverage (supply of debt) and thus the exclusion restriction can be applied (we also use an

alternative set of instruments computed by looking at the use of covenants by other companies

with the same lead arranger, see section 6.3). The results of this analysis suggest that contractual

use of accounting information affects the supply of debt financing and that the effect is due to the

use of profitability covenants. The economic magnitude of these results is substantial. As we

discuss later in the paper, we are able to identify the effect of covenant mix on the level of debt.

Contracts with 100% use of profitability covenants exhibit 0.21 higher level of leverage as

compared to 100% balance sheet covenants. Because our study is conditional on covenants

being used, more research is needed to understand the ex ante effect of covenant inclusion on the

supply of credit.

Overall, the results imply that credit agreements can either use capital structure covenants

that ex ante limit debt market access or, when accounting provides a good description of credit

quality, opt for profitability covenants that postpone the point at which financing is restricted to a

future adverse event. Indeed a recent study by Roberts and Sufi (2009) documents significant

decline in debt use/issuance following covenant violations. Our results complement theirs and

indicate that profitability covenants allow companies to borrow more ex ante. Overall the results

are consistent with accounting having a large effect on debt market access.

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The remainder of our paper is structured as follows: Section 2 discusses the roles of

accounting in credit agreements and develops hypotheses; Section 3 explains our empirical

strategy and the research design; Section 4 outlines the sample selection method and provides

descriptive statistics; Section 5 presents the results on covenant choices and the contracting value

of accounting; Section 6 examines accounting-based covenants affect on credit market access;

and Section 7 concludes our study.

2. Background and hypotheses

In the presence of agency and/or information problems between borrowers and lenders,

firms may not be able to raise the level of debt they would prefer to raise in a frictionless market.

Agency problems can lead to asset substitution (Jensen and Meckling 1976), debt overhang

(Myers 1977), or claim dilution problems (Smith and Warner 1979). These frictions become

more severe as leverage increases and, therefore, can make lending less attractive (or

substantially increase its costs), i.e., restrict credit market access. Additionally, the presence of

information asymmetry in the lending market opens up the scope for adverse selection and moral

hazard problems and thus can lead to credit rationing (Stiglitz and Weiss 1981), which arises

when firm‟s credit quality cannot be easily evaluated by the lender while increasing borrowing

costs attract more risky companies. Thus, irrespective the amount of interest a borrower is

willing to pay, banks may not be willing to lend funds to some borrowers. Covenants are a

contracting mechanism that limits both agency costs and adverse selection and thus is expected

to influence the supply of debt capital ceteris paribus. Reliance on covenants reduces the agency

problems because they limit managers‟ ability to take opportunistic actions that hurt debtholders

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(e.g., Smith and Warner 1979). In addition covenants may serve as a signaling/screening device

that helps lenders learn information about the borrower, which in turn alleviates credit rationing.

A key role of accounting in debt markets is to provide contractible information used to

detect financial distress and contractually transfer control rights to debtholders at times when the

value of their stakes is at risk.2 Depending on the industry a company operates in (i.e., type of

business, informational environment, growth opportunities, nature of assets and liabilities, etc.)

the extent to which accounting information reflects the credit quality is expected to vary. In turn,

while the use of covenants is determined by the investment opportunities set (Skinner 1992), we

also expect the reliance on types of covenants (as a mechanism that limits contracting frictions)

to vary with the contracting value of accounting information. Furthermore, covenants‟ inability

to control the agency problems when the contracting value of accounting information is poor is

expected to affect the supply of debt.

2.1. Capital structure versus profitability covenants

We classify covenants into two categories: capital structure covenants and profitability

covenants.3 Capital structure covenants typically restrict the maximum portion of debt (or

minimum amount of equity), for instance, by requiring a leverage ratio or net worth maintained.

Shareholders may contribute additional equity capital or cut back on dividends to meet the

capital structure covenants. In contrast, profitability covenants place no direct restrictions on

capital but require a minimum level of earnings-to-interest (or earnings-to-debt) and are more

difficult to relax by additional equity contributions or dividend cuts.

2 Other roles include providing information about liquidity, solvency, recovery of collateral, and other attributes that

help measuring credit risk.

3 Ratios based on balance sheet and income statement information (e.g., debt-to-cash flow) are classified as

profitability covenants. Thus we refrain using balance sheet vs. income statement classification.

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We argue that, while both groups of covenants limit agency problems, they accomplish

this in different ways. Capital structure covenants effectively force shareholders to participate

with a minimum fraction of their own capital in projects the firm invests in. This ensures that

shareholders' wealth is sensitive to actions that decrease firm value. This also encourages

shareholders to monitor managements' actions and aligns interests of shareholders with those of

debtholders. In other words, capital structure covenants reduce agency problems by requiring a

higher level of equity than shareholders may otherwise prefer. In contrast, provided a firm is

sufficiently profitable, profitability covenants do not directly require shareholders to maintain

any minimum level of shareholders' equity. Instead, profitability covenants transfer control

rights to lenders when the firm performs poorly and, consequently, agency conflicts become

more severe. By transferring control rights upon deteriorations in credit quality, profitability

covenants can limit the increased potential for debtholders‟ value expropriation, which is a rather

different way to deal with agency costs.

Another distinction between the two types of covenants that we identify is the timeliness

of control transfers. Capital structure covenants are based on the cumulative amount of

profitability plus shareholders' net contribution of capital. Profitability covenants, however, are

functions of current earnings only. This generally means that, for capital structure covenants to

become binding, a company needs a loss or even a series of losses. In contrast, profitability

covenants will generally become binding in the period when a company experiences a substantial

decline in profitability (which need not be a loss). This suggests that profitability covenants are

likely to detect economic distress and transfer decision rights to debtholders earlier than capital

structure covenants.

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Finally, profitability covenants require that accounting information reflects firm‟s credit

quality. If this is not the case, profitability covenants will be ineffective at limiting agency

problems because they will not transfer control rights to lenders when agency conflicts become

more severe. Capital structure covenants do not have as strong of a requirement for accounting

information to explain credit quality. In part this is because capital structure covenants reduce

agency costs by aligning shareholders‟ and debtholders‟ interests rather than by identifying the

timing of credit quality changes. And, in part, because accounting noise components in earnings

reverse over time and, therefore, have little long-term impact on the cumulative measures that

capital structure covenants rely on.

The above arguments imply that, when accounting information portrays credit quality

reasonably well, profitability covenants can detect deteriorations in credit quality before capital

structure covenants and thus will be used as a contracting mechanism. Early detection of credit

quality deterioration is important because shareholders have stronger incentives to expropriate

debtholders' wealth when a company moves closer to default. However, when contracting value

of accounting information is relatively poor, reliance on profitability covenants is likely to be

ineffective and costly and thus we predict lenders to resort to the less flexible but arguably more

robust mechanism to control the agency problems – capital structure covenants. Thus, our first

hypothesis is:

H1: The number of profitability (capital structure) covenants in credit agreements is

positively (negatively) associated with the extent accounting earnings‟ reflect credit

quality.

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2.2. Covenants and credit market access

It is often argued in the capital structure literature that firms appear to be underleveraged

and thus to forego an opportunity to increase firm value by exploiting the tax advantage of debt

financing (Graham 2000). Limited access to credit markets has been argued as one explanation

for this result (Faulkender and Petersen 2006). Frictions that arise in credit markets, such as

agency and/or information problems (that give rise to capital rationing or agency costs), are

likely to be key in explaining the limited use of debt financing. The contractual reliance on

accounting information serves the purpose of reducing these market frictions, and thus promotes

the access to debt financing. Hence we hypothesize:

H2: The use of accounting ratios in debt contracts increases access to debt financing.

It is not obvious, however, that accounting increases the use of debt financing. At loan

origination, provided that accounting is suitable for contracting purposes, lenders may be willing

to lend more when covenants are used. However, the very purpose of many covenants is

ultimately to limit the use of debt. Limitations on indebtedness is the mechanism that controls

the contracting frictions discussed earlier. Thus, empirically, one can find either a positive or a

negative association between leverage and covenants. Which of the two effects dominates in

practice is an empirical question and likely depends on the type of covenants at hand. Because

capital structure covenants reduce agency costs by limiting debt relative to equity (and thereby

increase the sensitivity of shareholder‟s wealth to changes in the market value of the firm) they

are more likely to serve as limitations on the level of debt. Profitability covenants, on the other

hand, are more likely to control leverage ex post, that is, by detecting when a firm performs

poorly and promptly reallocating decision rights to debthoders. Profitability covenants do not

require a maximum level of debt as long as the firm is sufficiently profitable. For example,

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increase in profitability implies greater latitude in issuing debt under profitability covenants then

under capital structure covenants. Provided a good accounting measure of default risk is

available, profitability covenants are more likely to promote debt market access as long as the

firm is not at risk of violating covenant thresholds. Our third hypothesis is:

H3: Firms that rely on profitability covenants more than on capital structure covenants

have increased access to credit markets.

Notice that hypotheses H1 and H3 are closely connected. When accounting does well in

reflecting companies‟ credit quality, a company is expected to use profitability covenants that

postpone the point at which credit market access is restricted (if an adverse event occurs in the

future) and thereby can afford higher levels of debt.

3. Empirical strategy and research design

3.1. Identification

Theoretical arguments presented earlier suggest that the use of accounting information in

debt contracts helps controlling the contracting frictions present in credit markets. Firms are thus

expected to enjoy increased access to debt financing when accounting information is available

for contracting purposes. The use of accounting information in the contracting process is likely

to depend on its ability to reflect the firm‟s underlying credit risk. The empirical strategy

discussed in this section seeks to identify the effect of accounting information use in contracts on

the level of debt financing obtained by firms. We start by specifying a basic regression model

and discuss the identification issues that arise when estimating this model. We consider firms‟

leverage to be a function of firms‟ access to debt markets (Faulkender and Petersen 2006) and

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the number of accounting-based benchmarks that a lending agreement employs.4 The model we

are interested in is the following:

1 1 1 1it it k kit itLeverage ACovenants X (1)

where Leverage is the ratio of long-term debt to total assets, ACovenants is a proxy for reliance

on accounting-based benchmarks of a certain type, and Xk represents a comprehensive set of

determinants of leverage (discussed later in the paper). The coefficient,1, is predicted to be

positive if covenants indeed facilitate access to debt financing, but its OLS estimates cannot be

interpreted as causal effects since covenants are endogenous to leverage. Specifically, increases

in leverage will be accompanied by increasing default risk and are, therefore, expected to

increase the use of covenants. However, to the extent the increase in the use of covenants makes

the increase in leverage possible, this result is still useful. While it does not bear a causal

interpretation, the association is still relevant as it suggests that covenants are a necessary,

although may not be a sufficient, condition to raise debt. Would firms be able to choose the

same level of debt if the use of covenants was not an option? Their strong association with

leverage implies that, at best such a choice would be more expensive, if at all feasible (e.g., due

to credit rationing). The problem may arise when some companies willing to use covenants are

not able to borrow (i.e., we do not observe the counterfactual). The question of whether a

random firm that is willing to rely on more covenants is able to borrow more is more difficult to

answer, as we discuss next.

4 There is a difference between access to debt financing and actual use of debt (e.g., zero leverage companies can

have access to debt financing). We assume that use of debt is a function of the access to debt financing. Therefore,

increased access to debt financing, ceteris paribus, should lead to higher level of debt, while higher levels of debt

should be indicative of credit market access.

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Identification of the causal effect,1, requires a set of instrumental variables that do not

directly influence leverage, but that are capable of inducing variation in the use of accounting

covenants. We construct our set of instruments by measuring properties of accounting

information. These properties, while theoretically having no direct relation to leverage, are

expected to exhibit association with the use of covenants. Specifically, the extent to which

accounting information reflects credit risk makes the use of accounting-based covenants, and

profitability covenants in particular, a more attractive contracting mechanism. Proxies for the

contracting value of accounting information, discussed next, are thus suitable instruments.

3.2. Measuring the contracting value of accounting information

To quantify the ability of accounting information to capture companies‟ default risk we

construct a number of “contracting value proxies”. We follow Ball et al. (2008) and base our

contracting value proxies on industry-level regressions of (quantified) long-term debt ratings on

quarterly accounting variables commonly used in lending agreements (i.e., earnings, cash flows,

interest coverage, etc).5 As the objective of credit ratings constructed by S&P is to describe

companies‟ credit risk profile, we use R-squares from these regressions to proxy for the ability of

accounting information to explain default risk. Debt ratings are constructed by S&P based on the

comprehensive analysis of both financial and non-financial information, as well as information

about other companies in the industry. Thus low explanatory power of accounting variables in

explaining credit ratings indicates the presence of a substantial information component not

reflected in the accounting information that credit agreements rely on (but which needs to be

taken into account in assessing credit risk). In contrast, high R-squared implies that accounting

5 Quarterly data measurement is justified by its use for quarterly compliance with accounting-based covenants

common for private credit agreements.

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benchmarks are sufficient statistics for determining credit risk within a particular industry. The

regressions are estimated at the industry level over the period 1988-2008.

Our approach differs from that in Ball et al. who measure the association of changes in

accounting earnings with changes in credit ratings (downgrades) for the following reasons. First,

our objective is different. We attempt to measure whether accounting information sufficiently

explains credit risk, or whether there is a significant non-accounting (orthogonal) information

component (residuals) in credit ratings. Changes in credit ratings are rare and for some

companies credit ratings do not change over many years. Given this, it is difficult to evaluate

whether accounting information explains (as a sufficient statistic) credit quality by examining

changes in credit ratings.6 Second, we cannot directly observe credit rating downgrades.

7 Next,

we describe the definitions of the specific contracting value proxies our analysis rely on.

CV1: Our first proxy, CV1, for the debt contracting value of accounting information, is

based on the following industry-level regression:

0 1 2 1 3 2 4 3 5 4it it it it it it itRating E E E E E

(2)

where Ratingit is constructed by assigning “1” to companies with the highest credit rating

following quarter t, “2” to companies with second highest credit rating, and so on, and

subsequently taking natural logarithm of this variable. it sE is earnings before extraordinary

items in quarter t s divided by average total assets over that quarter. CV1 equals R-squared

from model (2).

6 As an extreme example, consider firm/industry with no changes in credit ratings. Accounting information can still

do a good job indicating credit quality.

7 For example, suppose we observe credit rating as of February 1, 2000 and, subsequently, on April 1, 2002 a lower

rating. This can either mean that company was downgraded right before April 1, or alternatively that there was an

interruption in coverage.

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CV2: Our second contracting value proxy, CV2, is R-squared from the following

industry-level regression:

0 1 2 1 3 2 4 3 5 4

6 7 1 8 2 9 3 10 4

it it it it it it

it it it it it it

Rating E E E E E

CFO CFO CFO CFO CFO (3)

where it sCFO is quarter t s cash flow from operations scaled by average total assets in that

quarter; other variables are defined previously.

CV3: Our third proxy, CV3, is R-squared from the following industry-level regression:

0 1 2 1 3 2 4 3 5 4it it it it it it itRating Cover Cover Cover Cover Cover

(4)

where it sCover is quarter t s coverage of interest; other variables are defined previously.

CV4: Our fourth contracting value proxy, CV4, is R-squared from the following industry-

level regression:

0 1 2 3 1 4 2 5 3 6 4it it it it it it it itRating ED ED ED ED ED ED

(5)

where it sED is quarter t s ratio of earnings before extraordinary items to average total

liabilities; other variables are defined previously.

CV5: Our fifth, all-in-one, contracting value proxy, CV5, is based on R-squared the

following industry level regression:

0 1 2 3 4 5it it it it it it itRating E CFO Cover DE Debt

(6)

where Debt the ratio of total liabilities to assets in quarter t; the other variables are defined

previously.

Appendix A provides pooled sample estimates for models (2) – (6). In addition to the

individual contracting value proxies discussed above we construct a composite measure, CVALL,

by taking the average of all five contracting value proxies and use this measure as an alternative.

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3.3. Proxies for timely loss recognition

Timely loss recognition, or conditional conservatism, plays an important role in debt

markets (Watts 2003). In particular, timely loss recognition is expected to improve the

contracting value of accounting-based covenants by facilitating transfers of control to

debtholders when a company approaches financial distress (Ball and Shivakumar 2005). We

thus measure timely loss recognition and use it to validate our measures for the contracting value

of accounting.8

Timely loss recognition, TLR1, is measured by the coefficient 3 from the following

model based on Basu (1997):

1 0 1 2 3/ ( 0) ( 0)t t t t t t tE P D R R D R R (8)

where 1/t tE P is defined as a ratio of annual earnings before extraordinary items (scaled by

beginning of period market value of common stock, tR is stock return from CRSP compounded

over twelve months starting three months after the beginning of the fiscal year (to exclude prior

earnings announcement effects), and D(.) is an indicator function. This regression is estimated at

the industry level over the period 1988-2008.

As scaling by price can confound inferences (Patatoukas and Thomas 2009), we also use

earnings before extraordinary items scaled by average total assets as the dependent variable in

regression (8). The coefficient 3 from this regression is defined as our second proxy for timely

loss recognition, TLR2.

8 Ball et al. (2008) also use timeliness, R-squared from regression of stock returns on accounting earnings and their

changes. They find, however, that timeliness exhibits negative correlation with two other contracting proxies.

Given this, and that contracting benefit of higher return-earnings association is unclear, we do not include this

measure.

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4. Sample and summary statistics

4.1 Sample selection

Our data comes from several sources. We use Dealscan to measure reliance on

accounting-based covenants and other loan characteristics. Accounting information variables

and firm characteristics are constructed based on data from Compustat. We merge loan contracts

from Dealscan to fiscal years in which they are issued on Compustat, based on the link

constructed (and maintained) by Chava and Roberts (2008).9 If a deal package has more than

one credit facility, we randomly take one of the facilities; however, we leave out all facilities

with maturities of one year or less (as accounting covenants are less of a concern for short term

debt). Contracts for which covenant information is not available are excluded from the

analysis.10

The classification of accounting-based covenants into profitability covenants

(PRCovenants) and capital structure covenants (CSCovenants) is described in Appendix B.

To construct the proxies for contracting value of accounting information we link the S&P

Credit Ratings Database to the Compustat quarterly database and use S&P‟s local currency

firms‟ long-term credit ratings. Each credit rating observation is linked to accounting

information from the preceding fiscal quarter. If S&P did not update credit rating data during a

particular quarter, we use the most recent long-term credit rating. S&P Credit Ratings dates back

to the 1920s, however, coverage is sparse before 1986. As we further require cash flow

statement data, in the estimation of the contracting value proxies, we limit the sample to the

period 1988-2008. Over this period S&P rated over 5,500 companies and on average released

9 We thank the authors for generously sharing the link information.

10

Approximately, 50% of credit agreements in Dealscan are coded as having no covenants. It is unlikely that these

credit agreements do not employ covenants given that we know that almost all private credit agreements‟ rely on

covenants (e.g., Christensen and Nikolaev 2009). Thus, we exclude contracts with no covenant information rather

than set this number to zero.

19

credit rating (or economic outlook) information 1,900 times per year (this number ranges from

about 500 in 1988 to 3,300 in 2008). On average it takes more than a year for S&P to updates

the information about long-term credit of a given company.

Contracting value is measured based on SIC industry classification: 3-digit SIC is used in

cases where more than twenty five companies and two hundred fifty quarterly observations are

available; SIC codes that do not meet this requirement are combined and considered at the 2-

digit level. We further exclude resulting industry groups with less than twenty five companies or

two hundred and fifty quarterly observations to improve the reliability of estimates and avoid

over-fitting that may occur in small samples. This procedure results in 50 industry groups.

We employ analogous industry classification (and data requirements) when measuring

timely loss recognition. These properties are estimated using the intersection of CRSP and

annual Compustat data. 1% of observations for CRSP and Compustat variables used in this

study are left out at each tail and the sample is restricted to non-negative EBITDA (necessary to

compute coverage of interest).11

All variables are defined in Appendix C.

The final sample size varies from 5,000 to 7,000 debt contracts depending on data

availability in the specific regressions.

4.2. Summary statistics

Table 1 presents summary statistics for variables used in the subsequent analysis. With a

mean (median) market value of assets at $3,780 ($923) millions our sample is represented by

relatively large firms. Average book-to-market is 0.62 which is typical for an average

Compustat firm. Leverage, as measured by long-term debt divided by book (market) value of

assets is 27% (20%). This is substantially higher than the population of Compustat firms for

11

The results are not sensitive to this choice.

20

which long-term debt constitutes 17% (12%) of book (market) value of assets. Turning to

contracting value proxies, their averages range from 17% to 29%, which is the portion of

variance in credit ratings explained by accounting information variables.

Table 2 provides correlations among contracting value proxies as well as their

correlations with timely loss recognition proxies and industry level cash flow variability. All CV

proxies exhibit high positive correlations. CV proxies also exhibit positive and significant

correlations with TLR proxies. The correlation between TLR1 (TLR2) and CVALL is 28% (31%).

This result is in line with timely loss recognition being a desirable accounting income property in

debt markets, and serves as a validity check for our contracting value proxies.

Table 3 provides Pearson correlations among firm- and contract- level variables as well

as their correlation with the composite contracting value proxy, CVALL. Notably, CVALL

exhibits the highest correlations with PRCovenants (0.22) and CSCovenants (-0.19).

Profitability covenants (PRCovenants) and capital structure-based covenants (CSCovenants)

exhibit a significantly negative correlation of -0.44. This suggests that indeed these two types of

covenants serve as substitutes rather than complements, an issue we study in more detail next.

5. The contracting value of accounting information and covenants

This section studies the determinants of the covenant mix. Subsequently, we examine the

relation between contracting value proxies and the choice between profitability and capital

structure covenants (i.e., provide evidence on hypothesis H1).

5.1. Determinants of profitability and capital structure covenants

The correlation matrix in Table 3 suggests that profitability and capital structure

covenants are used in different situations. To establish how different profitability and capital

21

structure covenants are we explain their use by the most common variables shown by prior

research to determine the use of covenants in general (Skinner 1992; Nash et al. 2003; Billett et

al. 2007). We do not include leverage as an explanatory variable as the relation between

covenants and leverage is the focus of our analysis later in the paper (including instrumental

variable tests).

Table 4 presents the estimated coefficients for regressions of covenants of the two types

on their determinants. Model (1) explains the determinants of profitability covenants, while

Model (2) explains the use of capital structure covenants. Interestingly, except for ROA which

has a significantly positive coefficient in both of these models, the majority of determinants load

significantly but with the opposite signs in the models. Specifically, Size, book-to-market (B/M),

R&D, dividends (Div), tangibility (Tang), and Age are negatively related to the use of

profitability covenants but are positively related to capital structure covenants. The opposite is

true in the case of deal size (DealAmount), and loan maturity (Maturity). Model (3) analyses the

determinants of covenant mix, defined as the ratio of profitability covenants to the sum of all

covenants. The estimates indicate that the mix of covenants itself is non-random and exhibit

predictable associations with most firm- and contract-level characteristics.

The results above indicate that pooling all covenants is rarely meaningful, which has

direct implications for empirical research that combines covenants into one proxy to measure

their use. The documented relationship between the two classes of covenants or their mix and

other firm/contract characteristics is intuitive and demonstrates the importance of investment

opportunities set in determining the contract design (Skinner 1992). For example, firms that are

larger, older, and have higher levels of tangible assets are leaning towards capital structure

covenants, which can be explained by timely accounting information being lees important in

22

monitoring credit risk for these firms. In contrast, large loans and loans with longer maturities

are more likely to rely on profitability covenants. Interestingly, R&D intensive firms rely less on

profitability covenants and more on capital structure covenants, which is consistent with lower

contracting value of earnings for these firms.

Overall, the evidence in this sub-section confirms our prior that the two types of

covenants will be used in different situations and act as substitutes. More research is needed,

however, to enhance our understanding of the differential role of the two classes of covenants.

5.3. The choice of covenants and the contracting value of accounting information

In this subsection, we examine the relation between the proxies for debt contracting value

of accounting information and the use of covenants, and test the first hypothesis (H1).

According to H1, the use of profitability (capital structure) covenants is expected to increase

(decrease) in the CV proxies. We start with separate univariate analysis of both types of

covenants and subsequently employ multiple regression to explain their mix.

Profitability covenants. Table 5 presents the univariate regression results of profitability

covenants (PRCovenants) on CV proxies as well as TLR proxies. The estimates on all individual

proxies CV1 – CV5, as well as their composite measure, CVALL, are positive and statistically

significant. In addition, the coefficients on proxies for timely loss recognition, TLR1 and TLR2

are also positive and statistically significant. R-squares in many of these regressions are around

5%. The results are consistent with H1 and imply that the use of profitability covenants

increases with the contracting value of accounting information. Such relation is intuitive as

profitability covenants become more effective in reducing the underlying contracting frictions,

when they can promptly transfer control to debtholders following a decline in firm‟s credit

23

quality, i.e., when accounting information has contracting value. Next, we examine the capital

structure covenants.

Capital structure covenants. Table 6 presents the univariate regressions of capital

structure covenants on CV and TLR proxies. The findings closely mirror the evidence for

profitability covenants as all the contracting proxies, CV1 – CV5, as well as TLR1 and TLR2

exhibit a negative and significant association with the use of capital structure covenants. This

evidence is also in line with H1 and confirms prior evidence that the two types of covenants act

as substitutes. The results broadly suggest that companies retreat to capital structure covenants

in situations, where accounting information does not reflect the underlying default risk. As we

argued earlier, in such situations the capital structure covenants are expected to be more robust.

Covenant mix. Finally, Table 7 presents the univariate regressions of the mix of

covenants (CovenantMix = PRCovenants/(PRCovenants + CSCovenants)) on CV and TLR

proxies. As one would expect based on prior tests, CovenantMix exhibits a statistically

significant positive association with the contracting value proxies. The explanatory power in case

of CV1–CV4 exceeds 5%.

To isolate the confounding effects of other relevant firm- and contract-specific

characteristics on the relationship between covenants and contracting value proxies, we regress

the mix of covenants on the contracting value proxies and a wide range of control variables.

Table 8 presents the estimates for this model. Despite the presence of control variables, the

CovenantMix exhibits a significantly positive association with the CV and TLR proxies. This

corroborates prior evidence and implies that the mix of covenants is a function of inherent

properties of accounting information, and more specifically its ability to measure the underlying

default risk and its changes.

24

5.4. Robustness checks

Following Ball et al. (2008) we cluster the standard errors at firm level. One may argue

that standard errors should be clustered at industry level. The econometric argument is not

straightforward as measuring a right hand side variable at industry level does not in itself

introduce non-independence to the error term in regression. Specifically, when industry level

variables are measured without error, non-independence of the error term across different

companies within the same industry does not arise. Thus, industry clustering is likely to

overstate standard errors. When measurement error in industry level variables is present, the

non-independence of the error term, however, is of secondary importance as its presence

confounds the identification. We require a substantial number of observations within industries

to measure industry-level proxies more precisely (see Section 4) and thus report errors clustered

at firm level. To demonstrate the robustness of our results, we also verify that results in Tables 5

– 8 remain statistically significant when errors are clustered at industry level. We do not tabulate

these results to preserve space, however.

6. Accounting-based covenants and credit market access

This section attempts to identify the effect that the use of accounting information in debt

contracts plays in facilitating firms‟ access to debt financing. We begin with OLS analysis of

leverage as a function of its determinants known in the literature and the number of accounting-

based covenants. Since OLS precludes causal interpretations of our results we proceed by

performing IV analysis.

25

6.1. Covenants and leverage: Ordinary least squares

Table 9 presents the results of OLS regressions of leverage on covenants and their mix.

We employ both book- and market-based proxies for leverage: levbook and levmkt, respectively.

Columns (1) and (2) suggest that leverage has a statistically significant relation to the total

number of accounting based benchmarks used in lending agreements (TotalCovenants =

PRCovenants + CSCovenants), which is consistent with H2 but confounded by endogeneity of

covenants. However, when we separate the covenants into two classes, as suggested by models

(3) and (4), profitability covenants have a statistically significantly positive association with

leverage, while the opposite holds for capital structure covenants. On average, one profitability

covenant is associated with 5.4% (4.0%) higher level of levbook (levmkt), while one capital

structure covenant is associated with 2.6% (1.8%) lower levbook (levmkt). The economic

magnitudes of these estimates are large, especially, taking into account that we control for most

commonly used determinants of leverage. These associations are consistent with profitability

covenants facilitating access to debt financing, and capital structure covenants, in contrast,

limiting the level of debt, as we discuss in Section 2 and put forward in hypothesis H3. An

alternative interpretation of these results is that leverage increases default risk which in turn leads

to more heavy use of covenants, i.e., covenants may be a necessary, but not sufficient, condition

for increased borrowing. In this case, however, we would expect leverage to exhibit positive

associations with both types of covenants.

The models (5) and (6) of Table 9 examine the effect of the mix between profitability and

capital structure covenants on book- and market-leverage, respectively. Controlling, for the total

number of covenants, the mix of covenants has a statistically significant and economically

important association with leverage. Firms that use 100% profitability covenants have 12% (9%)

26

higher level of levbook (levmkt). Although the evidence from this analysis cannot be interpreted

as causal, it does highlight the key role of accounting-based covenants in determining firms‟

capital structure.

6.2. Covenants and leverage: Instrumental variable analysis with industry level instruments

Our identification strategy relies on correlations between the properties of accounting

information (CV1 – CV5) and the use of accounting-based covenants. The limitation of these

instruments is that, although they are useful in explaining the mix of profitability and capital

structure covenants, they cannot explain their independent inclusion into the contract. In other

words, as contracting value increase, we observe substitution of capital structure covenants with

profitability covenants. To test H2, however, we need instruments predicting each class of

covenants, while holding the other class unchanged (i.e., holding everything else constant). This

generally precludes the tests of H2 using instrument variables, however, enables the test of H3.

That is, we are able to determine whether relatively more heavy reliance on profitability

covenants affects the amount of leverage. While prior studies employed industry-level

instruments when examining debt market access (e.g., Faulkender and Petersen 2006), a caveat

applies. While conceptually appealing, industry based instruments may not be fully exogeneous,

and one should bear this in mind when interpreting the results. To alleviate this concern, we

perform the analysis using an alternative set of instruments as a robustness check in section 6.3.

Table 10 presents 2SLS analysis, where CV1 – CV5 are used as instruments. Across four

models, the partial R-squared is in the range 4.9-5.9%, which indicates a considerable

incremental explanatory power of the instruments in the first stage regressions (cluster adjusted

F-tests are highly significant throughout the four models). As other loan-characteristics are

generally also endogenous, and since we do not have a set of instruments to deal with this issue,

27

we show specifications with and without main loan characteristics. The results show that

CovenantMix has a statistically significant effect on both leverage proxies. The economic effect

is also large. Specifically, when loan characteristics are included, firms that rely fully on

profitability covenants reach 30% (21%) higher levels of book (market) leverage as compared to

firms that fully rely on capital structure covenants. The corresponding effects are similar if the

loan characteristics are excluded.

6.3. Robustness check: Instrumental variable analysis with lender level instruments

We also examine an alternative set of instruments to alleviate concerns with validity of

the instruments. Specifically, we measure lead arranger reliance on covenants (and their mix)

and use these as instruments in our analysis. Specifically, for each loan, we calculate the average

number of profitability and capital structure covenants across all contracts originated by a

particular lead arranger over the five years preceding a particular loan, excluding the current

borrower company. We find substantial variation with respect to covenants use across lenders.

In spirit, our procedure to construct instruments is similar to Christensen and Nikolaev (2009),

Faulkender and Petersen (2006), and Ivashina (2009), although their context differs.

The rationale for our instruments is the following: the lender-level averages aim at

determining a standard (boilerplate) contract offered by the lead arranger to various borrowers,

that is expected to vary with lender‟s preferences with respect to the use of accounting based-

covenants (and may in part be due to lender‟s specialization). Some lenders are known to rely on

covenants more than others (while others price protect instead). Thus, the use of covenants in a

particular loan will be correlated with lead lender preferences for monitoring via covenants in

general, while at the same time the lead lender‟s preference for covenant use should not directly

correlate with firm‟s leverage (unless via the use of covenants, controlling for other

28

characteristics). In other words, lender-level preferences/specialization conceptually is an

appropriate instrument. Partial R-squareds indicating explanatory power of the instruments vary

between 3.0 and 5.7%.

The results of this analysis are presented in Table 11. The results are similar to those in

Table 10 and even indicate a higher effect of CovenantMix on the level of debt financing.

Overall, the results of our analysis provide evidence on the importance of accounting for

debt contracting. The ability to use accounting-based benchmarks as an input into covenant

provisions has a large economic effect on firms‟ ability to access credit markets.

7. Conclusion

In this paper we examine the effect of reliance on accounting-based benchmarks in

private lending agreements on the level of debt. We argue that the use of accounting ratios in the

definitions of financial covenants increases firms‟ access to credit markets. Accounting-based

covenants alleviate agency and information problems that arise in credit markets and thus

facilitate borrowing. The ability of accounting information to reflect firms‟ credit risk is shown

to be an important determinant of covenants and their mix. Specifically, firms substitute to more

timely profitability covenants from capital structure covenants as the contracting value of

accounting information increases. This, as shown by our instrumental variable tests, leads to

increases in firms‟ debt capacity.

More research is needed, however, to understand the effect reliance on accounting based

covenants has on the level of debt as we are only able to identify the effect of covenant mix on

leverage.

29

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Appendix A: Estimation of CV proxies: Pooled regressions

This appendix presents parameter estimates for models (2) – (6) based on pooled sample. Ratingit

is constructed by assigning “1” to companies with the highest credit rating following quarter t,

“2” to companies with second highest credit rating, and so on, and subsequently taking natural

logarithm of this variable. Et-s is earnings before extraordinary items scaled by average total

assets in quarter t s divided by average total assets over that quarter. CFOt-s is quarter t s

cash flow from operations scaled by average total assets in that quarter; Covert-s is quarter t s

coverage of interest; EDt-s is quarter t s ratio of earnings before extraordinary items to average

total liabilities; Debt the ratio of total liabilities to assets in quarter t. (1) (2) (3) (4) (5)

VARIABLES Rating Rating Rating Rating Rating

Et -18.19*** -16.36*** -29.55***

(-5.910) (-5.592) (-4.339)

Et-1 -17.58*** -15.88***

(-19.68) (-18.38)

Et-2 -19.94*** -17.81***

(-21.31) (-19.77)

Et-3 -19.72*** -17.48***

(-21.66) (-19.42)

Et-4 -18.53*** -16.59***

(-20.05) (-18.16)

CFOt 1.391*** -4.562***

(2.782) (-6.554)

CFOt-1 -2.556***

(-6.461)

CFOt-2 -2.312***

(-5.600)

CFOt-3 -1.039***

(-2.589)

CFOt-4 -4.087***

(-9.474)

Covert -0.299*** -0.501***

(-7.192) (-5.540)

Covert -1 -0.255***

(-10.33)

Covert -2 -0.318***

(-15.10)

Covert -3 -0.345***

(-15.18)

Covert -4 -0.434***

(-11.37)

EDt -9.324*** 12.02***

(-3.569) (3.533)

EDt -1 -7.074***

(-12.36)

EDt -2 -7.965***

(-13.75)

EDt -3 -7.429***

(-12.88)

EDt -4 -6.351***

(-10.57)

Debtt 7.260***

32

(17.88)

Constant 10.32*** 10.70*** 11.90*** 10.14*** 8.207***

(124.0) (101.4) (67.07) (99.78) (33.64)

Observations 59532 56554 50577 59796 60610

R-squared 0.148 0.162 0.178 0.101 0.254

Robust t-statistics adjusted for clustering at firm level are in parentheses, *** p<0.01, ** p<0.05, * p<0.1

Appendix B: Covenant classification.

Profitability-based covenant benchmarks:

(1) Cash interest coverage ratio; (2) Debt service coverage ratio; (3) Level of EBITDA; (4) Fixed

charge coverage ratio; (5) Interest coverage ratio; (6) Debt to EBITDA; (7) Senior debt to EBITDA.

Capital structure-based covenant benchmarks:

(1) Quick ratio; (2) Current ratio; (3) Debt-to-equity ratio; (4) Loan-to-value ratio; (5) Debt-to-

tangible-net-worth ratio; (6) Leverage ratio; (7) Senior leverage ratio.

Appendix C: Variable definitions.

Levbook = ratio of long-term debt (Compustat item TLDT) divided by total assets (Compustat item

AT).

Levmkt = ratio of long-term debt (Compustat item TLDT) divided by market value of total assets

(Compustat: AT – SEQ + PRCC_F×CSHO).

Size = natural logarithm of “market value” of total assets (Compustat: log(AT – SEQ +

PRCC_F×CSHO)).

Margin = total revenue divided by- cost of goods sold (Compustat items REVT/COGS).

B/M = book-to-market ratio (Compustat: SEQ/(PRCC_F×CSHO)).

R&D = R&D expense divided by total revenue (Compustat: XRD/REVT). Missing R&D

expense is replaced with zeros.

Adv = Advertizing expense divided by total revenue (Compustat: XADV/REVT).

SG&A = SG&A expense divided by total revenue (Compustat: XSGA/REVT).

Capex = Capital expenditures divided by total assets net of capital expenditures (Compustat:

CAPX/(AT – CAPEX)).

ROA = Return on assets defined as a ratio of income before extraordinary items divided by total

assets.

Div = Dividend yield computed as a ratio of common dividends to market value of equity

(Compustat: DVC/(PRCC_F×CSHO)).

Tangible = Asset tangibility defined as a ratio of net value of property plant and equipment to total

assets (Compustat: PPENT/AT).

Marginal tax = Marginal tax rate based on method described in Graham (1996). See also Graham and

Mills (2009).

33

Age = Natural logarithm of the number of years on CRSP.

StdRet = Natural logarithm of the standard deviation of daily returns over the fiscal year.

Maturity = Years to maturity.

DealAmt = Natural logarithm of total deal amount (all facilities included).

Secured = An indicator variable that takes value of one if debt is secured, and zero otherwise.

LendFreq = Lending frequency computed as a number of loan deals a company have had over the

prior five years.

Revolver = An indicator variable that takes value of one if revolving facility exists in the deal

package, and zero otherwise.

DivRestrict = An indicator variable that takes value of one when restriction on dividend payments is

included.

PRCovenants = Number of profitability-based covenants (See Appendix B for classification).

CSCovenants = Number of capital structure-based covenants (See Appendix B for classification).

CovenantMix = PRCovenants/(PRCovenants + CSCovenants).

CV1 – CV5 = Debt contracting value of accounting information proxies, 1 through 6. Measure the

extent to which accounting information explains S&P‟s entity-level long-term credit rating.

See Section 4 for details.

TLR1–TLR2 = Proxies for timely loss recognition based on Basu (1997). See section 4 for details.

IndSize = Natural logarithm of the number of observations within a particular SIC industry (see

Section 3 for industry definitions).

StdROA = SIC industry-level standard deviation of ROA (see Section 3 for industry definitions).

StdCFO = SIC industry-level standard deviation of cash flow from operations (see Section 3 for

industry definitions).

Table 1: Summary Statistics Table 1 presents summary statistics for the variables used in the analysis in Tables 4 - 11. All variables are defined

in Appendix C. We obtain loan characteristics from Dealscan, credit ratings from S&P Credit Ratings Database,

accounting and firm characteristics from Compustat, and return data from CRSP. Contracts without covenant

information are excluded and if a deal package has multiple facilities we randomly select one. The sample sizes for

individual variables varies depending on data availability.

VARIABLES N Mean Std.Dev. p25 p50 p75

Size 7324 3780 9947 260 923 2956

Levbook 7289 0.27 0.19 0.11 0.25 0.39

Levmkt 7245 0.20 0.16 0.06 0.17 0.30

CV1 5938 0.17 0.14 0.04 0.14 0.28

CV2 5938 0.19 0.14 0.05 0.16 0.31

CV3 5938 0.31 0.17 0.16 0.30 0.40

CV4 5938 0.18 0.15 0.07 0.13 0.31

CV5 5938 0.29 0.15 0.18 0.26 0.39

CVALL 5938 0.23 0.14 0.11 0.20 0.35

TLR1 7332 0.21 0.10 0.15 0.21 0.27

TLR2 7332 7.63 4.40 4.74 6.78 9.23

StdCfo 7332 0.12 0.05 0.10 0.12 0.15

B/M 7292 0.62 0.55 0.31 0.51 0.78

Adv 7275 0.01 0.02 0 0 0.00

Rnd 7329 0.02 0.10 0 0 0.01

Margin 7310 1.76 0.96 1.27 1.47 1.84

ROA 7323 0.02 0.11 0.00 0.04 0.07

Div 7287 0.01 0.02 0 0 0.01

Marginal Tax 7332 0.30 0.08 0.3 0.34 0.35

Tangible 7059 0.32 0.25 0.12 0.25 0.48

Age 7332 2.29 1.03 1.61 2.30 3.14

StdRet 7309 0.12 0.06 0.07 0.11 0.15

PRCovenants 7332 1.60 0.98 1 2 2

ISCovenants 7332 0.54 0.66 0 0 1

CovenantMix 7205 0.73 0.34 0.5 1 1

DealAmount 7332 18.64 1.58 17.62 18.83 19.8

Maturity 7332 4.02 1.70 3 4.00 5.00

DivRestrict 7332 0.75 0.44 0 1 1

LendFreq 7332 2.36 2.20 1 2 3

Revolver 7332 0.88 0.33 1 1 1

Table 2: Pearson Correlation among Contracting Value proxies Table 2 presents Pearson correlations among the Contracting Value (CV) proxies and Timely Loss Recognition

(TLR) variables. All variables are defined in Section 4. We obtain loan characteristics from Dealscan, credit ratings

from S&P Credit Ratings Database, accounting and firm characteristics from Compustat, and return data from

CRSP. Contracts without covenant information are excluded and if a deal package has multiple facilities we

randomly select one. P-values are provided below the correlations.

VARIABLES CV1 CV2 CV3 CV4 CV5 CVALL TLR1 TLR2

CV2 0.99 1

CV3 0.84 0.84 1

CV4 0.91 0.89 0.89 1

CV5 0.83 0.84 0.96 0.83 1

CVALL 0.95 0.96 0.96 0.95 0.94 1

TLR1 0.28 0.26 0.28 0.2 0.31 0.28 1

TLR2 0.35 0.34 0.25 0.33 0.23 0.31 0.06 1

Table 3: Pearson Correlations

Table 3 presents Pearson correlations among the none-contract-value variables used in the analysis in Tables 4 - 11. Profitability covenants (PRCovenants) and capital

structure covenants (CSCovenants) are defined in Appendix A, all other variables are defined in Appendix C. We obtain loan characteristics from Dealscan, credit ratings

from S&P Credit Ratings Database, accounting and firm characteristics from Compustat, and return data from CRSP. Contracts without covenant information are excluded

and if a deal package has multiple facilities we randomly select one. P-values are provided below the correlations.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20)

(1) CVALL 1

(2) Levbook 0.09 1

(3) Levmkt 0.05 0.89 1

(4) Size -0.09 0.12 0.02 1

(5) B/M -0.06 -0.03 0.25 -0.3 1

(6) Adv 0.18 -0.04 -0.07 0.05 -0.05 1

(7) R&D 0.02 -0.12 -0.14 -0.07 -0.08 -0.01 1

(8) Margin -0.04 0.02 -0.05 0.06 -0.12 0.11 0.09 1

(9) ROA -0.03 -0.08 -0.12 0.23 -0.12 0.02 -0.28 0.06 1

(10) Div -0.13 0.11 0.15 0.24 0.05 -0.03 -0.08 -0.01 0.07 1

(11) Marginal tax 0 0 -0.03 0.2 -0.1 0.01 -0.1 -0.02 0.28 0.12 1

(12) Tangible -0.04 0.25 0.24 0.08 0.04 -0.06 -0.12 0.11 0.01 0.12 -0.03 1

(13) Age -0.06 -0.04 -0.04 0.31 -0.04 0.02 -0.06 -0.06 0.12 0.23 0.08 0.03 1

(14) StdRet 0.01 -0.04 0 -0.42 0.16 -0.04 0.12 0 -0.32 -0.3 -0.18 -0.09 -0.26 1

(15) PRCovenants 0.22 0.28 0.23 -0.04 -0.01 0.02 -0.1 -0.02 0.04 -0.11 0.01 -0.09 -0.14 0.03 1

(16) CSCovenants -0.19 -0.17 -0.13 -0.17 0.05 -0.07 0.08 0.02 -0.01 0.04 0 0.06 -0.02 0.07 -0.44 1

(17) Dealamount -0.02 0.28 0.21 0.82 -0.15 0.04 -0.16 0.02 0.19 0.19 0.17 0.11 0.25 -0.36 0.13 -0.27 1

(18) Loanmaturity 0.12 0.19 0.12 0.25 -0.11 0.02 -0.05 0 0.09 0.01 0.08 0.09 0.05 -0.2 0.12 -0.17 0.33 1

(19) DivRestrict 0.02 0.08 0.07 -0.1 0.02 -0.04 -0.02 -0.01 -0.06 -0.2 -0.04 -0.03 -0.08 0.12 0.24 -0.13 0 0.01 1

(20) LendFreq -0.07 0.2 0.2 0.41 -0.04 -0.01 -0.1 -0.02 0.06 0.09 0.07 0.08 0.15 -0.16 -0.02 -0.05 0.38 0.08 -0.03 1

(21) Revolver 0.01 -0.04 -0.03 0.07 0 0.02 -0.11 -0.03 0.07 0.02 0.02 -0.06 0.06 -0.04 0.06 -0.09 0.18 -0.22 0.06 0

Table 4: Determinants of mix of covenants Table 4 presents estimates from three regressions of the number of profitability covenants, capital structure covenants,

and covenant mix on firm-specific variables in columns (1) - (3), respectively. Profitability covenants (PRCovenants)

and capital structure covenants (CSCovenants) are defined in Appendix B, all other variables are defined in Appendix C.

We obtain loan characteristics from Dealscan, credit ratings from S&P Credit Ratings Database, accounting and firm

characteristics from Compustat, and return data from CRSP. Contracts without covenant information are excluded and if

a deal package has multiple facilities we randomly select one.

(1) (2) (3)

VARIABLES PRCovenants CSCovenants CovenantMix

Size -0.243*** 0.0704*** -0.0741***

[-12.16] [6.951] [-10.96]

B/M -0.140*** 0.0647*** -0.0444***

[-4.349] [3.727] [-4.863]

Adv 0.456 -1.165** 0.753***

[0.565] [-2.303] [2.721]

R&D -0.669*** 0.321*** -0.297***

[-4.877] [3.204] [-5.149]

Margin -0.0261 0.0236** -0.0163***

[-1.581] [2.384] [-2.692]

ROA 0.285** 0.288*** 0.0211

[2.109] [3.328] [0.417]

Div -7.462*** 2.689*** -2.235***

[-7.212] [3.370] [-5.065]

Tangible -0.380*** 0.173*** -0.129***

[-5.482] [4.033] [-5.111]

Age -0.134*** 0.0431*** -0.0325***

[-8.566] [4.284] [-5.837]

StdRet -0.375 0.00478 0.0185

[-1.426] [0.0288] [0.212]

LendFreq -0.00139 0.00494 -0.00369

[-0.174] [1.036] [-1.317]

DealAmount 0.285*** -0.147*** 0.0943***

[14.28] [-13.89] [14.40]

Maturity 0.0734*** -0.0517*** 0.0232***

[7.010] [-7.734] [7.065]

Revolver 0.0397 -0.101*** 0.0309**

[0.955] [-3.717] [2.202]

Constant -2.413*** 2.965*** -0.757***

[-9.063] [14.07] [-8.212]

Observations 6882 6882 6762

R-squared 0.184 0.204 0.201

Robust t-statistics in brackets, *** p<0.01, ** p<0.05, * p<0.1

Table 5: Contracting Value and Profitability Covenants Table 5 presents estimates from univariate regressions of the number of Profitability Covenants on Contracting Value (CV) proxies and Timely Loss Recognition

(TLR) variables. Profitability Covenants (PRCovenants) are defined in Appendix B and CV proxies are defined in Section 4. We obtain loan characteristics from

Dealscan, credit ratings from S&P Credit Ratings Database, accounting and firm characteristics from Compustat, and return data from CRSP. Contracts without

covenant information are excluded and if a deal package has multiple facilities we randomly select one.

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES PRCovenants PRCovenants PRCovenants PRCovenants PRCovenants PRCovenants PRCovenants PRCovenants

CV1 1.617***

(12.43)

CV2 1.539***

(11.97)

CV3 1.190***

(10.65)

CV4 1.470***

(11.59)

CV5 1.226***

(9.505)

CVALL 1.541***

(11.75)

TLR1 1.035***

(6.437)

TLR2 0.0123***

(2.696)

Constant 1.317*** 1.305*** 1.230*** 1.326*** 1.236*** 1.243*** 1.381*** 1.510***

(45.86) (42.95) (31.16) (44.55) (28.88) (35.26) (35.26) (41.76)

Observations 5938 5938 5938 5938 5938 5938 7332 7332

R-squared 0.053 0.049 0.043 0.049 0.035 0.050 0.012 0.003

Robust t-statistics adjusted for clustering at firm level are in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

39

Table 6: Contracting value proxies and Capital Structure Covenants Table 6 presents estimates from univariate regressions of the number of Capital Structure covenants on Contracting Value (CV) proxies and Timely Loss

Recognition (TLR) variables. Capital Structure Covenants (CSCovenants) are defined in Appendix B and CV proxies are defined in Section 4. We obtain loan

characteristics from Dealscan, credit ratings from S&P Credit Ratings Database, accounting and firm characteristics from Compustat, and return data from CRSP.

Contracts without covenant information are excluded and if a deal package has multiple facilities we randomly select one. (1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES CSCovenants CSCovenants CSCovenants CSCovenants CSCovenants CSCovenants CSCovenants CSCovenants

CV1 -0.851***

(-10.91)

CV2 -0.856***

(-11.05)

CV3 -0.672***

(-10.85)

CV4 -0.831***

(-11.43)

CV5 -0.678***

(-9.481)

CVALL -0.853***

(-11.33)

TLR1 -0.367***

(-3.750)

TLR2 -0.0120***

(-5.332)

Constant 0.705*** 0.720*** 0.764*** 0.710*** 0.757*** 0.753*** 0.623*** 0.636***

(39.84) (38.62) (34.06) (40.33) (30.94) (36.13) (25.75) (30.84)

Observations 5938 5938 5938 5938 5938 5938 7332 7332

R-squared 0.033 0.034 0.031 0.035 0.024 0.035 0.003 0.006

Robust t-statistics adjusted for clustering at firm level are in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

Table 7: Contracting value proxies and Mix of Covenants Table 7 presents estimates from univariate regressions of CovenantMix on Contracting Value (CV) proxies and Timely Loss Recognition (TLR) variables.

CovenantMix the fraction of PRCovenants in total number of covenants (CovenantMix =PRCovenants/(PRCovenants + CSCovenants)). CV proxies are defined in

Section 4, and other variables are defined in Appendix B. We obtain loan characteristics from Dealscan, credit ratings from S&P Credit Ratings Database,

accounting and firm characteristics from Compustat, and return data from CRSP. Contracts without covenant information are excluded and if a deal package has

multiple facilities we randomly select one. (1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES CovenantMix CovenantMix CovenantMix CovenantMix CovenantMix CovenantMix CovenantMix CovenantMix

CV1 0.565***

(13.01)

CV2 0.561***

(13.00)

CV3 0.452***

(12.39)

CV4 0.542***

(13.10)

CV5 0.450***

(10.98)

CVALL 0.564***

(13.21)

TLR1 0.329***

(5.679)

TLR2 0.00461***

(3.248)

Constant 0.619*** 0.610*** 0.578*** 0.617*** 0.584*** 0.587*** 0.654*** 0.690***

(57.08) (53.80) (40.42) (56.03) (38.94) (45.49) (44.71) (56.35)

Observations 5843 5843 5843 5843 5843 5843 7205 7205

R-squared 0.053 0.053 0.051 0.054 0.039 0.055 0.010 0.003

Robust t-statistics adjusted for clustering at firm level are in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

41

Table 8: Mix of Covenants and Contracting Value Proxies: Controlling for Firm and Contract characteristics Table 8 presents estimates from regressions of the covenant mix on Contracting Value (CV) proxies and Timely Loss Recognition (TLR) variables and control

variables. The covenant mix is defined as PRCovenants/(PRCovenants + CSCovenants), where PRCovenants and CSCovenants are Profitability- and Capital

Structure Covenants, respectively, defined in Appendix B. CV and TLR proxies are defined in Section 4 and all control variables are defined in Appendix C. We

obtain loan characteristics from Dealscan, credit ratings from S&P Credit Ratings Database, accounting and firm characteristics from Compustat, and return data

from CRSP. Contracts without covenant information are excluded and if a deal package has multiple facilities we randomly select one. (1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES CovenantMix CovenantMix CovenantMix CovenantMix CovenantMix CovenantMix CovenantMix CovenantMix

CV1 0.410***

(9.817)

CV2 0.411***

(9.731)

CV3 0.289***

(8.576)

CV4 0.381***

(9.525)

CV5 0.282***

(7.428)

CVALL 0.393***

(9.609)

TLR1 0.283***

(3.754)

TLR2 0.00209*

(1.647)

Size -0.0659*** -0.0658*** -0.0639*** -0.0647*** -0.0657*** -0.0646*** -0.0663*** -0.0682***

(-9.300) (-9.269) (-9.066) (-9.148) (-9.241) (-9.157) (-10.14) (-10.42)

B/M -0.0351*** -0.0351*** -0.0373*** -0.0353*** -0.0390*** -0.0359*** -0.0373*** -0.0401***

(-3.553) (-3.549) (-3.764) (-3.578) (-3.899) (-3.632) (-4.226) (-4.518)

Adv 0.123 0.166 0.348 0.0631 0.397 0.190 0.520* 0.446

(0.411) (0.557) (1.135) (0.206) (1.286) (0.627) (1.880) (1.615)

R&D -0.310*** -0.313*** -0.304*** -0.295*** -0.301*** -0.305*** -0.313*** -0.323***

(-5.514) (-5.508) (-5.239) (-5.224) (-5.337) (-5.380) (-5.201) (-5.339)

Margin -0.0144** -0.0147** -0.0161** -0.0131** -0.0170*** -0.0150** -0.0152** -0.0144**

(-2.249) (-2.288) (-2.526) (-2.044) (-2.661) (-2.350) (-2.515) (-2.380)

ROA 0.0345 0.0335 0.0279 0.0304 0.0286 0.0325 0.0134 0.0150

(0.637) (0.617) (0.522) (0.567) (0.531) (0.605) (0.266) (0.299)

Div -1.826*** -1.850*** -1.801*** -1.695*** -1.754*** -1.787*** -1.863*** -1.768***

(-3.797) (-3.828) (-3.716) (-3.532) (-3.633) (-3.692) (-4.319) (-4.103)

42

Tangible -0.120*** -0.122*** -0.104*** -0.108*** -0.113*** -0.116*** -0.0710*** -0.0644**

(-4.221) (-4.289) (-3.604) (-3.830) (-3.888) (-4.080) (-2.763) (-2.504)

Age -0.0309*** -0.0312*** -0.0335*** -0.0313*** -0.0331*** -0.0319*** -0.0316*** -0.0318***

(-5.030) (-5.093) (-5.442) (-5.060) (-5.351) (-5.179) (-5.740) (-5.736)

StdRet 0.0176 0.0192 -0.0221 0.00319 -0.0186 0.00409 0.0716 0.0365

(0.186) (0.203) (-0.232) (0.0336) (-0.196) (0.0432) (0.831) (0.426)

LendFreq -0.00161 -0.00139 -0.00171 -0.00144 -0.00194 -0.00155 -0.00403 -0.00384

(-0.532) (-0.460) (-0.568) (-0.479) (-0.636) (-0.515) (-1.459) (-1.361)

DealAmt 0.0889*** 0.0889*** 0.0867*** 0.0877*** 0.0879*** 0.0876*** 0.0904*** 0.0906***

(12.56) (12.53) (12.41) (12.48) (12.47) (12.49) (14.09) (14.06)

Maturity 0.0204*** 0.0203*** 0.0207*** 0.0207*** 0.0214*** 0.0203*** 0.0212*** 0.0213***

(5.991) (5.956) (6.068) (6.078) (6.253) (5.975) (6.602) (6.618)

Revolver 0.0241 0.0240 0.0263* 0.0253* 0.0261* 0.0248 0.0314** 0.0318**

(1.599) (1.588) (1.727) (1.671) (1.692) (1.637) (2.274) (2.299)

StdCFO 0.555*** 0.566*** 0.635*** 0.750*** 0.623*** 0.574*** 0.518*** 0.996***

(3.295) (3.352) (3.792) (4.565) (3.631) (3.423) (2.792) (6.803)

IndSize -0.0232*** -0.0208*** -0.0256*** -0.0231*** -0.0301*** -0.0220*** -0.0479*** -0.0465***

(-3.039) (-2.683) (-3.346) (-3.045) (-3.918) (-2.874) (-7.184) (-7.046)

Constant -0.779*** -0.808*** -0.728*** -0.782*** -0.708*** -0.774*** -0.609*** -0.626***

(-6.594) (-6.863) (-6.511) (-6.885) (-6.163) (-6.773) (-5.646) (-5.791)

Observations 5444 5444 5444 5444 5444 5444 6762 6762

R-squared 0.249 0.249 0.242 0.248 0.238 0.247 0.220 0.217

Robust t-statistics adjusted for clustering at firm level are in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

Table 9: Leverage and Covenants: Ordinary Least Squares Table 9 presents estimates from regressions of leverage on covenant characteristics and control variables. The

covenants characteristics are: acc_covenants that count all accounting-based covenants, PRCovenants counts

profitability covenants, CSCovenants counts capital structure covenants, and mix_covenants is defined as

PRCovenants/(PRCovenants + CSCovenants), where PRCovenants and CSCovenants are defined in Appendix B.

All other variables are defined in Appendix C. We obtain loan characteristics from Dealscan, credit ratings from

S&P Credit Ratings Database, accounting and firm characteristics from Compustat, and return data from CRSP.

Contracts without covenant information are excluded and if a deal package has multiple facilities we randomly

select one. (1) (2) (3) (4) (5) (6)

VARIABLES Levbook Levmkt Levbook Levmkt Levbook Levmkt

TotalCovenants 0.0398*** 0.0302*** 0.0345*** 0.0258***

[12.11] [10.99] [9.954] [8.913]

PRCovenants 0.0535*** 0.0402***

[16.43] [14.68]

CSCovenants -0.0262*** -0.0180***

[-5.858] [-4.702]

CovenantMix 0.121*** 0.0869***

[15.13] [13.10]

Size 0.0218*** 0.0150*** 0.0185*** 0.0127*** 0.0210*** 0.0144***

[11.23] [9.116] [9.824] [7.832] [11.04] [8.877]

B/M -0.00853 0.0733*** -0.00698 0.0753*** -0.00726 0.0765***

[-1.236] [10.49] [-1.067] [11.13] [-1.058] [10.96]

Adv -0.158 -0.281*** -0.240* -0.342*** -0.256* -0.352***

[-1.055] [-2.634] [-1.676] [-3.295] [-1.746] [-3.299]

R&D -0.212*** -0.180*** -0.161*** -0.142*** -0.165*** -0.147***

[-4.495] [-4.749] [-4.149] [-4.479] [-3.952] [-4.317]

Margin 0.000167 -0.00565** 0.00213 -0.00419* 0.00274 -0.00368

[0.0500] [-2.376] [0.656] [-1.818] [0.823] [-1.559]

ROA -0.285*** -0.244*** -0.266*** -0.230*** -0.306*** -0.263***

[-6.665] [-6.858] [-6.706] [-6.862] [-6.998] [-7.166]

Div 0.395* 0.576*** 0.713*** 0.807*** 0.685*** 0.777***

[1.843] [3.194] [3.326] [4.349] [3.264] [4.278]

Marginal tax -0.0337 -0.0279 -0.0275 -0.0217 -0.0196 -0.0170

[-0.949] [-0.859] [-0.820] [-0.705] [-0.560] [-0.530]

Tangible 0.173*** 0.136*** 0.188*** 0.146*** 0.185*** 0.143***

[12.74] [12.15] [14.02] [13.25] [13.68] [12.84]

Age -0.00738** -0.00344 -0.00205 0.000392 -0.00342 -0.000628

[-2.361] [-1.363] [-0.689] [0.160] [-1.122] [-0.253]

StdRet -0.0208 -0.0351 0.0206 -0.00360 -0.00741 -0.0217

[-0.365] [-0.725] [0.376] [-0.0767] [-0.131] [-0.450]

Constant -0.0604 -0.0658** -0.00607 -0.0276 -0.0978*** -0.0930***

[-1.443] [-2.003] [-0.157] [-0.897] [-2.971] [-3.464]

Observations 6854 6824 6854 6824 6734 6704

R-squared 0.164 0.210 0.227 0.257 0.205 0.243

Robust cluster adjusted at firm level t-statistics in brackets, *** p<0.01, ** p<0.05, * p<0.1

44

Table 10: Leverage and Covenants: Instrumental variable tests Table 10 presents estimates from regressions of leverage on covenant mix and control variables using instrumental

variables for covenant mix. Covenants mix is defined as PRCovenants/(PRCovenants + CSCovenants), where

PRCovenants and CSCovenants are Profitability- and Capital Structure Covenants, respectively, defined in

Appendix A. Contracting Value (CV) proxies, defined in Section 4, are used as instruments. All other variables are

defined in Appendix C. We obtain loan characteristics from Dealscan, credit ratings from S&P Credit Ratings

Database, accounting and firm characteristics from Compustat, and return data from CRSP. Contracts without

covenant information are excluded and if a deal package has multiple facilities we randomly select one.

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

VARIABLES Levbook Levbook Levmkt Levmkt

CovenantMix 0.332*** 0.289*** 0.244*** 0.213***

[6.926] [5.621] [5.989] [4.849]

Size 0.0187*** -0.0128* 0.0130*** -0.0140**

[8.002] [-1.859] [6.659] [-2.423]

B/M -0.00402 -0.0196** 0.0814*** 0.0683***

[-0.479] [-2.252] [9.685] [8.237]

Adv -0.334* -0.230 -0.408*** -0.318**

[-1.878] [-1.400] [-2.896] [-2.475]

R&D -0.111** -0.0784** -0.102*** -0.0696**

[-2.515] [-2.220] [-2.882] [-2.543]

Margin 0.00495 0.00551 -0.00249 -0.00176

[1.297] [1.571] [-0.907] [-0.701]

ROA -0.296*** -0.263*** -0.251*** -0.222***

[-6.785] [-6.287] [-6.758] [-6.293]

Div 1.127*** 1.346*** 1.189*** 1.382***

[3.838] [5.014] [4.759] [6.021]

Marginal tax 0.0102 -0.00705 -0.00276 -0.0145

[0.275] [-0.198] [-0.0830] [-0.461]

Tangible 0.207*** 0.183*** 0.157*** 0.138***

[11.55] [10.28] [10.55] [9.230]

Age -0.000693 -0.00202 0.00189 0.000766

[-0.179] [-0.553] [0.591] [0.257]

StdRet -0.0255 0.0162 -0.0111 0.0202

[-0.391] [0.263] [-0.202] [0.391]

DealAmount 0.0320*** 0.0271***

[4.250] [4.250]

Maturity 0.00534** 0.00251

[2.510] [1.381]

DivRestrict 0.0140* 0.0112

[1.653] [1.604]

LendFreq 0.0118*** 0.0114***

[6.984] [8.009]

Revolver -0.0413*** -0.0323***

[-4.656] [-4.411]

Constant -0.0982** -0.514*** -0.0957*** -0.460***

[-2.474] [-6.040] [-2.968] [-6.262]

Observations 5420 5420 5392 5392

R-squared 0.086 0.182 0.144 0.232

Robust cluster adjusted at firm level z-statistics in brackets, *** p<0.01, ** p<0.05, * p<0.1

45

Table 11: Leverage and Covenants: Alternative Instruments Table 11 presents estimates from regressions of leverage on covenant mix and control variables using instrumental

variables for covenant mix. Covenants mix is defined as PRCovenants/(PRCovenants + CSCovenants), where

PRCovenants and CSCovenants are Profitability- and Capital Structure covenants, respectively, defined in Appendix

B. Lender-level covenant characteristics are used as instruments. All other variables are defined in Appendix C. We

obtain loan characteristics from Dealscan, credit ratings from S&P Credit Ratings Database, accounting and firm

characteristics from Compustat, and return data from CRSP. Contracts without covenant information are excluded

and if a deal package has multiple facilities we randomly select one.

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

VARIABLES Levbook Levbook Levmkt Levmkt

CovenantMix 0.459*** 0.362*** 0.358*** 0.289***

[11.02] [6.674] [10.69] [6.566]

Size 0.0241*** -0.00997 0.0173*** -0.0102*

[9.089] [-1.429] [7.764] [-1.788]

B/M -0.00925 -0.0271*** 0.0785*** 0.0641***

[-1.127] [-3.224] [9.880] [8.233]

Adv -0.584*** -0.459*** -0.637*** -0.543***

[-3.005] [-2.600] [-4.320] [-4.050]

R&D -0.0715* -0.0731* -0.0787** -0.0749**

[-1.704] [-1.925] [-2.181] [-2.326]

Margin 0.00788* 0.00704* 0.000798 0.000447

[1.755] [1.772] [0.244] [0.154]

ROA -0.420*** -0.382*** -0.371*** -0.338***

[-8.368] [-7.838] [-9.026] [-8.482]

Div 1.335*** 1.333*** 1.307*** 1.332***

[4.172] [4.598] [4.701] [5.222]

Marginal tax -0.0296 -0.0429 -0.0168 -0.0253

[-0.729] [-1.137] [-0.481] [-0.772]

Tangible 0.225*** 0.196*** 0.172*** 0.149***

[11.74] [10.85] [11.17] [10.16]

Age 0.00482 0.000683 0.00734** 0.00408

[1.157] [0.169] [2.176] [1.250]

StdRet 0.0675 0.0897 0.0520 0.0639

[0.966] [1.373] [0.909] [1.183]

DealAmount 0.0330*** 0.0258***

[4.423] [4.262]

Maturity 0.00609*** 0.00273

[2.615] [1.442]

DivRestrict 0.00122 -0.000180

[0.128] [-0.0231]

LendFreq 0.0131*** 0.0127***

[7.644] [8.844]

Revolver -0.0485*** -0.0398***

[-4.823] [-4.893]

Constant -0.418*** -0.700*** -0.379*** -0.594***

[-6.652] [-8.458] [-7.343] [-8.805]

Observations 5252 5252 5230 5230

R-squared 0.086 0.104 0.135 0.142

Robust cluster adjusted at firm level z-statistics in brackets, *** p<0.01, ** p<0.05, * p<0.1