The Effect of Internal Control on Corporate Corruption: Evidence from China

Weili Ge Michael G. Foster School of Business

University of Washington Seattle, WA 98195 geweili@uw.edu

Zining Li

Cox School of Business Southern Methodist University

Dallas, TX 75275 zli@smu.edu

Qiliang Liu School of Management

Huazhong University of Science and Technology Wuhan, China, 430074

lql533@163.com

Sarah McVay Michael G. Foster School of Business

University of Washington Seattle, WA 98195 smcvay@uw.edu

November 4, 2014

                                                             Ge and McVay would like to thank the Moss Adams Professorship and Glen & Lucille Legoe Professorship, respectively, at the University of Washington for financial support. Liu acknowledges the financial support from the National Natural Science Foundation of China (Project No. 71172206 and 71328201). We thank workshop participants at the University of Missouri at Columbia for helpful comments. We thank Professor Hanwen Chen and his team for sharing the internal control index data developed under National Natural Science Foundation of China Project Nos. 71332008.

  

The Effect of Internal Control on Corporate Corruption: Evidence from China

Abstract

We examine whether internal control over financial reporting reduces the extent of corporate corruption (a form of corporate abuse in which controlling shareholders or managers expropriate resources from the firm). Using a sample of Chinese-listed firms, we find that after controlling for known determinants of corruption and internal control, firms with strong internal controls are significantly less likely to extract resources from the firm (either in the form of loans, reimbursement of personal consumption expenses, or receipt of bribes). We find this relation is concentrated within non-state-owned firms, providing some evidence of the need to establish substance over form when assessing the strength of internal control. Taken together, our results suggest that, on average, strong internal controls curb corporate corruption; however, institutional factors and the associated incentives play a significant role in the effectiveness of internal controls.

Keywords: Internal control over financial reporting; corporate corruption; state-owned enterprises

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1. Introduction

We examine the relation between the strength of internal control and corporate

corruption. We define corporate corruption as a form of corporate abuse in which controlling

shareholders or managers expropriate resources from the firm and thereby minority

shareholders. 1 For example, CEOs are often persuaded through bribes to contract with

inferior suppliers offering higher-priced or lower-quality inventories relative to other

suppliers. CEOs can also transfer funds to related parties as loans with indefinite loan terms.

Another common tactic for managers to tunnel cash from the firm is through the

reimbursement for their personal consumption expenses. Although corruption within China

has been well-documented (e.g., Jiang et al., 2010; Cai et al., 2011; Piotroski and Wong,

2012), our interest is in whether internal controls can effectively combat this type of

corruption.

The value of internal control is of considerable interest to academics, regulators and

practitioners. 2 Studies using internal control disclosures of U.S. firms have generally

concluded that stronger internal controls result in higher earnings quality (Doyle et al., 2007),

more accurate management forecasts (Feng et al., 2009), better investment decisions (Cheng

et al., 2013), and higher operating efficiency (Feng et al., 2015). There is more limited

                                                            1 The corporate corruption we investigate is related to government corruption, given China’s unique institutional environment. Shleifer and Vishny (1993) define government corruption as “the sale by government officials of government property for personal gain.” Over half of Chinese publicly traded firms are state-owned companies (often termed as stated-owned enterprises, SOEs), meaning that the government is the controlling shareholder. As a result, the government appoints CEOs of stated owned companies, who usually are current or former government bureaucrats. 2 Following the Committee of Sponsoring Organizations of the Treadway Commission (COSO) framework (2013), we define internal control as “a process, effected by an entity’s board of directors, management, and other personnel, designed to provide reasonable assurance regarding the achievement of objectives relating to operations, reporting, and compliance.” The focus of this paper is on internal control over financial reporting, but in our additional analyses, we also provide evidence on internal controls that are not related to financial reporting.

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evidence, however, on whether internal controls reduce self-dealing behavior. Although there

is some evidence in Skaife et al. (2013) that managers benefit from ineffective controls via

the profitability of their personal trades, Ashbaugh-Skaife et al. (2008) document that internal

control weaknesses are more likely to lead to unintentional errors than intentional

misstatements. The escalation to corruption has not been examined, although this type of self-

dealing behavior is likely to be far more costly to shareholders.

China is well-suited as a setting to examine the effect of internal control strength on

corporate corruption for several reasons. First, within China, corruption is common and

measurable. In particular, because the legal system offers only weak protection for minority

investors, corporate abuse by controlling shareholders and managers is wide-spread and

rather severe (Jiang et al., 2010) and there is a high risk of controlling shareholders

expropriating resources from minority investors (Djankov et al., 2008). Thus, although it is

difficult to measure corporate corruption within U.S. firms, we are able to measure corruption

relatively reliably in China using several well-documented forms of corruption used by

managers of Chinese firms. Specifically, we focus on tunneling (i.e., the use of inter-

corporate loans by controlling shareholders; Jiang et al., 2010), high travel and entertainment

costs (Cai et al., 2011), and publicly disclosed corruption cases. Second, required disclosures

in China allow for a continuous measure of internal control strength.3 This allows for greater

granularity than analyses using U.S. data, where only the small subset of firms with

                                                            3 We use a proprietary database that tracks listed Chinese firms’ internal control information from financial statements, filings to China Securities Regulatory Commission, government documents, and press releases (Chen et al., 2013). The database covers 99 percent of all Chinese public firms from 2007 through 2010 and allows us to measure the strength of internal control within the COSO framework.

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ineffective internal controls provide granularity on their underlying problems.4 This also

allows us to distinguish between the existence of internal controls, and the effectiveness of

internal controls. In particular, in the U.S., if managers override existing internal controls, the

firm must disclose ineffective internal controls. In contrast, in China we can assess the

existence of internal controls separately from the effectiveness of internal controls (i.e., form

versus substance).

We hypothesize that firms with stronger internal controls have a lower risk of

corporate corruption as a stricter control and monitoring environment would make it more

difficult for top management to engage in brazen activities that are harmful to minority

shareholders. For example, the separation of duties in the accounting department and controls

for authorizing and approving material matters would reduce the likelihood of removing cash

from corporate accounts for managers’ personal use. We do acknowledge, however, that we

might not find an association between internal control strength and corruption. In particular,

the effectiveness of internal controls would be compromised if managers are able to ignore

the internal control procedures, overriding them at will. In this case, the internal control

system is merely window dressing.

We study a sample of 4,775 firm-year observations of Chinese firms listed on the

Shanghai Stock Exchange and on the Shenzhen Stock Exchange from 2007 through 2010.

We first pare down the 144 items used in Chen et al. (2013) to focus on internal controls over

financial reporting (69 items). We next provide evidence on the validity of our measure of

                                                            4 For example, only 7.2% of firm-year observations disclose ineffective internal controls in the U.S. sample examined in Feng et al. (2015). The other 92.8% simply disclose they maintain effective internal controls, whereas our entire population exhibits variation in ICFR strength.

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internal control strength by confirming that firms with higher internal control scores exhibit

less earnings management, measured by the absolute value of discretionary accruals and the

incidence of financial statement restatements. This finding is consistent with that of Chen et

al. (2013) using the broader measure. Following prior literature, we then measure corporate

corruption in the following ways: other accounts receivable scaled by total assets to capture

the use of inter-corporate loans by controlling shareholders (Jiang et al., 2010), travel and

entertainment costs scaled by sales to capture the expenses incurred for managers’ personal

benefits (e.g., eating, drinking, sports club membership, and travel; Cai et al., 2011), and

public disclosures of corruption by top management.5 We find that, after controlling for

known determinants of corruption and internal control strength, firms with strong internal

controls appear to tunnel fewer resources out of firm and report lower travel and

entertainment expenses, consistent with our prediction that internal controls mitigate

corporate corruption. We do not, however, find a significant association between internal

control strength and the frequency of disclosed corruption for our full sample.

Next we examine whether the relation between internal control strength and corporate

corruption varies with whether the internal controls were more likely to be voluntarily

adopted or policy driven, proxied by state ownership structure. An important ownership

characteristic of China’s listed firms is state ownership, where the government is the

controlling shareholder and appoints the top management team. State-owned enterprises

(SOE) comprise more than half of all firms listed on China’s stock exchanges (Piotroski et al.,

                                                            5 Note that we remove the corruption cases in which managers bribe government officials to earn benefits for the firm to buy protection against government expropriation or buy government services (e.g., provide permits and licenses). We focus on corrupt behavior by management that is at the expense of minority shareholders.

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2014). Managers of state-owned enterprises tend to be former government officials who face

multiple—and potentially conflicting—objectives (e.g., political incentives versus incentives

to maximize minority shareholder welfare). In 2006, in an effort to reform state-owned

enterprises, the Chinese government issued numerous guidelines to underscore the

importance of internal controls for listed firms and applied a great deal of pressure on stated-

owned firms to follow the guidelines in establishing specific internal control policies and

procedures. It is likely that internal control procedures adopted by state-owned firms are

policy driven in that state-owned firms adopt in name a boilerplate list of internal control

procedures simply to satisfy government regulators but not actually implement or enforce

these internal control procedures.

This institutional difference could potentially allow us to examine differences in

substance versus form, as the strength of internal controls within state-owned enterprises

could have a smaller effect on the risk of corporate corruption than the strength of internal

controls within non-state-owned firms. We thus investigate whether these policy-driven

changes in internal control guidelines and procedures are effective in reducing corporate

corruption. Consistent with at least some window dressing (i.e., form over substance), we

find that for our measures of tunneling and private consumption, internal control strength is

less effective at curbing corporate corruption for state-owned enterprises relative to non-state-

owned firms. In addition, we find a significantly negative association between internal

control strength and the frequency of disclosed corruption within non-state-owned firms, but

not state-owned firms.

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We conclude our analysis with two additional tests. First, we employ two-stage least-

squares (2SLS) estimation procedures to address the concern that the internal control

procedures are chosen and implemented by management. We continue to find that internal

control strength is negatively associated with the risk of corporate corruption and this

association is significantly weaker within state-owned enterprises. Second, we examine

whether internal control strength is associated with the likelihood of CEOs receiving

promotions (being appointed to become either a higher-ranking government official or a CEO

of a larger public company). We find that within state-owned firms, CEOs of firms with

higher internal control scores are more likely to be promoted, but this is not the case within

non-state-owned firms. This finding corroborates our earlier results suggesting that the multi-

dimensional incentives of CEOs at state-owned firms likely contribute to less effective

internal controls (form over substance).

Our findings make several contributions to the literature. First, we document the link

between internal controls and corporate corruption, which has not been documented to date.

The internal control disclosure requirements of the Sarbanes Oxley Act in the U.S. have

triggered reforms of internal control disclosure requirements as well as other corporate

governance practices in many other countries. Our findings suggest that internal control

disclosures could be particularly important in emerging markets, where ownership structure is

highly concentrated and legal mechanisms to curb corporate abuse are largely absent. Second,

our results highlight that internal controls are significantly less effective in curbing corporate

corruption within state-owned enterprises. This suggests that institutional factors and the

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associated incentives play a significant role in the effectiveness of internal controls. Benefits

to firms are limited when internal control systems are undermined by management.

2. Background and predictions

Internal control over financial reporting is comprised of the processes and procedures

established by management to maintain records that accurately reflect the firm’s transactions

(Deloitte and Touche 2004).6 Effective on November 15, 2004 for accelerated filers, Section

404 of SOX requires companies to disclose management’s assessment of the effectiveness of

the internal control structure and procedures in the annual report and the firm’s auditor must

attest to this assessment. Following the implementation of Section 404, researchers have

examined various benefits of effective internal controls. Prior research has shown that higher

internal control strength reduces the unintentional errors in financial reporting (Ashbaugh-

Skaife et al., 2008; Doyle et al., 2007), leading to more accurate management forecasts as

managers use more accurate financial inputs to form their forecasts (Feng et al., 2009), and

improving investment decisions (Cheng et al., 2013) as well as firm operating efficiency and

firm performance (Feng et al., 2015). These studies rely on the notion that the reports

generated from a system with ineffective internal controls contain errors (often unintentional

errors), and thus the information management uses to create financial statements and make

decisions is faulty.

There is limited evidence, however, on whether internal controls reduce managers’

rent extracting behavior. Skaife et al. (2013) provide some evidence that the profitability of

insider trading is greater in firms with ineffective controls. They suggest that a weak internal

                                                            6 We use the term “internal controls” to refer to internal controls over financial reporting.

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control environment provides managers with an information advantage, enabling them to

profit from their private information by selling before stock price declines. They do not

provide evidence, however, of the more direct rent-extraction we examine. In particular,

although selling personal shares at a profit is evidence of opportunism, it is both more

indirect and less costly to shareholders than the more egregious and direct evidence of

corruption we examine.7 This is especially salient given Skaife et al. (2013) find only limited

evidence of managers managing earnings before selling their personal shares. The focus of

our study is whether internal controls curb the extent of corporate corruption, which is an

escalated form of managers’ rent extracting behavior that represents a much more direct and

quantifiable cost to shareholders.

The lack of evidence on the association between internal control strength and

managers’ corrupt behavior is likely due to two reasons. First, strong and well-enforced

investor protection in the U.S. constrains insiders’ ability to acquire private control benefits

(Leuz et al., 2003; La Porta et al., 2000); as a result, U.S. firms likely exhibit significantly

less corporate corruption, on average, than firms in countries with poor investor protection.

Second, there is no strong pattern of, or evidence on, how insiders engage in corporate

corruption in the U.S., making it difficult to measure corruption. Using a sample of Chinese

firms allows us to overcome both of these limitations. First, China has particularly weak

minority investor protection, in part because China’s legal system lacks enforcement (Allen

                                                            7 As an example, the CFO of South Airline Co., Liming Chen, tunneled numerous loans from the company to related parties. For instance, he took a corporate loan of $30 million Chinese Yuan from China CITIC Bank, and then moved the money in the form of other accounts receivable to a company controlled by his friend, Zhuangwen Yao. To return the favor, Zhuangwen Yao, gave Chen a BMW car worth $700,000 Chinese Yuan and a house worth $2.25 million Chinese Yuan as gifts. In addition, Chen took bribes of over $53 million Chinese Yuan from various sources.

  9

et al., 2005). Allen et al. (2005) show that even among seven developing countries, China’s

corruption index is ranked the worst. 8 As a result, corrupt behavior by controlling

shareholders and managers is wide-spread (Jiang et al., 2010). Second, prior research

provides evidence on common approaches through which controlling shareholders or

managers expropriate minority shareholders in China (Jiang et al., 2010; Cai et al., 2011).

Therefore, we are able to measure specific corporate corruption such as the tunneling of cash

from the firm through inter-corporate loans, the payment of personal entertainment or

consumption with firm resources, or the ex post revelation that managers accepted bribes (for

example to contract with inferior suppliers) or embezzled from the firm.

We predict that it is more difficult for managers and controlling shareholders to

undertake corrupt activities in a stricter internal control environment. For example, the

common technique of issuing loans to transfer cash (i.e., tunneling) would be curbed by the

requirement of a loan approval process that spells out interest and repayment terms. Related

controls would trigger personnel to follow up on expected interest/principal payments that

have not been received. With respect to private consumption by management, it is possible

that requiring separate personnel to approve versus pay for invoices (i.e., segregation of

duties) would curb much of the inappropriate reimbursement of personal consumption

expenses. In addition, beyond the establishment of segregation of duties, routine reviews of

expense reports or maintaining a clear reimbursement policy would likely further reduce

                                                            8 These seven countries are: China, India, Pakistan, South Africa, Argentina, Brazil, and Mexico. The corruption index used in Allen et al. (2005) is based on the International Country Risk Guide’s assessment of the corruption in government. Lower scores suggest that “high government officials are likely to demand special payments” and “illegal payments are generally expected throughout lower levels of government” in the form of “bribes connected with import and export licenses, tax assessment, policy protection, etc.”

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corrupt behavior of charging for items used for personal reasons or simply faking receipts. As

another example related to accepting bribes, stricter purchase order authorization would

ensure that managers are not over-ordering or ordering at an unreasonable price. Although

even in the presence of such internal controls, managers may simply override the controls, we

state our first hypothesis as the following, in the alternative form.

H1: Firms with stronger internal controls have less corporate corruption.

Next we examine whether the relation between internal control strength and corporate

corruption varies with whether the internal controls were more likely to be voluntarily

adopted or policy-driven. To proxy for this construct, we examine the ownership structure of

the firm. Specifically, we investigate whether state ownership in China influences the

effectiveness of internal control in curbing corporate corruption. As of 2010, sixty-five

percent of listed firms in China were state-owned enterprises, accounting for 89 percent of

total market capitalization in China (Piotroski et al., 2014). The government is the controlling

shareholder of stated-owned enterprises and appoints key executives such as the CEO and the

chairman of the board. As a result, the top managers of stated-owned enterprises have

multiple objectives. In addition to profit maximization, they might also be working to

improve employment rates or build relationships with government superiors; these other

objectives could cause significant inefficiencies for the firm (see Piotroski and Wong, 2012;

Piotroski et al., 2014).

Following the implementation of the Sarbanes Oxley Act in the U.S., the Chinese

government began to emphasize improving publicly listed firms’ internal controls. In June

2005, the Ministry of Finance, China Securities Regulatory Commission (CSRC), and the

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State-Owned Assets Supervision and Administration Commission (SOASAC) jointly issued

the “Report on Learning from Sarbanes Oxley to Strengthen Our Listed Firms’ Internal

Controls.” In the Fourth Plenary Meeting of the Tenth People’s Congress on March 5, 2006,

Premier Jiabao Wen stated that “we need to introduce and learn from other countries’

experiences in corporate governance, standardize governance mechanisms, and improve

internal control systems.” A series of governmental guidelines were issued following Premier

Wen’s address. The central government wanted to establish “role models” of state-owned

enterprises to benefit the social goals of the government. The guidelines issued by SOASAC

state that central government-controlled enterprises should develop internal control systems

and prevent corruption. Moreover, the “Basic Standards for Large and Median SOE on

Developing Modern Enterprises System and Strengthening Corporate Governance” issued by

the General Office of the State Council of PRC states that:

“Those state-owned enterprises classified as major enterprises by the central and local governments, are required to identify deficiencies according to the Standards and make improvements to comply with the Standards. All other enterprises should also follow the Standards and strive to meet all the requirements. The committee on trade and economy, and the budget committee at the central government and local governments are called to conduct research and investigation on the effectiveness in implementing the standards.”

Clearly, state-owned enterprises are under pressure from the government to establish certain

types of internal control procedures, which is compounded by managers of the stated-owned

enterprises (current or former government bureaucrats) having incentives to maintain strong

political connections with government officials.

The concern, then, is that state-owned firms will adopt in name a boilerplate list of

internal control procedures simply to satisfy government regulators and not actually

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implement or enforce these internal control procedures. As a result, such policy-determined

burdens (e.g., certain types of internal controls) are not necessarily effective in achieving the

stated goal of reducing corporate corruption. As suggested in Lin et al. (1998), policy burdens

reduce the efficiency of state-owned enterprises’ operations. In addition, as argued in

Piotroski and Wong (2012), greater state involvement in an economy creates incentives for

financial reporting opacity to hide the rent-seeking activities of politicians and related parties.

This means that, although management of state-owned enterprises have incentives to adopt

strong internal controls on paper, they likely have fewer incentives to actually realize the

benefits of these internal control practices, relative to firms that voluntarily choose to

establish strong controls. Because they have the power to override or simply ignore internal

controls in place, the most corrupt managers are unlikely to be restrained by internal controls.

As an example, Yunnan Copper Co. is a state-controlled enterprise with a market

capitalization of 14 billion Chinese Yuan. In 2007, it was disclosed that the CEO, Zhaolu

Zhou, abused his right in deciding whom to give contracts to and took at least 19 million

Chinese Yuan in bribes. In Zhou’s self-reflection report that he wrote during the detention

period, he acknowledged that he had too much power and was able to override the internal

control systems within the firm.9

We thus hypothesize that policy-driven changes in internal control guidelines and

procedures are less effective in reducing corporate corruption:

H2: Strong internal controls are less effective at curbing corporate corruption when they are policy-driven relative to when they are voluntarily adopted.

                                                            9 Bureaucratic prisoners in China are required to write self-reflection reports detailing their transgressions and the motivations for their actions.

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We use the adoption of internal controls within state-owned enterprises to proxy for policy-

driven adoptions.

3. Data, sample selection, and measures for corporate corruption 3.1. Internal control index and sample selection

Our ICFR measure is based on the underlying data used in Chen et al. (2013). These

data cover 99% of all Chinese listed firms from 2007 to 2010 and indicate whether 144

specific firm features exist within each firm. These 144 firm features each fall within the five

main aspects of internal control proposed by COSO: (1) Control Environment, (2) Risk

Assessment, (3) Control Activities, (4) Information and Communication, and (5) Monitoring

(see Appendix A).10 As our focus is ICFR, we focus on the existence of 69 of the 144 firm

features collected for the initial index (see Appendix B). Each of the 69 items receives a score

of one if it is in existence, which we then average within each control aspect (three-digit level

in Chen et al. 2013). Finally we aggregate the scores from each control aspect and calculate

the average ICFR score ranging from zero to one. See Appendix B for a more detailed

description of the calculation of our ICFR score.

Our sample begins with all Chinese firms listed on the Shanghai and Shenzhen stock

exchanges that have internal control index data from 2007 through 2010. We present the

                                                            10 These data were collected developed by the research team led by Hanwen Chen, supported by China NSF grant #7133200, and Ministry of Education Social Science Major Research grant #10JJD630003. Since its creation, the index has gained recognition from researchers, practitioners and regulators as the metric of Chinese firms’ internal control effectiveness. These data are considered the most comprehensive and authoritative detail on internal control by Chinese regulators and security market participants. For example, on June 11, 2010, all three of the most authoritative Chinese financial newspapers, the China Securities Journal, Shanghai Securities News, and Securities Times, featured articles introducing the index. The researchers who developed the internal control index annually publish the top 100 firms that have the highest internal control scores in the China Securities Journal. Deloitte highlighted this index on the website of its Corporate Governance Center; and the Public Company Monitoring Division of the Shanghai Stock Exchange also acknowledged that the index “has significant reference values for our efforts of monitoring internal control and corporate governance.”

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sample selection procedure in Table 1, Panel A. We remove 120 firm-years in the financial

industry as financial firms are under different internal control requirements issued by the

People’s Bank of China and the China Securities Regulatory Commission. We then delete

1,401 firm-year observations that are traded on the small and medium-sized enterprises

(SMEs) board because these firms are subject to different internal control and disclosure

requirements. Of the remaining firms, 468 firm-years do not have necessary data for the

analysis on the determinants of internal control, resulting in a sample of 4,775 firm-year

observations. We obtain the information on firms’ stock prices, company financials, industry

classification, ownership structure, auditors, and largest shareholders from the Chinese Stock

Market and Accounting Research (CSMAR) database. As shown in in Panel B of Table 1,

our sample is evenly distributed across our sample period, and about 67% of our sample firms

are state-owned enterprises, i.e. the controlling shareholders are either the central or local

government, or their agencies.

[Table 1]

3.2 Measures for corporate corruption

Our first measure of corporate corruption is intended to capture tunneling through the

use of inter-corporate loans. Jiang et al. (2010) document that tunneling is a prevalent and

persistent method used by controlling shareholders to expropriate funds from minority

shareholders. Following their paper, we use other receivables scaled by total assets (TUNNEL)

to measure the extent of tunneling. This measure captures one specific approach through

which controlling shareholders or managers can expropriate funds for personal gain.

Prior research has also shown that corporate executives in China often misuse the

corporate funds for their private consumption such as dining, travel and entertainment. In

China, entertainment providers generally are willing to provide any type of receipt that their

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clients prefer to have for reimbursement purposes; thus extremely lax accounting regulations

and enforcement allow the reimbursement of personal consumption expenses to be classified

as business related administrative expenses. We manually collect the information from the

notes to financial statements in the annual reports on the following expenditures: office

supplies, business travel, entertainment, communication, training abroad, board meetings,

automobile, conferences, and other expenses. Thus, our second proxy of corruption is the

sum of these expenditures scaled by sales (PRIVATE) which is intended to capture private

consumption (Cai et al., 2011).11

Our third measure of corruption is the ex post detection and disclosure of corruption

by either regulators or the media. We collect exposed corruption cases from both main stream

Chinese media sources and litigation cases to ensure the most comprehensive coverage.12 We

require that this disclosure indicates self-serving behavior (e.g., embezzlement or the receipt

of bribes) by management, and we set EXPOSED equal to one for the firm-years involved

with the exposed corruption cases, and zero otherwise.13 We analyze each corruption case to

determine the timing of corrupt behavior (i.e., when the management undertake the corrupt

behavior). One advantage of this measure (EXPOSED) is that we have a high level of                                                             11 PRIVATE could also capture the expenditures used to bribe external parties to negotiate deals that benefit firm. It is likely that internal controls will not curb such bribing behavior as it benefits the firm; therefore, this might weaken the effect of internal control on PRIVATE. 12 To collect publicly disclosed corruption cases, we first search the “China Economic News Database,” which consists of articles published in all Chinese newspapers and periodicals. We use the following keywords to conduct the news search: corruption, Shuanggui (detained and interrogated), stepping down, economic issues, embezzlement, misappropriation, bribery, job suspension, favoritism, property transfer, and Xiake (stepping down). Next we searched within all legal case documents that are relevant to corruption from the courts in accordance with “LawInfoChina” (LawInfoChina is a data center for court legal documents). Finally, we used the Baidu search engine to retrieve public companies’ corruption materials based on company names identified from our search. 13 We do not include those corruption cases that involve giving bribes to external parties (e.g., government) for the firm’s benefits (e.g., obtaining a contract with the government). Of the 159 cases involving exposed corruption, 120 relate to accepting bribes at the expense of shareholders (e.g., to buy inventory at above-market prices). The remaining cases involve various embezzlement actions. Because some corruption cases involve multiple years, we have 214 observations with EXPOSED = 1. To minimize the effect of Type II errors on our analyses (i.e., undetected corruption cases contaminating our control sample), we remove firms that involve other types of CSRC regulation violations and qualified audit opinions from the control sample.

  16

confidence regarding the existence of corrupt behavior (the type I error rate is low). These

detected and exposed corruption cases tend to be egregious in nature. One disadvantage,

however, is that many corruption cases likely remain unidentified (the type II error rate is

high) and there could be selection biases in the exposed cases. In contrast, it is likely that our

two other measures (TUNNEL and PRIVATE) have higher type I error rates, but lower type II

error rates. Thus, it is important to consider evidence from all three of our corruption

measures.

We report descriptive statistics of the main variables in Panel A of Table 2. In our

sample, the mean (median) of ICFR score is 0.453 (0.448). The mean (median) of tunneling

(measured by other receivables) is 2.7% (1.2%) of total assets, where the mean (median) of

total assets is 6,966 (2,504) million Chinese Yuan. The mean (median) of PRIVATE is 2.7%

(1.0%), where the mean (median) of sales revenue is 4,935 (1,444) million Chinese Yuan.

During our sample period, on average 4.5% of sample firms are detected and exposed for

corruption such as accepting bribes or embezzling assets.

[Table 2, Panel A]

We report descriptive statistics by state-owned enterprises (SOEs) and non-SOEs in

Panel B of Table 2. Consistent with the push by the government to establish internal control

procedures within SOEs, the ICFR scores are higher for SOEs than non-SOEs at both the

mean and median. For example, the mean score for SOEs is 0.463 versus 0.433 for non-SOEs.

With respect to our three corruption measures, tunneling and reimbursement of personal

consumption expenses appear more pervasive among non-SOEs whereas there are notably

more instances of publicly disclosed corporation within SOEs, with the mean value of

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EXPOSED equal to 0.062 for SOEs compared to a mean value of 0.011 for non-SOEs. Thus,

there is some evidence that managers of SOEs are more likely to accept bribes, relative to

managers of non-SOEs, who are more likely to extract funds via loans they do not expect to

repay or through questionable expense reimbursements.

SOEs are generally larger than non-SOEs, but are slightly less profitable, consistent

with these firms and managers having more objectives than solely profit maximization.

Finally, the Big4 audit firms have a much larger presence within SOEs, although the vast

majority of firms are audited by smaller audit firms, consistent with prior research (e.g., Chen

et al., 2011). Turning to the correlation table in Panel C of Table 3, we find that ICFR is

negatively correlated with TUNNEL and PRIVATE (consistent with H1), but not EXPOSED.

From the low correlations of our three corruption variables, it is clear that each captures a

distinct component of corruption.

[Table 2, Panel B]

4. Research design and empirical results

In this section, we first perform tests to validate our measure of ICFR strength in

Section 4.1. We present our main analyses examining the relation between internal controls

and corporate corruption in Section 4.2. We follow our main analysis with a discussion of

potential endogeneity concerns and present results using a two-stage design in Section 4.3.

Finally, we discuss our robustness tests in Section 4.4.

4.1. Internal control strength and earnings quality

As a validity test of our internal control measure, we first examine the association

between the internal control score (hereafter ICFR) and earnings quality. To the extent that

  18

our measure of internal control strength captures the strength of internal control, we expect to

observe a positive association between ICFR and earnings quality. We examine two measures

of earnings quality: the absolute value of discretionary accruals (ABSDA) and the occurrence

of accounting errors (RESTATE).

Earnings Quality = β0 +β1ICFR +β2CFO +β3 LEV +β4ROA +β5MB +β6EISSUE

+β7DISSUE +β8BIG4 +β9TOP1SHR+ ε (1)

The dependent variable is ABSDA or RESTATE. ABSDA is the absolute value of the residuals

from the performance-adjusted cross-sectional modified Jones model (Kothari et al., 2005).

RESTATE, equals one if the firm subsequently restated an accounting error made in the

current year, and zero otherwise. We control for variables documented in prior literature that

influence the magnitude of discretionary accruals including cash flows from operations

(CFO), leverage (LEV), firm accounting performance measured by returns on assets (ROA),

and growth opportunities measured by market-to-book ratio (MB). We also control for new

issuances of equity and debt (EISSUE and DISSUE) because firms have stronger incentives to

manage earnings in the presence of financing needs (Dechow et al., 1996). We also include

audit quality, which is an important factor in determining earnings quality; BIG4 is equal to

one if the auditor is one of the four largest U.S. accounting firms, and zero otherwise. Lastly,

we include the percentage of shares owned by the largest shareholder (TOP1SHR) to control

for potential effects of ownership structure on accounting quality (Dechow et al., 1996). We

again include year and industry fixed effects.

We present the results in Table 3. We find that ICFR is negatively associated with

both the absolute value of discretionary accruals (ABSDA) and the occurrence of accounting

errors (RESTATE), suggesting that earnings quality is lower for firms with weaker internal

  19

controls. The coefficients on the control variables are largely consistent with prior literature.

Overall, the findings in Table 3 provide support for the validity of our internal control

strength measure.14

[Table 3]

4.2. Internal control and corporate corruption

4.2.1 Internal control and tunneling

We begin our testing of H1 and H2 with tunneling (TUNNEL) as our measure of

corruption. Specifically, we estimate the following model:

TUNNEL = β0 +β1ICFR +β2ASSET +β3ROA +β4TOP1SHR +β5CEOCHAIR

+β6INDIRECTOR +β7MARKETIZATION +β8BIG4 +β9EXCHANGE + ε (2)

The dependent variable, TUNNEL, is other receivables scaled by total assets and is intended

to identify the existence of tunneling (the receipt of cash loans that are not expected to be

repaid). ICFR is a variable ranging from zero to one with a higher value indicating stronger

internal controls over financial reporting (see Appendix B). H1 predicts that effective internal

controls reduce the extent of corporate corruption; therefore, we expect the coefficient on

ICFR to be negative. Following Jiang et al. (2010), we include firm size (ASSET),

profitability (ROA), shares owned by the largest blockholder (TOP1SHR), and the

development of the regional market in which the firm is registered (MARKETIZATION) as

control variables. Jiang et al. (2010) show that tunneling decreases in firm size and firm

profitability. They also show that tunneling decreases in the ownership of the controlling

shareholder because a large ownership interest mitigates the controlling shareholder’s

incentives to extract private benefits that would destroy firm value. Tunneling is also

expected to be more serious in a less developed regional market as less developed local

markets are associated with weaker legal and regulatory environments. In addition to the

                                                            14 This is consistent with the findings in Chen et al. (2013) who examine the full internal control index, whereas we focus on ICFR.

  20

controls included in Jiang et al. (2010), we include CEOCHAIR which equals one if the CEO

is also the chairman of the board, because we expect more powerful CEOs to have more

opportunities to misappropriate corporate funds. For this reason, we also control for the

percentage of independent directors on the board (INDIRECTOR). In theory, the presence of

independent directors would protect minority shareholders’ (or the firm’s) interests and curb

tunneling behavior. However, because independent directors in many Chinese firms

(especially state-owned enterprises) have private connections with management or

blockholders, they may collude in tunneling. Thus we have no prediction on INDIRECTOR.

We include BIG4 to control differences in audit quality and EXCHANGE to control for

different regulations and requirements under the two exchanges, although we do not have a

directional prediction for this variable. Finally we include year and industry fixed effects in

the regression.

We report the regression results in Table 4. Consistent with H1, the coefficient on

ICFR is significantly negative (coefficient = –0.014; p-value = 0.036), suggesting that

stronger internal controls curb tunneling. The coefficients on the control variables are

consistent with those in Jiang et al. (2010). Specifically, tunneling is decreasing in firm size

(ASSETS), profitability (ROA), percentage of shares owned by the largest shareholder

(TOP1SHR), and the degree of development of regional markets (MARKETIZATION). We do

not find a significant association between tunneling and the CEO also being the Chairman,

nor between tunneling and the big-four audit firms being the auditor.

Next we examine H2 by estimating Equation (2) separately for SOEs and non-SOEs.

We present the regression results in Columns (2) and (3) of Table 4. Interestingly, ICFR is no

longer significant among SOEs (coefficient = –0.006; p-value = 0.212), but is a strong

predictor of tunneling within non-SOEs (coefficient = –0.033; p-value = 0.023). The

  21

coefficient on ICFR is statistically different between SOEs and non-SOEs (p-value =

0.066). 15 This result suggests that although internal control over financial reporting

effectively mitigates tunneling within non-SOEs, this is not the case within SOEs. This result

is consistent with our prediction (H2) that policy-driven adoptions of internal control are not

as effective as adoptions that are more voluntary in nature, highlighting the importance of

substance over form.

In summary, the results using tunneling as a measure of corporate corruption are

consistent with both H1 and H2. We find a negative association between internal control

strength and the extent of controlling shareholders harming minority shareholders by

extracting resources from the firm with inter-corporate loans. Within firms controlled by the

government (SOEs), however, internal controls do not appear to curb this corrupt behavior.

[Table 4]

4.2.2 Internal control and private consumption

The preceding analysis related to tunneling provides evidence on the effect of internal

control on corporate corruption specific to inter-corporate loans. Corporate corruption,

however, manifests in various ways. As discussed in Section 3.2, our next measure of

corporate corruption is private consumption expense reimbursement (PRIVATE). Thus, we

estimate the following model, where the dependent variable is PRIVATE.

PRIVATE= β0 +β1ICFR +β2PAYRATIO +β3MGMTSHR +β4ATO +β5ROA +β6EISSUE +β7DISSUE +β8BIG4 +β9TOP1SHR +β10CEOCHAIR +β11INDIRECTOR +β12MARKETIZATION (3)

                                                            15 To test whether the main coefficients are the same across different SOE types, we use the following Z-

statistics: ;

where bi and bj are coefficient estimates from the two sub-samples, and s2(b) is the squared standard errors of the coefficients (Clogg et al., 1995, Chen et al., 2010).

  22

PRIVATE is the sum of expenditures for office supplies, business travel, entertainment,

communication, training abroad, board meetings, automobile, conferences, and other

expenses scaled by sales. We predict a negative coefficient on ICFR because stronger internal

controls should limit the magnitude of private benefits resulting from the corruption. We

include several control variables from prior research. First, executives with lower pay have

stronger incentives to misappropriate corporate funds and assets for private consumption.

Chen et al. (2005) find that executives substitute lower pay with excessive perquisite

consumptions. We follow their measures of executive compensation and control for the ratio

of executive total compensation relative to other employees’ average compensation

(PAYRATIO). We also control for management ownership (MGMTSHR), measured as

outstanding shares owned by executives, because management ownership helps align

managers’ objectives with shareholders’ interests and reduces incentives for excessive

perquisite consumption. We next include asset turnover (ATO) as a control variable to control

for the effect of operating efficiency on selling and general administrative expenses. We

control for firm performance (ROA) but we do not have a clear prediction on this variable. On

one hand, more profits provide more resources for corrupt executives; on the other hand,

uncorrupt managers might generate better performance (e.g., they will buy the best-value

inventory). We control for new issuances of equity (EISSUE) and debt (DISSUE) since the

expenditures included in our PRIVATE measure may increase during financing activities.

Lastly we control for BIG4, TOP1SHR, CEOCHAIR, INDIRECTOR, and MARKETIZATION

as in the tunneling analysis.

  23

Table 5 presents the regression results for PRIVATE, our proxy for private

consumption expense reimbursement. We find a significant negative association between

ICFR and PRIVATE for the full sample (coefficient = –0.026; p-value = 0.039), suggesting

ICFR effectively reduces corruption related to private consumption, again supporting H1. We

again find that ICFR is insignificant within SOEs (coefficient = –0.001; p-value = 0.479), but

is a strong predictor of corruption within non-SOEs (coefficient = –0.099; p-value = 0.007).

The coefficient on ICFR within non-SOEs is statistically and economically larger in

magnitude than the coefficient on ICFR within SOEs, in support of H2.

[Table 5]

4.2.3 Internal control and exposure of corrupt behavior

Our third measure of corporate corruption is disclosed corruption cases (EXPOSED).

We estimate the following model, where the dependent variable is EXPOSED:

EXPOSED= β0 +β1ICFR +β2PAYRATIO +β3MGMTSHR +β4SALES +β5ROA +β6EISSUE +β7DISSUE +β8BIG4 +β9TOP1SHR +β10CEOCHAIR +β11INDIRECTOR +β12MARKETIZATION + ε (4)

EXPOSED equal to one if we identify an exposed case of corrupt self-dealing (e.g.,

embezzlement or the receipt of bribes to conduct suboptimal transactions) from either main

stream Chinese media sources or litigation cases. In addition to the corruption determinants

included previously, we also include SALES as a control variable because a majority of the

exposed cases (120 out of 159 cases) relate to accepting bribes (e.g., to buy inventory at

above-market prices). CEOs of firms with larger sales might have more opportunities to

conduct this type of corruption (e.g., more suppliers will vie for the opportunity to bribe these

managers as the gain is larger). We predict a negative coefficient on ICFR if stronger internal

controls limit the opportunity for corporate corruption.

  24

We provide the regression results for Equation (4) in Table 6. The association

between ICFR and EXPOSED is insignificant for the full sample and the SOE sample, but

significantly negative for the non-SOE sample. This is consistent with our results on

tunneling and private consumption in that internal controls appear to be effective at reducing

corruption within firms owned and controlled by entrepreneurs and private investors, but not

within state-owned enterprises. We find a significant negative association for the level of

executive pay relative to other employees (PAYRATIO) within the full, SOE and non-SOE

samples, suggesting that a lower relative executive pay creates incentives for managers to

accept bribes or embezzlement. We also find some evidence that powerful CEOs are more

likely to accept bribes or embezzle within non-SOEs. We find that the percentage of

independent directors (INDIRECTOR) is positively associated with EXPOSED within the full

and SOE samples, but negatively associated with EXPOSED within non-SEOs. One possible

explanation is that CEOs of state-owned firms appoint their friends to be independent

directors, which would compromise the independence of the board.

[Table 6]

Taken together, our results from Tables 4–6 provide evidence that the strength of

internal controls over financial reporting curb corporate corruption (H1). Internal controls

within state-owned enterprises, however, are much less effective at curbing corruption,

consistent with the importance of substance over form (H2).

4.3. Endogeneity of internal controls

In this section we discuss the potential endogeneity issue that arises because internal

controls are not exogenous; instead managers and the board of directors choose to establish

internal control policies and procedures. Managers with a tendency to misappropriate assets

  25

may be more likely to choose weaker internal controls. To shed some light on the direction of

causality, we regress all three lagged corruption variables on current ICFR and find no

relation, mitigating the concern of reverse causality (p-value ranging from 0.148 to 0.702; not

tabulated). In addition, the choice of internal controls is affected by firm characteristics such

as firm complexity and available resources. It is also possible that both corruption and

internal controls stem from firm characteristics, but that internal controls, per se, would not

curb corruption. To investigate whether endogeneity is a concern more generally, we conduct

a Hausman test following Larcker and Rusticus (2010) to test whether endogeneity is likely

to exist. We develop our internal control prediction model based on determinants considered

in the U.S. literature as well as factors specific to the China market. Specifically, we estimate

the following first stage regression:

Internal Control = β0 + β1SOE +β2ASSETS + β3ROA + β4 LOSS + β5 MB + β6 EXPORT

+ β7 SEGMENT + β8 M&A + β9 FOREIGNSHR + β10BIG4+ β11LIST2006

+ β12 MARKETIZATION + β13 EXCHANGE + ε (5)

The dependent variable is the score for internal control over financial reporting strength. Our

first determinant is SOE, based on our lengthy discussion above regarding the governmental

pressure on SOE firms to establish internal controls. Thus we include SOE which equals one

if the controlling shareholder of a firm is either a central or local government or their

agencies. We next follow prior literature and include the following variables that have been

shown to determine a company’s internal control strength. Doyle et al. (2007b) find that

internal control problems are more prevalent in firms that are smaller, financially weaker,

growing more quickly, and more complex. They argue that larger firms and more profitable

firms have more resources to invest in internal control, thus we include firm size (ASSETS),

  26

and return on assets (ROA), and expect the strength of internal controls to be increasing with

both ASSETS and ROA. Similarly, firms going through financial distress are less likely to

invest resources in internal control. We include LOSS which takes the value one if the

company reports a loss in the year to proxy for poor financial health. We include the ratio of

market value of equity to book value of equity (MB) to proxy for growth. We measure

financial reporting complexity by the existence of exporting transactions (EXPORT) and the

number of segments (SEGMENT). Ashbaugh-Skaife et al (2008) find that structural changes

within a company could affect the internal control strength, thus we include an indicator

variable M&A that equals one if a firm completes a merger or acquisition during the year. As

suggested in Ashbaugh-Skaife et al. (2007), shareholder composition and auditors may also

influence a firm’s choice in its internal control. We include the percentage of shares held by

foreigners and expect that a greater concentration of foreigners would reflect greater demand

for strong oversight and thus internal controls. We include an indicator variable equal to one

if the company is audited by one of the four biggest U.S. accounting firms to proxy for

auditor oversight.

In addition to the determinants established in prior research in the U.S., we include

the following variables specific to Chinese firms’ internal control strength. First, 2006 was

the year China moved to advance corporate internal control. In 2006 both the Shenzhen and

Shanghai exchanges issued several regulations regarding internal control for firms going

public. 16 As a result, any firm going public in 2006 or afterwards is subject to higher

regulatory standards for internal controls. Therefore, we include an indicator variable                                                             16 For example, in the IPO process, a new regulation requires a firm going public to disclose the management’s assessment as well as the auditor’s opinion on its internal control effectiveness.

  27

LIST2006 that takes a value of one for firms that listed on the Shenzhen and Shanghai

exchanges on or after 2006. Second, we recognize the difference in regulations on internal

control by the two Chinese stock exchanges in our sample (Shanghai vs. Shenzhen) by

including an indicator EXCHANGE that equals to one if a firm is listed on the Shenzhen

exchange, and zero otherwise. Finally, we include MARKETIZATION, which measures the

development of the regional market in which the firm is registered (Fan and Wang 2006,

Jiang et al. 2010). We also include year and industry fixed effects.

Table 7 reports the results from estimating the first-stage regression (Equation 5).

State-owned enterprises (SOE) tend to have stronger internal controls; the coefficient on SOE

is significantly positive (coefficient=0.010; p-value=0.095), consistent with SOEs

experiencing government pressure to establish internal controls. Consistent with our

predictions, we find that larger and more profitable firms have higher internal control scores.

The coefficient on firm size (ASSET) is significantly positive. Although the positive relation

between ROA and ICFR is not statistically significant, the coefficient on LOSS is significantly

negative (i.e., loss firms tend to have weaker internal controls). We find no evidence that the

composition of foreign shareholders is associated with internal control strength, but we see

that companies audited by the Big4 U.S. audit firms tend to maintain stronger internal

controls. Consistent with the higher internal control requirements for firms that listed on or

after 2006, and firms listed on the Shenzhen Stock Exchange, we find positive coefficients on

  28

LIST2006 and EXCHANGE. We do not find a significant association between internal control

over financial reporting and the development of regional markets (MARKETIZATION).17

[Table 7]

We then include both ICFR and the residual from Equation (5) (the first stage model)

in the second stage estimation of corruption (Equations 2–4). We find that the residual is

significant in explaining each of our corporate corruption variables. Thus the test rejects the

null of no endogeneity. We next follow prior research and employ a two-stage least-squares

(2SLS) estimation procedure (e.g., Gul, Fung and Jaggi, 2009). We obtain the predicted value

of ICFR from the first stage regression (Equation 5) and include it in the second stage

regressions (Equations 2–4).

We then repeat all of our analyses using the predicted internal control scores from the

first stage regression. Our results are substantially similar for all three proxies of corporate

corruptions (TUNNEL, PRIVATE and EXPOSED); that is, firms with higher internal control

scores have fewer inter-corporate loans and fewer expenses susceptible to private

consumption for the full sample, and the association is significantly weaker within SOEs

When examining EXPOSED as the dependent variable, we again only find evidence that

internal controls are negatively associated with ex post disclosures of corrupt behavior within

non-SOEs. These findings mitigate the concern that a particular firm feature drives both

                                                            17 Our instrumental variable (or exclusion restriction variable per Lennox, Francis, and Wang (2012)) in the first stage regression is LIST2006, which we expect to be significantly associated with ICFR but for which we do not have ex ante reasons to believe would directly affect corporate corruption (except indirectly through ICFR). Statistically, in the first stage regression, we find that internal control strength is significantly associated with LIST2006. When we include LIST2006 in the second stage regressions, it is insignificant in explaining our three corruption variables. Therefore, our instrumental variable appears to be significantly correlated with ICFR, but related to our corporate corruption variables mainly through ICFR.

  29

corruption and internal control strength, but that internal control strength alone would not

curb corruption.

[Table 8] 4.4. Additional analyses

We conduct three additional analyses. Our earlier results are consistent with H2 that

internal controls are not as effective in curbing corporate corruption within SOEs compared

to non-SOEs. This finding is likely due to SOE management having different incentives to

adopt internal control practices than non-SOE management, resulting in internal controls that

are effectively “window-dressing.” To provide further evidence on this, we examine whether

internal control strength is associated with the likelihood of CEOs receiving promotions.

Based on our full sample, we identify a sample of 788 CEO turnovers and estimate the

following model, where the dependent variable is PROMOTION.

PROMOTION= β0 +β1ICFR +β2SALE +β3 CFO +β4ROA +β5LEV +β6MB +β7CEOAGE

+β8EDUCATION +β9TENURE +β10TOP1SHR +β11MARKET + ε (6)

We consider the CEO as having been promoted if in the year subsequent to her

departure she became (1) the Chairman of the board at the same company, (2) the CEO or the

Chairman of the board in the parent company, (3) the CEO at a larger public company where

the assets of the new company are at least 20% larger than the former company, or (4) a high-

ranking government official.18

We first estimate Equation (6) using an Ordered Logit regression including all firm-

years. The dependent variable takes the value of one if the CEO is promoted in the following

                                                            18 Executive positions at Chinese SOEs tend to have an “equivalent ranking” within the government, which allows us to assess whether a new governmental position is a promotion. For example, Yongkang Zhou, CEO of Chinese Petroleum had an equivalent rank of the Vice Minister of the State Owned Assets and Resources from 1996 to 1998. In 1998, he was promoted to become the Minister of the State Owned Assets and Resources.

  30

year, zero if there is no CEO turnover, and –1 otherwise. Next we estimate a Logit regression

including only firm-years with CEO turnovers. The dependent variable takes the value of one

if the CEO is promoted in the following year, and zero otherwise. The results and the

definitions of control variables are presented in Table 9. For the full sample Ordered Logit

regressions, the coefficient on ICFR is significantly positive (p-value=0.024) for SOEs; in

contrast, ICFR is not associated with PROMOTION within non-SOEs. We find similar results

using a Logit estimation within the CEO turnover sample.19 This finding is consistent with

SOE management experiencing governmental pressure to establish certain internal control

procedures, and corroborates our earlier results suggesting that the multiple objectives of

SEO CEOs likely contribute to less effective internal controls (form over substance).

[Table 9]

Next we consider whether the inclusion of internal control unrelated to financial

reporting affects our results. To do so we select items related to internal control procedures

that are not related to financial reporting (e.g., whether the firm has a legal counselor) and use

the weighted average as the measure of internal control unrelated to financial reporting

(ICDIFF). We then include ICDIFF as an additional control in our main regressions of

corruption (Equation 2 – 5). Similar to our main results, we find the coefficient on ICFR

remains significantly negative for the regressions of PRIVATE (both full and non-SOE

samples) and EXPOSED (non-SOE sample only), while the coefficient on ICDIFF is

insignificant in these regressions for the full sample and both sub-samples. For TUNNELING,

however, the coefficients on ICFR become insignificant (yet negative) and the coefficients on

                                                            19 We note that PROMOTION for SOEs does not have the exact same meaning as PROMOTION for non-SOEs because only SOEs’ CEOs have the opportunity to be promoted to positions as government officials.

  31

ICDIFF are significantly negative. This result could be driven by the fact that firms

exhibiting strong internal controls over financial reporting tend to have strong other internal

controls, which is evident by a high correlation of 46% between ICDIFF and ICFR. It is also

possible that other controls are more effective at limiting these personal loans, but given the

descriptions of the controls, we believe this is unlikely. Overall our results are similar when

we control for internal control other than financial reporting, although the potential

multicollinearity problem affects the significance on ICFR in the case of tunneling.

Finally, we examine whether, within SOEs, the type of SOEs plays a role in how

internal control strength influences corporate corruption. In particular, we examine the

differences between SOEs controlled by the central government and SOEs controlled by the

providence, city, and county governments, because central SOEs are under tight control and

governance by the State Assets Supervision and Administration Commission (SASAC).

Across all the corruption measures, we do not find any significant differences in the effect of

internal control between central SOEs and local SOEs. 

5. Conclusion

We examine the relation between the strength of internal control over financial

reporting and corporate corruption. Using a sample of Chinese firms, we find that after

controlling for known determinants of corruption and internal control strength, firms with

strong internal controls exhibit significantly less corporate corruption. Specifically, high

internal control scores are associated with significantly less tunneling of resources out of the

firms and lower travel and entertainment costs (our proxy for personal consumption expense

reimbursement).

  32

We also use the setting of state ownership to explore substance versus form in the

internal control arena. Because SOEs experienced intense governmental pressure to establish

internal controls, we expect that these internal controls will be more likely to be form over

substance, as the management was forced to adopt the policies. Thus, we explore the

differences in the effectiveness of controls in mitigating corruption between SOEs and non-

SOEs. We find that the state ownership of Chinese firms significantly weakens the effect of

internal control strength on the extent of corporate corruption in the forms of tunneling

resources and private consumption. In addition, we find that internal control strength has a

significant negative association with the frequency of disclosed corruption within non-state-

owned firms, but not state-owned firms. This finding is consistent with the conjecture that

management of state-owned enterprises do not have incentives to actually realize the benefits

of these internal control practices even though they are under governmental pressure to adopt

strong internal control practices on paper.

In summary, our results suggest that, on average, strong internal controls seem to

reduce the extent of corporate corruption; however, institutional factors and the associated

incentives also play a significant role in the effectiveness of internal controls. Internal control

systems that are only form, without substance, are less effective.

  33

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  36

Appendix A: Overall Internal Control Index

This section provides a brief summary of the overall internal control index. For details about the index, please see Appendix 1 in Chen et al. (2013). The internal control index was created by a research team led by Professor Henwen Chen at the Xiamen University. The construction of the internal control index was based on the Basic Standard and the Guidelines for Chinese public companies,20 and the requirements issued by the Shenzhen and Shanghai Exchanges.21 The design of the index follows the Internal Control—Integrated Framework (released by the Committee of Sponsoring Organizations of the Treadway Commission in 1992). Under this framework, internal control contains five components: control environment, risk assessment, control activities, information and communication, and monitoring. The COSO framework has gained acceptance by regulators, such as the SEC and PCAOB, and forms the foundation of SOX 404.

The researchers adopted the analytic hierarchy process (AHP) developed by Thomas L. Saaty in the 1970s.22 Internal control begins with five first-level items, the same as the five components in the COSO framework. Each of the first-level items then contain more evaluation items. For example, the codes for the first item in the first three levels are IC1, IC11 and IC111, respectively. For the fourth level, the code is defined as, for example, IC11101. The first three numbers (111) represent the first three levels to which this item is affiliated. If there is no third level for a particular item, then the first two numbers are presented. Overall, there are five first-level items, 24 second-level items, 43 third-level items, and 144 fourth-level items.

In evaluating each of the 144 items of a firm, the researchers collect its internal control information from financial statements, CSRC filings, government documents, and press releases. In most cases, a value of zero or one is assigned to a fourth-level item based on the information obtained (see Appendix B for examples). The researchers follow AHP to assign weights for the items (see Chen et al. for details on calculating the item weights) and the overall index score is the weighted average of all items. We identify 69 of the144 items that most directly relate to ICFR. See Appendix B.

                                                            20 On July 15, 2006, five Chinese government authorities and regulatory bodies, the Ministry of Finance, the China Securities Regulatory Commission, the National Audit Office, the China Banking Regulatory Commission, and the China Insurance Regulatory Commission jointly established a Committee aiming to stipulate a set of universal, recognizable, and scientific rules governing firms’ internal controls. After two years of conducting research and seeking feedback, the Committee on Internal Control Standards issued The Basic Standards of Enterprise Internal Controls (the Basic Standards) requiring that a listed firm issue a self-assessment of its internal controls and that a Certified Public Accountant issue a report on the firms’ internal controls. Later, Supplemental Guidelines of Firms’ Internal Controls (the Guidelines) was released on April 26, 2010. The Basic Standards became effective on January 1, 2011 for firms listed both on an exchange in China and another exchange abroad (dual-listed), and on January 1, 2012 for firms listed on the Shanghai Stock Exchange or the main section of the Shenzhen Stock Exchange. 21 In July 2006, the Shanghai Stock Exchange (SSE) issued A Guideline of Internal Controls for Listed Firms on Shanghai Stock Exchange. Further, in September 2006, the Shenzhen Stock Exchange issued A Guideline of Internal Controls for Listed Firms on Shenzhen Stock Exchange. 22 AHP first decomposes a complex decision problem into a hierarchy of more easily comprehensible sub- problems, and then produces the qualitative and quantitative analyses of each sub-problem. AHP next makes pairwise comparisons of the same-level items in every sub-hierarchy, analyzing their relative impact on an element in the hierarchy above them.

  37

Appendix B: Composition of Measure of Internal Control over Financial Reporting

We select a subset of the index components that apply to internal control over financial reporting. We remove the items that are not directly related to internal control from the list (e.g., corporate governance items). Our score of ICFR covers 69 out of the 144 fourth-level evaluation items. The following provide a complete list of these components. Standardized scores are calculated as the actual value for the item scaled by the maximum value of the same item across all firms in the dataset. We note these as “to be standardized” in the following list. Since each third-level index evaluate a specific aspect, we first take the mean of the items under the same third-level index (second-level if there is no third-level), and then we take the mean over the third-level index as our measure of IFCR. For example, we will first take the average of IC11101, IC11102, and IC11103 as the score for IC111. Then ICFR is calculated using the mean of scores of all the third-level (second-level if there is no third-level) index items. As an alternative measure, we also calculate ICFR as the simple mean of the 69 fourth-level items. Our results remain unchanged using this alternative measure.

ICFR Component (fourth level items) Score IC1: Control Environment IC111: Institutional Arrangement IC11101: Existence of a manual or guideline on internal control in the company.

1 for yes, 0 for no

IC11102: Use of an outside professional to help the company improve its internal control.

1 for yes, 0 for no

IC11103: Cross-listed company. 1 for yes, 0 for no IC113: Board of Directors IC11303: A board-level committee charged with the oversight of internal control (audit committee or risk management committee).

1 for yes, 0 for no

IC11304: Number of audit committee members To be standardizedIC114: Board of Supervisors IC11404: Supervisors with legal and accounting expertise. 1 for yes, 0 for no IC121: Internal Control Implementation IC12101：Existence of an internal control department in the company.

1 for yes, 0 for no

IC12102：The internal audit department reports to the board. 1 for yes, 0 for no IC131: Recruiting and Training IC13102: The human resource policy contains provisions on recruiting.

1 for yes, 0 for no

IC13103: The human resource policy contains provisions on training.

1 for yes, 0 for no

IC13104: The human resource policy contains provisions on job rotations at critical levels.

1 for yes, 0 for no

IC132: Incentives IC13202: The human resource policy contains provisions on evaluations.

1 for yes, 0 for no

IC13205: Employee salary is linked to performance. 1 for yes, 0 for no

  38

ICFR Component (fourth level items) Score IC133: Severance IC13301: The human resource policy contains provisions on discharging employees.

1 for yes, 0 for no

IC13302: The human resource policy contains provisions on employee resignations.

1 for yes, 0 for no

IC13303: An audit is performed upon the departure of the chairman, CEO, and other executives.

1 for yes, 0 for no

IC142: Competence variable mean IC14201: Percentage of the staff with at least a junior college education.

To be standardized

IC14202: Existence of employee training by the company. 1 for yes, 0 for no IC2: Risk Assessment IC221: Risk evaluation IC22101: Existence of a risk management committee or department. 1 for yes, 0 for no

IC222:Internal risk IC22202: A human resource risk disclosed in public filings. 1 for yes, 0 for no IC22203: An operation risk disclosed in public filings. 1 for yes, 0 for no IC22205: A financial risk disclosed in public filings. 1 for yes, 0 for no

IC231: Risk Evaluation Method variable mean IC23101: A quantitative risk analysis in the annual report or in the internal control evaluation report.

1 for yes, 0 for no

IC23102: Economic risk disclosed? 1 for yes, 0 for no IC241: Response Strategies IC24101: Any response strategy disclosed? 1 for yes, 0 for no IC24102: Any analysis of risk tolerance? 1 for yes, 0 for no

IC242: Risk Management Measure IC24201: A risk management measure taken to control risk. 1 for yes, 0 for no

IC3: Control Activities IC31: Separation of Duties IC31101: Controls for incompatible separation of duties. 1 for yes, 0 for no IC31102: Controls for authorizing and approving. 1 for yes, 0 for no

IC31103: Approval of material matters is in accordance with existing procedures.

1 for yes, 0 for no

IC32: Accounting Control IC32101: Company has an accounting information system. 1 for yes, 0 for no

IC34: Budget Control variable IC34101: Existence of a budgeting committee or department. 1 for yes, 0 for no IC34102: A budget is implemented in the company. 1 for yes, 0 for no IC34103: The annual budget is discussed in the shareholder meeting or other similar setting.

1 for yes, 0 for no

   

  39

ICFR Component (fourth level items) Score IC35: Operating Control IC35101：Existence of an operational analysis. 1 for yes, 0 for no

IC36: Performance Control IC36101：Existence of a performance analysis committee or department.

1 for yes, 0 for no

IC36102：A report is issued from the performance analysis. 1 for yes, 0 for no IC37: Emergency Control IC37101：Existence of a system that generates early warnings for material risk.

1 for yes, 0 for no

IC37102：Existence of an emergency response system. 1 for yes, 0 for no IC4: Information and Communication IC41: Information Collection IC41101：Existence of a channel for internal communication of information (e.g. financial analysis and staff meeting)

1 for yes, 0 for no

IC41102：Channel for external collection of information (e.g. information from regulatory bodies).

1 for yes, 0 for no

IC42: Information Communication IC42201: A system/mechanism governing information disclosure. 1 for yes, 0 for no IC42203: A mechanism for managing relations with investors. 1 for yes, 0 for no IC42204: A link for investor relations on the corporate website. 1 for yes, 0 for no IC42205: Another platform for communicating with investors. 1 for yes, 0 for no

IC423: Internal Transparency IC42301：All resolutions from shareholder meetings are disclosed. 1 for yes, 0 for no IC42302：All resolutions from board meetings are disclosed. 1 for yes, 0 for no IC42303：All resolutions from meetings of the board of supervisors disclosed.

1 for yes, 0 for no

IC425: Internal Timeliness IC42501：Periodic reports are released on the scheduled date. 1 for yes, 0 for no

IC43: Information System IC43101：Existence of an information department or information security department.

1 for yes, 0 for no

IC441: Anti-Fraud Mechanism IC44101：Anti-fraud mechanisms in the company. 1 for yes, 0 for no IC44102：A channel for whistle blowing. 1 for yes, 0 for no

IC442: Anti-Fraud Priority IC44201：The company specifies the priorities for anti-fraud. 1 for yes, 0 for no

   

  40

IC5: Monitoring IC51: Internal Monitoring Function

IC511: Monitoring from the Internal Audit Department

ICFR Component (fourth level items) Score

IC51101：Inspection of internal control by the internal audit department.

1 for yes, 0 for no

IC512: Monitoring from the Board of Supervisors IC51201：The board of supervisors monitors operational activities, such as financing activities, asset sales, and acquisitions.

1 for yes, 0 for no

IC513: Monitoring from the Board of Directors IC51301：The audit committee discusses the internal control inspection in its responsibility report.

1 for yes, 0 for no

IC51302：The independent directors discuss the internal control inspection in their responsibility report.

1 for yes, 0 for no

IC514: Special Monitoring IC51401：Monitoring for suspicious special events. 1 for yes, 0 for no

IC52: Internal Control Deficiencies IC52101：A standard for deficiency recognition. 1 for yes, 0 for no IC52102：An analysis of the reasons for deficiencies. 1 for yes, 0 for no IC52103：A plan for rectifying internal control deficiencies. 1 for yes, 0 for no IC52104：The company tracks the reform of internal control procedures.

1 for yes, 0 for no

IC53: Internal Control Disclosure

IC53102：The internal controls are well designed. 1 for yes, 0 for no

IC53103：The internal controls function well. 1 for yes, 0 for no

IC53104：Performance of an inspection of the internal control system.

1 for yes, 0 for no

IC52106：The inspection of the internal control system was evaluated.

1 for yes, 0 for no

IC53107：A plan to improve internal control exists. 1 for yes, 0 for no

IC53108：A plan for next year’s internal control exists. 1 for yes, 0 for no

IC53110：The board of supervisors evaluates the internal control system.

1 for yes, 0 for no

IC53111：The independent directors evaluate the internal control system.

1 for yes, 0 for no

  41

Appendix C: Variable Definitions INTERNAL CONTROL MEASURES ICFR A measure of the strength of internal control over financial reporting in

year t based on 69 of the 144 items collected by Chen et.al. (2013); see Appendices A&B for details.

DEPENDENT VARIABLES TUNNEL Evidence of tunneling (removal of cash from the firm), measured as

other receivables scaled by year-end total assets; PRIVATE Personal consumption expenses scaled by sales, including specific

expenditures on office supplies, business travel, entertainment, tele-communication, training abroad, board meetings, automobile, conferences, and other expense. Data on these expenditures are collected from note disclosures.

EXPOSED An indicator variable equal to one if there is ex post public disclosure of corrupt self-serving behavior by management (e.g. receipt of bribes) in year t (where year t is the year management undertook the corrupt behavior, not necessarily the year it was revealed), zero otherwise;

ABSDA Evidence of earnings management, measured using the absolute value of discretionary accruals in year t based on the performance-adjusted cross-sectional modified Jones model (Kothari et al., 2005);

RESTATE Evidence of earnings management, measured as an indicator variable equal to one if there is a restatement of year t (discovered in subsequent years), zero otherwise;

CONTROL VARIABLES ASSETS The logarithm of total assets (in Chinese Yuan)； ATO Total sales in year t divided by average total assets. BIG4 An indicator variable, equal to one if the company is audited by one of

the four largest U.S. audit firms, zero otherwise; CEOCHAIR An indicator variable equal to one if the CEO is also the Chairman of

the board, zero otherwise; DISSUE Percentage change of the amount of total debt; EISSUE Percentage change of the number of outstanding shares; EXCHANGE An indicator variable equal to one if the company is traded on the

Shenzhen Stock Exchange, zero if the company is traded on the Shanghai Stock Exchange;

EXPORT An indicator variable equal to one if the company exports merchandise to foreign countries and districts (including Hong Kong and Taiwan), zero otherwise;

FOREIGHSHR Percentage of foreign institutional ownership； INDIRECTOR Percentage of independent directors on the board; LIST2006 An indicator variable equal to one if the company lists on the Shenzhen

or Shanghai Exchanges after 2005, zero if they were already listed; LOSS An indicator variable equal to one if the company reports a loss in the

year, zero otherwise; M&A An indicator variable equal to one if the company completed a merger

or acquisition during the year, zero otherwise; MARKETIZATION A comprehensive index measuring the development of the regional

market in which a firm is registered based on Fan and Wang (2006) and Jiang et al. (2010);

  42

MB Market value of equity divided by book value of equity. MGMTSHR Percentage of outstanding shares held by top executives (multiplied by

100); PAYRATIO Ratio of the average executive total compensation relative to other

employees’ average total compensation; ROA Earnings before extraordinary items divided by average total assets; SALES The logarithm of total sales (in Chinese Yuan); SEGMENTS The logarithm of the number of the geographic segments; SOE An indicator variable equal to one if the company is controlled by the

central or local government or their agencies, zero otherwise; TOP1SHR The percentage of outstanding shares held by the largest shareholder;

  43

Table 1

Panel A: Sample selection Firm-year

observations Firm-years listed on Shenzhen and Shanghai exchanges 2007-2010* 6,764

Less：Firms in the financial industry （120） Firms traded on small and medium-sized enterprises(SMEs) board （1,401） Firms with missing data required for the first stage regression （468） Total sample used in multivariate analysis 4,775

*: Firms traded on Entrepreneur Section of Shenzhen Exchange are excluded because the Entrepreneur Section was not established until 2009 and the Internal Control Index constructed by Chen et al. does not cover those firms.

Panel B: Distribution by year

Year SOE NonSOE Total

N % N %

2007 787 66.41 398 33.59 1,185

2008 802 67.28 390 32.72 1,192

2009 802 66.78 399 33.22 1,201

2010 805 67.25 392 32.75 1,197

Total 3,196 66.93 1,579 33.07 4,775

  44

Table 2, Panel A: Descriptive statistics for full sample

Variable N Mean Median SD Q1 Q3

ICFR score 4,775 0.453 0.448 0.109 0.376 0.525

TUNNEL 4,775 0.027 0.012 0.042 0.004 0.030

PRIVATE 2,128 0.027 0.010 0.056 0.004 0.024

EXPOSED 2,796 0.045 0.000 0.207 0.000 0.000

Total Assets 4,775 6,965,663 2,504,106 15,204,401 1,142,309 5,693,115

Sales Revenue 4,775 4,934,773 1,443,665 11,238,962 595,309 3,573,788

# Segment 4,775 3.486 2.000 2.840 2.000 5.000

ROA 4,775 0.036 0.033 0.080 0.011 0.065

LOSS 4,775 0.122 0.000 0.328 0.000 0.000

MB 4,775 4.342 3.387 5.379 1.980 5.496

EXPORT 4,775 0.414 0.000 0.493 0.000 1.000

M&A 4,775 0.780 1.000 0.414 1.000 1.000

FOREIGNSHR 4,775 0.019 0.000 0.073 0.000 0.000

BIG4 4,775 0.065 0.000 0.246 0.000 0.000

LIST2006 4,775 0.059 0.000 0.236 0.000 0.000

MARKETIZATION 4,775 8.737 8.660 2.043 7.190 11.040

EXCHENGE 4,775 0.377 0.000 0.485 0.000 1.000

TOP1SHR 4,775 0.357 0.337 0.154 0.232 0.476

CEOCHAIR 4,722 0.132 0.000 0.338 0.000 0.000

INDIRECTOR 4,684 0.363 0.333 0.051 0.333 0.375

PAYRATIO 3,954 3.734 2.763 2.940 1.822 4.592

MGMTSHR 4,773 0.530 0.002 3.258 0.000 0.017

ATO 4,775 0.746 0.625 0.546 0.364 0.963

EISSUE 4,762 0.117 0.000 0.280 0.000 0.009

DISSUE 4,773 0.241 0.107 0.659 -0.048 0.331 Total Assets and Sales Revenues are reported in thousands of Chinese Yuan.

  45

Table 2, Panel B: Descriptive statistics for the SOE and Non-SOE samples

SOEs

N=3,196

Non-SOEs

N=1,579

Test of Differences 

Mean  Median Mean Median Mean Test  Median Test

ICFR score 0.463 0.457 0.433 0.429 <0.001 <0.001

TUNNEL 0.022 0.011 0.038 0.017 <0.001 <0.001

PRIVATE 0.020 0.009 0.040 0.014 <0.001 <0.001

EXPOSED 0.062 0.000 0.011 0.000 <0.001 <0.001

Total Assets 8,867,397 2,958,579 3,116,430 1,530,566 <0.001 <0.001

Sales Revenue  6,297,742 1,812,571 2,176,033 882,171 <0.001 0.001

# Segments 3.422 2.000 3.616 2.000 0.027 <0.001

ROA 0.034 0.032 0.039 0.037 0.038 0.008

LOSS 0.114 0.000 0.140 0.000 0.009 0.009

MB 4.120 3.170 4.791 3.807 <0.001 <0.001

EXPORT 0.431 0.000 0.379 0.000 <0.001 <0.001

M&A 0.755 1.000 0.832 1.000 <0.001 <0.001

FOREIGNSHR 0.020 0.000 0.016 0.000 0.105 0.205

BIG4 0.084 0.000 0.027 0.000 <0.001 <0.001

LIST2006 0.068 0.000 0.042 0.000 <0.001 <0.001

MARKETIZATION 8.686 8.660 8.841 8.810 0.014 0.060

EXCHANGE 0.369 0.000 0.392 0.000 0.121 0.121

TOP1SHR 0.384 0.382 0.302 0.262 <0.001 <0.001

CEOCHAIR 0.095 0.000 0.205 0.000 <0.001 <0.001

INDIRECTOR 0.362 0.333 0.366 0.333 0.035 0.015

PAYRATIO 3.542 2.585 4.126 3.223 <0.001 <0.001

MGMTSHR 0.066 0.002 1.469 0.002 <0.001 0.001

ATO  0.784 0.652 0.668 0.537 <0.001 <0.001

EISSUE 0.107 0.000 0.138 0.000 <0.001 0.004

DISSUE 0.260 0.126 0.204 0.067 0.006 <0.001 Total Assets and Sales Revenues are reported in thousands of Chinese Yuan.

  46

Table 2, Panel C: Pearson (Spearman) Correlation

All variables are described in Appendix. Pearson correlations are reported above the diagonal, and Spearman correlations are reported off diagonal. All continuous variables are winsorized at 1% and 99% to mitigate outliers. a, b, indicate statistical significance at the 0.01, 0.05 level, respectively.

ICF

R

TUN

NE

L

PR

IVA

TE

EX

PO

SED

AB

SDA

RE

STA

TE

ASS

ET

ATO

BIG

4

CE

OC

HA

IR

DIS

SUE

EIS

SUE

EX

CH

AN

GE

EX

PO

RT

FO

RE

IGH

SHR

IND

IRE

CTO

R

LIST

2006

LOSS

M&

A

MA

RK

ETI

ZATI

ON

MB

MG

MTS

HR

PA

YRA

TIO

RO

A

SALE

S

SEG

ME

NTS

SOE

TOP

1SH

R

ICFR 1 -0.15a

-0.14 a

0.04b -0.02 -0.12

a0.38

a0.08

a0.17

a-0.07

a0.06

a0.06

a0.13

a0.07

a0.10

a0.06

a0.07

a-0.14

a 0.01 0.06 a

-0.06 a -0.01 0.11

a0.11

a0.35

a0.09

a0.13

a0.10

a

TUNNEL -0.12a 1 0.19

a0.06

a0.14

a0.06

a-0.27

a-0.12

a-0.09

a0.05

a-0.08

a-0.05

a0.05

a-0.08

a-0.06

a0.05

a-0.06

a0.11

a0.06

a-0.06

a0.09

a -0.01 -0.01 -0.14 a

-0.30 a 0.01 -0.17

a-0.20

a

PRIVATE -0.11 a

0.27 a 1 -0.01 0.04

b0.06

a-0.31

a-0.24

a-0.06

a0.09

a-0.08

a-0.06

a -0.03 -0.12 a -0.03 0 -0.05

a0.14

a0.03

b -0.03 -0.02 0.05 b

-0.07 a

-0.09 a

-0.43 a -0.02 -0.17

a-0.18

a

EXPOSED 0.03 0.03 -0.07 a 1 0.02 -0.01 0.11

a0.06

a0.05

a 0 0.02 -0.03 -0.01 -0.01 0.08 a

0.09 a

0.08 a 0.02 0.01 -0.01 0 -0.05

a-0.08

a -0.01 0.12 a 0 0.12

a0.12

a

ABSDA -0.02 0.08 a 0.01 0.01 1 0.01 -0.05

a-0.06

a-0.05

a0.05

a0.21

a0.13

a0.03

b-0.09

a-0.05

a0.04

a -0.02 -0.01 0.05 a

0.04 a

0.04 a 0 0.03

b0.08

a-0.12

a-0.04

a-0.11

a -0.02

RESTATE 0.12 a

0.08 a

0.05 b *0.01 0.03

b 1 -0.09 a

-0.03 b

-0.05 a 0.01 -0.02 0 0 -0.04

a-0.03

b 0.00 0.01 0.09 a 0.01 -0.05

a0.03

b 0 -0.03 -0.06 a

-0.09 a -0.02 -0.01 -0.05

a

ASSETS 0.34 a

-0.25 a

-0.39 a

0.07 a

-0.05 a

-0.09 a 1 0.12

a0.40

a-0.14

a0.22

a0.18

a-0.10

a 0.14 0.26 a

0.04 a

0.15 a

-0.20 a 0.00 0.10

a-0.18

a -0.02 0.21 a 0.17 0.86

a0.12

a0.29

a0.34

a

ATO 0.09 a

-0.08 a

-0.35 a

0.05 a

-0.05 a -0.03 0.12

a 1 0.02 -0.05 a

0.04 a 0.03 -0.01 0.17

a0.04

a-0.04

a 0.03 -0.12 a

-0.04 a

0.07 a 0.01 0.03 0.08

a0.17

a0.54

a0.09

a0.10

a0.11

a

BIG4 0.16 a

-0.08 a

-0.10 a

0.05 a

-0.05 a

-0.05 a

0.32 a

0.04 a 1 -0.05

a 0.01 -0.03 b

-0.04 a

0.07 a

0.40 a

0.05 a

0.13 a

-0.03 b

-0.04 a

0.13 a

-0.07 a

-0.04 a

0.11 a

0.06 a

0.33 a

0.03 b

0.11 a

0.14 a

CEOCHAIR -0.07 a

0.05 a

0.10 a 0 0.04

a 0.01 -0.14 a

0.07 a

0.05 a 1 -0.02 -0.03

b0.06

a -0.02 -0.01 0.03 b

-0.05 a

0.04 a 0 0.03

b 0.02 0.08 a

0.06 a

-0.04 a

-0.14 a

-0.03 b

-0.15 a

-0.13 a

DISSUE 0.14 a

-0.12 a

-0.13 a 0.01 0.11

a-0.04

a0.34

a0.08

a0.07

a-0.05

a 1 0.33 a

-0.03 b -0.02 0.01 0.01 0.01 -0.07

a0.03

b -0.02 0.01 0.01 0.05 a

0.11 a

0.14 a 0 0.04

a0.19

a

EISSUE 0.09 a

-0.05 a

-0.07 a

-0.04 b

0.06 a 0 0.23

a0.05

a -0.01 -0.03 b

0.18 a 1 -0.05

a 0.01 -0.01 0.01 0.05 a

-0.10 a

0.06 a

-0.04 a 0.01 0.06

a0.08

a0.16

a0.13

a0.04

a-0.05

a0.10

a

EXCHANGE 0.15 a

0.04 a 0.02 -0.01 0.03

b 0 -0.09 a

-0.03 b

-0.04 a

0.06 a

-0.06 a

-0.07 a 1 -0.13

a-0.05

a0.03

b-0.19

a0.04

a 0.01 -0.10 a

0.03 b

-0.10 a

0.07 a -0.01 -0.09

a 0.00 -0.02 -0.10 a

EXPORT 0.06 a

-0.07 a

-0.11 a -0.01 -0.09

a-0.04

a0.12

a0.23

a0.07

a -0.02 0.03 b 0.01 -0.13

a 1 0.13 a -0.01 0.05

a -0.02 -0.02 0.09 a -0.01 0.03

b0.05

a 0 0.22 a

0.03 b

0.05 a

0.04 a

FOREIGHSHR 0.07 a

-0.06 a

-0.06 a

0.07 a

-0.05 a -0.03 0.18

a0.04

a0.36

a 0 0.01 0.02 -0.04 a

0.13 a 1 0.04

a0.18

a 0 -0.04 a

0.09 a

-0.05 a -0.02 0.08

a 0.01 0.23 a

0.03 b

0.03 b

0.08 a

INDIRECTOR 0.04 a

0.03 b -0.02 0.09

a0.03

b 0 0.03 -0.03 b

0.04 a

0.03 b 0.01 -0.01 0.03 -0.02 0.05

a 1 0.05 a 0 0 -0.03 0.01 0.02 0.01 -0.01 0.01 0.01 -0.03

b 0.03

LIST2006 0.06 a

-0.08 a

-0.08 a

0.08 a -0.01 0.01 0.08

a0.04

a0.13

a-0.05

a0.05

a0.06

a-0.19

a0.05

a0.18

a0.04

a 1 -0.04 a -0.01 0.01 -0.02 0.12

a 0 0.05 a

0.13 a 0.02 0.05

a0.11

a

LOSS -0.14 a

0.10 a

0.14 a 0.02 0.02 0.09

a-0.21

a-0.14

a-0.03

b0.04

a-0.08

a-0.12

a0.04

a -0.02 0.01 0.01 -0.04 a 1 -0.01 -0.05

a 0.01 -0.02 -0.08 a

-0.65 a

-0.22 a -0.02 -0.04

a-0.12

a

M&A 0.01 0.13 a 0.03 0.01 0.05

a 0.01 0 -0.05 a

-0.04 a -0.00 0.03

b0.06

a 0.01 -0.02 -0.03 b 0.00 -0.01 -0.01 1 0.03

b 0.02 0.01 0.05 a 0 -0.03 0.02 -0.09

a-0.09

a

MARKETIZATION 0.05 a

-0.05 a

-0.04 b -0.01 0.01 -0.05

a0.10

a0.06

a0.14

a 0.02 -0.01 -0.03 b

-0.12 a

0.09 a

0.10 a -0.02 0.01 -0.05

a 0.03 1 -0.05 a

0.09 a

0.09 a

0.05 a

0.10 a 0.01 -0.04

a 0.02

MB -0.08 a

0.11 a

0.07 a -0.03 0.09

a 0.02 -0.29 a

0.07 a

-0.14 a

0.04 a

0.03 b 0 0.01 -0.03

b-0.10

a -0.01 -0.04 a -0.01 0.03

b-0.07

a 1 0 -0.03 0.07 a

-0.14 a

-0.03 b

-0.06 a 0.01

MGMTSHR 0.04 a

0.03 b -0.04 -0.06

a -0.01 0.02 0.11 a

0.07 a

-0.04 a

0.04 a 0.02 0.05

a0.05

a0.04

a-0.07

a-0.06

a-0.08

a-0.05

a0.04

a0.16

a-0.06

a 1 0.05 a

0.03 b 0 0.02 -0.20

a-0.11

a

PAYRATIO 0.11 a 0.01 -0.10

a-0.09

a 0.02 -0.03 0.20 a

0.12 a

0.09 a 0.03 0.09

a0.10

a0.06

a0.07

a0.08

a 0 0 -0.09 a

0.04 a

0.04 b 0 0.17

a 1 0.13 a

0.21 a

0.12 a

-0.09 a

-0.10 a

ROA 0.13 a

-0.16 a

-0.07 a -0.01 0.08

a-0.08

a0.20

a0.20

a0.09

a -0.02 0.15 a

0.21 a

-0.03 b -0.00 0.02 -0.02 0.06

a-0.57

a -0.01 0.07 a

0.14 a

0.06 a

0.19 a 1 0.20

a -0.01 -0.03 b

0.14 a

SALES 0.32 a

-0.23 a

-0.48 a

0.08 a

-0.09 a

-0.08 a

0.85 a

0.57 a

0.30 a

-0.14 a

0.25 a

0.18 a

-0.07 a

0.21 a

0.19 a 0 0.10

a-0.21

a -0.02 0.12 a

-0.21 a

0.11 a

0.22 a

0.24 a 1 0.17

a0.30

a0.32

a

SEGMENTS 0.09 a

0.12 a 0.03 0 -0.04

a -0.02 0.12 a

0.14 a

0.03 b -0.03 0.03

b0.08

a 0.01 0.03 b

0.04 a 0.01 0.01 -0.02 0.03

b 0 -0.04 a

0.05 a

0.12 a 0.01 0.16

a 1 -0.04 a 0.004

SOE 0.13 a

-0.16 a

-0.17 a

0.12 a

-0.10 a -0.01 0.27

a0.12

a0.11

a-0.15

a0.09

a-0.04

a -0.02 0.05 a 0.02 -0.04

b0.05

a-0.04

a0.09

a -0.03 -0.10 a

-0.05 a

-0.12 a

-0.04 a

0.27 a

-0.05 a 1 0.25

a

TOP1SHR 0.09 a

-0.23 a

-0.24 a

0.10 a

-0.03b

-0.05 a

0.30 a

0.13 a

0.14 a

-0.13 a

0.15 a

0.05 a

-0.10 a

0.05 a

0.06 a -0.01 0.11

a-0.12

a-0.09

a0.03

a -0.01 -0.23 a

-0.09 a

0.15 a

0.29 a 0.01 0.26

a 1

  47

Table 3: Internal Control and Financial Reporting Quality

Dependent Variable ABSDA RESTATE 2SLS 2SLS

Intercept 0.052*** -1.957***

(0.001) (0.000)

ICFR  -0.037** -1.715*** (0.013) (0.010) CFO -0.033 -1.068 (0.265) (0.178) LEV 0.043*** 0.117 (0.000) (0.501) ROA 0.122*** -1.601* (0.000) (0.055) MB 0.001** 0.016 (0.011) (0.163) EISSUE 0.018*** 0.163 (0.005) (0.492) DISSUE 0.027*** -0.055 (0.000) (0.581) BIG4 -0.006 -1.245** (0.208) (0.022) Top1SHR -0.017* -1.054** (0.073) (0.019)

Year effects Included Included

Industry effects Included Included No. of Obs. 4,749 4,772 Adjusted (or Pseudo) R2 0.214 0.063

CFO is the operating cash flows divided by end-of-year total assets; LEV is leverage calculated by total liabilities divided by total assets; All other variables are defined in Appendix C. All continuous variables are winsorized at 1% and 99% to mitigate outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, 0.10 level, respectively. P-values reported in parentheses are based on two-tailed tests. Standard errors are clustered by firm.

  48

Table 4: Internal Control and Tunneling

Dependent variable= TUNNEL Predicted

Signs Full Sample SOEs Non-SOEs

Intercept ? 0.154*** 0.102*** 0.255*** (0.000) (0.000) (0.000) ICFR − -0.014** -0.006 -0.033** (0.036) (0.212) (0.023) ASSETS − -0.006*** -0.004*** -0.008*** (0.000) (0.000) (0.000) ROA − -0.054*** -0.083*** -0.025 (0.000) (0.000) (0.284) TOP1SHR − -0.027*** -0.020*** -0.028*** (0.000) (0.001) (0.007) CEOCHAIR + 0.0001 0.003 -0.005 (0.485) (0.176) (0.854) INDIRECTOR ? 0.037** 0.024 0.026 (0.024) (0.145) (0.493) MARKETIZATION − -0.001*** -0.001* -0.003*** (0.002) (0.090) (0.001) BIG4 − 0.004 0.003 -0.006 (0.980) (0.871) (0.123) EXCHANGE ? 0.002 -0.001 0.007 (0.268) (0.763) (0.109) Year effects Included Included Included Industry effects Included Included Included No. of Obs. 4,638 3,097 1,541 Adjusted R2 0.145 0.146 0.139

Test of differences between coefficients SOEs vs. non-SOEs Coefficient Diff. Z-statistic P-value ICFR 0.027* 1.502 0.066 All variables are defined in Appendix C. All continuous variables are winsorized at 1% and 99% to mitigate outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, 0.10 level, respectively. P-values reported in parentheses are based on one-tailed tests for variables with predicted signs and two-tailed tests for variables without predicted signs. Standard errors are clustered by firm.

  49

Table 5: Internal Control and Private Consumption

Dependent variable= PRIVATE

Predicted

Signs Full Sample SOEs Non-SOEs

Intercept ? 0.052*** 0.084*** 0.155*** (0.002) (0.005) (0.000) ICFR − -0.026** -0.001 -0.099*** (0.039) (0.479) (0.007) PAYRATIO − -0.001*** -0.0003 -0.003*** (0.006) (0.206) (0.008) MGMTSHR − 0.088 -0.175** 0.107 (0.771) (0.031) (0.805) ATO − -0.019*** -0.015*** -0.023*** (0.000) (0.000) (0.002) ROA − -0.060*** -0.050** -0.048** (0.001) (0.033) (0.045) EISSUE + -0.002 0.001 -0.001 (0.683) (0.369) (0.538) DISSUE + -0.001 -0.001 0.001 (0.272) (0.209) (0.532) BIG4 − -0.0002 -0.006** 0.014 (0.476) (0.028) (0.829) TOP1SHR − -0.027*** -0.013 -0.061*** (0.004) (0.104) (0.007) CEOCHAIR ? 0.006 0.001 0.013 (0.342) (0.861) (0.310) INDIRECTOR ? 0.000 -0.035 0.059 (0.987) (0.210) (0.370) MARKETIZATION − -0.001 0.001 -0.004 (0.215) (0.711) (0.097) Year effects Included Included Included Industry effects Included Included Included No. of Obs. 1,802 1,240 562 Adjusted R2 0.120 0.096 0.164 Test of coefficient difference SOE vs. NonSOE Coefficient Diff. Z-statistic P-value ICFR 0.098*** 2.352 0.009 All variables are defined in Appendix C. All continuous variables are winsorized at 1% and 99% to mitigate outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, 0.10 level, respectively. P-values reported in parentheses are based on one-tailed tests for variables with predicted signs and two-tailed tests for variables without predicted signs. Standard errors are clustered by firm.

  50

Table 6: Internal Control and Exposed Corruption Cases

Dependent variable=EXPOSED Predicted

Signs Full SOE NonSOE

Intercept ? -10.875*** -11.243*** 11.097 (0.000) (0.000) (0.164) ICFR − 1.063 0.929 -4.633* (0.845) (0.791) (0.093) PAYRATIO − -0.132** -0.102** -0.390*** (0.011) (0.033) (0.002) MGMTSHR − -14.855* -13.720 -53.929*** (0.074) (0.360) (0.001) SALE + 0.316*** 0.346*** -1.020** (0.004) (0.003) (0.013) ROA − -2.345 -3.037 9.864*** (0.269) (0.199) (0.008) EISSUE + -0.720 -0.704 1.542** (0.981) (0.970) (0.039) DISSUE + 0.116 0.119 0.296 (0.191) (0.204) (0.323) BIG4 − -0.530 -0.598* -0.348 (0.107) (0.089) (0.432) TOP1SHR − 1.021 1.629 -6.692 (0.334) (0.168) (0.165) CEOCHAIR ? 0.345 0.056 1.284** (0.366) (0.894) (0.031) INDIRECTOR ? 4.615** 5.290*** -18.821*** (0.014) (0.008) (0.001) MARKETIZATION − -0.058 -0.092 0.369 (0.231) (0.150) (0.944) Year effects Included Included Included Industry effects Included Included Included No. of Obs. 2,398 1,732 666 Pseudo R2 0.114 0.120 0.391

Test of coefficient difference SOEs vs. non-SOEs Coefficient Diff. Z-statistic P-value ICFR 5.562* 1.508 0.066

All variables are defined in Appendix C. All continuous variables are winsorized at 1% and 99% to mitigate outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, and 0.10 level, respectively. P-values reported in parentheses are based on one-tailed tests for variables with predicted signs and two-tailed tests for variables without predicted signs. Standard errors are clustered by firm.

  51

Table 7: Determinants of Internal Control over Financial Reporting Strength The dependent variable is the score of internal control over financial reporting strength (ICFR), constructed based on the internal control index developed by Chen et al. (2013) and described in Appendices A and B.

All variables are defined in Appendix C. All continuous variables are winsorized at 1% and 99% to mitigate outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, 0.10 level, respectively. P-values reported in parentheses are based on one-tailed tests for variables with predicted signs and two-tailed tests for variables without predicted signs. Standard errors are clustered by firm.

Intercept ？ -0.177*** (0.001) SOE + 0.010*** (0.0095) ASSETS + 0.024*** (0.000) ROA + 0.030 (0.114) LOSS – -0.018*** (0.001) MB – 0.0003 (0.865) EXPORT – 0.004 (0.866) SEGMENT – 0.012*** (0.003) M&A – 0.003 (0.812) FOREIGHSHR + 0.016 (0.277) BIG4 + 0.015** (0.048) LIST2006 + 0.044*** (0.000) MARKETIZATION + 0.001 (0.135) EXCHANGE ？ 0.042*** (0.000) Year effects Included Industry effects Included No. of Obs. 4,775 Adjusted R2 0.312

  52

Table 8: Internal Control and Corporate Corruption: 2SLS

Panel A: Full Sample

Predicted

sign TUNNEL PRIVATE EXPOSED

ICFR − -0.122** -0.259*** -0.320 (0.011) (0.000) （0.473） Control variables Included Included Included # Obs. 4,638 1,802 2,398 Adjusted (Pseudo)R2 0.145 0.143 0.133

Panel B: SOEs versus Non-SOEs

TUNNEL PRIVATE EXPOSED

SOEs Non-SOEs SOEs Non-SOEs SOEs Non-SOEs ICFR 0.015 -0.274** -0.182*** -0.456*** -0.338 -39.296*** (0.631) (0.011) (0.000) (0.001) （0.474） （0.003） Control variables Included Included Included Included Included Included # Obs. 3,097 1,541 1,240 562 1,732 666 Adjusted (Pseudo)R2 0.145 0.139 0.117 0.186 0.124 0.410

Test of coefficient difference TUNNEL PRIVATE EXPOSED SOEs vs. Non-SOEs SOEs vs. Non-SOEs SOEs vs. Non-SOEs

Coefficient Diff. P-value Coefficient Diff. P-value Coefficient Diff. P-value ICFR 0.290** 0.011 0.273** 0.35 38.959*** 0.004

All variables are defined in Appendix C. All continuous variables are winsorized at 1% and 99% to mitigate outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, 0.10 level, respectively, under one-tailed tests for variables with predicted signs and two-tailed tests for variables without predicted signs. Standard errors are clustered by firm.

  53

Table 9: Internal Control and CEO Promotions

Dependent variable=PROMOTION Alla Turnover Onlyb Full

Sample SOEs Non-SOEs Full Sample SOEs Non-SOEs

ICFR 1.060** 1.187** 0.784 1.928** 1.913* 2.231 (0.012) (0.024) (0.285) (0.023) (0.069) (0.174) SALE 0.088*** 0.080** 0.138** 0.145** 0.137 0.207 (0.004) (0.048) (0.012) (0.029) (0.122) (0.105) CFO 0.266 -0.451 1.563** 0.406 -1.722 3.173* (0.557) (0.459) (0.023) (0.650) (0.158) (0.069) ROA 0.742 -0.223 1.189 -0.271 -0.019 0.148 (0.250) (0.826) (0.216) (0.814) (0.990) (0.938) LEV -0.294** -0.670*** -0.155 -0.395 -0.980** -0.347 (0.020) (0.001) (0.397) (0.226) (0.045) (0.441) MB -0.006 0.015 -0.024* -0.004 0.031* -0.040 (0.432) (0.161) (0.053) (0.794) (0.098) (0.119) CEOAGE -0.268** -0.484*** 0.282 -0.434 -0.732** 0.519 (0.036) (0.002) (0.235) (0.103) (0.019) (0.304) EDUCATION 0.023 0.021 0.028 0.089 0.136 -0.047 (0.657) (0.762) (0.728) (0.393) (0.335) (0.793) TENURE 0.009 0.027 -0.031 0.033 0.078 -0.111 (0.622) (0.253) (0.351) (0.417) (0.128) (0.216) TOP1SHR 0.136 0.334 -0.353 0.257 0.617 -0.283 (0.621) (0.300) (0.521) (0.650) (0.367) (0.802)MARKETIZATION 0.006 0.019 -0.020 0.005 0.018 -0.085 (0.760) (0.455) (0.574) (0.898) (0.741) (0.244) Constant1 -0.096 -0.484 0.643 (0.885) (0.566) (0.579) Constant 2 4.850*** 4.503*** 5.681*** (0.000) (0.000) (0.000) Constant -4.049*** -3.553** -4.629* (0.002) (0.038) (0.077) Year effects Included Included Included Included Included Included Industry effects Included Included Included Included Included Included No. of Obs. 4,573 3,071 1,502 722 506 266 Pseudo R2 0.012 0.019 0.035 0.043 0.065 0.101 a: Includes all firm-years; the dependent variable equals 1 if the CEO gets promoted in the following year, 0 if there is no CEO turnover, and –1 if the CEO gets demoted in the following year. b: Includes only firm-years of CEO turnover; the dependent variable equals 1 if the CEO gets promoted in the following year, and 0 otherwise. We collect the CEO turnover data during 2008-2011 and eliminate cases where the CEO left office due to retirement or health reason, and cases where we cannot identify her next appointment. We consider the CEO being promoted if in the following year she left the CEO position and became (1) the chairman of the board at the same company (2) the CEO or the chairman of the board in the parent company, (3) the CEO at a larger public company that the asset of the new company is at least 20% larger than the former company, or (4) a high-rank government official. CFO is the operating cash flows divided by end-of-year total assets. LEV is leverage calculated by total liabilities divided by total assets. CEOAGE is the logarithm of the CEO’s age. EDUCATION is a categorical variable measuring the highest degree of education the CEO holds; EDUCATION equals 4 if the CEO’s highest degree is doctorate, 3 if it is a master degree, 2 if it is a college degrees, 1 if it is a 3-year college or other. TENURE measures the number of years that the CEO has assumed the position. CEOAGE is an indicator variable that equals to one if the CEO is 55 years old or older, and zero otherwise. All other variables are defined in Appendix C. All continuous variables are winsorized at 1% and 99% to mitigate outliers. ***, **, * indicate statistical significance at the 0.01, 0.05, 0.10 level, respectively. P-values reported in parentheses are based on two-tailed tests. Standard errors are clustered by firm.