Managerial Incentive Structures, Conservatism and the Pricing of

Syndicated Loans∗

Florin P. Vasvari

London Business School Regent's Park

London, NW1 4SA United Kingdom

fvasvari@london.edu

June, 2006

Abstract

Drawing on previous theoretical research, this paper empirically explores the effect of manager-shareholder incentive alignment, as measured by the extent of equity compensation, on the pricing of syndicated loan contracts. First, I document that high levels of alignment are associated with larger risk premiums in loan spreads and with more restrictive covenants. Second, I document the effect of ex ante accounting conservatism on loan terms, conditional on managerial ex post reporting incentives as proxied by the level of manager-shareholder incentive alignment. I find that ex ante accounting conservatism decreases loan spreads and increases the number of financial covenants in the loan contract when managers receive average or below-average equity compensation. However, ex ante conservatism leads to banks demanding larger loan spreads as well as fewer and tighter financial covenants when managers receive above-average equity compensation. These latter results provide evidence consistent with banks viewing ex ante conservatism as an instrument for reducing the monitoring value of financial covenants when managers have incentives to over-report ex post due to large equity compensation. Keywords: managerial equity compensation, syndicated bank loans, covenants,

accounting conservatism, propensity score matching.

∗ This paper is part of my dissertation completed at University of Toronto. I am grateful to my dissertation committee members Varouj Aivazian, Jeffrey Callen (co-chair), Ole-Kristian Hope and Gordon Richardson (co-chair) for their guidance and support. I thank David Aboody, Sudipta Basu, Phil Berger, Peter Clarkson, Jerry Feltham, Gus De Franco, Jack Hughes, Bjorn Jorgensen, SP Kothari, Hai Lu, Ed Maydew, Bruce Miller, Steven Monahan, Mirela Predescu, Dan Segal, Lakshmanan Shivakumar, Joe Weber and seminar participants at INSEAD, London Business School, McGill University, MIT, UCLA, University of British Columbia, University of Illinois at Urbana-Champaign, University of North Carolina and University of Toronto for helpful suggestions and discussions. I also thank Moody’s Investors Service for providing historical data on ratings. I gratefully acknowledge the generous financial assistance of the Rotman School of Management, Ontario Graduate Scholarship Fund, and Home Capital Group. All errors are mine.

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1. Introduction

Managerial compensation plays a crucial role in aligning the interests of managers with those

of shareholders or debtholders. While equity compensation lowers the agency costs of equity, it

increases the agency costs of debt (e.g., Jensen and Mecking, 1976; John and John, 1993).

Higher agency costs of debt are reflected in the form of larger risk premiums, more restrictive

covenants, and less-advantageous non-price terms (e.g., shorter maturity or smaller amounts of

debt). In other words, lenders anticipate and rationally price risk-increasing managerial behavior

and potential wealth expropriation induced by the equity portion in the managerial incentive

structure. Further, lenders should price expected future aggressive accounting. Empirical

evidence suggests that greater managerial equity compensation leads to more aggressive

accounting practices that hinder creditors’ ability to properly monitor the solvency and liquidity

of the firm.1

This paper is motivated by the lack of empirical research that simultaneously explores the

role of debtholder-shareholder and manager-shareholder conflicts of interest on contracting

costs. The extant literature has investigated these conflicts separately although each generates

divergent demands for accounting information. To date, most of the accounting research focuses

on analyzing the role of financial statement information in equity markets, while the non-equity-

market demand is almost always ignored (Ball, 2001; Holthausen and Watts, 2001; Watts,

2003a,b). In particular, debt markets demand more accounting conservatism which understates

earnings and book value of net assets. These understatements limit manager-shareholder wealth

expropriations by restricting dividend payouts and managerial compensation and help lenders

better monitor borrowers since financial covenants are drawn tighter.

This paper sets out to investigate the effect of manager-shareholder incentive alignment on

agency costs of debt by providing direct empirical tests of debt contract design theories

developed by John and John (1993), Begley and Feltham (1999b), Sridhar and Magee (1997),

Douglas (2003), and Levine and Hughes (2005). Consistent with theoretical research, I view the

managerial compensation structure as a signal of borrowers’ commitment to avoid both wealth

1 For example, Cheng and Warfield (2005) and Ke (2005) find that managers receiving large equity compensation

engage in more earnings management. Consistent with this result, Efendi, Srivastava and Swanson (2004) find that restatements of aggressively reported financial statements are associated with firms whose CEOs had a sizable amount of “in-the-money” stock options.

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expropriations and overly risky investments and report accounting information conservatively in

the period following the negotiation of debt contracts. Consequently, I examine the impact of

manager-shareholder alignment, as proxied by managerial equity compensation, on the terms of

syndicated loan contracts (spreads and covenants) at the time of their origination. In addition, I

provide a direct test of the contracting demand for accounting conservatism discussed by Watts

(2003a,b) by documenting the impact of ex ante accounting conservatism on loan terms. Given

that accounting reporting choices are driven by managerial incentives, I investigate how

managerial incentives affect the relation between ex ante accounting conservatism and the terms

of syndicated loans.

In the empirical analysis, I define ex ante accounting conservatism as conditional accounting

conservatism measured in the period prior to the loan contracting date in the spirit of Beaver and

Ryan (2005) and Ball and Shivakumar (2005, 2006). Consistent with Basu (1997), conditional

conservatism refers to the asymmetry between gain and loss recognition timeliness. That is,

economic losses are more likely to be recognized on a timely basis, as accrued charges against

income, whereas the recognition of economic gains is more likely to be deferred until realized in

cash (e.g., the lower of cost or market rule, impairment accounting, or restructuring charges).2

Contracting theory predicts that only conditional conservatism reflects contemporaneous shocks

to economic income thus providing new information that could generate contracting responses

(Basu, 2005; Ball, Robin and Sadka, 2006). Unconditional conservatism does not enhance

contracting efficiency since it introduces an accounting bias not related to current economic

income (Ball, Robin and Sadka, 2006). Throughout the paper, I refer to ex ante conditional

conservatism as simply ex ante accounting conservatism. In the Sensitivity Analyses section, I

also investigate the role of ex ante unconditional accounting conservatism.

The empirical tests focus on recently-available syndicated bank loans during the period

January 1, 1993 to December 31, 2004. Syndicated bank debt is relevant for several reasons. Due

to its concentrated ownership, syndicated loan debt has more financial and non-financial

covenants that facilitate monitoring in the post contracting period than public debt (e.g., Begley

and Freedman, 2004). These contracting features ensure a greater role of accounting information

2 In contrast, unconditional conservatism is generated by the consistent application of GAAP, independent of

current economic events. Some examples of unconditional conservatism are the immediate expensing of R&D and internally developed intangibles, accelerated depreciation or historical cost accounting for positive net present value projects.

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in the monitoring process. In addition, most of the syndicated loans are floating rate loans thus

allowing an analysis of the spread above risk-free rates without concern about macroeconomic

factors that affect the interest rate environment. Last, syndicated loans are the most senior form

of debt and constitute a significant part of the overall debt market representing more than half of

new corporate financing since the late 1990s (Jones, Lang and Nigro, 2005). The syndicated

market has expanded rapidly due to a series of improvements such as development of loan

ratings and new risk management tools, adoption of flexible loan pricing terms and increased

liquidity in the secondary market.3

My primary empirical findings are as follows. First, I document that higher manager-

shareholder incentive alignment increases the level of syndicated loan spreads and the number of

covenants required by the bank syndicate. The manager-shareholder incentive alignment is

measured by the level of current year equity compensation and by the sensitivities of the entire

equity portfolio of top executives to stock price changes and volatility. The evidence is robust to

a series of sensitivity tests that attempt to mitigate the effects of potential measurement errors in

the covenant proxy and the model estimation method. In an analysis that allows for endogenous

managerial incentives, I employ a Heckman (1979) two-stage model and two Propensity Score

Matching methods, one of which follows recent developments in the labor economics literature

(Dehejia and Wahba, 2001).4 The results continue to be robust to these alternative tests.

Second, I find that a credible commitment to implement conservative accounting policies

consistently, as inferred from the managerial incentive structure, is rewarded by bank syndicates.

Specifically, I document that ex ante accounting conservatism, measured by alternative

conditional conservatism proxies, decreases the cost of syndicated loan debt when managers

receive average or below-average equity compensation. However, this benefit of accounting

conservatism is significantly lower when the borrowing firms closely align top executives with

shareholders (i.e., pay above-average equity compensation).

Although I find no empirical support that ex ante conservatism by itself affects the presence

of covenants in the contract, there is strong evidence that when managers are not highly aligned

with shareholders, more ex ante conservatism increases the number of covenants in the loan

3 The syndicated loan market reached a size of $1.6 trillion by the end of 2003 from only $0.2 trillion in 1991

(Yago and McCarthy, 2004). Bradley and Roberts (2004) document that during the past few years, the dollar amount of syndicated loans issued ranged from two to three times the amount of corporate public debt issued.

4 Propensity Score Matching is described in detail in Appendix B.

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agreement. In other words, bank syndicates rely more on monitoring based on accounting

reports. Conversely, when managers are highly aligned with shareholders, ex ante conservatism

leads to banks requiring fewer yet tighter financial covenants. This latter result is consistent with

financial covenants having a lower monitoring ability when managers have incentives to engage

in earnings management to avoid wealth losses due to covenant violations. Coupled with the

results for loan spreads, this finding suggests that bank syndicates demand a premium in terms of

higher interest rates (i.e., ex ante price protection) rather than use more inefficient financial

covenants that increase their monitoring costs when managerial compensation creates incentives

to over-report.

This paper makes several contributions. To begin with, it offers unique evidence on how the

structure of managerial compensation is priced in the syndicated-loan market. There is little

empirical evidence to date on the effects of managerial incentives on debt terms, despite the fact

that rating agencies have expressed concerns regarding the effect of managerial compensation on

the credit quality of borrowers.5 This evidence is only in the bond market. Using a small sample

of bond issues from 1970s, Begley and Feltham (1999a) find that equity compensation explains

the presence of covenants restricting dividends and additional borrowings. Financial covenants

and the price of debt are not included in their analysis. Also, Bagnani et al. (1994) and Ortiz-

Molina (2005) examine how managerial ownership affects bondholder returns and the cost of

corporate bond issues, respectively. Both papers fail to account for the possibility of endogenous

managerial compensation and do not use comprehensive measures of equity incentives.

In addition, this research is the first to provide empirical evidence consistent with the role of

executive compensation as a pre-commitment mechanism to conservatively report accounting

information ex post.6 I find that only when shareholders provide low equity compensation, a pre-

commitment signal that managers have incentives not to engage in aggressive reporting, does

accounting conservatism become credible ex post, decreasing the contracting cost of debt as

measured by syndicated loan spreads. In this respect, my paper documents the effect of

accounting conservatism on loan spreads conditional on post-contracting managerial reporting

5 For instance, Moody’s Investors Service (2005) recently issued a report linking abnormally high stock option grants with future downgrades and defaults. Also, Standard and Poor’s offers a Corporate Governance Scoring Service because it believes that there is a clear link between the credit quality of a borrower and its corporate governance. One of the key components of the governance score is the nature of the CEO compensation.

6 Bolton and Scharfstein (1990) and Katz (1991) provide a theoretical framework supporting the view that observable managerial compensation contracts could act as pre-commitment mechanisms when lending contracts are negotiated.

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incentives anticipated by lenders and thus contributes to the recent literature that investigates the

pricing of accounting conservatism in the debt market (see Ahmed et al., 2002; Zhang, 2004;

Ball, Robin and Sadka, 2006 and Moerman, 2006).

Furthermore, this paper contributes by providing unique large sample evidence not only on

the impact of ex ante accounting conservatism on the cost of debt but also on covenant

restrictiveness, an important choice variable in debt contracts. This relation has not yet been

investigated empirically. The result that accounting conservatism is associated with fewer and

tighter financial covenants when managers receive high equity compensation is consistent with

the recent findings of Beatty, Webber and Yu (2006) who document that lenders introduce

conservatism in debt covenants through contractual adjustments when borrowers’ financial

statements are more conservative ex ante.

The remainder of the paper is organized as follows. Section 2 provides the theoretical

background and develops the hypotheses to be tested. Section 3 describes the sample selection

procedure and variables. Section 4 presents the main empirical results. Section 5 provides

sensitivity analyses and Section 6 concludes. The two appendices describe the construction of

covenant proxies used in estimation and the Propensity Score Matching methods, respectively.

2. Theoretical Background and Hypotheses Development

2.1. The Effect of Managerial Incentives on Debt Terms 7

Shareholders do not necessarily prefer to closely align risk-averse managers’ incentives with

their own. Brander and Poitevin (1992) and John and John (1993) show theoretically that pay-

performance managerial compensation contracts based on equity that closely align the incentives

of managers and shareholders (i.e., decrease the agency cost of equity) generate higher

borrowing costs (i.e., increase the agency cost of debt). Rational lenders anticipate larger

monitoring costs due to managerial actions that expropriate debtholder wealth by under-investing

and increasing dividend payouts and share buybacks. Also, lenders anticipate that closely-

aligned managers increase the risk of debt. If most of the managerial compensation is in the form

of stock and stock options, then managers will be motivated to act in shareholders’ interest by

engaging in risk taking projects (e.g., Jensen and Meckling, 1976; John and John, 1993; Begley

7 I use “debt terms” to refer to the combination of loan spreads and covenants.

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and Feltham, 1999b).8 However, if most of the compensation is in the form of cash, managers

will be motivated to act in debtholders’ interest by engaging in risk reducing projects.9

The literature provides strong theoretical support for the assertion that the managerial

incentive structure affects the cost of debt. The theories of John and John (1993) and Levine and

Hughes (2005) predict that lenders ask for larger risk premiums if the pay-performance

sensitivity increases. Empirical evidence indirectly supports this claim. For instance, Yermack

(1995) finds that stock prices increase when executive stock grants are announced. His findings

indicate that agency costs of equity are reduced and that the current incentives are expected to

increase future firm performance. However, announcements of stock grants generate a negative

reaction in bond markets (De Fusco et al., 1990). This response is consistent with lenders

viewing these managerial compensation choices as increasing the risk of debt.

The managerial incentive structure also affects the presence of covenant restrictions that limit

the flexibility of the borrower. The model of Begley and Feltham (1999b) finds that lenders

require covenants in debt contracts when managers receive a high level of equity and option

holdings relative to cash compensation. Further, Sridhar and Magee (1997) demonstrate that

more stringent covenants bring firm investment decisions in line with the interests of debtholders

as opposed to shareholders. Theory thus suggests my first testable hypothesis on the effect of

managerial incentives on both loan spreads and covenants:

H1: Loan spreads and covenant restrictions are increasing in the level of manager-shareholder

alignment.

2.2. The Effect of Accounting Conservatism on Debt Terms

A high level of manager-shareholder alignment induces not only debtholder wealth

expropriation and risk increasing investments but also aggressive reporting of accounting

information that increases lenders’ monitoring costs (e.g., Cheng and Warfield, 2005; Ke, 2005;

Burns and Kedia, 2006). For this reason, debtholders want assurances that borrower’s accounting

choices are conservative and provide timely and reliable disclosure of adverse information (e.g.,

Ball, 2001). Accounting conservatism understates earnings, equity and book value of net assets

8 For example, Datta, Iskandar-Datta and Raman (2001), Rajgopal and Shevlin (2002) and Coles, Daniel and Naveen (2004) document that more managerial equity compensation is associated with riskier investment choices.

9 See Begley and Feltham (1999b) for a theoretical proof of this argument. Brander and Poitevin (1992) also show that by limiting the pay-performance sensitivity, shareholders can induce conservative managerial behavior.

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in the financial statements. As a result, the assets pledged as collateral to the loan provide a

greater margin of safety,10 financial covenants are drawn tighter and wealth expropriation is

limited in the post contracting period by reducing dividend payouts and managerial

compensation (Watts, 2003a,b). Although these arguments do not distinguish between

conditional and unconditional accounting conservatism, Ball and Shivakumar (2005, 2006), Ball,

Robin and Sadka (2006) and Basu (2005) argue that only the conditional accounting

conservatism (i.e., asymmetric timely recognition of gains and losses) is relevant in a debt

contracting setting since it provides new information to lenders.

The accounting literature presents two diverging views on the type of accounting information

demanded by lenders before debt contract inception. On the one hand, Sridhar and Magee (1997)

suggest that firms should implement aggressive accounting before entering the debt contract in

order to reduce any opportunities to over-report ex post. Accrual reserves, a consequence of ex

ante conservatism, can be used to circumvent the ability of the lender to intervene by increasing

reported performance to avoid covenant violations. A vast empirical literature finds that

managers engage in aggressive reporting to evade debt covenant violations thus suggesting that

accounting conservatism in the pre-contracting period may not be consistently applied in the

post-contracting period due to managerial incentives.11

On the other hand, while noting that accounting conservatism ex post is desirable in

enhancing the efficiency of the debt contract, Watts (2003a,b) suggest that lenders demand

accounting conservatism ex ante as well. In assessing a potential loan, a bank would be

interested in contracting on a verifiable lower bound of the borrower’s asset value which can be

provided by accounting conservatism. Early descriptive evidence by Leftwich (1983), Whittred

and Zimmer (1986) and Watts and Zimmerman (1986) documents that the rules involved in debt

contracting are even more conservative than GAAP. Consistent with this evidence, Zhang (2004)

finds that ex ante accounting conservatism lowers loan spreads. However, Bharath, Sunder and

Sunder (2004), although not testing for the effect of ex ante conservatism, find that low ex ante

accruals (a potential indicator of ex ante conservatism) are associated with high loan spreads.

Given the mixed current empirical evidence and the views presented above, I add to the

10 SFAC no. 2, paragraph 93 provides a similar view. 11 Among many papers, some notable references are Zmijewski and Hagerman (1981), Press and Weintrop

(1990), Duke and Hunt (1990), DeFond and Jimbalvo (1994), Sweeney (1994), and more recently Beatty and Weber (2003). In a single exception, Healy and Palepu (1990) find that firms do not make accounting changes to avoid the violation of one specific covenant, the dividend restriction.

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debate by investigating the relation between ex ante accounting conservatism and loan spreads. I

also analyze covenant restrictions since none of the cited papers provide evidence on the

implications of ex ante conservatism on covenant requirements even though covenants are a

major choice variable in debt contracts. This leads to my second hypothesis:

H2: Loan spreads and covenant restrictions are decreasing in the level of ex ante accounting

conservatism chosen by managers.

A natural question to ask then is when do banks reward ex ante accounting conservatism?

Using the level of manager-shareholder alignment to infer whether ex ante conservatism is

credible could provide an answer to this question and reconcile the two apparently contradictory

views previously outlined. Credible ex ante conservatism implies that borrowers report

understated earnings, equity and book value of net assets in the financial statements both before

the loan contract date and thereafter.

The managerial compensation structure can be interpreted as a pre-commitment mechanism

that signals managerial reporting incentives ex post. Ceteris paribus, a rational lender would

require some combination of more and/or tighter covenants and a higher risk premium if there is

reason to believe that closely aligned managers have incentives to use ex ante accounting

conservatism to create reporting reserves. Closely aligned managers are prone to aggressive

reporting in the post contracting period because positive stock price reactions to inflated

financial performance increase the value of their equity portfolio. Also, aggressive reporting

avoids covenant violations which further impact negatively on the stock price and the value of

their equity portfolio.12 Hence, the strength of incentives to over-report ex post is directly related

to the expected cost of covenant violations to managers (e.g., Beatty and Weber, 2003).

With respect to loan spreads, the theory of Sridhar and Magee (1997) shows that an increase

in the uncertainty and magnitude of management’s reporting discretion ex post causes higher

interest rates in equilibrium. In other words, lenders discount firms’ pre contracting (i.e., ex ante)

conservative accounting policies which facilitate a greater reporting discretion in the post

12 The anticipation of covenant violations and the actual violations are associated with significant stock price

declines (see Core and Schrand, 1999; Beneish and Press, 1995). Managers’ limited tenure also creates incentives to manipulate earnings to increase short term stock prices at the expense of long term firm value (e.g., Ke, 2005).

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contracting period.13 Managers who are highly aligned with shareholders are more likely to use

this discretion to aggressively report ex post. Thus, the pricing of ex ante accounting

conservatism in loan spreads should be affected by the manager-shareholder incentive alignment:

H3a: The predicted negative association between loan spreads and ex ante accounting

conservatism is mitigated by greater manager-shareholder incentive alignment.

With respect to covenants, Douglas (2003) expands the prior analytical work by allowing

renegotiations after covenant violations.14 He concludes that when accounting information is less

reliable (i.e., it does not reflect the true economic performance of the firm) more and tighter

covenants are required. Since highly aligned managers have incentives to report aggressively,

lenders should set covenants based on the expected ex post “reliability” of the accounting

information as inferred from the managerial incentive structure and the degree of reporting

discretion introduced by ex ante accounting conservatism. This suggests that ex ante accounting

conservatism is associated with more and tighter covenants when managers are highly aligned.

However, the direction of the relation suggested by Douglas (2003) could also go in the

opposite direction. Highly aligned managers’ use of accrual reserves generated by ex ante

conservatism to over report ex post reduce the monitoring ability of financial covenants (Sridhar

and Magee, 1997). Two potential implications emerge when the bank syndicate expects a low

monitoring value for financial covenants. First, financial covenants may not be included in the

contract while the loan spreads are increased (i.e., ex ante price protection is preferred). Second,

if financial covenants are required then they are more stringent (i.e., they have lower slack).

Sridhar and Magee’s (1997) model predicts the second implication. These theoretical

considerations lead to the following non-directional hypothesis with respect to covenants:

H3b: The predicted negative association between covenant restrictions and ex ante

accounting conservatism is affected by greater manager-shareholder incentive alignment.

To summarize, highly aligned managers could engage in aggressive accounting to avoid

wealth losses in case of a covenant breach or low financial performance. Ex ante conservatism

13 Consistent with this argument, Beatty, Ramesh and Weber (2002) find evidence that borrowers are willing to

pay greater interest rates to retain more reporting flexibility in the post contracting period. 14 Dichev and Skinner (2002), Beneish and Press (1993), Sweeney (1994) and others provide evidence that bank

debt is very often renegotiated.

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may facilitate this behavior since closely aligned managers have incentives to use it to build up

accrual reserves in advance. However, managers that are not closely aligned with shareholders

have a limited wealth loss when a covenant is breached or when low performance occurs because

a high proportion of their pay is in cash. In case of a covenant violation, they can renegotiate

with the lenders and pass the costs to shareholders. Therefore, they have fewer incentives to use

ex ante conservatism strategically.

3. Data and Variable Measurement

This section provides details on the sample selection process and variables. The first

subsection discusses the selection of firms in the sample and descriptive statistics on firm

characteristics. The second subsection presents measures for the cost of syndicated loan debt.

The third and fourth subsections present proxies for manager-shareholder alignment and ex ante

accounting conservatism, respectively while the last subsection describes the control variables

used in multivariate tests.

3.1. Sample Selection

The primary data source is a sample of syndicated loan agreements at the time of their

origination and is collected from Dealscan which is provided by the Loan Pricing Corporation

(LPC). Syndicated loans are private lending instruments but have features similar to public debt.

That is, they have credit ratings and trade in relatively liquid secondary markets. LPC collects

bank loan data for public companies from SEC fillings (13Ds, 14Ds, 13Es, 10ks, and 8ks) or

through its relationships with major banks. In general, loans (or facilities) are grouped in deals

(or packages) since most borrowers enter into multiple agreements at the same time with a single

lead bank or a group of lead banks. Once the deal terms are finalized, pieces of individual loans

may be sold to other banks or institutional investors on the secondary market.

Most of the loans in the database are term and revolving loans. In the case of term loans, the

amounts repaid must not be re-borrowed and the funds are usually drawn down all at once. They

have a fixed life and a fixed repayment schedule. Revolving loans allow the funds to be drawn

down at the discretion of the borrower and to be re-paid and re-borrowed over the term of the

agreement. The initial sample of loans with valid loan data available and borrower information

covered by Execucomp and Compustat consists of a total of 9,539 loans (6,964 deals) for 1,204

firms (Table 1, Panel A). Consistent with prior literature, the sample includes only non-financial

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US firms and the period covered is January 1, 1993 to December 31, 2004.15

The loan data is manually matched with Compustat firm identifiers using companies’ name,

location, and their 4-digit SIC industry classification codes. I require the firms to have available

Compustat quarterly data to compute firm-specific controls for the previous quarter, relative to

the loan’s start date. After merging the loan data with Compustat, the sample is reduced to 9,005

loans from 1,188 firms. I further limit the sample to firms for which previous fiscal year

compensation variables could be computed from Execucomp. The resulting panel of loans used

to test the first hypothesis has a size of 6,768 loan facilities (5,054 deals) from 1,111 firms and is

evenly distributed across the sample period (Table 1, Panel B). The percentage of term loans is

almost constant, around 22%, consistent with prior research that finds the majority of the loans in

the database are revolving. In terms of covenant requirements, Panel B of Table 1 documents a

high correlation between the presence of general covenants and that of financial covenants

(Appendix A provides a description of the covenants available in the database).

Although the screening process and data availability constraints tend to select large firms

thus imposing some survivorship bias, the final sample of loans is the largest to date when

compared to that of similar studies. This allows a great deal of cross-sectional variability.

Descriptive statistics presented in Table 2, Panel A suggest that sample firms are relatively large,

averaging around 6.2 billion in market capitalization. As a percentage of total assets, on average,

firms debt is about 32% while operating cash flows are about 4%.

3.2. Loan Data

For the typical loan, Dealscan contains data on borrowers and lenders, type of the loan (e.g.,

term, revolving), loan characteristics (e.g., starting date, maturity, size, rating, pricing, purpose),

and deal covenants. Since the loan rating is a first order effect control variable for borrower

specific credit quality, missing ratings in Dealscan are completed using a private historical rating

database provided by Moody’s Investors Service.16

I use two proxies for the cost of syndicated loan debt, loan spreads and a covenant index. The

15 In the context of this study, one reason for eliminating financial firms is that debt covenants for these firms are redundant. Black et al. (2004) argue that regulatory monitoring is more efficient than covenants, thus financial institutions that borrow have fewer covenants for this external reason.

16 Moody’s firm ratings are used for 645 loans. Using Moody’s historical firm ratings is appropriate because loans in general receive a rating that is the same as the issuer’s corporate credit rating (Standard & Poor’s, 2004). In some instances, the loan rating will be higher than the firm rating by one or more notches if the rating agency determines that the loan is secured (or has other enhancements) and 100% of the principal is likely to be repaid. The downward bias in ratings that might be introduced for these loans is mitigated by using group ratings in the analysis.

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loan spread is the so called All-in-Spread (AIS) Drawn which is computed by Dealscan as the

sum of the borrowing spread and the annual and utilization fees on the credit facility.17 Annual

fees are yearly charges against the entire loan amount whether used or unused. Utilization fees

are charges on the commitment amount that is drawn and are characteristic to revolving loans.

Thus, AIS Drawn is computed based on the assumption that the facility is fully used. It is a

composite way of reporting loan prices that enables comparisons across different types of loans,

regardless of their underlying fee structure. AIS Drawn is quoted in basis points over LIBOR or

an equivalent such as prime rates or T-bills.

The second proxy for the cost of debt, the covenant index, measures the extent to which the

terms of a particular loan constrain the flexibility of the borrowing firm. The stronger the

covenant package, the more control the lender can exercise over the issuer. This monitoring

mechanism allows banks to renegotiate the loan terms faster, before the loan becomes severely

impaired, and to preserve the value of the collateral. Similarly to Bradley and Roberts (2004) and

Bagnani et al. (1994), I measure the covenant index as the number of general and financial

covenants included in the deal agreement contract (Appendix A provides details). This measure

has the advantage of being transparent and easy to replicate while providing a summary

aggregate of many covenants that monitor different dimensions of firm’s performance and

solvency. Nevertheless, it has some limitations. First, non-financial affirmative covenants are not

covered by the database and are thus excluded.18 Second, all covenants receive an equal weight

in the index. In particular, the collateral requirement is expected to have a larger weight since it

greatly reduces lenders’ exposure to credit risk. Consistent with other papers, I find that when the

secured field in Dealscan is available, 80% of the loans in my sample are secured. This high

percentage could induce multicollinearity in multivariate tests. Syndicated loans are typically

senior to all other debt and most of them have a collateral requirement unless they are investment

grade loans. Therefore, of greater importance is not the collateral requirement per se but the

quality and the preservation of the collateral (e.g., Standard and Poor’s, 2004). I attempt to

measure the quality by the extent to which firm’s assets are tangible (control variables are

17 AIS Drawn is not computed on fixed-rate loans or notes, letters of credit and subordinated debt. To facilitate cross-sectional comparisons, I eliminate these facilities from the sample. This procedure eliminates only 2.8% of the loans, consistent with the fact that financial institutions prefer floating rate loans that provide a natural hedge against fluctuations in interest rates.

18 This omission is likely not important given that these covenants are usually boilerplate. Some notable examples are the maintenance of adequate insurance, payment of taxes, interest and fees on time, preparation of financial statements according to GAAP, proper maintenance of firm’s assets, etc.

13

discussed in the next section). In any case, I address potential measurement errors in the main

covenant index by using alternative covenant measures (see Section 5).

Table 2, Panel B presents descriptive statistics on the characteristics and pricing terms of

sample loans. On average, the loans are significant in size. The mean loan (deal) amount is about

$525 ($830) million and the mean number of lenders in the syndicate is 12 institutions. As

mentioned already, most of the loans are revolving thus the average maturity is only about three

years. The average loan has four covenants, one of them being a financial covenant.19 In the

empirical implementation, I control for skewness in the loan data by using the logarithm of the

loan (deal) size and of the number of lenders. Also, in unreported tests I use the logarithm of the

basis points spread and the inferences are not affected.

3.3. Manager-Shareholder Incentive Alignment Measures

I use equity-based incentives to proxy for the manager-shareholder incentive alignment.

Core, Guay and Verrecchia (2003) document that in their sample, for 80% of the CEOs, the

incentives provided by cash pay are less than 10% of the incentives provided by the stock and

stock option portfolio. In addition, Hall and Liebman (1998) show that fluctuations in the value

of stocks and options portfolio account for about 98% of the CEOs pay for performance

sensitivity. For this reason, similarly to Core and Guay (1999), I use comprehensive measures of

managerial incentives that take into account not only stock ownership but also the entire

portfolio of stock options.20 Using compensation measures that include changes in the total value

of the portfolio of stock and stock options better captures executives concerns regarding the

effect of their actions on their entire wealth and not just their current year pay (Hall and

Liebman, 1998; Core, Guay and Verrecchia, 2003).

I focus on the sensitivity of top executive compensation to stock price and stock volatility

percentage changes given that Brander and Poitevin (1992) and John and John (1993) provide

the theoretical framework on how pay-performance sensitivities affect debt pricing. Furthermore,

Baker and Hall (2004) argue that the sensitivity measure, defined as the change in equity

19 Pearson correlation statistics show that the presence of the collateral requirement is significantly positively

correlated with the presence of other general covenants (0.63 at 1%, two tailed t-tests) and financial covenants (0.48 at 1%, two tailed t-tests) This finding is consistent with banks viewing the collateral requirement and the other covenants as complements rather than substitutes.

20 Hanlon, Rajgopal and Shevlin (2003) document that stock option grants to executives are consistent with an incentive alignment between managers and shareholders. The inclusion of the value of the option portfolio is also consistent with Guay (1999) who argues that stock options provide better incentives than stock holdings.

14

portfolio value due to a 1% change in stock price, is the right measure to use when executives’

actions affect firm percentage returns.

The computation of pay-performance sensitivities follows two steps (see Core and Guay,

1999 and 2002). First, I compute the value of the stock option portfolio using the One-Year

Approximation Method. Briefly, the option portfolio value is computed by including current year

stock option grants as well as previously granted exercisable and unexercisable options using

average estimates of exercise prices and time to maturities for the previously granted options.

The algorithm computes the Black-Scholes value of the previously granted options using the

current year proxy statement data as reported by Execucomp.

Second, I compute sensitivities of the total equity portfolio with respect to changes in stock

price and stock volatility. The total equity portfolio is the sum of the stock option portfolio

(computed at the first step) and the restricted stock portfolio. The first proxy is Total Pay-

Performance Sensitivity (TPPS) and is calculated as the change in the value of the equity

portfolio due to a 1% change in the stock price. More equity compensation increases the

sensitivity of the equity portfolio to the stock price appreciation. The second proxy is Pay-Risk

Sensitivity (PRS), computed as the change in the value of the stock option portfolio due to a 0.01

change in the annualized standard deviation of stock returns. This sensitivity measure captures

another dimension of managerial incentives. Since options’ value increases with stock price

volatility, executives have incentives to engage in risky investments thus becoming more aligned

with shareholders.

The above two proxies for managerial incentives are potentially measured with error either

due to the inappropriateness of the Black-Scholes model to price executive stock options (e.g.,

Yermack, 1995) or due to the simplifying assumptions made by the One-Year Approximation

Method when calculating the value of previously granted options.21 To alleviate this concern and

to provide a more direct test of the theory developed by Begley and Feltham (1999b), I compute

a third measure, the Level of Current Year Equity Compensation as a percentage of total annual

compensation (EQ/TC). Total compensation includes salary, bonuses and long term benefits as

well as stock options and restricted stock granted.

I compute the three manager-shareholder alignment measures for both the CEO and the top

21 Rajgopal and Shevlin (2002) note that sensitivity measures computed using the Black-Scholes Model likely

overstate managerial incentives. However, any potential overstatement of managerial incentives implies that estimated coefficients are understated in multivariate tests.

15

five executives since there is some uncertainty about who has more control over financial

reporting and investment choices.22 In order to reduce the influence of outliers, I winsorize the

incentive variables at the 1st and 99th percent of their distribution. Alternative mechanical outlier

removal techniques (removal of observations with absolute studentized residuals in excess of 3.5

and/or Cook’s Distance measure greater than 1) do not affect the results. Table 2, Panel C

presents summary statistics on managerial incentive measures at the top executive level. The

mean change in the equity portfolio due to a 1% change in the stock price is $1.09 million while

the mean sensitivity to stock volatility is $0.29 million. Also, the average compensation package

pays 43% of the annual total in restricted stock or stock options. Similarly to the findings of

Aggarwal and Samwick (1999) and others, the statistics reveal skewness in the compensation

data with the medians being substantially lower than the means. Overall, the numbers are higher

than what other studies report (e.g., Core and Guay, 1999) because they are computed for the top

five executives instead of just the CEO. Another explanation is that borrowing firms in the

syndicated loan market are larger.

3.4. Ex Ante Conditional Conservatism Measures

I define ex ante accounting conservatism as conditional accounting conservatism measured at

the firm level in the period prior to the loan contracting date in the spirit of Beaver and Ryan

(2005) and Ball and Shivakumar (2005, 2006). Basu (2005), Ball, Robin and Sadka (2006) and

Ball and Shivakumar (2005, 2006) argue that conditional conservatism enhances contracting

efficiency by timely reflecting (negative) shocks to contemporaneous economic income. It thus

provides new information that triggers lenders’ responses in terms of debt contract design.

Although not providing insights into the role of managerial reporting incentives, recent research

documents that the debt market reflects conditional accounting conservatism choices. Ahmed et

al. (2002) document that accounting conservatism is associated with more favorable debt ratings

while Moerman (2006) finds that accounting conservatism decreases the bid-ask spreads of

syndicated loans traded in the secondary market thus reducing the information asymmetry among

debt market participants.

This paper does not address directly the issue as to what specific accounting methods are

used when engaging in conservative accounting reporting. It employs several conditional

22 Although the results are presented only for the top-five executive measures, I replicated successfully the main

findings with incentive measures computed only for CEOs.

16

accounting conservatism proxies suggested by prior research, each with its pros and cons. I

measure the differential timeliness (or conditional conservatism) by the accounting earnings’

response to bad news relative to good news, as proxied by signed stock returns (Basu, 1997). A

higher coefficient on bad news in an earnings reverse regression is consistent with more

accounting conservatism. In my setting, these timely recognition of losses improves the

effectiveness of loan contracts that are based on income statement and balance sheet variables.

I estimate ex ante conditional conservatism at the firm level using accruals instead of

earnings levels. I require at least 20 consecutive firm-quarterly observations (i.e., 5 years of data)

to be available before the loan date. In addition, to mitigate potential measurement errors due to

model misspecification, I employ three models for accruals which are well known in the

literature and which are tested recently by Ball and Shivakumar (2006): Basu (1997), Dechow

and Dichev (2002), and Jones (1991). There are few modifications to the standard models. First,

the dependent variable in the Basu (1997) model is total accruals instead of earnings levels since

accruals better capture conservative accounting choices. Second, the models of Dechow and

Dichev (2002) and Jones (1991) are extended to account for the asymmetric timelines in a

similar fashion as the one proposed by Basu (1997). The Dechow and Dichev model captures the

specific accrual mean reversion function and accruals’ correlation with cash flows while the

Jones model controls for factors that affect the level of accruals, independently of managerial

intervention:

-Basu (1997) Model

0 1 2ACC DC AR DC ARα β β β ε= + + + ⋅ + (1)

-Dechow and Dichev (2002) Model

0 1 2 3 4 1 5 1t t tACC DC AR DC AR CF CF CFα β β β β β β ε− += + + + ⋅ + + + + (2)

-Jones (1991) Model

0 1 2 3 4ACC DC AR DC AR REV GPPEα β β β β β ε= + + + ⋅ + ∆ + + (3)

where ACC is current year accruals, AR is quarterly market adjusted return,23 DC is an indicator

variable that equals one if AR is negative and zero otherwise, CF is cash flow from operations,

∆REV is the change in net revenues, and GPPE denotes gross property plant and equipment.

23 Removing market returns from firm specific raw returns controls to a limited extent for changes in discount

rates (i.e., only those changes driven by macro-economic conditions). For a more comprehensive approach see Callen, Hope and Segal (2006).

17

Consistent with prior research, I scale all Compustat variables by average total assets.

I measure asymmetric timeliness by the ratio (β1 + β2)/ β1, similarly to Pope and Walker

(1999) and others. However, this measure is not robust when estimated over short periods of

time and when estimated at the firm level (see Roychowdhury and Watts, 2004; Givoly, Hayn

and Natarajan, 2004). To minimize this issue without unnecessarily imposing strong data

availability constraints due to limited firm specific time series data, I rank the asymmetric

timeliness ratios obtained for each model within industry.24 I then compute the first conditional

accounting conservatism measure (Average Rank) as the average of the three ranked ratios.

In unreported analyses, I use the ranks of each model separately and the inferences do not

change. This result is not surprising since the measures are highly correlated. In addition, I

measure “news” by operating cash flow changes instead of abnormal stock returns, similarly to

Ball and Shivakumar (2005, 2006), and obtain similar results.

Other important limitations of asymmetric timeliness measures for conservatism are pointed

out by current research (e.g., Callen, Hope and Segal, 2006; Dietrich, Muller and Riedl, 2005;

Givoly, Hayn and Natarajan, 2004). To avoid these critiques, I measure the ex ante conditional

conservatism more directly using balance sheet items. The first measure is the accumulated non-

operating accruals over the five year period prior to the loan date (Givoly and Hayn, 2000).25

Although part of these non-operating accruals is mandated by GAAP, their timing and size are

left to management to decide upon. I standardize this measure by the size of total assets

accumulated over the same period to allow for cross-sectional comparisons and to control for

firm-specific growth. A more negative accumulation of non-operating accruals is consistent with

more accounting conservatism.

Finally, I use the level of special items scaled by total assets in the year just prior to the loan

24 To investigate the effect of measurement errors in the conservatism proxies derived from reverse regressions, I compute the quarterly DOM measure proposed by Givoly, Hayn and Natarajan (2004) using daily accumulation intervals. The measure captures the extent of the measurement error due to the fact that quarterly aggregated news (i.e., signed stock returns) is employed in the estimation: DOM=|CUM+ - |CUM-|| / max{|CUM+|, |CUM-|} where CUM+ and CUM- denote accumulation of positive and negative firm stock returns over the quarter, respectively. If DOM is close to 1 then the differential timeliness measures from the three models correctly indicate conservatism in accounting. I compute an average DOM measure across all firm-quarters used in the estimation of the three models for each firm. I then split the firms in three (or five) portfolios based on the magnitude of the average DOM. I find no significant differences (based on two-tailed t-tests) between the average timeliness measures of each portfolio. This suggests that if there is any measurement error due to return aggregation, then it is uniformly distributed across the firms in my sample. In other words, in-sample rankings are not affected.

25 Non-operating accruals are computed using annual Compustat data as follows: Non-Operating Accruals = Total Accruals – Depreciation & Amortization - Operating Accruals where Operating accruals = ∆Acc. Receivable + ∆Inventory + ∆Prepaid Expenses - ∆ Acc. Payable - ∆ Taxes Payable.

18

origination date as the third proxy capturing ex ante conditional conservative accounting.26

GAAP requires material non-recurring or unusual items to be reported separately as part of the

operating income. Therefore, the level of special items in the prior year is probably the most

direct measure of conditional conservatism because it reflects asset write-downs and write-offs,

restructuring charges and gains/losses on contingencies and asset sales. Recognition of these

items reflects managerial discretion due to the subjectivity in assessing both the probability and

the size of such losses and gains. A downside of this measure is that it does not capture

accounting conservatism impounded in working capital accruals and liabilities thus understating

the true level of conditional conservatism.

Table 2, Panel A presents summary statistics for the ex ante conservatism variables.

Consistent with Givoly, Hayn and Natarajan (2004), I find that asymmetric timeliness measures

estimated at the firm level for all three models yield negative first quartiles (even the median is

negative in the case of Basu’s Model). The noisiness of these measures is well known thus

justifying the ranking and the aggregation procedure described above. As expected, the

cumulated non-operating accruals mean is negative (Givoly and Hayn, 2000). Also, mean

special items are negative in the year prior to the loan date although the median is zero. In the

main tests below, I switch the sign of the non-operating accruals and special items measures such

that greater values mean more ex ante conditional conservatism. Unreported correlation statistics

show significant and positive correlations across all ex ante conservatism metrics.

3.5. Control Variables

In multivariate tests, I estimate two separate models (one for loan spreads and one for the

covenant index) that employ two types of controls, loan and firm-specific characteristics.

3.5.1 Loan Characteristics In the spread model, I use the logarithm of the loan size (Loan Size)

as a control for the syndicate’s risk exposure as well as a proxy for demand and supply factors

that drive market liquidity. Investors and banks put a premium on the ability to package and sell

loans, thus liquidity has become a very important factor lately (Standard and Poor’s, 2004).

Smaller loans are expected to be priced at a premium to larger ones although the premium is

mitigated by the lower syndicate exposure. In the covenant model, I use the logarithm of the deal

size (Deal Size) since the covenant requirement is set at the deal level (banks share the cost of

26 Recent studies have used special items to measure conservatism (e.g., Shroff, Venkataraman and Zhang, 2004 and Callen, Hope and Segal, 2006).

19

monitoring across all loans in the deal). Another loan specific characteristic is Maturity, defined

as the maturity of the loan in years. Shortening loan maturity limits the risk which results in

lower spreads and fewer covenants. Lenders, defined as the logarithm of the number of lenders

in the syndicate, controls for risk sharing of the bank syndicate. More banks in the syndicate

allow for a better diffusion of risk among syndicate participants hence lower yields are expected.

Also, the size of the syndicate is likely correlated with the loan (deal) size therefore its omission

could bias coefficients in multivariate regressions. I further control for Performance Pricing

features (indicator variable that equals one if the interest rate is tied to firm performance and/or

loan rating and zero otherwise) and Loan Type (indicator variable that equals one if the loan is

revolving and zero otherwise). Performance pricing features decrease the risk and the monitoring

costs of the lender in the post-contracting period since interest payments adjust automatically

based on the firm’s credit quality. Revolvers provide the option of renewing the loan in the

future, an option whose value increases with maturity thus lenders bear lower risk. Finally, I

control for loan rating (Rating Group) to account for the information collected by rating agencies

about firm quality and credit risk. A lower credit quality as reflected in ratings is likely

associated with higher loan spreads and more covenants. I transform the letter group ratings into

numbers such that investment grade loan ratings (Aaa to Baa ratings) are set from 1 to 4 while

speculative grade loan ratings (Ba to C ratings) are assigned large numbers (5 to 9).

3.5.2 Firm-Specific Characteristics Firm size, computed as the logarithm of market value, is

probably the most important control variable. Larger firms are able to obtain better terms given

their reputation and tangible asset size. Also, there is less information asymmetry associated with

them. Another control is Growth opportunities defined as the Book-to-Market ratio.27 Smith and

Watts (1992) predict that firms with more growth options employ more stock option based

compensation therefore this measure is an important correlated variable. Firms with low growth

opportunities (i.e. high B/M) may pay higher spreads given that lower future cash flows are

expected. However, firms with low growth are safer thus banks may ask for fewer covenants

and/or lower spreads. Profitability (return on Total Asset value) and Liquidity (ratio of Operating

Cash Flows to Total Assets) control for firm’s ability to generate profits to pay back the loans.

Firms with low liquidity and profitability are expected to pay larger interest rates and to have

27 Book-to-Market can also be interpreted as a control for the level of unconditional conservatism (Roychowdhury

and Watts, 2004).

20

more covenants. These firms are more tempted to issue additional debt and/or sell assets,

increasing the risk to the lender. Indebtedness, defined as the ratio of total liabilities to total

assets, controls for the firm’s financial flexibility. Firms with high debt have a higher probability

of future insolvency, regardless of the number of covenants required. Asset Tangibility computed

as the tangible asset ratio (ratio of Property, Plant and Equipment plus Inventories to Total

Assets) controls for the quality of the loan’s collateral; better collateral requires fewer covenants

to monitor.28 Firms with a higher proportion of tangible assets alleviate moral hazard problems

because they stand to lose more if they default (Strahan, 1999). Additionally, firms’ leverage

decision is highly correlated with the amount of assets that can be pledged as collateral when the

loans are secured. Finally, I use Firm Complexity (number of lines of business and geographic

segments reported) to control for lender’s expected monitoring costs (e.g., Hope et al., 2005).

Complex firms are harder to monitor with covenants thus banks may require a simpler covenant

structure while protecting themselves by demanding higher interest rates.

4. Results

This section explores in detail the relation between the managerial incentive structure and ex

ante conservatism and the terms of syndicated loans as measured by the loan spreads and the

covenant index.29 The first subsection presents tests for the first hypothesis while the second

discusses the tests of the remaining hypotheses.

4.1. Test of the First Hypothesis

To provide preliminary evidence on the relation between managerial incentives and loan

spreads, I construct portfolios of loans based on the three incentive proxies discussed in Section

3.3 (TPPS, PRS and EQ/TC) as well as firm specific characteristics which are important drivers

of loan spreads (leverage, profitability and liquidity). First, I run regressions of managerial

incentive measures on firm size (logarithm of market capitalization) because the incentive

proxies are affected by firm size (i.e., incentive measures are orthogonalized on firm market

28 In unreported analyses, I run the regressions in Table 4 using a dummy variable for the presence of the

collateral and the results are similar for all incentive measures used. However, I detected multicollinearity problems because most of the loans in my sample are secured.

29 I verify whether multicollinearity problems exist for all regressions presented in this section, given the number of control variables used. I find that the highest variance inflation factor for any regressor is about 3.5, well below 10 which is the threshold indicator for multicollinearity problems. Condition indices provide exactly the same conclusion.

21

value to remove the effect of firm size on equity compensation). I employ this methodology only

for the bivariate analysis since in the multivariate tests that follow I control separately for the

market capitalization. Second, residuals of these regressions are split in two groups, above and

below the median, and then intersected with quintile groups formed based on firm

characteristics. The results are reported in Table 3. Differences between average spreads of loan

portfolios corresponding to low managerial incentives (i.e., below the median equity

compensation) and loan portfolios with high managerial incentives are both statistically and

economically significant in all cases (Table 3, last three columns). The results indicate a positive

relation between loan spreads and the manager-shareholder incentive alignment measures,

irrespective of the firm specific controls used. Hence, Table 3 provides preliminary evidence on

the first hypothesis that banks demand higher spreads from borrowers with closely aligned

managers.

The results of t-tests in the bivariate analysis indicate systematic differences in the loan

spreads across the two groups reflecting the importance of managerial incentives. However, a

multivariate setting is more appropriate since relevant firm and loan characteristics can be

controlled for. The primary model specification that provides a test for the first hypothesis is

estimated in Table 4. For each of the three managerial incentive variables (Manag), I estimate

two separate models, one for loan spreads and one for the covenant intensity index:

0 1 2 3( . )Spread Cov Index Manag Loan Controls Firm Controlsα α α α ε= + + + + (4)

Dependent variables as well as the loan and firm controls are discussed in the prior section.

To account for industry specific impacts, I estimate the regressions using industry fixed effects.30

As a sensitivity test, I also present results for a fourth incentive variable, Pay-Performance

Sensitivity (PPS), which is defined as the sensitivity of the total stock option portfolio to a 1%

change in the stock price.

Consistent with the hypothesized predictions, I find that all coefficients of managerial

incentive proxies are significant and positive suggesting that high manager-shareholder

alignment is associated with larger loan spreads and more covenants (see Table 4). This evidence

30 As a sensitivity analysis, I have estimated the regressions using also annual fixed effects to control for time

changes in the syndicated loan market liquidity and/or other macroeconomic factors. In addition, I have eliminated loans that are in foreign currencies to eliminate the potential influence of foreign exchange risk on loan spreads (only 0.54% of the loans are not denominated in US dollars). Inferences are not affected by these alternative tests.

22

implies that bank syndicates price information about borrowers’ future aggressive behavior that

is embedded in the executive compensation structure.31

In general, the signs of the control variables are in the expected direction. The results show

that larger spreads are associated with more covenants and vice versa, consistent with Standard

and Poors’ (2004) views and with the findings of Bagnani et al. (1994) in the public debt market.

Loan spreads are negatively associated with the size of the loan, the presence of performance

pricing, the number of lenders and the firm profitability. The signs of these variables switch in

the covenant model, consistent with the fact that spreads and covenants fulfill different roles in

the debt contract. Loan maturity is not significant in the spread model (Bradley and Roberts

(2004) find a similar result) possibly because variables such as loan rating and the indicator

variable for revolving loans are correlated with the loan maturity. Among the control variables,

leverage is the most important driver of both loan spreads and the covenant intensity. The sign

in the covenant model is negative in agreement with the interpretation that, for highly leveraged

firms, banks require larger spreads instead of more covenants.

Although t-statistics are based on standard errors adjusted for heteroskedasticity (White

standard errors), as a sensitivity analysis, I compute Huber-White clustered standard errors (firm

clusters) and Newey-West standard errors (two year lags) and the inferences are the same. In a

latter section, I run additional analyses to mitigate concerns related to simultaneity between

spreads and covenants and related to the estimation methodology.

4.2. Tests for the Second and Third Hypotheses

Table 5 tests the predictions for the remaining hypotheses using the total pay sensitivity

measure (TPPS) as a proxy for managerial incentives alignment. I estimate the following models,

one for loan spreads and one for the covenant index:

0 1 2 3 4( . )Spread Cov Index Cons Manag Cons Manag Controlsβ β β β β ε= + + + ⋅ + + (5)

The models are estimated separately for each of the three ex ante conditional conservatism

proxies (Cons) discussed above. I introduce ex ante accounting conservatism both as a main

31 For higher levels of ownership, managers may be increasingly sensitive to non-diversifiable firm specific risk,

thus acting in the interest of bondholders (Bagnani et al. 1994, Ortiz-Molina, 2005). I measure the managerial ownership levels as the ratio of restricted firm shares owned and the number of shares related to exercisable and unexercisable stock option grants to the total number of common shares outstanding. I find a large percentage ownership (i.e., more than 25%) for only 275 loans (4.06% of the total sample). After removing these observations, coefficients of all incentive variables in Table 4 increase (also, the t-statistics are larger).

23

effect and as an interaction effect with the managerial incentive variable (Manag) which plays a

moderator role. Loan and firm-specific controls (Controls) are presented in Section 3.5. In order

to reduce multicollinearity concerns and to facilitate the interpretation of the coefficients

obtained, I center all variables on the independent side of the regression by subtracting out their

sample mean. This is the standard approach when continuous variables are interacted (see Aiken

and West, 1991).32

In Table 5, the results for loan spreads indicate that ex ante accounting conservatism provides

benefits to the borrower by lowering the cost of debt, consistent with predictions of contracting

theory (Watts, 2003a,b; Ball, Robin and Sadka, 2006) and the second hypothesis (H2). Most

importantly, this result is robust to alternative ex ante conditional conservatism specifications

(presented in different columns). To illustrate, consider the regression where accounting

conservatism is proxied by the average of within-industry ranks of timeliness measures estimated

for the three models presented in Section 3.4 (first column). The main effect (β1) shows a drop in

loan spreads of 14.30 basis points. This effect represents the impact of accounting conservatism

when the total pay-performance sensitivity measure is at the sample mean (since the data is

centered, the effect of the managerial incentives is zero). The decrease in spreads is even larger

when managerial equity incentives are below the mean. However, the strength of the negative

relation between ex ante conservatism and loan spreads weakens as managers’ pay-performance

sensitivity increases above the mean. The interaction effect coefficient (β3) is positive and

significant suggesting that, on average, the benefit of the accounting conservatism is reduced by

14.28 basis points per unit change in the managerial incentive measure above the mean or

increased by 14.28 basis points per unit change in the managerial incentive measure below the

mean.

Largely, all interaction coefficients are positive and significant in the spread models,

consistent with H3a. The results suggest that ex ante conservatism has positive economic

consequences to the borrower (i.e., the cost of debt as proxied by loan spreads is smaller).

Nevertheless, these benefits are trimmed when companies provide more equity compensation to

align the top management with the interests of shareholders. Although due to space limitations, I

report results only for the sensitivity of total equity portfolio with respect to a 1% change in the

32 I also orthogonalize the conservatism variables on the incentive proxies or center based on sample medians.

Results of these approaches as well as those from using non-centered data are very similar to the ones reported.

24

stock price (i.e., TPPS), the results are similar for the other two incentive variables.

Table 5 also provides the results of the effects of accounting conservatism on the covenant

restrictiveness in loan contracts (see Covenant Index columns). Contrary to the prediction of H2

with respect to covenants, I find almost no evidence that bank syndicates decrease the number of

covenants when firms choose ex ante conditional accounting conservatism and the total

sensitivity measure is equal to the mean (i.e., β1 is insignificant). However, in the test of H3b, I

find a negative and significant coefficient for the interaction coefficient (β3) meaning that ex ante

accounting conservatism is associated with more covenants when managerial equity

compensation is low. Alternatively, when managers receive high equity compensation, ex ante

accounting conservatism is associated with fewer covenants.

In Table 6, Panel A I find similar results when I rerun the regressions using as a dependent

variable the financial covenant index that counts the number of financial covenants in the

contract. One would expect that the ex ante accounting conservatism mainly affects the

monitoring ability of financial covenants. In conclusion, given ex ante conservatism, bank

syndicates’ monitoring relies more on accounting reports when equity compensation is low (i.e.,

more financial covenants) and less when equity compensation is high.

There are two possible interpretations of the result obtained for high equity incentives. First,

lenders require less monitoring in the post contracting period because they may view ex ante

accounting conservatism as a bonding mechanism of highly aligned managers. Second, the

expected managerial aggressive behavior of highly aligned managers coupled with reporting

reserves induced by ex ante conservatism might reduce the monitoring ability of financial

covenants as predicted by Sridhar and Magee (1997). Hence, lenders price-protect themselves by

demanding an ex ante premium in terms of interest rates. To discriminate between these two

alternatives, I perform a more in-depth analysis of few financial covenants (Panel B, Table 6).

Specifically, I compute the slack of six financial covenants: Debt to Equity, Debt to Tangible Net

Worth, Interest Coverage, Net Worth, Tangible Net Worth and Current Ratio (see Appendix A

for computational details). I then split the sample into four loan portfolios based on whether

managerial incentives and ex ante conservatism measures are below or above the median.

Although the six financial covenants are chosen because I expect a lower measurement error

in the slack due to their clear definition, there are still some caveats. First, it is possible that

accounting measures used in these financial covenants are the result of modified GAAP rules.

25

Leftwich (1983), Watts and Zimmerman (1990) and Beatty, Webber and Yu (2006) argue that

the modifications usually make the numbers more conservative. For this reason, the standard

formula used in the computation may not necessarily be the right one, despite the fact that I

follow Dealscan’s definitions closely. Second, the database does not indicate the first quarter in

which the firm has to meet the financial covenant. It can be the immediately following quarter,

two quarters ahead or more. To mitigate these potential measurement errors, I standardize the

slacks by the contract-required measures (see Appendix A). I also winsorize the top and bottom

1% of the slacks’ distribution to reduce the influence of outliers and discard those slacks with

negative values. By construction, slack measures are expected to be positive, as it is unlikely that

the firm is in technical default at loan’s origination. It is reasonable to assume that the

computation errors (especially when negative slacks are obtained) are randomly distributed

across the sample observations with no systematic effect on the reported results.

The results reported in Table 6, Panel B compare the slacks across portfolios with high

conservatism / low equity incentives and high conservatism / high equity incentives. In other

words, conditioning on the implementation of more ex ante conservative accounting policies, is

there a difference in slacks of financial covenants between high and low managerial incentives

groups? Overall, I find that bank syndicates tend to require tighter covenants (i.e., lower slacks)

when managers receive more equity incentives (differences in covenant slacks are statistically

significant across the two portfolios). The evidence is robust to alternative ex ante conservatism

measures used to construct the loan portfolios. The findings of Table 6, Panel B are consistent

with the second interpretation that financial covenants lose their monitoring value.

In conclusion, lenders not only require fewer covenants but they also tighten up the

remaining financial covenants when managers are highly aligned with shareholders. This result

is consistent with the recent findings of Beatty, Webber and Yu (2006) who document, without

conditioning on managerial reporting incentives, that lenders introduce conservatism in debt

covenants through contractual adjustments when borrowers’ financial statements are more

conservative ex ante.

26

5. Sensitivity Analyses

5.1. Alternative Measures for Covenant Restrictiveness

I verify whether the main results for the first hypothesis related to the effects of managerial

incentives on the terms of syndicated loan contracts (presented in Table 4) are sensitive to four

alternative covenant intensity measures which are presented in detail in Appendix A. The first

proxy is a demeaned measure of the covenant index used in the main test. Means are computed

annually within groups defined by 2-digit SIC industry codes and by rating categories.

Subtracting industry-ratings specific means is a simple way of removing rating-specific credit

risk and industry effects.

The second measure is an index that counts the number of financial covenants. Unreported

correlation statistics show that the number of financial covenants is highly correlated (but not

perfectly) with the number of general covenants. One advantage of this proxy (as well as the

main index measure used in Table 4) is that it quantifies the restrictiveness of covenants by

utilizing only loan specific data which are not influenced by potential sample selection biases

specific to relative measures.

A third proxy is measured using the slack of eleven financial covenants that could be

computed using Compustat quarterly data. In contrast to the previous section, I do not delete

slacks for which I obtain negative values to increase the sample size. To mitigate inherent

measurement errors (discussed in the previous section), I standardize the slacks by the contract

required measures (i.e., compute percentage slacks) and then rank them in sample within

industry and rating groups. Finally, I compute a loan specific average of these ranks. Low rank

averages suggest that the borrower faces tighter covenants (i.e., it has lower slacks relative to its

peers). The standard assumption is that the ranking procedure eliminates outliers and distributes

the measurement error evenly across loan observations. A potential advantage of this proxy is

that covenants’ tightness is computed relative to industry peers, thus providing a complementary

measure to the previous metrics.

The last alternative measure is a factor extracted from the set of covenant proxies presented

above using Principal Component Analysis (PCA). There is significant positive correlation

among the covenants, suggesting that factors affecting one covenant are likely to affect another

hence there may be relatively little loss of information in an aggregate measure. Using the

eigenvalue standard, I find only one factor with an eigenvalue greater than 1 (2.45). The factor

27

explains approximately 61.18% of the variation of the set of underlying variables.33

Table 7 presents the coefficients of the manager-shareholder incentive alignment variables

for each covenant measure. Recall that in the spread model the covenant intensity is a control

variable while in the covenant model it is the dependent variable. Control variables are included

in estimation of each regression but are not reported in the table. Overall, the results are quite

robust to these alternative covenant measures. Incentive variables continue to be significantly

positive in both the spread and the covenant models.

In unreported analyses, I perform two additional tests.34 First, I run a probit model to predict

the presence of the Asset Sales Sweep (see Appendix A) which directly captures the concern of

the bank syndicate over managerial wealth expropriation. Second, I run an ordered probit model

to predict the number of Sweep covenants in the contract (overall, sweeps limit over-investments

in risky projects). In both tests, I use the same managerial incentive proxies as well as loan and

firm specific controls. I find that the managerial incentive proxies load positively and

significantly in the models, consistent with the main results using the covenant index.

5.2. Alternative Estimation Methods

The bank syndicate determines both the loan spreads and the covenant intensity

simultaneously. Thus, the choice of one decision variable will affect the other, creating

simultaneous equation bias in the estimated managerial incentives slope coefficients. To address

this problem econometrically, I employ a two stage least squares (2SLS) method that estimates

jointly the spread and the covenant models. At the first stage, I estimate the predicted values of

the endogenous variables, using all exogenous variables in the system.

The results of this sensitivity test for the first hypothesis are presented in Table 8. The

managerial incentive coefficients continue to be significant and positive in all three models. To

control just for potential cross-equation correlations in the error terms, I also estimate Seemingly

Unrelated Regressions (not tabulated). The results are robust to this alternative estimation

technique. A Hausman Specification Test (m-statistic) is used to determine if it is necessary to

use an instrumental variables method (2SLS and 3SLS) rather than a more efficient OLS

33 To provide evidence that the set of four covenant proxies is measuring the same underlying construct (a single

latent variable) and to check the reliability of the construct, I compute the standardized Cronbach’s alpha which is equal to /[1 ( 1) ]N r N r⋅ + − (N is the number of variables used and r is the mean inter-item correlation). The value of the alpha is equal to 0.716 which is above the reliability benchmark (Nunnally, 1978).

34 I thank Jerry Feltham and Joe Webber for suggesting these tests.

28

estimation. In all models, the Hausman Test is insignificant thus the null hypothesis that OLS is

superior cannot be ruled out (tests are reported in Table 8 only for OLS and 2SLS

comparisons).35

Another potential problem with the main results reported in Table 4 is that cross-sectional

dependence in the error terms might bias the results due to the fact that a firm might have

multiple loans in a year. To avoid this problem, I construct 1,000 samples of unique firm-year

observations by randomly drawing loans from the initial sample. In case firms have more than

one loan facility in a year, the procedure will pick randomly only one of them. Each sample

drawn from the large pool of 6,768 loans has 4,138 unique firm-year observations.

Table 9 presents average coefficients across the 1,000 regressions for all three managerial

incentive variables and the percentage of coefficients in each significance level group. Panel A

presents average coefficients for the spread and covenant models estimated by OLS. The results

are robust to the random sampling procedure: most of the time the significance levels are at 1%. I

also estimate the covenant model by maximum likelihood since the dependent variable, the

covenant index, is not a continuous but a discrete count variable by construction. Panel B

presents the average coefficients from running Tobit and Poisson regressions. Tobit regressions

account for the truncation at zero where there is a mass point of observations while Poisson

regressions are the standard estimation procedure for count data (Wooldridge, 2002).36 Again,

the results are robust and even stronger in terms of significance levels for all three incentive

variables.

5.3. Endogenous Managerial Incentives

In the main results section, I assume that managerial incentives are optimally determined

before the loan contract date (or before the debt financing decision is made). However, the

assumption of exogenous managerial pay may not stand if managerial equity compensation is

offered jointly with the debt contract or in anticipation of the debt contract. Thus, an obvious

concern about causal inferences exists. One way to control for potential joint endogeneity

problems is to take advantage of external shocks outside the control of the firm or stakeholders.

35 Madalla (1977) indicates that OLS is more robust against specification errors and that predictions from OLS

models often compare favorably with those from simultaneous equations methods. Alternatively, the assumption that the instruments I use are truly exogenous may not apply (see Larcker and Rusticus (2005) for an investigation of the pitfalls of instrumental variable methods in the accounting research).

36 Negative binomial regressions produce similar results.

29

In the accounting research, the best such settings are usually regulatory pronouncements. A

second way, which is feasible in my setting, is to use available statistical techniques.

I implement two approaches that deal with the endogeneity of managerial incentives.37 The

first approach is the Heckman (1979) methodology which controls for managerial incentives’

endogeneity by eliminating the bias due to firms self selecting to pay high (or low) equity

compensation. Self-selection is based on unobservable variables (i.e., latent variables) that drive

the compensation decision. The approach of Heckman (1979) involves a two-stage estimation:

the first stage Probit model that models the decision to offer or not high incentive pay and the

second stage OLS model that explains the loan spreads and covenant intensity.

The second approach is due to recent econometric developments in the labor economics

literature and is called Propensity Score Matching (PSM) (Rosenbaum and Rubin, 1983; Dehejia

and Wahba, 2001; Benjamin, 2003). The method is new to the accounting literature therefore I

present a detailed description of the estimation in Appendix B. The procedure controls for

endogeneity of managerial incentives by eliminating the bias due to firms’ selection on

observable variables thereby supplementing Heckman’s approach. The basic idea is to match

treatment firms, that is, firms that offer high managerial incentives, with control firms based on

the probability of offering high managerial incentives.38 Matching on this probability (called

“propensity score”) is more efficient than the traditional matching methods that can search

among only few dimensions (e.g., firm size and profitability, industry, etc.) because it avoids the

“curse of dimensionality”. This problem occurs because it is difficult to find a proper match

when more dimensions are taken into account.

The purpose of PSM is to estimate the so called average treatment effects. In my case, these

are differences between loan spreads and covenant intensity across firms that choose to highly

align managers and shareholders and firms that do not make this choice. In the first stage, I use a

stepwise logistic analysis to estimate the probability that the firm offers high managerial

incentives using observable variables that are found by prior literature to predict equity

compensation. I use the following variables in the stepwise selection procedure: (1) Firm Size

37 I also implement a third approach by matching the loans with managerial incentive data from two or three years

prior. Estimating the spread and the covenant models using lagged compensation variables mitigates concerns that shareholders pay managers conditional on the terms of loan contracts. The results (unreported) are qualitatively similar to the ones reported in Table 4 although the significance is reduced in the covenant model (10%).

38 The control firms should be similar to the treatment firms along all dimensions used to estimate the probability of offering high managerial incentives except that they do not offer high managerial incentives.

30

(Log of Total Assets), (2) Percentage Sales Growth, (3) Return on Net Operating Assets

(Operating Income after Depreciation divided by the sum of Property, Plant and Equipment and

the Working Capital), (4) Loss Dummy (equal to 1 of there is an Operating Income Loss, 0

otherwise), (5) Growth Opportunities (Book to Market), (6) Leverage (Total Liabilities/Total

Assets), (7) R&D Intensity (R&D Expenditures/Total Sales), (8) Advertising Spending

(Advertising Expenditures/Total Sales), (9) Capital Intensity (Capital Expenditures/Total Sales),

(10) Manager Tenure, (11) Number of Board Meetings and (12) industry fixed effects. All these

variables represent the dimensions across which the firms are matched. The stepwise selection

procedure selects all variables and the estimation finds most to be significant in the expected

direction (results not reported). The same variables are used to estimate the first stage Probit for

the Two-Stage Heckman methodology.

Because both PSM and the Two-Stage Heckman use cross-sectional data, I select firms based

on their last year of loan data in the sample. To classify firms into the treatment group (firms

paying high equity compensation) and the control group (firms paying low equity

compensation), I first rank the three managerial incentives measures (using only the last year of

loan data). The top 50% of the firms based on the sum of these three ranks are assigned to the

treatment group (504 firms) while the rest are then assigned to the control group (510 firms).39

To implement PSM, I follow the six steps presented in detail in Appendix B. At Step 2, I

eliminate 52 treatment firms and 2 control firms that are not in the region of common support (in

the case of treatment firms, their Logit predicted scores are greater than the maximum predicted

score obtained for control firms). According to Step 4, I start with five groups of firms

constructed based on the magnitude of the Logit function computed at Step 3. Following the

balancing tests at Step 5, I split the last group of firms in three subgroups (the final number of

groups is 7). The average treatment effect (ATT1) and its significance are computed according to

the formulas presented at Step 6 by aggregating the treatment effects corresponding to each

group of firms (see Panel A, Table 10). In addition, I compute an alternative measure, ATT2,

using a caliper matching without replacement approach. Treatment firms are matched without

replacement with control firms based on an acceptable magnitude of the difference in the Logit

functions of the two firms (see bottom of Appendix B for details).

39 I obtain similar results when I use only the top and bottom quartiles to classify the firms in the treatment and

the control groups.

31

In Table 10, Panel A I find that all treatment effects, both for spreads and the covenant index,

are significant and positive. This result suggests that firms offering high equity compensation

pay higher loan spreads and receive more covenants relative to firms that offer low equity

compensation, after controlling for selection based on observable drivers of equity compensation

choices. Furthermore, Panel B of Table 10 presents a positive and significant Heckman estimator

for the spread and covenant models. Control variables (unreported) are the same as in Table 4.

Mills ratios do not load in the regressions indicating that the model did not detect any self-

selection based on unobservables. Taken together, the results in Table 10 suggest a causal

relation between managerial equity incentives and both the spreads and the number of covenants

in syndicated loan contracts.

5.4. Alternative Estimation of the Interaction Effects

As an alternative test of significance of the interactions between managerial incentives and

ex ante conditional conservatism proxies (used to test the third hypothesis), I implement a

moderator median split analysis which involves three stages (Jaccard, Turrisi and Wan, 1991).

First, I split the sample of loans in two groups based on median values of the three incentive

variables (TPPS, PRS and EQ/TC). Second, I run separate regressions of spreads (and the

covenant index) on the accounting conservatism proxies and the controls for both the low and

high managerial incentive samples. Third, using the t-test proposed by Jaccard, Turrisi and Wan

(1991),40 I verify whether the accounting conservatism slope coefficients in the two regressions

(high and low compensation) are significantly different. For the spread model, I find that the

coefficients of the accounting conservatism proxies are less negative in the high-managerial-

incentive regressions than in the low-managerial-incentive regressions (differences are

significant at 5% or less). For the covenant model, the sign of the differences reverses:

coefficients in high equity regressions are less positive then in the low equity regressions (the

significance is at 10% levels or less). In conclusion, this analysis provides an independent

statistical test that is consistent with the results presented in Table 5.

5.5. Ex Ante Unconditional Accounting Conservatism

Watts (2003a,b) suggests that both types of accounting conservatism, conditional and

40 The t-statistic is (blow-bhigh)/{[(SSElow+SSEhigh)/(nlow+nhigh-4)]·(ΣXlow+ ΣXhigh)/( ΣXlow· ΣXhigh)}1/2 where b is the coefficient of the accounting conservatism proxy, SSE is the sum of squared errors, n is the number of observations in the group and ΣX is the sum of squared accounting conservatism scores. Each of these variables is computed from the regressions corresponding to the “low” and “high” managerial incentives groups.

32

unconditional, are likely to improve contracting efficiency because they represent pre-

commitments by the firm. Recall that the main tests are based on conditional conservatism

proxies. Therefore, I re-estimate the models in Table 5 using two proxies for unconditional

conservatism measured before the loan contract date (results are not reported). The first proxy is

the “hidden accrual reserves” variable from Penman and Zhang (2002) (PZ measure).41 It is an

appropriate measure of unconditional conservatism because it does not capture current economic

income news once Advertising, R&D and Inventory related expenditures have occurred. As the

second proxy, I use intercepts from the three models presented in Section 3.4 (equations 1 to 3),

estimated using firm-specific regressions (Ball, Robin and Sadka (2006) use similar measures). I

do not use the Beaver and Ryan (2000) measure which captures the bias in book-to-market ratios

since Basu (2001) and Roychowdhury and Watts (2004) argue that it is a noisy proxy for

accounting conservatism.

I find that the interaction coefficients of managerial incentive variables with the PZ measure

are not significant in either the spread or the covenant index models. However, the main effect of

the PZ measure is positive and significant (at 10%) in the spread model and insignificant in the

covenant model. This finding is consistent with lenders viewing unconditional conservatism as a

tool to build up reporting reserves ex ante that could be used to over-report ex post, irrespective

of the managerial incentive structure. The results using the intercepts of the three models are not

significant suggesting either that they are very noisy measures of unconditional conservatism or

that unconditional conservatism is not priced in syndicated loan contracts. I obtain similar results

when I use in-sample ranks of the intercepts.

6. Conclusions

The extant literature provides limited empirical evidence on the pricing of managerial

incentive structures in debt markets. In particular, the evidence is non-existent for a very

important part of the debt market, the syndicated loan market.

This paper builds on the theoretical work of John and John (1993), Begley and Feltham

(1999b), Sridhar and Magee (1997), Douglas (2003) and Levine and Hughes (2005) by providing

direct empirical evidence on the important role of the manager-shareholder incentive alignment,

41 The “hidden accrual reserve” measure is computed as the sum of Inventory, R&D and Advertising reserves

divided by Net Operating Assets. A full description of the computation is presented by Penman and Zhang (2002).

33

as reflected in equity compensation, on the pricing of syndicated loan contracts. My findings are

consistent with the managerial incentive structure being perceived by the bank syndicate as a

pre-commitment mechanism. I find that bank syndicates anticipate and price managerial

incentives to invest in risk increasing projects and expropriate wealth induced by high equity

compensation. Higher managerial incentive alignment is associated with larger loan spreads and

more restrictive covenants. These results are robust to a series of sensitivity checks such as

allowing for endogenous managerial contracts, alternative covenant intensity measures, and

alternative estimation methodologies.

This paper also analyses the interaction between managerial and debt contracts to provide

evidence on the economic consequences of ex ante accounting conservatism on the terms of

syndicated loan debt. In this respect, I provide a direct test of the contracting demand for

accounting conservatism discussed by Watts (2003a,b). I employ ex ante measures of accounting

conservatism, that is, I measure conservatism in the period just prior to the loan date. Further, I

adopt measures of conditional accounting conservatism that capture the property of the

accounting earnings of recognizing bad news earlier than good news (Beaver and Ryan, 2005). I

find that ex ante conditional accounting conservatism decreases loan spreads and increases the

number of financial covenants when managers receive average or below-average equity

compensation. This is consistent with the view that low equity compensation provides a credible

commitment that managers will continue to report conservatively in the post contracting period.

In addition, I document that ex ante conditional accounting conservatism increases loan

spreads and decreases the number of financial covenants in the contract when managers are

highly aligned with shareholders (i.e., they receive above-average equity compensation). As a

result of an additional analysis on several financial covenants, I find evidence that banks require

tighter covenants when managers have large equity compensation and they implement ex ante

conditional accounting conservatism, consistent with the interpretation that financial covenants

have lower monitoring ability in the post contracting period. Coupled with the results on loan

spreads, it appears that bank syndicates prefer to demand a premium in terms of larger interest

rates rather than use more inefficient financial covenants that increase their monitoring costs

when high equity compensation creates incentives to over-report.

34

Appendix A: Covenant Proxies Dealscan classifies the covenant information for each deal (or package of loans) into Financial and General Covenants:

• Financial covenants are promises not to allow certain balance sheet or income statement items or ratios to fall below (or above) an agreed upon level. The first panel of the Table below presents all 15 different financial covenants available in the database. The most common are Net Worth, Current Ratio, Interest Coverage and Debt-to-Equity ratios. Only the first 11 covenant measures can be computed using Compustat data (annual or quarterly).

• General Covenants are standard assurances and undertakings that the syndicate obtains from the borrower and are classified into three categories. The Prepayment and Security group includes the “Sweeps” and the collateral requirement. Sweeps are percentages that state the fraction of the loan that must be repaid out of excess cash flows, debt, asset sales, equity issues or insurance proceeds. I include these covenants in covenant indices only when these percentages are greater than zero. The second group, Material Restrictions, contains restrictions on payments of dividends (e.g., frequency, maximum amounts, recipients) thus limiting the ability of the borrower to distribute cash to its shareholders. This covenant is stated as a binary variable in the database. The third group, Voting Rights, presents the percentage of lenders in the bank syndicate that must agree on potential amendments of the credit agreement in case of renegotiations. The greater the percentage, the harder and costlier it is for the borrower to renegotiate the contract during times of financial stress. The second panel in the Table below presents all 10 general covenants covered by the database.

The main results of the paper are estimated using the covenant intensity measure called Covenant Index. Sensitivity analyses (Table 8) use four alternative measures, Mean Adjusted Covenant Index, Financial Covenant Index, Covenant Slack Index and PCA Factor:

• Covenant Index: It is measured as the number of financial and general covenants attached to the loan (Bagnani et al.(1994) and Bradley and Roberts (2004) use a similar approach).

• Mean Adjusted Covenant Index: It is the previous covenant index measure adjusted by the means of the total number of covenants computed for industry-rating loan groups.

• Financial Covenant Index: It is measured as the number of financial covenants attached to the loan.

• Covenant Slack Index: It is computed as the average rank measure of the financial covenant slacks. Slacks for covenants that require a maximum accounting measure are computed as the percentage slack ratio, (Required– Actual)/Required (Press and Weintrop, 1990) where Required is the accounting ratio or number that has to be maintained as per the contract and Actual is the accounting ratio or number computed using the current balance sheet or income statement information. For covenants that require a minimum accounting measure, I calculate the negative of the ratio above. To mitigate measurement errors, percentage slacks are ranked within industry and rating groups on a scale from 0 to 1. The covenant slack index is then computed as the average rank across all financial covenants attached to a loan. Due to data limitations, the covenant slacks at the loan inception are calculated only for the first 11 covenants. The Compustat fields that enter the computation of slacks are reported in the table below.

• PCA Factor: It is computed using Principal Component Analysis using the four covenant measures above.

35

List of loan covenants available and their computation∗

∗ Numbers in parentheses represent Compustat Quarterly data items. The Cash Flow is the sum of Minority Interest (#3), Income before Extraordinary Items (#76), Depreciation and Amortization (#77), Extraordinary Items and Disc Operations (#78), Deferred Taxes (#79) and Equity in Net Loss (#80). Intangible Assets is the sum of Goodwill (#234) and Other Intangibles (#235). ** As an alternative, I have computed total debt as the sum of total long term debt (#51) and the debt in current liabilities (#45). Results are unchanged.

No. Financial Covenants Dealscan Definition and Computation Covenants with Compustat Data available

1 Max. Debt to Cash Flow** Total Debt (#51) / Cash Flow (#3+#76+#77+#78+#79+#80) 2 Max. Debt to Equity (Leverage ratio)** Total Debt (#51) / Equity (#59) 3 Max. Debt to Tangible Net Worth** Total Debt (#51) / [Total Assets (#44)–Total Liabilities(#54)-Intangible Assets(#234+#235)] 4 Min. Cash Interest Coverage Operating Cash Flow (#108) / Interest Expense (#22) 5 Min. Interest Coverage EBITDA (#21) / Interest Expense (#22) 6 Min. Net Worth Total Assets (#44)–Total Liabilities (#54) 7 Min. Tangible Net Worth Total Assets (#44) – Total Liabilities (#54) – Intangible Assets (#234+#235) 8 Min. Current Ratio Current Assets (#40) / Current Liabilities (#49) 9 Min. Quick Ratio (Cash and Short term Investments (#36) + Receivables (#37) ) / Current Liabilities (#49)

10 Max. Capital Expenditures Capital Expenditures (#90) 11 Min. EBITDA EBITDA (#21)

Covenants with no Compustat Data available 12 Min. Debt Service Coverage Flow EBITDA/ (Interest Expense + Principal Repayments) 13 Max. Loan to Value Loan Value / Asset Value 14 Max Sr Debt to Cash Flow Total Debt on a Senior Basis / Cash Flow 15 Min. Fixed Charge Coverage EBITDA / (Interest Expense + Fixed Charges)

No. General Covenants Explanation Prepayment and Security

1 Equity Issue Sweep The amount a loan must be repaid from an equity issuance 2 Excess Cash Flow Sweep The amount a loan must be repaid from excess cash flows 3 Asset Sales Sweep The amount a loan must be repaid from excess asset sales 4 Debt Issue Sweep The amount a loan must be repaid from excess debt issuance 5 Insurance Proceeds The amount a loan must be repaid from insurance proceeds related to the collateral 6 Collateral Requirement Requirement that the borrower pledges tangible assets against the loan

Dividend Restrictions 7 Material Restriction Restrictions on payment of dividends as stipulated in the credit agreement

Voting Rights 8 Required Lenders Percent of lenders that must approve non-material amendments and waivers 9 Term Changes Percent of lenders that must approve changes in the tenor or life of the loan

10 Collateral Release Percent of lenders that must approve the release of a lien on collateral associated with a deal

36

Appendix B: Propensity Score Matching (PSM)

Each borrower receives different loan spreads or covenant requirements (also called “outcome variables”) depending on the compensation structure offered to managers. Denote by Yi1 the outcome for the firm that offers high managerial incentives and Yi0 the outcome for the firm that offers low managerial incentives. Also, let Di=1 for firms offering high incentives (treatment firms) and Di=0 for firms offering low incentives (control firms). Using the terminology in the causal inference theory, the average treatment effect (ATT) labeled α is computed as a difference between two expected outcomes (Wooldridge, 2002):

1 1 0| ( | 1) ( | 1)iD i i i iE Y D E Y Dα = = = − = (B1)

The first term is the expected outcome (i.e., loan spread or covenant requirement) if the treatment firm offers high managerial incentives while the second term is the expected outcome the treatment firm would have had if it had not provided high managerial incentives. Because the latter term (also called ‘the counterfactual’) is unobservable, one has to rely on a control group of firms and use 0( | 0)i iE Y D = . This is the expected outcome for a non-treatment firm (i.e., Di=0) that does not provide high managerial incentives. When the assignment to the treatment and the control group is random, 0( | 0)i iE Y D = is equal to 0( | 1)i iE Y D = and thus α is unbiased. However, firms choose compensation policies non-randomly based on a decision making process which may take into account the costs in terms of loan spreads and covenants induced by high compensation. Therefore, a selection bias in estimating the treatment effect α is likely to occur.

Matching methods can be used to eliminate the selection bias as a function of some observable variables Zi (selection on observables). Traditional matching techniques pair the treatment firm with a control firm using the observable variables Zi. The assumption is that if one can observe enough information on the firm (contained in Zi) that determines the high compensation choice, then the outcomes are independent of the assignment to the treatment group. This solution works well only when Zi has few dimensions, however when the number of characteristics increases, it becomes very difficult if not impossible to find a proper match (i.e., there is a “curse of dimensionality”).42

Propensity score matching (PSM), proposed by Rosenbaum and Rubin (1983), avoids the curse of dimensionality. Unlike the usual matching methods, PSM matches based on a function of the characteristic variables Zi called propensity score (denoted p(Zi)):

( ) Pr( 1| )i i ip Z D Z≡ = (B2) In the above equation, the propensity score function is defined as the probability that the firm

is in the treatment group conditional on the observed firm variables Zi. If a random assignment to the treatment group is obtained conditional on Zi then it can also be obtained conditional on the propensity score function (see Rosenbaum and Rubin, 1983 for a proof). The only requirement is that the propensity score function has common support for both the treatment and the control units i.e., 0 < p(Zi) < 1. In other words, a proper match can be found based on the propensity score function, instead of Zi directly. Thus, ATT can be estimated without bias as follows:

1 ( ) 1 0| [ ( | ( ), 1) ( | ( ), 0)]iD p Z i i i i i iE E Y p Z D E Y p Z Dα = = = − = (B3)

Given that the propensity score is a scalar, the matching dimension is now reduced from the dimension of Zi (which can be as large as needed) to 1. Therefore, PSM is more efficient than the

42 Most accounting studies employing a matched control sample research design use only two or three variables to

match. However, these are not the only factors that affect the membership in the treatment group.

37

traditional matching methods in reducing the bias in the treatment effect. Based on Dehejia and Wahba (2001), I compute the ATT estimator by following the

algorithm below: Step 1: Estimate a stepwise Logit regression to predict the probability that the firm offers

high equity incentives ( ˆ ( )p Z ), conditional on firm specific characteristics measured in the year prior to the year when the level of managerial compensation is measured (a discussion of the covariates is presented in the text).

Step 2: Delete observations with propensity scores smaller than the minimum and larger than the maximum of the opposite group to ensure that the common support assumption is not violated.43

Step 3: Using the remaining propensity scores ˆ ( )p Z , compute the Logit function which follows a normal distribution (Rosenbaum and Rubin, 1985): ˆ ˆ ˆ( ) log[ ( ) /(1 ( ))]q Z p Z p Z= − .

Step 4: Split the sample in groups (strata) based on the magnitude of the Logit function across the treatment and control firms. Each stratum thus contains a variable number of treatment and control firms that have similar propensity scores. Start with 5 intervals and redefine the groups based on Step 5 (Imbens, 2004).

Step 5: Perform balancing tests within each stratum: test that means of the Logit function and the firm characteristics used at Step 1 do not differ significantly between treated and control firms (t-tests). If significant differences exist, divide the strata into finer partitions. If differences for a certain firm characteristic persist, go back to Step 1 and re-estimate the Logit model using higher order or interaction terms with that variable. The recursive procedure stops when all predictor variables from Step 1 are balanced for each stratum (balancing property).

Step 6: Estimate ATT as a weighted average of within-stratum mean differences in loan spreads and covenant intensity between high compensation firms and low compensation firms:

11 0

1 1

( )K

kk k

k

nATT Y YN=

= −∑ where, k is the number of final strata from Step 5, nk1 is the number of

treatment firms in stratum k, N1 is the total number of treatment firms across all strata and 1kY ( 0kY ) is the average spread or covenant intensity for the treatment (control) group in stratum k. Using standard errors within stratum (s), the estimated standard error of ATT is commonly calculated as (e.g., Benjamin, 2003):

2 2 21 1 02

1 1 1 0

ˆ( )K

k k k

k k k

n s ss ATTN n n=

⎛ ⎞= +⎜ ⎟

⎝ ⎠∑ (B4)

As an alternative to the Dehejia and Wahba (2001) algorithm, I perform direct matching without replacement. The first three steps are similar to the method above. However, instead of stratifying the sample, I match firms directly based on the Logit function, ˆ( )q Z . A control firm is matched with the treatment firm if ˆ ˆ| ( ) ( ) | / 4T Cq Z q Z σ− < (i.e., caliper matching without replacement) where ˆ ( )Tq Z and ˆ ( )Cq Z are Logit functions of treatment and control firms, respectively and σ is the in-sample standard deviation of the Logit measures. Matching on propensity scores directly (i.e., ˆ ( )p Z ) provides the same results. ATT is then estimated as the difference in the means of outcome variables across the two groups.

43 For example, if the scores obtained for the treatment group are within [0.10, 0.80] and the scores for the non-

treatment group are within [0.05, 0.60], then the common support will be [0.10, 0.60].

38

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42

Table 1 Sample Selection and Distribution

Panel A presents the sample selection process. Panel B presents the distribution of loans across the sample period. % with fin cov is the percentage of loans that have financial covenants, % with gen cov is the percentage of loans that have general covenants and % term loans is the percentage of term loans. A detailed description of financial and general covenants is presented in Appendix A.

Panel A: Sample Selection

Firms Packages Loans

Sample of loans of non-financial firms incorporated in US that are covered by Execucomp and Compustat 1,204 6,964 9,539

Sample of loans with previous quarterly Compustat data available 1,188 6,629 9,005

Sample of loans with top five executives sensitivity data available (for testing H1) 1,111 5,054 6,768

Sample of loans with top five executive sensitivity data and accounting conservatism data available (for testing H2-H3) 740 3,112 4,143

Sample of loans with financial ratios for which slack information could be computed (for testing H1) 862 2,372 3,344

Panel B: Sample distribution by year (6,768 loans)

Year Firms Packages Loans % with fin cov

% with gen cov

% term loans

1993 164 193 252 1.98 30.95 19.84 1994 270 315 419 16.47 43.44 19.33 1995 255 295 414 35.51 64.73 22.46 1996 290 342 478 71.55 76.36 23.43 1997 367 442 604 62.91 66.89 22.85 1998 276 327 491 63.95 68.23 27.70 1999 346 412 566 61.48 67.31 27.56 2000 402 512 699 51.36 55.51 23.75 2001 460 568 777 57.40 62.03 21.62 2002 457 537 712 60.39 62.22 20.79 2003 461 558 689 61.10 62.55 23.95 2004 460 530 667 58.32 59.52 23.39

43

Table 2 Descriptive Statistics

Panel A presents descriptive statistics for firm specific controls and accounting conservatism measures. Panel B presents loan characteristics and Panel C presents compensation variables. Firm size is firm’s market value. Profitability is income before extraordinary items on prior period total assets and Liquidity is the ratio of operating cash flows to total assets. Indebtedness is the ratio of total liabilities to total assets. Asset Tangibility is the tangible asset ratio (PPE + inventories / total assets). Growth Opportunities is the book to market ratio. Firm Complexity is the number of line of business and geographic segments reported. Basu Model, DD Model and Jones Model ratios are estimated using firm-specific time series regressions (see Section 3.4). Non-Oper Accruals is non-operating accruals cumulated over the previous five years scaled by cumulated average total assets. Special Items is the ratio of special items to average total assets in the prior year. Spread is loan’s spread in basis points. (Financial) Covenant index is the number of (financial) general and financial covenants in the deal contract. Loan Maturity is loan’s maturity in years. Loan (deal) size is the amount of loan’s (deal’s) principal. No of Lenders is the number of banks in the syndicate. Total pay-performance sensitivity (TPPS) is the change in the Black-Scholes value of top management total equity portfolio (stocks and options) for a 1% change in the stock price. Pay-risk sensitivity (PRS) is the change in the Black-Scholes value of top management stock option portfolio for a 0.01 change in the annualized standard deviation of the firm’s stock returns. EQ/TC is the proportion of equity compensation (stock and options) out of the total compensation (equity, salary, bonus and long term benefits) in the year just prior to the loan date. The value of previously granted options is computed using the One-Year Approximation Method (Core and Guay 1999; 2002). The loan data is matched with firm measures in the year (or quarter) just prior to loan’s date.

Panel A: Firm Controls and Conservatism Proxies

N Mean Median Q1 Q3 Firm Size ($ mil) 6768 6242.40 2213.32 752.82 6950.33 Profitability (%) 6768 1.74 1.46 0.73 2.63 Liquidity (%) 6768 4.14 2.92 0 7.24 Indebtedness (%) 6768 31.42 31.43 21.36 40.94 Asset Tangibility 6768 49.76 49.07 33.67 66.14 Growth Opportunities 6768 0.55 0.44 0.27 0.66 Firm Complexity 6768 5.46 5.00 3.00 7.00 Basu Model Ratio 6768 1.83 -0.05 -0.15 2.36 Dechow/Dichev Model Ratio 4143 2.52 1.87 -1.46 3.23 Jones Model Ratio 4143 5.94 1.97 -1.27 3.11 Non-Oper. Accruals (%) 4143 -5.97 -10.40 -13.50 -7.52 Special Items (%) 4143 -1.30 0 -1.48 0

Panel B: Loan characteristics

N Mean Median Q1 Q3 Loan Spread 6768 120.16 75.00 37.50 175.00 Covenant Index 6768 4.09 3.00 0 6.00 Financial Covenant Index 6768 1.35 1.00 0 2.00 Loan Size ($ mil) 6768 525.88 275.00 125.00 600.00 Deal Size ($ mil) 6768 830.64 404.00 200.00 1000.00 Loan Maturity 6768 3.31 3.01 0.99 5.00 No of Lenders 6768 12.22 10.00 5.00 17.00

Panel C: Top five executives compensation variables ($ mil.)

N Mean Median Q1 Q3 Value of Current Options 6768 8.28 2.55 5.99 7.61 Value of Previously Granted Options 6768 33.02 8.93 1.87 27.35 Value of Restricted Stock 6768 25.95 13.21 4.05 28.22 Total Pay Performance Sensitivity (TPPS) 6768 1.09 0.45 0.17 1.29

44

Pay Risk Sensitivity (PRS) 6768 0.29 0.11 0.03 0.32 Equity/Total Compensation (EQ/TC) (%) 6768 42.97 43.52 21.76 64.71

Table 3

Bivariate Analysis on Loan Spreads The table shows the average loan spreads (in basis points) across different loan portfolios constructed based on quintiles of firm characteristics (on rows) and managerial incentives proxies (on columns). Managerial Incentives groups are computed based on residuals from a regression of the managerial incentive proxy on firm size (logarithm of market value). Total pay-performance sensitivity (TPPS) is the change in the Black-Scholes value of top management total equity portfolio (stocks and options) for a 1% change in the stock price. Pay-risk sensitivity (PRS) is the change in the Black-Scholes value of top management stock option portfolio for a 0.01 change in the annualized standard deviation of the firm’s stock returns. EQ/TC is the proportion of equity compensation (stock and options) out of the total compensation in the year just prior to the loan date. Leverage is defined as the ratio of total liabilities to total assets, Profitability is return on assets and Liquidity is the ratio of operating cash flows to total assets. Significance levels are based on t-stats of differences between the above median and below median incentives loan portfolios (Satterthwaite – assuming unequal variances across groups). ***, ** and * denote significance at the 1%, 5% and 10% levels (two-sided tests), respectively.

Panel A: Leverage quintiles (rows) and Managerial Incentives (columns)

Below Median (1) Above Median (2) Difference [(2)-(1)] Groups TPPS PRS EQ/TC TPPS PRS EQ/TC TPPS PRS EQ/TC

1 (low) 81.03 74.02 99.02 127.84 142.90 112.18 46.82*** 68.88*** 13.16***

2 78.54 79.95 89.29 127.13 132.04 108.89 48.59*** 52.09*** 19.59***

3 82.31 78.66 98.33 135.04 133.29 122.16 52.73*** 54.63*** 11.70***

4 89.59 95.65 105.90 159.62 150.13 126.24 70.03*** 54.48*** 20.35***

5 (high) 131.13 125.43 138.27 194.31 188.36 166.66 63.18*** 62.93*** 28.39***

Panel B: Profitability quintiles (rows) and Managerial Incentives (columns)

Below Median (1) Above Median (2) Difference [(2)-(1)] Groups TPPS PRS EQ/TC TPPS PRS EQ/TC TPPS PRS EQ/TC

1 (low) 148.69 143.08 173.98 220.77 216.75 190.93 72.07*** 73.67*** 16.95***

2 102.83 101.62 106.77 153.02 148.21 136.29 50.18*** 46.58*** 29.52***

3 82.47 82.50 89.60 137.49 136.23 121.82 55.03*** 53.73*** 32.22***

4 68.91 71.63 81.36 115.50 116.48 91.53 46.59*** 44.85*** 10.17**

5 (high) 71.79 70.77 72.69 99.48 106.58 87.85 27.69*** 35.81*** 15.16***

Panel C: Liquidity quintiles (rows) and Managerial Incentives (columns)

Below Median (1) Above Median (2) Difference [(2)-(1)] Groups TPPS PRS EQ/TC TPPS PRS EQ/TC TPPS PRS EQ/TC

1 (low) 110.20 104.55 128.61 164.48 168.92 151.22 54.28*** 64.37*** 22.61***

2 106.33 103.08 106.57 149.64 148.20 131.26 43.31*** 45.12*** 24.69***

3 90.04 90.20 106.28 155.42 152.76 133.31 65.38*** 62.56*** 27.03***

4 85.11 86.59 99.38 144.02 146.16 111.98 58.91*** 59.57*** 12.59**

5 (high) 79.82 71.84 87.49 115.91 128.80 100.80 36.09*** 56.97*** 13.31***

45

Table 4 The Relation Between Management Incentives and the Pricing of Loans

The table presents results from tests of the relation between managerial incentives and the pricing of loans. Columns present results by model: the Spread model has loan’s spread (in basis points) as dependent variable and the Covenant model has the covenant index as the dependent variable. Manag. Incentive (on columns) is defined as follows. Total pay-performance sensitivity (TPPS) is the change in the Black-Scholes value of top management equity portfolio for a 1% change in the stock price. Pay-risk sensitivity (PRS) is the change in the Black-Scholes value of top management stock option portfolio for a 0.01 change in the annualized standard deviation of stock returns. Pay-performance sensitivity (PPS) is the change in the Black-Scholes value of top management stock option portfolio for a 1% change in the stock price. All sensitivity measures are in $ millions and the stock option portfolio includes both current and previous exercisable and unexercisable stock options. The value of previously granted options is computed using the One-Year Approximation Method (Core and Guay 1999; 2002). EQ/TC is the proportion of equity compensation (stock and options) out of the total compensation (equity, salary, bonus and long term benefits) in the year just prior to the loan date. Spread is loan spread in basis points. Covenant index is the number of general and financial covenants in the deal contract. Maturity is loan’s maturity in years. Loan (deal) size is the logarithm of the loan’s (deal’s) principal. Lenders is the logarithm of the number of lenders. Performance Pricing is a dummy variable that equals to one if the interest rate is tied to firm performance. Loan Type is a dummy that equals one if the loan is revolving. Rating Group is the loan’s rating. Firm size is the logarithm of market value. Growth opportunities is the book to market ratio. Profitability is return on assets and Liquidity is the ratio of operating cash flow on total assets. Indebtedness is the ratio of total liabilities on total assets. Asset Tangibility is the tangible asset ratio (PPE + Inventories / total assets). Firm Complexity is the number of line of business and geographic segments reported. t-statistics in parentheses are computed using White’s heteroskedasticity adjusted standard errors. Huber-White standard errors (firm clusters) do not change the significance levels. All regressions are run using industry fixed effects. ***, ** and * denote significance at the 1%, 5% and 10% levels (two-sided tests), respectively.

TPPS PRS PPS EQ/TC Variable Spread Covenant

index Spread Covenant

Index Spread Covenant

Index Spread Covenant

Index Intercept 143.65*** -7.27*** 143.18*** -7.25*** 144.56*** -7.23*** 139.21*** -7.42***

(5.43) (-7.28) (5.41) (-7.23) (5.45) (-7.22) (5.28) (-7.40)

Manag. Incentive 3.50*** 0.14*** 7.61*** 0.30*** 4.13*** 0.15*** 13.59*** 0.76***

(4.52) (4.34) (4.38) (3.64) (5.35) (3.88) (3.46) (4.32) Loan Specific Controls Spread 0.02*** 0.02*** 0.02*** 0.02*** (20.32) (20.35) (20.69) (20.36) Covenant Index 10.59*** 10.60*** 10.58*** 10.60*** (29.82) (29.86) (29.81) (29.53) Maturity 0.32 0.18*** 0.40 0.19*** 0.37 0.19*** 0.38 0.19*** (0.62) (8.96) (0.77) (9.18) (0.71) (9.10) (0.73) (9.15) Loan (Deal) Size -7.74*** 0.74*** -7.94*** 0.72*** -7.69*** 0.73*** -7.78*** 0.74*** (-5.08) (13.72) (-5.19) (13.57) (-5.02) (13.67) (-5.06) (13.72) Lenders -3.52*** 0.73*** -3.50*** 0.73*** -3.50*** 0.73*** -3.56*** 0.72***

46

(-3.02) (14.48) (-3.00) (14.54) (-3.01) (14.53) (-3.05) (14.37) Performance Pricing -46.28*** -46.05*** -46.05*** -46.17*** (-20.46) (-20.37) (-20.38) (-20.40) Loan Type -40.56*** -40.58*** -40.69*** -40.58*** (-13.19) (-13.19) (-13.22) (-13.24) Rating Group 27.89*** 0.36*** 28.28*** 0.37*** 28.12*** 0.38*** 28.19*** 0.36*** (21.14) (6.43) (21.23) (6.61) (21.10) (6.60) (21.39) (6.43) Firm Specific Controls Firm Size -3.41*** -0.93*** -3.26** -0.92*** -3.78*** -0.93*** -2.75** -0.92*** (-2.76) (-19.77) (-2.48) (-18.89) (-2.88) (-18.74) (-2.25) (-19.83) Growth Opportunities 10.64*** -0.25*** 10.53*** -0.25*** 10.54*** -0.25*** 11.08*** -0.23** (2.57) (-3.09) (2.57) (-3.05) (2.56) (-3.07) (2.62) (-2.95) Profitability -3.26*** 0.04* -3.14*** 0.04** -3.20*** 0.04** -3.23*** 0.04**

(-5.34) (1.89) (-5.11) (2.20) (-5.24) (2.03) (-5.32) (1.97) Liquidity 5.45 2.68*** 6.46 2.73*** 6.05 2.71*** 3.76 2.60*** (0.35) (4.19) (0.41) (4.29) (0.39) (4.24) (0.24) (4.07) Indebtedness 68.78*** -1.56*** 67.71*** -1.53*** 68.76*** -1.53*** 68.11*** -1.53*** (10.51) (-4.80) (10.47) (-4.71) (10.65) (-4.70) (10.46) (-4.69) Asset Tangibility -1.11*** -1.12*** -1.09*** -1.06*** (-4.70) (-4.77) (-4.62) (-4.52) Firm Complexity -0.10*** -0.10*** -0.10*** -0.10*** (-5.79) (-6.07) (-5.78) (-5.90) Industry Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Adjusted R2 58.78 40.84 58.73 44.30 58.76 44.31 58.72 44.38 N 6,768 6,768 6,768 6,768 6,768 6,768 6,768 6,768

47

Table 5 Effects of the Interaction Between Managerial Incentives and Ex Ante Conservatism

The table presents results from tests of the effect of the interaction between incentives and conservatism on spreads and the covenant index. All regressors have the same definitions as in Table 4. The dependent variable is Loan spreads or the Covenant Index. Manag. Incentive is TPPS as defined in Table 4. Cons is one of the three ex ante conservatism proxies used (columns). Average rank is the average of within industry specific ranks of (β1 + β2)/ β2 estimated from the Basu, Dechow-Dichev and Jones models. Each model is estimated from firm specific time series quarterly regressions (see Section 3.4). Non-Oper Accruals is non-operating accruals cumulated over the prior five years scaled by cumulated total assets. Special Items is the ratio of special items to average total assets in the year prior to the loan date. All ex ante conservatism proxies are set such that increasing values reflect more ex ante conservatism. t-statistics (in parentheses) are computed using White’s heteroskedasticity consistent standard errors. Huber-White standard errors (firm clusters) do not change the significance levels. ***, ** and * denote significance at the 1%, 5% and 10% levels (two-sided tests), respectively.

Average Rank Non-Oper Accruals Special Items

Variable Spread Covenant index Spread Covenant

index Spread Covenant index

Intercept 116.60*** 4.06*** 116.46*** 4.06*** 116.35*** 4.07*** (105.67) (81.95) (106.50) (81.87) (106.41) (82.13)

Cons -14.30** -0.32 -0.08** 0.01 -0.96*** -0.01 (-2.40) (-1.17) (-2.04) (0.93) (-3.73) (-0.28) Manag. Incentive 4.23*** 0.10** 4.32*** 0.10** 4.18*** 0.11*** (4.50) (2.47) (4.55) (2.40) (4.43) (2.69) Cons* Manag. Incentive 14.28*** -0.55*** 0.04** -0.01*** 0.56*** -0.03*** (3.39) (-3.57) (2.38) (-2.58) (4.44) (-3.74) Loan Specific Controls Covenant Index / Spread 9.08*** 0.01*** 9.08*** 0.01*** 9.16*** 0.01*** (20.62) (11.24) (20.62) (11.05) (20.81) (11.13) Maturity 0.83 0.23*** 0.81 0.23*** 0.83 0.23*** (1.31) (8.85) (1.28) (8.89) (1.33) (8.97) Loan Size -8.41*** 0.67*** -8.23*** 0.66*** -8.30*** 0.66*** (-4.15) (9.79) (-4.04) (9.68) (-4.07) (9.66) Lenders -2.84* 0.79*** -3.10** 0.79*** -2.67* 0.79*** (-1.89) (12.34) (-2.06) (12.20) (-1.78) (12.24) Performance Pricing -43.95*** 0.38*** -44.37*** 0.40*** -44.68*** 0.40*** (-16.07) (4.77) (-16.15) (5.12) (-16.24) (5.02) Loan Type -41.50*** -0.98*** -41.15*** -0.95*** -41.38*** -0.96*** (-10.21) (-15.21) (-10.12) (-14.93) (-10.20) (-14.91) Rating Group 30.84*** -0.31*** 30.89*** -0.30*** 30.39*** -0.31*** (17.96) (-2.74) (18.23) (-2.68) (17.91) (-2.69) Firm Specific Controls Firm Size -5.93*** 0.04* -6.15*** 0.04 -6.36*** 0.04 (-3.53) (1.78) (-3.63) (1.49) (-3.76) (1.56) Growth Opportunities 8.24** 1.97** 8.19** 2.09** 7.98** 1.98** (2.22) (2.41) (2.16) (2.57) (2.11) (2.43) Profitability -3.99*** -1.69*** -3.89*** -1.74*** -3.69*** -1.74*** (-4.73) (-3.86) (-4.76) (-4.00) (-4.36) (-3.98) Liquidity 9.36 -1.56*** 4.86 -1.58*** 4.45 -1.56*** (0.44) (-6.18) (0.23) (-6.20) (0.21) (-6.13) Indebtedness 74.39*** -0.10*** 75.46*** -0.10*** 74.12*** -0.11*** (8.56) (-5.51) (8.70) (-5.47) (8.50) (-5.57) Industry Fixed Effects Yes Yes Yes Yes Yes Yes Adjusted R2 60.88 43.60 60.82 43.51 60.90 43.56 N 4,143 4,143 4,143 4,143 4,143 4,143

48

Table 6 The Effects of Managerial Incentives and Ex Ante Conservatism on Financial Covenants

The table shows the effect of managerial incentives and ex ante conservatism on Financial Covenants. Panel A presents coefficients of managerial incentives and conservatism (control variables are not presented) in multivariate regressions. The dependent variable is the number of financial covenants in the loan contract. Manag. Incentive is TPPS as defined in Table 4. Cons is one of the three ex ante conservatism proxies used (columns). Panel B presents average standardized financial covenant slacks across loan portfolios constructed for high and low managerial incentives, conditional on the firm choosing a high level of ex ante conservatism. High Cons is based on above median values of Average Rank (the (1) rows), Non-Operating Accruals cumulated over the previous five years (the (2) rows) and Special Items in the year prior to the loan date (the (3) rows). All accounting measures are set such that higher values mean more conservatism. Slacks for Debt to Equity and Debt to Tangible Net Worth are computed as (Required– Actual)/Required where Required is the accounting measure that has to be maintained as per the contract and Actual is the accounting measure computed using the current balance sheet or income statement information (Compustat fields are in parentheses in the Appendix A Table). The slacks for the remaining covenants are computed as (Actual– Required)/Required. t-statistics (in parentheses) in Panel A are computed using White’s heteroskedasticity consistent standard errors. Significance levels in Panel B are based on t-statistics (Satterthwaite). ***, ** and * denote significance at the 1%, 5% and 10% levels (two-sided tests), respectively.

Panel A: Number of Financial Covenants Average Rank Non-Oper

Accruals Special Items

Cons -0.044 -0.001 -0.001 (-0.42) (1.30) (-0.20) Manag. Incentive 0.040** 0.039** 0.043** (2.70) (2.63) (2.90) Cons* Manag. Incentive -0.188** -0.001** -0.009** (-3.28) (-2.05) (-3.99) Industry Fixed Effects Yes Yes Yes Adjusted R2 29.93 29.86 29.92 N 4,143 4,143 4,143 Panel B: Tightness of Financial Covenants Financial Covenant Slacks

Financial Ratio High Cons/ Low Incentives

High Cons/ High Incentives Difference

Debt to Equity (1) 0.52 0.36 0.16***

(2) 0.51 0.38 0.13*** (3) 0.56 0.44 0.12*** Debt to Tangible Net Worth (1) 0.69 0.71 -0.02 (2) 0.65 0.57 0.08 (3) 0.70 0.60 0.09** Interest Coverage (1) 1.24 1.04 0.20** (2) 1.20 1.07 0.13** (3) 1.10 0.96 0.14** Net Worth (1) 0.90 0.58 0.31*** (2) 0.80 0.54 0.26*** (3) 0.94 0.51 0.43*** Tangible Net Worth (1) 1.22 0.92 0.30* (2) 1.14 0.75 0.39*** (3) 1.13 0.71 0.42*** Current Ratio (1) 0.86 0.75 0.11 (2) 0.73 0.49 0.24** (3) 0.82 0.49 0.33***

49

Table 7 Managerial Incentives Effects using Alternative Covenant Proxies

The table presents slope coefficients of managerial incentives in multivariate regressions that use alternative covenant measures (the model specification and control variables are presented in Table 4). TPPS, PRS, and EQ/TC are defined as in Table 4. The columns present results by model: the Spread model has loan’s spread (in basis points) as the dependent variable and the Covenant model has the covenant measure as the dependent variable. Different covenant measures used are presented on rows. Mean Adjusted Covenant is the total number of covenants index adjusted by industry-rating group means. Financial Covenant is an index that counts the number of financial covenants. Covenant Slack is computed as per Appendix A. PCA factor summarizes information in the four covenant proxies (including the main Covenant Index) and is generated using Principal Component Analysis. t-statistics in parentheses are computed using White’s heteroskedasticity adjusted standard errors. All regressions are run using industry fixed effects. Control variables are not reported. Complete results are available upon request.

TPPS PRS EQ/TC Covenant Proxy Spread Covenant

Proxy Spread Covenant

Proxy Spread Covenant

Proxy Mean Adjusted Covenant Index 3.68*** 0.06** 8.50*** 0.35*** 14.98*** 0.90***

(4.88) (2.04) (4.61) (4.20) (3.67) (5.40) Adjusted R2 54.18 21.12 54.13 21.27 54.12 21.46 N=6,768 Financial Covenant Index 3.42*** 0.04*** 9.82*** 0.09*** 14.81*** 0.43***

(4.62) (3.52) (5.36) (2.76) (3.55) (6.02) Adjusted R2 52.64 26.42 52.65 26.37 52.60 26.76 N=6,768 Covenant Slack Index 2.67*** 0.06** 11.63*** 0.35*** 25.92*** 0.23*

(2.82) (2.09) (3.35) (4.15) (4.51) (1.66) Adjusted R2 49.50 12.49 49.61 12.80 49.77 12.44 N=3,344 PCA Factor 1.83** 0.02** 6.72** 0.11** 16.10*** 0.21***

(2.18) (2.23) (2.47) (2.65) (3.13) (3.80) Adjusted R2 58.78 44.16 58.81 44.26 58.88 44.35 N=3,344

50

Table 8 The Relation between Managerial Incentives and the Pricing of Loans (2SLS)

The table presents results of the joint estimation of the Spread and the Covenant models when loan spreads and the covenant index are endogenously determined. Managerial incentives and control variables are used as instruments in the first stage. TPPS, PRS, and EQ/TC and the control variables are defined as in Table 4. Estimation using Seemingly Unrelated Regressions provides similar results. Hausman Specification Tests for 3SLS are also not significant. First stage outputs are available upon request. ***, ** and * denote significance at the 1%, 5% and 10% levels (two-sided tests), respectively.

TPPS PRS EQ/TC Variable

Spread Covenant index

Spread Covenant Index

Spread Covenant Index

Intercept 140.41*** -7.62*** 137.93*** -7.52*** 133.88*** -7.72*** (6.21) (-7.44) (6.11) (-7.34) (5.94) (-7.56)

Manag. Incentives 2.96*** 0.09*** 6.23*** 0.32*** 7.80** 1.01*** (3.95) (2.79) (2.99) (3.56) (2.00) (5.96) Loan Specific Controls Spread 0.01*** 0.01*** 0.01*** (8.30) (8.42) (8.13)

Covenant Index 16.02*** 15.88*** 15.98*** (13.94) (13.81) (13.89)

Maturity -0.65 0.22*** -0.54 0.22*** -0.59 0.22*** (-1.30) (10.52) (-1.08) (10.66) (-1.18) (10.74)

Loan (Deal) Size -7.82*** 0.72*** -8.01*** 0.71*** -7.91*** 0.72*** (-6.99) (14.19) (-7.18) (14.04) (-7.07) (14.34)

Lenders -7.55*** 0.77*** -7.46*** 0.78*** -7.55*** 0.76*** (-5.64) (15.19) (-5.57) (15.27) (-5.64) (15.04)

Performance Pricing -65.79*** -65.24*** -65.59*** (-15.10) (-14.97) (-15.08)

Loan Type -30.66*** -30.79*** -30.71*** (-9.47) (-9.53) (-9.52)

Rating Group 24.11*** 0.49*** 24.48*** 0.48*** 24.47*** 0.47*** (16.61) (6.43) (16.98) (6.29) (17.04) (6.24) Firm Specific Controls Firm Size -1.75 -0.89*** -1.29 -0.89*** -0.56 -0.91*** (-1.45) (-17.30) (-1.07) (-17.76) (-0.48) (-18.79)

Growth Opportunities 11.28*** -0.30*** 11.19*** -0.31*** 11.58*** -0.28*** (9.40) (-5.49) (9.32) (-5.66) (9.63) (-5.03)

Profitability -2.76*** 0.01 -2.66*** 0.02 -2.73*** 0.01 (-7.00) (0.55) (-6.75) (0.92) (-6.95) (0.65)

Liquidity -9.23 3.12*** -7.25 3.21*** -9.52 3.06*** (-0.62) (4.91) (-0.49) (-5.04) (-0.64) (4.81)

Indebtedness 74.98*** -1.37*** 73.40*** -1.35*** 73.34*** -1.32*** (11.81) (-4.37) (11.61) (-4.29) (11.58) (-4.20)

Asset Tangibility -1.30*** -1.29*** -1.21*** (-5.80) (-5.77) (-5.38)

Firm Complexity -0.09*** -0.10*** -0.09*** (-5.28) (-5.65) (-5.39)

Industry Fixed Effects Yes Yes Yes Yes Yes Yes Adjusted R2 52.46 36.89 52.50 36.97 52.44 37.19 N 6,768 6,768 6,768 6,768 6,768 6,768 Hausman Specif. Test χ2 (p-value)

34.45 (0.79)

32.87 (0.84)

34.56 (0.79)

51

Table 9 Random Sampling of Panel Data Sets

The table presents average coefficients of managerial incentive variables and the percentage of coefficients in each significance level group. The results use 1,000 randomly chosen panel data samples. Each sample drawn from the pool of 6,768 loans has 4,138 unique firm-years observations. The model estimated for each sample is similar to that reported in Table 4. PPS, PRS and TPPS are defined in Table 4. The specification of the Spread and Covenant Models is presented in Table 4. The table reports only the average incentive coefficients. Average coefficients for the control variables are available upon request. Significance levels in Panel A are measured using White heteroskedasticity consistent standard errors. Poisson (Tobit) means that the covenant model was estimated using Poisson (Tobit) regressions. ***, ** and * denote significance at the 1%, 5% and 10% levels (two-sided tests), respectively.

Panel A: Ordinary Least Squares (Spread and Covenant models)

TPPS PRS EQ/TC Spread Model (OLS) Average Coefficient 1.98** 6.54*** 13.68***

(Average t-statistic) (2.56) (3.13) (2.93) Significant at 1% 48.7% 95.1% 84.0% Significant at 5% 48.6% 4.9% 15.7% Significant at 10% 2.6% 0% 0.3% Insignificant 0.1% 0% 0% Covenant Index Model (OLS) Average Coefficient 0.14*** 0.29*** 0.63*** (Average t-statistic) (3.97) (2.84) (2.98) Significant at 1% 100% 76.6% 93.0% Significant at 5% 0% 17.4% 6.9% Significant at 10% 0% 5.2% 0.1% Insignificant 0% 0.8% 0%

Panel B: Maximum Likelihood (Covenant model)

TPPS PRS EQ/TC Covenant Index Model (Tobit) Average Coefficient 0.27*** 0.56*** 1.24*** (Average χ2-statistic) (22.37) (10.51) (17.40) Significant at 1% 100% 90.8% 100% Significant at 5% 0% 8.8% 0% Significant at 10% 0% 0.4% 0% Insignificant 0% 0% 0% Covenant Index Model (Poisson) Average Coefficient 0.07*** 0.10*** 0.26*** (Average χ2-statistic) (95.23) (21.91) (58.03) Significant at 1% 100% 98.7% 100% Significant at 5% 0% 1.2% 0% Significant at 10% 0% 0.1% 0% Insignificant 0% 0% 0%

52

Table 10 Endogenous Managerial Incentives

The table presents the effects of the managerial equity compensation on loan spreads and covenants after allowing for endogenous managerial incentives. Panel A presents the results of two Propensity Score Matching methods. ATT1 is the average treatment effect estimated using the method of Dehejia and Wahba (2001). ATT2 is the average treatment effect using a caliper matching without replacement (a complete description of both methods is presented in Appendix B). Variables that are used to compute the propensity scores are discussed in Section 5.3. Spread is the loan spread (in basis points) and Cov Index is the total number of covenants for treatment (T) and control firms (C). Differences among the two groups are presented for each stratum. Firms are grouped in strata based on the magnitude of the logit function of each firm. The first stratum corresponds to low values of the logit function while the last stratum to the highest values. Firms are assigned in the treatment group if they pay high equity compensation. Panel B reports the second stage results of the Heckman two-stage and the statistics of fit from the first stage. Control variables are not reported. Significance levels in Panel B are measured using White heteroskedasticity consistent standard errors. ***, ** and * denote significance at the 1%, 5% and 10% levels (two-sided tests), respectively.

Panel A: Propensity Score Matching (PSM)- matching on observables

SpreadT - SpreadC Cov IndexT - Cov IndexC No. of firms Propensity Stratum Mean t-stat Mean t-stat Treatment Control

Stratum 1 (low) 11.66 0.83 2.53*** 2.85 29 162

Stratum 2 33.22*** 2.23 1.24** 2.00 45 146

Stratum 3 6.79 0.71 0.26 0.48 88 103

Stratum 4 30.37*** 4.22 0.84* 1.79 123 68

Stratum 5 41.33** 2.39 1.70** 2.10 35 12

Stratum 6 37.82*** 2.95 1.56* 1.93 38 10

Stratum 7 (high) 14.20** 2.05 0.02 0.01 89 7

ATT1 23.07*** 5.85 0.84** 1.99 447 508

ATT2 16.69*** 3.09 0.74*** 2.63 259 259 Panel B: Two-Stage Heckman – matching on unobservables

Spread Covenant Index Coeff. t-stat Coeff. t-stat High Managerial Incentives Indicator 33.21*** 2.78 0.98** 1.97

Mills Ratios -4.73 -0.71 -0.21 -0.67 Adj. R2 30.72 29.24 N=No. firms 1,014 1,014 First Stage Statistics of Fit McFadden R2 25.08 Percent Concordant 82.10 Likelihood Ratio (p-value) 352.61 (<.001)