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DOI: 10.1111/j.1475-679X.2008.00301.x

Journal of Accounting ResearchVol. 46 No. 5 December 2008

Printed in U.S.A.

Corporate Governanceand Agency Conflicts

A I Y E S H A D E Y

Received 30 May 2006; accepted 3 March 2008

ABSTRACT

I investigate whether corporate governance is associated with the level ofagency conflicts in firms. I employ exploratory principal components analysison 22 individual governance variables to obtain seven factors that representthe different dimensions of governance for a firm. I measure the level ofagency conflicts in firms based on seven proxies for agency conflicts usedin the literature. I find that firms with greater agency conflicts have bettergovernance mechanisms in place, particularly those related to the board, auditcommittee, and auditor. I also find that thecomposition and functioning of theboard, the independence of the auditor, and the equity-based compensationof directors are significantly associated with firm performance, but primarilyfor firms with high agency conflicts. Overall, the results support the theorythat the existence and role of various governance mechanisms in a firm are afunction of the level of agency conflicts in the firm.

Graduate School of Business, University of Chicago. This paper is based on my dissertationand I would like to thank my committee members Ravi Jagannathan, Thomas Z. Lys, Robert P.Magee, and Beverly Walther for their valuable suggestions and guidance. I would also like tothank an anonymous referee, Brian Bushee, Marcus Caylor, Daniel Cohen, Ellen Engel, TomFields, Xiaohui Liu, N. V. Ramanan, Scott Richardson, Jonathan Rogers, Douglas Skinner (theeditor), Ewa Sletten, Abbie Smith, Suraj Srinivasan, Jayanthi Sunder, Shyam V. Sunder, andthe seminar participants at the 2005 AAA annual meeting, University of California at Berkeley,University of Chicago, Cornell University, Emory University, Georgia State University, HarvardBusiness School, University of Houston, University of Illinois, University of Michigan, MIT,Northwestern University, University of Pennsylvania, University of Southern California, Uni-

versity of Texas at Dallas, Tuck School of Business at Dartmouth, and University of Washingtonfor very useful comments. I am very grateful to the Zell Center for Risk Research and the

Accounting Research Center at the Kellogg School of Management, Northwestern Universityfor financial support. All errors are my own.

1143

Copyright C, University of Chicago on behalf of the Institute of Professional Accounting, 2008

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1. Introduction

In the wake of the recent scandals and the passage of the new regulationin 2002, the Sarbanes Oxley Act (SOX), the link between corporate gov-

ernance and shareholder value has re-emerged as a topic of considerableimportance among academics, practitioners, and regulators. A large bodyof research examines the association between various aspects of governanceand different features of organizational performance and decisions (Teohand Wong [1993], Vafeas [2000], Felo, Krishnamurthy, and Solieri [2003],Bushman et al. [2004]). However, there is little evidence on the factors thatdetermine the endogenous presence or effectiveness of these mechanisms.

The relationship between shareholders and corporate managers isfraught with conflicting interests that arise due to the separation of own-ership and control, divergent management and shareholder objectives, and

information asymmetry between managers and shareholders. Due to theseconflicting interests (collectively referred to as agency conflicts), managershave the incentives and ability to maximize their own utility at the expenseof corporate shareholders. Contracts alone are not always enough to re-solve these conflicts (Hart [1995]). Consequently, the owners (and in somesituations the managers themselves) have reason to establish mechanismsto monitor managerial activities and limit undesirable managerial behavior(Jensen and Meckling [1976]). As a result, corporate governance structuresevolve that help in mitigating these agency conflicts.

The magnitude of the agency conflicts varies cross-sectionally across firms

depending on the ease with which managers can exercise their own pref-erences as opposed to value maximization, the complexity of the firmsoperating environment, the attractiveness of perquisites, etc. (Jensen andMeckling [1976]). Because agency problems vary across firms, the gover-nance structures required to address these problems are also likely to vary.

As a result, any association between various governance mechanisms and var-ious aspects of organizational performance is unlikely to be uniform acrossallfirms. Consequently, in order to perform more meaningful analyses onthe role of governance in affectingfirm performance and other operatingdecisions, wefirst need to take a step back and examine how governance

mechanisms arise and vary acrossfirms.The purpose of this paper is to shed some light on this issue by investigat-ing how governance structures vary cross-sectionally. Specifically, I examine

whether the level of agency conflicts in a firm is associated with its gover-nance structure. Evidence on this relation will enhance our knowledge onthe role of governance infirms and whether improving various aspects ofgovernance will help in improving organizational performance for allfirmsor whether such measures will be more effective for certain types offirms.

Corporate governance has multiple dimensions. However, prior researchon the effect of governance on various organizational outcomes typically fo-

cuses on individual governance mechanisms or constructs one-dimensional

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GOVERNANCE AND AGENCY 1145

governance metrics by summing up individual variables.1 Simply aggregat-ing variables representing different aspects of boards of directors, auditors,managers, etc., or focusing on some of these variables in isolation is notlikely to be an appropriate approach to measuring the corporate gover-nance structure of afirm, and generally creates measurement error (Lar-cker, Richardson, and Tuna [2007]). Further, several individual governance

variables are likely to be interrelated, and ignoring such correlations canlead to spurious inferences (Agrawal and Knoeber [1996], Bowen, Rajgopal,and Venkatachalam [2005]).

I consider a large set of individual governance variables and rely on a vari-ety of traditional measures of governance used by academics and regulatorsin order to derive the various dimensions of afirms governance structure. Iapply exploratory principal components analysis (PCA) to 22 individual gov-ernance variables and obtain seven distinct governance factors representingthe composition and functioning of the board of directors, executive com-pensation, equity-based compensation of directors, independence of theauditor, structure and functioning of the audit committee, and the boardscontrol over financial reporting quality. I use these factors as representa-tions of the different underlying dimensions of governance. Given the lackof a theory on governance structures, this technique is more appropriatebecause the process identifies various factor structures with the individualgovernance indicators that are highly correlated. These factors can then beused to explain the underlying dimensions of governance. This method isalso employed in a recent study by Larcker, Richardson, and Tuna [2007]

to measure governance. Larcker, Richardson, and Tuna [2007] consider 39individual governance indicators and use PCA to form 14 governance fac-tors. They document that these governance indices have mixed association

with abnormal accruals, little relation to accounting restatements, and someability to explain future operating performance and future excess stockreturns.

Self-interested managerial behavior resulting from agency conflicts cancomprise a range of activities that are not optimal for shareholders. Someexamples of these include empire building, the consumption of corporateresources as perquisites, the avoidance of optimal risk investments, and ma-

nipulatingfinancialfigures to optimize compensation. I thus consider var-ious firm-specific attributes that are indicative of the existence of agencyconflicts. Specifically I include firm size, organizational complexity, own-ership structure, growth, leverage, operating risk, and free cash flows as

1 Some examples include Warfield, Wild, andWild [1995]on greater managerial ownership,Frankel, Johnson, and Nelson [2002] on the mix between consulting and audit fees paid toauditors, and Gompers, Ishii, and Metrick [2003] and Brown and Caylor [2004] who considerseveral governance variables to construct one-dimensional governance scores. While all theabove studies provide interesting evidence, a more meaningful analysis can be conducted

by considering a broad set of governance variables and the interaction among them whiledetermining the governance structure of afirm.

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measures of the level of agency conflicts infirms. Ifind evidence consistentwith the theory that governance structures, particularly those related to theboard of directors, the audit committee, and the auditor, are positively re-lated to the level of agency conflicts infirms. In particular, Ifind that largerfirms,firms with more diffuse ownership structures,firms with higher lever-age, and firms with more operating risk have better governance mechanismsin place.

Given this result, a natural hypothesis that emerges is that the relationbetween overall firm performance and governance is also unlikely to beuniform acrossfirms. Failing to control for this cross-sectional variation islikely to be one reason for the mixed evidence on the relation betweenoverallfirm performance and governance in prior studies (Hermalin and

Weisbach [1991], Mehran [1995], Bhagat and Black [2001], among oth-ers). I examine whether the relation between the governance structure and

overallfirm performance varies with the level of agency conflicts, andfindresults consistent with this claim. This supports the conclusion that gov-ernance structures arise endogenously to firms competitive and businessenvironments, and as a result the role of governance in affecting various as-pects of organizational performance and decisions is likely to differ acrossfirms.

My primary contribution is to document factors that are associated withthe determination of governance structures in firms, and to provide evi-dence that the role of governance is not uniform acrossfirms. I use a largeset of governance and agency variables to show thatfirms with higher agency

conflicts have stronger governance structures in place. Consequently, the re-lation between governance andfirm performance is also a function of thelevel of agency conflicts. These results are consistent with a fundamental hy-pothesis: The demand for higher quality governance is greater infirms withgreater need for oversight. The evidence supports claims that a uniform setof governance rules is unlikely to be efficient for all firms (Klein [2002a],Romano [2005], Ribstein [2005]).

The remainder of the paper is divided into the following sections. Sec-tion 2 discusses the theoretical background and the research objective, andprovides a brief overview of related research, followed by section 3 which

discusses the research design. Section 4 describes the tests and the results,section 5 discusses the analyses on the relation between governance andfirm performance, and section 6 concludes.

2. Theoretical Background, Research Objective, and Prior Research

Agency conflicts in organizations result from the separation of owner-ship and control, the conflicting objectives of owners and managers, andthe information asymmetry between owners and managers (Coase [1937],

Jensen and Meckling [1976], Fama and Jensen [1983a, b]). As a result of

these agency conflicts, and given that managers have sufficient latitude inapplying accepted accounting procedures, they are likely to have incentivesto take actions that maximize their utility, even when those actions do notmaximize shareholder wealth (Watts and Zimmerman [1986]).

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The governance structure of a firm involves mechanisms to minimizethese agency conflicts. Ceteris paribus, the demand for these control mech-anisms is likely to be higher for firms with greater need for oversight, orhigher degrees of agency conflicts. In other words, agency conflicts andgovernance mechanisms in afirm are likely to be complementaryhigherlevels of agency conflicts will result in stronger governance structures. Thepurpose of this paper is to test the hypothesis that the governance struc-tures in afirm are a function of the agency conflicts in thefirm. Specifically,I test the theory thatfirms with higher levels of agency conflicts have moreefficient governance mechanisms in place.

Prior research related in spirit to this study includes Lasfer [2002], whoexamines the association between board structure and firm value in theUnited Kingdom andfinds that this relation is a function of afirms growthopportunities. Hefinds that while low growthfirms are less likely to have an

independent board, their value is positively related to these board structurevariables. In contrast, for high growth firms, the relationship between boardstructure andfirm value is weak, suggesting that board structure does notalways mitigate agency conflicts for these firms. He concludes that imposingthe same board structures for all firms is likely to reduce the value offirmsthat are forced to depart from their optimal board structures. Klapper andLove [2004] and Durnev and Kim [2003] examine the association betweenthe Credit Lyonnais Securities Asia (CLSA) index of corporate governanceandfirm-level characteristics in emerging markets. These studies documentthatfirms with greater past growth, lower proportions offixed assets, shares

traded in the United States, better investment opportunities, higher owner-ship concentration, and greater needs for external financing have highergovernance rankings.

Related studies on U.S.firms include Gillan, Hartzell, and Starks [2003],who examine the extent to which industry characteristics are associatedwithfirms governance structures, namely, board structure, charter provi-sions (i.e., antitakeover devices), and state of incorporation. They documentthat industry factors contribute most of the explainable variation in theirgovernance indices, and dominate firm factors. Smith and Watts [1992] alsoargue that industry factors and the investment opportunity set determine

firms governance policies. Hermalin and Weisbach [1988, 2003] suggestthat boards of directors are an endogenous response to agency problems.Bushman et al. [2004] document that board structures, director equity in-centives, executive compensation, and ownership concentrations vary withfirmsaccounting systems and organizational complexity.

Klein [2002a] tests for the economic determinants of audit committee in-dependence, and documents that audit committee independence increases

with board size and the percentage of outsiders on the board. Klein [2002a]also finds that audit committee independence decreases with the firmsgrowth opportunities, when the firm experiences two or more consecu-

tive losses and in the presence of alternate monitoring mechanisms. In thecontext of audit committees, her findings also support the argument thatone size doesntfit alland suggest that it is desirable to allowfirms someflexibility in deciding on the composition of their audit committees. These

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studies provide motivation for further research on how governance mecha-nisms vary acrossfirms, and how this affects the role of governance in firmperformance and other firm decisions. This study contributes to the evi-dence in the prior literature by documenting thatfirms with higher agencyconflicts have better governance mechanisms in place, and the relation be-tween governance mechanisms and overall firm performance also varies

with the level of agency conflicts.One recent study that is related to this paper, particularly from the point of

view of the measurement of governance dimensions, is by Larcker, Richard-son, and Tuna [2007] (henceforth, LRT). LRT develop reliable indices of

various dimensions of governance using a large number of individual gov-ernance indicators. They argue that using a single indicator or summing upseveral indicators to form a one-dimensional index to measure a multidi-mensional construct such as governance is likely to introduce considerable

measurement error in the analyses. They consider 39 individual governanceindicatorsandusePCAtoform14governancefactors.Ialsoemploythesamemethod, PCA, to construct my governance dimensions. I consider 22 indi-

vidual governance variables and obtain seven factors that represent variousdimensions of governance.

A brief discussion of the main differences in the individual governancevariables in the two studies is as follows. LRT consider stock ownership byblockholders and activist institutions, debt-to-equity, and preferred stock-to-equity as governance indicators. I focus on internal governance mechanismsin my study and do not include these variables as governance indicators. I

consider institutional ownership and leverage as inputs in determining thelevel of agency conflicts in thefirm.

LRT also include antitakeover devices as additional governance indica-tors. Although I do not include antitakeover provisions in my main tests, inadditional analyses, I consider the 28 state, federal, and firm-specific pro-

visions included in the G-score computed by Gompers, Ishii, and Metrick[2003].2 For these antitakeover variables, the PCA mechanism reveals anadditionalfive factors representing the protection of officers and directorsfrom lawsuits, delay mechanisms management can use to prevent hostiletakeovers, protection available to management during a change in control,

protective mechanisms available to management during business combi-nations, and other state laws. The results indicate that two of the factors,namely, the protection of directors and officers from legal expenses andprotective mechanisms during a change in control, are significantly asso-ciated with the level of agency conflicts.3 Finally, I also include various at-tributes of the auditor among my individual governance variables, and LRT

2 I remove these variables as measures of governance from my main analyses based on thereferees advice that these variables are likely to measure takeover defenses rather than the

internal governance of afirm.3 These results are not tabulated but are available on request.

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include some additional board-related variables, such as the existence of alead director and the fraction of busy directors.

LRT focus on examining the association between their indices of gov-ernance and various accounting outcomes, namely, abnormal accruals, ac-counting restatements, and future operating performance. Theyfind thatthese governance indices exhibit mixed association with abnormal accrualsand have little relation to restatements, but some ability to explain futureoperating performance. In contrast, my primary focus is in understandinghow governance structures vary across forms. Specifically, I hypothesize andtest whether differences in governance structures are related to differencesin levels of agency conflicts across firms. Evidence on whether differentdimensions of governance vary with agency conflicts will provide an em-pirical basis for the theoretical predictions that governance structures ariseendogenously in response to specific problems faced byfirms.

3. Research Design

3.1 DATA

The analysis includes the years 2000 and 2001. I begin from year 2000 be-cause data on audit fees are unavailable prior to this period. I hand-collectdata on boards and auditors from proxy statements (schedule 14A). Sched-ule 14A requires firms to disclose all existing and nominated directorsnames, ages, all family relationships between directors, nominees, or execu-tive officers, any significant current or proposed transactions with manage-ment, any significant business relationships with thefirm, and the numberof shares held by the directors. Schedule 14A also requires firms to state

whether they have standing audit, nominating, compensation, and gover-nance committees. If such committees exist, firms are required to disclosetheir functions andresponsibilities, their members, andthe number of timesthe committees met during the lastfiscal year. Details regarding the auditfirm and audit and non-audit fees are also provided. Schedule 14A is typi-callyfiled byfirms in March of every year and reports on the preceding year(it also provides the names of the directors nominated and elected for thefollowing year). Accordingly, I code the information released by a firm in

March of yeart+1 as the governance characteristics of thefirm for yeart.I obtain the rest of the data from Compustat, Spectrum, and ExecuComp.

The initial sample consists of 1,887 firms with data available in the proxystatements. Merging these firms with Spectrum and ExecuComp reducesthe sample to 493 firms. After merging with Compustat, the final samplecomprises 371firms.4

4 My sample only covers two years and has relatively large firms. This limits my ability togeneralize the results. However, given that there is considerable cross-sectional variation in thegovernance measures and the agency measures during the period I examine, the statistical

analyses are likely to have sufficient power to detect the association between measures ofcorporate governance and agency conflicts.

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3.2 EMPIRICAL METHODOLOGY AND CONSTRUCTS

3.2.1. Corporate Governance. I consider 22 governance variables represent-ing various features of the board of directors, auditors, and executives. Ta-

ble 1 summarizes the descriptions and measurements of these variables.5

For the board of directors, I consider the following variables representingdifferent aspects of the whole board as well as of the audit committee inparticular. I measure the composition of the board as the percentage ofoutside directors on the board (%OUTDIR). Inside directors are directors

who are also employees of thefirm, and outside directors are nonexecutivedirectors, who are considered to be independent from management andfree from any business or other relationship that could materially interfere

with the exercise of their independent judgment. Typically a board withmore outside directors is considered to be more effective in monitoring

management.The next variable, INDP COMMITTEE, measures the existence of inde-pendent board committees, namely, the audit, compensation, nominating,and governance committees. This variable takes the value of two if the boardhas separate audit, compensation,nominating, and governance committees,one if either the nominating or governance committee is missing, and zeroif both the nominating and governance committees are missing. Typicallyall boards have standing audit and compensation committees, although notall boards have nominating and governance committees. It has been arguedthat nominating committees can improve the quality of appointments. Theyare likely to increase the independence of the board, in part by reducing thecontrol exercised by the CEO in appointments, and result in nominationsmore consistent with shareholder interests. The primary tasks performed bygovernance committees are to identify and recommend to the board appro-priate candidates who could serve as director nominees for the next annualmeeting of shareholders; to advise the board with respect to the board com-position, procedures, and committees; and to develop and recommend tothe CEO and the board a set of corporate governance guidelines applicableto the company and monitor such governance guidelines. When the nom-inating and governance committees are absent, these functions are eitherperformed by the whole board or not performed at all (particularly in the

case of the governance functions). The performance of nominating func-tions by the whole board, particularly if the CEO and other executives aremembers of the board (which is usually the case), could affect the qualityof board appointments.

The variableCEO COMMITTEEis set to zero if the CEO is a member ofthe nominating committee and/or the compensation committee, and one

5Although the inclusion of governance variables and the coding procedure in constructingthegovernance index aresubjective to an extent, they areguided by thedictatesof thecorporategovernance norms specified in SOX and other Securities and Exchange Commission (SEC)

rulings. Also, I exclude certain governance criteria specified in SOX due to the lack of suchactivity and/or disclosure prior to SOX (such as the requirement of auditor rotation).

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otherwise. If the CEO is a member of the nominating committee, then thatmeans that the CEO has control over the selection of the other membersof the board. As a result, the CEO might choose more sympathetic boardmembers if he has too much influence on the nominating committee. Themembership of the CEO in the compensation committee indicates thatthe CEO makes his own compensation decisions. This may compromisethe incentives and the actions of the CEO. Several studies examine theseparation of the CEO and chairman of the board (COB) positions positingthat agency problems are higher when the same person holds both positionsand documenting evidence consistent with this theory (Fama and Jensen[1983a], Berle and Means [1933], Yermack [1996]). The CEOs role as thechairman of the board of directors implies that the CEO has the final wordin many of the decisions made by the board. Moreover, to the extent that theother members take decisions that do not antagonize the chair, the role of

the CEO as the COB compromises the independence of the board. If theseroles of the CEO and the COB are combined, it is necessary to publicly justifythis combination. To capture the role of the CEO as the COB, I include the

variableCEO COB, which is set to zero if the CEO is also the chairman ofthe board of directors, and one otherwise.

The variableINDP AUDITCOMtakes the value of one if all audit commit-tee directors are independent, and zero otherwise. The audit committeeis primarily responsible for overseeing the financial reporting process onbehalf of the board of directors, reviewing the financial disclosures, andmeeting privately, outside the presence of management, with the firms au-

ditors to discuss the internal accounting control policies and procedures.Thus, the independent assessment of the audit committee is crucial for theeffective monitoring of afirmsfinancial reporting process, and there is ev-idence of a negative relation between audit committee independence andearnings management (Klein [2002b]). The existence of afinancially liter-ate member on the audit committee facilitates the proper oversight of thefinancial reporting process. It also ensures proper review and oversight ofthe audit functions, rather than complete reliance on the auditors represen-tation of thefirms policies. I code the variableFIN EXPERTas being equalto one if there exists at least one financial expert in the audit committee,

and zero otherwise.6Nonexecutive directors may have family and/or business relationships

with the company. They may also be associated with not-for-profit orga-nizations that receive support from the company. Such relations with the

6 The definition offinancial expert I use is as per SOX: Afinancial expert is any memberwho has the education or experience of a public accountant, auditor, principal financial officer,comptroller, or principal accounting officer of an issuer, or has been in a position requiring theunderstanding of generally accepted accounting principles and financial statements; experi-ence in the preparation and auditing offinancial statements of comparable issuers; experiencein the application of such principles in connection with the accounting for estimates, accruals,

and reserves; experience with internal accounting controls; and an understanding of auditcommittee functions.

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company may provide incentives to these directors to endorse certain de-cisions that they otherwise may object to. Thus, I include the variable%NONAFFIL OUTDIRas the percentage of outside directors that do nothave any relationship with the firm (family, business, not-for-profit organi-zations that receive support from thefirm). The number of meetings heldduring the year provides one indication of how effectively the board func-tions to the extent that this represents the regular attendance by the boardtofirm issues. I include the variables #BD MEET, which is the number ofmeetings held by the board during the year, and #AUDIT MEET, which isthe number of meetings held by the audit committee during the year, tocapture this aspect of the functioning of the board.

Smaller board sizes are considered to be more effective in attaining highermonitoring. Smaller boards have lesser disagreements among board mem-bers, and are likely to be more efficient and organized in carrying out board

functions, than larger boards (Lipton and Lorsch [1992], Jensen [1993],Yermack [1996]). To capture the effectiveness of the board based on itssize, I include the variableBD SIZE, which is defined as the negative of thenumber of members on the board.7

Drawing on prior studies, I include two variables to capture the age ofthe board members:DIR AGE, which is the negative of the average age ofthe directors, and DIR %UNDER70, which is the percentage of directorsunder 70 years (LRT).8 The presence of some form of evaluation of boardperformance is likely to enhance the quality of board functioning. In mostfirms, boards undertake self-evaluations on an annual basis, although in

some cases the audit committee is delegated the responsibility for evaluatingthe performance of the board. I set the variableBD EVALto take the valueone if there exists some form of evaluations for the performance of theboard, and zero otherwise.

Bhagat, Carey, and Elson [1999] document that directors with substantialstock ownership act more quickly to replace the CEO. Bhagat and Black[2001] also find evidence consistent with independent directors being moreeffective if motivated by significant stock ownership. To measure the equityownership of directors, I include the following stock-based compensation

variables:DIR STKCOMP, which is the number of shares of stock (including

restricted stock) received by a nonemployee director divided by the totalnumber of shares outstanding of the firm, andDIR OPTION, which is the

7 There is some evidence in the literature that board effectiveness declines as board sizeincreases above a moderate number, an optimal number being about seven to nine directors(Yermack [1996], Bhagat and Black [1999]). Given this prior evidence, I repeat my analysesby redefiningBD SIZEas a variable that takes the value one if the board has between fourand nine members, and zero otherwise. The board size in my sample offirms ranges from4 to 20 directors, with the mean (median) number being 8.85 (9). Thus, the coding of thedummy variable B SIZEtranslates to being equal to one if the number is less than nine andzero otherwise. However, this does not materially alter any of the results.

8 In companies where a retirement age of a director is speci fied, this variable equals thepercentage of directors who are under this retirement age.

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number of options received by a nonemployee director divided by the totalnumber of shares outstanding of thefirm.

Managerial ownership is an important mechanism to align managersin-centives to shareholders (Jensen and Meckling [1976], Morck, Shleifer, and

Vishny [1988]). One way to increase ownership is through stock-based com-pensation. As in Cheng and Warfield [2005], I include the following owner-ship variables of the top five executives: the average value of stock awards andrestricted stock grants divided by the total compensation,EXEC STKCOMP,and the average value of option grants divided by the total compensation,

EXEC OPTION. These variables measure the proportion of the executivescompensations that is sensitive to stock prices. I also include the bonus com-pensation of the topfive executives of thefirm,EXEC BONUS, defined asthe average ratio of bonuses to total compensation.

Provision of nonaudit related services may seriously compromise the in-

dependence of thefirms auditor. The empirical evidence on this relation,however, is mixed (Frankel,Johnson,and Nelson [2002], Ashbaugh, Lafond,and Mayhew [2003], Larcker and Richardson [2004]). The non-audit ser-

vices currently prohibited include: bookkeeping or other services relatedto accounting records orfinancial statements of the client;financial infor-mation systems design and implementation; appraisal or valuation services,fairness opinions, or contribution-in-kind reports; actuarial services; inter-nal audit outsourcing services; management functions or human resources;broker or dealer investment advisor, or investment banking services; andlegal services and expert services unrelated to the audit. I use the follow-

ing variables to represent the independence and functioning efficiency ofthefirms auditor. The variable NO NONAUDITtakes the value one if theauditor does not provide any of the non-audit services that are prohibitedunder SOX, and zero otherwise. The auditors independence may be fur-ther compromised if, in addition to the provision of non-audit services, thenon-audit fees exceed the audit fees received by the auditor for the year. Ithus include the variable %AUDIT FEES, which is the ratio of audit fees tonon-audit fees paid to the auditor for the year.

The SEC has declared it unlawful for a company to employ an auditfirmwhen the CEO and/or CFO and/or controller or an equivalent person was

an employee of that auditfirm in the recent past (in the preceding year)and participated in the audit of the company. The previous employmentof a director (particularly if the director is in the audit committee) by thecompanys auditfirm can also compromise the audit quality. Recent studiesindicate a threat to audit quality if officers previously worked for their com-panies auditfirms (Menon and Williams [2004], Lennox [2005]). Thereis also evidence suggesting that companies are more likely to appoint anauditfirm if the company has an officer who was an alumnus of thatfirm;however, the presence of an independent audit committee reduces the in-cidence of such officerauditor affiliations (Lennox and Park [2007]). To

capture this conflict of interest, I set the variable EXEC NONAUDITOR toone if the CEO/CFO/any other top management personnel/any director

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T A B L E 1

Individual Governance Variables

Area Variable Description (Name) Measurement

Board of

directors

Percentage of the board

comprising outsidedirectors (%OUTDIR)

This variable equals the percentage of outside

directors on the board

Existence of independent

audit, compensation,

nominating, andgovernance committees

(INDP COMMITTEE)

This variable takes the value +2 if the board has

separate audit, compensation, nominating,

and governance committees; +1 if either thenominating or governance committees are

missing; and 0 if both the nominating and

governance committees are missing

CEO membership on

nominating and/or

compensation committee(CEO COMMITTEE)

This variable takes the value +1 if the CEO is not

a member of the nominating committee nor

the compensation committee, and 0otherwise

The combination of the roles

of the CEO and thechairman of the board

(CEO COB)

This variable takes the value +1 if the CEO is not

the chairman of the board of directors, and 0otherwise

Independence of directors inthe audit committee (as

per NYSE/NASDAQ/SEC

rules) (INDP AUDITCOM)

This variable takes the value +1 if all auditcommittee directors are independent, and 0

if one or more of them are not independent

Existence of afinancial expert

in the audit committee

(FIN EXPERT)

This variable equals +1 if there exists at least one

financial expert in the audit committee, and

0 otherwise; afinancial expert is defined asany member who isfinancially literate as per

the definition in SOX

Outside directors with no

personal or businessrelationship/family

relationship/relationshipwith not-for-profit

organizations that receive

support from the

corporation

(%NONAFFIL OUTDIR)

This variable is the percentage of outside

directors that do not have any familyrelationship/business relationship/

relationship with not-for-profit organizationsthat receive support from the firm

Frequency of board meetings(#BD MEET)

This variable equals the number of meetingsheld by the board during the year

Frequency of audit committee

meetings (#AUDIT MEET)

This variable equals the number of meetings

held by the audit committee during the year

Size of the board (BD SIZE) This variable equals the negative of the number

of members in the boardAverage age of directors

(DIR AGE)

This variable is the negative of the average age of

the directors on the board

The presence of directors

under 70 years of age

(DIR %UNDER70)

This variable equals the percentage of directors

who are under 70 years old; in companies

where a retirement age of a director is

specified, this variable equals the percentageof directors who are under this retirement

age

Existence of board member

evaluations (BD EVAL)

This variable takes the value of+1 if there exists

some form of evaluations for the

performance of the board, and 0 otherwise

(Continued)

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T A B L E 1Continued

Area Variable Description (Name) Measurement

Director

ownership

Stock compensation for

directors (DIR STKCOMP)

This variable is the number of shares of stock

(including restricted stock) received by

nonemployee directors divided by the totalnumber of shares outstanding of the firm

Option-based compensation

for directors

(DIR OPTION)

This variable is the number of options received

by nonemployee directors divided by the total

number of shares outstanding of the firm

Executivecompensa-

tion

Stock awards (includingrestricted stock) as a

proportion of total

compensation for the top

managers of thefirm

(EXEC STKCOMP)

This variable equals the mean of the value ofstock awards and restricted stock grants

divided by the total compensation for the top

five executives of the firm

The average value of optionsgrants as a proportion of

total compensation for thetop managers

(EXEC OPTION)

This variable equals the mean of the value of theoptions granted divided by the total

compensation for the topfive executives ofthefirm

The average value of bonuscompensation as a

proportion of total

compensation for the top

managers of thefirm

(EXEC BONUS)

This variable is the mean of the bonuscompensation divided by the total

compensation for the topfive executives of

thefirm

Auditor Non-audit services provided byauditor (NO NONAUDIT)

This variable takes the value +1 if the auditordoes not provide any of the non-audit

services that are prohibited under SOX, and

0 otherwise

Proportion of fees received byauditor from audit services

(%AUDIT FEES)

This is the ratio of the audit fees to the non-auditfees paid to the auditor for the year; the

items categorized as non-audit servicesinclude those that are prohibited by SOX

Whether the CEO, CFO,

controller, or equivalent

person was employed by

auditor and participated in

the audit of the issuer inone year preceding

initiation of audit

(EXEC NONAUDITOR)

This variable takes the value +1 if the

CEO/CFO/any other top management

personnel/any director was not employed by

the auditor and participated in the audit of

the issuer in one year preceding initiation ofaudit, and 0 otherwise

If the auditor belongs to a

Big-5 auditfirm (BIG5)

This variable equals 1 if thefirm is audited by a

Big-5 auditfirm, and 0 otherwise

This table summarizes the descriptions and measurements of the 22 individual corporate governance variablesused in this study. These individual variables are used in the principal components analysis procedure to obtaingovernance factors.

Prior to SOX, companies were not required to disclose information regarding the prior employment of anexecutive in the companys auditfirm (EXEC NONAUDITOR) and the presence ofa financial expert (FIN EXPERT).Thus, if afirm does not disclose anything regarding these two items, then I assume that there are no executives whowere employees of the companys auditfirm (i.e., the variableEXEC NONAUDITORis coded as 1) and the companyhas nofinancial expert in the audit committee (i.e., the variableFIN EXPERTis coded as 0).

was not employed by the auditor and participated in the audit of the issuerin one year preceding the initiation of the audit, and to zero otherwise.

Finally, the reputation of an auditfirm could affect the quality of its audits.Although it is not clear whether the quality of audits of Big-5 auditors is

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1156 A.DEY

always superior, the brand value of the auditfirm could be associated withthe monitoring effectiveness of afirm. I set the variableBIG5to equal oneif thefirm is audited by a Big-5 auditfirm, and zero otherwise.

In the above variable construction process, other than the equityownership and compensation variables for directors and executives(DIR STKCOMP, DIR OPTION, EXEC STKCOMP, EXEC OPTION, and

EXEC BONUS), higher values of the variables indicate better governance.Predictions for the ownership and compensation variables are difficult, be-cause any interpretation depends on whether the existing levels are optimal,too high, or too low.9

Table 2 presents descriptive statistics of these individual governance vari-ables for the sample firms. The median firm has a majority of outsidedirectors on the board (the variable%OUTDIRhas a mean of 74% and amedian of 78%) and has at least one of the nominating or the governance

committees missing. The CEO of the median firm is not on the nominat-ing or the compensation committees nor is the COB. The median firmhas a fully independent audit committee but does not have anyfinancialexperts (as defined by SOX) in the committee. Most of the nonexecutivedirectors have no family or business relationship with thefirm (the variable%NONAFFIL OUTDIRhas a mean of 91% and a median of 92%). The mean(median) board meets seven (six) times a year and the mean (median) auditcommittee meetsfive (five) times a year. Both the mean and median boardhas about nine members, and most boards do not have any formal evalua-tions. The mean and median age of a director is 59 years and about 90% of

directors are under 70 years or the specified retirement age. Thus, from thepoint of view of the composition and functioning of the board, the averagesamplefirm appears to have reasonably good governance as measured bythese traditional governance variables.

The average outside director owns 0.01% of thefirm, when consideringboth stock-based and option-based compensation. However, a significantpercentage of the compensation of the top five executives is comprised ofequity-based compensation. On average, the top five executives receive 47%of their compensation in stock awards (including restricted stock) and 49%of their compensation in option grants. Bonus compensation comprises

13% of the total compensation package.The median firm in the sample hires its auditfirm to provide the var-

ious non-audit services prohibited by SOX. The median of the variable%AUDIT FEES is less than the mean, indicating positive skewness in the

9 The topic of the level of compensation is a controversial one, with two opposing maintheories regarding the efficiency of contracts. One view asserts that compensation contractsare on average efficient (e.g., Lazear [1995]), and the other view contends that compensationlevels are eithertoo highortoo low(e.g., Morck, Shleifer, and Vishny [1988]). If existinglevels are on average efficient, then any change in those levels lowers the quality of governance.On the other hand, if existing levels of compensationare inefficient, then whether higher levels

of compensation improve the quality of governance depends on whether the existing levelsare too high or too low.

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T A B L E 2Governance Variables: Descriptive Statistics

Variable Mean Median Std. Deviation

%OUTDIR 0.7401 0.7778 0.1421

INDP COMMITTEE 1.0768 1.0000 0.8138CEO COMMITTEE 0.5722 1.0000 0.4951CEO COB 0.3222 1.0000 0.4677INDP AUDITCOM 0.9518 1.0000 0.4602

FIN EXPERT 0.2244 0.0000 0.4175%NONAFFIL OUTDIR 0.9081 0.9177 0.1406#BD MEET 7.2891 6.0000 3.5349#AUDIT MEET 5.1144 5.0000 2.3442BD SIZE 8.8491 9.0000 0.4572

DIR AGE 58.6585 59.0000 4.3185DIR %UNDER70 0.8928 0.9091 0.1318BD EVAL 0.2665 0.0000 0.4425

DIR STKCOMP 0.00006 0.0000 0.00002DIR OPTION 0.0001 0.00006 0.0002EXEC STOCK 0.4678 0.3522 0.3188EXEC OPTION 0.4906 0.4973 0.3972EXEC BONUS 0.1317 0.1119 0.1190NO NONAUDIT 0.0150 0.0000 0.1219%AUDIT FEES 1.2976 0.5521 2.7652

EXEC NONAUDITOR 0.9849 1.0000 0.1219BIG5 0.9714 1.0000 0.1668

This table reports the descriptive statistics of the 22 individual corporate governance variables that areused in the principal components analysis procedure to obtain governance factors.

The variables BD SIZE and DIR AGEare used with a negative sign in the principal components anal-ysis but the descriptive statistics are presented without the negative sign for more meaningful interpretations.

proportion of fees received by the auditor from audit and non-audit ser-vices. While the mean of %AUDIT FEES indicates that the proportion offees from audit services is approximately 30% higher than that from non-audit services, the median value indicates that the audit fees are about 60%of the fees received from non-audit services. Mostfirms in the sample donot have any executives who were employed by the auditor. Finally, mostfirms employ a Big-5 auditor, which is not very surprising given the samplecomprises relatively largefirms.

3.2.1.1. The Governance Factors. I use PCA to form factors that capturedifferent dimensions of governance and determine which of the above gov-ernance indicators are associated with each factor. In this procedure theindividual variables are reduced into a smaller number of principal compo-nents (or artificial variables) that account for most of the variance in theobserved variables. I use a combination of the eigenvalue method and thescree test in order to determine the number of factors to retain. In the eigen-

value method, all factors with an eigenvalue greater than unity are retained.In the scree test, the eigenvalues associated with each componentare plottedand breaks between the components with relatively large eigenvalues and

those with small eigenvalues are identified. The components that appear be-fore the break are assumed to be meaningful and are retained for rotation,

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1158 A.DEY

and those appearing after the break are assumed to be unimportant andare not retained (Jolliffe [2002]).

This procedure results in seven factors that capture 62.4% of the totalvariance of the original data. These factors are rotated using an orthogonalrotation in order to better interpret the components. I perform an orthog-onal rotation instead of an oblique rotation, since the former is easier tointerpret in this case. In particular, since these governance components areused in a multiple regression, an orthogonal rotation, in which the factors re-main uncorrelated, avoids the complication of multicollinearity. Moreover,on examining the factor intercorrelations after performing an oblique ro-tation, Ifind that most of the factors are pairwise uncorrelated (0.3 or lowerin magnitude), which indicates that an orthogonal rotation over an obliqueone does not compromise the relation between the variables.

For each governance factor, I examine which of the individual governance

variables have high factor loadings (bivariate correlations between the vari-ables and the components). As is common in the PCA procedure, I considera factor loading to be large if its absolute value exceeds 0.40. The retainedfactors with the variables that are substantially associated with each factorand the corresponding factor loadings are summarized in table 3.

Each factor represents an underlying governance dimension, and the vari-ables that load measure the same underlying construct. The variables that donot load in any factor are measuring different constructs, and are unrelatedto the governance dimension measured by this factor. Consequently theseven factors obtained measure seven distinct dimensions of governance. I

assign a name to each governance factor based on the individual variablesthat load for an easier read and more meaningful interpretation of results.Thefirst two factors include variables related to the composition and func-tioning of the board, and I call themBoard IandBoard II. The next factor,Exec Comp, appears to capture the bonus and equity-based compensationof the top five executives of a firm. The factor Dir Comp appears to cap-ture the equity-based compensation provided to directors. The next factor,Auditor, appears to capture the independence of the auditor, and the fac-tor Audit Comm appears to capture the composition and functioning ofthe audit committee of the board of directors. Finally, the factor Fin Rep

appears to capture the boards control over thefinancial reporting quality,that is, whether there exists afinancial expert on the audit committee and

whether any top management or director has any association with the firmsauditor.10

10 In PCA, it is desirable to have at least three variables loading on each retained componentwhen the PCA procedure is complete (Rummel [1970]). Two of my components,Dir Compand Fin Rep, have only twovariables that load significantly. On measuringthe reliability of thesecomponents using Cronbachs alpha (discussed below), I find that the Cronbachs alphas forthese two components are 0.59 and 0.56, respectively, which are somewhat lower than the

benchmark of 0.70 suggested by Nunnally [1967]. However, these levels of reliability are notuncommon (LRT).

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T A B L E 3Principal Component Analysis: Factors and Factor Loadings

Principal Factor Significant Components Factor Loadings Cronbachs Alpha

Board I %OUTDIR 0.77 0.85

INDP COMMITTEE 0.82CEO COMMITTEE 0.72BD EVAL 0.64BD SIZE 0.68

Board II DIR %UNDER70 0.87 0.79DIR AGE 0.84#BD MEET 0.73CEO COB 0.43

Exec Comp EXEC STOCK 0.67 0.77EXEC OPTION 0.75EXEC BONUS 0.78

Dir Comp DIR STKCOMP 0.47 0.59

DIR OPTION 0.63Auditor NO NONAUDIT 0.60 0.54

%AUDIT FEES 0.75BIG5 0.43

Audit Comm INDP AUDITCOM 0.56 0.50#AUDIT MEET 0.42%NONAFFIL OUTDIR 0.43

Fin Rep FIN EXPERT 0.58 0.59EXEC NONAUDITOR 0.61

This table reports the factor loadings (absolute values) on each of the individual corporate governancevariables and the raw Cronbachs alphas for each factor. Factors are computed using exploratory principalcomponents analysis. For each factor, individual variables with absolute values of the loadings exceeding0.40 are reported. The reported factor loadings and the Cronbach s alphas are rounded off to two placesafter the decimal.

The method used to compute the individual governance variables ensuresthat a higher value of each of the governance factors, Board I, Board II,Auditor, Audit Comm, andFin Rep, indicates higher quality of the corre-sponding dimension of governance. However, in the case of the directorand executive compensation factors, namely,Dir CompandExec Comp, Ido not make any directional predictions.

To assess the reliability of the factors, I compute Cronbachs alpha, which

is a coefficient of reliability.11

The raw Cronbachs alpha coefficients arereported in table 3. Nunnally [1967] suggests that an alpha greater than0.70 is an acceptable reliability coefficient. The mean (median) value ofthe raw Cronbachs alpha is 0.65 (0.59). The alphas for the factors Board I,Board II,and Exec Comp are over 0.70, but those for the remaining factors,Dir Comp, Auditor, Audit Comm, and Fin Rep, are a bit lower than the

11 The formula for the standardized Cronbachs alpha is given by = N r/[1 + (N 1)r], whereN is the number of items andris the average interitem correlation among the items.Thus, if the interitem correlations are high, then there is evidence that the items are measuring

thesame underlying construct, that is, these items are measuringa single unidimensional latentconstruct.

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1160 A.DEY

benchmark of 0.70. However, as discussed in LRT, low reliability values arecommon in early measurement stages, and these factors are very likely tohave higherreliability than thesingle indicators in measuring various aspectsof governance. Thus, although not perfect measures of governance, thesefactors are likely to better capture the underlying governance dimensionsthan individual variables or indices compiled by summing up individual

variables.

3.2.2. Agency Variables. Measuring the agency conflicts in afirm is chal-lenging because there exists a variety offirm-specific situations where themanagers have the incentives and the ability to engage in maximizing theirown utilities at the cost of shareholders. Moreover, there is no widely ac-cepted measure of agency conflicts. Thus, I use several variables to proxy

for the scope of agency conflicts existing in a firm based on evidence inprior studies. A brief discussion of each of these variables along with relatedreferences is provided below.

3.2.2.1. Firm Size (SIZE). Size is measured as the natural logarithm ofsales. Large corporations are more likely to have highly diffuse ownershipstructures that effectively separate ownership of residual claims from controlof corporate decisions. Largerfirms also have a greater scale of operations,

which provides greater incentive and opportunities for managers to shirk(Demsetz and Lehn [1985]). Moreover, larger firms are more likely to be un-der greater political scrutiny, which provides managers of such firms greater

incentives to exercise discretion to minimize political costs (Watts and Zim-merman [1990]). As a result, I expect the level of agency con flicts to behigher in largerfirms.

3.2.2.2. Organizational Complexity (COMPLEX). Organizational com-plexity is measured as the number of industries thefirm operates in, wherethe industries are measured by two-digit Standard Industrial Classification(SIC) codes. Organizational complexity is an important component of thescope for moral hazard (Bushman et al. [2004]). Multi-industryfirms com-bine diverse operations. The resulting information aggregation problemscan lead to substantial information asymmetries within thefirm, or betweenfirm insiders and outside investors by suppressing the activities of informa-tion intermediaries (Habib, Johnson, and Naik [1997], Gilson et al. [2001]).Capital allocation in such firms may also be inefficient, and the CEOs of suchdiversefirms may have reduced focus (Stein [2000]). As Givoly, Hayn, andDSouza [1999] point out, though firms are required to disclose segmentdata, this information suffers from imprecisions of segment identification,cost allocations, and transfer pricing schemes. Accordingly, I expect agencyconflicts to be higher infirms that are more complex.

3.2.2.3. Volatility in Operating Environment (GROWTH; RISK).

Demsetz and Lehn [1985] conjecture that the scope for moral hazard isgreater for managers offirms with more volatile operating environments.

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They argue that more volatile environments result in greater difficulty inmonitoring managers. I use two variables to proxy for the volatility in theoperating environment of afirm: growth opportunities, as measured by thebook-to-market ratio, and operating risk, as measured by the standard de-

viation of quarterly operating cash flows deflated by total assets computedover the immediately preceding four quarters (measuring operating risk asthe standard deviation of sales deflated by total assets over the immediatelypreceding four quarters does not materially alter any results).

High growth firms have higher levels of information asymmetry and man-agers in thesefirms are likely to have more power due to a greater amountof resources under their control (Smith and Watts [1992], Jensen [1986]).Such firms are also expected to have greater incentives to meet earningstargets, since prior research has shown that the market severely penalizesgrowth firms for negative earnings surprises (Skinner and Sloan [2002]).

Riskierfirms are expected to have greater incentives to indulge in activitiesin order to reduce the perception of risk and thus lower the costs of equityand debt capital. Accordingly, agency conflicts are likely to be higher infirms with greater operating risk and growth opportunities.

3.2.2.4. Ownership Structure (OWNERSHIP). Ownership structure ismeasured as the percentage of capital owned by individual shareholders.It is calculated as one minus the value of shares held by executives, direc-tors, and institutional investors divided by the total market capitalization ofthefirm. This is a measure of how diffuse the ownership structure of the

firm is, or, how much management control is present in thefirm. As arguedby Berle and Means [1932], when managers hold little equity in the firmand shareholders are too dispersed to enforce value maximization, corpo-rate assets may be deployed to benefit managers rather than shareholders.In other words, management-controlledfirms have considerable discretionin guiding the affairs of corporations and this discretion could be used todivert some resources from corporate shareholders (Morck, Shleifer, and

Vishny [1988]). Jensen and Meckling [1976] argue that owner-controlledfirms do not have the same incentives to divert resources, since ownermanagers would suffer directly from reduced share values. I expectfirms

with more diffuse ownership or more management control to have higherlevels of agency conflicts.

3.2.2.5. Leverage (LEV). Leverage is measured as the ratio of long-termdebt to total assets. The agency costs related to debt are likely to be higherinfirms with greater leverage. Ownermanagers have an incentive to accepthigh-risk projects to transfer wealth from creditors to shareholders, and arealso likely to forego positive net present value projects because most of theircreated value is captured by bondholders (Myers [1977]). These activitiesare likely to reduce firm value. Furthermore, firms with higher leverageratios have greater incentives to manage earnings in order to avoid covenant

violations and/or to prevent adverse effects on their debt ratings (Bowen,Rajgopal, and Venkatachalam [2005], DeFond and Jiambalvo [1994], Watts

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1162 A.DEY

T A B L E 4Correlation Table for the Agency Variables

SIZE COMPLEX OWNERSHIP GROWTH LEV RISK FCF

SIZE 0.0032 0.1765 0.0250 0.0202 0.5563 0.0302

COMPLEX 0.0017 0.0109 0.0032 0.1052 0.1765 0.0021OWNERSHIP 0.1446 0.0074 0.0205 0.0153 0.1697 0.0509GROWTH 0.0628 0.0012 0.0488 0.0045 0.0427 0.0026LEV 0.1992 0.1276 0.0347 0.0257 0.3796 0.0006RISK 0.3012 0.1864 0.1362 0.0784 0.1343 0.0195

FCF 0.0415 0.0185 0.0414 0.0913 0.1457 0.0433

This table presents the Pearson and Spearman correlation coefficients between the seven agencyvariables. Pearson correlations are reported above the diagonal and Spearman correlations are reportedbelow the diagonal. SIZE is measured as the natural logarithm of sales; COMPLEX is measured as thenumber of industries the firm operates in, where the industries are measured by two-digit SIC code;OWNERSHIPis calculated as one minus the value of shares held by executives, directors, and institutionalinvestors divided by the total market capitalization of thefirm;GROWTHis measured by the book-to-marketratio; LEV is measured as the ratio of long-term debt to total assets; RISK is measured as the standard

deviation of quarterly operating cashflows divided by total assets computed over the immediately precedingfour quarters;FCFis measured as the free cash flow of thefirm scaled by current assets (free cash flows aredefined as the difference between the cash flow from operations of the previous quarter and the precedingthree quarter average of thefirms capital expenditures scaled by the current assets of the previous quarter).

indicates significance at the 1% level.

and Zimmerman [1990]). Suchfirms are thus likely to have higher agencyconflicts.

3.2.2.6. Free Cash Flow (FCF). Freecash flows are measured as the differ-ence between the cashflow from operations of the previous quarter and the

preceding three quarter average of the firms capital expenditures, scaled bythe current assets of the previous quarter. 12 Jensen [1986] argues that con-flicts of interests between shareholders and managers are especially severe

when there are substantial free cashflows generated in the organization.Thus, I expect that firms with greater levels of free cash flows will havehigher agency conflicts.

Table 4 presents the correlations (Pearson and Spearman) between theabove agency variables. The following correlations are statistically signifi-cant (at the 1% level). The variables SIZEand OWNERSHIPare significantlycorrelated (Pearson, 0.18; Spearman, 0.14), suggesting that for this samplemanagers of largerfirms have more control. Largerfirms also have higherleverage (only the Pearson correlation between SIZE and LEV is signifi-cant, 0.20). Both the Pearson (0.56) and Spearman (0.30) correlations forthe variablesSIZEandRISKare significant, indicating that for this samplelarger firms have greater operating risk. More riskyfirms also have greatermanagement control as indicated by the significant correlations betweenthe variablesOWNERSHIPandRISK(Pearson, 0.17; Spearman, 0.14). Onlythe Spearman correlation between GROWTHandFCF is negative and statis-tically significant (0.09). As expected, higher growthfirms have lesser free

12 I also use an alternative measure representing the cash on hand, defined as (cash +short-term investments)/total assets. This does not materially alter any results.

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cashflows. Samplefirms with higher leverage are also more risky (Pearson,0.38; Spearman, 0.13). Finally, only the Spearman correlation betweenLEVand FCF is negative and significant (0.15), indicating thatfirms in thesample with higher leverage also have lesser free cashflows.

3.2.3. Agency Conflict Groups. In order to examine how governance struc-tures offirms vary as a function of the level of agency conflicts in thefirm,Ifirst classifyfirms into homogeneous groups using cluster analysis basedon the above seven variables. Cluster analysis is an exploratory data analysistool which sorts different objects into groups by maximizing the degree ofassociation between two objects in the same group and minimizing the de-gree of association between two objects across groups. Using a data analysistool, such as cluster analysis, for separating thefirms into groups is more ef-ficient (than, for example, ranking thefirms and forming deciles) because

the process itself identifies the optimal firm clusters for the sample basedon the specified variables.

There are, however, no completely satisfactory methods for determiningthe optimal number of clusters for any type of cluster analysis. Based onsimulation studies by Cooper and Milligan [1985, 1988] three criteria thatperform best to determine the optimal number of clusters are the cubicclustering criterion (CCC) developed by Sarle [1983], a pseudo-F-statistic(PSF) developed by Calinski and Harabasz [1974], and a pseudo-t2-statistic(PST2) based on a statistic developed by Duda and Hart [1973]. A goodestimate for the optimal number of clusters is obtained by using a consensus

among these three statistics, that is, by determining local peaks of the CCCand PSF combined with a small value of the PST2 followed by a significantlylarger PST2 value for the next cluster fusion. A brief overview of the clusteranalysis procedure is provided in the appendix (see Everitt [1980], Massartand Kaufman [1983], Anderberg [1973] for greater detail).

Table 5, panel A reports the values of these statistics for the sample.13

The CCC and PSF statistics identify two possible numbers of clusters for thesample, three and seven. PST2 reveals possible clustering levels at three,seven, and nine. Thus, three and seven are the possible number of clustersfor this data set. I form three clusters due to limitations on the number of

firms in the sample. Next, I use Wards method of clustering, which formsclusters by minimizing the variance within each cluster. Wards method ofclustering is the most commonly used method among hierarchical cluster-ing algorithms, and produces clusters with roughly the same number ofobservations (Everitt [1980, 1993]).

In order to distinguish among the groups in terms of the level of agencyconflicts, I perform a principal factor analysis of the seven agency variablesthat are used to segregate the firms into clusters, andderive an overall agencyscore for eachfirm. This serves as an overall measure of the level of agency

13 I report the values for 10 clusters, but an analysis of the corresponding statistics for 20clusters does not reveal any additional clustering possibilities for the sample.

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1164 A.DEY

conflicts in a firm. The mean value of this agency score indicates that agencyconflicts are highest for the firms belonging to cluster 3 (mean agencyscore = 1.12), followed byfirms in cluster 2 (mean agency score = 0.26),and are lowest for firms in cluster 1 (mean agency score = 0.82). Basedon this score, cluster 3 represents the high agency conflict group (groupHIGH), cluster 2 represents the medium agency conflict group (groupMEDIUM), and cluster 1 represents the low agency conflict group(group LOW). The overall agency score is significantly different acrossthe three groups (t-statistic for the difference between groupsHIGH andMEDIUM = 21.35; t-statistic for the difference between groupsHIGH andLOW = 30.27; t-statistic for the difference between groups MEDIUMandLOW = 31.52).

Table 5, panel B presents the mean and median values of the seven indi-vidual agency variables across the three agency groups. It is interesting to see

which of these seven variables are significantly different across the groups.Compared tofirms in groupsMEDIUMandLOW,firms in groupHIGHarelargerthe mean (median)SIZEis 8.77 (8.95) for group HIGH, followedby 7.14 (7.08) for group MEDIUM, and 5.80 (5.81) for groupLOW. Thereis not much variation in organizational complexity across thefirms, as is evi-denced by the identical means and medians of the variable COMPLEXacrossall three agency groups. Firms in the higher agency groups also have a morediffuse ownership structure. The mean (median) values of the variable OWN-ERSHIPare 0.84 (0.66) for groupHIGH, 0.75 (0.55) for group MEDIUM,and 0.53 (0.41) for groupLOW. The values forGROWTH, although higher

in the highest agency group than in the other two lower groups, are moreor less comparable across the MEDIUMand LOWagency groups. The mean(median) values forGROWTHare 0.05 (0.05) for groupHIGH, 0.04 (0.03)for groupMEDIUM, and 0.04 [0.02] for groupLOW. Firms in groupHIGH

T A B L E 5

Agency Conflict Groups

Panel A: Optimal number of clusters

No. of Cubic Clustering Pseudo-F Pseudo-t2

Clusters Criterion (CCC) (PSF) (PST2)

10 7.2 76 103 2059 12 74 103 8228 15 73 103 1687 29 57 104 1,0516 19 37 103 4765 18 31 103 3544 14 30 103 2943 30 27 104 4,6552 28.4 9 102 17 102

1 0.0 NA 10 102

This table presents the values for the three statistics used to determine the optimal number of clustersfor the sample, namely, the cubic clustering criterion (CCC), the pseudo-F (PSF), and the pseudo-t2

(PST2). For each criterion, the rows in bold indicate the clustering possibilities identi fied by the criterion.(Continued)

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Panel B: Cluster-wise means/medians

Cluster 1 Cluster 2 Cluster 3(Low Agency (Medium Agency (High Agency

Group) Group) Group)

Variable Mean Median Mean Median Mean Median

SIZE 5.7978 5.8119 7.1426 7.0779 8.7658 8.9484COMPLEX 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000OWNERSHIP 0.5332 0.4190 0.7510 0.5543 0.8435 0.6634GROWTH 0.0422 0.0194 0.0392 0.0349 0.0468 0.0499LEV 0.2986 0.5901 1.2706 0.7638 1.6385 1.3964RISK 0.0246 0.0209 0.0450 0.0416 0.0664 0.0597

FCF 0.1035 0.0537 0.1967 0.0691 0.2276 0.0713

Overall Agency Score

AGENCY 0.3794 1.4509 1.5913No. offirms 111 143 117

This table presents the cluster-wise mean and median values of the seven variables that are used tosegregatefirms into three clusters and the overall agency scores for each cluster. SIZE is measured as thenatural logarithm of sales;COMPLEXis measured as the number of industries the firm operates in, wheretheindustries aremeasuredby two-digit SICcode; OWNERSHIPis calculated as oneminusthe value of sharesheld by executives, directors, and institutional investors divided by thetotal market capitalization of the firm;GROWTHis measured by the book-to-market ratio; LEVis measured as the ratio of long-term debt to totalassets;RISKis measured as the standard deviation of quarterly operating cash flows divided by total assetscomputed over the immediately preceding four quarters; FCFis measured as the free cash flow of thefirmscaled by current assets (free cashflows are defined as the difference between the cashflow from operationsof the previous quarter and the preceding three quarter average of the firms capital expenditures scaled bythe current assets of the previous quarter); the overall agency score, AGENCY, is computed by performingprincipal factor analysis of the four variablesSIZE,OWNERSHIP,LEV, andRISK.

indicates a variable that has significant factor loadings when principal factor analysis is performed onall of the seven agency variables.The results of Wilcoxon-Mann-Whitney two sample tests for differences in medians ofSIZE, OWNERSHIP,

LEV, andRISK andt-tests for differences in means ofSIZE,OWNERSHIP,LEV, andRISKand the agencyscore, AGENCY, between the three agency groups are reported below ( , , and indicate significance atless than the 10%, 5%, and 1% levels, respectively):

t-statistic for the differences in means and z-statistic for differences in medians between the HIGHagencygroup and theMEDIUMagency group:

SIZE: t= 24.35;z = 20.82

OWNERSHIP: t= 13.66;z = 12.34

LEV: t= 1.44;z = 2.33

RISK: t= 1.84;z = 2.04

AGENCY: t= 26.39

t-statistic for the differences in means and z-statistic for differences in medians between the HIGHagency

group and theLOWagency group:SIZE: t= 49.61;z = 27.51

OWNERSHIP: t= 15.34;z = 17.43

LEV: t= 2.27;z = 2.13

RISK: t= 2.25;z = 3.24

AGENCY: t= 43.64

t-statistic for the differences in means and z-statistic for differences in medians between the MEDIUMagency group and theLOWagency group:

SIZE: t= 36.09;z = 26.20

OWNERSHIP: t= 6.90;z = 8.60

LEV: t= 1.98;z = 0.19RISK: t= 0.70;z = 0.84

AGENCY: t= 40.82

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of control the manager has, and is a more traditional measure of agency con-flicts (Warfield, Wild, and Wild [1995], Morck, Shleifer, and Vishny [1988]).

As in these studies, I form agency conflict groups based on three levels ofmanagerial control: whenOWNERSHIP 95% (highest agency conflicts),

when 75% < OWNERSHIP

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1168 A.DEY

T A B L E 6Principal Factors: Descriptive Statistics

Agency Group

Principal Factor All HIGH MEDIUM LOW Board I 0.1563 0.3548 0.1992 0.0359Board II 0.0077 0.2529 0.1626 0.3859Exec Comp 0.1927 0.3151 0.2535 0.0227Dir Comp 0.1688 0.1464 0.2418 0.1616

Auditor 0.0397 0.1210 0.0782 0.0373Audit Comm 0.2012 0.4061 0.3138 0.1233Fin Rep 0.0208 0.1343 0.0263 0.0564

This table presents the median values of the seven governance factors for all firms, and forfirms in thethree agency conflict groups. The seven governance factors are obtained by performing principal compo-nents analysis on 22 individual corporate governance variables.

The results of Wilcoxon-Mann-Whitney two sample tests for differences in medians between the gover-nance factors for the three agency groups are reported below (, , and indicate significance at the less

than 10%, 5%, and 1% levels, respectively):z-statistic for differences in medians between the HIGHagency group and theMEDIUMagency group:

Board I: z= 5.41

Board II: z= 2.52

Exec Comp: z= 3.58

Dir Comp: z= 0.51Auditor: z= 1.17

Audit Comm: z= 3.15

Fin Rep: z= 3.88

z-statistic for differences in medians between the HIGHagency group and theLOWagency group:

Board I: z= 6.81

Board II: z= 2.24

Exec Comp: z= 7.33


Audit Comm: z= 7.58

Fin Rep: z= 3.01

z-statistic for differences in medians between the MEDIUMagency group and theLOWagency group:

Board I: z= 5.18

Board II: z= 2.21

Exec Comp: z= 4.92


Audit Comm: z= 8.24

Fin Rep: z= 1.37

LOWgroups. These results support the theory thatfirms with higher levelsof agency conflicts have higher quality of governance structures in place,particularly those related to the board of directors, the audit committee, theboards control over the financial reporting process, and the auditor.

In order to perform a more formal test of the relation between agencyconflicts and the governance factors, I estimate the following regression:

AGENCY j q = +7

i=1

i GOV FACTORi+ (1)

In the above equationAGENCYcorresponds to the principal factor analysisof the four significant agency variables,SIZE,OWNERSHIP,LEV, andRISK,

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T A B L E 7Agency Conflicts and Governance Quality

AGENCYjq = +7

i=1i GOV FACTORi +

Coefficient(t-Statistic)

Intercept 0.1768(7.01)

Board Ij 0.2967(11.53)

Board IIj 0.0587(2.38)

Exec Compj 0.3652(16.92)

Dir Compj 0.0026(0.09)

Auditorj 0.1468(5.63)

Audit Commj 0.2452(10.76)

Fin Repj 0.0680(1.45)

AdjustedR2 0.3602F-value (Pr>F) 78.58 (

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1170 A.DEY

governance structures vary as a function of the agency conflicts infirms isthat the role played by these governance mechanisms in affecting overallfirm performance is also likely to vary as a function of the agency conflicts.In the next section I investigate this conjecture.

5. Additional Analyses: Firm Performance and Governance

One important question that has been rigorously studied in the gover-nance literature is whether governance, or various aspects of governance,affects overallfirm performance. The empirical evidence on this relation,however, is mixed. For instance, Hermalin and Weisbach [1991] and Bhagatand Black [2001] find no relation between the proportion of outsider direc-tors and various performance measures. In contrast, Baysinger and Butler[1985] and Rosenstein and Wyatt [1990] show that the market rewards firms

for appointing outside directors.A few studies also suggest thatfirms with a high percentage of indepen-

dent directors may perform worse. For instance, Yermack [1996] reports asignificant negative correlation between the proportion of independent di-rectors and contemporaneous TobinsQ, but no significant correlation forseveral other performance variables (sales/assets, operating income/assets,operating income/sales). Yermack [1996] also documents an inverse rela-tion between board size and profitability, asset utilization, and Tobins Q.

Agrawal and Knoeber [1996] report a negative relation between the pro-portion of outside directors and Tobins Q. Klein [1998] does not find

a significant relation between firm performance and board structure asa whole, but documents that inside director representation on a boardsfinance and investment committees correlates with improvedfirm perfor-mance. She finds little evidence that the audit, compensation, and nomi-nating committees, which are usually dominated by independent directors,affect performance.18

More recently, LRT document that some of their governance indices areassociated with future return on assets (ROA). Theyfind that institutionalownership, long-term and bonus compensation of the CEO, and certain an-titakeover measures have a positive relation with future ROA, and board size,

audit committee size and compensation committee size, and the busynessof directors have a negative relation with future ROA.

I conjecture that one explanation for the mixed association between gov-ernance and performance is that governance mechanisms are likely to pos-itively affect overall firm performance only under certain circumstances.Specifically, I test whether the relation between governance mechanismsandfirm performance varies as a function of the level of agency conflictsinfirms. The governance mechanisms in place are important in monitor-ing the actions of managers infirms with high agency conflicts. In contrast,

18 The literature on governance and firm performance is vast, and I discuss only some ofthe important and relevant studies. See Shleifer and Vishny [1997], John and Senbet [1998],and Hermalin and Weisbach [2003] for more detailed literature reviews.

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managers in low agency conflictfirms are not likely to require as much mon-itoring, and so these governance mechanisms are not likely to be importantin affecting future performance.

I expect the performance of high agencyfirms to be positively related tothe factors Board I, Board II, Auditor, Audit Comm, and Fin Rep. As be-fore, the association with Exec Comp and Dir Comp is not clear. In contrast,for the lower agency companies the governance factors are not expected tobe important determinants of their performance. Based on prior researchI use two proxies for firm performance: TobinsQ (Q) defined as (market

value of equity+total debt)/total assets, and return on assets (ROA), de-fined as (net income before extraordinary items)/total assets (Agrawal andKnoeber [1996], Yermack [1996], Bhagat and Black [1999], among others).I use the one-year-ahead values of the above variables to control for potentialendogeneity issues.

The mean (median) values of the variable Q for the HIGH, MEDIUM,andLOWagency groups are 1.97 (1.77), 1.61 (1.14), and 1.24 (1.06), re-spectively. The mean (median) values of the variable ROAfor the HIGH,MEDIUM, and LOWagency groups are 0.04 (0.02), 0.02 (0.01), and 0.01(0.01), respectively. There is a monotonic relation between the performancemeasures and the level of agency conflicts, and as expected, the high agencygroup has higher values for both performance measures. Next, I formallyexamine whether the relation between governance and firm performanceis a function of agency conflicts. I perform the following regression for eachof the three agency groups:

PERFORMANCE MEASUREj,t+1

= +

7i=1

i GOV FACTORi,t+ L PERFORMANCE MEASUREj,t

+ RD/SALESj,t+ (INV + PPE)/TOTALASSETSj,t+ (2)

wherePERFORMANCE MEASURErepresents the variablesQandROA, andGOV FACTORrepresents the seven governance factors, Board I,Board II,Exec Comp, Dir Comp, Auditor, Audit Comm, and Fin Rep. Given that I

perform the above regression separately for each of the three agency groupsthat have similar characteristics of the various agency measures (particularlyfor size, operating risk, and leverage), I do not include these variables as ad-ditional controls in the regression. However, based on prior studies, I includethe lagged values of the dependent variables (L PERFORMANCE MEASUREin equation (2) is either the lagged value of Q or the lagged value ofROA), a proxy for the assets in place measured by the sum of inventoryand gross property, plant and equipment divided by total assets (INV +PPE)/TOTALASSETS, and a proxy for growth opportunities measured as theresearch and development expenses divided by sales (RD/SALES) (Mehran

[1995], Klein [1998]).Table 8 reports the results of this analysis. Panel A reports the results when

Q is the dependent variable, and panel B reports the results whenROAis

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1172 A.DEY

used as the dependent variable. The results in panel A indicate thatQ ispositively associated with the two factors related to the composition andfunctioning of the board, Board I and Board II; director compensation,Dir Comp; the independence of the auditor,Auditor; and audit committeeeffectiveness, Audit Comm, for theHIGHagencyfirms. The factorFin Repis positive and marginally significant. Only the factors Board I, Board II,

T A B L E 8

Firm Performance and Governance Quality

Panel A: Tobins Qand governance quality

Qj,t+1 = +7

i=1

i GOV FACTORi,t + L Qj,t + RD/SALESj,t

+ (INV + PPE)/TOTALASSETSj,t+ HIGH MEDIUM LOW

Agency Agency AgencyExpected Coef ficient Coef ficient Coef ficient

Sign (t-Statistic) (t-Statistic) (t-Statistic)

Intercept 4.2476 0.9307 0.8996(4.05) (2.08) (3.48)

Board I j,t + 0.1451 0.4609 0.0117(3.33) (3.76) (1.11)

Board II j,t + 0.1302 0.2857 0.0503(2.68) (2.35) (1.18)

Exec Comp j,t ? 0.0721 0.0299 0.0203

(1.18) (1.24) (0.51)Dir Comp j,t ? 0.2569 0.1352 0.1518

(3.51) (1.56) (1.89)Auditor j,t + 0.2407 0.1747 0.0278

(3.19) (1.02) (1.19)Audit Comm j,t + 0.1689 0.5972 0.1174

(3.10) (3.16) (1.43)Fin Rep j,t + 0.1832 0.0906 0.0143

(1.88) (0.50) (0.26)L Q j,t + 0.3677 0.4488 0.4266

(4.50) (7.58) (4.56)RD/SALES j,t + 0.1349 0.1172 0.0904

(1.47) (0.08) (1.09)(INV+PPE)/ + 0.2029 0.0429 0.1611

TOTALASSETSj,t (1.18) (0.48) (2.31)

AdjustedR2 0.4135 0.4338 0.2903F-value (Pr>F) 12.87 (

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Panel B: Return on assets and governance quality

ROAj,t+1 = +7

i=1

i GOV FACTORi,t+ L ROAj,t

+ RD/SALESj,t + (INV + PPE)/TOTALASSETSj,t+

HIGHAgency MEDIUMAgency LOW AgencyExpected Coef ficient Coef ficient Coef ficient

Sign (

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1174 A.DEY

groups. Thus, there is unlikely to be much variation in growth opportunitiesacrossfirms in each agency group. The proxy for assets in place, (INV+PPE)/TOTALASSETS, is positive and significant only for the LOW agencygroup and insignificant for the other two groups.

The results for the performance measure ROAhave a similar flavor as well.The factors representing the composition and functioning of the board,Board IandBoard II, director stock and option compensation,Dir Comp,and audit committee effectiveness, Audit Comm, are positive and signifi-cant for the HIGHagency group, while one of the board-related factors,Board I, and audit committee effectiveness, Audit Comm, is positive andsignificant for theMEDIUMagency group. For theLOWagency group onlyaudit committee effectiveness,Audit Comm, is positive and significant. ThefactorsExec Comp andFin Rep are not significant for any of the agencygroups.

The results for the control variables are similar to those obtained earlier.The lagged variable L ROAis positive and significant for all three agencygroups. The variable RD/SALESis not significant for any of the three agencygroups and (INV + PPE)/TOTALASSETSis positive and marginally signifi-cant only for theLOWagency group.

The above evidence suggests that the composition and functioning ofthe board, an effective audit committee, and the stock and option com-pensation provided to directors are significantly associated with futurefirmperformance. However, this relation holds primarily for firms in the high-est agency group for both the performance variables. Interestingly, for the

performance variableROA, the audit committee factor is significant for allagency groups, indicating that a strong audit committee is associated withhigher ROA for all firms. These results support the conjecture that the re-lation between various aspects of governance and firm performance is afunction of thefirms level of agency conflicts. In other words, the result inthe prior literature that there is no noticeable relation between the propor-tion of outside directors (which is part of the factor Board Iin my analysis)and firm performance is probably true only for firms where the level ofagency conflicts is low (Hermalin and Weisbach [1991], Bhagat and Black[2001]). Overall, these results provide greater reinforcement for the theory

that the presence and role of governance structures vary across firms de-pending on variousfirm-specific characteristics, one of which is the level ofagency conflicts present infirms.

6. Conclusion

I examine the relation between the level of agency conflicts in a firmand its governance structure. Ifind thatfirms with higher levels of agencyconflicts also have better governance mechanisms in place, particularly re-lated to the composition and functioning of the board of directors, the

audit committee, and the independence of the auditor. These results sup-port the theory on corporate governance that governance mechanisms are

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is that with any real data, the null hypothesis is implausible ev

dey (2008)

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