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The Relation Between the use of Accounting Measures in Debt

and Incentive Contracts

Adrienne Rhodes* Pennsylvania State University

Smeal College of Business

January 28, 2013

Abstract

The likelihood that tripping a debt covenant would precipitate the dismissal of top management provides an implicit incentive for managers to perform that is incremental to the explicit incentives in compensation contracts. I assess the sensitivity of the CEO’s cash compensation to earnings and earnings-based covenants in the firm’s debt contracts. When the debt contract contains an earnings-based covenant, the sensitivity of the CEO’s pay to earnings is muted. This reflects the influence of earning-based debt contract terms on the CEO’s incentives to focus on earnings. Additionally, I predict and find that the annual rebalancing of the CEO’s incentives in the compensation contract varies with the intensity of the firm’s pre-existing earnings-based debt covenants. For all firms, a one-percentage point increase in ROA increases the CEO’s cash compensation by an average of 3.6 percent. In contrast, in firms with earnings-based covenants, a one-percentage point increase in ROA is associated with an increase in cash compensation of only 1.9 percent.

JEL Classifications: G32, J31, J33, M12, M41

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! *!Contact e-mail: acr199@psu.edu. This paper is based on my dissertation at the Pennsylvania State University. I wish to thank my committee members, Keith Crocker, Dan Givoly, Guojin Gong and Steven Huddart (Chair), for their thoughtful comments and guidance. I would also like to thank Ningzhong Li (discussant), participants at the 2013 FARS Midyear Meeting, and workshop participants at Washington University in St. Louis, Georgetown University, Georgia State University, University of Colorado at Boulder, Pennsylvania State University and the 2012 PhD Project ADSA conference. I gratefully acknowledge financial support from the Pennsylvania State University and KPMG Foundation.

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1.0 Introduction

The board of directors hires a CEO and designs a compensation contract to mitigate

agency costs and motivate the CEO to make value-increasing decisions. In addition to the

compensation contract, the firm’s debt contract terms and the possibility that the firm’s creditors

may wrest control of the firm from the shareholders also motivate the CEO. From the

perspective of the board of directors, who each year revise the CEO’s compensation contract, the

incentive effects provided by long-term debt contracts are pre-existing. The board then adjusts

the compensation contract such that the compensation incentives, layered on top of the debt

contract incentives, modify the CEO’s interests to align with the firm’s owners. How do these

pre-existing debt contract provisions affect the compensation contract struck between the board

and the CEO? This paper is the first to investigate this issue. Specifically, I examine whether the

sensitivity of the CEO’s cash compensation to earnings varies with the presence of earnings-

based covenants in the firm’s debt contracts. Despite the numerous studies on the use of

accounting measures for both debt and compensation contracting (see Armstrong et al.’s 2010

survey of the literature), little evidence exists on how the use of accounting measures in one

contracting arrangement affects their use in another. This study identifies one such interaction,

namely, a setting where the debt contracts contain earnings-based covenants and the sensitivity

of the CEO’s cash compensation to earnings is muted.

In both debt and compensation contracts, earnings-based performance measures influence

the CEO to focus on earnings performance and make decisions with regard to their effect on

earnings. Beneish and Press (1993, 1995) find that technical defaults on debt contract provisions

result in costs to the firm (e.g., higher interest rates and renegotiation costs). In addition to these

costs, Nini, Sufi and Smith (2010) document an economically significant increase in the

likelihood of CEO turnover. Holding other performance variables constant, Nini et. al find the

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marginal likelihood of a forced CEO turnover increases by 60% with technical default.

Together, these findings suggest that the manager will strive to maintain earnings above the level

specified in the earnings-based covenant. Earnings are also used for compensation purposes.

Extant executive compensation literature illustrates the use of accounting earnings as an efficient

contracting tool. Tying CEO compensation to earnings motivates the CEO to focus on

maintaining or improving current earnings. Both the debt and compensation contracts influence

the CEO’s choice of actions. Therefore, in contracting with the CEO, the owners of the firm

likely consider how the debt contract provisions influence on the CEO.

I use details of the covenants in the firm’s private debt contracts and the CEO’s annual

cash compensation to test the following predictions. First, I predict the presence of earnings-

based covenants in the firm’s debt contracts weakens the sensitivity of the CEO’s cash

compensation to earnings. The presence of earnings-based covenants influences the CEO to

focus on earnings and avoid technical default. Weakening the pay sensitivity to earnings

effectively tilts the CEO’s incentives away from earnings performance. Second, I predict a

negative association between the earnings-based covenant intensity and the sensitivity of the

CEO’s cash compensation to earnings performance. I use two measures of earnings-based

covenant intensity: the fraction of firm debt subject to an earnings-based covenant and the total

number of earnings-based covenants in the firm’s debt contracts. The covenant intensity proxies

capture the emphasis on earnings within the array of covenants in the firm’s lending agreements.

I assume that the larger the fraction of the firm’s debt subject to earnings-based covenants, the

greater the CEO’s incentive to focus on earnings. Similarly, I assume that the larger the number

of earnings-based covenants (e.g., minimum earnings before interest, taxes, depreciation and

amortization (EBITDA), maximum debt-to-EBITDA, maximum senior debt-to-EBITDA, etc.),

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the greater the CEO’s incentive to focus on earnings. In response, I predict that the

compensation contract is written to tilt the CEO’s incentives away from earnings. Specifically,

the greater the intensity of earnings-based covenants, the lower the sensitivity of cash

compensation to earnings.

I test my hypotheses using details of earnings-based covenants from Thomson Reuters

Loan Pricing Corporation’s syndicated loan data in DealScan and CEO compensation data from

ExecuComp for the period 1992 through 2010. I find the CEO’s cash compensation sensitivity to

earnings is lower when the firm’s private debt agreement contains an earnings-based covenant.

The sensitivity of cash compensation to return on assets (ROA) is roughly half when an

earnings-based covenant is in place. For the full sample, a one-percentage point increase in

ROA implies an increase in cash compensation to the CEO of 3.6 percent or $46,000. In

contrast, for a one-percentage point increase in ROA the cash compensation of a CEO in a firm

with earnings-based covenants increases by only 1.9 percent or $23,950, a statistically significant

difference at the 5% level using a one-tailed test. A similar association holds for a one-

percentage point increase in alternative measures of earnings performance. For a subset of firms

that change from debt contracts with no earnings-based covenants to contracts including such

covenants, the CEO’s pay sensitivity to earnings decreases around the change to earnings-based

covenants. Further, in tests using a propensity-score matched sample, pay sensitivities to

earnings weaken in the presence of earnings-based debt covenants. Additionally, the sensitivity

of cash compensation to earnings varies with the intensity of earnings-based covenants. The

amount of firm debt tied to earnings-based covenants and the total number of earnings-based

covenants is negatively associated with the CEO’s pay sensitivity to earnings, statistically

significant at the 1% and 5% level, respectively.

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The main results of the paper are robust to three alternative specifications and tests. First,

I retest my hypotheses with an alternative research design to ensure my design choice does not

account for the reported results. Secondly, I test whether the negative association between

earnings-based debt covenants and pay-for-performance sensitivity varies with the degree to

which earnings-based covenants influence the manager’s incentives. For this test, I restrict the

sample to only those firms with the most common earnings-based covenant (i.e., maximum debt-

to-EBITDA) and partition the sample based on covenant slack (i.e., the distance between the

current debt-to-EBITDA ratio and the ratio specified in the covenant). As predicted, the negative

association with the CEO’s cash compensation sensitivity to earnings does not hold for firms

with the most slack in the earnings-based covenant. Conversely, less covenant slack (i.e., greater

influence on the CEO) is negatively associated with pay sensitivity to earnings. Lastly, to

control for the selection bias in debt contract design I use the Heckman two-step approach as

well as a sample of CEO turnovers. The Heckman two-step procedure yields qualitatively

similar results to those from the full sample, change, and propensity-score sample analyses.

Furthermore, I use a sample of CEO turnovers subsequent to the debt contract negotiation to

control for the possibility that the CEO influences both the debt contract and compensation

contract terms. The CEO turnover sample exhibits a negative relationship between (i) the

presence of earnings-based covenants and the CEO’s cash compensation sensitivity to earnings

and (ii) the intensity of earnings-based covenants and the CEO’s pay sensitivity to earnings.

Overall, results of the additional tests are consistent with those of the main analysis.

The evidence presented in this paper has several implications for academics and

practitioners. This study relates to the current issue of executive pay-for-performance where

critics spotlight weak associations between compensation and firm performance by providing

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insight for instances in which lower pay-for-performance sensitivities to earnings do not indicate

inefficient or sub-optimal CEO pay. Instead, lower pay-for-performance sensitivity in

compensation contracts may be a consequence of strong implicit incentives in the debt contracts

which motivate the CEO to focus on earnings performance. More generally, the findings

indicate that the use of accounting information in one contract setting alters the contracting

parties incentives and the subsequent contracts they enter into. The paper proceeds as follows:

section 2 summarizes the literature relevant to this study, section 3 develops the hypotheses and

presents empirical predictions, section 4 describes the research design, section 5 summarizes the

main findings, section 6 describes several robustness tests and section 7 concludes.

2.0 Background and Relevant Literature

2.1 Debt contracting

Jensen and Meckling (1976), Smith and Warner (1979) and Myers (1977) develop

theories of the firm and the contracts under which they operate. Debt contracting theory

describes the conflicts of interest between shareholders and creditors, and outlines the contract

features that protect the debtholder’s investment despite these conflicts. Bradley and Roberts

(2004), Garleanu and Zwiebel (2009), and Demiroglu and James (2010) examine the

determinants of covenant usage. These studies find that covenant usage relates to characteristics

associated with a greater risk of wealth shifting from the creditors to self-interested managers

and shareholders. These findings are consistent with theoretical predictions that covenants

protect creditors from downside risk. Restrictive covenants prohibit actions that would shift

wealth from debtholders to shareholders (e.g., paying dividends, repurchasing shares, or

increasing firm debt). In addition, debt contracts include affirmative covenants specifying

financial measures or credit ratings that alert debtholders of deteriorating firm performance or

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increased credit risk. Affirmative debt covenants specify thresholds of the borrower’s

performance required to avoid technical default (i.e., a default not associated with a failure to

make debt payments). Earnings-based covenants are affirmative debt covenants relying on

measures of accounting earnings (e.g., EBITDA and debt-to-EBITDA). Restrictive and

affirmative debt covenants, used to protect the creditors investment, in turn constrain and

motivate the manager’s action choice.

Recent empirical studies show debt contract terms consider the interests of the CEO, who

controls the use of the capital. Chava, Kumar and Warga (2010) and Anatharaman, Fang and

Gong (2011) empirically test associations between the manager’s long-term compensation (i.e.,

equity-based and debt-based compensation) and the structure of the firm’s debt contracts. Using

a probit analysis, Chava et al. find a positive relation between attributes of managerial

entrenchment and the likelihood of including investment, consolidation, and merger restricting

bond covenants. Anatharaman et al. find a negative relation between executive debt-based

compensation (pension and deferred compensation) and the number of covenants included in the

debt contracts. These studies suggest that, in practice, debtholders recognize the manager’s

incentives provided by his compensation and contract accordingly.

In contrast to these studies, this paper investigates the association between current

payments of cash compensation and long-term private debt contract terms. This distinction is

important for several reasons. First, the board can adjust the terms of the cash compensation

contract several times over the life of the debt contract. 1 The board has less control over

adjusting the balances of the CEO’s equity and debt-based compensation, which are the focus of

Chava et al. (2010) and Anatharaman et al. (2011). The board has the option to grant additional !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1 The average contract length for syndicated loan data in DealScan is long-term, with a maturity of 4 years, whereas; cash compensation contracts are revisited annually.

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equity and debt-based compensation, but can do little to reduce these balances. For these

reasons, it is easier for the board to adjust the cash compensation contract considerate of changes

in the firm’s debt contract terms. Secondly, upon payment, cash compensation provides no

further incentive to the CEO. This, coupled with the possibility of boards adjusting cash

compensation more readily, suggests it is unlikely for the debt contract terms to be influenced by

cash compensation arrangements. Additionally, the use cash compensation in this study follows

from the research question focusing on the use of earnings for contracting similar to Bushman,

Engel and Smith (2006).

This study examines the association between three contracting parties: two principals

(creditors and shareholders) and one agent (CEO). Consideration of these three contracting

parties relates to Stiglitz’s (1985) multiple-principal-agent problem. Stiglitz suggests the

multiple-principal-agent problem applies to creditors and shareholders contracting with one

manager. In this setting, creditors and shareholders contribute capital and each contract with the

manager to motivate him to act in their respective interests. With three distinct parties entering

into the contracts that make up the firm (shareholders, creditors and managers), the contracts

between any pair should consider the contracts each party has with the third. Despite this

economic intuition and prior evidence suggestive of covenants influencing the manager’s

behavior, executive compensation research largely ignores the influence of debt contract

provisions on the manager and his compensation contract. The evidence presented in this paper

empirically tests whether the variation in the agent’s incentive contract is associated with

existing debt contract terms.

2.2 Pay-for-performance

The design of the CEO’s compensation contract provides incentives to focus effort and

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make decisions in the best interest of the shareholders. Watts and Zimmerman (1978) outline the

importance of accounting information in contracting and suggest accounting information

provides verifiable, shared information to contracting parties, such as shareholders and

management. Holmstrom (1979), Lambert and Larcker (1987), Banker and Datar (1989), and

Sloan (1993) provide theoretical models of compensation contracting that suggest accounting

earnings provide contractible information to facilitate efficient contracting and risk sharing

between shareholders and management. Empirical evidence corroborates the use of accounting

in incentive contracts by showing strong correlations between earnings and executive cash

compensation (Lambert and Larcker 1987; Sloan 1993; Baber et al. 1996). Moreover, recent

critics of executive compensation argue that manager’s pay should more closely reflect firm

performance. Bebchuk and Fried (2006) criticize executive compensation practices as

reflections of CEO’s opportunistically extracting rents from shareholders without delivering

performance to match the extravagant pay. Likewise, the media frequently criticizes

compensation practices for not being a fair representation of firm performance. Taken together,

there exist strong arguments for incentive contracts to tie manager’s compensation to earnings

performance.

Smith and Watts (1982) and Dechow and Sloan (1991) claim that earnings encourages

myopic management decisions even though theoretical predictions, empirical evidence and the

media support the use of earnings for performance evaluation. Smith and Watts (1982) refer to

this short-term focus as the ‘horizon problem’ where managers choose lower net present value

projects to sustain earnings over higher net present value projects that reduce current earnings.

Managers with excessive incentives to maintain earnings may pass on riskier projects with

desirable upside potential due to the cost of reducing current earnings. Additionally, Healy

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(1985) and Gaver, Gaver and Austin (1999) provide empirical evidence of an association

between earnings manipulation and management’s earnings-based performance measures.

Crocker and Slemrod (2007) model the effect of the manager’s performance-contingent bonus

schemes on truthful financial reporting. Their model implies the use of earnings as an efficient

contracting tool necessitates tolerance of some degree of earnings management. Taken together,

the literature suggests earnings provide incremental information for efficient contracting at the

cost of encouraging myopic decisions and earnings manipulation. Therefore, the extent to which

the compensation contract relies on current earnings should reflect this tradeoff such that the

manager’s preferences are balanced between maintaining earnings performance and his other

corporate responsibilities. 2

3.0 Development of hypotheses

Affirmative earnings-based debt covenants stipulate a minimum level of earnings that

must be met in order to avoid technical default. Earnings-based covenants may include, but are

not limited to, maximum debt-to-EBITDA ratio, minimum EBITDA and several coverage ratios

relying on measures of earnings. The borrower triggers technical default when earnings fall

below the level specified in the covenant. Beneish and Press (1993, 1995) and Dichev and

Skinner (2002) find technical default results in costs to the firm through increased interest rates,

increased restrictions in the debt agreements, renegotiation costs, and a reduction in available

credit. In the event of technical default on earnings-based debt covenants, lenders have the right

to renegotiate the terms of the debt contract at the borrowing firm’s expense or call the debt prior

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"!The managerial accounting literature on the balanced scorecard highlights the use of many strategy-based performance measures that complement financial performance for management compensation. In addition to financial measures of performance, the balanced scorecard includes customer, business process and growth perspectives to measure management’s performance. For a detailed discussion of the balanced scorecard see Kaplan and Norton (1996).

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to its stated maturity. These studies suggest the inclusion of earnings-based debt covenants alters

the firm’s economic reality by imposing real costs to the firm when earnings fall below the

covenant threshold.

The existence of earnings-based covenants influence the manager’s interest in

maintaining earnings above the specified level in order to retain the shareholder’s control rights

to the firm and avoid costs associated with default. Additionally, the manager has personal

incentives to allocate effort towards maintaining earnings. Dichev and Skinner (2002) argue that

managers, in both firms with poor and good financial health, have incentives to avoid technical

default because of the time, effort and cost of renegotiations. Nini, Sufi and Smith (2010) find a

60% marginal increase in the likelihood of forced CEO turnover with violation of financial

covenants, incremental to the effect of firm performance. This suggests earnings-based

covenants increase the manager’s personal incentives to maintain earnings and avoid technical

default.

Several studies of executive compensation argue the short-term focus of earnings

motivate CEOs to make myopic decisions at the expense of firm value. Tilting the CEO’s

incentives away from earnings can serve the shareholders’ interests when debt covenants

influence the manager to act myopically to sustain earnings. The board of directors can

accomplish this objective through a variety of approaches. For example, the board could

increase the manager’s risk-taking incentives with stock options or restricted stock, thereby

shifting the manager’s effort away from a focus on earnings. Moreover, the board could increase

the sensitivity of the manager’s compensation to measures of current performance not captured

in earnings such as qualitative or customer satisfaction measures. Finally, the board of directors

could decrease the manager’s incentives to focus on earnings by directly reducing the CEO’s pay

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sensitivity to earnings. My first hypothesis considers the latter approach with respect to cash

compensation. In other words, the manager’s annual cash compensation tied to earnings is

adjusted when the firm’s debt contracts contain earnings-based covenants. This leads to my first

hypothesis stated in the alternative:

H1: The pay-for-performance sensitivity to earnings in the CEO’s compensation is lower for firms with an earnings-based debt covenant.

Pay-for-performance sensitivity to earnings captures the variation in compensation

explained by earnings. This proxy estimates the degree to which the terms of the compensation

contract motivate the manager to focus on current earnings. Pay-for-performance sensitivity to

earnings proxies for the ex-ante compensation contract using ex-post measures of the realized

cash compensation and earnings performance. Results consistent with H1 would suggest that the

board of directors recognizes the debt contract terms influence the CEO and rebalance his

incentives by reducing the pay-for-performance sensitivity to earnings. However, in the

presence of an earnings-based covenant and the threat of the creditors wresting control of the

firm, the board of directors’ incentive to maintain earnings performance may increase as well.

This change in shareholder incentives may influence the board of directors to increase the

sensitivity of the CEO’s pay to earnings performance, which would be reflected in an association

opposite to that predicted by H1. However, if the board takes any of the alternative approaches

to rebalancing the CEO’s incentives discussed above, then tests of H1 will fail to find a

significant relationship between pay-for-performance sensitivity and earnings-based covenants.

Additionally, the vastly different purposes for debt and compensation contracts may result in no

association between the use of earnings in the two contracting environments. Debt contract

provisions protect debtholders from the downside risk of losing their investment. On the other

hand, compensation contract terms motivate the manager to maximize firm value, which is often

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accomplished by choosing riskier projects with high upside potential.

My first hypothesis considers the association between the presence of earnings-based

covenants and the CEO’s cash compensation sensitivity to earnings. In contrast, my second set

of hypotheses recognizes the variation in covenant intensity. Greater covenant intensity refers to

a greater fraction of the firm’s debt subject to earnings-based covenants or a greater number of

earnings-based covenants in the firm’s debt contracts. Arguably, a single earnings-based

covenant in one lending agreement has a different effect than several earnings-based covenants

across multiple contracts. Multiple earnings-based covenants or earnings-based covenants that

cover a larger proportion of the firm’s debt should have a larger effect on the manager’s

incentives to focus on earnings. This leads to my second set of hypotheses stated in the

alternative:

H2a: A negative relationship exists between the fraction of firm debt subject to earnings-based covenants and the CEO’s pay-for-performance sensitivity to earnings.

H2b: A negative relationship exists between the number of earnings-based covenants in the firm’s debt contracts and the CEO’s pay-for-performance sensitivity to earnings.

Evidence consistent with the second set of hypotheses would indicate the board of

directors recognizes that the greater earnings-based covenant intensity results in greater influence

on the manager’s preferences. In other words, higher earnings-based covenant intensity shifts

the manager’s focus towards earnings performance to a greater extent. This greater shift in focus

may relate to the board of directors reducing the CEO’s cash compensation tied to current

earnings performance, as predicted by H2.

4.0 Research Design

4.1 Model of pay-for-performance sensitivity related to debt contract characteristics

The general model used to test my hypotheses regresses estimates of the CEO’s pay-for-

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performance sensitivity to earnings on the earnings-based covenant variable of interest:

!!"!" ! ! ! !"#$%!!"!#$%"&'$&#!" !!"#$%"&'!" !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

The subscripts i and e represent the firm and earnings-based covenant regime respectively.

Earnings-based covenant regime refers to a period of consecutive years with either one or more

earnings-based covenants in the firm’s debt contracts (e = 1) or a period of consecutive years

with no such covenant (e = 0). The dependent variable, PPS, equals the CEO’s pay-for-

performance sensitivity to earnings estimated over the earnings-based covenant or no earnings-

based covenant regime. DebtCharacteristic represents the debt contract term of interest. For

tests of the first hypothesis the debt characteristic of interest, EarnCovenant, equals one if the

firm’s debt contracts contain at least one earnings-based covenant and zero otherwise.

For the second set of hypotheses, DebtCharacteristic takes on values that proxy for the

earnings-based covenant intensity within the firm’s debt contracts. To test H2a, I use

EarnCovRatio to proxy for earnings-based covenant intensity, measured as the fraction of firm

debt subject to an earnings-based covenant. For example, if a firm has two loan packages with

borrowing allowances of $3 million subject to earnings-based covenants and $1 million without

earnings-based covenants then EarnCovRatio equals ! or equivalently 0.75. To test H2b, the

covenant intensity proxy equals the natural logarithm of the total number of earnings-based

covenants in the firm’s debt contracts, TotalEarnCovenants.

4.2 Variable measurement and sample construction

I begin with a sample of all firms covered by Compustat and ExecuComp from 1992

through 2010. Following a matching procedure, consistent with Chava and Roberts (2008), I

identify ExecuComp firms with syndicated loan data in the DealScan database. I then merge

details of the firm’s earnings-based covenants from DealScan to the firm-year financial and

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compensation data from Compustat, CRSP and Execucomp.

4.2.1 Estimating pay-for-performance sensitivity

I construct proxies for the pay-for-performance sensitivity with respect to earnings, PPS,

by estimating the degree to which the CEO’s cash compensation varies with earnings. I run

firm-specific ordinary least squares regressions of cash compensation on earnings for periods of

at least five consecutive years in which the firm’s debt contracts contain earnings-based

covenants. This provides estimates of PPSi,e=1 for each firm, i, under earnings-based covenant

regime e = 1. Similarly, I regress compensation on earnings for periods of at least five

consecutive years in which the debt contracts do not contain earnings-based covenants to

estimate the pay-for-performance sensitivity PPSi,e=0 for each firm, i, in regime e = 0. Requiring

at least five consecutive years of data, in which the firm’s earnings-based covenant status

remains constant, restricts the firm-year observations used to calculate PPS. For example, a firm

with an earnings-based covenant for three years, followed by five years without, has only one

estimate of pay-for-performance sensitivity, PPSi,e=0. I require at least five consecutive years to

mitigate poor estimates of pay-for-performance sensitivity from too few data points.3 I estimate

equation (2) by firm for each covenant regime with the minimum of five consecutive years.

!"#!!"#$%&'()*"&!" ! !!! ! !!!!"#$%$&'!" ! !!!" (2)

The subscripts i and t represent firm and year respectively. The coefficient estimates, !1, serve

as the pay-for-performance sensitivity proxy, PPS.4 CashCompensation equals the CEO’s cash

compensation (salary and bonus) in year t. Since the earnings measures used in compensation

contracts vary, I estimate PPS using three measures for Earnings. Following prior literature, I

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!3 Altering the five-year requirement to four or six years yields qualitatively similar results. 4 This research design choice relies on regression estimates for the dependent variable. To confirm that poor estimates for PPS do not drive the results, section 6 includes an alternative cross-sectional research design.

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set Earnings equal to ROA measured as earnings before interest, taxes, depreciation and

amortization divided by average assets. The coefficient estimates, !1, from regressions of

CashCompensation on ROA become the pay-for-performance sensitivity proxy PPS_ROA.

Several empirical studies use earnings performance measures scaled by sales. Therefore for a

second PPS proxy, I set Earnings equal to Margin, measured as earnings before interest, taxes,

depreciation and amortization divided by average sales. PPS_Margin equals the coefficient

estimates, !1, from regressions of compensation on Margin. Lastly, I use basic earnings per-

share, EPS, as reported by Compustat to estimate the pay sensitivity proxy PPS_EPS, which

takes the value of the coefficient estimate, !1, from regressions of CashCompensation on EPS.

This procedure results in the full sample of 2,118 estimates for each of the three pay-for-

performance proxies. Panel A of table 2 provides descriptive statistics for the pay-for-

performance sensitivity estimates (PPS_ROA, PPS_Margin and PPS_EPS) with 921 and 1,197

observations with and without earnings-based covenants respectively. Test of normality in the

PPS estimates reject the null hypothesis of a normal distribution. Therefore, I report Wilcoxon

tests of differences in panel A of table 2. One-sided tests show a significant difference between

PPS_ROA and PPS_Margin for observations with and without an earnings-based covenant. The

median values of PPS_ROA, PPS_Margin and PPS_EPS with earnings-based covenants are

0.75, 0.97 and 0.17 respectively. The estimates of PPS_ROA, PPS_Margin and PPS_EPS

without an earnings-based covenant have median values of 1.24, 1.37 and 0.32 respectively.

Consistent with H1, all three pay sensitivity measures are lower for observations with an

earnings-based covenant than observations without, significant at the 1% level.

4.2.2 Control variables

As control variables in equation (1), I include characteristics of the firm’s earnings, the

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firm itself and the CEO. To control for volatility in the firm’s earnings, I include the variance of

earnings before interest, taxes, depreciation and amortization over the previous five years,

EBITDA_Volatility. 5 Additionally, I include the variance of the firm’s cash from operations,

OpCF_Volatility, to control for volatile cash flows and operating risk. Both volatility measures

could be correlated with poor performance or financial distress in which case I would expect

these control variables to be positively associated with the CEO’s pay for earnings performance.

On the other hand, volatility may be indicative of unreliable earnings measures and hence poor

measures to use for contracting, in which case I would expect a negative association with the

pay-for-performance proxies. Lambert and Larcker (1987) argue that CEO compensation is less

sensitive to earnings in early stages of investment since managerial actions focus on future

period earnings. To control for firm growth, I include the market-to-book value (MTB) as a

control variable. I expect firms in growth stages to be less likely to use measures of current

earnings for executive compensation and therefore predict MTB to be negatively associated with

pay-for-performance sensitivity. Sales is included to control for sales growth, measured as the

percentage change in sales. I expect Sales to be positively associated with the PPS measures as

sales growth is indicative of favorable earnings performance. To control for the effect of the

firm’s capital structure on compensation I include Leverage measured as total debt (debt in

current liabilities and long-term debt) to total assets. Gabaix and Landier (2008) argue that

measures of market value are the most appropriate proxy of firm size for empirical studies of

executive compensation. Therefore, to control for firm size I include the natural logarithm of

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!5 Results are robust to the inclusion of abnormal accruals, from the modified Jones model (Dechow et al., 1995), as well as, the ratio of time-series variances of earnings and stock price (Sloan, 1993) as alternative measures for the noise in earnings. I choose not to include these control variables in the main analysis due to data limitations reducing the sample size.

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average market value, MarketValue.6 Consistent with prior literature I expect both Leverage and

MarketValue to be positively related to pay-for-performance. Lambert and Larcker (1987) argue

the more executive wealth tied to stock prices then the more likely the board of directors will tie

current compensation to earnings. Therefore, I include a control variable for changes in the

value of the CEO’s wealth for a 1% change in stock price and stock price volatility, Delta and

Vega respectively. To measure Delta and Vega, I first calculate the value of the CEO’s option

portfolio using the methodology described in Core and Guay (2002). Consistent with Lambert

and Larcker (1987), I expect Delta to be positively associated with PPS. However, Vega proxies

for risk-taking incentives, therefore firms with compensation practices and risk preferences

consistent with high levels of Vega are unlikely to motivate less risk taking by tying

compensation to earnings performance. Therefore, I expect Vega to be negatively related to

PPS. I include Age to control for changes in compensation due to CEOs nearing retirement,

measured as the natural logarithm of the CEO’s age. I also include 12 industry controls based on

Fama and French’s industry classifications.7 I calculate the control variables over the same time

period used to estimate the PPS proxies. Panel A of table 1 contains descriptive statistics for the

full sample of 2,118 observations.

4.2.3 DealScan Variables

Researchers have commented on the potential misclassification of a firm as either having

or not having an earnings-based covenant in any given year and incomplete information

regarding debt covenants in the DealScan database (Chava and Roberts, 2008). Incomplete

reporting of covenants in DealScan will result in classifying observations as not having an

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!6 Results are consistent using alternative proxies for firm size, such as average assets and average sales. 7Industry classifications available at: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_12_ind_port.html

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earnings-based covenant when in fact the debt contract contains such a covenant. Unless a

systematic process, correlated with CEO pay-for-performance, drives the incomplete covenant

data in DealScan, the misclassification will bias my tests against finding differences.

Another concern with the earnings-based covenant classification relates to the nature of

the DealScan data. The DealScan database contains information for syndicated loan contracts at

initiation, many of which relate to lines of credit. The loan amount reported in DealScan reflects

the credit available to the borrowing firm. This does not reflect when or to what degree the

borrower actually draws on the funds. Therefore, some earnings-based covenant observations

might be instances in which the borrower has access to but has not drawn from the credit subject

to earnings-based covenants. Additionally, Roberts and Sufi (2009) find approximately 90% of

debt contracts are renegotiated prior to maturity. Renegotiations may result in different debt

contract provisions than those in the original contract design. This may result in

misclassification of earnings-based and non earnings-based covenant observations, to the extent

that renegotiations include new earnings-based covenants or exclude earnings-based covenants

written into the initial contract.

The earnings-based covenant types reported by DealScan include maximum debt-to-

EBITDA, maximum senior debt-to-EBITDA, minimum EBITDA and several coverage ratios.

The frequency of covenant usage in the sample agrees with prior literature relying on the

DealScan database. The maximum debt-to-EBITDA ratio covenant appears in approximately

22% of the DealScan packages for firms with available Compustat and ExecuComp data.

Following in prevalence, minimum interest coverage ratio and minimum fixed charge ratio

covenants appear in 18.45% and 15.59% of packages respectively. On average, packages linked

to Compustat and ExecuComp contain 1.97 earnings-based covenants.

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Table 1 provides descriptive statistics on the variables constructed from the DealScan

database. The SecuredDebtRatio equals the ratio of secured debt to total debt. Similarly, the

SeniorDebtRatio equals the ratio of senior debt to total debt. DealScan provides information on

the primary purpose of the loan with the most frequent being corporate purposes, followed by

working capital, debt repay and takeovers. I measure CorpPurposes, WorkingCap, DebtRepay

and Takeovers as the fraction of debt for each of the respective purposes.

4.2.4 Sample for Change Analysis

To mitigate the effects of endogeneity I conduct an analysis using 259 firms with

sufficient data available during a period without earnings-based covenants followed by a period

with earnings-based covenants. This sample allows me to capture changes in the CEO’s cash

compensation sensitivity to earnings around a change in the firm’s debt contract provisions.

Panel B of table 2 reports descriptive statistics and results of non-parametric tests of the

differences between pay-for-performance sensitivity estimates in the period with earnings-based

covenants and the period without. I calculate differences in pay-for-performance sensitivities as

PPSi,e=1 minus PPSi,e=0. In other words, I subtract the estimate of the pay-for-performance

sensitivity without earnings-based covenants from the estimate for the period with earnings-

based covenants (PPSi,e=1 - PPSi,e=0 = "PPSi). Consistent with hypothesis H1, the signed rank

tests of the differences in pay-for-performance sensitivity indicate significantly negative values

at the 1% level for the three estimates, "PPS_ROA, "PPS_Margin and "PPS_EPS. This

suggests a decrease in pay sensitivity to earnings when the firm’s debt contract includes

earnings-based covenants.

4.2.5 Propensity-Score Matched Sample

To further address endogeneity, I use propensity score matching, an econometric method

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proposed by Rosenbaum and Rubin (1983), to match the treatment sample (earnings-based

covenant observations) to a control sample (without earnings-based covenants). Propensity score

matching uses observable characteristics of the treatment and control groups to match on

multiple dimensions. First, I use a probit specification to model the choice to include earnings-

based covenants in the firm’s debt contracts using the 2,118 observations in the full sample with

the following model:

!" !"#$%&'($"$) !" ! !! ! !!!"#$!" ! !!!"#$%!" ! !!!"#$%"!" ! !!!"#!" ! !!!"#$%&!" !!!!"#"$%&"!" ! !!!"#!" ! !!!"#$%&!!"#$%&#&%'!" !!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!"#$!!"#$%&#&%'!" ! !!"!!"!" ! !!!!"#$!" ! !!"!"#$%!" !!!!!!!!!!!!! !!!"!"#$!"!" !! !!"!"#$%&'"()%!" ! !!"!"#$%"&'()*+!" !!!!!!!!!!!!!!!!!! !!!"!"#$%&'()$%!" ! !!"!"#$%&#$"'('!" ! !!"!"#$%"&'(!" !!!!!!!!!!!! !!!"!"#$%&$'!" ! !!"!"#$%&'!" ! !!"!"!!"#$%!" ! !!" !!!!!!!!!!!!!!!!

The subscripts i and e represent firm and covenant regime respectively. I include individual and

compensation characteristics for the CEO (i.e. Vega, Delta, Tenure and Age) to capture the

extent to which earnings-based covenant inclusion relates to executive level characteristics.

Tenure equals the number of years the CEO has been in office and the remaining executive level

variables are measured as described in section 4.2. Following Christensen and Nikolaev (2012) I

include several firm level characteristics including EBITDA, Leverage, MTB, PPE, Return,

MarketValue and SecuredRatio. EBITDA equals earnings before interest, taxes, depreciation and

amortization divided by average assets, PPE equals net property plant and equipment divided by

average assets and Return equals the 12-month compound return. Leverage, MTB, MarketValue

and SecuredRatio are measured as described in section 4.2. I include additional firm-level

variables to capture the financial health (i.e. EBITDA_Volatility, OpCF_Volatility, OpCF and

Sales), existing debt contract design (i.e., SeniorRatio and OtherCov) and the purpose for

existing debt (i.e., CorpPurposes, DebtRepay, Takeover and WorkCap). OpCF equals operating

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21

cash flows divided by average assets, and OtherCov equals the number of non earnings-based

covenants in the firm’s debt contracts. Section 4.2 describes the measurement of the remaining

variables in equation (3).

I calculate the probability of having an earnings-based covenant for each of the 2,118

observations using the parameter estimates from equation (3). I match treatment observations to

control observations with the closest propensity score, equal to the estimated probability. The

resulting sample contains 492 matched pairs. Panel B of table 1 contains descriptive statistics for

the propensity-score matched sample and reports t-tests of differences between the treatment and

control groups. The t-tests show insignificant differences on several dimensions of firm and

executive characteristics. These comparisons indicate the propensity score matching provides a

well-suited matched sample of control observations. Panel C of table 2 reports tests of

differences in the pay-for-performance sensitivity proxies. Similar to the change analysis, I

calculate differences as the treatment observation values (with an earnings-based covenant)

minus the values for the control sample (without an earnings-based covenant). Consistent with

the full sample and change analysis, signed rank tests show negative values for the calculated

differences between pay-for-performance proxies significant at the 10% level or better. Table 3

contains Pearson and Spearman correlations between the estimates of pay-for-performance

sensitivity and the debt contract variables of interest (i.e. EarnCovenant, EarnCovRatio and

TotalEarnCovenants). Consistent with the hypotheses and the univariate tests, the correlations

reported in table 3 are negative.

5.0 Empirical results

5.1 Regression analysis: Earnings-based covenants and pay sensitivity to earnings

Hypothesis H1 predicts a lower pay-for-performance sensitivity in the CEO’s cash

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compensation when the firm’s debt contract includes an earnings-based covenant. The variable

of interest, EarnCovenant, equals one for observations with an earnings-based covenant and zero

otherwise. The model specifications to test the first hypothesis substitutes the dichotomous

variable, EarnCovenant, into the general specification of equation (1) such that:

!!"!" ! ! !! ! !!!"#$%&'($"$)!" ! !!!!"#$%"&'!" ! !!!" !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"!

!!!"!" ! ! !!!!"#$%&'($"$)!" ! !!!!!"#$%"&'!" ! !!!" !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"!

Subscripts i and e indicate firm and earnings-covenant regime respectively. Controls equals the

vector of control variables described above (MarketValue, Leverage, MTB, Sales,

EBITDA_Volatility, OpCF_Volatility, Delta, Vega and Age) and PPS equals one of the three pay-

for-performance proxies (PPS_ROA, PPS_Margin and PPS_EPS). Equation 4a is

operationalized for the full sample of 2,118 observations. I use equation 4b for analysis using

the 259 firms that switched to debt contracts with earnings-based covenants and the 492

propensity-score matched pairs. This specification allows for a change analysis with the 259

switching firms and controls for each of the pairs in the propensity-score matched sample. The

symbol ! represents the difference between observations with an earnings-based covenant and

those without, calculated as the treatment observation value minus the value for the control.

5.1.1 Full Sample analysis

Table 4 contains the results from OLS regressions of equation (4a) for the full sample.

The first hypothesis predicts a negative relationship between the dependent variable (PPS_ROA,

PPS_Margin and PPS_EPS) and EarnCovenant (i.e., !! ! !). A negative value for !! suggests

that the board of directors recognizes the earnings-based debt covenant motivates the manager to

focus on earnings and therefore, reduces the manager’s explicit earnings incentives in the

compensation contract. In the PPS_ROA, PPS_Margin and PPS_EPS models, coefficient

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23

estimates (t-statistics) for EarnCovenant are -2.21 (-1.66), -2.17 (-2.49) and -1.150 (-1.50)

respectively. This equates to one-tailed statistical significance at the 5%, 10% and 1% levels for

the PPS_ROA, PPS_Margin and PPS_EPS models respectively. The results are consistent with

those of the univariate tests reported in panel A of table 2 and the prediction of H1.

5.1.2 Change Analysis

Fundamental differences, not captured by the control variables, between firms with

earnings-based covenants and those without could lead to false inferences from the full sample

tests. In an attempt to control for these fundamental differences and isolate the relationship

between the CEO’s pay-for-performance sensitivity and the presence of earnings-based

covenants, I perform an analysis using equation (4b) and the 259 firms that switched to debt

contracts containing earnings-based covenants. I tabulate results from the change analysis in

panel A of table 5. Consistent with the full sample analysis, I find the change to earnings-based

covenants is negatively associated with the change in the CEO’s pay sensitivity to earnings. The

estimated change in the CEO’s cash compensation sensitivity to ROA (!PPS_ROA) related to

the change to earnings-based covenants is -5.89 (t-statistic of -2.61). Similarly, the change to

earnings-based covenants is associated with a -4.49 (t-statistic of -2.50) change in

!PPS_Margin. The coefficient estimates are significantly negative at the 1% level. In the

regression of !PPS_EPS, the estimated change in pay sensitivity related to the change in

earnings-based covenant regime is statistically significant at the 5% level with a coefficient

estimate (t-statistic) of -3.73 (-1.78). In all specifications, I find a negative change in pay-for-

performance sensitivity related to a change to debt contracts with earnings-based covenants.

These results agree with those of the univariate tests reported in table 2 and suggest the CEO’s

incentive contract adjusts for the presence of earnings-based covenants in the firm’s debt

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contracts.

5.1.2 Propensity-score matched sample

I tabulate the OLS regression results of equation (4b) with the propensity-score matched

sample in panel B of table 5. For all three models, "PPS_ROA, "PPS_Margin and "PPS_EPS, I

find negative coefficient estimates (t-statistics) on "EarnCovenant of -3.49 (-2.21), -1.99 (-1.82)

and -1.82 (-1.83), respectively. One-sided tests result in statistical significance at the 5% level

for all three models. This suggests selection bias, due to observable characteristics, does not

explain the results for the full sample analysis.

As discussed above, this empirical relationship could reflect the board recognizing the

CEO’s added incentives from the debt contract to focus on earnings thereby avoiding the

personal and firm costs associated with technical default. This decrease in the pay-for-

performance sensitivity to earnings can accomplish two things: (1) shift the CEO’s effort

allocation away from a focus on current earnings and (2) facilitate optimal contracting where the

board of directors does not contract with the CEO on earnings performance when the debt

contract covenants ex-ante implicitly provide incentive to maintain earnings.

5.2 Earnings-based covenant intensity and pay sensitivity to earnings

The second set of hypotheses predicts a negative relationship between two measures of

earnings-based covenant intensity (EarnCovRatio and TotalEarnCovenants) and pay-for-

performance sensitivity to earnings. EarnCovRatio equals the fraction of firm debt subject to an

earnings-based covenant as reported by DealScan. TotalEarnCovenants equals the natural

logarithm of the total number of earnings-based covenants in the firm’s debt contracts. The

regression equation used to test the second set of hypotheses follows:

!!"!" ! ! !! ! !!!"#$"%&#'!" ! !!!!"#$%"&'!" ! !!!" !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

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Subscripts i and e represent firm and covenant-regime respectively. PPS takes the value of one

of the pay-for-performance proxies, PPS_ROA, PPS_Margin and PPS_EPS. I tabulate

regression results from the three models using the full sample in table 6. Consistent with H2a

and H2b, I find negative and significant coefficient estimates (!! ! !! in one-sided tests. In

regressions of PPS_ROA and PPS_Margin the coefficient estimates (t-statistics) for

EarnCovRatio are -3.93 (-2.42) and -3.39 (-3.17) respectively. These results support the

prediction of H2a with statistical significance at the 1% level. Additionally, in the PPS_EPS

model, I find a coefficient estimate (t-statistic) of -1.24 (-1.32), statistically significant at the

10% level. These results consistently support the prediction of H2a and suggest a negative

relationship between the fraction of debt tied to earnings-based covenants and the CEO’s pay-

for-performance sensitivity.

To test hypothesis H2b, I use TotalEarnCovenants to proxy for covenant intensity. I find

a negative coefficient estimate of -0.104 on TotalEarnCovenants with statistical significance at

the 5% level (t-statistic of -1.70) in the model of PPS_ROA. In the PPS_Margin specification

the coefficient estimate (t-statistic) for TotalEarnCovenants is -0.103 (-2.57), statistically

significant at the 1% level. The PPS_EPS model results in a negative coefficient estimate

statistically significant at the 10% level. The results using TotalEarnCovenants to proxy for

earnings-based covenant intensity consistently support the prediction of H2b. Taken together,

the results for H2a and H2b suggest a negative relationship between the pay-for-performance

sensitivity of the CEO’s cash compensation and the intensity of earnings-based covenants in the

firm’s debt contracts.

6.0 Alternative and Robustness Tests

I run a battery of tests to confirm the main results of the paper, reported in tables 4, 5 and

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6. First, I re-test my hypotheses using a cross-sectional research design to ensure the main

results are not driven by design choice or reliance on poor firm-specific estimates for the

dependent variables, PPS_ROA, PPS_Margin and PPS_EPS. Second, I test whether the negative

relationship between pay sensitivity and the presence of earnings-based covenants holds for

firms with the greatest amount of slack in the earnings-based covenant. Lastly, to test the

robustness of the results regarding H1, I use Heckman’s two-step approach as an alternative

econometric technique to mitigate selection bias in the binary variable of interest, EarnCovenant.

Additionally, I use a sample of CEO turnovers as an exogenous shock, subsequent to the

initiation of firm debt contracts, to test the robustness of H1, H2a and H2b.8

6.1 Cross-sectional research design

As an alternative to the research design described in section 4, I use a cross-sectional

regression analysis to address my research questions. Similar to the research design in Banker,

Huang and Natarajan (2009), I include the main effects of the earnings performance measure,

earnings-based covenant characteristics and control variables as well as interacting earnings

performance with the covenant variable and controls. This research design captures the cross-

sectional variation in the CEO’s pay-for-performance sensitivity to earnings across firms. The

variable of interest is the interaction term between earnings performance and the earnings-based

covenant characteristic capturing the incremental effect on pay sensitivity to earnings of the

earnings-based covenant term.

!"#!!"#$%&'()*"&!" !! !! ! !!!"#$%$&'!" ! !!!!"#$%!!"!#$%"&'$&#!" !!!!!"#$%$&'!" ! !!"#$%!!"!#$%"&'$&#!"!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! !

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!8 In untabulated tests, I perform two-stage least squares analysis with qualitatively similar results using the several reported purposes of the loans as instrumental variables. Admittedly, these instruments may be correlated with pay-for-performance sensitivity making them poor instrumental variables. I rely on the robustness test reported in the text due to the unavailability of a good instrumental variable for two-stage least squares estimation.

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! ! !!!!!

!!!!"#$%"&'!"# !! !!!!"#$%$&'!" ! !"#$%"&!!"#!! !!!"!!

The subscripts i and t represent firm and year respectively. CashCompensation equals the

CEO’s annual salary and bonus, Earnings equals earnings before interest, taxes, depreciation and

amortization scaled by average assets. DebtCharacteristics equals EarnCovenant for the test of

H1, EarnCovRatio for the test of H2a and TotalEarnCovenants for the test of H2b. Controls

equals the vector of control variables described in the research design section above

(MarketValue, Leverage, MTB, Sales, EBITDA_Volatility, OpCF_Volatility, Delta, Vega and

Age). The hypotheses predict a negative coefficient estimate for the interaction of earnings

performance and the earnings-based covenant characteristic (!!< 0). I include the control

variables described in section 4.4, as well as industry and year fixed effects. Additionally, I

allow the pay-for-performance sensitivity to vary by industry and year. The sample consists of

18,336 firm-year observations for the cross-sectional analysis. Table 7 contains results from

tests of H1. The coefficient on the interaction between EarnCovenant and Earnings is -0.61,

significant at the 10% level (t-statistic of -1.28).9

In tests of H2a and H2b tabulated in table 7, the interaction terms, between earnings and

the covenant intensity proxies (EarnCovRatio and TotalEarnCovenants) have negative

coefficient estimates. For the regression of CEO cash compensation on the interaction between

EarnCovRatio and Earnings, the coefficient estimate of -1.13 (t-statistic of -2.18) is significant at

the 5% level. This finding is consistent with the prediction of H2a. In a test of H2b I find a

negative and significant coefficient of -0.15 on the interaction between TotalEarnCovenants and

earnings performance, statistically significant at the 5% level. The cross-sectional tests provide

results consistent with those reported in the main analysis. This suggests neither the research

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!9 In all cross-sectional regressions I cluster by firm and report significance using clustered standard errors.

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design nor poor estimates for pay-for-performance sensitivities drive the main results.

6.2 Covenant slack analysis

I construct a sample in which the firm’s debt contract contains the most common

earnings-based covenant, maximum debt-to-EBITDA. I calculate slack in the covenant as the

percentage change in the current period debt-to-EBITDA ratio necessary to violate the covenant.

Consistent with the arguments made throughout the paper I expect observations with less

covenant slack to influence the CEO’s preferences and therefore have a negative association with

the CEO’s pay-for-performance sensitivity to earnings. I partition the observations with the

greatest covenant slack (i.e., the observations where I expect the effect of the earnings covenant

on the CEO’s motivation to be the weakest) with the indicator variable, LowSlack. I set

LowSlack equal to one for observations in three quartiles with the least covenant slack and equal

to zero for observations in the quartile with the most covenant slack.10 Consistent with the

predictions in the main analysis, I expect the coefficient estimate on the indicator variable

LowSlack to be negative. Table 8 contains ordinary least squares regression results. LowSlack

has a negative coefficient estimate (t-statistic) of -4.81 (-2.92), significant at the 1% level. This

suggests that less slack in the debt-to-EBITDA covenant is associated with a weaker pay

sensitivity to earnings.

6.3 Heckman two-step model

In contrast to the propensity score matching technique used in the main analysis, the two-

step approach proposed in Heckman (1979) corrects for selection bias due to unobservables. I

use the Heckman two-step model to control for the selection bias in the choice of debt contracts

with or without earnings-based covenants. This econometric tool addresses the endogeneity

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!10 Qualitatively similar results obtain when partitioning out the uppermost quintile or decile.

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between the debt contract and compensation contract. In the first step I estimate the probit

model in equation (3) and calculate the inverse Mill’s ratio, IMR, to include in the second stage

regression of equation (4a) and correct for selection bias in the decision to include earnings-

based covenants. Untabulated results confirm the predictions of H1 with a negative coefficient (t-

statistic) of -6.35 (-2.49) on EarnCovenant. The results using the Heckman two-step model

suggest selection bias due to unobservable characteristics does not drive the main results of the

paper, with respect to H1.

6.4 CEO Turnovers

To address the concern that the CEO influences both the debt and compensation

contracts, I use CEO turnovers as an exogenous shock to the incentive contract. To be included

in the turnover sample, the firm’s debt contract (either with or without an earnings covenant)

must be in place at least one year prior to the new CEO taking office and persist over the

following 4 years. This ensures that the debt contract structure precedes the determination of the

compensation contract for the CEO. The resulting sample includes 758 CEO turnovers. Using

this sample, I estimate the pay-for-performance sensitivities for the new CEOs and measure

control variables with procedures identical to that described in sections 4.2. I use the CEO

turnovers to test the robustness of the results for H1, H2a and H2b. In untabulated tests

EarnCovenant has a negative and significant coefficient estimate (t-statistic) of -3.82 (-1.46).

Consistent with H1 this result suggests weaker CEO pay-for-performance sensitivities with

respect to earnings when the firm’s debt contracts contain earnings-based covenants.

In tests of H2a using the CEO turnover sample, EarnCovRatio has a negative coefficient

estimate (t-statistic) of -4.34 (-1.36), statistically significant at the 10% level. Similarly, in tests

of H2b, the coefficient estimate (t-statistic) on TotalEarnCovenants is -0.18 (-1.47), significant at

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the 10% level. The CEO turnover sample analysis, as a whole, supports the results of the main

analysis and provides confidence that the main results are not driven by CEOs influencing the

terms of both the debt contract and the compensation contract.

7.0 Conclusion

I consider a setting in which the firm’s existing debt contracts provide implicit incentives

for the CEO to maintain earnings. The compensation contract layered on top of the debt contract

adjusts the CEO’s incentives to best serve the interest of the shareholders. Prior literature

provides several indications of earnings-based debt contract provisions altering managers’

behaviors and preferences. Executive compensation is tasked with guiding managers’ behaviors

and preferences to benefit the firm owners with the optimal weight on earnings performance.

Therefore, boards of directors may counterbalance the motivating factors of earnings-based debt

contract terms by altering the earnings performance measures in the compensation contract.

Empirical executive compensation research has been silent on how earnings-based debt contract

terms influence the manager’s incentives for earnings performance. This paper provides

evidence consistent with the board of directors rebalancing the CEO’s cash compensation for

earnings in the presence of earnings-based covenants, thereby tilting the CEO’s incentives away

from earnings performance. By adjusting the CEO’s compensation contract, boards of directors

may re-establish the desired balance for the CEO’s effort allocation between earnings and his

other corporate responsibilities.

Specifically, earnings-based debt covenants are negatively associated with the CEO’s

cash compensation pay-for-performance sensitivity with respect to earnings. The findings of this

study highlight the interaction between the uses of accounting earnings in two different contract

settings and provide a possible explanation for low pay-for-performance sensitivities in

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31

executive compensation. To mitigate endogeneity bias in the full sample analysis, I use a sample

of firms that changed from debt contracts without earnings-based covenants to debt contracts

with earnings-based covenants. Additionally I use propensity score matching to re-test my first

hypothesis. Tests using these alternative research methods confirm the results of the full sample

analysis. I find a consistently negative relationship between earnings-based covenants and the

CEO’s pay-for-performance sensitivity. Additionally, the intensity of earnings-based covenants

is negatively related to the CEO’s cash compensation sensitivity to earnings. The CEO’s pay-for

performance sensitivity to earnings is negatively associated with fraction of firm debt subject to

earnings-based covenants and the total number of earnings-based covenants in the array of the

firm’s debt contracts.

The inferences drawn in this paper suggest that the presence of earnings-based debt

contract terms may increase the CEO’s incentives to focus on earnings such that compensation

contract terms are adjusted to shift the CEO’s motivation away from earnings. The findings in

this paper provide several opportunities for future research. First, the findings introduce new

insight into the role of accounting in corporate contracts and how existing debt contract design

relates to the design of compensation contracts. This insight may relate to other contract

settings, in which accounting measures used in a contract alter the preferences of one of the

contracting parties and thus influence the terms of future contracts the party enters into.

Additionally, the findings relate to the current issue of CEO pay-for-performance and the recent

public scrutiny of CEO compensation’s weak association with firm performance. In particular,

the results in this paper suggest low pay-for-performance sensitivities to earnings may indicate a

re-balancing of the CEO’s incentives away from earnings performance measures and not

inefficient or sub-optimal compensation practices.

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MeanStd.

DeviationFirst

Quartile MedianThird

QuartileFirm Variables

MarketValue (in millions) 6658.60 18394.88 669.43 1752.93 4741.54OpCF_Volatility 288.39 2055.30 25.27 58.75 166.67EBITDA_Volatility 212.20 634.34 23.35 53.48 162.05Market-to-Book 1.29 1.29 0.57 0.95 1.56Leverage 0.25 0.17 0.12 0.23 0.34Sales 0.13 0.26 0.04 0.09 0.16ROA 0.14 0.09 0.10 0.14 0.18Margin 0.15 0.82 0.10 0.15 0.24EPS 2.63 58.86 0.43 1.29 2.17

Compensation VariablesCashCompensation (in millions) 1.28 1.20 0.67 1.01 1.48Delta (in millions) 0.88 4.45 0.10 0.27 0.68Vega (in millions) 0.11 0.24 0.01 0.03 0.10Age 55.55 5.87 51.96 55.76 58.94

Deal Scan VariablesSecuredDebtRatio 0.31 0.36 0.00 0.12 0.60SeniorDebtRatio 0.94 0.11 0.92 1.00 1.00CorpPurposes 0.37 0.33 0.05 0.29 0.60DebtRepay 0.16 0.24 0.00 0.00 0.27Takeover 0.11 0.21 0.00 0.00 0.11WorkingCap 0.19 0.29 0.00 0.00 0.31EarnCovenant 0.57 0.50 0.00 1.00 1.00EarnCovRatio 0.42 0.40 0.00 0.49 0.82TotalEarnCovenants 1.97 2.43 0.00 1.40 3.19

Table 1 Descriptive Statistics

Panel A: Full Sample (N=2118)

Median Mean Std. Dev.N Median Mean Std. Dev. Diff.Firm Variables

MarketValue (in millions) 2335.87 6284.83 13741.76 1449.48 5897.75 17319.43 -387.08 0.702OpCF_Volatility 72.98 210.22 473.86 50.26 217.00 979.83 6.78 0.891EBITDA_Volatility 64.28 195.37 523.17 48.32 207.60 786.94 12.23 0.777Market-to-Book 1.03 1.30 1.07 0.93 1.34 1.02 0.040 0.560Leverage 0.21 0.22 0.17 0.25 0.22 0.15 0.002 0.806Sales 0.09 0.10 0.12 0.09 0.12 0.14 0.012 0.175ROA 0.13 0.14 0.09 0.14 0.15 0.08 0.006 0.237Margin 0.16 0.18 0.15 0.14 0.18 0.13 0.001 0.911EPS 1.52 1.47 1.85 1.15 1.25 2.38 -0.218 0.098

Pr > |t|

Panel B: Propensity-Score Matched Sample

(N = 492) (N = 492) DifferencesNo Earnings-based Earnings-based Covenant

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36

Median Mean Std DevN Median Mean Std Dev Diff.Compensation Variables

CashCompensation (millions) 1.01 1.26 1.11 1.01 1.27 1.12 0.00 0.961Delta (in millions) 0.28 1.18 7.97 0.30 0.93 2.71 -0.24 0.524Vega (in millions) 0.03 0.13 0.30 0.03 0.13 0.27 0.00 0.909Age 56.38 55.82 5.49 55.96 56.06 5.98 0.23 0.532

Deal Scan VariablesSecuredDebtRatio 0.00 0.28 0.39 0.36 0.28 0.33 0.00 0.997SeniorDebtRatio 1.00 0.94 0.12 0.98 0.94 0.09 0.00 0.970CorpPurposes 0.45 0.37 0.35 0.22 0.36 0.30 -0.01 0.696DebtRepay 0.00 0.17 0.28 0.05 0.18 0.25 0.01 0.769Takeover 0.00 0.10 0.24 0.00 0.09 0.18 -0.01 0.430WorkingCap 0.00 0.18 0.33 0.11 0.19 0.27 0.01 0.719EarnCovenant - - - 1.00 1.00 0.00 - -EarnCovRatio - - - 0.79 0.71 0.23 - -TotalEarnCovenants - - - 2.93 2.99 1.73 - -

Pr > |t|

Descriptive Statistics Table 1 (continued)

Panel B: Propensity-Score Matched Sample

Firm Variables: MarketValue equals the market value of equity (CRSP variables: prc * shrout). OpCF_Volatility and EBITDA_Volatility equal the standard deviation of operating cash flows and earnings before interest, taxes, depreciation and amortization over the previous five years, respectively. Market-to-Book equals MarketValue divided by the book value of assets. Leverage equals total debt divided by book value of total assets. Sales equals the percentage change in sales. ROA and Margin equal earnings before interest, taxes, depreciation and amortization scaled by average assets and average sales, respectively. EPS equals basic earnings per share. Compensation Variables: CashCompensation equals the sum of Execucomp variables salary and bonus. Delta (Vega ) equals the dollar change in CEO wealth for a 1% change in stock price (volatility). Age equals the natural logarithm of the CEO’s age. Deal Scan Variables: SecuredDebtRatio, SeniorDebtRatio, CorpPurposes, DebtRepay, Takeover, and WorkingCap are measured as the fraction of firm debt that is secured, senior or reported for the specified purposes. EarnCovenant equals one when the firm's debt contracts contain at least one earnings-based debt covenant and zero otherwise. EarnCovRatio equals the fraction of the firm's debt with an earnings-based covenants. TotalEarnCovenants equals the total number of earnings-based covenants in the firm's debt contracts.

The unit of observation is firm-regime such that all variables are measured over the same period used to calculate the pay-for-performance sensitivities as described in Table 2. For a detailed explanation refer to section 4 of the text. For ease of understanding I describe all variable definitions below in terms of firm-year.

(N = 492) (N = 492) DifferencesNo Earnings-based Earnings-based Covenant

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37

Median Mean Std. D. Median Mean Std. D. Diff.Pay Sensitivities

PPS_ROA 1.24 6.54 29.03 0.75 3.07 32.10 -0.49PPS_Margin 1.37 4.88 16.57 0.97 3.34 20.93 -0.40PPS_EPS 0.32 -0.07 19.55 0.17 -0.78 14.40 -0.15

Median Mean Std. D. Median Mean Std. D. Diff.Pay Sensitivities

PPS_ROA 1.12 5.56 24.34 0.86 -0.26 16.81 -0.44PPS_Margin 2.42 7.04 21.39 0.96 1.45 14.89 -0.98PPS_EPS 0.59 0.24 29.93 0.15 -3.10 13.77 -0.75

Median Mean Std. D. Median Mean Std. D. Diff.Pay Sensitivities

PPS_ROA 1.24 4.98 20.25 0.75 3.71 44.71 -1.27PPS_Margin 1.37 4.63 15.44 0.97 2.87 18.28 -1.76PPS_EPS 0.28 -0.15 12.69 0.18 -1.29 17.89 -0.20

TestVARIABLE Pr > |S|

Test

No Earnings-based Covenant (N=921)

Earnings-based Covenant (N=1197)

VARIABLE Pr > |S|

0.0090.0030.001

Panel C: Propensity-Score Matched Sample

0.0000.0040.008

No Earnings-based Covenant (N=259)

Earnings-based Covenant (N=259)

Signed Rank

Panel B: Within-Firm Matched Sample

PPS Variables: The pay-for-performance sensitivity proxy is the coefficient estimate, ! 1 , from firm-specific regressions estimated separately for spells of consecutive years with an earnings-based covenant and spells without an earnings-based covenant using the model:

CashCompensation it = ! 0 + ! 1 Earnings it + " itwhere i and t represent firm and year respectively, CashCompensation equals the sum of salary and bonus for the CEO and Earnings takes the value of one of the performance measures, ROA, Margin or EPS, to yield estimates for ! 1 which become the pay-for-performance sensitivity proxies PPS_ROA , PPS_Margin and PPS_EPS respectively. For a detailed explanation of PPS measurement see section 4.2 of the text.

I calculate differences for the within-firm and propensity-score matched sample as the value of the earnings-based covenant observation minus the value for the no earnings-based covenant observation. Results of t -tests have similar significance, however, non-normality in the pay-for-performance sensitivity variables suggest the appropriateness of tests of differences in medians.

No Earnings-based Covenant (N=492)

Earnings-based Covenant (N=492)

Signed Rank

0.0180.0210.098

WilcoxonTest

VARIABLE Pr > |Z|

Table 2Descriptive Statistics: Pay-for-performance sensitivities

Panel A: Full Sample

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38

Pay Sensitivities

Pay Sensitivities

Pay Sensitivities

where i and t represent firm and year respectively, CashCompensation equals the sum of ExecuComp variables salary and bonus for the CEO and Earnings equals one of the performance measures, ROA, Margin or EPS, yielding estimates for ! 1 which become the pay-for-performance sensitivity proxies PPS_ROA, PPS_Margin and PPS_EPS respectively. For a detailed explanation of PPS measurement see section 4.2 of the text. Debt Characteristics: EarnCovenant equals one when the firm's debt contracts contain at least one earnings-based debt covenant and zero otherwise. EarnCovRatio equals the fraction of firm debt subject to earnings-based covenants. TotalEarnCovenants equals the natural logarithm of the total number of earnings-based covenants in the firm's debt contracts.

-0.030

Spearman correlations reported in parenthesis. Bold represents significance at the 10% level or better.

Panel A: Full Sample (N=2118)

Panel B: Within-Firm Matched Sample (N=518)

Panel C: Propensity-Score Matched Sample (N=984)

-0.060

-0.016

(-0.051)

CashCompensation it = ! 0 + ! 1 Earnings it + " it

(-0.118)(-0.089)

(-0.125)

(-0.134)

(-0.073)

TotalEarnCovenants

-0.057

(-0.053)

(-0.082) (-0.073)

(-0.060)

(-0.042)

PPS Variables: The pay-for-performance sensitivity proxy equals the coefficient estimate, ! 1 , from firm-specific regressions estimated separately for periods of consecutive years with an earnings-based covenant and periods without using the model:

(-0.117)(-0.085)

(-0.053)

(-0.082) (-0.099) (-0.096)

(-0.069) (-0.079) (-0.094)

(-0.133)

-0.056 -0.079

-0.146

-0.035 -0.070

-0.150

-0.133 -0.140

-0.022-0.037

-0.072

-0.150

-0.138

(-0.081)

(-0.097)

(-0.128)

EarnCovenant EarnCovRatio

-0.066

TotalEarnCovenants

EarnCovenant EarnCovRatio TotalEarnCovenants

-0.021

-0.052

-0.056

-0.041

-0.020

-0.040

PPS_Margin

PPS_EPS

Table 3Pearson (Spearman) correlations between pay-for-performance sensitivities and earnings-

based covenant variables of interest.

-0.018 (-0.065)

PPS_ROA

-0.017

EarnCovenant EarnCovRatio

PPS_Margin

PPS_EPS

-0.046

PPS_ROA

PPS_Margin

PPS_EPS

PPS_ROA

(-0.057)

(-0.052)

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39

Intercept -49.867 (-2.05) ** -8.758 (-0.55) -17.811 (-1.27)

EarnCovenant -2.205 (-1.66) ** -2.173 (-2.49) *** -1.150 (-1.50) *

MarketValue 1.102 (2.06) ** 1.196 (3.40) *** -1.391 (-4.51) ***Leverage 10.221 (2.46) ** 4.519 (1.65) * 7.011 (2.93) ***Market-to-Book -1.038 (-1.81) * -0.432 (-1.14) 0.618 (1.86) *Sales 0.853 (0.34) -1.440 (-0.86) 1.730 (1.19)EBITDA_Volatility 0.757 (9.96) 0.061 (1.22) 0.124 (2.84) ***OpCF_Volatility 0.032 (6.74) 0.001 (0.33) 0.000 (0.33) Delta 0.036 (2.39) ** 0.009 (0.89) 0.000 (-0.16) Vega 0.085 (0.26) -0.032 (-0.15) -0.001 (-0.78)Age 11.275 (1.86) * 1.593 (0.40) 6.126 (1.76) *

R-square 0.136 0.031 0.035

Table 4

Full sample tests of H1 (N = 2118)This table reports parameter estimates and t -statistics (in parentheses) from OLS regressions of pay-for-performance sensitivity on the indicator variable for the presence of earnings-based covenants:

PPS ie = ! 0 + ! 1 EarnCovenant ie + ! k Controls ie + " ie

where i and e represent firm and earnings-based covenant regime respectively. H1 predicts a negative relationship between pay-for-performance senstivity and the presence of earnings-based covenants, such that ! 1 < 0.

All regressions include industry fixed effects. Statistical significance at the 1%, 5% and 10% levels represented by ***, ** and * respectively. Reported significance uses one-tailed tests of H1 (! 1 < 0).

PPS_ROA PPS_Margin PPS_EPS

The association between pay-for-performance sensitivity and earnings-based covenants

CashCompensation it = # 0 + # 1 Earnings it + " it

where i and t represent firm and year respectively, CashCompensation equals the sum of ExecuComp variables salary and bonus for the CEO and Earnings equals one of the performance measures, ROA, Margin or EPS, yielding estimates for # 1 which become the pay-for-performance sensitivity proxies PPS_ROA, PPS_Margin and PPS_EPS respectively. For a detailed description of PPS measurement see section 4.2 of the text. Debt Characteristics: EarnCovenant equals one when the firm's debt contracts contain at least one earnings-based debt covenant and zero otherwise. Control Variables: MarketValue equals the natural logarithm of the market value of equity (CRSP variables: prc * shrout). Leverage equals total debt (current debt and long-term debt) divided by average assets. Market-to-Book equals market value of equity divided by average assets. Sales equals the percentage change in sales. EBITDA_Volatility and OpCF_Volatility equal the variance of earnings before interest, taxes, depreciation and amortization and operating cash flows over the previous five years, respectively. Delta (Vega ) equals the dollar change in CEO wealth for a 1% change in stock price (stock price volatility). Age equals the natural logarithm of the CEO's age.

Dependent Variables: The pay-for-performance sensitivity proxy equals the coefficient estimate, # 1 , from firm-specific regressions estimated separately for spells of consecutive years with an earnings-based covenant and spells without using the model:

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40

EarnCovenant -5.891 (-2.61) *** -4.488 (-2.50) *** -3.729 (-1.78) **MarketValue 5.202 (2.04) ** 0.679 (0.33) -0.578 (-0.24) Leverage 22.540 (1.31) 6.278 (0.46) 34.767 (2.18) **Market-to-Book -2.305 (-1.34) -1.271 (-0.93) 3.328 (2.08) **Sales 17.135 (1.24) 7.068 (0.64) 12.809 (1.00) EBITDA_Volatility -0.596 (-1.51) -0.205 (-0.65) 1.190 (3.25) ***OpCF_Volatility -0.130 (-0.37) -0.379 (-1.37) -0.329 (-1.01) Delta -0.067 (-0.94) -0.026 (-0.46) 0.000 (-0.41) Vega -1.730 (-2.77) *** -1.040 (-2.09) ** 0.007 (1.28) Age 13.728 (0.82) 10.766 (0.81) 6.593 (0.42)

R-square 0.079 0.055 0.107

EarnCovenant -3.492 (-2.21) ** -1.985 (-1.82) ** -1.817 (1.83) **MarketValue 0.531 (0.54) 0.699 (1.02) -1.590 (-2.56) **Leverage 1.811 (0.23) -0.990 (-0.18) 1.050 (0.21) Market-to-Book -0.884 (-0.75) 0.213 (0.26) -1.014 (-1.39) Sales 16.668 (1.91) * -0.527 (-0.09) 17.104 (3.18) ***EBITDA_Volatility -0.602 (-4.75) *** -0.075 (-0.86) -0.200 (-2.60) ***OpCF_Volatility 2.570 (19.51) *** 0.228 (2.51) ** 0.510 (6.39) ***Delta 0.024 (1.16) 0.014 (1.02) 0.000 (0.25)Vega -0.219 (-0.45) -0.261 (-0.78) -0.005 (-1.73) *Age 23.853 (2.21) ** 16.431 (2.20) ** 11.638 (1.69) *

R-square 0.511 0.030 0.151

PPS_Margin PPS_EPSPanel A: Within-Firm Matched Sample (N=259)

PPS_ROA PPS_Margin PPS_EPSPanel B: Propensity-Score Matched Sample (N=492)

The association between pay-for-performance sensitivity and earnings-based covenantsChanges analysis and propensity-score matched sample tests of H1

All regressions include industry controls, suppressed for brevity. Statistical significance at the 1%, 5% and 10% levels represented by ***, ** and * respectively. Reported significance based on one-tailed tests of H1 (! 1 < 0).

Table 5

This table reports parameter estimates and t -statistics (in parentheses) from OLS regressions using the differences in the pairs of observations matched by firm and by propensity-score:

"PPS ie = ! 1 "EarnCovenant ie + ! k "Controls ie + # ie

where i and e represent firm and earnings-based covenant regime respectively. ! represents the difference calculated as the value for observation with an earnings-based covenant minus the value for the matched pair observations without an earnings-based covenant. H1 predicts a negative relationship between pay-for-performance sensitivity and earnings-based covenants, such that ! 1 < 0.

PPS_ROA

!

41

The association between pay-for-performance sensitivity and earnings-based covenantsChanges analysis and propensity-score matched sample tests of H1

Debt Characteristics: EarnCovenant equals one when the firm's debt contracts contain at least one earnings-based debt covenant and zero otherwise. Control Variables: MarketValue equals the natural logarithm of the market value of equity (CRSP variables: prc * shrout). Leverage equals total debt (current debt and long-term debt) divided by average assets. Market-to-Book equals market value of equity divided by average assets. Sales equals the percentage change in sales. EBITDA_Volatility and OpCF_Volatility equal the variance of earnings before interest, taxes, depreciation and amortization and operating cash flows over the previous five years, respectively. Delta (Vega ) equals the dollar change in CEO wealth for a 1% change in stock price (volatility). Age equals the natural logarithm of the CEO's age.

Dependent Variables: The pay-for-performance sensitivity proxy equals the coefficient estimate, ! 1 , from firm-specific regressions estimated separately for spells of consecutive years with an earnings-based covenant and spells without using the model:

CashCompensation it = ! 0 + ! 1 Earnings it + " it

where i and t represent firm and year respectively, CashCompensation equals the sum of ExecuComp variables salary and bonus for the CEO and Earnings equals one of the performance measures, ROA, Margin or EPS, yielding estimates for ! 1 which become the pay-for-performance sensitivity proxies PPS_ROA, PPS_Margin and PPS_EPS, respectively. Section 4.2 contains a detailed description of PPS measurement.

Table 5 (continued)

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42

Intercept -47.594 (-1.95) * -51.818 (-2.13) ** -7.201 (-0.45) -10.671 (-0.67) -17.763 (-1.26) -18.849 (-1.35)

EarnCovRatio -3.930 (-2.42) *** -3.386 (-3.17) *** -1.239 (-1.32) *TotalEarnCovenants -0.104 (-1.70) ** -0.103 (-2.57) *** -0.053 (-1.49) *

MarketValue 1.014 (1.89) * 1.100 (2.06) ** 1.132 (3.21) *** 1.192 (3.39) *** -1.398 (-4.52) *** -1.390 (-4.51) ***Leverage 9.820 (2.39) ** 10.307 (2.48) ** 4.057 (1.50) 4.612 (1.69) * 6.691 (2.82) *** 7.041 (2.94) ***Market-to-Book -1.042 (-1.82) * -1.042 (-1.81) * -0.425 (-1.13) -0.436 (-1.15) 0.633 (1.91) * 0.617 (1.86) *Sales 0.740 (0.29) 0.860 (0.34) -1.520 (-0.91) -1.434 (-0.86) 1.722 (1.18) 1.736 (1.19) *EBITDA_Volatility 0.755 (9.94) 0.757 (9.96) 0.060 (1.20) 0.061 (1.22) 0.124 (2.84) *** 0.124 (2.84) ***OpCF_Volatility 0.032 (6.74) 0.032 (6.74) 0.001 (0.33) 0.001 (0.33) 0.000 (0.34) 0.000 (0.34) Delta 0.035 (2.37) ** 0.036 (2.40) ** 0.008 (0.85) 0.009 (0.89) 0.000 (-0.17) 0.000 (-0.16) Vega 0.106 (0.33) 0.085 (0.26) -0.018 (-0.08) -0.031 (-0.15) -0.015 (0.78) -0.001 (-0.78) *Age 11.017 (1.82) * 11.235 (1.86) * 1.407 (0.35) 1.551 (0.39) 6.106 (1.75) * 6.110 (1.75)

R-square 0.138 0.137 0.033 0.032 0.035 0.035

PPS_ROA PPS_Margin PPS_EPS

All regressions include industry controls, suppressed for brevity. Statistical significance at the 1%, 5% and 10% levels represented by ***, ** and * respectively. Reported significance uses one-tailed for tests of H2a and H2b (! 1 < 0). Dependent Variables: The pay-for-performance sensitivity proxy equals the coefficient estimate, !1, from firm-specific regressions estimated separately for spells of consecutive years with an earnings-based covenant and spells without:

CashCompensation it = " 0 + " 1 Earnings it + # it

where i and t represent firm and year respectively, CashCompensation equals the sum of ExecuComp variables salary and bonus for the CEO and Earnings equals one of the performance measures, ROA, Margin or EPS, yielding estimates for PPS_ROA, PPS_Margin and PPS_EPS respectively. For a detailed explanation of PPS measurement see section 4.2 of the text. Intensity Proxies: EarnCovRatio equals the fraction of the firm's debt subject to an earnings-based covenant. TotalEarnCovenants equals the natural logarithm of the number of earnings-based covenants in the firm's debt

Table 6

Full sample tests of H2a and H2b (N = 2118)This table reports parameter estimates and t -statistics (in parentheses) from OLS regressions of pay-for-performance sensitivity on proxies of earnings-based covenant intensity:

PPS ie = ! 0 + ! 1 Intensity ie + ! kControls ie + # ie

Where i and e represent firm and earnings-based covenant regime respectively. H2 predicts a negative relationship between pay-for-performance sensitivity to earnings and earnings-based covenant intensity, such that ! 1 < 0.

The association between pay-for-performance sensitivity and earning-based covenant intensity

!

43

contracts. Control Variables: MarketValue equals the natural logarithm of the market value of equity (CRSP variables: prc * shrout). Leverage equals total debt (current debt and long-term debt) divided by average assets. Market-to-Book equals market value of equity divided by average assets. Sales equals the percentage change in sales. EBITDA_Volatility and OpCF_Volatility equal the variance of earnings before interest, taxes, depreciation and amortization and operating cash flows over the previous five years, respectively. Delta (Vega ) equals the dollar change in CEO wealth for a 1% change in stock price (volatility). Age equals the natural logarithm of the CEO's age.

Table 6 (continued)The association between pay-for-performance sensitivity and earning-based covenant intensity

Full sample tests of H2a and H2b (N = 2118)

!

44

Coeff. t-stat Coeff. t-stat Coeff. t-statIntercept -4.527 (-3.33) *** -3.986 (-2.73) *** -3.933 (-2.76) ***Earnings -7.061 (-1.39) -5.808 (-0.97) -5.814 (-0.99) EarnCovenant 0.075 (0.85) EarnCovRatio 0.175 (1.64) TotalEarnCovenants 0.008 (0.64) Earnings*DebtCharacter -0.61 (-1.28) * -1.13 (-2.18) ** -0.15 (-1.74) **MarketValue 0.340 (9.54) *** 0.368 (9.46) *** 0.361 (9.50) ***Leverage 0.592 (3.20) *** 0.658 (3.58) *** 0.683 (3.51) ***Market-to-Book -0.119 (-6.60) *** -0.194 (-5.96) *** -0.190 (-5.94) ***Sales 0.044 (2.35) ** 0.074 (3.59) *** 0.075 (3.60) ***EBITDA_Volatility 0.001 (0.82) 0.000 (0.57) 0.000 (0.52) OpCF_Volatility -0.002 (-1.63) -0.003 (-1.69) * -0.003 (-1.69) *Vega 0.179 (0.95) 0.182 (0.99) 0.177 (0.97) Age 0.729 (2.25) ** 0.587 (1.72) * 0.597 (1.77) *R-square 0.187 0.191 0.191

The association between pay-for-performance and earnings-based covenants

All regressions include year and industry controls, standard errors are clustered by firm and statistical significance at the 1%, 5% and 10% levels represented by ***, ** and * respectively. Reported significance uses one-tailed tests of H1, H2a and H2b (! 3 < 0). CashCompensation equals the sum of annual salary and bonus for the CEO. Earnings equals earnings before interest, taxes, depreciation and amortization scaled by average assets. DebtCharacteristic takes on the value of EarnCovenant equal to one when the firm's debt contracts contain at least one earnings-based debt covenant and zero otherwise, EarnCovRatio equal to the fraction of firm debt subject to earnings-based covenants, or TotalEarnCovenants equal to the natural logarithm of the total number of earnings-based covenants in the debt contracts. Control Variables: MarketValue equals the natural logarithm of the market value of equity (CRSP variables: prc * shrout). Leverage equals total debt (current debt and long-term debt) divided by average assets. Market-to-Book equals market value of equity divided by average assets. Sales equals the percentage change in sales. EBITDA_Volatility and OpCF_Volatility equal the variance of earnings before interest, taxes, depreciation and amortization and operating cash flows over the previous five years, respectively. Vega equals the dollar change in CEO wealth for a 1% change in stock price volatility. Age equals the natural logarithm of the CEO's age. I allow the pay-for-performance sensitivity vary with the control variables, coefficient estimates and t-statistics are untabulated for brevity.

Table 7

Cross-sectional tests of H1, H2a, and H2b (N = 18,336)This table reports parameter estimates and t -statistics (in parentheses) from OLS regressions of CEO cash compensation on earnings, the earnings-based covenant characteristic, and the interaction term:

CashCompensation it = ! 0 + ! 1 Earnings it + ! 2 DebtCharacteristic it + ! 3 Earnings it * DebtCharacteristic it + ! k Controls it + " it

where i and t represent firm and year respectively. The hypotheses predict a negative relationship between pay-for-performance sensitivity and (i) the presence of earnings-based covenants (H1) and (ii) the intensity of earnings-based debt covenants (H2a and H2b), such that ! 1 < 0.

!

45

Coeff. t -stat.Intercept -7.247 (-0.46)

LowSlack -4.810 (-2.92) ***

MarketValue -0.359 (-0.58) Leverage 5.330 (1.11) Market-to-Book -1.152 (-1.75) *Sales -1.225 (-0.53) EBITDA_Volatility 1.100 (1.52) OpCF_Volatility 0.300 (2.79) ***Delta 0.103 (1.77) *Vega -6.085 (-0.98) Age 3.551 (0.95)

R-square 0.360

Debt Characteristics: LowSlack equals one when the firm's debt-to-EBITDA covenant slack is in the lower quartiles, and zero for the fourth quartile. Covenant slack equals the percentage change in the debt-to-EBITDA ratio necessary to violate the covenant. Control Variables: MarketValue equals the natural logarithm of the market value of equity (CRSP variables: prc * shrout). Leverage equals total debt (current debt and long-term debt) divided by average assets. Market-to-Book equals market value of equity divided by average assets. Sales equals the percentage change in sales. EBITDA_Volatility and OpCF_Volatility equal the variance of earnings before interest, taxes, depreciation and amortization and operating cash flows over the previous five years, respectively. Delta (Vega ) equals the dollar change in CEO wealth for a 1% change in stock price (volatility). Age equals the natural logarithm of the CEO's age.

Industry controls suppressed for brevity. Statistical significance at the 1%, 5% and 10% levels represented by ***, ** and * respectively. Reported significance uses a one-tailed for test of ! 1 < 0. Dependent Variable: PPS equals the coefficient estimate, " 1 , from firm-specific regressions estimated seperately for spells of consecutive years with an earnings-based covenant and spells without using the model:

CashCompensation it = " 0 + " 1 Earnings it + # itwhere i and t represent firm and year respectively, CashCompensation equals the sum of the CEO's salary and bonus and Earnings equals earnings before interest, taxes, depreciation and amortization divided by average assets. For a detailed explanation of PPS measurement see section 4.2 of the text.

Table 8

Max debt-to-EBITDA covenant sample (N = 937)This table reports parameter estimates and t -statistics (in parantheses) from OLS regressions of pay-for-performance sensitivity on a dichotomous variable for the partition on debt-to-EBITDA covenant slack:

PPS ie = ! 0 + ! 1 LowSlack ie + ! k Controls ie + # iewhere i and e represent firm and earnings-based covenant regime respectively.

The association between pay-for-performance sensitivity and covenant slack