1
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: [email protected]. Connie Mao is an Associate Professor in Finance at Department of Finance, Fox
School of Business, Temple University. Email: [email protected]. Yuanzhi Li is an Assistant Professor in
Finance at Department of Finance, Fox School of Business, Temple University. Email:
[email protected]. Jian Zhang is an Assistant professor in Finance, School of Finance, Southwestern
University of Finance and Economics, China. Email: [email protected].
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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.
3
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.
4
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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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
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