Audit Firm Legal Form and Client Fraudulent Financial Reporting ∗
Chih-Ying Chen
School of Accountancy
Singapore Management University
Chen-Lung Chin
Department of Accounting
National Chengchi University
Hsin-Yi Chi
Department of Accounting
National Chung Hsing University
This version: April 2010
Abstract: In China, audit firms can be formed as either unlimited liability partnerships or limited
liability corporations. While the limited liability form of audit firms dominates the Chinese audit
market, the unlimited liability partnerships still audit between 10 and 15 percent of the listed
companies. This unique setting provides an opportunity for exploring the relation between audit
firm legal form and audit quality. We investigate this relation for a sample of Chinese listed
companies during the 2000-2004 period. We use the likelihood of client fraudulent financial
reporting as a proxy for audit quality and find that the likelihood is higher for the clients of the
limited liability audit firms than for the clients of the unlimited liability partnerships. The
difference in the likelihood of fraudulent financial reporting between the two types of audit
clients is greater in provinces where the legal environment is stronger. Overall, the results
suggest lower audit quality for the limited liability audit firms than for the unlimited liability
partnerships.
∗
Corresponding author: Chih-Ying Chen, School of Accountancy, Singapore Management University, Singapore
178900. Tel: (+65) 6828-0987; E-mail [email protected]. We thank Tom Lechner, Clive Lennox, and seminar
participants at the 2005 Inaugural Forum on Databases and Accounting and Finance Empirical Study (Peking
University), 2008 American Accounting Association Annual Meeting, National Central University, and National
Chengchi University for their comments and suggestions on previous versions of the paper. C-Y Chen appreciates
the Office of Research at Singapore Management University for providing financial support (07-C206-SMU-002).
1. Introduction
Unlimited liability partnership had been the only legal form for audit firms for long before
the limited liability form was permitted in certain countries. In the U.S., all the Big 6 audit firms
and most of the mid-tiers made switches to the form of limited liability partnership (LLP) shortly
after the regulatory change in 1994 that allowed audit firms to change their legal structure. The
situation was similar in the U.K. Based on 2009 data, about 97 percent of the U.S. companies
with a market capitalization over $75 millions (i.e., subject to SOX 404) are audited by limited
liability audit firms. The situation is similar in the U.K. based on the number of listed companies
in 2009.
Prior analytical research demonstrates that, under certain conditions, audit quality is lower
with limited or proportionate auditor liability (e.g., Dye 1995; Chan and Pae 1998). However,
there has been little empirical evidence in support of this conclusion. Since there is little crosssectional variation in the legal forms of the large and mid-sized audit firms in the U.S., prior
empirical research mostly investigates changes in audit firms’ or their clients’ characteristics
(e.g., the riskiness of audit client portfolios and underpricing of clients’ IPOs) after a change in the
auditor legal form (Choi, Doogar, and Ganguly 2004; Muzatko et al. 2004).
In China, audit firms can take the form of either unlimited liability (partnership) or limited
liability (corporation). 1 The limited liability form of audit firms dominates the Chinese audit
market, but the unlimited liability audit partnerships still have a market share between 10 and 15
percent, which is much larger than that observed in the U.S. and U.K. Meanwhile, the auditors in
China have faced increasing litigation risks since the first case of lawsuit against auditors
1
To be a limited liability legal entity, an audit firm must have a registered capital of no less than 300,000 Renminbi
(RMB, Chinese currency), have a certain number of full-time professional staff (exact number is not stipulated, but
must have at least five certified public accountants), and conform to the scope of professional activities and other
conditions as stipulated by the Finance Department of the State Council.
1
occurred in 2001. The “more balanced” distribution of audit firm legal forms and the increasing
auditor litigation risks provide a setting that allows for empirical analyses of the relation between
audit firm legal form and audit quality.
In this paper, we examine a sample of 5,462 observations of Chinese listed companies for
the 2000-2004 period. We first explore the company characteristics associated with the legal
form of the audit firm that the company hires. The results show that companies that are smaller
or have a longer listing age or higher leverage ratio are less likely to hire a limited liability form
of auditor. These results provide some evidence suggesting that companies select a particular
legal form of auditor depending on their own characteristics.
We use the likelihood of financial reporting frauds committed by audit clients as a proxy
for audit quality and investigate the association between this proxy and audit firm legal form. We
identify instances of financial reporting frauds based on the investigation reports issued by the
China Securities Regulatory Commission (CSRC). The reports show various types of corporate
frauds detected by the CSRC. Among these frauds, the following five types are related to
financial reporting: overstatement of earnings, overstatement of assets, delay of disclosure, false
statement, and omission of material information. Our regression results show that the likelihood
of fraudulent financial reporting is higher for the companies audited by the limited liability firms
than for those audited by the unlimited liability partnerships, after controlling for confounding
factors for frauds and companies’ selection of a particular legal form of auditor.
To enhance the likelihood that our findings are attributed to differences in auditor liabilities
instead of other confounding factors, we further investigate whether the association between
audit firm legal form and fraudulent financial reporting varies with the strength of the audit
client’s legal environment. The rationale is that the two legal forms are distinct in the auditors’
2
potential liabilities only if there are law enforcement and investor protection. To the extent that
the association between audit firm legal form and fraudulent financial reporting is attributed to
the differences in the auditor liability regimes, the association would be higher for audit clients in
a stronger legal environment.
We measure the strength of legal environment by the legal environment index constructed
by Fan and Wang (2003). This index has been used in prior research in accounting, finance, and
economics (e.g., Li, Meng, and Zhang 2006; Wang, Wong, and Xia 2008). The index varies by
province and a higher index indicates that the legal environment is stronger. We find that the
likelihood of fraudulent financial reporting is lower if the legal environment is stronger, and the
difference in the likelihood of fraudulent financial reporting between the audit clients of the
limited liability firms and those of the unlimited liability partnerships increases with the strength
of the legal environment. Collectively, our results suggest that audit quality is lower for the
limited liability firms than for the unlimited liability partnerships.
Since the likelihood of committing financial reporting frauds is not observable (only
detected frauds are observable), we analyze fraud propensity and fraud detection simultaneously
using bivariate probit regression in additional analysis. The results are generally consistent with
our primary findings. We also analyze each type of fraud separately and find that our results are
mainly attributed to the frauds that overstate earnings or assets.
The evidence in our study lends support to previous analytical research that shows that a
less strict legal regime for auditor liability is associated with lower audit quality. In addition, our
study extends empirical research on the consequences of changes in auditor liability regimes.
Previous studies in this line of research examine changes in the degree of the audit clients’ IPO
underpricing and the riskiness of the auditors’ client portfolios after the audit firms switch to the
3
limited liability legal form. However, the changes documented in these studies do not necessarily
indicate changes in audit quality. We extend these studies by comparing the companies audited
by different legal forms of auditors and investigating a more direct measure of audit quality, the
likelihood that the audit clients have fraudulent financial reporting.
Our study also contributes to research that relates auditor characteristics to audit quality.
Previous research has shown that certain auditor characteristics (e.g., size, tenure, and industry
specialization) are associated with audit quality. We investigate the auditor’s legal form and
show evidence that this auditor characteristic is associated with audit quality.
The results in our study should be interpreted with caution, however. One typical argument
in favor of the unlimited liability form is that auditors in general partnerships have stronger
incentives to monitor each other, so the audit quality is higher. 2 While we find that audit quality
is higher for the unlimited liability auditors, the limited liability firms likely have mechanisms to
control and maintain their audit quality (e.g., concurring partner review and professional
standards of conduct). To the extent that the limited liability auditors’ lower audit quality is
caused by ineffective design or implementation of these mechanisms but the ineffectiveness is
not due to the auditors’ lower legal liabilities, increasing auditor liability does not necessarily
lead to an improvement in audit quality. Therefore, the regulators should assess the effectiveness
of the mechanisms currently used (or potentially can be used) by audit firms to control or
maintain audit quality before making any policy changes to allow or disallow a particular legal
form of audit firms.
This paper proceeds as follows. Section 2 describes the institutional background and
develops the research hypotheses. Section 3 describes the research design. Section 4 describes
2
A similar argument was made by an official of the Chinese Institute of Certified Public Accountants (CICPA) in
2002 (see http://www.farsight.com.cn/FarsightBBS/boke.asp?4nlnn4o9.showtopic.134.html). CICPA is essentially a
government-sponsored organization.
4
the sample selection and data. Section 5 discusses the empirical results. Section 6 provides
conclusions.
2. Institutional Background and Development of Hypotheses
2.1 Institutional background
In 1980, the Chinese government issued its first regulation concerning incorporation of
audit firms. This regulation came in light of the demands for certified public accountants (CPAs)
due to direct foreign investments. Since then, the number of audit firms and CPAs nationwide
increased rapidly. The demand for CPAs further increased when the stock exchanges at Shanghai
and Shenzhen were established in 1990 and 1991, respectively. As of December 2006, the
Chinese Institute of Certified Public Accountants had 69,467 practicing members. 3
Before the enactment of the CPA Law in 1993, only the government agencies, corporations,
and universities were permitted to establish audit firms. Because the founding organizations had
to assume the legal responsibilities of the audit firms they established, and there was virtually no
competition among the audit firms, the CPAs generally did not have sufficient understandings of
audit techniques and risks (Li 1999). In addition, the close relationships between the audit firms
and their founding organizations could impair auditor independence because often the auditor
and the audited entity were administered by the same founding organization.
To address the above problems, the Chinese regulators took steps to disaffiliate audit firms
from their founding organizations. The first step, which was completed in 1999, targeted on the
audit firms that qualified to audit listed companies. The move toward disaffiliation created a
more competitive audit market, as the auditors no longer have affiliated organizations to bring in
clients. The disaffiliation has also increased auditors’ legal responsibilities because there are no
3
See China Accounting News Weekly (http://www.esnai.com/news/showdoc.asp?NewsID=28111).
5
founding organizations to assume these responsibilities. Meanwhile, the demand for high quality
auditors in China increases over time. For example, with the reduced government involvement in
setting the IPO prices since 2001, the average level of IPO underpricing has decreased
substantially, which created opportunities for reputable auditors to serve as a signaling device for
the quality of the IPO firms (Li et al. 2005).
The CPA Law in China allows audit firms to be formed as either unlimited liability general
partnerships or limited liability corporations (effective from 1994). Most of the large audit firms
are limited liability corporations (LLCs). However, the unlimited liability audit partnerships still
audit between 10 and 15 percent of the listed companies in China, and more than 15 percent of
the audit firms that audit listed companies are unlimited liability partnerships. 4 Therefore, the
market share of the unlimited liability audit firms is substantially higher in China than in the U.S.
or U.K.
Auditors’ exposure to litigation risks in China has increased since the first lawsuit against
auditors occurred in 2001. Before then, the punishment on auditors was confined to fines and
administrative penalties imposed by the CSRC (Li, et al. 2005). However, despite the overall
increases in litigation risks in recent years, auditors practicing in different parts of China could
face different levels of litigation risks. Compared with the inland provinces, the coastal regions
are more developed and have stronger law enforcement and investor protection (Fan and Wang
2003).
2.2 Development of hypotheses
Previous analytical research has examined the relation between auditor liability and audit
quality. Narayanan (1994) demonstrates that moving from the joint and several liability rule to a
4
These statistics only changed slightly over time during our sample period.
6
proportionate liability rule, if appropriately chosen, may actually motivate the auditor to work
harder and thus increase audit quality. However, other analytical research shows that audit
quality is lower with limited or proportionate auditor liability (e.g., Dye 1995; Chan and Pae
1998; Schwartz 1997).
In the U.S., general partnership was the only legal form for audit firms before the American
Institute of CPA’s by-law changes in 1992 that allowed audit firms to practice under other legal
forms. After the regulatory change, most of the large and mid-tier audit firms made switches from
unlimited liability general partnership to limited liability form of practice within a short period of
time. This regulatory change provides a setting that allows for empirical analyses on the
consequences of changes in audit firm legal forms.
Muzatko et al. (2004) investigate the relation between changes in the legal form of audit firms
and changes in IPO underpricing of their audit clients. Auditor liability is lower under the LLP form
of practice, and the level of underpricing varies inversely with both the amount of implicit insurance
and the quality of audit services provided by the audit firm. Muzatko et al. (2004) hypothesize that
the level of IPO underpricing increased after the Big 6 audit firms in the US made switches to the
LLP form in August/September 1994. Their findings support this hypothesis. However, they are
unable to distinguish between the lower implicit insurance explanation and the lower audit quality
explanation for their findings.
Another study related to changes in auditor liability regimes in the U.S. is Choi et al. (2004),
who examine the riskiness of audit firm client portfolios. They find risk decreases between 1990
and 1994 (a period of lobbying for regulatory reform on auditor liability) and risk increases between
1995 and 1999 (a post-relief period), which is consistent with their hypothesis that the riskiness of
Big-6 client portfolios responded to changes in the audit litigation liability environment.
7
Although the results of Muzatko et al. (2004) and Choi et al. (2004) provide insights into
understanding of what happens after audit firms switch from a more stringent legal form to a less
one, these results do not necessarily indicate changes in audit quality.5 Given the debates in several
countries on whether audit firms should be allowed to practice under the limited liability form, more
research on the relation between auditor liability and audit quality is needed, and the Chinese setting
provides an opportunity to explore this question.
We are aware of a concurrent study by Firth et al. (2006) who investigate the association
between audit firm legal form and audit opinion using a sample of Chinese companies for the
2000-2002 period. They find that unlimited liability audit partnerships are more likely to have a
higher threshold for issuing clean audit opinions than do LLCs, and companies switching their
auditor from an unlimited liability partnership to an LLC are less likely to receive a modified audit
opinion. Their findings suggest that audit quality is lower with the limited liability audit firms.
Although the likelihood of issuing a modified (non-clean) audit opinion has been used in prior
research as a proxy for audit quality (e.g., DeFond et al. 2002; Geiger and Raghunandan 2002), it
has a potential limitation in the Chinese setting because receiving a non-clean audit opinion is not
always a serious concern for some Chinese companies. According to the trading rules of the
Shanghai and Shenzhen Stock Exchanges, companies reporting net losses in two consecutive
years are labeled as “ST” (which stands for “special treatment”), and companies reporting net
losses for three years in a row are delisted. In addition, listed companies are not permitted to
raise capital if their annual return on equity is below ten percent in any one of the previous three
5
Previous research has also examined the impact of the change in litigation environment on auditor decisionmaking. For example, Geiger, Raghunandan, and Rama (2006) examine the impact of the Private Securities
Litigation Reform Act of 1995 on auditors’ going-concern modification decisions for companies entering
bankruptcy before and after the new legislation. They find that the likelihood of a going-concern modified opinion
decreased significantly after the Private Securities Litigation Reform Act, and the change was particularly
pronounced for the Big 6 audit firms.
8
years. However, receiving a non-clean audit opinion does not cause the company to be labeled as
ST, delisted, or suspended from equity offerings, if the company meets the regulatory
profitability thresholds. Therefore, some managers would engage in earnings management to
meet these thresholds even if they anticipate that their auditors would issue a non-clean audit
opinion (Chen, Chen, and Su 2001). To the extent that “anticipated” non-clean audit opinions do
not jeopardize the auditor-client relationships, the observed likelihood of issuing non-clean audit
opinions would not indicate the true likelihood and may not be a good proxy for audit quality.
The main objective of this paper is to explore whether audit firms of different legal forms
have different audit quality. We use the likelihood of financial reporting frauds committed by
audit clients as a proxy for audit quality. This proxy has been used in prior research (e.g.,
Carcello and Nagy 2004a and 2004b), and it is not subject to the aforementioned potential
limitation of measuring audit quality by the likelihood of non-clean audit opinions in the Chinese
setting.
Our first research question investigates the association between audit firm legal form and
the likelihood of fraudulent financial reporting. In light of the findings of prior analytical
research that audit quality is lower with limited auditor liability, we formulate the first
hypothesis as follows:
H1: Companies audited by limited liability firms are more likely to have fraudulent
financial reporting than are companies audited by unlimited liability firms.
If audit firms of different legal forms do have different audit quality, it could be due to
factors that are unrelated to auditor liability regimes. In the empirical test of H1, we control for
the factors that prior research has found to be associated with the likelihood of fraudulent
financial reporting (discussed below). However, as there is little distinction between the limited
9
and unlimited liability legal forms if the legal environment is weak, we further investigate the
association between audit firm legal form and audit quality conditional on the strength of legal
environment. We perform this analysis to enhance the likelihood that our findings are attributed
to differences in auditor liability regimes. Specifically, if the difference in the likelihood of
fraudulent financial reporting between the clients of the two different legal forms of audit firms
increases with the strength of legal environment, this would be further evidence that the results
for testing H1 are attributed to differences in auditor liability regimes. We formulate the second
hypothesis as follows:
H2: The difference in the likelihood of fraudulent financial reporting between the
companies audited by limited liability firms and those audited by unlimited liability
firms increases with the strength of legal environment.
3. Research Design
3.1 Factors associated with companies’ hiring of LLC auditors
Before examining the relation between audit firm legal form and fraudulent financial
reporting, we explore the company characteristics associated with the legal form of the auditor
the company hires. We do this because the LLC audit firms are generally larger than the
unlimited liability audit partnerships, hence there could be systematic differences between the
clients of these two forms of auditors. Without identifying the factors influencing companies’
selection of a particular legal form of auditor and controlling for these factors in the empirical
analyses, an observed association between auditor legal form and fraudulent financial reporting
could be attributed to these factors but not the auditor’s legal form.
10
To find the factors possibly associated with companies’ hiring of a particular legal form of
auditor, we first consider the view that auditors have “deep pockets” that may provide incentives
for plaintiffs to include auditors in class action lawsuits along with managers and other codefendants (e.g., Kothari et al. 1988; Schipper 1991). Specifically, we identify a set of company
characteristics that potentially are associated with the litigation risks faced by the company, and
examine the relations of these characteristics with the auditor’s legal form. These company
characteristics include the strength of legal environment, size, age, growth, leverage, net loss,
and state-owned enterprise. If companies’ decisions to hire an auditor with a particular legal
form reflect the “auditors’ deep pockets” view, the factors that suggest high (low) litigation risks
would be associated with hiring of an unlimited liability (a limited liability) form of auditor.
Companies’ hiring of auditors could also be influenced by factors unrelated to the auditors’
deep pockets. For example, larger companies tend to hire a larger audit firm (commensurate with
their size), which is more likely to be an LLC, whereas older companies are more likely to have
an unlimited liability auditor if the company has retained the same auditor since some year
before the LLC form was permitted. Considering the exploratory nature of this analysis, we do
not formulate formal hypotheses regarding the directional relationship between auditor legal
form and these potentially related factors.
In the empirical analyses, we compare the factors that potentially are associated with the
auditor legal form between the clients of the two legal forms of auditors. We also estimate the
following logistic regression of audit firm legal form on these potentially associated factors:
Prob(LLC = 1) =
1
, where
1 + e −Z
Z = α0 + α1LEGAL + α2SIZE + α3AGE + α4GROW + α5LEV + α6LOSS + α7SOE
+ δ⋅YEAR + φ⋅INDUSTRY.
(1)
11
In Eq. (1), LLC is an indicator variable that equals one if the observation’s auditor is a
limited liability firm and zero otherwise, and LEGAL equals the legal environment index for the
province where the company’s headquarter is located. The legal environment index is
constructed by Fan and Wang (2003) in a project sponsored by the National Economic Research
Institute and China Reform Foundation. The index is comprised of three components: the
number of lawyers as a percentage of the population of the province, the efficiency of local
courts (measured by the percentage of lawsuits pursued by the courts), and protection of property
rights. The raw index ranges between 2.62 and 7.97, where a higher value indicates that the legal
environment is stronger.
The other variables in Eq. (1) are defined as follows. SIZE equals the natural logarithm of
total assets at the end of the previous year. AGE equals the number of years since the company
was listed on a stock exchange. GROW equals the average annual percentage change in total
assets for the previous two years. LEV is total liabilities divided by total assets at the end of the
previous year. LOSS is an indicator variable that equals one if the company reported a net loss in
the preceding year and zero otherwise. SOE is an indicator variable that equals one if the
observation is a state-owned enterprise and zero otherwise. YEAR is a set of indicator variables
that represent year. INDUSTRY is a set of indicator variables that represent industry. 6 The
subscripts for company and year are suppressed for simplicity.
3.2 Factors influencing the auditor’s legal form of practice
Before testing the hypotheses, we also explore the possibility that auditors choose their
legal form of practice based on certain characteristics of their clients. If auditors decide to
6
Our industry classification is based on the industry code issued by the CSRC. In the primary analyses we use a
broader definition of industry which classifies the sample into 22 industries. The empirical results are similar when
we use more detailed industry classifications which give more than 80 industries.
12
practice under the limited liability legal form because the litigation risks associated with their
clients’ frauds are high, an association between audit firm legal form and fraudulent financial
reporting could be attributed to the client characteristics that cause frauds instead of the auditor’s
legal form. To examine this issue, we estimate logistic regression of each audit firm’s legal form
(LLC) on the profitability, short-term liquidity, long-term solvency, and Altman Z-score of the
clients in the preceding year. Each audit firm enters into the analysis only once in a year and the
independent variables are the means of the clients’ financial ratios indicated above for the audit
firm. The profitability measures we use include profit margin ratio and rate of return on total
assets. The short-term liquidity measures we use include current ratio and quick ratio. The longterm solvency ratios we use include debt ratio (total liabilities divided by total assets) and cash
flows adequacy ratio (net cash flows from operations to total liabilities).
3.2 Tests of Hypotheses
To test H1, we estimate a logistic regression of fraudulent financial reporting on auditor
legal form and control variables that represent company characteristics, board structure, and year
and industry. The variables for company characteristics include strength of legal environment,
size, age, growth, leverage, net loss, state-owned enterprise, and Big 4-affiliation. Except for Big
4-affiliation, the other company characteristics are the same as the factors potentially associated
with LLC that we examine previously (discussed in Section 3.1). Prior studies have found that
the likelihood of fraudulent financial reporting is negatively associated with company age
(Carcello and Nagy 2004a) and growth (Chen et al. 2006). Some studies also examine the size
and financial health of the company (Beasley 1996; Carcello and Nagy 2004a; Chen et al. 2006),
though they do not find these two factors to be associated with fraudulent financial reporting. We
consider two measures related to financial health: leverage and prior net loss. Reporting a net
13
loss in the preceding year also suggests that the company is under pressure to meet the regulatory
profitability thresholds so that it will not be delisted or labeled as ST. 7 We expect that companies
with a higher leverage or reporting a net loss in the preceding year are more likely to have
fraudulent financial reporting. We also expect a lower likelihood of fraudulent financial
reporting if the legal environment is stronger.
The control variables related to the board structure include board size, number of board
meetings held during the year, percentage of outside directors on the board, and dual position as
the board chair and CEO. Prior studies have found that the likelihood of fraudulent financial
reporting is positively associated with the number of board meetings (Chen et al. 2006) and the
board size and dual position of the board chair and CEO (Carcello and Nagy 2004a), and is
negatively associated with the percentage of outside directors on the board (Beasley 1996;
Carcello and Nagy 2004a; Chen et al. 2006).
The equation we estimate to test H1 is:
Prob(FRAUD = 1) =
1
, where
1 + e −Z
Z = β0 + β1LLC + β2LEGAL + β3SIZE + β4AGE + β5GROW + β6LEV + β7LOSS
+ β8SOE + β9BIG4 + β10NUMDIR + β11MEET + β12OUTDIR + β13DUAL
+ δ⋅YEAR + φ⋅INDUSTRY.
(2)
The variables in Eq. (2) are defined as follows. FRAUD is an indicator variable that equals
one if the observation has fraudulent financial reporting and zero otherwise. BIG4 is an indicator
7
As explained earlier, companies that report net losses in two and three consecutive years will be labeled as “ST”
(special treatment) and will be delisted, respectively. The regression results for Eq. (2) are unaffected when we add a
control variable that equals one if the company reported net losses in both of the preceding two years (i.e., would be
delisted if it reports a net loss for the current year) and zero otherwise. The coefficient for this indicator variable is
statistically significantly positive.
14
variable that equals one if the auditor is Big 4-affiliated and zero otherwise. 8 NUMDIR equals
the number of directors on the board. MEET equals the number of board meetings held during
the year. OUTDIR equals the percentage of outside directors on the board. DUAL is an indicator
variable that equals one if the same person holds a dual position as the board chair and CEO of
the company and zero otherwise. All other variables are as defined previously. A positive
coefficient on LLC in Eq. (2) would be consistent with H1.
To test H2, we add LLC*LEGAL to Eq. (2) and estimate the following equation:
Prob(FRAUD = 1) =
1
, where
1 + e −Z
Z = β0 + β1LLC + β2LEGAL + β3LLC *LEGAL + β4SIZE + β5AGE + β6GROW + β7LEV
+ β8LOSS + β9SOE + β10BIG4 + β11NUMDIR + β12MEET + β13OUTDIR
+ β14DUAL + δ⋅YEAR + φ⋅INDUSTRY.
(3)
A positive coefficient on LLC*LEGAL in Eq. (3) would be consistent with H2 that the
difference in the likelihood of fraudulent financial reporting between the clients of the limited
liability auditors and those of the unlimited liability auditors increases with the strength of legal
environment.
4. Sample Selection and Data
4.1 Sample selection
Our empirical analyses are based on two sets of observations: full sample and matchedpairs sample. In the full sample, each fraud company is compared with all of the non-fraud
companies. In the matched-pairs sample, each fraud company is matched with a non-fraud
8
The market share of the Big 4-affiliated audit firms in China is below ten percent. We obtain similar results when
Big4 is replaced by an indicator variable that equals one if the auditor is one of the top ten audit firms in China and
zero otherwise.
15
company. We obtain financial data and instances of fraudulent financial reporting from the China
Securities Markets and Accounting Research (CSMAR) database, and auditor names and legal
forms from the Chinese Center of Economic Research (CCER) database. 9
Table 1, Panel A, shows the selection process for the full sample. We start with 6,660
company-years that have A-shares traded on the stock exchanges in Shanghai and Shenzhen
from 2000 to 2004. 10 The sample period starts in 2000 because the CICPA did not complete a
program to disaffiliate audit firms from their founding organizations until the end of 1999. As
indicated earlier, before the disaffiliation, the founding organizations of audit firms (but not the
individual auditors) had to bear the legal responsibilities. We delete 173 and 298 observations
without sufficient data on auditor name and legal form, respectively. We further delete 440 and
287 observations due to insufficient data on the variables related to company characteristics and
board structure, respectively. The final sample consists of 5,462 observations.
Table 1, Panel B, shows that 86.8 percent of the sample is audited by LLCs and 13.2
percent is audited by unlimited liability partnerships. The proportion of LLCs in each year ranges
between 84.1 and 88.5 percent, and the proportion of unlimited liability partnerships ranges
between 11.5 and 15.9 percent.
In the full sample, 215 observations have fraudulent financial reporting. 11 To determine the
matched pairs, we follow a procedure similar to that used by Beasley (1996) and Carcello and
Nagy (2004a). Each fraud company is matched with a non-fraud company that is in the same
year and industry, is listed on the same stock exchange, has a different auditor, and has a size
9
We verify the legal form data by checking the full name of the audit firm stated in the audit report.
A-shares can only be owned and traded by Chinese citizens. Some companies that issue A-shares also have Bshares (which are traded on the stock exchanges at Shanghai and Shenzhen but can only be owned by qualified
foreigners with a security account in China), H-shares (traded in Hong Kong), or N-shares (traded in New York).
11
The auditors of these 215 fraud observations include more than 60 audit firms.
10
16
(total assets) closest to that of the fraud company. As a result, the matched-pairs sample consists
of 430 observations.
4.2 Descriptive statistics
Table 2 shows the means and medians of variables for the observations partitioned by
auditor legal form. Panel A shows that in the full sample, the clients of the limited liability audit
firms (LLC = 1) have a higher proportion of fraud companies compared to the clients of the
unlimited liability partnerships. In addition, the clients of the LLC audit firms are located in a
weaker legal environment, have larger company size and board size and a higher percentage of
outside directors on the board, are more likely to be state-owned enterprises and Big 4 audit
clients, have a shorter listing age and lower leverage ratio, and have a lower proportion that
reported a net loss in the preceding year and that has the same person as the board chair and CEO.
Panel B shows that in the matched-pairs sample, the observations with LLC = 1 have a higher
proportion of fraud companies, are more likely to be Big 4 audit clients, and have a shorter
listing age and lower leverage ratio. Overall, the statistics show some systematic differences
between the clients of audit firms that have different legal forms.
Table 3 shows the Pearson correlation coefficients of variables in the full sample (in upper
triangle) and the matched-pairs sample (in lower triangle). In both samples, FRAUD is positively
correlated with LLC, leverage, prior net losses, and the number of board meetings during the year,
and negatively correlated with the legal environment index and state-owned enterprise. LLC is
positively correlated with Big 4 affiliation and negatively correlated with listing age, leverage,
and dual position of board chair and CEO. Despite the above correlations, the independent
variables in the regression equations in general are not highly correlated with each other,
suggesting a low likelihood of serious multicollinearity problems.
17
5. Empirical Results
5.1 Company characteristics associated with hiring of an LLC auditor
Table 4 presents the results for the logistic regression of LLC on company characteristics
(Eq. (1)). The coefficient on SIZE is significantly positive and the coefficients on AGE and LEV
are significantly negative, 12 indicating that companies that are larger or with a shorter listing age
or lower leverage ratio are more likely to hire a limited liability form of auditor. Although the
univariate test results in Panel A of Table 2 show that companies hiring a limited liability auditor
are located in a weaker legal environment and are more likely to be state-owned and less likely
to have prior net losses, these relations are not statistically significant in the logistic regression.
We also examine whether companies change their auditor in favor of a particular legal
form. In our sample, there are 503 observations of auditor changes. Thirty percent of these
auditor changes (151 observations) also observe a change in auditor legal form (i.e. switching to
an auditor of a different legal form from the previous auditor’s). Among these 151 observations,
80 are switches to unlimited liability audit partnerships and 71 are switches to LLCs. We
examine the company characteristics (size, listing age, growth, leverage, occurrence of prior net
losses, and state-owned enterprise) for the above two groups of auditor/legal form changes, and
we find no significant differences in the means of these company characteristics between the two
groups. Overall, although Table 4 shows that companies audited by different legal forms of
auditors have some different characteristics, there is no evidence that when companies do change
12
In this paper, we test statistical significance of the regression coefficients based on robust standard errors adjusted
for clustering by company (i.e., observations from the same company are not treated as independent; see Rogers
1993). We use a one-tailed test when there is a directional prediction for the coefficient and a two-tailed test
otherwise.
18
auditor, they switch to a particular legal form of auditor depending on these company
characteristics.
5.2 Auditor legal form and client characteristics
As mentioned previously, we also estimate logistic regression of each audit firm’s legal
form on the clients’ profitability, short-term liquidity, long-term solvency, and Altman Z-score.
None of the coefficients for these financial ratios is statistically significantly different from zero
(results not tabulated). Therefore, we find no evidence that auditors choose to practice under the
limited liability legal form because their clients are more risky financially.
5.3 Univariate analyses of auditor legal form and fraudulent financial reporting
We first perform univariate analyses of the relation between auditor legal form and client
fraudulent financial reporting. Table 5 classifies FRAUD based on LLC. Panel A shows that, in
the full sample, the observations with LLC = 1 (0) have a higher-than-expected frequency of
FRAUD = 1 (0) and a lower-than-expected frequency of FRAUD = 0 (1). The difference between
the actual and expected frequencies is (marginally) statistically significant based on a χ2 test (p =
0.088). Panel B shows a similar but stronger result for the matched-pairs sample (p = 0.042).
These univariate analyses reveal that audit clients of limited liability firms are more likely to
have fraudulent financial reporting than are audit clients of unlimited liability firms.
5.4 Multivariate analysis of fraudulent financial reporting
Table 6, Panel A, shows the results from logistic regression of FRAUD on auditor legal
form and control variables for the full sample. Both Pearson and Hosmer-Lemeshow goodnessof-fit statistics (not tabulated) are not statistically significant at the 0.05 level, suggesting that the
data fits the model well. The coefficient on LLC is significantly positive, which indicates a
19
higher likelihood of fraudulent financial reporting when the company is audited by a limited
liability firm. To interpret the economic importance of this result, we estimate the change in the
odds of a company having fraudulent financial reporting in response to one unit increase in the
corresponding independent variable, LLC (which is binary). 13 We find that if the company is
audited by a limited liability firm as opposed to an unlimited liability firm, the odds of having
fraudulent financial reporting would increase by 74 percent.
Panel A also shows significantly negative coefficients on LEGAL and SOE, which suggest
that companies that are state-owned or located in a stronger legal environment are less likely to
have fraudulent financial reporting. The coefficients on SIZE, LEV, LOSS, and MEET are
significantly positive, suggesting that larger, highly leveraged, and loss companies, and
companies with more board meetings, are more likely to have fraudulent financial reporting.
These results are generally consistent with prior research findings.
Table 6, Panel B, shows the logistic regression results for the matched-pairs sample. The
coefficient on LLC is significantly positive, similar to the result shown in Panel A. In this sample,
if the company is audited by a limited liability auditor, the odds of having fraudulent financial
reporting would increase by 186 percent (not tabulated). Panel B also shows significantly
positive associations of FRAUD with prior net losses and the number of board meetings, and
significantly negative associations of FRAUD with legal environment index and state-owned
enterprise. Taken together, the results in Table 6 are consistent with H1 that the likelihood of
fraudulent financial reporting is higher for the companies audited by limited liability firms than
for those audited by unlimited liability firms.
Table 7 presents the results when an interaction effect between LLC and LEGAL is added to
the regression (Eq. (3)). In both Panels A and B, the coefficient on LLC*LEGAL is significantly
13
The odds equal the probability of occurrence of an event divided by the probability of no occurrence.
20
positive, which indicates that the association between FRAUD and LLC is higher when the legal
environment is stronger. The coefficient on LLC is (marginally) significantly negative. However,
note that this result has no bearing on the test of H2 and does not contradict the positive
coefficient on LLC shown in Table 6 (LEGAL is a continuous variable ranging between 2.62 and
7.97, with a median value of 6.24).
In the above analyses, the strength of legal environment is measured by the raw index. As a
robustness check, we replicate the regression of Eq. (3) after replacing the raw index by its decile
ranking that is scaled to range between zero and one (i.e., LEGAL = (decile ranking – 1)/9). The
regression results (not tabulated) show that, in both samples, the coefficient on LLC is not
significantly different from zero. This insignificant coefficient suggests that when the legal
environment is weak (i.e., the raw index is assigned to decile 1), there is no difference in the
likelihood of fraudulent financial reporting between the companies audited by limited liability
firms and those audited by unlimited liability partnerships. In both samples the coefficient on
LEGAL remains significantly negative and the coefficient on LLC*LEGAL remains significantly
positive. The sum of the coefficients on LLC and LLC*LEGAL is significantly positive,
suggesting that auditor legal form is associated with client fraudulent financial reporting when
the legal environment is strong. Taken together, the regression results provide evidence
consistent with H2 that the difference in the likelihood of fraudulent financial reporting between
the companies audited by the limited liability firms and those audited by the unlimited liability
partnerships increases with the strength of legal environment.
5.5 Simultaneous analysis of fraud occurrence and fraud detection
In this study, we are primarily interested in examining the relation between auditor legal
form and the probability of clients’ fraudulent financial reporting. However, the probability of
21
committing a fraud is not observable; only detected frauds can be observed. If the probability of
fraud detection is not one, our measure of fraudulent financial reporting would indicate the
probability of detected frauds but not the probability of committing frauds.
To check whether our results are robust to the issue of partial observability, we perform
bivariate probit analysis. The analysis has two regressions: one for fraud propensity (occurrence)
and one for fraud detection. These two regressions cannot have exactly the same independent
variables (Poirier 1980). In the regression of fraud propensity, we use the same independent
variables as those in Eqs. (2) and (3). In the regression of fraud detection, we drop auditor legal
form, legal environment index, and the interaction effect between these two variables, but retain
the variables for company characteristics and governance, as the regulators might pay more
attention to companies that have these characteristics when investigating frauds, which could
increase the likelihood of fraud detection. Meanwhile, we add two independent variables to the
regression of fraud detection: stock return (RET) and return volatility (VOLAT), where RET
equals stock return for the current year and VOLAT equals the standard deviation of monthly
returns during the year. These two variables are included because low stock returns and high
return volatility potentially could draw attention from the regulators and investors and lead to a
more thorough investigation on the company (Wang 2008).
Table 8 presents the results for the bivariate probit analysis. 14 In Panel A (test of H1), the
coefficient on LLC is significantly positive in the fraud propensity regression, which is consistent
with the result shown in Table 6. The results also show that companies that are located in a
stronger legal environment, state-owned, or observing a higher percentage of outside directors on
14
We assume a zero probability of alleged fraud when no fraud is committed. In the bivariate probit regressions, we
drop the industry indicator variables as the regressions do not converge when these indicator variables are included.
Eighteen observations in the full sample are dropped in this analysis due to missing data on stock returns and
volatility.
22
the board have a lower propensity of committing financial reporting fraud, but companies with
prior losses have a higher propensity of committing fraud. In the fraud detection regression, the
coefficient on RET is significantly negative and the coefficients on LEV and VOLAT are
significantly positive, suggesting that companies with lower stock returns, higher leverage, and
higher return volatility are more likely to be detected given that a fraud has been committed. In
Panel B (test of H2), the coefficient on LLC*LEGAL is (marginally) significantly positive in the
fraud propensity regression, consistent with the result shown in Table 7. Taken together, the
bivariate probit analysis shows that our conclusions based on the regressions of detected frauds
are unaffected after taking into account the partial observability of the frauds.
5.6 Additional analyses
In the primary analyses, we do not distinguish among different types of financial reporting
frauds. As additional analyses, we replicate the regressions in Tables 6 and 7 by fraud type.
Since overstatement of earnings usually leads to overstatement of assets, we merge these two and
examine the following four types of financial reporting frauds separately: overstatement of
earnings/assets, delay of disclosure, false statement, and omission of material information.
Table 9 presents the regression results by fraud type for the full sample. For brevity, the
results for the control variables are not presented, but they are similar to those shown in the
previous tables. Panel A of Table 9 shows that when the interaction effect between auditor legal
form and legal environment index is not analyzed, LLC is significantly positively associated with
only one type of fraud: overstatement of earnings/assets. Panel B shows that LLC*LEGAL is
significantly positively associated with two types of fraud: overstatement of earnings/assets and
delay of disclosure. The results for the matched-pairs sample (not tabulated) are similar. Overall,
the results indicate that the associations of fraudulent financial reporting with auditor legal form
23
and the strength of legal environment are not the same across the four types of frauds. The
associations revealed in Tables 6 and 7 are mainly attributed to the frauds that involve
overstatement of earnings or assets.
There are three types of punishment that can take place if a company is detected by CSRC
to have fraudulent financial reporting: public criticism, public condemnation, and administrative
penalty. 15 Among them, public criticism is the least severe and administrative penalty is the most.
As an additional analysis, we estimate ordered logistic regression of punishment on the
independent variables that are used in the logistic regressions in Tables 6 and 7. The dependent
variable equals one, two, and three if the punishment takes the form of public criticism, public
condemnation, and administrative penalty, respectively, and it equals zero if no financial
reporting fraud is detected. Therefore, the dependent variable increases as the punishment
becomes more severe. The regression results (not tabulated) are similar to those shown in Tables
6 and 7.
Since most of the large audit firms are LLCs, our results for the differences between the
two legal forms of auditors could be driven by the auditor’s size instead of legal form. To
examine this possibility, we replicate the regressions after removing the observations that are
clients of the Big 4-affiliated auditors (which are all LLCs). The results (not tabulated) are
similar to those presented in the paper.
6. Conclusions
15
These three categories of admonishments are based on classifications provided by the CSMAR database that we
use. According to Chen et al. (2005), there are four categories of admonishments: public criticism, public
condemnation, official warning, and monetary fines. The CSMAR database does not distinguish between official
warning and monetary fines.
24
Prior analytical research demonstrates that, under certain conditions, audit quality is lower
with limited auditor liability. However, there is little empirical evidence supporting this
conclusion. In this paper, we investigate whether audit quality is lower for limited liability audit
firms than for unlimited liability partnerships using a sample of Chinese companies during the
2000-2004 period. We investigate a Chinese sample because of the “more balanced” distribution
of auditor legal forms. The market share of the unlimited liability partnerships is between 10 and
15 percent (in terms of total listed companies), which is much greater than that in the U.S. and
U.K. where almost all of the large- and mid-sized audit firms are limited liability legal entities.
The empirical results show that companies audited by the limited liability firms have a
higher likelihood of fraudulent financial reporting than do companies audited by the unlimited
liability partnerships. In addition, we find that the difference in the likelihood of client fraudulent
financial reporting between the two legal forms of audit firms increases with the strength of legal
environment. That is, the distinction between limited and unlimited auditor liability in relation to
client fraudulent financial reporting becomes greater as law enforcement and investor protection
become stronger. These results suggest that audit quality is lower for limited liability firms than
for unlimited liability partnerships.
One limitation of using fraudulent financial reporting as a proxy for audit quality is that,
while we are interested in investigating the propensity to committing frauds, only detected frauds
are observable. To address this partial observability issue, we use bivariate probit regression to
analyze fraud propensity and fraud detection simultaneously, and the results are generally
consistent with our primary findings where only the detected frauds are analyzed.
25
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28
Table 1
Sample selection and distribution of audit firm legal forms
Panel A: Sample selection
Number of company-years that have A-shares traded on the stock
exchanges in Shanghai and Shenzhen from 2000 to 2004
Less: Observations without sufficient data on
Audit firm’s name
Audit firm’s legal form
Financial data (company characteristics)
Board structure
Number of company-years in the full sample
6,660
(173)
(298)
(440)
(287)
5,462
Panel B: Distribution of audit firm legal forms
Year
2000
2001
2002
2003
2004
Total
Limited liability
corporation
845
(84.1%)
915
(85.9%)
1,021
(87.9%)
1,081
(88.5%)
881
(87.2%)
4,743
(86.8%)
Unlimited liability
partnership
160
(15.9%)
150
(14.1%)
140
(12.1%)
140
(11.5%)
129
(12.8%)
719
(13.2%)
29
Total
1,005
1,065
1,161
1,221
1,010
5,462
Table 2
Descriptive Statistics of variables
Panel A: Full sample
Variables
FRAUD
LEGAL
SIZE
AGE
GROW (%)
LEV
LOSS
SOE
BIG4
NUMDIR
MEET
OUTDIR (%)
DUAL
LLC = 0 (N = 719)
Mean
Median
0.028
0.000
5.998
5.810
13.929
13.939
5.357
5.000
20.434
14.230
0.442
0.412
0.082
0.000
0.672
1.000
0.000
0.000
9.481
9.000
7.732
7.000
19.205
22.222
0.168
0.000
LLC = 1 (N = 4,143) t-value for test of Z-value for test of
diff. in mean
diff. in median
Mean
Median
*
0.041
0.000
−1.96
−1.71 *
5.893
6.240
2.14 **
1.94 *
***
14.039
13.968
−3.07
−2.52 **
4.895
5.000
3.64 ***
3.45 ***
22.000
14.560
−1.15
−1.20
0.400
0.388
4.95 ***
4.37 ***
**
0.061
0.000
1.97
2.19 **
0.709
1.000
−2.02 **
−2.02 **
***
0.078
0.000
−20.09
−7.78 ***
−3.04 ***
9.786
9.000
−3.30 ***
*
7.478
7.000
1.75
0.15
20.398
25.000
−1.97 **
−1.83 *
5.03 ***
0.105
0.000
4.35 ***
Panel B: Matched-pairs sample
Variables
FRAUD
LEGAL
SIZE
AGE
GROW (%)
LEV
LOSS
SOE
BIG4
NUMDIR
MEET
OUTDIR (%)
DUAL
LLC = 0 (N = 54)
Mean
Median
0.370
0.000
5.259
5.100
13.858
13.793
5.796
6.000
21.451
13.085
0.511
0.504
0.056
0.000
0.556
1.000
0.000
0.000
9.759
9.000
8.426
8.000
22.816
27.273
0.204
0.000
LLC = 1 (N = 376) t-value for test of Z-value for test of
diff. in mean
diff. in median
Mean
Median
**
0.519
1.000
−2.04
−2.03 **
5.178
5.160
0.38
0.17
13.917
13.870
−0.50
−0.29
4.713
5.000
2.55 **
2.32 **
19.364
14.215
0.35
−0.50
**
0.432
0.419
2.36
2.23 **
*
0.122
0.000
−1.87
−1.44
0.617
1.000
−0.86
−0.86
0.051
0.000
−4.47 ***
−1.69 *
9.582
9.000
0.51
0.55
8.117
7.000
0.61
1.07
*
18.082
22.222
1.66
1.55
0.120
0.000
1.45
1.71 *
Variable definitions:
= 1 if the auditor is a limited liability firm, 0 otherwise
LLC
= 1 if the observation has fraudulent financial reporting, 0 otherwise
FRAUD
= Legal environment index for the province where the company operates
LEGAL
= ln(book value of total assets at the year of the previous year)
SIZE
= Number of years since the company was listed
AGE
= Average annual percentage change in total assets for the previous two years
GROW
= Total liabilities divided by total assets at the end of the previous year
LEV
= 1 if the company reported a net loss in the previous year, 0 otherwise
LOSS
= 1 if the company is a state-owned enterprise, 0 otherwise
SOE
30
BIG4
NUMDIR
MEET
OUTDIR
DUAL
=
=
=
=
=
1 if the auditor is Big 4-affiliated, 0 otherwise
Number of directors on the board
Number of board meetings held during the year
Percentage of outside directors on the board
1 if the same person holds a dual position as the board chair and CEO of the
company, 0 otherwise
Statistical significance of the difference in the means and medians is based on a two-tailed test. ***, **, and
indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
*
31
Table 3
Pearson Correlation Coefficients of Variables
(Upper triangle for the full sample, lower triangle for the matched-pairs sample, and p-values in parentheses)
Variable
FRUAD
FRAUD
LLC
0.098
(0.042)
-0.478
(0.000)
-0.024
(0.625)
-0.002
(0.961)
0.045
(0.357)
0.173
(0.000)
0.139
(0.004)
-0.143
(0.003)
-0.034
(0.483)
-0.018
(0.707)
0.024
(0.619)
-0.122
(0.011)
-0.022
(0.653)
-0.131
(0.006)
0.070
(0.149)
0.042
(0.388)
0.082
(0.092)
0.016
(0.745)
-0.001
(0.983)
-0.071
(0.145)
-0.194
(0.000)
-0.070
(0.146)
0.065
(0.178)
0.042
(0.379)
0.165
(0.001)
0.194
(0.000)
0.003
(0.950)
-0.065
(0.182)
0.135
(0.005)
0.077
(0.110)
-0.158
(0.001)
0.163
(0.001)
0.076
(0.117)
-0.140
(0.004)
0.034
(0.479)
-0.201
(0.000)
-0.002
(0.964)
-0.029
(0.551)
-0.097
(0.044)
0.166
(0.001)
-0.063
(0.196)
-0.079
(0.103)
0.047
(0.329)
-0.042
(0.391)
-0.013
(0.782)
-0.047
(0.335)
0.124
(0.010)
-0.006
(0.907)
0.000
(1.000)
-0.024
(0.613)
-0.030
(0.541)
-0.080
(0.098)
-0.083
(0.087)
0.095
(0.048)
-0.099
(0.039)
-0.026
(0.586)
0.032
(0.514)
0.151
(0.002)
0.091
(0.061)
0.201
(0.000)
-0.023
(0.630)
-0.013
(0.786)
0.085
(0.078)
0.281
(0.000)
0.062
(0.198)
-0.009
(0.859)
0.057
(0.238)
-0.106
(0.028)
0.062
(0.201)
-0.069
(0.156)
0.043
(0.377)
0.172
(0.000)
0.059
(0.223)
-0.087
(0.070)
0.007
(0.883)
0.074
(0.125)
-0.074
(0.128)
-0.025
(0.610)
-0.059
(0.223)
-0.072
(0.135)
-0.087
(0.073)
LEGAL
SIZE
AGE
GROW
LEV
LOSS
SOE
BIG4
NUMDIR
MEET
OUTDIR
DUAL
LLC
0.023
(0.088)
LEGAL
-0.227
(0.000)
-0.028
(0.040)
SIZE
-0.030
(0.025)
0.042
(0.002)
0.150
(0.000)
AGE
-0.008
(0.573)
-0.052
(0.000)
0.161
(0.000)
0.172
(0.000)
GROW
-0.005
(0.741)
0.016
(0.250)
-0.036
(0.007)
0.183
(0.000)
-0.195
(0.000)
LEV
0.073
(0.000)
-0.073
(0.000)
-0.062
(0.000)
-0.023
(0.091)
0.129
(0.000)
-0.173
(0.000)
LOSS
0.079
(0.000)
-0.030
(0.029)
-0.013
(0.354)
-0.057
(0.000)
0.120
(0.000)
-0.048
(0.000)
0.143
(0.000)
SOE
-0.073
(0.000)
0.027
(0.044)
-0.021
(0.124)
0.152
(0.000)
-0.080
(0.000)
0.006
(0.636)
-0.037
(0.006)
-0.020
(0.138)
BIG4
-0.025
(0.067)
0.105
(0.000)
0.116
(0.000)
0.268
(0.000)
0.076
(0.000)
-0.050
(0.000)
-0.103
(0.000)
-0.041
(0.003)
-0.012
(0.359)
0.129
(0.007)
-0.046
(0.346)
0.044
(0.361)
0.018
(0.715)
NUMDIR
-0.021
(0.117)
0.043
(0.002)
0.012
(0.371)
0.241
(0.000)
0.018
(0.192)
0.031
(0.023)
0.004
(0.746)
-0.037
(0.006)
0.082
(0.000)
0.094
(0.000)
-0.027
(0.584)
-0.007
(0.881)
-0.092
(0.057)
MEET
0.070
(0.000)
-0.028
(0.042)
0.046
(0.001)
0.016
(0.242)
0.066
(0.000)
0.038
(0.005)
0.059
(0.000)
0.038
(0.005)
-0.055
(0.000)
-0.007
(0.613)
OUTDIR
-0.024
(0.079)
0.027
(0.049)
0.039
(0.004)
0.160
(0.000)
0.251
(0.000)
-0.059
(0.000)
0.020
(0.137)
0.046
(0.001)
-0.041
(0.003)
0.089
(0.000)
DUAL
0.011
(0.415)
-0.068
(0.000)
-0.006
(0.639)
-0.050
(0.000)
-0.027
(0.049)
-0.001
(0.933)
0.001
(0.949)
0.007
(0.624)
-0.078
(0.000)
-0.021
(0.126)
-0.039
(0.004)
0.036
(0.008)
0.024
(0.073)
-0.021
(0.118)
-0.004
(0.746)
-0.027
(0.045)
-0.002
(0.967)
0.054
(0.261)
0.021
(0.659)
This table shows the Pearson correlation coefficients of variables for the full sample (in upper triangle) and the matched-pairs sample (in lower
triangle). p-values presented in parentheses below the coefficients are based on a two-tailed test. See Table 2 for variable definitions. The full
(matched-pairs) sample consists of 5,462 (430) observations during the 2000-2004 period.
32
Table 4
Results from logistic regression of audit firm legal form on company characteristics
Prob(LLC = 1) = 1/(1 + e-Z), where
Z = α0 + α1LEGAL + α2SIZE + α3AGE + α4GROW + α5LEV + α6LOSS + α7SOE + δ⋅YEAR
+ φ⋅INDUSTRY
Variables
Intercept
LEGAL
SIZE
AGE
GROW
LEV
LOSS
SOE
N
Pseudo-R2
Coefficient
0.601
−0.050
0.157
−0.050
−0.162
−0.977
−0.135
−0.028
5,462
0.039
z-value
0.45
−0.90
1.88 *
−1.84 *
−1.03
−2.75 ***
−0.95
−0.19
LLC is an indicator variable that equals one if the auditor is a limited liability firm and zero otherwise.
LEGAL equals the legal environment index for the province where the company operates. SIZE equals the
natural logarithm of total assets at the end of the previous year. AGE equals the number of years since the
company was listed; GROW equals the average annual percentage change in total assets for the previous
two years. LEV is the total liabilities divided by total assets at the end of the previous year. LOSS is an
indicator variable that equals one if the company reported a net loss in the previous year and zero
otherwise. SOE is an indicator variable that equals one if the observation is a state-owned enterprise and
zero otherwise. YEAR is a set of indicator variables that represent year. INDUSTRY is a set of indicator
variables that represent industry. See Table 2 for detailed variable definitions. z-values are computed
using robust standard errors adjusted for clustering by company. Statistical significance in this table is
based on two-tailed test. Results for YEAR and INDUSTRY are not presented. ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% levels, respectively.
33
Table 5
Univariate comparison of fraudulent financial reporting based on audit firm legal form
Panel A: Full sample
FRAUD = 0
Frequency
Expected frequency
Cell percentage
LLC = 0
699.0
690.7
12.8
LLC = 1
4,548.0
4,556.3
83.3
Total
5,247.0
5,247.0
96.1
FRAUD = 1
Frequency
Expected frequency
Cell percentage
20.0
28.3
0.4
195.0
186.7
3.5
215.0
215.0
3.9
Total
Frequency
Expected frequency
Cell percentage
719.0
719.0
13.2
4,743.0
4,743.0
86.8
5,462.0
5,462.0
100.0
LLC = 1
181.0
188.0
42.1
Total
215.0
215.0
50.0
Pearson χ2
2.92
(p = 0.088)
Panel B: Matched-pairs sample
FRAUD = 0
Frequency
Expected frequency
Cell percentage
LLC = 0
34.0
27.0
7.9
FRAUD = 1
Frequency
Expected frequency
Cell percentage
20.0
27.0
4.7
195.0
188.0
45.3
215.0
215.0
50.0
Total
Frequency
Expected frequency
Cell percentage
54.0
54.0
12.6
376.0
376.0
87.4
430.0
430.0
100.0
Pearson χ2
4.15
(p = 0.042)
FRAUD is an indicator variable that equals one if the observation has fraudulent financial reporting and
zero otherwise. LLC is an indicator variable that equals one if the auditor is a limited liability firm and
zero otherwise. The last column shows the result from a Pearson χ2 test of the homogeneity across
classifications.
34
Table 6
Results from logistic regression of fraudulent financial reporting on audit firm legal form, legal
environment index, and control variables
Prob(FRAUD = 1) = 1/(1 + e-Z), where
Z = β0 + β1LLC + β2LEGAL + β3SIZE + β4AGE + β5GROW + β6LEV + β7LOSS + β8SOE + β9BIG4
+ β10NUMDIR + β11MEET + β12OUTDIR + β13DUAL + δ⋅YEAR + φ⋅INDUSTRY
Variables
Intercept
LLC
LEGAL
SIZE
AGE
GROW
LEV
LOSS
SOE
BIG4
NUMDIR
MEET
OUTDIR
DUAL
N
Pseudo-R2
Predicted sign
?
+
−
−
−
−
+
+
−
−
+
+
−
+
Panel A: Full sample
Coefficient
z-value
−2.773
−1.33
0.557
2.41 ***
−0.856
−12.32 ***
0.168
1.61 *
−0.012
−0.35
−0.228
−0.88
1.232
2.85 ***
1.018
4.57 ***
−0.791
−4.70 ***
−0.089
−0.24
0.002
0.07
0.077
3.63 ***
−0.284
−0.29
0.075
0.29
5,462
0.212
Panel B: Matched-pairs sample
Coefficient
z-value
2.806
1.01
1.051
3.18 ***
−0.893
−7.16 ***
0.098
0.54
−0.039
−0.76
0.329
0.69
0.938
1.25
0.995
2.51 ***
−0.741
−2.83 ***
−0.245
−0.41
0.021
0.39
0.060
1.72 **
−0.632
−0.38
−0.042
−0.10
430
0.260
FRAUD is an indicator variable that equals one if the observation has fraudulent financial reporting and
zero otherwise. LLC is a indicator variable that equals one if the auditor is a limited liability firm and zero
otherwise. LEGAL equals the legal environment index for the province where the company operates. SIZE
equals the natural logarithm of total assets at the end of the previous year. AGE equals the number of
years since the company was listed; GROW equals the average annual percentage change in total assets
for the previous two years. LEV is the total liabilities divided by total assets at the end of the previous
year. LOSS is an indicator variable that equals one if the company reported a net loss in the previous year
and zero otherwise. SOE is an indicator variable that equals one if the observation is a state-owned
enterprise and zero otherwise. BIG4 is an indicator variable that equals one if the auditor is Big 4affiliated and zero otherwise. NUMDIR equals the number of directors on the board. MEET equals the
number of board meetings held during the year. OUTDIR equals the percentage of outside directors on the
board. DUAL is an indicator variable that equals one if the same person holds a dual position as the board
chair and CEO of the company, and zero otherwise. YEAR is a set of indicator variables that represent
year. INDUSTRY is a set of indicator variables that represent industry. See Table 2 for detailed variable
definitions. z-values are computed using robust standard errors adjusted for clustering by company.
Statistical significance is based on one-tailed test if there is a directional prediction for the coefficient and
two-tailed test otherwise. Results for YEAR and INDUSTRY are not presented. ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% levels, respectively.
35
Table 7
Results from logistic regression of fraudulent financial reporting on audit firm legal form, legal
environment index, interaction effect between audit firm legal form and legal environment index,
and control variables
Prob(FRAUD = 1) = 1/(1 + e-Z), where
Z = β0 + β1LLC + β2LEGAL + β3LLC*LEGAL + β4SIZE + β5AGE + β6GROW + β7LEV + β8LOSS
+ β9SOE + β10BIG4 + β11NUMDIR + β12MEET + β13OUTDIR + β14DUAL + δ⋅YEAR + φ⋅INDUSTRY
Variables
Intercept
LLC
LEGAL
LLC*LEGAL
SIZE
AGE
GROW
LEV
LOSS
SOE
BIG4
NUMDIR
MEET
OUTDIR
DUAL
N
Pseudo-R2
Predicted sign
?
?
−
+
−
−
−
+
+
−
−
+
+
−
+
Panel A: Full sample
Coefficient
z-value
−0.346
−0.15
−2.028
−1.71 *
−1.384
−5.38 ***
0.564
2.21 **
0.164
1.57 *
−0.012
−0.34
−0.239
−0.93
1.174
2.68 ***
1.009
4.48 ***
−0.763
−4.50 ***
−0.127
−0.35
0.004
0.11
0.079
3.66 ***
−0.201
−0.21
0.065
0.26
5,462
0.215
Panel B: Matched-pairs sample
Coefficient
z-value
7.374
2.12 **
−3.804
−1.78 *
−1.819
−3.96 ***
0.996
2.20 **
0.081
0.44
−0.027
−0.53
0.243
0.52
1.031
1.33 *
0.927
2.33 **
−0.709
−2.72 ***
−0.321
−0.54
0.024
0.46
0.057
1.60 *
−0.676
−0.40
−0.022
−0.05
430
0.269
FRAUD is an indicator variable that equals one if the observation has fraudulent financial reporting and
zero otherwise. LLC is an indicator variable that equals one if the auditor is a limited liability firm and
zero otherwise. LEGAL equals the legal environment index for the province where the company operates.
SIZE equals the natural logarithm of total assets at the end of the previous year. AGE equals the number
of years since the company was listed; GROW equals the average annual percentage change in total assets
for the previous two years. LEV is the total liabilities divided by total assets at the end of the previous
year. CUR is the current ratio at the end of the previous year. LOSS is an indicator variable that equals one
if the company reported a net loss in the previous year and zero otherwise. SOE is an indicator variable
that equals one if the observation is a state-owned enterprise and zero otherwise. BIG4 is an indicator
variable that equals one if the auditor is Big 4-affiliated and zero otherwise. NUMDIR equals the number
of directors on the board. MEET equals the number of board meetings held during the year. OUTDIR
equals the percentage of outside directors on the board. DUAL is an indicator variable that equals one if
the same person holds a dual position as the board chair and CEO of the company, and zero otherwise.
YEAR is a set of indicator variables that represent year. INDUSTRY is a set of indicator variables that
represent industry. See Table 2 for detailed variable definitions. z-values are computed using robust
standard errors adjusted for clustering by company. Statistical significance is based on one-tailed test if
there is a directional prediction for the coefficient and two-tailed test otherwise. Results for YEAR and
INDUSTRY are not presented. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively.
36
Table 8
Results from bivariate probit regression of fraud propensity and fraud detection
Fraud propensity:
Prob.(FRAUD = 1) = Φ(β0 + β1LLC + β2LEGAL + β3LLC*LEGAL + β4SIZE + β5AGE + β6GROW + β7LEV
+ β8LOSS + β9SOE + β10BIG4 + β11NUMDIR + β12MEET + β13OUTDIR + β14DUAL + δ⋅YEAR)
Fraud detection:
Prob.(DETECT = 1|FRAUD = 1) = Φ(γ0 + γ1SIZE + γ2AGE + γ3GROW + γ4LEV + γ5LOSS + γ6SOE
+ γ7BIG4 + γ8NUMDIR + γ9MEET + γ10OUTDIR + γ11DUAL + γ12RET + γ13VOLAT + φ⋅YEAR)
Variables
Intercept
LLC
LEGAL
LLC*LEGAL
SIZE
AGE
GROW
LEV
LOSS
SOE
BIG4
NUMDIR
MEET
OUTDIR
DUAL
RET
VOLAT
Log Likelihood
χ2
N
Panel A
Fraud propensity
Fraud detection
Coef.
z-value
Coef.
z-value
−0.421
−0.36
−0.469 −0.10
0.321
2.11**
−0.481
−4.08***
0.095
−0.056
0.337
−0.100
0.420
−0.453
−0.319
−0.008
0.029
−2.153
0.105
−696.43
263.77
5,444
1.01
−1.33
1.45
−0.22
2.03**
−1.93*
−0.53
−0.14
1.12
−2.27**
0.26
−0.023
0.179
−0.760
2.140
0.446
0.071
0.956
0.024
0.072
6.957
−0.158
−1.239
0.085
−0.08
1.27
−1.34
2.17**
0.60
0.07
0.32
0.13
1.00
1.22
−0.14
−1.73*
1.90*
Panel B
Fraud propensity
Fraud detection
Coef.
z-value
Coef.
z-value
0.526
0.46
0.774
0.18
−0.941 −1.33
−0.687 −4.69***
0.257
1.75*
0.104
1.29
−0.079 −0.31
−0.056 −1.57
0.182
1.26
0.316
1.38
−0.870 −1.42
−0.105 −0.22
2.136
2.27**
**
0.408
1.97
0.365
0.54
−0.471 −2.69**
0.275
0.41
−0.254 −0.46
0.640
0.29
−0.017 −0.39
0.058
0.40
0.030
1.18
0.060
0.75
**
−2.084
−2.10
7.471
1.86*
0.056
0.15
−0.042
−0.03
−1.290 −2.03**
0.086
2.78***
-694.48
227.89
5,444
5,444
5,444
This table presents results from bivariate probit estimation of fraud propensity and fraud detection. Φ(⋅)
denotes a standard normal cumulative distribution function. FRAUD is an indicator variable that equals
one if the observation has fraudulent financial reporting and zero otherwise. DETECT is an indicator
variable that equals one if the observation is detected by CSRC to have committed financial reporting
fraud and zero otherwise. LLC is an indicator variable that equals one if the auditor is a limited liability
firm and zero otherwise. LEGAL equals the legal environment index for the province where the company
operates. SIZE equals the natural logarithm of total assets at the end of the previous year. AGE equals the
number of years since the company was listed; GROW equals the average annual percentage change in
total assets for the previous two years. LEV is the total liabilities divided by total assets at the end of the
previous year. LOSS is an indicator variable that equals one if the company reported a net loss in the
previous year and zero otherwise. SOE is an indicator variable that equals one if the observation is a stateowned enterprise and zero otherwise. BIG4 is an indicator variable that equals one if the auditor is Big 4-
37
affiliated and zero otherwise. NUMDIR equals the number of directors on the board. MEET equals the
number of board meetings held during the year. OUTDIR equals the percentage of outside directors on the
board. DUAL is an indicator variable that equals one if the same person holds a dual position as the board
chair and CEO of the company, and zero otherwise. RET equals the stock return over the fiscal year.
VOLAT equals the standard deviation of the monthly stock returns over the year. YEAR is a set of
indicator variables that represent year. See Table 2 for detailed variable definitions. Results for the
indicator variables that represent year are not presented. ***, **, and * indicate statistical significance at the
1%, 5%, and 10% levels, respectively, based on two-tailed test.
38
Table 9
Results from logistic regression of fraudulent financial reporting on audit firm legal form, legal
environment index, interaction effect between audit firm legal form and legal environment index,
and control variables (Regression by fraud type)
Prob.(FRAUD = 1) = 1/(1 + e-Z), where
Z = β0 + β1LLC + β2LEGAL + β3LLC*LEGAL + β4SIZE + β5AGE + β6GROW + β7LEV + β8LOSS
+ β9SOE + β10BIG4 + β11NUMDIR + β12MEET + β13OUTDIR + β14DUAL + δ⋅YEAR + φ⋅INDUSTRY
Fraud type
Overstatement
of earnings
Variables
Predicted sign
or assets
Panel A: LLC*LEGAL not included in the regression
+
2.070
LLC
(2.03)**
Pseudo-R2
0.209
Panel B: LLC*LEGAL included in the regression
?
−1.017
LLC
(−0.59)
+
0.700
LLC*LEGAL
(2.52)***
Pseudo-R2
0.211
Delayed
disclosure
False statement
Omission of
material
information
0.388
(1.21)
0.185
0.144
(0.29)
0.258
0.516
(1.27)
0.207
−3.284
(−2.26)**
0.823
(2.64)***
0.192
0.322
(0.21)
0.147
(0.11)
0.082
(0.29)
0.207
−0.041
(−0.11)
0.258
FRAUD is an indicator variable that equals one if the observation has the type of financial reporting fraud
indicated in the heading of the column and zero otherwise. LLC is an indicator variable that equals one if
the auditor is a limited liability firm and zero otherwise. LEGAL equals the legal environment index for
the province where the company operates. SIZE equals the natural logarithm of total assets at the end of
the previous year. AGE equals the number of years since the company was listed; GROW equals the
average annual percentage change in total assets for the previous two years. LEV is the total liabilities
divided by total assets at the end of the previous year. LOSS is an indicator variable that equals one if the
company reported a net loss in the previous year and zero otherwise. SOE is an indicator variable that
equals one if the observation is a state-owned enterprise and zero otherwise. BIG4 is an indicator variable
that equals one if the auditor is Big 4-affiliated and zero otherwise. NUMDIR equals the number of
directors on the board. MEET equals the number of board meetings held during the year. OUTDIR equals
the percentage of outside directors on the board. DUAL is an indicator variable that equals one if the same
person holds a dual position as the board chair and CEO of the company, and zero otherwise. YEAR is a
set of indicator variables that represent year. INDUSTRY is a set of indicator variables that represent
industry. See Table 2 for detailed variable definitions. Only the results for LLC and LLC*LEGAL are
presented. z-values (in parentheses) are computed using robust standard errors adjusted for clustering by
company. Statistical significance is based on one-tailed test if there is a directional prediction for the
coefficient and two-tailed test otherwise. ***, **, and * indicate statistical significance at the 1%, 5%, and
10% levels, respectively.
39