Employee treatment and corporate fraud

Journal Pre-proof Employee treatment and corporate fraud Jian Zhang, Jialong Wang, Dongmin Kong PII:

S0264-9993(18)31810-8

DOI:

https://doi.org/10.1016/j.econmod.2019.10.028

Reference:

ECMODE 5051

To appear in:

Economic Modelling

Received Date: 11 December 2018 Revised Date:

24 September 2019

Accepted Date: 29 October 2019

Please cite this article as: Zhang, J., Wang, J., Kong, D., Employee treatment and corporate fraud, Economic Modelling (2019), doi: https://doi.org/10.1016/j.econmod.2019.10.028. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

Employee Treatment and Corporate Fraud Jian Zhang School of Business and Management Shanghai International Studies University 550 Dalian West Road, Shanghai Shanghai, 200083 Email: [email protected] Phone: +86 13023270249

Jialong Wang School of Finance Southwestern University of Finance and Economics 555 Liutai Avenue, Chengdu Sichuan, 611130 Email: [email protected] Phone: +86 18516767986

Dongmin Kong1 School of Finance Zhongnan University of Economics and Law 182# Nanhu Avenue, Wuhan Hubei, 430073 P.R. China E-mail: [email protected] Phone: +86 15927068886

1

The corresponding author.

Employee Treatment and Corporate Fraud

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 since labor-friendly firms have incentives to signal their willingness to fulfill implicit contracts and maintain long-term relationships with employees. 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 a high-tech industry, when a firm in a less competitive industry, 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; Implicit Contracts

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Employee Treatment and Corporate Fraud 1. Introduction Recent high-profile corporate fraud 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. A large number of papers argue that shareholders can prevent managers from committing fraud by either improving the corporate governance quality (Beasley, 1996; Dechow et al., 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 fraud commitment. 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. In this paper, we attempt to investigate how a firm’s relations with its employees are related to its likelihood of committing fraud. Labor-friendly firms are more likely to be those that attach a high value to human capital. By definition, the investment in firmspecific human capital has limited economic value in other firms. Thus, to motivate employees to acquire firm-specific human capital, firms have to honor implicit contracts and maintain long-term relationships with employees (Dou et al., 2013; Wang et al., 2009). However, firms have a higher probability of reneging on the implicit contracts and

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hurting their long-term relationships with employees when they have financial difficulties resulting from the substantial fines and negative market reaction associated with fraud discovery. Therefore, to motivate employees to invest in human capital, labor-friendly firms have less incentives to commit fraud for honoring implicit contracts and ensuring a long-standing relationship with employees. In addition, Dechow et al. (2011) show that firms are more likely to engage in earning manipulation to disguise a moderate performance. Crutchley et al. (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. Given the arguments above, whether fair employee treatment lowers or increases the likelihood of fraud in 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 the firms’ employee treatment and is the widely used in academic research for evaluating a firm’s relations with its employees. First, we find that firms treating their employees friendly have a lower probability of committing fraud. This finding indicates that labor-friendly firms are reluctant to commit fraud to honor implicit contracts and maintain long-term relationships with employees to motivate employees to invest in firm-specific human capital. Second, combining each subcategory and regressing a measure of earnings management on such a 3

combined measure may not be insightful. In order to explore the economic mechanisms at work behind the relation between various aspects of employee treatment in the KLD measure and corporate fraud, we further investigate which component in the employee treatment (ET) plays the most important role in determining our findings. Our analysis shows that employee involvement 1 and cash profit-sharing 2 are the most important components in the employee treatment index to determine the results. The employee involvement aligns the interests between shareholders and employees since it encourages offering stock ownership to employees. In order to cater to the long-term incentives of employees, labor-friendly firms are less likely to commit fraud. Cash profit-sharing motivates both employees and managers to be more long-term oriented and committed to the firm, which lowers managers’ incentives to commit 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 a high-tech industry, when a firm is in a less competitive industry, and when employees have less outside employment opportunities. Finally, we show that our findings are not driven by the alternative explanations such as employees’ moral sensitivity or other labor related factors (i.e. labor wage, pension benefits, and labor union power). Furthermore, we adopt the collective bargaining and union membership at the industry level as our exogenous variables in the control function approach to alleviate the endogeneity problem.

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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. 2 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. 4

This paper contributes to the literature in several ways. First, its emphasis on the association between employee treatment and corporate fraud is a new addition to the literature highlighting the role of stakeholders in affecting management choices. Second, while the literature emphasizes the role of employees in fraud detection (Dyck et al., 2010, Bowen et al., 2010), our paper provides evidence about the role of employees in a firm’s fraud propensity. 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. We present the hypothesis development in Section 3. Section 4 presents our arguments on the impact of employee treatment on the likelihood of corporate fraud. Section 5 is the empirical testing. In Section 6, we present our regression results. In Section 7, we perform robustness analysis. Section 8 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 et al. (2007) find that there is a higher likelihood of financial misstatement when the CEO holds more in-the-money stock options. Johnson et al. (2009) find that the largest incentive source for firms to

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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 et al. (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. Several recent papers acknowledge the existence of undetected fraud cases and estimate the likelihood of fraud with the bi-variate probit model. Wang et al. (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 6

the simple probit model. Khanna et al. (2015) find that the connection between CEO and subordinate managers increases the firm’s fraud likelihood. 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 (2014) develops two 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 et al. (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 et al. (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 et al. (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. 7

3. Hypothesis Development First, labor-friendly firms are more likely to be those that attach a high value to human capital. Due to the limited economic value of firm-specific human capital in other firms, employees are reluctant to invest in human capital in a firm that has a higher probability of reneging on implicit contracts (Dou et al., 2013; Wang et al., 2009). 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.” Hence, to motivate employees to acquire firm-specific human capital, firms have to honor implicit contracts and maintain long-term relationships with employees. Bae et al. (2011) find that firms with fair employee treatment tend to adopt a lower level of leverage ratio to reduce the bankruptcy risk since they place a higher value on their reputation for honoring its implicit contracts. Similarly, it is hard for a fraud firm to honor its implicit contracts and maintain long-term relationships with employees since it has a higher probability of reneging implicit contracts due to the substantial fines and negative market reaction associated with fraud discovery. Thus, to motivate employees, labor-friendly firms are less likely to commit fraud. 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 8

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 et al. (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. H1: Firms treating their employees fairly have a lower probability to commit fraud.

4. Empirical Testing 4.1. Empirical Methodology We adopt a bivariate probit model with partial observability in our study, 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. Wang et al. (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. 9


, =  +  ∗    , +  ∗
_, +  ∗ !_", + #$   + % &, = γ + γ ∗    , + γ ∗ !_", + γ ∗ ! $_",' + #$   + ( where % and ( are noise terms following a zero-mean bivariate normal distribution. The correlation of % and ( is +. 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 γ 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 loglikelihood function is L(β1, β2)=∑(Zj×ln ( 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 10

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. Variable Construction Our main variable of interest is how a firm treats its employees, denoted as ET. 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 subcategories: 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. 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

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detection probability in the regression for p(Detection|Fraud) to achieve identification. We mainly follow Wang (2013) to construct the set of variables related to the expected benefit of fraud, the ex-ante fraud detection probability, and the ex-post fraud detection probability. The expected impact of each variables is listed in Table 1. [Table 1 here] 4.3. 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). 3 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. 3

For a detailed description about sample construction, please see Dyck, Morse, and Zingales (2010). 12

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 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 4. To make the fraud sample and control sample comparable, we follow Beasley (1996) to construct a 11 matched sample based on size of the firm, fraud year, and the industry. 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 et al., 2011; Landier et al., 2009). 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. Variable definitions are shown in Appendix I. All variables are winsorized at 1% and 99%. 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 4

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. 13

valuations. This is consistent with Wang et al. (2010) 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. [Table 2 here] 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 (ET), 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 MB (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. [Table 3 here] 5. Regression Results

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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 equation, we find that Employee Treatment (ET) 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. Labor-friendly firms are more likely to be those that rely on human capital to create value. To motivate human capital investment of employees, labor-friendly firms have to honor implicit contracts and maintain a long-term relationship with employees. However, a fraud firm has a higher probability of reneging on implicit contracts due to the substantial costs they have to pay if caught. Thus, in order to honor implicit contracts, managers are reluctant to commit fraud in labor-friendly firms. A firm’s profitability (ROA) is also negatively associated with fraud propensity. Firms with strong performance have less incentive to commit fraud. Leverage (LEV) is positively associated with fraud incentive. External finance need (FIN) plays a positive role in increasing the fraud likelihood (Dechow et al., 1996; Dechow et al. 2011). Among the set of variables related to both fraud benefit and ex-ante detection probability, firm size (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 et al. (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 15

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 (INSTOWN) and analyst coverage (ANALYST) depends on which channel of the two opposite directions dominates. We find that institutional ownership (INSTOWN) has a significant and positive coefficient, while analyst coverage (ANALYST) has a significant and negative sign in its coefficient. We find that Trade industries have a higher likelihood of being detected if commit fraud. This is consistent with Wang (2011) that Trade industries appear to have high fraud concentration. We show that both stock return volatility and stock turnover are positively related to fraud detection. Jones and Weingram (1996) argue that stock return volatility and stock turnover increase a firm’s litigation risk. Firms with higher stock return volatility have a higher chance of being sued due to investor’ loss in investment. Higher turnover rate implies that more investors are affected by the stock price fluctuation of the firms. Thus, there might be a higher chance of being sued by investors due to misconduct. [Table 4 here] Furthermore, combining each subcategory and regressing a measure of earnings management on such a combined measure may not be insightful. In order to explore the economic mechanisms at work behind the relation between various aspects of employee

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treatment in the KLD measure and corporate fraud, we further investigate which component in the employee treatment (ET) plays the most important role in determining our findings.. The results are presented in Table 5. 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 making5. The stock ownership aligns the interest of employees to the shareholders’. Chang et al. (2015) 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. Therefore, labor-friendly firms are reluctant to commit fraud in order to cater to the long-oriented employees. As Dyck et al. (2010) point out, employees are the major whistle-blowers for corporate fraud due to their best access to firm’s private information. The sharing of the financial information and participation in management decision making allows the employees to access to valuable private information and further lowers the information costs in identifying corporate fraud. Therefore, employee involvement is associated with a higher likelihood of fraud detection. Cash profit-sharing has a negative impact on both fraud propensity and detection. Vandenberghe et al. (2007) argue that the degree of employee commitment depends on the extent they are valued and cared about by the firm. Cash profit-sharing affects the perceptions of employees about the extent they are valued and cared about by the firm. 5

This is the definition for employee involvement in KLD database. 17

Thus, employees are more likely to be committed to the firm when the firm has a cash profit-sharing program. Faleye and Trahan (2011) argue that the employee commitment helps foster the convergence of labor and shareholder interest to keep the long-term value of the firm. 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 (Landier et al., 2009). This is clearly stated in the definition by their emphasis on a “majority” of the workforce. Therefore, managers are more long-term oriented and reluctant to commit fraud to hurt the long-term value of the firm in the firm with a cash profit-sharing program. In addition, Dyck, Morse, and Zingales (2010) argue that monetary incentive is an important determinant for employees to whistle-blow corporate fraud. Managers can bribe employees not to uncover the corporate misconducts through sharing cash profit. [Table 5 here] As argued above, since managers attempt to value their reputational capital for honoring implicit contracts to motivate employees, they have less incentives to commit fraud to harm the long-term value of the firm. However, the importance of employee motivation to a firm may vary across industries. In traditional industries, employees conduct unskilled work and are similar to other inputs such as raw materials. The employee motivation cannot improve the firm performance significantly. In high-tech industries that emphasize innovation, human, rather than physical, capital plays an important role in firms (Zingales, 2000). Employees can be viewed as valuable intangible assets in such industries and contribute substantially to the firm value. Thus, we expect 18

that the negative relation between employee treatment and a firm’s likelihood of fraud is more salient in the high-tech industry 6 . We rerun our bivariate probit regression by adding one interaction term between employee treatment and a dummy variable for hightech 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. The literature suggests that friendly employee relations promote employee trust and commitment to the firm (Blau, 1964; Etzioni, 1961; Mowday et al., 1982; Wang et al., 2009). 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 will forgo their human capital investment if they have to find new jobs in a new industry. In other words, if employees are in a less competitive industry, they have higher cost in leaving their jobs since their firm-specific human capital has limited value in firms in other industries. Therefore, in order to motivate employees to acquire firm-specific human capital, firms in a less competitive industry need to provide credible commitment to longterm value of the firm. We expect that the negative relation is more significant in less competitive industry7. We rerun our bivariate probit regression by adding one interaction term between employee treatment and a dummy variable for less competitive industries. We find that the interaction term in column 3 of Table 6 is negative and statistically significant. 6

The high-tech industry is defined as in Loughran and Ritter (2004). 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.

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The outside employment opportunities should be negatively related to the employee commitment. The more outside employment opportunities employees have, the less likely they are committed to the firm, and the less likely they invest in firm-specific human capital. 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, which implies more outside employment opportunities. In column 5 Table 6, we interact the dummy variable measuring the outside job options with our employee treatment variable. Our result shows that the impact of employee treatment on fraud likelihood even turns to positive when employees have more outside job options. More outside option of employees might indicate that firms face more pressure in attracting and retaining valuable employees. Thus, firms might boost short-term performance through committing fraud so that they can compete with other firms. [Table 6 here] 6. Robustness Analysis 20

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 7. We obtain qualitatively the same findings. [Table 7 here] Second, our findings might be driven by the heightened moral sensitivity of employees but not the quality of treatment. Bowen et al. (2010) 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 et al. (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 8. We find that our results are not altered after controlling the moral sensitivity in the regression. [Table 8 here] 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 21

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 are shown in Table 9. Our results are not changed. [Table 9 here] Fourth, employment treatment and the avoidance of corporate fraud are very likely to be the two sides of the same coin, social responsibility. A more socially responsible firm tend to treat their employees better and commit less frauds at the same time. Therefore, the association between employment treatment and corporate fraud may not be a causation, but just show that they are two of the indicators for social responsibility. Thus, our empirical finding might be subject to endogeneity issues such as simultaneous bias. To alleviate the endogeneity problem, most models that are linear in parameters are estimated using standard IV methods–two stage least squares (2SLS). However, we have a non-linear model in our study. Wooldridge (2010) offers a control function approach that relies on the same kind of identification conditions as 2SLS and offers some distinct advantages for models nonlinear in parameters.8 We adopt the collective bargaining and union coverage at the industry level to capture the exogenous variations of employee treatment. We believe that the collective bargaining and union coverage at the industry level should not affect a firm’s fraud likelihood beyond their correlation with employee treatment. The results are presented in Table 10. The first-stage regression shows that

8

In the 2SLS regression, one should plug the predicted value from the first stage to the second stage. However, in the control function approach, one should plug the predicted residual from the first stage to the second stage. That is the main difference between these two approaches. 22

our instrumental variables perform well in predicting a firm’s employee treatment. The coefficient estimates are positive and highly significant. In the second stage, we find that the coefficient on the employee treatment is negative and statistically significant, consistent with our early findings. [Table 10 here] 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. Labor-friendly firms are more likely to attach a high value to human capital, implying that labor-friendly firms have more incentives to motivate employees to acquire firm-specific human capital. To provide a credible commitment to honor implicit contracts, labor-friendly firms are less likely to commit fraud since the substantial cost associated with fraud commitment increases the likelihood of reneging on implicit contracts. 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 the firm is in a high-tech industry, when the firm is in a less competitive industry, and when employees have less outside employment opportunities. 23

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 intend to lower

the

likelihood

of

24

corporate

fraud.

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28

Appendix I: Variable Definitions Variables Fraud variables[t=0] Fraud

Definition

Data Source

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

SSCAC

Duration Ex-ante variables [t=-1]

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

SSCAC

ET

EMPNO HHI High-tech OUTJOB BOARD INDEP% ROA FIN LEV SIZE MB

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. A dummy variable, taking the value of one if the number of employees in the firm is more than the sample median. A dummy variable with value of 1, if the industry herfindahl index is greater than the median. A dummy defined as in Loughran and Ritter (2004). 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. The number of board members sitting on the board Fraction of independent directors on the board (Operating income after depreciation)/Assets Asset growth rate – ROA2/(1-ROA2), ROA2 = (income before extraordinary items)/Assets Long-term debt/total asset Log value of total asset Market value over book value of the firm

29

KLD

RiskMetrics RiskMetrics COMPUSTAT COMPUSTAT COMPUSTAT COMPUSTAT COMPUSTAT

INSTOWN

The percentage of shares held by institutions

ANALYST

The number of analysts following the firm The number of shares held by CEO divided by the total number of shares trading in the market. The average percentage of shares held by the non-CEO executives The fraction of the aggregate compensation of the top-five executive team captured by the CEO (Bebchuk, Cremers, and Peyer (2011)) Includes drug, drug proprietaries, and druggists’ sundries (SIC 5122), health care providers (8000-8099), and health care-related firms in Business Services. Includes drug, drug proprietaries, and druggists’ sundries (SIC 5122), health care providers (8000-8099), and health care-related firms in Business Services. Labor expense divided by the number of the employees at the industry level. Pension expense divided by the number of the employees at the industry level

CEOWN SUBOWN CPS Regulated industry

Health care industry Industry labor expense Industry pension expense Collective bargaining

Union membership Ex-post variables [t=1] RET VOL TURNOVER

Thomson-Reuters Institutional Holdings (13f) I/B/E/S EXECUMOP EXECUMOP EXECUMOP COMPUSTAT

COMPUSTAT COMPUSTAT COMPUSTAT

Hirsch The percentage of employees covered by a collective bargaining agreement Macpherson at the industry level. (2003) Hirsch The percentage of employees joined in labor union at the industry level Macpherson (2003) Annual buy-and-hold stock return Standard deviation of monthly stock returns in a year Average monthly turnover in a year

30

CRSP CRSP CRSP

and

and

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] +

31

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 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Total

Count 3 4 5 10 8 11 7 4 6 14 10 18 8 13 13 134

Percentage (%) 2.24 2.99 3.73 7.46 5.97 8.21 5.22 2.99 4.48 10.45 7.46 13.43 5.97 9.70 9.70 100.00

Min 0.21 0.25 0.24 0.11 0.21 0.32 0.21 0.58 0.21 0.03 0.38 0.21 0.24 0.10 0.00 0.00

Mean 1.67 1.74 1.86 2.00 2.07 1.42 1.79 3.25 1.34 1.42 1.95 1.29 0.93 0.69 0.46 1.43

32

Median 2.28 0.86 1.41 2.17 2.03 1.30 1.32 3.78 1.09 1.33 1.34 1.00 0.72 0.62 0.44 0.98

Max 2.53 4.97 4.36 3.98 4.96 4.95 4.72 4.85 3.60 3.52 4.82 3.57 1.97 1.38 1.00 4.97

Standard Deviation 1.27 2.19 1.71 1.37 1.64 1.25 1.68 2.04 1.18 1.04 1.64 0.93 0.63 0.37 0.31 1.29

Table 3: Comparison of Fraud Firms with Non-Fraud Firms in the COMPUSTAT Large Firms ET 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 is the number of board members sitting on the corporate board. INDEP% is the fraction of the independent directors on the board. SIZE is the log value of total assets. ROA is return on assets. LEV is long-term debt divided by total assets. MB is market value of equity plus books value of debt divided by book value of assets. FIN is equal to asset growth rate minus ROA2/(1-ROA2), where ROA2 is income before extraordinary items divided by total assets. CEOWN is the number of shares held by CEO divided by the total number of shares trading in the market. SUBOWN is the average percentage of shares held by the non-CEO executives in EXECUCOMP. CPS is the fraction of the aggregate compensation of the top-five executive team captured by the CEO (Bebchuk, Cremers, and Peyer, 2011). INSTOWN is the percentage of shares held by institutional investors from 13-f filings. ANALYST is the number of analysts following the firm. RET is the annual buy-and-hold stock return. VOL is standard deviation of monthly stock returns in a year. 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 nonfraud sample.

ET BOARD INDEP% SIZE ROA LEV MB FIN CEOWN SUBOWN CPS INSTOWN ANALYST RET VOL TURNOVER

Non-fraud #obs Fraud #obs 0.06(0.00) 134 -0.01(0.00) 134 11.02(11.00) 119 11.22(11.00) 118 0.72(0.75) 119 0.72(0.73) 118 9.26(9.09) 134 9.13(8.87) 134 0.11(0.10) 134 0.10(0.09) 134 0.20(0.18) 134 0.18(0.15) 134 1.63(0.88) 134 1.24(0.87) 134 0.04(-0.42) 132 -0.01(-0.10) 133 0.01(0.002) 128 0.02(0.002) 134 0.001(0.0004) 130 0.002(0.0004) 134 0.36(0.38) 133 0.35(0.37) 134 0.75(0.74) 134 0.73(0.74) 134 11.19(10.00) 134 10.00(9.00) 134 0.02(0.01) 121 0.07(0.07) 122 0.13(0.10) 121 0.11(0.09) 122 0.28(0.20) 121 0.22(0.17) 122

33

t-statistics 0.50 -0.20 0.001 0.67 0.48 1.12 1.91* 0.72 -0.33 -0.53 0.40 0.79 1.65 -0.70 1.59 2.07**

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. ET 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 is the number of board members sitting on the corporate board. INDEP% is the fraction of the independent directors on the board. SIZE is the log value of total assets. ROA is return on assets. LEV is long-term debt divided by total assets. MB is market value of equity plus books value of debt divided by book value of assets. FIN is equal to asset growth rate minus ROA2/(1-ROA2), where ROA2 is income before extraordinary items divided by total assets. CEOWN is the number of shares held by CEO divided by the total number of shares trading in the market. SUBOWN is the average percentage of shares held by the non-CEO executives in EXECUCOMP. CPS is the fraction of the aggregate compensation of the top-five executive team captured by the CEO (Bebchuk, Cremers, and Peyer, 2011). INSTOWN is the percentage of shares held by institutional investors from 13-f filings. ANALYST is the number of analysts following the firm. RET is the annual buy-and-hold stock return. VOL is standard deviation of monthly stock returns in a year. 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) ET

-1.02*** (0.000) 6.27* (0.075) 2.70** (0.019) 0.36 (0.193) 1.76*** (0.004) -3.98 (0.349) -0.43 (0.991) 1.51 (0.313) 0.64*** (0.000) 5.91***

ROA LEV MB FIN CEOWN SUBOWN CPS SIZE INSTOWN 34

0.09 (0.351)

-0.03 (0.639) -1.76**

(0.000) -0.06** (0.024) -0.56 (0.220) -2.05*** (0.000) -3.62*** (0.001)

ANALYST Technology Service Trade RET VOL TURNOVER Constant

-8.99*** (0.000) 232

Observations

35

(0.027) 0.03 (0.102) 0.23 (0.411) 0.38 (0.152) 4.29*** (0.000) 0.06 (0.813) 3.50** (0.031) 1.11* (0.086) 0.61 (0.549) 232

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. Labor union relation measures whether a firm has a good relationship with labor union. Retirement benefits measures whether a firm offer enough retirement benefits. Employee involvement measures whether a firm encourage employees to participate in management. Cash profit sharing measures whether a firm offer a cash profit-sharing program to a majority of workforce. P-value is in parentheses and robust standard error is adopted. *** p<0.01, ** p<0.05, * p<0.1

VARIABLES Labor union relation

(1) P(F=1)

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

-0.11 (0.741)

-0.48 (0.396)

Retirement benefits

(3) P(F=1)

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

-0.13 (0.491)

-1.43 (0.169)

Employee involvement

(5) P(F=1)

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

-1.47* (0.073)

5.02*** (0.000)

Cash profit sharing Controls Constant Observations

Yes -0.79 (0.777) 232

Yes -8.79 (0.748) 232

Yes -1.00 (0.360) 232

Yes -7.15 (0.140) 232

36

Yes -0.38 (0.813) 232

Yes -0.90 (0.474) 232

(7) P(F=1)

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

-1.49*** (0.000) Yes -0.08 (0.949) 232

-0.79* (0.054) Yes -7.38*** (0.000) 232

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. ET 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 is a dummy variable for high-tech industry, defined as in Loughran and Ritter (2004). HHI is the Herfindel index. OUTJOB measure the outside job option in a certain region. 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. Trade, Service, and Technology are defined as Wang (2013). Pvalue is in parentheses and robust standard error is adopted. *** p<0.01, ** p<0.05, * p<0.1

VARIABLES ET ET*High-tech High-tech

(1) P(F=1) -1.03*** (0.003) -0.95** (0.049) -0.25 (0.559)

(2) P(D=1|F=1) 0.15 (0.175)

ET*HHI

(3) P(F=1) -0.40* (0.050)

(4) P(D=1|F=1) 0.22* (0.057)

(5) P(F=1) -1.93*** (0.002)

(6) P(D=1|F=1) 0.12 (0.251)

Yes -0.42 (0.648) 232

6.69*** (0.002) -11.48*** (0.005) Yes 0.22 (0.937) 232

Yes -2.13** (0.020) 232

-0.41* (0.085) 0.53** (0.017)

HHI ET*OUTJOB OUTJOB Controls Constant Observations

Yes -7.60*** (0.001) 232

Yes 0.31 (0.758) 232

Yes -0.42 (0.648) 232

37

Table 7: 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. ET 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 is the number of board members sitting on the corporate board. INDEP% is the fraction of the independent directors on the board. P-value is in parentheses and robust standard error is adopted. *** p<0.01, ** p<0.05, * p<0.1 Bivariate probit P(F=1) P(D=1|F=1) -2.56*** 0.19* (0.004) (0.067) 0.01 (0.909) -0.37 (0.810) Yes Yes -0.74 -1.36 (0.853) (0.122) 227 227

VARIABLES ET BOARD INDEP% Controls Constant Observations

38

Table 8: 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. ET 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. 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 VARIABLES ET ET*Regulated industry Regulated industry Controls Constant Observations

39

(1) P(F=1)

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

-1.01*** (0.004) 0.08 (0.832) 0.08 (0.880) Yes -9.12*** (0.000) 232

0.11 (0.431) -0.03 (0.867) -0.03 (0.923) Yes 0.75 (0.461) 232

Table 9: 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. ET 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. 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 VARIABLES ET Industry labor expense

(1) P(F=1)

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

(3) P(F=1)

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

-2.97*** (0.002) -0.01 (0.414)

0.19* (0.067) -0.00 (0.663)

-1.83*** (0.001)

0.18* (0.093)

0.76*** (0.010) Yes 2.79 (0.256) 232

0.01 (0.882) Yes -1.95** (0.024) 232

Industry pension expense Controls Constant Observations

Yes -1.42 (0.522) 232

40

Yes -1.48* (0.087) 232

Table 10: Employee treatment and fraud propensity using control function approach 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. ET 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. 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 VARIABLES Collective bargaining

First stage Employee treatment 1.80*** (0.006)

Second stage P(F=1) P(D=1|F=1)

Union membership

First stage Employee treatment

1.88*** (0.007)

Employee treatment Predicted residual in collective bargaining

-2.47** (0.029) 1.41

-0.15 (0.703) 0.27

(0.169)

(0.506)

Predicted residual in union membership Controls Constant Observations R-square

Second stage P(F=1) P(D=1|F=1)

Yes 0.13 (0.868) 230 0.15

Yes -9.37*** (0.000) 230

Yes 0.81 (0.440) 230

41

Yes 0.11 (0.884) 230 0.15

-2.20** (0.047)

-0.18 (0.631)

1.17

0.31

(0.237) Yes -9.21*** (0.000) 230

(0.426) Yes 0.80 (0.441) 230

Highlights:




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 since labor-friendly firms have incentives to signal their willingness to fulfill implicit contracts and maintain long-term relationships with employees.




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 a high-tech industry, when a firm in a less competitive industry, 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).