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
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.
1
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 selfdealing 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 intercorporate 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.
2
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.
3
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.
4
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 statedowned 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-stateowned 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.
5
We conclude our analysis with two additional tests. First, we employ two-stage leastsquares (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 multidimensional 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
6
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 (AshbaughSkaife 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.
7
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.
8
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.”
9
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
10
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
11
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.
12
We use the adoption of internal controls within state-owned enterprises to proxy for policydriven 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.”
13
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
14
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.
15
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
16
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
17
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
18
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.
19
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
20
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).
21
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.
22
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.
23
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
24
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
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),
25
(5)
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.
26
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
27
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.
28
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 highranking government official.18
We first estimate Equation (6) using an Ordered Logit regression including all firmyears. 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.
29
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.
30
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).
31
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 nonSOEs. 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-stateowned 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.
32
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Skaife, H., D. Veenman, and D. Wangerin. 2013. Internal control over financial reporting and
managerial rent extraction: Evidence from the profitability of insider trading. Journal of
Accounting and Economics 55, 91-110.
35
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 selfassessment 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 subproblems, 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.
36
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 fourthlevel items. Our results remain unchanged using this alternative measure.
ICFR Component (fourth level items)
IC1: Control Environment
IC111: Institutional Arrangement
IC11101: Existence of a manual or guideline on internal control in
the company.
IC11102: Use of an outside professional to help the company
improve its internal control.
IC11103: Cross-listed company.
IC113: Board of Directors
IC11303: A board-level committee charged with the oversight of
internal control (audit committee or risk management committee).
IC11304: Number of audit committee members
IC114: Board of Supervisors
IC11404: Supervisors with legal and accounting expertise.
IC121: Internal Control Implementation
IC12101：Existence of an internal control department in the
company.
IC12102：The internal audit department reports to the board.
IC131: Recruiting and Training
IC13102: The human resource policy contains provisions on
recruiting.
IC13103: The human resource policy contains provisions on
training.
IC13104: The human resource policy contains provisions on job
rotations at critical levels.
IC132: Incentives
IC13202: The human resource policy contains provisions on
evaluations.
IC13205: Employee salary is linked to performance.
37
Score
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
To be standardized
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
ICFR Component (fourth level items)
IC133: Severance
IC13301: The human resource policy contains provisions on
discharging employees.
IC13302: The human resource policy contains provisions on
employee resignations.
IC13303: An audit is performed upon the departure of the chairman,
CEO, and other executives.
IC142: Competence variable mean
IC14201: Percentage of the staff with at least a junior college
education.
IC14202: Existence of employee training by the company.
IC2: Risk Assessment
IC221: Risk evaluation
IC22101: Existence of a risk management committee or department.
IC222:Internal risk
IC22202: A human resource risk disclosed in public filings.
IC22203: An operation risk disclosed in public filings.
IC22205: A financial risk disclosed in public filings.
IC231: Risk Evaluation Method variable mean
IC23101: A quantitative risk analysis in the annual report or in the
internal control evaluation report.
IC23102: Economic risk disclosed?
IC241: Response Strategies
IC24101: Any response strategy disclosed?
IC24102: Any analysis of risk tolerance?
IC242: Risk Management Measure
IC24201: A risk management measure taken to control risk.
IC3: Control Activities
IC31: Separation of Duties
IC31101: Controls for incompatible separation of duties.
IC31102: Controls for authorizing and approving.
IC31103: Approval of material matters is in accordance with
existing procedures.
IC32: Accounting Control
IC32101: Company has an accounting information system.
IC34: Budget Control variable
IC34101: Existence of a budgeting committee or department.
IC34102: A budget is implemented in the company.
IC34103: The annual budget is discussed in the shareholder meeting
or other similar setting.
38
Score
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
To be standardized
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
ICFR Component (fourth level items)
IC35: Operating Control
IC35101：Existence of an operational analysis.
IC36: Performance Control
IC36101：Existence of a performance analysis committee or
department.
IC36102：A report is issued from the performance analysis.
IC37: Emergency Control
IC37101：Existence of a system that generates early warnings for
material risk.
IC37102：Existence of an emergency response system.
IC4: Information and Communication
IC41: Information Collection
IC41101：Existence of a channel for internal communication of
information (e.g. financial analysis and staff meeting)
IC41102：Channel for external collection of information (e.g.
information from regulatory bodies).
IC42: Information Communication
IC42201: A system/mechanism governing information disclosure.
IC42203: A mechanism for managing relations with investors.
IC42204: A link for investor relations on the corporate website.
IC42205: Another platform for communicating with investors.
IC423: Internal Transparency
IC42301：All resolutions from shareholder meetings are disclosed.
IC42302：All resolutions from board meetings are disclosed.
IC42303：All resolutions from meetings of the board of supervisors
disclosed.
IC425: Internal Timeliness
IC42501：Periodic reports are released on the scheduled date.
IC43: Information System
IC43101：Existence of an information department or information
security department.
IC441: Anti-Fraud Mechanism
IC44101：Anti-fraud mechanisms in the company.
IC44102：A channel for whistle blowing.
IC442: Anti-Fraud Priority
IC44201：The company specifies the priorities for anti-fraud.
39
Score
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
IC5: Monitoring
IC51: Internal Monitoring Function
IC511: Monitoring from the Internal Audit Department
ICFR Component (fourth level items)
IC51101：Inspection of internal control by the internal audit
department.
IC512: Monitoring from the Board of Supervisors
IC51201：The board of supervisors monitors operational activities,
such as financing activities, asset sales, and acquisitions.
IC513: Monitoring from the Board of Directors
IC51301：The audit committee discusses the internal control
inspection in its responsibility report.
IC51302：The independent directors discuss the internal control
inspection in their responsibility report.
IC514: Special Monitoring
IC51401：Monitoring for suspicious special events.
IC52: Internal Control Deficiencies
IC52101：A standard for deficiency recognition.
IC52102：An analysis of the reasons for deficiencies.
IC52103：A plan for rectifying internal control deficiencies.
IC52104：The company tracks the reform of internal control
procedures.
IC53: Internal Control Disclosure
IC53102：The internal controls are well designed.
IC53103：The internal controls function well.
IC53104：Performance of an inspection of the internal control
system.
IC52106：The inspection of the internal control system was
evaluated.
IC53107：A plan to improve internal control exists.
IC53108：A plan for next year’s internal control exists.
IC53110：The board of supervisors evaluates the internal control
system.
IC53111：The independent directors evaluate the internal control
system.
40
Score
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
1 for yes, 0 for no
Appendix C: Variable Definitions
INTERNAL CONTROL MEASURES
A measure of the strength of internal control over financial reporting in
ICFR
year t based on 69 of the 144 items collected by Chen et.al. (2013); see
Appendices A&B for details.
DEPENDENT VARIABLES
Evidence of tunneling (removal of cash from the firm), measured as
TUNNEL
other receivables scaled by year-end total assets;
Personal consumption expenses scaled by sales, including specific
PRIVATE
expenditures on office supplies, business travel, entertainment, telecommunication, training abroad, board meetings, automobile,
conferences, and other expense. Data on these expenditures are
collected from note disclosures.
An indicator variable equal to one if there is ex post public disclosure of
EXPOSED
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;
Evidence of earnings management, measured using the absolute value
ABSDA
of discretionary accruals in year t based on the performance-adjusted
cross-sectional modified Jones model (Kothari et al., 2005);
Evidence of earnings management, measured as an indicator variable
RESTATE
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)；
Total sales in year t divided by average total assets.
ATO
An indicator variable, equal to one if the company is audited by one of
BIG4
the four largest U.S. audit firms, zero otherwise;
An indicator variable equal to one if the CEO is also the Chairman of
CEOCHAIR
the board, zero otherwise;
Percentage change of the amount of total debt;
DISSUE
Percentage change of the number of outstanding shares;
EISSUE
An indicator variable equal to one if the company is traded on the
EXCHANGE
Shenzhen Stock Exchange, zero if the company is traded on the
Shanghai Stock Exchange;
An indicator variable equal to one if the company exports merchandise
EXPORT
to foreign countries and districts (including Hong Kong and Taiwan),
zero otherwise;
FOREIGHSHR
Percentage of foreign institutional ownership；
Percentage of independent directors on the board;
INDIRECTOR
An indicator variable equal to one if the company lists on the Shenzhen
LIST2006
or Shanghai Exchanges after 2005, zero if they were already listed;
An indicator variable equal to one if the company reports a loss in the
LOSS
year, zero otherwise;
An indicator variable equal to one if the company completed a merger
M&A
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);
41
MB
MGMTSHR
PAYRATIO
ROA
SALES
SEGMENTS
SOE
TOP1SHR
Market value of equity divided by book value of equity.
Percentage of outstanding shares held by top executives (multiplied by
100);
Ratio of the average executive total compensation relative to other
employees’ average total compensation;
Earnings before extraordinary items divided by average total assets;
The logarithm of total sales (in Chinese Yuan);
The logarithm of the number of the geographic segments;
An indicator variable equal to one if the company is controlled by the
central or local government or their agencies, zero otherwise;
The percentage of outstanding shares held by the largest shareholder;
42
Table 1
Panel A: Sample selection
Firm-years listed on Shenzhen and Shanghai exchanges 2007-2010*
Firm-year
observations
6,764
Less：Firms in the financial industry
Firms traded on small and medium-sized enterprises(SMEs) board
Firms with missing data required for the first stage regression
（120）
（1,401）
（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
2007
2008
2009
2010
Total
SOE
N
787
802
802
805
3,196
NonSOE
%
66.41
67.28
66.78
67.25
66.93
43
N
398
390
399
392
1,579
Total
%
33.59
32.72
33.22
32.75
33.07
1,185
1,192
1,201
1,197
4,775
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
-0.048
0.331
DISSUE
4,773
0.241
0.107
0.659
Total Assets and Sales Revenues are reported in thousands of Chinese Yuan.
44
Table 2, Panel B: Descriptive statistics for the SOE and Non-SOE samples
SOEs
Non-SOEs
N=3,196
N=1,579
Test of Differences Mean Median
Mean
Median
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
0.006
<0.001
DISSUE
0.260
0.126
0.204
0.067
Total Assets and Sales Revenues are reported in thousands of Chinese Yuan.
45
Mean Test Median Test
a
1
a
-0.15
1
TUNNEL
-0.12
PRIVATE
EXPOSED
-0.11
0.03
ABSDA
-0.02
0.08
a
RESTATE
0.12
a
0.08
a
ASSETS
0.34
a
-0.25
a
ATO
0.09
a
-0.08
BIG4
0.16
a
-0.08
a
a
0.27
0.03
-0.14
a
0.19
1
a
-0.07
0.01
0.05
a
b
-0.39
a
a
-0.35
a
-0.10
b
0.04
a
0.06
-0.01
1
*0.01 0.03 b
0.07
a
-0.05
a
a
0.05
a
-0.05
a
a
0.05
0
a
-0.05
a
0.04
a
0.11
a
0.06
a
-0.07
0.05
0.10
DISSUE
0.14
a
-0.12
a
-0.13
a
0.01
EISSUE
0.09
a
-0.05
a
-0.07
0.02
a
-0.04
-0.01
a
0.15
a
0.04
a
EXPORT
0.06
a
-0.07
a
-0.11
FOREIGHSHR
0.07
a
-0.06
a
INDIRECTOR
0.04
a
0.03
-0.06
-0.02
LIST2006
0.06
a
-0.08
a
-0.08
LOSS
-0.14
0.01
a
0.10
a
0.14
0.03
b
a
a
a
b
ROA
0.13
a
-0.16
SALES
0.32
a
SEGMENTS
0.09
a
SOE
0.13
a
0.09
a
TOP1SHR
0.12
a
-0.05
0.01
a
0.32
a
-0.14
a
-0.04
0
a
0.34
a
0.23
a
b
a
0.06
a
-0.12
-0.09
0.05
-0.08
a
-0.05
a
a
-0.06
a
a
0.05
-0.08
0.02
a
a
0.09
0
-0.06
-0.03
-0.05
a
a
-0.07
a
-0.01
-0.23
a
0.08
0
a
a
-0.48
0.03
a
0.12
-0.16
a
-0.17
a
0.12
a
-0.23
a
-0.24
a
0.10
a
a
-0.05
0.40
0.02
-0.14
-0.05
a
0.04
a
1
a
0.07
a
-0.05
1
0.08
a
0.05
a
a
0.05
a
a
a
0.13
0
a
0.03
0
b
-0.04
a
a
a
0.05
0
a
0.09
a
-0.05
-0.04
0.14
a
-0.03
b
0.26
a
0.04
a
0.04
a
-0.04
a
a
0.40
-0.01
a
0.05
a
0.17
b
-0.14
a
-0.06
a
0.11
a
0.06
-0.05
a
0.14
0.02
a
0.03
0.01
-0.01
0.05
0.01
a
0.08
-0.02
a
0.15
0.03
0.13
a
0.01
0.03
0.01
-0.05
0.01
-0.01
0.01
0.05
0.04
a
0.03
0.13
a
-0.05
a
-0.03
b
-0.04
0.04 -0.08 -0.12 0.04
-0.00 0.03 b 0.06 a 0.01 -0.02 -0.03 b 0.00 -0.01
0.02 -0.01 -0.03 b -0.12 a 0.09 a 0.10 a -0.02 0.01
a
b
0
0.01 -0.03 b -0.10 a -0.01 -0.04 a
0.04 0.03
a
a
0.02 0.05 0.05 a 0.04 a -0.07 a -0.06 a -0.08 a
0.04
0.03 0.09 a 0.10 a 0.06 a 0.07 a 0.08 a
0
0
b
0.01
a
0.05
-0.05
a
-0.04
a
0.10
a
0.06
a
0.14
a
-0.29
a
0.07
a
-0.14
a
0.11
a
0.07
a
-0.04
a
0.20
a
0.12
a
0.09
a
a
0.20
a
0.20
a
0.09
a
-0.02
a
-0.14
-0.03
-0.09
a
-0.08
-0.02
0.85
a
0.57
a
0.30
a
a
0.12
a
0.14
a
0.03
b
a
-0.01
0.27
a
0.12
a
0.11
a
b
a
0.30
a
0.13
a
0.14
a
a
a
-0.15
a
-0.13
a
a
-0.13
-0.04
-0.01 0.03
a
0.06
a
0.15
a
a
-0.19
a
a
a
0.21
a
-0.03
b
-0.07
0.01
a
0.25
a
0.18
a
0.03
b
0.08
a
0.09
a
-0.04
a
-0.02
0.15
a
0.05
a
a
-0.10
a
0.13
-0.02
a
0.05
-0.02
0.13
1
a
0.05
a
a
a
-0.13
1
-0.05
a
0.36
a
a
a
0.18
0.01
-0.00
0.02
a
0.03 -0.19
-0.01 0.05 a
a
a
0.04
0.01
0.18
a
0.05
1
a
a
0.05
a
0.05
a
a
b
-0.04
-0.01
0.11
a
a
0.04
-0.07
a
0.03
-0.10
a
0.06
0.01
0
a
b
-0.01
a
-0.05
-0.01
a
-0.04
a
-0.12
a
-0.01
0.03
-0.02
-0.04
a
-0.10
a
b
0.04
a
0.01
a
0.16
a
0.04
-0.01
0.04
b
0.07
a
-0.02
0.12
0
a
0.03
b
0.09
a
-0.09
a
a
0.01
0.08
0.01
0.01
0.06
b
-0.02
-0.07
a
a
a
0.01
0.02
-0.05
1
a
-0.06
0
a
0.14
TOP1SHR
SOE
SEGMENTS
SALES
ROA
a
a
a
a
0.10
a
-0.20
a
-0.18
a
a
0.12
b
a
a
a
a
0.03
0.08 -0.12 -0.04 -0.11 -0.02
-0.03 -0.06 a -0.09 a -0.02 -0.01 -0.05 a
a
0.17 0.86 a 0.12 a 0.29 a 0.34 a
0.21
0.08
a
0.17
a
0.54
a
0.09
a
0.10
a
0.11
a
0.11
a
0.06
a
0.33
a
0.03
b
0.11
a
0.14
a
0.06
a
-0.04
a
-0.14
a
-0.15
a
-0.13
0.05
a
0.11
a
0.14
a
0.04
a
0.19
a
0.08
a
0.16 0.13 0.04 -0.05
-0.01 -0.09 a 0.00 -0.02
a
b
a
0
0.22 0.03
0.05
0.01 0.23 a 0.03 b 0.03 b
-0.01 0.01 0.01 -0.03 b
a
0.10
a
a
a
0.03 -0.10 0.07
-0.01 0.03 b 0.05 a
0.09
a
a
a
0.09 -0.05 -0.02 0.08
-0.03 0.01 0.02 0.01
1
0.03
0.03
-0.04
a
0.11 0.11 0.35 0.09 0.13
-0.01 -0.14 a -0.30 a 0.01 -0.17 a
a
a
a
a
-0.07 -0.09 -0.43 -0.02 -0.17
a
a
a
0
0.12
-0.08 -0.01 0.12
a
-0.01 -0.05 a
b
1
0.03
0.03
PAYRATIO
-0.07
0.02
a
-0.04
1
MGMTSHR
MB
-0.02
a
-0.21
-0.02
a
a
a
a
0.05
-0.18
0.01
0.13
a
0.10
0.01
0
0.10
0.07
a
-0.05
0
b
a
-0.04
0
a
0.03
a
0
b
a
-0.04
0
-0.02
0
0.05
a
-0.04
0.04
-0.02
0.09
-0.02
-0.05
b
a
-0.01
a
a
a
-0.01
0.04
-0.03
b
a
a
-0.06
a
0.04
-0.12
-0.57
0
0.04
0.02
-0.01
a
0.00
a
0.01
0.19
-0.06
-0.03
-0.20
a
a
a
b
b
a
a
-0.09
a
0.21
a
0.06
a
a
-0.05
0.06
0.01
0.09
a
-0.02
0.03
0.06
b
0.04
1
MARKETIZATION
a
0.01
a
M&A
0.07
0.07
1
a
a
0.04
0.00
LOSS
LIST2006
a
a
0.07
-0.02
a
0.06
a
0.06 -0.06 -0.07
-0.02 0.03 b 0.01
0
0.01 0.02
a
a
-0.04
-0.04
a
a
-0.09
-0.02 -0.03 0.06
a
b
1
0.33 -0.03 -0.02
a
a
1
0.01
-0.05
0.18
a
INDIRECTOR
FOREIGHSHR
EXPORT
b
-0.10
-0.01
-0.03
0.10
a
0.05 -0.08 -0.06
-0.03 -0.12 a -0.03
-0.01 -0.01 0.08 a
a
0.18
0.03
a
0.07
a
a
a
-0.14
a
a
a
0.13
b
0.07 -0.05
-0.01 -0.03 b
-0.08
-0.05
0.04
0.01
0.12
1
a
-0.03
0.22
a
a
0.08
-0.10
0.21
-0.02
a
b
a
a
0.05
0.01
-0.21
0
0.09
0.01
EXCHANGE
0.06
a
a
0.02
EISSUE
a
a
0
0.03
-0.09 -0.03
-0.01 -0.09 a -0.04 a 0.12 a 0.23 a
a
a
a
a
0.07 -0.05 -0.03 0.18 0.04
a
b
0
0.03 -0.03 b
0.09 0.03
a
a
a
-0.01
0.01
0.08 0.04
0.08
0.02
DISSUE
-0.07
a
a
1
-0.09
-0.03
CEOCHAIR
BIG4
ATO
ASSET
-0.27
a
a
0.01 0.05 a
0.13
MARKETIZATION 0.05 a -0.05 a -0.04 b -0.01 0.01 -0.05 a
a
a
a
MB
-0.03 0.09 a 0.02
-0.08 0.11 0.07
a
b
MGMTSHR
-0.04 -0.06 a -0.01 0.02
0.04 0.03
a
PAYRATIO
0.01 -0.10 a -0.09 a 0.02 -0.03
0.11
M&A
a
0.06 -0.31 -0.24
-0.01 0.11 a 0.06 a
0.01 -0.05 a -0.06 a
a
b
1
-0.09 -0.03
1
a
0.17
a
0.06
a
a
a
b
0.14
a
0.08
a
0.01
a
EXCHANGE
-0.02 -0.12 a 0.38 a
0.04
0.02
a
CEOCHAIR
RESTATE
ABSDA
PRIVATE
TUNNEL
ICFR
ICFR
EXPOSED
Table 2, Panel C: Pearson (Spearman) Correlation
a
b
-0.03
0
a
a
-0.10
0.04
a
a
a
a
0.08
0.03
a
a
a
0
0.02 0.05 a 0.11 a
0.12
0.05 0.13
-0.02 -0.08 a -0.65 a -0.22 a -0.02 -0.04 a -0.12 a
0.01 0.05 a
0
-0.03 0.02 -0.09 a -0.09 a
a
a
a
a
0.01 -0.04 a 0.02
0.09 0.09
0.05 0.10
0
-0.03
1
0.05
1
a
0.17
a
a
0.06
a
0.19
a
-0.21
a
0.11
a
0.22
a
-0.04
a
0.05
a
0.12
a
0.07
a
0.03
b
0.13
1
a
a
0.24
0.01
-0.14
0
a
0.21
a
0.20
1
a
0.16
a
-0.03
0.02
b
-0.06
a
0.01
-0.20
a
-0.11
a
a
a
a
0.30
a
-0.04
1
a
0.32
0.004
0.12 -0.09 -0.10
-0.01 -0.03 b 0.14 a
0.17
1
a
a
a
-0.03 -0.10 a -0.05 a -0.12 a -0.04 a 0.27 a -0.05 a
0.25
a
a
a
a
a
a
-0.01 -0.23 -0.09 0.15 0.29
0.01 0.26
1
0.03
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.
46
Table 3: Internal Control and Financial Reporting Quality
Dependent Variable
ABSDA
2SLS
0.052***
(0.001)
-0.037**
(0.013)
-0.033
(0.265)
0.043***
(0.000)
0.122***
(0.000)
0.001**
(0.011)
0.018***
(0.005)
0.027***
(0.000)
-0.006
(0.208)
-0.017*
(0.073)
RESTATE
2SLS
-1.957***
(0.000)
-1.715***
(0.010)
-1.068
(0.178)
0.117
(0.501)
-1.601*
(0.055)
0.016
(0.163)
0.163
(0.492)
-0.055
(0.581)
-1.245**
(0.022)
-1.054**
(0.019)
Year effects
Included
Included
Industry effects
Included
Included
4,749
0.214
4,772
0.063
Intercept
ICFR CFO
LEV
ROA
MB
EISSUE
DISSUE
BIG4
Top1SHR
No. of Obs.
Adjusted (or Pseudo) R2
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.
47
Table 4: Internal Control and Tunneling
Dependent variable= TUNNEL
Predicted
Signs
?
Intercept
ICFR
−
ASSETS
−
ROA
−
TOP1SHR
−
CEOCHAIR
+
INDIRECTOR
?
MARKETIZATION
−
BIG4
−
EXCHANGE
?
Year effects
Industry effects
No. of Obs.
Adjusted R2
Full Sample
SOEs
Non-SOEs
0.154***
(0.000)
-0.014**
(0.036)
-0.006***
(0.000)
-0.054***
(0.000)
-0.027***
(0.000)
0.0001
(0.485)
0.037**
(0.024)
-0.001***
(0.002)
0.004
(0.980)
0.002
(0.268)
Included
Included
0.102***
(0.000)
-0.006
(0.212)
-0.004***
(0.000)
-0.083***
(0.000)
-0.020***
(0.001)
0.003
(0.176)
0.024
(0.145)
-0.001*
(0.090)
0.003
(0.871)
-0.001
(0.763)
Included
Included
0.255***
(0.000)
-0.033**
(0.023)
-0.008***
(0.000)
-0.025
(0.284)
-0.028***
(0.007)
-0.005
(0.854)
0.026
(0.493)
-0.003***
(0.001)
-0.006
(0.123)
0.007
(0.109)
Included
Included
4,638
0.145
3,097
0.146
1,541
0.139
Test of differences between coefficients
ICFR
Coefficient Diff.
0.027*
SOEs vs. non-SOEs
Z-statistic
1.502
P-value
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.
48
Table 5: Internal Control and Private Consumption
Dependent variable= PRIVATE
Predicted
Signs
Intercept
?
ICFR
−
PAYRATIO
−
MGMTSHR
−
ATO
−
ROA
−
EISSUE
+
DISSUE
+
BIG4
−
TOP1SHR
−
CEOCHAIR
?
INDIRECTOR
?
MARKETIZATION
−
Year effects
Industry effects
No. of Obs.
Adjusted R2
Full Sample
SOEs
Non-SOEs
0.052***
(0.002)
-0.026**
(0.039)
-0.001***
(0.006)
0.088
(0.771)
-0.019***
(0.000)
-0.060***
(0.001)
-0.002
(0.683)
-0.001
(0.272)
-0.0002
(0.476)
-0.027***
(0.004)
0.006
(0.342)
0.000
(0.987)
-0.001
(0.215)
Included
Included
0.084***
(0.005)
-0.001
(0.479)
-0.0003
(0.206)
-0.175**
(0.031)
-0.015***
(0.000)
-0.050**
(0.033)
0.001
(0.369)
-0.001
(0.209)
-0.006**
(0.028)
-0.013
(0.104)
0.001
(0.861)
-0.035
(0.210)
0.001
(0.711)
Included
Included
0.155***
(0.000)
-0.099***
(0.007)
-0.003***
(0.008)
0.107
(0.805)
-0.023***
(0.002)
-0.048**
(0.045)
-0.001
(0.538)
0.001
(0.532)
0.014
(0.829)
-0.061***
(0.007)
0.013
(0.310)
0.059
(0.370)
-0.004
(0.097)
Included
Included
1,802
0.120
1,240
0.096
562
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. Pvalues reported in parentheses are based on one-tailed tests for variables with predicted signs and twotailed tests for variables without predicted signs. Standard errors are clustered by firm.
49
Table 6: Internal Control and Exposed Corruption Cases
Dependent variable=EXPOSED
Predicted
Signs
Intercept
?
ICFR
−
PAYRATIO
−
MGMTSHR
−
SALE
+
ROA
−
EISSUE
+
DISSUE
+
BIG4
−
TOP1SHR
−
CEOCHAIR
?
INDIRECTOR
?
MARKETIZATION
−
Year effects
Industry effects
No. of Obs.
Pseudo R2
Full
SOE
NonSOE
-10.875***
(0.000)
1.063
(0.845)
-0.132**
(0.011)
-14.855*
(0.074)
0.316***
(0.004)
-2.345
(0.269)
-0.720
(0.981)
0.116
(0.191)
-0.530
(0.107)
1.021
(0.334)
0.345
(0.366)
4.615**
(0.014)
-0.058
(0.231)
Included
Included
-11.243***
(0.000)
0.929
(0.791)
-0.102**
(0.033)
-13.720
(0.360)
0.346***
(0.003)
-3.037
(0.199)
-0.704
(0.970)
0.119
(0.204)
-0.598*
(0.089)
1.629
(0.168)
0.056
(0.894)
5.290***
(0.008)
-0.092
(0.150)
Included
Included
11.097
(0.164)
-4.633*
(0.093)
-0.390***
(0.002)
-53.929***
(0.001)
-1.020**
(0.013)
9.864***
(0.008)
1.542**
(0.039)
0.296
(0.323)
-0.348
(0.432)
-6.692
(0.165)
1.284**
(0.031)
-18.821***
(0.001)
0.369
(0.944)
Included
Included
2,398
0.114
1,732
0.120
666
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.
50
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.
Intercept
？
SOE
+
ASSETS
+
ROA
+
LOSS
–
MB
–
EXPORT
–
SEGMENT
–
M&A
–
FOREIGHSHR
+
BIG4
+
LIST2006
+
MARKETIZATION
+
EXCHANGE
？
-0.177***
(0.001)
0.010***
(0.0095)
0.024***
(0.000)
0.030
(0.114)
-0.018***
(0.001)
0.0003
(0.865)
0.004
(0.866)
0.012***
(0.003)
0.003
(0.812)
0.016
(0.277)
0.015**
(0.048)
0.044***
(0.000)
0.001
(0.135)
0.042***
(0.000)
Included
Included
Year effects
Industry effects
4,775
0.312
No. of Obs.
Adjusted R2
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.
51
Table 8: Internal Control and Corporate Corruption: 2SLS
Panel A: Full Sample
Predicted
TUNNEL
PRIVATE
sign
ICFR
-0.122**
-0.259***
−
(0.011)
(0.000)
Control variables
Included
Included
# Obs.
4,638
1,802
Adjusted (Pseudo)R2
0.145
0.143
Panel B: SOEs versus Non-SOEs
TUNNEL
SOEs
Non-SOEs
ICFR
0.015
-0.274**
(0.631)
(0.011)
Control variables
Included Included
# Obs.
3,097
1,541
Adjusted (Pseudo)R2 0.145
0.139
Test of coefficient difference
TUNNEL
SOEs vs. Non-SOEs
Coefficient Diff.
P-value
ICFR
0.290**
0.011
PRIVATE
SOEs
Non-SOEs
-0.182*** -0.456***
(0.000)
(0.001)
Included
Included
1,240
562
0.117
0.186
PRIVATE
SOEs vs. Non-SOEs
Coefficient Diff.
P-value
0.273**
0.35
EXPOSED
-0.320
（0.473）
Included
2,398
0.133
EXPOSED
SOEs
Non-SOEs
-0.338
-39.296***
（0.474） （0.003）
Included
Included
1,732
666
0.124
0.410
EXPOSED
SOEs vs. Non-SOEs
Coefficient Diff.
P-value
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.
52
Table 9: Internal Control and CEO Promotions
Dependent variable=PROMOTION
Alla
Full
SOEs
Non-SOEs
Sample
ICFR
1.060**
1.187**
0.784
(0.012)
(0.024)
(0.285)
SALE
0.088***
0.080**
0.138**
(0.004)
(0.048)
(0.012)
CFO
0.266
-0.451
1.563**
(0.557)
(0.459)
(0.023)
ROA
0.742
-0.223
1.189
(0.250)
(0.826)
(0.216)
LEV
-0.294**
-0.670***
-0.155
(0.020)
(0.001)
(0.397)
MB
-0.006
0.015
-0.024*
(0.432)
(0.161)
(0.053)
CEOAGE
-0.268**
-0.484***
0.282
(0.036)
(0.002)
(0.235)
EDUCATION
0.023
0.021
0.028
(0.657)
(0.762)
(0.728)
TENURE
0.009
0.027
-0.031
(0.622)
(0.253)
(0.351)
TOP1SHR
0.136
0.334
-0.353
(0.621)
(0.300)
(0.521)
MARKETIZATION
0.006
0.019
-0.020
(0.760)
(0.455)
(0.574)
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
Year effects
Industry effects
No. of Obs.
Pseudo R2
Turnover Onlyb
Full Sample
SOEs
Non-SOEs
1.928**
(0.023)
0.145**
(0.029)
0.406
(0.650)
-0.271
(0.814)
-0.395
(0.226)
-0.004
(0.794)
-0.434
(0.103)
0.089
(0.393)
0.033
(0.417)
0.257
(0.650)
0.005
(0.898)
1.913*
(0.069)
0.137
(0.122)
-1.722
(0.158)
-0.019
(0.990)
-0.980**
(0.045)
0.031*
(0.098)
-0.732**
(0.019)
0.136
(0.335)
0.078
(0.128)
0.617
(0.367)
0.018
(0.741)
2.231
(0.174)
0.207
(0.105)
3.173*
(0.069)
0.148
(0.938)
-0.347
(0.441)
-0.040
(0.119)
0.519
(0.304)
-0.047
(0.793)
-0.111
(0.216)
-0.283
(0.802)
-0.085
(0.244)
-3.553**
(0.038)
Included
Included
-4.629*
(0.077)
Included
Included
506
0.065
266
0.101
Included
Included
Included
Included
Included
Included
-4.049***
(0.002)
Included
Included
4,573
0.012
3,071
0.019
1,502
0.035
722
0.043
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.
53