board interlocking and em contagion ar 2013

THE ACCOUNTING REVIEW American Accounting AssociationVol. 88, No. 3 DOI: 10.2308/accr-503692013pp. 915–944

Board Interlocks and Earnings ManagementContagion

Peng-Chia ChiuSiew Hong Teoh

University of California, Irvine

Feng Tian

The University of Hong Kong

ABSTRACT: We test whether earnings management spreads between firms via

shared directors. We find that a firm is more likely to manage earnings when it shares

a common director with a firm that is currently managing earnings and is less likely to

manage earnings when it shares a common director with a non-manipulator. Earnings

management contagion is stronger when the shared director has a leadership or

accounting-relevant position (e.g., audit committee chair or member) on its board or

the contagious firm’s board. Irregularity contagion is stronger than error contagion.

The board contagion effect is robust to controlling for endogenous matching of firms

with directors, fixed firm/director effects, incidence of M&A, industry, and contagion via

a common auditor or geographical proximity. These findings support the view that

board monitoring plays a key role in the contagion and quality of firms’ financial

reports.

Keywords: earnings management; restatements; board interlocks; board networks;social networks; contagion; governance.

JEL Classifications: M40; M41; M49; G34; G39; D83.

Data Availability: Data are available from sources identified in the text.

We thank Steve Kachelmeier and Harry Evans (editors) and two anonymous referees; conference and workshopparticipants at the 20th Financial Economics and Accounting Conference (Rutgers, The State University of New Jersey),Annual Academic Corporate Reporting and Governance Conference (California State University, Fullerton), 2011 AAAWestern Region Conference (Lucile Faurel, discussant), 2011 AAA Annual Meeting (Rosemond Desir, discussant),Santa Clara University, Southern Methodist University, University of California, Irvine, and University of Toronto, andYe Cai, David Hirshleifer, Haidan Li, Hai Lu, Partha Mohanram, Carrie Pan, Mort Pincus, Devin Shanthikumar, and IvoWelch for very helpful comments.

Editor’s note: Accepted by John Harry Evans III, with thanks to Steven Kachelmeier for serving as editor on a previousversion.

Submitted: February 2011Accepted: November 2012

Published Online: December 2012

915

He that lies with dogs, shall rise up with fleas.

—Benjamin Franklin

I. INTRODUCTION

There is ample evidence that social interactions affect human behavior. We see imitation in a

wide range of situations, from teen activities to corporate decisions. Research on how

behavior spreads through social networks and affects outcomes has emerged in many

academic fields, such as psychology, sociology, anthropology, biology, economics, and computer

science, as well as various business disciplines, including accounting and finance (Rogers 2003;

Jackson 2010).

In the corporate world, behavior may spread through board of director networks.1 A board link

exists between two firms whenever a director sits on both firms’ boards. A typical board in our

sample has nine directors, and the median number of interlocks with other boards is approximately

five (see Table 1, Panel C). In this way, firms are widely connected by their board networks, which

potentially serve as conduits for spreading behaviors from firm to firm.

In this study, we investigate whether financial reporting behavior spreads through interlocking

corporate boards. Our test design emphasizes contagion of ‘‘bad’’ financial reporting choices,

specifically, earnings management that results in a subsequent earnings restatement, although it also

allows for inferences about ‘‘good’’ reporting contagion. We use restatements to identify firms that

have managed earnings and the period when the manipulation occurred. Timing issues are

important when studying contagion and Figure 1 illustrates our timeline of events for contagion.

We refer to a firm that later restates earnings as contagious. We define the contagious period as

starting in the first year for which earnings are restated and ending two years after. Any firm that

shares an interlocked director with the contagious firm during the contagious period is therefore

exposed to an earnings management infection via the board network. We consider a multiyear

contagious period to allow the earnings management infection to incubate, which is analogous to an

epidemiological setting for viral infections. Our key test investigates whether an exposed firm is

more likely to manage earnings during the contagious period as compared to an unexposed firm. If

shared directors transmit earnings management practices, then the answer would be yes.

Rogers (2003) suggests that opinion leaders are particularly influential in spreading behavior.

High-prestige board positions and those more directly responsible for monitoring financial reporting

are more likely to be opinion leaders about financial-reporting-related issues. Our second key test

examines whether earnings management contagion varies in strength by board leadership positions,

such as CEO, board chair, audit committee chair, audit committee member, and other board

positions, in either the exposed or contagious firm.

In testing for contagion, it is crucial to control carefully for alternative explanations for

commonalities in linked firms’ behavior. A firm that intends to manage earnings may intentionally

recruit a director who is earnings-management-friendly. Alternatively, firms facing similar

economic environments may have preferences for similar director characteristics. These similarities

or preferences could result in a common higher frequency of restatements in board-linked firms.

Section V discusses how we control for such endogenous matching of directors and firms using

variables that exploit the timing of the occurrence of earnings management and the timing of the

1 Our study concerns the behavior of individuals, and therefore networks in this study refer to social networksamong individuals (e.g., directors) or entities formed by individuals (firms), not networks of physical (e.g.,computer) objects. Specifically, we examine board networks formed from interlocked directors. In our tests inSections IV and V, we control for other networks, e.g., firms within a common industry, firms located in closegeographical proximity to other firms, and to accounting rule enforcers (auditors and SEC local office). Footnote 3discusses other networks studied in the literature, e.g., between CEO and directors, and between CEO and marketparticipants (analysts and investment fund managers).

916 Chiu, Teoh, and Tian

The Accounting ReviewMay 2013

creation of board linkages to contagious firms. Furthermore, we control for other channels for

spreading financial reporting behavior, such as networks formed within an industry, sharing a

common auditor, and geographical proximity to contagious firms and to the local Securities and

Exchange Commission (SEC) office. Finally, we control for situations for which previous studies

document an increased incentive to manage earnings, such as during mergers and acquisitions

(M&A) or new equity offerings and in high fraud-score firms.

The core question motivating our research is whether board networks spread financial reporting

behavior, and we focus on earnings management because regulators, market participants, and the

academic literature are concerned about bad financial reporting practices. Our test design choice

narrows the focus further on extreme forms of earnings management that are detected with

restatements. Whether our results generalize to undetected milder forms of earnings management

bears further study. For some insight into the effects of earnings management severity on board

contagion strength, in Section V we test whether board contagion differs between irregularity

restatements, which are more egregious forms of misreporting, and error restatements, which are

less serious violations.

In the 1997–2001 sample period, we find strong evidence that a firm is more likely to manage

earnings when exposed within a three-year period to earnings management from a common director

with an earnings manipulator. The contagion effect is economically substantial. The regression odds

ratio suggests that a board link to a manipulator doubles the likelihood that the firm will manage

earnings. Interestingly, we also find evidence for good financial reporting contagion. A board link

to a non-manipulator significantly decreases the likelihood of the firm being a manipulator. In sum,

both bad and good accounting behaviors are contagious across board networks.

FIGURE 1Illustration of Earnings Management Contagion

Board Interlocks and Earnings Management Contagion 917


Earnings management contagion is stronger when the shared director has a leadership position

as board chair or audit committee chair, or an accounting-relevant position as an audit committee

member, in the exposed firm. The contagion is also stronger when the linked director is the board

chair or CEO of the contagious firm, but not when the linked director is the CEO of the exposed

firm. A possible explanation is that other directors of the exposed firm, in their role as monitors of

its CEO, are more skeptical of reporting choices that are self-serving to the CEO.

We find contagion effects above and beyond endogenous firm-director matching effects,

firm-director fixed effects, alternative network effects from a common industry, geographical

proximity, and a common auditor. Interestingly, earnings management contagion is exacerbated

when the exposed firm is located within 100 miles of the contagious firm and shares a common

auditor with the contagious firm. Contagion effects from board links to contagious firms are also

incremental to a high accounting fraud-score effect and other earnings-management-related

situations such as during M&A or new issues. Board contagion effects are present for both error and

irregularity restatements, and are stronger for the latter. Overall, the evidence supports the

proposition that earnings manipulation spreads through board networks.

Our evidence on the firm-to-firm spread of financial reporting behavior via board networks

contributes to a little-studied area in accounting that should be important. As Granovetter (1985)

notes, economic choices are generally embedded within networks. Most prior studies that focus

mainly on firm-specific earnings management and ignore network effects disregard an important

determinant of economic choices.

Our study differs fundamentally from information transfer studies that focus on investor

reactions to other firm behavior and, hence, are about spillover of performance.2 In contrast, we

examine behavior contagion from firm to firm. Finally, we contribute to the corporate governance

literature by offering evidence that contagion effects vary with board positions. We show that board

supervision of management is important for ensuring high-quality financial reporting and that board

linkages affect the success of this supervision. Regulators concerned about improving financial

reporting quality should consider the board connectivity of companies.

We discuss the mechanisms for earnings management contagion via board networks, test

predictions and related past research, and their differences from the current study in Section II.

Section III presents the sample selection and empirical research design. Section IV presents results

for the main test of earnings management contagion via board networks, and for robustness tests

distinguishing board network from alternative networks. Section V presents additional robustness

tests to control for endogeneity issues, and tests that contrast error contagion from irregularity

contagion. Section VI concludes.

II. PAST LITERATURE AND TEST PREDICTIONS

Boards of directors monitor the operation of publicly traded companies and approve important

management decisions. Directors in the United States commonly serve on more than one board, and

each board meets several times a year—sometimes frequently, as for example, in the case of

Citibank, whose board met 16 times in 2002. Interlocking boards form a director network to carry

2 Past restatement studies report significant negative stock return spillover effects of restatement announcements tonon-restating firms (Srinivasan 2005; Gleason et al. 2008; Kang 2008; Durnev and Mangen 2009). This evidenceis potentially compatible with earnings management contagion, but does not definitely support behaviorcontagion, nor does it specify board network as the channel of contagion. The spillover return responses of non-restating firms at the time of a restatement announcement may also reflect other economic and psychologicaleffects on stock returns at the restatement announcement, rather than earnings management that occurred prior tothe restatement. Durnev and Mangen (2009), for example, suggest that negative spillovers at the restatementannouncement signal poor future industry prospects.



knowledge and corporate practices, bad or good, between companies. Interlocked directors,

therefore, are natural conduits for the spread of behavior between firms. We discuss below the

burgeoning accounting, finance, and strategy literatures that examine behavior contagion via board

networks across firms for a wide range of corporate decisions. We describe how our study differs

from these past studies and our incremental contribution. Finally, we describe mechanisms for

earnings management contagion via board networks based on both economic- and psychology-

based arguments, as well as the intuition for our key hypotheses.

Board Network Studies

Hirshleifer and Teoh (2003, 2009) review the theories and evidence of herding behavior,

incentives to herd, and thought and behavior contagion in capital markets. Since there are

challenging methodological issues in identifying contagion empirically, claims of contagion need to

be evaluated carefully. A linkage between common behaviors across board-linked firms has been

found for poison-pill adoption (Davis 1991), acquisitions (Haunschild 1993), NASDAQ to NYSE

listing migration (Rao et al. 2000), stock option backdating (Bizjak et al. 2009), and private equity

offers (Stuart and Yim 2010). Specifically with respect to contagion and accounting choices, board

networks are found to spread the adoption of corporate-owned life insurance (COLI) as a tax shelter

(Brown 2011) and stock option expensing (Reppenhagen 2010). These papers generally provide

evidence of a higher correlation in the behavior of firms that share common directors. None of these

studies, however, specifically addresses earnings management contagion, although option

backdating may be considered one form of earnings management. Since our sample period

precedes the earliest restatement for option backdating, the earnings management behaviors we

examine exclude options backdating.3

Our research design improves on prior studies of behavior contagion in several ways. We

include a more extensive set of control variables, distinguish board contagion from contagion via

alternative networks and from other explanations for correlated earnings management behavior, and

carefully address potential endogeneity issues. Since we are able to control for a wide range of

alternative explanations, our evidence provides more reliable inferences about earnings

management contagion via board networks.

A second distinctive feature of our study is that our setting permits stronger inferences about

causality from contagion, because earnings management is a type of action that is much less

publicly visible and difficult to verify at its occurrence than the actions considered in other studies.

For example, COLI adoption or stock option expensing are publicly observed in financial reports

upon adoption. Since these concrete actions are promptly revealed to the public, imitation by others

can occur without transmission via a board network. In contrast, earnings management actions, by

their nature, are less visible to the public at the time that they are occurring. Either good

fundamentals or upward earnings management can contribute to an observed high earnings

outcome, so outsiders cannot easily verify that earnings management has occurred. A board

network, hidden from the public eye, offers a feasible and important channel to communicate the

net benefits of earnings management, and encourage its spread. Thus, we study contagion of

earnings management during the contagious period when these actions are still difficult for the

public to detect, and not at a later time when the restatement is announced.

3 The accounting and finance literatures also study within-firm networks, such as social ties between the CEO andboard members via college alumni networks, churches, and golf clubs. Hwang and Kim (2010) find that social tiesbetween the CEO and audit committee board members facilitate earnings management. Fracassi and Tate (2009)find that firms with greater social ties between the CEO and its directors have fewer voluntary restatements. Seeour working paper version for further examples (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1723714).



Mechanisms for Contagion

Theoretical research on social influence suggests that a variety of different mechanisms

promote the spread of behaviors in networks (e.g., Bikhchandani et al. 1992). Some of these are

economically rational, while others are driven by human psychology. We next discuss how these

mechanisms can facilitate earnings management contagion.

Rational observers may follow the behavior of others based on direct observation of the

action or verbal communication about which action is preferred. An interlocked director

observing earnings management in another firm may estimate a lower perceived cost of

manipulation and a higher perceived benefit, potentially leading to rational herd behavior or

information cascades (Bikhchandani et al. 1992). For obvious reasons, earnings management in

firms is unlikely to be publicized externally by the firm and its directors. A link in the board

network, in contrast, can facilitate direct observation by one firm of the earnings management

technology of another firm that may otherwise be difficult to discern externally. Thus, this

behavior that, for example, could involve how and under what circumstances to manage a

specific accounting item, can diffuse quietly from one firm to another via conversations about

these reporting choices or observations of the preferences expressed for the reporting choices

from directors sitting on interlocked boards.

Rational observers may be encouraged to follow the actions of others when they learn the

rationales for the actions. Whether a firm decides to manage its earnings depends on the subjective

perceived cost and benefit to earnings management. A board network spreads information about the

net benefit of managing earnings, including the reward of upwardly biased stock returns to earnings

management behaviors of linked firms, and the probability of and the associated penalties with

being caught. Information about net benefits of earnings management in the contagious firm may

therefore encourage exposed firms to also engage in earnings management.

When businesses are complex, there is a gray area under Generally Accepted Accounting

Principles (GAAP) for what is acceptable versus deceptive financial reporting. A director of a firm

that is managing earnings can transfer different kinds of knowledge about earnings management,

including the technology for managing specific accounting items, and certain auditors’ degree of

tolerance for deceptive financial reporting, such as the materiality cut-off point, to a board-linked

firm. The latter can encourage earnings management in the exposed firm using a variety of reporting

decisions that may differ from those used in the contagious firm. Thus, earnings management

contagion can be present even when the particular reporting choices to manage earnings differ

between the board-linked firms. This is akin to the spread of innovation through a network wherein

firms may implement the innovation in significantly different ways that suit their particular

circumstances (Rogers 2003). Therefore, rather than focusing on whether board-linked firms use

similar accounting items to manage earnings, we test for contagion by examining whether firms

exposed to manipulators also subsequently have to restate their earnings.

Psychological forces may help drive earnings management contagion via board networks. The

social psychology literature (Asch 1951; Milgram 1963)4 demonstrates that individuals in groups

tend to conform to others’ behavior, sometimes even when the consensus is clearly incorrect or

dysfunctional, and tend to defer excessively to high-prestige leaders who are engaging in unethical

behavior.

4 Fich and Shivdasani (2007) find that firms are more likely to face a financial lawsuit if they have a board memberwho sits on the board of another firm that has previously been sued for fraud. The evidence on the higherfrequency of stock option backdating in board-interlocked firms (Bizjak et al. 2009) is also consistent with thiseffect.



Gino et al. (2009) suggest that observers are more likely to imitate individuals who are viewed

as belonging to the same group or an in-group than to imitate out-group individuals. Board directors

are likely to view other directors on the board as members of the in-group. Thus, a board connection

to firms that manage earnings may shift the directors and managers of the exposed firm toward

greater acceptance of earnings management. Furthermore, Sah (1991) finds that exposure to the

dishonesty of others whose actions are not caught leads individuals to change their subjectiveestimate of the probability of being caught and the benefits of crime, and so encourages imitation of

the dishonest behavior. Communication from board-linked directors that other firms manage

earnings could potentially shift upward the first board members’ subjective estimate of the net

benefits of managing earnings, and so encourages approval of the behavior.

These economic and psychology-based arguments lead to our primary test hypothesis:

H1: Exposure via board links to an earnings manipulator during the contagious period

increases the likelihood of a firm managing its earnings that later are restated.

Bikhchandani et al. (1992) argue that low-precision individuals tend to imitate high-precision

individuals. Psychology and sociology literatures suggest that opinion leaders exert greater

influence over group members (Rogers 2003). If directors in board leadership positions are viewed

as having more accurate information or are respected as opinion leaders, then we expect earnings

management contagion effects to be stronger when the board link is via a director who holds an

important board or corporate position. The leadership positions for financial reporting that we

consider are CEO, board chair, audit committee chair, and audit committee member.5 The fifth

group includes all other board positions. We test whether the strength of earnings management

contagion varies with the linked director’s board position in either the contagious firm or in the

exposed firm. This leads to our second test hypothesis.

H2: Board links to earnings manipulators during the contagious period by directors who hold

relatively important financial-reporting-related positions (CEO, board chairman, audit

committee chairman, audit committee members) in the contagious or exposed firm result

in a greater probability that the exposed firm manages earnings and restates earnings in

subsequent years.

It is important to distinguish board network contagion effects from alternative explanations for

correlation in earnings management behaviors between board-linked firms, such as endogenous

matching, and director fixed effects. In Sections IV and V, we control for other network

mechanisms for contagion, such as board-linked firms having common auditors or being located in

close geographical proximity or proximity to SEC local enforcement offices, as well other potential

endogeneity issues.

III. RESEARCH DESIGN AND DATA

This section discusses the sample selection, empirical research design, and empirical proxies. A

common challenge for research on earnings management behavior is identifying such behavior

reliably and contemporaneously. Following past literature (e.g., Kedia and Rajgopal 2011), we

address this problem by using ex post identification of earnings management from restatements.

Apart from technical restatements correcting for honest mistakes, a researcher can rely on the

restating firm’s own confession in the restatement that its earlier financial reporting choices had

violated GAAP to obtain a sample of earnings manipulators and the period when linked firms are

5 We do not consider the CFO subgroup, because CFOs rarely sit on boards in our sample period, which precedesthe Sarbanes-Oxley Act (SOX).



exposed to earnings management activities. Although no earnings management proxy is perfect, the

use of ex post restatement is less controversial than other earnings management proxies, such as

discretionary accruals (Dechow et al. 2010).

Sample Selection

Our test design compares the restatement rate of exposed firms with non-exposed firms, so we

require data on restatements and board links. We use the U.S. Government Accountability Office’s

(GAO) first release of restatements (GAO1) between January 1, 1997 to June 30, 2002 to identify

contagious firms and their contagious periods (U.S. GAO 2002).6 Because the contagious periods

precede the restatement announcements, our sample period ends earlier, at the end of 2001 instead

of 2002. We keep only the earliest restatement within the sample period when a firm has multiple

restatements. We read the restatement-date press release to identify the start year when earnings

violated GAAP. If the start date is unavailable in the initial press release, as is common, then we

search for news articles or subsequent press releases on LexisNexis within two days of the

restatement announcement. If the date is still unavailable, we search SEC EDGAR Form 10K/Q

filings subsequent to the restatement announcement.

To map the board network, we obtain director names from Risk Metrics, formerly the Investor

Responsibility Research Center (IRRC) database. The database coverage of relatively large firms in

the S&P 1500 and approximately 400 other widely held firms severely restricts our sample size.

Table 1, Panel A shows that we lose 606 (66 percent) observations from the original GAO1

restatements from unavailable Risk Metrics board data. We also use an alternative expanded sample

that includes smaller firms in robustness tests in Section V.

Test Design for Earnings Management Contagion

The following describes the test regression and measurement of the test variables. Appendix A

provides the definition of each variable and how the variable is calculated. The relevant dependent

variable measures the likelihood that a firm manages earnings that later have to be restated. The

indicator variable EM equals 1 for a firm in a given year if that year is the first year for which its

earnings are restated, and is 0 otherwise. In other words, EM turns on when the firm is infected.

Some firms restate earnings for more than one year. These observations are removed from the

sample after the initial year of infection and are not tested for infection again in later years to avoid

multiple-counting of the same infection.

The key independent variable is the indicator EMLINK, which equals 1 if the firm has a board

link to a contagious firm during the contagious period. Figure 2 shows an example where the

exposed firm and the contagious firm share a common director during the three contagious years

starting from the first year for which the contagion firm’s earnings are restated, so EMLINK equals 1

for this exposed firm in these three years.7 Because it takes time for an exposed firm to develop the

6 We do not use the second release of the GAO report on restatements covering the post-SOX period. Restatementsare more serious violations in the pre-SOX period than in the post-SOX period (Burks 2011), and a large fractionof post-SOX restatements pertain to technical issues reflecting changes in financial reporting rules after SOX thanto intentional accounting mistakes that are the focus of this study (Hennes et al. 2008).

7 We code EMLINKi,t observations for firm i year t as follows. Check whether firm i shares a director with acontagious firm at time t, t�1, or t�2. If yes, then EMLINKi,t ¼ 1, and if no, EMLINKi,t ¼ 0. Then the programmoves forward to the next firm-year observation. Note that if the board link is present in either t�1 or t�2 to acontagious firm, then it is still an EMLINK observation for year t that turns on. It is more efficient for codingpurposes to trace backward for any firm at a given point in time whether the firm has been exposed to a contagiousfirm over a multiyear incubation window. The setting is equivalent to the earlier text description tracing forwardboard contagion from a contagious firm at the time it is infectious.



infection, we assume a contagious period that begins in the earliest year for which earnings are

restated and lasts up to two years after.8 We also consider a measure for contagiousness using the

discrete variable #EMLINK, which measures the number of such links to contagious firms in a

given year.

TABLE 1

Descriptive Statistics

Panel A: Sample Selection

GAO sample released on Oct 4, 2002 (1/1/1997–6/30/2002) 919

Less:

Missing Gvkey 91

Not covered by Risk Metrics 606

Missing beginning EM date or outside of Risk Metrics coverage period 79

Duplicate restatements or multiple restatements per year 6

Multiple restatements per firm 19

Final usable sample for identifying earnings management 118

Panel B: Characteristics of Restatements

Variable

Combined Sample(n ¼ 118)

Non-Irregularity-Related(n ¼ 85)

Irregularity-Related(n ¼ 33)

Mean Median Mean Median Mean Median

Announcement Return �0.074 �0.027 �0.051 �0.014 �0.132 �0.086

Earn Decrease 0.763 1.000 0.718 1.000 0.879 1.000

MGR Prompt 0.390 0.000 0.329 0.000 0.545 1.000

Revenue Restate 0.475 0.000 0.459 0.000 0.515 1.000

Multiple 1.153 1.000 1.118 1.000 1.242 1.000

Restate Amt �0.054 �0.024 �0.022 �0.017 �0.137 �0.035

Duration 390 364 352 273 488 364

Variable Definitions:Announcement Return ¼market-adjusted restatement return over days (�1,þ1) relative to the announcement;Earn Decrease ¼ 1 if earnings are restated downward, and 0 otherwise;MGR Prompt ¼ 1 if the firm initiates the restatement, and 0 otherwise;Revenue Restate ¼ 1 if the restatement concerns revenue recognition, and 0 otherwise;Multiple ¼ number of restatement categories involved;Restate Amt ¼ cumulative magnitude of earnings correction scaled by total assets in fiscal year ended prior to the

restatement announcement date; andDuration ¼ length of restating period in calendar days.

(continued on next page)

8 A longer contagious period has the advantage of allowing the researcher to obtain a more complete response to theboard contagion if it exists. A longer contagious period, however, is more likely to include a public announcementof the restatement, which can contaminate the board contagion effect. We examine robustness in two ways (resultsare untabulated). First, we consider shorter contagious periods. When the contagious period is restricted to onlyone year, contemporaneous with the initial manipulation for the contagious firm, the EMLINK p-value is 0.079 forthe Table 2, Column (4) regression. The weaker significance is expected because there are fewer observations withboard links to contagious firms. Second, we drop observations when board links are observed after restatementannouncements. EMLINK is again significant with p-value of 0.044. Thus, we are able to infer with confidence thatour results are driven by board contagion and not from spillover effects from the public announcement ofrestatements.



TABLE 1 (continued)

Panel C: Comparison of Firm Characteristics for EM Sample and Control Sample

Variable

Control Group(n ¼ 8,035)

EM Group(n ¼ 118)

t-stats forMean

Difference

WilcoxonTest forMedian

DifferenceMean MedianStd.Dev. Mean Median

Std.Dev.

EMLINK 0.187 0.000 0.390 0.288 0.000 0.455 (�2.40)** (�2.79)***

#BOARDLINK 7.438 5.000 7.604 7.839 6.000 7.584 (�0.57) (�1.10)

ROA 0.026 0.037 0.111 0.004 0.025 0.132 (1.81)* (2.56)**

Loss 0.186 0.000 0.389 0.239 0.000 0.429 (�1.34) (�1.47)

Size 7.483 7.270 1.636 7.665 7.612 1.473 (�1.33) (�1.76)

Leverage 0.584 0.595 0.229 0.600 0.599 0.237 (�0.73) (�0.65)

Market-to-Book 3.346 2.246 3.450 3.362 2.338 3.074 (�0.06) (�0.10)

Ret Volatility 3.125 2.739 1.529 3.598 3.151 1.638 (�3.10)*** (�3.53)***

Operating Lease 0/1 0.771 1.000 0.420 0.855 1.000 0.354 (�2.53)** (�2.14)**

Firm Age 23.732 19.000 15.570 23.444 19.000 16.285 (0.19) (0.40)

Abnormal Employee �0.057 �0.039 0.229 �0.049 �0.021 0.275 (�0.31) (�1.18)

G-index 8.959 9.000 2.762 9.290 9.000 2.844 (�1.15) (�1.14)

Inst Holdings 0.153 0.134 0.123 0.149 0.139 0.125 (0.33) (0.46)

Board Size 9.600 9.000 3.180 9.356 9.000 2.833 (0.93) (0.45)

CEO Duality 0.670 1.000 0.470 0.703 1.000 0.459 (�0.79) (�0.77)

Pct Independent 60.263 62.500 19.342 62.717 66.667 19.086 (�1.39) (�1.42)

Director Tenure 9.218 8.727 4.231 8.431 8.174 3.623 (2.34)** (2.15)**

*, **, *** Significant at 10 percent, 5 percent, and 1 percent levels, respectively.t-statistics are in parentheses for mean difference tests and z-statistics are in parentheses for median difference tests.All variables are defined in Appendix A.

Panel D: Correlations

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

(2) #BOARDLINK 0.433(3) ROA 0.024 0.073(4) Loss �0.025 �0.101 �0.668(5) Size 0.269 0.564 0.083 �0.185(6) Leverage 0.104 0.256 �0.179 �0.002 0.506(7) Market-to-Book 0.068 0.129 0.170 �0.067 0.022 0.002

(8) Ret Volatility �0.061 �0.240 �0.454 0.494 �0.345 �0.237 0.074(9) Operating Lease (0/1) 0.018 0.001 �0.038 0.155 �0.266 �0.243 0.095(10) Firm Age 0.174 0.418 0.154 �0.179 0.333 0.196 �0.059(11) Abnormal Employee �0.017 �0.009 �0.141 0.071 �0.075 0.008 �0.075(12) G-index 0.075 0.228 0.048 �0.077 0.160 0.149 �0.041(13) Inst Holdings �0.052 �0.097 �0.025 0.090 �0.227 �0.059 �0.100(14) Board Size 0.197 0.489 0.082 �0.179 0.594 0.381 �0.007

(15) CEO Duality 0.065 0.152 0.036 �0.062 0.151 0.100 0.013

(16) Pct Independent 0.139 0.320 0.009 �0.062 0.161 0.138 �0.017




The variable #BOARDLINK is the number of links to other boards in general, regardless of

whether the linked firms manage earnings that require subsequent restatements. This measure

captures contagion effects via board networks in general. When included in a multiple regression

with #EMLINK, the #BOARDLINK variable captures contagion of relatively good accounting; i.e.,

it incrementally captures the effect of the number of links to firms that are not involved with

restatements. This allows us to test for contagion of both positive and negative financial reporting

behaviors.

In our setting, the event of interest occurs at discrete points in time and the key dependent

variable is the conditional probability that the event occurs, given that it has not yet happened.

The binary dependent variable EM and key independent variable EMLINK and other control

variables are all measured in annual fiscal year intervals. The empirical question is whether

EMLINK ¼ 1 incrementally raises the probability of observing EM ¼ 1. Our test sample is a

modified panel dataset because we drop firms from the sample in future years after the year they

are infected.

Allison (1982) recommends the discrete-time logistic model as the most appropriate model for

time-to-event settings such as ours. To test for board contagion of earnings management, we run the

discrete logistic regression of the EM indicator on the main independent variable X, which is either

EMLINK or #EMLINK, #BOARDLINK, and controls (regression time subscripts suppressed for

convenience):

LogitðEMÞ ¼ Fðb0 þ b1Xþ b2#BOARDLINK þX

bjControlsi þ Year Fixed Effects

þ Industry Fixed Effectsþ eÞ: ð1Þ

We consider whether the presence of a larger number of board links may dilute the contagion

effect from the board link to a contagious firm. To examine this, we include an additional

interaction variable between #EMLINK and #BOARDLINK as follows:

TABLE 1 (continued)

Panel E: Correlations (continued)

EMLINK (8) (9) (10) (11) (12) (13) (14) (15)

(9) Operating Lease (0/1) 0.302(10) Firm Age �0.443 �0.227(11) Abnormal Employee 0.017 0.003 �0.013

(12) G-index �0.214 �0.082 0.352 �0.009

(13) Inst Holdings 0.087 0.235 �0.137 0.011 �0.089(14) Board Size �0.382 �0.291 0.348 �0.025 0.218 �0.229(15) CEO Duality �0.099 �0.047 0.114 �0.009 0.111 �0.014 0.057(16) Pct Independent �0.144 �0.145 0.280 �0.016 0.274 �0.016 0.131 0.125

Correlation figures are shown in bold if they are significant at the 5 percent level. Definitions of all variables are inAppendix A.The table describes the selection process and summary statistics of the sample. The sample consists of all firms in RiskMetrics from 1997 to 2001. Panel A provides the number of observations obtained at each sample-selection step,beginning with the GAO (2002) restatement sample. Panel B reports the characteristics of these restatements. Panel Cprovides the summary statistics for two groups, the control sample versus the EM sample. In the EM sample, firms areidentified as earnings manipulators (i.e., contagious firms) if the earnings for that firm year had to be restated at a futuredate. The characteristics are measured at the time when the manipulators managed earnings (i.e., the contagious period).The control group consists of the remaining firms in Risk Metrics not identified as earnings manipulators. Panels D and Ereport correlations among all independent variables.



LogitðEMÞ ¼ Fðb0 þ b1#EMLINK þ b2#BOARDLINK þ b3#EMLINK 3 #BOARDLINK

þX

bjControlsi þ Year Fixed Effectsþ Industry Fixed Effectsþ eÞ:ð2Þ

The key coefficient of interest in Regressions (1) and (2) is b1 for EMLINK and #EMLINK. A

positive sign for these coefficients indicates that a board link to a contagious firm during the

earnings management period (i.e., an earnings manipulator that subsequently restated earnings)

increases the likelihood that an exposed firm also manages earnings to an extent that requires later

restatement.

The coefficient for #BOARDLINK indicates whether there is contagion of good financial

reporting. A significant negative coefficient would suggest that a firm whose board of directors is

linked to non-restating firms is less likely to manipulate its earnings. The interaction variable

#EMLINK 3 #BOARDLINK tests for whether the number of other non-manipulator board links

dilutes the earnings management contagion from the manipulator-board link.

As with most time-to-event data, there is right and left censoring of the data. Regarding

right censoring, we do not have data about whether a firm that is not infected during the sample

period will become infected after the sample period. Allison (1982) notes that discrete-time

logistic models handle right-censoring data appropriately. Regarding the unavoidable left-

censoring problem, the effect of truncation bias is less clear. On the one hand, exposure to a

contagious firm before the sample period begins but within the incubation period results in

undercounting exposure (EMLINK ¼ 0 instead of 1). This measurement error in the independent

variable biases against finding a positive association between EM and EMLINK. On the other

hand, a firm that restates prior to the start of the sample period is incorrectly included in the

sample when it should have been removed. Since EM is likely to be 0 after a restatement,

inclusion of observations where EMLINK ¼ 0 biases toward finding a positive association

between EM and EMLINK.

Next we examine whether the strength of contagion varies with the linked director’s board

position. A director’s influence over financial reporting varies with her leadership position on the

FIGURE 2Illustration of Timing for EMLINK ¼ 1



board. The CEO, the board chair, and audit-committee chair/members are likely to wield greater

influence than other directors on financial reporting issues. We therefore include additional

indicator variables EM_X_LINKy to represent links to contagious firms identified by the interlocked

board member position _X_ on firm y’s board, where _X_ is CEO, BOARDCHAIR, AUDITCHAIR,

AUDITCOM, or OTHER and y is the exposed firm or the contagious firm. We add these indicators

to Regression (1) separately in successive regressions:

LogitðEMÞ ¼ Fðb0 þ b1EMLINK þ b2EM X LINKy þ b3#BOARDLINK þX

bjControlsi

þ Year Fixed Effectsþ Industry Fixed Effectsþ eÞ:ð3Þ

The coefficients on board position _X_ variables measure the incremental strength of earnings

management contagion as a result of the shared director’s board position _X_ relative to the

average board member position in the exposed or contagious firms. Finally, we test jointly which

of the board positions dominate by substituting for EMLINK all five board position variables

pertaining to the exposed firm in one regression and then pertaining to the contagious firm in a

second regression.

Control Variables

Our tests require appropriate controls for other known determinants of restatements or

earnings management (e.g., Lee et al. 2006; Lennox and Pittman 2010). Details of their

measurement and data sources are summarized in Appendix A. We control for firm

performance with return on total assets (ROA), and a loss indicator variable, Loss. Large firms

are more visible and therefore politically more vulnerable to regulators wishing to send a

message of intolerance for earnings manipulation to the capital markets. Size is estimated as

the natural logarithm of total assets. Growth effects are controlled using Market-to-Book, the

firm’s equity market-to-book ratio. High-growth firms may be tempted to manage earnings to

sustain the perception of high growth when actual growth has slowed. High-growth firms may

also be less understood by investors and so may be more able to manipulate earnings without

detection.

We control for off-balance sheet activities that can be used to reduce reported liabilities and

inflate earnings using an indicator, Operating Lease, which equals 1 if the company’s future

operating lease obligations are greater than 0 (Ge 2006). We use Firm Age to control for older firms

being less likely to manage earnings because they have less information asymmetry and

information uncertainty. We use Abnormal Employee to control for the divergence between

financial and nonfinancial performance, following Brazel et al. (2009), who find that financial fraud

is higher where financial and nonfinancial performance measures diverge more. Firms facing higher

operating risks have greater incentives to manage earnings, so we control for operating risks using

Ret Volatility, measured as the standard deviation of the stock returns in the fiscal year. The variable

Leverage is measured as the ratio of total liabilities to total assets and controls for higher risk of firm

failure and higher incentive to manage earnings to avoid debt-related constraints imposed on

management.

To control for other governance-related variables that may separately affect earnings

management, we include a corporate governance score using the G-index (Gompers et al. 2003)

and the fraction of institutional holdings, Inst Holdings, from the Thompson Financial database. To

isolate the effect of contagion from board links conservatively, we include CEO duality, board size,

and board independence to control for other board characteristics that are proxies for the strength of

monitoring by the board in prior literature.



IV. RESULTS

Summary Statistics and Correlations

Table 1, Panel A documents the selection process for the earnings management sample. The

sample consists of 118 earnings management firms that eventually restated earnings.9 Adding the

remaining non-restating firms in Risk Metrics for the 1997 to 2001 period produces a total sample

of 8,153 firm-year observations with 2,406 distinct firms for the test sample. The number of

observations in each regression varies with data availability for the included variables. Panel B

presents mean and median statistics of the sample’s restatement characteristics. Although the

sample selection criteria retain only a small number of the restatement population, the restatement

characteristics are typical of the restatement population in our sample period (Burks 2011). The

mean announcement market reaction is significantly negative (�7.4 percent), the mean restated

amount as a percentage of assets is large (�5.4 percent), and the misreporting predominantly biases

earnings upward (76.3 percent). Irregularities are more severe violations than errors, consistent with

Hennes et al. (2008). The GAO and FASB, however, view even error restatements as serious

violations. Consistent with this view and Burks (2011), we find that errors are indeed serious

violations in our sample period. The mean announcement market reaction is �5.11 percent, the

mean restated amount is �2.17 percent of assets, and 72 percent of errors bias earnings upward.

Errors therefore merit inclusion in our tests.

Table 1, Panel C reports the summary characteristics separately for the sample of firms

identified as managing earnings based on subsequent restatements (EM group) versus the sample of

firm-year observations that did not manage earnings (control group). A significantly higher fraction

of EM firms (28.8 percent) have observations with EMLINK ¼ 1 than the control sample (18.7

percent). This univariate result, that earnings manipulators have greater exposure via board links to

firms that later restate earnings, provides preliminary support for H1. The number of interlocked

directors, #BOARDLINK, is not significantly different between the two groups and, thus,

differences in earnings management behavior do not have a univariate association with the level of

connectedness to other firms.

The remaining characteristics are similar along some, but not all, dimensions between the EMgroup and the control group and, therefore, we include these characteristics as control variables in

the regressions. EM firms have more volatile stock returns and worse performance, and use more

off-balance-sheet activities than the control sample firms, which is consistent with these firms

facing greater incentives and opportunities to manage earnings. The average directors’ tenure in EMfirms, 8.4 years, is shorter than the 9.2 years in control firms, which is consistent with higher

director turnover after restatements (Srinivasan 2005).

In untabulated analyses, we find that virtually all CEOs sit on their own boards and, compared

with other directors, interlocked directors have longer tenure, are more likely to serve on audit

committees, are more likely to be female, are less likely to be a CEO or a board chair, and hold a

lower percent of the company’s equity. In Table 1, Panel D, the high correlation of 0.43 between

EMLINK and #BOARDLINK suggests that opportunities for earnings management contagion

increase with greater board exposure to other companies. Therefore, it is especially important to

control for #BOARDLINK in all of our regressions. Pairwise correlations between EMLINK and the

other characteristics are also significant in some cases but not others, which again justifies including

the other characteristics as controls in the regressions.

9 The small number of firms with restatements in our sample is common in studies related to restatements. Ericksonet al. (2006) use 50 fraud events to study executive equity incentive effects on accounting fraud, and Lee et al.(2006) use 91 restatements to investigate the relation between earnings management and performance and growth.



Board Earnings Management Contagion

Table 2 presents the results of the logistic regressions of EM on EMLINK or #EMLINK and control

variables in Equations (1) and (2) to test H1. The results indicate that the likelihood that an exposed

firm manipulates earnings is significantly positively associated with the firm having a director who

serves on the board of a contagious firm. In all the model specifications, the coefficient estimate on

EMLINK is positive and significant at the 5 percent level. The results are robust to replacing EMLINKwith the discrete measure, #EMLINK, which measures the strength of these manipulator board links.10

In other words, an exposed firm that is board-linked to a contagious firm within two years of when

the contagious firm manipulated earnings, as evidenced by later restatement of that period’s earnings,

has a higher frequency of itself manipulating earnings, as reflected in a higher frequency of subsequent

restatements. Furthermore, the effect is economically significant. We calculate the economic magnitude

in two ways, using the following predicted probabilities from the regression in Table 2, Column (4):

P1 ¼ ProbabilityðEM ¼ 1jEMLINK ¼ 1; controlsÞ ¼ 2:04%:

P2 ¼ ProbabilityðEM ¼ 1jEMLINK ¼ 0; controlsÞ ¼ 1:02%:

This implies that the marginal effect of EMLINK is 1.02 percent, calculated as P1�P2. Compared with a

baseline unconditional probability of restatement-related earnings management of 1.76 percent (93 (EM¼1) observations divided by 5,279 total firm-year observations). Therefore, a board link to manipulators

(EMLINK ¼ 1) has a marginal affect that is 1.02/1.76 ¼ 58 percent as large as the unconditional

probability of managing earnings. Alternatively, the odds ratio [P1/(1� P1)]/[P2/(1� P2)]¼ 2.02,

which suggests that a board link to a manipulator doubles the firm’s likelihood of becoming an earnings

manipulator.11

Table 2, Column (5) uses the continuous measure #EMLINK to capture the effect of linkage

intensity on earnings management contagion. The coefficient estimate of 0.373 is significantly

positive (p-value ¼ 0.024), with a marginal effect of 0.44 percent.

Interestingly, the variable #BOARDLINK is significantly negative, �0.044 and �0.044 (p-

values , 0.10) in Columns (4) and (5), respectively, which implies that a firm with directors linked

to non-manipulators is less likely to manage earnings. This result is sensitive to the regression

specification, however, as Columns (1) to (3) do not report statistical significance for the variable.

This is some evidence that good financial reporting behaviors are also contagious. In our sample,

the average number of board links to other firms is 5, so the average marginal effect of 0.26 percent

is about 60 percent of the size of the marginal effect for #EMLINK.

Finally, we interact #BOARDLINK with #EMLINK and find that the interaction variable is

negative and statistically significant in Table 2, Column (6). This implies that a greater number of

board links for the exposed firm to relatively good financial reporting firms weakens earnings

management contagion.12

10 We infer results as being robust in the remainder of the study if the estimated coefficients have the same sign andare statistically significant at the 5 percent level in robustness tests.

11 The unconditional probability of a restatement is very small, so restatements are rare events in the sample. Ajackknife method to examine whether the marginal effect for EMLINK is sensitive to a handful of observationsshows that our results are robust. The jackknife estimate remains positive, with a p-value of 0.012.

12 Norton et al. (2004) recommend an adjustment to an interaction term coefficient in nonlinear models if theresearch objective is to assess its total effect at a point other than the center of the distribution. Our purpose forincluding the interaction term in the logit model is to examine whether earnings management contagion from aboard link to a manipulator varies with the number of board links to non-manipulators, and not to assess thecombined effect of board links to manipulators and non-manipulators on contagion, so it is correct to rely on theuncorrected interaction term coefficient and t-statistics to draw inferences (Kolasinski and Siegel 2010). A plot ofthe z-statistics for the interaction effect shows that the majority of the z-statistics are negative.



TABLE 2

Propensity to Manage Earnings on Board Links to Earnings Manipulators

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

#EMLINK 0.373** 1.053***

(0.024) (0.000)

EMLINK 0.639** 0.596** 0.591** 0.705**

(0.012) (0.023) (0.025) (0.013)

#EMLINK 3

#BOARDLINK�0.051**

(0.014)

#BOARDLINK �0.009 �0.009 �0.004 �0.044* �0.044* �0.014

(0.531) (0.524) (0.786) (0.062) (0.072) (0.572)

ROA �0.147 �0.173 �0.070

(0.908) (0.891) (0.957)

Loss 0.059 0.067 0.072

(0.879) (0.861) (0.853)

Size 0.383*** 0.388*** 0.378***

(0.000) (0.000) (0.000)

Leverage 1.024 1.011 1.014

(0.198) (0.200) (0.211)

Market-to-Book �0.054* �0.051* �0.060*

(0.079) (0.098) (0.050)

Ret Volatility 0.165 0.160 0.159

(0.122) (0.132) (0.136)

Operating Lease (0/1) 0.936** 0.924** 0.907**

(0.035) (0.034) (0.041)

Firm Age �0.011 �0.011 �0.010

(0.241) (0.242) (0.274)

Abnormal Employee �0.161 �0.172 �0.152

(0.739) (0.722) (0.751)

G-index 0.058 0.061 0.052

(0.219) (0.193) (0.263)

Inst Holdings 0.160 0.202 0.086

(0.869) (0.836) (0.930)

Board Size �0.021 �0.021 �0.027

(0.707) (0.717) (0.641)

CEO Duality 0.082 0.079 0.089

(0.730) (0.737) (0.707)

Pct Independent 0.006 0.006 0.005

(0.419) (0.394) (0.459)

Year Fixed Effects No Included Included Included Included Included

Industry Fixed Effects No No Included Included Included Included

Observations 8,153 8,153 7,001 5,279 5,279 5,279

Pseudo R2 0.006 0.016 0.036 0.070 0.067 0.075

*, **, *** Indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively.Robust p-values are in parentheses.The table presents results of logistic regressions of EM on EMLINK or #EMLINK based on Equations (1) or (2) inSection III. See Figures 1 and 2 for specification of the timing for these variables. EM equals 1 if this is the initial fiscalyear for which earnings are managed, and is 0 otherwise. EMLINK equals 1 when a firm becomes an exposed firm, and is0 otherwise. A firm is exposed when it has an interlocked board member with a contagious firm during the contagiousperiod. A firm is contagious when it is in the first year of its restating period and the subsequent two years. #EMLINK ismeasured as the number of board interlocks with other distinct earnings manipulators.Definitions of all variables are provided in Appendix A.



With regard to the control variables, Size, Operating Lease, and Market-to-Book are

statistically significant, indicating that firms that are larger and that have operating leases and low

growth are more likely to restate earnings, consistent with past literature. Finally, we note that 47

percent of the contagious-exposed firm pairs with restatements share at least one similar restated

item, and 30 percent of the firm pairs restate revenues. We do not provide formal tests because the

sample size is too small for meaningful statistical tests.

Board Positions and Earnings Management Contagion

Table 3, Panel A reports the results to test H2, which asserts that influential directors in

exposed firms who are linked to manipulator firms by virtue of their leadership or accounting-

related board positions have a disproportionately strong effect on spreading earnings management

across firms. Among the five types of positions, we find that links in which the tainted board

director is the board chair, audit committee chair, or audit committee member significantly raise the

likelihood that the firm manages earnings relative to other board positions.13

The CEO position in the exposed firm, however, does not have a significant incremental

influence on earnings management contagion. A CEO is expected to exert a strong influence over

the board on corporate policy in general. A CEO’s advocacy on financial reporting decisions may

seem transparently self-serving, however, and thereby be less persuasive.

Relative to an average director, an audit committee member has an economically substantial

incremental influence. The influence of the board chairman is even greater, and the audit committee

chairman’s influence is greatest. Compared to the marginal effect of EMLINK, the marginal effect

of EMAUDITCOMLINK in Table 3, Panel A, Column (4) is 1.5 times larger, that of

EMBOARDCHAIRLINK in Column (2) is 3 times larger, and that of EMAUDITCHAIRLINK in

Column (3) is 4.5 times larger. The regression in Column (5) that includes all five board positions

(CEO, board chair, audit chair, audit committee member, and all other board positions) achieves the

highest pseudo R2. Because different board positions vary in their contagion strength, the regression

that allows the board position weights to vary achieves greater overall explanatory power. Column

(5) shows that the audit chair and audit committee member positions dominate in statistical

significance relative to the three other positions. Since the audit committee has a supervisory role

over financial reporting, these results are intuitive and consistent with the board’s monitoring role.14

These results suggest that board governance does matter for the quality of financial statements. The

famous Milgram (1963) experiment indicated that an authority figure can potentially induce morally

unacceptable behavior in followers, even when the followers regard the actions as immoral. Even

though management is responsible for the financial reporting choices, the board plays an important role

in what is finally reported in the financial statements. In their role as monitors, they can permit or

prohibit aggressive accounting choices. An aggressive CEO can be controlled by a forceful board

chairman and, especially, also by a strict audit committee chairman, while a weak board may acquiesce

or even collaborate in aggressive earnings management. When the audit chair and audit committee

acquiesce to earnings management behaviors, the rest of the board is likely to acquiesce as well.

13 Information on audit-committee membership is not available until 1998 from Risk Metrics, so our sample periodis from 1998 to 2001 for Columns (3), (4), and (5) in Panels A and B of Table 3.

14 For brevity, the control variables are not tabulated in Table 3 onward. Apart from firm size (Size), which isstrongly significant in all the regressions, and Market-to-Book and Operating Lease, which are weakly significantin some regressions, none of the other control variables are significant. The regression of EM on the mainvariables, EMLINK and #BOARDLINK, and year and industry fixed effects and including only Size as the controlyields a pseudo R2 of 0.041. Including all the other control variables raises the pseudo R2 to 0.070. This impliesthat the other control variables are not individually significant because of multicollinearity, but that they arejointly important and so merit retention in all the regressions.



TABLE 3

Earnings Management Contagion and Board Positions of Interlocked Directors

Panel A: Earnings Management Contagion and Board Positions of Interlocked Directors inExposed Firms

(1) (2) (3) (4) (5)

EMLINK 0.621** 0.566* 0.559* 0.346

(0.030) (0.052) (0.065) (0.380)

EMCEOLINKExposed 0.630 �0.083

(0.230) (0.955)

EMBOARDCHAIRLINKExposed 0.951* 1.149

(0.054) (0.379)

EMAUDITCHAIRLINKExposed 1.320** 1.084*

(0.015) (0.058)

EMAUDITCOMLINKExposed 0.682* 0.660*

(0.097) (0.063)

EMOTHERLINKExposed �0.243

(0.626)

#BOARDLINK �0.044* �0.045* �0.052** �0.056** �0.053**

(0.063) (0.061) (0.039) (0.027) (0.045)

Control Variables Included Included Included Included Included

Year and Industry Fixed Effects Included Included Included Included Included

Observations 5,279 5,279 4,381 4,381 4,381

Pseudo R2 0.071 0.073 0.074 0.072 0.079

Panel B: Earnings Management Contagion and Board Positions of Interlocked Directors inContagious Firms

(1) (2) (3) (4) (5)

EMLINK 0.546* 0.584** 0.707** 0.443

(0.065) (0.047) (0.013) (0.195)

EMCEOLINKContagious 1.148** 1.413

(0.011) (0.118)

EMBOARDCHAIRLINKContagious 1.129** 0.074

(0.019) (0.939)

EMAUDITCHAIRLINKContagious 0.109 �0.421

(0.917) (0.637)

EMAUDITCOMLINKContagious 0.617 1.021***

(0.122) (0.005)

EMOTHERLINKContagious 0.242

(0.529)

#BOARDLINK �0.048** �0.049** �0.054** �0.054** �0.060**

(0.037) (0.037) (0.029) (0.031) (0.016)

Control Variables Included Included Included Included Included

Year and Industry Fixed Effects Included Included Included Included Included

Observations 5,279 5,279 4,381 4,381 4,381

Pseudo R2 0.075 0.074 0.069 0.071 0.078




Table 3, Panel B tests for the influence of board leadership positions of interlocked directors in

the contagious firm on earnings management in the exposed firm. The test mirrors that in Panel A,

but board positions of the interlocked directors pertain to the contagious firm. The statistical

significance of the board position variables in Columns (1) and (2) implies that an interlocked

director who is either the CEO or board chair in the contagious firm is more likely to transmit

earnings management to the exposed firm. When all five board positions are included jointly in

Column (5), the audit committee position dominates other positions (p-value¼ 0.5 percent) and the

CEO position is now marginally insignificant (p-value¼ 11.8 percent).15 To summarize, the results

suggest that linked directors who are the CEO, the board chairman, or an audit committee member

in the contagious firm are viewed as opinion leaders about financial reporting in the exposed firm.

They may also be viewed as having greater experience with financial reporting, a deeper

understanding of the technology needed to manage a particular accounting item, and more intimate

knowledge about auditor norms for materiality or general net benefits to earnings management,

potentially increasing their influence on the other directors in the exposed firm. A caveat to the

inferences in this subsection is that the number of observations for the various board positions for

the linked director is relatively small. Nevertheless, as discussed above, different board positions

have statistically and economically significant effects.

Controlling for Earnings Management Incentives

Previous studies document circumstances in which earnings management incentives are

especially strong. A correlation of board linkages with such strong incentives could potentially bias

our tests. Table 4, Panel A summarizes tests of whether earnings management contagion remains

significant after controlling for the presence of strong incentives for earnings management. We control

for the mergers and acquisitions indicator variable M&A in Column (1) (Louis 2004), for issuances of

new equity or debt indicator variable ISSUE (Teoh et al. 1998a, 1998b) in Column (2), and for the

likelihood of accounting fraud FSCORE (Dechow et al. 2011) in Column (3). All three variables are

included jointly in Column (4).

Table 4, Panel A shows that the incentive controls, M&A, ISSUE, and FSCORE, are

individually statistically significant, with FSCORE dominating the other two when considered

jointly. At the same time, the coefficient estimates of EMLINK and #BOARDLINK for earnings

management are robust to the inclusion of these further determinants of earnings management.

TABLE 3 (continued)

*, **, *** Indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively.Robust p-values are in parentheses.The table reports results of logistic regressions of EM on EMLINK and the interlocked director’s position (CEO, board

chair, audit-committee chair, audit-committee member, or other) in the exposed firm in Panel A and in the contagiousfirm in Panel B.Variable definitions are in Appendix A.

15 As a practical matter, every board has several audit committee members and only one CEO, one board chair, andone audit chair. Audit chair links comprise 6.9 percent (3.1 percent), CEO 9.0 percent (11.1 percent), board chair9.4 percent (9.2 percent), and audit member 43.9 percent (32.7 percent) of board positions in the exposed(contagious) firm in our sample. The greater availability of audit committee member board links suggests a higherpotential for audit committee members to spread financial reporting behavior. Table 3 results confirm that auditcommittee members dominate other board positions in spreading earnings management.



TABLE 4

Robustness Tests for Market Incentives and Alternative Networks

Panel A: Board Interlock Contagion Controlling for Market Incentives

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

EMLINK 0.676** 0.696** 0.612** 0.597**

(0.016) (0.014) (0.046) (0.048)

#BOARDLINK �0.039* �0.045* �0.058** �0.057**

(0.093) (0.053) (0.033) (0.033)

M&A 0.764*** 0.429

(0.002) (0.123)

ISSUE 0.483** 0.350

(0.050) (0.181)

FSCORE 0.982*** 0.770**

(0.009) (0.044)

Control Variables Included Included Included Included

Year and Industry Fixed Effects Included Included Included Included

Observations 5,279 5,279 4,807 4,807

Pseudo R2 0.078 0.074 0.083 0.088

Panel B: Board Contagion of Earnings Management Controlling for Alternative Networks:Common Industry, Geographical Proximity, or Shared Auditors

(1) (2) (3)

EMLINK—Different Ind. Only 0.502*

(0.092)

EMLINK 0.713** 0.7181**

(0.012) (0.011)

%EM firms 100 miles �1.590 �1.9637

(0.534) (0.444)

%EM firms 100 miles same auditor 0.6836**

(0.050)

SEC 100 miles 0.133 0.1234

(0.633) (0.656)

#BOARDLINK �0.037 �0.045* �0.0463**

(0.104) (0.057) (0.048)

Control Variables Included Included Included

Year and Industry Fixed Effects Included Included Included

Observations 5,279 5,269 5,269

Pseudo R2 0.066 0.070 0.074

*, **, *** Indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively.Robust p-values are in parentheses.Panel A reports results of logistic regressions of EM on EMLINK, with additional controls for indicators for incidence ofmergers and acquisitions (M&A), new issues (ISSUE), and Dechow et al.’s (2010) fraud score (FSCORE). Panel Breports results of logistic regressions of EM on EMLINK, EMLINK—Different Ind. Only, %EM firms 100 miles, %EMfirms 100 miles same auditor, and/or SEC 100 miles.Variable definitions are in Appendix A.



Contagion from Alternative Networks: Common Industry, Geography, or Common Auditor

We next discuss additional tests to control for contagion via other types of networks. Previous

literature documents contagion between firms in common industries, between spatially proximate

firms, between spatially proximate firms and monitors (i.e., distance to auditor and local SEC

office), and between firms sharing common auditors.

Firms in the same or similar industries often make similar accounting choices, and their

auditors and directors may be selected for their industry expertise. Kedia and Rajgopal’s (2011)

study of geographical network effects on restatements reports that the likelihood of misreporting by

a firm increases with the frequency of neighboring firm misreporting and decreases with the firm’s

distance from the SEC local office. Close proximity to an SEC enforcement office lowers

enforcement cost and, thus, increases enforcement. Geographic proximity and auditor sharing

measure the ease of information gathering by firms and monitoring costs associated with resource

constraints on auditors and the SEC staff. The earlier regressions already control for industry effects

and firm fixed effects. In addition to serving as controls for contagion via alternative networks,

geographical proximity variables can also control for common economic shocks, and for common

auditor and director expertise specific to local regions. These commonalities affect the net benefits

to managing earnings for firms in the same locale.

Table 4, Panel B, Column (1) modifies the EMLINK variable to the EMLINK—Different Ind.Only indicator variable. It takes a value of 1 when the tainted board link is to a contagious firm in a

different industry, based on the Fama-French 48 industry classification. The coefficient on

EMLINK—Different Ind. Only is statistically significant, so our earlier result of board contagion is

not driven solely by same-industry contagion. There is significant cross-industry earnings

management contagion via board links.

Column (2) regression includes a variable, %EM firms 100 miles, measured as the percentage of

contagious firms within 100 miles of the firm, and an indicator variable, SEC 100 miles, for whether a

local SEC office is within 100 miles of the firm. The evidence shows that EMLINK and

#BOARDLINK remain significant, and so board network contagion for both bad and good financial

reporting is incremental to the geography-related effects. Since having a contagious firm nearby and

a common auditor both encourage earnings management, we include a further variable, %EM firms100 miles same auditor, that measures the percentage of contagious firms within 100 miles that share

a common auditor with the firm in Column (3). Again, EMLINK and #BOARDLINK remain

significant. The new control variable for close proximity to other manipulators that share a common

auditor is also significant, consistent with prior findings (Kedia and Rajgopal 2011). Although

inclusion of the new controls in Table 4, Panel B makes our test for board contagion conservative, we

continue to be able to conclude that board networks contribute to earnings management contagion.

V. ALTERNATIVE EXPLANATIONS AND ERROR VERSUS IRREGULARITY

We examine additional tests in this section to distinguish board contagion from alternative

explanations, such as endogenous matching of firm-director and firm-director fixed effects. We also

examine board contagion for error restatements versus irregularity restatements.

Endogenous Matching of Firms and Directors

A firm that intends to manage earnings may purposely recruit a director that facilitates such

behavior. If this endogenous firm-director matching is present and not controlled for, then any

resulting higher frequency of restatements for the hiring firm would be incorrectly attributed to the

board network. In principle, we can control directly for endogenous matching by using proxies for

specific director characteristics that facilitate earnings management. There is no clear agreement in



the literature, however, on what these specific characteristics are, and as a practical matter, data for

these director characteristics may be difficult to obtain. We control for endogenous matching effects

indirectly, using two alternative indicator variables that identify when new directors are hired.

The indicator variable EMMIGRATEDLINK turns on whenever EMLINK ¼ 1 and the

interlocked director is new to the exposed firm during the contagious period. Figure 3 illustrates the

three cases when EMMIGRATEDLINK equals 1. The new director is therefore hired at the exposed

firm after s(he) has gained earnings management experience from the contagious firm. The second

indicator variable, EMNEWDIRECTORLINK, relaxes this requirement somewhat and allows for the

new contagious director to be hired at the exposed firm even before gaining earnings management

experience by as much as two years prior. Figure 4 shows that EMNEWDIRECTORLINK turns on

even if the new director is hired as early as two years prior to the start of the earnings management

event in the contagious firm.

Table 5, Columns (1) and (2) indicate that EMMIGRATEDLINK and EMNEWDIRECTOR-LINK are both insignificant. Recall that the instances when these variables turn on is a subset of the

instances when EMLINK ¼ 1, so EMMIGRATEDLINK and EMNEWDIRECTORLINK measure

incremental endogenous matching effects over board contagion effects. A lack of significance for

these variables suggests that endogenous matching has no incremental effect on board contagion

and not that there is no board contagion effect. The key variable of interest remains EMLINK in

Table 5 for whether the board contagion effect is robust to controls for the endogeneity matching

effect. EMLINK remains significant in both regressions and, therefore, the board contagion effect is

robust to controlling for endogeneity of director hiring.

The regressions in Table 5, Columns (1) and (2) are conservative tests for board contagion of

earnings management because inclusion of these two indicator variables biases against finding

significance for EMLINK. The two new indicator variables assume that board contagion effects

associated with any hiring of a new director from a firm that later restated earnings are for the

endogenous purpose of building an earnings-management-friendly board around the time of hiring.

In some cases, however, the exposed firm may not even be aware at the time of hiring that the

newly hired director is either earnings-management-friendly or experienced, because the contagious

firm may not yet have announced its restatement or the hiring of the new director occurred before

earnings management began in the contagious firm. Counting such situations as endogenous hiring

by the firm to build an earnings-management-friendly board favors the endogeneity effect over the

board contagion effect.

Director and Firm Fixed Effects

Despite the rich set of control variables in our earlier regressions, we may mis-measure or miss

entirely some firm or director characteristics that are associated with a higher likelihood of earnings

management and are common to the pair of contagious and exposed firms and cause them to have

common directors. Inadequate controls for these characteristics will lead to an erroneous inference

about board contagion.

We exploit the crucial role of timing in board network contagion to control for these fixed

effects. Board network contagion relies on communication between the contagious and exposed

firms by the interlocked director specifically during the contagious period. Fixed effects come from

fixed common characteristics that cause contagious and exposed firms to employ a common

director, and these characteristics are present in the exposed firm even at the times when the board

link is absent. We use new indicator variables to identify firm and director fixed effects. For a

contagious-exposed firm pair, Figure 5 shows that the indicator PRE_POST_FIRM for the exposed

firm turns on in all years in the sample period before or after the contagious period.



PRE_POST_DIR is more restrictive and turns on only when the interlocked director is employed at

the exposed firm before or after the contagious period.

In a regression that includes either of the new indicator variables with EMLINK, the new

variable coefficients measure fixed firm or director effects on earnings management by the exposed

firm, and the EMLINK variable measures the effect of board contagion as before. The coefficients

for PRE_POST_FIRM in Column (3) and PRE_POST_DIR in Column (4) in Table 5 are not

statistically significant and so fixed effects are weak in our sample. The coefficient on EMLINKremains positively significant, which indicates that the earlier inference that board contagion

spreads earnings management is robust. Earnings management is more likely if the exposed firm is

linked to the contagious firm at the time when the contagious firm is managing earnings, and not

before or after.16

Errors versus Irregularities

We next examine whether board contagion strength varies for two alternative types of

restatements, error versus irregularity, defined as in Hennes et al. (2008). We are unable to use the

original sample for this test. Board network data from Risk Metrics restrict the sample to 118

FIGURE 3Illustration of Timing for EMMIGRATEDLINK ¼ 1

16 EMLINK is robust to a control for busyness of directors (Fich and Shivdasani 2006). Shared directors sit onmultiple boards and so may be more distracted by the demands of multiple board appointments and be lesseffective monitors. EMLINK is also robust to using a multiplicative heterogeneous diffusion model regression(Strang and Tuma 1993) as an alternative to the discrete logistic model. As mentioned in Section III, the discretelogistic regression model is recommended by Allison (2010) to test event history models and is widely used inthe accounting literature (e.g., Brown 2011; Stuart and Yim 2010). These results are not tabulated for brevity.



restatements, and subdividing this into irregularity-only restatements, which are infrequent, yields

too few observations with board links for meaningful statistical tests. Therefore, we use Compact

Disclosure as an alternative source for board network data for the GAO1 restatements. Compact

Disclosure has the advantage of wider coverage, in that it contains all firms with assets exceeding

$5 million.17 The resulting new sample containing smaller firms also serves as a robustness test of

our earlier results for size. The disadvantage of our version of the Compact Disclosure dataset is

that detailed board position data are unavailable, and so we are able to test for H1 only and not

H2.18

In addition to EM as before, we define two new dependent indicator variables for irregularities

EM_IRR and errors EM_ERR. Similarly, we distinguish the board link independent variable

between links to a manipulator with a later irregularity restatement, EM_IRRLINK, versus links to a

FIGURE 4Illustration of Timing for EMNEWDIRECTORLINK ¼ 1

17 Our version of the Compact Disclosure sample covers 9,704 distinct firms and 77,475 distinct director names, andspans the period from 1995 to 2001. The final alternative sample consists of 114 irregularities and 202 errorobservations in the GAO1 sample. The earliest year when firms began earnings manipulation is 1995.

18 We examine the board positions for exposed firm-contagious firm pairs that share the same restatement types.Same irregularity contagion involves audit committee members more frequently than same error contagion. 62percent of the exposed firm audit committee members and 50 percent of contagious firm audit committeemembers provide the board link for same irregularity contagion versus 38 percent and 35 percent, respectively,for same error contagion. The contagious firm audit chair is also more frequently involved in same irregularitycontagion (14 percent) than same error contagion (0 percent). In contrast, CEOs are more often involved in sameerror contagion (13 percent of exposed firms and 19 percent of contagious firms) than same irregularity contagion(0 percent of exposed firms and 7.1 percent of contagious firms). These numbers hint that irregularity contagionoften requires acquiescence from the audit committee and its chair. As mentioned earlier, there are too fewobservations to permit robust statistical tests.



TABLE 5

Board Contagion Controlling for Endogenous Matching of Firm Director and Firm DirectorFixed Effects

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

EMLINK 0.681** 0.749** 0.726** 0.703**

(0.020) (0.016) (0.015) (0.018)

EMMIGRATEDLINK 0.205

(0.703)

EMNEWDIRECTOR �0.146

(0.719)

PRE_POST_FIRM 0.072

(0.821)

PRE_POST_DIR �0.009

(0.982)

#BOARDLINK �0.044* �0.043* �0.044* �0.044*

(0.060) (0.064) (0.058) (0.062)

Control Variables Included Included Included Included

Year and Industry Fixed Effects Included Included Included Included

Observations 5,279 5,279 5,279 5,279

Pseudo R2 0.070 0.070 0.070 0.070

*, **, *** Indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively.Robust p-values are in parentheses.The table reports results of logistic regressions of EM on EMLINK and one of the variables EMMIGRATEDLINK,EMNEWDIRECTOR, PRE_POST_FIRM, and PRE_POST_DIR from Columns (1) to (4).Variable definitions are as described in Appendix A, and results for control variables are suppressed for ease of reading.

FIGURE 5Illustration of Timing for PRE_POST_FIRM ¼ 1 and PRE_POST_DIR ¼ 1



later error restatement, EM_ERRLINK, during the contagious period. We regress EM on both

EM_IRRLINK and EM_ERRLINK, and then regress EM_IRR on EM_IRRLINK and EM_ERR on

EM_ERRLINK separately, all with controls as before, except G-index, which is unavailable from

Compact Disclosure. The results are in Table 6.

Table 6, Column (1) reports that contagion from an irregularity link dominates one from an

error link, which is consistent with stronger irregularity contagion than error contagion. Column (2)

shows that an irregularity contagion is especially strong. The irregularity link is significantly

positive, with an odds ratio of 2.4. A board link to an irregularity manipulator more than doubles

the exposed firm’s likelihood of being an irregularity manipulator itself. Interestingly, Column (3)

also shows a positive association between an error restatement and an error board link. This result

provides a hint that our evidence for board contagion of more severe earnings management

requiring a restatement likely generalizes to milder forms of undetected earnings management.

VI. CONCLUDING REMARKS

This paper studies the role of board interlocks in the propagation of both bad and good

corporate financial reporting practices, in the form of engaging or not engaging in earnings

management that later results in restatements. We provide evidence that a firm is more likely to

manage earnings during or soon after the period when it shares a common director with a firm that

is managing earnings. Similarly, we find evidence that a firm linked to a non-manipulator is less

likely to manage earnings. Furthermore, we find that more important board positions held by the

interlocked director in the exposed or contagious firm have a stronger contagion effect. This is

particularly the case with board positions that have influence over financial reporting, such as

membership on the audit committee. These findings support the view that board monitoring plays a

key role in the contagiousness and quality of firms’ financial reports.

Our study is careful to control for the possibility of contagion along alternative networks and

for alternative explanations, other than contagion, for why firms that are linked by directors might

TABLE 6

Earnings Management Contagion: Irregularities versus Errors

EM EM_IRR EM_ERR

(1) (2) (3)

EM_IRRLINK 0.442* 0.875**

(0.066) (0.012)

EM_ERRLINK 0.262 0.431*

(0.240) (0.087)

#BOARDLINK �0.008 0.004 �0.013

(0.506) (0.867) (0.314)

Control Variables Included Included Included

Year and Industry Fixed Effects Included Included Included

Observations 27,986 21,467 27,466

Pseudo R2 0.0523 0.0637 0.0557

*, **, *** Indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively.Robust p-values are in parentheses.The table presents results of logistic regressions of EM on EM_IRRLINK and EM_ERRLINK in Column (1), EM_IRR onEM_IRRLINK in Column (2), and EM_ERR on EM_ERRLINK in Column (3).Variable definitions are in Appendix A, and results for control variables are suppressed for ease of reading.



exhibit similar earnings management behavior. Our results are robust to endogenous hiring of

directors, firm and director fixed effects, industry networks, networks of close geographical

proximity, and common auditor networks. The effect of earnings management contagion via board

networks is also robust to controlling for events and circumstances that affect earnings management

incentives, such as mergers and acquisitions, new issues, or when firms have high fraud scores.

Finally, although our main sample primarily covers large companies, we verify the robustness of

our key result using an alternative sample that covers smaller firms.

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APPENDIX AVARIABLE DEFINITIONS

Variable Name Definition

Regression Variables: Measured at fiscal year-end; time subscript suppressed for convenience.

EM equals 1 if this is the first year for which the firm’s earnings are restated,

and 0 otherwise (Data source: GAO);

EM_x equals 1 if this is the initial year of the restating period for the firm that

later had to make a restatement classified as type x, and 0 otherwise; xis either an error (ERR) or an irregularity (IRR) (Hennes et al. 2008);

EMLINK equals 1 if the firm shares a director with a contagious firm during the

contagious period, and 0 otherwise. A firm is contagious when it is in

the first year of its restating period or in the subsequent two years; in

other words, the contagious period begins with the first year of the

restating period and lasts two years after it starts. We refer to the firm

that has EMLINK with a value of 1 as an exposed firm (Data source:

Risk Metrics and GAO);

EM_xLINK equals 1 if the firm has an interlocked board member with a contagious

firm during the contagious period, and the contagious firm later

announces a restatement of type x, and 0 otherwise. Here, x is either an

error (ERR) or an irregularity (IRR) (Data source: Compact Disclosure

and GAO);

#EMLINK number of EMLINKs to contagious firms during the contagious period. It

is the discrete version of the indicator EMLINK variable (Data source:

Risk Metrics and GAO);

EM_X_LINKy equals 1 if EMLINK with a value of 1 is via a director who holds _X_position on y firm’s board, and 0 otherwise; _X_ categories are CEO,

board chair (BOARDCHAIR), audit-committee chair (AUDITCHAIR),

audit-committee member (AUDITCOM), or other positions (OTHER). yrefers to either a contagious or exposed firm, e.g., EMCEOLINKExposed

equals 1 if EMLINK with a value of 1 is via a director who holds the

CEO position of the exposed firm (Data source: Risk Metrics and

GAO);

EMLINK—Different Ind.Only

equals 1 if EMLINK with a value of 1 is via a director from a contagious

firm that is in a different Fama-French 48 industry, and 0 otherwise

(Data source: Risk Metrics and GAO);

EMMIGRATEDLINK equals 1 if the director, who triggered EMLINK to equal 1, joined the

exposed firm subsequent to gaining earnings management (EM)

experience in the contagious firm, and 0 otherwise (Data source: Risk

Metrics and GAO);

EMNEWDIRECTORLINK equals 1 if the director joined the exposed firm no more than three years

before gaining earnings management experience from the contagious

firm board and if EMLINK equals 1, and 0 otherwise (Data source: Risk

Metrics and GAO);

PRE_POST_FIRM equals 1 if a firm shares a common director with a contagious firm during

the sample period other than the contagious period, and 0 otherwise

(Data source: Risk Metrics and GAO);




APPENDIX A (continued)

Variable Name Definition

PRE_POST_DIR equals 1 if PRE_POST_FIRM equals 1 and if the common director to the

contagious firm is currently serving on the exposed firm’s board, and 0

otherwise (Data source: Risk Metrics and GAO);

%EM firms 100 miles percentage of EM firms within 100 miles of the firm relative to the total

number of EM companies in the past three years, current year included

(Data source: GAO and Compustat);

%EM firms 100 milessame

auditor

percentage of EM firms within 100 miles of the firm that shared the same

auditor in the past three years, current year included (Data source: GAO

and Compustat);

SEC 100 miles equals 1 if any SEC regional office is within 100 miles of the firm’s

headquarters, and 0 otherwise (Data source: GAO and Compustat);

#BOARDLINK number of other firms that share common directors with the firm (Data

source: Risk Metrics);

ROA return on total assets, the ratio of net income to average total assets ([NI]/

[AT]) (Data source: Compustat); hereafter, Compustat labels are shown

in brackets;

Loss equals 1 if the firm has negative income before extraordinary items [IB],

and 0 otherwise (Data source: Compustat);

Size natural logarithm of firm total assets [AT] (Data source: Compustat);

Leverage total liabilities [LT] divided by total assets [AT] (Data source: Compustat);

Market-to-Book Market-to-book ratio ([CSHO] 3 [PRCC_F]/[CEQ]) (Data source:

Compustat);

Ret Volatility 100 times stock-return volatility, estimated as the standard deviation of

daily stock returns in the fiscal year (Data source: CRSP);

Operating Lease (0/1) equals 1 if future operating lease obligations ([MRC1-5]) are greater than

0, and 0 otherwise at year end (Data source: Compustat);

Firm Age number of years reported in Compustat for the firm (Data source:

Compustat);

Abnormal Employee percentage change in the number of employees [EMP] less percentage

change in total assets [AT] (Data source: Compustat);

G-index G-Score of Gompers et al. (2003) (Data source: Risk Metrics);

Inst Holdings percentage of institutional holdings (Data source: Thomson Financial);

Board Size number of directors on the firm’s board (Data source: Risk Metrics);

CEO Duality equals 1 if the CEO is the board chair of the firm, and 0 otherwise (Data

source: Risk Metrics);

Pct Independent percentage of independent directors on the board (Data source: Risk

Metrics);

M&A equals 1 if the firm has a M&A event [AQS] . 0 in the year, and 0

otherwise (Data source: Compustat);

ISSUE equals 1 if the sum of new long-term debt [DLTIS] and new equity

[SSTK] is greater than 2 percent of total assets [AT] (Data source:

Compustat);

FSCORE 100 times the average fraud score in the past three years, estimated from

Dechow et al.’s (2011) model 3; and

Director Tenure average tenure of directors on the firm’s board (Data source: Risk

Metrics).



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