# Internal governance mechanisms and corporate misconduct

Nicolas Eugster, Oskar Kowalewski, Piotr Śpiewanowski

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## To cite this version:

Nicolas Eugster, Oskar Kowalewski, Piotr Śpiewanowski. Internal governance mechanisms and corporate misconduct. International Review of Financial Analysis, 2024, 92, pp.103109. 10.1016/j.irfa.2024.103109. hal-04549564

HAL Id: hal-04549564

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Submitted on 21 May 2025

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# Internal governance mechanisms and corporate misconduct☆

Nicolas Eugster<sup>a</sup>, Oskar Kowalewski<sup>b,c,d,e,*</sup>, Piotr Śpiewanowski<sup>b</sup>

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## ARTICLE INFO

JEL classification:

G01

G34

G38

K22

L51

M41

Keywords:

Corporate misconduct

Internal governance mechanisms

Board of directors

Committees

Ownership

## ABSTRACT

This study aimed to provide new evidence linking internal corporate governance mechanisms and corporate misconduct by using a sample of 2,844 public US companies during the period 2007–2019. The results revealed that the optimal size and diversity of boards, including well-functioning audit committees, are negatively correlated with corporate violations. By contrast, it was shown that board members’ independence, activity, and ownership are positively correlated with a firm’s fraudulent activities. Therefore, not all internal governance mechanisms are related to reduced corporate misconduct. Moreover, the study shows that some internal governance mechanisms, such as the share of female board members, mitigate certain types of corporate misconduct.

## 1. Introduction

Dyck, Morse, and Zingales (2023) estimated that the cost of corporate fraud, detected and undetected, destroys 1.7% of the value of equity each year in the United States, which equaled US$ 744 billion in 2020. A common view is that corporate misconduct results from deficiencies in companies’ internal governance. Therefore, it is not surprising that legislative reforms have been implemented with the aim of improving company governance worldwide. These reforms aim to improve internal and external corporate governance mechanisms. The key internal mechanisms are the board of directors and the ownership structure. The primary external mechanisms are the market for corporate control and mass media (Heese, Pérez-Cavazos, & Peter, 2021), which are difficult to regulate. These different mechanisms work together in a system to affect the governance of firms but should be considered complementary (Cremers & Nair, 2005).

Acharya, Myers, and Rajan (2011) argue that internal governance mechanisms can mitigate agency problems and ensure that firms have

substantial value even with little or no external governance by investors. In this study, we investigate law violations in US public companies that are exposed to the same external governance factors as governmental regulatory bodies or media, which complement internal governance and improve efficiency. More importantly, this setting allows us to investigate the effect of various internal mechanisms on corporate misconduct and should not be biased by external mechanisms. In alignment with agency theory, the relationship between corporate governance and the reduction of misconduct remains a contentious issue, as highlighted by Velte (2023). Consequently, our research focuses on evaluating the various facets of internal governance and their impacts on different types of corporate misconduct. Additionally, we incorporate the evaluation of external audit quality as a critical factor in detecting financial irregularities, underscoring its pivotal role in our analysis.

In general, the literature shows the positive effect of internal governance reforms on firm performance and value (Fauver, Hung, Li,

<footnote><sup>a</sup> The University of Queensland, Brisbane, Australia</footnote>

<footnote><sup>b</sup> Institute of Economics, Polish Academy of Sciences, Warsaw, Poland</footnote>

<footnote><sup>c</sup> IESEG School of Management, UMR 9221-LEM-Lille Économie Management, F-59000 Lille, France</footnote>

<footnote><sup>d</sup> Univ. Lille, UMR 9221-LEM-Lille Économie Management, F-59000 Lille, France</footnote>

<footnote><sup>e</sup> CNRS, UMR 9221-LEM-Lille Économie Management, F-59000 Lille, France</footnote>

<footnote>☆ We thank Emmanuel Adegbite, Saqib Aziz, Muhammad Farooq Ahmad, Dusan Isakov, Thomas Kaspereit, Michael Troege, and the participants at the 38th AFFI Conference, Saint-Malo; the 29th AEFIN Finance Forum, Santiago de Compostela; the VSBF-2022, Hanoi; the 8th ICGS Conference, Miami; the Research Seminar at the Swansea University and University of Fribourg for their helpful comments and suggestions. We thank Philip Mattera at Good Jobs First for sharing his data on corporate misconduct. We acknowledge the support from the National Science Centre, Poland, research grant no. 2019/33/B/HS4/00369.</footnote>

<footnote>* Correspondence to: IESEG School of Management, Socle de la Grande Arche, 1 Parvis de La Défense F-92044 Paris La Défense cedex, France.</footnote>

<footnote>E-mail addresses: n.eugster@business.uq.edu.au (N. Eugster), o.kowalewski@ieseg.fr (O. Kowalewski), p.spiewanowski@inepan.waw.pl (P. Śpiewanowski).</footnote>
&Taboada, 2017). However, corporate fraud has increased recently, despite reforms to better align managers' and shareholders' interests and institutionalize codes of conduct that improve managers' ethical performance. A good example of this are recent corporate scandals such as the Volkswagen 2015 emission scandal (Bachmann, Ehrlich, Fan, Ruzic, & Leard, 2019) and the Wirecard bankruptcy (Jo, Hsu, Llanos-Popolizio, & Vergara-Vega, 2021), which generated billions of dollars in shareholder losses. These scandals have renewed research attention to the link between companies' perceived governance problems and fraudulent activities.

Existing research shows that poor governance can cause fraudulent management activities such as earnings restates (Agrawal & Chadha, 2005), environmental pollution (McKendall, Sánchez, & Sicilian, 1999), stock manipulation (Cumming, Leung, & Rui, 2015), and civil prosecution (Hutton, Jiang, & Kumar, 2015). Despite the considerable body of research on the influence of corporate governance on corporate misconduct, detailed evidence linking these two elements across different dimensions is lacking. Simultaneously, the literature recognizes the importance of internal governance as a method of preventing corporate misconduct, but it focuses on specific firm crimes, such as fraudulent financial reporting (Amiram et al., 2018; Amiram, Huang, & Rajgopal, 2020; Efendi, Srivastava, & Swanson, 2007).<fnref n="1" /> However, a broader perspective is required to better understand this complex relationship.

We investigated a wide range of internal corporate governance mechanisms and their relationships with various types of corporate misconduct by employing a holistic approach that sets our study apart from existing literature. Unlike prior research, which tends to focus on limited governance measures or specific types of misconduct, our analysis is more comprehensive and covers an expansive spectrum of governance mechanisms. By utilizing a single dataset of 2,844 non-financial US-listed companies merged with financial data and information on diverse categories of corporate violations from 2007 to 2020, we ensured a consistent and thorough analysis. The dataset included both the frequency of corporate violations and the value of the penalty imposed on the company. The granularity of the data allowed us to analyze the relationship between existing governance mechanisms and corporate misconduct more closely, highlighting the differential impact of governance measures on different types of misconduct.

Specifically, our findings provide nuanced insights into the dynamics of corporate governance and misconduct. We found a U-shaped relationship between board size and corporate misconduct. We also demonstrated that board diversity, including the presence of female board members, discourages corporate misconduct. A closer analysis of the types of misconduct revealed that the presence of female board members is negatively correlated with violations of environmental, safety, and financial laws. In contrast, we found that greater board gender diversity is positively correlated with violations of employment, government, and health-related laws. These results suggest that the type of legal violation plays an important role in understanding its relationship with a firm's governance factors. Additionally, we confirm that the composition and activity of audit committees are important in fraud prevention and that the use of one of the four largest accounting firms (Big 4) is associated with less corporate fraud. We also found that large blockholders and institutional investors help reduce corporate misconduct. By contrast, we show that board independence is positively correlated with corporate misconduct. Similarly, we find that a strong position of the Chief Executive Officer (CEO) on the board, proxied by CEO duality and board ownership, is also positively correlated with corporate misconduct. Such insights are vital for policymakers and decision makers, emphasizing the need for nuanced consideration when shaping governance structures and policies.

We conducted a series of tests to ensure the robustness of the baseline results. These include incorporating an extensive set of control variables related to board characteristics; employing alternative measurements for the main variables, such as a corporate governance index; and utilizing a matching method for the sample. Our results remain consistent after these robustness checks, thereby reinforcing our primary findings.

This study contributes to the literature on internal governance in several ways. First, to the best of our knowledge, this is the first empirical study to analyze the relationship between internal governance mechanisms and a wide range of corporate violations. Our results show that not all internal governance mechanisms work as expected to prevent fraud, which could explain the increasing number of corporate misconduct cases despite changes in laws in the past decade. Moreover, our findings align with those of a recent survey of the literature on corporate governance and firm misconduct, underscoring the importance of conducting a more detailed analysis of such cases of misconduct (Velte, 2023). Our analysis reveals that specific elements of governance exhibit divergent behavior depending on the type of misconduct, potentially clarifying previously ambiguous results in the literature.

Second, we employ a comprehensive dataset to analyze various categories of corporate misconduct, including labor violations, government fraud, worker discrimination, and securities fraud (Heese & Pérez-Cavazos, 2020). In contrast, the literature has predominantly focused on a single type of misconduct, such as accounting or security-related violations. Furthermore, while previous studies considered misconduct by individuals, physicians, and politicians (Heese et al., 2021), our data concentrate on corporate misconduct. Our analysis illuminates the nuanced relationship between internal governance mechanisms and misconduct, contingent on the type of misconduct, which may offer insights into the ambiguities prevalent in the literature.

Third, our results reaffirmed the positive influence of gender diversity on corporate misconduct. Studies by Arnaboldi, Casu, Gallo, Kalotychou, and Sarkisyan (2021), Cumming et al. (2015), Liao, Smith, and Liu (2019), and Luo, Peng, and Zhang (2020) collectively support the notion that the increased presence of female board members is likely to curtail instances of corporate misconduct. However, Liao et al. (2019) note that the gender effect of Chief Financial Officers (CFOs) is statistically significant only in firms with gender-diverse boards and not in those with male-only boards. Our contribution to the literature lies in demonstrating that increased female board membership may also exhibit a positive relationship with corporate misconduct, especially in the domains of employment, government, and health. Consequently, our findings suggest that while board diversity initially appears to reduce corporate misconduct, a closer examination reveals that female board membership does not necessarily mitigate all forms of corporate misconduct.

Finally, this study contributes to the literature by introducing an index constructed through principal component analysis and condensing a multitude of variables into a manageable set of governance indices (Larcker, Richardson, & Tuna, 2007). Our findings underscore the significance of the connection between internal governance and corporate misconduct, emphasizing the need for a more detailed examination to better comprehend this relationship.

The remainder of this paper is organized as follows: Section 2 reviews the existing literature and presents our main hypotheses. Section 3 describes the data and research methods. Section 4 presents and discusses the main empirical results. In Section 5, we present the results of an analysis in which we introduce additional control variables for firms' internal governance. Section 6 concludes the paper.

## 2. Literature review and hypothesis development

Pargendler (2016) highlights a significant increase in attention toward corporate governance since the late 1970s. She argues that governance reforms are the preferred policy response to a variety of

<footnote n="1">¹ Amiram et al. (2018) provides a review of the literature on financial reporting misconduct from the perspectives of law, accounting, and finance.</footnote>
corporate problems, including corruption and fraud. For example, the Sarbanes-Oxley Act of 2002 (SOX) was a legislative response to high-profile corporate scandals such as those involving Enron and WorldCom in the early 2000s. US president George W. Bush signed the Act into law, emphasizing that it incorporates stringent new provisions aimed at deterring and penalizing corporate and accounting fraud, as well as corruption. The objective of the Act is to ensure justice for wrongdoers and safeguard the interests of both workers and shareholders.<fnref n="2" />

However, these reforms did not prevent the occurrence of the 2007 financial crisis, which raised concerns about the effectiveness of corporate governance mechanisms. The Dodd-Frank Act of 2010 was enacted in response to the financial crisis, introducing further regulations, particularly in relation to the behavior of financial intermediaries and other aspects of the corporate sector.

One key area of focus in corporate governance reform is internal governance mechanisms, with boards playing a pivotal role in reducing instances of corporate wrongdoing (Liu, Ryan, Lin, & Xu, 2023). Boards serve as vital monitors of management behavior (Jensen, 1993; Jensen & Meckling, 1976), and their effectiveness can vary based on factors such as size, composition, structure, activity, and the number of committees.

Jensen (1993) observes that when corporate boards expand beyond seven or eight people, they are less likely to function effectively and monitor CEOs and managers. Similarly, Yermack (1996) presented evidence that small boards of directors are more effective than large boards. However, empirical studies on accounting fraud and board size yield mixed results. Beasley (1996) reports that board size is positively associated with the probability of changes in the financial statement. By contrast, Uzun, Szewczyk, and Varma (2004) did not find evidence of a relationship between board size and corporate fraud, while documenting that board composition is significantly correlated with the incidence of corporate fraud.

Their results support the findings of Beasley (1996) who documented that the presence of independent or external directors significantly reduces the probability of financial statement fraud. In a subsequent study, Beasley, Carcello, Hermanson, and Lapides (2000) reported that fraudulent companies have very weak governance mechanisms compared to non-fraudulent industry benchmarks and found that fraudulent companies have fewer independent boards. Similarly, Uzun et al. (2004) found that a higher proportion of independent outside directors is associated with a lower probability of corporate wrongdoing. Recently, Bai and Yu (2022) highlight the importance of independent directors in detecting and preventing corporate financial misconduct.

The existing literature highlights that board diversity can enhance decision-making processes and positively impact group dynamics (Ely & Thomas, 2020). According to Liu, Wei, and Xie (2014), female directors tend to monitor more actively. More importantly, previous research demonstrates that women tend to possess a higher degree of ethical sensitivity than men (Beji, Yousfi, Loukil, & Omri, 2021). Women are more inclined to raise concerns about unethical conduct and to act as internal whistleblowers. This divergence can be attributed to the fact that men often prioritize individual accomplishments, whereas women place greater emphasis on interpersonal relationships and collective objectives (Ho, Li, Tam, & Zhang, 2015). The stronger ethical standards upheld by female board members are expected to translate into more robust ethical leadership practices on the board, which, in turn, should discourage corporate fraud. These results led us to formulate our first hypothesis.

**Hypothesis 1: There is a relation between board size and its composition and firm's misconducts.**

As a key member of the board of directors, the CEO plays a crucial role in fraud detection owing to the consequences of misconduct discovery. Karpoff and Lott (1993) show that most managers, including CEOs, who are caught violating the law typically face severe consequences. These consequences encompass job loss, reduced employment opportunities, financial penalties, and, in some cases, even criminal sanctions. In general, these severe consequences are expected to serve as strong deterrents that motivate CEOs to take proactive measures to prevent fraud within their corporations. However, it is worth noting a contrasting finding by Beasley, Hermanson, Carcello, and Neal (2010), which indicates that CEOs are involved in approximately 70% of fraud cases. This suggests that, despite the potential consequences, a substantial proportion of corporate fraud cases directly implicate CEOs.

Furthermore, the risk of CEO wrongdoing appears to increase when the CEO also holds the position of the chairman of the board. Finkelstein and D'Aveni (1994) argue that CEO duality, where the CEO serves as both CEO and chairman, can promote CEO entrenchment, potentially reducing the effectiveness of board monitoring. This argument is supported by O'Connor, Priem, Coombs, and Gilley (2006), who demonstrate that, in certain contexts, CEO duality can exacerbate agency problems and increase the likelihood of fraudulent financial reporting. Farber (2005) also finds that firms involved in fraud cases are more likely to have dual CEOs.

Moreover, Khanna, Kim, and Lu (2015) emphasize the impact of CEO connections developed through appointment decisions with top executives and directors. These connections can heighten the risk of corporate fraud. Additionally, the study suggests that the greater the CEO's influence on the board and audit committee, the less likely the board is to detect corporate wrongdoing. Nevertheless, it is worth noting that CEO connectedness based on education, employment, or social network ties with top executives or directors has insignificant effects on both fraud occurrence and the expected costs of wrongdoing.

Based on these findings, we formulate the following hypotheses:

**Hypothesis 2: There is a relation between a strong CEO position within the board and firm's misconducts.**

A study conducted by Chen, Firth, Gao, and Rui (2006) focused on a sample of Chinese companies subjected to regulatory enforcement actions owing to fraud. Their analysis reveals interesting insights into the role of boards and ownership characteristics in explaining fraud. Initially, the results of the univariate analyses suggested that both board and ownership characteristics were significant factors in understanding fraud in these companies. However, a more sophisticated probit model analysis provides a clearer picture. In this advanced analysis, it became evident that boardroom characteristics, particularly the frequency of board meetings, played a crucial role in the incidence of fraud. This finding suggests that board activity, as measured by the frequency of meetings, is a significant factor in detecting and preventing corporate fraud.

Furthermore, this study examines another dimension of board activity, which involves the presence of board committees. Board committees offer various advantages over a full board, including flexibility and specialization, as noted by Jiraporn, Singh, and Lee (2009). These committees often make significant decisions, and their functioning and structure, such as the audit committee, can have a substantial impact on corporate performance, potentially more than the general composition of the board itself, as highlighted by Klein (1998).

Building on this, our hypothesis posits that the presence of specific board committees, such as executive or Corporate Social Responsibility (CSR) committees, can increase a company's awareness of the negative consequences of legal violations. This suggests that this committee play a role in fostering ethical conduct and corporate responsibility within an organization.

Interestingly, recent studies, such as (Ferrés & Marcet, 2021), reveal that companies involved in corporate misconduct tend to initiate new

<footnote n="2">² See https://georgewbush-whitehouse.archives.gov/news/releases/2002/07/text/20020730-10.html.</footnote>
CSR efforts that align with official investigations. These proactive CSR actions are seen as mechanisms for mitigating the potential costs associated with the revelation of fraud. In particular, colluding firms may intensify their CSR activities not only to reduce fines but also to maintain their CSR investments after facing official penalties. Therefore, CSR committees within companies can be viewed as a consequence of ongoing investigations and a strategic response to potential fraud revelations.

In summary, our hypotheses were as follows:

**Hypothesis 3: There is a relationship between board activity and firm misconduct.**

The audit committee is widely recognized as one of the most powerful committees within a corporation, responsible for overseeing corporate governance, financial reporting, internal control structures, and the audit function (Arthaud-Day, Certo, Dalton, & Dalton, 2006). It is not surprising that the literature consistently associates the presence and composition of an audit committee with fraudulent behavior within firms, as highlighted by Ghafran and O'Sullivan (2013).

Previous studies have provided valuable insights into this relationship. For instance, Agrawal and Chadha (2005) found that the independence of both the board and audit committees is correlated with the likelihood of a public company in the US restating its earnings. Companies with independent directors and financial experts on their boards or audit committees are less likely to engage in earnings restatements. Conversely, the probability of restatement is higher in companies in which the CEO has familial ties with the founding family, suggesting the critical importance of audit committee composition and independence.

Moreover, Johnson, Ryan, and Tian (2009) found that firms involved in fraudulent activities tend to have a higher proportion of insiders serving on their audit committees, which raises concerns about the effectiveness of audit committees with a significant number of individuals with direct ties to the company.

Additionally, Owens-Jackson, Robinson, and Shelton (2009) report an inverse relationship between fraudulent financial statements and the frequency of audit committee meetings. Their findings suggest that more frequent AC meetings by the audit committee are associated with a lower likelihood of fraudulent financial reporting, underscoring the significance of active and engaged audit committee in ensuring the integrity of financial information disclosure.

Considering these insights, we propose the following hypothesis:

**Hypothesis 4: There is a relationship between audit committee composition, its activity, and corporate misconduct.**

A firm's ownership structure can significantly impact the oversight and behavior of its executive board. Denis, Denis, and Sarin (1997) argue that when managers have a substantial ownership stake in the company, it can hinder external corporate control mechanisms and, consequently, reduce the effectiveness of internal monitoring efforts. Managerial ownership may create a situation in which managers have less incentive to monitor their actions rigorously.

Furthermore, Benmelech, Kandel, and Veronesi (2010) point out that stock-based compensation can have complex effects. While this incentivizes managers to put in effort, it can also motivate them to conceal negative information about the company's growth prospects and make suboptimal investment decisions to maintain strong performance.

However, there is a contradiction to this argument. Alexander and Cohen (1999) found that corporate misconduct occurs less frequently in firms where management holds a larger ownership stake. This finding supports the idea that misconduct can be viewed as an agency cost and that when managers have a significant financial interest in a company's success, they are more inclined to act in ways that benefit shareholders. In addition, Armstrong, Jagolinzer, and Larcker (2010) did not find strong evidence that CEO equity-based holdings and compensation

directly incentivize the manipulation of accounting reports. Instead, other factors may play a more significant role in such behavior.

On a related note, Chan, Tsai, and Li (2015) explore the relationship between different types of executive compensation and the likelihood of financial fraud. They find that providing financial and severance benefits to CEOs could help reduce the occurrence of fraud, suggesting that aligning CEO interests with the long-term success of a company may be a mitigating factor. However, it is worth noting that Haß, Müller, and Vergauwe (2015) found that the relative performance evaluation features of CEO promotion tournaments can lead to a higher likelihood of CEO misconduct. This suggests that the competitive nature of CEO promotions may incentivize unethical behavior.

Contrary to these arguments, Jensen (1993) proposed a different approach. He argued that encouraging outside board members to have substantial equity interests can provide better incentives to align their interests with those of shareholders. Moreover, large outside blockholders, as highlighted by Shleifer and Vishny (1986) and Holderness and Sheehan (1988), have a strong incentive to monitor corporate managers.

Finally, institutional investors play a crucial role in monitoring and constraining corporate managers' self-serving behavior, as documented by Chung, Firth, and Kim (2002). Their presence, especially large institutional shareholdings, can deter managers from manipulating reported profits to their advantage.

In light of these diverse findings and perspectives, we propose the following hypotheses:

**Hypothesis 5: There is a relation between the shareholder structure of the firm and corporate misconduct.**

In summary, the existing empirical research strongly indicates a significant relationship between internal governance and corporate fraud. These findings align with the notion that internal governance mechanisms, including board structure, are not just static components, but rather adaptive responses to the contractual and operational contexts of firms (Linck, Netter, & Yang, 2008).

It is important to note that much of the existing empirical research in this area has primarily concentrated on examining the relationship between specific elements of internal governance within a firm and instances of financial or accounting fraud. Our research seeks to expand this literature by investigating how various facets of internal governance relate to broader categories of corporate violations. This broader perspective allows a more comprehensive understanding of how internal governance mechanisms influence a wide range of corporate misconduct, thus contributing to a more holistic view of corporate governance and misconduct prevention.

# 3. Data and methodology

## 3.1. Sample composition

The sample was generated from an overlap between two comprehensive datasets. The first dataset, Violation Tracker, covers corporate misconduct from 2000 to 2020. The database was created and maintained by a nongovernmental organization called Good Jobs First. It includes a wide range of corporate misconduct by U.S.-incorporated companies, including subsidiaries of foreign companies. The second dataset includes information on firm board size and composition, board committees, audits, CEO, and ownership for the years 2007–2019. The data were retrieved from the NRG Metrics database, which sources publicly available documents, such as annual reports, corporate governance reports, firm presentations, SEC filings, and press releases.<fnref n="3" />

<footnote n="3">³ NRG Metrics was created by a team of expert analysts who manually enter, review, and cross check data with senior analysts, who perform frequent random audits. Custom software programs verify all levels of data entry for inconsistencies and errors using a combination of quality control measures (Miroshnychenko, De Massis, Miller, & Barontini, 2021).</footnote>
NRG Metrics have been validated in both management and finance literature (Attig, El Ghoul, Guedhami, & Zheng, 2021; Cho, Ibrahim, & Yan, 2019; Delis, Hasan, & Ongena, 2020; Eugster & Wang, 2023; Lozano-Reina, Sánchez-Marín, & Baixauli-Soler, 2022; Miroshnychenko et al., 2021). In our analysis, we concentrate only on listed US non-financial firms and merge the two databases using company names, ISIN numbers, and trackers. We supplement the database with firm-level financial information retrieved from DataStream and Worldscope. After merging, our final sample consists of 15,029 firm-year observations for 2,844 individual firms with data for the years 2007–2020. Because we do not have observations for all firms during this period, the panel is unbalanced.

The sample spans many sectors of the economy and has a distribution of firms consistent with the composition of the US stock market. Table A.1 in the Appendix presents the distribution of observations across sectors and years in the sample. The increase in observations over time is mainly due to NRG Metrics, which have increased the coverage of companies over the years.

## 3.2. Corporate misconduct measures

The variable of interest in our study is corporate misconduct, defined as the activities and actions in which organizational members engage to deceive or manipulate investors and other key stakeholders (Baucus, 1994; Baucus & Baucus, 1997; Neville, Byron, Post, & Ward, 2019). In our study, corporate misconduct, which we recovered from the Violation Tracker database, includes only acts that violate laws. The data cover banking, consumer protection, false claims, environmental, wage and hour, safety, discrimination, price fixing, and other cases resolved by federal regulatory agencies and all parts of the Justice Department as well as cases from state Attorney Generals and selected state regulatory agencies. Consequently, we do not cover legal acts that are considered morally wrong (Moore, Tetlock, Tanlu, & Bazerman, 2006).

Soltes (2019) used the Violation Tracker data for the period 2010–2017 and found that the probability that a publicly traded US firm will be sanctioned is 23% during a year. Thus, we can infer that corporate misconduct is considerably more common than indicated by previous studies, particularly in finance and accounting, using sanctions imposed by the Department of Justice or the Security and Exchange Commission. This difference implies that corporate misconduct may have been under-reported in past studies, as they ignored the sanctions of other administrative agencies.

Table 1 presents the distribution of the different categories of violations according to the number of violations and penalty values in the sample. The data showed that most violations were related to safety, employment, and the environment. These types of misconduct are quickly discovered and punished by regulators, which may explain their frequency in the sample. Furthermore, the highest average value of fines is related to the environment, reflecting tougher sentencing of environmental offenses. Karpoff and Lott (1993) present evidence that the reputational cost of corporate fraud is high and constitutes the majority of costs incurred by companies accused or convicted of fraud. Therefore, both the number and value of misconduct have negative impacts on a company's long-term value.

We follow (Heese et al., 2021) and use the following two alternative dependent variables in our model: The first variable is the log of the total value of violations by firm *i* in year *t* (*Value*). This indicates direct damage to shareholders, as fines do not present an expense position for companies. Indeed, they are paid from the net income and reduce the amount of cash available to shareholders. The second variable is the log of the total number of violations by firm *i* in year *t* (*Number*), which shows the dynamics of corporate violation frequency in a given year. Although the second variable does not directly damage shareholders, we assume that companies involved in numerous violations may have

<caption>**Table 1**</caption>

Penalties according to the type of violation in years 2007–2020.

Source: Good Jobs First's Violation Tracker and the authors' calculations.

<table><thead><tr><th>Violation category</th><th>Average</th><th>Max</th><th>Count</th><th>No. unique firms</th></tr></thead><tbody><tr><td>Environment</td><td>5.3</td><td>9357.0</td><td>4857</td><td>496</td></tr><tr><td>Safety</td><td>0.2</td><td>900.0</td><td>14,809</td><td>791</td></tr><tr><td>Employment</td><td>3.3</td><td>694.7</td><td>2453</td><td>519</td></tr><tr><td>Competition</td><td>19.9</td><td>500.4</td><td>240</td><td>133</td></tr><tr><td>Consumer</td><td>8.1</td><td>5002.4</td><td>1279</td><td>149</td></tr><tr><td>Financial</td><td>51.9</td><td>2300.0</td><td>151</td><td>110</td></tr><tr><td>Gov. Contracting</td><td>36.7</td><td>1156.0</td><td>416</td><td>131</td></tr><tr><td>Healthcare</td><td>107.1</td><td>3166.1</td><td>143</td><td>36</td></tr><tr><td>Other</td><td>8.0</td><td>50.0</td><td>7</td><td>7</td></tr><tr><td>Total</td><td>3.7</td><td>9357.0</td><td>24,355</td><td>988</td></tr></tbody></table>

The table shows the average and maximal value of the penalty paid by a company, total number of violations, and firms affected by violation type over the years 2007–2020. Value of fines in millions of USD.

a reputation problem. Therefore, the second variable was a proxy for general misconduct in companies.

In the sensitivity analysis, we also used the log of the value and number of different types of violations as dependent variables. Although we presume a negative relationship between corporate violations and governance, we may expect some variation because some violations may be partially justified by the interests of shareholders.

Our sample of observed corporate misconduct has a bias of excluding violations that have not been detected by authorities. However, our sample consists only of US listed companies under intense public scrutiny, which is likely to significantly reduce the problem of undetected misconduct (Dyck, Morse, & Zingales, 2010).

As we are unable to establish the date of misconduct, our empirical methodology uses the date of sanction as a proxy for corporate misconduct, not the date on which the violation of the law occurred. However, we may assume that corporate violations are quickly exposed because our sample covers the period in which US regulations were in place to encourage whistleblowers to expose firms' law violations. This included an award implemented under the Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010. The Act requires the Securities and Exchange Commission (SEC) to reward whistleblowers who voluntarily provide original information that leads to successful enforcement actions, yielding monetary sanctions of more than US$ 1 million. Moreover, in the United States, authorities have taken the most aggressive stance against white-collar crimes, as reflected in the short period between exposure to corporate misconduct and legal sanctions.

In addition, we did not observe corporate violations that were detected or prevented by companies' internal control mechanisms. Soltes (2019) argues that the vast majority of misconduct is detected by internal mechanisms but is not sanctioned by regulatory bodies for a variety of political and economic reasons. Thus, our dataset includes companies where corporate violations have not been reported, and companies where they have been repeatedly reported over the years. Nevertheless, we argue that our data allow us to evaluate the most effective internal mechanism for preventing corporate violations. This limited but important aspect of governance has received little attention in current literature.

## 3.3. Internal corporate governance measures

We used five variables to measure a firm's internal corporate governance. First, we include the log of the number of directors on the board (Board Size). Jensen (1993) argues that large boards are less effective in internal governance than small boards. In his opinion, large boards are less effective in coordination, communication, and decision-making and are more likely to be controlled by the CEO. Beasley (1996) reported that board size is positively associated with the probability of changes in financial statements. On the one hand, small boards
may not be efficient in detecting corporate misconduct because of missing knowledge and services. However, large boards may also be less efficient owing to coordination problems. Henceforth, we expect a nonlinear effect of the number of directors on monitoring, particularly for corporate violation prevention; therefore, we also include a variable representing the square of the board size (Board Size²).

However, Raheja (2005) argue that the optimal structure and effectiveness of the board in monitoring depend on the characteristics of the firm and its directors. Uzun et al. (2004) found no evidence of a relationship between board size and corporate fraud, while documenting that board composition is significantly related to the incidence of corporate fraud. Thus, the following variables control for board composition.

Second, we include a dummy variable that equals 1 if the chairperson is also the CEO of the company, and 0 otherwise (CEO Duality). Finkelstein and D'Aveni (1994) argue that CEO duality promotes CEO entrenchment by reducing the effectiveness of board monitoring. O'Connor et al. (2006) confirm this by showing that in certain contexts, CEO duality can lead to larger agency problems and the likelihood of fraudulent financial reporting. Moreover, Farber (2005) found that fraudulent firms have a higher percentage of dual CEOs. Similarly, Chen et al. (2006) show that firms with CEO duality have an increased propensity for fraud. Therefore, we expect the risk of CEO wrongdoing to increase when the CEO also serves as the chairman of the board.

Third, we include the percentage of independent directors on the board (Board Independence). Independent directors' monitoring role has been widely documented. Thus, increased board independence is a commonly offered solution for curbing corporate misconduct. However, empirical evidence so far has provided mixed results. In a meta-analysis of 135 studies on board independence and corporate misconduct, Neville et al. (2019) show that the relationship is generally negative but varies depending on the form of implementation that independence takes on; that is, the independence of the audit committee or between the roles of CEO and board chairman. Hence, we do not make strong assumptions about board independence.

Fourth, we control for the proportion of female board members (Female Director), because the relationship between gender and criminality is strong. Literature documents the predominance of men in virtually all forms of crime, including white-collar crime (Benson & Gottschalk, 2015). Benson and Gottschalk (2015) report that despite gender inequality being much lower in Norway than in the United States, the gender gap in Norwegian white-collar crimes appears to be nearly identical to that observed in the United States. They reported that, in Norway, only 6.7% of white-collar offenders were women, a rate similar to that observed in the United States. An explanation for these results is that women are less willing to take risks than men and less willing to hurt, victimize, or take advantage of others. It is not surprising that a growing number of studies show that women are more ethical and less likely to be involved in crime and litigation than men at the company level (Adhikari, Agrawal, & Malm, 2019). Similarly, Arnaboldi et al. (2021) reported that a greater representation of women on bank boards significantly reduces the frequency of misconduct fines. Therefore, we expected a negative relationship between female representation and company misconduct.

Fifth, we include a dummy variable Big4 that equals 1 if the company is audited by one of the Big 4 audit companies, and 0 otherwise. Auditors are associated with external governance, but the board decides which firm to choose. Therefore, we decided to control for internal decisions on audit quality by audit companies. We follow the literature and use the size of the auditor, measured as membership in the Big 4, as a proxy for audit quality because large auditors are expected to have stronger incentives and greater competencies to provide high-quality auditing (DeAngelo, 1981). DeFond and Zhang (2014) document that Big N auditors also have greater competence in delivering high audit quality because of advantages such as their ability to attract higher

<table><caption>Table 2
Descriptive statistics.</caption><thead><tr><th></th><th>Mean</th><th>p25</th><th>p50</th><th>p75</th><th>Obs.</th></tr></thead><tbody><tr><td colspan="6"><strong>Panel A: Internal governance measures</strong></td></tr><tr><td>Board Size</td><td>9.266</td><td>8.000</td><td>9.000</td><td>11.000</td><td>15,014</td></tr><tr><td>CEO Duality</td><td>0.434</td><td>0.000</td><td>0.000</td><td>1.000</td><td>15,029</td></tr><tr><td>Board Independence</td><td>0.809</td><td>0.750</td><td>0.850</td><td>0.880</td><td>15,029</td></tr><tr><td>Female Director</td><td>0.159</td><td>0.100</td><td>0.143</td><td>0.222</td><td>15,014</td></tr><tr><td>Big 4</td><td>0.894</td><td>1.000</td><td>1.000</td><td>1.000</td><td>14,975</td></tr><tr><td colspan="6"><strong>Panel B: CEO characteristics</strong></td></tr><tr><td>CEO Woman</td><td>0.048</td><td>0.000</td><td>0.000</td><td>0.000</td><td>15,029</td></tr><tr><td>CEO Age</td><td>55.914</td><td>51.000</td><td>56.000</td><td>60.000</td><td>14,813</td></tr><tr><td>CEO Tenure</td><td>10.010</td><td>4.000</td><td>7.000</td><td>13.000</td><td>14,825</td></tr><tr><td colspan="6"><strong>Panel C: Firm characteristics</strong></td></tr><tr><td>Size</td><td>10.970</td><td>0.818</td><td>2.687</td><td>8.973</td><td>14,832</td></tr><tr><td>Sales Growth</td><td>0.101</td><td>-0.007</td><td>0.058</td><td>0.143</td><td>14,605</td></tr><tr><td>ROA</td><td>0.033</td><td>0.022</td><td>0.058</td><td>0.097</td><td>14,797</td></tr><tr><td>Q</td><td>2.166</td><td>1.233</td><td>1.670</td><td>2.504</td><td>14,790</td></tr><tr><td>Leverage</td><td>0.229</td><td>0.065</td><td>0.212</td><td>0.337</td><td>14,801</td></tr></tbody></table>

The table provides the means and the 25th, 50th, and 75th percentiles of the main variables used in the analysis. The sample consists of 15,029 firm-year observations of 2844 public companies between 2007 and 2019. Board Size is the number of board members. CEO Duality is a dummy variable that takes the value of 1 if the positions of the CEO and chairman are held by the same person, and 0 otherwise. Board Independence is the ratio of independent members to board size. Female Director refers to the number of female directors on the board. Big 4 is a dummy variable that takes the value of 1 if the firm's audit company belongs to the top four auditor firms, and 0 otherwise. CEO Woman is a dummy variable that equals 1 if the CEO of the company is female and 0 otherwise. CEO Age is the log of CEO age. CEO Tenure is the log of the CEO tenure. Size is the log of total assets in millions of dollars. Sale growth is the percentage of change in sales from the previous year. ROA is the ratio of pre-tax income to the total assets. Q is the ratio of the market value to the book value of equity. Leverage is the ratio of total debt to assets.

quality input. Similarly, Che, Hope, and Langli (2020) show that the Big 4 firms provide higher-quality audits than non-Big 4 firms because they can recruit higher-quality personnel, place greater emphasis on learning, and have stronger incentives and monitoring systems. Therefore, Lennox et al. (2010) argue that there is a lower likelihood of fraudulent financial reporting if financial reports are audited by Big 4 accounting firms, because auditors are concerned about their reputations. Therefore, we expect the presence of the Big 4 audit companies to be negatively related to company misconduct.

Panel A of Table 2 presents the summary statistics for the five internal corporate governance measures at the firm level for the sample. The data show that the average board consists of nine members, the majority of whom are independent. Simultaneously, we find that female directors still represent a minority of US boards. Finally, most of our firms were audited by a Big 4 companies.

### 3.4. CEO characteristics

In the main analysis, we also use three CEO characteristics that can determine the functioning of the board and the violations of the firm. First, we use a dummy variable CEO Woman that takes the value of 1 if the CEO is female and 0 otherwise. The gender of the CEO may be considered one of the characteristics that can influence a firm's strategic decision-making process because of the different experiences, knowledge, and values held by women compared to men (Triana, Miller, & Trzebiatowski, 2014).

Second, we control for CEO Tenure (CEO Tenure) and age (CEO Age) because we expect these to determine CEO's incentives, reputations, and career concerns (Gibbons & Murphy, 1992). However, we can assume that CEOs who stay longer in a company have stronger connections with managers within the firm. Khanna et al. (2015) found that the connections developed by CEOs with top executives and directors through their appointment decisions increase the risk of corporate fraud. Furthermore, they argue that the greater the influence of a CEO
on the board and audit committee, the less likely the board is to detect corporate wrongdoing. However, Khanna et al. (2015) suggest that CEO connectedness based on education, employment, or social network ties with top executives or directors has insignificant effects on both fraud and the expected cost of wrongdoing. Consequently, CEO tenure can affect corporate fraud in two ways.

Panel B of Table 2 presents the summary statistics for the CEO characteristics. The average age of the CEO was 56 years, with a tenure of 10 years. In line with previous findings, we find that very few women still occupy positions as CEO (less than 5%).

### 3.5. Methodology

This study uses the following pooled ordinary least squares (OLS) model to estimate how corporate governance influences corporate misconduct.

$$ Violation_{i,t+j} = \alpha_0 + \alpha_1 CG_{i,t} + \alpha_2 CEO_{i,t} + \alpha_3 Finance_{i,t} + I_t + \eta_t + \epsilon_{i,t} \quad (1) $$

where the dependent variable Violation is measured by the log of the total value of fines (Value) or the log of the total number of prosecuted violations (Number) in firm i in year t, and j is equal to 0, 1, 2, or 3. CG, CEO, and Finance are the vectors of the governance, CEO-level, and firm-level control variables, respectively. Finally, I and η are industry and year fixed effects, respectively, and ε is an error term.

We followed the literature and controlled for firm characteristics related to corporate misconduct. Wang, Winton, and Yu (2010) argue that larger and high-growth firms are less likely to engage in fraudulent activity as these companies attract more investor attention. We measure firm size using the log of the book value of total assets (Size) and the Sales growth rate as the annual growth rate of sales for 1 year. We include the return on assets (ROA) and Tobin's Q (Q), which can also affect the incidence of corporate violations because fraud is more likely to occur when a company is experiencing operating difficulties (Arlen & Carney, 1992). We further control for firms' (Leverage) because banks and fixed-income investors are likely to closely monitor firms with higher financial leverage (Jensen & Meckling, 1976). Panel C of Table 2 presents the summary statistics for the control variables at the firm level and shows significant variations, particularly for the variables that proxy for firm growth, profitability, and size.

## 4. Empirical results

### 4.1. Main analysis

In Tables 3A and 3B, we show the results of the regression models using the log of the total value of fines and the total number of violations as measures of corporate misconduct. In both tables, the dependent variable is contemporaneous in column (1), as in Zaman, Atawnah, Baghdad, and Liu (2021), whereas in columns (2)–(4), it is forward-lagged by one, two, and three periods, respectively. On the one hand, we are mainly interested in the results using the one-period forward-lagged measures of misconduct, as we want to establish whether prior internal governance affects corporate misbehavior. On the other hand, some misconduct was minor in our study, and henceforth, the consequences follow soon after. Therefore, we focus mainly on the results presented in columns (1) and (2) of Tables 3A and 3B, where we find that the coefficients of interest are similar.

The results confirm that board size is related to the number of cases of corporate misconduct and penalty value. However, as expected, we find a nonlinear relationship between board size and corporate misconduct and the coefficients of board size are statistically significant at the 1% level in all specifications. In other words, the results indicate that the number and value of penalties will decrease as the number of directors increases to a point where the relationship reaches a maximum, from which point the relationship will change and misconduct will start to grow again as the number of directors increases. Similarly, and

consistent with the literature, we find that the likelihood of corporate fraud decreases in terms of both the number and volume of fines if an auditor is among the top four accounting firms. The coefficient of Big 4 firms is negative and statistically significant for all specifications at the 1% level.

We find that the coefficient of the ratio of independent board members is positively correlated with the amount and value of corporate misconduct. The coefficient is statistically significant for all the specifications in both tables. Therefore, board independence, commonly used to curb corporate misconduct, is ineffective. Neville et al. (2019) reported that the literature on board independence presents mixed evidence. However, they show that board composition is important, particularly for the separation of the CEO from the board chair. Our results confirm that the separation of the CEO and board chair is positively and significantly correlated with corporate misconduct. These findings are consistent with the argument that CEO duality increases the power of the CEO over the board, preventing independence between the board and management, which is necessary for controlling the managerial establishment (Fama & Jensen, 1983). Moreover, the greater the influence of a CEO on the board, the less likely it is to suspect irregularities that a more independent board may encounter.

Furthermore, the results suggest that leadership characteristics may occur at a cost. We found that the coefficient of CEO age is positive and significant in all specifications, at least at the 5% level. In our opinion, the results indicate that older and more reputed CEOs have more power and are, therefore, more likely to influence the board to ignore misconduct. This reasoning is supported by the coefficient of CEO duality, which is positive and statistically significant in columns (1)–(3). Thus, we confirm that corporate misconduct is more frequent in companies in which the CEO is also the board chairman. By contrast, we find that CEO tenure is negatively correlated with corporate fraud. These results indicate that new CEOs were appointed because of uncovered corporate fraud. This theory is supported by the fact that the coefficient of CEO tenure is significant only in columns (1) and (2) and only in panel B.

We did not find evidence that female CEO affect corporate violations. In all specifications, the coefficients for female CEO were insignificant. By contrast, the coefficient of female directors is negative in all specifications but significant only when the dependent variable is the number of company violations. Therefore, the results indicate that an increase in female directors is more likely to scale misconduct by firms but not the volume of fines. Most studies show that large fines could be attributed to male board members, which prevails; however, we did not observe a significant reduction in this area.

We also confirm that some firm-specific characteristics are related to corporate fraud. As expected, we find that company size is positively correlated with corporate misconduct, with coefficients significant at the 1% level in all specifications. Conversely, the coefficient of ROA is negative and statistically significant for all the specifications at the 1% level. In other words, the results show that companies with fewer ethics are larger and less profitable. Furthermore, we find that companies with lower leverage are more likely to commit fraud, because the coefficient of leverage is negative and significant in all specifications at the 1% level. Thus, the results confirm that debtor monitoring reduces corporate fraud. Generally, the results for the control variables are consistent with the existing literature on corporate fraud (Cumming et al., 2015; Khanna et al., 2015).

The potential endogeneity between governance measures and performance is a primary concern in literature. We address this problem using lagged periods between governance and violation measures, as we assume that prior internal governance may affect companies' current misconduct, yet we are also aware that the relationship could still go in both directions.

First, we extend the length of the lags. Columns (3) and (4) show the results. We use our dependent variable, forward-lagged by two or three periods. We find that the coefficients for the different lagged periods generally remain unchanged. The coefficients have the same signs but
<table><caption>**Table 3A**
Corporate governance and the total value of penalties.</caption><thead><tr><th></th><th>Value<sub>t</sub></th><th>Value<sub>t+1</sub></th><th>Value<sub>t+2</sub></th><th>Value<sub>t+3</sub></th></tr></thead><tbody><tr><td>Board Size</td><td>-18.545***<br>(1.997)</td><td>-19.118***<br>(1.970)</td><td>-17.641***<br>(2.256)</td><td>-14.607***<br>(2.654)</td></tr><tr><td>Board Size<sup>2</sup></td><td>4.597***<br>(0.479)</td><td>4.753***<br>(0.476)</td><td>4.324***<br>(0.539)</td><td>3.573***<br>(0.626)</td></tr><tr><td>CEO Duality</td><td>0.268***<br>(0.099)</td><td>0.245**<br>(0.098)</td><td>0.201*<br>(0.109)</td><td>0.055<br>(0.124)</td></tr><tr><td>Board Independence</td><td>2.217***<br>(0.437)</td><td>2.429***<br>(0.431)</td><td>1.989***<br>(0.496)</td><td>1.447**<br>(0.593)</td></tr><tr><td>Female Director</td><td>-0.505<br>(0.442)</td><td>-0.681<br>(0.440)</td><td>-0.531<br>(0.501)</td><td>-0.496<br>(0.593)</td></tr><tr><td>Big 4</td><td>-0.582***<br>(0.121)</td><td>-0.590***<br>(0.119)</td><td>-0.497***<br>(0.141)</td><td>-0.527***<br>(0.181)</td></tr><tr><td>CEO Woman</td><td>0.032<br>(0.212)</td><td>0.224<br>(0.215)</td><td>0.108<br>(0.244)</td><td>-0.037<br>(0.283)</td></tr><tr><td>CEO Age</td><td>0.749**<br>(0.349)</td><td>0.735**<br>(0.351)</td><td>0.823**<br>(0.401)</td><td>0.968**<br>(0.477)</td></tr><tr><td>CEO Tenure</td><td>-0.059<br>(0.054)</td><td>-0.031<br>(0.054)</td><td>0.023<br>(0.060)</td><td>0.157**<br>(0.071)</td></tr><tr><td>Size</td><td>1.519***<br>(0.040)</td><td>1.486***<br>(0.041)</td><td>1.553***<br>(0.045)</td><td>1.669***<br>(0.051)</td></tr><tr><td>Sales</td><td>-0.272***<br>(0.104)</td><td>-0.115<br>(0.099)</td><td>-0.061<br>(0.139)</td><td>-0.004<br>(0.218)</td></tr><tr><td>ROA</td><td>-2.073***<br>(0.228)</td><td>-2.375***<br>(0.225)</td><td>-2.002***<br>(0.300)</td><td>-1.064*<br>(0.583)</td></tr><tr><td>Q</td><td>-0.016<br>(0.027)</td><td>0.026<br>(0.026)</td><td>0.052<br>(0.033)</td><td>0.021<br>(0.045)</td></tr><tr><td>Leverage</td><td>-1.388***<br>(0.231)</td><td>-1.414***<br>(0.230)</td><td>-1.761***<br>(0.266)</td><td>-2.039***<br>(0.326)</td></tr><tr><td>Constant</td><td>-1.770<br>(2.618)</td><td>-0.782<br>(2.618)</td><td>-3.094<br>(2.996)</td><td>-7.830**<br>(3.517)</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>14,194</td><td>14,194</td><td>12,011</td><td>9,848</td></tr><tr><td>R<sup>2</sup></td><td>0.267</td><td>0.266</td><td>0.254</td><td>0.241</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.265</td><td>0.264</td><td>0.252</td><td>0.239</td></tr></tbody></table>

This table reports the results of the internal corporate governance measures. The dependent variable is the log of the total penalty value. Table 2 presents the independent variables. All specifications include constant, industry, and year fixed effects. Robust standard errors are in parentheses. ***, **, * indicate statistical significance at the 1, 5, and 10% levels, respectively.

are statistically weaker. Thus, our results are robust and likely present a causal relationship between governance and corporate misconduct. Therefore, we employ only one-period forward-lagged measures of misconduct in the remaining regressions.

Next, we employed matching methodologies to address the potential bias of the omitted variables. We use the coarsened exact matching (CEM) algorithm, which enables the estimation of causal effects by reducing the imbalance in covariates between the treated and control groups (Iacus, King, & Porro, 2009). We encoded the treated control group as companies that were engaged in misconduct and matched it with the control group based on industry, size, and book-to-market. The results for the matching sample are listed in Table 4, where we employ two CEM matching algorithms. In columns (1) and (3), the one-to-many matching method is used to maximize information on the sample, and weights are used in subsequent regressions. In columns (2) and (4), one-to-one matching is used, where non-uniquely matched control observations are dropped randomly. One drawback of this method is that the companies in the control sample differ when rerunning the regressions. Columns (1) and (2) present the results using the penalty value as the dependent variable, whereas columns (3) and (4) present the number of instances of misconduct.

In Table 4, the coefficients on the governance variables have the same sign as those in the main regression. In addition, the coefficients of board size and the ratio of independent board members are statistically significant in all specifications. Furthermore, the coefficient of CEO age was positively and significantly correlated with corporate misconduct. Hence, the results confirm that board size and composition

<table><caption>**Table 3B**
Corporate governance and the number of company violations.</caption><thead><tr><th></th><th>Number<sub>t</sub></th><th>Number<sub>t+1</sub></th><th>Number<sub>t+2</sub></th><th>Number<sub>t+3</sub></th></tr></thead><tbody><tr><td>Board Size</td><td>-2.803***<br>(0.258)</td><td>-2.865***<br>(0.254)</td><td>-2.569***<br>(0.284)</td><td>-2.212***<br>(0.333)</td></tr><tr><td>Board Size<sup>2</sup></td><td>0.686***<br>(0.062)</td><td>0.702***<br>(0.062)</td><td>0.623***<br>(0.069)</td><td>0.536***<br>(0.079)</td></tr><tr><td>CEO Duality</td><td>0.052***<br>(0.012)</td><td>0.047***<br>(0.012)</td><td>0.044***<br>(0.014)</td><td>0.023<br>(0.015)</td></tr><tr><td>Board Independence</td><td>0.399***<br>(0.049)</td><td>0.401***<br>(0.048)</td><td>0.347***<br>(0.055)</td><td>0.322***<br>(0.067)</td></tr><tr><td>Female Director</td><td>-0.153***<br>(0.053)</td><td>-0.166***<br>(0.052)</td><td>-0.153**<br>(0.059)</td><td>-0.159**<br>(0.070)</td></tr><tr><td>Big 4</td><td>-0.100***<br>(0.013)</td><td>-0.098***<br>(0.013)</td><td>-0.086***<br>(0.015)</td><td>-0.084***<br>(0.019)</td></tr><tr><td>CEO Woman</td><td>-0.021<br>(0.024)</td><td>-0.002<br>(0.024)</td><td>0.000<br>(0.027)</td><td>-0.014<br>(0.032)</td></tr><tr><td>CEO Age</td><td>0.211***<br>(0.041)</td><td>0.212***<br>(0.040)</td><td>0.219***<br>(0.046)</td><td>0.247***<br>(0.054)</td></tr><tr><td>CEO Tenure</td><td>-0.024***<br>(0.007)</td><td>-0.019***<br>(0.007)</td><td>-0.013*<br>(0.007)</td><td>0.004<br>(0.009)</td></tr><tr><td>Size</td><td>0.199***<br>(0.005)</td><td>0.190***<br>(0.005)</td><td>0.199***<br>(0.006)</td><td>0.214***<br>(0.007)</td></tr><tr><td>Sales</td><td>-0.010<br>(0.012)</td><td>0.006<br>(0.012)</td><td>0.019<br>(0.016)</td><td>0.024<br>(0.026)</td></tr><tr><td>ROA</td><td>-0.403***<br>(0.031)</td><td>-0.419***<br>(0.030)</td><td>-0.407***<br>(0.041)</td><td>-0.245***<br>(0.077)</td></tr><tr><td>Q</td><td>0.004<br>(0.003)</td><td>0.008***<br>(0.003)</td><td>0.012***<br>(0.004)</td><td>0.008<br>(0.005)</td></tr><tr><td>Leverage</td><td>-0.213***<br>(0.027)</td><td>-0.214***<br>(0.027)</td><td>-0.248***<br>(0.031)</td><td>-0.289***<br>(0.039)</td></tr><tr><td>Constant</td><td>-0.259<br>(0.329)</td><td>-0.061<br>(0.325)</td><td>-0.473<br>(0.367)</td><td>-1.102**<br>(0.431)</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>14,194</td><td>14,194</td><td>12,011</td><td>9848</td></tr><tr><td>R<sup>2</sup></td><td>0.289</td><td>0.286</td><td>0.278</td><td>0.270</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.287</td><td>0.284</td><td>0.276</td><td>0.268</td></tr></tbody></table>

This table reports the results of the internal corporate governance measures. The dependent variable is the log of the number of misconducts. Table 2 presents the independent variables. All specifications include constant, industry, and year fixed effects. Robust standard errors are in parentheses. ***, **, * indicate statistical significance at the 1, 5, and 10% levels, respectively.

are important for firms' governance, and consequently influence the likelihood of misconduct. Furthermore, we find that the coefficient for Big 4 is negative, but only significant in one of the specifications and only at the 10% level. Moreover, we find that the coefficient of the share of female directors is insignificant for all specifications. Therefore, the results indicate that the effect on female board members was limited.

Finally, we create an internal corporate governance (ICG) index using principal component analysis, as in Larcker et al. (2007). The ICG index condenses the information from the individual governance attributes into a smaller set of governance indices by identifying linear combinations of the original governance attributes that explain as much variation in the original dataset as possible. Consequently, the advantage of the ICG index lies in its foundation on a statistical procedure, in contrast to traditional indices that rely on equal or arbitrarily assigned weights.

We substitute our primary internal governance variables with the ICG index and proceed to replicate our primary regression analysis. The results are detailed in the Appendix, specifically in Tables A.3 and A.4, where the dependent variables are the total value and number of penalties, respectively. Across all the specifications in both tables, we find that the coefficient of the ICG index is positively and statistically significantly associated with corporate misconduct. This pattern suggests that, in general, a firm's internal governance provides only a limited defense against corporate misconduct. In line with our previous results, the coefficient for the Big 4 audit firms is consistently negative and statistically significant across all models, indicating that certain
<table><caption>**Table 4**
Coarsened exact matching.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Board Size</td><td>-12.335***<br>(3.937)</td><td>-8.956**<br>(4.527)</td><td>-1.643***<br>(0.490)</td><td>-1.176**<br>(0.535)</td></tr><tr><td>Board Size<sup>2</sup></td><td>3.175***<br>(0.909)</td><td>2.487**<br>(1.035)</td><td>0.416***<br>(0.113)</td><td>0.315**<br>(0.123)</td></tr><tr><td>CEO Duality</td><td>0.013<br>(0.154)</td><td>0.162<br>(0.170)</td><td>0.016<br>(0.019)</td><td>0.036*<br>(0.020)</td></tr><tr><td>Board Independence</td><td>4.210***<br>(0.635)</td><td>1.930**<br>(0.787)</td><td>0.562***<br>(0.074)</td><td>0.298***<br>(0.085)</td></tr><tr><td>Female Director</td><td>-0.117<br>(0.711)</td><td>-0.077<br>(0.813)</td><td>-0.097<br>(0.084)</td><td>-0.149<br>(0.092)</td></tr><tr><td>Big 4</td><td>-0.175<br>(0.254)</td><td>-0.096<br>(0.338)</td><td>-0.047*<br>(0.027)</td><td>-0.043<br>(0.035)</td></tr><tr><td>CEO Woman</td><td>0.097<br>(0.334)</td><td>0.179<br>(0.386)</td><td>-0.026<br>(0.038)</td><td>-0.041<br>(0.039)</td></tr><tr><td>CEO Age</td><td>1.501**<br>(0.595)</td><td>0.645<br>(0.686)</td><td>0.334***<br>(0.071)</td><td>0.284***<br>(0.074)</td></tr><tr><td>CEO Tenure</td><td>0.013<br>(0.084)</td><td>0.043<br>(0.096)</td><td>-0.014<br>(0.010)</td><td>-0.014<br>(0.011)</td></tr><tr><td>Firm controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>12,379</td><td>6843</td><td>12,379</td><td>6843</td></tr><tr><td>R<sup>2</sup></td><td>0.040</td><td>0.034</td><td>0.073</td><td>0.071</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.038</td><td>0.030</td><td>0.071</td><td>0.071</td></tr></tbody></table>

This table reports the results of the matched samples obtained using coarsened exact matching. The matching criteria are industry, firm size, and book-to-market ratio. In columns (1) and (3), the employed matching algorithm is one-to-many, whereas in columns (2) and (4), matching is performed one-to-one. In columns (1)-(2) and (3)-(4), the dependent variables are the log of the penalty value and number of penalties, respectively. Table 2 presents the independent variables. All specifications include constant, industry, and year fixed effects, as well as firm-level control variables, as in Table 3A, which are not presented here for brevity. Robust standard errors are in parentheses. ***, **, * indicate statistical significance at the 1, 5, and 10% levels, respectively.

factors can mitigate misconduct. This aspect will be explored further in subsequent sections.

## 4.2. Sensitivity analysis

Next, we analyze the sensitivity of our results by applying our main specifications separately to each type of corporate violation, as listed in Table 1. This allows us to test whether managers and monitors have different attitudes toward different types of fraud. In particular, financial fraud may be seen as positive by managers and supervisors, as it may increase profitability, and consequently, their remuneration.

The results in Tables 5A and 5B confirm that the relationship between the level of governance and firm misconduct, both in terms of volume and number of fines, is stable across violation types. We find that, for most types of violations, the coefficients of the governance variables have the same sign and statistical significance, as shown in Tables 3A and 3B. The only exceptions are offenses related to finance, including accounting fraud, economic sanction violations, tax evasion, and investor protection abuses. When we control for financial fraud, we find that only the share of female directors is negatively correlated with the volume of fines paid and number of financial offenses at the 5% level. Conversely, we find that the variable CEO Woman is positively correlated with offenses linked to finance and negatively correlated with CEO tenure, but only at the 10% level. Interestingly, being audited by a Big 4 company decreases all types of penalties except financial ones.

These results are consistent with the literature, showing that boards with greater gender differences are associated with lower tax avoidance (Richardson, Taylor, & Lanis, 2016), fewer instances of account misreporting (Lara, Osma, Mora, & Scapin, 2017), less frequent and less severe securities fraud (Cumming et al., 2015), and less bank misconduct (Arnaboldi et al., 2021). Furthermore, we found that the share

of female directors was negatively associated with violations related to environment and safety. In all the specifications, the coefficients are statistically significant, at least at the 5% level. In contrast, we find that the coefficient of the share of female directors is positively associated with crimes related to employment, government, and health. The coefficients are significant for all the specifications at the 5% level. Consequently, we found that the direction of the impact of gender diversity is strongly correlated with the type of corporate misconduct. The results also show that the relationship between governance and corporate misconduct differs depending on the type of offense. We attribute this to different perceptions of misconduct by the management board.

In all the specifications, the firm-level variables influence the dependent variable in the directions identified in Tables 3A and 3B. Furthermore, the statistical significance of these variables did not change, and their magnitudes were highly stable. Therefore, to keep the following tables concise, we include only the coefficients of the variables that present additional proxies for governance crucial to testing of our hypotheses.

To validate the robustness of our findings, we reanalyze the regressions using the ICG index in lieu of the internal governance variables. The results are presented in Appendix Tables A.5 and A.6, where the dependent variables are the total value and number of penalties, respectively. These results reveal that the coefficient of the ICG index is positively and statistically significantly associated with various types of corporate misconduct but not universally. Specifically, we observe that the coefficient for the ICG index turns negative in relation to consumer and financial misconduct, both in terms of the volume and number of fines. In addition, a negative association was noted with the number of health-related misconduct cases. However, in all these instances, the coefficient is statistically insignificant. This indicates that not all internal governance variables uniformly mitigate misconduct, and that their influence appears to be contingent on the type of misconduct. Consequently, we examined the relationship between various internal governance attributes and corporate misconduct through subsequent analyses.

# 5. Other governance mechanisms

The corporate governance literature uses additional proxy services to investigate the effectiveness of internal governance. We employ these variables to test the sensitivity of the main results. The summary statistics for these additional variables are presented in Table A.2 in the Appendix.

## 5.1. Board activity

Vafeas (1999) showed that the frequency of board meetings is related to corporate governance and ownership characteristics. Therefore, we control for the number of board meetings in a given year, indicating the strength of board supervision and monitoring (Meetings). Furthermore, we control for board members' participation in monitoring by considering their attendance at meetings (Attendance).

Corporate boards often delegate tasks under their responsibility to standing board committees, which often oversee a firm's important decisions. We controlled for the existence of an executive committee and a corporate social responsibility (CSR) committee using the dummy variable Executive Comm. and CSR Comm., respectively. The variables take a value of 1 if an executive or a CSR committee exists on the board, and 0 otherwise. An executive committee is a standing committee that often acts as the steering committee for the entire board. In general, they are responsible for overseeing board policies and ensuring good governance. The CSR committee formulates and recommends a CSR policy to the board. The absence of a CSR committee is associated with corporate misconduct (Zaman et al., 2021).
<table><caption>**Table 5A**
Corporate governance and the total value of penalties by offense group.</caption><thead><tr><th></th><th>Env</th><th>Safety</th><th>Empl</th><th>Comp</th><th>Cons</th><th>Fin</th><th>Gov</th><th>Health</th></tr></thead><tbody><tr><td>Board Size</td><td>-12.535***<br>(1.483)</td><td>-11.701***<br>(1.492)</td><td>-11.858***<br>(1.555)</td><td>-2.775***<br>(0.773)</td><td>-3.262***<br>(1.043)</td><td>-0.889<br>(0.665)</td><td>-3.233***<br>(1.055)</td><td>-1.772**<br>(0.803)</td></tr><tr><td>Board Size<sup>2</sup></td><td>3.092***<br>(0.362)</td><td>2.962***<br>(0.360)</td><td>2.892***<br>(0.381)</td><td>0.672***<br>(0.192)</td><td>0.671***<br>(0.256)</td><td>0.206<br>(0.162)</td><td>0.752***<br>(0.262)</td><td>0.379**<br>(0.192)</td></tr><tr><td>CEO Duality</td><td>0.139**<br>(0.069)</td><td>0.300***<br>(0.074)</td><td>0.015<br>(0.069)</td><td>-0.006<br>(0.029)</td><td>-0.069<br>(0.043)</td><td>0.030<br>(0.024)</td><td>0.161***<br>(0.042)</td><td>0.003<br>(0.024)</td></tr><tr><td>Board Indep.</td><td>1.795***<br>(0.266)</td><td>1.824***<br>(0.297)</td><td>0.844***<br>(0.285)</td><td>0.233**<br>(0.106)</td><td>0.465***<br>(0.164)</td><td>0.080<br>(0.086)</td><td>-0.058<br>(0.184)</td><td>0.200**<br>(0.078)</td></tr><tr><td>Female Dir.</td><td>-0.937***<br>(0.290)</td><td>-0.700**<br>(0.329)</td><td>0.723**<br>(0.307)</td><td>-0.121<br>(0.117)</td><td>0.143<br>(0.188)</td><td>-0.283**<br>(0.115)</td><td>0.475***<br>(0.162)</td><td>0.246***<br>(0.087)</td></tr><tr><td>Big 4</td><td>-0.325***<br>(0.069)</td><td>-0.179**<br>(0.088)</td><td>-0.390***<br>(0.068)</td><td>-0.119***<br>(0.030)</td><td>-0.290***<br>(0.039)</td><td>-0.012<br>(0.024)</td><td>-0.205***<br>(0.032)</td><td>-0.147***<br>(0.024)</td></tr><tr><td>CEO Woman</td><td>-0.173<br>(0.141)</td><td>-0.023<br>(0.156)</td><td>-0.197<br>(0.150)</td><td>-0.092*<br>(0.051)</td><td>0.015<br>(0.105)</td><td>0.120*<br>(0.071)</td><td>0.132<br>(0.108)</td><td>-0.071*<br>(0.038)</td></tr><tr><td>CEO Age</td><td>0.987***<br>(0.214)</td><td>0.851***<br>(0.246)</td><td>0.421*<br>(0.237)</td><td>-0.152*<br>(0.085)</td><td>-0.209<br>(0.175)</td><td>-0.059<br>(0.101)</td><td>0.187<br>(0.137)</td><td>0.094<br>(0.076)</td></tr><tr><td>CEO Tenure</td><td>-0.100***<br>(0.036)</td><td>-0.044<br>(0.039)</td><td>0.023<br>(0.039)</td><td>0.017<br>(0.015)</td><td>0.023<br>(0.024)</td><td>-0.025*<br>(0.015)</td><td>-0.037<br>(0.023)</td><td>-0.025*<br>(0.013)</td></tr><tr><td>Firm controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td></tr><tr><td>R<sup>2</sup></td><td>0.193</td><td>0.195</td><td>0.115</td><td>0.025</td><td>0.096</td><td>0.012</td><td>0.055</td><td>0.045</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.191</td><td>0.193</td><td>0.112</td><td>0.023</td><td>0.093</td><td>0.010</td><td>0.052</td><td>0.043</td></tr></tbody></table>

This table reports the results of the internal corporate governance measures and diverse corporate violations. The dependent variable is the log of the total penalty value for the main violation categories: environment, safety, employment, competition, consumer, financial, government contracting, and healthcare. Table 2 presents the independent variables. All specifications include constant, industry, and year fixed effects, as well as firm-level control variables, as in Table 3A, which are not presented here for brevity. Robust standard errors are in parentheses. ***, **, * indicate statistical significance at the 1, 5, and 10% levels, respectively.

<table><caption>**Table 5B**
Corporate governance and the number of company violations by offense group.</caption><thead><tr><th></th><th>Env</th><th>Safety</th><th>Empl</th><th>Comp</th><th>Cons</th><th>Fin</th><th>Gov</th><th>Health</th></tr></thead><tbody><tr><td>Board Size</td><td>-1.209***<br>(0.155)</td><td>-1.813***<br>(0.209)</td><td>-0.934***<br>(0.120)</td><td>-0.151***<br>(0.043)</td><td>-0.233**<br>(0.107)</td><td>-0.034<br>(0.033)</td><td>-0.180***<br>(0.062)</td><td>-0.074*<br>(0.042)</td></tr><tr><td>Board Size<sup>2</sup></td><td>0.299***<br>(0.038)</td><td>0.451***<br>(0.051)</td><td>0.227***<br>(0.029)</td><td>0.037***<br>(0.011)</td><td>0.043<br>(0.026)</td><td>0.008<br>(0.008)</td><td>0.041***<br>(0.015)</td><td>0.015<br>(0.010)</td></tr><tr><td>CEO Duality</td><td>0.017**<br>(0.007)</td><td>0.049***<br>(0.010)</td><td>0.004<br>(0.005)</td><td>-0.001<br>(0.002)</td><td>-0.015***<br>(0.004)</td><td>0.002<br>(0.001)</td><td>0.008***<br>(0.002)</td><td>-0.000<br>(0.001)</td></tr><tr><td>Board Indep.</td><td>0.176***<br>(0.026)</td><td>0.264***<br>(0.036)</td><td>0.059***<br>(0.021)</td><td>0.015**<br>(0.006)</td><td>0.052***<br>(0.014)</td><td>0.003<br>(0.005)</td><td>-0.000<br>(0.010)</td><td>0.014***<br>(0.004)</td></tr><tr><td>Female Dir.</td><td>-0.089***<br>(0.029)</td><td>-0.159***<br>(0.043)</td><td>0.057**<br>(0.023)</td><td>-0.008<br>(0.006)</td><td>0.013<br>(0.015)</td><td>-0.017***<br>(0.006)</td><td>0.024***<br>(0.009)</td><td>0.014***<br>(0.005)</td></tr><tr><td>Big 4</td><td>-0.035***<br>(0.006)</td><td>-0.046***<br>(0.010)</td><td>-0.031***<br>(0.005)</td><td>-0.006***<br>(0.002)</td><td>-0.029***<br>(0.004)</td><td>-0.001<br>(0.001)</td><td>-0.012***<br>(0.002)</td><td>-0.008***<br>(0.001)</td></tr><tr><td>CEO Woman</td><td>-0.018<br>(0.013)</td><td>-0.017<br>(0.018)</td><td>-0.015<br>(0.011)</td><td>-0.004<br>(0.003)</td><td>0.014<br>(0.012)</td><td>0.005<br>(0.003)</td><td>0.005<br>(0.005)</td><td>-0.004*<br>(0.002)</td></tr><tr><td>CEO Age</td><td>0.107***<br>(0.021)</td><td>0.176***<br>(0.032)</td><td>0.025<br>(0.017)</td><td>-0.008*<br>(0.005)</td><td>-0.001<br>(0.014)</td><td>-0.003<br>(0.005)</td><td>0.016**<br>(0.008)</td><td>0.008*<br>(0.004)</td></tr><tr><td>CEO Tenure</td><td>-0.010***<br>(0.004)</td><td>-0.018***<br>(0.005)</td><td>0.001<br>(0.003)</td><td>0.001<br>(0.001)</td><td>0.002<br>(0.002)</td><td>-0.002*<br>(0.001)</td><td>-0.003**<br>(0.001)</td><td>-0.001*<br>(0.001)</td></tr><tr><td>Firm controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td></tr><tr><td>R<sup>2</sup></td><td>0.193</td><td>0.194</td><td>0.118</td><td>0.023</td><td>0.084</td><td>0.012</td><td>0.052</td><td>0.041</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.191</td><td>0.192</td><td>0.116</td><td>0.021</td><td>0.082</td><td>0.010</td><td>0.050</td><td>0.039</td></tr></tbody></table>

This table reports the results of the internal corporate governance measures and diverse corporate violations. The dependent variable is the log of total number of misconducts by the main violation categories: environment, safety, employment, competition, consumer, financial, government contracting and healthcare. Table 2 presents the independent variables. All specifications include constant, industry, and year fixed effects, as well as firm-level control variables, as in Table 3A, which are not presented here for brevity. Robust standard errors are in parentheses. ***, **, * indicate statistical significance at the 1, 5, and 10% levels, respectively.

We expand our analysis and add new variables that control board activity, which are proxies for member participation in the company's monitoring. We present the results in Table 6, which includes all governance- and firm-level variables shown in Tables 3A and 3B. In panels A and B, the dependent variables are the total value of penalties and the total number of violations, respectively.

We found that the number of board meetings and board member attendance were positively correlated with the volume of fines paid by the company and the scale of corporate misconduct. In columns (1) and (2), the coefficients of the variables are positive and statistically

significant, at least at the 5% level. Our results are in line with those of Chen et al. (2006), who argue that this may imply that the board discusses the questionable or illegal activities of the firm.

The results in columns (3) and (4) show that the presence of an executive committee or a CSR committee is positively and statistically correlated with corporate misconduct. In column (5), we perform a horse race analysis and employ all variables simultaneously. We find that the coefficient of the CSR committee variable is positively correlated with both the value and number of penalties and is statistically significant at the 1% level. One explanation for these results is that
<table><caption>**Table 6**
Board activity.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td colspan="6"><strong>Panel A: Total value of penalties</strong></td></tr><tr><td>Meetings</td><td>0.318***<br>(0.106)</td><td></td><td></td><td></td><td>0.205*<br>(0.109)</td></tr><tr><td>Attendance</td><td></td><td>1.554***<br>(0.255)</td><td></td><td></td><td>0.070<br>(0.461)</td></tr><tr><td>Executive Comm.</td><td></td><td></td><td>0.274**<br>(0.127)</td><td></td><td>0.152<br>(0.128)</td></tr><tr><td>CSR Comm.</td><td></td><td></td><td></td><td>1.477***<br>(0.206)</td><td>1.399***<br>(0.207)</td></tr><tr><td>Governance Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>13,829</td><td>14,171</td><td>12,441</td><td>12,441</td><td>12,130</td></tr><tr><td>R<sup>2</sup></td><td>0.269</td><td>0.267</td><td>0.260</td><td>0.264</td><td>0.267</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.267</td><td>0.265</td><td>0.258</td><td>0.262</td><td>0.265</td></tr><tr><td colspan="6"><strong>Panel B: Total number of violations</strong></td></tr><tr><td>Meetings</td><td>0.029**<br>(0.012)</td><td></td><td></td><td></td><td>0.019<br>(0.012)</td></tr><tr><td>Attendance</td><td></td><td>0.209***<br>(0.034)</td><td></td><td></td><td>0.104*<br>(0.057)</td></tr><tr><td>Executive Comm.</td><td></td><td></td><td>0.054***<br>(0.016)</td><td></td><td>0.038**<br>(0.016)</td></tr><tr><td>CSR Comm.</td><td></td><td></td><td></td><td>0.220***<br>(0.028)</td><td>0.205***<br>(0.028)</td></tr><tr><td>Governance Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>13,829</td><td>14,171</td><td>12,441</td><td>12,441</td><td>12,130</td></tr><tr><td>R<sup>2</sup></td><td>0.290</td><td>0.288</td><td>0.276</td><td>0.282</td><td>0.286</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.288</td><td>0.286</td><td>0.274</td><td>0.280</td><td>0.284</td></tr></tbody></table>

This table reports the results for the additional variables that control for board activity. The dependent variable is the log of the total penalty value and the log of the total number of misconducts in Panels A and B. The independent variables are defined in Table A.2 of the Appendix. All specifications include constant, industry, and year fixed effects, as well as the governance and firm-level control variables, as in Table 3A, which are not presented here for brevity. Robust standard errors are in parentheses. ***, **, * indicate statistical significance at the 1, 5, and 10% levels, respectively.

companies heavily involved in misconduct try to mitigate the consequences of evolving the board, including the establishment of a CSR committee. In fact, we observe that the coefficients for board meetings and attendance are also positive but statistically significant at the 10% level in panels A and B, respectively. Therefore, the results support (Farber, 2005), who show that fraudulent companies take action to improve corporate governance after revealing misconduct.

## 5.2. Audit committee

The audit committee is one of the most powerful committees responsible for supervising corporate governance, financial reporting, internal control structures, and audit functions (Arthaud-Day et al., 2006). Several studies report that audit committees play an important role in fraud detection (Deli & Gillan, 2000; Johnson et al., 2009). Moreover, Owens-Jackson et al. (2009) report an inverse relationship between fraudulent financial statements and the number of meetings. Therefore, to provide effective supervision of the disclosure of financial information, the committee must hold frequent meetings. We controlled for the committee's activity using *Comm. Meetings*, which measures the frequency of committee meetings during the fiscal year.

Agrawal and Chadha (2005) document that the presence of an audit committee is related to the probability that a public company in the United States restates its earnings. They showed that the probability of restatement is lower in companies whose boards or audit committees have independent directors with financial expertise. Johnson et al. (2009) find that fraudulent firms have a higher proportion of insiders

on their audit committees. They suggest that the number of insiders on a committee reduces the likelihood that the committee and the external auditors they hire will detect corporate violations. Similarly, Ghafran and O'Sullivan (2013) associated firms' fraudulent behavior with the existence and composition of an audit committee.

Therefore, we controlled for the number of directors on the audit committee (*Comm. Size*), and share of independent members in the committee (*Comm. Independent*). Additionally, we control for the share of members with financial backgrounds (*Mem. Financial*) and share of female representatives (*Mem. Female*).

The results in Table 7 complement the existing results by documenting that the structure and activity of the audit committee are closely correlated with the volume of penalties (panel A) and the number of fraudulent activities (panel B). Column (1) shows that the share of independent members in the audit committee is negatively correlated with the value and number of penalties. The coefficients are significant at the 1% level. Consistent with this result, we found that the number of committee meetings is negatively correlated with corporate misconduct. The coefficient is significant at the 1% level but only when the dependent variable is the number of corporate violations. In contrast, we find that the coefficient of the size of the audit committee is positive and statistically significant at the 1% level. Therefore, the results suggest that, as an audit committee grows in size, its inefficiency increases, leading to additional corporate misconduct. In column (6), we run a horse race analysis and employ all variables simultaneously. We find that all three variables remain significant at least at the 5% level. Thus, we confirm the importance of audit committees in proving corporate misconduct. We demonstrate that audit committees, including independent board members who meet frequently, are more likely to report corporate misconduct. By contrast, we find that large audit committees are positively correlated with corporate misconduct, which we attribute to their inefficiency. Interestingly, we find that the coefficients of committee members with financial backgrounds or female committee members are statistically insignificant for all specifications.

## 5.3. Additional CEO characteristics

In subsequent analyses, we controlled for CEO position and ownership. We controlled for CEO origin using dummy variables. The dummy variable *Founder* takes the value of 1 if the CEO is the founder of the company and 0 otherwise. Similarly, we control for whether the CEO is a descendant of the founding family or an external hire, using the variables *Descendant* and *Hire*, respectively. Agrawal and Chadha (2005) show that the probability of restating earnings is higher when the CEO belongs to a founding family. Moreover, the CEO's influence on the board should be greater when belonging to a founding family. The influence of a CEO on the board can reduce the effectiveness of these mechanisms for monitoring managers.

Thus, on the one hand, we may expect that the greater the influence of a CEO on the board, the less likely the board is to suspect irregularities that a more independent board may have detected. However, family members, particularly founders, identify with the firm and are highly motivated to protect the reputation of the firm and the family (Deephouse & Jaskiewicz, 2013). In fact, Dyer and Whetten (2006) document that family companies are more socially responsible than non-family companies, which they attribute to concerns about reputation and the desire to protect family assets. On the other hand, Agrawal and Chadha (2005) show that the probability of restatement is higher in companies in which the CEO belongs to the founding family.

Similarly, CEO connections with other boards or social connections can influence board effectiveness. The more distracted a CEO, the less likely their attention will be directed toward the identification of corporate violations. However, the counterargument is that busy CEOs are more experienced and connected than single-firm executives, which may benefit the company. Thus, the trade-off between these two effects on board effectiveness is mixed in the literature. We control for the
<table><caption>**Table 7**
Audit committee.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A: Total value of penalties</strong></td></tr><tr><td>Comm. Independent</td><td>-3.641<sup>***</sup><br>(1.239)</td><td></td><td></td><td></td><td></td><td>-3.598<sup>***</sup><br>(1.312)</td></tr><tr><td>Comm. Meetings</td><td></td><td>-0.169<br>(0.129)</td><td></td><td></td><td></td><td>-0.160<br>(0.129)</td></tr><tr><td>Comm. Size</td><td></td><td></td><td>1.001<sup>***</sup><br>(0.223)</td><td></td><td></td><td>0.931<sup>***</sup><br>(0.230)</td></tr><tr><td>Mem. Financial</td><td></td><td></td><td></td><td>0.145<br>(0.192)</td><td></td><td>0.247<br>(0.199)</td></tr><tr><td>Mem. Female</td><td></td><td></td><td></td><td></td><td>-0.212<br>(0.317)</td><td>-0.193<br>(0.321)</td></tr><tr><td>Governance Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>12,446</td><td>12,135</td><td>12,446</td><td>12,446</td><td>12,446</td><td>12,135</td></tr><tr><td>R<sup>2</sup></td><td>0.260</td><td>0.260</td><td>0.260</td><td>0.259</td><td>0.259</td><td>0.262</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.258</td><td>0.258</td><td>0.258</td><td>0.257</td><td>0.257</td><td>0.260</td></tr><tr><td colspan="7"><strong>Panel B: Total number of violations</strong></td></tr><tr><td>Comm. Independent</td><td>-0.368<sup>***</sup><br>(0.133)</td><td></td><td></td><td></td><td></td><td>-0.359<sup>**</sup><br>(0.140)</td></tr><tr><td>Comm. Meetings</td><td></td><td>-0.049<sup>***</sup><br>(0.015)</td><td></td><td></td><td></td><td>-0.048<sup>***</sup><br>(0.015)</td></tr><tr><td>Comm. Size</td><td></td><td></td><td>0.089<sup>***</sup><br>(0.026)</td><td></td><td></td><td>0.077<sup>***</sup><br>(0.027)</td></tr><tr><td>Mem. Financial</td><td></td><td></td><td></td><td>0.008<br>(0.023)</td><td></td><td>0.018<br>(0.024)</td></tr><tr><td>Mem. Female</td><td></td><td></td><td></td><td></td><td>-0.012<br>(0.036)</td><td>-0.009<br>(0.036)</td></tr><tr><td>Governance Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>12,446</td><td>12,135</td><td>12,446</td><td>12,446</td><td>12,446</td><td>12,135</td></tr><tr><td>R<sup>2</sup></td><td>0.275</td><td>0.277</td><td>0.275</td><td>0.275</td><td>0.275</td><td>0.278</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.273</td><td>0.275</td><td>0.274</td><td>0.273</td><td>0.273</td><td>0.276</td></tr></tbody></table>

This table reports the results of additional variables controlling for audit committee. The dependent variable is the log of the total penalty value and the log of the total number of misconducts in Panels A and B. The independent variables are defined in Table A.2 of the Appendix. All specifications include constant, industry, and year fixed effects, as well as the governance and firm-level control variables, as in Table 3A, which are not presented here for brevity. Robust standard errors are in parentheses. ***, **, * indicate statistical significance at the 1, 5, and 10% levels, respectively.

multi-directorship of CEOs using a dummy *Other Boards* that takes the value of 1 if the CEO is on another board of a company and 0 otherwise.

Finally, we control for the (*Voting Rights*) and (*Ownership*). The literature shows that top managers with substantial stakes in their companies are relatively difficult to dismiss, even in poorly performing companies (Denis et al., 1997). We assume that higher voting rights or ownership increases CEO power, which may help cover fraud or at least mitigate its consequences.

Our main regression suggests that a CEO's leadership role may influence companies' fraudulent activities. This is supported by the results in Table 8, whereas in panels A and B, the dependent variables are the total value of penalties and the total number of violations, respectively.

In columns (1) and (2), we find that the coefficient of CEO founders is negatively correlated with corporate misconduct, yet the coefficient is only significant in panel A. In contrast, in columns (3) and (4), we find that the coefficient for CEO descendants is positively correlated with corporate misconduct, yet the coefficients are again only statistically significant in panel A. Thus, the results indicate that companies with CEO founders are less likely to be involved in corporate fraud, while the opposite is true for those with CEO descendants. Columns (5) and (6) show that the coefficient of CEO hire, which indicates an outside member, is positively correlated with corporate fraud. However, the coefficients are insignificant for all the specifications. In general,

the results indicate that founders are more attached to the company and are less likely to engage in fraud.

In addition, the coefficient for a CEO with multiple directorships is negative, indicating that CEOs from less fraudulent companies are more likely to be offered a directorship position in another company. However, the coefficient is statistically insignificant for all specifications. By contrast, we find that a high share of CEO voting rights is positively correlated with corporate fraud. Similarly, we find a positive relationship between corporate misconduct and CEO shareholdings. The coefficients of voting rights and ownership are positive and statistically significant, at least at the 5% level in panel A but only significant for ownership in panel B. These results supplement the existing literature by showing that CEO ownership can weaken a company's internal governance (Weisbach, 1988).

## 5.4. Ownership structure

Previous results have indicated that CEO ownership and voting rights are positively correlated with corporate misconduct. We expand the analysis by controlling for board shareholdings and the shareholdings of executive and independent board members. Moreover, we introduce variables that control for large external and institutional shareholders, as well as the use of dual-class shares.

Denis et al. (1997) show that management ownership structure has an important influence on internal monitoring efforts and that this
<table><caption>**Table 8**
Additional CEO characteristics.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A: Total value of penalties</strong></td></tr><tr><td>Founder</td><td>-0.378***<br>(0.137)</td><td>-0.441***<br>(0.139)</td><td></td><td></td><td></td><td></td></tr><tr><td>Descendant</td><td></td><td></td><td>0.483**<br>(0.231)</td><td>0.482**<br>(0.230)</td><td></td><td></td></tr><tr><td>Hire</td><td></td><td></td><td></td><td></td><td>0.125<br>(0.133)</td><td>0.171<br>(0.134)</td></tr><tr><td>Other boards</td><td>-0.036<br>(0.077)</td><td>-0.034<br>(0.077)</td><td>-0.017<br>(0.077)</td><td>-0.012<br>(0.077)</td><td>-0.029<br>(0.077)</td><td>-0.026<br>(0.077)</td></tr><tr><td>Voting rights</td><td>1.850***<br>(0.608)</td><td></td><td>1.262**<br>(0.588)</td><td></td><td>1.608***<br>(0.617)</td><td></td></tr><tr><td>Ownership</td><td></td><td>3.683***<br>(0.894)</td><td></td><td>2.625***<br>(0.847)</td><td></td><td>3.218***<br>(0.897)</td></tr><tr><td>Governance controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>12,384</td><td>12,384</td><td>12,384</td><td>12,384</td><td>12,384</td><td>12,384</td></tr><tr><td>R<sup>2</sup></td><td>0.260</td><td>0.260</td><td>0.260</td><td>0.259</td><td>0.259</td><td>0.259</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.258</td><td>0.258</td><td>0.258</td><td>0.257</td><td>0.257</td><td>0.257</td></tr><tr><td colspan="7"><strong>Panel B: Total number of violations</strong></td></tr><tr><td>Founder</td><td>-0.009<br>(0.016)</td><td>-0.017<br>(0.016)</td><td></td><td></td><td></td><td></td></tr><tr><td>Descendant</td><td></td><td></td><td>0.027<br>(0.024)</td><td>0.025<br>(0.024)</td><td></td><td></td></tr><tr><td>Hire</td><td></td><td></td><td></td><td></td><td>-0.004<br>(0.015)</td><td>0.003<br>(0.015)</td></tr><tr><td>Other boards</td><td>-0.013<br>(0.009)</td><td>-0.013<br>(0.009)</td><td>-0.013<br>(0.009)</td><td>-0.012<br>(0.009)</td><td>-0.013<br>(0.009)</td><td>-0.013<br>(0.009)</td></tr><tr><td>Voting rights</td><td>0.095<br>(0.061)</td><td></td><td>0.076<br>(0.058)</td><td></td><td>0.080<br>(0.061)</td><td></td></tr><tr><td>Ownership</td><td></td><td>0.276***<br>(0.094)</td><td></td><td>0.234***<br>(0.090)</td><td></td><td>0.252***<br>(0.094)</td></tr><tr><td>Governance controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>12,384</td><td>12,384</td><td>12,384</td><td>12,384</td><td>12,384</td><td>12,384</td></tr><tr><td>R<sup>2</sup></td><td>0.275</td><td>0.275</td><td>0.275</td><td>0.275</td><td>0.275</td><td>0.275</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.273</td><td>0.273</td><td>0.273</td><td>0.273</td><td>0.273</td><td>0.273</td></tr></tbody></table>

This table reports the results of additional variables controlling for further CEO characteristics. The dependent variable is the log of the total penalty value and the log of the total number of misconducts in Panels A and B. The independent variables are defined in Table A.2 of the Appendix. All specifications include constant, industry, and year fixed effects, as well as the governance and firm-level control variables, as in Table 3A, which are not presented here for brevity. Robust standard errors are in parentheses. ***, **, * indicate statistical significance at the 1, 5, and 10% levels, respectively.

influence is mainly due to the effect of the ownership structure on external control threats. Alexander and Cohen (1999) examined the relationship between ownership structure and corporate misconduct, and found that it occurs less frequently among firms in which management has a greater ownership stake. Based on these results, they argue that the ownership structure plays an important role in aligning the hidden actions of top management with shareholder interests. Similarly, Armstrong et al. (2010) find evidence that accounting irregularities occur less frequently in firms in which CEOs have relatively higher levels of equity incentives. In contrast, (Dechow, Sloan, & Sweeney, 1996) find that firms convicted by the SEC for fraudulent financial reporting tend to have a large ownership interest by insiders.

Similarly, Jensen (1993) argued that encouraging outside board members to have substantial equity interests provides better incentives to align their interests with those of shareholders. By contrast, Dechow et al. (1996) document that the ownership of outside directors relative to insiders contributes to poor corporate governance and fraudulent reporting.

Consequently, existing empirical results on the impact of insiders and outsiders on misconduct are ambiguous. Thus, we introduce a

variable that controls for board members' share of ownership in the company Board. We then break this variable up and control for the share of ownership held by (Executive) directors and (Independent) board members.

Another significant internal control mechanism is monitoring by holders of a large block of shares. Large external shareholders have an incentive to replace inefficient managers to improve company performance. They have a general interest in maximizing profits and have sufficient control over a firm's assets to respect their interests (Shleifer & Vishny, 1997). Owing to a sufficiently large stake in the company, major shareholders find it profitable to monitor top management because their return on their own shares suffices to cover their monitoring costs (Holderness & Sheehan, 1988; Shleifer & Vishny, 1986). Moreover, they not only have an incentive to exercise control but also the power to activate it through their voting rights. Similarly, institutional shareholders have an incentive to exercise control and prevent the management from engaging in fraudulent behavior. Institutional investors can influence corporate decisions by playing an active role in decision-making processes. Chung et al. (2002) document that
<table><caption>**Table 9**
Ownership and cash flow rights.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A: Total value of fines</strong></td></tr><tr><td>Board</td><td>1.448***<br>(0.485)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Executive</td><td></td><td>1.591**<br>(0.624)</td><td></td><td></td><td></td><td>0.738<br>(0.639)</td></tr><tr><td>Independent</td><td></td><td></td><td>2.612*<br>(1.450)</td><td></td><td></td><td>2.348<br>(1.454)</td></tr><tr><td>Blockholders</td><td></td><td></td><td></td><td>-2.457***<br>(0.492)</td><td></td><td>-3.072***<br>(0.516)</td></tr><tr><td>Institutional</td><td></td><td></td><td></td><td></td><td>-1.254***<br>(0.304)</td><td>-1.611***<br>(0.322)</td></tr><tr><td>Dual Class</td><td>-0.531***<br>(0.164)</td><td>-0.527***<br>(0.164)</td><td>-0.485***<br>(0.163)</td><td>-0.373**<br>(0.163)</td><td>-0.507***<br>(0.162)</td><td>-0.415**<br>(0.164)</td></tr><tr><td>Governance Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>14,184</td><td>14,184</td><td>14,184</td><td>14,184</td><td>14,184</td><td>14,184</td></tr><tr><td>R<sup>2</sup></td><td>0.267</td><td>0.267</td><td>0.266</td><td>0.267</td><td>0.267</td><td>0.269</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.265</td><td>0.265</td><td>0.265</td><td>0.266</td><td>0.265</td><td>0.267</td></tr><tr><td colspan="7"><strong>Panel B: Total number of violations</strong></td></tr><tr><td>Board</td><td>0.086*<br>(0.050)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Executive</td><td></td><td>0.064<br>(0.064)</td><td></td><td></td><td></td><td>-0.063<br>(0.066)</td></tr><tr><td>Independent</td><td></td><td></td><td>0.334**<br>(0.157)</td><td></td><td></td><td>0.255<br>(0.158)</td></tr><tr><td>Blockholders</td><td></td><td></td><td></td><td>-0.206***<br>(0.063)</td><td></td><td>-0.328***<br>(0.066)</td></tr><tr><td>Institutional</td><td></td><td></td><td></td><td></td><td>-0.245***<br>(0.036)</td><td>-0.292***<br>(0.038)</td></tr><tr><td>Dual Class</td><td>-0.079***<br>(0.019)</td><td>-0.078***<br>(0.019)</td><td>-0.077***<br>(0.019)</td><td>-0.067***<br>(0.019)</td><td>-0.082***<br>(0.019)</td><td>-0.068***<br>(0.019)</td></tr><tr><td>Governance Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>14,184</td><td>14,184</td><td>14,184</td><td>14,184</td><td>14,184</td><td>14,184</td></tr><tr><td>R<sup>2</sup></td><td>0.287</td><td>0.287</td><td>0.287</td><td>0.287</td><td>0.289</td><td>0.290</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.285</td><td>0.285</td><td>0.285</td><td>0.285</td><td>0.287</td><td>0.288</td></tr></tbody></table>

This table reports the results of additional variables controlling for firms' ownership and cash flow rights. The dependent variable is the log of the total penalty value and the log of the total number of misconducts in Panels A and B. The independent variables are defined in Table A.2 of the Appendix. All specifications include constant, industry, and year fixed effects, as well as the governance and firm-level control variables, as in Table 3A, which are not presented here for brevity. Robust standard errors are in parentheses. ***, **, * indicate statistical significance at the 1, 5, and 10% levels, respectively.

institutional investors monitor and constrain corporate managers' self-serving behavior. They report that the presence of large institutional shareholdings inhibits managers from manipulating reported profits toward the desired level or range. Therefore, they argued that institutional investors contribute to improving corporate governance. Thus, we control for the shares of external shareholders, *Blockholders*, and *Institutional*, respectively.

Finally, we introduce a dummy variable, *Dual Class*, which takes the value of 1 if the company has dual-class shares, and 0 otherwise. Companies with dual-class share structures are usually those in which a separate class of stock offers its holders superior voting rights over common shareholders. The holders of superior shares are typically the company founder and/or top executives who maintain company control even as their economic stake in the business may diminish. The divergence between voting and cash flow rights in dual-class firms exacerbates the agency conflicts between managers and external investors. Gompers, Ishii, and Metrick (2010) document that firm value is positively associated with insider cash flow rights, and negatively associated with insider voting rights. Furthermore, Masulis, Wang, and Xie (2009) show that large excess control rights in dual-class firms

lead to greater private benefits of control and reduced market value for external shareholders. Therefore, we assume that managers of dual-class share companies are more prone to violating laws because their governance structures are weaker.

We present the results in Table 9, where in panels A and B, the dependent variables are the total penalties and total number of violations, respectively. Consistent with our previous results, we find that board member ownership is positively correlated with corporate fraud. The coefficient on board ownership in column (1) is positive and significant in both specifications. Our analysis shows that this result can be explained by the shares of both executive and independent board members. The coefficients for both board member groups are positive. The coefficients are statistically significant at the 5% level, except for executive board members in panel B. Therefore, our results confirm that board members' independence and ownership are not strongly protected against corporate fraud.

By contrast, we find that the ownership of large external shareholders and/or institutional shareholders is negatively correlated with corporate fraud. The coefficients of blockholders and institutional shareholders are negative and statistically significant at the 1% level for
<table><caption>**Table A.1**
Frequency table by year and industry.</caption><thead><tr><th>Industry</th><th>2007</th><th>2008</th><th>2009</th><th>2010</th><th>2011</th><th>2012</th><th>2013</th><th>2014</th><th>2015</th><th>2016</th><th>2017</th><th>2018</th><th>2019</th><th>Total</th></tr></thead><tbody><tr><td>Basic Materials</td><td>26</td><td>28</td><td>28</td><td>31</td><td>80</td><td>83</td><td>81</td><td>83</td><td>76</td><td>78</td><td>75</td><td>110</td><td>104</td><td>883</td></tr><tr><td>Consumer Goods</td><td>57</td><td>59</td><td>57</td><td>60</td><td>145</td><td>146</td><td>149</td><td>150</td><td>145</td><td>151</td><td>153</td><td>260</td><td>257</td><td>1789</td></tr><tr><td>Consumer Services</td><td>69</td><td>74</td><td>74</td><td>82</td><td>206</td><td>211</td><td>205</td><td>198</td><td>203</td><td>213</td><td>212</td><td>356</td><td>350</td><td>2453</td></tr><tr><td>Health Care</td><td>49</td><td>48</td><td>48</td><td>52</td><td>132</td><td>134</td><td>133</td><td>134</td><td>136</td><td>136</td><td>137</td><td>424</td><td>474</td><td>2037</td></tr><tr><td>Industrials</td><td>81</td><td>89</td><td>87</td><td>86</td><td>291</td><td>304</td><td>309</td><td>312</td><td>309</td><td>304</td><td>302</td><td>527</td><td>510</td><td>3511</td></tr><tr><td>Oil & Gas</td><td>38</td><td>58</td><td>56</td><td>57</td><td>93</td><td>97</td><td>99</td><td>103</td><td>100</td><td>94</td><td>98</td><td>171</td><td>159</td><td>1223</td></tr><tr><td>Technology</td><td>63</td><td>67</td><td>66</td><td>72</td><td>193</td><td>201</td><td>192</td><td>178</td><td>170</td><td>161</td><td>162</td><td>339</td><td>333</td><td>2197</td></tr><tr><td>Telecommunications</td><td>8</td><td>9</td><td>9</td><td>10</td><td>18</td><td>19</td><td>19</td><td>20</td><td>19</td><td>19</td><td>17</td><td>28</td><td>28</td><td>223</td></tr><tr><td>Utilities</td><td>34</td><td>39</td><td>39</td><td>39</td><td>66</td><td>63</td><td>61</td><td>59</td><td>59</td><td>55</td><td>51</td><td>76</td><td>72</td><td>713</td></tr></tbody><tfoot><tr><td>Total</td><td>425</td><td>471</td><td>464</td><td>489</td><td>1224</td><td>1258</td><td>1248</td><td>1237</td><td>1217</td><td>1211</td><td>1207</td><td>2291</td><td>2287</td><td>15,029</td></tr></tfoot></table>

This table reports the distribution across the industries and years for our sample of 15,029 firm-year observations.

all specifications. Our results are consistent with those of Chung et al. (2002), who find that large institutional ownership is associated with less earnings management. The results show that large shareholders and institutional investors have the incentive and power to impose effective management monitoring (Shleifer & Vishny, 1997), and consequently, reduce corporate misconduct. However, another explanation could be that institutional shareholders prefer to invest in companies with good governance and a low level of corporate misconduct. Thus, ownership by blockholders and institutional shareholders might be the result of good governance by companies, which in turn leads to lower misconduct.

On the one hand, contrary to our expectations, we find that the coefficient of the dual-class dummy variables is negative and statistically significant at the 1% level in all specifications. Although often depicted as indicating poor governance characteristics, the results show that the use of dual-class shares is negatively correlated with corporate misconduct. On the other hand, a closer analysis of our data shows that the dual-class shares in our sample are in companies where the founder's descendants and their affiliates hold at least 5% of the vote in the company and are represented on the board of directors. Hence, in our dataset, companies with dual-class shares represent a subsample of family firms. Thus, the results are in line with our previous findings and show that companies managed or controlled by the founder descend are more likely to be involved in misconduct.

Column (6) shows the results of the horse race analysis. The coefficients of the variables for blockholders, institutional investors, and dual-class shares are negative and statistically significant. These results confirm that ethical companies are more likely to have large external shareholders, including institutional shareholders. By contrast, we find that the existence of dual-class shares in a company, which in our dataset is a subsample of family firms, is positively correlated with corporate misconduct.

We further analyze the impact of ownership on the company and repeated this analysis using voting rights instead of ownership rights. We present the results in the Appendix of Table A.3 and find that employing an alternative ownership variable does not change our main results. The coefficients of the variables in Table A.3 have almost the same signs and statistical significance as shown in Table 9. Moreover, a horse race analysis using voting rights reconfirms the importance of external shareholders, including institutional shareholders, and the use of dual-class shares to explain the level of corporate misconduct.

## 6. Conclusion

Over the last two decades, there has been a comprehensive overhaul of corporate governance regulations in the United States and worldwide. Reforms were initiated after large scandals and were aimed at preventing future fraud. Despite legal and public efforts to improve corporate governance, corporate misconduct in the US has not decreased

in recent decades. Our results show that one reason for this is that not all governance mechanisms work as expected.

Although we show that board size and composition are important for fraud prevention, we do not find that improving board independence reduces corporate misconduct. We find that gender diversity may reduce corporate misconduct, but only for female directors as opposed to female CEOs, and not for all types of offenses. We further demonstrate that audit committees and their activities are important in fraud prevention. By contrast, we document that the strong position of the CEO, proxied by their dual position on the board and voting or ownership rights, and board ownership are positively correlated with corporate misconduct. By contrast, we find that founder CEO, large external blockholders, and institutional shareholders prevent companies from violating laws.

Our findings are important for understanding the different mechanisms of internal governance in corporate misconduct, a relevant topic in the ongoing debate on how to further improve corporate governance and mitigate instances of corporate misconduct.

## Funding

This work was supported by the National Science Center, Poland, under grant OPUS 17 no. 2019/33/B/HS4/00369.

## CRediT authorship contribution statement

Nicolas Eugster: Conceptualization, Methodology, Formal analysis, Writing – review & editing. Oskar Kowalewski: Conceptualization, Methodology, Formal analysis, Writing – review & editing. Piotr Śpiewanowski: Conceptualization, Methodology, Formal analysis, Writing – review & editing.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

The authors do not have permission to share data.

## Appendix

See Tables A.1 to A.7.
<table><caption>**Table A.2**

Descriptive statistics of additional variables.</caption><thead><tr><th></th><th>Mean</th><th>p25</th><th>p50</th><th>p75</th><th>Obs.</th></tr></thead><tbody><tr><td><strong>Board</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Meetings</td><td>7.831</td><td>5.000</td><td>7.000</td><td>9.000</td><td>14,823</td></tr><tr><td>Attendance</td><td>0.747</td><td>0.750</td><td>0.750</td><td>0.750</td><td>15,000</td></tr><tr><td>Executive Comm.</td><td>0.214</td><td>0.000</td><td>0.000</td><td>0.000</td><td>13,169</td></tr><tr><td>CSR Comm.</td><td>0.098</td><td>0.000</td><td>0.000</td><td>0.000</td><td>13,169</td></tr><tr><td><strong>Audit Committee</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Comm. Meetings</td><td>7.401</td><td>5.000</td><td>7.000</td><td>9.000</td><td>12,841</td></tr><tr><td>Comm. Size</td><td>3.828</td><td>3.000</td><td>4.000</td><td>4.000</td><td>13,175</td></tr><tr><td>Comm. Independent</td><td>0.995</td><td>1.000</td><td>1.000</td><td>1.000</td><td>13,175</td></tr><tr><td>Mem. Financial</td><td>0.787</td><td>0.667</td><td>0.800</td><td>1.000</td><td>13,175</td></tr><tr><td>Mem. Female</td><td>0.176</td><td>0.000</td><td>0.200</td><td>0.333</td><td>13,175</td></tr><tr><td><strong>Additional CEO characteristics</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Founder</td><td>0.123</td><td>0.000</td><td>0.000</td><td>0.000</td><td>14,863</td></tr><tr><td>Descendant</td><td>0.043</td><td>0.000</td><td>0.000</td><td>0.000</td><td>14,863</td></tr><tr><td>Hire</td><td>0.833</td><td>1.000</td><td>1.000</td><td>1.000</td><td>14,863</td></tr><tr><td>Other boards</td><td>0.386</td><td>0.000</td><td>0.000</td><td>1.000</td><td>13,169</td></tr><tr><td>Voting rights</td><td>0.030</td><td>0.002</td><td>0.006</td><td>0.018</td><td>15,025</td></tr><tr><td>Ownership</td><td>0.024</td><td>0.002</td><td>0.006</td><td>0.018</td><td>15,015</td></tr><tr><td><strong>Ownership and cash flow rights</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Board Own.</td><td>0.054</td><td>0.006</td><td>0.017</td><td>0.053</td><td>15,015</td></tr><tr><td>Executive Own.</td><td>0.035</td><td>0.002</td><td>0.008</td><td>0.026</td><td>15,015</td></tr><tr><td>Independent Own.</td><td>0.012</td><td>0.001</td><td>0.003</td><td>0.009</td><td>15,015</td></tr><tr><td>Blockholders Own.</td><td>0.029</td><td>0.000</td><td>0.000</td><td>0.000</td><td>15,015</td></tr><tr><td>Institutional Own.</td><td>0.256</td><td>0.153</td><td>0.250</td><td>0.351</td><td>15,015</td></tr><tr><td>Dual Class</td><td>0.094</td><td>0.000</td><td>0.000</td><td>0.000</td><td>15,029</td></tr></tbody></table>

The table provides the means and the 25th, 50th, and 75th percentiles of the additional variables used in the study. In the Panel Board, Meetings refers to the number of board meetings, Attendance is the average percentage of meeting attendance of board members, and Executive Comm. equals 1 if the company has an executive committee and 0 otherwise; CSR Comm equals 1 if the company has a CSR committee and 0 otherwise. In the Panel Audit Committee, Comm. Meetings refers to the number of committee meetings; Comm. Size is the number of committee members; Comm. Independent is the ratio of independent committee members to its size; Mem. Financial is the number of financial experts participating in the committee; Mem. Female is the number of female directors on the committee. In the Panel Additional CEO characteristics: Founder equals 1 when the CEO is a founder and 0 otherwise; Descendant equals 1 when the CEO is a founder descendant and 0 otherwise; Hire equals 1 when the CEO is an outsider and 0 otherwise; Other boards equals the number of other public companies that the CEO is a member of. Voting rights and ownership are the percentage of voting rights and shares held by the CEO, respectively. In Panel Ownership and cash flow rights, Board is the percentage of shares held by board members. The Executive variable is the percentage of shares held by the executive directors of the board. Independent is the percentage of shares held by independent directors. Blockholders refers to the percentage of shares held by the external blockholders. Institutional is the percentage of shares held by the institutional investors. Dual Class equals 1 if a company has more than one share class and 0 otherwise.

<table><caption>**Table A.3**

Corporate governance index and the total value of penalties.</caption><thead><tr><th></th><th>Value<sub>t</sub></th><th>Value<sub>t+1</sub></th><th>Value<sub>t+2</sub></th><th>Value<sub>t+3</sub></th></tr></thead><tbody><tr><td>ICG</td><td>0.286***<br>(0.045)</td><td>0.297***<br>(0.044)</td><td>0.241***<br>(0.050)</td><td>0.177***<br>(0.059)</td></tr><tr><td>Big 4</td><td>-0.834***<br>(0.121)</td><td>-0.846***<br>(0.119)</td><td>-0.731***<br>(0.140)</td><td>-0.712***<br>(0.180)</td></tr><tr><td>CEO Woman</td><td>-0.203<br>(0.207)</td><td>-0.039<br>(0.210)</td><td>-0.094<br>(0.239)</td><td>-0.179<br>(0.279)</td></tr><tr><td>CEO Age</td><td>0.945***<br>(0.349)</td><td>0.929***<br>(0.351)</td><td>0.974**<br>(0.401)</td><td>1.024**<br>(0.475)</td></tr><tr><td>CEO Tenure</td><td>-0.023<br>(0.050)</td><td>-0.001<br>(0.050)</td><td>0.050<br>(0.056)</td><td>0.162**<br>(0.066)</td></tr><tr><td>Sales</td><td>-0.256**<br>(0.105)</td><td>-0.099<br>(0.100)</td><td>-0.034<br>(0.140)</td><td>0.037<br>(0.218)</td></tr><tr><td>ROA</td><td>-2.366***<br>(0.230)</td><td>-2.676***<br>(0.227)</td><td>-2.252***<br>(0.304)</td><td>-1.180**<br>(0.590)</td></tr><tr><td>Tobins q</td><td>-0.014<br>(0.027)</td><td>0.027<br>(0.026)</td><td>0.052<br>(0.033)</td><td>0.020<br>(0.045)</td></tr><tr><td>Leverage</td><td>-1.419***<br>(0.231)</td><td>-1.442***<br>(0.230)</td><td>-1.793***<br>(0.266)</td><td>-2.037***<br>(0.325)</td></tr><tr><td>Size</td><td>1.588***<br>(0.037)</td><td>1.559***<br>(0.037)</td><td>1.614***<br>(0.041)</td><td>1.724***<br>(0.047)</td></tr><tr><td>Constant</td><td>-19.555***<br>(1.503)</td><td>-18.948***<br>(1.510)</td><td>-20.204***<br>(1.711)</td><td>-22.053***<br>(2.000)</td></tr><tr><td>Observations</td><td>14,194</td><td>14,194</td><td>12,011</td><td>9,848</td></tr><tr><td>R<sup>2</sup></td><td>0.262</td><td>0.260</td><td>0.250</td><td>0.239</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.260</td><td>0.259</td><td>0.248</td><td>0.237</td></tr></tbody></table>

This table reports the results of the internal corporate governance index. The dependent variable is the log of the total penalty value. Table 2 presents the independent variables. All specifications include constant, industry, and year fixed effects. Robust standard errors are in parentheses. ***, **, * indicate statistical significance at the 1, 5, and 10% levels, respectively.

<table><caption>**Table A.4**

Corporate governance index and the number of company violations.</caption><thead><tr><th></th><th>Number<sub>t</sub></th><th>Number<sub>t+1</sub></th><th>Number<sub>t+2</sub></th><th>Number<sub>t+3</sub></th></tr></thead><tbody><tr><td>ICG</td><td>0.040***<br>(0.005)</td><td>0.039***<br>(0.005)</td><td>0.033***<br>(0.006)</td><td>0.028***<br>(0.007)</td></tr><tr><td>Big 4</td><td>-0.140***<br>(0.014)</td><td>-0.139***<br>(0.013)</td><td>-0.123***<br>(0.015)</td><td>-0.115***<br>(0.019)</td></tr><tr><td>CEO Woman</td><td>-0.065***<br>(0.023)</td><td>-0.047**<br>(0.023)</td><td>-0.038<br>(0.027)</td><td>-0.046<br>(0.032)</td></tr><tr><td>CEO Age</td><td>0.244***<br>(0.041)</td><td>0.244***<br>(0.040)</td><td>0.247***<br>(0.046)</td><td>0.259***<br>(0.054)</td></tr><tr><td>CEO Tenure</td><td>-0.016**<br>(0.006)</td><td>-0.012**<br>(0.006)</td><td>-0.006<br>(0.007)</td><td>0.008<br>(0.008)</td></tr><tr><td>Sales</td><td>-0.007<br>(0.013)</td><td>0.009<br>(0.013)</td><td>0.023<br>(0.017)</td><td>0.030<br>(0.026)</td></tr><tr><td>ROA</td><td>-0.447***<br>(0.031)</td><td>-0.463***<br>(0.031)</td><td>-0.443***<br>(0.042)</td><td>-0.259***<br>(0.078)</td></tr><tr><td>Tobins q</td><td>0.004<br>(0.003)</td><td>0.008***<br>(0.003)</td><td>0.012***<br>(0.004)</td><td>0.007<br>(0.005)</td></tr><tr><td>Leverage</td><td>-0.216***<br>(0.028)</td><td>-0.218***<br>(0.027)</td><td>-0.252***<br>(0.031)</td><td>-0.287***<br>(0.039)</td></tr><tr><td>Size</td><td>0.207***<br>(0.005)</td><td>0.198***<br>(0.005)</td><td>0.206***<br>(0.006)</td><td>0.221***<br>(0.006)</td></tr><tr><td>Constant</td><td>-2.930***<br>(0.186)</td><td>-2.791***<br>(0.183)</td><td>-2.950***<br>(0.206)</td><td>-3.185***<br>(0.240)</td></tr><tr><td>Observations</td><td>14,194</td><td>14,194</td><td>12,011</td><td>9,848</td></tr><tr><td>R<sup>2</sup></td><td>0.281</td><td>0.277</td><td>0.271</td><td>0.266</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.279</td><td>0.276</td><td>0.270</td><td>0.264</td></tr></tbody></table>

This table reports the results of the internal corporate governance index. The dependent variable is the log of the number of misconducts. Table 2 presents the independent variables. All specifications include constant, industry, and year fixed effects. Robust standard errors are in parentheses. ***, **, * indicate statistical significance at the 1, 5, and 10% levels, respectively.
<table><caption>**Table A.5**
Corporate governance index and the total value of penalties by offense group.</caption><thead><tr><th></th><th>Env</th><th>Safe</th><th>Empl</th><th>Comp</th><th>Cons</th><th>Fin</th><th>Gov</th><th>Health</th></tr></thead><tbody><tr><td>ICG</td><td>0.163***<br>(0.029)</td><td>0.227***<br>(0.032)</td><td>0.174***<br>(0.031)</td><td>0.021*<br>(0.012)</td><td>-0.005<br>(0.020)</td><td>-0.010<br>(0.011)</td><td>0.048**<br>(0.019)</td><td>0.017*<br>(0.010)</td></tr><tr><td>Big 4</td><td>-0.497***<br>(0.070)</td><td>-0.348***<br>(0.088)</td><td>-0.533***<br>(0.069)</td><td>-0.154***<br>(0.031)</td><td>-0.334***<br>(0.039)</td><td>-0.029<br>(0.023)</td><td>-0.260***<br>(0.035)</td><td>-0.172***<br>(0.026)</td></tr><tr><td>CEO Woman</td><td>-0.387***<br>(0.136)</td><td>-0.255*<br>(0.151)</td><td>-0.206<br>(0.144)</td><td>-0.117**<br>(0.047)</td><td>0.040<br>(0.103)</td><td>0.091<br>(0.068)</td><td>0.157<br>(0.104)</td><td>-0.050<br>(0.038)</td></tr><tr><td>CEO Age</td><td>1.109***<br>(0.213)</td><td>1.036***<br>(0.245)</td><td>0.461*<br>(0.237)</td><td>-0.140*<br>(0.084)</td><td>-0.251<br>(0.173)</td><td>-0.037<br>(0.100)</td><td>0.251*<br>(0.137)</td><td>0.086<br>(0.077)</td></tr><tr><td>CEO Tenure</td><td>-0.082**<br>(0.034)</td><td>-0.002<br>(0.037)</td><td>0.018<br>(0.036)</td><td>0.015<br>(0.014)</td><td>0.016<br>(0.022)</td><td>-0.018<br>(0.014)</td><td>-0.006<br>(0.021)</td><td>-0.022*<br>(0.012)</td></tr><tr><td>Sales</td><td>-0.043<br>(0.070)</td><td>0.157**<br>(0.075)</td><td>0.039<br>(0.064)</td><td>-0.055**<br>(0.022)</td><td>0.040<br>(0.041)</td><td>-0.000<br>(0.021)</td><td>-0.053<br>(0.042)</td><td>0.011<br>(0.022)</td></tr><tr><td>ROA</td><td>-2.152***<br>(0.149)</td><td>-1.993***<br>(0.160)</td><td>-1.490***<br>(0.142)</td><td>-0.199***<br>(0.062)</td><td>-0.720***<br>(0.101)</td><td>-0.178***<br>(0.058)</td><td>-0.074<br>(0.104)</td><td>0.033<br>(0.049)</td></tr><tr><td>Tobins q</td><td>0.083***<br>(0.014)</td><td>0.052***<br>(0.018)</td><td>0.058***<br>(0.018)</td><td>-0.004<br>(0.005)</td><td>-0.021**<br>(0.010)</td><td>0.006<br>(0.006)</td><td>-0.027**<br>(0.011)</td><td>-0.021***<br>(0.005)</td></tr><tr><td>Leverage</td><td>-0.713***<br>(0.141)</td><td>-0.922***<br>(0.165)</td><td>-0.829***<br>(0.163)</td><td>-0.203***<br>(0.063)</td><td>-0.791***<br>(0.098)</td><td>-0.185***<br>(0.058)</td><td>-0.158<br>(0.099)</td><td>-0.267***<br>(0.052)</td></tr><tr><td>Size</td><td>0.721***<br>(0.026)</td><td>0.783***<br>(0.027)</td><td>0.598***<br>(0.029)</td><td>0.129***<br>(0.015)</td><td>0.362***<br>(0.023)</td><td>0.077***<br>(0.012)</td><td>0.230***<br>(0.020)</td><td>0.113***<br>(0.014)</td></tr><tr><td>Constant</td><td>-9.808***<br>(0.993)</td><td>-11.110***<br>(1.090)</td><td>-8.976***<br>(1.049)</td><td>-0.902**<br>(0.377)</td><td>-3.913***<br>(0.733)</td><td>-0.762*<br>(0.424)</td><td>-3.993***<br>(0.650)</td><td>-1.723***<br>(0.379)</td></tr><tr><td>Observations</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td></tr><tr><td>R<sup>2</sup></td><td>0.187</td><td>0.190</td><td>0.111</td><td>0.024</td><td>0.094</td><td>0.011</td><td>0.052</td><td>0.043</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.185</td><td>0.188</td><td>0.109</td><td>0.021</td><td>0.092</td><td>0.009</td><td>0.050</td><td>0.041</td></tr></tbody></table>

This table reports the results of the internal corporate governance index and diverse corporate violations. The dependent variable is the log of the total penalty value for the main violation categories: environment, safety, employment, competition, consumer, financial, government contracting, and healthcare. Table 2 presents the independent variables. All specifications include constant, industry, and year fixed effects, as well as firm-level control variables, as in Table 3A, which are not presented here for brevity. Robust standard errors are in parentheses. ***, **, * indicate statistical significance at the 1, 5, and 10% levels, respectively.

<table><caption>**Table A.6**
Corporate governance index and the number of company violations by offense group.</caption><thead><tr><th></th><th>Env</th><th>Safe</th><th>Empl</th><th>Comp</th><th>Cons</th><th>Fin</th><th>Gov</th><th>Health</th></tr></thead><tbody><tr><td>ICG</td><td>0.017***<br>(0.003)</td><td>0.027***<br>(0.004)</td><td>0.013***<br>(0.002)</td><td>0.001*<br>(0.001)</td><td>-0.002<br>(0.002)</td><td>-0.001<br>(0.001)</td><td>0.002**<br>(0.001)</td><td>0.001<br>(0.001)</td></tr><tr><td>Big 4</td><td>-0.051***<br>(0.006)</td><td>-0.073***<br>(0.011)</td><td>-0.043***<br>(0.005)</td><td>-0.008***<br>(0.002)</td><td>-0.031***<br>(0.004)</td><td>-0.001<br>(0.001)</td><td>-0.015***<br>(0.002)</td><td>-0.009***<br>(0.001)</td></tr><tr><td>CEO Woman</td><td>-0.039***<br>(0.013)</td><td>-0.055***<br>(0.017)</td><td>-0.015<br>(0.010)</td><td>-0.006**<br>(0.003)</td><td>0.018<br>(0.012)</td><td>0.004<br>(0.003)</td><td>0.006<br>(0.005)</td><td>-0.003<br>(0.002)</td></tr><tr><td>CEO Age</td><td>0.120***<br>(0.021)</td><td>0.206***<br>(0.032)</td><td>0.029*<br>(0.017)</td><td>-0.007<br>(0.005)</td><td>-0.009<br>(0.014)</td><td>-0.002<br>(0.005)</td><td>0.019**<br>(0.008)</td><td>0.007*<br>(0.004)</td></tr><tr><td>CEO Tenure</td><td>-0.007**<br>(0.003)</td><td>-0.010**<br>(0.005)</td><td>0.001<br>(0.003)</td><td>0.001<br>(0.001)</td><td>-0.000<br>(0.002)</td><td>-0.001<br>(0.001)</td><td>-0.001<br>(0.001)</td><td>-0.001**<br>(0.001)</td></tr><tr><td>Sales</td><td>-0.006<br>(0.008)</td><td>0.022**<br>(0.009)</td><td>0.005<br>(0.005)</td><td>-0.003***<br>(0.001)</td><td>0.005<br>(0.005)</td><td>0.000<br>(0.001)</td><td>-0.001<br>(0.003)</td><td>0.001<br>(0.001)</td></tr><tr><td>ROA</td><td>-0.217***<br>(0.016)</td><td>-0.341***<br>(0.026)</td><td>-0.117***<br>(0.010)</td><td>-0.012***<br>(0.003)</td><td>-0.042***<br>(0.009)</td><td>-0.009***<br>(0.003)</td><td>-0.005<br>(0.005)</td><td>0.002<br>(0.003)</td></tr><tr><td>Tobins q</td><td>0.009***<br>(0.001)</td><td>0.011***<br>(0.002)</td><td>0.004***<br>(0.001)</td><td>-0.000<br>(0.000)</td><td>-0.005***<br>(0.001)</td><td>0.000<br>(0.000)</td><td>-0.002***<br>(0.001)</td><td>-0.001***<br>(0.000)</td></tr><tr><td>Leverage</td><td>-0.081***<br>(0.015)</td><td>-0.115***<br>(0.022)</td><td>-0.059***<br>(0.012)</td><td>-0.011***<br>(0.003)</td><td>-0.080***<br>(0.008)</td><td>-0.011***<br>(0.003)</td><td>-0.008<br>(0.006)</td><td>-0.015***<br>(0.003)</td></tr><tr><td>Size</td><td>0.072***<br>(0.003)</td><td>0.110***<br>(0.004)</td><td>0.043***<br>(0.002)</td><td>0.007***<br>(0.001)</td><td>0.033***<br>(0.002)</td><td>0.004***<br>(0.001)</td><td>0.013***<br>(0.001)</td><td>0.006***<br>(0.001)</td></tr><tr><td>Constant</td><td>-1.000***<br>(0.100)</td><td>-1.745***<br>(0.149)</td><td>-0.609***<br>(0.075)</td><td>-0.042**<br>(0.021)</td><td>-0.408***<br>(0.070)</td><td>-0.038*<br>(0.022)</td><td>-0.236***<br>(0.040)</td><td>-0.106***<br>(0.022)</td></tr><tr><td>Observations</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td><td>14,194</td></tr><tr><td>R<sup>2</sup></td><td>0.187</td><td>0.187</td><td>0.114</td><td>0.022</td><td>0.081</td><td>0.011</td><td>0.050</td><td>0.039</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.185</td><td>0.185</td><td>0.112</td><td>0.020</td><td>0.079</td><td>0.009</td><td>0.048</td><td>0.037</td></tr></tbody></table>

This table reports the results of the internal corporate governance index and diverse corporate violations. The dependent variable is the log of total number of misconducts by the main violation categories: environment, safety, employment, competition, consumer, financial, government contracting and healthcare. Table 2 presents the independent variables. All specifications include constant, industry, and year fixed effects, as well as firm-level control variables, as in Table 3A, which are not presented here for brevity. Robust standard errors are in parentheses. ***, **, * indicate statistical significance at the 1, 5, and 10% levels, respectively.
<table><caption>**Table A.7**
Ownership and voting rights.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A: Total value of penalties</strong></td></tr><tr><td>Board</td><td>1.142***<br>(0.399)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Executive</td><td></td><td>1.504***<br>(0.474)</td><td></td><td></td><td></td><td>0.372<br>(0.492)</td></tr><tr><td>Independent</td><td></td><td></td><td>0.790<br>(1.313)</td><td></td><td></td><td>0.713<br>(1.313)</td></tr><tr><td>Blockholders</td><td></td><td></td><td></td><td>-2.694***<br>(0.396)</td><td></td><td>-3.092***<br>(0.421)</td></tr><tr><td>Institutional</td><td></td><td></td><td></td><td></td><td>-1.157***<br>(0.304)</td><td>-1.657***<br>(0.323)</td></tr><tr><td>Dual Class</td><td>-0.640***<br>(0.173)</td><td>-0.651***<br>(0.170)</td><td>-0.484***<br>(0.163)</td><td>-0.170<br>(0.167)</td><td>-0.568***<br>(0.164)</td><td>-0.300*<br>(0.177)</td></tr><tr><td>Governance Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>14,192</td><td>14,192</td><td>14,192</td><td>14,192</td><td>14,192</td><td>14,192</td></tr><tr><td>R<sup>2</sup></td><td>0.267</td><td>0.267</td><td>0.266</td><td>0.268</td><td>0.267</td><td>0.270</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.265</td><td>0.265</td><td>0.264</td><td>0.267</td><td>0.265</td><td>0.268</td></tr><tr><td colspan="7"><strong>Panel B: Total number of violations</strong></td></tr><tr><td>Board</td><td>0.025<br>(0.040)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Executive</td><td></td><td>0.031<br>(0.046)</td><td></td><td></td><td></td><td>-0.124**<br>(0.049)</td></tr><tr><td>Independent</td><td></td><td></td><td>0.132<br>(0.142)</td><td></td><td></td><td>0.067<br>(0.141)</td></tr><tr><td>Blockholders</td><td></td><td></td><td></td><td>-0.234***<br>(0.051)</td><td></td><td>-0.347***<br>(0.055)</td></tr><tr><td>Institutional</td><td></td><td></td><td></td><td></td><td>-0.242***<br>(0.036)</td><td>-0.316***<br>(0.039)</td></tr><tr><td>Dual Class</td><td>-0.079***<br>(0.021)</td><td>-0.079***<br>(0.020)</td><td>-0.076***<br>(0.019)</td><td>-0.049**<br>(0.020)</td><td>-0.094***<br>(0.019)</td><td>-0.046**<br>(0.021)</td></tr><tr><td>Governance Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>14,192</td><td>14,192</td><td>14,192</td><td>14,192</td><td>14,192</td><td>14,192</td></tr><tr><td>R<sup>2</sup></td><td>0.287</td><td>0.287</td><td>0.287</td><td>0.288</td><td>0.288</td><td>0.291</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.285</td><td>0.285</td><td>0.285</td><td>0.286</td><td>0.287</td><td>0.289</td></tr></tbody></table>

This table reports the results of an alternative variable controlling for firm ownership using voting rights. The dependent variable is the log of the total penalty value and the log of the total number of misconducts in Panels A and B. The independent variables are defined in Table A.2 of the Appendix. All specifications include constant, industry, and year fixed effects, as well as the governance and firm-level control variables, as in Table 3A, which are not presented here for brevity. Robust standard errors are in parentheses. ***, **, * indicate statistical significance at the 1, 5, and 10% levels, respectively.

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