Corporate Governance Spillovers�

Ing-Haw Chengy

University of MichiganRoss School of Business

August 2010

Abstract

Failures of corporate governance at one �rm spill over into short-termism and incentives formanagers at other �rms to manipulate earnings fraudulently due to career concerns and relativeperformance evaluation. The model predicts that (i) peer governance matters and that the averagerate of earnings fraud should be higher when peer governance is weaker; (ii) managers should reactmore aggressively to changes in relative performance when peer governance is weaker; and (iii)earnings fraud should be most sensitive to peer governance when career concerns are strong. Usingdata on event-periods associated with fraudulent restatements, I �nd evidence corroborating allthree predictions and the implication that a few "bad apples" can lead to increased misbehavior atother �rms. By studying a speci�c managerial action, the paper highlights the importance of therole of career concerns and implicit incentives as well as the governance environment in in�uencingmanagerial behavior.

Keywords: corporate governance; spillovers; earnings fraud; peer e¤ects; career concerns

JEL Classi�cation: G30, G34

�For helpful comments and encouragement, I thank Wei Xiong, Markus Brunnermeier, Hyun Shin, Harrison Hong,Jonathan Parker, Motohiro Yogo, Darius Palia, Jakub Jurek, Zhiguo He, Martin Oehmke, Konstantin Milbradt, AdamZawadowski, Alice Hsiaw, an anonymous referee, as well as seminar participants at the Civitas Foundation Finance Sem-inar at the Bendheim Center for Finance, Princeton University, University of Michigan (Ross), University of Colorado-Boulder (Leeds), University of Pennsylvania (Wharton), Dartmouth College (Tuck), Harvard Business School, Washing-ton University-St Louis (Olin), University of Notre Dame (Mendoza), University of Rochester (Simon), Boston University,University of California-San Diego (Rady), and the London School of Economics. I also thank Jonathan Parker for gen-erous funding during this research. All errors and omissions are my own.

yRoss School of Business, University of Michigan, 701 Tappan St. R5466, Ann Arbor, MI 48109, e-mail: in-gcheng@umich.edu, http://webuser.bus.umich.edu/ingcheng.

Are there spillover e¤ects in corporate governance? Recent research has taken a renewed interest

in the e¤ects of governance at competitors on managerial behavior (Acharya and Volpin, 2010; Dicks,

2009; John and Kadyrzhanova, 2008). The basic idea is that poor governance at one �rm may in�uence

managerial behavior at competing �rms through externalities in the labor market, leading to lower

valuations and ine¢ ciencies.

In this paper, I take a new approach to understanding the role of externalities in corporate gover-

nance by studying whether a speci�c managerial action - earnings fraud - is related to peer governance.

The basic insight is that poor governance increases the ability of managers to engage in fraudulent

earnings manipulation, so that competing managers, who are motivated by their own career concerns,

are more likely to engage in their own earnings fraud as a response. If managers are motivated to

pump their stock price by in�ating earnings, this puts pressure on competitors to in�ate earnings

as well in order to avoid bad inferences about their ability (Holmstrom, 1982; Stein, 1989; Meyer

and Vickers, 1997). Indeed, survey evidence suggest that career concerns and external labor mar-

ket reputations are a �rst order concern for managers (Graham, Harvey, and Rajgopal, 2005), and a

large literature has empirically documented the relationship between relative performance evaluation

in stock price performance and observably poor labor market outcomes such as being �red (Gibbons

and Murphy, 1990; Jenter and Kanaan, 2008). The contribution of this paper is to show that gover-

nance exerts an externality on other �rms through these career concerns and that this has observable

implications for how earnings fraud is in�uenced by governance and fraud at competitors.

Intuitively, career concerns in the labor market and the resulting relative performance evaluation

create a natural mechanism whereby the fortunes of one manager are tied to the fortunes of another.

Indeed, the basic idea that managers in�ate earnings because "others do it" may help explain why

earnings fraud tends to be observed in clusters, for example with telecommunications industry in

2002. After WorldCom - whose stellar stock price performance through 2002 threatened to eclipse

industry stalwarts such as AT&T and other competitors - was revealed as a fraud, there was a wave of

scandals throughout the industry at competitors, such as Qwest Communications. Rumors had already

circulated that Michael Keith of AT&T had been ousted in 1999 for failing to match WorldCom�s

performance, and executives in the aftermath subsequently described competing with WorldCom as

"running track against an athlete who is later discovered to be using steroids," and taking extraordinary

measures because "it was and is a brutally competitive business."1

Studying the e¤ect of governance and governance spillovers on earnings fraud is advantageous as

1Quotes from a June 30, 2002 New York Times article titled "Trying to Catch WorldCom�s Mirage" by Seth Schiesel,and an August 2005 Fortune Magazine article titled "The Other Victims of Bernie Ebber�s Fraud," by Geo¤rey Colvin,respectively.

1

it gives a concrete link between failures of corporate governance at competitors and value-destroying

activity that is directly within the control of management. Research in governance typically studies

the e¤ect of governance on valuation ratios such as Tobin�s q (so-called "performance on structure"

studies), or the e¤ect of industry governance on an individual �rm�s governance (so-called "structure

on structure" studies). In contrast, studying earnings fraud provides an example of one speci�c

mechanism - one that is directly in control of management - for how governance at competitors can

in�uence behavior.

To model the e¤ect of peer governance on earnings fraud, I �rst develop a model motivated by the

career concerns models of Holmstrom (1982) and Meyer and Vickers (1997). In these models, managers

take actions to manipulate the learning process of the market about their ability. As in Stein (1989), I

interpret such actions as earnings in�ation. The model contains the following elements. First, as in the

career concerns literature, I construct a dynamic model that captures the e¤ect of future performance

movements on managers� decision-making today. To focus ideas on how governance in�uences a

competing manager�s behavior through relative performance, I take the learning process in reduced

form and focus only on relative performance between two �rms, which is subject to manipulation by

either manager. This assumes that earnings in�ation is not immediately unwound in the underlying

beliefs, an assumption which has basis in empirical evidence (Sloan, 1996; Xie, 2001). Managers of

the two �rms observe the relative performance and decide how much to in�ate earnings based on the

performance di¤erential.

Second, I introduce negative and positive career outcomes based on performance. Speci�cally,

a manager earns a wage while employed, but is �red if he underperforms the competing manager�s

performance by an amount exceeding a certain threshold. A �red manager receives a severance pay-

ment, while the outperforming manager is rewarded. This reward may be interpreted as either a

direct increase in compensation, or an indirect reward through perquisites, increased job security, or

a possibility of acquiring the lagging �rm. More broadly, I interpret these incentives as negative and

positive career outcomes based on underperforming or outperforming industry-benchmarks. Third, I

introduce governance in the form of a detection mechanism (such as independent audit boards) that

makes earnings in�ation costly. If a manager�s earnings in�ation is detected, he is terminated and re-

ceives a punishment, which I normalize to zero. As a simplifying assumption, I assume that detection

mechanisms focus on the current level of manipulation.

I begin with a benchmark case where only one manager may manipulate earnings. Here, a manager

has a strong incentive to in�ate earnings when he is signi�cantly behind in relative performance. This

simple prediction highlights the idea that managerial horizons are an endogenous function of how a

2

manager expects performance to evolve in the future. A manager trades o¤ improving his current

career�s prospects by in�ating earnings against the possibility of a severe negative consequence if the

in�ation is detected. The model neatly summarizes a manager�s career concerns in a single number,

what I term his "value-adjusted horizon," equal to the expected discounted value of future wage

payments of his current job net of any expected losses from the possibility of detection.

I next introduce governance spillovers by allowing the second manager to in�ate earnings and

examining the e¤ect of poor peer governance on a manager�s incentive to in�ate earnings. Here,

when a manager evaluates the trade-o¤ between increasing performance through earnings in�ation

and the possibility of being detected, his decision will be tilted by any anticipated earnings in�ation

by the competing manager. In particular, this anticipated earnings in�ation leads a manager to expect

relative performance to deteriorate in the near future. This leads to more short-termism on the part

of the manager as his career is now less secure and his value-adjusted horizon decreases, which tilts

his decision-making more towards in�ating earnings, ceteris paribus.

How much a manager�s decision-making is tilted depends crucially on the strength of governance

at his competitor. If his competitor has weak governance, the manager will expect aggressive in�ation

by his competitor, which will tilt his decision-making towards more earnings in�ation in order to keep

up in performance. Intuitively, weak governance at the competing �rm may not re-assure the manager

that the competitor�s earnings fraud will be revealed before his career su¤ers signi�cant damage. The

manager is thus more inclined to take matters into his own hands and not only increase the level

of his earnings in�ation as a response, but also to in�ate more aggressively with respect to relative

performance. In equilibrium, this actually feeds back into more short-termism and more earnings

in�ation at both �rms, as both managers engage in an arms race to respond to each other�s increased

performance through more in�ation.

These insights translate into several predictions, which I test empirically. The �rst prediction

is that peer governance matters, so that earnings in�ation should be sensitive to peer governance.

Weaker peer governance at competitors should be associated with higher (or more likely) earnings

in�ation at a �rm level. At an industry level, industries with a higher dispersion of governance should

experience higher rates of earnings in�ation, even when comparing industries with similar average or

median governance levels. The idea is that industries where a disproportionate number of �rms have

poor governance should have stronger governance spillovers and thus higher rates of earnings in�ation.

The second set of predictions deal with the model�s implication that poor peer governance motivates

managers to respond more aggressively to relative performance with earnings in�ation. First, this

implies that the slope of earnings in�ation to relative underperformance should be steeper when peer

3

governance is weaker, as managers with poor peer governance react more quickly to underperformance

relative to managers with strong peer governance. Second, managers who are outperforming in relative

performance will also be motivated to in�ate earnings, so that the manipulation pro�le should form

a tilted "U-shape" with respect to relative performance. The intuition is that a manager leading

in relative performance expects aggressive manipulation from underperforming competitors, which

will cut into the relative performance advantage. For su¢ ciently weak levels of peer governance, the

manipulation is aggressive enough that this loss will not be compensated su¢ ciently through the

possibility that the competing manager is detected, so that on the margin, an outperforming manager

will have an incentive to "take matters into his own hands" and manipulate more, not less.

Finally, the model predicts earnings in�ation to be high, and for earnings in�ation to be the

more sensitive to peer governance, when managers face strong career concerns in the form of tight

performance boundaries. This prediction is similar in spirit to the competitive e¤ect of Acharya

and Volpin (2010), where competition in the labor market leads to spillovers in governance. Here,

the spillovers should result in observable di¤erences in managers�actions. Intuitively, in industries

where labor markets are competitive, career concerns are the strongest as it is very easy to replace an

underperforming manager with a new one with similar experience in the industry. The model predicts

that this creates a role for peer governance, as managers are more sensitive to poor peer governance

when career concerns are stronger.

I use data on �nancial restatements from Audit Analytics to test these predictions. This dataset

is advantageous in two ways. First, the data identify restatements that were associated with fraud

or subsequent SEC investigations, allowing me to focus on egregious cases of earnings manipulation,

�ltering out more routine cases due to clerical errors. Second, the data contains the event period of the

restatement, yielding a �rm-quarter panel on whether or not a �rm was fraudulently in�ating earnings.

The data allow me to construct a quarterly 0/1 panel of whether a �rm was in�ating earnings, which

is in contrast to other studies in the literature, which often only study e¤ects around the restatement

date.

To empirically implement a measure of governance, I focus on an aspect of governance that is

speci�cally relevant for earnings in�ation, the independence of the �rm�s auditor. Using data on

fees paid to auditors also from Audit Analytics, I proxy for auditor independence by computing the

percentage of total fees paid to the �rm�s auditor that were for actual audit services, as opposed

to other lucrative consulting services. The idea is that �rms that pay a high percentage of fees for

audit services have more independent auditors. Although auditor independence is not a conventional

measure of governance in the literature, focusing on a speci�c aspect of governance allows for a sharper

4

picture of how di¤erent aspects of governance matter.

Using these measures, I �nd evidence consistent with the model�s three main predictions. I �rst

establish that the spread or distribution of governance predicts the rate of earnings fraud within an

industry beyond just what the mean level of governance would predict. At the �rm level, earnings

fraud is associated with earnings fraud at peers, and, more to the point of the model, a decrease in

peer governance is associated with a positive increase in the probability of earnings in�ation, even

controlling for the �rm�s own governance. In all tests, I proxy for a �rm�s competitors using �rms

sorted in its industry-size quintile. For example, in this context, whether or not a �rm is engaged in

earnings fraud is negatively associated with the average governance of �rms in its industry-size quintile

portfolio, where the �rm�s own governance has been removed from the average.

Second, I �nd that earnings in�ation is sensitive to relative performance, measured using stock

returns relative to a �rm�s peer group. First, I �nd that both underperformance and outperformance

are associated with subsequent earnings in�ation. Speci�cally, the slope of earnings in�ation with

respect to relative performance is negative when a �rm is underperforming (relative performance

less than zero), and positive when a �rm is outperforming (relative performance greater than zero).

However, relative outperformance itself may be associated with prior earnings in�ation that drove up

the stock price, so that the outperformance-earnings in�ation relationship may re�ect a need to "keep

in�ating" due to previous in�ation. To distinguish this from a career concerns story, I also look only

at the �rst quarter in which a �rm begins to in�ate earnings (the sub-sample of "0 to 1" changes),

and �nd that the results hold even in this sub-sample.

I then dig deeper and �nd that this e¤ect is driven by �rms with poor peer governance. To test this,

I examine whether the slope of earnings in�ation with respect to relative performance is steeper for

�rms with poor peer governance (below the sample median) versus those with stronger peer governance

(above the sample median). I �nd that the relationship between manipulation and underperformance

among �rms with poor peer governance is over twice the economic magnitude of the same relationship

for �rms with good peer governance, even when controlling for a �rm�s own governance levels. I also

�nd a positive relationship between earnings in�ation and outperformance primarily in the low peer

governance sub-sample. As a further robustness check, I again examine the smaller sub-sample that

looks at only when �rms begin to in�ate earnings, and �nd a similar interaction. Overall, the evidence

is consistent with the prediction of the model that �rms react to relative performance and that they

do so more aggressively when peer governance is weak.2

2A previous version of the paper that only found that underperformers were more likely to manipulate earnings,whereas this paper �nds results supporting the outperformers hypothesis as well. The discrepancy is worth explaining.There are several major changes in this version that improve the statistical analysis. First, I use quarterly as opposed

5

Testing the third prediction that earnings in�ation is more likely when and career concerns are

strong is di¢ cult as this is not directly observed. Drawing on existing research, I use three di¤erent

proxies for labor market competitiveness that attempt to capture the depth of the replacement pool

of managers. I �rst look at industries which are more homogeneous using a ranking method developed

by Parrino (1997), who �nds that turnover is higher and more sensitive to performance in industries

where stock returns track their industry benchmark more closely (more homogeneous) than other

industries (less homogeneous). One interpretation consistent with this �nding is that news a¤ects

cash �ows of stocks in homogeneous industries very similarly, implying that �rms are very similar,

making it is easier to �nd replacement managers. Consistent with the model�s predictions, earnings

fraud is more common and more sensitive to peer governance in more homogeneous industries. I also

look at less-concentrated industries (DeFond and Park, 1999; Engel, Hayes, and Wang, 2003) and

larger industries, and �nd similar e¤ects.

While the empirical results are not causal, they provide correlations linked to the speci�c predic-

tions of the model. One concern may be that I am simply picking up clustering of manipulation by

�rms in industries where it is easy to manipulate. As a robustness check, I �nd that the results are

robust to the inclusion of factors such as asset tangibility and also industry �xed e¤ects which should

help capture any heterogeneity in the earnings manipulation "technology" across industries. I also

�nd that the results are similar using accounting-based measures of performance, and that the results

are weaker in the post-Sarbanes-Oxley period as expected, suggesting the results are not spurious.

Overall, the paper stresses the importance of e¤ects such as competitive pressure and career con-

cerns in explaining why managers engage in value-destroying activity even if governance at their

own �rm is strong. This notion of "competitive pressure," and the model presented, echoes the

spirit that competition can be a form of "rivalry" (Vickers, 1995) and has implications for behav-

ior (Shleifer, 2004); indeed, the model is related to "arms race" models in the literature on R&D

(e.g., Harris and Vickers, 1987), as well as models analyzing the e¤ects of relative performance

(Zwiebel, 1995) and herding (Scharfstein and Stein, 1990). The paper contributes by showing that

these general insights have speci�c implications for peer governance and fraudulent accounting, an

activity that destroys real value (Kedia and Philippon, 2009), and also adds to the growing literature

to annual data so are able to pin down the timing of relative performance and when �rms were manipulating earnings.Second, this version looks at slopes of earnings in�ation with respect to relative performance, which is a better test ofthe model than the previous version where I looked at whether average rates of earnings fraud in extreme portfolios ofrelative performance are di¤erent from the mean in a much larger "center" portfolio. This has relatively low power totest for a "U-shape" since mean of the larger center portfolio will still re�ect the U-shaped region. Finally, the resultsin this paper focus more on the general implication that any association between relative performance (both under andoutperformance) and earnings in�ation should be stronger among poor peer governance �rms, an interaction e¤ect whichwas not in the previous paper but which further supports the model.

6

on peer e¤ects in governance (aforementioned; e.g., Acharya and Volpin, 2010).

In Section 1, I setup the model, and Section 2 describes the equilibrium. Section 3 analyzes the

main empirical predictions, and Section 4 discusses the evidence. Section 5 provides discussion on

additional topics, and Section 6 concludes.

1 Model Setup

The model is motivated by models of career concerns (Holmstrom, 1982; Meyer and Vickers, 1997).

Consider an environment with two �rms, A and B, operating in one industry. The industry may

contain many �rms as a whole, but these two �rms are viewed as competitors who are particularly

similar in business structure. In a standard model of career concerns and learning about managerial

ability, the managers of the two �rms will be benchmarked against each other using relative perfor-

mance evaluation. In order to focus attention on how governance a¤ects strategic interactions in the

two managers�actions, I take the learning process in reduced form and simply assume that the two

risk-neutral managers have tenure which is tied to the observable relative (A minus B) stock price

performance of the two �rms, xt. The stock price performance is reported continuously on [0;1),

and, in the absence of any earnings in�ation, evolves as an Ito process with no drift: dxt = � dZt.

Managerial Tenure and Compensation. More speci�cally, suppose the two incumbent man-

agers earn wage �ow w per unit of time dt (discounted at rate �) as long as the two �rms are within a

range of relative performance, kxtk < �. However, when one manager signi�cantly underperforms his

competitor (kxtk = �), that manager is terminated for poor performance, and receives severance equal

to wL=�, with 0 < wL < w. One may interpret this as a literal severance payment, or the reduced

career prospects of a manager who has just been �red for underperformance. In contrast, the compet-

ing manager earns a �ow of w thereafter, which may be interpreted as the manager experiencing an

increase in job security. To best separate the e¤ect of governance spillovers from payo¤ e¤ects, I focus

on the case where w � wL. This highlights the fact that the loss from termination are typically large

relative to the expected compensation over the full tenure of a job, consistent with existing empirical

evidence Yermack (2005).

In other words, when xt = ��, manager A is terminated and receives wL=�, while manager B

receives the equivalent of w=�. When xt = �, the reverse occurs: manager B is terminated and

receives wL=�, while manager A receives the equivalent of w=�. Figure 1 illustrates simulated paths

and payo¤s for the game.

7

This setup deliberately focuses on the e¤ects of potential termination and career concerns, rather

than compensation. This is motivated by the observation that the literature supports the use of

relative performance evaluation in CEO turnover, but not necessarily compensation. In this sense,

the tie between compensation and targets may often be implicit, rather than explicitly set out in

contracts; for example, pressure may grow to oust a CEO after consecutive quarters of poor stock

price performance. Such a compensation scheme also re�ects the practical reality that compensation

plans are infrequently adjusted.

Manipulation. Each manager may in�uence the relative performance metric xt by an amount

mi;t < �m by in�ating earnings. For example, by in�ating earnings, manager B can favorably in�uence

the rate at which the stock price di¤erential xt moves and improve his expected tenure in o¢ ce. In

terms of the model, this represents a favorable change in the drift of xt:

dxt = (mA;t �mB;t) dt+ � dZt (1)

This assumes that markets fail to completely unravel earnings in�ation so that it actually in�uences

prices, an observation supported by empirical evidence (Sloan, 1996; Xie, 2001). In practice, managers

have many tools at their disposal to create the perception of enhanced pro�tability. The accrual process

of accounting was designed to allow managers to match expenses and revenues in order to create a

more accurate portrayal of current �rm performance. However, this process of "creative matching"

leaves much room for discretion to e¤ectively mis-statement or omit information in an attempt to

deceive users of �nancial statements. For example, one common technique is to decrease expenses by

improperly capitalizing short-term expenditures (a technique employed to great e¤ect by WorldCom),

or to recognize revenue prematurely. Bergstresser, Desai, and Rauh (2006) �nd that managers adjust

their assumed long-run rate of return on pension assets when it has a large impact on reported earnings.

More obvious cases include simply faking revenue or holding the books open past the end of a reporting

period to increase sales in a quarter.

Governance and Detection. Manipulation is costly, since it may be detected by the �rm�s

internal governance system, or by external auditors. I model the detection mechanism at �rm i as

a Poisson process N it with intensity

12�im

2i , and de�ne detection to occur at the �rst arrival time of

N it . In other words, the governance controls at �rm i can detect manipulation in a small dt interval

with probability 12�im

2i;t dt, which is convex in the level of manipulation. I make the simplifying

assumption that detection mechanisms operate on current manipulation rather than the accumulated

8

level, an assumption which I return to in Section 5. The parameters �i should be interpreted as the

strength of corporate governance at each �rm - higher �i implies stronger audit controls.

If a manager is caught manipulating earnings, that manager is immediately �red with prejudice -

he receives no severance payment, so that he earns zero thereafter, equivalent to an extremely poor

labor market outcome that re�ects the aggressive nature of manipulation I am considering. If this

type of aggressive manipulation is uncovered, the o¤ending manager is disgraced and loses much more

earnings power than if he were terminated for poor performance. In contrast, the competing manager

earns w thereafter, a stylized assumption but one which can be interpreted in a setting where managers

who are caught �rst receive a harsher punishment than others. For example, the relative performance

mechanism may break down after a scandal becomes public, or a manager�s job security may improve

due to the elimination of a competitor.

Assumptions. The model focuses attention the strategic interaction of earnings in�ation as a

function of governance (�) and performance (xt), and takes other parameters as exogenous. One way

to think about the setting is one where the governance and compensation plans are very slow-moving

compared to performance. I discuss some of these assumptions here.

First, the model takes � as exogenous. One interpretation is that � proxies for the strength of career

concerns in the industry and hence is �xed by industry-level labor market conditions. For example,

di¤erent industries may have di¤ering levels of labor market competitiveness which give rise to di¤erent

values of � but which are exogenous from each �rm�s perspective. In industries where there are many

managers that are close substitutes, shareholders would tolerate less underperformance (resulting in

a smaller �) than in industries where labor markets are fairly uncompetitive and managers possess

highly specialized, �rm-speci�c skills. Additionally, the symmetry of � and �� is made for simplicity.

The bene�t of these simplifying assumptions is that it allows the model to capture elements of how

expected future performance a¤ects managers�choices today, generating speci�c predictions about the

role of relative performance.

An interesting question remains, however, of how shareholders would optimally choose to replace

managers through � in a full optimal contracting model in order to mitigate earnings in�ation, and

similarly, how investors would choose among di¤ering governance schemes through � and whether

their choice would be in�uenced by that of peers. Acharya and Volpin (2010) develops a "structure-

on-structure" model and �nds that peer e¤ects in governance may lead to ine¢ ciently low levels

of overall governance in the economy. I view this as largely complementary; rather than focus on

structures as an equilibrium outcome by imposing an incentive compatibility constraint, I focus on

9

interactions in managerial misbehavior as the equilibrium outcome. In that sense the focus of this

paper is the determinants of the observable pattern of earnings in�ation, rather than the observable

pattern of governance; although these are no doubt intricately linked in a more general model, I leave

these questions for future research.

Another stylized feature of the model is that detection mechanisms operate on current manipula-

tion rather than the accumulated level, and that manipulation does not revert over time. Although

realistically the trade-o¤ between career concerns and manipulation is a¤ected by knowing that any

manipulation today may add up and be caught in the future, I conjecture that the primary e¤ect of

this is to add richer, additional dimensions to the main predictions of the model. I discuss this in

more detail in Section 5.

2 Earnings In�ation

Managerial Objective Functions. Managers maximize the expected discounted future �ow of

payments net of the possibility of losing these payments through detection. Since in�ating earnings

has no cost to �ow utility, a manager�s choice of manipulation is a trade-o¤ between the marginal

amount that it improves their current career through improved performance and the marginal amount

that it increases his chance of losing his career altogether from detection. There are essentially four

possible outcomes of the game from the setup described above: either a performance target is met (xt

hitting � or ��), or detection of manipulation at either �rm occurs. Until then, each manager earns

a �ow w. Figure 1 illustrates various sample paths associated with these outcomes.

I assume a setup where managers observe the current spread between themselves and their com-

petitors, and decide how much manipulation to employ based on this spread. All parameters of the

model are also common knowledge (manipulation need not be observable). A straightforward argu-

ment shows that such a problem is stationary, and therefore I can con�ne the space of admissible

strategies to those that omit time as an argument, mit = mi (x) : (��; �)! R. Normalizing w to one,

I can write each manager�s objective function as

JA (x) = maxmA

TA (x;mA;mB) (2)

JB (x) = maxmB

TB (x;mA;mB) (3)

10

TA (x;mA;mB) = Ext=x

26664R ��^���^�A^�Bt e��(s�t)w ds+ 1 [� � < (��� ^ �A ^ �B)] e��(���t)w�+1 [��� < (� � ^ �A ^ �B)] e��(����t)wL�+1 [�B < (�A ^ � � ^ ���)] e��(�B�t)w�

37775

TB (x;mA;mB) = Ext=x

26664R ��^���^�A^�B0 e��(s�t)w ds+ 1 [��� < (� � ^ �A ^ �B)] e��(����t)w�+1 [� � < (��� ^ �A ^ �B)] e��(���t)wL�+1 [�A < (�B ^ � � ^ ���)] e��(�A�t)w�

37775subject to the law of motion for xt in (1), where the various ��s denote �rst hitting times for the four

events:� � = inft>0 fxt = �g �A = inft>0

�NAt > 0

��� = inft>0 fxt = ��g �B = inft>0

�NBt > 0

For the purposes of interpretation, it is easier to write the value function of manager A as the sum

of three components:

TA (x;mA;mB) = THZNA (x;mA;mB) + TSEVA (x;mA;mB) + T

WAITA (x;mA;mB)

THZNA (x;mA;mB) = Ext=x�Z ��^���^�A^�B�t

te��(s�t) ds+ 1 [� � < (��� ^ �A ^ �B ^ ��)]

1

�

�TSEVA (x;mA;mB) =

wL�Ext=x [1 [��� < (� � ^ �A ^ �B ^ ��)]]

TWAITA (x;mA;mB) =

1

�Ext=x [1 [�B < (�A ^ � � ^ ��� ^ ��)]]

where �� is an independent, exponentially-distributed stopping time with rate �. Manager B�s value

function is analogous. The above equation makes clear that manager A chooses his manipulation

in order to maximize the amount of time he expects to spend on the job (THZNA ), adjusted by the

value he would receive if he is �red (TSEVA ) or if the other manager is caught manipulating earnings

(TWAITA ). The sum of these three numbers summarizes manager A�s career concerns, and I term it

manager A�s value-adjusted horizon. On the one hand, earnings in�ation increases THZNA by extending

the manager�s time on the job and reduces the expected value of severance payments, TSEVA , which

is good for the manager because w > wL. On the other hand, manipulation also increases the

risk of detection. Mathematically, this acts through an increase in the intensity of �A; intuitively,

manipulation increases the rate at manager discounts future wage �ows. In this sense, the model

captures short-termism through an endogenous discount rate that is determined in equilibrium by the

manipulation of both managers.

11

Equilibrium. Given the objective functions above, I look for a solution that is a Markov-Perfect

Nash equilibrium:

De�nition 1 (Equilibrium.) A Markov-Perfect Nash equilibrium is a pair of bounded functions

fmA (x) ;mB (x)g such that mA (x) : (��; �)!R is optimal for (2) taking mB (x) as given, and

mB (x) : (��; �)!R is optimal for (3) taking mA (x) as given, subject to the law of motion for xt

given in (1).

In equilibrium, the functions JA and JB that determine manipulation solve the following Hamilton-

Jacobi-Bellman equations, respectively:

JA : maxmA

8<: w + 12�

2J 00A + gJ0A � �JA

+(mA �mB) J0A � 1

2�Am2AJA +

12�Bm

2B

�w� � JA

�9=; = 0 (4)

JB : maxmB

8<: w + 12�

2J 00B + gJ0B � �JB

+(mA �mB) J0B � 1

2�Bm2BJB +

12�Am

2A

�w� � JB

�9=; = 0 (5)

with boundary conditions

JA (�) = w=� JB (�) = wL=�

JA (��) = wL=� JB (��) = w=�

subject to the law of motion for xt in (1). The �rst four terms of each HJB equation re�ect the

standard equation de�ning the evolution of xt. The term involving (mA �mB) re�ects the fact that

manipulation by manager A is o¤set by manipulation by manager B in the drift of xt. The term

involving 12�im

2i represents the detection mechanism at �rm i: if a manager is detected, he is �red and

receives zero compensation thereafter. The �nal term represents the assumption that the manager

earns w after a competing manager is caught manipulating earnings.

From the two HJB equations, the necessary �rst order conditions for maximization imply that JA

and JB must satisfy

A : mA = minn1�A

J 0AJA; �mo; B : mB = min

n� 1�B

J 0BJB; �mo

(6)

which makes clear that manipulation equalizes the the marginal improvement in the expected dis-

counted value of future payments, J0i , with an increase in the rate at which future payments are

discounted, �iJi. Su¢ cient second-order conditions are given by

A : ��AJA < 0; B : ��BJB < 0

12

Since �i > 0 and JA; JB > wL=� > 0, these conditions are satis�ed.3

The �rst order conditions (6), in conjunction with the HJB equations (4) and (5), imply a non-linear

pair of ordinary di¤erential equations that I solve using numerical methods to compute the equilibrium.

Veri�cation that the HJB equations are su¢ cient is a standard proof (Fleming and Rishel, 1975); full

technical details are available from the author. To compute an equilibrium, I choose a consumption

discount rate � to normalize the expected tenure of a manager who does not manipulate earnings to 8

years. This is motivated by evidence in Kaplan and Minton (2006) that the average CEO tenure since

1998 is roughly six years; since presumably some of these managers were terminated due to engaging

in fraudulent activity, I choose 1=� = 8 years. I choose wL = 0:25; with w normalized to 1, this

corresponds to a severance payment of roughly a quarter of their expected earnings. This is roughly

in line with reported �gures from Yermack (2005) on severance payments. Importantly, w � wL > 0,

highlighting that termination for underperformance is an undesirable labor market outcome, but also

that being �red for unethical behavior is even worse. I choose � = 10%, and also choose �m = 0:1,

so that managers may not manipulate more than one instantaneous standard deviation. Given these

parameters, I begin with � =p0:08 (� 0:28) to be consistent with an 8-year no-manipulation tenure.

Table 1 summarizes.

A Benchmark Case. To illustrate the e¤ect of spillovers, I begin with a benchmark case where

manager B cannot manipulate earnings. In the model, this corresponds to �B very large. I allow man-

ager A to manipulate earnings but where he faces a detection intensity of �A = 20, which corresponds

to a constant 10% manipulation level being caught, in expectation, within 10 years, indicating a weak

governance structure.

When manager B cannot manipulate earnings, manager A will strongly in�ate earnings when he

is underperforming manager B and very weakly in�ate earnings when he is outperforming. Figure 2

illustrates and plots the equilibrium level of manipulation on the vertical axis as a function of relative

performance on the horizontal axis, where the dark dashed line labeled mA;BM denotes the level of

manipulation by manager A when manager B cannot manipulate. Intuitively, manager A manipulates

to "catch up" when he is underperforming because he risks a negative career outcome if he falls too

far behind. In contrast, when manager A is outperforming, his career is fairly secure and thus he has

much to lose if he is caught in�ating earnings.

Figure 3 decomposes the motivation behind this pressure to manipulate earnings. Consider a

3Note that in this model, manipulation does not a¤ect the volatility of xt, so that the equilibrium value-functionsmay be convex in some regions.

13

thought experiment if manager A chooses to never in�ate earnings. Panel A illustrates the components

of manager A�s value-adjusted horizon under this case. Panel B illustrates that playing the optimal

benchmark level of earnings in�ation mA;BM increases his value-adjusted horizon. The dark bold lines

represent this new value-adjusted horizon from playing mA;BM while the lighter lines represent the

value from never in�ating earnings. The �gure demonstrates that, by in�ating earnings, manager A

increases his expected time in o¢ ce, THZNA;BM , and reduces the expected value of his severance pay,

TSEVA;BM . Intuitively, managers prefer to substitute towards staying in o¢ ce as opposed to experiencing

a negative career outcome. How much manipulation he is willing to employ depends on the relative

preference of being �red (and receiving severance wL=�) versus that of being caught manipulating

earnings, the probability of being caught manipulating earnings (determined by �A), and the current

amount of value-adjusted time he can expect to stay in o¢ ce. Manipulation is cheapest precisely

when a manager is underperforming, so that managers are endogenously short-termist when lagging

in performance.

3 Corporate Governance Spillovers

In this section, I analyze the e¤ect of governance spillovers, which I de�ne to be the change in manager

A�s manipulation when I allow manager B to also manipulate earnings. The central result is that

weaker governance at �rm B (lower �B) strengthens the incentive for manager A to in�ate earnings.

This has three speci�c predictions. First, earnings in�ation will be higher at both �rms. Second,

managers will respond to changes in relative performance with earnings in�ation more aggressively,

both when they are underperforming and outperforming. Finally, earnings in�ation should be more

pronounced when labor markets are very competitive and career concerns are strongest.

3.1 The Mechanics of Spillovers

I now allow �rm B to have the same governance level as �rm A, so that �B = �A = 20. Returning to

Figure 2, the solid lines labeled m�A and m

�B show the new equilibrium manipulation for this case. I

plot manager B�s manipulation below the horizontal axis. Comparing the equilibrium manipulation

pro�le of manager A, m�A, with that of the benchmark case, the dashed line labeled mA;BM , shows

that weak governance at �rm B leads manager A to increase his manipulation and to react more

aggressively to relative performance. In other words, managers have an incentive to manipulate since

they feel that "everyone else is doing it."

To dig deeper into the intuition, consider a thought experiment where manager A ignores manager

14

B�s manipulation m�B and sticks with the benchmark manipulation strategy mA;BM instead. Why is

this suboptimal? Figure 4, Panel A demonstrates the damage that manager B�s earnings in�ation

in�icts on manager A�s career. I �rst plot, in the lighter grey lines, manager A�s value-adjusted horizon

when manager B plays zero and manager A plays the benchmark strategy mA;BM . The �gure then

plots, in dark bold lines, manager A�s value-adjusted horizon when manager B in�ates earnings under

m�B but manager A sticks with mA;BM . Evidently, manager B�s earnings in�ation decreases the value-

adjusted horizon by manager A, as it decreases the value of manager A�s current career (the inward

shift of THZNA , the dashed line) and increases the "value" manager A attaches to a negative career

outcome (the shift in TSEVA ). Manager B�s earnings in�ation also provides some value to manager A,

as evidenced by the positive values of TWAITA , since manager A will implicitly bene�t if manager B is

caught in�ating earnings.

Figure 4, Panel B plots the gain to manager A from playing the new equilibrium strategy m�A over

that of sticking with the benchmark strategy mBMA for each of the three components of value. On

the margin, it is optimal for manager A to risk a bit more earnings in�ation to increase the value

of his current career, as evidenced by the positive values of �THZNA and negative values of �TSEVA .

Anticipating this reaction, manager B will also up the ante with more manipulation; in equilibrium,

this implies that the earnings in�ation by both managers. Managers will manipulate because "everyone

else is doing it," and short-termism is an endogenous function of both �rms�governance levels.

A more speci�c implication is that manager A will in�ate earnings more aggressively as a function

of relative performance when �rm B has poor governance. First, manager A will respond more

aggressively to underperformance. Intuitively, manager B�s weak governance tilts the trade-o¤between

earnings in�ation and the risk of being detected even more towards more earnings in�ation when

manager A is underperforming, as manager A cannot a¤ord to "wait around" for manager B to be

detected since he may be �red soon. Figure 4, Panel B demonstrates that playing the new equilibrium

strategy improves the value of his career in the underperformance region by showing that �THZNA is

humped and positive, while �TSEVA is negative, for negative xt.

Additionally, manager A in�ates earnings when he is outperforming as well. The intuition is as

follows. When both manager A and manager B�s governance levels are weak, manager B will be

especially aggressive in in�ating earnings when he is underperforming in order to avoid a poor career

outcome, using the logic just outlined. On the margin, this aggressive manipulation does a large

amount of damage to manager A�s career; furthermore, manager A does not derive much comfort

from the idea that manager B might be caught, precisely because manager B�s manipulation is weak.

Therefore, it becomes optimal on the margin for A to risk a bit more manipulation of his own just to

15

protect his outperformance in this region and substitute away from relying on B�s governance.

Figure 4, Panel B demonstrates more formally. When manager B is underperforming (xt is high),

manager B�s manipulation is very aggressive. In this region, the component changes in a manager�s

value-adjusted horizon are given by �THZNA , which is positive, and �TWAITA , which is negative. The

manager is making a net gain by substituting away from value derived from B�s weak governance

system and towards his career by manipulating slightly more even though he is in the lead. This is

because the rate at which manager A is implicitly compensated by manager B�s governance system,

TAWAIT , does not increase fast enough as B falls behind (and manipulates more aggressively) to o¤set

the damage manager B is doing to manager A�s career value, TAHZN , precisely because �rm B�s

governance is weak. At some point manager A "takes matters into his own hands" and in�ates

earnings more to protect his outperformance.

Note that the assumption in the model that manager A obtains w=� when B is terminated is not

su¢ cient to generate this e¤ect, since it is not observed in the benchmark case. In other words, only

very mild assumptions about what happens to manager A when manager B experiences a negative

outcome are needed. Nevertheless, to be careful in interpretation, I interpret weak peer governance and

an increase in job security from relative outperformance as a su¢ cient (but not necessary) condition

to generate such a "U-shaped" pro�le in manipulation.

3.2 Empirical Predictions

These insights translate directly into empirical predictions. The �rst set concern the relationship

between earnings in�ation, own-governance, and peer-governance. The model predicts that weak

�rm governance and weak peer governance should both be associated with more earnings in�ation.

Increasing the strength of detection at �rm A (equivalent to increasing �A in the model) makes it

more di¢ cult for a manager A to manipulate, so manipulation should, on average, decrease with the

strength of a �rm�s own governance. More to the point of the model, manipulation will also decrease

with the strength of peer governance as well, as described above: weak governance at �rm B feeds

back, in equilibrium, into higher levels of manipulation at both �rms.

This logic can also be tested at an industry level, as it implies that the spread of governance across

�rms within an industry should predict which industries experience high levels of earnings in�ation.

This should hold even controlling for the average or median levels of governance.

Prediction 1 (Peer Governance E¤ects.) At a �rm-level, earnings in�ation should be decreasing

in the strength of governance mechanisms that detect such manipulation. Moreover, earnings in�ation

16

should be decreasing in the governance strength of industry competitors, as managers have an incentive

to manipulate because "everybody else is doing it." At an industry-level, larger spreads of governance

across �rms should be associated with more earnings in�ation.

The second set of predictions concern the relationship between earnings in�ation, relative perfor-

mance, and the interaction of this relationship with peer governance. As discussed, managers should

respond to relative performance more aggressively when peer governance is weak. The model predicts

that relative underperformance should be associated with earnings in�ation for both �rms with strong

and peer governance, but that the relationship should be much stronger among �rms with weak peer

governance. Additionally, relative outperformance should be associated with earnings in�ation for

�rms with weak peer governance but not so among �rms with strong peer governance.

Prediction 2 (Relative Performance and Peer Governance.) Relative underperformance

should be associated with earnings in�ation, particularly among �rms with weak peer governance.

Relative outperformance should be associated with earnings in�ation among �rms with weak peer gov-

ernance, but not among �rms with strong peer governance.

The third set of predictions relates earnings in�ation to the labor market environment. In the

model, earnings in�ation is sensitive to �, the amount of underperformance tolerated before a manager

experiences a negative career outcome. Intuitively, a given amount of earnings in�ation by manager B

does more and more damage to manager A�s career as the performance threshold � shrinks. This not

only implies that manager A�s equilibrium earnings in�ation will be higher for small �, but it should

also be more sensitive to manager B�s governance level. Figure 5 demonstrates by plotting equilibrium

earnings in�ation for two values of �. Panel A indicates that earnings in�ation will be higher when �

is small. This is because manager A�s gains from reacting to manager B�s manipulation are highest

when � is small, indicated by Panel B. Intuitively, manager A can quickly su¤er a poor career outcome

in this case, so that his incentive to react to weak peer governance is stronger.

Empirically, small � should not only be associated with higher earnings in�ation, but an interaction

between peer governance and a proxy for � should indicate that peer governance has the strongest

relationship with earnings in�ation when � is small. As mentioned at the end of Section 1, one

interpretation of � is the labor market competitiveness of the industry. When labor markets are

competitive and managers are close substitutes, the tolerance for relative underperformance will be

smaller and career concerns will be strong.

17

Prediction 3 (Competition E¤ects.) Earnings in�ation should be highest in industries associated

with competitive labor markets. Furthermore, the sensitivity of earnings in�ation to peer governance

should be strongest in these industries.

4 Empirical Evidence

4.1 Methodology

The empirical methodology explicitly follows the model�s predictions, and I test them in order. The

key methodological challenge is measurement: how to measure earnings fraud, how to measure gov-

ernance as it relates to ex ante detection probabilities, and how to form peer groups for measuring

relative performance and peer governance. To address the �rst two issues, I turn to data from Au-

dit Analytics. Audit Analytics is a commercial �rm that tracks accounting information for a wide

universe of companies. For example, the Audit Analytics "Audit Fee" �le, which tracks how much a

�rm pays its auditor for various services, covers 79% of the nearly 190,000 total �rm-quarters tracked

by the CRSP-COMPUSTAT Fundamentals Quarterly �le during the period 2000-2006. In their earn-

ings restatements �le, they provide information about the nature of the restatement, including which

�nancial quarters are being restated and whether the restatement is associated with fraud or a sub-

sequent SEC investigation. A restatement is classi�ed as fraudulent if it uses words such as "fraud,"

"irregularities," or "misrepresentation," among others. Although they unfortunately do not include a

measure of the total dollar size of the restatement, these two pieces of information are a key advantage

of the data, since I can not only �lter out restatements associated with routine clerical errors, but also

determine when the fraudulent manipulation was occurring.

I use data from Audit Analytics in two ways. First, I use the earnings restatements �le to construct

a 0/1 variable, which I call the "Manipulation Indicator," that contains information about whether a

�rm was fraudulently manipulating earnings. Since the �le provides a speci�c range of calendar quar-

ters associated with the restatement, I am able to construct this variable for each �rm at a quarterly

frequency. I code a �rm-quarter as a "one" (manipulation occurring) if there exists a restatement that

indicates a �rm was engaged in fraud or an activity that resulted in an SEC investigation during that

quarter. Otherwise, I code a �rm-quarter as "zero" (manipulation not occurring) if it is in the Audit

Analytics universe but does not have a restatement associated with it. I consider a �rm-quarter in

the Audit Analytics universe if Audit Analytics tracks it in its Audit Fees �le.

Second, I use their Audit Fee �le to construct the primary governance variable, the percentage of

audit fees paid to a �rm�s auditor for actual audit services, which I call "Audit Fee %." Intuitively, this

18

variable measures whether a �rm maintains an independent relationship with its auditor. A low value

of Audit Fee % indicates that the �rm also employs its auditor for various other lucrative consulting

services, suggesting that auditor independence is compromised due to the presence of "side payments."

Indeed, former-SEC Chairman Arthur Levitt expressed this exact concern before the Senate in 2000,

testifying that, "As auditing becomes an ever-smaller portion of a �rm�s business with an audit client,

it becomes harder to assume that the auditor will challenge management when he or she should, if to

do so might jeopardize a lucrative consulting contract for the auditor�s �rm." (Levitt, 2000). Although

di¤erent than traditional measures of corporate governance - such as board size (Yermack, 1996) or the

Gompers, Ishii, and Metrick (2003) "G Index," this measure is much more likely to capture aspects of

governance related to the likelihood of detection. For example, Frankel, Johnson, and Nelson (2002)

�nds that earnings management is negatively related to a similar measure of auditor independence.

The third major challenge requires the researcher to form peer groups. The peer group should

capture the �rms a manager is most likely to be benchmarked against in the labor market. Following

the relative performance/CEO turnover literature, I focus on industry competitors (e.g., Jenter and

Kanaan, 2008), and I use the Fama-French 49 industry classi�cation. I de�ne a peer group as the group

of �rms in its associated industry-size portfolio, where I form portfolios every quarter by splitting all

�rms within a Fama-French 49 industry into quintiles of beginning-of-quarter market capitalization.

In order to avoid very small portfolios, when the number of �rms in an industry-quarter is less than

20, I do a simple high/low split; when the number of �rms is less than 10, I do not split on size. I

then measure the peer governance of the �rm as the average Audit Fee % in this portfolio, and relative

performance as the di¤erence between the previous four-quarter stock return of the �rm and that of

its peers, where I always take care to �rst exclude the �rm itself when computing peer governance and

peer performance.

Of course, true earnings in�ation, ex ante detection probabilities, and peer groups are all unob-

served to the econometrician. At best, the Manipulation Indicator and Audit Fee % measures of

earnings fraud and governance are proxies; furthermore, the true peer groups are unknown to the

researcher, so a number of inherent endogeneity issues attach to all tests (Manski, 1993). Rather

than ask whether shocking the auditor independence or peer performance of a random group of �rms

causes a change in whether they manipulate earnings fraudulently, the thought experiment instead is

to ask whether observed correlations and patterns between fraudulent earnings manipulation, relative

performance and peer governance are consistent with the speci�c predictions of the model, where I

attempt to rule out obvious alternative explanations. Nevertheless, it is important to note that I do

not attach a causal interpretation to the results. With this caveat in mind, however, I nevertheless

19

�nd a pattern of evidence consistent with the predictions.

4.2 Sample Construction and Summary Statistics

The sample frame is a quarterly panel of �rms in the intersection of the Audit Analytics and CRSP-

COMPUSTAT Fundamentals Quarterly universes for 2000-2006. Since the Audit Analytics �les pro-

vide Central Index Keys (CIK), a unique EDGAR identi�er, for each �rm, merging these �les into

CRSP-COMPUSTAT is straightforward and requires little additional cleaning. I exclude utilities (FF

#31), �nancials (FF #45-48), and also industry #49 ("Almost Nothing"). More speci�cally, the panel

consists of �rm-quarters for which I have data for the Manipulation Indicator and Audit Fee % vari-

ables, prior four-quarter stock performance, and data on market capitalization and the market/book

ratio, since these are obvious sources of heterogeneity across �rms.

The Manipulation Indicator and Audit Fee % are de�ned as I just discussed; the remaining variables

are de�ned as follows. I de�ne the Peer Audit Fee % as the average Audit Fee % among �rms in its

peer group (excluding itself). To compare governance measures, I also obtain the G Index as well as

the percentage of independent directors on the board from RiskMetrics, where I de�ne a director as

independent using the RiskMetrics classi�cation. I compute Market Capitalization as the share price

(PRC) times number of shares outstanding (SHROUT) from the CRSP Monthly File, and obtain

Total Book Assets (variable ATQ) directly from COMPUSTAT. I de�ne a �rm�s Market/Book ratio

as its Market Capitalization divided by the book value of equity, where the book value of equity is

total stockholder equity (SEQQ) plus deferred taxes and investment tax credits (TXDITCQ), less the

stated value of preferred stock (PSTKQ).

In robustness tests, I will attempt to rule out an alternative explanation that I am simply picking

up industries where it is simply easier to manipulate earnings because of the intangible nature of their

products or assets by using asset tangibility as a proxy for this earnings manipulation "technology."

I de�ne asset tangibility as net property, plants and equipment (PPENTQ) divided by Total Book

Assets. Finally, for each �rm in the sample, I compute their four-quarter stock performance using

data from the CRSP Monthly File. Peer Performance is the average four-quarter stock performance of

its peer group (excluding itself), and I de�ne Relative Performance as the di¤erence between a �rm�s

stock performance and peer stock performance. The timing convention is to measure the Manipulation

Indicator at the end of calendar-quarter and other variables at the beginning of calendar-quarter. The

caveat here is that Audit Fee % is reported at an annual frequency, so I merge based on contempora-

neous �scal years. The results are very similar when lagging Audit Fee % by four quarters instead. I

20

winsorize performance and ratio variables at the 5% and 95% levels.

The �nal sample consists of 85,425 �rm-quarters spanning 4,808 �rms and 43 industries. Table

2, Panel A presents summary statistics. The sample tracks 3,124 quarters of earnings fraud across

293 �rms; the implied rate of earnings fraud in the whole period is then 3.6% using �rm-quarters

and 6.1% using �rms. The data come from 538 restatements, since some �rms restate more than

once. Although this is lower than the number of restatements provided in a GAO report on �nancial

restatements (GAO, 2006), the wider group of restatements (including those not associated with fraud)

includes many of the GAO restatements. The key advantage of the Audit Analytics data is that it

provides data on the event period of the restatement. The average (median) length of a fraudulent

restatement event period is 3.0 (2.5) years, which highlights that fraudulent manipulation can persist

for quite some time. In contrast, the average and median lengths of manipulation for non-fraudulent

restatements are 1.7 and 1.0 years, respectively, a di¤erence that is statistically signi�cant at the 1%

level. Since the Audit Analytics and COMPUSTAT universes overlap signi�cantly, a wide variety of

�rms are represented in the sample - the average market capitalization of a �rm in the sample is $2.8B,

with the maximum at $449B.

Table 2, Panel B correlates Audit Fee % with traditional measures such as the G Index and the

percentage of outside directors. The correlation with the G Index is nearly zero, so Audit Fee %

is capturing a distinct element of governance. Audit Fee % is weakly positively correlated with the

percentage of outside directors, with a correlation of 0.13, suggesting the �rms with more independent

boards also have more independent auditors. Table 2, Panel C shows that values of Audit Fee % tend

to be correlated with that of their peers, which suggests that ex ante detection mechanisms are very

similar across peers.

Table 3 breaks down fraud by industries. The key observation from this table is the that there is

a wide variety of industry heterogeneity how often manipulation is observed, or the "Industry Rate."

Some industries such as Agriculture have no �rm-quarter associated with manipulation; others such

as Precious Metals have a rate of 8%. Moreover, the heterogeneity in the rate of manipulation is not

driven a "denominator e¤ect," the number of �rms in the industry. For example, the largest industry,

Computer Software, has a rate of 5.43%. Although the data is not informative about the size of

the restatements, the frequency of restatements is nevertheless highly heterogeneous across industries.

The �nal column of the table, "% Quarters HHI > 0.18," yields a measure of the competitiveness of

the industry. I measure the beginning-of-period Hirschman-Her�ndahl Index of sales for each industry

quarter and compute the number of quarters where the value is greater than 0.18, the value that

the Department of Justice uses to classify an industry as "highly concentrated" for the purposes of

21

merger and anti-trust considerations. The model predicts that high rates of manipulation are in more

competitive industries and that the spread of governance matters the least in these more uncompetitive

industries, a prediction I test shortly.

4.3 Empirical Tests

Peer Governance E¤ects. I �rst test Prediction 1, that peer governance is associated with

earnings fraud. Table 4 performs a "between-industry" test and assesses whether industries with a

high spread of governance are also high manipulation industries. I �nd that industries with high

spreads of Audit Fee % are more likely to manipulate, controlling for the level of Audit Fee % as

well as the average �rm size and market/book of the industry. Speci�cally, I �rst aggregate the

�rm-quarter panel into an industry-quarter panel, and compute the industry rate of manipulation for

every quarter. The test is to examine whether the spread of Audit Fee % is positively associated

with this rate even when controlling for the level. Since Audit Fee % is bounded between 0 and 1,

its mean and standard deviation are not independent. I therefore use the 90-10 percentile spread as

the primary measure of governance spread, and the median as the primary measure of the industry

level. Furthermore, since the industry rate is also bounded between 0 and 1, I estimate a Tobit model

and report average marginal e¤ects. Column 1 shows that the 90-10 spread is positively associated

with the rate of industry manipulation, even when controlling for the median level of Audit Fee %.

This relationship is signi�cant at the 5% level. Column 2 shows that this result is not dependent on

the particular type of spread I have chosen, as the e¤ect persists even when I measure spread using

the standard deviation and the level using the mean. All speci�cations include time e¤ects for each

quarter in the sample.

An alternative explanation is that the tests are simply picking up technological di¤erences across

industries, since it may be easier to in�ate revenues in industries where products or assets are in-

tangible. To proxy for this, columns 3 and 4 include an additional control for the average Asset

Tangibility of �rms in the industry. As expected, high asset tangibility is associated with low rates

of manipulation. The governance spread e¤ects are statistically stronger once this control is included;

more importantly, the coe¢ cients are largely stable. The R-squared with asset tangibility is 29% and

is 21% without, suggesting that governance spreads are an important factor in the overall �t of the

regression.

Table 5 tests Prediction 1 at the �rm level. The left-hand side variable is the Manipulation

Indicator, and I �t a logit model to the right hand side variables, which are the governance variables,

22

time e¤ects, as well as controls for market capitalization and market/book. Column 1 demonstrates

that the beginning-of-period peer rate of manipulation positively predicts end-of-period earnings fraud,

where a 100-basis point increase in the rate of earnings fraud at peers is associated with a 17-basis point

increase in the likelihood of a �rm fraudulently manipulating earnings, on average. This demonstrates

that fraud is clustered within industries and time, a fact corroborated by Palmrose, Richardson, and

Scholz (2004) and Dechow, Sloan, and Sweeney (1996). Column 2 demonstrates that Audit Fee % is

negatively correlated with earnings fraud; a one-standard deviation (SD) increase in Audit Fee % is

associated with an average 43-basis point decrease in the probability of earnings fraud. This represents

an 12% decrease over the unconditional mean rate of earnings fraud, which, from Table 2, is 366 basis

points. Column 3 demonstrates that peer governance matters, even when controlling for the �rm�s own

level of governance. As predicted by the model, the sign on Peer Audit Fee % is negative, indicating

that stronger peer governance is associated with a lower likelihood of earnings in�ation. The e¤ect is

both larger and more statistically signi�cant than the e¤ect for own governance. A one-SD increase

in Peer Audit Fee % is associated with a 68-basis point decrease in the probability of earnings fraud,

or 19% of the mean.

Alternatively, I can also examine whether peer governance matters in predicting when �rms begin

in�ating earnings fraudulently. Columns 4 thru 6 perform these tests but only include �rms who were

not fraudulently manipulating earnings in the previous period. Such an exercise discards information

about �rms who began in�ating earnings prior to 2000, as well as �rms for which the data indicate the

�rm was manipulating earnings from the very �rst time that it appears in COMPUSTAT. Altogether,

I only observe 145 changes, or roughly half of the 293 �rms for whom I observe any earnings fraud, and

the overall sample size decreases to 82,197 observations (the sample is even smaller in a logit regression

due to the inclusion of time e¤ects, as some periods experience no changes). Nevertheless, the data

indicate that peer governance is informative about these "0 to 1" changes as well. In terms of economic

signi�cance, a one-SD increase in peer governance is now associated with a 6.4-basis point decrease in

the probability of beginning earnings fraud; since the average probability of beginning earnings fraud

is (145=82197) = 17:6 basis points, this represents a 36% decrease over the mean. Overall, the results

illustrate the importance of the governance environment in the industry.

Relative Performance and Peer Governance. One concern may be that peer governance is

simply picking up other unobserved characteristics about the peer group which are correlated with

earnings fraud. A stronger test of the model is Prediction 2, which suggests that managers with poor

peer governance have particularly aggressive responses to relative performance. For ease of interpreting

23

this interaction, I report OLS results in this section; all results are identical when tests are performed

using average marginal e¤ects estimated from a logit model.

Table 6 tests Prediction 2 by exploring the relationship between relative performance and fraud-

ulent manipulation. The left-hand-side variable is the Manipulation Indicator, and I �t relative per-

formance, governance, peer governance, and controls for market capitalization and market/book. Im-

portantly, I distinguish between relative underperformance and relative outperformance in the tests

by �tting a continuous piecewise linear function to relative performance, where I allow a kink at zero.

In other words, I allow for di¤erential slopes when relative performance is less than zero and for when

relative performance is greater than zero. Results are nearly identical when I allow for a jump at zero

in addition to a kink. All speci�cations include time e¤ects.

The model predicts that the slope on relative performance when it is less than zero is negative; that

is, worsening performance is associated with a higher likelihood of earnings in�ation, and that this

relationship is more negative for �rms with poor peer governance compared to �rms with stronger peer

governance. Similarly, the model predicts that the slope on relative performance when it is greater

than zero is positive for �rms with poor peer governance and indistinguishable from zero for �rms

with stronger peer governance.

Table 6, Column 1 tests for the e¤ect of relative performance in the full sample and �nds that

worsening performance is associated with a higher likelihood of earnings in�ation when relative per-

formance is already negative. In other words, relative underperformance is associated with earnings

in�ation. Economically, a 1-SD decrease in relative performance (when it is negative) is associated with

a 66-basis point increase in the likelihood of earnings fraud, or 18% of the unconditional mean (366

basis points). Additionally, relative performance is positively associated with earnings in�ation when

relative performance is positive; in other words, relative outperformance is also associated with more

earnings in�ation. A 1-SD increase in relative performance (when it is positive) is associated with a

42-basis point increase in the likelihood of earnings fraud, or 12% of the unconditional mean, consistent

with the notion that the e¤ect for outperformance should be weaker than that for underperformance.

According to the model, both e¤ects should be driven by �rms with poor peer governance. In order

to test this prediction, I split the sample into two groups, those with peer governance below the sample

median ("low peer governance") and those with peer governance above ("high peer governance"). Of

course, since governance and peer governance are correlated, it is di¢ cult to split on peer governance

without splitting on own-governance as well. To mitigate this, I continue to include controls for a

�rm�s own-governance.

Columns 2 and 3 demonstrate that the relationship for underperformance and outperformance in

24

the low peer governance sample is statistically signi�cant at the 1% and 5% levels, respectively, while in

the high peer governance sample the relationship is statistically signi�cant only for underperformance,

and at the 10% level. More striking is the economic magnitude of the di¤erence in coe¢ cients across

the two groups. In the low peer governance sub-sample, a 1-SD decrease in relative performance

(when it is negative) is associated with a 97-basis point increase in the likelihood of fraud (26% of the

unconditional mean), while the corresponding number for the high peer governance sub-sample is 27

basis points (7% of the mean). Not only is the impact of relative underperformance over three times

as large in the poor peer governance sample, the di¤erence (70 basis points) corresponds to 19% of the

mean rate of earnings in�ation. Similarly, virtually all of the e¤ect for the outperformers is associated

with the low peer governance group. Column 4 interacts an indicator for whether a �rm is in the low

peer governance group with the relative performance coe¢ cients in the full sample, and �nds that the

di¤erences across the two groups is statistically signi�cant as well.4

One concern is that outperformance, rather than being a motivating factor for manipulation, is

simply a symptom of previous earnings in�ation that has "built up." To better distinguish this from

a career concerns story, I again look at only the sub-sample of "0 to 1 changes," or �rm-quarters for

which a �rm was not fraudulently manipulating in the previous quarter. I again �nd that there is a

relationship between earnings in�ation and relative performance (Column 5), and that this relation-

ship is driven almost entirely by the low peer governance group (Columns 6 and 7). The economic

magnitudes are very similar to the analysis above where I include all �rm-quarters. In particular,

a 1-SD decrease in relative performance when it is negative is associated with a 7.5-basis point in-

crease in the likelihood of earnings in�ation for the low peer governance group (42% of the mean); the

corresponding number for the high peer governance group is 2.8-basis points (16% of the mean), or

nearly three times lower. Similarly, the outperformance relationship is virtually negligible in the high

peer governance group. Although the di¤erences across the two groups are not statistically signi�cant

(Column 8), the similarity in economic magnitudes between the two groups is reassuring. Overall,

the evidence is consistent with the idea that managers is low peer governance groups respond more

aggressively to relative performance than managers in high peer governance groups.

Competition E¤ects. Table 7 tests Prediction 3 of the model, which predicts that industries

with tight labor market competition (smaller �) have higher earnings in�ation and higher sensitivity

of earnings in�ation to peer governance. In our panel, I regress our �rm-level Manipulation Indicator

4Note that the coe¢ cient on the indicator for being in the low peer governance group is negative, not positive. This isbecause on average �rms in this group also have low values of Audit Fee % (which has a negative coe¢ cient), as expected.On average, the low peer governance group has a higher rate of manipulation than the high peer governance group.

25

as the dependent variable on Audit Fee %, Peer Audit Fee %, controls for Market Capitalization

and Market/Book, and an interaction of Peer Audit Fee % with a proxy that is an indicator for

whether labor markets are competitive in the industry. The model predicts a positive sign on the level

e¤ect of whether an industry has a competitive labor market and a negative sign on the interaction

with peer governance. Drawing from existing research, I use three di¤erent proxies for labor market

competitiveness and the depth of the pool of potential replacement managers.

The �rst proxy is a measure of industry homogeneity. As suggested by Parrino (1997) and Gillan,

Hartzell, and Parrino (2009), �rms in more homogeneous industries should have stock prices that track

their industry benchmark more closely than in non-homogeneous industries, since this indicates that

news tends to a¤ect the future cash �ows of many �rms together. Managers of �rms in homogeneous

industries should be closer substitutes than in non-homogeneous industries. Furthermore, relative

performance should be more strongly used in homogeneous industries, since the performance of com-

peting �rms is fairly informative about managerial ability. Consistent with this idea, Parrino (1997)

�nds that CEO turnover is higher and that the frequency of outside appointments are more sensitive

to performance in more homogeneous industries.

I follow the Parrino (1997) methodology and rank each Fama-French 49 industry by homogeneity

by scoring how closely stocks in each industry track their industry benchmark. Speci�cally, I select

a random sample of stocks for each industry from the universe of stocks in the CRSP Monthly File

during the period 2000-2006, and I regress each stock�s monthly return on the value-weighted CRSP

market return as well as the value-weighted Fama-French 49 industry return. I then compute the

industry homogeneity as the average coe¢ cient on the industry return across these stocks. Following

Parrino (1997), I omit industries that do not contain at least 35 stocks and use a maximum of 50

stocks. Table 2 lists the computed homogeneity score for each industry, with a higher score indicating

a higher degree of homogeneity.

Table 7, Column 1 reports results using this industry homogeneity score as a measure of labor

market competitiveness. I split industries into two groups, those whose industry homogeneity score is

above the median (more homogeneous) and those whose score is below (less homogeneous). Consistent

with the model, the coe¢ cient on an indicator for whether an industry is homogeneous is positive,

indicating that earnings fraud is more common in homogeneous industries. Furthermore, the e¤ect of

Peer Audit Fee% is markedly more negative in homogeneous industries, as indicated by the interaction

between the indicator and Peer Audit Fee %. The economic magnitude of the association between

peer governance and earnings in�ation is nearly three times as large in homogeneous industries.

I also proxy for labor market competitiveness with the Her�ndahl index of sales for the industry,

26

which measures industry concentration. The idea draws from Engel, Hayes, and Wang (2003) and

DeFond and Park (1999), who �nd that sensitivity of turnover to relative performance is stronger

in industries with low Her�ndahl indices, and is similar to Parrino (1997) in that less-concentrated

industries may have more substitutable managers. Motivated by this �nding, I split industry-quarters

into two groups, those where the Her�ndahl index of sales for the industry is greater than 0.18 (the

level at which the Department of Justice classi�es an industry as "concentrated") and those where the

index is less than 0.18. I compute Her�ndahl indices using the entire CRSP-COMPUSTAT universe

and use a running average of the Her�ndahl indices over the previous four quarters in order to smooth

out noise in the data. Table 7, Column 2 tests whether peer governance matters more in industries

where the Her�ndahl index is less than 0.18. The results are consistent with the previous results using

the Parrino (1997) industry homogeneity score and indicate that the e¤ect of Peer Audit Fee % is

driven by less-concentrated industries.

One �nal measure of labor market competitiveness is a basic measure of number of �rms in the

industry. This is the least sophisticated proxy but is motivated by the simple observation that more

�rms in an industry may correlate with more potential replacements, and should serve as a useful

back-check. Table 7, Column 3 tests whether peer governance matters more in the larger industries,

where I rank industries as large or small based on whether the number of �rms in the industry exceeds

the median number across all industries in a quarter. I compute the number of �rms in an industry-

quarter using the entire CRSP-COMPUSTAT universe. Consistent with the other measures, peer

governance matters the most in larger industries, as demonstrated by the interaction term between

Peer Audit Fee % and an indicator for whether an industry is large. However, I interpret this result

and the result using Her�ndahl indices with caution as the theoretical implications for whether RPE

should be used more in larger or less-concentrated industries is less clear. However, the consistency of

the result across various proxies, particularly using industry homogeneity, is re-assuring.

When examining the sub-sample that only includes �rms who were not manipulating in the pre-

vious quarter (not reported), both homogeneous and less-homogeneous industries exhibit signi�cant

sensitivity of earnings in�ation to peer governance, where the e¤ect is larger for homogeneous indus-

tries. Although the di¤erence between the two industries is not statistically signi�cant, the economic

magnitude of the point estimates is not trivial. For example, a one-SD decrease in Peer Audit Fee %

is associated with a 6.2-basis point increase in the likelihood of earnings fraud, or 35% of the mean,

for non-homogeneous industries. Being in a homogeneous industry increases this sensitivity to 8.1-

basis points, or an additional 11% of the mean. Overall, the evidence suggests that the sensitivity of

earnings in�ation to peer governance is higher in industries where proxies indicate labor markets are

27

competitive.

Robustness. An alternative explanation for the results in Tables 5-7 is that our proxies for peer

governance and labor market competitiveness are correlated with how easy it is to in�ate earnings

in an industry. Table 8 conducts robustness checks by testing Predictions 1-3 with additional con-

trols for asset tangibility as well as industry �xed e¤ects. As expected, asset tangibility is strongly

negatively associated with earnings in�ation in all the exercises. However, the statistical signi�cance

and economic magnitudes of the relationship between earnings fraud and peer governance predicted

by Prediction 1 are virtually unchanged, as demonstrated by Panel A. Similarly, Panel B reveals that

the relationship between earnings in�ation and the aggressiveness of how �rms respond to relative

performance predicted by Prediction 2 is also virtually unchanged by the inclusion of asset tangibility.

Panel C reveals that the relationship between earnings and peer governance interaction is driven by

homogeneous industries even when controlling for asset tangibility, as predicted by Prediction 3. The

e¤ects are also very similar when including industry �xed e¤ects.5 Overall, while di¤erences in earn-

ings manipulation technologies are likely to explain di¤erences in the level of earnings in�ation across

industries, it does not seem to explain the sensitivity to peer governance.

I also test whether the results in Table 6 are speci�c to stock-based measures of relative performance

as opposed to accounting-based measures of performance. Although the two are related, there are

conceptual di¤erences between the two (Jenter and Kanaan, 2008; Weisbach, 1988). The idea is that

stock prices may re�ect expectations of future management so that accounting-based performance

may be more informative about the performance of current management. This is important because

the more pronounced turnover-performance relationship found in homogeneous industries (Parrino,

1997) and less-concentrated industries (DeFond and Park, 1999) focus primarily on accounting-based

measures. In unreported results, I repeat all tests using a �rm�s four-quarter return on assets (ROA),

de�ned as the sum of earnings over the previous four quarters divided by book assets at the beginning

of those four quarters, as the primary performance measure. The e¤ects documented in Table 6

between earnings in�ation and relative performance are robust when performance is computed using

ROA. Importantly, the economic magnitudes of the e¤ects of relative performance are very similar as

well. A one-SD decrease in relative ROA (when it is negative) is associated with a 107-basis point

increase in the likelihood of observing fraudulent manipulation, or 29% of the mean, for the low peer

governance group, and a one-SD increase in relative ROA (when it is positive) is associated with a

5 In Panel A, the magnitude of the coe¢ cient on Peer Audit Fee % is much larger when including industry �xed e¤ectssince industries which never experience in�ation are not included in the logit model, and the e¤ect of Peer Audit Fee %is much higher outside of these industries.

28

with a 53-basis point increase in the likelihood of manipulation, or 15% of the mean.

Additionally, the Sarbanes-Oxley ("SOX") Act also provides a way to test the robustness of whether

peer governance matters. SOX was passed largely as a result to the wave of scandals in the early

2000�s at WorldCom and Enron. Sections 201, 202, 203 and 206 speci�cally restrict a �rm�s auditor

from providing a wide swath of non-audit services without pre-approval from the company�s audit

committee. Indeed, the main component of non-audit services provided by audit �rms after 2003 are

tax advisory services, and Audit Fee % exhibits an upward trend over time. One can view this as

a shift in the governance environment towards that of better governance with respect to stringent

controls on earnings manipulation, and the e¤ects should be muted after 2003. Indeed, in results not

reported, I �nd that the rate of earnings in�ation has come down after SOX, and that peer governance

plays a markedly less important role from 2003-onwards, suggesting that the results are not spurious.

5 Discussion

In this section, I explore the implications of some of the speci�c assumptions in the model and empirical

results and how the analysis relates to the literature.

5.1 Modeling Assumptions

The analysis in this paper takes a number of parameters in the environment, such as � or �, as

exogenous, and examines their comparative statics. An interesting question would be how investors

would choose these parameters when setting the optimal contract. As discussed at the end of Section

1, I view this question as complementary and for future research, and focus in this study on managerial

actions as the equilibrium outcome, in contrast to studies such as Acharya and Volpin (2010) and John

and Kadyrzhanova (2008) that focus on governance structures as the equilibrium outcome. However,

the model is nevertheless stylized, in the sense that 1) the accumulated component of manipulation

does not decay over time, and 2) detection probabilities depend on current manipulation instead

of manipulation that has built up over time. More realistic modeling would deliver a richer set of

empirical predictions, although I believe it would be unlikely to overturn the broad implication that

peer governance matters.

Speci�cally, reversion in manipulation implies that managers should expect their own earnings

in�ation to revert in the future. This dampens the overall incentive for managers to in�ate earnings. As

an empirical matter, though, fraudulent manipulation can persist for quite some time. The mean length

of restatement period for fraudulent restatements in the empirical analysis is 3 years; furthermore,

29

this manipulation can add up to signi�cant mispricing (Richardson, Sloan, Soliman, and Tuna, 2005).

Further evidence that fraud can persist until it has large e¤ects includes the signi�cant adverse stock

price reaction to such restatements (Dechow, Sloan, and Sweeney, 1996) and the economic consequences

thereof (Kedia and Philippon, 2009). Given that the average CEO tenure is roughly six years (Kaplan

and Minton, 2006), the trade-o¤ between career concerns and manipulation is non-trivial. The speci�c

prediction of the model most a¤ected by the reversion assumption is that outperforming managers may

manipulate. However, I conjecture that the model would still imply that managers are more aggressive

in responding to relative performance when peer governance is weaker, and that the magnitude is an

empirical matter that depends on how weak peer governance actually is, since on the margin, peer

governance would still matter.6

Introducing a detection technology that operates on the stock of manipulation introduces similar

considerations. First, the incentive to manipulate depends on the other manager�s stock of manipula-

tion as well as a manager�s own stock of manipulation. When the other manager�s stock of manipulation

is high, the incentive to react is low because the manager is con�dent the competing manager will

be caught soon anyway. Second, managers know that manipulation today adds up to a more likely

chance of detection. However, the trade-o¤ between career concerns and manipulation still persists,

since manipulation may not be revealed before a manager is forced out, even when detection occurs on

the stock of manipulation. In fact, there would be states of the world when manipulation incentives are

ampli�ed, rather than tamped down: if a manager has accumulated signi�cant manipulation already,

his incentive to react will be even stronger because he knows he will be detected soon anyway. These

considerations add considerably more richness to the model, but I conjecture that they likely do not

imply that peer governance does not matter.

5.2 Empirical Evidence

The empirical evidence in this paper dovetails with a number of �ndings in the literature. The predic-

tion that poor governance is associated with earnings manipulation is widely supported in the empirical

literature. Klein (2002) �nds that earnings management is negatively related to audit committee in-

dependence. Beasley (1996) compares �rms who committed fraud with matched no-fraud �rms and

6 Introducing reversion in accumulated manipulation also implies managers will expect competing managers�manip-ulation to revert, so that the incentive for a manager to react to another manager�s manipulation depends on how muchmanipulation the other manager has built up. If competitors have built up a large stock of manipulation, a managerhas less of an incentive to react because he knows that xt will be moving favorably soon anyway. Although this providesricher insight into when peer governance matters in these di¤erent cases, again, on the margin peer governance wouldstill matter. Although outperforming managers will certainly be less likely to manipulate when they expect that theircompetitor has built up manipulation, this is more of a special case.

30

�nds that no-fraud �rms have signi�cantly higher percentages of outside directors than fraud �rms.

Leuz, Nanda, and Wysocki (2003) �nds international evidence that weak outsider protection and en-

forcement of regulations leads to poor reporting. The critical contribution here is that Prediction 1

predicts that peer governance also matters. In addition to echoing results from the existing empiri-

cal literature, the results in Tables 5-7 support the idea that the governance environment in�uences

managerial behavior as well.

The case of the telecoms industry in 2002 is illustrative. By the end of 2002, a stream of accounting

scandals in the telecoms industry - with WorldCom, Qwest, and Global Crossing leading the way,

among others - had led to what then-SEC chairman Harvey Pitt referred to as a "crisis of con�dence"

in corporate governance (Pitt, 2002).

The model o¤ers a new twist on these events by linking them to the intense pressure that executives

were under to match the astronomical (but fraudulent) rise of WorldCom in the late 1990�s and early

2000�s. Congress enacted the Telecommunications Act in 1996 with the intent of increasing competition

by eliminating many barriers to entry; for example, many cross-ownership restrictions were lifted.

What ensued, however, was a frenzy of consolidation, with MCI WorldCom emerging as the new star

versus competitors such as AT&T and Lucent, which seemed relatively aged in comparison. Pressure

rose on executives to replicate similar stellar performance; implicit in this pressure was the threat that

their jobs depended on matching WorldCom. Indeed, rumors circulated that Michael Keith, CEO of

AT&T in 1999, was reportedly ousted and replaced by Michael Armstrong precisely because AT&T

was failing to match WorldCom�s performance, and Sidak (2003) argues that WorldCom�s fraudulent

behavior was intended to exploit it�s competitors and force them out of business.

In this spirit, Sadka (2006) argues that the growth of WorldCom put pressure on competitors such

as AT&T and Sprint through ine¢ ciently low pricing in the product market in order to support its

fraudulent accounting claims. The model complements this idea by suggesting that the subsequent

wave of accounting scandals revealed throughout the industry, at competitors like Qwest, Adelphia,

and Global Crossing, were related to this pressure to deliver performance in-line with WorldCom.

Essentially, the weak controls at WorldCom, both external and internal, "spilled over" into a stronger

incentive to manipulate earnings at competitors. In this sense, although governance may have been

extremely poor in only a few "bad apples" (as former-President Bush famously put it in a 2002

Newsweek article), these "bad apples" were driving the entire industry towards higher and higher

levels of misbehavior and fraud. Indeed, in news accounts afterwards, executives were quoted as

describing competing with WorldCom like "running track against an athlete who is later discovered to

be using steroids", and taking extraordinary measures because "it was and is a brutally competitive

31

business." The sports analogy delivers the message quite appropriately: in win-or-lose situations,

doping increases the pressure on competitors to dope.

Another context in which the model may be particularly relevant is in mergers and acquisitions.

Managers may in�ate earnings aggressively in order to gain better terms for stock mergers (Shleifer and

Vishny, 2003). In this context, outperforming managers are likely the managers of the acquiring �rm,

while the underperforming managers are managers of the target. Since there are large rewards, both

explicit and implicit, for managers to complete an acquisition, this provides a nice setting to examine

if outperforming managers in�ate earnings. The AOL-Time Warner merger of 2001 is illustrative; the

model suggests that the revelation of accounting malpractices that followed the $164-billion merger

was related to the incentive for managers to in�ate earnings and actually increase the overvaluation of

AOL in order to increase the chances of a successful takeover. More systematically, Erickson and Wang

(1999) and Louis (2004) �nd that earnings manipulation rises the quarter before an acquisition, and

that the negative post-announcement return is signi�cantly correlated with abnormal accruals. The

model would additionally imply that the magnitude of this earnings in�ation is related to governance

at both the acquirer and target. I leave these interesting questions for future research.

6 Conclusion

The paper highlights that managerial actions are in�uenced not only by the governance at a �rm

but also by the governance environment in its industry. Managers whose competitors have poor peer

governance may be motivated by career concerns to in�ate earnings, and to do so more aggressively

in responses to changes in relative performance. This is particularly so in industries where career

concerns are likely very strong. The empirical evidence seems to support these predictions, and these

insights o¤er a new interpretation on the events surrounding the implosion of WorldCom in 2002.

Indeed, revelations of accounting fraud at Lehman Brothers in the more recent �nancial crisis have

highlighted that implicit incentives and career concerns are very strong motivators for management

and may lead to large and serious consequences. Further research into the role of career concerns and

strategic interactions in managerial behavior and governance may shed even more light on why these

large-scale disasters occur.

32

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35

0 1 2 3 4 5 6 7 8 9 10

­0.3

­0.2

­0.1

0

0.1

0.2

0.3

t (years)

x t

Simulated Paths and Payoffs

Manager A Terminated

Manager B Terminated

Manager A Caught

δ

(w/ β,wL/β)

­ δ

(0,w/ β)

(Flow w, Flow w)

(wL/β,w/β)

Figure 1: Simulated Paths and Payo¤s. Three of the four possible outcomes are shown: manager B isterminated, manager A is terminated, and manager A is caught in�ating earnings. (The fourth case,where manager B is caught, is omitted.) At a point on the green line, I show the equilibrium horizondecomposition for each manager into its three parts: the tenure value, the severance value, and thegovernance value.

36

­0.3 ­0.2 ­0.1 0 0.1 0.2 0.3

­0.1

­0.08

­0.06

­0.04

­0.02

0

0.02

0.04

0.06

0.08

0.1

xt

mi

Manipulation

mA*

mA,BM

­mB*

Figure 2: Equilibrium Manipulation. This �gure shows optimal levels of earnings in�ation. Thedashed line indicates optimal earning in�ation for manager A when manager B is constrained andcannot in�ate earnings, mA;BM . The solid lines indicate the optimal equilibrium earnings in�ationwhen both managers can in�ate earnings, m�

A and m�B. Manager B�s earnings in�ation is plotted

below the horizontal axis and in a lighter grey.

37

­0.3 ­0.2 ­0.1 0 0.1 0.2 0.30

1

2

3

4

5

6

7

8

xt

Yea

rs (C

ash 

Com

pens

atio

n­E

quiv

alen

t)

Panel A: Play Zero

TA(x;0,0)

TAH ZN(x;0,0)

TASEV (x;0,0)

TAW AIT(x;0,0)

­0.3 ­0.2 ­0.1 0 0.1 0.2 0.30

1

2

3

4

5

6

7

8

xt

Yea

rs (C

ash 

Com

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quiv

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t)

Panel B: Play Optimal Strategy

TA(x;m

A,BM,0)

TAH ZN(x;m

A,BM,0)

TASEV (x;m

A,BM,0)

TAW AIT(x;m

A,BM,0)

Figure 3: Benchmark Case. This �gure shows the gain in manager A�s career value from in�atingearnings when manager B cannot in�ate earnings. Panel A illustrates manager A�s value-adjustedhorizon as a function of relative performance in the solid line, TA, when he never in�ates earnings. Itdecomposes this into its component parts, the value of his current career in the dashed line, THZNA ,the value of a negative career outcome due to underperformance in the dashed-dot line, TSEVA , and thevalue of �rm B�s governance (identically zero since manager B cannot in�ate earnings). Panel B thendemonstrates the new value-adjusted horizon from in�ating earnings using the benchmark strategy,mA;BM , in the bold dark lines, and decomposes this gain into its component parts. The decompositionshows that in�ating earnings is optimal for manager A by substituting away from the negative careeroutcome and towards his current career.

38

­0.3 ­0.2 ­0.1 0 0.1 0.2 0.30

1

2

3

4

5

6

7

8

xt

Yea

rs (C

ash 

Com

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t)

Panel A: No Reaction

TA(x;m

A,BM,m

B∗ )

TAH ZN(x;m

A,BM,m

B∗ )

TASEV (x;m

A,BM,m

B∗ )

TAW AIT(x;m

A,BM,m

B∗ )

­0.3 ­0.2 ­0.1 0 0.1 0.2 0.3­0.15

­0.1

­0.05

0

0.05

0.1

0.15

0.2

xt

Yea

rs (C

ash 

Com

pens

atio

n­E

quiv

alen

t)

Panel B: Gain from Reaction

∆ Value∆ HZN∆ SEV

∆ WAIT

Figure 4: Mechanics of Spillovers. This �gure illustrates the motivation for manager A to react tomanager B�s earnings in�ation. Panel A demonstrates the negative impact of manager B�s earningsin�ation on manager A�s career prospects. The lighter grey lines illustrate the value of manager A�scareer when manager A plays the benchmark strategy and manager B cannot in�ate earnings. Thedark bold lines illustrate the new value of manager A�s career when manager A plays the benchmarkstrategy but now manager B plays the new equilibrium strategy, m�

B. It decomposes these changesinto their component parts- the expected value of manager A�s current job THZNA , the expected valueof manager A�s negative career outcome TSEVA , and the expected value to manager A of manager B�sgovernance system, TWAIT

A . Panel B demonstrates the change in value for manager A from playingthe new equilibrium strategy, m�

A over the benchmark strategy mA;BM , in response to manager B�searnings in�ation. The dark line is the total gain in value for manager A. The component gains arethe change in value of manager A�s current job (dashed line), change in value of manager A�s negativecareer outcome (dashed-dot line), and the change in value to manager A of manager B�s governancesystem (dotted line).

39

­0.3 ­0.2 ­0.1 0 0.1 0.2 0.3

­0.1

­0.08

­0.06

­0.04

­0.02

0

0.02

0.04

0.06

0.08

0.1

xt

mi

Panel A: Manipulation

­0.3 ­0.2 ­0.1 0 0.1 0.2 0.3­0.3

­0.2

­0.1

0

0.1

0.2

0.3

0.4

0.5

xt

Year

s (C

ash 

Com

pens

atio

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quiv

alen

t)

Panel B: Gain from Reaction

∆ HZN

∆ W A IT

­mB

mA*  (Small δ)

mA*  (Larger δ)

Figure 5: Spillovers in Competitive Industries. This �gure illustrates the e¤ect of tighter performanceboundaries on spillovers. Panel A demonstrates equilibrium earnings in�ation when performanceboundaries are looser and � is larger (dashed lines), and performance boundaries are tighter and � issmaller (thick dark lines). Panel B demonstrates the component gains in value to manager A fromplaying the equilibrium strategy m�

A over the benchmark strategy mA;BM for larger � (thin lines) andsmaller � (dark thick lines).

40

 

41

Table 1: Model Parameter Values

Parameter Value Interpretation

1/8 Discount rate of managers

√0.08 Performance thresholds consistent with 8-year expected tenure

1 Flow value of career, normalized to 1

1/4 Value of negative career outcome, e.g., termination due to

underperformance

0.10 Volatility of relative stock performance

0.10 Maximum feasible manipulation

 

42

Table 2: Summary Statistics

I report summary statistics for the panel of firm-quarters from 2000-2006. Manipulation Indicator is 1 if a firm has a subsequent restatement indicator a firm was engaged in fraudulent accounting practices or practices that resulted in an SEC investigation during a firm-quarter. The Manipulation Indicator is measured at the end-of-quarter while other variables are measured at end-of-quarter. Audit Fee % is the percentage of fees a firm pays to its auditor for actual audit services. Peer groups for a firm are defined as firms in its associated industry-size portfolio, where portfolios are formed by splitting all firms within an industry-quarter into quintiles based on market capitalization, and peer group characteristics such as Peer Audit Fee % are computed as the mean characteristic in that portfolio excluding the firm itself. G Index is the Gompers-Ishii-Metrick (2003) measure of governance. % Outside Directors is the percentage of directors on the board classified as Independent by RiskMetrics. Market Capitalization is price times shares outstanding and Total Book Assets is taken from the firm's balance sheet. Market/Book is market capitalization divided by book equity. Asset Tangibility is Net Property, Plants and Equipment divided by Total Book Assets. One-Year Stock Return is the firm's stock return measured over the previous four quarters, and Relative Performance is the difference between the firm's and peer group's average one-year stock returns.

Panel A: Summary Statistics

Variable Mean SD Skew. Min. Max. Median IQR N

Manipulation Indicator 0.0366 0.1877 4.9379 0.0000 1.0000 0.0000 0.0000 85425 Audit Fee % 0.6976 0.2236 -0.5935 0.0000 1.0000 0.7351 0.3388 85425 Peer Audit Fee % 0.6604 0.1422 -0.6162 0.0000 0.9087 0.6861 0.2090 85425 G Index 9.0285 2.5784 0.1633 1.0000 19.0000 9.0000 4.0000 32664 % Outside Directors 0.6659 0.1757 -0.6182 0.0000 1.0000 0.6667 0.2444 28485 Market Cap ($M) 2780.0 14200.0 0.0 0.6 449000.0 265.0 983.5 85425 Total Book Assets ($M) 2210.5 11023.5 18.7 0.9 479921.0 243.7 923.3 85425 Market/Book 2.7994 2.4849 2.1202 0.3372 19.3143 1.9677 2.2934 85425 Asset Tangibility (PPENT/A) 0.2446 0.2202 1.2484 0.0000 0.9953 0.1717 0.2717 85253 One-Year Stock Return 0.1679 0.7019 2.0374 -0.9134 4.7083 0.0500 0.6819 85425 Relative Performance 0.0045 0.5365 1.2359 -1.5536 2.9330 -0.0714 0.5793 85425

Panel B: Correlation of Different Governance Measures

Audit Fee % 1.0000

G Index 0.0093 1.0000

% Outside Directors 0.1333 0.2356 1.0000

Panel C: Correlation of Own-Governance and Peer-Governance Peer Audit Fee %

Audit Fee % 0.5860

 

43

Table 3: Industry Rates of Manipulation

For each Fama-French industry portfolio, I compute the number of firm-quarters associated with fraudulent manipulation, the industry homogeneity score (Parrino 1997), which is the average partial correlation of stock returns in that industry with their industry benchmark (computed for industries with at least 35 stocks), and also their product market competitiveness, as measured by their Hirschman-Herfindahl (HHI) index of sales.

Homo- FF49 # Firm Manip- Industry geneity % Quarters Code Description Quarters % Sample ulation Rate Score HHI < 0.18

1 Agriculture 355 0.42 0 0.00 56.90 2 Food Products 1409 1.65 27 1.92 0.1907 100.00 3 Candy & Soda 198 0.23 14 7.07 0.00 4 Beer & Liquor 349 0.41 0 0.00 26.07 5 Tobacco Products 100 0.12 0 0.00 0.00 6 Recreation 843 0.99 11 1.30 0.1987 0.00 7 Entertainment 1255 1.47 55 4.38 0.6062 100.00 8 Printing and Publishing 870 1.02 14 1.61 0.3631 100.00 9 Consumer Goods 1343 1.57 34 2.53 0.0419 100.00

10 Apparel 1448 1.70 34 2.35 0.0683 100.00 11 Healthcare 1809 2.12 98 5.42 0.0388 100.00 12 Medical Equipment 3731 4.37 58 1.55 0.0947 100.00 13 Pharmaceutical Products 6795 7.95 196 2.88 0.2136 100.00 14 Chemicals 1695 1.98 32 1.89 0.3847 100.00 15 Rubber and Plastic Products 851 1.00 24 2.82 0.3059 100.00 16 Textiles 345 0.40 14 4.06 51.88 17 Construction Materials 1746 2.04 34 1.95 0.0208 100.00 18 Construction 1145 1.34 55 4.80 0.3775 100.00 19 Steel Works, Etc. 1244 1.46 27 2.17 0.3456 100.00 20 Fabricated Products 338 0.40 0 0.00 61.83 21 Machinery 3255 3.81 71 2.18 0.8483 100.00 22 Electrical Equipment 1628 1.91 11 0.68 -0.8788 0.00 23 Automobiles and Trucks 1246 1.46 86 6.90 0.5513 100.00 24 Aircraft 500 0.59 0 0.00 0.00 25 Shipbuilding, Railroad Equip. 237 0.28 0 0.00 0.00 26 Defense 271 0.32 0 0.00 0.00 27 Precious Metals 217 0.25 18 8.29 0.8847 100.00 28 Mines 290 0.34 6 2.07 0.9057 6.21 29 Coal 174 0.20 3 1.72 74.14 30 Petroleum and Natural Gas 3413 4.00 37 1.08 1.0098 100.00 32 Communication 2504 2.93 139 5.55 0.5112 100.00 33 Personal Services 1203 1.41 71 5.90 0.4155 100.00 34 Business Services 6066 7.10 307 5.06 0.3937 100.00 35 Computers 2687 3.15 183 6.81 0.6537 100.00 36 Computer Software 9685 11.34 526 5.43 1.0450 100.00 37 Electronic Equipment 6783 7.94 386 5.69 0.7169 100.00 38 Measuring and Control Equip. 2540 2.97 52 2.05 0.6816 100.00 39 Business Supplies 1077 1.26 22 2.04 0.1691 100.00 40 Shipping Containers 269 0.31 0 0.00 100.00 41 Transportation 2368 2.77 59 2.49 0.4208 100.00 42 Wholesale 3519 4.12 118 3.35 0.0620 100.00 43 Retail 5638 6.60 274 4.86 0.4902 100.00 44 Restaurants, Hotels, Motels 1986 2.32 28 1.41 0.4419 100.00

Total 85425 3124 3.66

 

44

Table 4: Governance Distributions and Observed Industry Rates of Manipulation

For each quarter and every industry, I compute the percentage of firms manipulating earnings fraudulently, the log average market capitalization, log average market/book ratio, average asset tangibility, and the mean, standard deviation, median, and 90-10 percentile spread of Audit Fee %. In the resulting industry-quarter panel, I test Prediction 1 of the model at the industry level by fitting this percentage of firms manipulating earnings as the dependent variable on these characteristics using a Tobit censored regression model, weighting by the number of firms in an industry-quarter. The first column reports the relationship between industry rates of manipulation and the median and 90-10 percentile spread of Audit Fee %. The second column reports the relationship for the mean and standard deviation. Columns 3 and 4 repeat the exercise including asset tangibility as an additional control. Average marginal effects are reported. Variables and peer groups are as defined in Table 2. Standard errors are clustered at the industry level. */**/*** denotes significant at the 10, 5 and 1-percent levels.

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

LHS: Industry Rate of Manipulation 90-10 SD 90-10 SD

Tobit, Average Marginal Effects

Median Audit Fee % -0.0496 -0.0224

[0.0641] [0.0554]

90-10 Percentile Spread of Audit Fee % 0.0851** 0.0850**

[0.0354] [0.0330]

Mean Audit Fee % -0.0669 -0.0109

[0.0886] [0.0763]

SD Audit Fee % 0.2330** 0.2716***

[0.1174] [0.1022]

Log Average Market Cap 0.0028 0.0027 0.009 0.009

[0.0057] [0.0057] [0.0056] [0.0056]

Log Average Market/Book 0.0178 0.017 -0.0058 -0.0069

[0.0119] [0.0120] [0.0114] [0.0114]

Asset Tangibility -0.0721*** -0.0738***

[0.0180] [0.0177]

Observations 1,192 1,182 1,192 1,182

Industries 43 43 43 43

Log LH 1119 1130 1167 1182

OLS R-Squared 0.2105 0.2115 0.2902 0.2948

 

45

Table 5: Peer Governance Effects

In the firm-quarter panel, I test Prediction 1 of the model at the firm level by estimating a logit model that fits the Manipulation Indicator as the dependent variable on peer manipulation and peer governance characteristics, controlling for own-governance characteristics, market capitalization, and market/book. The first column fits the Manipulation Indicator on the Peer Manipulation Rate, defined as the average rate of manipulation among its peers. The second column fits the Manipulation Indicator on Audit Fee %, the percentage of fees paid to its auditor for actual audit services. The third column adds the Peer Audit Fee %, which is the average Audit Fee % in its peer group, excluding itself. The second set of three columns repeats these regressions in the subsample of firm-quarters where a firm was not manipulating earnings in the previous quarter, effectively looking at the determinants of when firms first begin to manipulate earnings. I report average marginal effects. Variables and peer groups are defined in Table 2. All right-hand side variables are measured at the beginning of the quarter while the left-hand side variables are measured at the end of quarter. All specifications include period-specific time effects. Standard errors are clustered at the firm level. */**/*** denotes significant at the 10, 5 and 1-percent levels.

(1)-(3): Full Sample (4)-(6): Changes Only

LHS: Firm-Level Indicator (1) (2) (3) (4) (5) (6)

of Fraudulent Manipulation M-M M-OwnG M-G M-OwnG M-G M-M-G

Logit, Average Marginal Effects

Peer Manipulation Rate 0.1627*** 0.0026

[0.0336] [0.0028]

Audit Fee % -0.0192** -0.0163* -0.0030*** -0.0028*** -0.0028***

[0.0088] [0.0089] [0.0009] [0.0009] [0.0009]

Peer Audit Fee % -0.0478** -0.0045** -0.0043**

[0.0197] [0.0020] [0.0021]

Log Market Cap 0.0067*** 0.0076*** 0.0065*** 0 -0.0001 -0.0001

[0.0012] [0.0013] [0.0013] [0.0001] [0.0001] [0.0001]

Log Market/Book -0.0036 -0.0031 -0.0031 -0.0005** -0.0005** -0.0005**

[0.0031] [0.0032] [0.0032] [0.0003] [0.0003] [0.0003]

Observations 85,425 85,425 85,425 77,049 77,049 77,049

Firms 4808 4808 4808 4762 4762 4762

Log LH -12806 -12890 -12877 -967.5 -965.4 -965.1

Pseudo R-Squared 0.0444 0.0382 0.0392 0.0828 0.0848 0.0851

 

46

Table 6: Relative Performance and Peer Governance

In the firm-quarter panel, I test Prediction 2 of the model by regressing the Manipulation Indicator as the dependent variable on a continuous piecewise linear function of relative performance, governance, and peer governance, controlling for market capitalization and market/book. Column 1 fits the Manipulation Indicator on relative performance, allowing for differential slopes when relative performance is less than zero (underperformance) and greater than zero (outperformance), in the full panel. Column 2 performs this exercise for the half of the sample where peer governance is below the sample median, and Column 3 performs this exercise for the half of the sample where peer governance is above the sample median. Column 4 tests for a differential relative underperformance/outperformance effect across these two groups. Columns 5 thru 8 perform the same exercise in the subsample of firm-quarters where a firm was not manipulating earnings in the previous quarter, effectively looking at the determinants of when firms first begin to inflate earnings. Variables and peer groups are defined in Table 2. All right-hand side variables are measured at the beginning of the quarter while the left-hand side variables are measured at the end of quarter. All specifications include period-specific time effects. Since these tests involve interactions, OLS coefficients are reported for ease of interpretation. Standard errors are clustered at the firm level. */**/*** denotes significant at the 10, 5 and 1-percent levels.

(1)-(4): Full Sample (5)-(8): Changes Only LHS: Firm-Level Indicator (1) (2) (3) (4) (5) (6) (7) (8) of Fraudulent Manipulation Full Low PG High PG Interaction Full Low PG High PG Interaction Relative Performance, RP < 0 -0.0260*** -0.0364*** -0.0115* -0.0136** -0.0022*** -0.0028*** -0.0012 -0.0015

[0.0052] [0.0071] [0.0060] [0.0058] [0.0008] [0.0011] [0.0010] [0.0010] Relative Performance, RP > 0 0.0090*** 0.0166*** -0.0004 -0.0006 0.0010** 0.0015** 0.0002 0.0005

[0.0035] [0.0047] [0.0039] [0.0039] [0.0005] [0.0008] [0.0005] [0.0006] Audit Fee % -0.0165* -0.0173 -0.0174 -0.0202** -0.0029*** -0.0046*** -0.0008 -0.0031***

[0.0092] [0.0116] [0.0121] [0.0089] [0.0009] [0.0014] [0.0010] [0.0009] Peer Audit Fee % -0.0500** -0.0041*

[0.0198] [0.0021] Lowest Peer Audit Fee %, Indicator -0.0070* 0.0002

[0.0042] [0.0006] Lowest Peer Audit Fee % x -0.0215*** -0.0012

Relative Performance, RP < 0 [0.0079] [0.0015] Lowest Peer Audit Fee % x 0.0173*** 0.0008

Relative Performance, RP > 0 [0.0050] [0.0009] Log Market Cap 0.0073*** 0.0094*** 0.0069*** 0.0084*** -0.0001 0.0000 0.0000 0.0000

[0.0014] [0.0019] [0.0013] [0.0014] [0.0001] [0.0002] [0.0001] [0.0001] Log Market/Book -0.003 -0.0015 -0.0057* -0.003 -0.0005** -0.0008** -0.0003 -0.0005**

[0.0031] [0.0041] [0.0030] [0.0031] [0.0002] [0.0004] [0.0002] [0.0002] Constant -0.0273 -0.0792*** -0.0406** -0.0574** 0.0123 0.0112 -0.0003 0.0095

[0.0258] [0.0288] [0.0193] [0.0239] [0.0093] [0.0088] [0.0013] [0.0089]

Observations 85,425 42713 42712 85425 82197 40,827 41370 82197 R-Squared 0.0128 0.0116 0.0112 0.0127 0.0032 0.0039 0.0023 0.0031 Firms 4,808 4144 4215 4808 4776 4,053 4182 4776

 

47

Table 7: Competition Effects

In the firm-quarter panel, I test Prediction 3 of the model by regressing the Manipulation Indicator as the dependent variable on governance and peer governance, controlling for market capitalization and market/book, and testing for differential effects of peer governance across various proxies for the competitiveness of industry labor markets. Column 1 tests whether the Manipulation Indicator is more sensitive to Peer Audit Fee % in more homogeneous industries. Industry homogeneity is ranked using the Parrino (1997) methodology and measures how closely the returns of a random sample stocks in the industry track its industry benchmark over the 2000-2006 time period. Homogeneity scores are reported for each industry in Table 3, and we split industries into homogeneous and non-homogeneous groups according to the sample median. Column 2 tests whether the Manipulation Indicator is more sensitive to Peer Audit Fee % in industries with less concentrated product markets. An industry is classified as “concentrated” if its Herfindahl index is above 0.18 and “less concentrated” otherwise, following the Department of Justice guidelines. We use a running 4-quarter average of the industry Herfindahl index of sales, and Table 3 reports whether an industry is concentrated or not. Column 3 tests whether the Manipulation Indicator is more sensitive to Peer Audit Fee % in industries with more firms, where the number of firms is computed every quarter. Variables and peer groups are defined in Table 2. Left-hand-side variables are measured at the end-of-quarter while right-hand-side variables are measured at the beginning of the quarter, except for the industry classifications which are described above. All specifications include period-specific time effects. For ease of interpretation on the interaction term, OLS coefficients are reported. Standard errors are clustered at the firm level. */**/*** denotes significant at the 10, 5 and 1-percent levels.

LHS: Firm-Level Indicator (1) (2) (3)

of Fraudulent Manipulation Homog. HHI # Firms

Audit Fee % -0.0145 -0.0160* -0.0153*

[0.0096] [0.0092] [0.0092]

Peer Audit Fee % -0.0345 0.0354 -0.0381

[0.0228] [0.0313] [0.0286]

Homogenous Industry Indicator 0.0721***

[0.0187]

Homogenous x Peer Audit Fee % -0.0800***

[0.0241]

Non-Concentrated Industry Indicator 0.0941***

[0.0173]

Non-Concentrated x Peer Audit Fee % -0.1061***

[0.0287]

Large # of Firms Indicator 0.0508***

[0.0196]

Large # of Firms x Peer Audit Fee % -0.0558**

[0.0271]

Log Market Cap 0.0065*** 0.0065*** 0.0063***

[0.0014] [0.0014] [0.0014]

Log Market/Book -0.0031 -0.003 -0.0042

[0.0031] [0.0030] [0.0030]

Observations 82,289 85,425 85,425

Firms 4645 4808 4808

R-Squared 0.0162 0.0133 0.0131

 

48

Table 8: Robustness

I test Predictions 1-3 of the model with asset tangibility and industry fixed effects as additional controls. Panel A tests whether manipulation is related to peer governance, and Panel B tests for a piecewise linear response of manipulation to relative performance across high and low peer governance groups. Panel C tests whether the relationship between manipulation and peer governance varies across homogeneous and non-homogeneous industries, ranked using the Parrino (1997) methodology. All specifications also include controls for log market capitalization, log market/book, and time effects; for brevity, these effects are omitted. Variables and peer groups are defined in Table 2. Left-hand-side variables are measured at the end-of-quarter while right-hand-side variables are measured at the beginning of the quarter. All specifications include period-specific time effects. Standard errors are clustered at the firm level. */**/*** denotes significant at the 10, 5 and 1-percent levels.

Panel A: Peer Governance Effects LHS: Firm-Level Indicator (1) (2) of Fraudulent Manipulation Tangibility Industry FE

Logit, Average MFX Audit Fee % -0.0144 -0.0104

[0.0089] [0.0091] Peer Audit Fee % -0.0461** -0.1040***

[0.0201] [0.0253] Asset Tangibility -0.0554***

[0.0136] Observations 85,253 83,006 Firms 4804 4685 Pseudo R-Squared 0.0488 0.0727

Panel C: Competition Effects (Homogeneity Measure) LHS: Firm-Level Indicator (1) (2) of Fraudulent Manipulation Tangibility Industry FE

OLS OLS Audit Fee % -0.0123 -0.0104

[0.0095] [0.0095] Peer Audit Fee % -0.0349 -0.0594**

[0.0225] [0.0281] Homogenous Industry Indicator 0.0757***

[0.0185] Homogenous x Peer Audit Fee % -0.0807*** -0.0628**

[0.0238] [0.0260] Asset Tangibility -0.0501***

[0.0100] Observations 82,121 82,289 Firms 4641 4645 R-Squared 0.0194 0.023

Panel B: Relative Performance and Peer Governance LHS: Firm-Level Indicator (1) (2) of Fraudulent Manipulation Tangibility Industry FE

OLS OLS Relative Performance, RP < 0 -0.0107* -0.008

[0.0058] [0.0058] Relative Performance, RP > 0 -0.0013 -0.0022

[0.0039] [0.0039] Audit Fee % -0.0188** -0.0167*

[0.0089] [0.0089] Lowest Peer Audit Fee %, Indicator -0.0070* -0.0027

[0.0042] [0.0040] Lowest Peer Audit Fee % x -0.0207*** -0.0152**

Relative Performance, RP < 0 [0.0078] [0.0076] Lowest Peer Audit Fee % x 0.0168*** 0.0137***

Relative Performance, RP > 0 [0.0049] [0.0048] Log Market Cap 0.0091*** 0.0089***

[0.0015] [0.0015] Log Market/Book -0.0056* -0.0052

[0.0031] [0.0033] Asset Tangibility -0.0431***

[0.0093] Constant -0.0520** -0.0988***

[0.0237] [0.0257]

Observations 85253 85,425 R-Squared 0.0151 0.0224 Firms 4804 4,808