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Employee Treatment and Corporate Fraud

J. Jay Choi, Yuanzhi Li, Connie X. Mao, and Jian Zhang

Current Draft: April, 2014

Abstract:

This paper examines the association between a firm’s relations with its employees and its

likelihood of committing fraud. We find that firms treating their employees fairly (as measured by

employee treatment index) have a lower likelihood of committing fraud. Further analysis shows

that employee involvement and cash profit-sharing are the most important components in employee

treatment to determine our results. Moreover, we show that the negative association between

employee treatment and fraud propensity is more prominent when a firm is in high-tech industry

or less competitive industry, when a firm has less employees, and when employees have less outside

employment opportunities. Finally, we show that our results are not driven by the employee’s moral

sensitivity or other labor related factors (i.e. labor wage, pension benefits, and labor union power).

JEL classification: G34

Key words: Employee treatment; Corporate fraud; Stakeholder

J. Jay Choi is a Professor in Finance at Department of Finance, Fox School of Business, Temple University.

Email: jjchoi@temple.edu. Connie Mao is an Associate Professor in Finance at Department of Finance, Fox

School of Business, Temple University. Email: cmao@temple.edu. Yuanzhi Li is an Assistant Professor in

Finance at Department of Finance, Fox School of Business, Temple University. Email:

yuanzhi.li@temple.edu. Jian Zhang is an Assistant professor in Finance, School of Finance, Southwestern

University of Finance and Economics, China. Email: jianzhang@temple.edu.

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Employee Treatment and Corporate Fraud

1. Introduction

Recent high-profile corporate fraud1 scandals in U.S. result in tremendous losses to both

shareholders (i.e. the owners of corporations) and stakeholders (i.e. employees, customers, and

suppliers). Both shareholders and stakeholders have incentives to limit fraud commitment and

enhance fraud detection efficiency. A large number of papers argue that shareholders can prevent

managers from committing fraud by either improving the corporate governance quality (Beasley,

1996; Dechow, Sloan, and Sweeney, 1996; Agrawal and Chadha, 2005) or limiting managers’

incentives for self-interest behaviors (Bergstresser and Philippon, 2006; Burns and Kedia, 2006).

While these studies strengthen our understanding of shareholders’ interest to prevent fraud, they

pay almost no attention on stakeholders’ incentive to limit the likelihood of fraud. Particularly, no

paper studies the role of employees in hindering the fraud commitment in the literature. This lack

of evidence is surprising due to the fact that employees are major stakeholders and their personal

benefits are closely tied to the firm performance. Anecdotal evidence also suggests that employees

are one of the major whistle-blowers to bring the fraud to light. For example, Sherron Watkins

plays an important role in uncovering accounting fraud of Enron. Also, Cynthia Cooper is treated

as the whistle-blower of the WorldCom fraud scandal.

In this paper, we attempt to investigate how a firm’s relations with its employees2 is related

to its likelihood of committing fraud. Dechow, Ge, Larson, and Sloan (2011) show that firms are

more likely to engage in earning manipulation to disguise a moderate performance. Crutchley,

1 The Antifraud Rule 10b-5 of Securities Exchange Act of 1934 defines corporate financial fraud as the intent

to deceive or manipulate with misstatements or omissions of material information relating to financial

condition, solvency, and profitability (see SEC Administrative Proceeding 3-9588, April 27, 1998). McLucas,

Taylor and Mathews (1997) find that financial fraud is mostly due to the use of false financial information

or the failure to disclose material facts relating to a public company’s financial condition. 2 We view the firm’s relations with its employees and employee treatment as the same issue. Thus, those

terms are interchangeable throughout the paper.

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Jensen, and Marshall (2007) find that firms tend to have significant growth before committing fraud.

Edmans (2011) argues that employees motivated by the fair treatment contribute more effort in

working, resulting in strong firm performance. With motivated employees, managers have less

incentive to commit fraud to boost firm performance. Furthermore, as important stakeholders,

employees are capable of monitoring the managers’ self-interest decisions.3 Employees can be

treated as inside stakeholders due to their participation in daily-operation and direct observation on

daily management decisions. They are able to collect information about the firm at a low cost. Fair

employee treatment 4 encourages employee involvement, which enhances the corporate

transparency and lowers the information cost of employees to identify and collect fraud-relevant

information.

However, employee-friendly treatment policy is also likely to harm the governance quality

of the firm and increase the likelihood of fraud. Pagano and Volpin (2005) find that CEOs who

want to enjoy higher private benefits can ensure their job security by offering employees generous

long-term contracts to increase their loyalty. The labor-management alliance can serve as an anti-

takeover device for entrenched managers to deter value-adding takeover bids. Cronqvist, Heyman,

Nilsson, Svaleryd, and Vlachos (2008) find that entrenched CEOs are more likely to pay more to

employees so that they can enjoy labor-market related private benefits such as lower effort wage

bargaining and improved social relations with employees. Thus, whether fair employee treatment

lowers or increases the likelihood of fraud by a firm becomes an empirical issue.

To measure the extent of a firm’s relations with its employees, we adopt a firm-level index

of employee treatment. Our employee treatment index is drawn from the KLD Research &

Analytics, Inc. (Hereafter, KLD) database. This database provides a variety of information about

3 See Acharya, Myers, and Rajan (2011), Bae, Kang, Wang (2011), Chang et al. (2013), Landier, Sraer, and

Thesmar (2009). 4 In our paper, the employee treatment is evaluated from several areas: union relations, employee involvement,

cash-profit sharing, retirement benefits, health and safety benefits, and layoff policy.

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the firms’ employee treatment and is the widely used in academic research for evaluating a firm’s

relations with its employees.

However, when we implement the empirical testing, one caveat is that we only observe the

detected fraud. We do not observe the fraud propensity and detection separately. To address the

identification problem complicated by undetected fraud, we first follow Dyck, Morse, and Zingales

(2010) to limit our sample in large firms, which are the ones with more intense public scrutiny.

Dyck, Morse, and Zingales (2010) and Yu and Yu (2011) argue that due to the intense public

scrutiny, and the strong incentives to sue by plaintiff lawyers, large firms have fewer undetected

frauds. We then follow Wang, Winton, and Yu (2010) to use a bivariate probit with partial

observability model proposed by Poirier (1980) to account for the undetected fraud. The bivariate

probit with partial observability model allows us to study the impact of employee treatment on the

likelihood of fraud committed by a firm without worrying about the undetected fraud. To the best

of our knowledge, this is the first paper to study the association between a firm’s relations with its

employees and its likelihood of committing fraud.

First, we find that firms treating their employees friendly have a lower probability of

committing fraud. Second, we look at each sub-category of our employee treatment index to

investigate which component is the most important determinant for our findings. Our analysis

shows that employee involvement5 and cash profit-sharing6 are the most important components in

the employee treatment index to determine the results. Employee involvement lowers the likelihood

of fraud and facilitates the fraud detection due to employees’ information advantage and bottom-

up governance7. Cash profit-sharing reduces both fraud propensity and fraud detection because of

5 Employee involvement measures whether the company strongly encourages worker involvement and

ownership through stock options available to a majority of its employees, sharing of financial information,

or participation in management decision making. 6 Cash profit-sharing measures whether the company has a cash profit sharing program through which it has

recently made distributions to a majority of its workforce. 7 See Acharya, Myers, and Rajan (2011), Landier, Sraer, and Thesmar (2009), Landier, Sauvagnat, Sraer,

and Thesmar (2013).

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employees’ monetary incentive. None of the other factors (i.e. union relations and retirement

benefits) plays a significant role in lowering the likelihood of fraud. Moreover, we find that the

negative impact of employee treatment on a firm’s likelihood of fraud is more significant when a

firm is in high-tech industry or less competitive industry, when a firm has less employees, and when

employees have less outside employment opportunities.

Finally, we include additional control variables in the regression to alleviate the

endogeneity problem due to the omitted variable bias. We show that our findings are not driven by

the omitted variables such as employees’ moral sensitivity or other labor related factors (i.e. labor

wage, pension benefits, and labor union power). We further adopt the collective bargaining and

union membership at the industry level as our instruments in the regression to rule out the

possibility of reverse causality.

The remainder of this paper is organized as follows. In Section 2, we give a brief literature

review on the topic of corporate fraud and employee treatment. Section 3 presents our arguments

on the impact of employee treatment on the likelihood of corporate fraud. Section 4 is the empirical

testing. In Section 5, we present our regression results. In Section 6, we perform robustness analysis.

Section 7 concludes.

2. Related Literature

The current literature on corporate fraud is mostly empirical and focus on explaining the

likelihood of fraud with factors such as the CEO's compensation structure, board characteristics,

and corporate governance quality. Bergstresser and Philippon (2006) find that earnings

manipulation is more pronounced at firms where the CEO's total compensation consists of more

stock and option holdings. Similarly Burns and Kedia (2006) show that the propensity of

misreporting is positively related to the sensitivity of the CEO's option portfolio value to stock

price. Efendi, Srivastava, and Swanson (2007) find that there is a higher likelihood of financial

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misstatement when the CEO holds more in-the-money stock options. Johnson, Ryan, and Tian

(2009) find that the largest incentive source for firms to commit fraud comes from managerial

unrestricted stock holdings. Beasley (1996) examines the relation between board compositions and

financial statement fraud. He finds that lower likelihood of fraud is associated with smaller board

size and higher board independence. Agrawal and Chadha (2005) study the relation between

corporate governance and earnings restatement. They find that the probability of restatement is

lower in companies whose boards or audit committees have an independent director with financial

expertise, and is higher in companies where the CEO belongs to the founding family. Dechow et

al. (2011) develop a scaled probability (F-score) that can be used as a red flag for earnings

misstatement. The composite score is based on accrual quality, financial performance, nonfinancial

measures such as abnormal reduction of number of employees, off-balance-sheet activities such as

the use of operating leases, and stock and debt market incentives such as stock issuances. Crutchley,

Jensen, and Marshall (2007) study the impact of governance, earnings quality, growth, dividend

policy, and executive compensation structure on the likelihood of fraud. They find that fast growing

firms with fewer outsiders on the audit committee and more overcommitted outside directors are

more likely to commit accounting fraud. These papers assume 100% detection rate for fraud cases

and use a simple logit or probit model in the regression equation.

Two recent papers acknowledge the existence of undetected fraud cases and estimate the

likelihood of fraud with the bi-variate probit model. Wang, Winton, and Yu (2010) examine a firm's

incentive to commit fraud when going public and find that fraud propensity increases with the level

of investor beliefs about industry prospects but decreases when beliefs are extremely high. Wang

(2013) shows that using the bi-variate probit model reveals new insight about the factors behind

corporate fraud compared to the simple probit model.

A few authors have studied the detection of fraud. Yu and Yu (2011) find that the fraud

committed by politically connected firms is less likely to be detected. Correia (2009) develops two

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theoretical models and finds that politically connected firms are less likely to make a financial

restatement initiated by a common letter from the SEC, have lower probability to be involved in an

SEC enforcement action and face lower penalties on average. Karpoff and Lou (2010) find that

short sellers can help uncover the misconduct of management. Dyck, Morse, and Zingales (2010)

find that fraud detection does not rely on standard corporate governance actors such as investors,

the SEC, and auditors, but rather it depends on several non-traditional players such as employees,

media, and industry regulators. Karpoff, Lee, and Marin (2008a, 2008b) find that both managers

and firms suffer substantial reputation loss following the revelation of fraud.

There are only limited papers studying the role of a firm’s employee relations in firm. Bae,

Kang, and Wang (2011) investigate the role of employees on shaping firm’s capital structure. They

find that firms with fair employee treatment maintain low debt ratios. They conclude that employee

treatment plays an important role in shaping firm’s financing policy. Edmans (2011) finds that

employee satisfaction is associated with higher long-run stock return, more positive earnings

surprises, and announcement returns. He further argues that stock market does not fully value

intangibles, and that certain socially responsible investing (SRI) screens have a positive effect on

investment returns. Jiao (2010) finds that employees represent intangible assets and better

employee relations can enhance firm value substantially.

3. Employee treatment and corporate fraud

3.1 The potential negative impact of fair employee treatment on a firm’s likelihood of fraud

First, employees can be treated as inside stakeholders due to their participation in daily-

operation and direct observation on daily management decisions. They are able to collect

information about the firm at a low cost. Fama (1985) points out that inside stakeholders have

access to private information, which provides significant information advantage in monitoring

managers. As Landier, Sraer, and Thesmar (2009) argued, employees can force decision-makers

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(managers) to use more “objective” information to make the “right” decision, the one maximizing

shareholders’ value since the management needs the effort of employees to implement their

decisions. Similarly, Acharya, Myers, and Rajan (2011) propose that employees can serve as an

internal governance mechanism for the management. Treating employees fairly not only improves

employment conditions (i.e. cash compensation and retirement benefits) but also encourages

employee involvement (i.e. sharing financial information, participation in management, and

granting employee stock ownership and option).The employee involvement enhances the corporate

transparency and further reinforces employees’ information advantage in monitoring managers.

Chang, Fu, Low, and Zhang (2013) find that non-executive employee option plan directs employees’

attention to the firm’s long-term success, encourage employees’ long-term human capital

investment, and spur employees’ long-term commitment to the firm. To some extent, employees

hold a larger stake over the firm due to the long-term human capital investment and commitment.

Thus, fair employee treatment strengthens employees’ capability and willingness to monitor

managers for the long-term value of the firm.

Second, human relations theories (Maslow, 1943; Hertzberg, 1959; McGregor, 1960) argue

that employee satisfaction improves corporate performance since it induces working efforts and

retains valuable human-capital, especially in modern technological industries such as

pharmaceuticals and IT. Employees view the fair treatment as a “gift” from the firm and contribute

more effort in working as a response (Akerlof, 1982). To avoid from being fired from a satisfying

job, employees intend to exert more effort in working (Shapiro and Stiglitz, 1984). Edmans (2011)

finds that employee satisfaction leads to higher long-run stock return and motivated employees

create substantial value to the firm. Dechow, Ge, Larson, and Sloan (2011) shows that firms are

more likely to engage in earning manipulation to disguise a moderate performance. Poor

performance is an important fraud motivator. Thus, firms treating employee fairly have less

incentive to commit fraud since motivated employees lead to strong corporate performance.

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Third, Maksimovic and Titman (1991) argue that stakeholders are reluctant to do business

with firms who cannot honor its implicit contracts with them, when they develop their reputational

model of the firm to produce a high-quality product. Maksimovic and Titman (1991, p.194) also

note that their “analysis can be applied to many types of implicit contracts other than product quality

by reputation considerations. Examples include a firm’s reputation for treating suppliers and

employees fairly.” Bae, Kang, and Wang (2011) find that firms with fair employee treatment

maintain low debt ratios since they place a higher value on their reputation for honoring its implicit

contracts with employees. They further point out that a firm’s reputational loss imposes notable ex

ante costs on its employees and these costs will be transferred to the firm in the end. For example,

once a firm is detected for committing fraud, it will face substantial monetary fines and has

difficulties in maintaining the current level of employee welfare. Because rational employees with

inside information recognize the negative outcome, they require higher wages for their labor to

compensate future welfare loss or even change jobs as soon as possible to avoid potential legal

liability, resulting in a reduction in firm value. Thus, firms value their reputation for implementing

employee-friendly policies should limit their incentives to commit fraud. Because firms with

employee-friendly policies are more likely to value their reputational capital, they prefer to commit

to fair employee treatment credibly. Therefore, these firms are expected to have less incentives to

commit fraud than those that do not offer fair employee treatment.

3.2 The potential positive impact of employee treatment on a firm’s likelihood of fraud

Cronqvist, Heyman, Nilsson, Svaleryd, and Vlachos (2008) find that entrenched CEOs are

more likely to pay more to employees so that they can enjoy labor-market related private benefits

such as lower effort wage bargaining and improved social relations with employees. Pagano and

Volpin (2005) find that CEOs who want to enjoy higher private benefits can ensure their job

security by offering employees generous long-term contracts to increase their loyalty. The labor-

management alliance can serve as an anti-takeover device for entrenched managers to deter value-

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adding takeover bids. Similarly, Rauh (2006) argues that entrenched CEOs can utilize the large

employee stock holdings to insulate themselves from market discipline. Faleye, Mehrotra, and

Morck (2006) find that labor's voice in corporate governance lowers the firm's equity value, sales

growth, and job creation. With higher labor wage and longer job security, employees are loyal to

the managers and reluctant to monitor the managers.

3.3 Research Focus

Our main question is the association between a firm’s relations with its employees and its

likelihood of committing fraud. The fair employee treatment facilitates information sharing and

bottom-up governance. Motivated employees respond to increase their working efforts, which

boosts firm performance and lower the need of the firm to commit fraud. Those firms implementing

employee-friendly policies are less likely to commit fraud because they place a high value to their

reputational capital. Yet, arguments of the monetary incentives of employees suggest that

employees in firms with employee-friendly policies are loyal to the managers and reluctant to

monitor the managers. Ultimately, the impact of fair employee treatment on the fraud likelihood is

an empirical issue that we attempt to study in this paper by addressing several issues. First, does

fair employee treatment lower or increase a firm’s likelihood of fraud? Second, which component

in our employee treatment index is the most important in determining our results? Third, whether

the impact of employee treatment on a firm’s likelihood of fraud is influenced by the characteristics

of the firm and industry? Our study provides detailed answers to these questions.

4. Empirical Testing

4.1 Empirical Methodology

We adopt a bivariate probit model, which implies that the ex-post fraud detection

probability can be less than 100%. Thus, some fraud cases remain undetected. Since we only

observe detected fraud in the data, there exists a partial observability problem. The nature of the

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problem is depicted in Figure 2. Wang, Winton, and Yu (2010) provide a bivariate probit model as

the solution for the partial observability problem and offer a new insight estimating the likelihood

of fraud. In a bivariate probit model, we estimate two dependent variables simultaneously. The first

dependent variable, fraud commitment denoted as F, takes the value of one if firm i commits fraud

in year t, and zero otherwise. Then, conditional on the fact that a firm commits fraud, the second

dependent variable, the fraud detection denoted as D, takes the value of one if the firm is caught,

and zero otherwise.

𝐹𝑖,𝑡 = 𝛽0 + 𝛽1 ∗ 𝐸𝑚𝑝𝑙𝑜𝑦𝑒𝑒 𝑇𝑟𝑒𝑎𝑡𝑒𝑚𝑒𝑛𝑡𝑖,𝑡−1 + 𝛽2 ∗ 𝐹𝑟𝑎𝑢𝑑_𝑏𝑒𝑛𝑒𝑓𝑖𝑡𝑖,𝑡−1

+ 𝛽3 ∗ 𝐸𝑥𝑎𝑛𝑡𝑒_𝑑𝑒𝑡𝑒𝑐𝑡𝑖𝑜𝑛𝑖,𝑡−1 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑦 𝑑𝑢𝑚𝑚𝑦 + 𝜇

𝐷𝑖,𝑡 = 0

+ 1

∗ 𝐸𝑚𝑝𝑙𝑜𝑦𝑒𝑒 𝑇𝑟𝑒𝑎𝑡𝑒𝑚𝑒𝑛𝑡𝑖,𝑡−1 + 2

∗ 𝐸𝑥𝑎𝑛𝑡𝑒_𝑑𝑒𝑡𝑒𝑐𝑡𝑖𝑜𝑛𝑖,𝑡−1 + 3

∗ 𝐸𝑥𝑝𝑜𝑠𝑡_𝑑𝑒𝑡𝑒𝑐𝑡𝑖𝑜𝑛𝑖,𝑡+1 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑦 𝑑𝑢𝑚𝑚𝑦 + 𝜈

where 𝜇 and 𝜈 are noise terms following a zero-mean bivariate normal distribution. The correlation

of 𝜇 and 𝜈 is 𝜌. Denote the vector of explanatory variables in the regression for F as xF , for D as

xD , and the vector of coefficients as β and respectively.

The partial observability problem is that we do not observe F and D directly, but only

observe Z=F D. Z takes the value of one if the firm commits fraud and is detected, and the value

of zero if the firm does not commit fraud or commits fraud but not detected. Let Φ denote the

bivariate standard normal cumulative distribution function. The empirical model for Zj is,

P(Zj =1)= P(Fj =1 & Dj =1) =P(Fj =1)P(Dj =1| Fj =1)=Φ (x1j β1, x2j β2 )

P(Zj =0)= P(Fj =0 or Dj =) = P(Fj =0)+P(Fj =1)P(Dj =0| Fj =1)=1-Φ (x1j β1, x2j β2 )

The above model can be estimated by using maximum likelihood estimator. The log-likelihood

function is

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L(β1, β2)=∑(Zjln ( Φ (x1j β1, x2j β2 ))+(1-Zj)ln( 1-Φ (x1j β1, x2j β2 ))

According to Poirier (1980), the condition for the full identification of the model parameters

are, (1) xF and xD do not contain exactly the same set of variables, and (2) the explanatory variables

exhibit substantial variations in the sample. Condition (1) is satisfied according to the equations

listed above. Condition (2) means that when explanatory variables include continuous variables,

the identification is strong. Most of our explanatory variables are continuous variables.

4.2 Sample Construction

We obtain a sample of large fraud studied in Dyck, Morse, and Zingales (2010), who

collect the fraud sample from Stanford Securities Class Action Clearinghouse (SSCAC). 8 To

control for frivolous lawsuits, they restrict their sample from 1996 to 2004. In 1995, Private

Securities Litigation Reform Act was passed to reduce frivolous lawsuits. They further filter the

sample by the following criteria: (i) exclude all cases dismissed during the judicial review process;

(ii) the settlement amount is at least $3 million; (iii) firms’ assets are higher than $750 million in

the year before the fraud is detected. They argue that this reduces the chance of undetected fraud

as large firms face more intense public scrutiny and lawyers have stronger incentives to investigate

their fraudulent activities. We follow the same criteria and extend their sample to 2011. In line with

Wang, Winton, and Yu (2010), we only include a firm's earliest committed fraud in our analysis

for the firms having multiple convictions in different years. The total number of fraud satisfying

all above criteria is 392. After merging with variables about firm characteristics, we have 134 fraud-

year observations left.

For the comparison sample, we attempt to obtain a random sample of firms that are

litigation-free. Thus, we start with all the firms in the CRSP/COMPUSTAT Merged database

8 For a detailed description about sample construction, please see Dyck, Morse, and Zingales (2010). Their

sample is also available on Alexander Dyck’s personal website http://www.rotman.utoronto.ca/dyck/.

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excluding firms that are in the detected fraud sample and firms that have total asset less than 750

million dollars one year before the fraud is detected 9. To make the fraud sample and control sample

comparable, we follow Beasley (1996) to construct a 1-1 matching sample based on size of the firm,

fraud year, and the industry10. Within the same industry of the fraud firms, we define a non-fraud

firm as the matching firm if it is the closest in size. The industry is defined by the two-digit SIC

code.

The employee treatment index is obtained from KLD Database, which provides a variety

of information on the firm’s employee friendliness. KLD Database is widely used in academic

research to evaluate a firm’s relations with its employees (Bae, Kang, and Wang, 2011; Landier,

Nair, and Wulf, 2009). KLD database is constructed on multiple data sources such as company

filings, government data, media information, and direct communication with company officers.

Once KLD collects the information, its sector-specific analysts adopt a proprietary framework to

rate the firms.

Firm financial data is obtained from CRSP/COMPUSTAT Merged Database. Executive

compensation data is collected from the EXECUCOMP Database. Institutional ownership data is

acquired from Thomson-Reuters Institutional Holdings (13f) Database. Analyst coverage data is

obtained from I/B/E/S Database.

Table 2 shows the number of fraud cases by the fraud starting year and the distribution of

fraud duration. The litigation documents from SSCAC record the time fraud activities start and end.

Starting year is the first year when fraudulent activities occur. The table shows that the number of

fraud cases significantly increases in 2001, 2005, and 2007. These are the time periods that the

equity market achieves highest valuations. This is consistent with Wang, Winton, and Yu (2010)

9 Our fraud sample only covers the large firm with total asset greater than 750 million dollars one year before

the fraud is detected. Thus, we only include large non-fraud firms in our comparison sample. 10 Figure 1 shows the Kernel density plot of size across fraud and non-fraud sample.

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that managers have stronger incentives to misrepresent firm performance in order to get better

valuations when financing valuations are high. Fraud started in the later part of the sample period,

especially after 2008, tends to have lower fraud duration. It is likely due to the selection bias, as

some of the fraud activities with longer duration after 2008 are not detected yet.

4.3 Variable Construction

Our main variable of interest is how a firm treats its employees, denoted as Employee

Treatment. We adopt ratings in all the sub-categories of employee relations in KLD to measure

how firms treat their employees. KLD rates the employee relations in the following sub-categories11:

union relation strength (weakness), cash profit-sharing strength, employee involvement strength,

retirement benefit strength (weakness), health and safety strength (weakness), layoff policy strength

(weakness), supply chain policy strength (weakness), and other strength (weakness). The KLD

assigns 0/1 in the strength and weakness of each sub-category. Our employee treatment index is

measured by using the total employee relation strength score minus total employee relation

weakness. The total employee relation strength score is calculated as the total points a firm

receiving on criteria for employee strength in KLD, while the total employee relation weakness

score is obtained from the total points a firm receiving on criteria for employee relation weakness

in KLD. A higher score on the employee treatment index indicates that the firm treats its employees

fairly.

11 Union relation measures whether the company has taken exceptional steps to treat its unionized workforce

fairly. Employee involvement measures whether the company encourages worker involvement and

ownership through stock options available to a majority of its employees, gain sharing, stock ownership,

sharing of financial information, or participation in management decision making. Cash profit-sharing

measures whether the company has a cash profit sharing program through which it has recently made

distributions to a majority of its workforce. Retirement benefit measures whether the company has a notably

strong retirement benefits program. Health and safety measures whether the company has strong health and

safety programs. Layoff policy measures whether or not the company has made significant reductions in its

workforce in recent years. Supply chain policy measures whether the company has strong supply chain

program.

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Besides employee treatment, the likelihood of fraud p(Fraud) depends on the variables

related to the expected benefit of fraud for the managers and the variables related to the ex-ante

fraud detection probability perceived by the managers. When we use a bivariate probit model, we

include the variables related to the ex-ante and ex-post detection probability in the regression for

p(Detection|Fraud) to achieve identification. We discuss how we construct these variables as

follows.

We mainly follow Wang (2013) to construct the set of variables related to the expected

benefit of fraud for CEOs and subordinate managers, the ex-ante fraud detection probability, and

the ex-post fraud detection probability.

4.3.1 Variables related to expected fraud benefit

We include profitability (ROA), leverage, the firm's external financing need, insider

ownership of the CEO, insider ownership of subordinate executives, firm size, institutional

ownership, and analyst coverage. Dechow, Ge, Larson, and Sloan (2011) find that earnings

manipulating firms tend to show strong financial performance prior to the manipulations. We use

ROA to measure profitability. Leverage has been used as a proxy for closeness to covenant

restrictions in the accounting literature, and firms that are close to the restrictions in debt covenants

have more incentives to manipulate earnings (see Healy and Wahlen, 1999 and Dechow, Sloan,

and Sweeny, 1996). Leverage is calculated as the ratio of long term debt over total assets. Cox,

Thomas, and Kiku (2003) find that firms that get involved in securities litigation tend to be larger

firms. Wang (2013) shows that firm size is positively related to the incentive to commit fraud as

estimated in the bivariate probit model. We measure firm size as the log value of total assets.

Another important fraud motivator is the need for external financing. Teoh, Welch, and

Wong (1998) find that firms have incentives to engage in earnings management before public

equity offers. Dechow, Ge, Larson, and Sloan (2011) find that firms actively seeking new financing

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are more likely to commit fraud. We calculate a firm's external financing need as suggested by

Demirguc-Kunt and Maksimovic (1998). Specifically, it is a firm's asset growth rate in excess of

the maximum internal growth rate sustained by retained earnings ROA/(1-ROA). Crutchley, Jensen,

and Marshall (2007) find that fraud firms tend to have significant growth before committing fraud.

We use market to book (M/B) as a proxy for growth opportunities, which is also related to external

financing need.

The literature documents that executive equity incentives affect the fraud motivation as

reviewed in Section 2. Thus we include the insider ownership of the CEO and other executives as

control variables. CEO ownership is the number of shares held by the CEO divided by the total

number of shares outstanding, while non-CEO executive ownership is the average percentage

holding in stocks for non-CEO executives.

Burns and Kedia (2010) find that the likelihood and severity of financial misreporting is

positively related to aggregate institutional ownership. They attribute this finding to the short

investment horizons of institutional investors. Firms are motivated to either make myopic

investment decisions or inflate current performance to prevent institutions from selling their shares.

Institutional ownership is the aggregate percentage holdings by institutional investors from 13-f

filings. A similar effect can be expected for firms facing expectation pressure from analyst

following, as in the case of WorldCom. We measure analyst coverage intensity by the number of

analysts following the firm.

4.3.2 Variables related to ex-ante fraud detection probability

We include leverage, firm size, institutional ownership, and analyst coverage in the set of

variables related to ex-ante fraud detection probability.12 Larger firms have more information

12 Note that leverage, firm size, institutional ownership, and analyst coverage are factors related to both the

benefit of fraud and the ex-ante fraud detection probability. Their effects on the likelihood of fraud through

17

disclosure and are under more scrutiny from the capital market, thus it is harder for larger firms to

hide fraud. Leverage can affect the ex-ante fraud detection probability as creditors are normally

viewed as the delegated monitor for firms (see Diamond, 1984). Firms with higher leverage face

more intensive monitoring from creditors, which leads to a higher fraud detection probability.

Shleifer and Vishny (1997) argue that institutional investors have stronger incentives and

more resources to monitor the management. Monitoring by institutional investors thus should lead

to higher detection rate for fraud. Yu (2008) find that analyst coverage leads to less earnings

management. Dyck, Morse, and Zingales (2010) document the active role analysts play in

uncovering fraud. Security analysts follow a firm's financial disclosure with greater scrutiny and

interact with the management more frequently, thus firms with more analysts covering their stocks

will find it harder to hide fraud activities.

Wang (2013) documents that there are clear industry patterns in securities litigation.

Technology firms (software and programming, computer and electronic parts, and biotech), service

firms (financial services, business services, and telecommunication services), and the trade industry

(wholesale and retail) appear to have high fraud concentration. We include three dummy variables

for these industries as controls in the regression.

4.3.3 Variables related to ex-post fraud detection probability

The eventual fraud detection probability depends on the ex-ante fraud detection probability

plus factors that affect fraud detection after the fraud is committed. These factors cannot be

predicted by managers or market participants at the time of committing fraud, therefore must be

measured in year t+1, where t is the year fraud activities start.

these two channels are of opposite directions. Thus the overall result depends on which channel has the

dominant effect.

18

When managers misrepresent firm information, subsequent firm performance is likely to

fall short of investors' expectation. And this is likely to trigger fraud detection. Jones and Weingram

(1996) show that firms that have recently experienced large negative stock returns are often subject

to high litigation risk. They also show that litigation risk increases with stock return volatility and

stock turnover. Firms that experience higher return volatility are more likely to be sued because the

probability of a large investment loss for the investors is higher. A higher stock turnover implies

that more investors are affected by the firm's stock prices and it is easier to identify a class of

plaintiff investors. We measure the stock return, stock volatility of monthly returns, and average

monthly stock turnover in the year following the fraud starting year.

4.4 Sample Characteristics

Table 3 compares the characteristics of the fraud sample and the non-fraud sample in large

COMPUSTAT firms with total assets above $750 million. Our main variable of interest, Employee

Treatment, does not show any difference across two samples. Most of control variables are not

significantly different across the two samples, implying that the fraud and non-fraud sample are

very similar due to our matching. When growth opportunity is measured by the market to book

ratio, fraud firms have significantly higher M/B (1.63 vs. 1.24). This is consistent with the view

that firms with more growth opportunities have stronger incentives for fraud, since they have a

larger demand for external financing and are more motivated to misrepresent performance to take

advantage of high valuations. Fraud firms experience higher turnover compared to non-fraud firms

in the year following fraud. These factors contribute to higher fraud detection probability, which

leads to observed fraud.

5. Regression Results

In Table 4, we present the result of bivariate probit regression. P(F) stands for the fraud

propensity equation. P(D|F) represents the fraud detection equation. In the fraud propensity

19

equation, we find that Employee Treatment is negatively related to a firm’s likelihood of fraud at

1% significance level. This finding implies that the firm treating its employees fairly has a lower

probability of committing fraud. The negative impact of employee treatment on a firm’s fraud

likelihood dominates its positive effect on fraud likelihood. ROA is also negatively associated with

fraud propensity. Firms with strong performance have less incentive to commit fraud. Leverage is

positively associated with fraud incentive. External Finance Need plays a positive role in increasing

the fraud likelihood. Dechow, Ge, Larson and Sloan (2011) and Dechow, Sloan and Sweeney (1996)

argue that firms subject to AAERs are more active in seeking new financing.

Among the set of variables related to both fraud benefit and ex-ante detection probability,

firm size is a significant fraud motivator. Larger firms tend to have more incentive to commit fraud,

consistent with Wang (2013). This implies size effect on fraud benefit dominates its effect on the

ex-ante fraud detection. Large and sophisticated institutional investors should have both incentive

and power to impose effective monitoring on the management (Shleifer and Vishny,1997).

However, Burns, Kedia, and Lipson (2010) find that the likelihood and severity of financial

misreporting is positively related to aggregate institutional ownership due to the short investment

horizons of institutional investors who are reluctant to involve in costly monitoring activities.

Similarly, financial analysts are able to improve the governance quality by their financial expertise

and regular communication with the management team. Yet, firms might be more likely to commit

fraud due to pressures from meeting analysts’ expectations, as was in the case for WorldCom.

Therefore, the sign of the coefficient of Institutional Ownership and Analyst Coverage depends on

which channel of the two opposite directions dominates. We find that Institutional Ownership has

a significant positive coefficient, while Analyst Coverage has a significant negative sign in its

coefficient.

20

Furthermore, we aim to analyze the underlying reasons for our findings. We look at four

sub-categories of our employee treatment index respectively13. The results are presented in Table

5. Neither Labor Union Relation nor Retirement Benefits has any impact on either fraud propensity

or fraud detection. We find that Employee Involvement is negatively related to fraud propensity and

positively associated with fraud detection. Extensive employee involvement indicates that the

company strongly encourages worker involvement and ownership through stock options available

to a majority of its employees, gain sharing, stock ownership, sharing of financial information, or

participation in management decision making14. The stock ownership and option plan align the

interest of employees to the shareholders’. Chang, Fu, Low, and Zhang (2013) find that non-

executive employee option plan directs employees’ attention to the firm’s long-term success,

encourage employees’ long-term human capital investment, and spur employees’ long-term

commitment to the firm. The sharing of the financial information and participation in management

decision making allows the employees to access to valuable private information to monitor

managerial performance. Therefore, employees have incentives and capabilities to monitor

managers and force them to make decisions at the interest of shareholders. Landier, Sraer, and

Thesmar (2009) develop a so-called “bottom-up governance” model in which decision-makers (i.e.

management) are in charge of selecting projects and implementers (i.e. employees) are in charge

of its execution. Implementers can force decision-makers to use more “objective” information to

make the “right” decision, the one maximizing shareholders’ value since management needs the

effort of implements to execute the projects. Similarly, Acharya, Myers, and Rajan (2011) propose

that employees can serve as an internal governance mechanism for the management.

13 We only look at union relations, employee involvement, retirement benefits, and cash-profit sharing for

two reasons. First, Bae, Kang, and Wang (2011) measures employee treatment by only including union

relations, employee treatment, retirement benefits, cash-profit sharing, and health and safety benefits. Second,

the dummy variable of health and safety benefits, layoff policy, and supply chain policy are mainly assigned

zeros. 14 This is the definition for employee involvement in KLD database.

21

As Dyck, Morse, and Zingales (2010) point out, employees are the major whistle-blower

for corporate fraud due to their best access to firm’s private information. Employee involvement

lowers the information cost for employees to identify and gather fraud-relevant information. Our

finding is consistent with the employee’s information advantage argument.

Cash profit-sharing has a negative impact on both fraud propensity and detection.

According to the definition, cash profit-sharing measures whether the company has a cash profit

sharing program through which it has recently made distributions to a majority of its workforce.

KLD collects the employee information at the firm level, including both management team and

ordinary employees (see Landier, Nair, and Wulf, 2009). This is clearly stated in the definition by

their emphasis on a “majority” of the workforce. Pursuing personal benefit is one of the most

important reasons for managers to commit fraud. When a firm has a cash profit-sharing program

through which it makes distributions to the management, the managers have lower incentive to

commit fraud since their personal benefits are well satisfied. In addition, employees motivated by

the cash profit-sharing contribute more efforts in their working, results in strong firm performance.

The strong firm performance further lowers the need of a firm to commit fraud.

Dyck, Morse, and Zingales (2010) argue that monetary incentive is an important

determinant for employees to whistle-blow corporate fraud. Thus, employees have less incentive

to whistle-blow the existing fraud since their monetary incentive is reduced due to the cash-profit

sharing program.

As argued above, one potential reason why we have the negative relation between

employee treatment and a firm’s likelihood of fraud is that motivated employees contribute more

effort in working, resulting in strong performance and less need for a firm to commit fraud.

However, the impact of employees’ motivation on firm performance may vary across industries. In

traditional industries, employees conduct unskilled work and are similar to other inputs such as raw

22

materials. The motivation of employees cannot improve the firm performance too much. In high-

tech industries emphasizing innovation, human, rather than physical, capital plays an important role

in firm (see Zingales, 2000). Employees can be viewed as valuable intangible assets in such

industries and contribute substantial to the firm value. Thus, we expect that the negative relation

between employee treatment and a firm’s likelihood of fraud is more salient in the high-tech

industry 15 . We rerun our bivariate probit regression by adding one interaction term between

Employee Treatment and a dummy variable for high-tech industry. We find that the interaction

term in Column 1 Table 6 is negative and statistically significant, consistent with the view that fair

employee treatment lowers a firm’s likelihood of fraud on a larger scale, if human capital is

important in firm’s daily operation.

Another potential explanation for the negative relation between employee treatment and

fraud likelihood is the bottom-up governance by the employees. However, such bottom-up

governance is accompanied with cost, such as retaliation from the managers and the need to change

one’s career (Dyck, Morse, and Zingales, 2010). Thus, a natural question is why the employees

choose to bear the cost to monitor the managers rather than simply find a new job in another firm.

Employees invest a large amount of time and effort in acquiring firm-specific or industry-specific

human capital during their daily work year by year. They are reluctant to forgo their human capital

investment and find new jobs in a new industry. Even if they are willing to restart their career in a

new industry, firms in the new industry prefer to hire experienced employees. Thus, if employees

are in a less competitive industry, they have difficulties in changing their jobs. If that happens,

employees hold a higher stake over the firm value and are likely to bear the cost to monitor

managers. We expect that the negative relation is more significant in less competitive industry16, in

15 The high-tech industry is defined as in Loughran and Ritter (2004). 16 The industry competition is measured by the herfindahl index. HHI is a dummy variable with value of one,

if the industry herfindahl index is greater than the median. The greater HHI is, the less competition the

industry has.

23

which employees have few outside job opportunities. We rerun our bivariate probit regression by

adding one interaction term between Employee Treatment and a dummy variable for less

competitive industry. We find that the interaction term in Column 3 Table 6 is negative and

statistically significant.

Hochberg and Lindsey (2010) document that the positive relation between employees’

incentive compensation and firm performance exists only in firms with a weaker free-riding

problem such as firms with fewer employees. Employees are reluctant to bear the cost to monitor

managers, but let others enjoy the benefits. Thus, we interact a dummy variable17 measuring the

degree of free-riding problem with our employee treatment variable and present the result in

Column 1 Table 7. We find that the free-riding problem lowers the negative impact of employee

treatment on the fraud likelihood.

Employees prefer to find a job near their home because they are familiar with the community,

because they are reluctant to forgo their social networks in the current community, and because

they have difficulties in moving the whole family. We do not have the data for location of

employees’ houses. However, we know that employees prefer to live near where they work. Thus,

we obtain the zip code of headquarter of the firm. In addition, due to the geographic proximity, it

is easier for employees to find new jobs near their original jobs. We first compute a fraction using

the total number of firms in the same industry and under the same zip code divided by the total

number of firms under the same zip code. Then, we define the outside option as one if this fraction

is greater than the sample median, otherwise zero. The higher of this fraction means there are more

similar firms in the same location. Thus, employees should have more outside job opportunities in

this area. In Column 3 Table 7, we interact the dummy variable measuring the outside job options

17 Free-riding is a dummy variable, taking the value of one if the number of employees in the firm is more

than the sample median.

24

with our employee treatment variable. Our result shows that the negative impact of employee

treatment on fraud likelihood is less significant when employees have more outside job options.

6. Robustness Analysis

To mitigate the endogeneity problem caused by the omitted variable, we perform several

additional tests. First, Beasley (1996) emphasizes the importance of board characteristics in

lowering fraud likelihood. They find that larger board is associated with higher fraud likelihood.

They further find that independent directors are able to lower the fraud likelihood. Thus, we include

Board Size and Independent Director% as additional controls in our regression. The results are

presented in Table 8. We obtain qualitatively the same findings.

Second, our findings might be driven by the heightened moral sensitivity of employees but

not the quality of treatment. Bowen, Call, and Rajgopal (2009) find that employee whistleblowing

is more likely in firms in “moral sensitive” industries including pharmaceuticals, health care,

medicine, the environment, oil, utilities, and banks. All these “moral sensitive” industries are

regulated industries. Following Dyck, Morse, and Zingales (2010), we define a dummy variable

called Regulated industry to control the moral sensitivity. We then rerun our analysis with

Regulated industry and an interaction term between Employee treatment and Regulated industry.

The results are presented in Table 9. We find that our results are not altered after controlling the

moral sensitivity in the regression.

Third, our results might be due to other labor related factors such as labor wage and pension

benefits. Due to the limited observations for labor wage and pension expense at the firm level, we

group the labor wage and pension expense at the industry level. We then scale the industry labor

wage and pension expense by the total number of workers in the industry. Finally, we rerun the

analysis by adding the industry labor wage per worker (Industry labor expense) and industry

pension expense per worker (Industry pension expense) as additional control variables. The results

25

are shown in Table 10. Our Employee Treatment still plays an important in limiting the fraud

propensity.

Fourth, labor union power is viewed to be correlated with both employee treatment and

managerial decisions. Therefore, we add two proxies for labor union power in our analysis.

Collective Bargaining is the percentage of employees covered by a collective bargaining agreement

at the industry level. Union Coverage is the percentage of employees joined in labor union at the

industry level18. Our results are also not changed in Table 11.

To mitigate the endogeneity problem caused by reverse causality problem, we first adopt pre-

determined (one-period lagged) independent variables in all regressions. It is possible that fraud

firm adjusts its employee treatment after the fraud happens in order to reduce the monitoring from

employees. However, it is very unlikely that future fraud commitment results in the adjustment in

employee treatment in the current period since the firm cannot predict the future fraud commitment

in the current period.

Additionally, we adopt either Collective Bargaining or Union Coverage as instrument

variables in the regressions by using the control function approach19. Those two instruments are

highly related to the employee treatment in a firm. However, those two variables are measured at

the industry level, and thus they should not be related to a firm’s fraud likelihood. The results are

presented in Table 12 and our results do not change.

We also run the regression reversely to examine whether fraud firms also tend to offer fair

employee treatment. We then perform an OLS regression in Table 13. The results show that the

18 It is very hard to get the labor union data at the firm level. We only can get those labor union data at the

industry level at best. Labor union data is obtained from Hirsch and Macpherson (2003). 19 See Wooldridge (2010, p126).

26

fraud dummy variable does not seem to have any explanatory power on how the firms treat their

employees.

In the main regression, we obtain the result that fair employee treatment lowers the fraud

likelihood. Following Yu and Yu (2011), we conduct a survival analysis to examine whether firms

treating employee fairly further lowers the fraud duration. Columns 1 Table 14 present the results

from the Weibull regression. Columns 2 present the results from the Cox regression. All the

coefficients are reported in the unexponentiated form. We observe the same results in two

regressions: Employee treatment seems have no relation with the hazard rate of fraud being detected.

7. Conclusion

Despite abundant evidence documented on the shareholders’ interest to limit the likelihood

of corporate fraud, few empirical studies investigate the stakeholders’ incentive to lower a firm’s

likelihood of fraud. Using the KLD database, we empirically examine the effect of a firm’s relations

with its employees on the likelihood of fraud.

We find that firms treating their employees fairly (as measured by employee treatment index)

have a lower probability of committing fraud. Further analysis shows that employee involvement

and cash profit-sharing are the most important components in employee treatment to determine our

results. As the inside stakeholders, fair employee treatment lowers the cost of employees to collect

information about the firm and facilitates the bottom-up governance. Motivated employees by the

fair treatment contribute more to the value of the firm, leading to strong performance and less need

to commit fraud. Moreover, we show that the negative association between employee treatment

and fraud propensity is more prominent when the firm is in high-tech industry or less competitive

industry, when firms have less employees, and when employees have less outside employment

opportunities.

27

Overall, these results suggest that employees, as important stakeholders, play an important

role in lowering the firm’s likelihood of fraud. Consequently, it is optimal for the regulators to take

the stakeholders’ interest into consideration, when they make the policies to lower the likelihood

of corporate fraud. Our findings are consistent with Dyck, Morse, and Zingales (2010) who argue

that stakeholders are the major players in uncovering corporate fraud.

28

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31

Appendix I: Variable Definitions

Variables Definition Data Source

Fraud variables[t=0]

Fraud A dummy variable equal to one, if a firm commits fraud SSCAC

Duration The number of days from the start of fraud date to the end of the fraud date SSCAC

Ex-ante variables [t=-1]

Employee treatment

A firm’s total employee relation strength score minus its total employee relation

weakness score. The total employee relation strength score is formed by adding the

points a firm receives on criteria for employee relation strength in the KLD

database, and the total employ relation weakness score is formed by adding the

points the firm receives on criteria for employee relation weakness.

KLD

Free-riding A dummy variable, taking the value of one if the number of employees in the firm

is more than the sample median.

HHI A dummy variable with value of 1, if the industry herfindahl index is greater than

the median.

High-tech industry A dummy defined as in Loughran and Ritter (2004).

Outside option

We first compute a fraction using the number of firms in the same industry and

under the same zip code divided by the total number of firms under the same zip

code. Then, we define the outside option as one if this fraction is greater than the

sample median, otherwise zero.

Board size The number of board members sitting on the board RiskMetrics

Independent director% Fraction of independent directors on the board RiskMetrics

ROA (Operating income after depreciation)/Assets COMPUSTAT

External finance need Asset growth rate – ROA2/(1-ROA2), ROA2 = (income before extraordinary

items)/Assets COMPUSTAT

Leverage Long-term debt/total asset COMPUSTAT

Size Log value of total asset COMPUSTAT

M/B Market value over book value of the firm COMPUSTAT

32

Institutional ownership The percentage of shares held by institutions

Thomson-Reuters

Institutional

Holdings (13f)

Analyst coverage The number of analysts following the firm I/B/E/S

CEO ownership The number of shares held by CEO divided by the total number of shares trading in

the market. EXECUMOP

Non-CEO Executive ownership The average percentage of shares held by the non-CEO executives EXECUMOP

CEO pay slice The fraction of the aggregate compensation of the top-five executive team captured

by the CEO (Bebchuk, Cremers, and Peyer (2011)) EXECUMOP

Regulated industry Includes drug, drug proprietaries, and druggists’ sundries (SIC 5122), health care

providers (8000-8099), and health care-related firms in Business Services. COMPUSTAT

Health care industry Includes drug, drug proprietaries, and druggists’ sundries (SIC 5122), health care

providers (8000-8099), and health care-related firms in Business Services. COMPUSTAT

Industry labor expense Labor expense divided by the number of the employees at the industry level. COMPUSTAT

Industry pension expense Pension expense divided by the number of the employees at the industry level COMPUSTAT

Collective bargaining The percentage of employees covered by a collective bargaining agreement at the

industry level.

Hirsch and

Macpherson (2003)

Union membership The percentage of employees joined in labor union at the industry level Hirsch and

Macpherson (2003)

Ex-post variables [t=1]

Stock return Annual buy-and-hold stock return CRSP

Return volatility Standard deviation of monthly stock returns in a year CRSP

Stock Turnover Average monthly turnover in a year CRSP

33

Figure 1: Kernel Density Plot of Size across Fraud and Non-fraud Sample

0.1

.2.3

De

nsity

6 8 10 12 14Size

Fraud Non-Fraud

Panel A: Kernel density estimate (Raw sample)

.05

.1.1

5.2

.25

.3

De

nsity

6 8 10 12 14Size

Fraud Non-Fraud

Panel B: Kernel density estimate (Matching sample)

34

Figure 2: Partial Observability of Fraud Cases

Only detected fraud cases are observed in the data (Z=1), while undetected fraud is not (D=0|F=1).

Thus the probability of observed fraud Prob (Z=1) is less than the probability for a firm to commit

fraud Prob (F=1).

35

Table 1: Model specification

The first column contains variables in the fraud propensity equation. The fourth column contains

the variables in the fraud detection equation. The second and last columns show the predicted

direction of the influence. The arrows in the third column show the feedback effect of detection

on the fraud propensity. The number after each variable indicates the year when the variables

are measured relative to the fiscal year when the fraud happens.

Fraud Propensity (XF) βF Fraud Detection (XD) βD

Variables of interest Variables of interest

Benefit from fraud

Employee treatment [-1] -/+ Employee treatment [-1] +/-

Board characteristics [-1] -

Growth & profitability [-1] +

External financing need [-

1] +

Leverage [-1] +

Insider equity incentive [-

1] -/+

Feedback from Detection Ex-ante Detection

Institutional ownership [-1] - Institutional ownership [-1] +

Analyst coverage [-1] - Analyst coverage [-1] +

Firm size and industry [-1] Firm size and industry [-1]

Ex-post Detection

Stock return [1] -

Return volatility [1] +

Stock turnover [1] +

36

Table 2: Number of Fraud Cases by Year and Durations of Fraud

We follow Dyck, Morse, and Zingales (2010) and collect fraud cases filed from 1996 to 2011 in Stanford Securities Class Action Clearinghouse.

The filing date is the data when shareholders file the federal class action securities fraud litigation, and thus it is after the ending data of fraud

activities. Starting Year of fraud is the first year when fraudulent activities happen. Duration is defined as the number of years from the start of fraud

date to the end of the fraud date, as shown in the litigation documents.

Fraud Duration (years)

Starting Year Count Percentage (%) Min Mean Median Max

Standard

Deviation

1996 3 2.24 0.21 1.67 2.28 2.53 1.27

1997 4 2.99 0.25 1.74 0.86 4.97 2.19

1998 5 3.73 0.24 1.86 1.41 4.36 1.71

1999 10 7.46 0.11 2.00 2.17 3.98 1.37

2000 8 5.97 0.21 2.07 2.03 4.96 1.64

2001 11 8.21 0.32 1.42 1.30 4.95 1.25

2002 7 5.22 0.21 1.79 1.32 4.72 1.68

2003 4 2.99 0.58 3.25 3.78 4.85 2.04

2004 6 4.48 0.21 1.34 1.09 3.60 1.18

2005 14 10.45 0.03 1.42 1.33 3.52 1.04

2006 10 7.46 0.38 1.95 1.34 4.82 1.64

2007 18 13.43 0.21 1.29 1.00 3.57 0.93

2008 8 5.97 0.24 0.93 0.72 1.97 0.63

2009 13 9.70 0.10 0.69 0.62 1.38 0.37

2010 13 9.70 0.00 0.46 0.44 1.00 0.31

Total 134 100.00 0.00 1.43 0.98 4.97 1.29

37

Table 3: Comparison of Fraud Firms with Non-Fraud Firms in the COMPUSTAT Large Firms Employee treatment is calculated by using a firm’s total employee relation strength score minus its total employee relation weakness score. The total employee

relation strength score is formed by adding the points a firm receives on criteria for employee relation strength in the KLD database, and the total employ relation

weakness score is formed by adding the points the firm receives on criteria for employee relation weakness. Board size is the number of board members sitting on

the corporate board. Independent director% is the fraction of the independent directors on the board. Firm Size is the log value of total assets. ROA is return on

assets. Leverage is long-term debt divided by total assets. M/B is market value of equity plus books value of debt divided by book value of assets. External Finance

need is equal to asset growth rate minus ROA2/(1-ROA2), where ROA2 is income before extraordinary items divided by total assets. CEO ownership is the number

of shares held by CEO divided by the total number of shares trading in the market. Non-CEO executive ownership is the average percentage of shares held by the

non-CEO executives in EXECUCOMP. CEO pay slice is the fraction of the aggregate compensation of the top-five executive team captured by the CEO (Bebchuk,

Cremers, and Peyer, 2011). Institutional ownership is the percentage of shares held by institutional investors from 13-f filings. Analyst coverage is the number of

analysts following the firm. Stock return is the annual buy-and-hold stock return. Stock volatility is standard deviation of monthly stock returns in a year. Stock

turnover is average monthly turnover in a year. The table shows the mean values and median values in parenthesis of each variable of the fraud sample and non-

fraud sample.

Fraud #obs Non-fraud #obs t-statistics

Employee treatment 0.06(0.00) 134 -0.01(0.00) 134 0.50

Board size 11.02(11.00) 119 11.22(11.00) 118 -0.20

Independent director% 0.72(0.75) 119 0.72(0.73) 118 0.001

Firm size 9.26(9.09) 134 9.13(8.87) 134 0.67

ROA 0.11(0.10) 134 0.10(0.09) 134 0.48

Leverage 0.20(0.18) 134 0.18(0.15) 134 1.12

M/B 1.63(0.88) 134 1.24(0.87) 134 1.91*

External finance need 0.04(-0.42) 132 -0.01(-0.10) 133 0.72

CEO ownership 0.01(0.002) 128 0.02(0.002) 134 -0.33

Non-CEO executive ownership 0.001(0.0004) 130 0.002(0.0004) 134 -0.53

CEO pay slice 0.36(0.38) 133 0.35(0.37) 134 0.40

Institutional ownership 0.75(0.74) 134 0.73(0.74) 134 0.79

Stock return 0.02(0.01) 121 0.07(0.07) 122 -0.70

Analyst coverage 11.19(10.00) 134 10.00(9.00) 134 1.65

Stock volatility 0.13(0.10) 121 0.11(0.09) 122 1.59

Stock turnover 0.28(0.20) 121 0.22(0.17) 122 2.07**

38

Table 4: Employee treatment and fraud propensity in bivariate probit model with partial

observability The dependent variable is the dummy variable for detected fraud. Fraud is the dependent variable, and it is

a dummy variable equal to one, if a firm commits fraud. Employee treatment is calculated by using a firm’s

total employee relation strength score minus its total employee relation weakness score. The total employee

relation strength score is formed by adding the points a firm receives on criteria for employee relation strength

in the KLD database, and the total employ relation weakness score is formed by adding the points the firm

receives on criteria for employee relation weakness. Size is the log value of total assets. ROA is return on

assets. Leverage is long-term debt divided by total assets. M/B is market value of equity plus books value of

debt divided by book value of assets. External Finance need is equal to asset growth rate minus ROA2/(1-

ROA2), where ROA2 is income before extraordinary items divided by total assets. CEO ownership is the

number of shares held by CEO divided by the total number of shares trading in the market. Non-CEO

executive ownership is the average percentage of shares held by the non-CEO executives in EXECUCOMP.

CEO pay slice is the fraction of the aggregate compensation of the top-five executive team captured by the

CEO (Bebchuk, Cremers, and Peyer, 2011). Institutional ownership is the percentage of shares held by

institutional investors from 13-f filings. Analyst coverage is the number of analysts following the firm. Stock

return is the annual buy-and-hold stock return. Stock volatility is standard deviation of monthly stock returns

in a year. Stock turnover is average monthly turnover in a year. Trade, Service, and Technology are defined

as Wang (2013). P-value is in parentheses and robust standard error is adopted. *** p<0.01, ** p<0.05, *

p<0.1

Bivariate probit

VARIABLES P(F=1) P(D=1|F=1)

Employee treatment -1.02*** 0.09 (0.000) (0.351) ROA 6.27*

(0.075)

Leverage 2.70**

(0.019)

M/B 0.36

(0.193)

External finance need 1.76***

(0.004)

CEO ownership -3.98

(0.349)

Non-CEO Executive ownership -0.43

(0.991)

CEO pay slice 1.51

(0.313)

Firm size 0.64*** -0.03 (0.000) (0.639) Institutional ownership 5.91*** -1.76** (0.000) (0.027) Analyst coverage -0.06** 0.03 (0.024) (0.102) Technology -0.56 0.23 (0.220) (0.411) Service -2.05*** 0.38 (0.000) (0.152)

39

Trade -3.62*** 4.29*** (0.001) (0.000) Stock return 0.06 (0.813) Stock volatility 3.50** (0.031) Stock turnover 1.11* (0.086) Constant -8.99*** 0.61 (0.000) (0.549) Observations 232 232

Pseudo R-square X X

40 Table 5: Subcategory of the Employee treatment and fraud propensity in bivariate probit model with partial observability The dependent variable is the dummy variable for detected fraud. Fraud is the dependent variable, and it is a dummy variable equal to one, if a firm commits fraud. Employee

treatment is defined as a firm’s total employee strength score minus its total employee weakness score. The total employee strength score is formed by adding the points a firm

receives on criteria for employee strength in the KLD database, and the total employee weakness score is formed by adding the points the firm receives on criteria for employee

weakness. Labor union relation, Retirement benefits, Employee involvement, and Cash profit sharing are four subcategories forming the employee relation index. Size is the log

value of total assets. ROA is return on assets. Leverage is long-term debt divided by total assets. M/B is market value of equity plus books value of debt divided by book value of

assets. External Finance need is equal to asset growth rate minus ROA2/(1-ROA2), where ROA2 is income before extraordinary items divided by total assets. CEO ownership is the

number of shares held by CEO divided by the total number of shares trading in the market. Non-CEO executive ownership is the average percentage of shares held by the non-CEO

executives in EXECUCOMP. CEO pay slice is the fraction of the aggregate compensation of the top-five executive team captured by the CEO (Bebchuk, Cremers, and Peyer, 2011).

Institutional ownership is the percentage of shares held by institutional investors from 13-f filings. Analyst coverage is the number of analysts following the firm. Stock return is the

annual buy-and-hold stock return. Stock volatility is standard deviation of monthly stock returns in a year. Stock turnover is average monthly turnover in a year. Trade, Service, and

Technology are defined as Wang (2013). P-value is in parentheses and robust standard error is adopted. *** p<0.01, ** p<0.05, * p<0.1

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES P(F=1) P(D=1|F=1) P(F=1) P(D=1|F=1) P(F=1) P(D=1|F=1) P(F=1) P(D=1|F=1)

Labor union relation -0.11 -0.48

(0.741) (0.396)

Retirement benefits -0.13 -1.43

(0.491) (0.169)

Employee involvement -1.47* 5.02***

(0.073) (0.000)

Cash profit sharing -1.49*** -0.79*

(0.000) (0.054)

ROA -3.20 -3.27 -4.64* -6.20**

(0.189) (0.103) (0.088) (0.029)

Leverage 2.66*** 2.35*** 3.91** 2.17***

(0.001) (0.001) (0.011) (0.008)

M/B 0.42*** 0.40*** 0.58*** 0.86***

(0.005) (0.005) (0.003) (0.000)

External finance need 0.70 0.50 0.39 0.82

(0.704) (0.263) (0.353) (0.163)

CEO ownership 3.69 5.27* 5.12 10.50

(0.336) (0.071) (0.257) (0.138)

Non-CEO Executive ownership 25.23 31.30 -0.32 62.37

(0.390) (0.283) (0.992) (0.159)

CEO pay slice 0.53 0.48 0.34 -0.03

(0.481) (0.464) (0.692) (0.970)

Firm size 0.08 0.83 0.09 0.94* 0.17 -0.02 0.08 0.51***

41 (0.560) (0.653) (0.257) (0.057) (0.141) (0.841) (0.381) (0.000)

Institutional ownership -0.96 10.25 -0.85 9.92*** -2.40* 1.90 -1.87** 4.84***

(0.625) (0.743) (0.252) (0.001) (0.062) (0.116) (0.027) (0.000)

Analyst coverage -0.02 0.13 -0.01 0.07 0.04 -0.01 -0.01 0.04

(0.468) (0.816) (0.553) (0.242) (0.392) (0.549) (0.542) (0.142)

Technology 0.25 -6.28 0.23 -7.90*** 0.16 -0.20 0.89** -1.27***

(0.742) (0.667) (0.551) (0.000) (0.672) (0.651) (0.017) (0.007)

Service 0.20 -5.12 0.26 -7.18*** 0.60 -0.28 0.38 -0.97**

(0.776) (0.528) (0.299) (0.000) (0.338) (0.513) (0.138) (0.014)

Trade 1.11 -7.34 1.08* -9.30*** 0.37 -0.00 1.47** -1.66***

(0.621) (0.604) (0.067) (0.000) (0.555) (0.996) (0.011) (0.002)

Stock return 0.49 0.39 -0.23 0.26

(0.840) (0.466) (0.440) (0.286)

Stock volatility -6.49 -3.48 -0.13 -1.37

(0.792) (0.490) (0.934) (0.429)

Stock turnover 3.92 2.61 1.62 2.51***

(0.707) (0.123) (0.128) (0.001)

Constant -0.79 -8.79 -1.00 -7.15 -0.38 -0.90 -0.08 -7.38***

(0.777) (0.748) (0.360) (0.140) (0.813) (0.474) (0.949) (0.000)

Observations 232 232 232 232 232 232 232 232

42 Table 6: Employee treatment and fraud propensity in bivariate probit interacted with industry characteristics The dependent variable is the dummy variable for detected fraud. Fraud is the dependent variable, and it is a dummy variable equal to

one, if a firm commits fraud. Employee treatment is calculated by using a firm’s total employee relation strength score minus its total

employee relation weakness score. The total employee relation strength score is formed by adding the points a firm receives on criteria

for employee relation strength in the KLD database, and the total employ relation weakness score is formed by adding the points the

firm receives on criteria for employee relation weakness. High-tech industry is a dummy defined as in Loughran and Ritter (2004). HHI

is a dummy variable with value of 1, if the industry herfindahl index is greater than the median. Size is the log value of total assets. ROA

is return on assets. Leverage is long-term debt divided by total assets. M/B is market value of equity plus books value of debt divided

by book value of assets. External Finance need is equal to asset growth rate minus ROA2/(1-ROA2), where ROA2 is income before

extraordinary items divided by total assets. CEO ownership is the number of shares held by CEO divided by the total number of shares

trading in the market. Non-CEO executive ownership is the average percentage of shares held by the non-CEO executives in

EXECUCOMP. CEO pay slice is the fraction of the aggregate compensation of the top-five executive team captured by the CEO

(Bebchuk, Cremers, and Peyer, 2011). Institutional ownership is the percentage of shares held by institutional investors from 13-f filings.

Analyst coverage is the number of analysts following the firm. Stock return is the annual buy-and-hold stock return. Stock volatility is

standard deviation of monthly stock returns in a year. Stock turnover is average monthly turnover in a year. Trade, Service, and

Technology are defined as Wang (2013). P-value is in parentheses and robust standard error is adopted. *** p<0.01, ** p<0.05, * p<0.1

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

VARIABLES P(F=1) P(D=1|F=1) P(F=1) P(D=1|F=1)

Employee treatment -1.03*** 0.15 -0.40* 0.22*

(0.003) (0.175) (0.050) (0.057)

Employee treatment*High-tech -0.95**

(0.049)

High-tech industry -0.25

(0.559)

Employee treatment*HHI -0.41*

(0.085)

HHI 0.53**

(0.017)

ROA -0.98 -4.93***

(0.819) (0.009)

Leverage -0.05 1.59**

(0.934) (0.016)

M/B 4.08*** 3.95***

(0.000) (0.000)

External finance need 0.61*** 0.27**

(0.001) (0.021)

CEO ownership 0.35 0.62***

(0.260) (0.000)

Non-CEO Executive ownership 7.08*** 2.69***

(0.000) (0.000)

CEO pay slice -0.11*** -0.03

(0.003) (0.168)

Firm size -3.36 -0.02 3.42 0.01

(0.307) (0.798) (0.169) (0.874)

Institutional ownership -0.95 -1.39* 18.46 -0.24

(0.982) (0.071) (0.614) (0.705)

Analyst coverage -0.36 0.04** 1.43** 0.04**

(0.843) (0.048) (0.038) (0.038)

Technology 0.60 0.07 0.15 -0.10

(0.237) (0.808) (0.702) (0.733)

Service -2.45*** 0.35 -1.29*** 0.45*

(0.000) (0.171) (0.001) (0.063)

Trade -3.73*** 4.65*** -1.51*** 2.19***

(0.001) (0.000) (0.005) (0.002)

Stock return -0.01 -0.22

43 (0.955) (0.222)

Stock volatility 1.74 -0.23

(0.268) (0.836)

Stock turnover 1.18* 0.88

(0.074) (0.121)

Constant -7.60*** 0.31 -0.42 -0.42

(0.001) (0.758) (0.648) (0.648)

Observations 232 232 232 232

44

Table 7: Employee treatment and fraud propensity in bivariate probit interacted with firm

characteristics The dependent variable is the dummy variable for detected fraud. Fraud is the dependent variable, and it is a dummy

variable equal to one, if a firm commits fraud. Employee treatment is calculated by using a firm’s total employee

relation strength score minus its total employee relation weakness score. The total employee relation strength score is

formed by adding the points a firm receives on criteria for employee relation strength in the KLD database, and the

total employ relation weakness score is formed by adding the points the firm receives on criteria for employee relation

weakness. Free-riding is a dummy variable, taking the value of one if the number of employees in the firm is

more than the sample median. We first compute a fraction using the number of firms in the same industry

and under the same zip code divided by the total number of firms under the same zip code. Then, we define

the outside option as one if this fraction is greater than the sample median, otherwise zero. Size is the log

value of total assets. ROA is return on assets. Leverage is long-term debt divided by total assets. M/B is market value

of equity plus books value of debt divided by book value of assets. External Finance need is equal to asset growth

rate minus ROA2/(1-ROA2), where ROA2 is income before extraordinary items divided by total assets. CEO

ownership is the number of shares held by CEO divided by the total number of shares trading in the market. Non-CEO

executive ownership is the average percentage of shares held by the non-CEO executives in EXECUCOMP. CEO pay

slice is the fraction of the aggregate compensation of the top-five executive team captured by the CEO (Bebchuk,

Cremers, and Peyer, 2011). Institutional ownership is the percentage of shares held by institutional investors from 13-

f filings. Analyst coverage is the number of analysts following the firm. Stock return is the annual buy-and-hold stock

return. Stock volatility is standard deviation of monthly stock returns in a year. Stock turnover is average monthly

turnover in a year. Trade, Service, and Technology are defined as Wang (2013). P-value is in parentheses and robust

standard error is adopted. *** p<0.01, ** p<0.05, * p<0.1

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

VARIABLES P(F=1) P(D=1|F=1) P(F=1) P(D=1|F=1)

Employee treatment -2.02** 0.05 -1.93*** 0.12

(0.047) (0.628) (0.002) (0.251)

Employee treatment*Free-riding 1.83*

(0.082)

Free-riding 0.21

(0.883)

Employee treatment*Outside option 6.69***

(0.002)

Outside option -11.48***

(0.005)

ROA -13.39* -10.78***

(0.056) (0.004)

Leverage 0.90 1.79

(0.474) (0.133)

M/B 5.72*** 8.47***

(0.003) (0.004)

External finance need 1.10*** 0.62**

(0.009) (0.035)

CEO ownership 0.89** 1.94***

(0.035) (0.003)

Non-CEO Executive ownership 11.43** -3.13

(0.035) (0.131)

CEO pay slice 0.13* -0.14*

(0.080) (0.059)

45

Firm size 3.07 0.01 -13.23** 0.02

(0.647) (0.913) (0.013) (0.802)

Institutional ownership 0.62 -0.80 -39.27 1.38**

(0.996) (0.265) (0.144) (0.026)

Analyst coverage -1.39 -0.01 -2.48 0.06***

(0.542) (0.559) (0.150) (0.005)

Technology -10.30*** 1.07 -0.98* 0.20

(0.000) (0.154) (0.069) (0.498)

Service -8.55*** 0.25 -1.41** 0.03

(0.000) (0.317) (0.037) (0.889)

Trade -11.74*** 5.16*** -1.84** 0.26

(0.000) (0.000) (0.038) (0.451)

Stock return -0.12 0.21

(0.635) (0.334)

Stock volatility 1.27 2.92**

(0.413) (0.047)

Stock turnover 0.59 0.62

(0.330) (0.247)

Constant -9.03* 0.25 0.22 -2.13**

(0.079) (0.794) (0.937) (0.020)

Observations 219 219 232 232

46

Table 8: Employee treatment and fraud propensity with board characteristics The dependent variable is the dummy variable for detected fraud. Fraud is the dependent variable, and it is a dummy

variable equal to one, if a firm commits fraud. Employee treatment is calculated by using a firm’s total employee

relation strength score minus its total employee relation weakness score. The total employee relation strength score is

formed by adding the points a firm receives on criteria for employee relation strength in the KLD database, and the

total employ relation weakness score is formed by adding the points the firm receives on criteria for employee relation

weakness. Board size is the number of board members sitting on the corporate board. Independent director% is the

fraction of the independent directors on the board. Size is the log value of total assets. ROA is return on assets. Leverage

is long-term debt divided by total assets. M/B is market value of equity plus books value of debt divided by book value

of assets. External Finance need is equal to asset growth rate minus ROA2/(1-ROA2), where ROA2 is income before

extraordinary items divided by total assets. CEO ownership is the number of shares held by CEO divided by the total

number of shares trading in the market. Non-CEO executive ownership is the average percentage of shares held by the

non-CEO executives in EXECUCOMP. CEO pay slice is the fraction of the aggregate compensation of the top-five

executive team captured by the CEO (Bebchuk, Cremers, and Peyer, 2011). Institutional ownership is the percentage

of shares held by institutional investors from 13-f filings. Analyst coverage is the number of analysts following the

firm. Stock return is the annual buy-and-hold stock return. Stock volatility is standard deviation of monthly stock

returns in a year. Stock turnover is average monthly turnover in a year. Trade, Service, and Technology are defined as

Wang (2013). P-value is in parentheses and robust standard error is adopted. *** p<0.01, ** p<0.05, * p<0.1

Bivariate probit

VARIABLES P(F=1) P(D=1|F=1)

Employee treatment -2.56*** 0.19*

(0.004) (0.067)

Board size 0.01

(0.909)

Independent director% -0.37

(0.810)

ROA -15.03**

(0.033)

External finance need 8.13***

(0.001)

Leverage 9.07**

(0.047)

Market-to-book 3.06**

(0.023)

CEO ownership -0.50

(0.987)

Executive ownership 0.55

(0.994)

CEO pay slice -1.34

(0.410)

Firm size 1.05** -0.02

(0.032) (0.770)

Institutional ownership -8.14*** 1.58**

(0.008) (0.014)

Analyst coverage -0.15* 0.03*

(0.071) (0.059)

Technology 0.83 -0.13

(0.392) (0.625)

47

Service 0.62 0.13

(0.288) (0.552)

Trade -0.81 0.02

(0.278) (0.961)

Constant -0.74 -1.36

(0.853) (0.122)

Observations 227 227

Pseudo R-square X X

48

Table 9: The industry effect on employee treatment and fraud propensity

The dependent variable is the dummy variable for detected fraud. Fraud is the dependent variable, and it is

a dummy variable equal to one, if a firm commits fraud. Employee treatment is defined as a firm’s total

employee strength score minus its total employee weakness score. The total employee strength score is

formed by adding the points a firm receives on criteria for employee strength in the KLD database, and the

total employee weakness score is formed by adding the points the firm receives on criteria for employee

weakness. ROA is return on assets. Leverage is long-term debt divided by total assets. M/B is market value

of equity plus books value of debt divided by book value of assets. External Finance need is equal to asset

growth rate minus ROA2/(1-ROA2), where ROA2 is income before extraordinary items divided by total

assets. CEO ownership is the number of shares held by CEO divided by the total number of shares trading

in the market. Non-CEO executive ownership is the average percentage of shares held by the non-CEO

executives in EXECUCOMP. CEO pay slice is the fraction of the aggregate compensation of the top-five

executive team captured by the CEO (Bebchuk, Cremers, and Peyer, 2011). Institutional ownership is the

percentage of shares held by institutional investors from 13-f filings. Analyst coverage is the number of

analysts following the firm. Stock return is the annual buy-and-hold stock return. Stock volatility is standard

deviation of monthly stock returns in a year. Stock turnover is average monthly turnover in a year. Trade,

Service, and Technology are defined as Wang (2013). Regulated industry is defined as Dyck, Morse and

Zingales (2010). P-value is in parentheses and robust standard error is adopted. *** p<0.01, ** p<0.05, *

p<0.1

(1) (2)

VARIABLES P(F=1) P(D=1|F=1)

Employee treatment -1.01*** 0.11

(0.004) (0.431)

Employee treatment*Regulated industry 0.08 -0.03

(0.832) (0.867)

Regulated industry 0.08 -0.03

(0.880) (0.923)

ROA 5.29

(0.233)

Leverage 1.55**

(0.041)

M/B 2.94***

(0.001)

External finance need 0.64*** -0.04

(0.000) (0.569)

CEO ownership 0.38*

(0.090)

Non-CEO Executive ownership 5.88*** -1.80**

(0.000) (0.024)

CEO pay slice -0.06* 0.03

(0.055) (0.137)

Firm size -4.56**

(0.039)

Institutional ownership 0.70

(0.985)

Analyst coverage 1.69

49

(0.297)

Technology -0.42 0.20

(0.392) (0.482)

Service -2.04*** 0.39

(0.005) (0.252)

Trade -3.45*** 4.06***

(0.002) (0.000)

Stock return 0.08

(0.764)

Stock volatility 3.47**

(0.049)

Stock turnover 1.10*

(0.086)

Constant -9.12*** 0.75

(0.000) (0.461)

Observations 232 232

50

Table 10: Employee treatment and fraud propensity with labor and pension expense

The dependent variable is the dummy variable for detected fraud. Fraud is the dependent variable, and it is

a dummy variable equal to one, if a firm commits fraud. Employee treatment is defined as a firm’s total

employee strength score minus its total employee weakness score. The total employee strength score is

formed by adding the points a firm receives on criteria for employee strength in the KLD database, and the

total employee weakness score is formed by adding the points the firm receives on criteria for employee

weakness. ROA is return on assets. Leverage is long-term debt divided by total assets. M/B is market value

of equity plus books value of debt divided by book value of assets. External Finance need is equal to asset

growth rate minus ROA2/(1-ROA2), where ROA2 is income before extraordinary items divided by total

assets. CEO ownership is the number of shares held by CEO divided by the total number of shares trading

in the market. Non-CEO executive ownership is the average percentage of shares held by the non-CEO

executives in EXECUCOMP. CEO pay slice is the fraction of the aggregate compensation of the top-five

executive team captured by the CEO (Bebchuk, Cremers, and Peyer, 2011). Institutional ownership is the

percentage of shares held by institutional investors from 13-f filings. Analyst coverage is the number of

analysts following the firm. Stock return is the annual buy-and-hold stock return. Stock volatility is standard

deviation of monthly stock returns in a year. Stock turnover is average monthly turnover in a year. Trade,

Service, and Technology are defined as Wang (2013). Industry labor expense is the labor expense divided

by the number of the employees at the industry level. Industry pension expense is the pension expense

divided by the number of the employees at the industry level. P-value is in parentheses and robust standard

error is adopted. *** p<0.01, ** p<0.05, * p<0.1

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

VARIABLES P(F=1) P(F=1|D=1) P(F=1) P(F=1|D=1)

Employee treatment -2.97*** 0.19* -1.83*** 0.18*

(0.002) (0.067) (0.001) (0.093)

ROA -15.40** -11.57**

(0.021) (0.042)

Leverage 8.66* 7.30**

(0.054) (0.012)

M/B 3.14*** 2.62***

(0.004) (0.002)

External finance need 9.13*** 4.35***

(0.001) (0.008)

CEO ownership -16.94*** 34.02

(0.003) (0.475)

Non-CEO Executive ownership 9.00 5.76

(0.893) (0.941)

CEO pay slice -1.90 -4.34**

(0.178) (0.030)

Industry labor expense -0.01 -0.00

(0.414) (0.663)

Industry pension expense 0.76*** 0.01

(0.010) (0.882)

Firm size 1.31*** 0.00 0.46** 0.02

(0.002) (0.961) (0.020) (0.807)

Institutional ownership -9.47** 1.50** -9.37*** 1.83***

(0.010) (0.012) (0.002) (0.004)

51

Analyst coverage -0.16** 0.03* -0.09 0.03*

(0.019) (0.069) (0.148) (0.073)

Technology 1.28* -0.14 1.39 -0.20

(0.086) (0.601) (0.320) (0.476)

Service -0.41 0.11 1.01 -0.08

(0.447) (0.620) (0.120) (0.732)

Trade 1.30 0.01 2.44*** 0.04

(0.237) (0.987) (0.004) (0.906)

Stock return -0.09 0.00

(0.641) (0.993)

Stock volatility 0.47 0.69

(0.687) (0.625)

Stock turnover 0.65 1.12*

(0.211) (0.054)

Constant -1.42 -1.48* 2.79 -1.95**

(0.522) (0.087) (0.256) (0.024)

Observations 232 232 232 232

52

Table 11: Employee treatment and fraud propensity with labor union power

The dependent variable is the dummy variable for detected fraud. Fraud is the dependent variable, and it is

a dummy variable equal to one, if a firm commits fraud. Employee treatment is defined as a firm’s total

employee strength score minus its total employee weakness score. The total employee strength score is

formed by adding the points a firm receives on criteria for employee strength in the KLD database, and the

total employee weakness score is formed by adding the points the firm receives on criteria for employee

weakness. ROA is return on assets. Leverage is long-term debt divided by total assets. M/B is market value

of equity plus books value of debt divided by book value of assets. External Finance need is equal to asset

growth rate minus ROA2/(1-ROA2), where ROA2 is income before extraordinary items divided by total

assets. CEO ownership is the number of shares held by CEO divided by the total number of shares trading

in the market. Non-CEO executive ownership is the average percentage of shares held by the non-CEO

executives in EXECUCOMP. CEO pay slice is the fraction of the aggregate compensation of the top-five

executive team captured by the CEO (Bebchuk, Cremers, and Peyer, 2011). Institutional ownership is the

percentage of shares held by institutional investors from 13-f filings. Analyst coverage is the number of

analysts following the firm. Stock return is the annual buy-and-hold stock return. Stock volatility is standard

deviation of monthly stock returns in a year. Stock turnover is average monthly turnover in a year. Trade,

Service, and Technology are defined as Wang (2013). Collective bargaining is the percentage of employees

covered by a collective bargaining agreement at the industry level. Union membership is the percentage of

employees joined in labor union at the industry level. P-value is in parentheses and robust standard error is

adopted. *** p<0.01, ** p<0.05, * p<0.1

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

VARIABLES P(F=1) P(D=1|F=1) P(F=1) P(D=1|F=1)

Employee treatment -2.67*** 0.19* -2.88** 0.18*

(0.004) (0.067) (0.010) (0.074)

ROA -15.69** -16.24*

(0.041) (0.057)

Leverage 9.14** 9.48**

(0.018) (0.024)

M/B 3.15** 3.37**

(0.022) (0.021)

External finance need 8.53*** 9.01***

(0.005) (0.006)

CEO ownership -0.32 -12.50

(0.992) (0.714)

Non-CEO Executive ownership 4.05 8.90

(0.950) (0.910)

CEO pay slice -1.81 -1.47

(0.129) (0.308)

Collective bargaining 0.70 0.95

(0.686) (0.266)

Union membership 1.04 0.99

(0.674) (0.274)

Firm size 1.07** -0.01 1.20** -0.02

(0.022) (0.828) (0.037) (0.789)

Institutional ownership -8.64** 1.62*** -9.54** 1.57***

(0.026) (0.008) (0.027) (0.009)

53

Analyst coverage -0.15** 0.03* -0.17* 0.03*

(0.036) (0.061) (0.074) (0.065)

Technology 0.86 -0.05 0.91 -0.06

(0.218) (0.852) (0.260) (0.836)

Service -0.68 0.19 -0.59 0.16

(0.227) (0.410) (0.317) (0.484)

Trade 1.09 0.11 1.22 0.11

(0.271) (0.752) (0.232) (0.763)

Stock return -0.08 -0.08

(0.683) (0.686)

Stock volatility 0.50 0.52

(0.682) (0.704)

Stock turnover 0.69 0.72

(0.202) (0.194)

Constant -0.63 -1.61* -0.88 -1.55*

(0.862) (0.076) (0.847) (0.087)

Observations 230 230 230 230

54

Table 12: Employee treatment and fraud propensity using control function approach

We adopt the control function approach to deal with the endogeneity of employee treatment. The dependent variable

in the first stage is the Employee treatment. The dependent variable is the dummy variable for detected fraud in the

second stage. Fraud is the dependent variable, and it is a dummy variable equal to one, if a firm commits fraud.

Employee treatment is defined as a firm’s total employee strength score minus its total employee weakness score.

The total employee strength score is formed by adding the points a firm receives on criteria for employee strength

in the KLD database, and the total employee weakness score is formed by adding the points the firm receives on

criteria for employee weakness. ROA is return on assets. Leverage is long-term debt divided by total assets. M/B is

market value of equity plus books value of debt divided by book value of assets. External Finance need is equal to

asset growth rate minus ROA2/(1-ROA2), where ROA2 is income before extraordinary items divided by total assets.

CEO ownership is the number of shares held by CEO divided by the total number of shares trading in the market.

Non-CEO executive ownership is the average percentage of shares held by the non-CEO executives in

EXECUCOMP. CEO pay slice is the fraction of the aggregate compensation of the top-five executive team captured

by the CEO (Bebchuk, Cremers, and Peyer, 2011). Institutional ownership is the percentage of shares held by

institutional investors from 13-f filings. Analyst coverage is the number of analysts following the firm. Stock return

is the annual buy-and-hold stock return. Stock volatility is standard deviation of monthly stock returns in a year.

Stock turnover is average monthly turnover in a year. Trade, Service, and Technology are defined as Wang (2013).

Collective bargaining is the percentage of employees covered by a collective bargaining agreement at the industry

level. Union membership is the percentage of employees joined in labor union at the industry level. Predicted

residual in collective bargaining is the predicted residual from the first stage using the Collective bargaining as the

instrument. Predicted residual in union membership is the predicted residual from the first stage using the Union

membership as the instrument. P-value is in parentheses and robust standard error is adopted. *** p<0.01, ** p<0.05,

* p<0.1

First stage Second stage First stage Second stage

VARIABLES Employee

treatment

P(F=1) P(D=1|F=1) Employee

treatment

P(F=1) P(D=1|F=1)

Collective bargaining -1.80***

(0.006)

Union membership -1.88***

(0.007)

Employee treatment -2.47** -0.15 -2.20** -0.18 (0.029) (0.703) (0.047) (0.631) ROA 1.93 6.13 1.92 5.65

(0.199) (0.218) (0.202) (0.133)

Leverage -0.10 2.20** -0.09 2.29**

(0.846) (0.031) (0.857) (0.022)

M/B -0.13 0.28 -0.13 0.31

(0.113) (0.336) (0.115) (0.242)

External finance need 0.51** 2.40** 0.51** 2.23**

(0.045) (0.020) (0.046) (0.014)

CEO ownership -2.22 -6.81* -2.19 -6.43*

(0.213) (0.057) (0.220) (0.078)

Executive ownership -15.10 -31.33 -15.41 -21.50

(0.410) (0.400) (0.400) (0.557)

CEO pay slice 0.50 2.04* 0.51 1.93*

(0.315) (0.078) (0.310) (0.093)

Firm size 0.04 0.76*** -0.05 0.04 0.74*** -0.04

55

(0.429) (0.000) (0.592) (0.413) (0.000) (0.608) Institutional ownership -0.69 4.96*** -1.85** -0.69 4.98*** -1.86** (0.128) (0.000) (0.014) (0.125) (0.000) (0.016) Analyst coverage 0.01 -0.05* 0.03 0.01 -0.05* 0.03 (0.510) (0.061) (0.148) (0.535) (0.064) (0.182) Technology 0.41* 0.24 0.33 0.42* 0.11 0.34 (0.063) (0.717) (0.302) (0.060) (0.882) (0.294) Service -0.24 -2.23*** 0.37 -0.24 -2.18*** 0.37 (0.162) (0.000) (0.187) (0.156) (0.000) (0.165) Trade -0.57** -4.13*** 4.47*** -0.56** -3.95*** 4.45*** (0.033) (0.001) (0.000) (0.035) (0.001) (0.000) Predicted residual in

collective bargaining

1.41 0.27

(0.169) (0.506)

Predicted residual in union

membership

1.17 0.31

(0.237) (0.426) Stock return -0.08 0.03 -0.09 0.02 (0.610) (0.906) (0.589) (0.930) Stock volatility 0.61 3.50* 0.61 3.49** (0.535) (0.053) (0.537) (0.034) Stock turnover -0.14 1.17* -0.14 1.16* (0.730) (0.061) (0.726) (0.061) Constant 0.13 -9.37*** 0.81 0.11 -9.21*** 0.80 (0.868) (0.000) (0.440) (0.884) (0.000) (0.441) Observations 230 230 230 230 230 230

R-square 0.15 X X 0.15 X X

56

Table 13: Do fraud firms tend to offer better employee treatment?

The dependent variable is the Employee treatment defined as a firm’s total employee strength score

minus its total employee weakness score. The total employee strength score is formed by adding

the points a firm receives on criteria for employee strength in the KLD database, and the total

employee weakness score is formed by adding the points the firm receives on criteria for employee

weakness. Fraud is the independent variable, and it is a dummy variable equal to one, if a firm

commits fraud. ROA is return on assets. Leverage is long-term debt divided by total assets. M/B is

market value of equity plus books value of debt divided by book value of assets. External Finance

need is equal to asset growth rate minus ROA2/(1-ROA2), where ROA2 is income before

extraordinary items divided by total assets. CEO ownership is the number of shares held by CEO

divided by the total number of shares trading in the market. Non-CEO executive ownership is the

average percentage of shares held by the non-CEO executives in EXECUCOMP. CEO pay slice is

the fraction of the aggregate compensation of the top-five executive team captured by the CEO

(Bebchuk, Cremers, and Peyer, 2011). Institutional ownership is the percentage of shares held by

institutional investors from 13-f filings. Analyst coverage is the number of analysts following the

firm. Stock return is the annual buy-and-hold stock return. Stock volatility is standard deviation of

monthly stock returns in a year. Stock turnover is average monthly turnover in a year. Trade,

Service, and Technology are defined as Wang (2013). Collective bargaining is the percentage of

employees covered by a collective bargaining agreement at the industry level. Union coverage is

the percentage of employees joined in labor union at the industry level. P-value is in parentheses

and robust standard error is adopted. *** p<0.01, ** p<0.05, * p<0.1

(1)

VARIABLES OLS

Fraud 0.02

(0.901)

ROA 0.79

(0.533)

External finance need 0.48***

(0.006)

Leverage -0.23

(0.576)

Firm size 0.05

(0.348)

M/B -0.07

(0.347)

Institutional ownership -0.42

(0.249)

Analyst coverage 0.01

(0.471)

CEO ownership -1.90*

(0.053)

Non-CEO Executive ownership -17.70

(0.220)

CEO pay slice 0.67

(0.144)

Technology 0.50**

57

(0.047)

Service -0.15

(0.339)

Trade -0.46**

(0.037)

Constant -0.36

(0.589)

Observations 258

R-square 0.12

58

Table 14: Employee treatment and fraud duration

The dependent variable is the fraud duration, measured by the number of years from the start of fraud date

to the end of the fraud date, as shown in the litigation documents. Employee treatment is defined as a firm’s

total employee strength score minus its total employee weakness score. The total employee strength score

is formed by adding the points a firm receives on criteria for employee strength in the KLD database, and

the total employee weakness score is formed by adding the points the firm receives on criteria for employee

weakness. ROA is return on assets. Leverage is long-term debt divided by total assets. M/B is market value

of equity plus books value of debt divided by book value of assets. External Finance need is equal to asset

growth rate minus ROA2/(1-ROA2), where ROA2 is income before extraordinary items divided by total

assets. CEO ownership is the number of shares held by CEO divided by the total number of shares trading

in the market. Non-CEO executive ownership is the average percentage of shares held by the non-CEO

executives in EXECUCOMP. CEO pay slice is the fraction of the aggregate compensation of the top-five

executive team captured by the CEO (Bebchuk, Cremers, and Peyer, 2011). Institutional ownership is the

percentage of shares held by institutional investors from 13-f filings. Analyst coverage is the number of

analysts following the firm. Stock return is the annual buy-and-hold stock return. Stock volatility is standard

deviation of monthly stock returns in a year. Stock turnover is average monthly turnover in a year. Trade,

Service, and Technology are defined as Wang (2013). Collective bargaining is the percentage of employees

covered by a collective bargaining agreement at the industry level. Union coverage is the percentage of

employees joined in labor union at the industry level. P-value is in parentheses and robust standard error is

adopted. *** p<0.01, ** p<0.05, * p<0.1

(1) (2)

VARIABLES Weibull regression Cox regression

Employee treatment -0.16 -0.14

(0.215) (0.236)

ROA 5.63*** 5.63***

(0.006) (0.005)

External finance need 0.38 0.30

(0.332) (0.473)

Leverage 0.13 0.39

(0.849) (0.572)

Firm size -0.20*** -0.18**

(0.007) (0.011)

M/B -0.36*** -0.36***

(0.001) (0.001)

Institutional ownership -0.26 -0.13

(0.655) (0.825)

Analyst coverage 0.05*** 0.05***

(0.005) (0.002)

CEO ownership 1.38 1.54

(0.425) (0.378)

Non-CEO Executive ownership -80.25* -75.52*

(0.084) (0.083)

CEO pay slice -1.17* -1.08*

(0.065) (0.075)

59

Technology 0.29 0.25

(0.383) (0.422)

Service -0.29 -0.39

(0.279) (0.141)

Trade -0.36 -0.30

(0.316) (0.388)

Constant -6.23***

(0.000)

Observations 124 124